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Record W2147205113 · doi:10.1093/hmg/ddu142

Myogenic program dysregulation is contributory to disease pathogenesis in spinal muscular atrophy

2014· article· en· W2147205113 on OpenAlexafffund
Justin G. Boyer, Marc‐Olivier Deguise, Lyndsay M. Murray, Armin Yazdani, Yves De Repentigny, Céline Boudreau‐Larivière, Rashmi Kothary

Bibliographic record

VenueHuman Molecular Genetics · 2014
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversity of OttawaLaurentian UniversityOttawa Hospital
FundersMultiple Sclerosis Society of CanadaCanadian Institutes of Health ResearchMultiple Sclerosis SocietyUniversity of OttawaMuscular Dystrophy Association
KeywordsSpinal muscular atrophySMA*SMN1BiologyMotor neuronMyocyteTrichostatin AMuscle atrophyAtrophyPathogenesisNeuroscienceEndocrinologyHistone deacetylaseSpinal cordImmunologyGeneticsGeneHistone

Abstract

fetched live from OpenAlex

Mutations in the survival motor neuron (SMN1) gene lead to the neuromuscular disease spinal muscular atrophy (SMA). Although SMA is primarily considered as a motor neuron disease, the importance of muscle defects in its pathogenesis has not been fully examined. We use both primary cell culture and two different SMA model mice to demonstrate that reduced levels of Smn lead to a profound disruption in the expression of myogenic genes. This disruption was associated with a decrease in myofiber size and an increase in immature myofibers, suggesting that Smn is crucial for myogenic gene regulation and early muscle development. Histone deacetylase inhibitor trichostatin A treatment of SMA model mice increased myofiber size, myofiber maturity and attenuated the disruption of the myogenic program in these mice. Taken together, our work highlights the important contribution of myogenic program dysregulation to the muscle weakness observed in SMA.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
GPT teacher head0.330
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), 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

Citations83
Published2014
Admission routes2
Has abstractyes

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