Local insulin application on the carotid artery inhibits neointima formation
Bibliographic record
Abstract
Anti-mitogenic agents currently used to prevent restenosis in drug-eluting stents delay re-endothelialization. Delayed re-endothelialization is now considered as the main cause of late stent thrombosis with drug-eluting stents, which emphasizes the need for new treatments. We have shown that systemic insulin treatment decreases neointimal growth and accelerates re-endothelialization after arterial injury in a rat model of restenosis. However, systemic insulin treatment cannot be given to non-diabetic individuals because of the risk of hypoglycemia. Thus, we investigated whether local insulin treatment is also effective in reducing neointimal growth after arterial injury. Rats were given local vehicle or local insulin delivered via Pluronic gel applied around the carotid artery immediately following balloon injury. Plasma glucose and systemic insulin levels were not affected by local insulin treatment. Insulin decreased intimal area at 28 days (P < 0.05) and also inhibited vascular smooth muscle cell migration by 60% at 4 days (P < 0.05). NPH (a longer-lasting insulin) also decreased neointimal area. These results indicate that local insulin treatment can lead to decreased restenosis, suggesting a protective vascular effect of insulin in vivo and that local insulin treatment, possibly via insulin-eluting stents, may be clinically relevant.
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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) | 0.001 | 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".