Unraveling SARS-CoV-2 Host-Response Heterogeneity through Longitudinal Molecular Subtyping
Bibliographic record
Abstract
Hospitalized COVID-19 patients exhibit diverse immune responses during acute infection, which are associated with a wide range of clinical outcomes. However, understanding these immune heterogeneities and their links to various clinical complications, especially long COVID, remains a challenge. In this study, we performed unsupervised subtyping of longitudinal multi-omics immunophenotyping in over 1,000 hospitalized patients, identifying two critical subtypes linked to mortality or mechanical ventilation with prolonged hospital stay and three severe subtypes associated with timely acute recovery. We confirmed that unresolved systemic inflammation and T-cell dysfunctions were hallmarks of increased severity and further distinguished patients with similar acute respiratory severity by their distinct immune profiles, which correlated with differences in demographic and clinical complications. Notably, one critical subtype (SubF) was uniquely characterized by early excessive inflammation, insufficient anticoagulation, and fatty acid dysregulation, alongside higher incidences of hematologic, cardiac, and renal complications, and an elevated risk of long COVID. Among the severe subtypes, significant differences in viral clearance and early antiviral responses were observed, with one subtype (SubC) showing strong early T-cell cytotoxicity but a poor humoral response, slower viral clearance, and greater risks of chronic organ dysfunction and long COVID. These findings provide crucial insights into the complex and context-dependent nature of COVID-19 immune responses, highlighting the importance of personalized therapeutic strategies to improve both acute and long-term outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".