Local Delivery of 17-Beta-Estradiol Modulates Collagen Content in Coronary Porcine Arteries after PTCA and Stent Implantation
Bibliographic record
Abstract
BACKGROUND: Percutaneous transluminal coronary angioplasty (PTCA) and stent implantation are associated with intimal hyperplasia and extracellular matrix (ECM) accumulation, resulting in restenosis. We showed that local delivery of 17-beta-estradiol (17betaE) reduced restenosis following PTCA and stent implantation by 47 and 23%, respectively. Because estrogens decreased type I and type III collagen synthesis in vitro, we hypothesized that local delivery of 17betaE may influence intimal hyperplasia formation by modulating ECM expression. METHODS: Porcine coronary arteries underwent PTCA or stenting and were randomly assigned to 17betaE or placebo. After 28 days, animals were sacrificed for histology and collagen type I and III content analysis. RESULTS: Both collagen subtypes increased in the media by 1.7 to 2.6-fold after PTCA and by 15.7 to 16.1-fold after stenting, as compared to PTCA segments. In the neointima, the ratio of collagen type III to type I was 2.7 in stented arteries and only 0.3 in PTCA arteries. In the neointima of 17betaE-treated animals, collagen type I (but not type III) content upregulation was limited by 53% after PTCA and by 74% after stenting. CONCLUSION: Local delivery of 17betaE reduces restenosis, in part by decreasing the density of collagen type I in the neointima in PTCA and stented arteries.
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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 | 0.001 |
| 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".