Passive skeletal muscle can function as an osmotic engine
Bibliographic record
Abstract
Muscles are composite structures. The protein filaments responsible for force production are bundled within fluid-filled cells, and these cells are wrapped in ordered sleeves of fibrous collagen. Recent models suggest that the mechanical interaction between the intracellular fluid and extracellular collagen is essential to force production in passive skeletal muscle, allowing the material stiffness of extracellular collagen to contribute to passive muscle force at physiologically relevant muscle lengths. Such models lead to the prediction, tested here, that expansion of the fluid compartment within muscles should drive forceful muscle shortening, resulting in the production of mechanical work unassociated with contractile activity. We tested this prediction by experimentally increasing the fluid volumes of isolated bullfrog semimembranosus muscles via osmotically hypotonic bathing solutions. Over time, passive muscles bathed in hypotonic solution widened by 16.44 ± 3.66% (mean ± s.d.) as they took on fluid. Concurrently, muscles shortened by 2.13 ± 0.75% along their line of action, displacing a force-regulated servomotor and doing measurable mechanical work. This behaviour contradicts the expectation for an isotropic biological tissue that would lengthen when internally pressurized, suggesting a functional mechanism analogous to that of engineered pneumatic actuators and highlighting the significance of three-dimensional force transmission in skeletal muscle.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".