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Record W4294579442 · doi:10.1177/10738584221120182

Cyclin F, Neurodegeneration, and the Pathogenesis of ALS/FTD

2022· article· en· W4294579442 on OpenAlexfundno aff
Stephanie L. Rayner, Alison Hogan, Jennilee M. Davidson, Flora Cheng, Luan Luu, Marco Morsch, Ian P. Blair, Roger S. Chung, Albert Lee

Bibliographic record

VenueThe Neuroscientist · 2022
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsnot available
FundersSnow FoundationNational Health and Medical Research CouncilNatural Sciences and Engineering Research Council of CanadaMacquarie UniversityMotor Neurone Disease AustraliaFightMND
KeywordsAmyotrophic lateral sclerosisFrontotemporal dementiaNeurodegenerationUbiquitin ligaseNeuroscienceBiologyMotor neuronC9orf72UbiquitinDementiaDiseaseGeneticsGeneSpinal cordMedicinePathology

Abstract

fetched live from OpenAlex

Amyotrophic lateral sclerosis (ALS) is the most common form of motor neuron disease and is characterized by the degeneration of upper and lower motor neurons of the brain and spinal cord. ALS is also linked clinically, genetically, and pathologically to a form of dementia known as frontotemporal dementia (FTD). Identifying gene mutations that cause ALS/FTD has provided valuable insight into the disease process. Several ALS/FTD-causing mutations occur within proteins with roles in protein clearance systems. This includes ALS/FTD mutations in CCNF, which encodes the protein cyclin F: a component of a multiprotein E3 ubiquitin ligase that mediates the ubiquitylation of substrates for their timely degradation. In this review, we provide an update on the link between ALS/FTD CCNF mutations and neurodegeneration.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.260
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

Citations10
Published2022
Admission routes1
Has abstractyes

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