Local delivery of 17β-estradiol improves reendothelialization and decreases inflammation after coronary stenting in a porcine model
Bibliographic record
Abstract
In the current study, we investigated the effect of local intravascular delivery of 17beta-estradiol (17beta-E) on subsequent in-stent neointimal hyperplasia. Twenty-seven stents were implanted in coronary arteries of juvenile swine. Coronary arteries were randomized to local treatment with 17beta-E or no drug therapy (control-vehicle treated). Twenty-eight days post-treatment, angiographic images revealed an improved minimal lumen diameter (2.2 +/- 0.2 vs. 1.3 +/- 0.2 mm, P < 0.005) and a reduction of late lumen loss (1.7 +/- 0.2 vs. 2.3 +/- 0.1 mm, P < 0.01) in 17beta-E-treated vessels compared to control-vehicle treated. Histological analyses showed a reduction of stenosis (51.49 +/- 6.75 vs. 70.86 +/- 6.24%, P < 0.05), mean neointimal thickness (0.51 +/- 0.07 vs. 0.83 +/- 0.14 mm, P < 0.05) and inflammation score (1.29 +/- 0.28 vs. 2.85 +/- 0.40, P < 0.05) in 17beta-E-treated arteries compared to control-vehicle treated arteries. Immunohistochemistry analyses revealed a reduction of proliferating smooth muscle cells and increased in-stent reendothelialization in 17beta-E-treated arteries. Finally, we observed a correlation between neointimal hyperplasia and inflammation score, which in turn, was inversely related to reendothelialization. Locally delivered, 17beta-E is inhibiting the inflammatory response and smooth muscle cells proliferation and improving vascular reendothelialization which together are contributing to reduce in-stent restenosis in a porcine coronary injury model. Together, these data demonstrate the potential clinical application of 17beta-estradiol to improve vascular healing and prevent in-stent restenosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".