Common Beans and Their Non-Digestible Fraction: Cancer Inhibitory Activity—An Overview
Bibliographic record
Abstract
The US Department of Agriculture’s MyPyramid guidelines introduced a near doubling of the dietary recommendations for vegetables including dry beans—an important food staple in many traditional diets that can improve public health and nutrition. Populations with high legume (peas, beans, lentils) consumption have a low risk of cancer and chronic degenerative diseases. Common beans (Phaseolus vulgaris L.) are known as a rich, reliable source of non-digested compounds like fiber, phenolics, peptides and phytochemicals that are associated with health benefits. Emerging evidence indicates that common bean consumption is associated with reduced cancer risk in human populations, inhibiting carcinogenesis in animal models and inducing cell cycle arrest and apoptosis in cell cultures. Fiber may reduce the risk of premature death from all causes, whereas the whole non-digestible fraction from common beans exhibits anti-proliferative activity and induces apoptosis in vitro and in vivo colon cancer. The mechanisms responsible for this apparently protective role may include gene-nutrient interactions and modulation of proteins’ expression. This review investigates the potential health benefits and bioactivity of beans on tumor inhibition, highlighting studies involving functional compounds, mainly non-digestible fractions that modulate genes and proteins, thereby, unraveling their preventive role against the development of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".