Exercise and Prebiotic Fiber Provide Gut Microbiota-Driven Benefit in a Survivor to Germ-Free Mouse Translational Model of Breast Cancer
Bibliographic record
Abstract
The gut microbiota plays a role in shaping overall host health and response to several cancer treatments. Factors, such as diet, exercise, and chemotherapy, can alter the gut microbiota. In the present study, the Alberta Cancer Exercise (ACE) program was investigated as a strategy to favorably modify the gut microbiota of breast cancer survivors who had received chemotherapy. Subsequently, the ability of post-exercise gut microbiota, alone or with prebiotic fiber supplementation, to influence breast cancer outcomes was interrogated using fecal microbiota transplant (FMT) in germ-free mice. While cancer survivors experienced little gut microbial change following ACE, in the mice, tumor volume trended consistently lower over time in mice colonized with post-exercise compared to pre-exercise microbiota with significant differences on days 16 and 22. Beta diversity analysis revealed that EO771 breast tumor cell injection and Paclitaxel chemotherapy altered the gut microbial communities in mice. Enrichment of potentially protective microbes was found in post-exercise microbiota groups. Tumors of mice colonized with post-exercise microbiota exhibited more favorable cytokine profiles, including decreased vascular endothelial growth factor (VEGF) levels. Beneficial microbial and molecular outcomes were augmented with prebiotic supplementation. Exercise and prebiotic fiber demonstrated adjuvant action, potentially via an enhanced anti-tumor immune response modulated by advantageous gut microbial shifts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".