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Sex Differences in Skeletal Muscle Force Production, Fatiguability and Recovery in Slow and Fast Twitch Muscles

2022· article· en· W4225326813 on OpenAlexafffund
Nicole M. Fletcher, Kinley S. Gee, C L Murrant

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIsometric exerciseContraction (grammar)Skeletal muscleInternal medicineEndocrinologyMuscle contractionStimulationChemistrySexual dimorphismStimulus (psychology)AnatomyMedicinePsychology

Abstract

fetched live from OpenAlex

Sex differences in skeletal muscle fatiguability have been well documented during isometric contractions in humans but whether this sexual dimorphism occurs in skeletal muscle at the cellular level is not well understood. Therefore, we aimed to determine if sex differences persisted in skeletal muscle function in isolated tissue. We hypothesized that sexual dimorphisms would not be apparent in twitch and tetanic contraction characteristics or fatigue and recovery of slow and fast twitch muscles. Soleus (SOL) and extensor digitorum longus (EDL) muscles were isolated from male and female CD‐1 mice. Muscle force production at optimal length was measured during 5 protocols: 1) force‐frequency relationship (1‐120Hz), 2) twitch contraction (1Hz), 3) non‐fatiguing contraction protocol (1 contraction/90sec, for 22.5 mins at submaximal (SOL‐25Hz; EDL‐50Hz) and maximal (SOL‐80Hz; EDL‐120Hz) stimulus frequencies), 4) fatiguing contraction protocols at submaximal and maximal stimulus frequencies using contraction frequencies of 15, 30, 45 and 60 contractions per min (cpm) over 5 mins and 5) recovery protocols (1contraction/90sec for 30 mins). The force‐frequency relationship revealed that females generated significantly larger force compared to males at stimulation frequencies of 10‐120Hz (SOL) and 30‐120Hz (EDL). Female SOL and EDL produced significantly higher forces during twitch contractions compared to males (SOL: male 27.8+/‐2.0 mN/mm 2 vs. female 34.7+/‐2.4 mN/mm 2 ; EDL: male 32.5+/‐2.1 mN/mm 2 vs. female 41.2+/‐2.2 mN/mm 2 ). No significant differences in time to peak contraction or time to half relaxation during the twitch were found between males and females. There were no differences in fatigue rates between male and female SOL or EDL during the non‐fatiguing contraction protocols. Fatiguing contraction protocols revealed that females were less fatiguable than males at submaximal stimulus frequencies at 30 and 45cpm (SOL) and at maximal stimulus frequencies at 45cpm (EDL). No significant differences in fatigue between males and females were found in the remaining 13 fatiguing contraction protocols. Significant differences between males and females in recovery protocols were found in only 4 of the 16 recovery protocols. Female SOL had less recovery of force compared to males at 25Hz at 30 and 45cpm, and at 80Hz at 45 and 60cpm. Collectively, these data demonstrate that there are sexual dimorphisms in absolute force production during twitch and a range of tetanic contractions in slow and fast twitch muscles. Although there were specific contraction parameters that showed enhanced fatigue resistance of females and less recovery of female slow twitch muscles, these findings were not systematic and not present in the vast majority of the fatigue/recovery protocols. Our data show that force production and fatiguability at the in vitro, cellular level cannot explain the in vivo observations in the literature and the lack of sexual dimorphisms present during the majority of fatigue/recovery protocols in vitro indicate that systemic influences may be driving these documented differences at the whole‐body level.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.204
Teacher spread0.191 · 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 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

Citations0
Published2022
Admission routes2
Has abstractyes

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