Selected article for: "acute respiratory syndrome and machine 1000"
Author: Ng, Dianna L.; Granados, Andrea C.; Santos, Yale A.; Servellita, Venice; Goldgof, Gregory M.; Meydan, Cem; Sotomayor-Gonzalez, Alicia; Levine, Andrew G.; Balcerek, Joanna; Han, Lucy M.; Akagi, Naomi; Truong, Kent; Neumann, Neil M.; Nguyen, David N.; Bapat, Sagar P.; Cheng, Jing; Martin, Claudia Sanchez-San; Federman, Scot; Foox, Jonathan; Gopez, Allan; Li, Tony; Chan, Ray; Chu, Cynthia S.; Wabl, Chiara A.; Gliwa, Amelia S.; Reyes, Kevin; Pan, Chao-Yang; Guevara, Hugo; Wadford, Debra; Miller, Steve; Mason, Christopher E.; Chiu, Charles Y.
Title: A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood
  • Cord-id: lnlwoazm
  • Document date: 2021_2_3
    • ID: lnlwoazm
    Snippet: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease-19 (COVID-19), has emerged as the cause of a global pandemic. We used RNA sequencing to analyze 286 nasopharyngeal (NP) swab and 53 whole-blood (WB) samples from 333 patients with COVID-19 and controls. Overall, a muted immune response was observed in COVID-19 relative to other infections (influenza, other seasonal coronaviruses, and bacterial sepsis), with paradoxical down-regulation of several key di
    Document: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease-19 (COVID-19), has emerged as the cause of a global pandemic. We used RNA sequencing to analyze 286 nasopharyngeal (NP) swab and 53 whole-blood (WB) samples from 333 patients with COVID-19 and controls. Overall, a muted immune response was observed in COVID-19 relative to other infections (influenza, other seasonal coronaviruses, and bacterial sepsis), with paradoxical down-regulation of several key differentially expressed genes. Hospitalized patients and outpatients exhibited up-regulation of interferon-associated pathways, although heightened and more robust inflammatory responses were observed in hospitalized patients with more clinically severe illness. Two-layer machine learning–based host classifiers consisting of complete (>1000 genes), medium (<100), and small (<20) gene biomarker panels identified COVID-19 disease with 85.1–86.5% accuracy when benchmarked using an independent test set. SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for COVID-19 diagnosis.

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