Weight loss with exercise improves muscle architecture and progenitor cell populations compared with weight loss alone in mice with preneoplastic colorectal lesions
Bibliographic record
Abstract
Weight loss and exercise reduce colorectal cancer (CRC) risk in persons with obesity. Whether weight loss and exercise effect myofiber characteristics and muscle stem/progenitor cell populations in mice with preneoplastic colorectal lesions, a model of CRC risk, is unknown. To address this gap, male C57Bl/6J mice were fed a high-fat diet (HFD) to induce obesity or a control (CON) diet prior to azoxymethane injection to induce preneoplastic colorectal lesions. The HFD group was then randomized to weight loss conditions that included (1) switching to the CON diet only (HFD-SED) or switching to the CON diet with treadmill exercise training (HFD-EX). Average myofiber cross-sectional area was not different between groups. There were more smaller-sized fibres in HFD-EX (p < 0.05 vs. CON), and more fibrosis in HFD-SED (p < 0.05 vs. HFD-EX and CON). There was a trend for more committed (Pax7+MyoD+) myoblasts (p = 0.059) and more fibro-adipogenic progenitors in HFD-EX (p < 0.05 vs. CON). Additionally, the canonical pro-inflammatory marker p-NF-κB was markedly reduced in the interstitium of HFD-EX (p < 0.05 vs. CON and HFD-SED). Our findings suggest that in mice with preneoplastic colorectal lesions, HFD followed by weight loss with exercise reduces muscle fibrosis and results in a higher content of muscle stem/progenitor cells. Novelty: Exercise improves muscle architecture in mice with preneoplastic colorectal lesion Exercise increases fibro/adipogenic progenitors and reduces inflammatory signaling in mice with preneoplastic colorectal lesions
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".