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Record W4414533302 · doi:10.1139/cjpp-2025-0019

Aerobic and/or resistance exercise in restoring metabolic dysregulation induced by chronic sleep restriction in rats

2025· article· en· W4414533302 on OpenAlexvenueno aff
Gülhan Cansu Şen, Muhammed Ali Aydın, Selen Yıldız

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2025
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsnot available
Fundersnot available
KeywordsSleep restrictionGlycemicAerobic exerciseInsulin resistanceCarbohydrate metabolismGlucose tolerance testResistance trainingImpaired glucose toleranceInsulin tolerance test

Abstract

fetched live from OpenAlex

Chronic sleep restriction (SR) disrupts blood glucose regulation, leading to glucose intolerance and insulin resistance. Regular exercises, however, are known to enhance glycemic control. This study aimed to evaluate the regulatory effects of three distinct exercise protocols on blood glucose alterations caused by chronic rapid eye movement SR. Thirty-four Sprague-Dawley rats were allocated into five groups: control (CTRL), SR, SR plus aerobic exercise (SR + Ex A ), SR plus resistance exercise (SR + Ex R ), and SR plus combined exercises (SR + Ex C ). Except for the control group, all rats underwent 18 h of SR daily for 8 weeks using a modified multi-platform model. Exercise protocols included 30 min of swimming and/or vertical ladder climbing (15 repetitions/day) performed 3 days per week for 8 weeks. Following the intervention, glucose and insulin tolerance tests were conducted. Chronic SR increased blood glucose levels, while aerobic and/or resistance exercises effectively reduced or prevented this elevation. Glucose tolerance was significantly improved in all exercise groups compared to the sedentary group (intraperitoneal glucose tolerance test blood glucose 120 min: SR + Ex A = 95 ± 7.7, SR + Ex R = 100 ± 7.3, SR + Ex C = 90 ± 12.6, SR = 119 ± 14.5 mg/dL; P < 0.05). Regular exercise may mitigate adverse metabolic effects of SR.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.310
Teacher spread0.294 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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