Hirulog-1 Reduces Expression of Platelet-Derived Growth Factor in Neointima of Rat Carotid ArteryInduced by Balloon Catheter Injury
Bibliographic record
Abstract
Vascular restenosis is one of the major concerns for the treatment of atherosclerotic cardiovascular diseases using therapeutic vascular procedures. Hirulog-1, a synthetic thrombin inhibitor, effectively reduced ischemic events in coronary heart disease patients and caused less hemorrhagic complications compared to heparin. Thrombin stimulated the expression of platelet-derived growth factor (PDGF) in vascular cells. PDGF receptor blockers reduced angioplasty-induced restenosis in the swine model. The present study examined the effects of hirulog-1 on vascular stenosis, platelet deposition and the expression of PDGF in rat carotid arteries injured by balloon catheter. Multiple intravenous infusions of hirulog-1 (1 mg/kg/h for 4 h for 6 times), but not bolus injection or 1-2 times of infusion, reduced neointima/media ratio by 50% in balloon-injured carotid arteries compared to injured animals receiving saline alone. Activated partial thromboplastin time in hirulog-1-treated rats was significantly prolonged compared to saline controls but shorter than that in animals receiving heparin (50 U/kg/h). One of heparin-treated rat, but none of hirulog-1-treated, died from bleeding complication. Hirulog-1 injection transiently reduced platelet deposition on denuded intima visualized by scanning electron microscopy. Abundance of PDGF in neointima of injured carotid arteries detected by immunohistochemistry was significantly decreased following infusions of hirulog-1. The results suggest that balloon catheter injury induced neointima formation and the overexpression of PDGF in the neointima of rat carotid artery may be effectively suppressed by infusions with hirulog-1, a thrombin-specific inhibitor.
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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) | 0.001 | 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 | 0.001 |
| 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".