Characterization of the Biological Roles of the Estrogen Receptors, ERα and ERβ, in Estrogen Target Tissues<i>in Vivo</i>through the Use of an ERα-Selective Ligand
Bibliographic record
Abstract
Estrogens elicit many biomedically important responses in different target tissues, and the respective roles of the two estrogen receptors, ERalpha and ERbeta, in mediating these bioactivities is incompletely understood. In this study, we investigated the activity of an ERalpha-selective agonist ligand, propyl pyrazole triol (PPT), in several rat animal models to define the involvement of ERalpha in these biological responses. In a short-term (4 d) uterotrophic assay, PPT was found to be as efficacious as 17alpha-ethinyl-17beta-estradiol in stimulating uterine weight gain and up-regulating complement 3 gene expression. In a 6-wk chronic model, PPT completely prevented the ovariectomy-induced body weight increase and loss of bone mineral density. It also increased uterine weight and markedly reduced plasma cholesterol levels in these mature animals. PPT was also effective in the brain. It increased progesterone receptor mRNA in the arcuate and ventromedial nuclei of the hypothalamus and prevented experimentally induced hot flushes. Our findings indicate that several physiologically relevant estrogen-induced tissue responses can be effectively evoked via ERalpha alone. By providing an approach that is complementary to that of analyzing the phenotype and response of ER knockout animals, our findings also demonstrate that ER subtype-selective ligands can play a valuable role in enhancing our understanding of how estrogens work through the two ER subtypes.
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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 | 0.001 |
| 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".