G‐protein Coupled Receptor 30 (GPR30) in the Human Endothelium: New Roles for a Novel Estrogen Receptor
Bibliographic record
Abstract
Estrogen is a modulator of endothelial function. Traditionally the effects of estrogen were believed to be mediated through its classical receptors alone. There is increasing evidence of a novel G‐protein coupled receptor GPR30 in mediating some estrogenic actions. While there is evidence of vascular actions of GPR30, this protein has not been identified to date in the human endothelium and its endothelium‐specific roles are poorly understood. We used human umbilical vein endothelial cells (HUVECs) as a model system. A specific pharmacological agonist (G‐1) and an antagonist (G‐15) were used to characterize roles of GPR30. Inflammatory changes were induced by stimulation with the pro‐inflammatory cytokine tumor necrosis factor (TNF). NF‐kappaB activation was determined by degradation of inhibitor kappaB and nuclear translocation of p65. We detected GPR30 protein in HUVECs by both western blotting and immunofluorescence. GPR30 activation by G‐1 increased F‐actin and stress fibre formation in HUVECs which were prevented by G‐15 administration. G‐1 treatment also inhibited TNF‐mediated adhesion molecule expression in these cells, without altering NF‐kappaB activity. Our results suggest that endothelial GPR30 is a novel mediator of actin polymerization and anti‐inflammatory effects downstream of NF‐kappaB. This work was supported by CIHR.
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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".