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Record W3091839684 · doi:10.1016/j.ebiom.2020.103032

Recent insights into neuromuscular junction biology in Duchenne muscular dystrophy: Impacts, challenges, and opportunities

2020· review· en· W3091839684 on OpenAlexafffund
Sean Y. Ng, Vladimir Ljubicic

Bibliographic record

VenueEBioMedicine · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchOntario Ministry of Economic Development, Job Creation and TradeCanada Research ChairsMuscular Dystrophy CanadaNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsDuchenne muscular dystrophyNeuromuscular junctionMuscular dystrophyContext (archaeology)NeuroscienceWastingNeuromuscular diseaseBiologyDystrophinBioinformaticsMedicineDiseaseGeneticsPathologyEndocrinology

Abstract

fetched live from OpenAlex

Duchenne muscular dystrophy (DMD) is the most common and relentless form of muscular dystrophy. The pleiotropic effects of dystrophin deficiency include remarkable impacts on neuromuscular junction (NMJ) structure and function. Some of these alterations contribute to the severe muscle wasting and weakness that distinguish DMD, while others attempt to compensate for them. Experimental approaches that correct NMJ biology in pre-clinical models of DMD attenuate disease progression and improve functional outcomes, which suggests that targeting the NMJ may be an effective therapeutic strategy for DMD patients. The objectives of this review are to 1) survey the distinctions in NMJ structure, function, and gene expression in the dystrophic context as compared to the healthy condition, and 2) summarize the efforts, opportunities and challenges to correct NMJ biology in DMD. This information will expand our basic understanding of neuromuscular biology and may be useful for designing novel NMJ-targeted drug or behavioural strategies to mitigate the dystrophic pathology and other disorders of the neuromuscular system.

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 score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.058
GPT teacher head0.307
Teacher spread0.248 · 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

Citations44
Published2020
Admission routes2
Has abstractyes

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