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Record W2784259796 · doi:10.1002/mds.27270

The emerging role of Rab GTPases in the pathogenesis of Parkinson's disease

2018· review· en· W2784259796 on OpenAlexaff
Yujing Gao, Gabrielle R. Wilson, Sarah Stephenson, Kiymet Bozaoglu, Matthew J. Farrer, Paul J. Lockhart

Bibliographic record

VenueMovement Disorders · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsBritish Columbia Centre of Excellence for Women's HealthUniversity of British Columbia
FundersNational Health and Medical Research CouncilMichael J. Fox Foundation for Parkinson's Research
KeywordsRabGTPaseLRRK2PathogenesisBiologyCell biologyNeuroscienceParkinson's diseaseGuanine nucleotide exchange factorDiseaseMedicineImmunologyPathology

Abstract

fetched live from OpenAlex

The identification of pathogenic mutations in Ras analog in brain 39B (RAB39B) and Ras analog in brain 32 (RAB32) that cause Parkinson's disease (PD) has highlighted the emerging role of protein trafficking in disease pathogenesis. Ras analog in brain (Rab) Guanosine triphosphatase (GTPase) function as master regulators of membrane trafficking, including vesicle formation, movement along cytoskeletal networks, and membrane fusion. Recent studies have linked Rab GTPases with α-synuclein, Leucine-rich repeat kinase 2, and Vacuolar protein sorting 35, 3 key proteins in PD pathogenesis. In this review, we discuss the various RAB GTPases associated with PD, current progress in the research, and potential future directions. Investigations into the function of RAB GTPases will likely provide significant insight into the etiology of PD and identify novel therapeutic targets for a currently incurable disease. © 2018 International Parkinson and Movement Disorder Society.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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

Citations63
Published2018
Admission routes1
Has abstractyes

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