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Record W2972426770 · doi:10.1016/j.bbrep.2019.100686

Effect of different sources of dietary protein on muscle hypertrophy in functionally overloaded mice

2019· article· en· W2972426770 on OpenAlexfundno aff
Shinya Aoyama, Rina Hirooka, Takeru Shimoda, Shigenobu Shibata

Bibliographic record

VenueBiochemistry and Biophysics Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
FundersBio-oriented Technology Research Advancement InstitutionJapan Society for the Promotion of ScienceCouncil for Science, Technology and InnovationNational Agriculture and Food Research OrganizationSwine Innovation Porc
KeywordsMuscle hypertrophyMuscle proteinBiologyDietary proteinInternal medicineCell biologyEndocrinologySkeletal muscleMedicine

Abstract

fetched live from OpenAlex

Dietary protein intake is important for skeletal muscle protein synthesis. In this study, we investigated the differential effect of protein sources on hypertrophy of plantaris muscle induced by surgical ablation of gastrocnemius and soleus muscles. Six-week old mice were fed diets containing caseinate, whey, or soy as protein sources for 2 weeks. Plantaris muscle hypertrophy was induced by a unilateral ablation of synergistic muscles after a week. Food intake of soy protein-fed mice was higher than that of caseinate and whey-fed mice, resulting in higher body and fat weights. Plantaris muscle weight in sham-operated mice was not different across the groups. Overload-operated plantaris muscle weight and increased ratio of overloaded muscle to sham-operated muscle weights were higher in caseinate-fed mice than in whey- and soy protein-fed mice, suggesting caseinate as a promising protein source for muscle hypertrophy.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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