Kefir Extracts Suppress <i>In Vitro</i> Proliferation of Estrogen-Dependent Human Breast Cancer Cells but Not Normal Mammary Epithelial Cells
Bibliographic record
Abstract
Anti-tumorigenic effects have been demonstrated in animal studies from the intake of kefir, a traditional fermented milk product believed to originate from the Caucasian mountains of Russia. In the present study, the antiproliferative effects of extracts of kefir, yogurt, and pasteurized cow's milk on human mammary cancer cells (MCF-7) and normal human mammary epithelial cells (HMECs) was investigated at doses of 0.31%, 0.63%, 1.25%, 2.5%, 5%, and 10% (vol/vol). After 6 days of culture, extracts of kefir-fermented milk depressed MCF-7 cell growth in a dose-dependent manner, showing 29% inhibition of proliferation at a concentration as low as 0.63%, whereas yogurt extracts began to show dose-dependent antiproliferative effects only at the 2.5% dose. Moreover, at the 2.5% dose, kefir extracts decreased the MCF-7 cell numbers by 56%, while yogurt extracts decreased MCF-7 cell proliferation by only 14%. No antiproliferative effects of kefir extracts were observed in the HMECs, while the yogurt extracts exerted antiproliferative effects on HMECs at the 5% and 10% doses. Unfermented milk extracts stimulated proliferation of MCF-7 cells and HMECs at concentrations above 0.31%. Peptide content and capillary electrophoresis analyses showed that kefir-mediated milk fermentation led to an increase in peptide concentrations and a change in peptide profiles relative to milk or yogurt. The present findings suggest that kefir extracts contain constituents that specifically inhibit the growth of human breast cancer cells, which might eventually be useful in the prevention or treatment of breast cancer.
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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".