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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".