Acetylsalicylic acid inhibits intravascular coagulation during Staphylococcus aureus–induced sepsis in mice
Bibliographic record
Abstract
Antiplatelet therapies have been proposed for the treatment of sepsis, a syndrome resulting from a dysregulated immune response and inappropriate activation of coagulation. Acetylsalicylic acid (ASA) may serve as a potential therapeutic strategy to prevent infection-induced coagulopathy and associated tissue damage. Using intravital microscopy, we found that Staphylococcus aureus infection induced neutrophil recruitment, platelet aggregation, and neutrophil extracellular trap (NET) release in the liver. Mice pretreated with ASA, or animals receiving ASA 3 hours postinfection, had significantly reduced platelet aggregation and NET release. Additionally, ASA-treated mice had reduced intravascular thrombin activity and microvascular occlusion as compared with untreated S aureus-infected mice. This inhibition of coagulation was accompanied by decreased levels of alanine aminotransferase and aspartate aminotransferase in the plasma, indicating less liver damage. Finally, bacterial loads (colony-forming units per milliliter) in liver, lung, and spleen were not different between groups, and the phagocytic capacity of Kupffer cells was preserved following ASA treatment. These results suggest that ASA may serve as a therapeutic approach to sepsis through its ability to reduce the deleterious action of immunothrombi while maintaining innate immune functions.
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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) | 0.001 | 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".