Specific Contribution of Estrogen Receptors on Mitogen-Activated Protein Kinase Pathways and Vascular Cell Activation
Bibliographic record
Abstract
Randomized clinical trials have not provided conclusive data that hormone replacement therapy confers cardioprotection against coronary artery disease in postmenopausal women. However, other studies have shown that estrogens can induce beneficial effects on the vasculature. Nevertheless, the specific contribution of estrogen receptors (ERs) alpha and beta on vascular cells is not well characterized. Therefore, we used an antisense gene therapy approach to investigate the contribution of ERalpha and ERbeta on p38 and p42/44 mitogen-activated protein kinase (MAPK) activation and on vascular cell responsiveness. Treatment of porcine smooth muscle cells (PSMCs) with platelet-derived growth factor-BB induced p38 and p42/44 MAPK activation and their migration and proliferation. These effects were prevented by pretreatment with 17beta-estradiol (17betaE). The inhibitory effects of 17betaE on PSMCs were abrogated by the downregulation of ERbeta protein expression with selective ERbeta mRNA antisense oligomers, whereas the downregulation of ERalpha had no effect. However, treatment of porcine aortic endothelial cells with 17betaE promoted p38 and p42/44 MAPK phosphorylation and their migration and proliferation. These effects were ERalpha dependent, as defined by antisense gene therapy. These results suggest that in PSMCs, 17betaE reduces p42/44 and p38 MAPK activity through ERbeta stimulation, whereas in contrast, in porcine aortic endothelial cells, 17betaE induces p42/44 and p38 MAPK through ERalpha activation. 17betaE may contribute to the vascular healing process and to the prevention of restenosis by improving reendothelialization through ERalpha activation and by decreasing smooth muscle cell migration and proliferation through ERbeta stimulation.
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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".