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Version: 0.3.1
\nRelease date: 3 July 2020
\nOverview\nThis model simulates replication dynamics of SARS-CoV-2 (coronavirus / COVID19) in a layer of epithelium with an initial immune reaction. It is being rapidly prototyped and refined with community support (see below).
\nIn this model, SARS-CoV-2 (coronavirus / COVID19) infects a single cell, or a solution of virions is administered to the extracellular space. The virus is uncoated to explose viral RNA, which synthesizes viral proteins that are assembled into a virion. Assembled virions are exported to the environment, where they can diffuse and infect other cells. In the extracellular space, virions adhere to ACE2 receptors and get internalized through endocytosis. Internalized ACE2 receptors release their virus cargo and are recycled back to the surface.
\nResident macrophages ingest apototic cells and release a pro-inflammatory cytokine that recruits additional macrophages, neutrophils, and CD8+ T cells. CD8+ T cells chemotax towards cytokines released by infected cells and adhere. Cumulative CD8+ T cell contact time can induce apoptosis in infectd cells. Activated macrophages and neutrophils chemotaxis chemotax along chemokine and debris gradients and continue to phagocytose dead cells. Neutrophils also absorb free (extracellular) virus.
\nThe model includes a basic pharmacodynamic response (to assembled virions) to cause cell apoptosis. Apoptosed cells release some or all of their internal contents, notably including virions.
\nCaveats and disclaimers:\nThis model is under active development using rapid prototyping:
\nThis model will be continually refined with input from the community, particularly experts in infectious diseases. The validation state will be updated as this progresses.
\nKey makefile rules:\nmake : compiles the project.
\nmake clean : removes all .o files and the executable, so that the next \"make\" recompiles the entire project
\nmake data-cleanup : clears out all simulation data
\nMore references\nPreprint: https://doi.org/10.1101/2020.04.02.019075
\nModel details: https://github.com/MathCancer/COVID19/wiki/About
\nHomepage: http://covid19.PhysiCell.org
\nSupport: https://sourceforge.net/p/physicell/tickets/
\nLatest info: follow @PhysiCell and @MathCancer on Twitter (http://twitter.com/MathCancer)
\nSee changes.md for the full change log.
\n\nRelease summary:\n0.3.1:\nThis release improves parameter estimates for digestion of phagocytosed material and has an immune model refinement to prevent runaway macrophage death.
\n0.3.0:\nThis release incorporates major v2 model feedback and adds the first immune submodel.
\nNOTE: OSX users must now define PHYSICELL_CPP system variable. See the documentation.
\nNew features and changes:\n0.3.1:\nRefactored modular design to include refinements from immune model.
\nFirst integration of new immune submodel.
\nUpgrade to PhysiCell Version 1.7.1, allowing use of XML-based cell definitions to define the behavior of immune cell types.
\nUpgrade to PhysiCell Version 1.7.2beta to improve multithreaded performance, add new cell-cell interaction features, and fix concurrency issues on some platforms.
\nContinue to vet model biology with collaborators.
\nAdd lymph node module.
\nAdd tissue damage models.
\nIntegrate SBML support for submodels.
\nRefine viral replication model.
\nRefine immune model (including more cell types and improved parameter estimates).
\nAdd interferon response model.
\nThe Coronavirus Disease Research Community - COVID-19 is curated by a selected team of experts nominated by OpenAIRE* (see list below). Each time a Zenodo user wants to add a record into the community, an email is sent to the curators that will decide whether to include the record or not.
\r\n\r\nOnly records that may be relevant to the Corona Virus Disease (COVID-19) or the SARS-CoV-2 should be included in this community. The Community curators are not able to edit records, therefore they may ask the corresponding authors to modify the record metadata when necessary, to provide the readers/users with more detailed information according to the FAIR principle of Open Science.
\r\n\r\nIf after its acceptance, a record is subsequently found not to be compliant, we reserve the right to remove it from the community.
\r\n\r\nThe curation team is reachable through the following email address for further clarification or information: covid19@openaire.eu.
\r\n\r\nCurator List:
\r\n\r\n* OpenAIRE: open access and open science training and support since 2009. OpenAIRE is the largest aggregator of European Commission funded research outputs and beyond, also delivering on-demand services for research communities.
\r\n", "page": "This community collects research outputs that may be relevant to the Coronavirus Disease (COVID-19) or the SARS-CoV-2. Scientists are encouraged to upload their outcome in this collection to facilitate sharing and discovery of information. Although Open Access articles and datasets are recommended, also closed and restricted access material are accepted. All types of research outputs can be included in this Community (Publication, Poster, Presentation, Dataset, Image, Video/Audio, Software, Lesson, Other).
\r\n\r\nThe recent Corona Virus Disease (COVID-19) outbreak is requiring unseen efforts of collaboration of the scientific community that need to act fast and to share results in an unpredictable manner. In order to facilitate the Scientist efforts, this community was created to collect all research results that could be relevant for the scientific community working on the Corona Virus Disease (COVID-19) and SARS-CoV-2.
\r\n\r\nAlthough Open Access articles and datasets are recommended, also closed and restricted access material are accepted. All types of research outputs can be included in this Community (Publication, Poster, Presentation, Dataset, Image, Video/Audio, Software, Lesson, Other).
\r\n\r\nWhen depositing a resource that is linked to other resources (not limited to the records deposited in Zenodo but also in other repositories), please make sure that your record is linked to all the other related elements already available, in order to adhere to the FAIR principles of Open Science to maximise the reusability of research results.
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