Delayed re-endothelialization with rapamycin-coated stents is rescued by the addition of a glycogen synthase kinase-3β inhibitor
Bibliographic record
Abstract
AIMS: Drug-eluting stents (DESs) reduce neointima area and in-stent restenosis but delay re-endothelialization. Recently, we demonstrated that pharmacological expansion and functional enhancement of endothelial progenitor cells (EPCs) can be achieved by treatment with a glycogen synthase kinase-3beta inhibitor (GSKi)-even for feeble cells derived from coronary artery disease patients. GSKi treatment enhanced EPC adhesion via up-regulated expression of the alpha-4 integrin, ameliorated re-endothelialization, and reduced neointima formation in denuded murine arteries. Hence, we hypothesized that GSKi-coated stents (GSs) will enhance EPC adhesion and attenuate delayed vascular healing associated with rapamycin, a key DES agent. METHODS AND RESULTS: In vitro human EPCs adhered to GS with affinities that were 2x, 14x, and 13x greater than vehicle (VSs)-, rapamycin (RSs)-, and rapamycin plus GSKi (RGSs)-coated stents, respectively. Stents were inserted in rabbit carotid arteries, and at 14 days, neointima area was 45 and 49% lower in GSs compared with bare metal stents (BMSs) and VSs. Moreover, RSs had a 47% larger neointima area than GSs, but RGSs reduced neointima area to a level comparable to GSs. Seven days after stenting, GSs displayed re-endothelialization that was 40, 33, and 42% greater than BMSs, VSs, and RSs, respectively. Moreover, RGSs had 41% more re-endothelialization than RSs. At 14 days, the 7-day re-endothelialization patterns persisted. CONCLUSION: GSKi efficiently ameliorates the vascular response to stent implantation and has an important redeeming effect on the deleterious endothelial effects of rapamycin-coated stents.
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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".