Disruption of androgen receptor activity by synthetic and dietary aryl hydrocarbon receptor ligands
Bibliographic record
Abstract
There is a growing concern that many of the environmental and dietary chemicals that are ingested by humans but have not been thoroughly tested for endocrine activity may pose a significant health threat. These chemicals may have the potential to mimic or antagonize hormone action, possibly through binding to steroid receptors and interfering with transcriptional activity. The prostate is highly sensitive to androgens and requires precise hormonal control to regulate its growth and function. Prostate cells express both androgen receptor and aryl hydrocarbon receptor (AhR) and cross‐talk between the two pathways may affect the function of the prostate cell. A number of methods were used to characterize the impact of dietary and environmental AhR ligands on androgen action in a prostate cancer‐derived cell line. Our in vitro cell culture‐based assay utilizes androgen‐responsive promoter constructs in conjunction with a luciferase reporter gene. These results combined with those from a binding assay indicate that several of the AhR ligands, including resveratrol, indole 3 carbinol, curcumin and hexachlorobenzene antagonize androgen‐initiated transcriptional activity without binding to the androgen receptor. Environmental and dietary compounds have been implicated in the rising incidence of prostate cancer and insight into the mechanisms of endocrine disruption will help to clarify their role.
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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".