Paternal Influence on Developmental Toxicity Following Administration of Therapeutic Drugs and Direct Impact on Developmental Toxicity
Bibliographic record
Abstract
In the last few decades, a new concept of developmental origins of health and disease was introduced based upon the paternal contribution to developmental toxicity. This was attributed to the first reports on the incidence of leukemia in children whose parents worked at Sellafield Nuclear Plant in West Cumbria, England, which created awareness to the scientific community to the possibility of male-mediated developmental toxicity. Thus, animal studies commenced, which demonstrated that genetic damage to paternal DNA following exposure to radiation or chemical products (mutagens) may be transmitted to the offspring. Several studies highlighted the paternal impact on the development of toxicity following exposure to endocrine disruptors, alcohol, nicotine, radiation as well as antineoplastic drugs, but other factors including environmental factors, social factors, and chemicals that men are also exposed to might also directly influence sperm quality, resulting in DNA damage and consequently affecting the development of offspring, which have not yet received much attention. In this chapter, paternal exposure to various risk factors including obesity, stress, anxiety, and medications used to treat several conditions, such as anxiolytics, antidepressants, glucocorticoids, anorexigens, antirheumatics, antiepileptics and analgesic drugs, is described with associated potential impact on the development of offspring toxicity. Even though many studies still need to be carried out, it is known that spermatozoa might constitute one of the crucial keys in the development of health or disease of the 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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".