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Record W2517382074 · doi:10.1111/dmcn.13250

Loss‐of‐function mutation in <i>RUSC2</i> causes intellectual disability and secondary microcephaly

2016· article· en· W2517382074 on OpenAlexaff
Ali H. Alwadei, Ruba Benini, Adel Mahmoud, Ali Alasmari, Erik‐Jan Kamsteeg, Majid Alfadhel

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsMontreal Children's Hospital
Fundersnot available
KeywordsMicrocephalyRabIntellectual disabilityBiologyGeneticsMutationHypotoniaNonsense mutationExomeExome sequencingLoss functionNonsenseGTPaseNeuroscienceGeneMissense mutationPhenotype

Abstract

fetched live from OpenAlex

Inherited aberrancies in intracellular vesicular transport are associated with a variety of neurological and non-neurological diseases. RUSC2 is a gene found on chromosome 9p13.3 that codes for iporin, a ubiquitous protein with high expression in the brain that interacts with Rab proteins (GTPases implicated in intracellular protein trafficking). Although mutations in Rab proteins have been described as causing brain abnormalities and intellectual disability, until now no disease-causing mutations in RUSC2 have ever been reported in humans. We describe, to our knowledge for the first time, three patients with inherited homozygous nonsense mutations identified in RUSC2 on whole-exome sequencing. All three patients had central hypotonia, microcephaly, and moderate to severe intellectual disability. Two patients had additional features of early-onset epilepsy and absence of the splenium. This report adds to the ever-expanding landscape of genetic causes of intellectual disability and increases our understanding of the cellular processes underlying this important neurological entity.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.204
Teacher spread0.197 · 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 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

Citations16
Published2016
Admission routes1
Has abstractyes

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