Thixotropy and Rheopexy of Muscle Fibers Probed Using Sinusoidal Oscillations
Bibliographic record
Abstract
Length changes of muscle fibers have previously been shown to result in a temporary reduction in fiber stiffness that is referred to as thixotropy. Understanding the mechanism of this thixotropy is important to our understanding of muscle function since there are many instances in which muscle is subjected to repeated patterns of lengthening and shortening. By applying sinusoidal length changes to one end of single permeabilized muscle fibers and measuring the force response at the opposite end, we studied the history-dependent stiffness of both relaxed and activated muscle fibers. For length change oscillations greater than 1 Hz, we observed thixotropic behavior of activated fibers. Treatment of these fibers with EDTA and blebbistatin, which inhibits myosin-actin interactions, quashed this effect, suggesting that the mechanism of muscle fiber thixotropy is cross-bridge dependent. We modeled a half-sarcomere experiencing sinusoidal length changes, and our simulations suggest that thixotropy could arise from force-dependent cross-bridge kinetics. Surprisingly, we also observed that, for length change oscillations less than 1 Hz, the muscle fiber exhibited rheopexy. In other words, the stiffness of the fiber increased in response to the length changes. Blebbistatin and EDTA did not disrupt the rheopectic behavior, suggesting that a non-cross-bridge mechanism contributes to this phenomenon.
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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) | 0.001 | 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".