Current Genetic Survey and Potential Gene-Targeting Therapeutics for Neuromuscular Diseases
Bibliographic record
Abstract
Neuromuscular diseases (NMDs) belong to a class of functional impairments that cause dysfunctions of the motor neuron-muscle functional axis components. Inherited monogenic neuromuscular disorders encompass both muscular dystrophies and motor neuron diseases. Understanding of their causative genetic defects and pathological genetic mechanisms has led to the unprecedented clinical translation of genetic therapies. Challenged by a broad range of gene defect types, researchers have developed different approaches to tackle mutations by hijacking the cellular gene expression machinery to minimize the mutational damage and produce the functional target proteins. Such manipulations may be directed to any point of the gene expression axis, such as classical gene augmentation, modulating premature termination codon ribosomal bypass, splicing modification of pre-mRNA, etc. With the soar of the CRISPR-based gene editing systems, researchers now gravitate toward genome surgery in tackling NMDs by directly correcting the mutational defects at the genome level and expanding the scope of targetable NMDs. In this article, we will review the current development of gene therapy and focus on NMDs that are available in published reports, including Duchenne Muscular Dystrophy (DMD), Becker muscular dystrophy (BMD), X-linked myotubular myopathy (XLMTM), Spinal Muscular Atrophy (SMA), and Limb-girdle muscular dystrophy Type 2C (LGMD2C).
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 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".