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Record W2090769822 · doi:10.1155/2012/181687

Effects of Exercise Training on Molecular Markers of Lipogenesis and Lipid Partitioning in Fructose-Induced Liver Fat Accumulation

2011· article· en· W2090769822 on OpenAlexafffund
Siham Yasari, Denis Prud’homme, Frédérique Tesson, Marek Jankowski, Jolanta Gutkowska, Émile Lévy, Jean-Marc Lavoie

Bibliographic record

VenueJournal of Nutrition and Metabolism · 2011
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsUniversité de MontréalHôtel-Dieu de MontréalMontfort HospitalUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsLipogenesisMedicineFructoseInternal medicineFat accumulationEndocrinologyLipid metabolismBiochemistryBiologyObesity

Abstract

fetched live from OpenAlex

The present study was designed to investigate the impact of exercise training on lipogenic gene expression in liver and lipid partitioning following the ingestion of a high fructose load. Female rats were exercise-trained for 8 wk or kept sedentary before being submitted to a fasting/refeeding protocol. Rats were further subdivided as follow: rats were fasted for 24 h, refed a standard diet for 24 h, starved for another 24 h, and refed with a standard or a high-fructose diet 24 h before sacrifice. Fructose refeeding was associated with an increase in hepatic lipid content, endocannabinoid receptor 1, sterol regulatory element-binding protein1c, and stearoyl-CoA desaturase1 gene expression in both Sed and TR rats. However, desaturation indexes measured in liver (C16 : 1/C16 : 0 and C18 : 1/C18 : 0) and plasma (C18 : 1/C18 : 0) were higher (P < 0.01) in TR than in Sed rats following fructose refeeding. It is concluded that exercise training does not significantly affect fat accumulation and the molecular expression of genes involved in lipogenesis after fasting and fructose refeeding but does modify the partitioning of lipids so as to provide more unsaturated fatty acids in liver without affecting liver fat content.

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 score0.759
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.047
GPT teacher head0.285
Teacher spread0.238 · 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

Citations16
Published2011
Admission routes2
Has abstractyes

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