Fiber Type and Size as Sources of Variation in Human Single Muscle Fiber Passive Elasticity
Bibliographic record
Abstract
Studies on single muscle fiber passive material properties often report relatively large variation in elastic modulus (or normalized stiffness), and it is not clear where this variation arises. This study was designed to determine if the stiffness, normalized to both fiber cross-sectional area and length, is inherently different between types 1 and 2 muscle fibers. Vastus lateralis fibers (n = 93), from ten young men, were mechanically tested using a cumulative stretch-relaxation protocol. SDS-PAGE classified fibers as types 1 or 2. While there was a difference in normalized stiffness between fiber types (p = 0.0019), an unexpected inverse relationship was found between fiber diameter and normalized stiffness (r = -0.64; p < 0.001). As fiber type and diameter are not independent, a one-way analysis of covariance (ANCOVA) including fiber diameter as a covariate was run; this eliminated the effect of fiber type on normalized stiffness (p = 0.1935). To further explore the relationship between fiber size and elastic properties, we tested whether stiffness was linearly related to fiber cross-sectional area, as would be expected for a homogenous material. Passive stiffness was not linearly related to fiber area (p < 0.001), which can occur if single muscle fibers are better represented as composite materials. The rule of mixtures for composite materials was used to explore whether the presence of a stiff perimeter-based fiber component could explain the observed results. The model (R2 = 0.38) predicted a perimeter-based normalized stiffness of 8800 ± 2600 kPa/μm, which is within the range of basement membrane moduli reported in the literature.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".