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Sci Rep ; 11(1): 21126, 2021 10 26.
Article in English | MEDLINE | ID: covidwho-1493210

ABSTRACT

Rapid identification of SARS-CoV-2-infected individuals is a cornerstone for the control of virus spread. The sensitivity of SARS-CoV-2 RNA detection by RT-PCR is similar in saliva and nasopharyngeal swabs. Rapid molecular point-of-care tests in saliva could facilitate, broaden and speed up the diagnosis. We conducted a prospective study in two community COVID-19 screening centers to evaluate the performances of a CE-marked RT-LAMP assay (EasyCoV) designed for the detection of SARS-CoV2 RNA from fresh saliva samples, compared to nasopharyngeal RT-PCR, to saliva RT-PCR and to nasopharyngeal antigen testing. Overall, 117 of the 1718 participants (7%) tested positive with nasopharyngeal RT-PCR. Compared to nasopharyngeal RT-PCR, the sensitivity and specificity of the RT-LAMP assay in saliva were 34% and 97%, respectively. The Ct values of nasopharyngeal RT-PCR were significantly lower in the 40 true positive subjects with saliva RT-LAMP (Ct 25.9) than in the 48 false negative subjects with saliva RT-LAMP (Ct 28.4) (p = 0.028). Considering six alternate criteria for reference tests, including saliva RT-PCR and nasopharyngeal antigen, the sensitivity of saliva RT-LAMP ranged between 27 and 44%. The detection of SARS-CoV-2 in crude saliva samples with an RT-LAMP assay had a lower sensitivity than nasopharyngeal RT-PCR, saliva RT-PCR and nasopharyngeal antigen testing.Registration number: NCT04578509.


Subject(s)
Ambulatory Care/methods , COVID-19 Nucleic Acid Testing/methods , COVID-19/diagnosis , COVID-19/metabolism , SARS-CoV-2 , Saliva/metabolism , Adult , Diagnostic Tests, Routine , False Negative Reactions , False Positive Reactions , Female , Humans , Male , Middle Aged , Molecular Diagnostic Techniques , Molecular Medicine , Nasopharynx/virology , Nucleic Acid Amplification Techniques , Point-of-Care Systems , Point-of-Care Testing , Prospective Studies , RNA, Viral/genetics , Real-Time Polymerase Chain Reaction , Reproducibility of Results , Sensitivity and Specificity
2.
Sci Rep ; 11(1): 20864, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1479817

ABSTRACT

Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19's clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation's molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19's clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19's severe adverse events and lesser ones such as loss of taste/smell.


Subject(s)
COVID-19 Drug Treatment , COVID-19/complications , COVID-19/diagnosis , Computational Biology/methods , Neoplasms/complications , Nervous System Diseases/complications , Thrombosis/complications , Virus Replication , Benchmarking , Comorbidity , Computer Simulation , Cytokine Release Syndrome , Drug Discovery , Humans , Machine Learning , Molecular Medicine , Phenotype , SARS-CoV-2 , Treatment Outcome
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