Exposure to the pesticide linuron affects androgen-dependent gene expression in the three-spined stickleback (<i>Gasterosteus aculeatus</i>)
Bibliographic record
Abstract
Previous research demonstrated that exposure to exogenous androgens and effluents with androgenic activity can induce spiggin mRNA production in the kidney of the three-spined stickleback (Gasterosteus aculeatus). In the present study, we determine whether a short-term exposure to a known antiandrogenic pesticide, linuron (LN), suppresses spiggin mRNA in male stickleback and in androgenized female stickleback. Primers were designed from previously characterized sequences for each androgen receptor (AR) isoform in stickleback, arα and arβ, to assess whether these receptors are differentially regulated by androgen or antiandrogen exposure. Fish were exposed for 72 h to one of four treatments: control, LN (250 µg/L), 17α-methyltestosterone (MT, 500 ng/L), and an LN-MT mixture at those same concentrations. There was no effect of LN on spiggin and arβ mRNA levels in male kidney, while levels of arα were significantly increased twofold. Exposure to LN significantly inhibited MT-induced spiggin RNA production in female kidney with no effect on expression of arα and arβ. The present study is the first to demonstrate the antiandrogenic effect of LN at the transcript level and to examine androgenic/antiandrogenic responsiveness of the two ARs in the stickleback. From the present study, it was determined that measurement of spiggin RNA is a reliable and sensitive screening tool for the detection of both androgenic and antiandrogenic compounds.
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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".