Establishing the relationship between Polycyclic Aromatic Hydrocarbons (PAHs) exposure and male infertility: A systematic review
Bibliographic record
Abstract
It has been demonstrated that human exposure to environmental chemicals may have sperm genotoxic potentiality. Among the different classes, Polycyclic Aromatic Hydrocarbons (PAHs) have been receiving attention in recent years due to reports of sperm geno-toxicity, a series of reproductive defects and male infertility. This review aims to substantiate the effects of PAHs exposure on male infertility, with focus on the sperm characteristics (count, concentration, volume, motility, DNA damage, and morphology). To this end, international databases such as Cochrane Library, PubMed, Web of Science, Embase Ovid, Scopus, and Google Scholar were used to conduct a systematic search for papers on the subject, based on PRISMA guidelines, published up to 24 March 2022. The Newcastle-Ottawa Scale was subsequently used to assess the quality of the studies. The results showed that there is a significant negative relationship between PAHs metabolites and sperm volume, concentration, motility, morphology, as well as an observed DNA degeneration. Also, the CYP1A1 genotype polymorphisms were considered as a representative of PAHs exposure to infertility; the review highlights that polymorphisms of this genotype were more common in the infertile people. In overall, this work provides a solid summary of the existing works correlating PAHs exposure and male infertility, which could impulse further protective measures and informative campaigns on users, workers, and general population.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".