Canadian Arctic Contaminants and Their Effects on the Maternal Brain and Behaviour: A Scoping Review of the Animal Literature
Bibliographic record
Abstract
Background: Environmental toxicants such as methylmercury, polychlorinated biphenyls, and organochlorine pesticides are potentially harmful pollutants present in contaminated food, soil, air, and water. Exposure to these ecologically relevant toxicants is prominent in Northern Canadian populations. Previous work focused on toxicant exposure during pregnancy as a threat to fetal neurodevelopment. However, little is known about the individual and combined effects of these toxicants on maternal health during pregnancy and post-partum. Methods: A scoping review was conducted to synthesize the current knowledge regarding individual and combined effects of methylmercury, polychlorinated biphenyls, and organochlorine pesticides on maternal behaviour and the maternal brain. Relevant studies were identified through the PubMed, Embase, and Toxline databases. Literature involving animal models and one human cohort were included in the review. Results: Research findings indicate that exposures to these environmental toxicants are associated with neurochemical changes in rodent models. Animal models provided the majority of information on toxicant-induced alterations in maternal care behaviours. Molecular and hormonal changes hypothesized to underlie these alterations were also addressed, although studies assessing toxicant co-exposure were limited. Conclusion: This review speaks to the limited knowledge regarding effects of these persistent organic pollutants on the maternal brain and related behavioural outcomes. Further research is required to better comprehend any such effects on maternal brain and behaviour, as maternal care is an important contributor to offspring neurodevelopment.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".