Disulfiram inhibits neutrophil extracellular trap formation and protects rodents from acute lung injury and SARS-CoV-2 infection
Bibliographic record
Abstract
Severe acute lung injury has few treatment options and a high mortality rate. Upon injury, neutrophils infiltrate the lungs and form neutrophil extracellular traps (NETs), damaging the lungs and driving an exacerbated immune response. Unfortunately, no drug preventing NET formation has completed clinical development. Here, we report that disulfiram - an FDA-approved drug for alcohol use disorder - dramatically reduced NETs, increased survival, improved blood oxygenation, and reduced lung edema in a transfusion-related acute lung injury (TRALI) mouse model. We then tested whether disulfiram could confer protection in the context of SARS-CoV-2 infection, as NETs are elevated in patients with severe COVID-19. In SARS-CoV-2-infected golden hamsters, disulfiram reduced NETs and perivascular fibrosis in the lungs, and it downregulated innate immune and complement/coagulation pathways, suggesting that it could be beneficial for patients with COVID-19. In conclusion, an existing FDA-approved drug can block NET formation and improve disease course in 2 rodent models of lung injury for which treatment options are limited.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".