Effect of Hirulog-Like Peptide on Balloon Catheter Injury-Induced Neointimal Formation in Femoral Arteries of Minipigs and Relationship with Inflammatory Mediators
Bibliographic record
Abstract
Vascular intervention-induced neointimal formation is a major drawback for managing atherosclerotic cardiovascular diseases using invasive vascular procedures. Our previous studies demonstrated that hirulog-like peptide (HLP) reduced balloon catheter dilation-induced neointimal formation or restenosis in carotid arteries of rats or atherosclerotic rabbits with less interruption in coagulation or bleeding than heparin or hirulog-1. The present study examined the effect of HLP on balloon catheter injury-induced neointimal formation in femoral arteries of minipigs. Intravenous infusion of HLP (1.6 mg/kg/h for 4 h started 0.5 h before the intervention) or unfractured heparin (50 U/kg/h for 4 h) significantly reduced neointimal formation in femoral arteries 4 weeks after intervention compared with the vehicle. Heparin, but not HLP, significantly prolonged activated partial thromboplastin time. HLP or heparin significantly reduced vascular intervention-induced increases in C-reactive protein, P-selectin and interleukin-6 in serum. HLP, but not heparin, normalized vascular injury-induced increase in P-selectin in platelets. The results of the present study suggest that HLP is an effective agent for preventing balloon catheter injury-induced neointimal formation in femoral arteries of minipigs. The beneficial effects of HLP on vascular injury-induced neointimal formation may partially result from its inhibition on inflammatory mediators.
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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".