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10.1002/rmv.2140 http://scihub22266oqcxt.onion/10.1002/rmv.2140 32686248!7404608!32686248     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Rev+Med+Virol 2020 ; 30 (5): e2140 Nephropedia Template TP {{tp|p=32686248|t=2020. Novel coronavirus SARS-CoV-2 (Covid-19) dynamics inside the human body |pdf=|usr=}}{{32686248}} gab.com Text Novel coronavirus SARS-CoV-2 (Covid-19) dynamics inside the human body JO: Rev Med Virol http://www.kidney.de/pget.php?&tw=1&pmid=32686248 #FOAvip A knowledge-based cybernetic framework model representing the dynamics of SARS-CoV-2 inside the human body has been studied analytically and in silico to explore the pathophysiologic regulations. The following modeling methodology was developed as a platform to introduce a predictive tool supporting a therapeutic approach to Covid-19 disease. A time-dependent nonlinear system of ordinary differential equations model was constructed involving type-I cells, type-II cells, SARS-CoV-2 virus, inflammatory mediators, interleukins along with host pulmonary gas exchange rate, thermostat control, and mean pressure difference. This formalism introduced about 17 unknown parameters. Estimating these unknown parameters requires a mathematical association with the in vivo sparse data and the dynamic sensitivities of the model. The cybernetic model can simulate a dynamic response to the reduced pulmonary alveolar gas exchange rate, thermostat control, and mean pressure difference under a very critical condition based on equilibrium (steady state) values of the inflammatory mediators and system parameters. In silico analysis of the current cybernetical approach with system dynamical modeling can provide an intellectual framework to help experimentalists identify more active therapeutic approaches. Twit Text FOAVip Novel coronavirus SARS-CoV-2 (Covid-19) dynamics inside the human body ????Rev Med Virol http://www.kidney.de/pget.php?&tw=1&pmid=32686248 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32686248 #FOAvip English Wikipedia <ref>{{Cite pmid|32686248}}</ref> Novel coronavirus SARS-CoV-2 (Covid-19) dynamics inside the human body #MMPMID32686248 Saha A; Saha B Rev Med Virol 2020[Sep]; 30 (5): e2140 PMID32686248show ga A knowledge-based cybernetic framework model representing the dynamics of SARS-CoV-2 inside the human body has been studied analytically and in silico to explore the pathophysiologic regulations. The following modeling methodology was developed as a platform to introduce a predictive tool supporting a therapeutic approach to Covid-19 disease. A time-dependent nonlinear system of ordinary differential equations model was constructed involving type-I cells, type-II cells, SARS-CoV-2 virus, inflammatory mediators, interleukins along with host pulmonary gas exchange rate, thermostat control, and mean pressure difference. This formalism introduced about 17 unknown parameters. Estimating these unknown parameters requires a mathematical association with the in vivo sparse data and the dynamic sensitivities of the model. The cybernetic model can simulate a dynamic response to the reduced pulmonary alveolar gas exchange rate, thermostat control, and mean pressure difference under a very critical condition based on equilibrium (steady state) values of the inflammatory mediators and system parameters. In silico analysis of the current cybernetical approach with system dynamical modeling can provide an intellectual framework to help experimentalists identify more active therapeutic approaches. |*Nonlinear Dynamics[MESH] |Acute-Phase Proteins/antagonists & inhibitors/genetics/immunology[MESH] |Angiotensin-Converting Enzyme 2[MESH] |Anti-Inflammatory Agents/therapeutic use[MESH] |Antiviral Agents/therapeutic use[MESH] |Betacoronavirus/drug effects/growth & development/*pathogenicity[MESH] |Body Temperature[MESH] |COVID-19[MESH] |Coronavirus Infections/drug therapy/*immunology/pathology/virology[MESH] |Cytokines/antagonists & inhibitors/genetics/immunology[MESH] |Epithelial Cells/drug effects/immunology/virology[MESH] |Gene Expression Regulation[MESH] |Host-Pathogen Interactions/drug effects/genetics/*immunology[MESH] |Humans[MESH] |Lung/drug effects/*immunology/virology[MESH] |Macrophages, Alveolar/drug effects/immunology/virology[MESH] |Pandemics[MESH] |Peptidyl-Dipeptidase A/genetics/immunology[MESH] |Pneumonia, Viral/drug therapy/*immunology/pathology/virology[MESH] |Pulmonary Gas Exchange/drug effects/immunology[MESH] |SARS-CoV-2[MESH]Novel coronavirus SARS-CoV-2 (Covid-19) dynamics inside the human body(epmc).pdf DeepDyve Pubget Overpricing