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Record W2419387337 · doi:10.1038/npjregenmed.2016.6

Targeting muscle stem cell intrinsic defects to treat Duchenne muscular dystrophy

2016· article· en· W2419387337 on OpenAlexafffund
Nicolas A. Dumont, Michael A. Rudnicki

Bibliographic record

Venuenpj Regenerative Medicine · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Institutes of Health ResearchStem Cell NetworkMuscular Dystrophy CanadaE-RareGovernment of OntarioMuscular Dystrophy Association
KeywordsDuchenne muscular dystrophyDystrophinMuscular dystrophyBiologyCell biologyITGA7MyocyteSkeletal muscleStem cellRegeneration (biology)WastingGeneticsEndocrinology

Abstract

fetched live from OpenAlex

gene that encodes for the protein dystrophin. Dystrophin has critical roles in myofiber stability and integrity by connecting the actin cytoskeleton to the extracellular matrix. Absence of dystrophin leads to myofiber fragility and contributes to skeletal muscle degeneration in DMD patients, however, accumulating evidence also indicate that muscle stem cells (also known as satellite cells) are defective in dystrophic muscles, which leads to impaired muscle regeneration. Our recent work demonstrated that dystrophin is expressed in activated satellite cells, where it regulates the establishment of satellite cell polarity and asymmetric cell division. These findings indicate that dystrophin-deficient satellite cells have intrinsic dysfunctions that contribute to muscle wasting and progression of the disease. This discovery suggests that satellite cells could be targeted to treat DMD. Here we discuss how these new findings affect regenerative therapies for muscular dystrophies. Therapies targeting satellite cells hold great potential and could have long-term efficiency owing to the high self-renewal ability of these cells.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations68
Published2016
Admission routes2
Has abstractyes

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