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10.1016/j.csbj.2021.03.020 http://scihub22266oqcxt.onion/10.1016/j.csbj.2021.03.020 33815693!7997026!33815693     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33815693&cmd=llinks): Failed to open stream: HTTP request failed! 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Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity |pdf=|usr=}}{{33815693}} gab.com Text Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity JO: Comput Struct Biotechnol J http://www.kidney.de/pget.php?&tw=1&pmid=33815693 #FOAvip The global pandemic caused by the SARS-CoV-2 virus continues to spread. Infection with SARS- CoV-2 causes COVID-19, a disease of variable severity. Mutation has already altered the SARS-CoV-2 genome from its original reported sequence and continued mutation is highly probable. These mutations can: (i) have no significant impact (they are silent), (ii) result in a complete loss or reduction of infectivity, or (iii) induce increase in infectivity. Physical generation, for research purposes, of viral mutations that could enhance infectivity are controversial and highly regulated. The primary purpose of this project was to evaluate the ability of the DeepNEU machine learning stem-cell simulation platform to enable rapid and efficient assessment of the potential impact of viral loss-of-function (LOF) and gain-of-function (GOF) mutations on SARS-CoV-2 infectivity. Our data suggest that SARS-CoV-2 infection can be simulated in human alveolar type lung cells. Simulation of infection in these lung cells can be used to model and assess the impact of LOF and GOF mutations in the SARS-CoV2 genome. We have also created a four- factor infectivity measure: the DeepNEU Case Fatality Rate (dnCFR). dnCFR can be used to assess infectivity based on the presence or absence of the key viral proteins (NSP3, Spike-RDB, N protein, and M protein). dnCFR was used in this study, not to only assess the impact of different mutations on SARS-CoV2 infectivity, but also to categorize the effects of mutations as loss of infectivity or gain of infectivity events. Twit Text FOAVip Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity ????Comput Struct Biotechnol J http://www.kidney.de/pget.php?&tw=1&pmid=33815693 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33815693 #FOAvip English Wikipedia <ref>{{Cite pmid|33815693}}</ref> Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity #MMPMID33815693 Esmail S; Danter WR Comput Struct Biotechnol J 2021[]; 19 (ä): 1701-1712 PMID33815693show ga The global pandemic caused by the SARS-CoV-2 virus continues to spread. Infection with SARS- CoV-2 causes COVID-19, a disease of variable severity. Mutation has already altered the SARS-CoV-2 genome from its original reported sequence and continued mutation is highly probable. These mutations can: (i) have no significant impact (they are silent), (ii) result in a complete loss or reduction of infectivity, or (iii) induce increase in infectivity. Physical generation, for research purposes, of viral mutations that could enhance infectivity are controversial and highly regulated. The primary purpose of this project was to evaluate the ability of the DeepNEU machine learning stem-cell simulation platform to enable rapid and efficient assessment of the potential impact of viral loss-of-function (LOF) and gain-of-function (GOF) mutations on SARS-CoV-2 infectivity. Our data suggest that SARS-CoV-2 infection can be simulated in human alveolar type lung cells. Simulation of infection in these lung cells can be used to model and assess the impact of LOF and GOF mutations in the SARS-CoV2 genome. We have also created a four- factor infectivity measure: the DeepNEU Case Fatality Rate (dnCFR). dnCFR can be used to assess infectivity based on the presence or absence of the key viral proteins (NSP3, Spike-RDB, N protein, and M protein). dnCFR was used in this study, not to only assess the impact of different mutations on SARS-CoV2 infectivity, but also to categorize the effects of mutations as loss of infectivity or gain of infectivity events. äLung organoid simulations for modelling and predicting the effect of m(epmc).pdf DeepDyve Pubget Overpricing