Effects of obesity and exercise on colon cancer induction and hematopoiesis in mice
Bibliographic record
Abstract
Obesity-induced inflammation is associated with increased risk for colorectal cancer (CRC). The role of diet and exercise in modulating increased CRC risk in obesity and the potential role of altered hematopoiesis as a contributor to these effects remain unknown. The purpose of this study was to examine how weight loss induced during CRC induction with or without exercise alters CRC initiation and its relationship to altered hematopoiesis. Mice consumed either a control (CON) or a high-fat diet to induce obesity. All mice were then placed on the control diet during CRC induction with azoxymethane (AOM). Following AOM injection, mice originally on the high-fat diet were randomized into sedentary (HF-SED) or exercise trained (HF-EX) conditions. At euthanasia, body weight and fat mass were similar among all three groups ( P < 0.05). Compared with CON and HF-EX, HF-SED developed increased content of preneoplastic lesions ( P < 0.05), and HF-SED had significantly increased markers of colon inflammation compared with CON. Compared with both CON and HF-EX, HF-SED had decreased content of short-term hematopoietic stem cells and increased content of common myeloid progenitor cells (both P < 0.05). Similarly, HF-SED had increased bone marrow adiposity compared with CON and HF-EX ( P < 0.05), and proteomics analysis revealed an increased marker of bone marrow inflammation in HF-SED compared with CON and HF-EX. Our results suggest that the early removal of a high-fat diet reduces CRC incidence when combined with an exercise training intervention. This reduction in risk was related to lower colon inflammation with anti-inflammatory changes in hematopoiesis induced by exercise.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".