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Record W4413757518 · doi:10.3390/cells14171326

Cardiac Cell and Animal Models for Duchenne Muscular Dystrophy in the Era of Gene Therapy and Precision Medicine

2025· review· en· W4413757518 on OpenAlexafffund
Hidenori Moriyama, Toshifumi Yokota

Bibliographic record

VenueCells · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMuscular Dystrophy CanadaUniversity of Alberta
FundersMuscular Dystrophy CanadaUniversity of AlbertaCanadian Institutes of Health ResearchParent Project Muscular DystrophyFaculty of Medicine and Dentistry, University of AlbertaFoundation to Eradicate DuchenneWomen and Children's Health Research InstituteChildren's Health Research InstituteHeart and Stroke Foundation of CanadaU.S. Department of Defense
KeywordsDuchenne muscular dystrophyMuscular dystrophyMedicineGenetic enhancementPrecision medicineCardiologyBioinformaticsInternal medicineGenePathologyBiologyGenetics

Abstract

fetched live from OpenAlex

is rapidly progressing. Patient-derived induced pluripotent stem cells and animal models that mimic patient-specific mutations have significantly contributed to the advancement of precision medicine based on individual genetic profiles. Currently, no approved disease-specific therapy exists for DMD cardiomyopathy, which remains one of the leading causes of death in DMD patients. Therefore, the development of effective cardiac therapies represents a critical milestone in DMD research. In this review, we provide an overview of essential cellular and animal models used in DMD research, with a specific focus on the heart. We describe their key characteristics, advantages, and limitations. It is considered that a comprehensive and strategic integration of these models-based on a clear understanding of their respective strengths and weaknesses-will be important for advancing the development and clinical application of targeted therapies for DMD cardiomyopathy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.283
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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