A new rapamycin-abluminally coated chitosan/heparin stent system accelerates early re-endothelialisation and improves anti-coagulant properties in porcine coronary artery models
Bibliographic record
Abstract
PURPOSE: Drug-eluting stents (DES) in percutaneous coronary intervention are more effective in preventing in-stent restenosis compared with bare metal stents (BMS); however, DES may cause late stent thrombosis, which has limited its use. In this study, the functional properties of a newly developed DES (RAP/CS/HEP), in which rapamycin was abluminally-loaded onto a chitosan/heparin coating stent (CS/HEP), were investigated in large animal artery injury models. METHODS: The effectiveness of BMS, RAP (the traditional version of rapamycin DES), CS/HEP and RAP/CS/HEP stents in preventing coagulation and promoting re-endothelialisation was examined and compared in the porcine coronary artery models with arteriovenous shunt, high load thrombus and coronary balloon injury at day 7 and 28, respectively, after stent implantation. The re-endothelialisation on these stents was further evaluated in terms of endothelial gene expression using quantitative RT-PCR. RESULTS: In the porcine coronary artery injury models, both RAP and RAP/CS/HEP stents were potent in reducing neointimal thickness, thus enlarging lumen area efficiently in the stented artery region compared with BMS and CS/HEP. RAP/CS/HEP stents facilitated re-endothelialisation and inhibited thrombosis more efficiently than BMS and RAP. Consistent with this, the expression of endothelial genes, such as CD31, CD34, eNOS and VEGF, was significantly elevated with RAP/CS/HEP stents compared with RAP and BMS stents. CONCLUSION: Abluminal coating of rapamycin onto the endothelialisation-accelerated CS/HEP stent and may prove to be an efficient treatment for tackling the late stent thrombosis associated with the traditionally circumferential RAP stent. This new RAP/CS/HEP stent system exhibits considerably improved therapeutic activity.
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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) | 0.000 | 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) | 0.001 | 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".