The adhesion of clots in wounds contributes to hemostasis and can be enhanced by coagulation factor XIII
Bibliographic record
Abstract
The adhesion of blood clots to wounds is necessary to seal injured vasculature and achieve hemostasis. However, it has not been specifically tested if adhesive failure of clots is a major contributor to rebleeding and what mechanisms prevent clot delamination. Here, we quantified the contribution of adhesive and cohesive failure to rebleeding in a rat model of femoral artery injury, and identified mechanisms that contribute to the adhesive strength of bulk clots in a lap-shear test in vitro. In the rat bleeding model, the frequency of clot failures correlated positively with blood loss (R = 0.81, p = 0.014) and negatively with survival time (R = - 0.89, p = 0.0030), with adhesive failures accounting for 51 ± 14% of rebleeds. In vitro, adhesion depended on fibrinogen and coagulation factor XIII (FXIII), and supraphysiological FXIII improved adhesive strength. Furthermore, when exogenous FXIII was topically applied into the wound pocket of rats, eleven adhesive failures occurred between eight rats, compared to seventeen adhesive failures between eight untreated rats, whereas the number of cohesive failures remained the same at sixteen in both groups. In conclusion, rebleeding from both adhesive and cohesive failure of clots decreases survival from hemorrhage in vivo. Both endogenous and exogenous FXIII improves the adhesive strength of clots.
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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 | 0.001 |
| 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".