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Record W2867704153 · doi:10.1139/apnm-2018-0146

Low-intensity resistance training with partial blood flow restriction and high-intensity resistance training induce similar changes in skeletal muscle transcriptome in elderly humans

2018· article· en· W2867704153 on OpenAlexvenueno aff
Felipe C. Vechin, Cleiton Augusto Libardi, Miguel Soares Conceição, Felipe Damas, Cláudia Regina Cavaglieri, Mara Patrícia Traina Chacon‐Mikahil, Luiz Lehmann Coutinho, Sónia C. S. Andrade, M T Neves, Hamilton Roschel, Valmor Tricoli, Igor L. Baptista, Anselmo A. Moriscot, Carlos Ugrinowitsch

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsnot available
Fundersnot available
KeywordsBlood flow restrictionTranscriptomeResistance trainingSkeletal muscleBiologyGeneHigh-intensity interval trainingMedicinePhysical medicine and rehabilitationPhysiologyGene expressionEndocrinologyGenetics

Abstract

fetched live from OpenAlex

We aimed to investigate the mechanisms underlying muscle growth after 12 weeks of resistance training performed with blood flow restriction (RT-BFR) and high-intensity resistance training (HRT) in older individuals. Participants were allocated into the following groups: HRT, RT-BFR, or a control group. High-throughput transcriptome sequencing was performed by the Illumina HiSeq 2500 platform. HRT and RT-BFR presented similar increases in the quadriceps femoris cross-sectional area, and few genes were differently expressed between interventions. The small differences in gene expression between interventions suggest that similar mechanisms may underpin training-induced muscle growth.

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 categoriesMeta-epidemiology (narrow)
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 score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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