Effects of exercise and corticotrophin‐releasing factor 2 receptor agonist on skeletal muscle of mdx mice
Bibliographic record
Abstract
Exercise and corticotrophin‐releasing factor 2 receptor (CRF2R) agonist treatment improves skeletal muscle function, however the potential synergistic transcriptional effects of both treatments in dystrophic muscle are not characterized. Mdx (C57BL/10ScSn‐Dmdmdx) and WT (C57BL/6) mice were treated with placebo (PL) or CRF2R agonist and remained sedentary or performed treadmill exercise (EX; 3x/week, 30 minutes at 8–12m/min) for 12 weeks. RNA from tibialis anterior was prepared for gene array analysis using the Affymetrix Mouse 430 array and functional cluster analysis was performed on differentially expressed (DE) genes using DAVID. The Mdx genotype had 224 DE genes, many associated with muscle differentiation and development of the actin cytoskeleton and extracellular matrix. Exercise resulted in 79 DE genes, primarily those involved in transcriptional regulation and circadian rhythms, whereas the CRF2R‐agonist produced 122 DE genes, many involved in chromatin remodeling. The combination of exercise and CRF2R‐agonist resulted in 87 DE genes compared to MDX‐PL‐EX mice, influencing genes involved in protein transport and neuronal nitric oxide synthase (nNOS). These results indicate that the effects of exercise and CRF2R agonist treatment are not identical and that combination treatment may result in a favorable transcriptional signature that holds therapeutic potential. (Funded by P&G)
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".