Adverse effects of perinatal nicotine exposure on reproductive outcomes
Bibliographic record
Abstract
Nicotine exposure during pregnancy through cigarette smoking, nicotine replacement therapies or e-cigarette use continues to be a widespread public health problem, impacting both fetal and postnatal health. Yet, at this time, there remains limited data regarding the safety and efficacy in using these nicotine products during pregnancy. Notably, reports assessing the effect of nicotine exposure on postnatal health outcomes in humans, including reproductive health, are severely lacking. Our current understanding regarding the consequences of nicotine exposure during pregnancy is limited to a few animal studies, which do not comprehensively address the underlying cellular mechanisms involved. This paper aims to critically review the current knowledge from human and animal studies regarding the direct and indirect effects (e.g. obesity) of maternal nicotine exposure, regardless of its source, on reproductive outcomes in pregnancy and postnatal life. Furthermore, this review highlights several key cellular mechanisms involved in these adverse reproductive deficits including oxidative stress, inflammation, and endoplasmic reticulum (ER) stress. By understanding the interplay of the cellular mechanisms involved, further strategies could be developed to prevent the reproductive abnormalities resulting from exposure to nicotine in utero and influence informed clinical guidelines for pregnant women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".