Influence of residual force enhancement and elongation of attached cross‐bridges on stretch‐shortening cycle in skinned muscle fibers
Bibliographic record
Abstract
Abstract Increased muscle force during stretch‐shortening cycles (SSCs) has been widely examined. However, the mechanisms causing increased muscle force in SSCs remain unknown. The purpose of this study was to determine the influence of residual force enhancement and elongation of attached cross‐bridges on the work enhancement in SSCs. For the Control condition, skinned rabbit soleus fibers were elongated passively from an average sarcomere length of 2.4 to 3.0 μm, activated and then actively shortened to 2.4 μm. For the Transition condition, fibers were elongated actively from an average sarcomere length of 2.4 to 3.0 μm. Two seconds after the end of active lengthening, fibers were actively shortened to 2.4 μm. In the SSC condition, fibers were lengthened actively from an average sarcomere length of 2.4 to 3.0 μm, and then immediately shortened actively to 2.4 μm. Increased muscle force in the SSCs was quantified by the increase in mechanical work during active shortening compared to the mechanical work measured during the purely active shortening contractions. Work enhancement was significantly greater in the SSC compared to the Transition conditions. This difference was associated with the pause given between the active lengthening and shortening phase in the Transition test, which likely resulted in a reduction of the average elongation of the attached cross‐bridges caused by active stretching. Since some work enhancement was still observed in the Transition condition, another factor, for example the stretch‐induced residual force enhancement, must also have contributed to the work enhancement in SSCs.
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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".