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Record W2492410565 · doi:10.1093/hmg/ddw225

Muscle dysfunction caused by loss of<i>Magel2</i>in a mouse model of Prader-Willi and Schaaf-Yang syndromes

2016· article· en· W2492410565 on OpenAlexaff
Ain A. Kamaludin, Christa Smolarchuk, Jocelyn M. Bischof, Rachelle Eggert, John J. Greer, Jun Ren, Joshua J. Lee, Toshifumi Yokota, Fred B. Berry, Rachel Wevrick

Bibliographic record

VenueHuman Molecular Genetics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHypotoniaInternal medicineEndocrinologyAtrophyPhenotypeSarcopeniaBiologyObesityLean body massMedicineGeneBody weightGenetics

Abstract

fetched live from OpenAlex

Prader-Willi syndrome is characterized by severe hypotonia in infancy, with decreased lean mass and increased fat mass in childhood followed by severe hyperphagia and consequent obesity. Scoliosis and other orthopaedic manifestations of hypotonia are common in children with Prader-Willi syndrome and cause significant morbidity. The relationships among hypotonia, reduced muscle mass and scoliosis have been difficult to establish. Inactivating mutations in one Prader-Willi syndrome candidate gene, MAGEL2, cause a Prader-Willi-like syndrome called Schaaf-Yang syndrome, highlighting the importance of loss of MAGEL2 in Prader-Willi syndrome phenotypes. Gene-targeted mice lacking Magel2 have excess fat and decreased muscle, recapitulating altered body composition in Prader-Willi syndrome. We now demonstrate that Magel2 is expressed in the developing musculoskeletal system, and that loss of Magel2 causes muscle-related phenotypes in mice consistent with atrophy caused by altered autophagy. Magel2-null mice serve as a preclinical model for therapies targeting muscle structure and function in children lacking MAGEL2 diagnosed with Prader-Willi or Schaaf-Yang syndrome.

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 score0.020
Threshold uncertainty score0.776

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 head0.015
GPT teacher head0.234
Teacher spread0.220 · 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

Citations47
Published2016
Admission routes1
Has abstractyes

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