Characterization of the passive component of force enhancement following active stretching of skeletal muscle
Bibliographic record
Abstract
The mechanisms causing the steady-state force enhancement following active skeletal muscle stretching are not well understood. Recently, we found direct evidence that part of the force enhancement is associated with the engagement of a passive component. In this study, we reproduced the conditions that give consistent passive force enhancement and evaluated the mechanical properties of this passive force enhancement so as to gain insight into its source. The three primary results were that (1). the passive force enhancement is long lasting (>25 s), (2). passive force enhancement was reduced in a dose-dependent manner by the amount of shortening preceding active muscle stretching, and (3). passive force enhancement could be abolished 'instantaneously' by shortening-stretching the passive muscle by an amount equivalent to the active stretch magnitude. Together with the remaining results, we conclude that the source of the passive force enhancement must be arranged in parallel with the contractile force, it must consist of a viscoelastic molecular spring whose stiffness characteristic can be reset by shortening, and it must have a characteristic length that is governed by the length of the contractile components, possibly the sarcomeres. Based on these results, the molecular spring titin emerges as a possible candidate for the passive component of the steady-state force enhancement observed in this and previous studies.
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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".