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3.
J Clin Microbiol ; 59(6)2021 05 19.
Article in English | MEDLINE | ID: covidwho-1236418

ABSTRACT

Real-time PCR (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data are traditionally analyzed by estimating the threshold cycle (CT ) at which the fluorescence signal produced by emission of a probe crosses a baseline level. Current models used to estimate the CT 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 CT 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 CT interpretive paradigm.


Subject(s)
COVID-19 , Deep Learning , Humans , Real-Time Polymerase Chain Reaction , SARS-CoV-2 , Sensitivity and Specificity
4.
J Clin Microbiol ; 59(6)2021 05 19.
Article in English | MEDLINE | ID: covidwho-1140316

ABSTRACT

Real-time PCR (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data are traditionally analyzed by estimating the threshold cycle (CT ) at which the fluorescence signal produced by emission of a probe crosses a baseline level. Current models used to estimate the CT 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 CT 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 CT interpretive paradigm.


Subject(s)
COVID-19 , Deep Learning , Humans , Real-Time Polymerase Chain Reaction , SARS-CoV-2 , Sensitivity and Specificity
5.
Am J Clin Pathol ; 154(2): 190-200, 2020 07 07.
Article in English | MEDLINE | ID: covidwho-377954

ABSTRACT

OBJECTIVES: To report methods and findings of 2 autopsies with molecular evaluation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive individuals. METHODS: Postmortem examination was completed following Centers for Disease Control and Prevention public guidelines. Numerous formalin-fixed paraffin-embedded (FFPE) tissue types from each case were surveyed for SARS-CoV-2 RNA by quantitative reverse transcription polymerase chain reaction (qRT-PCR). SARS-CoV-2 viral genome was sequenced by next-generation sequencing (NGS) from FFPE lung tissue blocks. RESULTS: Postmortem examinations revealed diffuse alveolar damage, while no viral-associated hepatic, cardiac, or renal damage was observed. Viral RNA was detected in lungs, bronchi, lymph nodes, and spleen in both cases using qRT-PCR method. RNA sequencing using NGS in case 1 revealed mutations most consistent with Western European Clade A2a with ORF1a L3606F mutation. CONCLUSIONS: SARS-CoV-2 testing and viral sequencing can be performed from FFPE tissue. Detection and sequencing of SARS-CoV-2 in combination with morphological findings from postmortem tissue examination can aid in gaining a better understanding of the virus's pathophysiologic effects on human health.


Subject(s)
Betacoronavirus/isolation & purification , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Lung/pathology , Molecular Diagnostic Techniques/methods , Pneumonia, Viral/diagnosis , Aged, 80 and over , Autopsy , Betacoronavirus/genetics , COVID-19 , COVID-19 Testing , Coronavirus Infections/pathology , Coronavirus Infections/virology , Fatal Outcome , High-Throughput Nucleotide Sequencing , Humans , Lung/virology , Male , Middle Aged , Pandemics , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , RNA, Viral/analysis , RNA, Viral/isolation & purification , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Sequence Analysis, RNA/methods , Tissue Fixation/methods
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