Time course of isotonic shortening and the underlying contraction mechanism in airway smooth muscle
Bibliographic record
Abstract
Although the structure of the contractile unit in smooth muscle is poorly understood, some of the mechanical properties of the muscle suggest that a sliding-filament mechanism, similar to that in striated muscle, is also operative in smooth muscle. To test the applicability of this mechanism to smooth muscle function, we have constructed a mathematical model based on a hypothetical structure of the smooth muscle contractile unit: a side-polar myosin filament sandwiched by actin filaments, each attached to the equivalent of a Z disk. Model prediction of isotonic shortening as a function of time was compared with data from experiments using ovine tracheal smooth muscle. After equilibration and establishment of in situ length, the muscle was stimulated with ACh (100 μM) until force reached a plateau. The muscle was then allowed to shorten isotonically against various loads. From the experimental records, length-force and force-velocity relationships were obtained. Integration of the hyperbolic force-velocity relationship and the linear length-force relationship yielded an exponential function that approximated the time course of isotonic shortening generated by the modeled sliding-filament mechanism. However, to obtain an accurate fit, it was necessary to incorporate a viscoelastic element in series with the sliding-filament mechanism. The results suggest that a large portion of the shortening is due to filament sliding associated with muscle activation and that a small portion is due to continued deformation associated with an element that shows viscoelastic or power-law creep after a step change in force.
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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".