Hirulog-Like Peptide Reduces Balloon Catheter Injury Induced Neointima Formation in Rat Carotid Artery without Increase in Bleeding Tendency
Bibliographic record
Abstract
Vascular restenosis is one of the major concerns for the management of coronary artery disease using therapeutic vascular procedures. Treatments with thrombin-specific inhibitors, hirudin or hirulog-1, reduced ischemic events in coronary artery disease patients. Early started and prolonged infusions of these thrombin inhibitors partially prevented balloon catheter injury induced restenosis or neointima formation in experimental animal models, but increased the bleeding tendency. Hirulog-like peptide (HLP) was rationally designed to enhance the inhibition of the binding of thrombin to its receptor with less interruption of coagulation activity in comparison to hirulog-1. A single infusion of HLP for 4 h started 0.5 h before balloon catheter injury reduced neointima formation by 36% in rat carotid artery compared to vehicle controls. Tail bleeding time and activated partial thromboplastin time during HLP infusion were not significantly different from vehicle controls, but were significantly shorter than during heparin or hirulog-1 infusion. HLP treatment attenuated the expression of platelet-derived growth factor in the neointima of injured arteries. HLP also inhibited thrombin-induced thymidine incorporation in cultured baboon aortic smooth muscle cells. The findings suggest that HLP may substantially inhibit balloon catheter injury induced neointima formation without noticeable increase in bleeding tendency in rats. The inhibition by HLP of the expression of platelet-derived growth factor and of the smooth muscle cell proliferation in the vascular wall potentially contributes to the preventive effect of the new thrombin inhibitor on injury-induced neointima formation in the vascular wall.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".