MétaCan
Menu
Back to cohort
Record W4385260020 · doi:10.1016/j.bj.2023.100636

Exercise, mitochondrial dysfunction and inflammasomes in skeletal muscle

2023· review· en· W4385260020 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomedical Journal · 2023
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSkeletal muscleMedicineMitochondrionBiologyPhysiologyPhysical medicine and rehabilitationInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

In the broad field of inflammation, skeletal muscle is a tissue that is understudied. Yet it represents about 40% of body mass in non-obese individuals and is therefore of fundamental importance for whole body metabolism and health. This article provides an overview of the unique features of skeletal muscle tissue, as well as its adaptability to exercise. This ability to adapt, particularly with respect to mitochondrial content and function, confers a level of metabolic "protection" against energy consuming events, and adds a measure of quality control that determines the phenotypic response to stress. Thus, we describe the particular role of mitochondria in promoting inflammasome activation in skeletal muscle, contributing to muscle wasting and dysfunction in aging, disuse and metabolic disease. We will then discuss how exercise training can be anti-inflammatory, mitigating the chronic inflammation that is observed in these conditions, potentially through improvements in mitochondrial quality and function.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.043
GPT teacher head0.339
Teacher spread0.296 · 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