Dexamethasone modulates immature neutrophils and interferon programming in severe COVID-19
Bibliographic record
Abstract
While critical for host defense, innate immune cells are also pathologic drivers of acute respiratory distress syndrome (ARDS). Innate immune dynamics during COVID-19 ARDS, compared to ARDS from other respiratory pathogens, is unclear. Moreover, mechanisms underlying beneficial effects of dexamethasone during severe COVID-19 remain elusive. Using scRNA-seq and plasma proteomics, we discovered that compared to bacterial ARDS, COVID-19 was associated with distinct neutrophil polarization characterized by interferon (IFN) and prostaglandin (PG) active states. Dexamethasone during severe COVID-19 depleted circulating neutrophils, altered IFNactive neutrophils, downregulated interferon-stimulated gene, and activated IL1R2+ve neutrophils. Dexamethasone also expanded immature neutrophils expressing immunosuppressive molecules and remodeled cellular interactions by changing neutrophils from information receivers into information providers. Male patients had higher proportions of IFNactive neutrophils, preferential steroid-induced immature neutrophil expansion, and possibly different effects on outcome. Our single-cell atlas (www.biernaskielab.ca/COVID_neutrophil) defines COVID-19-enriched neutrophil states and molecular mechanisms of dexamethasone action to develop targeted immunotherapies for severe COVID-19.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.018 |
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".