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Record W3116546115 · doi:10.1097/mco.0000000000000723

Omega-3 fatty acids and human skeletal muscle

2020· review· en· W3116546115 on OpenAlexaff
Emily J. Ferguson, Joel Seigel, Chris McGlory

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsQueen's University
Fundersnot available
KeywordsSkeletal muscleInternal medicineEndocrinologyBiologyFatty acidSarcopeniaMuscle atrophyAnabolismBiochemistryChemistryMedicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To examine recent findings related to the influence of omega-3 (ω-3) fatty acid supplementation on skeletal muscle anabolism with a particular focus on situations of skeletal muscle disuse. RECENT FINDINGS: Skeletal muscle disuse results in a reduction in fed and fasted rates of skeletal muscle protein synthesis leading to the loss of skeletal muscle mass. Recent evidence has suggested that supplementation with ω-3 fatty acids during a period of skeletal muscle disuse increases the ω-3 fatty acid composition of skeletal muscle membranes, heightens rates of skeletal muscle protein synthesis, and protects against skeletal muscle loss. The protective effects of ω-3 fatty acids towards skeletal muscle during disuse appear to be related to changes in mitochondrial bioenergetics suggesting crosstalk between mitochondria and the regulation of skeletal muscle protein synthesis. SUMMARY: ω-3 fatty acid ingestion is a potential preventive therapy to combat skeletal muscle-disuse atrophy but additional, appropriately powered randomized controlled trials are now needed in a range of populations before firm conclusions can be made.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.103
GPT teacher head0.440
Teacher spread0.337 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2020
Admission routes1
Has abstractyes

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