Effects of endocrine disrupting chemicals on gonad development: Mechanistic insights from fish and mammals
Bibliographic record
Abstract
Over the past century, evidence has emerged that endocrine disrupting chemicals (EDCs) have an impact on reproductive health. An increased frequency of reproductive disorders has been observed worldwide in both wildlife and humans that is correlated with accidental exposures to EDCs and their increased production. Epidemiological and experimental studies have highlighted the consequences of early exposures and the existence of key windows of sensitivity during development. Such early in life exposures can have an immediate impact on gonadal and reproductive tract development, as well as on long-term reproductive health in both males and females. Traditionally, EDCs were thought to exert their effects by modifying the endocrine pathways controlling reproduction. Advances in knowledge of the mechanisms regulating sex determination, differentiation and gonadal development in fish and rodents have led to a better understanding of the molecular mechanisms underlying the effects of early exposure to EDCs on reproduction. In this manuscript, we review the key developmental stages sensitive to EDCs and the state of knowledge on the mechanisms by which model EDCs affect these processes, based on the roadmap of gonad development specific to fish and mammals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".