ArticleBMC medical genomics2021
Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants.
Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Host Subcellular Organelles: Targets of Viral Manipulation.International journal of molecular sciences · 2024Review
- Gene Expression Risk Scores for COVID-19 Illness Severity.The Journal of infectious diseases · 2023Article
- Cilia-related gene signature in the nasal mucosa correlates with disease severity and outcomes in critical respiratory syncytial virus bronchiolitis.Frontiers in immunology · 2022Article
- A systems genomics approach uncovers molecular associates of RSV severity.PLoS computational biology · 2021Article
- Gene Expression Risk Scores for COVID-19 Illness Severity.bioRxiv : the preprint server for biology · 2021Article
- Temporal Dysbiosis of Infant Nasal Microbiota Relative to Respiratory Syncytial Virus Infection.The Journal of infectious diseases · 2021Article
- Airway Gene Expression Correlates of Respiratory Syncytial Virus Disease Severity and Microbiome Composition in Infants.The Journal of infectious diseases · 2021Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundA substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness.
methodWe defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2).
resultsNGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%.
conclusionAirway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.