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Record W2530086384 · doi:10.1016/j.celrep.2016.09.030

Voluntary Running Triggers VGF-Mediated Oligodendrogenesis to Prolong the Lifespan of Snf2h-Null Ataxic Mice

2016· article· en· W2530086384 on OpenAlexafffund
Matías Alvarez-Saavedra, Yves De Repentigny, Doo Yang, Ryan W. O’Meara, Keqin Yan, Lukas E. Hashem, Lemuel Racacho, Ilya Ioshikhes, Dennis E. Bulman, Robin J. Parks, Rashmi Kothary, David J. Picketts

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsChildren's Hospital of Eastern OntarioOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsHindbrainAtaxiaNeuroscienceNeurogenesisCerebellumBiologyConditional gene knockoutCentral nervous systemPhenotypeGeneGenetics

Abstract

fetched live from OpenAlex

Exercise has been argued to enhance cognitive function and slow progressive neurodegenerative disease. Although exercise promotes neurogenesis, oligodendrogenesis and adaptive myelination are also significant contributors to brain repair and brain health. Nonetheless, the molecular details underlying these effects remain poorly understood. Conditional ablation of the Snf2h gene impairs cerebellar development producing mice with poor motor function, progressive ataxia, and death between postnatal days 25-45. Here, we show that voluntary running induced an endogenous brain repair mechanism that resulted in a striking increase in hindbrain myelination and the long-term survival of Snf2h cKO mice. Further experiments identified the VGF growth factor as a major driver underlying this effect. VGF neuropeptides promote oligodendrogenesis in vitro, whereas Snf2h cKO mice treated with full-length VGF-encoding adenoviruses removed the requirement of exercise for survival. Together, these results suggest that VGF delivery could represent a therapeutic strategy for cerebellar ataxia and other pathologies of the CNS.

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 score0.005
Threshold uncertainty score0.721

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.229
Teacher spread0.207 · 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

Citations47
Published2016
Admission routes2
Has abstractyes

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