Increased kidney, liver, and testicular cell death after chronic exposure to 17α-ethinylestradiol in medaka (<i>Oryzias latipes</i>)
Bibliographic record
Abstract
Sublethal effects observed in fish exposed to environmental estrogens may be mediated via stimulation of cell death. To investigate whether cell death is induced in fish after chronic exposure to estrogenic chemicals, Japanese medaka (Oryzias latipes) were exposed from hatch until sexual maturity to 10 ng/L 17alpha-ethinylestradiol (EE2) or acetone solvent (control). Cell death was evaluated in blinded histological sections of whole medaka using terminal dideoxynucleotidyl-mediated dUTP nick end-labeling (TUNEL), which labels nuclei of cells containing apoptotic or necrotic (fragmented) DNA. The major impact of EE2 exposure in both male and female medaka was to significantly increase the number of TUNEL-positive hepatocytes and kidney tubule cells compared to control. Cell morphology was consistent with apoptosis in the liver and cloudy swelling or necrosis in the tubule cells. The number of TUNEL-positive interstitial (hematopoietic) and glomerular cells was significantly greater in the kidneys of EE2-exposed male, but not female, medaka. The EE2 exposure also significantly increased the number of TUNEL-positive testicular cells in medaka compared to corresponding controls, namely Leydig cells, Sertoli cells, spermatocytes, and spermatids. In medaka with gonadal intersex, areas of fibrosis and areas containing female gonadal cells were relatively unstained with TUNEL. No effect of EE2 exposure on the number of TUNEL-positive ovarian somatic cells or on the rate of female ovarian follicle atresia was found. These results suggest that chronic exposure to EE2 in medaka is hepatotoxic and nephrotoxic in both sexes, whereas gonadal toxicity is specific to males.
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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".