Turnover and fate of fibrinogen and platelets at the rabbit aorta wall immediately after a balloon de-endothelializing injury in vivo
Bibliographic record
Abstract
A de-endothelializing injury to the artery wall in vivo results in a rapid procoagulant response at the surface of the exposed subendothelium. Activated tissue factor (TF)-bearing cells and hemostasis factors located at the site of injury respond by producing thrombin, and within minutes the principal thrombus-forming, blood-borne components (platelets, fibrinogen) accumulate at the site. To compare their behaviors, the rates of uptake and turnover of rabbit (51)Cr-platelets and rabbit (125)I-fibrinogen were quantified simultaneously during the initial 100-min interval after a balloon catheter injury to the rabbit aorta in vivo. Platelets ( approximately 70,000/mm(2)) and fibrin(ogen) ( approximately 2.8 pmol/cm(2)) saturated the ballooned aorta surface within five minutes after injury. Whereas the adherent platelet and fibrinogen concentrations remained steady at the aorta surface, fibrin(ogen)-related products continued to accumulate slowly in the tunica media (TM) for at least 100 minutes. A relatively small proportion (3.7%/min) of adhered platelets turned over at the ballooned aorta surface at 10 minutes, decreasing to 1.2%/min at 100 minutes. By contrast, a larger proportion of fibrin(ogen) ( approximately 20%/min) was turned over within the platelet layer at 10 minutes, decreasing to 6%/min at 100 minutes. As verified by immunostaining aorta sections and by protein analysis of TM extracts, the uptakes of platelets and fibrinogen at the site of injury contributed to an accumulation of products of platelet releasate and fibrin(ogen) degradation (FDPs) within the TM. These observations improve our understanding of the hemostatic processes and subsequent events that occur after an arterial injury in vivo.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".