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10.1128/JCM.02959-20 http://scihub22266oqcxt.onion/10.1128/JCM.02959-20 33731417!8316125!33731417     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Clin+Microbiol 2021 ; 59 (6): ä Nephropedia Template TP {{tp|p=33731417|t=2021. Specificity of SARS-CoV-2 Real-Time PCR Improved by Deep Learning Analysis |pdf=|usr=}}{{33731417}} gab.com Text Specificity of SARS-CoV-2 Real-Time PCR Improved by Deep Learning Analysis JO: J Clin Microbiol http://www.kidney.de/pget.php?&tw=1&pmid=33731417 #FOAvip Real-time PCR (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data are traditionally analyzed by estimating the threshold cycle (C(T) ) at which the fluorescence signal produced by emission of a probe crosses a baseline level. Current models used to estimate the C(T) value are based on approximations that do not adequately account for the stochastic variations of the fluorescence signal that is detected during RT-PCR. Less common deviations become more apparent as the sample size increases, as is the case in the current SARS-CoV-2 pandemic. In this work, we employ a method independent of C(T) value to interpret RT-PCR data. In this novel approach, we built and trained a deep learning model, qPCRdeepNet, to analyze the fluorescent readings obtained during RT-PCR. We describe how this model can be deployed as a quality assurance tool to monitor result interpretation in real time. The model's performance with the TaqPath COVID19 Combo Kit assay, widely used for SARS-CoV-2 detection, is described. This model can be applied broadly for the primary interpretation of RT-PCR assays and potentially replace the C(T) interpretive paradigm. Twit Text FOAVip Specificity of SARS-CoV-2 Real-Time PCR Improved by Deep Learning Analysis ????J Clin Microbiol http://www.kidney.de/pget.php?&tw=1&pmid=33731417 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33731417 #FOAvip English Wikipedia <ref>{{Cite pmid|33731417}}</ref> Specificity of SARS-CoV-2 Real-Time PCR Improved by Deep Learning Analysis #MMPMID33731417 Alouani DJ; Rajapaksha RRP; Jani M; Rhoads DD; Sadri N J Clin Microbiol 2021[May]; 59 (6): ä PMID33731417show ga Real-time PCR (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data are traditionally analyzed by estimating the threshold cycle (C(T) ) at which the fluorescence signal produced by emission of a probe crosses a baseline level. Current models used to estimate the C(T) value are based on approximations that do not adequately account for the stochastic variations of the fluorescence signal that is detected during RT-PCR. Less common deviations become more apparent as the sample size increases, as is the case in the current SARS-CoV-2 pandemic. In this work, we employ a method independent of C(T) value to interpret RT-PCR data. In this novel approach, we built and trained a deep learning model, qPCRdeepNet, to analyze the fluorescent readings obtained during RT-PCR. We describe how this model can be deployed as a quality assurance tool to monitor result interpretation in real time. The model's performance with the TaqPath COVID19 Combo Kit assay, widely used for SARS-CoV-2 detection, is described. This model can be applied broadly for the primary interpretation of RT-PCR assays and potentially replace the C(T) interpretive paradigm. |*COVID-19[MESH] |*Deep Learning[MESH] |Humans[MESH] |Real-Time Polymerase Chain Reaction[MESH] |SARS-CoV-2[MESH]Specificity of SARS-CoV-2 Real-Time PCR Improved by Deep Learning Anal(epmc).pdf DeepDyve Pubget Overpricing