Probiotics and mediterranean diet for breast cancer management and prevention?
Bibliographic record
Abstract
The human gut microbiota, a diverse community of beneficial normal flora microorganisms, significantly influences physiological function and the immune response. Various microbiota strains have shown promise in supporting clinical treatment of chronic diseases, including cancer, by potentially providing antioxidative and anti-tumorigenic effects in both in vivo and in vitro studies. Breast cancer, which ranks amongst the top five cancer types common worldwide and particularly in Mediterranean countries, has been showing high incidence and prevalence. In breast cancer, microbiota composition, hormonal dynamics, and dietary choices are believed to play significant roles. Hence, the Mediterranean diet, known for its microbiota-friendly features, emerges as a potential protective factor against breast cancer development, highlighting the potential for personalized dietary strategies in cancer prevention. This comprehensive review highlights the emerging mechanisms by which probiotics support our immune system during different physiological activities. It also discusses their potential role, along with nutrition intervention, in improving essential clinical treatment outcomes in breast cancer patients and survivors, suggesting potential supportive strategies that go hand in hand with clinical strategies. Unfortunately, very little research addresses the possible clinical implications of probiotics and dietary habits on breast cancer, despite the promising results, calling for further studies and actions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".