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Record W2785441464 · doi:10.1038/aps.2018.11

Proteostasis in Huntington's disease: disease mechanisms and therapeutic opportunities

2018· review· en· W2785441464 on OpenAlexafffund
Rachel Harding, Yufeng Tong

Bibliographic record

VenueActa Pharmacologica Sinica · 2018
Typereview
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersFundação de Amparo à Pesquisa do Estado de São PauloNovartis PharmaWellcome TrustOntario Genomics InstituteEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAGenome CanadaOntario GenomicsNatural Sciences and Engineering Research Council of CanadaPfizer
KeywordsProteostasisProteasomeAutophagyProtein aggregationBiologyUbiquitinHuntington's diseaseDiseaseCell biologyMechanism (biology)NeuroscienceMedicineBiochemistryApoptosisPathologyGene

Abstract

fetched live from OpenAlex

Many neurodegenerative diseases are characterized by impairment of protein quality control mechanisms in neuronal cells. Ineffective clearance of misfolded proteins by the proteasome, autophagy pathways and exocytosis leads to accumulation of toxic protein oligomers and aggregates in neurons. Toxic protein species affect various cellular functions resulting in the development of a spectrum of different neurodegenerative proteinopathies, including Huntington's disease (HD). Playing an integral role in proteostasis, dysfunction of the ubiquitylation system in HD is progressive and multi-faceted with numerous biochemical pathways affected, in particular, the ubiquitin-proteasome system and autophagy routes for protein aggregate degradation. Unravelling the molecular mechanisms involved in HD pathogenesis of proteostasis provides new insight in disease progression in HD as well as possible therapeutic avenues. Recent developments of potential therapeutics are discussed in this review.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.234
GPT teacher head0.395
Teacher spread0.161 · 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

Citations109
Published2018
Admission routes2
Has abstractyes

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Same venueActa Pharmacologica SinicaSame topicGenetic Neurodegenerative DiseasesFrench-language works237,207