Xenobiotic Effects on Ovarian Preantral Follicles1
Bibliographic record
Abstract
Women are born with a finite population of ovarian follicles, which are slowly depleted during their reproductive years until reproductive failure (menopause) occurs. The rate of loss of primordial follicles is determined by genetic and environmental influences, but certain toxic exposures can accelerate this process. Ionizing radiation reduces preantral follicle numbers in rodents and humans in a dose-dependent manner. Cigarette smoking is linked to menopause occurring 1-4 yr earlier than with nonsmokers, and components of smoke, polycyclic aromatic hydrocarbons, can cause follicle depletion in rodents or in ovaries in vitro. Chemotherapeutic agents, such as alkylating drugs and cisplatin, also cause loss of preantral ovarian follicles. Effects depend on dose, type, and reactivity of the drug, and the age of the individual. Evidence suggests DNA damage may underlie follicle loss induced by one common alkylating drug, cyclophosphamide. Occupational exposures have also been linked to ovarian damage. In an industrial setting, 2-bromopropane caused infertility in men and women, and it can induce ovarian follicle depletion in rats. Solvents, such as butadiene, 4-vinylcyclohexene, and their diepoxides, can also cause specific preantral follicle depletion. The mechanism(s) underlying effects of the latter compound may involve alterations in apoptosis, survival factors such as KIT/Kit Ligand, and/or the cellular signaling that maintains primordial follicle dormancy. Estrogenic endocrine disruptors may alter follicle formation/development and impair fertility or normal development of offspring. Thus, specific exposures are known or suspected of detrimentally impacting preantral ovarian follicles, leading to early ovarian failure.
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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) | 0.004 | 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".