Training Induced Changes to Skeletal Muscle Passive Properties Are Evident in Both Single Fibers and Fiber Bundles in the Rat Hindlimb
Bibliographic record
Abstract
Introduction: The passive mechanical behavior of skeletal muscle represents both important, and generally underappreciated, biomechanical properties with little attention paid to their trainability. These experiments were designed to gain insight into the trainability of muscle passive mechanical properties in both single fibres and fibre bundles. Methods: Rats were trained in two groups: 4 weeks of either uphill (UH) or downhill (DH) treadmill running; with a third group as sedentary control. After sacrifice, the soleus (SOL), extensor digitorum longus (EDL), and vastus intermedius (VI) were harvested. 179 bundles and 185 fibres were tested and analyzed using a cumulative stretch-relaxation protocol to determine the passive stress and elastic modulus. Titin isoform expression was analysed using SDS-VAGE. Results: Single fibres: passive modulus and stress were greater for the EDL at sarcomere lengths (SLs) ≥ 3.7 µm (modulus) and 4.0 µm (stress) with DH compared to UH training, and lesser for the SOL (SLs ≥ 3.3 µm) with DH training compared with control; there was no effect of UH training. VI was not affected by either training protocol. Fibre bundles: passive modulus and stress were greater for the EDL at SLs ≥ 2.5 µm (modulus) and 3.3 µm (stress) in the DH training group as compared with control, while no affects were observed in either the SOL or VI for either training group. No effects on titin isoform size were detected with training. Conclusion: This study demonstrated a trainability of passive muscle properties at both the single fibre and fibre bundle levels were not accompanied by any detectable changes to titin isoform size.
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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) | 0.002 | 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".