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Record W1946841469 · doi:10.1139/apnm-2015-0222

A comparison of PGC-1α mRNA and protein expression in response to 1-week endurance training on alternate days or 4 consecutive days

2015· article· en· W1946841469 on OpenAlexvenueno aff
Liping Huang, Min Yao, Yali Wang, Allan J Davie, Shi Zhou

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
FundersSouthern Cross University
KeywordsMessenger RNAEndurance trainingCoactivatorTreadmillGene expressionProtein expressionInternal medicineEndocrinologyMedicineBiologyGeneBiochemistryTranscription factor

Abstract

fetched live from OpenAlex

To understand the molecular mechanisms of adaptation to different training protocols, this study compared the effects of 4 sessions of 90 min treadmill exercise on alternate days or consecutive days in 1 week on messenger RNA (mRNA) and protein expression of proliferator-activated receptor-γ coactivator 1-α in rat gastrocnemius muscle. The mRNA significantly increased by 25.8-fold after alternate-day and 10.1-fold after consecutive-day training, while the protein showed no significant cumulative effect, 1.5-1.7-fold above baseline, in the 2 protocols.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.044
GPT teacher head0.306
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 source (direct Gemma or distilled Codex), 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

Citations2
Published2015
Admission routes1
Has abstractyes

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