How Per- and Poly-Fluoroalkyl Substances Affect Gamete Viability and Fertilization Capability: Insights from the Literature
Bibliographic record
Abstract
There has been emerging research linking per- and poly-fluoroalkyl substances (PFAS) to gamete viability and fertility. PFAS, prevalent in the environment and water supplies, undergo slow degradation due to their C-F bond and a long half-life (2.3-8.5 years). In females, PFAS inhibit the hypothalamic-pituitary-gonadal (HPG) axis, reducing follicle-stimulating hormone (FSH) and luteinizing hormone (LH) levels, leading to the inhibition of androgen and estradiol production. PFAS have been found to cause detrimental effects on egg quality through impairing folliculogenesis. In males, PFAS can impair sperm motility and morphology: two fundamental qualities of successful fertilization. PFAS exposure has been proven to inhibit testosterone production, sperm capacitation, and acrosomal reaction. After fertilization, the results of PFAS exposure to embryos have also been investigated, showing reduced development to the blastocyst stage. The aim of this review is to report the main findings in the literature on the impact of PFAS exposure to gamete competency and fertilization capability by highlighting key studies on both male and female fertility. We report that there is significant evidence demonstrating the negative impacts on fertility after PFAS exposure. At high doses, these environmentally abundant and widespread compounds can significantly affect human fertility.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.000 |
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".