Receptor activities of persistent pollutant serum mixtures and breast cancer risk
Bibliographic record
Abstract
Studies on associations between persistent organic pollutants (POPs) and breast cancer risk are inconclusive. The majority of studies have evaluated the effect of single compounds, without considering multiple exposures to and interactions between different POPs. The present study aimed at evaluating breast cancer risk related to combined effects of serum POP mixtures on cellular receptor functions. Data on breast cancer cases ( n = 77) and controls ( n = 84) were collected among Greenlandic Inuit women. Serum mixtures of lipophilic POPs (lipPOPs), perfluoroalkyl acids (PFAAs) and dioxin-like POPs were extracted. The effect of the mixture extracts on the estrogen receptor (ER), androgen receptor (AR) and aryl hydrocarbon receptor (AhR) was determined using cell culture reporter gene assays. The serum mixtures were analyzed alone and upon co-exposure with natural receptor ligands to determine agonistic and antagonistic/competitive activity. We found that the frequency of lipPOP mixtures eliciting no, decreasing, or agonizing xenoandrogenic effect differed by breast cancer status. Using lipPOP mixtures with no effect on AR as reference, the mixtures with decreasing effects reduced breast cancer risk (OR: 0.30 (0.12; 0.76)). The AhR-toxic equivalent of serum mixtures was significantly lower in cases than in controls, and a reduced breast cancer risk was found when comparing the third tertile to the first (OR: 0.34 (0.14; 0.83)). We found no association between the xenoestrogenic activities of lipPOPs or PFAAs and breast cancer risk. Serum lipPOP mixtures are hormone disruptive and may influence breast cancer risk, whereas PFAAs seem to influence breast cancer risk through other pathways.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".