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Record W2471046935 · doi:10.1038/srep29489

4-aminopyridine reverses ataxia and cerebellar firing deficiency in a mouse model of spinocerebellar ataxia type 6

2016· article· en· W2471046935 on OpenAlexafffund
Sriram Jayabal, Hui Ho Vanessa Chang, Kathleen E. Cullen, Alanna J. Watt

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsMcGill University
FundersNational Institutes of HealthNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health ResearchRoyal SocietyMcGill University
KeywordsSpinocerebellar ataxiaAtaxiaCerebellumMotor coordinationCerebellar ataxiaPurkinje cellNeuroscienceMedicineBiology

Abstract

fetched live from OpenAlex

Spinocerebellar ataxia type 6 (SCA6) is a devastating midlife-onset autosomal dominant motor control disease with no known treatment. Using a hyper-expanded polyglutamine (84Q) knock-in mouse, we found that cerebellar Purkinje cell firing precision was degraded in heterozygous (SCA6(84Q/+)) mice at 19 months when motor deficits are observed. Similar alterations in firing precision and motor control were observed at disease onset at 7 months in homozygous (SCA6(84Q/84Q)) mice, as well as a reduction in firing rate. We further found that chronic administration of the FDA-approved drug 4-aminopyridine (4-AP), which targets potassium channels, alleviated motor coordination deficits and restored cerebellar Purkinje cell firing precision to wildtype (WT) levels in SCA6(84Q/84Q) mice both in acute slices and in vivo. These results provide a novel therapeutic approach for treating ataxic symptoms associated with SCA6.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.039
GPT teacher head0.258
Teacher spread0.219 · 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

Citations107
Published2016
Admission routes2
Has abstractyes

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