Adverse health effects and stresses on offspring due to paternal exposure to harmful substances
Bibliographic record
Abstract
Recent epidemiological investigations report that environmental factors, such as exposure to harmful substances and stress related to unhealthy lifestyles, can result in health risks to organisms. The involvement of paternal exposure to chemicals and stresses, which leads to a negative impact on physiological responses and developmental processes in descendants, has drawn scientific attention. In this comprehensive review, we systematically describe different exposure sources of intergenerational and transgenerational health effects, including smoking, atmospheric fine particulate matter, alcohol, obesogenic diet and chemical toxicants, as well as stress related to unhealthy lifestyles, such as early stress and trauma during early development. Furthermore, effects on paternal lineages through epigenetic mechanisms mediated by germ cells, including effects on reproduction, oxidative stress, nervous and immune systems, were reviewed. Collectively, these effects can affect genetic materials through DNA methylation, small noncoding RNAs and histone modifications in later generations. Specifically, neurotoxicity or brain development caused by paternal inheritance was reviewed for this research. To better understand the epigenetic mechanisms of phenotypic changes and pathological lesions, further studies should emphasize research on the effects on third or fourth generation (i.e., F3 or F4) of offspring, rather than only the first or second generation (i.e., F1 or F2), which is generally done. In particular, how the toxic effects of new pollutants on paternal heritage are transmitted to successive generations (i.e., F3 and onward) should be more fully explored, and attempts should be made to find ways to alleviate the effects of the exposures on offspring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".