Antioxidant Capacity of Food Mixtures Is Not Correlated with Their Antiproliferative Activity Against MCF-7 Breast Cancer Cells
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Bibliographic record
Abstract
Combining different foods may produce additive, synergistic, or antagonistic interactions that may modify certain physiological effects (i.e., anticancer properties). For investigating these interactions and potential synergetic combinations, thirteen foods from three categories, including fruits (raspberries, blackberries, apples, grapes), vegetables (broccoli, tomatoes, mushrooms, purple cauliflowers, onions), and legumes (soy beans, adzuki beans, red kidney beans, black beans), were evaluated for their inhibitory activity against MCF-7 breast cancer cells. Grape, onion, and adzuki bean showed maximal growth inhibition of MCF-7 from the fruit, vegetable, and legume groups, respectively. When these three foods were combined in pairs, unique interactions were observed that were not seen when individual extracts were used. Combining onion and grape resulted in a synergistic antiproliferative effect (APE) against MCF-7 compared with either onion or grape treatment alone. In contrast, combining grape and adzuki bean resulted in an antagonistic interaction. Additionally, four antioxidant assays (total phenolic contents, ferric reducing antioxidant power, 2,2-diphenyl-1-picrylhydrazyl, and oxygen radical absorbance capacity) were further used to evaluate the antioxidant capacities (AC) of individual foods and their combinations. Combining raspberry and adzuki bean extracts demonstrated synergistic AC in all four assays, but they did not show synergistic APE against the MCF-7 cells. Combining broccoli and soy produced antioxidant antagonism, but did not have an antagonistic APE against MCF-7. The synergistic or antagonistic AC of food mixtures did not correlate with the synergistic or antagonistic APE against MCF-7. Further investigation is to determine the mechanisms of these interactions and to predict and enhance the therapeutic benefits of foods and food components through strategic food combinations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it