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Record W4282027745 · doi:10.3390/genes13060963

Multisystem Proteinopathy Due to VCP Mutations: A Review of Clinical Heterogeneity and Genetic Diagnosis

2022· review· en· W4282027745 on OpenAlexaff
Gerald Pfeffer, Grace Lee, Carly Sabine Pontifex, Roberto D. Fanganiello, Allison Peck, Conrad C. Weihl, Virginia Kimonis

Bibliographic record

VenueGenes · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsUniversité LavalUniversity of Calgary
FundersNational Center for Advancing Translational Sciences
KeywordsAmyotrophic lateral sclerosisFrontotemporal dementiaPhenotypeDiseaseBiologyMedicineDementiaStress granuleGenetic heterogeneityParkinsonismGeneGeneticsNeuroscienceBioinformaticsPathology

Abstract

fetched live from OpenAlex

In this work, we review clinical features and genetic diagnosis of diseases caused by mutations in the gene encoding valosin-containing protein (VCP/p97), the functionally diverse AAA-ATPase. VCP is crucial to a multitude of cellular functions including protein quality control, stress granule formation and clearance, and genomic integrity functions, among others. Pathogenic mutations in VCP cause multisystem proteinopathy (VCP-MSP), an autosomal dominant, adult-onset disorder causing dysfunction in several tissue types. It can result in complex neurodegenerative conditions including inclusion body myopathy, frontotemporal dementia, amyotrophic lateral sclerosis, or combinations of these. There is also an association with other neurodegenerative phenotypes such as Alzheimer-type dementia and Parkinsonism. Non-neurological presentations include Paget disease of bone and may also include cardiac dysfunction. We provide a detailed discussion of genotype-phenotype correlations, recommendations for genetic diagnosis, and genetic counselling implications of VCP-MSP.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.065
GPT teacher head0.393
Teacher spread0.328 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations55
Published2022
Admission routes1
Has abstractyes

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