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Record W4411441661 · doi:10.1101/2025.06.15.659256

From muscle fibres to muscle gears: How dynamic fascicle orientation and shape change impact skeletal muscle function

2025· preprint· en· W4411441661 on OpenAlexaff
Matheus D. Pinto, James M. Wakeling, Javier A. Almonacid, Anthony J. Blazevich

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsSimon Fraser University
FundersAustralasian Society for Human Biology
KeywordsRotation (mathematics)Function (biology)Materials scienceGeometryMathematicsBiologyCell biology

Abstract

fetched live from OpenAlex

Abstract Skeletal muscle architectural design strongly affects force-generating capacity and excursion range, and its functional importance to animal and human movement is well founded. Traditionally, this ‘structure−function relation’ has been inferred from architecture measurements with muscles at rest (from anatomical dissections or medical imaging techniques such as ultrasonography or magnetic resonance imaging) and muscle function tested under quasi-steady force conditions (e.g., isometric). A contemporary view recognises that, during active, dynamic contractions, muscles undergo load- and velocity-dependent changes in architecture and three-dimensional shape while remaining near-constant volume. Consequently, whole-muscle length change and velocity can become decoupled from fascicle length change and velocity, such that the whole muscle cannot be treated as a simple linear velocity transmission system but instead as a geared system where the input fascicle length change and velocity differ from the output whole muscle length change and velocity. This phenomenon is described by the concept of ‘muscle gearing’ and can be quantified as the ratio of the velocities or displacement of the muscle to the fascicle. Gear ratios are not fixed; instead, gear shifts with force and velocity, allowing the whole-muscle output to better match the mechanical demands of the task. Variable gearing is thought to emerge from load-dependent changes in fascicle angle and three-dimensional muscle deformations under volume-preserving and tissue constraints. Variable gearing has important functional implications, as it influences fascicle operating length and shortening velocity for a given task and thereby shifts where fascicles operate along their intrinsic force–length and force–velocity relationships. Through these effects, along with changes in the projection of fascicle force onto the muscle line of action as pennation varies, variable gearing can broaden whole-muscle force and power output across movement conditions. Accordingly, muscle gearing should be considered an integral component of a comprehensive framework for understanding dynamic muscle function in both normal and pathological states. This review aims to (i) provide a brief historical overview of how the muscle structure−function relation has been understood from resting muscle structure measurements and modelling and steady-force contractions to observations during dynamic, time-varying active contractions, and (ii) synthesise evidence on muscle gearing, including its measurement, functional consequences, and current understanding of the factors that influence it by reviewing findings from animal and human studies and raising hypotheses to explain the variation in gearing identified across muscle, species, and contraction conditions. Given the challenges in experimentally isolating the contribution of individual factors, we also employ a previously validated three-dimensional finite-element muscle model to interrogate the mechanical phenomena underpinning variable gearing and for the first time report effects from muscle activation. Finally, we briefly discuss the potential role of gearing in the observed changes in muscle function with ageing, injury, and exercise training. Overall, muscle gearing is a distinctive feature of skeletal muscles with important functional implications, and our modelling demonstrates that it is an emergent property of the physics of muscle contraction.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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 designObservational
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

Citations2
Published2025
Admission routes1
Has abstractyes

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