Comparison of the effect of coagulation and platelet functionimpairments on various mouse bleeding models
Bibliographic record
Abstract
Haemostatic impairments are studied in vivo using one of several murine bleeding models. However it is not known whether these models are equally appropriate for assessing coagulation or platelet function defects. It was our study objective to assess the performance of arterial, venous and combined arterial and venous murine bleeding models towards impaired coagulation or platelet function. Unfractionated heparin (UFH) or αIIbβ3inhibitory antibody (Leo.H4) were administered to mice, and their effects on bleeding in saphenous vein, artery, and tail tip transection models were quantified and correlated with their effects on plasma clotting and ADP-induced platelet aggregation, respectively. All models exhibited similar sensitivity with UFH (EC50 dose = 0.19, 0.13 and 0.07 U/g, respectively) (95% CI = 0.14 - 0.27, 0.08 - 0.20, and 0.03 - 0.16 U/g, respectively). Maximal inhibition of ex vivo plasma clotting could be achieved with UFH doses as low as 0.03 U/g. In contrast, the saphenous vein bleeding model was less sensitive to αIIbβ3 inhibition (EC50 = 6.9 μg/ml) than tail transection or saphenous artery bleeding models (EC50 = 0.12 and 0.37 μg/ml, respectively) (95% CI = 2.4 - 20, 0.05 - 0.33, and 0.06 - 2.2 μg/ml, respectively). The EC50 of Leo.H4 for ADP-induced platelet aggregation in vitro (8.0 μg/ml) was at least 20-fold higher than that of the tail and arterial, but not the venous bleeding model. In conclusion, venous, arterial and tail bleeding models are similarly affected by impaired coagulation, while platelet function defects have a greater influence in models incorporating arterial injury.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".