Discrete and conserved inflammatory signatures drive thrombosis in different organs after <i>Salmonella</i> infection
Bibliographic record
Abstract
Abstract Inflammation-induced thrombosis is a common consequence of bacterial and viral infections, such as those caused by Salmonella Typhimurium (STm) and SARS-CoV-2. The identification of multi-organ thrombosis and the chronological differences in its induction and resolution raises significant challenges for successfully targeting multi-organ infection-associated thrombosis. Here, we identified specific pathways and effector cells driving thrombosis in the spleen and liver following STm infection. Thrombosis in the spleen is independent of IFN-γ or the platelet C-type lectin-like receptor CLEC-2, while both molecules were previously identified as key drivers of thrombosis in the liver. Furthermore, we identified platelets, monocytes, and neutrophils as core constituents of thrombi in both organs. Depleting neutrophils or monocytic cells independently abrogated thrombus formation. Nevertheless, blocking TNFα, which is expressed by both myeloid cell types, diminished both thrombosis and inflammation which correlates with reduced endothelial expression of E-selectin and leukocyte infiltration. Moreover, tissue factor and P-selectin glycoprotein ligand 1 inhibition impair thrombosis in both spleen and liver, identifying multiple common checkpoints to target multi-organ thrombosis. Therefore, organ-specific, and broad mechanisms driving thrombosis potentially allow tailored treatments based on the clinical need and to define the most adequate strategy to target both thrombosis and inflammation associated with systemic infections.
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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) | 0.002 | 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".