Treatment of Mcf-7 Breast Cancer Cells with a Red Grape Wine Polyphenol Fraction Results in Disruption of Calcium Homeostasis and Cell Cycle Arrest Causing Selective Cytotoxicity
Bibliographic record
Abstract
Food components influence the physiology by modulating gene expression and biochemical pathways within the human body. The disease-preventive roles of several fruit and vegetable components have been related to such properties. Polyphenolic components such as flavonoids are strong antioxidants and induce the expression of several xenobiotic-detoxifying enzymes. The mechanism of selective cytotoxicity induced by red grape wine polyphenols against MCF-7 breast cancer cells was investigated in relation to their interference with calcium homeostasis. MCF-7 cells showed an increase in cytosolic calcium levels within 10 min of treatment with the polyphenols. Immunohistochemical localization of calmodulin with secondary gold-labeled antibodies showed similar levels of gold labeling in both MCF-7 cells and the spontaneously immortalized, normal MCF-10A cell line. MCF-7 cells treated with the red wine polyphenol fraction (RWPF) showed swelling of endoplasmic reticulum, dissolution of the nucleus, and loss of plasma membrane integrity as well as reduced mitochondrial membrane potential. These cells were arrested at the G2/M interphase. By contrast, MCF-10A cells did not show such changes after RWPF treatment. The results suggest that polyphenol-induced calcium release may disrupt mitochondrial function and cause membrane damage, resulting in selective cytotoxicity toward MCF-7 cells. This property could further be developed toward breast cancer prevention strategies either independently or in conjunction with conventional prevention therapies where a positive drug-nutrient interaction can be demonstrated.
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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 | 0.000 | 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 | 0.000 |
| 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".