Exposure to brominated flame retardants during pregnancy and lactation increases the prevalence of breast lesions and cancer-associated pathways in sprague-dawley rats
Bibliographic record
Abstract
The mammary gland undergoes significant changes during pregnancy, lactation, and involution, making it highly susceptible to endocrine-disrupting chemicals such as brominated flame retardants (BFRs). Despite being restricted in many countries, some BFRs persist in the environment and accumulate in human tissues, including the mammary gland and human milk. This study investigates the effects of BFRs exposure during pregnancy and lactation on mammary gland development and breast cancer risk in a rat model. Dams were exposed to a mixture of polybrominated diphenyl ethers (PBDEs) and hexabromocyclododecane (HBCDD), formulated based on relative congener levels found in house dust. Post-weaning, dams were treated with 7,12-dimethylbenz[a]anthracene (DMBA) to initiate tumor formation. The results revealed that both low and high doses of BFRs induced lesions in mammary epithelium, with an increase in total lesion number in low dose. Molecular analysis revealed disruptions in the Wnt/β-catenin signaling pathway, leading to an increase in oncogene expression, including c-Myc and c-Jun. RNA sequencing also indicated dysregulation in calcium signaling and glucose metabolism pathways. Our findings suggest that BFR exposure during the critical window of mammary gland involution compromises the cancer-protective effects of pregnancy and lactation. These effects are particularly significant at low exposure levels, demonstrating a non-monotonic dose-response. The study underscores the potential long-term health risks associated with environmental BFR exposure and highlights the need for further research on its implications on the risks of developing breast cancer later in life.
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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 | 0.001 | 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 | 0.001 |
| 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".