Genetic therapy for congenital myopathies
Bibliographic record
Abstract
PURPOSE OF REVIEW: There has been an explosion of advancement in the field of genetic therapies. The first gene-based treatments are now in clinical practice, with several additional therapeutic programs in various stages of development. Novel technologies are being developed that will further advance the breadth and success of genetic medicine.Congenital myopathies are an important group of neuromuscular disorders defined by structural changes in the muscle and characterized by severe clinical symptoms caused by muscle weakness. At present, there are no approved drug therapies for any subtype of congenital myopathy.In this review, we present an overview of genetic therapies and discuss their application to congenital myopathies. RECENT FINDINGS: Several candidate therapeutics for congenital myopathies are in the development pipeline, including ones in clinical trial. These include genetic medicines such as gene replacement therapy and antisense oligonucleotide-based gene knockdown. We highlight the programs related to genetic medicine, and also discuss congenital myopathy subtypes where genetic therapy could be applied. SUMMARY: Genetic therapies are ushering in an era of precision medicine for neurological diseases. Congenital myopathies are conditions ideally suited for genetic medicine approaches, and the first such therapies will hopefully soon be reaching congenital myopathy patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".