Gene therapy for Duchenne muscular dystrophy: an update on the latest clinical developments
Bibliographic record
Abstract
INTRODUCTION: Duchenne muscular dystrophy (DMD) is one of the most severe and devastating neuromuscular hereditary diseases with a male newborn incidence of 20 000 cases each year. The disease caused by mutations (exon deletions, nonsense mutations, intra-exonic insertions or deletions, exon duplications, splice site defects, and deep intronic mutations) in the DMD gene, progressively leads to muscle wasting and loss of ambulation. This situation is painful for both patients and their families, calling for an emergent need for effective treatments. AREAS COVERED: In this review, the authors describe the state of the gene therapy approach in clinical trials for DMD. This therapeutics included gene replacement, gene substitution, RNA-based therapeutics, readthrough mutation, and the CRISPR approach. EXPERT OPINION: Only a few drug candidates have yet been granted conditional approval for the treatment of DMD. Most of these therapies have only a modest capability to restore the dystrophin or improve muscle function, suggesting an important unmet need in the development of DMD therapeutics. Complementary genes and cellular therapeutics need to be explored to both restore dystrophin, improve muscle function, and efficiently reconstitute the muscle fibers in the advanced stage of the disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.006 | 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".