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Record W1970897515 · doi:10.1055/s-0030-1255065

Regulation of Muscle Genes by Moderate Exercise

2010· article· en· W1970897515 on OpenAlexaff
Yuichiro Nishida, Hiroki Tanaka, Takuro Tobina, Kiyohide Murakami, Naoko Shono, M. Shindo, Wataru Ogawa, Mayumi Yoshioka, Jonny St‐Amand

Bibliographic record

VenueInternational Journal of Sports Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsCentre hospitalier de l'Université LavalUniversité Laval
FundersFukuoka University
KeywordsGene expressionEndocrinologyCycle ergometerInternal medicineSkeletal muscleMedicineGeneMetabolic syndromeTime trialBiologyGeneticsObesityBlood pressureHeart rate

Abstract

fetched live from OpenAlex

Moderate-intensity exercise at the lactate threshold (LT) is considered to be a safe and effective training regimen for improving metabolic syndrome. The aim of the current study was to investigate the effects of moderate exercise performed at the LT on skeletal muscle gene expression. 6 healthy men participated in cycle ergometer training at LT, 60 min/d, 5 d/wk for 12 wks. Muscle samples were collected after 5 d of training, and then 2 d after training at wks 6 and 12. Quantitative real-time PCR analysis revealed that the expression of peroxisome proliferator activated receptor co-activated 1alpha was significantly increased at 1 h after the training session on day 5. Moreover, using serial analysis gene expression, we found that moderate training for 6 and 12 wks simultaneously induced the expression of a number of metabolic genes involved in the TCA cycle, beta-oxidation, and electron transport. Furthermore, several genes encoding antioxidant enzymes and contractile apparatus were induced. The expression levels of 233 novel transcripts were also altered in response to moderate exercise. Thus, moderate training at the LT is a sufficient stimulus to induce the expression of numerous genes implicated in the development of metabolic syndrome, transcripts involved in the contractile apparatus, and novel transcripts.

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 categoriesInsufficient payload (model declined to judge)
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 score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.011
GPT teacher head0.287
Teacher spread0.275 · 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 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

Citations26
Published2010
Admission routes1
Has abstractyes

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