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Voluntary long-term exercise by female mice modulates anxiety-like behavior and motor function but minimally impacts acute oxidative injury in the central nervous system

2025· article· W7128204892 on OpenAlexaff
Yifei Dong

Bibliographic record

Venuenot available
Typearticle
Language
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCentral nervous systemMotor functionMotor activityFunction (biology)Motor controlNervous system

Abstract

fetched live from OpenAlex

Physical and cognitive decline from a sedentary lifestyle and aging are detrimental to the health and function of the central nervous system (CNS). As people living in developed societies adopt more sedentary lifestyles with age, identifying cost-efficient strategies to mitigate physical and cognitive decline is critical for improving long-term health care outcomes. While accumulating evidence suggests that moderate aerobic exercise can acutely enhance cognitive decline and improve physical function, the ability of voluntary long-term exercise (VLTE) to improve CNS health and resilience remains less well understood. Here, we assessed how VLTE affected the health and function of the CNS by comparing female mice with access to a functional or disabled running wheel for 6-months. Notably, VLTE limited weight gain in mice and significantly upregulated gene expression in pathways related to synapse function and ion transport in neuroglial cells from the brain. While mice with VLTE had similar short-term memory performance as sedentary mice, VLTE significantly reduced anxiety-like behavior and altered motor function by 6 months. Despite these transcriptomic and behavioral changes, VLTE did not modulate acute oxidative injury induced by oxidized phosphatidylcholine in the spinal cord white matter of mice, suggesting that VLTE may not be sufficient to overcome severe oxidative injury in 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.271
Teacher spread0.262 · 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.

Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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