Atrazine‐induced changes in aromatase activity in estrogen sensitive target tissues
Bibliographic record
Abstract
Atrazine (ATR) is a pesticide used widely throughout North America. Although not directly estrogenic, ATR treatment has been shown to increase aromatase activity in tumor cell lines. Thus, it is suggested that ATR can increase local tissue estrogen levels in estrogen sensitive target tissues through increased aromatase activity. Therefore the effect of ATR on aromatase activity was measured in human granulosa-lutein cell cultures, cells that abundantly express aromatase, and endometrial stromal cell (ESC) cultures, cells that do not express aromatase. Aromatase activity was quantified by the tritiated water method and the specificity of the assay was confirmed by co-incubation with 4-hydroxyandrostenedione, an irreversible inhibitor of the catalytic activity of aromatase. Aromatase activity in ATR treated (1-10 microm) granulosa-lutein cells was increased more than 2-fold compared with control cultures. There were no treatment related changes in cellular protein and thus it is suggested that the ATR-induced change in aromatase activity was not due to an increase in cell number. ATR-treatment had no effect on ESC aromatase activity at any concentration tested. Similarly, there was no effect of ATR treatment on human recombinant aromatase activity in our cell-free test system. Therefore it is concluded that microm concentrations of ATR can increase aromatase activity of human granulosa cells but not ESC and this effect is not elicited at the enzyme level.
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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".