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Record W1901673166 · doi:10.1002/glia.22582

A mutation in the canine gene encoding folliculin‐interacting protein 2 (FNIP2) associated with a unique disruption in spinal cord myelination

2013· article· en· W1901673166 on OpenAlexaff
Trevor J. Pemberton, Sunju Choi, Joshua A. Mayer, Fangyuan Li, Nolan G. Gokey, John Svaren, Noa Safra, Danika L. Bannasch, Katrina Sullivan, Babetta A. Breuhaus, Pragna I. Patel, Ian D. Duncan

Bibliographic record

VenueGlia · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicWnt/β-catenin signaling in development and cancer
Canadian institutionsUniversity of Manitoba
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Allergy and Infectious Diseases
KeywordsBiologyMyelinFrameshift mutationMutationSpinal cordGeneticsGeneCentral nervous systemNeuroscience

Abstract

fetched live from OpenAlex

Novel mutations in myelin and myelin-associated genes have provided important information on oligodendrocytes and myelin and the effects of their disruption on the normal developmental process of myelination of the central nervous system (CNS). We report here a mutation in the folliculin-interacting protein 2 (FNIP2) gene in the Weimaraner dog that results in hypomyelination of the brain and a tract-specific myelin defect in the spinal cord. This myelination disruption results in a notable tremor syndrome from which affected dogs recover with time. In the peripheral tracts of the lateral and ventral columns of the spinal cord, there is a lack of mature oligodendrocytes. A genome-wide association study of DNA from three groups of dogs mapped the gene to canine chromosome 15. Sequencing of all the genes in the candidate region identified a frameshift mutation in the FNIP2 gene that segregated with the phenotype. While the functional role of FNIP2 is not known, our data would suggest that production of truncated protein results in a delay or failure of maturation of a subpopulation of oligodendrocytes.

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 score0.347
Threshold uncertainty score0.298

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 head0.013
GPT teacher head0.265
Teacher spread0.251 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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