Rapid vascular effects of estrogen and aldosterone on MAP kinase activation: A role for GPR30.
Bibliographic record
Abstract
Recent studies have suggested that for EST, the rapid and non‐genomic effects may in part be mediated through the GPR30 receptor. However, the role of GPR30 in VSMC MAP kinase regulation is unclear. Therefore, we examined the role of GPR30, MR and ERa in mediating the rapid effects of EST and ALDO on ERK activation via western blotting. In native VSMCs maintained in primary culture, short‐term exposure to ALDO stimulated ERK activation (146±7% of control), whereas EST inhibited of ERK phosphorylation (62±5% of control). Both GPR30 and MR gene transfer enhanced the effect of ALDO to stimulate ERK phosphorylation (GPR30:172±10% of control; MR: 156±6% of control). Interestingly, GPR30 gene transfer reversed the effect of EST from ERK inhibition to ERK activation (149±11% of control), whereas gene transfer of ERa further inhibited ERK activation (45±6% of control). Additionally, in freshly isolated aortic tissue, which express higher endogenous levels of GPR30 (as compared to cultured VSMCs), EST exposure mediated ERK activation‐ consistent with a GPR30‐predominant effect. Overall, these data support the hypothesis that the vascular expression of GPR30 plays an important role in mediating the common and rapid vascular effects of ALDO and EST. Further, the net effect of EST to regulate ERK activation is dependent on the balance between ERα‐mediated signaling and GPR30‐mediated signaling in VSMCs.
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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 | 0.001 |
| 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) | 0.002 | 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".