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10.1016/j.xcrm.2021.100192 http://scihub22266oqcxt.onion/10.1016/j.xcrm.2021.100192 33495756!7816879!33495756     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cell+Rep+Med 2021 ; 2 (2): 100192 Nephropedia Template TP {{tp|p=33495756|t=2021. Use of machine learning to identify a T cell response to SARS-CoV-2 |pdf=|usr=}}{{33495756}} gab.com Text Use of machine learning to identify a T cell response to SARS-CoV-2 JO: Cell Rep Med http://www.kidney.de/pget.php?&tw=1&pmid=33495756 #FOAvip The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease (n = 17) and SARS-CoV-2 infection-naive (control) individuals (n = 39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/control samples with a training sensitivity, specificity, and accuracy of 88.2%, 100%, and 96.4% and a testing sensitivity, specificity, and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (immunoglobulin heavy chain; IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity. Twit Text FOAVip Use of machine learning to identify a T cell response to SARS-CoV-2 ????Cell Rep Med http://www.kidney.de/pget.php?&tw=1&pmid=33495756 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33495756 #FOAvip English Wikipedia <ref>{{Cite pmid|33495756}}</ref> Use of machine learning to identify a T cell response to SARS-CoV-2 #MMPMID33495756 Shoukat MS; Foers AD; Woodmansey S; Evans SC; Fowler A; Soilleux EJ Cell Rep Med 2021[Feb]; 2 (2): 100192 PMID33495756show ga The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease (n = 17) and SARS-CoV-2 infection-naive (control) individuals (n = 39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/control samples with a training sensitivity, specificity, and accuracy of 88.2%, 100%, and 96.4% and a testing sensitivity, specificity, and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (immunoglobulin heavy chain; IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity. |*Machine Learning[MESH] |Amino Acid Sequence[MESH] |COVID-19/*immunology/pathology/virology[MESH] |Cluster Analysis[MESH] |Complementarity Determining Regions/chemistry/genetics[MESH] |High-Throughput Nucleotide Sequencing[MESH] |Humans[MESH] |Principal Component Analysis[MESH] |Receptors, Antigen, B-Cell/chemistry/metabolism[MESH] |Receptors, Antigen, T-Cell/chemistry/metabolism[MESH] |SARS-CoV-2/immunology/isolation & purification[MESH] |Sequence Analysis, DNA[MESH]Use of machine learning to identify a T cell response to SARS-CoV-2 (epmc).pdf DeepDyve Pubget Overpricing