Assessment of longitudinal changes in immune responses in critically ill adults with COVID-19
Bibliographic record
Abstract
Abstract Clinically, COVID-19 is often a mild or asymptomatic illness. However, in a subset of patients, a more severe illness with one or more organ dysfunction requiring intensive care (ICU) admission occurs (stated as critical COVID-19). Most studies assessing the immune responses in COVID-19 focus on patients with non-critical COVID-19, often assessing single biological domain (such as cytokines, leukocytes, proteomics, or transcriptomics) at single time point in patient’s illness. In this context, our cohort study of patients with critical COVID-19 with demographically similar pre-pandemic controls, characterised the longitudinal changes in multiple biological domains (28 plasma cytokines, 30 immune cell subsets identified using mass cytometry and pan-leukocyte transcriptome) at four clinically relevant timepoints between ICU admission and discharge. When compared with controls, on ICU admission day, patients with critical COVID-19, had altered cytokine/chemokine profile (high interleukin-6 (IL-6), IL-10, IL-13, CXCL10, with low CCL17, and CXCL5)), raised histones (H3.1, H3R8), robust plasmablast response despite lymphopenia, with enrichment of immunoglobulin production and interferon pathways in the transcriptome. Analyses of longitudinal transcriptome data highlights three immunologically distinct clusters that were discordant to clinical time points, indicating that the clinical time points do capture immune response trajectory. Complete integration of this multi-domain longitudinal data indicated that ~ 70% of immunological heterogeneity is explained by the transcriptome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".