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10.3390/v13040610 http://scihub22266oqcxt.onion/10.3390/v13040610 33918368!8066066!33918368     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33918368&cmd=llinks): Failed to open stream: HTTP request failed! 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Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 |pdf=|usr=}}{{33918368}} gab.com Text Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 JO: Viruses http://www.kidney.de/pget.php?&tw=1&pmid=33918368 #FOAvip Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes COVID-19 and is responsible for the ongoing pandemic. Screening of potential antiviral drugs against SARS-CoV-2 depend on in vitro experiments, which are based on the quantification of the virus titer. Here, we used virus-induced cytopathic effects (CPE) in brightfield microscopy of SARS-CoV-2-infected monolayers to quantify the virus titer. Images were classified using deep transfer learning (DTL) that fine-tune the last layers of a pre-trained Resnet18 (ImageNet). To exclude toxic concentrations of potential drugs, the network was expanded to include a toxic score (TOX) that detected cell death (CPETOXnet). With this analytic tool, the inhibitory effects of chloroquine, hydroxychloroquine, remdesivir, and emetine were validated. Taken together we developed a simple method and provided open access implementation to quantify SARS-CoV-2 titers and drug toxicity in experimental settings, which may be adaptable to assays with other viruses. The quantification of virus titers from brightfield images could accelerate the experimental approach for antiviral testing. Twit Text FOAVip Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 ????Viruses http://www.kidney.de/pget.php?&tw=1&pmid=33918368 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33918368 #FOAvip English Wikipedia <ref>{{Cite pmid|33918368}}</ref> Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2 #MMPMID33918368 Werner J; Kronberg RM; Stachura P; Ostermann PN; Muller L; Schaal H; Bhatia S; Kather JN; Borkhardt A; Pandyra AA; Lang KS; Lang PA Viruses 2021[Apr]; 13 (4): ä PMID33918368show ga Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes COVID-19 and is responsible for the ongoing pandemic. Screening of potential antiviral drugs against SARS-CoV-2 depend on in vitro experiments, which are based on the quantification of the virus titer. Here, we used virus-induced cytopathic effects (CPE) in brightfield microscopy of SARS-CoV-2-infected monolayers to quantify the virus titer. Images were classified using deep transfer learning (DTL) that fine-tune the last layers of a pre-trained Resnet18 (ImageNet). To exclude toxic concentrations of potential drugs, the network was expanded to include a toxic score (TOX) that detected cell death (CPETOXnet). With this analytic tool, the inhibitory effects of chloroquine, hydroxychloroquine, remdesivir, and emetine were validated. Taken together we developed a simple method and provided open access implementation to quantify SARS-CoV-2 titers and drug toxicity in experimental settings, which may be adaptable to assays with other viruses. The quantification of virus titers from brightfield images could accelerate the experimental approach for antiviral testing. |*Deep Learning[MESH] |*Drug-Related Side Effects and Adverse Reactions[MESH] |*Machine Learning[MESH] |Adenosine Monophosphate/analogs & derivatives/pharmacology[MESH] |Alanine/analogs & derivatives[MESH] |Animals[MESH] |Antiviral Agents/*pharmacology[MESH] |COVID-19[MESH] |Chlorocebus aethiops[MESH] |Coronavirus Nucleocapsid Proteins[MESH] |Drug Evaluation, Preclinical/*methods[MESH] |Phosphoproteins[MESH] |SARS-CoV-2/*drug effects[MESH] |Vero Cells[MESH]Deep Transfer Learning Approach for Automatic Recognition of Drug Toxi(epmc).pdf DeepDyve Pubget Overpricing