The therapeutic potential of skeletal muscle plasticity in Duchenne muscular dystrophy: phenotypic modifiers as pharmacologic targets
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a life-limiting, neuromuscular disorder that causes progressive, severe muscle wasting in boys and young men. Although there is no cure, scientists and clinicians can leverage the fact that slower, more oxidative skeletal muscle fibers possess an enhanced degree of resistance to the dystrophic pathology relative to their faster, more glycolytic counterparts, and can thus use this knowledge when investigating novel therapeutic avenues. Several factors have been identified as powerful regulators of muscle plasticity. Some proteins, such as calcineurin, peroxisome proliferator-activated receptor (PPAR) γ coactivator 1α (PGC-1α), PPARβ/δ, and AMP-activated protein kinase (AMPK), when chronically stimulated in animal models, remodel skeletal muscle toward the slow, oxidative myogenic program, whereas others, such as receptor-interacting protein 140 (RIP140) and E2F transcription factor 1 (E2F1), repress this phenotype. Recent studies demonstrating that pharmacologic and physiological activation of targets that shift dystrophic muscle toward the slow, oxidative myogenic program provide appreciable molecular and functional benefits. This review surveys the rationale behind, and evidence for, the study of skeletal muscle plasticity in preclinical models of DMD and highlights the potential therapeutic opportunities in advancing a strategy focused on remodeling skeletal muscle in patients with DMD toward the slow, oxidative phenotype.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".