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Record W3083521980 · doi:10.1073/pnas.2003798117

Ataxin-1 regulates B cell function and the severity of autoimmune experimental encephalomyelitis

2020· article· en· W3083521980 on OpenAlexaff
Alessandro Didonna, Ester Canto Puig, Qin Ma, Atsuko Matsunaga, Brenda Ho, Stacy J. Caillier, Hengameh Shams, Nicholas Lee, Stephen L. Hauser, Qiumin Tan, Scott S. Zamvil, Jorge R. Oksenberg

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversity of Alberta
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthFondazione Italiana Sclerosi MultiplaNational Multiple Sclerosis Society
KeywordsExperimental autoimmune encephalomyelitisMultiple sclerosisSpinocerebellar ataxiaBiologyPathogenesisAtaxiaAutoimmunityGeneticsImmunologyNeuroscienceImmune system

Abstract

fetched live from OpenAlex

Significance Over 200 genomic loci have been found associated with the risk of developing multiple sclerosis (MS). Despite this important body of data, limited information exists on the cellular pathways and molecular mechanisms underlying MS genetic complexity. In this study, we report the functional characterization of the ataxin-1 encoding ATXN1 susceptibility locus. Ataxin-1 is a polyglutamine protein that is classically associated with the neurodegenerative disorder spinocerebellar ataxia type 1 (SCA1). Here, we show that ataxin-1 also exerts a protective activity against autoimmune demyelination in a preclinical model of MS. This function is associated with an immunomodulatory role mainly targeting the B cell compartment. Altogether, these findings expand our current knowledge on both MS pathogenesis and ataxin-1 biology.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.043
GPT teacher head0.274
Teacher spread0.231 · 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 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

Citations47
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicGenetic Neurodegenerative DiseasesFrench-language works237,207