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Record W2006861467 · doi:10.1517/14712598.2012.693469

Exon skipping for nonsense mutations in Duchenne muscular dystrophy: too many mutations, too few patients?

2012· review· en· W2006861467 on OpenAlexaff
Toshifumi Yokota, William Duddy, Yusuke Echigoya, Hanna Kolski

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMuscular Dystrophy CanadaUniversity of Alberta
Fundersnot available
KeywordsExon skippingNonsense mutationExonDuchenne muscular dystrophyDystrophinGeneticsNonsenseMutationBiologyMuscular dystrophyBioinformaticsMedicineGeneAlternative splicingMissense mutation

Abstract

fetched live from OpenAlex

INTRODUCTION: Duchenne muscular dystrophy (DMD), one of the most common and lethal genetic disorders, is caused by mutations of the dystrophin gene. Removal of an exon or of multiple exons using antisense molecules has been demonstrated to allow synthesis of truncated 'Becker muscular dystrophy-like' dystrophin. AREAS COVERED: Approximately 15% of DMD cases are caused by a nonsense mutation. Although patient databases have previously been surveyed for applicability to each deletion mutation pattern, this is not so for nonsense mutations. Here, we examine the world-wide database containing notations for more than 1200 patients with nonsense mutations. Approximately 47% of nonsense mutations can be potentially treated with single exon skipping, rising to 90% with double exon skipping, but to reach this proportion requires the development of exon skipping molecules targeting some 68 of dystrophin's 79 exons, with patient numbers spread thinly across those exons. In this review, we discuss progress and remaining hurdles in exon skipping and an alternative strategy, stop-codon readthrough. EXPERT OPINION: Antisense-mediated exon skipping therapy is targeted highly at the individual patient and is a clear example of personalized medicine. An efficient regulatory path for drug approval will be a key to success.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.110
GPT teacher head0.376
Teacher spread0.266 · 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.

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

Citations43
Published2012
Admission routes1
Has abstractyes

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