A Novel in Vivo Bioassay for (Xeno-)estrogens Using Transgenic Zebrafish
Bibliographic record
Abstract
Adverse trends in the reproductive health of male fish, including testis abnormalities and intersex gonads, have been increasingly reported over recent years. These effects have been associated with the exposure of fish to natural, synthetic, and xenobiotic estrogens present in the aquatic environment. A novel in vivo test system using transgenic zebrafish has been developed to rapidly determine the effects of estrogenic chemicals on critical life stages and sensitive target organs in the fish. In the transgenic zebrafish, an estrogen binding sequence linked to a TATA box and luciferase reporter gene was stably introduced. Binding of a substance to endogenous estrogen receptors (ER) and the subsequent transactivation of the ER result in luciferase gene induction that is easily measured in tissue lysates. Exposure to estradiol (E2) during juvenile stages of the transgenic zebrafish revealed the period of gonad differentiation to be the most responsive early life stage. In adult male transgenic zebrafish, the testis was the most sensitive and responsive target tissue to estrogens. Partial sequences of zebrafish estrogen receptor subtypes α and β were cloned for the first time and were found to be differentially expressed in developing fish and tissues of adult male zebrafish. The transgenic zebrafish assay is a promising new tool to rapidly determine the estrogenic potency of chemicals in vivo.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".