Shifting gears: dynamic muscle shape changes and force-velocity behavior in the medial gastrocnemius
Bibliographic record
Abstract
When muscles contract, they bulge in thickness or in width to maintain a (nearly) constant volume. These dynamic shape changes are tightly linked to the internal constraints placed on individual muscle fibers and play a key functional role in modulating the mechanical performance of skeletal muscle by increasing its range of operating velocities. Yet to date we have a limited understanding of the nature and functional implications of in vivo dynamic muscle shape change under submaximal conditions. This study determined how the in vivo changes in medial gastrocnemius (MG) fascicle velocity, pennation angle, muscle thickness, and subsequent muscle gearing varied as a function of force and velocity. To do this, we obtained recordings of MG tendon length, fascicle length, pennation angle, and thickness using B-mode ultrasound and muscle activation using surface electromyography during cycling at a range of cadences and loads. We found that that increases in contractile force were accompanied by reduced bulging in muscle thickness, reduced increases in pennation angle, and faster fascicle shortening. Although the force and velocity of a muscle contraction are inversely related due to the force-velocity effect, this study has shown how dynamic muscle shape changes are influenced by force and not influenced by velocity. NEW & NOTEWORTHY During movement, skeletal muscles contract and bulge in thickness or width. These shape changes play a key role in modulating the performance of skeletal muscle by increasing its range of operating velocities. Yet to date the underlying mechanisms associated with muscle shape change remain largely unexplored. This study identified muscle force, and not velocity, as the mechanistic driving factor to allow for muscle gearing to vary depending on the contractile conditions during human cycling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".