Perinatal Exposure to Environmental Contaminants Detected in Canadian Arctic Human Populations Changes Bone Geometry and Biomechanical Properties in Rat Offspring
Bibliographic record
Abstract
Arctic inhabitants consume large proportions of fish and marine mammals, and are therefore continuously exposed to levels of environmental toxicants, which may produce adverse health effects. Fetuses and newborns are the most vulnerable groups. The aim of this study was to evaluate changes in bone geometry, mineral density, and biomechanical properties during development following perinatal exposure to a mixture of environmental contaminants corresponding to maternal blood levels in Canadian Arctic human populations. Sprague-Dawley rat dams were dosed with a Northern Contaminant Mixture (NCM) from gestational day 1 to postnatal day (PND) 23. NCM contains 27 contaminants comprising polychlorinated biphenyls, organochlorine pesticides, and methylmercury. Femurs were collected on PND 35, 77 and 350, and diaphysis was analyzed by peripheral quantitative computed tomography and three-point bending test, while femoral neck was assessed in an axial loading experiment. Dose-response modeling was performed to establish the benchmark dose (BMD) for the analyzed bone parameters. Exposure to the high dose of NMC resulted in short and thin femur with reduced mechanical strength in offspring at PND35. BMD of femur length, cortical area, and stiffness were 3.2, 1.6, and 0.8 mg/kg bw/d, respectively. At PND77 femur was still thin, but at PND350 no treatment-related bone differences were detected. This study provides new insights on environmental contaminants present in the maternal blood of Canadian Arctic populations, showing that perinatal exposure induces bone alterations in the young offspring. These findings could be significant from a health risk assessment point of view.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".