Acute Respiratory Distress Syndrome Molecular Phenotypes Have Distinct Lower Respiratory Tract Transcriptomes
Bibliographic record
Abstract
Abstract Rationale Two molecular phenotypes of the acute respiratory distress syndrome (ARDS) with divergent clinical trajectories and responses to therapy have been identified. Classification as “hyperinflammatory” or “hypoinflammatory” depends on plasma biomarker profiling. Limited data are available about the differences in the pulmonary biology of the molecular phenotypes. Objectives To identify differences in the pulmonary biology of ARDS molecular phenotypes Methods We compared tracheal aspirate gene expression between hyperinflammatory and hypoinflammatory phenotypes in bulk RNA sequencing (RNASeq) from coronavirus disease (COVID-19) and non–COVID-19 ARDS and single-cell RNASeq from non–COVID-19 ARDS. In a subset of subjects, we also compared plasma proteomic data. Measurements and Main Results In bulk RNASeq analyses, 1,157 genes were differentially expressed (false discovery rate < 0.1) between phenotypes in non–COVID-19 ARDS, and 85 genes were differentially expressed between phenotypes in COVID-19 ARDS. Eighteen genes were reproducibly differentially expressed between phenotypes in both cohorts, including greater expression of IL32, HSPA8, and PPP3CC in hyperinflammatory ARDS. A total of 195 pathways were reproducibly enriched across the two cohorts by gene set enrichment analysis, including greater expression of granulopoiesis, T-cell and IFN signaling, and integrated stress response pathways in hyperinflammatory ARDS. Network analysis of single-cell RNASeq in a third group of patients identified greater T-cell signaling to other immune cells in hyperinflammatory ARDS. Conclusions Hyperinflammatory and hypoinflammatory ARDS molecular phenotypes have distinct respiratory biology. Hyperinflammatory ARDS is characterized by an increased IFN-stimulated gene expression and T-cell activation in the lungs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".