Infection-Stimulated Fibrin Deposition Controls Hemorrhage and Limits Hepatic Bacterial Growth during Listeriosis
Bibliographic record
Abstract
Bacterial infections are major causes of human mortality. The activation of coagulation pathways leading to the deposition of insoluble fibrin frequently accompanies bacterial infection, and much attention has focused upon the pathological attributes of infection-stimulated fibrin deposition. Nevertheless, here we present conclusive evidence that infection-stimulated fibrin deposition can perform critical protective functions during bacterial infection. Specifically, we demonstrate that coagulation-impaired fibrin(ogen)-deficient mice, in comparison with genetically matched control mice, display increased mortality upon peritoneal infection with the gram-positive facultative intracellular bacterium Listeria monocytogenes. To distinguish effects of fibrinogen from those of fibrin, we treat wild-type mice with warfarin, an anticoagulant that suppresses fibrin formation without impacting fibrinogen levels. Warfarin treatment exacerbates listeriosis, suggesting that fibrin is the key mediator of protection. With regard to the underlying protective mechanisms, we demonstrate that fibrin(ogen) suppresses anemia, reduces hemorrhagic pathology, and limits bacterial growth during listeriosis. Despite confirming a prior report that fibrin(ogen) promotes the peritoneal clearance of the extracellular bacterium Staphylococcal aureus, we demonstrate that fibrin(ogen) plays little role in controlling peritoneal numbers of L. monocytogenes bacteria or the dissemination of L. monocytogenes bacteria from the peritoneal cavity. Rather, fibrin(ogen) primarily limits the growth of these intracellular bacteria within hepatic tissue. While the pathological potential of excessive infection-stimulated fibrin deposition is well appreciated, our findings reveal that fibrin can function protectively, via multiple mechanisms, during bacterial infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".