MétaCan
Menu
Back to cohort
Record W2088832657 · doi:10.1002/mds.21895

Large deletions account for an increasing number of mutations in <i>SGCE</i>

2007· article· en· W2088832657 on OpenAlexaff
Fabin Han, Lemuel Racacho, Howard H. Yang, Tara Read, Oksana Suchowersky, Anthony E. Lang, David A. Grimes, Dennis E. Bulman

Bibliographic record

VenueMovement Disorders · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsUniversity Health NetworkOntario Brain InstituteUniversity of CalgaryToronto Western HospitalUniversity of Ottawa
Fundersnot available
KeywordsGeneticsBiologyMutationProbandDenaturing high performance liquid chromatographyGeneMolecular biology

Abstract

fetched live from OpenAlex

Myoclonus-dystonia (M-D) (MIM 159900) is a rare "dystonia plus" syndrome, characterized by rapid myoclonic jerks, predominantly in the neck and upper limbs, in combination with dystonia. Mutations in the gene epsilon-sarcoglycan (SGCE) are known to be responsible for approximately one-third of cases. We screened 21 probands diagnosed with M-D for large deletions who were mutation negative as determined by PCR-direct sequencing. Multiplex PCR and quantification of PCR products was performed using a modified application of denaturing high performance liquid chromatography (dHPLC). We have identified two novel large multiexonic deletions of SGCE, which were confirmed by amplifying and sequencing the deletion breakpoints. Five other families were found to harbor small mutations identified by direct sequencing. Analysis of the region surrounding the deletions demonstrates that both deletions are the result of nonhomologous recombination with homologous end joining. This is only the second report of intragenic deletions with SGCE and it highlights the need to include exonic copy number variation when performing mutational analysis of SGCE.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.499

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 head0.277
Teacher spread0.268 · 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 designObservational
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

Citations17
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueMovement DisordersSame topicGenetics and Neurodevelopmental DisordersFrench-language works237,207