Corticosteroids reduce pathologic interferon responses by downregulating STAT1 in patients with high-risk COVID-19
Bibliographic record
Abstract
We do not yet understand exactly how corticosteroids attenuate hyperinflammatory responses and alleviate high-risk coronavirus disease 2019 (COVID-19). We aimed to reveal the molecular mechanisms of hyperinflammation in COVID-19 and the anti-inflammatory effects of corticosteroids in patients with high-risk COVID-19. We performed single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from three independent COVID-19 cohorts: cohort 1 was used for comparative analysis of high-risk and low-risk COVID-19 (47 PBMC samples from 28 patients), cohort 2 for longitudinal analysis during COVID-19 (57 PBMC samples from 15 patients), and cohort 3 for investigating the effects of corticosteroid treatment in patients with high-risk COVID-19 (55 PBMC samples from 13 patients). PBMC samples from healthy donors (12 PBMC samples from 12 donors) were also included. Cohort 1 revealed a significant increase in the proportion of monocytes expressing the long noncoding RNAs NEAT1 and MALAT1 in high-risk patients. Cohort 2 showed that genes encoding inflammatory chemokines and their receptors were upregulated during aggravation, whereas genes related to angiogenesis were upregulated during improvement. Cohort 3 demonstrated downregulation of interferon-stimulated genes (ISGs), including STAT1, in monocytes after corticosteroid treatment. In particular, unphosphorylated STAT-dependent ISGs enriched in monocytes from lupus patients were selectively downregulated by corticosteroid treatment in patients with high-risk COVID-19. Corticosteroid treatment suppresses pathologic interferon responses in monocytes by downregulating STAT1 in patients with high-risk COVID-19. Our study provides insights into the mechanisms underlying COVID-19 aggravation and improvement and the effects of corticosteroid treatment.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".