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Record W2107408901 · doi:10.1002/mus.24215

Effect of eccentric versus concentric exercise training on mitochondrial function

2014· article· en· W2107408901 on OpenAlexaff
Marie-Ève Isner-Horobeti, Laurence Rasseneur, Evelyne Lonsdorfer‐Wolf, Stéphane Dufour, Stéphane Doutreleau, Jamal Bouitbir, Joffrey Zoll, Sophia Kapchinsky, Bernard Gény, Frédéric Daussin, Yan Burelle, Ruddy Richard

Bibliographic record

VenueMuscle & Nerve · 2014
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsConcentricEccentricSkeletal muscleCitrate synthaseRespirationOxidative phosphorylationAnatomyInternal medicineEndocrinologyBiologyChemistryMedicineBiochemistryMathematics

Abstract

fetched live from OpenAlex

INTRODUCTION: The effect of eccentric (ECC) versus concentric (CON) training on metabolic properties in skeletal muscle is understood poorly. We determined the responses in oxidative capacity and mitochondrial H2 O2 production after eccentric (ECC) versus concentric (CON) training performed at similar mechanical power. METHODS: Forty-eight rats performed 5- or 20-day eccentric (ECC) or concentric (CON) training programs. Mitochondrial respiration, H2 O2 production, citrate synthase activity (CS), and skeletal muscle damage were assessed in gastrocnemius (GAS), soleus (SOL) and vastus intermedius (VI) muscles. RESULTS: Maximal mitochondrial respiration improved only after 20 days of concentric (CON) training in GAS and SOL. H2 O2 production increased specifically after 20 days of eccentric ECC training in VI. Skeletal muscle damage occurred transiently in VI after 5 days of ECC training. CONCLUSIONS: Twenty days of ECC versus CON training performed at similar mechanical power output do not increase skeletal muscle oxidative capacities, but it elevates mitochondrial H2 O2 production in VI, presumably linked to transient muscle damage.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.289
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations27
Published2014
Admission routes1
Has abstractyes

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