ReviewGenomics & informatics2024
Neuromuscular diseases: genomics-driven advances.
Review in Genomics & informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Implementation of a neuromuscular clinical trial network: a rare disease model for enhancing clinical trial readiness, capacity, and access in Canada.Orphanet journal of rare diseases · 2026Article
- Molecular Insights and Orthopedic Management in Muscular Dystrophies: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Gene Therapy and Gene Editing: Current Trends and Future Prospects of Molecular Medicine.Current gene therapy · 2026Review
- From Variant Interpretation to Biomarker Translation: Multi-omics Integration in Inherited Neuromuscular Diseases.Human mutation · 2026Review
- MyomiRs Expression in Limb Girdle Muscular Dystrophy.IUBMB life · 2025Review
- Integrative Transcriptomic and Network-Based Analysis of Neuromuscular Diseases.International journal of molecular sciences · 2025Article
- Decoding Neuromuscular Disorders: The Complex Role of Genetic and Epigenetic Regulators.Genes · 2025Review
- Editorial for the special collection: frontiers in rare disease genetics.Genomics & informatics · 2025Article
- Feasibility and usability of a wearable sensor system for gait assessment in children with neuromuscular diseases.Frontiers in rehabilitation sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuromuscular diseases (NMDs) are a group of rare disorders characterized by significant genetic and clinical complexity. Advances in genomics have revolutionized both the diagnosis and treatment of NMDs. While fewer than 30 NMDs had known genetic causes before the 1990s, more than 600 have now been identified, largely due to the adoption of next-generation sequencing (NGS) technologies such as whole-exome sequencing (WES) and whole-genome sequencing (WGS). These technologies have enabled more precise and earlier diagnoses, although the genetic complexity of many NMDs continues to pose challenges. Gene therapy has been a transformative breakthrough in the treatment of NMDs. In spinal muscular atrophy (SMA), therapies like nusinersen, onasemnogene abeparvovec, and risdiplam have dramatically improved patient outcomes. Similarly, Duchenne muscular dystrophy (DMD) has seen significant progress, most notably with the FDA approval of delandistrogene moxeparvovec, the first micro-dystrophin gene therapy. Despite these advancements, challenges remain, including the rarity of many NMDs, genetic heterogeneity, and the high costs associated with genomic technologies and therapies. Continued progress in gene therapy, RNA-based therapeutics, and personalized medicine holds promise for further breakthroughs in the management of these debilitating diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.