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Record W2618468792 · doi:10.1111/cge.13056

Disruption of <i><scp>YWHAE</scp></i> gene at 17p13.3 causes learning disabilities and brain abnormalities

2017· article· en· W2618468792 on OpenAlexaff
Abdul Noor, S. Bogatan, Nicholas A. Watkins, Wendy S. Meschino, Dimitri J. Stavropoulos

Bibliographic record

VenueClinical Genetics · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topic14-3-3 protein interactions
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoNorth York General HospitalMount Sinai Hospital
Fundersnot available
KeywordsHaploinsufficiencyLissencephalyBiologyPhenotypeCancer researchGeneGenetics

Abstract

fetched live from OpenAlex

There is a broad phenotypic spectrum of patients with 17p13.3 deletions. One of the most prominent feature is lissencephaly caused by haploinsufficiency of the gene PAFAH1B1. The deletion of this gene and those distal to it, results in Miller-Dieker syndrome, however there have been many reports of patients with haploinsufficiency of the distal genes alone. The deletions of these genes including YWHAE CRK and TUSC5 have been studied extensively and YWHAE has been postulated to be the cause of neurological abnormalities. The patients with deletions of the Miller-Dieker syndrome distal region present with variable clinical features including brain abnormalities, growth retardation, developmental delay, facial dysmorphisms and seizures. While there have been many patients reported to have deletions involving the YWHAE gene along with other genes, here we present the first detailed clinical description of a patient with deletion of YWHAE alone, allowing a more accurate characterization of the pathogenicity of YWHAE haploinsufficiency. The patient reported here demonstrated brain abnormalities, learning disabilities, and seizures supporting the role of YWHAE in these features. We review the literature and use this case report to better characterize and further confirm the genotype-phenotype relationship of the genes within the critical region of Miller-Dieker 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)0.006
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 score0.504
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.046
GPT teacher head0.369
Teacher spread0.322 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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