Evidence map›Paper›PMID 41524923›Full record

ReviewMolecular biology reports2026

Emerging therapeutic strategies in muscular dystrophy: an updated review on pathogenesis and treatment advances.

Shahid Parwez, Khurshid Ahmad, Eun Ju Lee, Inho Choi

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shahid ParwezDepartment of Medical Biotechnology, Yeungnam University, Gyeongsan, 38541, South Korea.
Khurshid AhmadDepartment of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia.
Eun Ju LeeDepartment of Medical Biotechnology, Yeungnam University, Gyeongsan, 38541, South Korea.
Inho ChoiDepartment of Medical Biotechnology, Yeungnam University, Gyeongsan, 38541, South Korea. inhochoi@ynu.ac.kr.

Funding

Basic Science Research Program through the National Research Foundation of Korea (NRF) RS-2020-NR049591
6 · The paper itself

Abstract

Muscular dystrophy (MD) comprises a class of genetic conditions characterized by the progressive degeneration and weakness of skeletal muscle. Genetic etiologies differ among the major muscular dystrophies: myotonic dystrophy type 1 (DM1) is linked to CTG repeat expansion in DMPK whereas DM2 is linked to CCTG repeat expansion in CNBP; facioscapulohumeral muscular dystrophy (FSHD1) is linked to contraction of the D4Z4 repeat to cause inappropriate DUX4 expression whereas FSHD2 is linked to mutations in chromatin modifier SMCHD1 that derepress DUX4 expression. Despite advancements in investigations into the molecular mechanisms, effective treatments for MD remain limited. This review study aims to elaborate on the pathogenesis of each type of MD, including the underlying genetic mutations, cellular dysfunction, and pathway deregulation. We also conduct comprehensive research on various breakthroughs in treatment strategies, including protein replacement therapies, stem-cell-based, exon skipping, gene therapy, and recently discovered drugs for MD. Furthermore, this study focuses on the artificial intelligence (AI)-based improvement in the diagnosis, management, and treatment of MD. The AI-based discovery of compounds has provided novel treatment modalities that hold potential for managing MD conditions.

Indexed as

Muscular DystrophiesAnimalsGenetic TherapyHumansMuscular Dystrophy, FacioscapulohumeralMutationMyotonic DystrophyArtificial intelligenceDuchenne muscular dystrophyDystrophinMachine learningMuscular dystrophies

Identifiers

PMID41524923

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.