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 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 | 0.000 | 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) | 0.000 | 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".