Coronary stent CD31-mimetic coating favours endothelialization and reduces local inflammation and neointimal development <i>in vivo</i>
Bibliographic record
Abstract
AIMS: The rapid endothelialization of bare metal stents (BMS) is counterbalanced by inflammation-induced neointimal growth. Drug-eluting stents (DES) prevent leukocyte activation but impair endothelialization, delaying effective device integration into arterial walls. Previously, we have shown that engaging the vascular CD31 co-receptor is crucial for endothelial and leukocyte homeostasis and arterial healing. Furthermore, we have shown that a soluble synthetic peptide (known as P8RI) acts like a CD31 agonist. The aim of this study was to evaluate the effect of CD31-mimetic metal stent coating on the in vitro adherence of endothelial cells (ECs) and blood elements and the in vivo strut coverage and neointimal growth. METHODS AND RESULTS: We produced Cobalt Chromium discs and stents coated with a CD31-mimetic peptide through two procedures, plasma amination or dip-coating, both yielding comparable results. We found that CD31-mimetic discs significantly reduced the extent of primary human coronary artery EC and blood platelet/leukocyte activation in vitro. In vivo, CD31-mimetic stent properties were compared with those of DES and BMS by coronarography and microscopy at 7 and 28 days post-implantation in pig coronary arteries (n = 9 stents/group/timepoint). Seven days post-implantation, only CD31-mimetic struts were fully endothelialized with no activated platelets/leukocytes. At day 28, neointima development over CD31-mimetic stents was significantly reduced compared to BMS, appearing as a normal arterial media with the absence of thrombosis contrary to DES. CONCLUSION: CD31-mimetic coating favours vascular homeostasis and arterial wall healing, preventing in-stent stenosis and thrombosis. Hence, such coatings seem to improve the metal stent biocompatibility.
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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 | 0.000 | 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 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".