Added mass in rat plantaris muscle causes a reduction in mechanical work
Bibliographic record
Abstract
Most of what we know about whole muscle behaviour comes from experiments on single fibres or small muscles that are scaled up in size without considering the effects of the additional muscle mass. Previous modelling studies have shown that tissue inertia acts to slow the rate of force development and maximum velocity of muscle during shortening contractions and decreases the work and power per cycle during cyclic contractions; however, these results have not yet been confirmed by experiments on living tissue. Therefore, in this study we conducted in situ work-loop experiments on rat plantaris muscle to determine the effects of increasing the mass of muscle on mechanical work during cyclic contractions. We additionally simulated these experimental contractions using a mass-enhanced Hill-type model to validate our previous modelling work. We found that greater added mass resulted in lower mechanical work per cycle relative to the unloaded trials in which no mass was added to the muscle (p=0.041 for both 85% and 123% increases in muscle mass). We additionally found that greater strain resulted in lower work per cycle relative to unloaded trials at the same strain to control for length change and velocity effects on the work output, possibly due to greater accelerations of the muscle mass at higher strains. These results confirm that tissue mass reduces muscle mechanical work at larger muscle sizes, and that this effect is likely amplified for lower activations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".