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Record W2171890134 · doi:10.1096/fj.04-1956rev

Molecular, cellular, and pharmacological therapies for Duchenne/Becker muscular dystrophies

2005· review· en· W2171890134 on OpenAlexafffund
Joe V. Chakkalakal, Jennifer Thompson, Robin J. Parks, Bernard J. Jasmin

Bibliographic record

VenueThe FASEB Journal · 2005
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsDystrophinDuchenne muscular dystrophyUtrophinContext (archaeology)Muscular dystrophyBioinformaticsDiseaseMedicineComputational biologyNeuroscienceBiologyGeneticsPathology

Abstract

fetched live from OpenAlex

Although the molecular defect causing Duchenne/Becker muscular dystrophy (DMD/BMD) was identified nearly 20 years ago, the development of effective therapeutic strategies has nonetheless remained a daunting challenge. Over the years, a variety of different approaches have been explored in an effort to compensate for the lack of the DMD gene product called dystrophin. This review not only presents some of the most promising molecular, cellular, and pharmacological strategies but also highlights some issues that need to be addressed before considering their implementation. Specifically, we describe current strategies being developed to exogenously deliver healthy copies of the dystrophin gene to dystrophic muscles. We present the findings of several studies that have focused on repairing the mutant dystrophin gene using various approaches. We include a discussion of cell-based therapies that capitalize on the use of myoblast or stem cell transfer. Finally, we summarize the results of several studies that may eventually lead to the development of appropriate drug-based therapies. In this context, we review our current knowledge of the mechanisms regulating expression of utrophin, the autosomal homologue of dystrophin. Given the complexity associated with the dystrophic phenotype, it appears likely that a combinatorial approach involving different therapeutic strategies will be necessary for the appropriate management and eventual treatment of this devastating neuromuscular disease.

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 score0.989
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.030
GPT teacher head0.316
Teacher spread0.286 · 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

Citations129
Published2005
Admission routes2
Has abstractyes

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