17-Epiestriol, an Estrogen Metabolite, Is More Potent Than Estradiol in Inhibiting Vascular Cell Adhesion Molecule 1 (VCAM-1) mRNA Expression
Bibliographic record
Abstract
17-beta estradiol (17-beta E(2)) attenuates the expression of vascular cell adhesion molecule 1 (VCAM-1) in vivo at physiological levels (pg/ml), whereas supraphysiological concentrations of 17-beta E(2) (ng/ml) are required in vitro. We assessed whether a metabolite of estrogen, which could only be generated in vivo, might be a more potent inhibitor of VCAM-1 expression and thereby explain this discrepancy. We report here that 17-epiestriol, an estrogen metabolite and a selective estrogen receptor (ER) beta agonist, is approximately 400x more potent than 17-beta E(2) in suppressing tumor necrosis factor (TNF) alpha-induced VCAM-1 mRNA as well as protein expression in human umbilical vein endothelial cells. Genistein, an ERbeta agonist, at low concentrations (1 and 10 nm) also suppressed TNFalpha-induced VCAM-1 mRNA expression. These actions of 17-epiestriol and genistein were significantly attenuated in the presence of the estrogen receptor antagonist ICI-182780. Other estrogenic compounds such as ethinyl estradiol and estrone did not have any effect on TNFalpha-induced VCAM-1 expression at the concentrations tested. We further show that, 1) 17-epiestriol induces the expression of endothelial nitric-oxide synthase mRNA and protein, 2) 17-epiestriol prevents TNFalpha-induced migration of NFkappaB into the nucleus, 3) N(G)-nitro-l-arginine methyl ester, an inhibitor of NO synthesis, abolishes 17-epiestriol-mediated inhibition of TNFalpha-induced VCAM-1 expression and migration of NFkappaB from the cytoplasm to the nucleus. Our results indicate that 17-epiestriol is more potent than 17-beta E(2) in suppressing TNFalpha-induced VCAM-1 expression and that this action is modulated at least in part through NO.
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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) | 0.002 | 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".