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 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 | 0.000 |
| 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".