Empowering Muscle Stem Cells for the Treatment of Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a devastating and debilitating muscle degenerative disease affecting 1 in every 3,500 male births worldwide. DMD is progressive and fatal; accumulated weakening of the muscle tissue leads to an inability to walk and eventual loss of life due to respiratory and cardiac failure. Importantly, there remains no effective cure for DMD. DMD is caused by defective expression of the DMD gene, which encodes for dystrophin, a component of the dystrophin glycoprotein complex. In muscle fibers, this protein complex plays a critical role in maintaining muscle membrane integrity. Emerging studies have shown that muscle stem cells, which are adult stem cells responsible for muscle repair, are also affected in DMD. DMD muscle stem cells do not function as healthy muscle stem cells, and their impairment contributes to disease progression. Deficiencies in muscle stem cell function include impaired establishment of cell polarity leading to defective asymmetric stem cell division, reduced myogenic commitment, impaired differentiation, altered metabolism, and enhanced entry into senescence. Altogether, these findings indicate that DMD muscle stem cells are dysfunctional and have impaired regenerative potential. Although recent advances in adeno-associated vector and antisense oligonucleotide-mediated mechanisms for gene therapy have shown clinical promise, the current therapeutic strategies for muscular dystrophy do not effectively target muscle stem cells and do not address the deficiencies in muscle stem cell function. Here, we discuss the merits of restoring endogenous muscle stem cell function in degenerating muscle as a viable regenerative medicine strategy to mitigate DMD.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".