Estrogen Regulation of Endothelial and Smooth Muscle Cell Migration and Proliferation
Bibliographic record
Abstract
OBJECTIVE: Restenosis is a major limitation of percutaneous coronary intervention. Migration and proliferation of vascular cells remain a cornerstone in neointimal formation. The cardioprotection of estrogen is well recognized, but the intracellular mechanisms related to these beneficial effects are not completely understood. METHODS AND RESULTS: We investigated the effects of 17beta-estradiol (17betaE) on mitogen-activated protein kinase (MAPK) activity and the migration and proliferation of porcine aortic endothelial cells (PAECs) and porcine smooth muscle cells (PSMCs). Treatment with 17betaE (10(-8) mol/L) abrogated p38 and p42/44 MAPK phosphorylation mediated by platelet-derived growth factor-BB as well as the migration and proliferation of PSMCs. In contrast, treatment with 17betaE (10(-8) mol/L) induced the phosphorylation of p38 and p42/44 MAPK and the migration and proliferation of PAECs. Interestingly, the effects of 17betaE on PSMCs and PAECs were reversed by selective estrogen receptor antagonists (tamoxifen, 4-OH-tamoxifen, and raloxifen). These results suggest that in PSMCs, 17betaE inhibits chemotactic and mitogenic effects of platelet-derived growth factor-BB as well as p38 and p42/44 MAPK phosphorylation. In contrast, 17betaE promotes in PAECs the phosphorylation of p42/44 and p38 MAPK as well as the migration and proliferation of these cells. CONCLUSIONS: Treatment with 17betaE has a dual beneficial effect: the improvement of vascular healing and the prevention of restenosis after angioplasty.
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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".