In Utero and Lactational Exposure to a Mixture of Environmental Contaminants Detected in Canadian Arctic Human Populations Alters Retinoid Levels in Rat Offspring with Low Margins of Exposure
Bibliographic record
Abstract
Arctic inhabitants are highly exposed to persistent organic pollutants (POP), which may produce adverse health effects. This study characterized alterations in tissue retinoid (vitamin A) levels in rat offspring and their dams following in utero and lactational exposure to the Northern Contaminant Mixture (NCM), a mixture of 27 contaminants including polychlorinated biphenyls (PCB), organochlorine (OC) pesticides, and methylmercury (MeHg), present in maternal blood of the Canadian Arctic Inuit population. Further, effect levels for retinoid system alterations and other endpoints were compared to the Arctic Inuit population exposure and their interrelationships were assessed. Sprague-Dawley rat dams were dosed with NCM from gestational day 1 to postnatal day (PND) 23. Livers, kidneys and serum were obtained from offspring on PND35, PND77, and PND350 and their dams on PND30 for analysis of tissue retinoid levels, hepatic cytochrome P-450 (CYP) enzymes, and serum thyroid hormones. Benchmark doses were established for all endpoints, and a partial least-squares regression analysis was performed for NCM treatment, hepatic retinoid levels, CYP enzyme induction, and thyroid hormone levels, as well as body and liver weights. Hepatic retinoid levels were sensitive endpoints, with the most pronounced effects at PND35 though still apparent at PND350. The effects on tissue retinoid levels and changes in CYP enzyme activities, body and liver weights, and thyroid hormone levels were associated and likely driven by dioxin-like compounds in the mixture. Low margins of exposure were observed for all retinoid endpoints at PND35. These findings are important for health risk assessment of Canadian Arctic populations and further support the use of retinoid system analyses in testing of endocrine-system-modulating compounds.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".