Entropy, harmony, synchronization and their neuro-endocrine-immune correlates
Authors/Creators
Description
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UkrNDI OF MEDICINE OF TRANSPORT MOH
TERNOPIL STATE MEDICAL UNIVERSITY named after I. Y. HORBACHEVSKY
A. I. GOZHENKO
M. M. KORDA
O. O. POPADYNETS
I. L. POPOVYCH
ENTROPY, HARMONY, SYNCHRONIZATION
AND THEIR NEURO-ENDOCRINE-IMMUNE CORRELATIONS
Monograph
Odesa Phoenix 2021
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UDC 616-003.96: 616-084: 536(075.8) G
57
Recommended for publication by the Academic Council of
the State Enterprise Ukrainian Research Institute of Transport Medicine of the Ministry of Health of Ukraine (protocol No. 5 dated June 30, 2021)
Reviewers: Ivan
Volodymyrovych Savytskyi, doctor of medical sciences, professor, pro-rector for scientific and pedagogical work, head of the department of medical and biological disciplines of Odesa International Medical University
Ruslan Serhiyovych Vastyanov, Doctor of Medical Sciences, Professor, Honored Worker of Science and Technology of Ukraine, Head of the Department of General and Clinical
Pathological Physiology named after V.V. Pidvysotskyi of Odessa National Medical University
G 57
Gozhenko A.I. [etc.]. Entropy, harmony, synchronization and their neuro- endocrine-immune correlates: monograph / A.I. Gozhenko, M. M. Korda,
O. O. Popadynets, I. L. Popovych. – Odesa: Phoenix, 2021. – 232 p. DOI https://doi.org/10.5281/zenodo.7764769 ISBN 978-966-928-715-1
The monograph summarizes data from the literature and the results of own research on the relationship of information parameters - entropy, harmony and synchronization - with the parameters of the neuro-endocrine-immune complex of experimental animals and patients of the Truskavets resort. It has been demonstrated that information parameters, primarily entropy, are markers of the integral state of the neuro-endocrine-immune complex, which is responsible for the adaptation of the organism to adverse factors of a physical, chemical, and biological nature.
For medical rehabilitation specialists, immunologists, endocrinologists, pathophysiologists.
UDC 616-003.96: 616-084: 536(075.8)
ISBN 978-966-928-715-1
DOI https://doi.org/10.5281/zenodo.7764769
© A.I. Gozhenko, M. M. Korda, O.
O. Popadynets, I. L. Popovych, 2021
© UkrNDI of transport medicine, 2021 © Ternopil State Medical
University named after I. Ya. Gorbachevsky, 2021
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Content
Information about the authors. 4
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
INTRODUCTION 7
CHAPTER 1. APPLICATION OF ENTROPY DETERMINATION IN MEDICAL
RESEARCH. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SECTION 2. 12
INFORMATIONAL EFFECTS OF BIOACTIVE OIL WATER. . . . 45
CHAPTER 3. RELATIONSHIPS BETWEEN ENTROPYS OF MORPHO-FUNCTIONAL IMMUNE SUBSYSTEMS (THYMUS, SPLEEN, PERIPHERAL BLOOD) AND PARAMETERS OF THE NEURO-ENDOCRINE-IMMUNE COMPLEX IN RATS OF BOTH
SEXES. 72 CHAPTER 4. FACTOR ANALYSIS
OF THE INFORMATION FIELD OF PARAMETERS OF NERVOUS REGULATORY STRUCTURES (EEG/VRS) AND IMMUNITY. 107
CHAPTER 5. CORRELATION RELATIONS BETWEEN ENTROPY OF NERVOUS REGULATORY STRUCTURES (EEG/VRS) AND PARAMETERS OF IMMUNITY. . CHAPTER
6. INDIVIDUAL CHARACTERISTICS OF ENTROPY PARAMETERS OF NERVOUS REGULATORY STRUCTURES (EEG/VRS). . . . . . . . . . . . . . . . . . .
122
132
CHAPTER 7. CHARACTERISTICS OF AMPLITUDE-FREQUENCY AND SPECTRAL PARAMETERS OF EEG/VRS IN PERSONS WITH DIFFERENT STATE OF ENTROPY. 143
CHAPTER 8. FEATURES OF THE NEURO-IMMUNE COMPLEX IN PERSONS WITH DIFFERENT STATE OF ENTROPY OF NERVOUS REGULATORY STRUCTURES. . 157
CHAPTER 9. OPTIONS OF CHANGES IN EEG, HRV, LCG AND ICG ENTROPY UNDER THE INFLUENCE OF ADAPTOGENEIC BALNEOTHERAPY AND THEIR PREDICTIONS. 169
CHAPTER 10. IMMUNE SUPPORT OF POLYVARIANT REACTIONS OF ENTROPY TO ADAPTOGENIC BALNEOTHERAPY. CHAPTER 11.
188
RELATIONSHIPS BETWEEN THE ENTROPY OF THE PARAMETERS OF THE NEURO-IMMUNE COMPLEX AND GAS DISCHARGE VISUALIZATION. 207
CONCLUSION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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215
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Information about the authors
Anatoly Ivanovich Gozhenko, Doctor of Medical Sciences, Professor, Honored Worker of Science and Technology of Ukraine, Director of the State Enterprise "Ukrainian Research Institute of Transport Medicine of the Ministry of Health of Ukraine", President of the Scientific Society of Pathophysiologists of Ukraine, Chairman of the Public
Organization "Ukrainian Association of Medical Science". Author of more than 1,650 scientific works (more than 95 publications in scientific journals indexed in the Web of Science Core Collection
and Scopus), including 67 monographs, 82 author's certificates and patents, 8 textbooks, 4 training manuals, 2 methodological manuals.
A. I. Gozhenko created the international scientific school "Pathophysiology of water- salt exchange and kidney function". Under his scientific guidance and counseling, 30 doctors and 72 candidates of sciences were trained.
Mykhailo Mykhailovych Korda, Doctor of Medical Sciences, Member-Cor of the National Academy
of Sciences of Ukraine, Honored Worker of Science and Technology of Ukraine, Professor of the Department of Medical Biochemistry, Rector of Ternopil National Medical University named after I. Ya. Gorbachevsky.
Field of scientific interests: biochemistry of free radicals; hepatotoxicology; the role of
nitric oxide in the pathology of the cardiovascular system; endothelial dysfunction mechanisms in obesity, hypertension, atherosclerosis, COVID-19; nanotoxicology,
application of nanomaterials in medicine and pharmacy; epidemiology, pathogenesis, treatment of borrelio
Korda M. M. is the author of 455 scientific works, including 11 monographs, 4 textbooks
Kiev, 9 teaching and methodical manuals.
M. Korda is the chief editor of the scientific journals "Medical and Clinical Chemistry", "International Journal of Medicine and Medical Research, a member of the editorial boards of 3 foreign journals.
Ihor Lvovych Popovych is a famous Ukrainian scientist in the field of balneology and physiology.
Employee (1983–2009) and head (2010–2016) of the laboratory of experimental balneology of the Institute of Physiology named after O.O. Bogomolets National Academy of Sciences of Ukraine. Scientific (2003–2011) and chief (2012–2013) editor of the journal "Medical Hydrology and Rehabilitation". Chairman of the Council of the Truskavets Association of Scientists (since 1999). Honors: Prize named
after O.O. Bogomolets of the National Academy of Sciences of Ukraine in the field of physiology (1998),
Prize named after Teodor Torosevich of Truskavetskurort CJSC in the field of balneology (2000), Certificate
of Honor of the Presidium of the National Academy of Sciences of Ukraine (2004), medal "25 years of independence of Ukraine"
Oleksandr Oleksiyovych Popadynets - vice-rector for infrastructure development of the Southern Ukrainian National Pedagogical University named after K.D. Ushin
of
He has more than 40 years of practical work experience in clinical medical and preventive institutions of Ukraine (CLPU) in the specialty "Physician", of which more than 30 years he was engaged in the organization of medical assistance to the population, and the development of the activities of medical universities in the positions of chief physician and vice-rector.
The author of more than 30 scientific works devoted to the study of various problems of medicine. He has more than 10 years of teaching experience.
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Preface
The development of civilization is based on continuous human knowledge
(mankind) of the world. The main objects of knowledge are the person himself and the environment environment. At the same time, the basic logic of this process has developed and exists: from general ideas to an analytical approach that develops and deepens them, that and
allows further formation of an objective picture of the world. Medical science also has a general logico-historical approach to understanding the essence of a person. Indeed, the primary idea of the human body as a holistic phenomenon of nature gradually moved to the analytical study of its essence from its structure, i.e. morphology, to the function of individual components and finally to their chemical (biochemical) nature. Despite the huge amount of material collected by medicine, we see many questions that need to be answered
understanding as the essence of a biochemical component and especially a physical basis life. But the more data accumulates about the human body and
our knowledge about the structure and function of its individual parts deepens components, the more we move away from understanding the human body as holistic phenomenon, its main characteristics, which are health and life in
as a whole Although they are the main subject of medicine. Such a dialectic
holistic and its structure, and therefore the accumulation of knowledge on elementary components logically requires understanding and characteristics of whole objects nature
Taking into account that every object of nature exists due to the fact that it is a system, the concept of the main general characteristic of any system, its entropy, which characterizes the general energy principle of existence - a constant direction to decrease the energy level,
i.e. essentially destruction, was formed in science of this system.
It goes without saying that biological organisms are the most complex systems that have evolved in nature, and man is the most complex, i.e.
super system That is why the human body is relatively stable over time thanks to metabolism, which ensures this temporary state, that is, life.
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Medicine itself aims to ensure the maximum stability of human existence in the environment. Moreover, the main indicators of a person's existence are his health and life expectancy, which are the subject of the scientific and practical activity of medicine, which, in order to achieve this goal, studies individual morpho-functional indicators that allow us to understand the state of the body
and influence him.
At the same time, studying (diagnosing) all these valuable components of the body of man, we are moving more and more away from the general characteristics of the self of a complex supersystem, but instead from ideas about its stability. On our
view, continuing the development of an analytical approach to the human body there comes a time when it becomes necessary to define general characteristics organism, namely its entropy as an indicator of the energy state, and instead the ability to ensure relative stability of existence.
However, in modern medicine there are only separate attempts to characterize it organism according to its entropy indicators in connection with the existing priorities determination of specific changes. However, in the case when physiological loads on the human body is not significant or the pathological changes are also not large,
it is rather difficult to characterize its condition. Exactly in these cases
the entropy indicator can give an idea of the general state of the organism.
In this regard, the authors set the goal of determining the entropy changes of the main systems that ensure the functional stability of the body in the conditions of sanatorium-resort treatment, because in these cases, pathological changes, as a rule, are not significant, and the operating factors also do not cause significant
differences
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INTRODUCTION
Despite the fact that information medicine, like homeopathy and biophotonics, still did not receive academic status (and not so long ago this was the case
acupuncture and more recently - theoneurology), it has an ancient background.
If we translate the first lines of the Gospel of John from religious language to scientific language (logos = word = information; God = World Mind = information space of the Universe), we will get: "The first was Information, and the World Mind had Information , and the World Mind was Information. It was primary in the World Mind ... Everything arose through Information ...
Information became a body." By the way, here is the answer to the main question of philosophy regarding the primacy of the ideal or the material.
In a general sense, bioinformation is a message about events from external and internal environment, perceived stimuli
biosystems and affect life processes. Biloshitsky PV and others.
[2005] suggest that the very appearance of life on our planet is a result of it
informational "fertilization" - the triune unity of the conditions of the Earth, the energy of the Sun, etc information of the universe. The reliability of the functioning of the organism is ensured by conditions for coordinating the states and activities of its systems, organs, cells, and others components This consistency can be achieved by the transfer of bioinformation by
intra- and intersystem communications for the formation of control signals.
Bioinformation from the surrounding world and internal environment is perceived by the biosystem through sense organs and specialized receptors, that is, it can pass both on a conscious level and involuntarily through the autonomic nervous system. The authors do not exclude the interaction of the unidentified DNA of the genome and the "redundant" neurons of the cerebral cortex, on the one hand, with the "invisible" world - hidden matter and fields of unknown nature - on the other hand, because their fates are comparable, moreover, "lion" - 70 -90% Bioinformation can be constructive and destructive, which gave the authors a reason to highlight
information diseases and information methods of treatment.
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One of the cornerstones of information medicine is entropy. The concept of entropy is multifaceted. In physics, entropy is a quantity that, in observed phenomena and processes, characterizes the devaluation (dissipation) of energy caused by the transformation of all its types into thermal energy with uniform distribution of heat between bodies; in chemistry and thermodynamics, it is a measure of the amount of energy in a physical system that cannot be used to perform work; in mathematics, it is a measure of the uncertainty of a random function; in information theory is a measure of uncertainty
situation, any experience (trial), which may have different consequences; entropy is also a measure of disorder, the degree of chaos present in a system.
Shannon SE [1948] connected the concepts of information and
entropy, which characterizes the degree of system order. This assessment
amount of information coincides with the assessment of the quantitative measure of elimination uncertainty, degree of organization of the system. According to Biloshitsky PV
[2007], the mathematical formula directly indicates the possibility of a quantitative change information can change the orderliness of the system, which in relation to biosystems can to mean a change in quality (stability, efficiency, health, etc.) and thereby
to indicate the way of purposeful use of bioinformation in medical practice. By chance, the author suggests instead of the term entropy to use the term reliability of the functioning of the organism, which is very impressive to us, as well as his assumption that the dependence of the reliability of the biosystem on information is the elusive vis vitalis: [Biloshytsky PV et al., 2003; 2005]).
Calculation of entropy is acceptable, in particular, in relation to the leukocytogram of peripheral blood, which is a closed system of various forms
elements. Informational analysis of the cytogram allows to assess the state of morpho functional adaptive protective systems, information about which is contained
in the cytogram [GG Avtandilov, 1990; Yushkovska OG, 2001].
IL Popovych [2007] first used this approach for evaluation
immunocytograms of peripheral blood, as well as splenocytograms and thymocytograms smear-imprints in rats. The creativity of this approach is demonstrated in
subsequent studies of I.L. Popovych [2008, 2008a, 2009, 2011, 2018, 2019] and
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other representatives of the Truskavet Scientific School [Kostiuk PG et al., 2007; Phil VM, 2008; Flunt IS et al., 2008; Guchko BYA, 2008; Bilas VR, Popovych IL, 2009; OI Chebanenko and others; 2011; Polovynko IS et al, 2013, 2016, 2016a; Kozyavkina OV and others, 2015; Zajats LM et al, 2017, 2017a; Zavidnyuk YV et al, 2018; Sydoruk HO et al., 2018; Popovych AI, 2018, 2019; Gozhenko AI et al, 2018, 2019; Struk ZD
et al., 2019].
In 2016 Popovych I.L. proposed to calculate the relative entropy spectral powers of HF, LF, VLF and ULF components of rhythm variability
heart rate (HRS) and ÿ-, ÿ-, ÿ- and ÿ-rhythms of the electroencephalogram (EEG). This offer was implemented in a number of clinical and physiological studies and also showed its value productivity, despite the fact that entropy was not the focus of the analysis
[Popovych IL et al, 2016, 2017, 2017a, 2018; Kul'chyns'kyi AB et al, 2016, 2017,
2017a; Kyrylenko IG, 2018; Popovych AI, 2018, 2019; Lukyanchenko OI et al, 2019; Mel'nyk OI et al, 2019].
The results of experimental and clinical-physiological studies showed that that, on the one hand, the increase in entropy is not uniquely physiological
unfavorable process, and on the other hand, the decrease in entropy is not unambiguously physiologically favorable for the human body and animals. By the way, according to the literature, this also applies to the physiological assessment of deterministic chaos, which is close to, but not identical to, entropy.
There is a theoretical position [Baevsky R.M. et al., 1984] that in a complete organism any reaction is carried out by a coordinated ensemble of various systems. Often, especially under conditions of sub-extreme influences, which do not cause "obvious" changes, but still reduce the resistance of the integral
body and disrupt its homeostasis, affecting internal connections in life process, the body's reaction cannot be recorded at all,
if we analyze only the changes in the average values of certain parameters. For these circumstances, the informational components of biological systems should be analyzed by application of methods of information analysis.
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This is, first of all, a correlational approach that allows you to assess the relationship between indicators and, from a physiological point of view, reflects the processes of synchronization in the work of regulatory systems. A decrease in synchronization (that is, a decrease in correlation coefficients) while maintaining a high degree of tension in the regulation mechanisms indicates the phenomena of overvoltage and astenization in the control system. Therefore, the revealed regularities of changes in the correlation between numerous indicators have diagnostic and prognostic value. For
obtaining an integral indicator that would reflect numerous
relationships of homeostasis indicators, it is suggested to study not in pairs correlation of indicators, and the sum of correlation coefficients, which, as multiparametric indicator, comprehensively reflects complex regulatory processes during the development of general adaptation reactions.
Another group of authors [Peredery V.G. et al., 1995] suggests for detection hidden imbalance of the immune system, calculate two coefficients:
conjugation (the ratio of the number of probable correlations to the number
of possible correlations) and closeness of connections (the ratio of the number
probable correlations to such improbable ones). Our previous experience showed that the informativeness of both coefficients is duplicated, so we can limit ourselves to the first of them [I.S. Flunt. et al., 2001].
V.P. Voytenko also speaks about the actual diagnostic value of the analysis of systemic correlations of the organism. [1991]. With regard to different body systems, cause-and-effect relationships between individual functions (and correlations as a statistical manifestation of these relationships) have different dynamics in the process of disease formation and recovery. There are several nodal positions all around
which events occur. Functional tension determines the "mobilized" mutual connection of life processes, and functional fatigue
accompanied by "demobilization". The disease is accompanied by "emergency" a strategy of coordinated subordination of all resources to the goal of survival; the inconsistency in its decompensated and irreversible stages reflects it
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the disintegration of the organism as a whole, its transition to a state of competition, and not
interactions, subsystems.
Another information parameter is harmony. According to the concept of Shannon S. [1963], developed by Suvorov N.P. and Suvorova I.G. [2003; 2014], the mathematical interpretation of the harmony of any complex component of the energy-informational structure
- technical or biological, including the animal organism - is unified, and the principles and criteria of harmony are also unified
technical optimality and perfection of biological structures. the only one
the universal criterion of optimality and perfection is the maximum of harmony - the best internal and external harmony, which is equivalent to the maximum autocorrelation (ÿ) and minimum cross-correlation (r).
For the first time (according to the authors' personal testimony) in biology and medicine applied the mentioned concept to quantify the degree of harmony
information components of neuroendocrine-immune morpho-functional system and metabolism. Unfortunately, there are still no followers of this trend
there is no
Therefore, research in the direction of information medicine remains relevant
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CHAPTER
1 APPLICATION OF ENTROPY DETERMINATION IN MEDICAL RESEARCH
Entropy is a thermodynamic variable that depends on temperature and heat. According to the Boltzmann equation, entropy can be interpreted as a measure of the disorder of a system, including living organisms. Based on these positions, one of the founding fathers of quantum mechanics, Erwin Schrödinger, in his
the well-known book "What is life?", published in 1944 [Schrödinger E, 1944], suggested that entropy might be the key to explaining that
such is life This interpretation of entropy corresponds to the interpretation that Claude Shannon introduced in his theory of communication in 1948, when he defined
entropy as a measure of information stored in the system [Shannon CE, 1948]. These
the different interpretations of entropy are equivalent, and the choice to apply one or the other is straightforward
depends on the question under investigation. The main difficulty with which encountered when applying entropy in medical research, consists in
development of a method capable of calculating the entropy associated with the patient's condition
[Melis M et al, 2019].
The growing awareness that many real systems exhibit complex dynamics that are difficult to quantify has sparked great interest in the development of frequency and time series analysis tools and approaches to characterize these systems. In this context, the use of tools taken from information theory has become extremely popular to assess the degree of complexity of physical, biological, physiological, social and econometric
systems A variety of metrics have been proposed, rooted in the concept of entropy and implemented according to a number of assessment approaches [Xiong W et al, 2017].
The central concept for deriving a measure of entropy is definition
information content of Shannon random variable [Shannon CE, 1948].
The classical information entropy of Shannon CE [1948] is:
H(X) = -ÿ Pr(x)log2[Pr(x)]. xÿX
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The term X is a random variable with n possible outcomes, while pi=Pr(x), for i=1,2,3,...n, is the probability distribution of Pr over a finite set. Shannon entropy states that under ergodic conditions, if we know the value of Pr(x), we can obtain the value of H(X). In other words, H(X) is a probability density function that defines the general probability distribution Pr. Thus, if we modify Pr, we get a different entropy value on the Shannon curve. Hence, the change in probabilities usually occurs,
when we have prior information.
According to this definition, the information content will be low for very of probable outcomes of the observed random variable and high for unlikely results. Entropy can be defined as the expected value
Shannon's informative content. Entropy quantifies information as
average uncertainty about the outcomes of a variable: if all observations of variable takes the same value, there is no uncertainty, but entropy
equal to zero; if, on the contrary, the variable takes different values, all with the same value probability of occurrence, the entropy is maximum and reflects the maximum
uncertainty The concept of entropy defined above is based on the main works of Shannon CE [1948; 1997], performed in the field of communication theory.
The relevant measure has been extended to define many alternative information measures, such as the Rényi entropy A [1966] and the Tsallis C entropy [1988], for which the Shannon entropy CE is a limiting case possessing all the desired properties of an information measure. Minimum Entropy Rényi A [1961]
Hÿ(X) = (1-ÿ)
N
-1 ÿ pi ÿ (x),
i=1
xÿX
where 0ÿÿ<ÿ and pi ÿ (x) is the probability of event x. Reny entropy approaches Shannon entropy as ÿ approaches 1. In other words, Shannon entropy is a case of Reny entropy in which ÿ=1.
Tozzi A et al [2018] make their case for why it should be preferred Reny entropy for evaluating brain activity. Yes, Shannon entropy
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displays the discrete probabilities of an event through a single curve. However, in many physical and biological cases, diversity cannot be reduced to a single index of information, since all its aspects cannot be reflected in a single statistic. The Reny entropy, in turn, reflects the discrete probabilities of an event through many curves. Therefore, in contrast to Shannon entropy, Reny entropy allows to describe the state of the system not only at a specific moment,
but also when its trend changes over time.
Rahman MA et al [2020] also showed that their proposed approach,
based on Reny minentropy, outperforms conventional methods (entropy
Shannon and methods of mutual information) classification of multiple EEGs signals
Nevertheless, in the lion's share of medical and biological research
it is the Shannon entropy that is used, however, in different modifications
including approximate entropy [Pincus SM, 1991], sample entropy [Richman
JS, Moorman JR, 2000], corrected conditional entropy [Porta A et al, 1998],
fuzzy entropy [Chen W et al, 2007], compression entropy [Truebner S et al,
2006], entropy of permutations [Bandt C, Pompe B, 2002; Müller A et al, 2013], distribution entropy [Li P et al, 2015], multiscale entropy [Costa M et al, 2002; Baumert M et al, 2012; Angelini L et al, 2007;. Costa M et al, 2005], self-entropy and information storage [Faes L et al, 2015; Gómez C et al, 2014].
These approaches turned out to be a less ambitious but more practical alternative to classical methods of analyzing nonlinear dynamical systems, such as correlation dimension, Lyapunov exponents, and nonlinear methods
forecasting [Farmer JD, Sidorowich JJ, 1987]. In fact, the popularity of metrics
entropy comes from their applicability to short and noisy processes with
by important stochastic components such as those describing
dynamic activity of real systems. These metrics were applied with great
success in numerous studies, including heart rate variability
[Porta A et al, 1998; Kurths J et al, 1995; Vikman S et al, 1999; Vigo DE et al, 2010;
Voss A et al, 2015; Wessel N et al, 2000], cardiovascular control [Porta A et
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et al., 2016; Porta A et al, 2015], cerebrovascular dynamics [Faes L et al, 2013;
Hornero R et al, 2005], cardiac arrhythmias [Alcaraz R, Rieta JJ, 2010], electromyography [Xie HB et al, 2010], electroencephalography [Abásolo D et al, 2006; Ferenets R et al, 2007; Bai Y et al, 2015], functional neuroimaging [Gómez C et al, 2014; Sokunbi MO, 2014; Wang Z et al, 2014], as well as immunity and receptor expression [Chung YR et al, 2017;
Laurinavicius A et
et al., 2012; 2016; Melis M et al, 2019; Plancoulaine B et al, 2018; Zilenaite D et al, 2020].
In the focus of the interests of the Truskavet Scientific School and ours in particular parameters of immunity, electroencephalogram and rhythm variability are found
hearts that reflect the state of the neuroendocrine-immune complex, study of its role in the mechanism of adaptogenic action on the body
ballroom attendants was recognized by an expert in 2015 as the main trend of Ukrainian balneology of the last decade [Portnychenko AG, 2015].
Of special interest are the parameters of gas discharge visualization, around of which the discussions regarding verification and relevance continue.
In the future, we will consider in more detail the data from the literature on the definition of entropy for assessing the state of immunity, electroencephalogram, heart rate variability and gas discharge visualization.
1.1. Entropy and immunity
According to Melis M et al [2019], the definition of entropy introduced by Claude Shannon in 1948 could even then be adapted to different disciplines,
including studying the complex underlying genetic mechanisms
development of the disease. Unfortunately, the understanding of genetics in those years was insufficient for
using entropy as a measure of disorder associated with genetic systems,
as Shannon himself probably hoped. And only in 2019 these authors adapted determination of Shannon entropy for the study of such complex genetic systems, such as human leukocyte antigens (HLA) and immunoglobulin-like receptors
killer cells (KIR), and their influence on immune response mechanisms.
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The authors proposed the following adaptation of the entropy determination method
Shannon. Here is her presentation.
If we have a set of possible events, the probability of occurrence of which is equal to f1,f2,...,fn, then the Shannon entropy , measuring the amount of uncertainty, associated with the result is given by:
n
S = -kÿ filogfi ,
i=1
where k is a positive constant depending on the choice of measurement units. In all in calculations, we set k = 100.
In the case of two possible events, with probabilities f (with 0ÿfÿ1) and q = 1ÿf,
the Shannon entropy becomes:
S = -k(flogf+qlogq).
The entropy S has a maximum for f=1/2, i.e. when the probability f that an event occurs is equal to the probability q that it did not occur. S disappears
only when f is equal to one or zero, that is, only when we are sure of the result; in all other cases, the entropy S is strictly positive.
The Shannon entropy S associated with HLA haplotypes or combinations of two KIR genes (KIR gene pairs) of a subject was obtained using the following equation:
n
S = -k/Nÿ[filogfi +(1-fi)log(1-fi)],
i=1
where N is the number of HLA haplotypes or KIR gene pairs for each subject, and f1,f2,
...,fn, are the frequencies of n different HLA haplotypes or KIR gene pairs, which observed in the control.
HLA entropy was estimated considering sixteen HLA-A haplotypes, - B, -C, -DR, which occurred in each subject. Therefore, we set N = 16 in
Shannon entropy equations . The entropy of KIR was estimated by considering for each subject six possible pairs of KIR genes from a set of four
inhibitory KIR genes. Therefore, we set N = 6 in the Shannon entropy equation .
The absolute value of the SHLA or SKIR entropy depends on the investigated system and the selected reference level. The purpose of entropy calculation is to achieve
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comparison between different systems (eg, healthy controls and patients), so only the relative value of entropy is important. For this reason, it seems appropriate to consider the RHLA or RKIR ratio between the HLA or KIR entropy in each patient group and the average HLA or KIR entropy in the control group. Moreover, the authors analyzed the total entropy of the HLA and KIR systems by calculating the total entropy coefficient Rtot, which is determined by the average value of the corresponding HLA entropy coefficients
and KIR: Rtot = 0.5(RHLA + RKIR).
In this study , the entropy associated with the HLA and KIR systems compared between a cohort of healthy and a group of patients affected by diffuse sclerosis, the latter was stratified into patients with primary progressive
multiple sclerosis and patients with relapsing remitting multiple sclerosis sclerosis
At the initial stage of the analysis, the authors applied standard methods, i.e evaluated the HLA association with multiple sclerosis by comparing the frequency haplotypes and HLA alleles in patients and controls. The results
confirmed the association of multiple sclerosis with the extended haplotype HLA- A*30, - B*18, -C*05, -DR*03 (19.1% vs. 12.4%; Pc=0.023) and the well-known HLA susceptibility allele HLA-DR*03 (30.9% vs. 22.0%; Pc=9.2•10-4). However, these standard methods showed only minor differences in frequency between patients and controls. Overall, they provide insufficient information to reliably predict multiple sclerosis risk , especially in individuals who do not have the above genetic characteristics. Therefore, the innovative approach described here was further applied ,
based on entropy.
It was found that HLA entropy in patients who suffered from recurrent relapsing multiple sclerosis, was significantly higher than the entropy of HLA y controls (p=0.002). A similar finding was obtained for the KIR entropy, but with lower level of significance (p=0.043). In patients with primary progressive
no differences were observed in multiple sclerosis . When analyzing combined
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HLA and KIR systems total entropy was significantly higher in patients suffering from relapsing remitting multiple sclerosis (P=0.001) compared to controls. On the other hand, no significant differences were observed when comparing the total entropy of healthy people and patients suffering from primary progressive multiple sclerosis.
On the basis of HLA and KIR entropy , the authors established three degrees of risk for the development of multiple sclerosis based on three different intervals of total entropy coefficient . The method based on total entropy coefficients
HLA or KIR, made it possible to calculate the individual risk of the subject in relation to
development of multiple sclerosis, especially its relapsing remitting form option
Melis M et al [2019] showed that in addition to standard statistical methods used to assess immunogenetic parameters,
associated with immune-mediated disease, entropy analysis measures the state of global disorder derived from these parameters. Improved assessment
risk is especially important for family members of patients with multiple sclerosis. This
innovative approach, according to its authors, can become a useful additional
tool for assessing the risk of immune-mediated disorders, such as type 1 diabetes,
Hashimoto's thyroiditis, celiac disease, psoriasis, rheumatoid
arthritis and systemic lupus erythematosus.
Another object of research using entropy calculation is intratumor heterogeneity ( ITH), i.e
phenotypic differences between cancer cells within the same
tumors This affects important behavioral features, including metastatic potential, angiogenesis, migration, evasion of antitumor immunity, and activation of metabolic pathways [Fidler IJ, 1978; Yap TA, 2012]. Such intratumoral diversity leads to therapeutic resistance and is a major obstacle to treatment [Turner NC, Reis-Filho JS, 2012]. Although the importance of intratumoral heterogeneity in tumors is evident, difficulties in measuring its extent and interpreting its impact on clinical
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results have limited its use in clinical settings. Park SJ et al. [2010] investigated the cellular and genetic heterogeneity of breast cancer using two diversity indices: the Shannon index and the Simpson index, but their prognostic value was not evaluated.
Therefore, Chung YR et al [2017] decided to investigate for the first time
intratumoral genetic heterogeneity using the c-MYC gene,
since the c-MYC locus (8q24) is in one of the most unstable
chromosomal regions and shows increased copy number in all subtypes of breast cancer. c-MYC amplification , defined as a mean c-MYC copy number of 6.0 or higher, was detected in 22 (7.8%) of 283 invasive cancer samples
mammary gland. Increase in c-MYC gene copy number , defined as no
copies greater than or equal to three were detected in 115 cases (40.6%). Regional heterogeneity was observed in 32 cases (11.3%), and genetic heterogeneity - in 77 cases (27.2%). Then the authors calculated the Shannon index H of the number of copies of the c-MYC gene according to the modified formula: H = -ÿpilnpi, where
pi equals the frequency of a tumor cell with the same number of gene copies.
A preliminary analysis showed that the Simpson index correlated with the index
Shannon is very tight (r=0.966), so in the future the authors limited themselves to the study correlations between the Shannon index for gene copy number variations and clin pathological features of breast cancer and evaluated it
prognostic value in breast cancer.
It was found that the Shannon index, which ranged from 0.071 to 2.827, with
with a median of 1.034, was closely correlated with mean c-MYC copy number (r=0.849). When the authors analyzed the distribution of the Shannon index for heterogeneity and of c-MYC amplification , its average value was greater in tumors with
genetic heterogeneity than those who had neither heterogeneity nor amplification, but was lower than in tumors with amplification but without
heterogeneity The authors further assessed the relationship between copy number variation c
MYC and clinical and pathological features. Amplification of c-MYC was associated with
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unfavorable pathological features: high histological level, excessive expression of p53, high proliferation index Ki-67 and negative status of hormone receptors. c-MYC copy number gain was also associated with all clinicopathological features associated with c-MYC amplification in addition to HER2 amplification. When the authors divided the samples into high and low index groups using the median
value, a high Shannon index was associated with a high histological degree, lymphovascular invasion, overexpression of p53, high
Ki-67 index, negative status of hormone receptors and
HER2 amplification . The Shannon index also differed significantly depending on breast cancer subtype: it was significantly higher in Lumen B cases,
HER2+ and triple-negative subtypes than the most favorable subtype
Lumen A. In addition, a high Shannon index showed a significant association with
poor disease-free survival.
Chung YR et al [2017] reasonably believe that the data obtained by them suggest that the Shannon diversity index is a measure
intratumoral heterogeneity and can be used as a prognostic factor in breast cancer.
Lithuanian authors [Laurinavicius A et al, 2012; 2015; Plancoulaine B et al, 2015] applied hexagonal tiling (HexT) methodology to improve digital immunohistochemistry in general and breast cancer in particular. With its help, the data of digital analysis of images of breast cancer samples stained with the Ki67 method were selected. Factor analysis of the set
data, including the total number of tumor cells, proliferative
activity of cancer tissue as assessed by the Ki67 labeling index and indicators textures, singled out 4 factors defined as entropy, proliferation,
bimodality and cellularity. Indicators of factors were further used in cluster analysis, delineating subcategories of heterogeneous tumors with by predominant entropy, bimodality, or both at different levels proliferative activity. The methodology also allowed visualization
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heterogeneity of the Ki67 labeling index in tumors, which reflects aspects of intratumoral
heterogeneity.
Zilenaite D et al [2020] applied the described methodology for assessing intratumoral heterogeneity (entropy) in relation to the expression of estrogen receptors (ER) and progesterone receptors (PR) and Ki67 to search for predictors of overall survival in
breast cancer patients. Multiple Cox regression analysis revealed three independent predictors of overall patient survival:
Haralik texture entropy with progesterone receptors, Ki67 bimodality Ashman D (calculated for the intratumoral distribution of ER, PR expression
and Ki67 in hexagonal grids) and the density of CD8+SATB1+ cells in the tumor fabric It is noteworthy that indicators of intratumoral heterogeneity
(entropies) of progesterone receptors and Ki67 were prognostically more more informative than indicators of their severity. In particular, a clear
non-linear relationship between the level of expression of progesterone receptors and its intratumoral heterogeneity, which revealed a nonlinear prognostic
the effect of progesterone receptor expression. To study the influence of nonlinear
the relationship between the severity of PR expression and its intratumoral heterogeneity for the prognostic stratification of patients was divided into three groups: low expression (<20%) and low entropy, moderate expression (20–80%) and high entropy and high expression (above 80%) and low entropy. Tumors with moderate PR expression (20– 80%) were associated with the best overall survival (OS) (91% OS probability at 143 months), followed by high (>80%) expression (71% OS) and low (< 20%) (63% OS).
Zilenaite D et al [2020] concluded that it is intratumoral
heterogeneity (entropy) of progesterone receptors, together with indicators
of the immune response (density of CD8+SATB1+ cells), exceeded the usual ones indicators of breast cancer immunohistochemistry and clinical and pathological indicators (expression of Ki67, HIF1ÿ, estrogen receptors, histological
stage, stage T, condition of lymph nodes) as predictors of general survival rate
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As a summary of the subsection, we cite the statement of Melis M et al [2019] that
entropy can be adapted to study complex genetic systems and
multifactorial diseases, but this requires standard analysis methods to identify the genetic parameters needed to build an entropy-based algorithm. The implementation of entropy in clinical practice can provide valuable support to currently existing methods of risk assessment of immune-mediated diseases. The entropy specificity is probably
will grow in proportion to the number of analyzed immunogenetic data. You can express the assumption that the more complex and data-rich systems are, the the more entropic picture of the disease will be clear and concrete. ideally,
joint efforts of researchers can contribute to the construction of exquisite entropy
models for many immune-mediated disorders. Refined samples entropies can improve discrimination between immune-mediated disorders that share etiological similarities and genetic factors
receptivity Therefore, the study of the effectiveness of the entropy approach at pathologies with a strong immune component are very relevant.
1.2. Entropy and electroencephalogram
Analysis of brain activity is an important area of research in the field of human neurology. Moreover, a subcategory in this field is the classification of brain activity in terms of various brain disorders. Since the electroencephalography (EEG) signal is actually a non-linear time series, the use of methods to study its non-linear structure is quite
important
To determine the EEG entropy, the developed Pincus SM [Pincus
SM, 1991; 1995; Pincus SM, Goldberger AL, 1994] statistics that are quantitative determines the regularity and complexity of time series - approximate entropy (Approximate Entropy). It is shown that the approximate entropy can classify complex systems, including both deterministic chaotic and stochastic ones processes, including data on EEG, heart rate, and secretion
endocrine hormones. The method studies time series for similar epochs: more
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frequent and similar epochs lead to a decrease in the approximate entropy values. Informally, given N points, the family of approximate entropy statistics (m, r, N) is approximately equal to the negative mean natural logarithm of the conditional probability that two sequences that are similar for m points remain similar, that is, within tolerance r, at the next point. Thus, a low value of approximate entropy reflects a high degree of regularity.
The approximate entropy (ApEn) is calculated as follows [Pincus SM,
1995]. Time series of N data points are divided into m sub-segments according to order of data points, and a total of (N-m+1) fragments can be obtained
sequence Sequence fragments are denoted by the sign X(i), where 1<i<N-M+1. Then the distance dXm(i,j) between the current sequence of the i-th is calculated subsegment and other subsegments X(j), where 1<j<N-M+1 and jÿi. When dXm(i,j)<r (r is a threshold, 0.2 times the standard deviation is used here
sequence), it is assumed that X(i) and X(j) are similar. Calculate the share of others sequences similar to the current sequence of the i-th subsegment:
Cmi(r)=num(dXm(i,j)<r)/(N-m+1)
The above analysis is performed on all sub-segments to obtain the average value of the sequence similarity coefficient on their scale
data:
N-m+1
ÿm(r) = ÿ log(Cmi(r))/(N-m+1)
i=1
Similarly, building the sequence m+1, repeat the above steps, calculate ÿm+1(r), and the approximate entropy becomes:
ApEn = ÿm(r) – ÿm+1(r)
Currently, to quantify the complexity of the time series
proposed a wide range of entropy measurements and estimates [Xiong W et al, 2017]. These metrics include Sample Entropy
[Richman JS, Moorman JR, 2000; Lake DE, Moorman JR, 2011], fuzzy entropy
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(Fuzzy Entropy) [Chen W et al, 2007], corrected conditional entropy [Porta A et al, 1998], permutational entropy [Bandt C, Pompe B, 2002], etc.
One of the most important entropy metrics is sample entropy [Richman JS, Moorman JR, 2000]. To briefly
describe the sample entropy, when m, r , and N are denoted by the pattern length, the normalized threshold (normalized by the standard deviation of the original sequence), and the signal length, respectively, assume that Bm(r) is
the probability that two sequences will match at m points, and Am(r) is the probability that two sequences match at m+1 points. Spark (match) is considered within tolerance, and independent sparks
are excluded The parameter is estimated according to statistics:
Sample Entropy(m,r,N) = - ln[Am(r)/Bm(r)].
The sample entropy will be zero when Am(r) = Bm(r). Entropy of the sample is not defined when the conditional probability Am(r) or Bm(r) = 0. At least
the conditional probability value that can be calculated is 1/2[(Nm)(Nm-1)],
which results in a maximum value for the sample entropy equal to ln(Nm) + ln(Nm-1) - ln(2). The sample
entropy is the most used recently because it has several advantages, one of which is that its values are stable with the size of the time series. Sample entropy was proposed because the first kernel-based conditional entropy introduced, the approximate entropy, was typically skewed [Xiong W et al, 2017]. One of the most recent and important papers on the calculation of conditional entropy is that of Xiong W et al [2017], in which
the dependence of various entropies on specific parameters was analyzed estimator, as well as the effects of three types of non-stationarity due to artifacts, which are most often found in real data (trends, random peaks and
changes in local dispersion). In this paper, they present for the first time a quant assessment of the impact on entropy indicators of trends originating from the internal dynamics of systems with multifractal properties.
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The literature on EEG entropy is very extensive, so we will limit ourselves to a mention
the latest publications.
Cuesta-Frau D et al [2017] conducted a comparative evaluation of the performance of different entropy metrics: approximate, fuzzy and sample entropy
- in the context of classifying artifacts of real EEGs, such as white noise, as well as muscle, heart and eye artifacts. The results showed that the qualitative
behavior of the two datasets is similar, with the approximate and fuzzy entropies showing the best results, instead the low efficiency achieved by approx
entropy, suggests that this metric should not be used in these
contexts.
Liu Q et al [2017] applied multiscale entropy to create EEG artifact filtering algorithm. The proposed method works
more efficient than the commonly used wavelet noise method. This is research provides a fully adaptive and automated EEG filter to
measure the depth of anesthesia with greater accuracy and thus reduce
the risk associated with the maintenance of anesthetics.
Liu M. et al [2019] to find out whether there is temporal and spatial variability temporal synchrony as a valid and reliable marker of spatiotemporal variability used optical voltage imaging in anesthetized and awake mice to monitor cortical voltage activity with both high spatial and temporal resolution to investigate functional connectivity as a measure of spatiotemporal synchronization, multiscale entropy as a measure of temporal variability and their connection with
regional entropy as a measure of spatio-temporal variability. The authors observed that there is a multiscale entropy model in the cortical space can largely explain the regional entropy pattern at small and
large time scales with high positive and negative correlation
respectively, while the picture of functional connectivity is strongly negative is related to the picture of regional entropy. Time course of functional
connectivity and multi-scale entropy clearly followed the course of the regional
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entropy Functional magnetic resonance imaging (fMRI) and EEG data modeled by reducing the spatiotemporal resolution of the voltage imaging data or accounting for hemodynamics yielded measures of multiscale entropy and functional connectivity that contained regional entropy information from the high-resolution voltage imaging data. resolution. This proves that multiscale entropy and functional connectivity can be effective measures to fix
of spatio-temporal variability with limited visualization methods, which applied to people. According to the authors, their results confirm
the notion that functional connectivity and multiscale entropy are effective biomarkers for the state of the brain, and provide perspective combining these two main domains in the analysis of human brain data.
Migliorelli C et al [2019] investigated changes in brain EEG connections during sleep in healthy subjects and compared them with slow-wave activity
and entropy. Four different connectivity metrics were calculated: coherence, synchronization probability, mutual information and phase lock value, focusing on their correlation with sleep depth. These metrics provide different
information and perspectives on functional connectivity. The averaged mutual information score appeared to be a more reliable connectivity metric for measuring sleep depth (correlations of 0.78 and 0.84 with slow-wave activity and entropy, respectively), conveying greater linear and nonlinear interdependence between brain regions, especially during slow-wave sleep.
sleep
Jun MR et al [2019] conducted a study on the effectiveness of the application
determination of the entropy of the phase lag (PLE) (diversity of the time pattern in the phase relationship between two EEG signals) to estimate depth sedation PLE values were found to be closely correlated with depth estimates
sedation according to the OAA/S scale (Spearman's ÿ=0.755), which coincides with the correlation
scale data with the generally recognized EEG indicator of sleep depth
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bispectral index™ (BIS) (Spearman's ÿ=0.788). The values of the probability of prediction (Pk) were 0.731 and 0.718, respectively. Later, Park JH et al
[2020] obtained strikingly close results. Partial correlation coefficients between OAA/ S and PLE scores and between OAA/S and BIS scores were 0.778 and 0.846, respectively. During the entire period of anesthesia, PLE and BIS showed a significant positive correlation. The partial correlation coefficient before unconsciousness was 0.838 and 0.669 after unconsciousness.
The intraclass correlation between the two indices was 0.889 and 0.791 to and after losing consciousness, respectively. PLE exhibited strong and predicted correlation with both BIS and OAA/S scores. These results, together with preliminary data from Ki S et al [2019], suggest that PLE is
reliable for assessing the level of consciousness. Therefore, the entropy of the phase lag is a new and reliable system of consciousness control during sedation and general anesthesia (induced by propofol), which is comparable to the bispectral index.
Zhu L et al [2019] first presented a hybrid EEG processing method for differentiation of brain death and coma patients based on canonical
correlation analysis of spectral power density, features of complexity and synthesis of features for group analysis. The results showed three main differences in the EEG signal between the brain death and coma groups: slowing, increased complexity, and improved classification accuracy using feature fusion. Therefore, the relative density of the delta band power spectrum and the permutation entropy can be effectively considered as potential discriminating features for brain death and comatose patients.
Josefsson A et al [2019] showed that a functional network created on
based on non-linear measurement of the connection of beta-filtered EEG recordings, can be used for early diagnosis of mild cognitive disorders.
Helakari H et al [2019] used spectral entropy as a metric
to study whether the irregularity of the spectral signal in the range frequencies of brain signals based on synchronous multimodal brain signals to give new insights into the neural basis of epileptiform activity.
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A significantly higher magnetic resonance encephalography spectral entropy index was found in the right thalamus, transverse gyrus, inferior frontal gyrus, and frontal pole in patients with epilepsy compared to healthy controls. Epileptic patients also showed a significant increase in entropy of direct current electroencephalography in the full-band range (0-5 Hz) in the fronto-central and parietal-occipital regions and in the very low- frequency range (0.009-0.08 Hz) in the parietal-central region , what
was accompanied by a significant decrease in entropy in the high-frequency range (0.12-0.4 Hz) in the frontal pole (Fp) and parietal-occipital (O2, Oz) regions.
According to the authors, higher entropy indicators in patients with epilepsy in anterior transverse cingulate gyrus together can be associated with
by a change in parasympathetic function and respiratory pulsations of the brain, and a higher level entropy in the thalamus is associated with anatomical and functional disorders connections in epilepsy.
Namazi H et al [2020] analyzed the variations of fractal dynamics and
of the approximate entropy of the EEG signals between the four data sets that were collected from healthy subjects with eyes open and closed, and epilepsy patients without seizures and with seizures. The obtained results showed that the EEG signal during the seizure has the highest complexity, and the EEG signal during the non-seizure interval has the lowest complexity. The obtained results in the case of approximate entropy confirmed the results of the fractal analysis, which shows the effectiveness of the fractal theory for studying the nonlinear structure of the EEG signal under different conditions.
In another study, Namazi H et al [2019] compared approx
EEG entropy between healthy and schizophrenic adolescents. Result entropy analysis showed that the EEG signal in healthy subjects is less random (more complex) compared to the EEG signal in schizophrenics.
Racz FS et al [2020] analyzed the resting EEG recordings of the groups schizophrenic patients and healthy age- and sex-matched controls. The authors reconstructed dynamic functional networks from the delta band (0.5-4 Hz)
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neural activity and recorded their spatio-temporal dynamics in different topological dimensions of the global network. The obtained time series of network measurements were subjected to dynamic analysis, including multifractal analysis and entropy estimation. As a result, a stronger connection of the delta band was found, as well as an increased dispersion of dynamic functional connectivity in patients with schizophrenia. Entropy analysis indicated their reduced temporal complexity of dynamic functional connectivity. The results obtained by the authors indicate
that multifractal properties and entropy are powerful markers
of altered neuronal dynamics in patients with schizophrenia and carry significant the potential not only for a better understanding of its pathophysiology, but also for improvement of diagnostics. The proposed system, according to the authors, easily used for neuropsychiatric disorders other than
schizophrenia
In a study by Xiang J et al [2019] to study the complexity of the brain activities of patients with schizophrenia under baseline and paradigm conditions of the auditory paired stimulus (states S1 and S2), fuzzy entropy was used.
Generally, schizophrenic patients showed significantly higher entropy values in the frontal and occipital regions of interest. Compared with the baseline condition, the normalized entropy values of the normal controls were greatly reduced in the S1 condition and showed less variance in the S2 condition. Schizophrenic patients showed less reduction in normalized values in the S1 condition. Moreover, schizophrenic patients showed a significant decrease in entropy suppression coefficients, which is explained by its
higher values in state S1. Based on these results, the authors hypothesized that that entropy modulation during the process of sensory information and sensory test gate was evident in normal control groups and significantly deficient
in patients with schizophrenia. In addition, the entropy values measured in the frontal areas of interest, were positively correlated with positive indicators of the scale positive and negative syndrome (PANSS), indicating that frontal entropy
was a potential indicator in the assessment of clinical symptoms. However, there were
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found negative relationships between entropy values of the occipital region of interest and total PANSS scores, which probably reflects a compensatory effect in visual processing. Thus, the authors' findings provided a deeper understanding of deficits in sensory information processing and the sensory gating test that contribute to cognitive deficits and symptoms in schizophrenia patients.
A study by Gao Y et al [2019] presents a multiscale entropy measurement of permutation transfer that was used to
communication characteristics between EEG signals measured from bilateral motor and sensory areas. Patients after stroke and healthy volunteers participated in the handshake task with different levels of contraction.
Entropy values were calculated and analyzed in different ranges frequencies for all subjects. The results showed that for healthy controls the connection between motor and sensory areas was bidirectional and, as usually strongest in the beta range. In particular, the dominant hand had
a larger beta entropy range was found and the coupling strength decreased with
by increasing the contraction force. In addition, the connection between motor and sensory areas of stroke revealed weaker beta bands of entropy than in healthy controls. The obtained data indicate that the level of entropy is capable of quantitatively characterizing the properties of communication between many areas of the brain, providing a promising approach to the study of the main mechanisms of functional restoration of movements.
Entropy calculations are used to analyze changes in EEG and electromyogram synchronous coupling caused by various factors [Li S et al, 2019; Li M
et al., 2019].
Chen X et al [2019] proposed a new method called transmission spectral entropy for the study of functional cortical
of muscle coupling by analyzing the correlation between EEG and EMG signals during stationary force output. Analysis of experimental data showed
that bands ÿ1 (15-25 Hz) and ÿ2 (25-35 Hz) were visible in the cortical
muscle coupling both for EEG to EMG and from EMG to EEG directions. Except
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moreover, the statistical analysis of the significant area showed that the connection in the EEG- EMG direction was higher in the ÿ1 and ÿ2 bands than in the EMG-EEG direction, and the connection in the ÿ1 range (35-45 Hz) in the EMG-EEG direction was higher than in the opposite.
Zhao J et al [2019] showed that the EEG complexity of children with autism was lower than that of normal controls. Among the four measured entropies, the wavelet entropy showed the best classification performance, compared to the approximate, permutation and sample entropy. The results
the classifications differ in different regions, and the frontal region performed the best Indexes. After selecting the features, six features and degree were filtered classification accuracy is increased to 84.55%, which can be convincing for promoting early diagnosis of autism.
Hadoush H et al [2019] explored features and patterns
of multiscale entropy in children with mild and severe course of the disorder of the autism spectrum using a high-energy 64-channel EEG system.
Mean entropy values in children with a mild course were higher than in children with a severe course, in the right frontal (0.37 vs. 0.22), right parietal (0.31
vs. 0.13), left parietal (0.37 vs. 0.17) and central (0.36 vs. 0.21) areas of the cortex. In addition, children with a mild course showed a clear and more pronounced increase in sample entropy values compared to an increase in scale factor values than children with a severe course. The obtained data showed different features, values and topographic indicators of brain complexity (estimated by entropy) in children with a mild course of autism spectrum disorder compared
to children with a severe course. The authors believe that
multiscale entropy can serve as a sensitive method to det level of severity of this disease.
Li X et al [2019] based on the author's algorithm analyzed 19-
channel EEG signals of children with autism spectrum disorder and healthy children.
The results showed that the weighted multiple multiscale entropy
of healthy children was slightly higher than that of patients, with the exception of channel Fp2, and
numerical differences of channels F3, F7, F8, C3 and P3 were statistically significant.
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By classifying the weighted multiple multiscale entropy of each brain region, the authors found that the accuracy of the anterior temporal lobe (F7, F8) was
the highest This indicates that the anterior temporal lobe can
to be used as a sensitive area of the brain to assess brain function in children with
autism spectrum disorder.
Candra H et al [2017] applied a combination of wavelet entropy and mean wavelet coefficient as a potential EEG emotion feature for
classification of valence and arousal emotions. Chen DW et al [2019]
introduced an innovative method of obtaining reliable distinguishing features from EEG signals of emotions. This feature extraction method combines differential entropy
with linear discriminant analysis that can be applied for extraction of emotional EEG signals.
Keshmiri S [2020] conducted a comparison of multiscale and permutational entropy of EEG recordings of people who watched short video clips with negative, neutral and positive content. First, the author found a significant
anticorrelation between two entropy metrics, and what such an anticorrelation is stronger for negative rather than positive or neutral effects of video clips. Second, he found that multiscale entropy significantly distinguished these three affective states, whereas the use of permutational entropy did not guarantee such significant differences. These results highlight the level of association between brain entropy in response to affective stimuli, on the one hand, and its quantification in terms of various metrics, on the other hand. This, in turn, makes it possible to draw more reasonable conclusions about
the usefulness of various metrics for the study and analysis of brain variability signal in naturalistic scenarios.
1.3. Entropy and heart rate variability
Heart rate variability (HRV), as fluctuations in time between successive heart contractions, is a reliable reflection of many
physiological factors that modulate heart rhythm in healthy conditions, as well as changes in these factors associated with pathological conditions [Kleiger RE et al,
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2005]. It is widely accepted that the assessment of HRV on time scales ranging from seconds to several minutes allows us to indirectly investigate the short-term mechanisms underlying cardiovascular control [Akselrod S et al, 1981; Malliani A et al, 1994; Cohen MA, Taylor JA, 2002]. HRV, obtained by measuring changes in the
duration of the RR interval of the electrocardiogram (ECG), is the result of a combination of various physiological
control systems that operate on different temporal scales and allow
adapt the functioning of the body to physical, environmental and other changes. Such oscillations were presented as superimposition of rhythms that contribute neuroautonomic modulation of the heart rhythm in healthy conditions and changes
a wide range of diseases.
Traditional approaches to measuring the complexity of biological signals do not take into account the multiple time scales inherent in such time series. These algorithms have given conflicting results when applied to real sets
data obtained in states of health and disease.
In fact, there is a consensus among the scientific community that long-term RR interval time series are nonlinear and multifractal, and the behavior of the HRV scale changes with aging, during exercise, and in pathological conditions [Huikuri HV et al, 2000; Bernaola-Galván PA et al, 2017; Gómez-Extremera M et al, 2018; Faes L et al, 2019]. It is also widely accepted that the assessment of HRV on different time scales made it possible to give a completely satisfactory explanation of the short-term mechanisms underlying cardiovascular
control [Malliani A et al, 1991; Cohen MA, Taylor JA, 2002; Xiong W et al, 2017]. Long-term measurement makes it easy to assess the impact on HRV factors of everyday life, such as physical exercise. It was widely reported about HRV changes caused by low or intense physical activity
[Taylor KA et al, 2017; Weippert M et al, 2015]. Cute during exercises
the system dominates and the pendulum oscillates around this level, producing anti- correlated behavior on short time scales. However, since
the level of engagement changes over time, and as the pendulum follows these changes, the oscillations increase on intermediate time scales, causing a shift to more correlated behavior [Karasik R et al, 2002].
Tachograms are non-linear time series, highly heterogeneous and non-stationary, they oscillate in a complex manner, assuming that different parts of the signal have different scaling properties, so non-linear methods can better capture changes in
HRV that cannot be captured by linear
methods [Shekatkar SM et al, 2017]. For the analysis of these time series, it was used many non-linear methodologies, for example, decentralized
oscillation analysis [Peng CK et al, 1995], time irreversibility [Porta A et al, 2008; Visnovcova Z et al, 2014], fractal dimension [Eke A et al, 2002],
multifractal spectra [Goldberger AL et al, 2002; Aguilar-Molina AM et al, 2019].
Costa M et al, [2005] detailed the framework and implementation of the method multiscale entropy. They expanded and developed the previous ones
findings showing its suitability for human heart rate fluctuations in
physiological and pathological conditions. The method consistently shows loss of complexity with aging, with unstable cardiac arrhythmia (atrial fibrillation), and with life-threatening syndrome (congestive heart failure). Furthermore, these different conditions have different profiles of the multiscale entropy curve, suggesting diagnostic uses. The results support the general "loss of complexity" theory of aging and disease. By the way, the authors also applied the method for the analysis of coding and
non-coding DNA sequences and found that the latter have a higher
multi-scale entropy, which is consistent with the new idea that the so-called "junk DNA" sequences contain important biological information.
A viable and widely used research approach
of these short-term dynamics and their structural complexity is the use methods based on entropy [Peng CK et al, 1995; Shekatkar SM et al, 2017; Shi B et al, 2017].
The entropy of a dynamical system measures the information contained in its current state [Xiong W et al, 2017], higher entropy values indicate a more complex signal and lower
Xiong W et al. [2017] considered the study of human heart rate fluctuations in various physiological and pathological conditions, and their results have been proven advantages and pitfalls of entropy metrics and estimates, and provided guidance and recommendations for their optimal use in the study of real
time series. When analyzing the HRV series, when measuring entropy applied correctly, they can characterize changes of certain types
of the cardiac system, which are associated with various physiological and clinical conditions.
The main conclusion of the authors regarding entropy is that it was
was significantly lower in patients with chronic heart failure (CHF) than in healthy subjects both during sleep and during wakefulness, and was higher during sleep than during wakefulness in both groups. Moving on to the analysis of measures of dynamic HRV complexity, the first main conclusion is a significant increase in conditional entropy and a decrease in the amount of information observed during the transition from wakefulness to sleep in healthy subjects. The second main result is significantly higher conditional entropy and less measured information in CHF patients than in healthy subjects in most physiological conditions
Xiong W et al. [2017] found that the correct interpretation of the behavior measurement of entropy requires a clear understanding of the properties of the chosen specific measure and estimator and adequate choice of preprocessing,
applied to the measured signals. This is due to the fact that when the methods entropy is applied directly to the original HRV signals in the data
there may be factors such as long-range trends or correlations that differ
affect entropy measurements and estimates, and therefore can lead to conflicting results and make interpretation difficult.
In a recently published paper by Solís-Montufar EE [2020], several entropy metrics were used as a HRV measure: Sample Entropy, Approximate Entropy, and Fuzzy Entropy. Tachograms were obtained during a cardiac stress test consisting of a period of rest followed by a period of moderate exercise. Subjects were grouped by physical activity according to the data
IPAQ questionnaire. Entropy measures for each group showed that for
of sedentary subjects, the values are high at rest and noticeably decrease at moderate physical exertion. This happens to both young people and adults
middle age These results are highly reproducible. In the case of subjects who exercise regularly, an increase in entropy is observed, or they, as
as a rule, retain the value of entropy that they had in a state of rest. Exist correlation between a person's physical condition with an increase or decrease entropy during moderate exercise compared to entropy at rest.
The authors also noticed that entropy during long tests of physical
activity tends to decrease as fatigue accumulates, but this decrease is small compared to the change that occurs during the transition from rest to physical activity. The results showed that there is a statistically significant difference between the mean entropies for patients with chronic heart failure (CHF) and healthy patients, this behavior is observed for Sample Entropy (0.6567±0.2923 vs 0.8398±0.2039; p< 10 -3), Approximate Entropy (0.8067±0.2602 vs 0.9967±0.1851; p=10-4) and Fuzzy Entropy (0.3273±0.0938 vs 0.3995±0.0823; p <10-3). A decrease in entropy associated with CHF was also observed
other authors [Martinis M et al, 2004; Goya-Esteban R et al, 2012].
The model of neurovisceral integration [Thayer GF, Lane RD, 2009] suggests a neural network that connects heart rhythm activity and cognitive
Indexes. This model assumes that the central nervous system and the ANS are interconnected,
so information flows bidirectionally [Smith R et al, 2017]. Very heavy
evidence suggests that prefrontal cortical activity is involved in the modulation
vagal efferent input to the heart, and HRV is an indicator of cardiac activity associated with cognitive flexibility in tasks related to attention, working memory, and inhibitory control [Hansen AL et al, 2009]. That's not it
less, the relationship between HRV and cognitive indicators is determined type of task and is mediated by the functional variability of brain connectivity.
Relationships between HRV and endogenous dynamics of brain regions involved in autonomic control and emotional
regulation during the state of rest. These studies showed that high and
low-frequency components of HRV are closely related to functional connectivity [Chang C et al, 2013; Jennings JR et al, 2016; Sakaki M et al, 2016]. However, these studies did not consider the relationship between the variability of functional connectivity and HRV or whether both factors can predict outcomes
cognitive tasks.
Therefore, Alba G et al [2019] investigated the relationship between variability functional connectivity of EEG and HRV at rest and later
cognitive test indicators. The procedure used to
study of the individual functional variability of the connection in the resting state (RSVFC), was as follows. First, coherence was calculated as an index of functional connectivity (FC) in each frequency range (ÿ, ÿ, ÿ, ÿ). This index measures the linear correlation between two EEG signals, x(t) and y(t), as a function of frequency, f. Thus, coherence
(C) is the ratio of the transverse power spectral density Sxy(f) between both signals and their separate power spectral densities, Sxx(f) and Syy(f): Chÿy(f) = Shÿy(f)/[Sÿÿ (f)Syy(f)]0.5.
Entropy of the sample FC (SampEn-FC) allows to obtain the variability of the bond in time and interdependence between pairs of nodes (electrodes) in brain networks.
SampEn-FCm values in the delta range and alpha range showed significant correlations with LF, SDNN and the number of errors in the positive network.
Theta-band SampEn-FCm was correlated with RMSSD, while the number of errors and
SampEn-FCm beta bands were correlated with the number of errors in the negative network,
delta-band and theta-band SampEn-FCm values were correlated with SDNN and number of errors, beta-band values were correlated with HF, LF, SDNN and number of errors, and alpha-band values were only correlated with number
errors
Very convincing evidence suggests that HRV is an indicator of adaptation of the ANS to various psychological and behavioral situations [Thayer JF et al, 2010; Zahn D et
suggest that levels of variability in brain signals can predict cognitive flexibility in cognitive tasks. HRV and variability
of functional communication are related to cognitive efficiency. The results were given significant differences between high, medium and low error groups in LF and SDNN indices and RSVFC (positive and negative networks). Thus, participants with more errors on the CAMBIOS test showed greater LF and SDNN values, greater variability in the positive network, and less variability in the negative network across all frequency ranges. The authors summarize that levels of brain signal variability can predict cognitive flexibility in a cognitive task, whereas HRV can only predict cognitive flexibility when it is mediated by neuronal oscillations.
Dong X et al [2020] found that the average value of approximate and imprecise the entropy of sleep apnea signals is less than that of normal sleep signals. With the difference represented by the approximate entropy is larger than the fuzzy one
entropy Sympathetic VRS markers SD2 and LF of sleep apnea signals are larger, and SD1/SD2 and HF/LF indices are smaller than normal signals which means that
the sympathetic-vagal balance of sleep apnea signals shifts towards sympathotonia.
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In contrast, the vagal HRS markers SD1, HF, and RMSSD did not significantly differ between normal sleep signals and sleep apnea signals. Therefore, the authors concluded that low-frequency (sympathotonic) information may better reflect the occurrence of sleep apnea, indicating that complexity is lower because sympathetic arousal increases signal specificity and thus entropy value decreases.
Ma C et al [2020] reviewed existing approaches to application
determination of the entropy of RR intervals to distinguish between fibrillation and flutter atria from sinus rhythm and other arrhythmias. This is, first of all, a coefficient
entropy of the sample, which is able to encode the irregular character of short segments of the RR interval during atrial fibrillation and the average interval heartbeat, which adds additional independent information to the discrimination [Lake DE, Moorman JR, 2011; Richman JS, Moorman JR, 2000].
Based on sample entropy, Liu C et al [2011] developed a fuzzy method entropy, and then proposed normalized fuzzy entropy - a new measure entropy, suitable for detection of atrial fibrillation on the basis
short-term time series RR [Liu C et al, 2018]. The method uses
fuzzy function to determine vector similarity, replaces the probability estimate with a density estimate to approximate the entropy, uses a flexible distance threshold parameter, and adjusts the heart rate by subtracting the natural logarithm of the mean RR intervals.
Zhao L et al [2018] proposed an algorithm that combines the distance normalization function and the concept of atrial fibrillation detection based on
entropy and uses flexible threshold parameters. The method is characterized high values of sensitivity (92.77%), specificity (85.17%), accuracy
(87.10%), positive predictability (68.09%)
predictability (97.18%).
1.4. Entropy and gas discharge visualization
and
negative
As declared by Muehsam D et al [2015], the achievements of biophysics, biology, functional genomics, neurology, psychology, psychoneuroimmunology and
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other fields allow us to assume the existence of a subtle system of interactions of "biofields", which organizes biological processes from subatomic, atomic, molecular, cellular and organismal to interpersonal and cosmic levels. The interaction of biofields can cause the regulation of biochemical, cellular, and neurological processes through means related to electromagnetism, quantum fields, and possibly other means of modulating biological activity and information flow. Biofield paradigm, on
in contrast to the reductionist, chemistry-oriented point of view, emphasizes the information content of biological processes. It is believed that interactions of biofields are partially carried out with the help of low-energy or "thin"
processes, such as weak, non-thermal electromagnetic fields (or processes, potentially related to consciousness and non-locality. Interactions of biofields may also operate through better understood information processes,
detected in EEG and ECG data. Recent advances have led to the development of a wide range of therapeutic and diagnostic registration devices
biofields defined as physical instruments best understood with
from the point of view of the biofield paradigm. The authors offer a broad overview of biofield devices, with an emphasis on those devices for which there is strong, peer- reviewed evidence. A subset of these devices, such as those based on EEG and ECG, function by mechanisms that are well known and widely used in clinical settings. Other devices, such as gas discharge imaging and biophoton emission, according to the
authors of the review, work by mechanisms that are not fully understood and have unclear
clinical significance. The operating modes of the devices include EMF light, EMF heat, EMF-non-heat, electric current, vibration and sound, physical-mechanical, intentional and non-local, gas and plasma and others (mode of operation no clear). Devices play an important cultural and scientific role in
our society, and, according to the authors, it is quite likely that technology devices will become one of the most influential access points for the future biofield research and dissemination of biofield concepts. This field of research,
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emerging represents new fields of research that have many important implications for both basic science and clinical practice
of medicine
One of the directions of research that has provided a large amount of information about the activity of biofields is the study of biophoton emission (biophoton emission, BE), which is also called ultra-weak photon emission. BE is the spontaneous emission of light that comes from all living organisms,
including man [Popp FA, 1984]. In a systematic review by Ives JA et al [2014] reported on VE intercellular signaling, expressed
the assumption that such signaling by coherent biophotons can explain many regulatory functions, including detection of cell orientation,
biophotonic regulation of the release of neurotransmitters, respiratory activity leukocytes The authors of the review assume that the detection of radiation biophotons can be useful as a medical diagnostic approach and as
research tool.
An important example of the use of plasma in the science of biofields is visualization of gas discharges (GDV). Based on the effect of the Kirlian couple to stimulate the weak emission of photons is used
high-frequency high-voltage field with further application of modern
optics, electronics and computer processing for forming images of weak photon radiation. Since the 1930s, this technique has been called electrography, electrophotography, corona photography, bioelectrography, gas discharge imaging
discharge,
(GDV),
electrophoton images (EPI) and Kirlianography [Boyers DC, Tiller WA, 1973; Bankovskii NG et al, 1986; Korotkov KG, 2001-2014].
GDV/EPI methods are currently used on a diagnostic basis characteristics of fingertip images and often using their own
means of correlating these data with acupuncture systems or other means assessment of biological condition. Almost 1000 articles have been published (mostly in Russian) about GDV research and several hundred more in the West
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[reviews: Korotkov KG et al, 2010; Yakovleva E, Korotkov K, 2013; Korotkov KG, 2014].
The most recent study found significant differences in GDV scans in cancer patients compared to healthy participants, and after 6 weeks of treatment, including surgery, chemotherapy, and radiation, the trends in GDV profiles of healthy patients changed [Yakovleva EG et al,
2016].
So, the method of HVD, the essence of which is the registration of photoelectronic skin radiation induced by high-frequency electromagnetics
impulses, allows to evaluate the integrated psychosomatic state of the organism.
It is believed that a gas discharge image (GDR), taken without a filter, characterizes functional changes of the body, and removed with a filter - organic changes. First
the basic parameter of the DHW is the area of the DHW in the right, frontal and left projections, registered both with a polyethylene filter and without it. Second base
the parameter is the shape factor (the ratio of the square of the length of the external
outline
H.R.Z to
its area), which characterizes the measure
serrations/fractalities of the outer contour. Entropy is the third basic parameter of RHZ. The program also assesses the energy and asymmetry of the virtual chakras
[Korotkov KG, 2001; 2007; 2014; Korotkov KG et al, 2010]. It should
be noted that in the interpretation of Korotkov KG [2001], the entropy of the RHZ calculated by the formula: E =
-ÿg(f)log{g(f)}df, f ÿ [max, fmin]. An
important characteristic of the behavior of the function is the degree of repeatability of the properties of the function f(x) at a certain distance. In particular, the presence of duplicates elements of the HSR along its perimeter. In the opinion of the author, the value of GRV-entropy allows to introduce the classification of GDV-grams according to the degree of "imbalance". AND Namely: for very unbalanced RHV-grams (corresponding to an unstable state
homeokinesis) the function f(x) is random, which leads to a high value of entropy. Instead, the level, "calm" RHV-grams, which corresponds to a low level uncertainties of the function f(x), have smaller entropy values.
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Since the attitude towards the GDV method in the academic circles of Ukraine is ambiguous, our laboratory conducted research that proved its relevance [Popovych IL et al, 2010; Babelyuk VY, 2013; Babelyuk VY et al, 2017b; 2018].
In addition, the response of the parameters of the gastrointestinal tract to the regular use of Naftusya bioactive water [Gozhenko AI et al, 2016], electrical stimulation with the "VEB" device [Babelyuk NV, 2020; Babelyuk NV et al, 2015; 2016; 2016a;
2018; Babelyuk VYe et al, 2018; 2020; Kindzer BM et al, 2019], rehabilitation course according to Kozyavkin's method [Babelyuk VY et al, 2018a; Popovych IL et al, 2018; Kozyavkina OV et al, 2018; 2018a], and an immediate response has also been demonstrated on the kata of the kyokushin karate operator [Babelyuk VY et al, 2017a].
Our informative search of PubMed and PMC found only two works that deal with the entropy of RHZ.
Kushwah KK et al [2016] to compare the effects of cyclic meditation and control (lying in a meditation position) used three parameters:
the integral area of the RHZ on the right and on the left, which shows the general condition
the health of the examinees; the Korotkoff activation coefficient is the ratio between the areas of the RHZ taken without a filter and with a filter, which characterizes the level of stress; and the integral entropy on the right and left, showing the level of chaos. The results showed the following. The stress level in the meditation group decreased by 14.51% (p=0.005) and did not significantly decrease in the supine control group (-7.21%; p=0.15). The entropy parameter on the right side decreased by 3.76% (p=0.04) after the meditation session, but did not change significantly (by 0.92%) on the left side.
In contrast, a decrease was observed after passively lying in the meditation position
entropy by 8.36% (p<0.005) on the left side without significant changes (by 1.1%) on the right side The area of the GHZ also showed a significant increase of 18.48%
(p=0.05) and 30.56% (p=0.03) of the right and left sides in the meditation group, while in in the control group, an increase was observed only on the right side (by 23.29%; p=0.02) in the absence of a significant change (3.03%) on the left side.
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In a study with the participation of the author of the method, Korotkov KG [Yakovleva EG et al, 2016], it was found that in patients with colon cancer, the normalized area of the colon cancer was 22.7% (p<0.001) smaller than that of healthy controls, while the entropy was 10.8 times greater % (p<0.05). It is important that in patients with colon polyps, deviations from control levels are much less pronounced, although statistically significant: -9.9% (p<0.05) and +5.1% (p<0.05), respectively.
These intriguing data, according to Muehsam D et al [2015], with which we fully solidarity, suggest that device-based informatics for
measuring biofields such as GDV can be useful for deeper
understanding the state of the disease and directing the practitioners of their patients to the states,
having a higher level of health.
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SECTION 2
INFORMATIONAL EFFECTS OF BIOACTIVE OIL WATER
The experiment was performed on 58 white rats of both sexes of the Wistar line by weight 200-250 g, divided into 4 groups: conditionally intact, control, experimental and
reference Before the start of the watering course, the state of the vegetative state was assessed regulations For this, under light ether anesthesia, an ECG was recorded by injecting
needle electrodes under the skin of the paws, followed by the calculation of parameters variational cardiointervalogram: modes (Mo), mode amplitudes (AMo) and
variation range (ÿÿ) - correlates of the humoral channel of regulation, sympathetic and vagal tones, respectively [Baevsky R.M. and others, 1984].
The animals of the first group were practically not exposed to stressful influences, receiving only tap water at the rate of 2% of body weight once a day for seven days. The animals of the control group were subjected to water immersion stress (WAS) one day after the end of the course of watering with tap water according to the method of J. Nakamura et al. [1977] in our modification, which consists in reducing the duration of the stay of rats in cold water (to 20-21o C) from 8 to 4 hours. Rats of the experimental group received
instead of tap water, bioactive Naftusya water (St. 21N) according to the same method scheme, followed by VIS. In the reference group, rats were given tincture
ginseng (in "Lubnikhimpharm") in a dose of 0.5 ml/kg, dissolved in tap water water of the same volume as in the previous groups.
The next day after VIS, a peripheral blood sample was first taken (by
incision of the tip of the tail), in which the leukocytogram was counted, determined
parameters of phagocytosis and immunograms according to WHO level I and II tests [Lapovets
L.E. et al., 2002; Perederii V.G. and others, 1995; Khaitov R.M. et al., 1995]:
the relative content of the T-lymphocyte population in the blood according to the spontaneous test rosette formation with ram erythrocytes according to M. Jondal et al. [1972], their theophylline- resistant and theophylline-sensitive subpopulations (according to the test of sensitivity of rosette formation to theophylline according to S. Limatibul et al. [1978]), population B-
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lymphocytes - according to the test of complementary rosette formation with ram erythrocytes according to S. Bianco [1970]. Natural killers were identified as large granular lymphocytes. Natural killer activity (NKA) was assessed in the chicken erythrocyte lysis test with the addition of 10% fetal calf serum to the incubation medium (ratio of effector cells and target cells - 10:1, incubation time - 4 hours) according to S.M. Gordienko. [1983].
About the state of the phagocytic function of neutrophils (microphages) and monocytes (macrophages) were judged by the phagocytic index, microbial (phagocytic)
number and killing index in relation to Staphylococcus aureus, with calculation derived indicators: phagocytic capacity (the number of phagocytes per unit
volume of blood absorbed by microbes), microbial capacity (the number of microbes that able to absorb phagocytes contained in a unit of blood volume) and
bactericidal capacity (the number of microbes that neutrophils can neutralize, contained in a unit of blood volume).
After blood sampling, the ECG was recorded again.
The experiment was completed by decapitation of the animals in order to collect as much as possible of the possible amount of blood, which was divided into two test tubes to obtain serum and plasma by centrifugation. Hormonal status indicators were determined in biofluids: cortisol, thyroxine, and triiodothyronine (by the immunoenzymatic method, using reagent kits from CJSC Alkor Bio, RF), as well as metabolism.
Lipid metabolism was judged by the level of triacylglycerides in the plasma (metaperiodic acetylacetone colorimetric method), total cholesterol (direct method according to the Zlatkis-Zak reaction) and its distribution in
of the composition of ÿ-lipoproteins (the enzymatic method of Hiller G. [1987] after precipitation of pre-ÿ- and ÿ-lipoproteins using dextran sulfate/Mg2+) and
pre-ÿ- and ÿ-lipoproteins (turbidometric method of Burstein-Samai) [Horyachkovsky A.M., 1998].
The state of lipoperoxidation was assessed by the content of its products: dienes in the serum of conjugates (spectrophotometry of the heptane phase of lipid extract) [Gavrilov
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V.B. et al., 1983] and malondialdehyde (thiobarbituric acid test) [Andreeva L.I. et al., 1988], and the activity of antioxidant defense enzymes: catalase of serum and erythrocytes (according to the rate of decomposition of hydrogen peroxide) [Korolyuk M.A. et al., 1988], erythrocyte peroxidases (according to the rate of hydrogen peroxide oxidation by n- phenylenediamine) and erythrocyte superoxide dismutase (according to the degree of inhibition of nitroblue tetrazolium reduction in the presence of N-methylphenazonium metasulfate and NADH) [E.E. Dubinina etc.,
1988; E.V. Makarenko, 1988]. Electrolyte exchange was judged by the level of calcium plasma (by reaction with arsenazo III), phosphates (molybdate phosphate method), chloride (mercury-rhodanide method), potassium and sodium (flame method photometry), the latter electrolytes were also determined in erythrocytes.
The total antiprotease activity of plasma (ZAPA) was estimated by inhibition of N-benzoyl-L-arginine ethyl ester esterase by trypsin
[Veremeenko K.N. et al., 1988], activity of AlT, AsT, alkaline and acid phosphatase, creatine phosphokinase - by unified methods.
"Tecan" (Oesterreich), "Pointe-180" analyzers were used ("Scientific", USA), "Reflotron" ("Boehringer Mannheim", BRD) and flame spectrophotometer.
After decapitation, the spleen, thymus, and stomach were removed from the animals.
Immune organs were weighed and smears-imprints were made from them for counting the spleno- and thymocytogram [Bazarnova M.A., 1988]. The stomach was cut along the greater curvature, it was mounted on a gastroluminoscope, and erosive and ulcerative lesions were evaluated under a magnifying glass according to the method developed by
I.L. Popovich. [2007] scale using Harrington EC [1965] scale indices.
2.1. Entropy of morpho-functional immune systems
Calculation of entropy is acceptable, in particular, in relation to immuno-, leuko-, spleno- and thymocytograms, which are closed systems of various form elements. Informational analysis of cytograms allows to assess the state of morpho-functional adaptive and protective systems with the help of generalized indices,
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information about which is contained in their cytograms [Avtandylov H.G., 1990;
O.G. Yushkovska, 2001].
Informational analysis of cytograms was carried out using the Shannon equation S.E. [1948] to calculate the value H - the entropy of the set of probabilities (information entropy):
n
H=- ÿ pi•log2 pi,
i=1
where: and - the number of groups of form elements; p
- the fate of the ith group of elements in the cytogram.
Since the value of entropy depends on the number of constituent elements, to level this fact and enable the comparison of systems with different numbers of constituents, the relative entropy index (h) was calculated, that is, the fate
of the actual entropy (ÿ) in the maximum entropy (Hmax) of the system of n elements:
Hmax=log2n; h=H/Hmax.
To assess the degree of relative organization of the system, it is recommended to calculate
redundancy factor R:
R=(1-h)•100%.
The redundancy coefficient shows the fate of morpho-functional information,
excessive compared to optimal. This fate provides backup reliability,
increases the adaptive and compensatory capabilities of the system [Avtandylov H.G.,
1990]. However, in essence, R is the inverse measure of h, that is, it does not carry additional information.
In order to assess the effects, the informational impact indicator (IPV) was calculated according to
by the equation [H.G. Avtandylov, 1990]: IPV=1-NB/NI,
where NO - entropy in the intact group; HB
- entropy in influence groups.
Previous experience shows that the impact assessment is mathematically more correct
(effect) of this or that factor according to the sigmoidal deviation of the indicator from
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norm, that is, Euclid's distance [I.L. Popovich et al., 2003]. Therefore, the index d was used:
d=IPV/CV,
where CV is the coefficient of variation of the indicator in
the intact group. In the peripheral blood of rats of the intact group, the relative content of the theophylline-resistant T-lymphocyte subpopulation is in the range of 29÷30%, the theophylline-sensitive subpopulation - 12÷18%, B-lymphocyte population - 11÷16%, plasma cells - 0÷2%, natural killers - 0.5÷2.5%, and 0-lymphocytes -
34÷45%. Calculated level of relative entropy (h) of the immunocytogram varies between 0.72÷0.79, and the redundancy factor (R) - between 28÷21%.
As can be seen from the table. 2.1, under the influence of VIS, the average value of entropy practically does not change (+0.5%), as does the redundancy ratio (24.2% vs. 24.6% in intact), therefore the IPV and index d are quasi-zero.
Table 2.1
Entropy parameters of morpho-functional immune systems of rats of different exposure groups
|
Group |
Entropy n parameter |
Immunocytogram |
Leukocyte gram of blood of blood56 |
Splenocyte gram 8 |
Thymocyte gram 8 |
Immune system in general |
|
intact |
I 10 h±m |
0.75±0.01 |
0.68±0.02 |
0.53±0.02 |
0.60±0.02 |
0.88±0.01 |
|
(tap water) Control |
|
0 0 |
0 0 |
0 0 |
0 0 |
0 0 |
|
(water + VIS) |
IPV±md±m |
0.76±0.01 |
0.67±0.01 |
0.55±0.01 |
0.61±0.01 |
0.90±0.01 |
|
|
30 h±m |
-0.01±0.01 |
+0.02±0.01* |
-0.03±0.02 |
-0.02±0.02 |
-0.02±0.01* |
|
|
IPV±md±m |
-0.12±0.27 |
+0.32±0.14* |
-0.24±0.21 |
-0.24±0.20 |
-0.51±0.23* |
|
Experimental |
10 h±m |
0.73±0.01 |
0.66±0.01 |
0.57±0.01 |
0.63±0.02 |
0.93±0.01* |
|
(Naftusya + VIS) |
|
+0.03±0.02 |
+0.03±0.02 |
-0.07±0.03* |
-0.06±0.03 |
-0.05±0.01* |
Notes: 1. n - number of animals in the group; And - the number of elements of the system.
2. h - relative entropy; IPV - informative impact indicator; d - normalized IPV. 3.
Indicators that probably differ from intact ones are marked with *, from control ones - #.
Instead, against the background of preventive use, Naftusi causes stress
a tendency to decrease entropy by 2.9% relative to the intact (I) group and by 3.4%
- relative to the control (K) group and an increase in R to 26.8%, i.e. makes insignificant negentropic effect, which is +2.9% for IPV and +0.62ÿ for
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index d. In this regard, Naftusia is inferior to ginseng, which probably reduces h by 6.2% and 6.7% (relative to I and K, respectively), increases R to 29.3%, and therefore causes a significant negentropic effect: +6 .3% for IPV and
+1.36ÿ for d.
The elements of the blood leukocytogram of intact rats are in the following intervals: lymphocytes - 47÷57%, segmentonuclear neutrophils - 38÷31%, rod-nuclear neutrophils - 1÷3%, eosinophils - 3÷7%, monocytes - 4÷8%, yes
that the relative entropy ranges from 0.63÷0.73, and the redundancy coefficient - 37÷27%. Stress causes a tendency for h to decrease by 2.3% and R to increase from 31.8% to 33.4%, that is, it has a slight negentropic effect on
leukocytogram: +2.4% and +0.32ÿ for IPV and d, respectively. No Naftusya, no gin they do not significantly affect the described effects of VIS.
During histological examination of splenocytograms of intact animals
it was established that 63÷73% of cells are lymphocytes, 5÷12% are lymphoblasts, 1÷2% - plasma cells, 2÷3% - macrophages, localized mainly in the white zone pulp, reticulocytes - 2÷3%, located in the capsule of lymphatic follicles, and
also segmented neutrophils - 10÷15%, rod-shaped neutrophils -1÷3% and eosinophils - 1÷3%, which make up the basis of the red pulp of the spleen.
This distribution of splenocytogram elements is characterized by entropy within 0.47÷0.59 and R value within 53÷41%. Under the influence of stress, h increases by 2.8%, accordingly, R decreases from 46.7% to 45.1%, the proentropic effect is -2.7% and -0.24ÿ, but these changes are insignificant. Instead, the preventive use of Naftusi causes a natural increase in entropy by 7.3% in combination with a decrease in the redundancy factor to 42.7%, so that
the proentropic effect reaches -7.3% and -0.64ÿ. The effect of ginseng on considered
the parameters are uncertain.
In rats of the intact group, the cellularity of the main element of the thymocytogram - lymphocytes (by definition, T-population), compactly localized in the cortex
substance, varies in the range of 62÷70%, they are surrounded by macrophages (4÷7%), reticulocytes (2÷6%), epitheliocytes (6÷10%), as well as lymphoblasts (5÷10%),
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located mainly in the subcapsular zone. Another 3-7% of cells are fibroblasts, 2-4% are basophils and only 1% are Hassall's bodies.
The relative entropy is in the range of 0.55÷0.65, R - 45÷35%. The effect of stress per se is quasi-zero, while both Naftusia and ginseng, used preventively, almost equally cause an increase in entropy parameters by 6.3% and 6.0% and 0.76ÿ and 0.72ÿ, which, however, is on the verge of significance. A continuous correlation
analysis shows that the values of h of each individual
cytograms have regular connections both within their morpho-functional systems (intra-system) and inter-system.
In particular, the entropy of the immunocytogram is primarily related to its own elements: the content of 0-lymphocytes (r=-0.84), natural killers (r=0.70),
plasma cells (r=0.63), theophylline-sensitive T-lymphocytes (r=0.42) and B-lymphocytes (r=0.37), but not theophylline-resistant T-lymphocytes (r=0.19). From among the elements thymocytograms deserve attention in this aspect of Hassal's body (r=-0.29),
reticulocytes (r=-0.27), epitheliocytes (r=0.26) and lymphoblasts (r=-0.22); splenocytograms - rod-shaped neutrophils (r=0.36), eosinophils (r=-0.27), lymphoblasts (r=0.24), as well as spleen mass (r=-0.26); leukocytograms - only rod- shaped neutrophils (r=-0.22). At the same time, correlations between h thymocytogram and indicators of phagocytosis were revealed: the microbial number of monocytes
(r=-0.42), the phagocytic index of neutrophils (r=0.27) and their killing index (r=-0.27). Among the indicators of the neurohormonal-metabolic (NGM) galaxy, the total antiprotease activity of plasma (r=-0.44), malondialdehyde (r=-0.34), activity of AsT (r=-0.29) and catalase of plasma are noteworthy (r=-0.27), erythrocyte potassium (r=-0.28), humoral channel of heart rate regulation (r=-0.25),
sympathetic tone (r=0.21) and adrenal mass index (r=0.22).
Leukocytogram entropy has significant connections with three of its own elements: eosinophils (r=0.71), monocytes (r=0.43) and rod cells
with neutrophils (r=0.33) and weak - with lymphocytes (r=-0.25) and segmentonuclear neutrophils (r=-0.22). Here it is appropriate to note the connection with intensity phagocytosis by monocytes (r=0.30), phagocytosis activity by neutrophils (r=-
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0.23) and the activity of natural killers (r=0.24). Worthy of attention are the relationships with rod- (r=0.35) and segmentonuclear (r=0.28) neutrophils and plasma cells (r=-0.31) of the splenocytogram, reticulocytes (r=-0.24) and lymphoblasts (r =-0.22) thymocytograms, as well as 0-lymphocytes (r=0.26) immunocytograms. Among the indicators of NGM of the Pleiades, significant relationships were found with phosphatemia (r=-0.31), superoxide dismutase activity (r=0.29) and humoral regulation channel (r=0.25).
Connections
entropy
splenocytograms
have
in mainly
intrasystemic character. According to the strength of connections, its elements are located in in the following order: lymphocytes (r=-0.97), CYAN (r=0.64), lymphoblasts (r=0.60), macrophages (r=0.47), reticulocytes (r=0.40), PAN (r=0.35), eosinophils (r=0.31),
for the loss of only plasma cells (r=0.16). Of the other systems, it is worth noting only theophylline-resistant T-lymphocytes (r=0.26) and 0-lymphocytes (r=-0.23) and activity creatine kinase (r=0.32) and antiprotease (r=-0.29) plasma.
Intrasystem connections of entropy thymocytogram are characterized next row: lymphocytes (r=-0.97), reticulocytes (r=0.78), basophils
(r=0.52), macrophages (r=0.47), lymphoblasts (r=0.44), Hassall bodies (r=0.34), fibroblasts (r=0.31) for the loss of only epitheliocytes (r =-0.10). There is also a connection with the mass index of the thymus (r=-0.34). Intersystemic connections are represented by indicators of immunity: intensity (r=0.30) and activity (r=0.22) of phagocytosis by neutrophils, activity of natural killers (r=-0.23), blood plasma cells (r=-0.22) - and NGM Pleiades: erythrocyte catalase activity (r=0.32) and adrenal gland mass index (r=-0.25).
Entropy immune morpho-functional connections are worth a separate analysis systems with indicators of stress erosive-ulcerative mucosal damage
stomach It was found that only the entropy of the thymocytogram is significantly inverse correlates with the number of ulcers (r=-0.33), their total length (r=-0.27) and
severity of damage (r=-0.28), while connections in this direction leuko- and immunocytograms are weak (r=-0.23÷-0.18 and 0.20÷0.15, respectively), and splenocytograms are practically absent (r=0.15÷0.07).
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It is significant that the entropies of the considered four immune morpho-functional systems are practically unrelated (r=-0.21÷+0.06). Given this circumstance, as well as the direction of entropy changes of these systems under the influence of stress, we calculated the entropy of the immune system as a whole ( htot) using the formula:
htot=(hTh•hSp/hIm•hLeu)
0.25. Entropy calculated in this way was found to be significantly related to 6 thymus indicators: reticulocytes (r=0.61), lymphocytes (r=-0.54), corpuscles Gassal (r=0.39), basophils (r=0.36), lymphoblasts (r=0.32) and its massive index (r=-0.39); 6 indicators of the spleen: lymphocytes (r=-0.57),
macrophages (r=0.38), eosinophils (r=0.36), reticulocytes (r=0.32), SYAN (r=0.30) and plasma cells (r=0.27); 6 immune indicators of blood: eosinophils (r=-0.29), monocytes (r=-0.29), natural killers (r=-0.28), microbial
by the number of neutrophils (r=0.28), plasma cells (r=-0.26) and the phagocytic index monocytes (r=-0.25), as well as 4 indicators of metabolism: creatine kinase
(r=0.35), erythrocyte catalase (r=0.31) and plasma (r=0.23) and malonate plasma dialdehyde (r=0.27).
As can be seen in the table. 2.1, the parameters of the total (integral) entropy of the immune system change more likely and significantly compared to such partial entropies. In particular, stress per se causes a slight but regular increase in integral entropy by 2.0±0.9% or 0.51±0.23ÿ, and the preventive use of Naftusi increases this effect to 5.0±1.5% or 1, 28±0.37ÿ. Ginseng has an almost similar proentropic effect: 4.4±1.3% and 1.12±0.33ÿ. The coefficient of redundancy under the influence of stress decreases to 9.9±0.8% against 11.7±1.1% in
intact rats, Naftusya causes its further decrease to 7.3±1.3%, and ginseng - up to 7.8±1.2%.
To justify one's own interpretation of ambiguous, at first glance,
results, we consider it necessary to make a preamble. According to Gozhenko A.I.
and Gozhenko E.A. [2007], the development of the disease should be imagined as a linear grid
a structure based on the holistic nature of the body's response to
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damage. At the same time, self-developing pathological processes are formed, which largely determines the state of the disease. This is consistent with the conclusion of A.Sh. Zaichyk. and Churylova L.P. [1999] that pathogenesis can be imagined as the development of a disease in the form of parallel, branched and crossed chains of causal relationships and that pathogenesis considers diseases as mosaic combinations of more elementary or less specific components - pathological processes. However, many work simultaneously in the body
adaptation mechanisms, i.e. are turned on, and then grow and even arise
"reserve forces" (VV Podvysotsky's term). The latter have varying degrees of influence on the course of pathological processes, reduce the degree of damage, i.e
modulate the course of the disease, promoting the recovery of the body. The totality of these mainly compensatory and adaptive mechanisms is nominated as sanogenesis.
According to Frolov V.A. [1987], sanogenesis is a dynamic protective process adaptive mechanisms (of a physiological and pathological nature), which
develops as a result of the action of an extraordinary stimulus on the body, functions throughout the entire pathological process (from the premorbid stage to recovery) and aimed at restoring self-regulation of the body. So, according to the modern paradigm of pathophysiology, the development of the disease is determined by the relationship and interaction of pathogenetic and sanogenetic mechanisms; all processes take place in the sick body in an organized manner and are carried out according to the protective-damaging principle; the development of these processes is significantly influenced by sanogenetic mechanisms. The presence of the latter not only expands modern ideas about the disease, but also is the theoretical basis of preventive and
of rehabilitation medicine [Gozhenko A.I., Gozhenko E.A., 2007].
It is accepted [Avtandylov H.G., 1990] that the increase in entropy (resp. a decrease in the redundancy ratio) indicates the inadequacy of the response morpho-functional system to exogenous influences and its transition to premorbid or pathological condition. According to an alternative interpretation
[Yushkovska O.G., 2001], an increase in entropy, for example, under the influence of running training, may indicate the inferiority of adaptive mechanisms,
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as well as on the significant biological strength (and/or duration of action) of stimuli. In both cases, there is a need to involve significant reserves of homeostatic systems.
Based on the stated provisions, we tend to interpret
stress-induced decrease in entropy of leukocytogram as activation of neuro-hormonal adaptive systems, the mirror of which it (leukocytogram) is considered [Harkavy L.Kh. and others, 1990; O.M. Radchenko, 2004], and the increase in the entropy of the spleno- and thymocytogram (with a decrease in their structural reserve) - as
mobilization of reserve protective sanogenetic mechanisms, burdened, however, pathological process, in particular erosive and ulcerative lesions
gastric mucosa. At the same time, the informational component of the morpho-functional the condition of peripheral blood immunocytes remains practically intact, judging
by quasi-zero entropy changes of the immunocytogram. Oil used before
by stress and acting as an adaptogen, causes a more complete stressor mobilization structural reserves of immune systems (spleen and thymus), which is combined with
the development of non-genotropic changes in the immunocytogram (and therefore - an increase
reliability of immune functioning) while maintaining stressors
non-genotropic changes in the leukocytogram (and therefore - increased adaptive and compensatory capabilities), which is manifested in the mitigation of stressor erosive- ulcerative lesions of the gastric mucosa. The reference adaptogen ginseng has an almost similar integral entropy effect (by 4 modules of d indices): 2.53±0.28ÿ versus 2.40±0.31ÿ in the experimental group, while in the absence of differences between the effects on the entropies of leuko- and thymocytograms (+0.04ÿ and -0.04ÿ, respectively), the significantly stronger effect of ginseng on the entropy of the immunocytogram (+0.73ÿ) is compensated by its much weaker effect on
entropy of the splenocytogram (-0.60ÿ).
2.2. Intra- and intersystem synchronization of immune and neurohormonal and metabolic indicators
Based on methodological approaches to the assessment of synchronization [Baevsky R.M. and others, 1984; V.P. Voitenko, 1991; Perederii V.G. and others, 1995; Flunt I.S. et al., 2001], we proposed our own modification of it. IN
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quality of the preparatory stage for each group of animals, matrices of linear correlation coefficients are created between indicators within separate pleiads, for example, immune and neurohormonal-metabolic (NGM), which characterizes intrasystemic synchronization (conjugation), and between indicators of the immune and NGM pleiads, which characterize intersystem synchronization. At the next stage, based on such matrices, histograms of correlation coefficient modules (|r|) were constructed using generally accepted intervals [Sepetliev D.,
1968], which characterize the gradation of tightness (strength) of connections: very weak (<0.10), weak (0.10÷0.29), moderate (0.30÷0.49), significant (0.50÷0.70), strong
(0.71÷0.90) and very strong (>0.90).
Because the probability of the correlation coefficient is determined not only by the value by its module, but also by the number of objects of analysis, in order to level the differences
of the quantitative compositions of the groups, we propose to estimate the measure (coefficient) conjugation (CS) by frequencies (or fates) |r| ÿ0.30 and ÿ0.50, nominated as
KS0.30 and KS0.50 , respectively. In addition, it was calculated for each galaxy indicators, the average value |r|m and its error ÿ according to the formula:
ÿ=(1-r2 )/(Nr-2)0.5,
where Nr is the number of correlation
coefficients. Finally, using the well-known formula for calculating the stress index of the variational cardiointervalogram [Baevsky R.M. et al., 1984], we introduced the calculation of the stress index of the interaction of indicators (INVP) in each galaxy in particular and in the information field as a whole:
INVP=ÿÿÿ/2•ÿÿ•ÿÿ,
where Mo is the most frequent interval |r|; AMo - fate of Mo; ÿX is the difference between midpoints of extreme intervals |r|.
The immune galaxy consisted of 38 indicators: 6 elements of the blood immunocytogram together with the activity of natural killers; 5 leukocytogram elements together with
phagocytic indices of neutrophils and monocytes, their microbial numbers, neutrophil killing index, as well as general leukocytosis; 8 elements each
spleno- and thymocytograms together with the absolute and relative masses of the spleen and
56
thymus As a result of continuous correlation analysis, 703 coefficients (38•37/2=703) were calculated for each of the 4 groups of rats.
It was established (Table 2.2, Fig. 2.1) that in intact rats weak correlation is the most frequent, that is, the value of Mo histogram is 0.20, and its amplitude is 0.352. The next two ranks with almost the same AMo occupy intervals |r|, which characterize both very weak and moderate correlation, a significant correlation was established for 92 pairs of indicators, strong - for 21, on the other hand, very strong -
only for 2. The variation range of the histogram reaches 0.90.
Group stress index of the interaction of indicators (INVP) of the immune galaxy turns out to be equal to 0.978 (INVP=0.352/2•0.90•0.20). KS0.30, i.e. fate in the galaxy significant |r|, is 0.409, respectively, the coefficient of closeness of connections (KTZ) - the ratio of the shares of essential and non-essential |r| - 0.692. Average value |r|
is on the border between weak and moderate correlation. So, intrasystem synchronization (conjugation) in an intact group of rats can be characterized in general as moderate.
Fig. 3.11. Histograms of modules of correlation coefficients between
immunity parameters
%
Stress causes quantitative and qualitative changes in histogram parameters. In particular, for similar to the intact mode group |r| its amplitude in the control group increases by
13 absolute % or by 37 relative %; this is accompanied by an increase in the frequency of very weak (by 13.5% and 57%) and a reciprocal decrease in the frequencies of moderate (by 13.6% and 56%), significant (by 10.5% and 80%) and strong (by 2.1 % and 70%) of connections and the disappearance of very strong correlation connections, and therefore - a decrease in the range of variation from 0.90 to 0.75. CS0.30 decreases by 65%, CT3 - by 78% (to 0.168), and INVP - increases by 64%. Average value |r| shifts
to the middle of the interval of weak correlation. The figures given in the aggregate testify to this
desynchronizing effect of stress.
Preventive use of Naftusi almost completely averts stressful changes parameters of the histogram and its integral characteristics: KS0.30, KS0.50, KTZ,
GDP, |r|m are 106%, 102%, 110%, 108% and 106% compared to corresponding values of the intact group. So, Naftusya turns away
the desynchronizing effect of stress on the immune constellation, that is, it does resynchronizing effect.
Table 2.8
Intra- and intersystem synchronization (conjugation) of immune and neurohormonal metabolic indicators of rats of different exposure groups
|
Standard |
Immune p |
15.9 |
25.6 |
25.0 |
24.1 |
8.5 |
0.9 |
58.5 |
33.5 |
0.711 |r| 0.380 0.032is |
|
(Jen |
38,703 m |
1.4is |
1.6s |
1.6s |
1.6is |
1.1is |
0.4s |
1.9is |
1.8is |
r| ÿ 0.388 | |
|
Shen + |
Neurohormone. - 38 p |
13.4 |
25.0 |
27.9 |
23.0 |
8.5 |
2.1 |
61.5 |
33.6 |
0.394 0.032s ÿ 0.340 |
|
AXIS) |
metabolic. (ÿÿÿ) 703 Immunna m |
1.3is |
1.6s |
1.7s |
1.6is |
1.1is |
0.5 |
1.8is |
1.8is |
0.023s |
|
n=8 |
- Nÿÿ 76 1444 p |
16.3 |
33.1 |
26.1 |
18.1 |
5.6 |
0.8 |
50.6 |
24.5 |
0.919 |r| ÿ 0.363 |
|
|
m |
1.0is |
1.2s |
1.2s |
1.0is |
0.6is |
0.2s |
1.3is |
1.1is |
0.016s |
|
|
In general 76 p |
15.5 |
29.3 |
26.3 |
20.8 |
7.0 |
1.1 |
55.2 |
28.9 |
0.814 |r| ÿ |
|
|
2850 m |
0.7is |
0.8s |
0.8s |
0.8is |
0.5is |
0.2s |
0.9is |
0.8is |
|
Notes: 1. nr - number of indicators subjected to correlation analysis; Nr - the number of correlation coefficients; p - frequency (in %) of intervals |r|, m - its standard error, calculated according to the formula: m=[p•(100-p)/(Nr-1)]0.5 ; |r| - the average
value of the correlation coefficients without taking into account the sign, ÿ - its standard
error. 2. For each galaxy, the probable difference from the intact (i) and control (c) groups of the experimental and reference groups and the experimental group from the reference (e) is indicated.
Instead, ginseng not only prevents stressful desynchronization, but also reverses it into hypersynchronization: the histogram takes on a trapezoidal shape due to a significant decrease in the frequency of weak and reciprocal increases - significant correlations, which together with an increase in the amplitude of strong
Fig. 3.12. Conjugation of neurohormonal and metabolic indicators of rats
%
40
35
30
25
20
15
10
5
0
0 0.1
intact
0.2
0.3
0.4
BB+stress
0.5 0.6
0.7
H+ stress
0.8
0.9 1 |r|
F+stress
connections gives an increase in KS0.30 by 43%, KS0.50 - by 104%, KTZ - by 104%, |r|m - by 33% and a decrease in GDP by 27%.
The neurohormonal-metabolic (NGM) galaxy also consists of 38
indicators: 4 of them relate to adreno-cholinergic nervous regulation, 8 -
hormonal regulation, 23 - protein, lipid and electrolyte exchanges, this includes the weight of the rat, the sex index and the severity index of stressor erosive ulceration of the gastric mucosa, i.e. indicators related to neurohormonal regulation and metabolism.
In the intact group of rats, the conjugation parameters of the NGM pleiad indicators are close to those of the immune pleiad (Fig. 2.2, Table 2.1), that is, they indicate moderate synchronization. Stress also causes desynchronization, but somewhat less
perceptible due to the preservation of the frequency of very strong connections, and therefore - and
variation range of the histogram. Resynchronization also appears
the effect of Naftusi and the hypersynchronizing effect of ginseng-shen. At the same time, the last one much more noticeable than in relation to the immune galaxy, which is caused by the quality by changing the histogram - moving the fashion into the interval of moderate |r|.
Intersystem immuno-neurohormonal-metabolic synchronization analyzed by 1444 correlation coefficients between 38 indicators of immune and
Fig. 3.13. Intersystem immuno-neurohormonal-metabolic conjugation of
% indicators of rats
38 - NGM Pleiades. And in this case, fundamentally similar to these were found previous desynchronizing effect of stress and preventive
the resynchronizing effect of Naftusi, on the other hand, the hypersynchronizing effect of ginseng is less noticeable and close to the effect of Naftusi.
The above provides grounds for analyzing the synchronization of indicators of everything
information field. It was found (Fig. 2.3, Table 2.1) that the immuno-neurohormonal-metabolic morpho-functional supersystem in rats of the intact group is characterized by a moderate synchronization (conjugation) of its
elements.
Under the influence of stress, neither the mode (frequency of weak connections) of the histogram, nor its
Fig. 3.14. Total conjugation of indicators of the immuno-
% neurohormonal metabolic morpho-functional system
the variation range does not change, but the amplitude of the mode increases by 11.2 absolute % or by 32 relative %, and the frequency of very weak connections - by 12.0% or 53%, on the other hand, moderate frequencies decrease (by 11.0% or 44%), significant (by 9.2% or 71%) and strong (by 2.9% or 72%) connections. This causes reduction of KS0.30 by 55%, KS0.50 - by 70%, KTZ - by 68%, |r|m - by 32% and
an increase in INVP by 32%, which indicates a noticeable desynchronizing effect stress
Preventive use of Naftusi leads to maintenance of synchronization parameters at levels almost similar to those in the intact group, which are 106%, 113%, 112%, 103% and 106%, respectively, that is, it causes resynchronization. Ginseng causes a qualitatively different state of interaction of elements
- hypersynchronization, which is characterized by a decrease (relative to the intact group) of the frequencies of very weak and weak connections and an increase of significant and strong ones, in the absence of changes on the part of moderate and very strong connections and is manifested in an increase in the average values of CS0.30 to 130 % , KS0.50 - up to 166%, KTZ - up to 167%, |r|m - up to 123% and a reciprocal reduction of INVP up to 84% from the corresponding ones in the intact group.
At the next stage of the analysis, it was found that the average group modules
correlation coefficients (|r|m) are related to indicators of both one's constellation and another (table 2.2 and 2.3).
Table 2.2
Correlation-regression analysis (CRA) of relationships between average group coefficients
correlations of the immune constellation of indicators and indicators of morpho-functional systems
|
Determinant variables |
r |
b ±m t |
|
|
Blood lymphocytes |
0.54 |
0.00719 0.00331 2.17 -0.00010 |
ÿ |
|
Blood segmented neutrophils |
-0.53 |
0.00272 0.05 -0.01085 0.00405 |
0.035 |
|
Phagocytic index of monocytes |
-0.42 |
2.68 -0.01281 0.00491 2.61 |
0.97 |
|
Blood natural killers |
-0.41 |
0.00071 0.00049 1.45 0, 00084 |
0.01 |
|
Cortisolemia |
-0.39 |
0.00100 0.85 -0.00729 0.00252 |
|
|
Natural killer activity Length of gastric |
0.38 |
2.90 0.00047 0.00063 0.75 |
|
|
mucosal ulcers Plasma malondialdehyde |
-0.37 |
-0.00136 0.00088 1.55 -0.82800 |
0.012 |
|
Killing index of blood neutrophils |
0.37 |
1.0095 0.82 a=-0 .0138 0.2634 |
0.15 |
|
Adrenal mass index |
0.28 |
0.05 |
0.40 |
|
|
-0.27 |
|
|
|
|
|
|
0.006 0.45 0.13 0.42 0.96 |
Standard error for the dependent variable: ±0.053; R=0.766; R2 = 0.587; F(10.5)=6.69; p<10-5
Table 2.3 Correlation-regression analysis (CRA) of relationships between the average group correlation coefficients
of the neurohormonal-metabolic galaxy of indicators and indicators of morpho-functional systems
|
Determinant variables r |
±m t |
|
|
Blood lymphocytes 0.53 Blood segmented neutrophils |
b 0.00438 0.00261 1.68 -0.00165 |
ÿ |
|
-0.51 Monocyte phagocytic index -0.43 Blood natural |
0.00220 0.75 -0.00674 0.00315 |
0.10 |
|
killers -0.41 Cortisolemia -0.38 Plasma |
2.14 -0.00845 0.00374 2.26 |
0.46 |
|
malondialdehyde 0.36 Natural killer activity 0.35 |
0.00073 0.00038 1.90 0 .00035 |
0.038 |
|
Length of gastric mucosal ulcers - 0.35 |
0.00049 0.71 0.00060 0.00078 |
|
|
Phosphatemia 0.28 Adrenal mass index -0.28 |
0.77 -0.00504 0.00184 2.74 |
|
|
|
0.49519 0.19856 2.49 -0.00347 |
|
|
|
0.77627 0.005 a=-0.5025 0.2988 |
0.029 |
|
|
1.68 |
0.06 |
|
|
|
0.48 |
|
|
|
0.44 0.009 0.016 0.996 0.10 |
Standard error for dependent variable: ±0.041; R=0.773; R2 = 0.598; F(10,6)=6,98; p<10-5
A similar situation is observed for intersystem (Table 2.4) and total
(Table 2.5) correlations. This makes it possible according to the corresponding equations of the multiple
regression to calculate the individual modules of the correlation coefficients (|r|i) as a measure
synchronization. This is a kind of "second-order correlation", that is, between |r|m and | r|and
for the immune constellation it is characterized by a value of 0.71, for NGM - 0.77;
intersystem correlation - 0.78, total - 0.77, which determines the fundamental
similarity of patterns of individual integral parameters of synchronization in rats
different groups of influence .
Table 2.4
Correlation-regression analysis (CRA) of relationships between average group correlation coefficients of the immune-neurohormonal-metabolic galaxy of indicators and indicators of morpho-functional systems
|
Determining variables |
r |
±m t |
p |
|
Blood lymphocytes |
0.56 |
b 0.00603 0.00268 2.25 -0.00062 |
0.029 |
|
Blood segmented neutrophils |
-0.56 |
0.00219 0.28 0.00066 0.00039 |
0.78 |
|
Cortisolemia |
-0.41 |
1.69 0.00087 0.00080 1.08 |
0.10 |
|
Natural killer activity Monocyte |
0.40 |
-0.00794 0.00327 2.42 -0 .00636 |
0.28 |
|
phagocytic index Length of gastric |
-0.40 |
0.00204 3.12 -0.00940 0.00397 |
0.019 |
|
mucosal ulcers Natural blood killers |
-0.40 |
2.37 0.00051 0.00050 1.02 |
0.003 |
|
Plasma malondialdehyde |
-0.39 |
-0.00094 0.00069 1.37 a=-0.0016 |
0.022 |
|
Neutrophil killing index |
0.38 |
0.2108 0.01 |
0.31 |
|
|
0.29 |
|
0.18 |
|
|
|
|
0.99 |
Standard error for the dependent variable: ±0.043; R = 0.774; R2 = 0.598; F(9,48)=7,94; p<10-5
Table 2.5 Correlation-regression analysis (CRA) of relationships between average group coefficients
total correlation of indicators and indicators of morpho-functional systems
|
Determinant variables |
r |
|
±m t |
ÿ |
|
Blood lymphocytes |
0.55 |
b |
0.00280 2.23 |
0.031 |
|
Blood segmented neutrophils Length of |
-0.54 |
0.00624 |
0.00230 0.11 |
0.91 |
|
gastric mucosal ulcers Phagocytic index |
-0.41 |
-0.00026 |
0.00213 2.98 |
0.005 |
|
of monocytes Blood natural killers |
-0.40 |
-0.00635 |
0.00344 2.58 |
0.013 |
|
Cortisolemia Natural killer |
-0.40 |
-0.00886 |
0.00416 2.54 |
0.014 |
|
activity Plasma |
-0.40 |
-0.01058 |
0.00041 1.53 |
0.13 |
|
malondialdehyde Neutrophil killing |
0.39 |
0.00063 |
0.00084 0.89 |
0.38 |
|
index Adrenal mass index |
0.38 |
0.00075 |
0.00053 0.83 0, |
0.41 |
|
|
0.28 |
0.00044 |
00075 1.52 |
0.13 |
|
|
-0.26 |
-0.00114 |
0.85552 0.63 |
0.53 |
-0.53990 a=0.0203.26232 0.11 0.92
Standard error for the dependent variable: ±0.045; R=0.770; R2 = 0.593; F(10,5)=6,85; p<10-5
Machine Translated by Google
According to the concept of K.A. Lebedeva and I.D. Poniakinoi [1990], the degree of activity of the immune system is closely related to the level of conjugation of its components. In healthy individuals with a relatively calm state of the immune system, the number and intensity of relationships between components are minimal; during the development of the inflammatory process, during the period of active work of the immune system, the conjugation of components increases sharply, several times; at a favorable end of the process, the connectivity decreases again. If we consider that the level of conjugation
parameters reflect the degree of stress of the immune system, then increase conjugation in the course of an infectious disease can be regarded as a "syndrome tension". Bonding is strengthened by accumulation
immunocompetent cells in certain places, activation of the synthesis of mediators, an increase in the number of receptors that perceive regulatory signals.
Connectivity doubles in the period of 7-12 years compared to 3 years of age, c
in the period of 18-40 years, stabilization is observed, after which - repeated growth, yes that in 70-90-year-olds, its degree doubles again compared to the average age.
If the strengthening of conjugation in children is due to the inclusion of new components and elements of hormonal regulation, then in old age - acceleration of the processes of destruction, catabolism, an increase in the number of micro-inflammatory processes.
Remission of chronic inflammation ("practically healthy") is characterized by the maintenance of a high level of connectivity of immune components, when transitioning to the phase of exacerbation of the conjugation of the components of the immune system, compared to its already high level, it not only does not increase, but can even significantly decrease. When remission is restored, connectivity increases again. High level of conjugation in
the authors explain the phase of remission as an ongoing struggle with persistent an infection that maintains a compensated level. Decline of connectivity at
decompensation, i.e. during exacerbation, is explained by the breakdown of effective work of the immune system after prolonged stress in remission. Connectivity
decreases also in severe cases of an acute process that passes into
decompensated phase, while a decline to values lower than
such in healthy people.
64
Machine Translated by Google
Proceeding from the given passage, the desynchronizing effect of acute stress revealed in this experiment should be interpreted as a manifestation of decompensation of regulatory and morpho-functional systems, which is prevented by Naftusa, and transformed into supercompensation under the influence of ginseng . Note that |r|i of different pleiads are significantly related only to the entropy of the immunocytogram (r=-0.34÷-0.38).
2.3. Harmony of informational components of the neuro-endocrine-immune system morpho-functional supersystem
To achieve the goal, a factor analysis was carried out (the method of principals component) of the information field of registered indicators of leukemia and immunocytograms of blood, spleno- and thymocytograms, neuroendocrine and metabolic statuses of different groups of rats. To calculate group
coefficients ÿ and r, an extended matrix of factor loadings was used, which contains correlations of clusters of variables (oblique factors) with secondary and primary factors [Kim JO, Mueller Ch.W., 1989].
At the preparatory stage, using the factor analysis method, it was found that the entire information field of 76 indicators of rats of the intact group is condensed into 9 clusters of variables, which to one degree or another correlate with 11 factors; 93% of the information of the control group was condensed into a matrix of 19 clusters and 27 factors; 100% of the information of the research group is represented by 9 clusters and 13 factors, and the entire information field of the reference group is reduced to 7 clusters and 9 factors (Table 2.6). It was found that
in intact animals, ÿ is 0.86±0.03 (ideally - 1), and r -
0.06±0.02 (ideally - 0), that is, the value (ÿ-r) as a quantitative measure of harmony is equal to
0.80. One day after stress, ÿ drops to 0.69±0.02 (p<0.001) in the absence
significant changes in r (0.05±0.01), so that the degree of harmony decreases to 0.64 (by 20%).
Prophylactic use of Naftusi minimizes the drop in ÿ to 0.79±0.03 (p>0.1 relative to the intact and <0.02 relative to the control group), again almost not affecting r (0.07±0.02), i.e. limiting the disharmonizing effect of stress on 13% (up to 0.72).
65
Table 2.6 Extended matrix of factor loadings. Correlations of clusters of variables (oblique factors)
with secondary (S) and primary (P) factors.
|
Intact group |
|
||||||||
|
Cl S1 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
S2 |
0.68 |
-0.22 |
|
0.01 |
0.66 |
0.04 |
0.03 |
-0.33 |
-0.29 |
|
S3 |
0.02 |
0.15 |
|
-0.40 |
-0.16 |
-0.30 |
0.48 |
0.54 |
0.32 |
|
S4 |
0.73 |
0.00 |
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S5 |
0.00 |
0.97 |
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S6 |
0.00 |
0.00 |
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S7 |
0.00 |
0.00 |
|
0.92 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S8 |
0.00 |
0.00 |
|
0.00 |
0.73 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P1 |
0.00 |
0.00 |
|
0.00 |
0.00 |
0.95 |
0.00 |
0.00 |
0.00 |
|
P2 |
0.00 |
0.00 |
|
0.00 |
0.00 |
0.00 |
0.88 |
0.00 |
0.00 |
|
P3 |
0.00 0.00 |
0.00 0.00 |
|
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.77 0.00 |
0.00 0.90 |
-0.52 -0.06 0.00 0.00 0.85 0.00 0.00 0.00
0.00 0.00 0.00 ÿ9=0.856±0.030; r90=0.058±0.016 Control group
|
Clu |
|
|
|
|
|
|
|
|
|
10 |
|
ster |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
|
S1 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
-0.19 |
|
|
0.23 |
-0.44 |
0.06 |
-0.53 |
-0.57 |
0.04 |
-0.09 |
0.16 |
0.14 |
|
|
S2 |
-0.53 |
0.00 |
0.40 |
0.06 |
0.01 |
0.04 |
-0.06 |
0.17 |
-0.08 |
0.14 |
|
|
0.02 |
0.08 |
-0.02 |
-0.16 |
0.05 |
0.44 |
0.47 |
0.54 |
0.10 |
|
|
S3 |
-0.06 |
0.05 |
-0.38 |
-0.75 |
0.02 |
-0.11 |
-0.02 |
-0.05 |
-0.04 |
0.44 |
|
|
-0.03 |
0.09 |
0.09 |
0.48 |
-0.04 |
0.37 |
0.08 |
0.01 |
0.06 |
|
|
S4 |
-0.04 |
0.64 |
0.24 |
-0.01 |
-0.11 |
0.06 |
0.04 |
-0.14 |
-0.48 |
0.38 |
|
|
-0.04 |
0.38 |
0.14 |
0.02 |
0.07 |
0.10 |
0.10 |
0.04 |
-0.59 |
|
|
S5 |
0.16 |
-0.21 |
0.00 |
0.08 |
0.10 |
-0.67 |
0.08 |
0.04 |
-0.14 |
0.14 |
|
|
0.55 |
-0.10 |
0.00 |
-0.27 |
0.04 |
0.07 |
-0.23 |
-0.19 |
-0.10 |
|
|
S6 |
0.19 |
0.03 |
0.02 |
-0.07 |
0.11 |
-0.00 |
0.64 |
-0.18 |
-0.02 |
0.39 |
|
|
-0.12 |
0.01 |
-0.72 |
0.03 |
0.11 |
0.02 |
0.39 |
0.01 |
0.03 |
|
|
S7 |
0.06 |
-0.07 |
0.17 |
0.00 |
-0.01 |
0.18 |
0.01 |
-0.11 |
0.41 |
0.29 |
|
|
-0.42 |
0.07 |
0.09 |
-0.13 |
-0.01 |
-0.12 |
0.14 |
-0.21 |
0.10 |
|
|
S8 |
0, 07 |
-0.03 |
-0.25 |
-0 .09 |
0.68 |
-0.29 |
0.15 |
0.08 |
0, 23 |
0.19 |
|
|
0.42 |
0 .14 |
-0 |
-0.13 |
0 ,12 |
- 0.03 |
0, 11 |
0.44 |
0.15 |
|
|
S9 |
-0.41 |
0.15 |
.04 |
-0.01 |
0.01 |
-0.03 |
0.03 |
-0.61 |
-0.01 |
0.00 |
|
|
0.52 |
0.00 |
0.02 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S10 |
0.69 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.79 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S11 |
0.00 |
0.72 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S12 |
0.00 |
0.00 |
0.66 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.74 |
0.58 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S13 |
0.00 |
0.00 |
0.00 |
0.64 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.80 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S14 |
0.00 |
0.00 |
0.00 |
0.00 |
0.71 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.80 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S15 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.65 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.72 |
0.00 |
0.00 |
0.00 |
|
S16 |
0.00 |
0.00 |
0.00 |
0 .00 |
0.00 |
0.00 |
0.74 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0 .00 |
0.00 |
0 .64 |
0.00 |
0.00 |
|
S17 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0 .00 |
0.73 |
0.00 |
0, 00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.76 |
0.00 |
|
S18 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.72 |
0.57 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
P1 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P2 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P3 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P4 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P5 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P6 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0, 00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0, 00 |
0.00 |
|
P7 |
0.00 |
0, 00 |
0.00 |
0.00 |
0, 00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0, 00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P8 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
|
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P9 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0, 00 |
|
|
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.000.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 |
ÿ19=0.693±0.019; r494=0.053±0.006
Research group
|
Cl |
|
||||||||
|
S1 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
|
S2 |
-0.08 |
0.12 |
0.06 |
-0.68 |
0.15 |
-0.14 |
0.01 |
0.49 |
0.59 |
|
S3 |
0.59 |
0.01 |
0.09 |
-0.05 |
0.03 |
-0.42 |
0.41 |
0.03 |
-0.52 |
|
S4 |
0.03 |
0.06 |
-0.59 |
0.17 |
0.06 |
0.28 |
-0.04 |
0.44 |
0.07 |
|
S5 |
0.11 |
0.43 |
0.00 |
0.08 |
-0.56 |
0.00 |
-0.07 |
0.06 |
0.14 |
|
S6 |
0.80 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S7 |
0.00 |
0.89 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S8 |
0.00 |
0.00 |
0.80 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P1 |
0.00 |
0.00 |
0.00 |
0.71 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P2 |
0.00 |
0.00 |
0.00 |
0.00 |
0.81 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P3 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.85 |
0.00 |
0.00 |
0.00 |
|
P4 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.91 |
0.00 |
0.00 |
|
P5 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.75 0.00 |
0.00 0.59 |
ÿ9=0.791±0.032; r108=0.071±0.015
Reference group
Cl
|
S1 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
|
S2 |
0.43 |
0.34 |
-0.82 |
0.07 |
0.01 |
-0.48 |
0.43 |
|
S3 |
0.66 |
-0.01 |
-0.19 |
-0.49 |
0.08 |
0.08 |
-0.64 |
|
S4 |
0.62 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S5 |
0.00 |
0.94 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
|
S6 |
0.00 |
0.00 |
0.54 |
0.00 |
0.00 |
0.00 |
0.00 |
|
P1 |
0.00 |
0.00 |
0.00 |
0.87 |
0.00 |
0.00 |
0.00 |
|
P2 |
0.00 |
0.00 |
0.00 |
0.00 |
1.00 |
0.00 |
0.00 |
|
P3 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.00 0.00 |
0.88 0.00 |
0.00 0.64 |
ÿ7=0.783±0.068; r56=0.084±0.026
This ability of Naftusa is similar to the following ginseng: ÿ=0.78±0.07; r=0.08±0.03; (ÿ-r)=0.70, which is additional evidence of its adaptogenic properties, revealed in our previous studies. The measure of group harmony is directly correlated with blood lymphocytosis, activity of natural killers, level of MDA, pre-
ÿ and ÿ-LP cholesterol, lymphoblastosis of the thymus, and the killing index of blood neutrophils, and inversely - with blood neutrophilia, the index of SSFS and cortisolemia (Table 2.7).
Table 2.7
Correlation-regression analysis (CRA) of relationships between the harmony index and indicators morpho-functional systems
|
Determining variables |
r |
|
±m t |
p |
|
Blood segmented neutrophils Blood |
-0.52 |
b -0.00257 |
0.00220 1.17 |
0.25 |
|
lymphocytes Activity |
0.46 |
0.00135 |
0.00275 0.49 |
0.63 |
|
of natural killers Gastric mucosa |
0.45 |
0.00097 |
0.00082 1.18 |
0.24 |
|
damage index Cortisolemia Plasma |
-0.45 |
-0.08885 |
0.02772 3.21 |
0.002 |
|
malondialdehyde |
-0.35 |
0.00022 |
0.00038 0.58 |
0.56 |
|
Cholesterol of pre-ÿ- and ÿ- |
0.34 |
0.00051 |
0.00052 0.99 |
0.33 |
|
lipoproteins Thymus lymphoblasts Killing |
0.32 |
0.03311 |
0.02069 1.60 |
0.12 |
|
index of blood neutrophils |
0.30 |
0.00461 |
0.00367 1.25 0, |
0.22 |
|
|
0.29 |
-0.00041 |
00065 0.36 0.2129 |
0.53 |
|
|
|
a=0.6771 |
3.18 |
0.003 |
Standard error for the dependent variable: ±0.043; R=0.757; R2 = 0.574; F(10,5)=6,33; p<10-5
On this basis, multiple regression equations were created, which enable the calculation of
individual harmonic values, as well as coefficients ÿ (Table 2.8) and r (Table 2.9).
Table 2.8 KRA of relationships between the autocorrelation coefficient (ÿ) and indicators of morpho-functional
systems
|
Determining variables |
r |
b |
±m |
t |
ÿ |
|
Blood segmented neutrophils Blood |
-0.55 |
-0.00235 |
0.00232 1.01 |
|
0.31 |
|
lymphocytes Activity |
0.51 |
0.00340 |
0.00283 1.20 |
|
0.23 |
|
of natural killers Index of damage to |
0.46 |
0.00122 |
0.00088 1.40 |
|
0.17 |
|
the gastric mucosa Cortisolemia Plasma |
-0.44 |
-0.08652 |
0.02842 3.04 |
|
0.004 |
|
malondialdehyde |
-0.38 |
0.00034 |
0.00041 0.84 |
|
0.41 |
|
Cholesterol of pre-ÿ- and ÿ- |
0.36 |
0.00098 |
0.00052 1.90 |
|
0.06 |
|
lipoproteins Killing index of blood |
0.31 |
0.04261 |
0.02171 1.96 |
|
0.055 |
|
neutrophils |
0.30 |
-0.00034 |
0.00069 0.45 0, |
|
0.65 |
|
|
|
a=0.5553 |
2202 2.52 |
|
0.015 |
Standard error for the dependent variable: ±0.046; R = 0.749; R2 = 0.561; F(8,49)=7,84; p<10-5
Table 2.9 KRA of relations between the coefficient of mutual correlation (r) and indicators of morpho-functional
systems
|
Determining variables |
r |
±m t |
p |
|
Blood lymphocytes |
0.46 |
b 0.00061 0.00054 1.13 -0.00158 |
0.26 |
|
Phagocytic index of neutrophils |
-0.44 |
0.00067 2.37 -0.00034 0.00043 |
0.02 |
|
Segmented neutrophils of blood Natural |
-0.42 |
0.78 -0.00169 0.00079 2.15 |
0.44 |
|
killers of blood Phosphatemia |
-0.40 |
0.08374 0.04136 2.02 0 .00010 |
0.037 |
|
Cortisolemia |
0.33 |
0.00008 1.26 0.00007 0.00010 |
0.048 |
|
Malondialdehyde |
-0.33 |
0.66 -0.11574 0.16192 0.71 |
0.21 |
|
of plasma Adrenal mass index |
0.31 |
a=-0.0549 0.0584 0.94 |
0.51 |
|
|
-0.28 |
|
0.48 |
|
|
|
|
0.35 |
Standard error for the dependent variable: ±0.009; R=0.690; R2 = 0.476; F(8,49)=5,56; p<10-4
"Factorial" (fac) and "individual" (ind) harmonic values, ÿ and r closely
connected in pairs, which is evidenced by the numbers 0.75; 0.77 and 0.69, respectively. Such connections determine the fundamental similarity of the patterns of information components (Fig. 2.5): the disharmonizing effect of stress (mainly due to the weakening of autocorrelation) and the significant mitigation of this effect under the conditions of preventive use of both Naftusi and ginseng.
Figure 3.17. Information components of the morpho-functional supersystem of rats of different
exposure groups
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
rÿ (within) fac
rÿ (within) ind
r (between) fac
r (between) ind
harmony fac
harmony ind
intact
BB+stress H+ stress
F+stress
The measure of harmony practically does not correlate with the entropy of immune systems (except very weakly with h immunocytogram: r=-0.20), at the same time there is a direct strong correlation with |r|i - an individual measure of synchronization - immune (r=0 ,
79) and NGM (r=0.72) Pleiades and intersystem (r=0.83) and total (r=0.80) synchronization of indicators. With other synchronization parameters - conjugation coefficients and stress indices - the relationship of the measure of harmony is weaker (r=0.72÷0.44).
Connections of informational parameters with difficulty are worthy of special attention stress erosive-ulcerative lesions of the gastric mucosa. It was found that
it is determined by the combined influence of the indices of harmony and tension intersystemic interaction of immuno-neurohormonal-metabolic indicators and entropy of the thymocytogram by 44.4% (Table 2.10, Fig. 2.6).
Table 2.10
Correlation-regression analysis (CRA) of relationships between the severity of mucosal damage stomach and information indicators of morpho-functional systems
Determining variables
r b ±mt
Individual index of harmony -0.60 -3.810 Index of tension of interaction in the immuno-NGM galaxy 0.37 -0.142 Entropy of thymocytogram
-0.29 -1.236
ÿ 0.780 4.88 =10-5
0.122 1.16 0.25
0.463 2.67 0.01
a=3.795 0.734 5.17 <10-5
Standard error for the dependent variable: ±0.19; R=0.666; R2 = 0.444; F(3.54)=14.4; p<10-5
Z=12.37-33.6*X+2.91*Y+19.5*X2 +5.71*XY-6.50*Y2
Fig. 2.6. Dependence of the severity of stress injuries of the gastric mucosa on the state of harmony, the stress index of immune and neurohormonal interactions of metabolic indicators (IT IHM) and entropy of thymocytogram
CONCLUSION
Methods of quantitative assessment of information parameters applied and tested in the field of balneophysiology can be successfully applied in medicine for diagnosis and evaluation of the effectiveness of prevention and treatment.
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Naftusya bioactive water, like ginseng, without changing the stress-induced decrease in the entropy of the blood leukocytogram, increases the stress-induced increase in the entropy of the thymocytogram; the entropy of the splenocytogram against the background of Naftusi also increases relative to the control (stress), while the effect of ginseng is uncertain; the entropy of the blood immunocytogram is not affected by stress, while the preventive use of Naftusi causes a negentropic effect, which is inferior to such ginseng. Naftusya warns against stress
desynchronization of morpho-functional systems, and ginseng even reverses it its relative to intact rats. The disharmonizing effect of stress, estimated by
a 20% decrease in the difference in the coefficients of auto- and intercorrelation of indicators, significantly weakens to approximately the same extent against the background of both Naftusi and ginseng The index of stress damage to the gastric mucosa decreases
approximately to the same extent against the background of the use of both Naftusi and ginseng;
it correlates inversely with the index of harmony and entropy of the thymocytogram and directly with an index of tension of the interaction in the immune-neurohormonal-metabolic galaxy
indicators
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SECTION 3
RELATIONSHIPS BETWEEN ENTROPYS OF MORPHO-FUNCTIONAL IMMUNE SUBSYSTEMS (THYMUS, SPLEEN, PERIPHERAL BLOOD) AND PARAMETERS OF THE NEURO-
ENDOCRINE-IMMUNE COMPLEX IN RATS OF BOTH SEXES
The experiment was performed on 108 healthy Wistar rats (60 females and 48 males). Of these, 10 females and 10 males remained intact, consuming tap water from drinking troughs ad libitum. The rest of the rats were given a single dose of 1.5 ml/100 g of tap water, Naftusya bioactive water, Sophia mineral water of the Truskavetsky deposit, Hertz water and its artificial salt analogue through a probe for 6 days. The last two groups received applications on the tail of ozoketite and applications of ozoketite and Naphtusya.
The next day after the end of the course, all rats were first sampled of peripheral blood (by cutting the tip of the tail) for counting
by the unified method of the number of leukocytes and leukocytogram analysis. With this one smears were prepared for the purpose, dried in air, fixed for 3 minutes in methanol, and
then in absolute alcohol, stained according to Pappenheim. 200 were counted cells
According to the leukocytogram data, its entropy was calculated:
h =-[E•log2E+PYAN•log2PYAN+SYAN•log2SYAN+ÿ•log2ÿ+ÿ•log2ÿ+ÿ•log2ÿ]/log26 Next, the state of autonomic regulation was assessed by the method of variational
cardiointervalography [Baevsky R.M. and others, 1984]. For this, under light ether anesthesia, an ECG was recorded (speed 50 mm/sec) in the II lead for 15-20 seconds, inserting needle electrodes under the skin of the paws. A series of approximately 100 cycles, the duration of which was determined with a caliper with an accuracy of 0.1 mm (2 msec), was divided into 6- millisecond intervals, with the following calculation of the parameters of the variation cardiointervalogram: Mo, AMo and MxDMn.
Next, the animals were placed in individual chambers with a perforated bottom for collection of daily urine. The experiment was completed by decapitation of rats for the purpose
collection of the maximum possible amount of blood, the content of which was determined in the plasma
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indicators of endocrine status: corticosterone, triiodothyronine (T3) and testosterone
Hormonal studies were carried out by the method of solid-phase competitive enzyme- linked immunosorbent assay (ELISA) on the "Tecan" analyzer
(Oesterreich) using appropriate kits ("Alcor Bio", St. Petersburg, RF).
The principle of the kit is that during the incubation of plasma in a well with immobilized mouse monoclonal antibodies to a certain hormone
this plasma hormone competes with the conjugated hormone for binding to antibodies on the surface of the well. As a result, it is formed bound with plastic sandwich containing peroxidase. During incubation with the substrate solution of tetramethylbenzidine, the solution in the wells is stained.
The intensity of the color is inversely proportional to the concentration of the hormone the studied sample. The concentration of the hormone in the sample is determined by calibration graph of dependence of optical density on hormone content in
calibration samples. The coefficient of variation of the results of determining the content in in the same plasma sample does not exceed 8% for T3 , 8% for corticosterone,
for testosterone 8%. Accuracy according to the percentage of "opening" is 90-110%, 90-110% and 90-110%, respectively. The minimum reliably determined concentration does not exceed 0.2 nM/l for T3 , 5 nM/l for corticosterone, and 5 nM/l for testosterone
0.15 nM/l.
In order to further evaluate the androgenic function of the adrenal glands, the content of 17-ketosteroids was determined in daily urine. The method is based on the quantitative measurement of specifically purple colored chromogens extracted with chloroform, formed as a result of the reaction of 17-ketosteroids with meta
dinitrobenzene in an alkaline medium. Color intensity
was measured with a Pointe-180 analyzer (Scientific, USA) at the wavelength 500-560 nm. Androsterone was used to construct the calibration graph [Horyachkovsky A.M., 1998].
Another approach to assessing the morpho-functional state of the adrenal glands, used in this study is the determination of their mass with the following
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preparation of smears-prints, in which the thickness of the glomerular, fascicular, reticular and medullary zones was measured under a microscope [Bilas V.R., Popovych I.L., 2008; Bilas V.R., Popovych I.L., 2008, 2011].
The content of electrolytes in blood plasma and daily urine was determined: calcium (by reaction with arsenazo III), phosphates (by the phosphate-molybdate method), sodium and potassium (by the flame photometry method).
"Tecan" (Oesterreich), "Pointe-180" analyzers were used
(“Scientific”, USA) and “Reflotron” (“Boehringer Mannheim”, BRD) with the corresponding ones sets and flame spectrophotometer "SF-47".
Hormonal activity was assessed by indicators of electrolyte metabolism:
parathyroid - according to the coefficients (Car/Pr) 0.5, (Pu/Cau)0.5 and (Car•Pu/Pr•Cau)0.25,
calcitonin - (Cau•Pu/ÿÿÿ•ÿÿ)
0.25
by coefficients (1/Car•Pr) 0.25,
and mineralocorticoid - by coefficients (Nap/Kp)0.5,
(Cau•Pu)0.5
and
(Ku/Nau)0.5 and (Nap•Ku/Kp•Nau)0.25, based on classical principles and recommendations [I.L. Popovich, 2011].
Immunogram parameters were determined in the blood according to WHO level I and II tests, as
it is described in the manual [Peredery V.G. et al., 1995].
Isolation of lymphocytes was carried out on ficoll-verografin (density 1.077 g/cm3 ).
The relative content of the T-lymphocyte population in the blood was determined by the test of spontaneous rosette formation with ram erythrocytes according to Jondal M. et al. [1972]. At the same time, erythrocytes were brought to a concentration of 0.5% with medium
199. 0.1 ml of lymphocyte suspension (2•106 /ml) was added to silicone tubes , the mixture was incubated at 370 C for 5 min, then it was centrifuged at 750 rpm for 5 min and incubated at 120 C for 60 min. After incubation, the cells were fixed
glutaraldehyde (0.1 ml of 0.8% solution). Smears were fixed in methanol and
dyed according to Romanovsky Giemse for 20 minutes. Then the smears were washed in distilled water, dried, microscoped in an immersion system,
counted the number of lymphocytes that fixed on its surface 3 and more erythrocytes than 200 lymphocytes.
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The relative content of the theophylline-sensitive subpopulation of T-lymphocytes (T-killers) was determined by the test of sensitivity of rosette formation to theophylline according to Limatibul S. et al. [1978]. At the same time, 0.1 ml of 2•106 lymphocytes were mixed in a test tube with 0.1 ml of theophylline solution (1.8 mg/ml) in medium 199 and kept in a thermostat for 60 minutes at 370 C. After that, the operations described above were repeated.
The content of the theophylline-resistant subpopulation of T-lymphocytes (T-helpers) was determined by the difference between the content of the T-population and its theophylline-sensitive subpopulations.
The relative content of the B-lymphocyte population was determined by the test complementary rosette formation with ram erythrocytes according to Bianco C.
[1970]. At the same time, 0.1 ml of lymphocyte suspension was introduced into silicone tubes (2•106 /ml) and 0.1 ml of 0.5% EAS complex, the mixture was centrifuged at 150-200 g within 5 min. The formed rosettes were fixed by adding 0.05 ml to the test tubes
of a 3% solution of glutaraldehyde in phosphate buffer, for 20 minutes at at room temperature and stopped by adding an excess of distilled
water Cells were precipitated by centrifugation (150-200 g for 5 min), the liquid above
the sediment was sucked off. The remaining suspension was pipetted onto degreased glass, which was then air-dried and fixed in methanol for 5 min. The following staining and counting procedures were performed as described
above algorithm.
Natural killers (NK lymphocytes) were identified as large granulocytic lymphocytes in the leukocytogram. The content of 0-lymphocytes in the immunocytogram was calculated using the balance (residual) method with 100% of the amount.
The entropy of the immunocytogram was calculated using a similar algorithm:
hICG =
- [Tc•log2Tc+Th•log2Th+B•log2B+Pla•log2Pla+NK•log2NK+0•log20]/log26
In addition, the reaction of blast transformation of T-lymphocytes was evaluated mitogen phytohemagglutinin (FHA) according to N.A. Samoilova (1970), as described in management [Perederii V.G. et al., 1995]. For this, up to 0.1 ml of selected
20% of inactivated serum and 80% of medium 199 with antibiotics and FHA were added to washed lymphocytes (1.5•106 /ml). The mixture is inclined at an angle of 450
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test tubes were cultivated for 3 days at 370 C in an incubator with CO2. Next, a smear was made, stained according to Romanovsky-Giemse, and the number of blast forms per 200 lymphocytes was counted.
The state of the phagocytic function of neutrophils (microphages) and monocytes (macrophages) was judged by the phagocytic index, the microbial (phagocytic) number and the killing index with the calculation of the bactericidal capacity (the number of microbes that can be neutralized by neutrophils and monocytes contained in
blood volume units) [Douglas SD, Quie PG, 1981; Bilas V.R., Popovych I.L.,
2008]. The object of phagocytosis was a strain of Staphylococcus aureus (ATCC N25423 F49), obtained in the chemical and bacteriological laboratory of the branch of GGRES PrJSC "Truskavets resort". The daily culture of these was used in the research
microorganisms
After decapitation, the spleen and thymus were removed from the animals. Immune organs were weighed and smears-imprints were made from them for counting spleno- and thymocytogram [Belousova O.I., Fedorova M.I., 1968; Bazarnova M.A., 1988;
Bilas V.R., Popovych I.L., 2008].
The components of the thymocytogram are T-lymphocytes, lymphoblasts, epitheliocytes, endotheliocytes, reticulocytes, macrophages and Hassall's bodies.
Splenocytogram includes lymphocytes (T and B), lymphoblasts, plasma cells, reticulocytes, fibroblasts, macrophages, microphages and eosinophils.
The entropy of thymocytogram (TCG) and splenocytogram (SCG) was calculated by
by the algorithm described above, taking into account the number of elements.
hTCG = - [Lc•log2Lc+Lb•log2Lb+Ret•log2Ret+Mac•log2Mac+En•log2En+Ep•log2Ep+Has•log2Has]/log27 hSCG = - [Lc•log2Lc+Lb•log2Lb+P•log2P+R•log2R+Ma•log2Ma+F•log2F+Mi•log2Mi+Eo•log2Eo]/log28
It is obvious that the leukocytogram and immunocytogram of peripheral blood reflect the redistribution of immune cells between the bone marrow, thymus,
spleen, lymph nodes, as well as non-encapsulated lymphoid tissue of mucous membranes, liver, skin, etc. through controlled migration and recirculation.
76
Given the impossibility of analyzing the cell composition in practice immune organs in humans, we resorted to an experiment on rats.
Calculation of the entropy of the immunocytogram of peripheral blood, as well as the splenocytogram and thymocytogram of smear-imprints in rats was first used by I.L. Popovych back in 2007, using the idea of O.G. Yushkovskaya [2001] on the application of entropy calculation of leukocytogram of peripheral blood of athletes to assess their adaptive reactions. Such creativity
approach is demonstrated in the following studies by I.L. Popovich [] and others representatives of the Truskavet Scientific School []. However, Entropy is not was in the focus of the analysis that she took in this study.
At the first stage of the analysis, we will find out the gender differences in entropy of cytograms. To enable a quantitative assessment of the role of gender, males were conditionally given one point, and females - two points.
Fig. 3.1 illustrates significant sexual dimorphism of cytogram entropy
of the central body of immunity, which is documented by the correlation coefficient between entropy and gender index.
H TCG = 0.709 - 0.1385*Sex
Correlation: r = - 0.889
0.65
0.60
0.55
0.50
0.45
0.40
0.35
Fig. 3.1. Gender differences in entropy of rat thymocytograms
On the other hand, regarding Splenocytogram (Fig. 3.2) and Immunocytogram (Fig. 3.3), gender differences are insignificant, and regarding Leukocytogram (Fig. 3.4) they are completely absent (however, different dispersion draws attention).
Fig. 3.3. Gender differences in Entropy immunocytograms of rats
Fig. 3.4. Gender differences in entropy of leukocytograms of rats
Given the information about the existence of a whole series of sexual dimorphism parameters of the neuroendocrine-immune complex of rats [Popovych IL et al,
2019], in order to level it, all registered parameters were normalized, that is, listed in Z-units according to the formula:
Z=(V/N-1)/Cv, where
V is an actual individual value; N is the
average of intact animals; Cv is the coefficient of variation in intact animals.
Figures 3.5-3.8 illustrate the leveling (along the Y axis) of sex differences in cytograms of morpho-functional immune subsystems.
Fig. 3.6. Current (X-axis) and normalized (Y-axis) Entropy values Splenocytograms of rats of both sexes
Fig. 3.8. Current (X-axis) and normalized (Y-axis) Entropy values Leukocytograms of rats of both sexes
When comparing the actual levels of entropy of the thymocytogram, it was found that the applied course of balneofactors, judging by the average values, did not affect them either in males or in females (Fig. 3.9). A similar situation applies to the Entropy levels of Splenocytogram (Fig. 3.10), Immunocytogram (Fig. 3.11) and Leukocytogram (Fig. 3.12).
hT
0.61
0.57
0.53
0.49
0.45
0.41
0.37
0.33
Males Intact (10)
Males Louded (38)
Females Intact (10)
hT
Females Loud (50)
Fig. 3.9. Current Entropy levels of thymocytograms in intact and balneofactor- loaded rats of both sexes
hS
0.77
0.75
0.73
0.71
0.69
0.67
0.65
0.63
Males Intact (10)
Males Loaded
(38) (10)
Females Intact
hS
Females Loaded (50)
Fig. 3.10. Actual levels of Entropy of splenocytograms in intact and rats of both sexes loaded with balneofactors
Fig. 3.12. Actual levels of entropy of leukocytograms in intact and balneofactor- loaded rats of both sexes
So, Entropy of all four morpho-functional immune subsystems
under the conditions of our experiment, it was found to be stable.
According to the result of the screening of the correlations of the sex index with normalized neuro-endocrine parameters with step-by-step
as an exception, a regression model was constructed (Table 3.1). It was found that gender determines the level of constellation of endocrine parameters by 43%. The negative correlation of the sex index with calcitonin and parathyroid activity and corticosteronemia indicates their lower level in females, on the other hand, in
mineralocorticoid activity and daily excretion of 17-ketosteroids are higher than in males.
Table 3.1. Regression summary for endocrine variables against gender index R=0.675; R2 = 0.456; Adjusted R2 =0.429; F(5,1)=17.1; p<10-5
Variables, Z
Beta St. Err. B St. Err. t(102) p-level 29.9 10-6
r Intercpt 1.347 0.045
(Cau•Pu/Cap•Pp)0.25 as CTA -0.59 -0.322 0.097 -0.067 0.020 -3.33 0.001
(Cap•Pu/Cau•Pp)0.25 as PTA -0.40 -0.163 0.085 -0.071 0.037 -1.92 0.058
Corticosterone Plasma
(Nap/Kp)0.5 as MCA
17-Ketosteroides Urine
-0.22 -0.087 0.075 -0.034 0.029 -1.16 0.249
0.47 0.271 0.082 0.114 0.035 3.30 0.001
0.31 0.181 0.076 0.064 0.027 2.38 0.019
Among the registered immune parameters, two sets were found, with positive and negative correlation with the sex index. The first set (table 3.2)
contains 6 blood parameters, 2 spleen parameters and one thymus parameter,
the levels of which are higher in females, that is, subject to activating regulation by factors, linked to the female gender. It has the right to exist and an alternative statement about the subordination of these parameters to inhibitory regulation by factors linked to the male sex. Be that as it may, gender determines the level of this immune constellation by 34%.
Table 3.2. Regression summary for immune variables activated by factors associated with female gender R=0.630; R2 =
0.397; Adjusted R2 =0.342; F(10,0)=7.2; p<10-6
|
Beta |
St. Err. |
B |
St. Err. |
t(98) p-level |
|
Variables, Z r |
Intercpt |
1,571 |
0.046 |
10-6 34.47 |
|
Natural Killers Blood 0.35 0.132 |
0.086 |
0.040 |
0.026 |
1.54 0.127 |
|
PhI Monocytes Blood 0.33 0.278 |
0.085 |
0.130 |
0.040 |
3.27 0.002 |
|
Thymus endotheliocytes 0.32 0.165 |
0.086 |
0.073 |
0.038 |
1.91 0.059 |
|
Reticulocytes Spleen 0.30 0.231 |
0.083 |
0.112 |
0.040 |
2.79 0.006 |
|
Killing Ind Neutr Blood 0.27 0.201 0.26 |
0.082 |
0.091 |
0.037 |
2.45 0.016 1.89 |
|
Basophiles Blood 0.193 |
0.102 |
0.090 |
0.047 |
0.061 |
|
Lymphoblasts Spleen 0.26 0.113 |
0.085 |
0.054 |
0.041 |
1.33 0.189 |
|
T-cytolytic L Blood 0.22 0.153 |
0.098 |
0.069 |
0.044 |
1.57 0.121 |
|
Blood plasmocytes 0.21 -0.152 |
0.113 |
-0.058 |
0.043 |
-1.35 0.180 |
Instead, the second set (Table 3.3) consists of other 5 blood parameters, 2 spleen
parameters and 2 thymus parameters, the levels of which are lower in females, i.e.
subject to suppressor regulation by factors linked to the female sex or activated by factors linked to the male sex. The gender determination rate of this immune constellation is 41%.
Table 3.3. Regression summary for immune variables suppressed by factors associated with female gender R=0.678; R2
= 0.460; Adjusted R2 =0.410; F(10.0)=9.3; p<10-6
Beta St. Err. B St. Err. t(98) p-level
|
Variables, Z |
r Intercpt 1.644 0.046 35.90 10-6 |
|
Thymus Macrophages |
-0.40 -0.192 0.087 -0.050 0.023 -2.21 0.029 -0.39 |
|
Monocytes Blood |
-0.308 0.076 -0.172 0.042 -4.05 10-4 |
|
0-Lymphocytes Blood |
-0.30 -0.103 0.085 -0.042 0.034 -1.22 0.224 |
|
Microb Count Monocytes Blood -0.29 -0.130 0.088 -0.031 0.021 -1.48 0.143 |
|
|
Thymus Mass Index |
-0.27 -0.179 0.077 -0.092 0.040 -2.32 0.022 |
|
Leukocytes Blood |
-0.26 -0.191 0.075 -0.091 0.036 -2.54 0.013 |
|
Plasmocytes Spleen |
-0.23 -0.160 0.078 -0.085 0.042 -2.05 0.044 |
|
Macrophages Spleen |
-0.22 -0.093 0.081 -0.043 0.037 -1.15 0.252 |
|
Blood Eosinophiles |
-0.20 -0.204 0.078 -0.106 0.040 -2.62 0.010 |
In order to assess the integral impact on Entropy of immune subsystems with on the part of neuro-endocrine factors, the canonical procedure was carried out correlation analysis. The resulting (right) set is formed from partial sets
entropy Within the set, a significant correlation was found only between entropies Thymocytograms and Immunocytograms (Table 3.4), i.e. morpho-functional subsystems are sufficiently independent of each other.
Table 3.4. Correlation matrix (right set)
Variables, Z
Entropy Leukocytogram 1
H LCG H ICG H TCG H SCG
0.140 -0.046 0.078
Entropy Immunocytogram 0.140 1
Entropy Thymocytogram -0.046 0.265 1
Entropy Splenocytogram 0.078 -0.183 0.125 1
0.265 -0.183
0.125
The factor (left) set is formed by parameters of the autonomic nervous system, calcitonin, parathyroid and mineralocorticoid activities, as well as plasma testosterone (Table 3.5).
Table 3.5. Correlation matrix (left set vs. right set)
Variables, Z AMo DX Mode CTAu PTAu GloZAC MCAp Testost
Entropy Leukocytogram 0.098 0.051 0.186 0.157 0.153 0.073 0.025 -0.172
Entropy Immunocytogram 0.080 0.130 0.052 -0.189 0.026 -0.009 0.284 0.205
Entropy Thymocytogram 0.294 -0.221 -0.199 -0.161 -0.138 -0.014 0.073 0.146
Entropy Splenocytogram 0.155 -0.166 -0.170 0.191 -0.205 -0.184 -0.127 0.002
As a result, two pairs of canonical roots were selected (Table 3.6). Factorial
the root of the first pair, judging by the obtained moderate negative loads, represents the inverse
of the six endocrine parameters (however, the mode reflects the level of circulating catecholamines inversely). The resulting root of the first pair directly represents the entropy of the Leukocytogram and Immunocytogram, but inversely – the Splenocytogram. The canonical correlation between
the roots is of moderate strength, but significant (Fig. 3.13).
The factorial structure of the second pair of canonical roots has both common and distinctive features compared to those of the first pair. Canonical correlation between the roots are somewhat weaker, but also significant (Fig. 3.14).
Table 3.6. Factorial structure matrix for canonical correlation between endocrine parameters (left set) and entropies of morpho-functional immune subsystems (right set)
|
Right set Entropy Leukocytogram |
R 1 R 2 -0.918 -0.262 |
|
Entropy Splenocytogram |
0.170 0.175 |
|
Entropy Immunocytogram |
-0.419 0.770 |
|
Entropy Thymocytogram |
-0.182 0.651 |
|
Left set |
R 1 R 2 |
|
Mode HRV as Humoral channel -0.338 -0.335 |
|
|
(Cap/Pp)0.5 as PTA |
-0.310 -0.323 |
|
AMo HRV as Sympathetic tone |
-0.242 0.395 |
|
(Nap/Kp)0.5 as MCA |
-0.229 0.427 |
Glomerular Zone of Adrenal Cort -0.197 -0.200
MxDMn HRV as Vagal tone (Cau•Pu)0.5 as CTA
-0.123 -0.116
-0.058 -0.450
Testosterone
0.168
0.597
2
1
0
-1
-2
-3
-3 -2 -1 0 1 2
Neuroendocrine factors
R=0.533; R2 = 0.284; ÿ2 (32)=83; p<10-5; ÿ Prime=0.413
Fig. 3.13. Canonical correlation between the first pair of roots representing neuroendocrine factors (line X) and entropy of immune subsystems (line Y)
3
2
1
0
-1
-2
-3
-3 -2 -1 0
1 2 3
Neuroendocrine factors
R=0.471; R2 = 0.222; ÿ2 (21)=49; p<10-3; ÿ Prime=0.611
Fig. 3.14. Canonical correlation between the second pair of roots representing neuroendocrine factors (line X) and entropy of immune subsystems (line Y)
Therefore, the entropy of the immune cells of the thymus and blood, but not the spleen, was subject to the modulating influence of the average force of sympathetic
nerves, circulating catecholamines, mineralocorticoids, testosterone, parathyrin, and calcitonin.
Using the terminology of factor analysis, we will consider Entropy as a hypothetical general factor (vis vitalis). Adopting this approach, we will consider the Entropy connections of each of the immune subsystems, firstly, with their components, and secondly, with other parameters of immunity.
As we can see (Table 3.7), the Entropy level of the Thymocytogram is the largest determined, by definition, by its major component – T-lymphocytes.
Table 3.7. Entropy correlation matrix of immune subsystems with parameters immunity
|
|
H |
H |
H |
H |
|
Variables, Z |
TCG |
SCG |
LCG |
ICG |
|
Thymus Lymphocytes |
-.64 |
-.01 |
.08 |
-.07 |
|
RETTZ |
-.26 |
-.19 |
-.06 |
-.12 |
|
LBTZ |
-.02 |
-.22 |
.01 |
-.02 |
|
HASZ |
.62 |
.13 |
-.04 |
.17 |
|
ENDTZ |
.47 |
.01 |
-.16 |
.29 |
|
MACTZ |
.40 |
.17 |
.07 |
-.06 |
|
Thymus Epitheliocytes |
.35 |
.08 |
.03 |
-.06 |
|
LCSZ |
-.22 |
-.88 |
-.09 |
.10 |
|
PLASZ -,19 |
|
.40 |
.06 |
-.26 |
|
FIBSZ -,03 |
|
.34 |
.22 |
-.10 |
|
MACSZ , 26 |
|
.30 |
.24 |
.07 |
|
ESZ -,03 |
|
.26 |
-.21 |
-.11 |
|
RETSZ , 31 |
|
.13 |
-.12 |
-.02 |
|
NEUSZ -,04 |
|
.10 |
-.05 |
.05 |
|
LBSZ -,06 |
|
.05 |
-.14 |
.15 |
|
Blood Lymphocytes , 04 |
|
-.11 |
-.83 |
-.14 |
|
Rodnuclear Neutrophils B ,06 |
|
.02 |
.60 |
.13 |
|
EZ -.10 -.03 -.05 .26 .34 .05 .10 .12 -.09 |
-.12 |
.12 |
.59 |
.09 |
|
SNNZ |
-.06 |
.13 |
.45 |
.11 |
|
Monocytes Blood |
-.11 |
-.10 |
.43 |
- ,06 |
|
BASZ |
-.15 |
-.07 |
,27 |
,42 |
|
PLAZ |
-.26 |
-.21 |
,17 |
,65 |
|
TSZ |
|
-.22 |
-,12 |
,55 |
|
NKZ |
|
.11 |
,18 |
,24 |
|
BZ |
|
.05 |
-,11 |
,18 |
|
T-helper Lymphocytes |
-.27 |
-.06 |
,04 |
-,39 |
|
OZ |
-.17 |
.13 |
-,00 |
-,34 |
|
THYMZ |
-.11 |
-.03 |
-,05 |
-,04 |
|
THYM%Z |
-.05 |
-.04 |
-,11 |
-,04 |
|
SPLZ |
|
.11 |
,01 |
-,22 |
|
SPL%Z |
|
.09 |
-,08 |
-,23 |
|
RBTZ |
|
.13 |
,05 |
- ,10 |
|
LEUZ |
|
.02 |
-,14 |
-,13 |
|
FIMZ |
|
-.37 |
-,06 |
,14 |
|
FNMZ |
|
.18 |
-,03 |
-,29 |
|
BCMZ |
-.18 |
-.34 |
.14 |
|
|
Phagocytosis Ind Neutr |
-.02 |
-.10 |
-.00 |
|
|
FNNZ |
.02 |
-.20 |
-.08 |
|
|
IKZ |
-.05 |
-.12 |
-.17 |
|
|
BCNZ |
-.18 |
.01 |
.16 |
|
|
MASS |
.16 |
.03 |
.08 |
-,11,10,16,09 -,01 -, 04 |
The content of pan-lymphocytes on the right of the major component also determines to the maximum extent the Entropy of the Splenocytogram and the Leukocytogram. Instead, the maximum determination of the entropy of the immunocytogram is carried out by its minor component - plasma cells.
With regard to another aspect of the relationship, it was found that the entropy of the thymocytogram has an activating effect on the content of plasma cells, basophils and B-lymphocytes
in the blood, as well as the content of reticulocytes and macrophages in the splenocytogram, on the other hand suppressive effect on the relative weight of the spleen and its content in the splenocytogram
lymphocytes, as well as the total content of leukocytes in the blood and intensity
blast transformation of T-lymphocytes to phytohemagglutinin. The measure of determination
of the listed immunity parameters is 32% (Table 3.8 and Fig. 3.15).
Table 3.8. The summary of the regression of the immune variables of the blood and spleen against the entropy of the
thymocytogram R=0.615; R2 = 0.378; Adjusted R2 =0.321; F(10)=6.6; p<10-6
|
Variables, Z |
r |
Beta |
St. Err.
Intercpt |
B
-0.006 |
St. Err.
0.107 |
t(98)
-0.06 |
p-level 0.955 |
|
Blood plasmocytes |
0.33 |
0.129 |
0.112 |
0.110 |
0.096 |
1.15 |
0.255 |
|
Reticulocytes Spleen |
0.29 |
0.282 |
0.095 |
0.308 |
0.104 |
2.97 |
0.004 |
|
Macrophages Spleen |
0.25 |
0.230 |
0.093 |
0.237 |
0.095 |
2.48 |
0.015 |
|
Basophiles Blood |
0.25 |
-0.114 |
0.113 |
-0.120 |
0.118 |
-1.01 |
0.314 |
|
B-Lymphocytes Blood |
0.11 |
0.161 |
0.085 |
0.153 |
0.080 |
1.90 |
0.060 |
|
Blasttransformation T-Lym -0.28 -0.393 0.094 |
-0.434 |
0.104 |
-4.17 |
10-4 |
|||
|
Spleen Mass Index -0.27 -0.190 |
|
0.098 |
-0.258 |
0.132 |
-1.95 |
0.054 |
|
|
Lymphocytes Spleen |
-0.23 -0.122 |
0.101 |
-0.134 |
0.112 |
-1.20 |
0.232 |
|
|
Leukocytes Blood |
-0.17 -0.110 |
0.092 |
-0.118 |
0.099 |
-1.19 |
0.236 |
3
2
1
0
-1
-2
-2 -1 0
1 2 3
Entropy of Thymocytogram
R=0.615; R2 = 0.378; ÿ2 (9)=48; p<10-6; ÿ Prime=0.622
Fig. 3.15. Dot plot of the canonical correlation between entropy of the thymocytogram (line
X) and parameters of immunity (line Y)
An even stronger immunomodulatory effect is exerted by the hypothetical X-factor Entropies of splenocytograms (Table 3.9 and Fig. 3.16).
Table 3.9. Regression summary of blood and thymus immune variables against splenocytogram entropy
R=0.721; R2 = 0.520; Adjusted R2 =0.460; F(13)=8.6; p<10-6
Variables, Z
Beta St. Err. B St. Err. t(95) p-level
r Intercpt -0.076 0.086 -0.88 0.378
Phagocytosis Ind Monocytes Blood -0.36 -0.375 0.091 -0.342 0.083 -4.13 10-4 -0.33
BactericidityMonocytes Blood Lymphoblasts Thymus
Blood plasmocytes
-0.338 0.088 -0.351 0.092 -3.84 10-3
-0.21 -0.127 0.075 -0.098 0.058 -1.69 0.094
-0.20 -0.256 0.087 -0.191 0.065 -2.93 0.004
Microbian Count Neutrophils Blood -0.19 -0.112 0.084 -0.066 0.050 -1.32 0.190
Reticulocytes Thymus -0.18 -0.108 0.080 -0.130 0.096 -1.35 0.180
Thymus Macrophages 0.19 0.103 0.091 0.052 0.046 1.13 0.263
Microbian Count Monocytes Blood 0.17 0.339 0.102 0.157 0.047 3.33 0.001
Segmentonuclear Neutr Blood Hassal's corpuscles Thymus
0.16 0.298 0.089 0.326 0.098 3.33 0.001
0.15 0.170 0.080 0.181 0.085 2.12 0.037
Blasttransformation T-Lymphocytes 0.13 0.139 0.082 0.134 0.079 1.70 0.092
0-Lymphocytes Blood 0.11 -0.121 0.091 -0.096 0.073 -1.32 0.190
R=0.721; R2 = 0.520; ÿ2 (12)=73; p<10-6; ÿ Prime=0.480
Fig. 3.16. Dot plot of the canonical correlation between the entropy of the splenocytogram (line X) and the parameters of immunity (line Y)
At the same time, the suppressive effect prevails, in particular, the phagocytosis activity of blood macrophages decreases with a decrease in their bactericidal ability, despite an increase in the intensity of phagocytosis, on the other hand, the latter decreases in macrophages. The content of plasma cells in the blood and lymphoblasts and reticulocytes in the thymus also decreases. At the same time, the X- factor of the splenocytogram has an activating effect on the content of macrophages and Hassal bodies in the thymus,
polymorphonuclear neutrophils in the blood, as well as blast transformation of T
lymphocytes under the influence of a mitogen. The measure of determination of the listed immune
parameters from the Entropy side of the splenocytogram is 52%.
The entropy of the immunocytogram correlates positively with the content of basophils in the blood,
endotheliocytes in the thymocytogram and lymphoblasts in the splenocytogram, instead
negatively with the content of leukocytes and pan-lymphocytes in the blood, intensity
phagocytosis of monocytes, as well as the mass index of the spleen and the content of
Splenocytograms of plasma cells. The measure of determination of the listed immune
parameters from Entropy Immunocytogram is only 29% (Table 3.10 and
Fig. 3.17).
Table 3.10. Regression summary of immune variables of blood, spleen and thymus
against entropy of immunocytogram
R=0.586; R2 = 0.343; Adjusted R2 =0.290; F(9)=6.5; p<10-5
R=0.586; R2 = 0.343; ÿ2 (8)=43; p=10-5; ÿ Prime=0.657
Fig. 3.17. Dot plot of the canonical correlation between the entropy of the immunocytogram (line X) and the parameters of immunity (line Y)
Finally, the Leukocytogram Entropy regression model includes as many as 12 immune parameters, but due to weak correlations, the degree of determination of this immune constellation by the Entropy X factor is only 32% (Table 3.11 and Fig. 3.18).
Table 3.11. Summary of regression of immune variables of blood, spleen and thymus
against leukocytogram entropy
R=0.631; R2 = 0.398; Adjusted R2 =0.322; F(13)=5.2; p<10-6
|
Macrophages Spleen |
0.24 0.122 |
0.105 |
0.138 |
0.119 |
1.16 |
0.249 |
|
Natural Killers Blood |
0.18 0.235 |
0.086 |
0.176 |
0.065 |
2.72 |
0.008 |
|
Blood plasmocytes |
0.17 0.198 |
0.108 |
0.186 |
0.101 |
1.84 |
0.069 |
|
BactericidityNeutrophils Blood |
0.16 0.399 |
0.109 |
0.648 |
0.177 |
3.66 |
10-3 |
|
BactericidityMonocytes Blood |
0.15 0.175 |
0.099 |
0.230 |
0.129 |
1.78 |
0.079 |
|
Eosinophils Spleen |
-0.19 -0.130 |
0.090 |
-0.153 |
0.107 |
-1.43 |
0.155 |
|
Killing Index Neutroph |
-0.17 -0.243 |
0.091 |
-0.271 |
0.102 |
-2.67 |
0.009 |
|
Thymus endotheliocytes |
-0.16 -0.232 |
0.091 |
-0.254 |
0.099 |
-2.56 |
0.012 |
|
T-cytolytic Lymphocytes |
-0.14 -0.113 |
0.101 |
-0.127 |
0.113 |
-1.12 |
0.265 |
|
Reticulocytes Spleen |
-0.13 -0.152 |
0.089 |
-0.182 |
0.107 |
-1.70 |
0.092 |
|
Leukocytes Blood |
-0.12 -0.418 |
0.119 |
-0.492 |
0.140 |
-3.53 |
0.001 |
|
Lymphoblasts Spleen |
-0.12 -0.119 |
0.105 |
-0.141 |
0.125 |
-1.13 |
0.260 |
Fig. 3.18. Dot plot of canonical correlation between leukocytogram entropy (X line) and immunity parameters (Y line)
Mutual independence of entropies of immune subsystems actualized the use of another approach. Using the method of cluster analysis (k-means clustering), four homogeneous groups of rats were created , which is documented
by the Euclidean distances of the members of each individual cluster from its centroid (Table 3.12). On the other hand, the clusters are significantly different from each other in the constellation of entropies of the four morpho-functional immune subsystems, which is documented by the Euclidean distances between them (Table 3.13).
Table 3.12. Cluster members and their distance (D) from the corresponding cluster center
Cluster Number 3 contains 28 cases
|
Case 1 |
13 14 17 1.82 |
21 22 25 .58 |
26 27 30 .73 |
52 61 63 .84 1.06 |
64 65 66 1.23 .86 |
|
D, 55, 28 |
.95 |
1.61 1.14 .79 |
.59 |
41,72 .71 |
.69 |
|
69 78 ,79 |
81 82 86 1.16 |
87 |
99 |
|
|
|
,94 |
80,71 ,77,57 |
1.77 88,73 |
90, 41 91, 93 1.04 |
|
|
|
D .67 .95 1.51 .89 069 .65 |
|
|
19, 40 |
.63 |
|
28, 37 ,77 ,77 |
|||
|
57 59 62 71 ,70 ,41 1,28 |
77 79 ,47 |
|
|
|
|
|
|||
|
,29 |
,70 |
83, 85 |
84, 81 |
85, 94 |
93,25 |
97,41 |
|||
|
Cluster Number 2 contains 21 cases |
|||||||||
|
Case 9 32 34 16 |
31 |
36 42 |
44 |
50 |
53 |
54 55 |
58 |
70 |
74 75 76 |
|
D, 58, 92 |
,34 ,47,68 |
,57 ,65 |
,77 |
,83 |
,79 |
,95 |
,37 ,58 |
,43 |
,36 ,98 ,88 |
|
Case 3 8 |
5 |
|
10 |
18 24 33 |
35 |
37 |
38 43 |
47 |
|
56 60 67 .89 1.36 |
|
D, 49 |
7 1.01 .76 |
,60,99 |
|
.48 1.04 1.13 .59 .86 |
|
|
,58 |
1.10 .58 |
49, 58 |
.40 |
|
68 72 73 89 .63 .87 1.10 |
92 94 .73 |
100 101 103 .68 |
|
|||||||
|
.70 ,83 |
|
96, 88 98, 66 .77 .62 |
105,81 106,75 108,74 |
|
No. 1 |
0.00 |
1.52 1.56 0.00 |
2.09 |
|
No. 2 |
1.23 |
2.07 1.44 0.00 |
1.50 |
|
No. 3 |
1.25 |
1.22 1.15 |
1.33 |
|
No. 4 |
1.45 |
|
0.00 |
|
Entropy of |
Between SS |
Within SS |
ÿ2 |
R |
F |
meaning p |
|
Immunocytogram 105.9 65.4 |
0.618 0.786 57.2 0.526 |
10-6 |
||||
|
Thymocytogram 72.7 65.6 |
0.725 39.1 0.525 0.725 |
10-6 |
||||
|
Leukocytogram 86.8 78.5 |
39.1 0.092 0.303 3.6 |
10-6 |
Plot of Means for Each Cluster
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
-1.4
-1.6
HLZ
HIZ
Variables
HTZ
HSZ
Cluster no. 1
Cluster No. 2
Cluster no. 3
Cluster no. 4
Fig. 3.19. Normalized average values of entropies for each cluster
Based on the directionality and degree of deviation Entropy morpho
of functional immune subsystems from the norm (±0.5 Z), the first cluster is marked with the code I+T+L+Sn, the second L-SnI+T+, the third T-SnInLn, the fourth I-LnSnTn (Fig. 3.19 and
Table 3.15) . Table 3.15. Matrix of connections between balneofactors and entropy clusters
Balneofactor Absent (intact rats, both sexes)
Entropy clusters of immune subsystems
T-SnInLn I+T+L+Sn I-LnSnTn L-SnI+T+ Total (ÿn2 /Nx)/N 9 20 4.05 6.30 4 18 0.89 5.00 7 20 2.48
n 5.40 1 10 0.10 4.604 3 10 0.90 3.40 3 10 2
|
Tap water (both sexes) |
n2 /Nx |
|
0.80 |
5 |
0.20 |
|
0.315 |
|
Bioactive water |
n |
|
4 |
|
7 |
|
|
|
Naphtusya (both sexes) Ozokerite applications |
n2 /Nx n |
|
0.89 |
1.25 |
2.72 |
|
0.278 |
|
(males) Water Naphtusya + Ozokerite (males) Mineral water |
n2 /Nx n n2 /Nx |
|
5
1.25 |
3
0.50 |
3 |
|
0.270
0.460 |
|
Sophia (females) |
n |
|
|
5 1.25 6 3.60 4 |
|
|
|
|
Mineral water Hertz |
n2 /Nx |
|
3 |
1.60 |
0.45 0 0 0 0 |
|
0.340 |
|
(females) Salt analogue of |
n |
|
0.90 3 0.90 5 |
1 |
1 |
|
|
|
Hertz |
n2 /Nx |
0.90 |
2.50 |
0.10 |
0.10 |
3.60 |
0.360 |
|
water (females) Total |
n |
|
4 |
1 |
|
10 |
|
|
|
n2 /Nx |
0 0 |
1.60 |
0.10 |
5 |
4.20 |
0.420 |
|
|
n |
1 |
3 |
3 |
2.50 3 |
10 |
|
|
|
n2 /Nx |
0.10 |
0.90 |
0.90 |
0.90 |
2.80 |
0.280 |
|
|
Ny |
28 |
31 |
28 |
21 |
108 |
2.303 |
ÿ2 = [(ÿn2 /Nx)/N] – 1 = 1.303 ÿ2 =
ÿ2 – (x-1)(y-1)/N = 1.303 – (8-1)(4-1)/108 = 1.109
R = {ÿ2 /(1+ ÿ2 )[xy/(x-1)(y-1)]0.5}0.5 = [1.109/2.109•(32/21)0.5] 0.5 = 0.806 ÿR = (1-R2 )/ (N-2)0.5 = 0.034
A visual analysis of the distribution of rats from different exposure groups among the four clusters creates the impression that there is no connection between the type of balneofactor received (or without it) and the characteristic features of the cluster. Thus, both intact rats and Naftusya water-drenched rats have almost the same representation in all clusters. Rats that received only ozokerite or ozokerite with Naphtuseia look quite similar, as do animals fed native Hertz water or its saline analogue (without trace elements and organic substances).
However, statistical analysis revealed a significant relationship between the type of balneofactor
and a mosaic of entropies in clusters.
Next, in order to visualize the members of each cluster, Entropy was applied discriminant analysis (forward stepwise). Separating information that
contained in four variables, condensed in three canonical ones discriminant roots. At the same time, the first root contains 43.4% discriminating capabilities, representing a thymocytogram and
Splenocytogram, the second - 40.5%, representing the Immunocytogram, the third - 16.1%, representing the Leukocytogram (Table 3.16 - 3.18).
Table 3.16. Summary of the analysis of discriminant functions for cytogram entropies
Step 4, N of vars in model: 4; Grouping: 4 grps Wilks' Lambda: 0.082; approx. F(12)=34.9; p<10-6
|
Thymocytogram 0.162 0.509 32.5 10-6 0.950 35.1 10-6 0.092 |
45.9 10-6 |
|
Splenocytogram 0.092 0.895 4.0 0.010 0.909 4.0 0.010 0.082 |
34.9 10-6 |
|
Immunocytogram 0.194 0.425 45.5 10-6 0.937 54.8 10-6 0.387 |
54.8 10-6 |
|
Leukocytogram 0.180 0.459 39.6 10-6 0.926 36.6 10-6 0.187 |
45.0 10-6 |
Table 3.18. Standardized and raw coefficients and constants for cytogram entropies
Coefficients
Standardized
Raw
Entropy of
Root 1 Root 2 Root 3 Root 1 Root 2 Root 3
Immunocytogram -0.260 -0.903 -0.397 -0.329 -1.143 -0.503
Leukocytogram -0.803 0.013 0.659 -0.933 0.015 0.766
Thymocytogram 0.673 -0.328 0.638
Splenocytogram 0.389 -0.169 0.060
0.857 -0.418 0.813
0.412 -0.178 0.063
Constants -0.406 0.234 -0.161
Eigenvalues 1.765 1.649 0.655
Cumul. Report. 0.434 0.839 1.000
Using non-standardized coefficients for variables and constants given in the table. 3.18, individual entropy values of cytograms were transformed into individual values of discriminant roots, which made it possible to visualize each animal in the information field of these roots (Figs. 3.20 and 3.21).
4
3
2
1
0
-1
-2
-3
-4
-4 -3 -2 -1 0
1 2 3
III IV II I
Root H 1(43.4%)
Fig. 3.20. Individual values of the first and second roots of Entropies for each cluster
3
2
1
0
-1
-2
-3
-4 -3
-2 -1 0
1 2 3
III IV II I
Root H 1(43.4%)
Fig. 3.21. Individual values of the first and third roots of Entropies for each cluster
Despite the visual impression of a not entirely clear mutual separation of clusters, the calculation of the squared Mahalanobis distances between clusters documents the statistical significance of the mutual separation (Table 3.19).
Table 3.19. Squares of Mahalanobis distances between clusters (above the diagonal) and the value of the F criterion (below the diagonal); for all pairs p<10-6
|
Clutters |
III |
IV |
II |
I |
|
(n) |
(28) |
(28) |
(21) |
(31) |
|
T-SnInLn |
0.0 |
10.7 |
15.2 |
9.1 |
|
I-LnSnTn+ |
35.2 |
0.0 |
9.9 |
12.3 |
|
L-SnI+T+ |
42.3 |
27.6 |
0.0 |
8.8 |
|
I+T+L+Sn |
31.3 |
42.5 |
23.8 |
0.0 |
Accuracy of retrospective recognition of animal belonging to
of a certain cluster by calculating classification functions according to coefficients and constants given in the table. 3.20, is 96.3% (table 3.21).
Table 3.20. Coefficients and constants for classification functions
III IV II
Entropy of
I (.259) ((..129559) )(.287)
Immunocytogram -0.156 -2.326 1.330 1.526
Leukocytogram 0.832 -0.899 -2.580 0.904
Thymocytogram -1,772 1,549 1,413 1,254
Splenocytogram -0.681 0.508 0.968 0.260
Constants -2.522 -3.747 -4.837 -3.110
Table 3.21. Classification matrix Rows:
observed classifications, columns: predicted classifications
|
Clutters |
III |
IV |
II |
I |
|
Percent (n) (.259) (.259) (.195) (.287) 0 0 |
||||
|
T-SnInLn 96.4 |
27 |
|
1 |
|
|
I-LnSnTn+ 100 |
0 |
28 |
0 |
0 |
|
L-SnI+T+ 85.7 |
0 |
1 |
18 2 |
|
|
I+T+L+Sn 100 |
0 |
0 |
0 |
31 |
|
Total 96.3 |
27 |
29 |
19 |
33 |
Discriminant analysis was also used to identify those
parameters of the neuroendocrine-immune complex, the aggregate of which clusters Entropy cytograms differ from each other. The program included
model of 15 parameters, in particular, by definition, Entropy of immune subsystems, and also one thymus parameter, 3 spleen parameters, 4 blood parameters and 3 neuroendocrine parameters. A number of other parameters that appeared outside the model, apparently as carriers of redundant or duplicative discriminant information, are worthy of attention (tables 3.22 and 3.23). Table
3.22. Summary of the analysis of discriminant functions for entropies of cytograms and parameters of the neuroendocrine-immune complex Step 15, N of
vars in model: 15; Grouping: 4 grps Wilks' Lambda: 0.0433; approx. F(45)=11.2; p<10-6
Variables currently in the model
Root 1 (43.5%)
L-SnI+T+ (21)
-1.64
I-LnSnTn (28)
-0.77
I+T+L+Sn (31)
-0.47
T-SnInLn (28)
2.52
Wilks' ÿ Parti
al ÿ
F-re move
p level
Tolerate rancy
|
Entropy Thymocytogr 0.81±0.15 0.60±0.15 1.00±0.14 -1.01±0.17 |
0.080 0.543 25.3 10-6 0.454 |
|
Entropy Splenocytogr 0.13±0.16 0.49±0.15 0.14±0.16 -0.33±0.23 |
0.047 0.921 2.59 0.058 0.766 |
|
Reticulocytes Spleen 0.27±0.18 0.46±0.19 0.27±0.16 -0.68±0.18 |
0.045 0.959 1.30 0.280 0.778 |
|
AMo as Sympathotone 0.35±0.29 0.53±0.20 0.47±0.22 0.12±0.22 |
0.045 0.956 1.39 0.251 0.645 |
|
Lymphocytes Thymus -0.59±0.24 -0.68±0.22 -0.66±0.19 0.24±0.25 |
0.046 0.934 2.11 0.105 0.522 |
|
(Cap•Pu/Cau•Pp)0.25 -1.05±0.28 -0.38±0.22 -0.91±0.21 -0.19±0.14 |
0.046 0.944 1.77 0.159 0.834 |
|
Spleen Mass -0.28±0.10 -0.07±0.14 -0.19±0.09 0.15±0.19 |
0.045 0.958 1.30 0.279 0.755 |
|
Blasttransform T-Lym -0.29±0.23 -0.18±0.19 -0.41±0.20 -0.01±0.18 1.89 |
0.045 0.962 1.19 0.318 0.513 |
|
Root 2 (35.8%) -0.02 -1.89 -0.20 |
|
|
Entropy Immunocytogr 0.80±0.17 -1.36±0.14 1.12±0.16 -0.19±0.14 0.47±0.20 |
0.082 0.527 26.9 10-6 0.656 |
|
Blood plasmocytes -0.31±0.19 1.60 ±0.25 -0.15±0.16 |
0.047 0.921 2.57 0.059 0.578 |
|
Basophiles Blood -0.29±0.16 -0.32±0.13 0.94±0.24 -0.32±0.14 |
0.045 0.958 1.31 0.276 0.523 |
|
Microphages Spleen -0.33±0.28 -0.22±0.24 -0.07±0.32 -0.34±0.23 |
0.045 0.969 0.98 0.408 0.830 |
Root 3 (20.7%)
-1.85
0.95
0.80
-0.45
Entropy Leukocytogra -1.59±0.16 -0.52±0.16 0.88±0.12 0.35±0.20 0.080 0.538 25.8 10-6 0.792
Testosterone
1.28±0.46 0.21±0.29 0.34±0.27 0.29±0.21
0.045 0.956 1.38 0.254 0.756
Microb Count Neutroph -0.18±0.20 -1.25±0.45 -0.54±0.25 -0.39±0.26 0.047 0.917 2.71 0.050 0.732
Variables currently not in the model
L-SnI+T+ (21)
I-LnSnTn (28)
I+T+L+Sn (31)
T-SnInLn (28)
Wilks' ÿ Parti F p
al ÿ to enterlevel
Tolerate rancy
|
Endotheliocytes Thymus 0.23±0.18 -0.39±0.22 0.08±0.21 -0.94±0.19 |
0.043 0.984 0.47 0.701 0.607 |
|
Hassal corpuscles Thym 0.57±0.17 0.54±0.15 0.77±0.15 -0.48±0.15 |
0.043 0.990 0.29 0.831 0.565 |
|
Epitheliocytes Thymus 0.27±0.32 0.43±0.20 0.33±0.17 -0.09±0.16 |
0.042 0.981 0.56 0.642 0.551 |
|
Killing Index Neutroph 0.59±0.28 0.15±0.21 0.18±0.21 0.17±0.16 |
0.042 0.979 0.63 0.600 0.825 0.043 |
|
Macrophages Thymus 0.74±0.19 1.46±0.42 1.13±0.41 0.33±0.32 |
0.994 0.17 0.914 0.509 |
|
Sex Index 0.43±0.20 -0.06±0.19 0.36±0.17 -0.21±0.19 |
0.042 0.981 0.58 0.629 0.613 |
|
Reticulocytes Thymus -0.07±0.11 0.01±0.14 -0.18±0.10 0.56±0.21 |
0.043 0.985 0.44 0.727 0.165 |
|
Monocytes Blood -0.65±0.17 -0.14±0.16 0.16±0.13 0.36±0.18 |
0.042 0.980 0.61 0.609 0.759 |
|
Mode as Humoral chan -0.83±0.31 -0.81±0.18 -0.33±0.24 -0.21±0.19 |
0.042 0.976 0.74 0.531 0.235 |
|
(Cau•Pu/Cap•Pp)0.25 -2.39±0.60 -0.85±0.37 -1.62±0.47 -0.84±0.39 |
0.042 0.980 0.61 0.613 0.595 |
|
(Nap•Ku/Kp•Nau)0.25 0.16±0.29 -0.11±0.17 0.42±0.24 0.15±0.18 |
0.042 0.974 0.79 0.502 0.872 |
|
T-cytolytic Lymphocyte 0.59±0.19 -0.59±0.22 0.34±0.16 0.04±0.21 |
0.043 0.983 0.50 0.681 0.614 |
|
Phagocytosis Ind Neutr 0.06±0.26 -0.52±0.36 -0.01±0.24 -0.17±0.22 |
0.042 0.972 0.87 0.460 0.559 |
|
T-helper Lymphocytes -0.83±0.23 0.14±0.31 -0.74±0.21 -0.25±0.23 |
0.042 0.977 0.70 0.554 0.673 |
|
Rodnuclear Neutrop B -1.14±0.31 -0.48±0.26 0.52±0.22 -0.17±0.24 |
0.043 0.996 0.11 0.953 0.689 |
|
Segmentonucl Neutr B -0.49±0.15 -0.08±0.14 0.38±0.15 0.03±0.20 |
0.042 0.974 0.78 0.507 0.722 |
|
Eosinophils Blood -0.69±0.11 -0.36±0.15 0.29±0.20 0.16±0.19 |
0.043 0.994 0.19 0.902 0.674 |
|
Lymphocytes Blood 1.13±0.18 0.34±0.14 -0.70±0.15 -0.22±0.23 |
0.043 0.983 0.50 0.685 0.379 |
Table 3.23. Summary of step-by-step analysis of discriminant functions for entropy of cytograms and parameters of the neuroendocrine-immune complex
Variables currently in the model
F that p
enter level
Lambda
F p value level
Entropy Immunocytogram 54.83 10-6 0.387 54.8 10-6
Entropy Leukocytogram 36.64 10-6 0.187 45.0 10-6
Entropy Thymocytogram 35.12 10-6 0.092 45.9 10-6 4.00 0.010
Blood plasmocytes 0.082 35.0 10-6
Entropy Splenocytogram 4.23 0.007 0.073 29.1 10-6 2.76 0.046
Reticulocytes Spleen AMo as Sympathotone Thymus Lymphocytes
0.067 24.8 10-6 2.72 0.049 0.062
21.9 10-6 1.80 0.151 0.059 19.4
10-6
Microbial Count Neutroph 1.84 0.144 0.056 17.6 10-6
(Cap•Pu/Cau•Pp)0.25 as PTA 1.62 0.191 0.053 16.0 10-6
Microphages Spleen 1.50 0.219 0.051 14.7 10-6 1.32 0.272
|
Testosterone |
0.049 13.6 10-6 |
|
Spleen Mass |
1.27 0.289 0.047 12.7 10-6 1.10 |
|
Basophiles Blood |
0.353 0.045 11.9 10-6 |
Blasttransformation T-Lym 1.19 0.318 0.043 11.2 10-6
The separating information contained in 15 variables is condensed in three canonical discriminant roots (Table 3.24). At the same time, the first root contains 43.5% of the discriminating capabilities (r*=0.844; Wilks' ÿ=0.043; ÿ2 (45)=306; p<10-6), the second - 35.8% (r*=0.819; Wilks' 'ÿ=0.151; ÿ2 (28)=184; p<10-6), the third - 20.7% (r*=0.736; Wilks' ÿ=0.459; ÿ2 (13)=76; p<10-6) .
Table 3.24. Standardized, structural and raw coefficients and constants for cytogram entropies and neuroendocrine-immune parameters
of the complex
According to the already used algorithm, using non-standardized coefficients and constants (Table 3.24), members of all clusters were visualized in the information field of discriminant roots.
The localization of members of the T-SnInLn cluster in the positive zone of the first root axis (Figs. 3.22 and 3.23) reflects, first of all, their reduced level of entropy in the thymocytogram, as well as the reduced content of reticulocytes in the splenocytogram, while in animals of other clusters these parameters are more or
less elevated . This is combined with normal levels of T-lymphocytes in the thymus and their
the ability to blast transformation, as well as the mass of the spleen, while in other clusters these immune parameters are reduced to a greater or lesser extent.
Fig. 3.23. Patterns of parameters, information about which is condensed in the first canonical discriminant root, and also not included in the model
Such an immune constellation is accompanied by normal levels of sympathetic tone and parathyroid activity, while in members of other clusters they are respectively increased and decreased (Table 3.22).
Members of the I+T+L+Sn cluster are separated from others along the second axis root, occupying the top position, which reflects their maximum for the sample
Entropy level Immunocytogram. This is accompanied by the maximum levels of plasma cells and basophils in the blood, as well as the normal content of microphages in the spleen, while it is slightly reduced in other clusters.
The opposite position is occupied by members of the I-LnSnTn cluster with minimal or reduced levels of the listed immune parameters (Figs. 3.22 and 3.24 and Table 3.22).
1
0.5
0
y = 0.140x + 0.16 R2 = 0.998
not yes
-0.5
-1
y = 0.384x + 0.03 R2 = 0.919
-2 -1.5
-1 -0.5 0
0.5
1 1.5 2
Root 2
Fig. 3.24. Patterns of parameters, information about which is condensed in the second canonical discriminant root, and also not included in the model
The last L-SnI+T+ cluster is separated from the others along the axis of the third root (Figs. 3.25 and 3.26), occupying the lower zone, which reflects their minimum
for the sample, the Entropy level of the Leukocytogram (Table 3.22).
3
2
1
0
-1
-2
-3
-4
-4 -2
0 2 4
Root 1 (43.5%)
T-SnInLn I-LnSnTn L-SnI+T+
6 I+T+L+Sn
Fig. 3.25. Individual values of the first and third roots of Entropy and neuroendocrine-immune complex parameters in rats of different clusters
1
0.5
0
-0.5
-1
T-cytol PhIN
T-help MCN
Testost
-1.5
-2
-2 -1.5 -1
-0.5 0
0.5 1
Root 3
Fig. 3.26. Patterns of parameters, information about which is condensed in the third canonical discriminant root, and also not included in the model
This is accompanied by a normal level of intensity of phagocytosis blood microphages, while in other clusters it is reduced and also increased
the level of plasma testosterone against the background of its normal levels in other members
clusters.
Despite separate mutual penetrations, there are three in the information field discriminant roots, all four clusters are quite clearly demarcated,
which is documented by Mahalanobis distances between them (Table 3.25).
Table 3.25. Squares of Mahalanobis distances between clusters (above the diagonal) and the value of the F criterion (below the diagonal); for all pairs p<10-6
|
Clutters |
III |
IV |
II |
I |
|
(n) |
(28) |
(28) |
(21) |
(31) |
|
T-SnInLn |
0.0 |
16.3 |
20.1 |
15.5 |
|
I-LnSnTn |
12.7 |
0.0 |
12.6 |
15.0 |
|
L-SnI+T+ |
13.3 |
8.3 |
0.0 |
12.5 |
|
I+T+L+Sn |
12.7 |
12.3 |
8.7 |
0.0 |
Selected discriminant parameters can be used to
identification of the belonging of this or that rat to a certain cluster. This is the goal discriminant analysis is implemented with the help of classifiers
(discriminant) functions (Table 3.26). These functions are special linear combinations that maximize the variance between groups and minimize the variance within groups. The coefficients of the classification functions are not standardized, so they are not interpreted. The object belongs to the group with the maximum value of the function, calculated by summing the products of the values of the variables and the coefficients of the classifying functions plus a constant. Table 3.26. Coefficients and constants
for classification functions
Variables currently in the model
III (28)
IV (28)
II (21)
I (31)
Entropy Immunocytogram 0.092 -2.857 1.667 1.083
Entropy Leukocytogram 1.070 -1.125 -2.649 1.021
Entropy Thymocytogram -3,391 2,128 1,703 1,604
Blood plasmocytes -0.008 0.465 0.042 1.304
Entropy Splenocytogram -0.664 0.441 1.104 0.507
|
Reticulocytes Spleen |
-0.665 0.303 0.325 0.387 |
|
AMo as Sympathotone |
0.674 0.166 -0.178 -0.336 |
|
Thymus Lymphocytes |
-0.908 0.428 0.356 0.251 |
|
Microbial Count Neutroph 0.284 -0.688 -0.250 -0.302 |
|
|
(Cap•Pu/Cau•Pp)0.25 |
-0.279 -0.231 -1.092 -1.039 |
|
Microphages Spleen |
-0.183 0.124 -0.306 0.183 |
|
Testosterone |
0.288 -0.010 0.139 -0.366 |
|
Spleen Mass |
0.492 -0.793 -0.839 0.225 |
|
Basophiles Blood |
-0.653 0.021 -1.149 -0.114 |
Blasttransformation T-Lym -0.917 0.342 -0.265 -0.202
Constants -3.708 -4.673 -6.126 -4.475
The accuracy of retrospective classification with respect to different clusters varies from 85.7% to 100%, and in general it is 95.4% (Table 3.27).
Table 3.27. Classification matrix Rows:
observed classifications, columns: predicted classifications
Clutters
Percent III
IV II I
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CONCLUSION
Entropy has been demonstrated to possess a real life force which
is quantified by the canonical correlation coefficient of entropy levels of morpho- functional immune subsystems with the immunity parameters of other subsystems. That is, entropy acts as a subject of influence. On the other hand, entropy is the object of the regulatory influence of the autonomic nervous and endocrine systems.
Each constellation of independent thymocytogram entropies, accompanied by splenocytogram, immunocytogram and leukocytogram specific constellation of parameters of the neuroendocrine-immune complex.
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SECTION 4
FACTOR ANALYSIS OF THE INFORMATION FIELD OF PARAMETERS
NERVOUS REGULATORY STRUCTURES (EEG/VRS) AND IMMUNITY
The object of clinical and physiological observation were 37 men and 14 women aged 23-76 years who underwent restorative treatment of chronic pyelonephritis and cholecystitis in the remission phase at the Truskavets resort
dysfunctions of the neuroendocrine-immune complex. The survey was conducted twice, before and after a 7-10-day course of balneotherapy (drinking bioactive water Oil, ozokerite applications, mineral fountains).
In the morning, under basal conditions, an ECG was recorded in lead II for 7 minutes (hardware and software complex "CardioLab+VSR", in "KHAI-Medyka",
Kharkiv) to determine the spectral and time parameters of VRS. Frequency Domain Methods: HF (0.4÷0.15 Hz), LF (0.15÷0.04 Hz), VLF (0.04÷0.015 Hz), ULF
(0.015÷0.003 Hz) components. Time Domain Methods: HR, SDNN, RMSSD, pNN50 [HRV, 1996; Berntson GG et al, 1997].
At the same time, EEG was recorded (hardware and software complex "NeuroCom
Standard" of the same article) monopolar in 16 loci (Fp1, Fp2, F3, F4, F7, F8, C3, C4, T3, T4, P3, P4, T5, T6, O1, O2) according to the international system "10 -20”, with the location of reference electrodes A and Ref on the tufts of the ears. Average amplitude (ÿV), average frequency (Hz), frequency deviation (Hz), indices (%), asymmetry coefficients (%), as well as absolute (ÿV2 /Hz) and relative (%) spectral power density (PSD) were measured and calculated ) of the main rhythms: ÿ (35÷13 Hz), ÿ (13÷8 Hz), ÿ (8÷4 Hz) and ÿ (4÷0.5 Hz) in all loci, according to
with instructions.
In addition, we calculated the lateralization index (LI) of the SHSP of each rhythm according to by the formula [Newberg AB et al, 2001]:
LI, % = ÿ [200•(Right – Left)/(Right + Left)]/8.
107
The entropy (h) of the normalized SPD, HRV and EEG loci was calculated according to the formula of Popovych IL [2016], derived on the basis of the formula
Shannon CE [1963]:
hHRV =
- [SPHF•log2SPHF+SPLF•log2SPLF+SPVLF•log2SPVLF+SPULF•log2SPULF]/log24; hEEG =
- [SPDÿ•log2SPDÿ+SPDÿ•log2SPDÿ+SPDÿ•log2SPDÿ+SPDÿ•log2SPDÿ]/log24
Leukocytogram (LCG) was calculated in the capillary blood smear, based on
which calculated two variants of the strain index (Strain Index) and deduced two variants of the adaptation index of IL Popovych [Popovych I.L. et al., 2000; Kostyuk P.G. et al., 2006; Barylyak LG et al, 2013; Petsyukh SV et al, 2016].
The algorithm for quantification of indicators of Popovych's adaptation index is based on those proposed by L.H. Garkava. et al. [1990, 1998] ranges of the relative content in the leukocytogram of lymphocytes, which determines the type of general adaptation of the
organism's reaction (Zÿÿÿ), as well as other components of the leukocytogram and the general level leukocytes, which indicate the harmonious or disharmonic nature of ZARO
(Table 4.1).
Table 4.1. The first scale for the quantitative assessment of pathological, disharmonious and harmonious ZARO and the formula for calculating the stress index of the leukocytogram [Popovych I.L. et al., 2000; Kostyuk P.G. et al., 2006].
Strain Index-1 = [(Eo/3,5-1)2 + (SN/3,5-1)2 + (Mon/5,5-1)2 + (Leu/6-1)2 ]/4
Later, L.H. Harkavy et al. [2000] proposed slightly different ranges of leukocytogram components, on the basis of which we, together with Popovych I.L. a second scale was created (Table 4.2).
Table 4.2. The second scale for the quantitative assessment of pathological, disharmonious and harmonious ZARO and the formula for calculating the stress index of the leukocytogram
Strain Index-2 = [(Eo/2.75-1)2 + (SN/4.25-1)2 + (Mon/6-1)2 + (Leu/5-1)2 ]/4
The entropy of the leukocytogram was calculated according to the formula:
hLCG = - [Lym•log2Lym+Mon•log2Mon+Eos•log2Eos+SNN•log2SNN+StubN•log2StubN]/log25
Immune status was assessed by the relative content of lymphocytes in the blood, which was determined by the method of rosette formation with ram erythrocytes, on which monoclonal antibodies against CD3, CD4, CD8, CD22 and CD56 receptors were adsorbed (from the Granum company, Kharkiv) with visualization under a microscope with immersion system. The subpopulation of T- lymphocytes with high receptor affinity was determined in the test of "active" rosette formation. In serum
the content of immunoglobulins G, A, M was determined (ELISA, analyzer "Immunochem",
USA) and circulating immune complexes (precipitation method with
polyethylene glycol) [Lapovets LE, Lutsik BD, 2002].
The entropy of the immunocytogram (ICG) was calculated using a similar algorithm:
hICG =
- [CD4•log2CD4+CD8•log2CD8+CD22•log2CD22+CD56•log2CD56]/log24
The parameters of the phagocytic function of neutrophils were determined by the Douglas method
SD, Quie PG. [1981], modified by Kovbasnyuk MM [Kul'chyns'kyi AB et al, 2016].
Here is the author's description of the modification. Freshly collected venous blood was used. 5 drops of this blood, immediately after collection, were placed in glass centrifuge tubes with 2 ml of 4% sodium citrate solution. Since blood was collected from patients within 2 hours, ready-made blood samples were stored in a refrigerator at a temperature of 40 C. Subsequently, the samples were centrifuged (5,000 rpm, for 5 minutes). The supernatant was removed using a Pasteur pipette. The leukocyte fraction with traces of the erythrocyte fraction was used for the study.
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The objects of phagocytosis were day cultures of Staphylococcus aureus (ATCC N 25423 F49) and Escherichia coli (O55 K59), obtained in the chemical and bacteriological laboratory of the GGRES branch of PrJSC "Truskavetskurort". In order to prepare a suspension of microbial bodies, the respective cobs were rinsed with a sterile physiological solution, the test tubes were immersed in boiling water for 3 seconds, and cooled to room temperature. The integrity of the microbial bodies was monitored using a microscope. For this, a drop of suspension St. aureus and E. coli were applied to skim
the slide was fixed in the flame of the alcohol bottle. Finished preparations were painted according to Pappenheim, microscoped under immersion, objective x90, eyepiece x10.
Test samples were prepared as follows. In plastic Vidalivsk
tubes were introduced in the following sequence: 0.05 ml of heparin, 0.05 ml sterile physiological solution, 0.1 ml leukocyte suspension, 0.05 ml
suspensions with microbial bodies St. aureus or E. coli. Samples were shaken and
were placed in a thermostat at a temperature of 370 C for 30 min, shaking them at the same time every 10 min. After that, to stop phagocytosis, the samples were cooled
under running water for 10 min. Later, the samples were centrifuged (5
000 rpm for 5 min), the supernatant was removed using a Pasteur pipette. Smears were prepared from the suspension of leukocytes (with traces of erythrocytes), air-dried at room temperature
and stained according to Pappenheim. Microscopy under immersion, objective x90, eyepiece x10.
The phagocytic activity of peripheral blood neutrophils was evaluated according to the following indicators. Phagocytic activity was calculated (the number of phagocytes per 100 neutrophils); microbial number (the number of microbes absorbed by each specific phagocyte) and the index of digestion (killing) of absorbed
microorganisms (% of completely digested microorganisms to the total
the number of absorbed microorganisms). Microbial number and index of their digestion was determined for each phagocyte and fixed in the phagocytic frame.
Using these individual data, average microbial counts were calculated
number and index of keeling.
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According to the theory of factor analysis [Kim JO, Mueller Ch.W., 1989], it is believed that the observed parameters (variables) are a linear combination of some latent (hypothetical, unobservable) factors. In other words, factors are hypothetical, not directly measurable, latent variables in terms of which the measured variables are described.
Some of the factors are allowed to be shared by two or more variables, others are characteristic of each parameter separately. Characteristic (unique) factors are orthogonal
to each other, i.e. do not contribute to the covariance between the variables. Others in words, only general factors, the number of which is much less than the number variables, contribute to the covariance between them. Can be accurately identified latent factor structure by examining the resulting covariance
matrices. In practice, it is impossible to obtain the exact structure of the factor model, one can only find estimates of the parameters of the factor structure. Therefore, for
by the principle of the postulate of parsimony, accept the model with the minimum number common factors.
One of the methods of factor analysis is principal component analysis. The main ones components (GC) are linear combinations of observed variables that have the properties of orthogonality, that is, they are natural orthogonal functions. Therefore, GCs are the opposite of general factors, since the latter are hypothetical and are not expressed through a combination of variables, while GCs are linear functions of observed variables.
The essence of the GC method is the linear transformation and condensation of the initial information. On the basis of correlation matrices, a system of orthogonal, linearly independent functions, nominated by eigenvectors, is determined
correspond to a system of self-nominated independent random variables
numbers of the correlation matrix (ÿ). The first few eigenvalues of the correlation coefficient matrices exhaust the main part of the total dispersion of the field, therefore in the analysis of the decomposition results, special attention is paid to the first eigenvalues and corresponding to their components. And how large-scale processes are
111
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functional systems of the body are characterized by a large dispersion, it is fair to assume that they are reflected in the first components.
GC analysis is a method of transforming a given sequence of observed variables into another sequence of variables. The method of obtaining the directions of the main axes is based on finding eigenvalues and vectors of correlations (covariances). The eigenvalue (ÿ) is the most important characteristic of the matrix (R); is used in the decomposition of the covariance matrix and at the same time - as a criterion for determination
number of isolated factors and as a measure of dispersion corresponding to this factor.
An eigenvector (V) is a vector associated with the corresponding eigenvalue i
is obtained in the process of selection of primary factors. These vectors are presented in normalized form, are factor loads. The connection between the mentioned characteristics is expressed by the equation: RV=ÿV.
The first eigenvalue represents the value of the variance corresponding to the first
to the main axis, the second to the second, etc. The sum of eigenvalues is equal to the number of variables, a the proportion of variance corresponding to a given direction or GC is obtained from the division eigenvalue by the number of variables. The task of the GC is to explain
of the maximum share of the variance of observations, and the task of common factors is the
explanation of correlations between variables.
In the n-dimensional factor space, the first GC is a representation of points (data) along the selected main axis, it reproduces the maximum proportion of dispersion of experimental data. If you describe each point in a new coordinate system, there is no loss of information. In the case of a linear relationship between the variables, the first GC contains all the information to describe each point, but if the variables are independent, then the main axis is absent, and the GC analysis does not contribute
even minimal compression of observation results. In the presence
more or less close relationship between the variables, the rest of the information is contained in subsequent GCs, while the axis of the second GC is perpendicular to the axis of the first GC and a smaller part of the data is located along it, that is, the second GC reproduces
the next largest share of dispersion; even less information is contained along axis of the third GC, perpendicular to the first two, etc. It is believed that for
112
the study of the factor structure of the studied field can be limited to the consideration of such a number of GCs, the total contribution of which to the total variance of the initial data exceeds 2/3. Another approach to determining the number of GCs is the use of the Kaiser (ÿ>1) and Cattell criteria (by the maximum deceleration of the
value of the eigenvalue ÿ, visualized graphically) [Kim JO, Mueller Ch.W., 1986].
At the first stage of the factor analysis, we found out that the variance 20 of the information field of 229 registered parameters are absorbed factors (Fig. 4.1).
Fig. 4.1. The values of the eigenvalues of the main components
Applying Cattel's technique, the number of factors analyzed by us
limited to twelve (Table 4.3), the total contribution of which to the total variance of the initial data is 66.2%, that is, it just reaches the necessary critical level.
Table 4.3. The values of the eigenvalues of the main components and the fraction of variance absorbed by them
Extraction: Principal components
|
PC |
Eigen value |
% total Variance |
Cumul. Eigenval |
|
|
|
37.7 |
16.8 |
37.7 |
|
|
1 2 |
31.4 |
14.0 |
69.1 |
|
|
3 |
20.8 |
9.3 |
89.9 |
|
|
4 |
14.4 |
6.4 |
104.3 |
|
|
5 |
11.2 |
5.0 |
115.5 |
|
|
6 |
7.3 |
3.2 |
122.8 |
|
|
7 |
5.8 |
2.6 |
128.5 |
|
|
|
5.6 |
2.5 |
134.1 |
|
|
8 |
5.3 |
2.4 |
139.5 |
|
|
9 |
4.6 |
2.1 |
144.1 |
|
|
10 11 |
4.2 |
1.9 |
148.3 |
Cumulative % 16.8 30.9 40.1 46.6 51.6 54.8 57.4 59.9 62.3 64.3 66.2 |
A factorial structure is considered simplest if all variables have unit factorial complexity, that is, when each variable has a non-zero loading on only one general factor. If there are at least two factors, then each row contains only one non-zero element, each column has
several zeros, for each pair of columns the zero elements do not match. But such a simple structure is unattainable for real data. Simplicity of structure defined if for each factor there are at least three variables that have
there is a significant load on this factor. In the orthogonal case, it is simple the structure is given by the set of points that have non-zero loads only
by one factor (axis).
To achieve a simpler interpretation of decisions, the concept is used oblique (non-orthogonal) factors, which makes it possible to better present clusters of variables without renouncing orthogonality (independence) of factors.
In order to find the matrix of the factor mapping closest to the simplest ideal structure, the procedure of orthogonal rotation using the quartimax, varimax and equamax methods is carried out. Varimax is a method of obtaining an orthogonal solution, which is reduced to the simplification of the factorial structure using the criterion of minimizing the column of the factorial mapping matrix; quartimax - the criterion for obtaining an orthogonal solution, which comes down to simplifying the description of the rows of the matrix, and equamax - combines the properties of both first, therefore, at the next stage of factor analysis, the procedure is orthogonal
Machine Translated by Google
rotation was carried out by us using the Equamax normalized method in order to find the matrix of the factor mapping closest to the simplest ideal
structures.
The summary of the factor analysis by the GC method of the field of variables is shown in the table. 4.4, which is, in essence, a matrix of factor mapping, the elements of which are
factor loadings - correlation coefficients between factors (GC) and variables
It is known that indicators (variables) are combined in one GC, as much as possible interrelated and minimally related to other indicators. Ago
we used factor analysis as a heuristic selection method
among the registered variable clusters, as structures were found
are considered as hypotheses reflecting some trends in the obtained data to the clustering of variables into clusters [Kim JO, Mueller Ch.W., 1986].
After orthogonal rotation in the GC, there were slight changes in the fractions absorbed dispersion, with the exception of the eighth GC, the share of which doubled - from 4.1% to 8.0%. Therefore, for the sake of convenience, when characterizing the elements of the GC, we adhere to one exception, primary order.
The summary of the factor analysis by the GC method of the field of variables is shown in the table. 4.2, which is, in fact, a factor mapping matrix, the elements of which are factor loadings - correlation coefficients between GC and variables. We took 0.5 as the lower limit of inclusion (except for entropy). For ease of perception, we applied pseudo-coloring of parameters: delta rhythm, theta rhythm, alpha rhythm, beta rhythm, immunity, entropy.
As we can see, the first GC, by definition, explains the maximum fate variability - 14.9%, and reflects, first of all, the absolute density
spectral power (SPS) of alpha rhythm, to a lesser extent - theta rhythm and more
smaller - beta rhythm. The second GC (12.2% of the variance) represents the relative SHSP alpha and delta rhythms. The third GC absorbs 8.2% of the variability given by the relative Theta-rhythm's SHSP, as well as the SHSP entropy of 12 EEG loci out of 16 registered.
The fourth GC explains 7.9% of the variance, representing the relative SHSP beta-
115
rhythm Another 4.6% of the variability is absorbed by the absolute SHSP of the delta rhythm, presented in the eighth GC. Identical in terms of absorbed dispersion is the fifth GC, in which VRS-markers of vagus tone are collected. In the next GC, the elements of the leukocytogram and the adaptation index determined by them and its entropy are collected. HRS markers of sympathetic tone, stress index of the leukocytogram, as well as HRS entropy make up the twelfth GC, which absorbs 2.8% of the variance. An
identical fate of the dispersion is absorbed by the sixth GC, which represents the entropy of the SHSP other 3 EEG loci. The seventh GC is represented by only four parameters, which
reflect the lateralization of the main EEG rhythms. The following GC represents
natural and T-killers. And the last GC is the entropy of the SHSP of the last EEG locus.
Table 4.4. Factor loadings (after Equamax normalized rotation). Clusters of loads that determine the oblique factors for the hierarchical parameter analysis
|
Variable GK1 |
GK2 GK3 |
GK4 GK5 |
GK8 GK9 |
GK12 F6 |
F7 F10 F11 |
|
T3A |
-.183 -.073 -.050 |
,087 |
-.016 |
,009 |
-.136 .042 .017 .001 .072 .019 -.176 -.029 -.043 .077 |
|
F7A |
.014 .054 .119 |
,055 |
-.060 |
,091 |
.115 .029 -.101 -.026 -.024 .007 .106 .017 -.147 |
|
P3T |
-.120 -.155 |
,159 |
.030 |
-,241 |
-.146 , 028 -.061 .021 .053 -.061 -.019 -.008 .202 |
|
T5A |
-.200 -.050 -.276 |
,155 |
-.018 |
-,132 |
.134 -.010 -.099 -.077 -.049 .039 .118 .052 -.035 |
|
T4A |
-.092 -.281 -.115 |
,117 |
-.002 |
,005 |
-.075 -.030 .102 ,130 ,024 -,037 ,038 -,006 ,092 ,103 |
|
Fp1A |
-.355 -.033 -.203 |
,207 |
-.045 |
-,091 |
,022 -,097 -,024 -,030 ,221 ,025 -,005 ,114 ,024 ,068 |
|
Fp2A |
-, 077 -.199 |
,193 |
-.029 |
,004 |
-,118 -,125 ,045 ,079 ,018 ,070 -,221 -,143 ,120 -,098 |
|
F4A |
-.085 -.151 -.079 |
,183 |
.003 |
,131 |
-,100 -,065 -,125 ,067 ,039 -,059 ,018 ,064 ,088 -,047 |
|
T6A |
.235 .069 -.013 |
,159 |
.011 |
-,000 |
-,115 -,189 -,015 - ,070,245,167 |
|
P4B |
.305 -.016 -.050 |
-,084 |
-.072 |
,150 |
-,001,030,062,135,048,059 -,093,052,074,085,110,060 |
|
P3B |
-.011 .364 .037 |
-,075 |
-.073 |
-,124 |
-,118 -,093 -,226 -,032,029,047,073 .022 -.015 .065 |
|
T5T |
.394 -.278 -.159 |
,147 |
-.029 |
-,232 |
.099 .046 .015 -.091 .011 -.066 -.113 .099 -.016 -.013 |
|
P4T |
-.404 -.021 , |
,217 |
.093 |
,014 |
-.011 .026 -.055 .013 .066 .043 .051 .087 014 067 |
|
F8A |
202 ,148 -,464 |
-,019 |
.010 |
,080 |
-122 118 046 110 272 095 055 187 042 014 039 196 |
|
C3T |
-,069 ,156 ,379 |
,212 |
.029 |
,020 |
021 030 141 121 031 004 051 019 ,040 ,082 -,032 |
|
C4T |
-,151 ,085 -,153 |
,185 |
.052 |
-,016 |
-,052 ,052 -,046 -,102 -,157 -,069 ,027 -,047 ,122 |
|
O1A |
-,069 -,357 -,180 |
,164 |
-.008 |
,071 |
-,000 ,025 -,176 -,050 -,083 ,088 -, 132 ,050 -,006 |
|
C4A |
-,425 -,066 ,247 |
,283 |
.015 |
,099 |
-,070 ,049 ,091 ,075 ,138 -,340 -,119 ,080 -,099 -,036 |
|
F7T |
,063 ,236 ,352 |
,151 |
-.050 |
-,162 |
-,019 ,122 ,145 ,068 -,105 ,102 -,048 -, 1 55,118 -,124 |
|
F3A |
-,442 -,196 -.191 |
,270 |
- ,027 |
,099 |
-,053 -,021,006 -,030 -,087 -,002 |
|
Fp1T |
.197 .264 .030 |
,218 |
-,000 |
-, 226 |
|
|
C4B |
.152 .217 -.006 |
-,120 |
-,137 |
,026 |
|
|
O2B |
.210 |
- |
,004 |
,020 |
|
|
P3A |
|
|
-,019 |
,053 |
|
|
C3A |
|
|
-,001 |
,097 |
|
|
O1T |
|
|
-,050 |
-,094 |
|
|
T ÿV |
|
|
,063 |
-,152 |
|
|
A ÿV |
|
|
-,011 |
-,027 |
|
|
C3B |
|
|
-,134 |
,036 |
|
|
F8T |
|
|
,075 |
-,244 |
|
|
T3T |
|
|
,005 |
-,389 |
|
O2T
,918,893,888,886,879,876,872,184622,2,87428,2,88,421,141,93854,02,6843,37-,2,081139-5,1,8023,21,0802,80,3802,139,8611,797,794,792,787,785,782,781,770 ,768 ,758 ,744 ,731 ,728 ,
|
P4A ,700 |
-.430 -.155 |
,315 |
-.045 |
.134 |
.080 -.175 .104 .048 -.035 .023 .031 .049 .011 |
|
T4T ,697 |
.190 .453 |
,168 |
.109 |
-.194 |
.138 .054 -.160 -.004 -.216 .017 .025 -.015 .065 |
|
O2A ,696 |
-.391 -.135 |
,238 |
.037 |
.139 |
.227 .041 .119 - 195 -.057 .028 -.092 .027 -.087 |
|
F4B ,691 |
-.165 .078 |
-,173 |
-.187 |
.076 |
.157 .034 .007 .105 -.050 -.081 .344 -.103 -.036 |
|
T6T ,682 |
.169 .266 .188 |
,162 |
.037 |
-.074 |
.113 .187 -.065 -.201 -.071 , 121,205,142,160 |
|
F8B ,651 |
-.012 -.055 |
-,289 |
-.167 |
-.170 |
-,277 -,161,089,174,126,148 -,107 -,053 |
|
Fp1B ,642 |
-.069 -.144 |
-,128 |
-.161 |
-.470 |
-,186,221,140 -,048 -,128,038,071,142,060,139 |
|
F3B ,640 |
.123 .107 .550 |
-,132 |
-.210 |
.026 |
,013 ,010 -,115 -,211 ,016 -,238 ,185 ,141 -,011 |
|
F3T ,640 |
-.068 -, |
,147 |
.020 |
.007 |
,084 -,143 -,073 -,265 -,295 -,083 ,174 ,158 ,164 |
|
Fp2B ,631 |
167,190,370,352 |
-,157 |
-.153 |
-.353 |
-,011 -, 023 -.150 .257 -.081 .003 -.083 -.412 |
|
Fp2T ,618 |
-,051,058 |
,225 |
.075 |
-.365 |
-.094 .252 -.046 -.115 -.052 -.232 .170 .241 -.185 |
|
C3D ,578 |
|
,261 |
.008 |
.067 |
.072 -.309 -.202 , 023 ,273 ,050 -,090 ,131 -,210 |
|
O1B ,576 |
-,162,052,609,087 |
-,094 |
-.137 |
.057 |
,059 -,152 ,044 -,335 ,192 ,116 -,080 -,097 -,039 |
|
F4T ,566 |
|
,127 |
.024 |
-.040 |
-,110 ,075 -,156 -,203 ,028 - |
|
B ÿV .562 |
|
-,368 |
-.240 |
-.077 |
,004,032,020,091,102,063,016 -,011 -,037,108 |
|
T3B ,557 |
|
-,265 |
-.161 |
-.314 |
-,062,085,039,000 -,056,091,062,127 -,013,199 |
|
T5B ,530 |
-,061,079,142,136 |
-,336 |
-.148 |
-.069 |
-,020,056 -.015 -.024 -.045 -.117 .029 -.058 -.053 |
|
T4B ,528 |
-,080,146 |
-,396 |
-.177 |
-.251 |
-.010 -.068 -.024 -.025 .024 .169 .165 .082 .104 |
|
C4D ,510 |
-,118,434 |
,261 |
|
-.302 |
-.096 -.013 .011 -.049 -.010 -.198 .008 .045 .122 |
|
P4D ,491 |
-,072,349 -,120 |
,274 |
.031 , |
-.161 |
-.157 -.087 -.104 -.030 -.149 -.119 .017 -.053 |
|
F3A% .109 |
- , 903 -.188 |
|
122 |
-.046 |
-.156 .080 -.058 -.079 - ,094,123 .136 -.002 .023 |
|
T4A% .093 |
-.883 -.114 |
|
.025 |
.052 |
-.032 -.095 -.030 -.197 .012 -.095 -.110 .158 -.031 |
|
F4A% .121 |
-.869 -.179 |
|
-.074 |
.072 |
.133 -.011 .000 -.025 -.127 .025 -.275 ,002 ,005 |
|
C4A% .045 |
-.867 -.196 |
|
.045 |
.011 |
-,076 ,211 -,001 ,190 ,008 -,093 -,094 -,173 -,070 |
|
O1A% .200 |
-.865 .004 |
|
-.037 |
.065 |
-,205 -,008 ,008 ,023 ,175 ,020 ,048 -,028 -,112 |
|
Fp1A% .135 |
-.858 -.089 |
|
.099 |
.128 |
-.003 -.077 .002 -.227 -.026 -.124 -.183 -.186 |
|
C3A% .097 |
-.835 -.213 |
|
-.052 |
-.037 |
.043 -.352 .191 -.063 .005 -.154 .006 -.160 .178 |
|
Fp2A% .149 |
-.826 -.020 |
|
.022 |
.159 |
-.087 -, 059 -.194 .049 -.251 .039 .089 .167 -.248 |
|
T5A% .231 |
-.817 .023 |
|
-.078 |
.063 |
.005 -.205 .258 -.024 .047 .012 -.031 -.190 -.106 |
|
O2A% .168 |
-.811 .038 |
|
.053 |
.135 |
.054 -.128 -.190 - ,006 -,169 ,106 -,123 ,010 -,142 |
|
T3A% .176 |
-.810 -.158 |
|
-.056 |
.026 |
-,073 ,018 ,098 -,064 ,032 ,091 ,020 ,143 -,082 |
|
T6A% .090 |
-.809 -.021 |
|
.087 |
.117 |
,019 ,054 -,054 -,045 -,046 , 046 -.057 -.208 -.006 |
|
P4A% .106 |
-.803 -.210 |
|
.026 |
.074 |
-.089 .179 .072 -.097 -.130 -.018 .033 .196 -.111 |
|
F8A% .136 |
-.797 -.028 |
|
-.175 |
.038 |
-.001 .172 .029 .030 .068 -.064 .133 , |
|
P3A% .154 |
-.783 -.239 |
|
-.038 |
.036 |
145,052,023,188 -,122,066,119 -,017 -,051,253 |
|
F7A% .190 .011 |
-.751 .132 |
|
-.034 |
.029 |
-,130,005,125,053,088,109-,117,040,042,105 |
|
T Hz |
-.633 .045 |
|
-.049 |
-.046 |
-,047 -,173 -.180 -.211 -.428 -.038 .018 -.206 .071 |
|
F3D% .003 |
.822 -.016 .773 |
|
.087 |
.109 |
-.007 -.202 .112 .033 .097 .123 -.091 -.002 -.089 |
|
C4D% .009 |
.002 .764 |
|
-.081 |
-.017 |
-.023 .170 .049 - .032 -.071 .199 .097 .321 .047 |
|
F4D% -.005 |
-.013 .718 .053 |
|
.025 |
-.047 |
-.052 .024 .170 .063 .118 .036 -.016 -.002 .076 |
|
P3D% -.047 |
.717 .005 .715 |
|
- |
-, 079 |
.030 .162 .052 .065 -.092 |
|
C3D% -.012 |
-.098 .698 |
|
.060 |
,037 |
|
|
T3D% .032 |
.045 .693 .008 |
|
.030 |
,003 |
|
|
P4D% -.012 |
.686 -.223 |
|
-.046 |
-,159 |
|
|
T4D% .103 |
.677 -.099 |
|
-.089 |
-,110 |
|
|
O1D% .026 |
.670 -.098 |
|
.138 |
-,172 |
|
|
Fp1D% .033 |
-.246 .651 ,649 |
|
.067 |
-,158 |
|
|
Fp2D% .039 |
- ,189,617 - , |
|
-.070 |
-,180 |
|
|
O2D% -.015 |
|
|
.057 |
-,223 |
|
|
T5D% -.002 |
162,590 - , |
|
.085 |
-,125 |
|
|
T6D% .143 |
170,560 - , |
|
-.008 |
-,226 |
|
|
F8D% .062 |
300,524 - , |
|
-.046 |
-,061 |
|
|
F7D% .064 |
078,517 - , |
|
.002 |
-,006 |
|
|
F3D , 312 |
|
|
.120 |
,111 |
|
|
F4D ,377 |
|
|
.036 |
-,448 |
|
|
Fp2T% .037 |
|
|
-.082 |
,008 |
|
|
P4T% .111 |
|
|
-.067 |
-,070 |
|
|
F4T% .110 |
|
|
.080 |
-,029 |
|
|
Fp1T% ,121 |
|
|
.068 |
,049 |
|
T4T% .076 132,074,841,212,80,514- 4,,011480,,075.91173,1,9088.208,40,47858.0,6,080,6505193,020758,0454,049,110,090,149,196 -,084,188,003 -,114,286,271,405,252,295,396,330 ,407 ,4
|
T6T% .008 |
- , |
.154 |
,145 |
.092 |
-.066 -.029 -.182 .085 .005 .095 .189 -.021 .370 |
|
C4T% .167 |
020,780,015,763 |
-.009 |
,079 |
-.052 |
.081 -.039 -.091 .076 -.006 -.208 .030 .065 .186 |
|
F8T% .049 |
- , 015,758 - , |
.009 |
,117 |
.056 |
.181 .060 .331 ,012 ,025 ,045 -,156 -,140 ,112 |
|
F3T% .097 |
|
.041 |
,201 |
-.107 |
-,129 -,002 -,180 -,127 -,038 -,132 -,058 -,024 |
|
P3T% .072 |
|
-.073 |
,048 |
-.063 |
-,073 ,131 -,050 ,367 ,075 -,024 -,183 -,134 -,063 |
|
T5T% .015 |
|
.051 |
,011 |
.143 |
-,195 -,059 -,148 -,041 -,174 ,043 -,209 -,174 ,114 |
|
C3T% .074 |
|
.020 |
,098 |
-.122 |
,112 -,061 -,073 -,282 -, 185,039,083 -,157,124,306 |
|
O2T% .058 |
|
-.095 |
,010 |
.078 |
-,123,194 -,213 -,116,021 -,282,092 -,035,162 |
|
F7T% .020 |
|
.021 |
,041 |
.038 |
-,092,105 -,226 -,042,095,174 -.096 .005 -.170 |
|
O1T% -.049 |
|
-.061 |
,006 |
.106 |
.047 -.076 -.007 -.099 -.040 -.146 .023 -.112 -.046 |
|
T3T% .046 -.090 |
|
-.034 |
,198 |
.028 |
-.057 .058 -.164 .153 -.040 .355 .005 ,001 -,385 |
|
T6H |
|
.043 |
,068 |
.424 |
,115 ,003 ,150 -,178 -,032 ,282 -,037 -,028 -,040 |
|
O1H -,195 |
|
-.203 |
,022 |
.339 |
-,192 -,036 -,267 ,062 ,007 ,194 ,019 ,136 ,029 , |
|
O2H -,154 |
|
-.330 |
-,099 |
.376 |
044 -.086 .352 -.112 .098 .340 .062 .021 .236 |
|
T5H -,036 |
|
-.093 |
-,001 |
.370 |
-.139 .057 .460 .057 .134 .166 .020 .011 .137 .005 |
|
Fp2H -.040 |
|
-.132 |
-,082 |
.378 |
.035 .198 .044 .008 .003 .117 .006 -.071 .019 |
|
F8H -,002 |
|
-.149 |
-,016 |
.296 |
.110 .043 -.059 .067 .139 .156 .056 .048 .139 |
|
P3H -,017 |
|
-.233 |
,018 |
.165 |
-.033 -.065 .013 .122 -.101 -.153 .127 .069 .141 |
|
F7H -,049 |
|
-.049 |
-,050 |
.261 |
-.101 .063 -.331 .067 -.024 .081 .065 -.008 -.163 |
|
Fp1H -.014 |
|
-.100 |
-,091 |
.363 |
.199 .049 .021 -.028 .050 -.030 .014 -.049 .045 |
|
P4H .008 .044 |
|
-.172 |
-,029 , |
.174 |
.047 -.024 , 0 79,137 -,153,082 -,053,207 |
|
T3H -.011 |
|
-.082 |
059 |
.260 |
-,363,057,053,080 -,096,131 -,262,070,022,015 |
|
T4H |
|
.033 |
-.108 |
.390 |
-,030,211,015,095,181 -,082 ,054 ,038 ,140 ,038 |
|
T6B% -.266 |
|
-.830 |
-.068 |
.146 |
-,034 ,100 -,077 ,058 ,207 -,088 -,101 ,115 ,077 |
|
Fp1B% -.239 |
|
-.826 |
-.052 |
.083 |
-,064 -,118 -,030 ,041 ,109 ,058 -,091 ,185 -.031 |
|
T5B% -.234 |
|
-.808 |
.009 |
.072 |
-.179 .127 -.115 .035 .190 -.160 .280 .027 -.004 |
|
Fp2B% -.206 |
|
-.795 |
-.075 |
.102 |
.060 .022 -.065 .056 -.002 -.092 .009 .068 -.045 |
|
C3B% -.139 |
|
-.782 |
.009 |
.033 |
.040 - ,008 ,044 -,022 ,040 -,087 ,036 ,187 ,265 |
|
P4B% -.185 |
|
-.778 |
.033 |
.155 |
,029 ,017 ,030 ,034 ,116 ,208 ,081 -,009 ,007 |
|
P3B% -.234 |
|
-.776 |
-.011 |
.101 |
-,060 -,061 -,022 -,031 030 -,035 -,033 ,181 ,246 |
|
O2B% -.257 |
|
-.765 |
.098 |
.139 |
,088 ,015 ,088 ,033 -,010 -,253 -,008 ,123 -,058 |
|
C4B% -.156 |
|
-.753 |
-.031 |
.039 |
,188 ,013 -,056 ,080 -,022 -,044 -, 266 ,008 ,036 |
|
F4B% -.175 |
|
-.750 |
-.009 |
.013 |
-,078 -,013 ,062 ,008 ,022 ,026 -,012 -,035 ,065 |
|
F3B% -.200 |
|
-.742 |
.017 |
-.087 |
,011 ,011 ,053 -,226 -,027 ,065 -,022 ,028 -,009 , |
|
O1B% -.294 |
|
-.729 |
-.034 |
.131 |
107 -.064 .105 .044 .032 .002 .044 -.026 .085 |
|
F7B% -.235 |
|
-.722 |
-.022 |
-.022 |
-.008 .038 .008 -.048 -.019 .061 -.108 -.058 .036 |
|
T4B% -.252 |
|
-.709 |
-.050 |
.087 |
-.178 .094 .079 - ,014 ,058 ,008 ,174 -,016 ,063 |
|
F8B% -.220 |
|
-.706 |
-.183 |
.043 |
,056 ,107 -,047 ,152 -,127 -,070 -,151 -,084 ,030 |
|
T3B% -.232 |
|
-.633 |
-.025 |
-.038 |
-,156 -,019 ,117 ,114 ,031 -, 003 .079 -.057 .010 |
|
IRA ,364 |
|
.589 |
|
-.101 |
-.034 -.733 -.181 -.038 -.124 -.014 -.096 |
|
TP -,072 |
|
.014 |
.031 , |
-.010 |
|
|
SDNN -,138 |
|
.001 |
929 |
-.002 |
|
|
RMSSD -.079 |
|
.051 |
,903 |
.065 |
|
|
HF -.003 -.079 |
|
.063 |
,889 |
.040 |
|
|
LF -.037 |
|
.114 |
,889 |
.020 |
|
|
pNN50 -.080 |
|
.058 |
,842 |
,057 |
|
|
VLF -.006 |
|
-.098 |
,839 |
-,052 |
|
|
ULF .199 |
|
.027 |
,677 |
-,020 |
|
|
F8D .232 |
|
.125 |
,601 |
-,858 |
|
|
T6D .217 |
|
.159 |
-,019 |
-,841 |
|
|
T3D .192 |
|
.096 |
-,052 |
-,806 |
|
|
T5D .287 |
|
.207 |
-,045 |
-,780 |
|
|
O1D .246 |
|
.208 |
-,074 |
-,769 |
|
|
Fp1D .362 |
|
.204 |
-, 089 |
-,768 |
|
|
T4D .194 |
|
.117 |
-,031 |
-,759 |
|
|
O2D .219 |
|
.257 |
,005 |
-,729 |
|
|
Fp2D .515 |
|
.236 |
-,049 |
-,556 |
|
|
P3D |
|
.211 |
,001 |
-,548 |
|
|
D ÿV .307 |
|
.414 |
-,004 |
-,539 , |
|
SN N% .048 053,747,288,722,034,,078122,041,6-9,024,242-,91,5688807-1, 180,681,248,663 - , 073,631 , 230,630,131,616,205,606 - , 160,597
|
IgA -.028 PAI-1 |
,098 -,019 ,135 |
,223 |
,119 |
,116 -,587 -,086 -,083 -,031 ,021 ,266 -,074 -, 550 -,095 ,085 |
|
-.210 PAI -2 -.102 |
-,131 ,074 |
,155 |
-,008 |
-,114 ,082 -,093 -,092 -,528 -,106 ,242 -, 160 -.084 .004 .044 |
|
Stub N -.070 CD4 |
-,100 ,015 |
,008 |
-,215 |
-.498 .169 .131 .147 -.184 .039 .040 -.498 -.086 -.133 -.036 |
|
.046 Lym % -.075 |
-,106 ,274 ,105 |
,227 |
-,152 |
-.423 .039 -.094 .680 .291 -, 008 ,009 ,034 ,034 -,019 ,478 |
|
Mon % -.127 |
-,209 -,019 |
,212 |
,117 |
-,015 ,141 ,160 -,092 -,080 -,026 ,405 -,106 , 171 ,336 -,038 |
|
LCG H -.020 HR |
-,127 -,133 |
-,104 |
,182 |
,185 ,008 ,050 -,671 - ,116 ,042 -,044 -,035 -,028 -, 409 -,614 |
|
-.010 LFnu -.105 |
-,142 -,193 |
-,170 |
-,045 |
-,018 -, 040 ,020 -,147 -,087 -,347 -,568 , 157 -,068 -,031 ,023 |
|
VL/( L+H -.039 |
,004 ,032 ,039 |
,073 |
,039 |
- ,022 -,254 -,566 ,091 -,134 -,000 -,133 ,145 ,074 -,495 - ,003 |
|
LF/HF -.067 PSI-2 |
-, 118,020,000 |
-,016 |
-,071 |
,000 ,208 ,292 ,076 ,069 -,484 -,045 ,206 ,129 , 320 ,089 ,388 |
|
.379 Eos % .279 |
-,016 -,071 |
-,004 |
-,272 |
,558 ,056 -,036 ,081 ,028 -,060 ,289 ,519 -,066 ,087 -,052 - , |
|
HF% -.006 KI |
-,110 -,074,007 |
-,174 |
-,296 |
072 ,041 ,243 ,484 - ,045 -,188 ,049 -, 295,185,146 -,071,565 |
|
Esch -.132 HRV |
-,056 -,022,162 |
-,076 |
-,234 |
- ,031 -,151,210 -,020,187 -,120,514 -,043 -,104,117,248,138 -, |
|
H -.054 C4H .112 |
-,213 -,251,004 |
,000 |
,053 |
013,411,005 - , 072 ,325 ,063 ,170 -,025 ,105 ,729 -,071 ,123 |
|
C3H .090 F4H |
-,094 -,164,396 |
,133 |
,063 |
,198 ,187 -,033 -,041 ,716 ,144 -,038 -,124 ,057 -, 121 ,211 , |
|
.088 -.034 -, 157 |
- , 079,403 - , |
,045 |
,389 |
614 -,123 ,156 ,104 ,183 ,082 -,071 ,538 ,196 -,084 -,097 ,380 |
|
.018 -.015 CD56 |
|
-,079 |
-,008 |
,022 ,077 ,041 , 711 ,055 ,109 -,055 ,061 ,024 -,018 -,666 - 137 |
|
-.038 CD8 .010 |
|
,188 |
,367 |
-,0 95 -.018 .043 .430 .107 .062 .508 10.2 7.19 6.26 6.12 5.19 |
|
F3H .021 |
|
-,131 |
-,021 |
4.97 4.57 .046 .032 .028 .028 .023 .022 .021 |
|
Exp.Var 33.0 |
|
-,155 |
-, |
|
|
Prp.Totl .149 |
|
-,128 |
017 , |
|
|
LIT |
|
.010 |
|
|
|
LIA |
|
-.052 |
|
|
|
LID |
|
.043 |
|
|
|
LIB |
|
-.072 |
|
|
|
|
|
-.186 |
|
|
|
|
|
.057 |
|
|
|
|
|
-.062 |
|
|
|
|
|
17.6 |
|
|
So, judging by the fate of the explained variance, the entropy of the SHSP of the EEG loci
in terms of its informativeness, such commonly accepted EEG parameters as rhythm frequency deviation, rhythm index, its asymmetry and lateralization.
At the next stage, a correlation matrix for obliques is obtained factors was subjected to further analysis to identify a set
of orthogonal factors that divide the variability in the variables into that which refers to the total variance (secondary factors) and to individual variances that refer to clusters or similar variables (primary factors).
Four general (secondary) hypotheticals were identified (Table 4.5), i.e directly not measured factors.
It is significant that the first factor receives significant loads from the entropy of the SSC in the C4 and C3 loci, which project the right and left hippocampus, T3 and
T4, which reflect the activity of the left and right amygdala [Romodanov AP, 1993] and F4 and F3, which record cortical activity in the anterior cingulate [Cahn BR et al, 2003] and orbito-frontal regions associated with VRS-markers of parasympathetic
HF activity [Matthews SC et al, 2004; Winkelmann T et al, 2017] and RMSSD [Yoo HJ et al, 2018].
Table 4.5. Loadings on common (S) factors
|
|
S1 S2 S3 S4 |
|
C4H |
,600 -,227 ,027 ,086 , 590 -,307 |
|
F4H |
-,024 ,112 , 581 -,081 -,000 ,104 , |
|
F3H |
504 -,315 ,024 -,007 ,472 -,090 |
|
T3H |
,058 ,121 , 360 -.358 .050 -.032 |
|
C3H |
-.186 .794 .067 .086 -.158 .793 |
|
T4H |
.105 .095 -.136 .782 .048 -.032 |
|
T6D |
-.239 .772 .061 .067 -.220 , |
|
O1D |
771,043,080 -,211,770,061,025 - |
|
D ÿV |
,130,757,098,100 -, 076,740,060,117 |
|
Fp1D |
- ,286,731,047,053 - , 078,727,106 |
|
F8D |
- , 024 -.276 .681 .053 -.101 -.135 |
|
T4D |
.653 .027 .048 -.221 .612 .059 -.137 |
|
T5D |
|
|
O2D |
|
|
T3D |
|
|
P3D |
|
|
F4D |
|
|
Fp2D C4D |
|
Machine Translated by Google
The second factor receives a load from SHSP theta and delta rhythms, and the third - from SHSP VRS markers of vagus tone. It is known about the positive correlation between theta rhythm (generated by the anterior cingulate cortex) in the frontal loci Fz, FCz and Cz-ÿ and HFnu [Tang YY et al., 2009]. Popovych IL et al. [2013, 2014] revealed the correlation of vagal markers HFnu, HF%, HF, SDNN, RMSSD, and pNN50 with theta-rhythm SSC in loci Fp1, Fp2, F3, F4, C3, C4, T4, P4, and O1, as well as delta- rhythm in T4 and O1 loci.
The last factor represents 5 parameters of immunity. According to the concept of the immunological homunculus Tracey KJ [2007], neural structures which
are projected to certain loci responsible for certain immune functions, in particular, regulation of T-lymphocytes (T5 and/or T6), activation of memory B-lymphocytes (Fp1 and/or Fp2), maturation of dendritic cells (T3 and/or T4), clonal expansion
(P3 and/or P4) release of cytokines by immune compartments (F3 and/or F4).
CONCLUSION
The results obtained in this study give us reason to consider
the entropy of the relative EEG EEG is a completely relevant parameter not only of the EEG, but and neuro-immune complex.
121
SECTION 5
CORRELATION RELATIONS BETWEEN NERVOUS ENTROPY
REGULATORY STRUCTURES (EEG/VRS) AND PARAMETERS
IMMUNITY
At the first stage, a screening of correlations between the levels of HRS entropy and EEG loci, on the one hand, and immunity parameters, as well as leukocytogram and immunocytogram entropy, on the other hand, was carried out.
According to calculations using the
formula: |r|={exp[2t/(n - 1.5)0.5] - 1}/{exp[2t/(n - 1.5)0.5]
+ 1} for samples with n=102 critical level |r| at p<0.05 (t>2.00) is 0.20, at p<0.02 (t>2.39) 0.23, at p<0.01 (t>2.66) 0, 26, at p<0.001 (t>3.46) 0.33.
Based on the results of the screening, a matrix was created (Table 5.1), which includes only those parameters that have at least one significant correlation with
parameters of another set.
Table 5.1. Correlation matrix between VRS/EEG entropy (horizontally) and immunity parameters (vertically)
0.05|r|ÿ0.20; 0.02|r|ÿ0.23; 0.01|r|ÿ0.26; 0.001|r|ÿ0.33
|
LCGH -,03 |
-.07 |
-.12 |
,19 ,09 -,09 -,03 -,03 ,12 ,03 -,23 -,17 -, 22 - , 31 -,20 -,16 -,10 ,16 -,07 -,04 -,13 |
|
ICGH -,03 |
.09 |
.15 |
-.03 .20 .22 .22 .13 .18 .23 .23 .18 .14 .08 .13 .27 .10 .16 .02 .13 .09 -.09 -.06 |
|
MC Sa -,12 |
.21 |
.24 |
-.20 -.23 - ,21 -,08 -,09 -,16 -,22 -, 19 -,20 -,23 ,28 , 27 , 18 ,13 , 20 ,10 ,17 ,11 |
|
KI Sa , 28 |
-.14 |
-.29 |
,00 ,03 -,09 -,11 - .12 -.05 -.07 -.05 -.08 -.01 -.23 -.12 -.14 .18 .19 .02 .03 .09 .07 |
|
MC Ec -,27 |
.25 |
.23 |
-.10 -.07 -.11 -.21 -.11 -.25 -.,2140,.1089 -.02 -.01 -.24 -.17 .12 .14 .24 .15 .19,1.601 -.00 |
|
KI Ec ,27 |
-.07 |
-.04 |
.03 -.04 -.01 .00 .01 -.11 -.02 -.20 -.18 -.15 .21 .24 -.09 .03 .23 .18 -.03 -.08 -.20 |
|
Leukoc -,18 |
.09 |
-.00 |
-.01 -.15 -.23 -.18 , 11 -.10 -.,091 -.11 -.07 .10 .13 .17 .01 .08 .02 .16 .06 .07 .11 .08 |
|
SNN -.25 |
-.03 |
.06 |
.20 .20 .18 .09 .03 .11 -.02 -.16 .21 -.08 .01 -.19 -.10 .01 .07 .08 .12 .18 -.17 -.28 |
|
Eosin -,21 |
-.10 |
-.07 |
-.16 -.06 .00 -.06 -.16 -.06 - ,06 -,05 -,14 -,09 -,08 -,20 ,12 ,02 -,03 -,23 -,16 -,06 |
|
Lymph , 36 |
.06 |
-.05 |
,09 -,00 ,04 ,05 ,13 ,04 , 11 ,,081 ,09 ,25 ,17 ,12 ,11 ,10 ,17 ,11 -,26 -, 22 ,17 -,04 |
|
CD4 -.03 |
-.02 |
.01 |
-,04 -,18 -,07 ,16 ,25 , 21 ,08 ,20 ,05 ,10 .04 .07 .10 .12 .09 -.07 -.09 .01 -.23 -.10 |
|
CD8, 17 |
.16 |
.14 |
.03 .07 .04 .07 .09 .10 .08 -.06 -.07 -.07 -.26 -,17 -,12 -,29 -,05 -,01 -,17 -,16 -,17 |
|
Ta -,13 |
.07 |
.17 |
-,03 -,14 -,01 -,08 -,05 ,06 -,11 -,06 ,10 -,09 , 01 -.07 .04 -.11 .20 -.04 .03 |
|
CIC, 04 |
-.11 |
-.07 |
|
|
IgA -,10 |
-.07 |
.00 |
|
|
IgM, 09 |
.14 |
.14 |
|
|
CD56 -.04 |
.02 |
.12 |
|
|
PSI-1 -.18 -.23 |
.00 |
.07 |
|
|
PSI-2 |
-.03 |
.04 |
|
|
PAI-1 -.04 |
- , |
-.28 |
|
|
PAI-2 -,21 |
28 -,07 |
- ,14 |
|
As you can see, the F8 and P4 loci were outside the matrix, on the one hand, and
phagocytic index, spheroid neutrophils, monocytes, CD22+ B lymphocytes, IgG - on the other hand.
Correlation between HRS entropies and LCG and ICG is completely absent, instead between the latter is the maximum for the matrix (r=-0.40; p<0.001).
At the second stage, coefficients of multiple correlation of entropy indicators were calculated among themselves (Tables 5.2-5.4) and with immunity parameters (Tables 5.5-5.19) according to the summary of the regression model with stepwise elimination until achievement
of the maximum Adjusted R2.
Table 5.2. Regression summary for HRV entropy R=0.268; R2 = 0.072; Adjusted R2 =0.049; F(2,8)=3,2; p=0.046
|
|
Beta St. Err. of St. Err. Beta B of B |
p t(83) level |
|
Variable r |
Intercpt .940 .088 -0.21 |
10.7 10-6 |
|
Fp2H |
-.169 .109 -.141 .091 -0.21 -.170 .109 -.130 |
-1.55 .126 |
|
F3H |
.084 |
-1.55 .124 |
|
|
Beta St. Err. of Beta B |
St. Err. of B |
|
p level |
|
Variable r |
Intercpt .669 |
|
t(83) |
10-6 |
|
O1H |
-0.31 -.360 .102 -.100 0.19 .257 .102 |
|
22.0 |
.001 |
Table 5.6. The result of the regression for the entropy of the SHSP in the locus Fp2 R=0.470; R2 = 0.220; Adjusted R2 =0.182; F(4,8)=5,7; p<10-3
Beta St. Err. St. Err.
Variable
of Beta B of B Intercpt ,8547
r ,1842 -,0052 ,0018 -,0734
p level t(81) 4.64
Killing Index vs Staphyl. aureus -0.29 -.287 .100 Popovych's
Adaptation Index-1 -0.28 -.289 .099 Microbial Count vs Staph. aur.
0.24 160 100 Immunoglobulin M 0.14 134 098
,0252 ,0030
,0019 ,0705 ,0518
10-5 -2.87 .005
-2.91 .005 1.60
.114 1.36 .177
Table 5.7. The result of the regression for the entropy of SHSP in the locus F3 R=0.444; R2 = 0.197; Adjusted R2 =0.157; F(4,8)=5,0; p=0.001
Beta St. Err. of
Beta B Variable Intercpt,1954 Microbian Count vrs E. coli,314,101,0066 Entropy of Leukocytogramm,180,102,5989 CD4+ T-h0e.2lp8er Lymphocytes -,221,104 -,0046
Circulating Immune Complexes -0,17 -, 1301.1,1902 -,0013
-0.23
St. Err. p of B t(81)
level .2813 .69 .489 .0021
3.11 .003 .3401 1.76 .082
.0021 -2.13 .036 .0010
-1.29 .200
Table 5.8. The result of the regression for the entropy of the SHSP in the locus F4 R=0.529; R2 = 0.280; Adjusted R2 =0.215; F(7,8)=4.3; p<10-3
|
Beta St. Err. of Beta B |
St. Err. of B |
p t(78) level |
|
Variable Intercpt .5714 Popovych's Adaptration Index-1 -0.29 -.170 .103 -.0491 |
,1915 |
2.98 .004 -1.65 |
|
Circulating Immune Complexes -0.28 -.197 .098 -.0021 CD16+ NK Lymphocytes |
,0298 |
.103 -2.02 .047 |
|
-0.22 -.164 , 124 -.0048 T Active Lymphocytes -0.16 -.157 .109 -.0052 Microbian |
,0011 |
-1.32 .189 |
|
Count vs E. coli 0.27 .235 .101 .0052 Leukocytes 0.19 .128 .101 .0212 CD8+ T- |
,0037 |
-1.44 .153 2.33 |
|
cytolytic Lymphocytes 0 .16 .175 .109 .0055 |
,0036 |
.022 1.27 .207 |
|
|
,0022 |
1.60 .114 |
|
|
,0167 |
|
|
|
,0035 |
|
Table 5.9. The result of the regression for the entropy of the SHSP in the locus F7 R=0.333; R2 = 0.111; Adjusted R2 =0.067; F(4,8)=2.5; p=0.047
Beta St. Err.
of Beta B of B Variable
St. Err.
p
Intercpt ,3465 ,2121 T Active Lymphocyters ,116 ,0062 ,0048 Microbial Count vs Staph. aur.
.106 .0041 .0028 CD16+ NK Lymphocytes0..21117 .0047 .0043 Circulating Immune Complexes
level t(81) 1.63
.106 1.31 .194
-0.16 -.180 .106 -.0024 .0014
0.18
0.17
,153,157,128
1.48 .142 1.10
.275 -1.70 .093
Table 5.10. The result of the regression for the entropy of SHSP in the locus F8 R=0.270; R2 = 0.073; Adjusted R2 =0.051; F(2,8)=3,3; p=0.043
Beta St. Err.
of Beta B of B Variable
St. Err.
p
Intercpt .5860 .2113 Microbian Count vs Er. coli -0.16 .135 .106 .0039 .0031 Popovych's
Adaptation Index-1 -0.23 -.222 .106 -.0844 .0404
level t(83) 2.77
.007 1.27 .206
-2.09 .040
Table 5.11. The result of the regression for the entropy of the SHSP in the locus T3 R=0.233; R2 = 0.054; Adjusted R2 =0.043; F(1,8)=4,8; p=0.031
Beta
St. Err. of Beta B
St. Err.
of B p
Variable r
Killing Index vs Staphyl. aureus -0.23
Intercpt 1.0430 .1048 -.233
.106 -.0046 .0021
l9e.v9e5l t(84) 10-6
-2.19 .031
Table 5.12. The result of the regression for the entropy of the SHSP in the locus T4 R=0.282; R2 = 0.080; Adjusted R2 =0.057; F(2,8)=3.6; p=0.032
Beta
St. Err. of Beta B
St. Err. of B
p level
Variable
r Intercpt 1.0583 .1011 -.224
t(83)
10-6
Killing Index vs Staphyl. aureus -0.21 Popovych's Adaptation Index-1 -0.17
.106 -.0040 .0019 -.193 .106 -.0473 .0260
10.5 -2.12 .037
-1.82 .072
Table 5.13. The result of the regression for the entropy of the SHSP in the C3 locus R=0.390; R2 = 0.152; Adjusted R2 =0.110; F(4,8)=3.6; p=0.009
|
|
Beta St. Err. St. Err. of Beta B of B Intercpt .7265 |
|
p level |
|
Variable r |
.1317 .210 .106 .0926 .0464 |
t(81) |
10-6 |
|
Immunoglobulin M 0.25 Microbian Count |
.161 .106 .0026 .0017 -.175 .109 -.0455 |
5.52 |
.049 |
|
vs E. coli 0.18 Immunoglobulin A -0.23 |
.0283 -.158 .109 -.0039 .0027 |
1.99 |
.133 |
|
T Active Lymphocytes -0.19 |
|
1.52 |
.112 |
-1.61 -1.45 .152
Table 5.14. The result of the regression for the entropy of the SHSP in the C4 locus R=0.358; R2 = 0.129; Adjusted R2 =0.085; F(4,8)=3.0; p=0.024
Beta St. Err. of
Beta B
St. Err. of B t(81)
p level
Variable r
Microbial Count vs Staph. aur. 0.27
Intercpt. .209 -.110 .105 -.0009 .0009 -1.05 .298
Immunoglobulin M 0.17 Popovych's Adaptation Index-1 -0.17 Circulating Immune Complexes
-0.16
Table 5.15. The result of the regression for the entropy of the SHSP in the locus T5 R=0.302; R2 = 0.091; Adjusted R2 =0.058; F(3,8)=2,7; p=0.049
Variable
Beta
r
St. Err. of Beta B
Intercpt 1.192 .111
St. Err. of B
,2985
t(82)
p level
Entropy of LCG -0.23 Killing Index vs Staphyl.
aureus -0.16 CD8+ T-cytolytic Lymphocytes 0.20
-,165
-,121,173
-.6284 .110 -.0027
.107 .0060
,4257
,0025
,0037
3.99
-1.48
-1.10 1.61 .0001 .144 .274 .111
Table 5.16. The result of the regression for the entropy of the SHSP in the locus T6 R=0.329; R2 = 0.108; Adjusted R2 =0.075; F(3,8)=3,3; p=0.024
|
|
Beta St. Err. St. Err. of Beta B of B Intercpt ,6442 |
p |
|
Variable r |
,2218 ,218 ,108 ,0067 ,0033 |
level t(82) |
|
CD16+ NK Lymphocytes 0.25 Microbial Count |
,142 ,106 ,0032 ,0024 -,140 ,109 -,0030 |
2.90 .005 2.02 |
|
vs Staph. aur. 0.16 Killing Index vs Staphyl. |
,0024 |
.047 1.34 .185 |
|
aureus -0.22 |
|
-1.28 .204 |
Table 5.17. The result of the regression for the entropy of the SHSP in the locus P3 R=0.389; R2 = 0.152; Adjusted R2 =0.099; F(5,8)=2.9; p=0.020
|
Beta St. Err. of Beta B |
St. Err. of B |
|
p |
|
Variable Intercpt 2.589 SN Neurtrophils -.684 .699 -.0111 Killing Index |
1.501 |
t(80) |
level |
|
vs E. coli -0.23 -.246 .123 -.00240.E24ntropy of LCG -0.22 -.355 .337 -.9487 |
.0114 |
1.72 |
.089 |
|
Eosinophils -0.20 -.244 .119 -.0164 Lymphocytes -0.20 -.661 .545 -.0117 |
.0012 |
-.98 |
.331 |
|
|
.8993 |
-2.00 |
.048 |
|
|
.0080 |
-1.06 |
.295 |
|
|
.0096 |
-2.04 -1.21 |
.044 .229 |
Table 5.18. The result of the regression for the entropy of the SHSP in the O1 locus R=0.464; R2 = 0.215; Adjusted R2 =0.156; F(6,8)=3.6; p=0.003
|
|
Beta St. Err. St. Err. of Beta B of B Intercpt 1.645 |
p |
|
Variable |
r .3186 -0.31 -.427 .157 -1.541 |
l5e.v1e7l t(79) 10-5 |
|
Entropy of LCG |
.5677 -0.26 -1.289 .576 -.5618 .2510 -0.23 1.040 .526 |
-2.71 .008 -2.24 |
|
Popovych's Strain Index-2 |
.5748 .2908 -0.20 -.151 .107 -.0032 .0023 -0.18 .444 |
.028 1.98 .052 |
|
Popovych's Strain Index-1 |
.276 .0404 .0251 .17 .199 .101 .1235 .0630 |
-1.41 .163 1.61 |
|
Killing Index vs Staphyl. aureus |
|
.111 1.96 .053 |
|
Eosinophils |
|
|
|
Immunoglobulin M |
|
|
Table 5.19. The result of the regression for the entropy of SHSP in the O2 locus R=0.321; R2 = 0.103; Adjusted R2 =0.071; F(3,8)=3,2; p=0.027
Variable
Beta St. Err. of
Beta B
r Intercpt .5817 Killing
St. Err. of B
,3440
p level t(84) 1.69 .095
Index vs Staphyl. aureus -0.23 -.233 .104 Popovych's Strain Index-2
-0.17 -.130 .105 Entropy of ICG 0.18 .141 .105
-,0048 ,0022 -,0558
,0448 ,4419 ,3292
-2.23 .028 -1.24
.217 1.34 .183
As we can see, the coefficients of multiple correlation despite statistical significance very moderate, being in the range of 0.233÷0.529.
A similar situation regarding the influence of the entropy of the constellation of EEG loci on entropy of LCG and ICG is revealed in the result of the canonical correlation
analysis (Table 5.20).
Table 5.20. The factorial structure of the matrix for the entropy of the SHSP loci of EEG (left set) and LCG and ICG (right set)
Left set R O1H -,681
T5H -,564
P3H -.563
O2H -,507
T6H -, 481,491
F3H
Right set R LCGH ,952
ICGH -,661
and
3
2
1
0
-1
-2
-2 -1 0 1 2 3
h HRV
R=0.597; R2 = 0.355; ÿ2 (9)=35; p<10-4; ÿ Prime=0.645
Figure 5.2. Canonical correlation between VRS entropy (X-axis) and parameters of immunity (Y axis)
A special canonical analysis proved that the recently proposed I.L. Popovych modifications of stress and adaptation indices are more closely correlated with entropies
of nervous regulatory structures compared to the previous version (PSI-1 and PAI-1) (Table
5.22 and Fig. 5.3).
Table 5.22. The factorial structure of the matrix for the entropy of the EEG and HRS loci of the SHSP (left set) and the Popovych adaptation and stress indices (right set)
Left set R HRVH -.657
O1H -.444 -.112
FP1H FP2H P3H , 204
F4H ,018
Right set R PSI-2 .739
PSI-1 .709
PAI-2 ,510
PAI-1,100
-.051
R=0.540; R2 = 0.291; ÿ2 (24)=54; p=0.0005; ÿ Prime=0.514
Figure 5.3. Canonical correlation between VRS entropy and SHSP of EEG loci (axis
X) and Popovych adaptation and stress indices (Y axis)
At the final stage, the canonical correlation between the entropies of HRS and EEG, on the one hand, and relevant and informational parameters of immunity -
on the other hand (Table 5.23 and Fig. 5.4). It was found that the entropic radical receives the maximum positive factor load from HRS entropy, significantly
less - from the SSC locus C3 and very little - from the other 5 loci. in return Perceptible negative factor loadings are given by the entropy of the EEG in loci P3, O2 and F7, and very little - in the other 2 loci.
The factor structure of the immune radical is formed by positive loads
from, above all, pan-lymphocytes, as well as from the completion of phagocytosis by neutrophils of both gram-negative and gram-positive bacteria, the content of IgM and circulating immune complexes in the serum and T-killers in the blood. The entropy of the leukocytogram completes the list. Negative loads on the canonical root are mainly provided by segmented neutrophils, T-lymphocytes with high affinity, natural killers, IgA and T-helpers, as well as informative indices of the leukocytogram.
Table 5.23. Matrix of factor structure of canonical roots of HRV and EEG entropy (left set) and immune status (right set)
|
Left set |
R |
|
HRVH |
,635 |
|
C3H |
,214 |
|
F3H |
,102 |
|
C4H |
,100 |
|
F4H |
,073 |
|
T5H |
,046 |
|
T6H |
,037 |
|
P3H |
- |
|
O2H |
,290 |
A canonical correlation between entropy and immunity radicals is revealed very strong (Fig. 5.4). In general, the constellation of entropy parameters determines the immune status by 66%.
R=0.814; R2 = 0.663; ÿ2 (240)=296; p=0.008; ÿ Prime=0.013
Figure 5.4. Canonical correlation between HRS entropy and EEG loci (X axis) and immunity parameters (Y axis)
CONCLUSION
The entropy of the HRV components and the spectral power density of the EEG loci is logically related to a number of relevant and informational parameters of immunity, which gives reason to consider entropy as a neuro factor
immunomodulation.
Machine Translated by Google
CHAPTER 6
INDIVIDUAL FEATURES OF THE ENTROPY OF PARAMETERS
OF NERVOUS REGULATORY STRUCTURES (EEG/VRS)
1. The preliminary analysis showed wide variability of entropy
parameters of nervous regulatory structures. That's why we set before ourselves
the goal is to divide the observed contingent into four homogeneous groups. For
cluster analysis was used to achieve the goal.
2. While the routine methodical approach allows only alternately
to analyze one or another feature of statistical sampling, the use of clustering
analysis makes it possible to simultaneously take into account all signs. Taking into account everything
the set of signs of persons taken in their interrelationship and the conditioning of some of them (derivatives) by others (main, determining) makes it possible to carry out a natural classification that reflects the nature of things, their essence. It is believed that knowing the essence of an object boils down to identifying its quality properties, which actually define this object, distinguish it from others [Aldenderfer MS, Blashfield RK, 1985; Mandel I.D., 1988]. Clustering by entropy parameters is implemented by the iterative k-means method. In this method, the object
are assigned to the class to which the Euclidean distance is minimal. Main
the principle of the structural approach to the selection of homogeneous groups is that
objects of one class are close, and objects of different classes are distant. In other words, a cluster
(image) is such an accumulation of points in an n-dimensional geometric space, c
for which the average distance between points is less than the average distance from the given points
to the rest
3. As a result, the observed sample was divided into 4 clusters (table.
6.1). Cluster No. 2 turned out to be the most numerous, which included 61 members, significantly
Cluster No. 4 with 24 members turned out to be smaller, while Clusters No. 3 and No. 1 contain only 9 and 8 members respectively.
4.
5.
132
Table 6.1. Cluster members and distances from the center of the respective clusters
Cluster Number 2 contains 61 cases
C_1 C_2 C_3 C_5 C_8 C_13 C_14 C_15 C_16 C_18 C_19 C_20 C_22
Distance ,13,06,11,18 ,14
,09
,08
,15
,13
,09
,11
,09
,02
C_23 C_25 C_28 C_30 C_31 C_32 C_33 C_34 C_36 C_37 C_41 C_42 C_47
Distance ,12,13,13,11 ,06
,01
,08
,03
,08
,07
,02
,11
,08
C_51 C_54 C_55 C_57 C_58 C_59 C_61 C_62 C_63 C_64 C_65 C_67 C_68
Distance ,12,11,03,16 ,09
,12
,02
,09
,09
,08
,02
,14
,10
C_70 C_71 C_73 C_75 C_77 C_79 C_80 C_81 C_82 C_84 C_86 C_87 C_89
Distance ,13,04,07,01 ,01
,07
,10
,09
,13
,12
,11
,06
,08
C_90 C_91 C_92 C_93 C_95 C_97 C_100 C_101 C_102
Distance ,10,11
,10
,16
,10
,07
,02
,01
,02
Cluster Number 4 contains 24 cases
C_56 C_60 C_66 C_69 C_78 C_85 C_88 C_94 C_96 C_98 C_7 C_9 C_10
Distance ,06,09,11,12 ,09
,08
,04
,08
,09
,05
,07
,05
,05
C_17 C_26 C_27 C_29 C_35 C_43 C_45 C_46 C_48 C_52 C_53
Distance ,08,08,10
,06
,08
,11
,06
,08
,06
,07
,05
Cluster Number 3 contains 9 cases
|
|
C_4 |
C_11, |
C_21 |
C_38 |
C_39 |
C_49 |
C_50 |
C_76 |
C_83 |
|
Distance 11 |
|
20 |
,15 |
,14 |
,21 |
,11 |
,14 |
,18 |
,14 |
Cluster Number 1 contains 8 cases
|
|
C_6 |
C_12 |
C_24 |
C_40 |
C_44, |
C_72 |
C_74 |
C_99 |
|
Distance |
,13 |
,21 |
,14 |
,16 |
16 |
,20 |
,17 |
,13 |
Further, analysis of variance and ranking of variables was carried out. The maximum contribution to the distribution into clusters, judging by the criterion ÿ2 , which
reflects the proportion of intergroup variance in the total variance, contributed by entropy
SHSP in the EEG loci T6 and Fp2, on the other hand, the minimum contribution is given by the entropy of SHSP
in the P4 and T3 loci. Very small, but statistically significant contributions to clustering
Leukocytograms and Immunocytograms give entropy instead of entropy contribution
of VRS components is scarce (Table 6.2).
Table 6.2. Dispersion analysis of entropy of SHSP EEG, HRV, Leukocytogram and Immunocytogram
|
Variables |
Between |
Within |
ÿ2 R |
F |
meaning |
|
|
SS |
SS |
|
|
p |
|
T6H |
1,662 |
,923 |
0.643 0.802 |
49.2 |
10-6 |
|
Fp2H |
1,167 |
,652 |
0.642 0.801 |
49.0 |
10-6 |
|
F3H |
1,249 |
,893 |
0.583 0.764 |
38.2 |
10-6 |
|
T5H |
1,455 |
1,365 |
0.516 0.718 |
29.2 |
10-6 |
|
O1H |
1,228 |
1,295 |
0.487 0.698 |
25.9 |
10-6 |
|
F4H |
1,143 |
1,208 0,486 0,697 |
25.9 |
10-6 |
|
F8H |
1,961 |
2,125 0,480 0,693 1,924 |
25.2 |
10-6 |
|
F7H |
1,636 |
0,460 0,678 1,385 |
23.2 |
10-6 |
|
O2H |
1,143 |
0,452 0,672 1,207 |
22.6 |
10-6 |
|
Fp1H |
,752 |
0,384 0,620 ,811 0,358 |
17.0 10-6 |
|
|
C3H |
,453 |
0,599 ,976 0,350 0,592 |
15.3 |
10-6 |
|
C4H |
,526 |
1,127 0,338 0,582 ,925 |
14.7 |
10-6 |
|
T4H |
,576 |
0,329 0,573 1,040 |
14.0 |
10-6 |
|
P3H |
,453 |
0,298 0,546 1,523 |
13.4 |
10-6 |
|
P4H |
,441 |
0,289 0,538 ,172 0,109 |
11.6 |
10-5 |
|
T3H |
,620 |
0,330 ,236 0,092 0,304 |
11.1 |
10-5 |
|
LCGH ,021 |
|
1,235 0,022 0,149 |
3.3 |
,024 |
|
ICGH ,024 |
|
|
2.8 |
,045 , |
|
HRVH ,028 |
|
|
0.6 |
602 |
Note. The parameters of dispersion analysis are calculated according to the following formulas:
ÿ2 =Sb2 /(Sb2 +Sw2 ); R=ÿ; F=[Sb2 (nk)]/[Sw2 (k-1)],
where Sb2 is the intergroup
variance; Sw2 – intragroup variance; n – number of persons (102);
k is the number of cluster groups (4).
Current average values of entropy of the SSC in EEG loci, as well as entropy VRS, LCG and ICG in members of different clusters are shown in fig. 6.1.
1.0
0.9
0.8
0.7
0.6
0.5
I(8)
III(9)
IV(24)
II(61)
0.4
0.3
0.2
T4 T6 O1 F8 O2 Fp2 F7 T5 Fp1 F4 F3 C4 C3 T3 P3 P4 HRV LCG ICG
Fig. 6.1. The actual mean values (M±SE) of the entropy of the SHSP in the EEG loci, as well as HRV, LCG and ICG in members of different clusters
However, as demonstrated in numerous studies of Truskavetska scientific school, more adequate in terms of physiological significance is
expression of parameters in the format of Z-scores, i.e. taking into account their variability normal [I.L. Popovych, 2011; Kozyavkina O.V. et al., 2015; Gozhenko AI et al, 2019; Popovych IL et al, 2020].
This approach is implemented and visualized in fig. 6.2.
Fig. 6.2. Z-scores (M±SE) of the entropy of the SHSP in the loci of EEG, VRS, LCG and ICG in members of different clusters
It turned out that in the members of the second major cluster, the entropy of the EEG, VRS, LCG and ICG fluctuates in the range of the narrowed norm (-0.5ÿ÷+0.5ÿ). The members of the fourth cluster , next in number, are characterized by a moderately increased entropy of the SHSP in all EEG loci in combination with a normal entropy of the ICH and a moderately reduced entropy of the VRS and LCH. Members of two minor clusters turned out to be the most colorful.
In particular, the members of the third cluster were found to have significantly reduced entropy (negenropia) SHSP in paired loci F3 and F4 responsible, as suggested by KJ
Tracey [2007], for the release of cytokines from the immune compartment, T3 locus (but not T4), responsible for the maturation of dendritic cells, and also in the locus
C4, responsible, according to another assumption [Mel'nyk OI et al, 2019], for increasing the intensity of phagocytosis by gram-positive neutrophils and
gram-negative microbes, and in the C3 locus responsible for increasing the content of
blood IgM and total lymphocytes and a decrease in the content of IgA and segmentonuclear
neutrophils In addition, there is a moderate decrease in the entropy of LCG. Entropy
other EEG loci, as well as HRV and ICG are within the normal range.
Instead, the members of the first cluster were found to be heterozygous in the paired loci Fp1 and Fp2, responsible, according to KJ Tracey [2007], for the activation of memory B-lymphocytes; T5 and T6, responsible for the regulation of T-lymphocytes; T3 and
T4, responsible, as already mentioned, for the maturation of dendritic cells; as well as less pronounced than in the third cluster, a decrease in entropy in the F3 and F4 loci responsible for the release of cytokines from the immune compartment. In addition, there is a moderate decrease in entropy in the O1 and O2 loci, which are responsible for stressing the leukocytogram, as well as for inhibiting the completion of Staph phagocytosis. aureus, but not E. coli [Mel'nyk OI et al, 2019]. At the same time, the responsibility for immunomodulation of the structures that are projected onto the F7 loci
and F8 with maximum negentropy remains unknown to us. Except
moreover, there is a moderate decrease in the entropy of ICG. Entropy of other EEG loci, a also HRV and LCG are within the normal range.
The average normalized entropy values of SHSP loci of EEG, VRS, immunocytograms and leukocytograms are shown in Fig. 6.3.
Fig. 6.3. Normalized entropy of SHSP loci of EEG, VRS, immunocytogram and leukocytogram
As we can see, the characteristic features of the image of the members of the first cluster
are pronounced negentropy of the EEG as a whole, moderate negentropy of the immunocytogram,
the lower bound level of HRS entropy and the normal level of entropy leukocytograms. Members of the third cluster are characterized by moderate negentropy EEG and leukocytogram in combination with normal levels
HRS entropy and immunocytogram. Instead, members of the fourth cluster are characterized by increased EEG entropy combined with decreased
by HRV entropy and leukocytogram at a normal level of entropy
immunocytograms. However, for the vast majority of people who make up the second cluster, the normal entropy of all analyzed systems is characteristic.
In order to determine exactly these parameters (variables), the constellation of which is characteristic for each cluster, the available information field was subjected to discriminant analysis using the forward stepwise method. For inclusion in the model (tables 6.3 and 6.4), the program selected only 15 variables, while the other 4 were outside the discriminant model.
Table 6.3. Summary of the analysis of discriminant functions for entropies
Step 15, N of vars in model: 15; Grouping: 4 grps Wilks' Lambda: 0.0376; approx. F(45)=11.2; p<10-6
|
Variables currently in |
IV (24) |
II III I Wilks' ÿ (61) (9) (8) |
Parti al ÿ |
F-re Norm move p level Tolerate ranclyevel Cv |
|
the model |
|
|
|
(3.8) (88), |
|
T6H |
0.900 |
0,820 0,814 0,338 ,042 0,813 0,760 |
.890 3.45 |
.934 020, 681 0,742 0,199, 125, 689 0,782 |
|
Fp2H |
0.896 |
0,420 ,040 0,731 0,775 0,246 ,042 |
1.97 .898 |
3.20 0,161, 028, 707 0.757 0.226, 138, 706 |
|
F8H |
0.877 |
0,800 0,688 0,301 ,040 0,799 0,723 |
.937 1.89 |
.964 0.772 0,207, 374, 682 0,756 0,169, |
|
F7H |
0.865 |
0,432 ,039 0,728 0,812 0,420 ,043 |
1.05 .869 |
4.22 008, 657 0.68. 0,131, 002, 631 0,809 |
|
T5H |
0.904 |
0,741 0,769 0,479 ,041 0,800 0,527 |
.924 2.30 |
.949 0,146, 018, 559 0.782 0.159, 029, 501 |
|
O2H |
0.865 |
0,613 ,040 0,789 0,792 0,585 ,045 |
1.49 .844 |
5.19 0.823 0.126, 245, 890 0,960 0,059, |
|
O1H |
0.904 |
0,788 0,738 0,690 ,042 0.819 0.627 |
.888 3.52 |
.899 0004, 622 0,830 0,115, 0001 |
|
F4H |
0.927 |
0.659 .042 0.937 0.970 0.897 .039 |
3.16 .952 |
1.41 |
|
T4H |
0.903 |
0.858 0.597 0.758 .047 0.844 0.530 |
.808 6.66 |
.782 |
|
P3H |
0.913 |
0.676 .048 0.717 0.787 0.727 .041 |
7.81 .925 |
2.27 |
|
T3H |
0.911 |
|
|
|
|
ICGH |
0.948 |
|
|
|
|
C4H |
0.934 |
|
|
|
|
F3H |
0.905 |
|
|
|
|
HRVH |
0.689 |
|
|
|
|
Variables |
IV |
II III I Wilks' |
parties |
F to Tole |
|
currently not in the model |
(24) |
(61) (9) (8) ÿ |
al ÿ enter |
p level rancy |
|
Fp1H |
0.905 |
0.812 0.732 0.508 .036 0.790 0.709 |
.969 |
.498 0.781 0.157 .465 0.761 |
|
P4H |
0.908 |
0.681 .037 0.848 0.653 0.793 .037 |
.991 |
0.184 .537 0.827 0.114 .755 |
|
C3H |
0.915 |
0.661 0.637 0.669 .037 |
.985 |
0.681 0.070 |
|
LCGH |
0.644 |
|
.993 |
,90,25,43,1,9446,864,734,905 |
|
Variables currently in the model |
F to enter |
ÿ p level |
F value |
p level |
|
T6H |
69.8 |
10-6 ,319 10-6 |
70 |
10-6 |
|
C4H |
32.7 |
,159 10-5 ,121 |
49 |
10-6 |
|
F3H |
10.0 |
10-3 , 098 |
36 |
10-6 |
|
O2H |
7.4 |
10-3 ,081 ,010 |
29 |
10-6 |
|
T4H |
6.5 |
,072 ,003 ,062 |
26 |
10-6 |
|
P3H |
4.0 |
,040 ,056 ,036 |
23 |
10-6 |
|
F8H |
5.1 |
,051 |
21 |
10-6 |
|
T3H |
2.9 |
|
19 |
10-6 |
|
F7H |
3.0 |
|
17 |
10-6 |
|
HRVH |
1.7 |
,177 ,048 |
16 |
10-6 |
|
ICGH |
1.6 |
,206 ,046 |
15 |
10-6 |
|
O1H |
1.5 |
,230 ,044 |
13 |
10-6 |
|
Fp2H |
2.0 |
,123 ,041 |
13 |
10-6 |
|
F4H |
1.4 |
,249 ,039 |
12 |
10-6 |
|
T5H |
1.1 |
,374 ,038 |
11 |
10-6 |
Next, the 15-dimensional space of discriminant variables is transformed into a 3- dimensional space of canonical discriminant functions (canonical roots),
which are a linear combination of the discriminant variables. Discriminating
the (separative) ability of the root is quantitatively characterized by the canonical coefficient correlation (r*) as a measure of connection, the degree of dependence between groups (clusters) and discriminant function.
For the first root r*=0.926 (Wilks' ÿ=0.038; ÿ2 (45)=300; p<10-6), for the second r*=0.800
(Wilks' ÿ=0.265; ÿ2 (28)=121; p< 10-6), and for the third r*=0.512 (Wilks' ÿ=0.738; ÿ2 (13)=28; p=0.0097).
The first root contains 73.9% of discriminating capabilities, the second - 21.8%, and the third - only 4.3%, so it will be ignored in the future.
In the table 6.4 presents raw (actual) and standardized (normalized) coefficients for discriminant variables. The actual coefficient gives information about the absolute contribution of this variable to the value of the discriminant function, while the standardized coefficients represent
the relative contribution of a variable, regardless of the unit of measurement. They enable identify those variables that contribute the most to the value
discriminative function.
Full structural coefficients, i.e. coefficients , are also given there correlations between the discriminant root and the variables. Structural coefficient shows how closely related variables and discriminant functions are, i.e. which
a fraction of the information about the discriminant function (the root) is contained in this variable.
Calculation of the values of the discriminant roots for each individual as a sum products of raw coefficients on the values of individual discriminant variables, together with a constant, allows you to visualize each patient in the information space of two roots (Fig. 6.4).
Table 6.5. Standardized, structural and raw coefficients and constants for entropy variables
Fig. 6.4. Individual values of two entropy roots for members of four clusters
The localization of the members of the first cluster along the axis of the first root in the extreme right (positive) zone (centroid: +7.24) reflects the pronounced integral negentropy of the SHSP of 11 EEG loci, as well as ICH, which are negatively related to the root (tables 6.2 and 6.4 ) .
Instead, the fourth cluster occupies the extreme left (negative) zone (centroid: -2.58), which reflects the increased integral entropy of the same parameters. Members of the other two clusters occupy an intermediate position and their projections on the axis are mixed. However, the positive value of the centroid of the third cluster (+1.23) reflects the lower limit level of the integral entropy of its parameters, and the quasi-zero value of the
centroid of the second cluster (-0.12) characterizes the entropy fluctuations of its parameters around zero.
Instead, along the axis of the second discriminant root, the terms of the third
cluster (centroid: -4.08) are clearly distinguished from the members of both the second and two other clusters, the projections of which are mixed on the axis (centroids: +0.20;
+0.55 and +1.39 for II, IV and I clusters, respectively). Such disposition reflects
minimum for the cohort values of the entropy of the SHSP in loci C4 and F3, which are associated
with a positive root, instead the maximum HRS entropy associated with root is negative (Tables 6.2 and 6.4).
In general, all four entropy clusters on the plane of the first two roots, which together contain 95.7% of discriminative information, quite clearly
delimited, which is documented by calculating Mahalanobis distances (Table 6.5).
Table 6.5. Squares of Mahalanobis distances between entropy clusters (above the diagonal) and F-indexes (df=15.8; for all pairs p<10-6)
The same discriminant variables can be used to identify (classify) the belonging of one or another person to one or another cluster. This goal of discriminant analysis is implemented using classification (discriminant) functions (Table 6.6).
These functions are special linear combinations that maximize differences between groups and minimize dispersion within groups.
The coefficients of the classification functions are not standardized, so they are not
are interpreted. The object belongs to the group with the maximum value of the function,
calculated by summing the products of the values of the variables by the coefficients of the classification functions plus a constant.
Table 6.6. Coefficients and constants for classification functions of entropy clusters
Clusters
III I
IV II
Variables p=.088 p=.078 p=.235 p=.598
T6H 26.05 -6.14 27.87
C4H 9.42 47.02 47.48
F3H 130.5 119.1 164.4
O2H 69.63 33.67 65.33
T4H 126.7 91.80 125.9
P3H 95.94 75.42 115.2
F8H 67.86 50.39 76.51
T3H -72.19 -49.94 -86.48 24.45 11.70
26.11
46.63
152.4
58.56
113.7
98.86
68.22
-77.83
F7H
31.76
28.86
HRVH 117.4 93.28 110.7
ICGH 421.7 384.8 418.8 24.04 34.52 32.14
O1H
Fp2H 21.35 -2.79 12.96
F4H -6.27 .09 10.13
T5H -0.85 1.31 10.27
Constants -442.5 -325.5 -523.1
107.9
408.0
25.26
15.73
2.90
9.19
-455.6
In this case, we can unmistakably recognize the members of the third and of the first clusters, the second cluster is classified with one error, and only the fourth cluster - with three errors. Overall classification accuracy
is 96.1% (Table 6.7). Table
6.7. Classification matrix for entropy clusters Rows: observed classifications; columns: predicted classifications
Clusters Percent
III I
II IV
correct
p=.088 p=.078 p=.598 p=.235 0 9 0 8
CONCLUSION
It was found that the entropy levels of HRS and SHSP of EEG loci, as well as ICH and LCH in the vast majority of the observed individuals, who make up the second cluster (59.8%), are within the normal range. Instead, members of the fourth cluster (23.5%) are characterized by increased EEG entropy in combination with reduced HRS entropy and leukocytogram at a normal entropy level
immunocytograms. Members of the third cluster (8.8%) are characterized by moderate
Machine Translated by Google
negentropy EEG and leukocytogram in combination with normal levels of HRS entropy and immunocytogram. Characteristic features of the image of the members of the first cluster (7.8%) are pronounced negentropy of the EEG as a whole, moderate negentropy of the immunocytogram, lower limit level of HRS entropy and normal level of leukocytogram entropy.
142
SECTION 7
FEATURES OF AMPLITUDE-FREQUENCY AND SPECTRAL PARAMETERS OF EEG/ VRS IN PERSONS WITH DIFFERENT STATE OF ENTROPY
In Chapter 4, we proved that the entropy of the spectral power density (ShSP) of rhythms, judging by the results of the factor analysis, is quite relevant and a very informative EEG parameter. Therefore, the next logical step is
elucidation of the peculiarities of the amplitude-frequency and spectral parameters of the EEG, and together with VRS, in persons with different states of entropy, pre-distributed to
clusters.
In order to level the units of measurement of amplitude-frequency and
EEG and HRS spectral parameters (ÿV, Hz, ÿV2 /Hz, %, msec2 ), comparison with the norm, as well as taking into account their variability as a criterion of "physiological weight" [Cook IA et al, 1998; I.L. Popovych 2011] actual values were listed in Z-unit.
As a result of the screening of relationships between normalized EEG entropy levels, on the one hand, and EEG and HRV parameters and indicators, on the other hand, three pairs of quasi- mirror patterns were revealed.
moderately elevated parameters, and in persons with normal and high entropy these parameters are quasi-normal.
12
11 y = -1.54x3 + 1.027x2 + 0.001x + 0.70
R2 = 1
10
9
y = 0.370x3 + 0.472x2 + 0.235x - 0.06
8 R2 = 1
7
6 R1+
5 R1-
4
3
2
1
0
-1
-2 -1
0 1
EEG Entropy, Z
Fig. 7.1. The first pair of patterns of EEG parameters and HRV at different levels of EEG entropy
The patterns of the second pair are parabolas (Fig. 7.2), which are usually called straight or inverted letter U. They represent extreme levels
EEG and HRS parameters in individuals with moderate negentropia. In particular, the inverse U the pattern reflects a significantly increased level of ÿ-rhythm SHSP in loci C4, F3 and
F4 and its index and asymmetry, as well as ÿ-rhythm index and asymmetry and asymmetry ÿ-rhythm. This is accompanied by increased ULF spectral power
components of VRS. Pronounced negentropy is accompanied by the upper limits of these parameters, and normal and increased entropy - completely normal levels. Instead, the actual U-shaped
pattern reflects moderately reduced levels of the ÿ-rhythm SSC in loci T3, F3, C4, T5, Fp1, F4 and T6 and its amplitude,
the amplitude of the ÿ-rhythm and its SHSP in the P4 locus, as well as the frequency of the ÿ- rhythm. However, the amplitude of the ÿ-rhythm and its SHSP in the Fp2, P3, and T6 loci are within norms
This state of EEG parameters is accompanied by increased indices of centralization and sympathetic/vagal balance, but with a minimal degree for the contingent. In general, the average level of EEG and HRV parameters in persons of the third cluster is at the lower limit, while in persons of the first cluster it is
upper borderline, and the other two - completely normal.
1.6
1,2
0.8
0.4
= 0.748x3 + 0.396x2 - 1.558x + 0.62 y R2 = 1
R2+ R2-
0.0
-0.4
-0.8
= -0.431x3 - 0.124x2 + 0.828x - 0.01 y R2 = 1
-2 -1.6
-1.2
-0.8
-0.4 0
0.4
0.8
EEG Entropy, Z
Fig. 7.2. The second pair of patterns of EEG parameters and HRV at different levels of EEG entropy
The patterns of the third pair are sinusoids (Fig. 7.3). The first reflects
a situation where individuals with a normal level of entropy have a normal level ÿ-rhythm SSC in the O2 and Fp2 loci in combination with an elevated level
spectral power of the HF and LF bands of the VRS. People with high entropy have these the parameters are accordingly moderately reduced or increased to a lesser extent, and negentropy is accompanied by a deeper decrease in EEG parameters
combined with normal levels of HRV parameters.
Another pattern reflects a combination of normal entropy with normal levels of ÿ-rhythm frequency and sympathetic tone, while a deviation of entropy level in one direction or another is accompanied by a modest increase in both.
0.5
0.0
-0.5
-1.0
= 0.344x3 + 0.449x2 - 0.46x + 0.16 y R2 = 1
= -0.425x3 - 0.604x2 + 0.67x - 0.11 y R2 = 1
R3+ R3-
-2 -1.5 -1
-0.5 0
0.5 1
EEG Entropy, Z
Fig. 7.3. The third pair of patterns of EEG and HRS parameters at different levels of EEG entropy
Special attention should be paid to the analysis of rhythm lateralization patterns. As we
see in fig. 7.4, pronounced negentropy is associated with pronounced left-sided (negative index) lateralization of ÿ- and ÿ-rhythms and moderate lateralization of ÿ- and ÿ-rhythms, which is nullified in individuals with both normal and increased entropy.
0.5
0
-0.5
-1
Theta Delta Beta
Alpha
-1.5
-2
-1.5
-1 -0.5
0 0.5 1
EEG Entropy
Fig. 7.4. Lateralization of the EEG rhythm at different levels of EEG entropy
However, in subjects with moderate negentropy, there is still a tendency for left lateralization of the ÿ- and ÿ-rhythms, whereas the ÿ-rhythm tends to show a right- sided (positive index) lateralization.
According to the results of the discriminant analysis, only 37 parameters were identified as characteristic of entropy clusters, 11 of which relate to the delta rhythm, 8 to the theta rhythm, 8 to the beta rhythm, and 4 to the alpha rhythm of the EEG, and another 6 parameters represent VRS. Other 16 considered EEG parameters and VRS were not included by the program in the discriminant model (Table 7.1 and 7.2).
Table 7.1. Summary of the analysis of discriminant functions for EEG and HRV variables, as well as their actual rate and coefficients of variability Step 37, N of vars in model: 37; Grouping: 4 groups; Wilks' ÿ: 0.0047; approx. F(111)=8.4; p<10-6
|
F7-ÿ SPD, ÿV2 /Hz |
92 |
134 |
453 |
3774 .0073 .651 11.1 3071 .0111 |
10-4 ,041 72 1,836 10-4 ,153 94 |
|
O2-ÿ SPD, ÿV2 /Hz |
116 |
186 |
136 |
.426 27.8 1992 .0068 .696 9.0 |
1,063 10-4 ,023 110 2,162 ,002 |
|
Fp2-ÿ SPD, ÿV2 /Hz 81 |
|
110 |
125 |
.0060 .791 5.45 651 .0057 .838 |
,059 89 0,994 ,011 ,083 15 0,894 |
|
F4-ÿ SPD, ÿV2 /Hz |
115 |
196 |
152 |
4.01 .0104 .457 24, 6 |
10-4 ,102 25 0,786 10-1 ,4207 ± |
|
O2-ÿ SPD, % |
28 |
26 |
39 |
5.05066 .724 7.90 .0061 .780 5.83 |
.001 .292 6.5 0.477 .062 .332 7.6 |
|
F7-ÿ SPD, % ÿ- 33 |
35 |
55 |
58 |
.0053 .889 2.57 .0091 |
0.564 10-4 .081 10.3 0.424 .007 |
|
Laterality, % -5 |
-6 |
-15 |
-19 |
.519 19.1 .0058 .824 |
.200 8.7 0.463 10-3 .054 39 |
|
T6-ÿ SPD, % 12.5 |
8.5 |
7.9 |
1.3 |
4.41 .0064 .741 7.23 |
0.630 .084 .446 1397 0.578 0.8196 |
|
F7-ÿ SPD, % 12.0 |
8.7 |
8.9 |
2.0 |
.0053 .899 2.32 .0050 |
22 0,525, 353, 053 23 0,692, 003, |
|
F4-ÿ SPD, % 16.0 |
9.1 |
5.0 |
4.9 |
.943 1.26 .0050 .949 |
071 23 0,606, 002, 437 50 0.868, |
|
T4-ÿ SPD, % 13.1 |
7.9 |
9.5 |
4.2 |
1.11 .0060 .796 5.28 |
002, 596 4.3 0,926, 009, 383 17 |
|
F4-ÿ SPD, ÿV2 /Hz 76 |
40 |
31 |
29 |
.0061 .784 5.69 .0061 |
0,590, 003, 423 87,9 .073 26.3 |
|
VLF HRV SP, msec2 1865 1229 1163 |
795 |
|
|
.782 5.75 .0057 .833 |
0.609 10-3 .086 27.4 0.583 .056 |
|
C4-ÿ SPD, % 30 |
31 |
61 |
31 |
4.16 .0060 .797 5.26 . |
.192 66.5 0.484 .042 .142 76 0.443 |
|
F3-ÿ SPD, % 34 30 |
34 |
64 |
39 |
0055 .862 3.32 .0057 |
10-3 .376 92.5 0.839 .214 .174 37 |
|
F4-ÿ SPD, % ÿ- |
37 |
60 |
43 |
.831 4.20 .0063 .755 |
0.618 .007 212.08 ,004 ,173 39 |
|
Index, % 28 |
67 |
99 |
72 |
6.70 .0054 .886 2.66 |
0,715 ,075 ,315 6,5 0,188 ,065 |
|
ULF HRV SP, % 3.4 ÿ-Asymmetry, |
4.0 |
11.2 |
5.5 |
.0054 .877 2.91 .0063 |
,162 7,5 0,506 ,032 ,163 2,76 |
|
% 20 ÿ-Index, % 92.0 |
19 |
35 |
20 |
.753 6.77 .0051 .931 |
0,675 10-3 ,060 54,5 0,453 ,002 |
|
|
91.9 |
96.4 |
95.1 |
1.54 51 . 0058 .822 4.46 |
,122 40 0,492 |
|
T3-ÿ SPD, % 32 |
29 |
15 |
16 |
78 .0059 .811 4.83 5.2 |
|
|
F3-ÿ SPD, % 25.1 |
22.0 |
11.4 |
26.5 |
5.7 .0053 .895 2.41 11.9 |
|
|
C4-ÿ SPD, % 26.1 |
23.4 |
14.1 |
24.9 |
16.5 .0053 .891 2.54 |
|
|
Fp1-ÿ SPD, ÿV2 /Hz 75 |
66 |
43 |
103 |
4.35 6.10 .0055 .869 |
|
|
F4-ÿ SPD, ÿV2 /Hz 91 |
78 |
57 |
83 |
3.12 27 26 .0065 .730 |
|
|
T6-ÿ SPD, ÿV2 /Hz 78 |
76 |
47 |
133 |
7.63 20 16 .0060 .792 |
|
|
T5-ÿ SPD, % 30 |
27 |
19 |
22 |
5.42 |
|
|
Fp2-ÿ SPD, ÿV2 /Hz 43 |
27 |
16 |
|
|
|
|
P3-ÿ SPD, ÿV2 /Hz 59 ÿ- |
59 |
24 |
|
|
|
|
Frequency, Hz 6.0 |
6.4 |
|
|
|
|
|
(VLF+LF)/HF 17 |
12.2 |
|
|
|
|
|
LF/HF 5.35 |
4.80 |
|
|
|
|
|
O2-ÿ SPD, % 35 |
50 |
|
|
|
|
|
Fp2-ÿ SPD, % 28 |
35 |
|
|
|
|
|
HF HRV SP, msec2 |
481 |
596 279 1089 |
.0053 .894 2.44 .0052 |
.073 .110 347 1.358 .146 .122 640 |
||
|
LF HRV SP, msec2 ÿ- |
953 |
954 10.4 10.7 |
.917 1.86 .0055 .867 3.17 |
.529 .031 .385 10.4 .069 |
||
|
Frequency, Hz |
10.8 |
|
318,623 10.5 |
|
||
|
VARIABLES |
IV |
II III |
I Wilks Par F to |
p That's it Norm Cv |
||
|
CURRENTLY NOT |
(24) |
(61) (9) |
(8) ÿ tial enter ÿ .977 .49 .959 |
le wound (88) |
||
|
IN THE MODEL ÿ- |
|
|
|
vel cy |
||
|
Amplitude, ÿV 15.0 ÿ-Frequency, |
19.7 |
28.9 |
59.8 |
.005 |
.87 .984 .33 |
.691 .126 13.3 0.442 .463 .413 19.2 |
|
Hz 19.0 |
17.7 |
18.0 |
15.5 |
.005 |
.962 .79 |
0.179 .804 .249 7.1 0.425 .502 .204 |
|
P4-ÿ SPD, % 12.8 ÿ-Laterality, % |
7.5 |
7.8 |
5.5 |
.005 |
.974 .79 |
-5 ±3 .706 .259 -2 ±2 .706 .259 122 |
|
-11 ÿ-Laterality, % -11 |
-15 |
-21 |
-37 |
.005 |
.978 .47 |
1.021 , 649,153 87 0,792,706,259 |
|
|
-11 |
-5 |
-28 |
.005 |
.974 .55 |
19,8 0,717,502,204 -8,575,183 |
|
ULF HRV SP, msec2 98 |
151 |
218 |
152 |
.005 |
.962 .79 |
33,399,166 341,481,101 |
|
C4-ÿ SPD, ÿV2 /Hz 126 ÿ- |
163 |
443 |
315 |
.005 |
.953 1.00 |
22,1,399,166 7,2,419,214 17,458 |
|
Asymmetry, % 20.7 ÿ-Laterality, |
23.0 |
29.3 |
21.3 |
.005 |
.968 .67 |
.190 13.6 .502 .204 66.3 |
|
% ÿ-Asymmetry, -14 |
-12 |
+2 |
-15 |
.005 |
.953 1 .00 |
±3 |
|
% 34 |
49 |
65 |
33 |
.005 |
.961 .83 |
0.812 |
|
P4-ÿ SPD, ÿV2 /Hz 157 ÿ- |
350 |
92 |
319 |
.005 |
.955 .96 .955 |
1.013 |
|
Amplitude, ÿV 13.8 ÿ-Amplitude, |
20.7 |
11.3 |
21.1 |
.005 |
.96 .959 .88 |
0.657 |
|
ÿV 9.8 |
8.5 |
7.7 |
9.4 |
.005 |
.953 1.00 |
0.315 |
|
T6-ÿ SPD, ÿV2 /Hz 38 ÿ- |
28 |
16 |
26 |
.005 |
|
0.642 |
|
Amplitude, ÿV 12.7 100•LF/ |
12.4 |
11.0 |
14.3 |
.005 |
|
0.313 |
|
(LF+HF), % 70.8 |
70.1 |
77.7 |
78.8 |
.005 |
|
0.210 |
Table 7.2. Summary of step-by-step analysis of EEG and HRV variables. Variables are ranked according to the ÿ criterion
|
Variables currently in the model |
F that enter |
p ÿ level |
F p value level 10-6 22.5 |
|
O2-ÿ SPD, ÿV2 /Hz 22.5 |
|
10-6 ,593 |
10-6 19.4 |
|
F4-ÿ SPD, % 16.7 |
|
10-6 ,391 |
10-6 16.9 10-6 |
|
C4-ÿ SPD, % 10.3 |
|
10-5 ,296 |
10-6 15.9 |
|
F7-ÿ SPD, ÿV2 /Hz 9.9 |
|
10-5 ,225 |
14.2 15.3 10-6 |
|
ULF band HRV Spectral Power, % 9.0 |
|
10-4 ,175 |
10-6 10-6 13.2 |
|
F3-ÿ SPD, % 5.8 ÿ-Index, % 4.8 |
|
,001 ,147 ,004 |
11.9 12 .5 |
|
|
|
,127 ,005 ,111 |
10-6 10-6 11.6 |
|
T6-ÿ SPD, % 4.6 |
|
,007 ,097 ,005 |
11.0 11.3 10-6 |
|
T3-ÿ SPD, % 4.3 |
|
,084 ,005 , |
10-6 10-6 10.7 |
|
F7-ÿ SPD, % 4.6 |
|
|
10-6 10.6 10.4 |
|
Fp2-ÿ SPD, ÿV2 /Hz 4.5 ÿ-Asymmetry, % 3.8 |
|
|
10-6 10.4 10-6 |
|
|
|
|
10-6 10.3 10-6 |
|
Fp2-ÿ SPD, % 3.7 |
|
|
10.0 10.2 |
|
F3-ÿ SPD, % 4.2 |
|
|
9.7 10-6 10-6 |
|
O2-ÿ SPD, % 3.8 |
|
|
10-6 9.5 9.3 |
|
O2-ÿ SPD, % 4.5 |
|
|
10-6 9.0 10-6 |
|
HF band HRV Spectral Power, msec2 3.9 |
|
|
10-6 9.0 |
|
T4-ÿ SPD, % 3.5 |
|
|
10-6 10- 6 8.9 |
|
F4-ÿ SPD, % 2.6 ÿ-Frequency, Hz 2.1 ÿ-Index, % 2.0 |
|
|
8.8 10-6 8.8 |
8.7 10-6
10-6 8.6
F7-ÿ SPD, % 2.1
F4-ÿ SPD, ÿV2 /Hz 1.7 P3-ÿ SPD, ÿV2 /Hz 3.3 Fp2-ÿ SPD, ÿV2 /Hz 2.3 Fp1-ÿ SPD, ÿV2 /Hz 2.9 F4-ÿ SPD, ÿV2 /Hz 2.5 F4-ÿ SPD, ÿV2 /Hz 2.4
T6-ÿ SPD, ÿV2 /Hz 2.4 ÿ-Laterality Index, % 4.0
10-6 8.8 10-6
8.8 10-6 8.8
C4-ÿ SPD, % 2.9 ÿ-Frequency, Hz 2.6
073,013,064,014,057,008,050,013,044,006,037,012,033,018,029,059,026,105,024,122,023,103,021
|
(VLF+LF)/HF as Centralization Index 2.2 |
,092 ,006 |
8.8 |
10-6 |
|
LF/HF as Sympatho/Vagal Balance 2.1 |
,108 ,006 |
8.7 |
10-6 |
|
VLF band HRV Spectral Power, msec2 1.4 |
,240 ,006 |
8.6 |
10-6 |
|
LF band HRV Spectral Power, msec2 1.8 |
,149 ,005 |
8.5 |
10-6 |
|
T5-ÿ SPD, % 1.5 |
,214 ,005 |
8.4 |
10-6 |
Next, according to the algorithm, the 37-dimensional space of discriminant variables turns into a 3-dimensional space of canonical roots. We bring
root parameters. R1*=0.958 (Wilks' ÿ=0.005; ÿ2 (111)=431; p<10-6); R2*=0.912 (Wilks' ÿ=0.058; ÿ2 (72)=230; p<10-6); R3*=0.811 (Wilks' ÿ=0.343; ÿ2 (35)=86; p<10-5).
The first root contains 61.8% of discriminating capabilities, the second - 27.6%,
and the third - only 10.6%.
Calculation of the values of the discriminant roots for each person as a sum products of raw coefficients on individual values
discriminant variables together with a constant, allows you to visualize each patient in the information space of the roots (Figs. 7.5 and 7.8). Table 7.3.
Standardized and raw coefficients and constants for EEG
and HRV variables
Coefficients
Standardized
Raw
Variables currently in the model
Root 1 Root 2 Root 3 Root 1 Root 2 Root 3
O2-ÿ SPD, ÿV2 /Hz 1.978 .116 .483 .00208 .00012 .00051
F4-ÿ SPD, % -2.313 1.121 -.160 -.46666 .22621 -.03236
C4-ÿ SPD, % -.047 -.914 -.135 -.00265 -.05146 -.00763
F7-ÿ SPD, ÿV2 /Hz 2.322 -2.048 .119 .00195 -.00172 .00010
ULF band HRV Spectral Power, % -.221 -.608 .140 -.04936 -.13584 .03127
F3-ÿ SPD, % .737 1.482 -.027 .06465 .13003 -.00236 ÿ-Index, % .076 -.715 -.311 .00218 -.02061
-.00896
T6-ÿ SPD, % -.194 .787 -.05042 .20417 .14450 .557
T3-ÿ SPD, % -.749 .073 -.120 -.05188 .00508 -.00829
F7-ÿ SPD, % -2.377 .377 .167 -.09908 .01572 .00695
Fp2-ÿ SPD, ÿV2 /Hz -1.468 3.640 -.086 -.00234 .00581 -.00014 ÿ-Asymmetry, % -.594 -.357 -.105
-.05491 -.03295 -.00974
Fp2-ÿ SPD, % -1.338 -.115 -.269 -.09671 -.00833 -.01945
F3-ÿ SPD, % .077 1.018 -.359 .00373 .04934 -.01741
O2-ÿ SPD, % -.236 1.382 -.697 -.01133 .06623 -.03340
O2-ÿ SPD, % -.169 1.759 -1.701 -.00883 .09171 -.08869
HF band HRV Spectral Power, msec2 ,971 ,258 -,236 ,00122 ,00032 -,00030
T4-ÿ SPD, % .119 -.796 .718 .02903 -.19435 .17519
F4-ÿ SPD, % -.937 1.042 -1.335 -.04176 .04643 -.05951 .215 -.583 -.102 .17005 -.46091 -.08095 ÿ-
,02385 Frequency, Hz ÿ-Index, % .679 .065 ,283 ,05716 ,00550
F7-ÿ SPD, % -.539 -.281 -.019 -.14300 -.07445 -.00504
F4-ÿ SPD, ÿV2 /Hz -.133 -2.007 .548 -.00041 -.00625 .00171
P3-ÿ SPD, ÿV2 /Hz -.393 .942 -.571 -.00621 .01488 -.00902
Fp2-ÿ SPD, ÿV2 /Hz -.235 -1.443 -.581 -.00665 -.04076 -.01641
Fp1-ÿ SPD, ÿV2 /Hz -.730 .353 -.043 -.01569 .00758 -.00093
F4-ÿ SPD, ÿV2 /Hz 2.137 .872 -.013 .03837 .01565 -.00024
|
F4-ÿ SPD, ÿV2 /Hz -.823 -.280 |
.529 -.01738 -.00591 .01116 .304 |
|
T6-ÿ SPD, ÿV2 /Hz .804 .052 ÿ-Laterality Index, % -.811 |
.01058 .00068 .00400 -.940 -.02178 |
|
-.425 |
-.01141 -.02524 -1.518 -.11814 -.00381 |
|
C4-ÿ SPD, % -1.202 -.039 ÿ-Frequency, Hz .454 -.255 |
-.14915 .394 .60641 -.340654 .529 ,497 |
|
|
-,00062 -,05843 ,03696 ,053 ,05838 |
|
(VLF+LF)/HF as Centralization Index -.008 -.786 |
,18084 ,01030 ,466 ,00007 ,00017 |
|
LF/HF as Sympatho/Vagal Balance ,300 ,931 |
,00027 -,256 -,00054 -,00021 -,00018 |
|
VLF band HRV Spectral Power, msec2 ,116,293 |
,200 -,02571 ,03154 ,0 |
|
LF band HRV Spectral Power, msec2 -.778 -.300 |
|
|
T5-ÿ SPD, % -.414 .508 |
|
Constants 5.753 -5.076 .883
Eigenvalues 11,122 4,961 1,917 ,894 1,000
Cumulative Properties ,618
Table 7.4 presents the full structural coefficients, as well as the average values (centroids) of the roots and Z-scores of the EEG and VRS variables, including those not included in the model, since the experience of the Truskavet Scientific School shows that the variable’s failure to enter the model can be due to , that it is a carrier, except for characteristic, redundant or duplicative information.
Table 7.4. Correlations between variables and roots, mean roots and Z-scores of variables
Correlations
IV II
III I
Variables
Variables-Roots
(24) (61) (9) -2.57
(8)
Root 1 (61.8%)
R1 R2 R3
-0.51 +0.85 +10.66 .241 .055 .103
F7-ÿ SPD, ÿV2 /Hz O2-ÿ SPD, ÿV2 /Hz
Fp2-ÿ SPD, ÿV2 /Hz F4-ÿ SPD, ÿV2 /Hz O2-ÿ SPD, %
F7-ÿ SPD, %
ÿ-Amplitude, ÿV ÿ-Laterality, % T6-ÿ SPD, %
F7-ÿ SPD, % F4-ÿ SPD, % T4-ÿ SPD, %
F4-ÿ SPD, ÿV2 /Hz P4-ÿ SPD, %
ÿ-Laterality, %
ÿ-Frequency, Hz
VLF band HRV SP, msec2
+0.15 +0.47 +2.88 +28.0 .239 .090 .076 +0.22 +0.92 +0.42
+29.8 .082 .086 -0.12 0.00 +0.06 +7.91 .236 .130 -.076 .029
+0 .30 +1.21 +4.10 +6.35 .107 -.029 .103 +1.02
+0.85 +1.86 +3.09 .083 -.078 .065 +0.43 +0 .48 +1.51 +1.66
Currently not in model +0.29 +1.10 +2.65 +7.91 -.024 .011
-.041 -0.18 -0.21 -0.45 -0.59 -.207 .043 , 175 +1.93 +0.63
+0.46 -1.69 -.189 .008 .147 +1.02 +0.26 +0.30 -1.31 -.152
.186 .252 +1, 31 -0.28 -1.22 -1.23 -.141 .022 .280 +1.08 -0.19
+0.20 -1.11 -.053 .064 .131 +1.51 +0, 06 -0.32 -0.39
Currently not in model +1.88 +0.12 +0.22 -0.52
Currently not in model -0.19 -0.36 -0.55 -1.13
Currently not in model -0.06 -0.44 -0.35 -1.08 -.038 .027
.078 +0.58 -0.21 -0.29 -0.75
Root 2 (27.6%)
R1 R2
R3 +1.54 +0.15 -6.69 +1.77
C4-ÿ SPD, ÿV2 /Hz C4-ÿ SPD, %
F3-ÿ SPD, % F4-ÿ SPD, %
ÿ-Index, %
ÿ-Asymmetry, % ÿ-Index, %
ÿ-Asymmetry, % ÿ-Laterality, %
ÿ-Laterality, %
ÿ-Asymmetry, %
Currently not in model +0.56 +1.11 +5.17 +3.31 .015 -.213
.085 +0.75 +0.79 +3.48 +0.84 .029 -.169 .082 +0.73 +0.74
+2.63 +1.08 .046 -.145 -.014 +0.47 +1.01 +2.67 +1.43 .083
-.196 -.211 -0 .51 +0.40 +1.13 +0.52
Currently not in model +0.03 +0.60 +1.20 0.00 .023 -.039
+0.24 +0.23 +0.49 ,+003.142
Currently not in model +0.06 +0.21 +0.67 +0.10
Currently not in model -0.24 -0.15 +0.38 -0.28
Currently not in model -0.41 -0.38 -1.16 -0.14+.00.1323 -.170
+0.22 +1.81 +0.34 .043 -.201 -0.24 -0.08 +1.73 +0.29
ULF band HRV SP, % ,105,065
ULF band HRV SP, msec2 T3-ÿ SPD, %
F3-ÿ SPD, % C4-ÿ SPD, % T5-ÿ SPD, %
Fp1-ÿ SPD, ÿV2 /Hz F4-ÿ SPD, ÿV2 /Hz T6-ÿ SPD, ÿV2 /Hz
ÿ-Amplitude, ÿV ÿ-Amplitude, ÿV
P4-ÿ SPD, ÿV2 /Hz
ÿ-Frequency, Hz Fp2-ÿ SPD, ÿV2 /Hz P3-ÿ SPD, ÿV2 /Hz T6-ÿ SPD, ÿV2 /Hz
ÿ-Amplitude, ÿV
(VLF+LF)/HF as Centralization Index LF/HF as Sympatho/Vagal Balance Root 3 (10.6%)
O2-ÿ SPD, % Fp2-ÿ SPD, %
HF band HRV SP, msec2
LF band HRV SP, msec2 100•LF/(LF+HF), % ÿ-
Frequency, Hz
Currently not in model -0.19 +0.23 +0.77 +0.24 -.087 .106
-0.13 -0.27 -1.07 -1-..00431.009 .147 -0.07 - 0.27 -0.93 +0.01
-.010 .137 -0.08 -0..20256-0.83 -0.16 -.038 .063 -0.31 -0.44 -0.77
- 0.67 .048 .098 +0..02168-0.02 -0.71 +1.14 -.010 .079 +0.44
+0.05 -0.57 +0.21 ..005064 .070 -0 .19 -0.22 -0.58 +0.52
.045
.044
.010
Currently not in model -0.22 -0.28 -0.61 +0.16
Currently not in model -0.57 -0.10 -0.74 -0.07
Currently not in model -0.53 +0.03 -0.72 +0.06 -.029 .072
-.134 -0.46 -0.15 -1.07 -0.69 .022 .093 .108 +1.53 +0.39 -0.43
+2.05 .020 .076 -.018 +0.71 +0.71 -0.53 +1.38
Currently not in model +1.82 +0.94 -0.16 +1.03
Currently not in model +1.15 +0.56 +0.23 +0.98 .003 +2.25
+1.63 +1.59 +2.20 .011 +1.49 +1.22 +1 ,21 +2.63
,038,026 ,093,029
R1 +1.98R-12.09 +1.R273 +0.92 -.062 .049 -.273 -0.77 -0.20 -1.10
-1.17 -.084 .049 -.230 - 0.63 -0.24 -1.00 -1.24 -.019 .029
-.067 +0.28 +0.53 -0.14 -0.06 -.018 -.004 -.037 +0 .92 +1.33
+0.92 -0.05
Currently not in model +0.32 +0.27 +0.82 +0.90 -.017 -.009
.155 +0.50 -0.07 +0.41 +0.10
The localization of the members of the first cluster along the axis of the first root (Figs. 7.5 and 7.6) in the extreme right (positive) zone (centroid: +10.66) reflects sharply increased EEG parameters that are positively related to the root, as well as maximally reduced EEG parameters and HRV, which are negatively related to the root (Table 7.4).
4
3
2
1
0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12
Root EEG&HRV 1 (61.8%)
III I IV II
Fig. 7.5. Individual values of I and II roots of EEG and HRV of members of four clusters
Fig. 7.6. Normalized values (Z±SE) of EEG and HRV parameters, condensed in the first root, which correlate with it positively or
negatively
At the same time, the fourth cluster occupies the extreme left (negative, centroid: -2.57) zone displaying the minimum/maximum levels of these parameters. members
the other two clusters occupy an intermediate position, and their projections on the axis mixed up Nevertheless, the positive value of the centroid of the third cluster
(+0.85) reflects a higher level of parameters than the members of the second cluster (centroid: -0.51).
Instead, along the axis of the second root, members of cluster III (centroid: -6.69)
clearly separated from the members of II and two other clusters, the projections of which are on the axis
mixed (centroids: +1.54; +0.15 and +1.77 for IV, II and I cluster, respectively).
This disposition of the III cluster reflects the maximum values of the EEG and HRS parameters for the sample, which are negatively related to the root , and the minimum values of the parameters that are positively related to the root, while the members of the other clusters do not differ significantly in terms of these parameters from each other (Table
7.4 and Fig. 7.7).
Fig. 7.8. Individual values of I and III roots of EEG and HRV of members of four clusters
0.8
0.4
0.0
-0.4
y = -0.114x2 + 0.209x + 0.46 R2 = 0.940
y = 0.304x2 - 0.391x - 0.44 R2 = 0.958
R3+ R3-
-0.8
-1.2
-0.8
-0.4 0
0.4
0.8
1,2
1.6 2
Root Centroides
Fig. 7.9. Normalized values (Z±SE) of EEG and HRV parameters condensed in the third root, which correlate with it positively or
negatively
In general, all four EEG&VSR clusters on the planes of the three roots are quite
clearly delineated, which is confirmed by the calculation of Mahalanobis distances between them (Table 6.5).
Table 7.5. Squares of Mahalanobis distances between EEG/VRS clusters (above the diagonal) and F-criteria (df=37.6; for all pairs p<10-6)
|
Clusters |
III I IV II 0 |
|
III |
175 83 56 11.2 0 |
|
I |
183 137 8.4 16.8 0 |
|
IV II |
16 6.8 14.7 4.6 0 |
Use of coefficients and constants to calculate classification
functions (table 7.6), makes it possible to retrospectively recognize the members of the third
and the first clusters without error, the second cluster is classified with one error, and only the fourth cluster - with three errors. Overall accuracy classification is 96.1% (table 7.7).
Table 7.6. Coefficients and constants for classification functions of EEG/VRC clusters
|
CLUSTERS III |
I IV II |
|
Variables currently in the model p=.088 |
p=.078 p=.235 p=.598 .0188 |
|
O2-ÿ SPD, ÿV2 /Hz -.0024 |
-.0081 -.0056 .5799 6.6657 |
|
F4-ÿ SPD, % 3.2303 |
5.4926 -1.4043 -1.3655 |
|
C4-ÿ SPD, % -.9456 |
-1.2759 .0094 -.0159 -.0098 |
|
F7-ÿ SPD, ÿV2 /Hz .0049 |
-1 ,1989 -,4813 -,4897 |
|
ULF band HRV Spectral Power, % .4455 |
4,7258 3,8381 3,7972 |
|
F3-ÿ SPD, % 2.9908 ÿ-Index, % .0774 |
-,0724 -,1061 -,0454 |
|
T6-ÿ SPD, % -.6276 |
... |
1.3285 .4972 |
|
T3-ÿ SPD, % .4068 |
2654 |
.6202 .5321 |
|
F7-ÿ SPD, % .4226 |
-.1726 |
.8960 .6494 |
|
Fp2-ÿ SPD, ÿV2 /Hz -.0466 ÿ-Asymmetry, % .6653 |
.0470 |
.0091 -.0034 |
|
|
.0339 |
.5750 .5380 |
|
Fp2-ÿ SPD, % 2.1118 |
-.1112 |
2.3603 2.2332 |
|
F3-ÿ SPD, % 2.4689 |
-1.0422 |
2.8498 2.8422 |
|
O2-ÿ SPD, % 1.2255 |
34.90 |
1.7855 1.7728 |
|
O2-ÿ SPD, % 1.0700 |
-.3692 |
1.7917 1.9184 |
|
HF band HRV Spectral Power, msec2 -.0064 |
|
-.0081 - - ,6263 |
|
T4-ÿ SPD, % 3.0162 |
3.1609 |
,2902 ,2285 |
|
F4-ÿ SPD, % .1915 ÿ-Frequency, Hz 14.95 ÿ- |
|
-,1020 -,0935 |
|
Index, % .5922 |
|
,1527 ,2014 |
|
|
-.0011 |
,3624 ,5833 |
|
F7-ÿ SPD, % 1.8221 |
-.0008 |
27,52 27,62 |
|
F4-ÿ SPD, ÿV2 /Hz .0905 |
.2239 |
-,3081 -,3416 |
|
P3-ÿ SPD, ÿV2 /Hz .0128 |
-477.9 |
2,3579 2,1946 |
|
Fp2-ÿ SPD, ÿV2 /Hz .7055 |
|
-,0017 -,0026 |
|
Fp1-ÿ SPD, ÿV2 /Hz -.1760 |
|
,0061 ,0058 |
|
F4-ÿ SPD, ÿV2 /Hz -.6814 |
|
,5699 ,4352 |
|
F4-ÿ SPD, ÿV2 /Hz ,2714 |
|
-498.5 -475.7 |
|
T6-ÿ SPD, ÿV2 /Hz -.0743 ÿ-Laterality, % .1901 |
|
|
|
C4-ÿ SPD, % .0960 32.03 ÿ-Frequency, Hz |
|
|
|
(VLF+LF)/HF as Centralization Index ,1443 |
|
|
|
LF/HF as Sympatho/Vagal Balance 1.0620 |
|
|
|
VLF band HRV Spectral Power, msec2 -.0031 |
|
|
|
LF band HRV Spectral Power, msec2 .0061 |
|
|
|
T5-ÿ SPD, % .2135 Constants -455.7 |
|
|
|
III 100 0 |
|
9 |
0 |
|
0 |
|
I |
100 |
0 |
8 |
0 |
0 |
|
IV |
87.5 |
0 |
0 |
21 |
3 |
|
II |
98.4 |
0 |
0 |
1 |
60 |
|
Total |
96.1 |
9 |
8 |
22 |
63 |
CONCLUSION
We identified quantitatively and qualitatively different clusters
entropies of the spectral power density of EEG rhythms are clearly different
from each other according to at least 37 amplitude-frequency and spectral parameters of EEG and VRS, information about which is summarized in three discriminant roots. Importantly, each root contains information about both EEG and VRS parameters, which is consistent with the proposition about connections
Machine Translated by Google
HRS parameters with electrical and morpho-functional correlates of the activity of the cortex and subcortical structures.
Taking into account the previously established relationships between entropy and immunity parameters, it can be assumed that each of the entropy clusters is characterized by a specific constellation of immunity parameters. The next section will be devoted to testing this hypothesis.
156
CHAPTER
8 FEATURES OF THE NEURO-IMMUNE COMPLEX IN PERSONS WITH DIFFERENT STATE OF ENTROPY OF NERVOUS REGULATORY STRUCTURES
Because there is a close two-way connection between the nervous and immune systems, the next stage of research was to find out the features
of the neuro-immune complex in individuals with different states of EEG entropy and VRS as one from manifestations of the activity of nervous regulatory structures.
Based on discriminant analysis, 39 parameters are included in the model,
in particular, 9 parameters of SHSP delta and theta rhythm, 3 parameters of alpha rhythm, 4 parameters of beta rhythm, 5 HRV parameters, as well as 9 immunity parameters.
The remaining 19 immunity parameters were found to be outside the model. In addition, attention should be paid to 6 EEG parameters and 3 HRV parameters, which are not formally included in the discrimination model, but are actually cognitive (tables 8.1 and 8.2). Table 8.1. Summary of the
analysis of discriminant functions for neuro-immune variables, their actual level for clusters and norms and coefficients of variability Step 39, N of vars in model: 39; Grouping: 4 groups; Wilks' ÿ:
0.0053; approx.
F(117)=7.3; p<10-6
|
SPD, ÿV2 /Hz 3071 ,008 F7-ÿ S11P6D, ÿV2 /1H3z63774 ,010886Fp2-ÿ SPD, ÿV2 /Hz |
.640 11.3 |
10-5 ,252 94 1,063 10-4 ,038 72 |
|
81 1992 ,008 F4-ÿ SPD, ÿV2 /H92z 115 65145, 3007 Fp1-1ÿ34SPD, % 24 47 .006 F7- |
.686 9.1 |
1,836 10-4 ,020 110 2,162 10-3 |
|
ÿ SPD, % 33 58 .009 Killing In St. aur, %12456.4 53.61.10006 BC St. aur., 109 B/ |
.663 10.2 .734 |
,058 89 0,994 ,386 ,216 18,9 0,701 |
|
L 96 114 .006 F7-ÿ SPD, % 12.0 2.0 .0081T542-ÿ SPD,1%9613.1 4.2 .007 F4-ÿ |
7.2 .951 1.0 |
10-5 ,165 25 0,786 ,018 8,24 9 |
|
SPD, % 16.0 4, 9 .008 P4-ÿ SPD, % 12.8458.5 .006 F239-ÿ SPD, % 15.3 6.2 .006 |
.599 13.4 |
0.071 .041 .368 106 0.200 10-4 |
|
F4-ÿ SPD, ÿV2 /Hz 76 29 .007 VLF HRV 5S5P, msec2351865 1163 1229 795 |
.846 3.6 .873 |
.370 7.6 0.564 .005 .211 8.7 0.463 |
|
.006 0.948 0.970 0.937 0.897 .006 Entrop5y0o.5f ICG C5D12.12+ B Lymph, % 24.5 |
2.9 .706 8.3 |
10-4 .080 10.3 0.424 .152 .287 7.1 |
|
24.1 23.7 22.5 .006 CD3+ Tac Lymph, %9209.3 30.6 9268.3 26.7 , 006 C4-ÿ SPD, |
,806 4,8 |
0.425 .101 .161 9.02 0.4 10-3 ,054 |
|
% 61 31 31 .007 F3-ÿ SPD, % 64 34 39 .080.97 F4-ÿ SP8.D7, % 60 37 43 .007 |
,663 10,2 |
39 0,630 ,185 ,415 1397 0,578 |
|
ULF HRV SP, % 3.4 ULF HRV SP, 11.2 4.0 5.5 ,007 |
.736 7.2 .852 |
10-3 .327 4.3 0.926 .021 .220 122 |
|
msec2 98 CIC, units 35 Killing I 218 151 152 ,006 |
3.5 .900 2.2 |
1.021 .096 .545 45 0.389 .292 .247 |
|
E. coli, % 43.5 50.4 F3-ÿ SPD, % 43 34 40 ,006 |
.940 1.3 .740 |
62.0 0.078 10-3 .094 26.3 0.609 |
|
25.1 11.4 T3-ÿ SPD, % 32 15 F4-ÿ SPD, ÿV2 / 47.9 46.8 ,006 |
7.0 .612 12.7 |
10-5 .236 34 0.509 135-3 76 0.443 |
|
Hz 57 T6-ÿ SPD, ÿV2 /Hz Fp2-ÿ SPD, ÿV2 /Hz 22.0 26.5 ,007 |
.709 8.2 .849 |
.020 .432 92.5 0.839 .048 .077 22 |
|
43 T6-ÿ SPD, ÿV2 /Hz P3-ÿ SPD, ÿV2 /Hz 59 29 16 ,009 |
3.6 .877 2.8 |
0.631 .179 .219 17 0.642 .003 .102 |
|
P4-ÿ SPD, ÿV2 /Hz 157 (VLF+L9F1)/HF 17 78 83 ,008 |
.922 1.7 .792 |
39 0.715 .032 .144 341 1.013 .099 |
|
Monocytes, % 6.06 MC 78 47 76 133 ,006 |
5.3 .865 3.1 |
.394 7.5 0.85 ,366 ,596 54,7 0,097 |
|
vs E. coli, M/Ph 67.1 O2-ÿ SPD, 16 27 51 ,006 |
.901 2.2 .901 |
,010 ,170 54,5 0,453 10-5 ,090 40 |
|
% Fp2-ÿ SPD, % LF 38 16 28 26 ,006 |
2.2 .949 1.1 |
0,492 ,303 ,406 640 0,529 |
|
HRV SP, msec2 VARIABLES 24 59 78 350 ,007 |
.828 4.1 .636 |
|
|
CURRENTLY NOT IN THE MODEL 92 319 11.9 12.2 ,006 |
11.5 .942 1.2 |
|
|
O2-ÿ SPD, % 28 .005 LF/HF 5.35 16.5 5.81 6.36 6.46 57.6 ,006 |
|
|
|
.005 Popovych's AI-1, pts 1.10 .007 64.3 62.0 27 50 26 20 35 ,006 |
|
|
|
Leukocytes, 109 /L 5.67 .007 BC 16 954 1089 623 ,006 |
|
|
|
E. coli, 109 B/L 35 ,006 |
|
|
|
92 .007 CD56+ NK 28 ,008 |
|
|
|
Lym, % 19.2 .007 0 953 ,006 Lymphocytes, % IV III II I Wilks ÿ |
Par F to |
p That's it Norm Cv |
|
1.1 .007 T6-ÿ SPD, % 12.5 (24) (9) (61) (8) |
tial enter |
le wound (88) |
|
.007 CD8+ T-cytolytic, |
ÿ |
vel cy |
|
% 24.2 .007 Phag Ind St. aur., % 98.7 .00739C4-ÿ SPD2,6ÿV2 /Hz 15256 .007 SegN |
.961 .79 .979 |
,503 ,103 15 0,894 ,739 ,171 2,76 |
|
Neutrophils, % 54.7 .007 Stub Neutrophil4s.,3%5 2.45 .40.0870 CD4+ T6-.1h0elpers, % |
.42 .989 .23 |
0,675 ,877 ,470 1,70 0,147 ,877 |
|
31.0 .005 Immunoglob A, g/L 1.73 .007 T51-.ÿ33SPD, %13.105.005 C41-.ÿ40SPD, % 26.1 |
.989 .23 .959 |
,264 5,00 0,100 ,465 ,069 99 0,200 |
|
.007 Fp1-ÿ SPD, ÿV2 /Hz 75 .007 Immuno5g.l2o4b M, g/L5.16.451 1.3661.1.456 1.41 .007 |
.86 .974 .54 |
,660 ,102 17,0 ,0617 ,104 0 0,576 |
|
MC vs St. aur, M/Ph 65.0 57.6 62.5 61.8 .08097 Pan Ly9m3phocytes9,9% 33.8 30.3 |
.973 .54 .979 |
,734 ,344 6,5 0,477 ,756 ,261 23,5 |
|
35.1 34.0 .007 Entropy of LCG 0.644 0.631760.8.661 0.6169 0.007 HF2H3.R5V SP, |
.43 .981 .40 |
0,138 ,509 ,499 98,3 0,018 ,421 |
|
msec2 481 279 596 318 .005 Eosinophile-s0,.1% 2.97 21.9.80 3.46 3.43.19.007 |
.962 .78 .955 |
,136 87 0,792 ,910 ,372 55,0 0,100 |
|
Popovych 's SI-1, pts 0.21 0.15 0.23 0.12 .70.097 Popov8y.c5h's SI-2,1p.3ts 0.25 0.16 |
.95 .991 , |
,741 ,274,564 4 ,583 ,224 39,5 0,082 |
|
0.30 0.18 .007 100•LF/(LF+HF), % 70.8 772.73.790.1 78.823.0.605 Phag21In.4vs E. coli, |
|
,722 ,425 1,875 0,167 ,562 ,192 37 |
|
% 99.3 99.4 98.8 99.1 .005 Popovych's AI9-28,.2pts 0.89980..385 0.81 908.9.02 .007 |
|
0,618 ,573 ,100 27,4 0,583 ,667 |
|
Immunoglob G, g/L 14.8 14.4 14.2 15.0 , 040473 163 315 |
|
,197 66,5 0,484 ,573 , 532 1,15 |
|
58.2 52.4 53.3 |
|
0,6,361,369,361 6 0,080, 918, 413 |
|
2.84 2.71 2.79 |
|
32,0 0,174 , 925, 327 0,681 0,070, |
|
35.3 32.0 27.9 |
|
972, 119 347 1.358 , 851, 561 2.75 |
|
2.00 1.73 1.59 |
|
0,318, 498, 498, 481 0,067 067, 652 |
|
19 27 22 |
|
.409 .482 98.3 0.012 .571 .627 1.70 |
|
14.1 23.4 24.9 |
|
0.147 .568 .733 12.75 0.206 |
|
43 66 103 |
|
|
18,980,42,968,66,978,44,966,69,968,67,974,52,968,67,975,52,992,17,992,16,996
Table 8.2. Summary of the step-by-step analysis of neuro-immune variables ranked by the ÿ criterion
|
Variables currently in F that the model O2- enter |
p level |
ÿ F-value 22.5 |
p level |
|
ÿ SPD, ÿV2 /Hz 22.5 F4-ÿ SPD, % 16.7 C4-ÿ SPD, % 10.3 |
10-6 |
19.4 |
10-6 |
|
F7-ÿ SPD, ÿV2 /Hz 9.9 ULF band HRV SP, % 9.0 F3-ÿ SPD, |
10-6 |
16.9 |
10-6 |
|
% 5.8 T3-ÿ SPD, % 4.4 P4-ÿ SPD, % 3.3 Fp2-ÿ SPD, ÿV2 / |
10-5 |
15.9 |
10-6 |
|
Hz 3.2 F7-ÿ SPD, % 4, 6 F7-ÿ SPD, % 5.4 F3-ÿ SPD, % 3.7 |
10-5 |
15.3 |
10-6 |
|
Fp2-ÿ SPD, % 3.5 O2-ÿ SPD, % 4.3 T4-ÿ SPD, % 3.3 |
10-4 |
14.2 |
10-6 |
|
Killing Index vs E coli, % 3.3 (VLF+LF)/HF as |
,001 |
13.1 |
10-6 |
|
Centralization Index 2.9 F4-ÿ SPD, % 2.7 F4-ÿ SPD, ÿV2 / |
,006 |
12.1 |
10-6 |
|
Hz 2.5 P3-ÿ SPD, ÿV2 /Hz 4, 3 F3-ÿ SPD, % 3.5 T6-ÿ SPD, |
,023 |
11.4 |
10-6 |
|
ÿV2 /Hz 3.1 Fp2-ÿ SPD, ÿV2 /Hz 2.2 Killing Index vs |
,026 |
11.0 |
10-6 |
|
Staphylococ. aureus, % 2.7 Bactericidity vs Staph. aur., |
,005 |
11.0 |
10-6 |
|
109 Bact/L 2.0 F4-ÿ SPD, ÿV2 /Hz 1.9 F4-ÿ SPD, ÿV2 /Hz |
,002 |
10.6 |
10-6 |
|
6.3 P4-ÿ SPD, ÿV2 /Hz 3.0 CD22+ B Lymphocytes, % 1, |
,014 |
10.3 |
10-6 |
|
7 CD3+ T Active Lymphocytes, % 1.6 T6-ÿ SPD, ÿV2 /Hz |
,018 |
10.2 |
10-6 |
|
1.5 Fp1-ÿ SPD, % 1.7 Monocytes, % 1.4 ULF band HRV |
,007 |
10.0 |
10-6 |
|
SP, msec2 1.3 Circulating Immune Complexes, units 1.7 |
,024 |
9.8 |
10-6 |
|
LF band HRV SP, msec2 1.5 VLF band HRV SP, msec2 |
,023 |
9.6 |
10-6 |
|
1.2 Entropy of Immunocytogram 1.0 Microbial Count for |
,042 |
9.4 |
10-6 |
|
E. coli, M/PhC 1.1 |
,053 |
9.2 |
10-6 |
|
|
,067 |
9.3 |
10-6 |
|
|
,007 |
9.2 |
10-6 |
|
|
,020 , |
9.2 |
10-6 |
|
|
|
9.0 |
10-6 |
|
|
|
8.9 |
10-6 |
|
|
|
8.8 |
10-6 |
|
|
|
8.6 |
10-6 |
|
|
|
9.0 |
10-6 |
|
|
|
9.0 |
10-6 |
|
|
|
8.9 |
10-6 |
|
|
|
8.7 |
10-6 |
|
|
|
8.5 |
10-6 |
|
|
|
8, |
10-6 |
|
|
|
4 |
10-6 |
|
|
|
8.2 |
10- |
|
|
|
8.1 |
6 |
|
|
|
8.0 |
10-6 |
|
|
|
7.8 |
10-6 |
|
|
|
7.7 |
10-6 |
|
|
|
7.5 |
10-6 |
030,095,050,,152933,1,3359110,2-397,06.33,52,21583,1,179581,,12024-677,1,1062-96,92,5131,267,01,01573,0,29316,,033757,3,0964,83,6066_0,052,047,042,038,034,032,027,02
We condense information from 39 neuro-immune variables in a well-worn way into three discriminant roots with the following characteristics. R1*=0.956 (Wilks' ÿ=0.005; ÿ2 (117)=416; p<10-6); R2*=0.906 (Wilks' ÿ=0.061; ÿ2 (76)=222; p<10-6);
R3*=0.810 (Wilks' ÿ=0.344; ÿ2 (37)=85; p<10-5). The first root contains 61.8% discriminative capabilities, the second - 27.0%, and the third - 11.2%.
Calculation of the discriminant values of the roots for each person as a sum products of raw coefficients on separate values of discriminants
of variables together with the constant (Table 8.3) again makes it possible to visualize each patient in the information space of the roots (Fig.
8.1). Table 8.3. Standardized and raw coefficients and constants for neuro- immune variables
Coefficients currently in the model O2-ÿ SPD,
Standardized
Root 1 Root 2 Root 3 Root 1 Variables
Raw
Root 2 Root 3
|
ÿV2 /Hz 1.229 -.111 F4-ÿ SPD, % -1.170 1.819 C4-ÿ SPD, % -.128 |
,239 ,0013 ,604 |
-.0001 .0003 |
|
-1.323 F7-ÿ SPD, ÿV2 /Hz ,911 -2,876 ULF band HRV SP, % -,602 |
-,2360 ,644 -,0072 |
.3670 .1219 |
|
-,755 F3-ÿ SPD, % 1,014 1,434 T3-ÿ SPD, % -1,229 ,372 P4-ÿ SPD, |
1,062 ,0008 -,102 |
-.0745 .0363 |
|
% ,310 -,129 Fp2- ÿ SPD, ÿV2 /Hz .490 4.279 F7-ÿ SPD, % -1.409 |
-,1346 -,433 |
-.0024 .0009 |
|
.848 F7-ÿ SPD, % -.805 -.387 F3-ÿ SPD, % .154 .066 Fp2-ÿ SPD, % |
,0889 -,484 -,0852 |
-.1688 -.0228 |
|
-1.282 1.751 O2-ÿ SPD, % ,750 -,082 T4-ÿ SPD, % -,934 ,041 Killing |
,539 ,0894 -1,354 |
.1259 -.0380 .0257 |
|
Index vs E. coli, % -,452 -,197 (VLF+LF)/HF as Centralization |
,0008 ,172 -,0588 |
-.0336 -.0373 |
|
Index -,467 , 222 F4-ÿ SPD, % -.109 1.188 F4-ÿ SPD, ÿV2 /Hz -.619 |
,348 - .2135 -.943 |
.1552 .0068 |
|
-1.888 P3-ÿ SPD, ÿV2 /Hz -.727 .936 F3-ÿ SPD, % .017 1.489 T6-ÿ |
.0294 -.225 -.0927 |
-.0022 0354 ,0072 |
|
SPD , ÿV2 /Hz ,515 -,303 Fp2-ÿ SPD, ÿV2 /Hz -,541 -1,159 Killing |
-.865 .0391 .427 |
-,1025 ,0922 |
|
Index vs Staphyl. aureus, % -.158 -.343 Bactericidity vs. Staph. |
-.2280 .192 -.0360 |
,0126 -,1795 |
|
aur., 109 Bact/L .173 .448 F4-ÿ SPD, ÿV2 /Hz -1.167 .933 F4-ÿ |
.127 -.0347 -1.703 |
,1265 -,0163 |
|
SPD, ÿV2 /Hz 2.103 -.410 P4-ÿ SPD, ÿV2 /Hz .763 -.693 CD22+ B |
-.0049 1.429 |
-,0043 -,0451 |
|
Lymphocytes, % -.139 .276 CD3+ T Active Lymphocytes, % -.197 |
-.0019 -1.125 |
,0100 ,1041 -,0157 |
|
-.120 T6-ÿ SPD, ÿV2 /Hz -.127 .645 Fp1-ÿ SPD, % -.145 .487 |
-.0115 -.899 |
,0153 ,0165 ,0095 |
|
Monocytes, % -.439 -,121 ULF band HRV SP, msec2 ,486 ,680 |
,0008,220,0068,587 |
,0529 -,0759 |
|
Circulating Immune Complexes, units ,312 ,295 LF band HRV |
-,0153 -,887 |
-,0059 ,0045 |
|
SP, msec2 ,073 -,059 VLF band HRV SP, msec2 -,344 -,247 |
-,0192,484,0071,546 |
,0148 - ,0178,0722 |
|
Entropy of Immunocytogram -, 314 -,317 Microbial Count vs E. |
-,0246 -,732,0378 |
-,0436 -,0040,0029 |
|
coli, M/PhC ,244 ,193 |
-,149,0029,344 |
-,0328,0166 -,0416 |
|
|
-,0310 -,336 |
|
|
|
-,0395,014 - |
|
|
|
.0034 .135 -.0069 |
|
|
|
.035 -.1306 .344 |
|
|
|
.0014 .184 .0205 |
|
|
|
-.456 .0001 .203 |
|
|
|
-.0002 .081 |
|
|
|
-5.9414 -.035 |
|
|
|
.0307 |
|
Constants 17.76
Eigenvalues 10.54
Cumulative Properties ,618 -,1076,0182,0197,0197,0115 -,0074 -,0131 -,0027 -,
Fig. 8.1. Individual values of neuro-immune discriminant roots of members of four clusters
It can be seen that all four clusters are quite clearly demarcated on the planes discriminant roots. Difference after calculation of root centroids
becomes even clearer (Fig. 8.2). In particular, the first root sharply highlights the first cluster, the second - the third and, to a lesser extent, the fourth cluster, and the third root
highlights the second cluster. The visual impression is documented by calculating Mahalanobis distances between centroids (Table 8.4).
10
9
8
7
6
5
4
3
2
1
0
-1
-2
-3
-4
-5
-6 -7
Root 1 (62%)
Root 2 (27%)
Root 3 (11%)
IV (24)
III (9)
II (61)
I (8)
Fig. 8.2. Centroids of neuro-immune discriminant roots of members of four clusters
Table 8.4. Squares of Mahalanobis distances between neuro-immune clusters (above the diagonal) and F-criteria (df=39.6; for all pairs p<10-6)
Clusters
III I
IV II 202
III I
0
11.8 0
72 53 173 118
IV 6.7 14.6 0 5.8 19
II 11.6 4.9 0
Table 8.5 presents the full structural coefficients and average values (centroids) of roots, as well as Z-scores of neuro-immune variables.
Table 8.5. Correlations between variables and canonical roots, root centroids and Z- scores of neuro-immune variables
Variables currently in the
Correlations
Variables-Roots
IV
(24) (9)
III
II I
(61) (8)
model Root 1 (61.8%)
R1 R2
R3 -2.46 -2.21
-0.05 +10.3
O2-ÿ SPD, ÿV2 / Hz Fp2-ÿ SPD,
ÿV2 /Hz F7-ÿ SPD, ÿV2 /Hz F4-
ÿ SPD, ÿV2 / Hz Fp1-ÿ SPD,
% F7-ÿ SPD, % LF/HF Killing Index vs
.246 .000 .162 +0.22 +0.42 +0.92 +29.8 .241 -.006 .171
-0.12 +0 .06 0.00 +7.91 .240 -.035 .192 +0.15 +2.88 +0.47
+28.0 -.126 .084 +0.30 +4.10 +1.21 + 6.35 .112 .065 -.136
.067 +0.39 +2.17 +0.72 +2.12 .063 -.108 .102
+0.43 +1.51 +0.48 +1, 66
Currently not in model +1.49 +1.21 +1.22 +2.63 -.055
-.111 -1.50 -1.00 -0.94 -0.63 .029 .034 -0.95
Staphyl. aureus, % .058 Bactericidity vs -1.50 -0.96 +0.75
Staph. aur., 109 Bac/L ,063 Popovych's
Adaptation Index-1, pts Leukocytes, 109 /L Bactericidity vs
Currently not in model -2.40 -1.49 -2.18 -1.19
Currently not in model +1.34 +0.48 +1.28 +2.29
Currently not in model -0.70 -0.96 -0.61 +0.03
Currently not in model +0.75 -0.07 +0.68 +2.20
Currently not in model +0.20 -0.03 +0.32 +0.86 -.193
E. coli, 109 Bact/L CD56+ NK Lymphocy.0t9e0s,+%1.002L+y0m.3p0h,+o00c7.y12t6e-s1,.3%1F7-ÿ SPD, %
T4-ÿ SPD, % +1.08 +0.20 -0.19 -1.11 P4-ÿ-,1S5P1D,0,9%3,2+210.8-8 +0.22 +0.12 -0.52 F4-ÿ SPD, % +1.31
-1.22 -0.28 -1.23 F3-ÿ SPD, % +1.64 -0.97,1+304.,51361-,02.8803 -F,4-ÿ SPD, ÿV2 /Hz +1.51 -0.32 +0.06 -0.39
VLF band HRV SP, msec2 +0.58 -0.29 -01.2311,2-06.07,517E7n-tr, opy of Immunocytogram -0.21 +0.18
-0.40 - 1.12 CD3+ T Active Lymphocytes1,0%0,2-107.1,043+30-.,11 -0.33 -0.65 CD22+ B Lymphocytes, %
+1.30 +1.17 +1.04 +0.71 CD8+ T-cytolytic04L9y,0m9p2,h1o0c4y-t,es, % Currently not in model +0.22 +0.12
+0.04 -0.66 Phagocytosis Index vs Stap0h3. 7a,u04r.6, ,%060Cu-,0rr8e5ntly not in model +0.20 -0.02 +0.02
-0.14 +2.11 -6.18 +0.08 +0.02 Root 2 (27.-0,0%5)4,040 -.045 -.034
.054 -.034 .010 .034
R1 R2 R3
C4-ÿ SPD, % -.029 -.214 .105 +0.75 +3.48 +0.79 +0.84 F3 -ÿ SPD, % -.007 -.177 .104 +0.73 +2
.63 +0.74 +1.08 F4-ÿ SPD, % .020 -.165 .014 +0.47 +2.67 +1.01 +1.43 ULF band HRV SP, % .001
-.214 .095 -0.24 +1.73 -0.08 +0.29 ULF HRV SP, msec2 .005 -.041 -.019 -0.19 +0.77 +0.23 +0.24
Circulating Immune Complexes , units ,020 -,065 ,068 -0,59 -0,13 -0,60 -0,26 Killing Index vs
E. coli, % ,009 -,063 -,071 -1,91 -1,20 - 1.45 -1.57 Segmentonuclear Neutrophils, % Currently not in model -0.06 +0.58 -0.48 -0.31 Stubnuclear Neutrophils, % Currently not in model -2.87
-2.26 -2, 46 -2.33 CD4+ T-helper Lymphocytes, % Currently not in model -2.64 -1.29 -2.33
-3.59 Immunoglobulins A, g/L Currently not in model -0.47 +0.40 -0.48 -0.92 F3-ÿ SPD, % .034
-0.07 -0.93 -0.27 +0.01 .145 T3-ÿ SPD, % -.064 .138 -0.13 - 1.07 -0.27 -1.03 F4-ÿ SPD, ÿV2 /Hz
.002 .085 +0.44 -0.57 +0.05 +0.21 T6-ÿ SPD, ÿV2 /Hz .065 , 049 -0.19 -0.58 -0.22 +0.52 Fp2-ÿ
SPD, ÿV2 /Hz ,032 ,090 +1.53 -0.43 +0.39 +2.05 T6-ÿ SPD, ÿV2 /Hz -.007 .070 +1.82 -0.16 +0.94
+1.03 P3-ÿ SPD, ÿV2 /Hz .034 .067 -.016 +0.71 -0.53 +0.71,0+191.38 P4-ÿ SPD, ÿV2 /Hz .052 .039
-.218 -0.53 -0.72 +0.03 +0.06 (VLF+LF)/HF .004 .042 .091 -+,028.125 +1.59 + 1.63 +2.20 Microbial
Count of E. coli, Micr/PhC -.027 .144 -.000 +1.17 +0.27 +0,0.9340 +0.69 Monocytes, % .010 .010 -,
030 +0.12 -0.38 +0.72 +0.92 Immunoglobulins M, g/L Cur,0re26ntly not in model +1.31 +0.77 +1.11
+0.93 Microbial Count of Staph. aur., M/Ph Currently not,1i0n9 model +0.35 -0.40 +0.09 +0.02 Currently not in model +0.33 -0.31 +0.56 +0.36 Pan Lymp,0h3o3 cytes, % Entropy of Leukocytogram Currently not in model -0.78 -0.92 -0.42 -0.25 Root 3 (11.2%)
R1 R2 R3 +1.74 +1.49 -1.11 +1.57 O2-ÿ SPD, % -.035
.058 -.297 -0.77 -1.10 -0.20 -1.17 Fp2 -ÿ SPD, % -.060 .069 -.264 -0.63 -1.00 -0.24 -1.24 LF band
HRV SP, msec2 -.016 .001 -.044 +0.92 +0, 92 +1.33 -0.05 HF band HRV SP, msec2 Currently
not in model +0.32 -0.22 +0.39 -0.17 Eosinophiles, % Currently not in model +0.25 +0.17
+0.82 +0.64 Popovych's Strain Index-1, points Currently not in model +2.71 +1.53 +3.20 +1.09
Popovych's Strain Index-2, points Currently not in model +4.51 +2.39 +5.83 +2.97 100•LF/ (LF+HF), % Currently not in model +0.50 +1.07 +0.44 +1.12 Phagocytosis Index vs E. coli, % Currently not in model +0.88 +0.90 +0.41 +0.70 Currently not in model -3.24 -3.41 -3.56 -3.12 Popovych's Adaptation Index-2, pts Immunoglobulins G , g/L Currently not in model +0.80
+0.62 +0.55 +0.84
The location of the variables in the composition of each root is carried out according to algorithm of the traditional hierarchy of systems: central nervous system, autonomic system nervous, immune, which, however, is relatively conditional in the light of modern ideas about bilateral relations between the three systems.
The localization of the members of the first cluster along the axis of the first root (Figs. 8.1 and 8.3) in the extreme right zone (centroid: +10.3) reflects the maximum parameters of the SHSP ÿ-rhythm, as well as the LF/HF-index and immunity, which are positively correlated with the root (Table 8.5), as well as the minimum parameters for the sample of the SHSP ÿ
rhythm, VLF and other immune parameters, which are negatively correlated with the root (Fig. 8.4).
Instead, the fourth cluster occupies the extreme left zone (centroid: -2.46), which reflects
the minimum/maximum levels of these parameters. Members of the second
of the cluster occupy an intermediate position (centroid: -0.05), while the centroid of the third cluster is almost the same (-2.21) as the fourth and their projection on are mixed up.
12
11
10
9
8
7
6
5
4
3
2
1
0 -1
y = 0.952x + 2.49 R2 = 0.944
y = 0.001x2 + 0.085x - 0.36 R2 = 0.977
-3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11
y = 0.106x + 1.49 R2 = 0.892
Root Centroides
LF/HF R1+
Imm
Fig. 8.3. Normalized values (Z±SE) of the ÿ-rhythm SSC, LF/HF and immune parameters condensed in the first root, which are positively correlated with it
1.5
1.0
0.5
0.0
-0.5
y = 0.0143x2 - 0.23x - 0.02
y = 0.011x2 - 0.16x - 0.26 R2 = 0.530 R2 = 0.630
= 0.003x2 - 0.085x - 0.31 y
R2 = 0.918
Imm VLF R1-
-1.0
-3 -2 -1
0 1 2 3 4 5 6 7
8 9 10 11
Root Centroides
Fig. 8.4. Normalized values (Z±SE) of ÿ-rhythm SSC, VLF and immune parameters condensed in the first root, which are negatively correlated with it
A clear distinction between the members of the third and fourth (as well as the second and first) clusters occurs along the axis of the second root (centroids: -6.18 versus +2.11; +0.08 and +0.02, respectively) (table 8.5 and fig. . 8.1), which reflects the maximum levels of the ÿ-rhythm, ULF , and immune parameters for the sample , which are negatively correlated with the root (Fig. 8.5), and the minimum levels of the ÿ-, ÿ- , and ÿ- rhythms, index (VLF+LF) /HF and other immune parameters that correlate positively with the root (Fig. 8.6).
3
2 = 0.018x2 - 0.202x + 0.99y
R2 = 0.990
1
0 = -0.002x2 - 0.184x + 0.18y R2 = 0.987
-1
y = 0.005x2 - 0.057x - 1.08
-2 R2 = 0.999
R2+ ULF
Imm
-7 -6
-5 -4
-3 -2 -1 0 1 2
Root Centroides
Fig. 8.5. Normalized values (Z±SE) of ÿ-rhythm SSC, ULF and immune parameters condensed in the second root, which are negatively correlated with it
3
2
y = 0.095x + 2.253
R2 = 0.2208
1
y = -0.015x2 + 0.008x + 0.47 R2 = 0.996
0
(VLF+LF)/HF
Imm R2-
y = -0.014x2 + 0.074x + 0.35 R2 = 0.921
-1
-7 -6
-5 -4
-3 -2 -1 0 1 2
Root Centroides
Fig. 8.6. Normalized values (Z±SE) of SSC ÿ-,ÿ-and ÿ-rhythms, (VLF+LF)/HF and
immune parameters condensed in the second root, which are correlated with it
positively
The members of the second cluster differ from the other three also along the axis of the third root (centroids: -1.11 versus +1.74; +1.49 and +1.57) (table 8.5 and fig.
8.1 ), which reflects the maximum levels of the ÿ-rhythm, LF , and HF for the sample, and
as well as immune parameters that are negatively correlated with the root (Fig. 8.7), and minimum sample levels of LFnu and other immune parameters that are positively correlated with the root (Fig. 8.8).
4
3
3
2 = -0.514x + 2.65 y
R2 = 0.622
2
1
1 = -0.264x + 1.02 y
R2 = 0.374
0
Imm LF R3+
-1
-1 = -0.271x - 0.544 y
R2 = 0.7101
-2
-1.5
-0.5
0.5
1.5
Root Centroides
Fig. 8.7. Normalized values of ÿ-rhythm SSC, HRS and immune parameters condensed in the third root, which are negatively correlated with it
1.5
1.0
0.5
0.0
-0.5
y = 0.153x + 0.64 R2 = 0.329
y = 0.115x - 0.74 R2 = 0.934
Imm LFnu
-1.0
-1.5 -1
-0.5 0 0.5
1 1.5 2
Root Centroides
Fig. 8.8. Normalized values of LFnu and immune parameters condensed in the third root, which are positively correlated with it
The same discriminant variables were used to identify affiliation of this or that person to this or that cluster (Table 8.6).
Table 8.6. Coefficients and constants for classification functions of neuroimmune clusters
CLUSTERS III I IV II
Variables currently in the model p=.088 p=.078 p=.235 p=.598 O2-ÿ SPD, ÿV2 /Hz
-.009 .006 -.010 -.008 F4-ÿ SPD, % 6.258 5.600 9.390 7.730 C4-ÿ SPD, % -.730
-1.280 -1.337 -1.307 F7-ÿ SPD, ÿV2 /Hz -.009 -.014 -.029 -.025 ULF band HRV SP,
% 3.429 .700 2.058 2.141 F3-ÿ SPD , % 2.157 4.044 3.168 3.236 T3-ÿ SPD, % 1.625
.719 1.851 1.690 P4-ÿ SPD, % -4.415 -3.518 -4.707 -4.858 Fp2-ÿ SPD, ÿV2 /Hz
-.027 .025 .029 F7-ÿ SPD , % 1.495 .982 1.805 F7-ÿ SPD, % 5.143 1.851 4.371 F3-
ÿ SPD, % 1.652 2.082 1.704 Fp2-ÿ SPD, % 4.105 3.733 5.174 -.550 -.093 -.607 O2- ÿ SPD, % T4- ÿ SPD, % 6.149 3.375 6.316 Killing Index vs E. coli, % .182 -.363
.065 (VLF+LF)/HF as Centralization Index .441 .111 .589 F4-ÿ SPD, % .294 .556
.715 F4-ÿ SPD, ÿV2 /Hz ,045 -,015 -,002 P3-ÿ SPD, ÿV2 /Hz ,272 ,220
,394 F3-ÿ SPD, % 1,725 2,179 2,312 T6-ÿ SPD, ÿV2 /Hz -,173 -.113
-.207 Fp2-ÿ SPD, ÿV2 /Hz -.161 -.553 -.424 Killing Index vs Staphyloc.
aureus, % 3.083 2.578 2.716 Bactericidity vs. Staph. aur., 109 Bact/L
-.528 -.325 -.373 F4-ÿ SPD, ÿV2 /Hz .439 .254 .611 F4-ÿ SPD, ÿV2 /Hz
.023
1.571
3.801
2.260
4.740
SPD, ÿV2 /Hz
-.621 -.196 -.694 P4-ÿ
-.375
-.062 -.042 -.085 CD22+ B Lymphocytes, % -.750 -.750 -.214 CD3+ T
Active Lymphocytes, % -.389 -1.037 -.595 T6-ÿ SPD, ÿV2 /Hz ,159
,224 ,303 Fp1-ÿ SPD, % ,852 ,910 1,046 Monocytes, % 5,787 3,935
5,523 ULF band HRV SP, msec2 -,058 -,028 -,042 Circulating Immune
Complexes, units -,676 -,299 - .517 LF band HRV SP, msec2 -.002
-.002 -.003 VLF band HRV SP, msec2 .004 .001 .003 Entropy of
Immunocytogram 765.4 654.3 717.7 .455 .989 .647 Microbial Count for E. coli, M/PhC
Constants -671.7 -539.5 -736.1
5.450
-.033
.445
.812
-.007
.386
2.292
-.191
-.442
3.061
-.449
.479
-.551
-.071
-.632
-.450
.259 ,
965
5,252
-,045
-,542
-,002
,002
711,
,685
-681,0
As a result, we can retrospectively recognize the members of the third and first clusters without error, while the members of the second and fourth clusters are classified with one error. The overall classification accuracy is
98%
Machine Translated by Google
CONCLUSION
Individuals with quantitatively and qualitatively different levels of entropy of the spectral power density of EEG rhythms are characterized by specific sets of EEG parameters, HRV and immunity, which indicates the modulating effect of entropy on the neuro-immune complex.
168
SECTION 9
OPTIONS OF CHANGES IN EEG, VRS, LCG AND ICG ENTROPY UNDER THE INFLUENCE OF ADAPTOGENEIC BALNEOTHERAPY AND THEIR FORECAST
In the previous sections, the subject of research was the entropy parameters of individuals,
registered before and after the balneotherapy course. In this section will be
entropy changes caused by balneofactors are considered .
The preliminary analysis revealed a variety of entropy changes, which prompted us, following the previously created algorithm, to conduct a cluster analysis of the entropy changes of the EEG, HRV, ICH and LCH using the k-means clustering method. As a result, three groups of individuals were created, significantly different from each other in terms of entropy changes (Table 9.1), while the differences between the members of each group are much smaller (Table 9.2). Table 9.1. Euclidean distances between clusters Distances below the diagonal. Squared distances above the diagonal
The maximum contributions to the distribution of individuals, or rather changes in their entropy, to clusters
give the changes in the entropy of the SHSP in the C3 and C4 loci, minimal but significant contributions
give changes in the F8 and T6 loci, instead, the contributions of changes in the entropy of ICH, LCH and HRS
insignificant (Table 9.3).
Table 9.3. Variance analysis of entropy changes (H)
|
Change in Variables |
Between SS |
Within SS |
ÿ2 R F |
meaning p |
|
C3H |
,584 |
,552 |
0,514 0,717 25,4 0,462 |
10-6 |
|
C4H |
,650 |
,758 |
0.679 20.6 0.425 0,652 |
10-6 |
|
O1H |
,867 |
1,174 |
17,7 0,364 0,603 13,7 |
10-5 |
|
Fp2H |
,751 |
1,314 |
0,340 0,583 12,4 0,315 |
10-4 |
|
P3H |
,292 |
,567 |
0,561 11,0 0,306 0,553 |
10-4 |
|
O2H |
,539 |
1,172 |
10,6 0,277 0.242 0.491 |
10-3 |
|
F3H |
,340 |
,770 |
7.65 0.239 0.489 7.53 |
10-3 |
|
F4H |
,557 |
1,453 |
0.238 0,488 |
10-3 |
|
F7H |
,961 |
2,551 |
|
10-3 |
|
P4H |
,253 |
,789 |
|
,001 , |
|
T5H |
,534 |
1,677 |
|
001 |
|
T4H |
,412 |
1,314 |
|
,001 |
|
Fp1H |
,523 |
1,672 |
|
,001 |
|
T3H |
,416 |
1,400 |
|
,002 |
|
F8H |
,885 |
3,250 |
|
,003 |
|
T6H |
,486 |
1,805 |
|
,003 |
|
ICG H |
,003 |
,036 |
|
,185 |
|
LCG H |
,004 |
,127 |
|
,494 |
|
HRV H |
,014 |
,708 |
|
,618 |
In fig. 9.1 shows the profiles of changes in actual entropy values in individuals different clusters, and in fig. 9.2 shows the profiles of changes in normalized values.
As you can see, individuals of the major first cluster (66.7% of the cohort)
are characterized by a moderate and approximately equal decrease in the entropy of the SHSP in all EEG loci in the absence of significant changes in HRS entropy, ICH and LCH. In persons
the second cluster (19.6% of the cohort) is the sphere of the absence of significant entropy changes VRS, ICG and LCG are complemented by loci C4, C3, F3, F4, T4 and T3, and in others 10
loci, the level of entropy increases moderately. In the members of the third cluster (13.7% of the cohort), with similar stability of HRV entropy, ICG and LCG,
balneotherapy does not have a significant effect on the entropy of SHSP in loci F8 and O2, increasing it in loci Fp2, T6, O1 to a lesser extent than in the second cluster, in loci F7, T5, Fp1, P3, P4 and T4 almost similarly, and in loci T3, F4, F3, C3 and C4 much more pronounced. The integral proentropy effect of balneotherapy is found to be greater in the members of the third cluster, but not significantly so (Fig. 9.3).
0.25
0.20
0.15
0.10
0.05
0.00
II (10)
III (7)
I (34)
-0.05
-0.10
-0.15
C4 C3 F3 F4 P3 Fp1 T5 F7 O1 T6 Fp2 O2 F8 P4 T4 T3 HRV LCG ICG
Fig. 9.1. The actual mean values (M±SE) of the changes in the entropy of the SHSP in the EEG loci, as well as HRV, LCG and ICG in members of different clusters
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
II (10)
III (7)
I (34)
-1.0
-1.5
C4 C3 F3 F4 P3 Fp1 T5 F7 O1 T6 Fp2 O2 F8 P4 T4 T3 HRV LCG ICG
Fig. 9.2. Z-scores (M±SE) of changes in the entropy of the SHSP in EEG loci, as well as HRV, LCG and ICG in members of different clusters
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
EEG HRV
ICG LCG
I (34)
II (10)
III (7)
Fig. 9.3. Changes in the normalized entropy of SHSP loci of EEG, HRS, immunocytogram and leukocytogram in members of different clusters
Therefore, balneotherapy has a generalized negative effect on
EEG of 2/3 patients. On the other hand, the members of the other two clusters have an EEG entropy of
as a whole, it significantly increases to approximately the same extent, but there are significant differences in relation to individual loci. Changes in the entropy of HRV, ICG and LCG are in the range of ±0.5 ÿ, which we interpret as insignificant.
According to the results of the discriminant analysis of entropy changes, only 11 EEG loci, as well as ICH, were determined to be characteristic of clusters. The other 5 EEG loci, as well as changes in the entropy of LCG and VRS, were not included in the discriminant model (tables 9.4 and 9.5).
Table 9.4. Summary of analysis of discriminant functions of entropy changes in clusters Step 12, N of vars in model: 12; Grouping: 3 grps Wilks' Lambda: 0.119;
approx. F(24.7)=5.8; p<10-6
|
Variables currently in |
Cluster no. 3 |
Cluster No.1 |
Cluster No.2 |
Wilks' ÿ |
Parti al ÿ |
F-re move |
p level |
That's it
rancy |
|
the model |
(7) |
(34) |
(10) |
|
|
|
|
|
|
C3H |
,253 |
-.065 |
-.012 |
,178 |
,670 |
9.10 |
,001 |
,432 |
|
C4H |
,269 |
-.065 |
.009 |
,121 |
,991 |
.17 |
,842 |
,470 |
|
F4H |
,241 |
-.060 |
.053 |
,146 |
,818 |
4.10 |
,025 |
,224 |
|
T3H |
,156 |
-.111 |
-.055 |
,124 |
,960 |
.78 |
,466 |
,626 |
|
P3H |
,144 |
-.039 |
.103 |
,152 |
,786 |
5.05 |
,012 |
,435 |
|
T4H |
,093 |
-.127 |
.038 |
,132 |
,907 |
1.89 |
,165 |
,553 |
|
O1H |
,108 |
-.071 |
.251 |
,140 |
,852 |
3.22 |
,051 |
,386 |
|
Fp2H |
,063 |
-.108 |
.190 |
,129 |
,924 |
1.52 |
,231 |
,185 |
|
T5H |
,147 |
-.049 |
.181 |
,134 |
,890 |
2.28 |
,116 |
,330 |
|
Fp1H |
,145 |
-.052 |
.175 |
,125 |
,957 |
.83 |
,442 |
,295 |
|
F8H |
-,117 |
-.111 |
.220 |
,131 |
,910 |
1.82 |
,176 |
,433 |
|
ICG H |
-,002 |
-.000 |
.018 |
,124 |
,966 |
.66 |
,525 |
,781 |
|
Variables |
Cluster |
Cluster |
Cluster |
Wilks' ÿ |
Parti |
F that |
p |
That's it |
|
currently not in the model |
No. 3 (7) |
No.1 (34 ) |
No. 2 (10) |
|
al ÿ |
enter |
level |
rancy |
|
F3H |
.193 |
-.049 |
-.017 |
,114 |
,958 ,79 ,973 |
,460 |
,608 |
|
|
F7H |
.176 |
-.060 |
.262 |
,116 |
,50 1,000 ,00 |
,610 |
,550 |
|
|
T6H |
.077 |
-.066 |
.172 |
,119 |
,988 ,22 ,976 |
,995 |
,282 |
|
|
P4H |
.098 |
-.054 |
.093 , |
,118 |
,45 ,997 ,06 |
,805 |
,476 |
|
|
O2H |
.022 |
-.071 |
191 |
,116 |
,974 ,49 |
,643 |
,533 |
|
|
LCG H |
-.015 |
-.001 |
-.022 |
,119 |
|
,941 |
,835 |
|
|
HRV H |
.011 |
.030 |
-.012 |
,116 |
|
,619 |
,789 |
Table 9.5. Summary of step-by-step analysis of changes in entropy ranked by the ÿ criterion
|
Changes in Variables |
F that enter |
p level |
ÿ F value |
25.4 |
p level |
|
C3H |
25.4 |
10-6 |
,486 |
19.5 |
10-6 |
|
O1H |
14.8 |
10-5 |
,298 |
15.0 |
10-6 |
|
T3H |
3.8 |
,030 |
,256 |
12.4 |
10-6 |
|
ICG H |
3.0 |
,060 |
,226 |
10.6 |
10-6 |
|
C4H |
2.2 |
,122 |
,206 |
9.2 |
10-6 |
|
P3H |
1.5 |
,228 |
,192 |
8.1 |
10-6 |
|
T4H |
1.3 |
,273 |
,180 |
7.7 |
10-6 |
|
F8H |
2.7 |
,077 |
,159 |
7.0 |
10-6 |
|
F4H |
1.2 |
,302 |
,150 |
|
10-6 |
|
Fp1H |
1.7 |
6.6 |
10-6 |
|
T5H |
1.2 |
6.2 |
10-6 |
|
Fp2H |
1.5 |
,191,300,,213318,129,51.189 |
10-6 |
Next, the 12-dimensional space of discriminant variables is usually transformed into a two-dimensional space of canonical roots. The canonical correlation coefficient for the major root is 0.839 (Wilks' ÿ=0.119; ÿ2 (24)=90; p<10-6), and for the minor - 0.773 (Wilks' ÿ=0.402; ÿ2 (11)=39; p< 10-6).
The main root contains 61.5% of the discriminating capabilities, the secondary - 38.5%.
Calculation of the values of the discriminant roots for each person by
according to the table 9.6 made it possible to visualize each patient in the information root spaces (Fig. 9.1).
Table 9.6. Standardized and raw coefficients and constants for variables
Coefficients Standardized
Variables Root 1 Root 2 Root 1 Root 2
Raw
C3H O1H T3H ICGH C4H P3H T4H F8H F4H
Fp1H T5H
Fp2H
-1,003 ,306 -9,345 2,848 -,010 -,802
-,063 -5,127 -,300 ,049 -1,756 ,286 ,071
-,260 2,560 -9,425 -,117 -,130 -,931
-1,035 ,378 -, 811 3,478 -7,460 -,216
-,475 -1,308 -2,871 ,466 ,302 1,791 1,159
-,670 ,909 -3,853 5,225 ,450 -,079 2,413
-,425 -,340 ,649 -1,817 3,0470 3,0470
1,155 -4,850
Constants -,237 -,082
Eigenvalues 2.370 1.485
Cumulative Properties ,615 1,000
As you can see, all three clusters are quite clearly mutually demarcated. Visual impression is documented by calculating Mahalanobis distances between clusters (Table 9.7).
Table 9.7. Squared Mahalanobis distance between entropy changes in clusters (above the diagonal), F-criterion (df=12.4) and p-levels
Clusters III III
I II
0
6.6
<10-5
6.1
<10-5
I II
20 27
0 10
4.5 0
<10-3
2
1
0
-1
-2
-3
-4
-7 -6
-5 -4
-3 -2 -1 0
Root 1
1 2 3
III II I
Fig. 9.4. Individual values of the roots of entropy changes in members of three clusters
In the table 9.8 the correlation coefficients of entropy changes (discriminant variables) with canonical discriminant roots, cluster centroids for
of both roots and normalized values of entropy changes of discriminant variables, as well as those not included in the discriminant model, but worthy of attention.
Table 9.8. Correlations of entropy changes with canonical roots, root centroids and Z-scores of entropy changes for clusters
Change in
Correlations
III I II
Variables
Variables-Roots
(7) (34) (10) -3.74
Root 1 (61.5%) R1 R2 +0.53 +0.80 +2.68 -0.89
C3H C4H F4H T3H P3H T4H F3H
-.645 -.219 -.569
-.245 -.352
-.246 -.336 -.139
-.294 -.457 -.235
-.351 currently not in model
+1.74 -0.57
-0.13 +2.82 -0.91 +0.09
+2.21 - 0.77 +0.49 +1.51
-1.24 -0.53 +1.16 -0.46
+0.83 +0.78 -1.27 +0.32
-0.15 -0, 21 +0.72 -2.31
-.118 -.690 +0.60 -0.56
+1.38
Root 2 (38.5%) R1 R2 -.111 +0.50 -1.10 +1.51
O1H
Fp2H T5H
Fp1H F8H
ICG H F7H T6H P4H O2H HRV H LCG H
-.604 -.154 - , 420 +1.15 -0.59 +1.42 -.157 -.414
+1.17 -0.62.0+517.41 .096 -.411 -0.68 -0.78 +1.29
-.209 -0.03 -0.01 +0.31 currently not in model +1.10
-0.59 +1.64 currently not in model +0.52 -0.61
+1.16 currently not in model +0.70 -0.52 +0.66
currently not in model +0.12 -0.53 +1.06 currently
not in model +0.11 +0.41 -0.10 currently not in
model - 0.01 +0.10 -0.55
The localization of the members of the third cluster in the extreme left zone of the first root axis (Fig. 9.4) reflects the maximum increase in the entropy of the SHSP for the sample EEG in loci that represent the root inversely. The members of the other two clusters are located in the opposite zone of the axis and are practically not demarcated, which reflects the absence of clear differences between the quasi-zero entropy changes. At the same time, these clusters are clearly
demarcated along the axis of the second root.
In particular, the lower zone is occupied by the second cluster, which reflects significant growth of the entropy of the EEG SSC in the loci that represent the root inversely. Instead, in
members of the first cluster are located in the upper zone of the axis, which reflects moderate reduction of entropy in these loci.
The use of cluster centroid values for marking the abscissa axis, a
for the ordinate axis of the normalized average values of entropy changes gives an opportunity visualize their patterns (Fig. 9.5 and 9.6).
Fig. 9.5. Patterns of changes in the entropy of SHSP in EEG loci representing the first root
1.6
1,2
0.8
ICG F8
0.4 Fp1
T5
0
-0.4
Fp2 O1
-0.8
-1.2
-2.4
-2 -1.6
-1.2
-0.8
-0.4 0
0.4
0.8 Root 2
Fig. 9.6. Patterns of changes in the entropy of the immunocytogram and SHSP in the EEG loci representing the second root
In fig. 9.7 visualized average values of both roots (abscissa axis) and average values of entropy changes (ordinate axis) for three clusters. With
thick lines contain information about the variables included in the discriminant model, and thin lines refer to variables not included in the model. As we can see, for
they are almost the same for each root.
2
1.5
1
0.5
0
-0.5
-1
-4 -3.5 -3 -2.5
-2 -1.5
-1 -0.5 0 0.5 1
Root Centroid
Fig. 9.7. Patterns of integral entropy changes for the first and second roots
Calculation of classification functions by coefficients and constants, given in the table. 9.9, allows retrospective identification of members
the third cluster without error, the first one with one error, and the second one with two errors (Table 9.10).
Table 9.9. Coefficients and constants for classification functions for entropy changes
Clusters III I II
Variables p=.137 p=.667 p=.196
C3H 31.48 -5.746 -16.92
O1H 4.249 -.801 14.72
T3H 1.333 -5.896 -7.240
ICG H 5.526 7.664 36.91
C4H 4.459 -.479 2.403
P3H -12.67 -4.784 18.76
T4H 3,253 -5,006 3,335 -7,201 1,523
F8H F4H
Fp1H
T5H
-1,501 16,81 5,237 -11,64
-5,110 4,792 6,738 10,108
5,589 -5,420
Fp2H -11.81 -11.40 3.608
Constants -8.576 -1.487 -5.111
Table 9.10. Classification matrix for entropy changes Rows: observed classifications; columns: predicted classifications
Clusters Percent III II I
correct
p=.137 p=.196 p=.667 7 0
III II I
Total
100
80.0
97.1
94.1
0 0 7 0
8
1
9
2
33 35
Now let's try to give a qualitative physiological assessment of the identified changes in the entropy of the SHSP of the EEG loci. A general impression is given by the petal diagrams (Figs. 9.8-9.10), and more detailed information is given by Figs. 9.11 and 9.12.
Fp1 O2 1,000
O1 0.000
P4 -1,000
-2,000
P3 -3,000
T6
Fp2
F3
F4 F7
F8
III Before III After
T5 T3
C4 T4
C3
Fig. 9.8. Petal diagram of the entropy of SHSP EEG loci before and after balneotherapy in members of the third cluster
Fp1
O2 1,000
O1 0.500
0.000
P4 -0.500
-1.000
P3 -1,500
T6
Fp2
F3
F4 F7
F8
II Before II After
T5 T3
C4 T4
C3
Fig. 9.9. Petal diagram of the entropy of the SHSP EEG loci before and after balneotherapy in the members of the second cluster
Fp1 O2 1,000
O1 0.500
P4 0.000
-0.500
P3 -1,000
T6
Fp2
F3
F4 F7
F8
I Before
And After
T5 T3
C4 T4
C3
Fig. 9.10. Petal diagram of the entropy of the SHSP EEG loci before and after balneotherapy in the members of the first cluster
As we can see, the members of the third cluster have both reduced and lower-limit values entropy levels of 15 out of 16 registered loci are increasing, while in 13
loci to the range of -0.5÷+0.5 ÿ, which we have taken as the narrowed norm. On the other hand on the other hand, members of the first cluster have upper limit and moderately elevated levels entropies decrease to the zone of the narrowed norm or slightly below. In general, how increase of initially significantly reduced entropy in persons of the third cluster, as well as reduction of the initially moderately elevated level of entropy in persons of the first
clusters have a normalizing character. In other words, in 80.4% of patients, EEG entropy changes occur according to the classic "initial level law".
|
1 |
y = 0.695x3 + 0.244x2 - 0.48x + 0.52 |
|
|
0.5 |
R2 = 0.376 |
|
|
0 |
|
|
|
-0.5 |
y = 0.213x3 + 0.75x2 + 0.64x + 0.27 |
I |
|
-1 |
R2 = 0.305 |
III |
|
|
y = 0.866x2 + 0.132x - 0.47 |
II |
|
-1.5 |
R2 = 0.365 |
|
-2
-2.5
-3
-3 -2.5
-2 -1.5
-1 -0.5 0
0.5 1
EEG H before
Fig. 9.11. Normalized mean entropy levels of the EEG loci of SHSP before (X-axis) and after (Y-axis) balneotherapy in members of different clusters
Instead, in the second cluster, similar to the first cluster, they change
only 4 EEG parameters, while the entropy of most parameters rises
higher than the upper limit of the norm, that is, a true pro-entropic effect occurs
balneotherapy.
1.5
1.3
1.1
0.9
0.7
0.5
0.3
0.1
-0.1
-0.3
-0.5
-0.7
-0.9
-1.1
-1 ,3
-1.5
Before
After
I (34)
II (10)
III (7)
Fig. 9.12. Normalized integral entropy levels of the SHSP EEG before and after balneotherapy in members of different clusters
Responses of the entropy change of HRS (Figs. 9.13 and 9.14), ICG (Figs. 9.15 and 9.16) and LCG
(Figs. 9.17 and 9.18) also occur according to the "initial level law", but they
are not very pronounced, especially against the background of changes in the entropy of the EEG SHSP.
2
1
0
-1
-2
y = -0.045x2 - 0.038x - 0.78
-3 R2 = 0.007
y = -0.426x2 + 0.32x + 0.41
-4 R2 = 0.794
y = 0.083x2 + 0.32x - 0.19
-5 R2 = 0.075
I III II
-5 -4 -3 -2 -1
0 1 2
HRV H before
Fig. 9.13. Normalized mean HRV entropy levels before (X-axis) and after (Y-axis) balneotherapy in members of different clusters
1.5
1.3
1.1
0.9
0.7
0.5
0.3
0.1
-0.1
-0.3
-0.5
-0.7
-0.9
-1.1
-1 ,3
-1.5
Before
After
I (34)
II (10)
III (7)
Fig. 9.14. Normalized integral levels of HRS entropy before and after balneotherapy in members of different clusters
1
y = 2.10x2 - 0.25x - 0.02 R2 = 0.295
0.5
0
-0.5
y = 0.0074x2 + 0.52x + 0.18 R2 = 0.207
y = -0.14x2 + 0.42x + 0.04 R2 = 0.357
II I III II
-1
-1.5
-1.5
-1 -0.5
0 0.5 1
ICG H before
Fig. 9.15 Normalized mean entropy levels of the immunocytogram before (X axis) and after (Y axis) balneotherapy in members of different clusters
1.5
1.3
1.1
0.9
0.7
0.5
0.3
0.1
-0.1
-0.3
-0.5
-0.7
-0.9
-1.1
-1 ,3
-1.5
Before
After
I (34)
II (10)
III (7)
Fig. 9.16 Normalized integral entropy levels Immunocytograms before and after balneotherapy in members of different clusters
1.5
1
y = -0.091x2 + 0.353x - 0.20 R2 = 0.226
0.5
0
-0.5
-1
y = 0.153x2 + 0.585x - 0.52 R2 = 0.300
I III II
-1.5
-2
y = 1.34x2 + 0.95x - 1.7 R2 = 0.430
-2.5
-3
-3 -2.5
-2 -1.5 -1
-0.5 0
0.5
1 1.5
LCG H before
Fig. 9.17 Normalized mean entropy levels of leukocytograms before (X axis) and after (Y axis) balneotherapy in members of different clusters
1.5
1.3
1.1
0.9
0.7
0.5
0.3
0.1
-0.1
-0.3
-0.5
-0.7
-0.9
-1.1
-1 ,3
-1.5
Before After
II (10)
III (7)
I (34)
Fig. 9.18. Normalized integral entropy levels of leukocytograms before and after balneotherapy in members of different clusters
From the foregoing, it follows the assumption that the directionality of the entropy responses to balneotherapy is determined by its initial levels. And indeed, the discriminant analysis program selected 12 of 16 EEG loci, as well as ICH, LCH and VRS, as predictors of the entropy of SHSP . At the same time, the second version of the LCG Stress Index and the second, but not the first version of the LCG Adaptation Index, as well as the gender, but not the age of the patients, were predictive (tables 9.11 and 9.12).
Table 9.11. Summary of analysis of discriminant functions for initial variables, their actual levels for clusters, as well as norms and coefficients of variability Step 18, N of vars in model: 18; Grouping: 3 groups; Wilks' Lambda: 0.041; approx. F(39)=6.2; p<10-6
|
Variables currently in |
II I III Wilks' ÿ (10) (34) (7) |
Parti al ÿ |
F-re move |
p level |
That's it rancy |
Norm level Cv |
|
the model |
|
|
(2.3) |
|
|
(88) |
|
C4H |
0,907 0,887 0,583, 046 0.901 |
,906 |
1.57 |
,226 |
,233 |
0.830 0.830 |
|
C3H |
0.888 0,593, 045 0.838 0.843 |
,913 |
1.43 |
,256 |
,160 |
0.115 0.827 |
|
F4H |
0,506, 054 0.884 0.870 0,634, |
,763 |
4.65 |
,017 |
,316 |
0.114 0,828 |
|
T3H |
049 0.855 0.844 0,654, 062 |
,837 |
2.92 |
,069 |
,231 |
0.131 0.823 |
|
F3H |
0,736, 045 0.678 0.864 0.762, |
,662 |
7.66 |
,002 |
,217 |
0.126 0.810 |
|
Sex Index |
047 0.57610.2.8416 0.659, 061 |
,763 |
4.65 |
,017 |
,341 |
0.137 1.5 0.250 |
|
ICG H |
0.572 0,813 0,746, 057 057 |
,864 |
2.36 |
,112 |
,407 |
0.960 0.059 |
|
PSI-2 |
0.634 0,802 0,688, 048 048 0406 |
,881 |
2.02 |
,150 |
,157 |
0.065 0.618 |
|
LCG H |
0,806 0.7 |
,837 |
2.91 |
,070 |
,445 |
0,681 0,070 |
|
O1H |
|
,928 |
1.16 |
,326 |
,235 |
0.682 0,266 |
|
Fp2H |
|
,879 |
2.06 |
,145 |
,269 |
|
|
F7H |
|
,674 |
7.26 |
,003 |
,193 |
|
|
F8H |
|
,725 |
5.68 |
,008 |
,130 |
|
|
O2H |
|
,856 |
2.52 |
,097 |
,193 |
|
|
T4H |
|
,759 |
4.76 |
,016 |
,322 |
|
|
P4H |
|
,880 |
2.04 |
,148 |
,243 |
|
|
HRV H |
|
,906 |
1.55 |
,228 |
,463 |
|
|
PAI-2 Variables |
0.70 0.91 II I III Wilks' ÿ |
,937 Parti |
1.00 F that |
,379 p |
,569 That's it |
Norm |
|
currently not in the model |
(10) (34) (7) |
al ÿ |
enter |
level |
rancy |
level Cv (88) |
|
T6H |
0,651 0,871 0,731, 041 1.27 |
.989 .16 |
1.000 |
,849 |
,240 |
0.742 0.199 |
|
PAI-1 |
1.22, 04110..11051 0.151 0,121, |
.00 .942 |
.93 |
,999 |
,467 |
1.70 0.147 |
|
PSI-1 |
044 044 0.709 0.848 0.685, 041 |
.983 .26 |
.956 |
,406 |
,162 |
0.067 0.722 |
|
Fp1H |
0.664 0.840 0.648, 040 040 040 |
.67 .987 |
.19 |
,777 |
,235 |
0.781 0.157 |
|
T5H |
040 040 040 040 040 |
.955 .68 |
|
,522 |
,284 |
0.756 0.169 |
|
P3H |
|
|
|
,827 |
,126 |
0.782 0.159 |
|
Age, ys |
|
|
|
,512 |
,691 |
49.8 0.275 |
|
Variables F to |
enter |
p level |
ÿ |
F- |
p level |
|
C4H 32.4 |
|
10-6 |
,425 |
value |
10-6 |
|
O1H 9.8 |
|
10-3 |
,300 |
32.4 |
10-6 |
|
Sex Index 4.0 |
|
,026 |
,255 |
19.4 |
10-6 |
|
T4H 2.9 |
|
,064 |
,226 |
15.0 |
10-6 |
|
F8H 3.8 |
|
,031 |
,193 |
12.4 |
10-6 |
|
PSI-2 3.8 4.1 2.3 |
,031 |
,164 |
11.2 |
10-6 |
|
|
F7H |
2.5 |
,024 |
,137 |
10.5 |
10-6 |
|
Fp2H |
2.9 |
,109 |
,123 |
|
10-6 |
|
C3H |
,097 |
,110 |
10-6 |
||
|
F3H |
,070 |
,096 |
10-6 |
||
|
F4H 3.9 |
,029 |
,080 |
10-6 |
||
|
LCG H 2.1 |
,135 |
,071 |
10-6 |
||
|
O2H 1.8 |
,175 |
,065 |
10-6 |
||
|
P4H 1.7 |
,189 |
,059 |
10-6 |
||
|
T3H 1.0 |
,378 |
,052 |
10-6 |
||
|
ICG H 1.6 |
,215 |
,047 |
10-6 |
||
|
HRV H 1.1 |
,340 |
,044 |
10-6 |
||
|
PAI-2 1.0 |
,379 |
,041 |
10.2 9.5 9.0180-.76 8.8 8.5 8.1 7.8 7.0 6 .8 6.5 6.2 |
Prognostic information is condensed in two roots, in particular in the major 68.3% (r*=0.922; Wilks' ÿ=0.041; ÿ2 (38)=124; p<10-6), in the minor 31.7% (r*= 0.851; Wilks' ÿ=0.276; ÿ2 (18)=50; p<10-4).
Having applied the previous algorithm, according to non-standardized coefficients and constants from the table. 9.13 we visualize the initial state of each member of the three clusters (Fig. 9.19). Table 9.13.
Standardized and raw coefficients and constants for predictor variables
|
C4H -.691 |
, 010 -7,441, 111, 389 |
|
O1H -,480 |
-3,540 2,871 -, 723 -1,372 |
|
Sex Index -.610 |
-1,627, 541 -7,815 5,325 -, |
|
T4H -.795 1.392 -.732 |
805 7,345 -4,248 -, 644 |
|
F8H -.177 |
-1,59 -1,046 7,804 -8,582 |
|
PSI-2 .308 |
,452 -7,473 4,017 -,188 |
|
F7H -.528 |
-13,81 -4,101 -,726 -4,487 |
|
Fp2H .951 |
-4,975 ,604 4,448 5,196 |
|
C3H -.842 |
-,970 -1,893 -10,96 -,666 |
|
F3H -.632 |
-2,053 -11,40 ,375 2,860 |
|
F4H -.654 |
3,119 ,351 ,431 ,962 |
|
LCG H .517 |
|
|
O2H -.168 |
|
|
P4H -.120 |
|
|
T3H .344 |
|
|
ICG H .157 |
|
|
HRV H |
|
|
PAI-2 |
|
As we can see, the normalizing growth of the entropy of the EEG in the third cluster, which is located in the extreme right zone of the axis of the first root, is conditioned by its minimum initial levels (maximum negentropy) in the loci C4, C3, F4, T3 and F3, which represent the root inversely, and as well as the maximum entropy of the ICH for the sample, which is positively related to the root (Table 9.14).
Another predictor is male gender (6 out of 7), which is quantified by the minimum sex index (male=1, female=2). Members of two other clusters
are localized in the opposite zone of the axis and their projections are mixed.
These clusters are demarcated along the axis of the second root, which reflects the increased levels of entropy of the EEG in the O1, Fp2, F7, F8, O2, T4 and P4 loci in combination with the minimally reduced VRS entropy and the maximally reduced Adaptation Index-2 in the members of the first cluster , then as in the members of the second cluster,
the levels of entropy of the EEG SHSP in these loci are reduced, and the negentropy of the HRS
maximum
3
2
1
0
-1
-2
-3
-4
-5
-4 -3 -2 -1 0
1 2 3 4
5 6 7
II III I
Root 1 (68%)
Fig. 9.19. Individual values of two roots containing predictor information for clusters of entropy changes
Table 9.14. Correlations of predictors with canonical roots, centroids of roots, and Z- scores of predictors for clusters of entropy changes
Variables
Correlations
II I
III
initial
Root 1 (68.3%) R 1 R 2
C4H -, 481,120
C3H .131 - .473
F4H -, 420,160
T3H -, 391,101
F3H -,228 ,059
Sex Index -.076 -.122
PSI-2 -.077 -.232
LCG H -.039 -.123
ICG H .059 - .118
Root 2 (31.7%) R 1 R 2
Variables-Roots
(10) (34) (7) -2.10
-0.53 +5.59 +0.80 +0.60
-2.59 +0.79 +0.64 -2.48
+0.09 +0.14 -2.97 +0.59
+0.45 -1.82 +0.40 +0.30
-1.41 0.00 -0.71 -0.95
+5.87 +3, 32 +1.86 -0.18
-0.63 -0.85 -0.27 0.00
+0.25 -2.84 +1.05 -1.04
-0.51 +0.75 +0 .30 -0.83
+0.65 -0.16 -1.23 +0.27
-0.71 -1.08 +0.33 -0.06
O1H ,392 ,366 ,298 ,29.004,9287 ,211 ,196
-0.30 +0.64 0.00 - 0.02
|
Fp2H |
.003 |
,053 |
+0.53 -0.74 +0.11 +0.60 |
|
F7H |
-.015 |
|
-0.54 -1.01 -0.76 -0.97 |
|
F8H |
.051 |
|
|
|
O2H |
-.018 |
|
|
|
T4H |
-.158 |
|
|
|
P4H |
-.147 |
|
|
|
HRV H |
-.009 |
|
|
PAI-2
,046
-,099
-3.36
-4.01
-3.16
In the information space of two roots, all three clusters are delimited very clearly (Fig. 9.19 and Table 9.15).
Table 9.15. Squares of Mahalanobis distances between predictors of clusters of entropy changes (above the diagonal), F-values (df=19.3) and p-levels
Clusters II
II 0
III 7.8 <10-6 4.4
<10-3
I
III I
66 19
0 44
7.4 0
<10-6
This means that with the help of predictors and classification functions (Table 9.16), the belonging of a specific person to one or another cluster of entropy changes is predicted almost without error (Table 9.17). Table 9.16. Coefficients
and constants for classification functions for predictors of entropy changes
Clusters II
Variables p=.196
C4H 384.1
O1H 249.6
Sex Index 21.48
T4H 297.8
F8H -236.7
PSI-2 55.48 -55.26
III I
p=.137 p=.667 327.1
372.8 227.5 255.2
8.00 12.99 247.3
306.3 -187.9
-241.7 40.80 47.56
-48, 37 -26.94
21.81 18.63 -157.9
F7H
Fp2H C3H
F3H
F4H 121.95
LCG H 1166
O2H 152.8
P4H -389.7
T3H 381.3
ICG H 792.8
-7.88
-126.0
-143.3
-107.1 -98.72
-164.4 71.72
125.85 1053 1128
109.4 126.4 -346.1
-362.4 347 ,1 335.7
756.5 745.2
-81.10 -92.05 5.63
5.01 -847.0 -970.4
HRV H -108.7
PAI-2 .59
Constants -1038
Table 9.17. Classification matrix for predictors of changes in entropy Rows: observed classifications; columns: predicted classifications
Percent II III
Clusters II
III I
Total
correct 90
100
100
98
I p=.196 p=.137 p=.667 9 1
0 0 0 34 9 30 5
7
0
7
Machine Translated by Google
CONCLUSION
Three variants of entropy changes under the influence of balneofactors were revealed. In 66.7%
persons upper limit and moderately increased levels of entropy decrease to the zone
normal or slightly below. 13.7% of people have both reduced and subliminal levels
the entropy of the SHSP of 15 loci out of 16 is increasing, while in 13 loci it is up to normal. IN
in general, both the increase of the initially significantly reduced entropy in the persons of the third cluster and the decrease of the initially moderately increased level of entropy in the persons of the first cluster have a normalizing character. In other words, in 80.4% of patients, EEG entropy changes occur according to the classic "initial level law". On the other hand, in 19.6% of patients, the entropy of most parameters rises above the upper limit of the norm, that is, there is a true pro-entropy effect of balneotherapy. The nature of the reaction of entropy to balneotherapy is determined
the initial state of EEG entropy, immunocytogram and leukocytogram, as well as
article and predicted with 98% accuracy.
187
Machine Translated by Google
SECTION 10
IMMUNE SUPPORT OF POLYVARIANT ENTROPY REACTIONS ON ADAPTOGENIC BALNEOTHERAPY
In view of the previously identified relationships between the entropy parameters of the EEG loci and immunity, the question arises whether changes in entropy induced by balneofactors are reflected in changes in the body's immune status? For him
clarification, let's compare the normalized profiles of integral entropy and parameters immunity before and after balneotherapy (Fig. 10.1).
As we can see, the members of the first cluster have complete normalization of the upper boundary level of the EEG integral entropy is accompanied
normalization of the significantly reduced bactericidal ability of neutrophils relative to
both types of microbes. At the same time, the levels of B-lymphocytes and IgM are moderately increased
practically do not change, and other parameters of immunity remain stable normal
Instead, the opposite entropy effect of balneotherapy in members of the second cluster, namely, the movement of its level from the middle normal zone to the upper borderline is accompanied by a decrease in the initially normal levels of IgA, monocytes and leukocytogram entropy, as well as an initially drastically increased level of eosinophils. Instead, the upper limit level of B lymphocytes becomes even higher, in the absence of significant changes in other immune parameters, regardless of their initial levels.
In the members of the third cluster, the complete normalization of initial negentropy is accompanied by a normalizing decrease in elevated levels of IgA and
bactericidal activity of neutrophils against E. coli, on the one hand, and a decrease normal levels of monocytes and bactericidal activity of neutrophils against Staph. aureus
- on the other hand. At the same time, the level of eosinophils rises from the lower zone normal to upper, and moderately increased levels of IgM and B-lymphocytes become more
higher
188
Fig. 10.1. Profiles of normalized levels of EEG integral entropy, as well as leukocytograms and immunocytograms and immunity parameters in members of different clusters before and after balneotherapy
In order to quantify the influence of entropy changes of neural factors on
changes in immunity parameters, both relevant and informational, have been created correlation matrix (Table 10.1).
Table 10.1. Matrix of correlations between changes in HRS entropy and SHSP of EEG loci and immunity parameters
HRV
Fp1
Fp2 F3 F4
F7 F8
T3 T4
C3 C4 T5
T6 P3
P4 O1 O2
Variables H
H H H H H H H H
H H H H H
H H H
LCGH ,03 -,13 -,24 ,13 -,13 -,01 -,13 -,15 -,19 -, 06 -,11 -,30 -,10 -,03 -,02 - ,34 -,24
ICGH - , 13,18,15,34,01,07 _ _,09 ,12 ,10 ,15 ,04 -,02 ,07 -,06 -,10 ,06 ,18
PSI-1 .10 -.03 -.02 -.09 -.04 .03 -.04 -.14 -.02 -.01 -.03 -.15 -.00 .00 -.10 -.32 - , 12 .07 -.11 -.10 -.09 -.09 -.02 -.12 -.16
PSI-2 PAI-1
-.02 .04 .00 -.15 -.11 -.17 -.22 -.39 -.28 - ,24 -,20 -,12 ,10 -,05 ,06 -,20 ,18 -,00 -,07 -,02 ,04 -,26 -,10 -,06 -,01
-,29
PAI-2 -.11 -.11 -.21 .18 -.04 -.04 -.17 .21 -.26 -.09 -.18 -.06 -.26 .17 -.00 -.11 -, 20
PhIA -.05 .07 -.06 .19 -.06 .01 -.27 .04 -.11 .23 .18 -.02 -.06 .10 -.08 -.03 -.16
MCA -.33 .06 .14 .03 .15 .13 .05 .11 .06 .13 .30 .06 .06 .05 .01 .01 -.13
KIA ,08 -,03 -,06 -,03 -,14 -,01 -,11 -,20 -, 12 -,14 - ,20 -,09 -,04 -,03 ,14 -,18,03 - ,19 ,09 -,08 -,17 -,06 -,08 -,07 -,07
PhIE
-,23 -,11 -,20 -,16 ,02 -, 04 - , 05 - ,20 -,22 ,08 .19 .08 .05 -.01 -.05 .25 .11 .04 .04 .22 -.08 .02 -.24 .08 -.20
MCE -.30 .26 .12
IKE
-.08 -.09 -.26 .01 -.08 - ,13 -,21 -,20 -,24 -,08 ,15 -,15 -,02 -,22 ,02
BCA -.08 .05 -.11 -.07 .00 -.18 -.25 -.19 -.14 -.15 -.07 -.20 -.18 -.06 .11 -.30 -.31
BCE -.16 -.04 -.12 -.09 -.07 -.12 -.21 -.15 -.25 -.17 -.18 -.14 .02 -.04 -.03 -.32 - , 32
Leukoc -,03 -,02 -,26 -,06 -,09 -,34 -,28 -,26 -,20 -,22 -,10 -,39 -,25 - ,16 ,07 -,37 -, 37
SNN .00 .08 .12 -.19 .28 -.13 -.10 .09 .17 -.01 .03 .23 -.00 .12 -.04 .08 -.09
RNN -.09 .14 -.09 .04 .20 .05 .02 .23 -.10 .04 -.05 .02 -.09 .32 -.00 .05 -.07
Eosin ,00 -,27 -,23 ,04 -,11 -,11 -,18 -,13 -,02 ,05 ,11 -,14 -,21 -, 36 -,17 -,27 -,30
Monoc ,06 ,03 -,03 ,05 -,10 ,04 -,00 - ,21 -,11 -,10 -,12 -,21 ,10 ,06 ,05 -,20 ,02
Lymph -.07 -.02 .02 -.06 .06 .0,612.0-1, 2.024,1-4.1,138.,05 .25 .25
CD3A ,18 -,07 -,23 -,11 -,13 -,08 -,10 -,22 -,06 -, 11 -,06 -,35 -,19 -,17 -,18 -,25 -, 05
CD4 -.18 -.02 -.08 .10 -.08 .12 .08 -.03 -.13 .09 -.04 .08 -.03 -.01 -.14 -.02 -.13
CD8 ,17 ,19 ,15 ,06 ,13 ,11 ,02 -,14 ,22 -,03 ,05 ,12 ,15 ,08 -,03 ,11
CD56 -.05 -.20 -.11 -.15 -.08 -.21 -.08 .18 -.16 -.03 -.02 -.20 -.15 -.07 .01 .05 -.03
0-Lym ,18 -,12 -,06 -,11 -,09 -,12 -,10 -,22 -, 03 - ,29 -,04 ,01 -,07 -, 19 -,12 ,04 -, 10
CD22 -.16 .10 .06 .07 .10 .06 .07 .26 .03 .27 .05 -.06 .05 .19 .16 -.03 .13
,07
CIC ,00 ,05 -,13 -,21 - ,06 ,06 -,08 ,04 -,11 -, 20 -,01 ,08 -,09 - ,22 -,10 ,12 ,06 -,17 -, 01 -,11 -,07 -,15 -,08 -,01 ,00 -,23
IgG IgA IgM
-,19 -,22 -,18 -,07 -,19 - , 16 - ,23 -,29 -,22 -, 15 -.29 -.30 .03 -.01 -.14 -.09 -.42 -.33 -.24 -.12 -.35 -.20
-.08 -.01 .35 .23 .14 .19 .08 .26 .13 -.02
,00 ,03 ,16 -,27 ,24 ,23 ,36 ,24 ,20 ,11
Then, on the basis of the matrix, by stepwise elimination until the maximum of
Adjusted R2 was reached, regression models were built for changes in the entropy of HRS and SHSP of EEG loci, on the one hand, and informational and relevant parameters of immunity, on the other hand (Tables 10.2-10.18).
Table 10.2. Regression Summary for change in O2 Entropy R=0.618; R2 = 0.381; Adjusted R2 =0.297; F(6,4)=4.52; p=0.001
|
St. Err. Beta of Beta B |
St. Err. of B |
p level |
|
Var r Intercpt .0035 |
,0247 |
t(44) .14 .887 |
|
MCE -.24 -.196 .127 -.0033 |
,0021 |
-1.55 .129 |
|
BCE -.32 -.306 .124 -.0018 |
,0007 |
-2.47 .018 |
|
Eos -.30 -.402 .131 -.0395 |
,0128 |
-3.08 .004 |
|
PAI-1 -.29 -.208 .133 -.0604 |
,0386 |
-1.56 .125 |
|
PAI-2 -.20 -.186 .138 -.0603 |
,0448 |
-1.35 .185 |
|
IgA -,20 -,325,128 -,1394 |
,0548 |
-2.55 .015 |
Table 10.3. Regression Summary for change in O1 Entropy R=0.602; R2 = 0.363; Adjusted R2 =0.259; F(7,4)=3.50; p=0.005
St. Err. St. Err.
Var r
Beta
of Beta B of B Intercpt,0127,0281
-,251,128 -,0036,0018 -,150,130
p level t(43) .45
KIE -,22
-,5912,5117,222,129,0073,0043,681,310,4288,195.6352 -1.96 .057
|
LCGH -,34 |
-,830, 296 -.4352 .1551 -.195 .127 -.0078 |
-1.16 .254 1.71 |
|
Lymph , 25 |
.0050 -.279 .128 -.1305 .0601 |
.094 2.20 .034 |
|
PSI-1 -.32 |
|
-2.80 .008 -1.54 |
|
PSI-2 -.39 |
|
.131 -2.17 .035 |
|
CD3 A -,25 |
|
|
|
IgA -,19 |
|
|
Table 10.4. Regression Summary for changes in C3 Entropy R=0.583; R2 = 0.340; Adjusted R2 =0.266; F(5,5)=4,63; p=0.002
|
|
Beta |
St. Err. of Beta B |
St. Err. of B |
p |
|
Var r |
|
Intercpt -.0192 .126 |
,0200 |
level t(45) -.96 |
|
PhIA , 23 |
,343 |
.0467 .122 -.0022 |
,0172 |
.341 2.72 .009 |
|
KIE -,20 |
-,205 |
.124 -.0219 .133 |
,0013 |
-1.68 .100 -1.34 |
|
Leukoc -,22 |
|
.0104 .128 .1497 |
,0163 |
.186 3.05 .004 |
|
CD22 , 27 |
|
|
,0034 |
3.13 .003 |
IgM ,35
-,167,407,400
,0478
Table 10.5. Regression Summary for change in T5 Entropy R=0.562; R2 = 0.316; Adjusted R2 =0.240; F(5,5)=4,16; p=0.003
St. Err. St. Err.
Var r
Beta
of Beta B of B Intercpt .0178
.0285 -.151 .132 -.0010 .0009
p level t(45) .63
BCA -,20
CD3 A -,35
IgA -.42
CD56 -,20
LCGH -,30
-.354 .132 -.0147 .0055 -.221 .128 -.1076
.0624 -.298 .133 -.0162 .0072 -,140,133
-,5729,5444
.535 -1.15 .256
-2.68 .010 -1.72
.092 -2.24 .030
-1.05 .298
Table 10.6. Regression Summary for change in P3 Entropy R=0.524; R2 = 0.274; Adjusted R2 =0.194; F(5,5)=3.40; p=0.011
Var r
Beta
St. Err. of Beta B
Intercpt -.0004 .228
St. Err. of B
,0174
p level t(45) -.03 .980
Table 10.7. Regression Summary for change in T6 Entropy R=0.522; R2 = 0.273; Adjusted R2 =0.192; F(5,5)=3.38; p=0.011
St. Err. St. Err.
Beta of Beta B of B Var Intercpt p
.0032 .0292 rEosin -.21 -.327 .140 -.0372 .0159 PAI-1 -.26
-.194 .149 -.0650 .0502 PAI-2 -.26 -.217 .146 -.0812 .0548
CD3 A -.19 -.237 .137 -.0100 .0058 IgA -.33 -.350 .135 -.1735
.0671
level t(45) .11
.912 -2.33 .024
-1.30 .201
-1.48 .146 -1.73
.090 -2.58 .013
Table 10.8. Regression Summary for change in T3 Entropy R=0.509; R2 = 0.259; Adjusted R2 =0.194; F(4,5)=4,01; p=0.007
|
|
Beta |
St. Err. of Beta B |
St. Err. of B |
|
p |
|
Var r |
|
Intercpt |
-.0667 .0272 |
t(46) |
level |
|
KIA -.20 -.327 |
|
,131 |
-.0058 .0023 .0747 |
-2.46 |
.018 |
|
PAI-2 , 21 , 224 |
|
,130 |
.0435 .0114 .0043 |
-2.50 |
.016 |
|
CD22,26,356 _ _ |
|
,132 |
-.0059 .0049 |
1.72 |
.092 |
|
CD3 A -,22 -,156 |
|
,131 |
|
2.69 -1.19 |
.010 .241 |
Table 10.9. Regression Summary for change in F8 Entropy R=0.488; R2 = 0.238; Adjusted R2 =0.153; F(5,4)=2.81; p=0.027
Beta
St. Err. of Beta B
St. Err. of B
p level
Var PhIA
r
.133 -.0509 .0346
Intercpt -.0224 .0420 -.27 -.196
t(45) -.53 .597
-1.47 .148 -1.78
BCA -.25 -.236 .133 -.0022 .0012
Eosin -.18 -.274 .143 -.0419 .0218
IgA -.30 -.300 .139 -.1996 .0925
PAI-2 -.17 -.161 .136 -.0809 .0685
.082 -1.92 .061
-2.16 .036 -1.18
.244
Table 10.10. Regression Summary for change in Fp1 Entropy R=0.479; R2 = 0.229; Adjusted R2 =0.162; F(4,5)=3.42; p=0.016
|
St. Err. St. Err. Beta of Beta B of B Var Intercpt |
|
p |
|
,0287 ,0299 rEosin -,27 -,333 ,137 -,0371 ,0152 IgA -,23 -,272 |
|
level |
|
,137 -,1318 ,0665 KIE -,20 -,277 ,131 -, 0041 .0020 PAI-1 -.20 |
t(46) |
.342 |
|
-.187 .131 -.0614 .0431 |
.96 |
.019 |
|
|
-2.44 |
.054 |
-1.98 -2.11 -.01.4412.161
Table 10.11. Regression Summary for change in F7 Entropy R=0.476; R2 = 0.226; Adjusted R2 =0.169; F(3,4)=3.51; p=0.025
Beta
St. Err.
of Beta B of B Var Intercpt
St. Err.
p
0.024 0.043rLeukoc -.34 -0.354 0.154 -0.096 0.042 CD56 -.21
-0.261 0.152 -0.019 0.011 IgA -.29 -0.175 0.153 -0.128 0.111
level t(36) 0.55
0.585 -2.29 0.028
-1.72 0.095 -1.15
0.259
Table 10.12. Regression Summary for change in Fp2 Entropy R=0.460; R2 = 0.211; Adjusted R2 =0.143; F(4,5)=3.08; p=0.025
St. Err. St. Err.
Beta of Beta B of B Var Intercpt p
-.0253 .028r5 Eosin -.23 -.340 .143 -.0367 .0154 PAI-2
-.21 -.235 .137 -.0834 .0487 CD3 A -.23 -.198 .134
-.0079 .0054 IgA -.29 -.322 .139 -.1515 .0656
level t(46)
-.89 .380
-2.38 .021
-1.71 .093
-1.48 .147 -2.31 .026
Table 10.13. Regression Summary for change in T4 Entropy R=0.455; R2 = 0.207; Adjusted R2 =0.138; F(4,5)=3.00; p=0.028
Beta
St. Err. of Beta B
St. Err. of B
p level
Var r
BCE -.25
SNN, 17
PAI-2 -.26
CD56 -,16
Intercpt -.0417 .0270 -.334
.133 -.0020 .0008 .242 .138 .0075
.0042 -.251 .137 -.0817 .0446 -.208
.134 -.0100 .0064
t(46) -1.55
.129 -2.50
.016 1.76
.085 -1.83
.073 -1.55 .127
Table 10.14. Regression Summary for changes in C4 Entropy R=0.450; R2 = 0.202; Adjusted R2 =0.114; F(5,5)=2.29; p=0.062
|
|
Beta |
St. Err. of Beta B |
St. Err. of B |
p t(45) level |
|
Var r |
|
Intercpt |
,0256 |
.49 .626 1.84 |
|
PhIA, 18 |
|
|
,0220 |
.072 -1.26 |
|
PhIE -,20 |
|
|
,0148 |
.214 -1.51 |
|
KIE -,24 |
|
|
,0017 |
.138 -1.78 |
|
IgG -,23 |
|
|
,0059 |
.082 1.62 .111 |
|
IgM , 23 |
|
|
,0588 |
|
Table 10.15. Regression Summary for change in F3 Entropy R=0.444; R2 = 0.197; Adjusted R2 =0.108; F(5,5)=2.21; p=0.070
|
St. Err. St. Err. Beta of Beta B Var of B |
p |
|
Intercpt -.03r 19 .0214 PhIA .19 .303 .145 .0408 .0195 |
level t(45) |
|
PhIE -.17 -.229 .145 -.0202 .0128 PAI-2 .18 .205 .135 |
-1.49 .144 |
|
.0534 .0351 IgM , 36 .197 .135 .0730 .0498 IgA -.22 |
2.09 .043 |
|
-.159 .135 -.0549 .0466 |
-1.58 .121 |
|
|
1.52 .136 |
|
|
1.47 .149 -1.18 .246 |
Table 10.16. Regression Summary for change in P4 Entropy R=0.421; R2 = 0.177; Adjusted R2 =0.124; F(3,5)=3,37; p=0.026
|
|
St. Err. Beta of Beta B |
St. Err. of B |
p |
|
Var r |
Intercpt -.0172 |
,0199 |
t(47) level |
|
ICGH , 34 |
.307 .133 1.5857 -.169 |
,6865 |
-.87 |
|
PSI-2 -.22 |
.133 -.0634 .268 .133 |
,0497 |
2.31 |
|
IgM , 26 |
.0960 |
,0477 |
-1.27 2.01 ,391,025,209,050 |
Table 10.17. Regression Summary for change in F4 Entropy R=0.387; R2 = 0.150; Adjusted R2 =0.095; F(3,5)=2.76; p=0.053
The result of the regression analysis is visualized in fig. 10.2 and 10.3.
Fp2 0.6
HRV
Fp1
F4 0.5 F3
0.4
F8 0.3 F7
0.2
T4 0.1 T3
0
C4 C3
T6 T5
P4 P3
O2 O1
Fig. 10.2. Petal diagram of multiple correlation coefficients between changes in the entropies of SHSP loci of EEG and HRS and immunity parameters
0.6
0.55
0.5
0.45
0.4
Left Right
0.35
0.3
O1/2 F7/8 Fp1/2 F3/4 P3/4 T3/4 C3/4 T5/6 HRV
Fig. 10.3. Asymmetry of coefficients of multiple correlation between changes in entropies of SHSP of EEG loci and parameters of immunity
As we can see, the maximum immunomodulatory effect is exerted by balneofactor- induced entropy changes in the occipital loci, and almost
equally on both sides. Entropy changes in the lateral and anterior frontal loci have a weaker, but also symmetrical, immunomodulating effect. Instead, the immunomodulatory effect of entropy changes in most loci is characterized by left- sided asymmetry, maximally expressed in parietal and central
loci
The immunomodulatory effect of changes in HRS entropy deserves special
consideration (Table 10.18). A moderate suppressive effect on the intensity of phagocytosis was found
Staphylococcus aureus, Escherichia coli phagocytosis activity, IgM level, and
as well as the leukocyte adaptation index of Popovych, instead, the effect on the level of
activator of blood T-killers.
Table 10.18. Regression Summary for changes in HRV Entropy R=0.544; R2 = 0.296; Adjusted R2 =0.192; F(5,3)=2.86; p=0.029
R=0.544; R2 = 0.296; ÿ2 (5)=12.5; p=0.029; ÿ Prime=0.704
Fig. 10.4. Canonical correlation between HRS entropy change (X axis) and immunity parameters (Y axis)
However, HRS entropy changes are weakly and insignificantly related to EEG entropy changes (R=0.353; R2 =0.125; Adjusted R2 =0.078; F(2,4)=2.64; p=0.085). We interpret this as evidence of a separate role of HRS entropy changes in the mechanism of the immunomodulatory effect of balneofactors.
At the end of the relationship research, a canonical correlation was conducted. First, about the correlation between the balneofactor-induced changes in the entropy of the VRS and SHSP of the EEG loci, on the one hand, and the informational parameters of immunity (entropies and leukocyte indices) - on the other hand (Table 10.19, Fig. 10.5).
Table 10.19. Factor structure of entropy (left set) and information immune (right set) canonical roots
R=0.509; R2 = 0.289; ÿ2 (16)=24.5; p=0.079; ÿ Prime=0.491
Fig. 10.5. Canonical correlation between the change in EEG entropy (X axis) and immune information parameters (Y axis)
The program includes only 4 EEG loci in the factor structure of the entropic (causal) root, and the resulting root receives the maximum load from the leukocyte adaptation index (the first option), as well as from the entropies of the leukocytogram and immunocytogram and the stress index of the leukocytogram (the second option). The canonical correlation was of medium strength and on the border of significance (Fig. 10.5).
Next, the canonical correlation between the induced was found out
balneofactors by changes in the entropy of HRS and SHSP of EEG loci, on the one hand, and all (informational and relevant) parameters of immunity - on the other hand.
The program generated two pairs of canonical roots (Table 10.20). Entropic the root of the first pair receives the maximum factor loads from changes
entropy in loci T6 and P3 and the opposite direction - from changes in HRS entropy. The immune root is directly represented by the parameters of E. coli phagocytosis, adaptation index (option II) and B-lymphocytes, and inversely – IgA,
eosinophils and active and total lymphocytes. Canonical correlation between roots turns out to be very strong (Fig. 10.6).
Table 10.20. Factor structure of entropy (left set) and total immune (right set) canonical roots
|
Left set (SPD Entropies) |
R1 R2 0,417 |
|
T6 H |
0,273 0,338 |
|
P3 H |
0,198 0,176 |
|
T3 H |
0,052 0,144 |
|
Fp2 H |
0,179 0,083 |
|
Fp1 H |
0,084 -0,384 |
|
HRV H |
0,353 0,001 |
|
O2 H |
0,620 0,239 |
|
F8 H |
0,423 0,049 |
|
O1 H |
0,339 0,189 |
|
T5 H |
0,292 -0,121 |
|
T4 H |
0,232 0,160 |
|
F7 H |
0,229 0,048 |
|
P4 H |
-0,211 0,030 |
|
C3 H |
-0,044 R1 R2 |
|
Right set (Immunity) |
0,462 -0,396 |
|
Bactericidity vs E. coli |
0,329 -0,199 |
|
Microbial Count E. coli |
Killing Index vs |
R=0.993; R2 = 0.987; ÿ2 (294)=384; p=0.0003; ÿ Prime<10-6
Fig. 10.6. Canonical correlation between the first pair of roots, which represent changes in the entropies of the SHSP of the EEG and HRS loci (X axis) and immunity parameters (Y axis)
The factorial structure of the second root is formed by another constellation of EEG loci and again by HRS entropy. Their changes have a suppressive effect on
another constellation of immune parameters, in particular informational, as well as
responsible for phagocytosis of Staph. aureus. The strength of the correlation is somewhat weaker, but
nevertheless, it is very significant (Fig. 10.7).
3
2
1
0
-1
-2
-3
-3 -2 -1 0 1 2 3
Change in EEG&HRV Entropy
R=0.978; R2 = 0.956; ÿ2 (260)=293; p=0.077; ÿ Prime=10-6
Fig. 10.7. Canonical correlation between the second pair of roots, which represent the changes in the entropies of the EEG and VRS loci (X axis) and immunity parameters (Y axis)
Further, discriminant analysis was applied to identify, firstly,
precisely those loci in which the clusters differ from each other in terms of entropy changes one, and secondly, constellations of immune parameters, the changes of which are characteristic of of each cluster.
The program selected only nine entropy changes as characteristic
loci from 16, which are accompanied by changes in ten actual parameters
immunity and the integral immune index, as well as the stress index of the leukocytogram. It is interesting that the entropies of the immunocytogram, leukocytogram, and HRV were outside the discriminant model (tables 10.21 and 10.22). Table 10.21. Summary of the analysis of discriminant functions for changes in entropy and immunity parameters in clusters Step 21, N of vars in model:
21; Grouping: 3 grps Wilks' Lambda: 0.0187; approx. F(42)=8.4; p<10-6
|
Variables |
Cluster Cluster Cluster No.2 No. |
Wilks' ÿ |
Parti |
F-re |
That's it |
|
currently in the model |
3 No.1 (10) (7) (34) +0.251 +0.108 -0.071 +0.103 +0.144 |
|
al ÿ |
move 2.28 |
p level rancy |
|
O1H |
-0.039 +0.191 +0.022 -0.071 |
,025 |
,736 |
5.0 |
,014 ,331 ,0003 |
|
P3H |
+0.262 +0.176 -0.060 +0.038 |
,033 |
,559 |
11.0 |
,337 ,002 ,230 |
|
O2H |
+0.093 -0.127 +0.172 +0.077 |
,029 |
,641 |
7.8 |
,0002 ,264 ,023 |
|
F7H |
-0.066 + 0.220 -0.117 -0.111 |
,034 |
,550 |
11.5 |
,536 ,002 ,175 |
|
T4H |
|
,024 |
,763 |
4.4 |
,0002 ,182 |
|
T6H |
|
,029 |
,637 |
8.0 |
|
|
F8H |
|
,034 |
,546 |
11.6 |
|
|
Phagocytosis Ind vs Staph. aur., % +0.23 Immunity +0.24 +0.10 -0.16 |
,023 |
,805 |
3.4 |
.048 .475 .0002 |
|
Index-11 +0.11 Leukocytes, 109 /L -0.33 Stub +0.41 -0.42 +0.13 |
,035 |
,541 |
11.9 |
.060 .017 .227 |
|
Neutrophils, % -0.10 CD3+ T active Lymphocytes, -0.07 +0.35 +0.1 |
,025 |
,747 |
4.7 |
10-5 .124 .001 |
|
% -0.6 C3H T3H +0.7 -0.012 +0.253 |
,052 |
,360 |
24.9 |
.356 .0002 .395 |
|
-0.065 -0.055 |
,030 |
,625 |
8.4 |
.001 .491 .448 |
|
+0.156 -0.111 -1.50 +0.91 -0.10 |
,034 |
,549 |
11.5 |
.505 .0002 .346 |
|
-0.004 -0.032 +3.8 +0.35 +1.3 |
,030 |
,622 |
8.5 |
.002 .292 .136 |
|
Eosinophiles, % +1.2 +6.2 -5.7 -5.9 +7.7 -1.12 |
,020 |
,944 |
.8 |
.489 10-4 .107 |
|
Popovych's Strain Index-2, points -0.163 Micr Count +0.01 Cluster |
,034 |
,546 |
11.6 |
.095 .264 .0002 |
|
vs St. aur., Bact/Phag +0.6 CD4+ T-helper Cluster No. 3 No.1 |
,030 |
,630 |
8.2 |
.223 |
|
Lymphocytes, % +0.3 Killing Index vs Staph. |
,022 |
,867 |
2.1 |
|
|
aureus, % +3.0 Killing Index vs E. coli, % +5.5 |
,041 |
,453 |
16.9 |
|
|
Phagocytosis Index vs E. coli, % +0.22 Variables |
,022 |
,845 |
2.6 |
|
|
Cluster currently not in the model No.2 Df for all F- |
,035 |
,537 |
12.1 |
|
|
tests: 2, 27 Immunocytogram H Leukocytogram H HRV H |
Wilks' ÿ |
Parti al ÿ |
F to enter |
p level Tolerate rancy |
|
(10) (7) +0.018 (34) |
|
|
|
|
Next, the 21-dimensional space of discriminant variables is transformed into a 2- dimensional space of canonical roots. The canonical correlation coefficient for the first root is 0.945 (Wilks' ÿ=0.019; ÿ2 (42)=151; p<10-6), and for the second 0.909 (Wilks' ÿ=0.174; ÿ2 (20)=66; p=10 -6). The main root contains 63.8%
discriminatory capabilities, secondary - 36.2%.
Calculation of the values of the discriminant roots for each person on the basis
coefficients from the table 10.23 makes it possible to visualize each patient in the information space of the roots (Fig. 10.8). Table 10.23.
Standardized and raw coefficients and constants for canonical variables
Variables C3H
O1H T3H
Coefficients
Standardized Raw Root 1
Root 2 Root 1 Root 2 ,047 -1,175 ,440
-10,95 -,714 ,644 -4,569 4,120 ,474
-,831 2,777 -4,868 Immunity
Index-11 -2,179 2,045 -3,624 3,401
Eosinophils, % - ... 670 1.598 -.463 Killing Index vs Staph.
aureus, % 2,223 -,908 ,217 -,089 P3H -1,175 ,304 -10,81 2,794 F7H -1,317
,437 -5,715 1,897 Phagocytosis
Index vs E. coli, % -1,429 ,551 -,854 ,329 T6H 1,038 - 1.162 5.353 -5.993
Phagocytosis Ind vs Staph. aur., % ,624 ,275 ,553 ,244 CD4+ T-helper
Lymphocytes, % -,337 -,454 -,067 -,090 CD3+ T active Lymphocytes, %
,801 -,763 ,156 -,148 -1,165 , 648 -7.457 4.145 Micr Count vs St. aur., Bact/
Phag 1,019 -,640 ,113 -,071 Leukocytes, 109 /L ,873 -,725 ,762 -,633 Killing
Index vs E. coli, % ,806 -,077 ,060 -,006 Constants , 208 -.911 Eigenvalues
8.334 4.736 Cum. Prop ,638 1,000
O2H
Table 10.24 shows the correlation coefficients of changes in entropy and immunity with canonical discriminant roots, as well as centroids of both clusters
roots and normalized values of entropy and immunity changes as discriminants parameters not included in the discriminant model.
Table 10.24. Correlations between parameters and canonical roots, root centroids and Z- scores of parameter changes for clusters
|
Change in Variables |
Correlations Variables-Roots |
II III I (10) (7) (34) -4.43 |
|
Root 1 (63.8%) |
R1 R2 |
-3.22 +1.97 +1.38 +0.60 |
|
O1H |
-.292 .076 -.239 |
-0.56 +0.83 +1.16 -0.46 |
|
P3H |
-.090 -.220 .111 |
+1.06 +0.12 -0.53 +1.64 |
|
O2H |
-.212 .016 -.186 |
+1.10 -0.59 +0.32 +0.78 |
|
F7H |
-.075 -.178 .037 |
-1.27 +1.16 +0.52 -0.61 |
|
T4H |
-.139 .153 -.019 |
+1.29 -0 .68 -0.78 +0.13 |
|
T6H |
-.005 currently not |
+0.13 +0.06 +1.51 |
|
F8H |
in model currently |
+0.50 -1.10 +1.42 +1.15 |
|
Phagocytosis Ind vs Staph. |
not in model |
-0.59 +1.41 +1.17 -0.62 |
|
aureus Fp2H T5H |
currently not in model |
|
|
Fp1H |
|
|
Localization of members of the first cluster along the axis of the first root (Fig. 10.8 and 10.9) in the far right zone reflects a decrease in the entropy of EEG loci, as well as
minimal increase in Staph phagocytosis activity. aureus, i.e. parameters,
which are negatively correlated with the root , and the maximum increase of immune parameters that are positively correlated with the root (Table 10.24).
Members of the other two clusters occupy the extreme left position and their projections on are mixed up. Nevertheless, a greater shift of the centroid to the left of the second cluster,
as a rule, it manifests itself in a greater increase in entropy.
Instead, along the axis of the second root, the members of these clusters are separated clearly due to the extremely low position of the members of the third cluster, which reflects a significant increase in the entropy of the EEG loci, as well as the immune parameters that are associated with the root negatively, combined with a decrease in the immune parameters associated with the root positively (Figs. 10.8 and 10.10) .
4
3
2
1
0
-1
-2
-3
-4
-5
-6
-7
-6 -5
-4 -3
-2 -1 0
1 2 3 4
III I II
Root 1
Fig. 10.8. Individual values of the first and second roots of EEG/LCH/ICH entropy changes and immunity in members of the three clusters
1.5
1.0
0.5
0.0
-0.5
-1.0
-5
-4 -3
-2 -1 0
1 2 R1 Mean
Imm R1 H R1
Fig. 10.9. Patterns of changes in EEG entropy parameters and immunity, information about which is condensed in the first root
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-5 -4
-3 -2
-1 0
ImR2 ImR2 H R2
1 2 3 R2 Mean
Fig. 10.10. Patterns of changes in EEG entropy parameters and immunity, information about which is condensed in the second root
A clear demarcation of three clusters is documented (Table 10.25).
Table 10.25. Squares of Mahalanobis distances between clusters, F-criteria and p are equal
|
|
III |
I |
II |
|
III |
0 |
54 |
59 |
|
I |
7.6 |
0 |
50 |
|
|
10-6 |
|
|
|
II |
5.9 |
9.8 |
0 |
|
|
10-5 |
10-6 |
|
The use of predictors and classification functions (Table 10.26) allows
the belonging of a specific person to one or another cluster of changes can be predicted unmistakably (Table 10.27).
Table 10.26. Coefficients and constants for cluster classification functions
III I II
Change in Variables
C3H O1H T3H
p=.137 p=.667 p=.196 37.73 -13.51 -43.56 3.000
-.555 38.91 7.153 -2.234
-32.10 Immunity Index-11
2.169 -.004 31.63 Eosinophils, % 1.292 -.143 .361 F8H -33.07
11.68 -16.51 T4H 17.58 -11.18 9.596 Popovych's Strain
Index-2, points -12.71 6.476 -19.39 Stub Neutrophils, % -3.813
2,210 -9,157 Killing Index vs Staph. aureus, % -441 ,249 -1,356
P3H 30,18 -12,24 63,85 12,77 -7,594 33,66 Phagocytose Index
vs E. coli, % 1,119 -1,701 4,581 T6H 5,829 4,302 -44,83
Phagocytose Index vs Staph. aur., % -2,504 1,554 -1,375 CD4+ T-helper Lymphocytes, % ,580 -,207 -,004 CD3+ T active
F7H Lymphocytes, % ,046
,129 -1,235 O2H 9,373 -9,038 48,95 Micr Count vs St. aur., Bact/
Phag -.124 .114 -.783 Leukocytes, 109 /L -.816 .044 -6.406
Killing Index vs E. coli, % -.190 .093 -.304 Constants -15.63
-3.122 - 19.36
Table 10.27. Classification matrix for clusters Rows: observed classifications; columns: predicted classifications
Percent correct
III I II
p=.137 p=.667 p=.196 7 0
|
III |
100 |
0 0 0 |
|
|
I |
100 |
34 |
|
|
II |
100 |
0 0 |
10 |
|
Total |
100 |
7 34 |
10 |
At the final stage of the analysis, we created three patterns of connections between the changes in the entropy of the SHSP of individual EEG loci caused by adaptogenic balneotherapy, on the one hand, and immune parameters, the information about which is condensed in two canonical discriminant roots, on the other hand (Fig. 10.11). As we can see, both inverse patterns are quite clear, instead, the direct connection occurs only on the segment of entropy growth, while its decrease is accompanied by the absence of changes in immune parameters.
Fig. 10.11. Patterns of relationships between changes in EEG entropy parameters and immunity parameters, information about which is condensed in discriminant roots
CONCLUSION
Maximum immunomodulatory effect do induced
balneofactors of EEG entropy changes in occipital loci, and almost equally on both sides. Weaker, but also symmetrical immunomodulating
entropy changes in the lateral and frontal frontal loci have an effect. in return the immunomodulatory effect of entropy changes in most loci is characterized left-sided asymmetry, most pronounced in the parietal and central
loci Changes in HRS entropy are accompanied by opposite changes
the intensity of phagocytosis of Staphylococcus aureus, the activity of phagocytosis of Escherichia coli, the level of IgM and the leukocyte adaptation index, and unidirectional changes in the level of T-killers in the blood.
Machine Translated by Google
Three variants of the effect of balneotherapy on EEG entropy and VRS were identified, which are accompanied by characteristic changes in ten relevant parameters of immunity and the integral immune index, as well as the stress index leukocytograms.
206
Machine Translated by Google
SECTION 11
RELATIONSHIPS BETWEEN ENTROPY OF NEURO-IMMUNE PARAMETERS
COMPLEX AND GAS DISCHARGE VISUALIZATION
The subjects of the second observation were 10 men and 10 women aged 23-76, residents of Truskavets, without a clinical diagnosis, but with signs of dysfunction of the neuroendocrine- immune complex. The examination was carried out twice, before and
after a 7-day course of drinking Naftusya bioactive water, according to the same algorithm, but with the additional use of the gas discharge visualization method (Kirlianography, biophotonics) [K.G. Korotkov, 2001, 2007].
The bioelectrogram of the tips of all fingers was recorded with the device "GRV
Camera" (manufactured by "Biotechprogress", St. Petersburg).
According to the instructions, 21 parameters were analyzed: the area of the gas discharger image (GRZ), form factor (ratio of the square of the length
of the external contour of the RHZ to its area), which characterizes the degree of jaggedness (fractality) of the outer contour, and the entropy of this right contour,
of the frontal and left projections without a filter and with a filter, the symmetry of the frontal projection under both conditions, as well as the activation coefficient, calculated from the GDV diagrams with a filter and without a filter.
Entropy was calculated using a special program implemented in
instrument.
Korotkov K.G. [2001], the founder of the method of gas discharge visualization (GRV), according to by analogy with the concept of thermodynamic entropy, he introduced the term entropy of RHV grams and created the appropriate software for its calculation. Author
introduced the classification of GDV-grams according to the degree of "imbalance", namely: high
the value of the entropy indicates strongly unbalanced GRV-grams, which
corresponds to an unstable state of homeokinesis, on the other hand, equal, "calm" RHV grams have a lower entropy value. The author believes that GRV-grams
reflect the "state of internal production of negative entropy". This product depends on the functional state of the body and energy flows and
207
information from the outside. The experiments of the author's laboratory have shown that the entropy of the GRV-gram is an informative characteristic of the state of the organism.
Based on the above, it would be interesting to analyze the connections between entropy parameters of the neuro-immune complex and gas discharge imaging.
But first, let's analyze the connections between the entropy of the GRV-gram, removed without
filter and with filter (Table 11.1).
Table 11.1. Correlation matrix for gas-discharge entropies image in different projections, taken without a filter and with a filter
|
Entropy |
Right |
Right |
Frontal |
Frontal |
Left |
Left |
|
|
GDI |
GDI (f) |
GDI |
GDI (f) |
GDI |
GDI (f) |
|
Right GDI |
1.00 |
.46 |
.71 |
.42 |
.58 |
.31 |
|
Right GDI (f) |
.46 |
1.00 |
.50 |
.69 |
.49 |
.64 |
|
Frontal GDI |
.71 |
.50 |
1.00 |
.58 |
.77 |
.36 |
|
Frontal GDI (f) |
.42 |
.69 |
.58 |
1.00 |
.50 |
.64 |
|
Left GDI |
.58 |
.49 |
.77 |
.50 |
1.00 |
.44 |
|
Left GDI (f) |
.31 |
.64 |
.36 |
.64 |
.44 |
1.00 |
Note. For a sample of 40 people, the critical value of the modulus of the correlation coefficient
|r| for p<0.05 (t>2.02) is 0.31, for p<0.01 (t>2.70) 0.41, for p<0.001 (t>3.55) 0.52.
As we can see, for each projection, the pairwise correlation coefficients are only
of medium strength, which is consistent with the position of K.G. Korotkov. [2001] that GRV gram, taken without a filter, reflects the current state of the body, instead taken with
filter - its basic state. A certain analogy between reactive and personal anxiety.
Also, the correlation between the entropies of the RHV was only average in strength grams in the right and left projections, which indicates their lateralization, similar to this density of spectral power of EEG loci.
To visualize the correlations, we had to decide on factorial (argument) and effective (functional) parameters. From the point of view of mathematics this does not matter, while from the point of view of physiology there is a perennial problem of the nature of cause-and- effect relationships. We, having taken the position of idealism in medicine, chose the parameters of the GDV as a factor
grams
According to the results of the screening (Table 11.2), the strongest relationship was found between the entropy of the HV-gram in the left projection, taken with the filter, and the entropy of the EEG spectral power density in the right lateral
frontal locus (Fig. 11.1). Table 11.2. Correlation matrix for Entropies of gas discharge image, spectral power density of EEG loci, HRV, Leukocytogram and Immunocytogram
|
Entropy |
Right |
Right |
Frontal |
Frontal |
Left |
Left |
|
|
GDI |
GDI (f) |
GDI |
GDI (f) |
GDI |
GDI (f) |
|
Fp2 |
,09 |
,10 |
,18 |
,16 |
,08 |
,06 |
|
F4 |
,08 |
-,07 |
,16 |
-,04 |
,07 |
-,14 |
|
F8 |
-,01 |
-,19 |
-,02 |
-,24 |
-,07 |
-,46 |
|
T4 |
-,22 |
-,08 |
-,06 |
-,27 |
-,02 |
-,16 |
|
C4 |
,03 |
-,07 |
-,06 |
-,04 |
-,17 |
-,19 |
|
T6 |
,17 |
|
,03 |
|
-,05 |
|
|
P4 |
,24 |
|
,19 |
|
,13 |
|
|
O2 |
,21 |
|
-,02 |
|
,04 |
|
|
Fp1 |
-,00 |
|
-,06 |
|
-,15 |
|
|
F3 |
,19 |
|
,28 |
|
,25 |
|
|
F7 |
-, 03 |
|
,08 |
|
-,19 |
|
|
T3 |
-,22 |
|
-,07 |
|
-,24 |
|
|
C3 |
,01 |
|
-,17 |
|
-,24 |
|
|
T5 |
,10 |
|
-,07 |
|
-,07 |
|
|
P3 |
,20 |
|
,09 |
|
,04 |
|
|
O1 |
,16 |
|
,13 |
|
,17 |
|
|
HRV |
-,10 |
|
-,08 , |
|
|
|
|
LCG |
,30 |
|
30 |
|
|
|
ICG
- ,25
-,01,20,21 -,0-7,,1246 -,03 -,0-5,1-2,1,247-,0,167,1,11-2,,21,2194,,11-0,80-5- ,,0--28,.070-8,,10-12.1,-1,8110,0,250-,,3012--,214,-1,822- -,2,119 -,14,05,19 -,10 14 - 30
F8H = 2.363 - 0.4318*ELF
Correlation: r = -.4553
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
3.3 3,4
3.5
3.6
3.7
3.8 3.9
4.0
4.1
Entropy Left (f)
95% confidence
GDV Fig. 11.1. Correlation dot plot between the entropy of the HRV-gram (filtered) in the left projection (X-axis) and the entropy of the EEG SHSP in the F8 locus (Y-axis)
F8h=2.22-0.472•ELf+0.081•EFf; R=0.458; R2 = 0.210; F(2,4)=4.9; p=0.013
Fig. 11.2. A dot plot of the dependence of the entropy of the EEG in the F8 locus (Z axis) on the entropies of the HRV-gram (filtered) in the left (X axis) and frontal (Y axis) projections
The inclusion in the multiple regression model of the second-strongest connection option (entropy of the GRV-gram in the frontal projection) only brought aesthetic satisfaction from the three-dimensional image (Fig. 11.2), but no more, judging by the value of R, which practically did not increase.
Intrigue about a cross connection like corticospinal
of the pyramid tract, unfortunately, later dispelled by other facts.
At the next stage, the canonical correlation between
by the entropy indicators of the gas discharge image taken without a filter and with filter in three projections, on the one hand, and the entropy of SHSP in 16 EEG loci - on the other
By means of step-by-step elimination in the structure of the canonical GVD-root 5 variables are included, and EEG-root - 6 variables (Table 11.3).
Table 11.3. The factorial structure of the canonical roots representing the Entropy of the gas discharge image (left set) and the SSC of electroencephalogram loci (right set)
|
Left set |
R |
|
Left GDI (f) |
.536 |
|
Left GDI |
.158 |
|
Right GDI |
-.420 |
|
Frontal GDI (f) |
-.084 |
|
Frontal GDI |
-.055 |
|
Right set |
R |
|
F8H |
-.514 |
|
P4H |
-.478 |
|
C3H |
-.454 |
|
F3H |
-.237 |
|
O1H |
-.141 |
|
T4H |
.360 |
Judging by the factor loadings, the causal root represents directly, mainly, the entropy of the GDV-gram (with a filter) in the left projection, instead, inversely - the entropy of the GDV-gram (taken without a filter) in the right projection. On the other hand, the EEG-root reflects the entropy of the SHSP in five loci inversely and in only one - directly. In general, the entropy of the GRV- gram determines
the entropy of the EEG SSC by 33% (Fig. 11.3).
2
1
0
-1
-2
-2 -1 0 1 2
hGDV
R=0.575; R2 = 0.330; ÿ2 (30)=38; p=0.158; ÿ Prime=0.341
Fig. 11.3. Dot plot of the canonical correlation between the entropy of the HRV-gram (X-axis) and the entropy of the EEG SHSP (Y-axis)
Additional inclusion in the right set of entropy parameters of HRS, leukocytogram, and immunocytogram gives a significant increase in the strength of canonical relationships between roots. This also changes the factorial structure of the roots. In particular, the leukocytogram entropy occupies a prominent place. Contrary to expectations, the HRS entropy turned out to be outside the model (Table 11.4 and Fig. 11.4).
Table 11.4. The factorial structure of canonical roots, which are represented by the entropy of the HRV (left set) and the entropies of the EEG, LCG and ICG (right set)
R=0.699; R2 = 0.489; ÿ2 (48)=54; p=0.262; ÿ Prime=0.181
Fig. 11.4. Dot plot of the canonical correlation between the entropy of the HRV-gram (X axis) and EEG, LCG, and ICG (Y axis)
Machine Translated by Google
CONCLUSION
The results of the conducted research allow us to come to some conclusions
important conclusions.
First, with the help of existing in the literature, as well as proposed ones
with these indicators, there is a possibility of determining nervous and endocrine entropy and nervous systems.
Secondly, there are general connections between these systems, which, with special known regulation mechanisms, allow us to assert that along with
this changes the general state of these systems according to those indicators characterize their entropy.
Thirdly, it should be emphasized that entropy changes in the studied systems occur in both humans and animals.
Moreover, it is important that the complex influence of the factors of resort treatment with the use of "Naftusya" mineral water causes changes in entropy as in
both healthy animals and sick ones.
Fourthly, the central nervous system is the object of these influences, but
at the same time, it is a subject in relation to the endocrine and immune systems. It allows us to state that the entropy changes of regulatory systems
(central and autonomic nervous systems) cause entropy changes executive systems, for example, the immune system.
Fifth, changes in the entropy of the studied systems are not always unidirectional, which is due to the different participation of the studied systems in relation to the influence of sanatorium-resort factors. Sixth,
changes in entropy of regulatory and immune systems occur in connection with changes in gas discharge imaging, which gives us the opportunity to state that under the influence of sanatorium-resort factors, systemic, general changes in the body occur, judging by the related dynamics of entropy " biophysical" system, which occurs, judging by changes in indicators
gas discharge visualization.
213
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In general, we should come to the conclusion that the general functional state of the organism of animals and people can be determined by changes in the entropy indicators of these systems. Undoubtedly, further research can provide more complete information about the general state of the organism, and by the nature of the changes in relationships that occur in various functional systems, it is possible to trace the causal relationships and the causal relationships between them. It is important that further theoretical studies of the problem of entropy changes
will be able to lay the foundation for clinical understanding of changes in the future the general state of the human body, both in normal and pathological conditions.
214
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SCIENTIFIC PUBLICATION
Anatoly Ivanovych GOZHENKO Mykhailo Mykhailovych
KORDA Oleksandr Oleksiyovych
POPADYNETS Ihor Lvovich POPOVYCH
ENTROPY, HARMONY, SYNCHRONIZATION
AND THEIR NEURO-ENDOCRINE-IMMUNE CORRELATIONS
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