Brief research report pesticide occupational exposure leads to significant inflammatory changes in normal mammary breast tissue
Bibliographic record
Abstract
Studies have documented the high occurrence of several tumors, including female breast cancer, in populations occupationally exposed to pesticides worldwide. It is believed that in addition to direct DNA damage, other molecular alterations that indicate genomic instability are associated, such as epigenetic modifications and the production of inflammation mediators. The present study characterized the profile of inflammatory changes in the breast tissue of women without cancer occupationally exposed to pesticides. In samples of normal breast tissue collected during biopsy and evaluated as negative for cancer by a pathologist, oxidative stress levels were assessed as inflammatory markers through measurements of lipoperoxides and total antioxidant capacity of the sample (TRAP) by high-sensitivity chemiluminescence, as well as levels of nitric oxide (NOx) metabolites. The levels of inflammation-modulating transcription factors PPAR-γ (peroxisome proliferator-activated receptor gamma) and NF-κB (nuclear factor kappa B) were also quantified, in addition to the pro-inflammatory cytokines tumor necrosis factor-alpha (TNF-α) and interleukin 12 (IL-12). The levels of lipoperoxides, TRAP, and NOx were significantly lower in the exposed group. On the other hand, PPAR-γ levels were increased in the breast tissue of exposed women, with no variation in NF-κB. There was also a rise of TNF-α in exposed women samples without significant variations in IL-12 levels. These findings suggest an inflammatory signature of the breast tissue associated with pesticide exposure, which may trigger mechanisms related to mutations and breast carcinogenesis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".