The Interplay between Microbiota and Chemotherapy-Derived Metabolites in Breast Cancer
Bibliographic record
Abstract
The most common cancer in women is breast cancer, which is also the second leading cause of death in this group. It is, however, important to note that some women will develop or will not develop breast cancer regardless of whether certain known risk factors are present. On the other hand, certain compounds are produced by bacteria in the gut, such as short-chain fatty acids, secondary bile acids, and other metabolites that may be linked to breast cancer development and mediate the chemotherapy response. Modeling the microbiota through dietary intervention and identifying metabolites directly associated with breast cancer and its complications may be useful to identify actionable targets and improve the effect of antiangiogenic therapies. Metabolomics is therefore a complementary approach to metagenomics for this purpose. As a result of the combination of both techniques, a better understanding of molecular biology and oncogenesis can be obtained. This article reviews recent literature about the influence of bacterial metabolites and chemotherapy metabolites in breast cancer patients, as well as the influence of diet.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".