Functional Significance of Nuclear Estrogen Receptor Subtypes in the Liver of Goldfish
Bibliographic record
Abstract
Estrogens work by binding to and activating specific estrogen receptors (ERs). Although mammals have two major nuclear ER subtypes (ERalpha and ERbeta), three subtypes have been shown in teleost fish (ERalpha, ERbeta-I, and ERbeta-II). 17beta-Estradiol stimulates the production of an egg yolk precursor protein (vitellogenin) in the liver of oviparous species, including the goldfish. However, the functional involvement of the ER subtypes in this process is not fully understood. Here, using primary goldfish hepatocytes, we test the hypothesis that all three ER subtypes are functionally involved in the liver of goldfish by using RNA interference to specifically knock-down the different ER subtypes. The results suggest that ERalpha is induced by estradiol through activation of the ERbeta subtypes. This induction serves to sensitize the liver to further stimulation by estradiol. The knock-down results were supported by use of ER subtype specific antagonists. Sensitization by up-regulation of ERalpha is likely to be important for seasonal spawners such as goldfish, to bring about a change from somatic growth to reproductive development, and vitellogenesis. The novel data presented in this study provide strong support for the hypothesis that the goldfish ER subtypes play functional roles in the regulation of vitellogenin and ERalpha and provide a framework for the better understanding of ER signaling in fish and other vertebrates.
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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".