4D intravital imaging studies identify platelets as the predominant cellular procoagulant surface in a mouse model of hemostasis
Bibliographic record
Abstract
Interplay between platelets, coagulation/fibrinolytic factors, and endothelial cells (ECs) is necessary for effective hemostatic plug formation. This study describes a novel four-dimensional (4D) imaging platform to visualize and quantify hemostatic plug components with high spatiotemporal resolution. Fibrin accumulation following laser-induced endothelial ablation was observed at the EC-platelet plug interface, controlled by the antagonistic balance between fibrin generation and breakdown. Phosphatidylserine (PS) was first detected in close physical proximity to the fibrin ring, followed by exposure across the endothelium. Impaired PS exposure in cyclophilinD -/- mice resulted in a significant reduction in fibrin accumulation. Adoptive transfer and inhibitor studies demonstrated a key role for platelets, but not ECs, in fibrin generation during hemostatic plug formation. Inhibition of fibrinolysis with tranexamic acid (TXA) led to increased fibrin accumulation in WT mice, but not in cyclophilinD -/- mice or WT mice treated with antiplatelet drugs. These studies implicate platelets as the functionally dominant procoagulant surface during hemostatic plug formation. In addition, they suggest that impaired fibrin formation due to reduced platelet procoagulant activity is not reversed by TXA treatment.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".