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Record W4296164563 · doi:10.1126/sciadv.abh3260

Activation of TrkB-Akt signaling rescues deficits in a mouse model of SCA6

2022· article· en· W4296164563 on OpenAlexaff
Anna A. Cook, Sriram Jayabal, Jacky Sheng, Eviatar Fields, Tsz Chui Sophia Leung, Sabrina Quilez, Eileen McNicholas, Lois Lau, Shixia Huang, Alanna J. Watt

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsMcGill University
FundersNational Cancer InstituteNational Institutes of Health
KeywordsTropomyosin receptor kinase BNeurotrophic factorsSpinocerebellar ataxiaNeuroscienceProtein kinase BAtaxiaBrain-derived neurotrophic factorMotor coordinationCerebellumAgonistPurkinje cellCerebellar ataxiaCerebellar cortexNeurotrophinTrk receptorMedicineInternal medicinePsychologyReceptorBiologySignal transductionCell biology

Abstract

fetched live from OpenAlex

Spinocerebellar ataxia type 6 (SCA6) is a neurodegenerative disease resulting in motor coordination deficits and cerebellar pathology. Expression of brain-derived neurotrophic factor (BDNF) is reduced in postmortem tissue from SCA6 patients. Here, we show that levels of cerebellar BDNF and its receptor, tropomyosin receptor kinase B (TrkB), are reduced at an early disease stage in a mouse model of SCA6 (SCA6 84Q/84Q ). One month of exercise elevated cerebellar BDNF expression and improved ataxia and cerebellar Purkinje cell firing rate deficits. A TrkB agonist, 7,8-dihydroxyflavone (7,8-DHF), likewise improved motor coordination and Purkinje cell firing rate and elevated downstream Akt signaling. Prolonged 7,8-DHF administration persistently improved ataxia when treatment commenced near disease onset but was ineffective when treatment was started late. These data suggest that 7,8-DHF, which is orally bioavailable and crosses the blood-brain barrier, is a promising therapeutic for SCA6 and argue for the importance of early intervention for 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.374
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.306
Teacher spread0.257 · 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 teacher head, 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

Citations31
Published2022
Admission routes1
Has abstractyes

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