Comparison of lengthening and shortening stiffness tests in single skeletal muscle fibres
Bibliographic record
Abstract
Studies of skeletal muscle fibre function often incorporate stiffness tests to provide information on contractile performance. These tests are most often performed by applying rapid small (<0.5 % fibre length) lengthening or shortening steps and measuring the corresponding change in force. Despite these stiffness tests being regularly performed in studies of contractile function, their repeatability during contractions has not been evaluated, and reported differences in stiffness measured from lengthening versus shortening tests have not been fully evaluated. Single muscle fibres were chemically permeabilized and maximally activated at three different lengths. During maximal activation at each length three lengthening and three shortening tests were performed; these were then repeated with the fibre relaxed. Both stiffness and force measures were normalized to fibre size (stiffness normalized to modulus and force normalized to stress (i.e. specific force)) to best represent the intrinsic properties of the fibres. Active modulus tests were highly repeatable with mean coefficient of variations (CoV) less than 0.028 (2.8 %). Active modulus was on average 28 % higher in all fibres in response to the lengthening compared to the shortening tests. Interestingly, correlations between specific force and active modulus were significantly (p < 0.01) higher for the shortening (r = 0.86) compared to lengthening (r = 0.78) tests. Relaxed modulus tests were less repeatable with mean CoVs ranging from 0.089 to 0.151 (8.9 to 15.1 %). Relaxed modulus was not significantly affected by the direction (lengthening versus shortening) of the test.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".