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Record W4400651339 · doi:10.1080/15548627.2024.2373676

Key considerations for investigating and interpreting autophagy in skeletal muscle

2024· review· en· W4400651339 on OpenAlex
Fasih A. Rahman, Brittany L. Baechler, Joe Quadrilatero

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

VenueAutophagy · 2024
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyAutophagySkeletal muscleKey (lock)Cell biologyComputational biologyNeuroscienceAnatomyGeneticsEcologyApoptosis

Abstract

fetched live from OpenAlex

Skeletal muscle plays a crucial role in generating force to facilitate movement. Skeletal muscle is a heterogenous tissue composed of diverse fibers with distinct contractile and metabolic profiles. The intricate classification of skeletal muscle fibers exists on a continuum ranging from type I (slow-twitch, oxidative) to type II (fast-twitch, glycolytic). The heterogenous distribution and characteristics of fibers within and between skeletal muscles profoundly influences cellular signaling; however, this has not been broadly discussed as it relates to macroautophagy/autophagy. The growing interest in skeletal muscle autophagy research underscores the necessity of comprehending the interplay between autophagic responses among skeletal muscles and fibers with different contractile properties, metabolic profiles, and other related signaling processes. We recommend approaching the interpretation of autophagy findings with careful consideration for two key reasons: 1) the distinct behaviors and responses of different skeletal muscles or fibers to various perturbations, and 2) the potential impact of alterations in skeletal muscle fiber type or metabolic profile on observed autophagic outcomes. This review provides an overview of the autophagic profile and response in skeletal muscles/fibers of different types and metabolic profiles. Further, this review discusses autophagic findings in various conditions and diseases that may differentially affect skeletal muscle. Finally, we provide key points of consideration to better enable researchers to fine-tune the design and interpretation of skeletal muscle autophagy experiments.Abbreviation: AKT1: AKT serine/threonine kinase 1; AMPK: AMP-activated protein kinase; ATG: autophagy related; ATG4: autophagy related 4 cysteine peptidase; ATG5: autophagy related 5; ATG7: autophagy related 7; ATG12: autophagy related 12; BECN1: beclin 1; BNIP3: BCL2 interacting protein 3; CKD: chronic kidney disease; COPD: chronic obstructive pulmonary disease; CS: citrate synthase; DIA: diaphragm; EDL: extensor digitorum longus; FOXO3/FOXO3A: forkhead box O3; GAS; gastrocnemius; GP: gastrocnemius-plantaris complex; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MAPK: mitogen-activated protein kinase; MYH: myosin heavy chain; PINK1: PTEN induced kinase 1; PLANT: plantaris; PRKN: parkin RBR E3 ubiquitin protein ligase; QUAD: quadriceps; RA: rectus abdominis; RG: red gastrocnemius; RQ: red quadriceps; SOL: soleus; SQSTM1: sequestosome 1; TA: tibialis anterior; WG: white gastrocnemius; WQ: white quadriceps; WVL: white vastus lateralis; VL: vastus lateralis; ULK1: unc-51 like autophagy activating kinase 1.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.386
Teacher spread0.318 · 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