The origin of passive force enhancement in skeletal muscle
Bibliographic record
Abstract
The aim of the present study was to test whether titin is a calcium-dependent spring and whether it is the source of the passive force enhancement observed in muscle and single fiber preparations. We measured passive force enhancement in troponin C (TnC)-depleted myofibrils in which active force production was completely eliminated. The TnC-depleted construct allowed for the investigation of the effect of calcium concentration on passive force, without the confounding effects of actin-myosin cross-bridge formation and active force production. Passive forces in TnC-depleted myofibrils (n = 6) were 35.0 +/- 2.9 nN/ microm(2) when stretched to an average sarcomere length of 3.4 microm in a solution with low calcium concentration (pCa 8.0). Passive forces in the same myofibrils increased by 25% to 30% when stretches were performed in a solution with high calcium concentration (pCa 3.5). Since it is well accepted that titin is the primary source for passive force in rabbit psoas myofibrils and since the increase in passive force in TnC-depleted myofibrils was abolished after trypsin treatment, our results suggest that increasing calcium concentration is associated with increased titin stiffness. However, this calcium-induced titin stiffness accounted for only approximately 25% of the passive force enhancement observed in intact myofibrils. Therefore, approximately 75% of the normally occurring passive force enhancement remains unexplained. The findings of the present study suggest that passive force enhancement is partly caused by a calcium-induced increase in titin stiffness but also requires cross-bridge formation and/or active force production for full manifestation.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".