Long‐Term Effects of Early‐Life Exposure to Environmental Oestrogens on Ovarian Function: Role of Epigenetics
Bibliographic record
Abstract
Oestrogens play an important role in development and function of the brain and reproductive tract. Accordingly, it is considered that developmental exposure to environmental oestrogens can disrupt neural and reproductive tract development, potentially resulting in long-term alterations in neurobehaviour and reproductive function. Many chemicals have been shown to have oestrogenic activity, whereas others affect oestrogen production and turnover, resulting in the disruption of oestrogen signalling pathways. However, these mechanisms and the concentrations required to induce these effects cannot account for the myriad adverse effects of environmental toxicants on oestrogen-sensitive target tissues. Hence, alternative mechanisms are assumed to underlie the adverse effects documented in experimental animal models and thus could be important to human health. In this review, the epigenetic regulation of gene expression is explored as a potential target of environmental toxicants including oestrogenic chemicals. We suggest that toxicant-induced changes in epigenetic signatures are important mechanisms underlying the disruption of ovarian follicular development. In addition, we discuss how exposure to environmental oestrogens during early life can alter gene expression through effects on epigenetic control potentially leading to permanent changes in ovarian physiology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".