Paclitaxel Stent Coating Inhibits Neointimal Hyperplasia at 4 Weeks in a Porcine Model of Coronary Restenosis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Despite limiting elastic recoil and late vascular remodeling after angioplasty, coronary stents remain vulnerable to restenosis, caused primarily by neointimal hyperplasia. Paclitaxel, a microtubule-stabilizing drug, has been shown to inhibit vascular smooth muscle cell migration and proliferation contributing to neointimal hyperplasia. We tested whether paclitaxel-coated coronary stents are effective at preventing neointimal proliferation in a porcine model of restenosis. METHODS AND RESULTS: Palmaz-Schatz stents were dip-coated with paclitaxel (0, 0.2, 15, or 187 microgram/stent) by immersion in ethanolic paclitaxel and evaporation of the solvent. Stents were deployed with mild oversizing in the left anterior descending coronary artery (LAD) of 41 minipigs. The treatment effect was assessed 4 weeks after stent implantation. The angiographic late loss index (mean luminal diameter) decreased with increasing paclitaxel dose (P<0.0028 by ANOVA), declining by 84.3% (from 0.352 to 0.055, P<0.05) at the highest level tested (187 microgram/stent versus control). Accompanying this change, the neointimal area decreased (by 39.5%, high-dose versus control; P<0.05) with increasing dose (P<0.040 by ANOVA), whereas the luminal area increased (by 90.4%, high-dose versus control; P<0.05) with escalating dose (P<0.0004 by ANOVA). Inflammatory cells were seen infrequently, and there were no cases of aneurysm or thrombosis. CONCLUSIONS: Paclitaxel-coated coronary stents produced a significant dose-dependent inhibition of neointimal hyperplasia and luminal encroachment in the pig LAD 28 days after implantation; later effects require further study. These results demonstrate the potential therapeutic benefit of paclitaxel-coated coronary stents in the prevention and treatment of human coronary restenosis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it