Molecular, cellular, and pharmacological therapies for Duchenne/Becker muscular dystrophies
Bibliographic record
Abstract
Although the molecular defect causing Duchenne/Becker muscular dystrophy (DMD/BMD) was identified nearly 20 years ago, the development of effective therapeutic strategies has nonetheless remained a daunting challenge. Over the years, a variety of different approaches have been explored in an effort to compensate for the lack of the DMD gene product called dystrophin. This review not only presents some of the most promising molecular, cellular, and pharmacological strategies but also highlights some issues that need to be addressed before considering their implementation. Specifically, we describe current strategies being developed to exogenously deliver healthy copies of the dystrophin gene to dystrophic muscles. We present the findings of several studies that have focused on repairing the mutant dystrophin gene using various approaches. We include a discussion of cell-based therapies that capitalize on the use of myoblast or stem cell transfer. Finally, we summarize the results of several studies that may eventually lead to the development of appropriate drug-based therapies. In this context, we review our current knowledge of the mechanisms regulating expression of utrophin, the autosomal homologue of dystrophin. Given the complexity associated with the dystrophic phenotype, it appears likely that a combinatorial approach involving different therapeutic strategies will be necessary for the appropriate management and eventual treatment of this devastating neuromuscular 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".