MétaCan
Menu
← Back to cohort

Why does a defect in choline kinase beta cause muscular dystrophy in mice?

2008· article· en· W139605025 on OpenAlexaffabout
Gengshu Wu, Dennis Vance

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMuscular dystrophyPhosphocholineSkeletal muscleEndocrinologyInternal medicineBiologyDuchenne muscular dystrophyPhosphatidylcholineMedicineBiochemistryPhospholipid

Abstract

fetched live from OpenAlex

Muscular dystrophies (MD) are a group of more than 30 genetic diseases characterized by progressive weakness and degeneration of the skeletal muscles. There is no specific treatment to stop or reverse any form of MD due to limited knowledge. We recently uncovered a natural mutation (rmd) in the Chkb gene that resulted in a complete loss of choline kinase (CK) activity in skeletal muscle, and the mice developed severe and progressive hindlimb MD. It is the first demonstration of a defect in a phospholipid biosynthetic enzyme causing MD. Why does muscular dystrophy occur? We observed decreased phosphatidylcholine (PC)/phosphatidylethanolamine (PE) ratio both in hindlimb muscle and in mitochondria associated with sarcolemmal integrity and megamitochondria formation. Decreased PC level is due to three factors: down‐regulated CTP:phosphocholine cytidylyltransferase (CT) activity, the rate‐limiting enzyme for PC biosynthesis; increased PC turnover due to increased phospholipase activity, and a limited source of phosphocholine in Chkb −/– mice. In vivo experiments further demonstrated that the incorporation ability of 3 H‐choline into PC was significantly decreased in affected muscle. Decreased regeneration ability of Chkb −/– mice may also contribute to muscular dystrophy. In affected muscle there was increased expression of myostatin and decreased proliferating cell nuclear antigen (PCNA). This research was supported by a grant from Alberta Heritage Foundation for Medical Research and Canadian Institutes of Health Research.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Citations0
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicMuscle Physiology and Disorders→French-language works237,207→