Effect of pre-activation force on active force generation in skeletal muscle
Bibliographic record
Abstract
Cross bridges play a central role in skeletal muscle force generation. The level of force per cross bridge and the number of attached cross bridges are thought to determine muscle performance. Recent studies propose the so-called myosin-activation hypothesis, which suggests that stress exerted on myosin filaments increases the number of attached cross bridges and, hence, active force. This study was aimed at investigating the influence of passive stress magnitude exerted at the onset of activation on active force in a whole muscle preparation. The tibialis anterior (TA) muscle-tendon unit (MTU) of mice (N = 8) was stretched uniaxially in situ to long lengths where substantial viscoelastic passive force relaxation occurs. Muscle stress upon activation was varied by activating the TA either immediately at the end of the passive stretch (high passive force), or following nearly complete passive force relaxation (low passive force). Total forces with and without activation were measured from every MTU. Active forces were calculated by subtracting the passive force relaxation curve from the total force measured over a 1.13-s activation. We found that active force generated by the TA at low passive stress was 5-13 % higher than that at high passive stress. While the results seem contradictory to the myosin-activation hypothesis, we speculate that the results arose either from length adjustments between muscle and tendon during passive force relaxation, from excessive lattice spacing compression, or from unfavourable alterations of myosin conformation by high passive stress. Further research is required to improve our understanding of active force generation under the influence of viscoelasticity of muscle and tendon.
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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 | 0.001 |
| 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".