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Record W2793958848 · doi:10.1152/ajpregu.00025.2018

Sex differences in mitochondrial respiratory function in human skeletal muscle

2018· article· en· W2793958848 on OpenAlexafffund
Paula M. Miotto, Chris McGlory, Tanya M. Holloway, Stuart M. Phillips, Graham P. Holloway

Bibliographic record

VenueAmerican Journal of Physiology-Regulatory, Integrative and Comparative Physiology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMcMaster UniversityUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsBioenergeticsSkeletal muscleRespirationMitochondrionBiologyContext (archaeology)EndocrinologyInternal medicineCarnitineATP–ADP translocaseRespiratory systemBiochemistryInner mitochondrial membraneAnatomyMedicine

Abstract

fetched live from OpenAlex

Mitochondrial bioenergetic contributions to sex differences in human skeletal muscle metabolism remain poorly defined. The primary aim of this study was to determine whether mitochondrial respiratory kinetics differed between healthy young men and women in permeabilized skeletal muscle fibers. While men and women displayed similar ( P > 0.05) maximal respiration rates and abundance of mitochondrial/adenosine diphosphate (ADP) transport proteins, women had lower ( P < 0.05) mitochondrial ADP sensitivity (+30% apparent K m ) and absolute respiration rates at a physiologically relevant ADP concentration (100 μM). Moreover, although men and women exhibited similar carnitine palmitoyl transferase-I protein content- and palmitoyl-CoA-supported respiration, women displayed greater sensitivity to malonyl-CoA-mediated respiratory inhibition. These data establish baseline sex differences in mitochondrial bioenergetics and provide the foundation for studying mitochondrial function within the context of metabolic perturbations and diseases that affect men and women differently.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.025
GPT teacher head0.288
Teacher spread0.263 · 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 teacher head, not a consensus.

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

Citations108
Published2018
Admission routes2
Has abstractyes

Explore more

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