Succinobucol‐eluting stents increase neointimal thickening and peri‐strut inflammation in a porcine coronary model
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the efficacy of stent-based delivery of succinobucol alone and in combination with rapamycin in a porcine coronary model. BACKGROUND: Current drugs and polymers used to coat coronary stents remain suboptimal in terms of long term efficacy and safety. Succinobucol is a novel derivative of probucol with improved antioxidant and anti-inflammatory properties. METHODS: Polymer-free Yukon stents were coated with 1% succinobucol (SucES), 2% rapamycin (RES), or 1% succinobucol plus 2% rapamycin solutions (SucRES) and compared with a bare metal stent (BMS). RESULTS: The in vivo release profile of SucES indicated drug release up to 28 days (60% drug released at 7 days); 41 stents (BMS, n = 11; SucES, n =10; RES, n = 10; SucRES, n = 10) were implanted in the coronary arteries of 17 pigs. After 28 days, mean neointimal thickness was 0.31 ± 0.14 mm for BMS, 0.51 ± 0.14 mm for SucES, 0.19 ± 0.11 mm for RES, and 0.36 ± 0.17 mm for SucRES (P < 0.05 for SucES vs. BMS). SucES increased inflammation and fibrin deposition compared with BMS (P < 0.05), whereas RES reduced inflammation compared with BMS (P < 0.05). CONCLUSION: In this model, stent-based delivery of 1% succinobucol using a polymer-free stent platform increased neointimal formation and inflammation following coronary stenting.
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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 | 0.001 |
| 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".