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The impact of quadriceps disuse atrophy on motor unit properties

2021· article· en· W3168235742 on OpenAlexaff
Thomas B. Inns, Joseph J. Bass, Edward Hardy, Daniel W. Stashuk, Philip J. Atherton, Bethan E. Phillips, Mathew Piasecki

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Waterloo
FundersBiotechnology and Biological Sciences Research Council
KeywordsIsometric exerciseMotor unitAtrophyMuscle atrophyMedicineElectromyographySarcopeniaPhysical medicine and rehabilitationMotor unit recruitmentCardiologyPeripheralSkeletal muscleInternal medicineAnatomyPhysical therapy

Abstract

fetched live from OpenAlex

Disuse atrophy describes the reduction in skeletal muscle mass and strength accompanying periods of inactivity (1). This impairment is of interest in the lower limbs due to their high functional importance in activities of daily living. Similar to the age‐related loss of muscle mass associated with sarcopenia, disuse atrophy has widespread impact throughout the muscle tissue, and whilst mechanisms of muscle atrophy are reasonably well described (2), data on adaptations of the peripheral neuromuscular system are scant. The aim of the current study was to explore neuromuscular changes in individual motor units (MUs) of the vastus lateralis (VL) following 15 days of whole‐leg immobilisation. Intramuscular electromyography (iEMG) was performed in 8 healthy males (18‐25 years) before and after 15 days of unilateral leg immobilisation. Individual MU potentials (MUPs) were sampled from near the VL motor point during isometric contractions held at 25% of maximum voluntary contraction (MVC). MUP complexity was quantified by the number of significant slope changes within the MUP template (turns), and MUP duration as the time in ms between MUP onset and offset. The non‐immobilised limb served as a control. Multi‐level mixed effects linear regression models were used to examine effects of the intervention, with leg and time as factors. Significance was accepted as p<0.05. MVC decreased by 30% in the immobilised leg (p<0.05) with no difference in the control leg (p=0.23). Analysis of the number of MUP turns revealed significant interaction effects between leg and time (p<0.05); the number of MUP turns increased in the immobilised leg (β = 0.442, 95% CI: 0.160 – 0.724, p<0.01), and did not differ in the control leg (p=0.861). Similarly, there was a significant interaction between leg and time point for MUP duration (p<0.05), which was greater post intervention in the immobilised leg (β = 1.349, 95% CI: 0.72 – 1.97, p<0.001), and did not differ in the control leg (p=0.183). The increased MUP complexity and duration in the immobilised leg can be caused by increased electrophysiological temporal dispersion across MU fibres, which in turn can be related to increased differences in conduction times along axonal branches and/or MU fibres. These findings therefore indicate that short‐term immobilisation of large limb muscles can exert notable effects on MU morphology and electrophysiology. Clinical pre‐/rehabilitation regimes targeting disuse atrophy should also aim to specifically target peripheral motor nerves to mitigate the MU changes shown to occur following periods of disuse, potentially facilitating enhanced improvements in functional outcomes. 1. Rudrappa SS et al, J. Front Physiol. 2016;7(AUG):1–10. 2. Brook MS et al, J. Curr Opin Clin Nutr Metab Care. 2017;20(6):433–9.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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
Published2021
Admission routes1
Has abstractyes

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