Evaluation of Antioxidant Intakes in Relation to Inflammatory Markers Expression Within the Normal Breast Tissue of Breast Cancer Patients
Bibliographic record
Abstract
Chronic inflammation may be a causative factor in breast cancer. One possible underlying mechanism is the generation of oxidative stress, which may favor tumorigenic processes. Antioxidant consumption may, therefore, help reduce tissue inflammation levels. However, few studies have explored this relation in breast tissue. We aimed to evaluate correlations between antioxidant (vitamin A/retinol, vitamin C, vitamin E, β-carotene, α-carotene, lycopene, lutein/zeaxanthin, β-cryptoxanthin, selenium, and zinc) intakes and protein expression levels of interleukin (IL)-6, tumor necrosis factor-α, C-reactive protein, cyclooxygenase-2, leptin, serum amyloid A1, signal transducer and activator of transcription 3, IL-8, IL-10, lactoferrin, and transforming growth factor-β measured in the normal breast tissue of 160 women diagnosed with breast cancer. Antioxidant intakes were collected using a self-administered food frequency questionnaire. Inflammation marker expression was assessed by immunohistochemistry. Correlations between antioxidant intakes and inflammatory marker expression were evaluated using Spearman's partial correlation coefficients ( r) for all women and for premenopausal and postmenopausal women separately. After Bonferroni correction, negative correlations were observed between dietary β-tocopherol and IL-10 expression in all women combined ( r = -0.26, P = .003) and among postmenopausal women ( r = -0.39, P = .003). For all women, a negative correlation was found between total zinc intakes and IL-10 ( r = -0.26, P = .002). Among postmenopausal women, dietary selenium intake was negatively correlated with the expression of lactoferrin ( r = -0.39, P = .003). No associations were observed in premenopausal women. Our findings suggest that consumption of specific antioxidants, including β-tocopherol, zinc, and selenium, may act on the breast tissue through mechanisms affecting the expression of some inflammation markers, particularly among postmenopausal women.
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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 | 0.001 |
| 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".