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Record W3093074122 · doi:10.1007/s00018-020-03662-0

The connection between the dynamic remodeling of the mitochondrial network and the regulation of muscle mass

2020· review· en· W3093074122 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.

Bibliographic record

VenueCellular and Molecular Life Sciences · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMcGill University
FundersAssociazione Italiana per la Ricerca sul CancroAFM-TéléthonUniversità degli Studi di Padova
KeywordsMitophagySarcopeniamitochondrial fusionMitochondrionSkeletal muscleMuscle atrophyBiologyAtrophyMitochondrial fissionCell biologyNeuroscienceBioinformaticsEndocrinologyMitochondrial DNAAutophagyGeneticsApoptosisGene

Abstract

fetched live from OpenAlex

The dynamic coordination of processes controlling the quality of the mitochondrial network is crucial to maintain the function of mitochondria in skeletal muscle. Changes of mitochondrial proteolytic system, dynamics (fusion/fission), and mitophagy induce pathways that affect muscle mass and performance. When muscle mass is lost, the risk of disease onset and premature death is dramatically increased. For instance, poor quality of muscles correlates with the onset progression of several age-related disorders such as diabetes, obesity, cancer, and aging sarcopenia. To date, there are no drug therapies to reverse muscle loss, and exercise remains the best approach to improve mitochondrial health and to slow atrophy in several diseases. This review will describe the principal mechanisms that control mitochondrial quality and the pathways that link mitochondrial dysfunction to muscle mass regulation.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.019
GPT teacher head0.262
Teacher spread0.243 · 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