ArticleiScience2026
Analysis of severity in COVID-19 patients by using longitudinal immune profiles.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
COVID-19 severity varies due to immune heterogeneity, complicating treatment; therefore, we modeled oxygen saturation, CRP, and blood urea as severity markers for pulmonary, inflammatory, and renal functions. We analyzed cytokine, T cell receptor (TCR), and B cell receptor (BCR) expressions in 106 patients (NIH, Korea) across three stages (T1-T3) and applied a joint modeling framework, using linear mixed-effects models to identify immune features associated with severity. Models incorporating correlations among severity variables outperformed models assuming independence. IL-6 was significantly associated with all markers (negatively with oxygen saturation and positively with CRP and blood urea), whereas IL-8 was linked to oxygen saturation, and IGLV1-44 was consistently associated with CRP. Overall, accounting for correlations improved model performance and highlighted IL-6, IL-8, and IGLV1-44 as candidate immune features that may help refine severity assessment in heterogeneous COVID-19 cases.
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Registered trials
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 Socratic graph.