Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1038/s41598-021-93608-8 http://scihub22266oqcxt.onion/10.1038/s41598-021-93608-8 34253751!8275616!34253751     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 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       Sci+Rep 2021 ; 11 (1): 14275 Nephropedia Template TP {{tp|p=34253751|t=2021. Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires |pdf=|usr=}}{{34253751}} gab.com Text Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34253751 #FOAvip SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database. We identified two cohorts within the database that had clinical outcomes data reflecting severity of illness and utilized DeepTCR, a multiple-instance deep learning repertoire classifier, to predict patients with severe SARS-CoV-2 infection from their repertoire sequencing. We demonstrate that patients with severe infection have repertoires with higher T-cell responses associated with SARS-CoV-2 epitopes and identify the epitopes that result in these responses. Our results provide evidence that the highly variable clinical course seen in SARS-CoV-2 infection is associated to certain antigen-specific responses. Twit Text FOAVip Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34253751 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34253751 #FOAvip English Wikipedia <ref>{{Cite pmid|34253751}}</ref> Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires #MMPMID34253751 Sidhom JW; Baras AS Sci Rep 2021[Jul]; 11 (1): 14275 PMID34253751show ga SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database. We identified two cohorts within the database that had clinical outcomes data reflecting severity of illness and utilized DeepTCR, a multiple-instance deep learning repertoire classifier, to predict patients with severe SARS-CoV-2 infection from their repertoire sequencing. We demonstrate that patients with severe infection have repertoires with higher T-cell responses associated with SARS-CoV-2 epitopes and identify the epitopes that result in these responses. Our results provide evidence that the highly variable clinical course seen in SARS-CoV-2 infection is associated to certain antigen-specific responses. |Asymptomatic Infections/epidemiology[MESH] |COVID-19/*immunology/pathology/virology[MESH] |Deep Learning[MESH] |Epitopes/*immunology[MESH] |Humans[MESH] |Receptors, Antigen, T-Cell/genetics/*immunology[MESH] |SARS-CoV-2/*immunology/pathogenicity[MESH]Deep learning identifies antigenic determinants of severe SARS-CoV-2 i(epmc).pdf DeepDyve Pubget Overpricing