Discrete and conserved inflammatory signatures drive thrombosis in different organs after Salmonella infection
Bibliographic record
Abstract
Inflammation-induced thrombosis is a common consequence of bacterial infections, such as those caused by Salmonella Typhimurium (STm). The presentation of multi-organ thrombosis post-infection that develops and resolves with organ-specific kinetics raises significant challenges for its therapeutic control. Here, we identify specific inflammatory events driving thrombosis in the spleens and livers of STm-infected mice. IFN-γ or platelet expression of C-type lectin-like receptor CLEC-2, key drivers of thrombosis in liver, are dispensable for thrombosis in the spleen. Platelets, monocytes, and neutrophils are identified as core constituents of thrombi in both organs. Depleting either neutrophils or monocytic cells abrogates thrombus formation. Neutrophils and monocytes secrete TNF and blocking TNF diminishes both thrombosis and inflammation, which correlates with reduced endothelial expression of E-selectin and leukocyte infiltration. Moreover, inhibiting tissue factor and P-selectin glycoprotein ligand-1 pathways impairs thrombosis in both spleen and liver. Therefore, we identify organ-specific, and shared mechanisms driving thrombosis within a single infection. This may inform on tailoring treatments towards infection-induced inflammation, and single- or multi-organ thrombosis, based on the clinical need.
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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 | 0.000 | 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 | 0.001 |
| 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".