The effect of storing fiber, fiber bundle, and whole muscle in glycerinated solution on their passive elastic modulus
Bibliographic record
Abstract
Storing skeletal muscle specimens in glycerinated solution is a common preservation method before biomechanical testing. This study examined the impact of glycerinated solution on passive elastic modulus in muscle tissues at three scales: fiber, fiber bundle, and whole muscle. Tensile testing was conducted on Tibialis Anterior muscle specimens obtained from nine male Sprague-Dawley rats. In total, 36 fibers, 48 fiber bundles, and 12 whole muscles were tested. Half of the specimens were tested immediately, while the other half were stored in glycerinated solution at -20 °C for 2 weeks prior to testing. The elastic moduli of all specimens were determined from stress-strain curves at 10%, 20%, and 30% strains. The results showed glycerinated solution led to about 50% decrease in elastic modulus for fibers and bundles (p < 0.001) compared to fresh muscle, while whole muscle storage caused fiber damage in the tissue center. Furthermore, the slack sarcomere length of the stored fibers and fiber bundles decreased while their cross-sectional area increased (p < 0.041). For the whole muscles, storing reduced both mass and physiological cross-sectional area of the samples (p < 0.002). These findings highlight the effect of glycerinated storage solution on muscle specimens of different sizes; and indicate that tensile testing of stored fibers and fiber bundles primarily evaluates their passive properties, while testing fresh fibers and fiber bundles assesses both passive and some active mechanical properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".