Force maintenance and myosin filament assembly regulated by Rho-kinase in airway smooth muscle
Bibliographic record
Abstract
Smooth muscle contraction can be divided into two phases: the initial contraction determines the amount of developed force and the second phase determines how well the force is maintained. The initial phase is primarily due to activation of actomyosin interaction and is relatively well understood, whereas the second phase remains poorly understood. Force maintenance in the sustained phase can be disrupted by strains applied to the muscle; the strain causes actomyosin cross-bridges to detach and also the cytoskeletal structure to disassemble in a process known as fluidization, for which the underlying mechanism is largely unknown. In the present study we investigated the ability of airway smooth muscle to maintain force after the initial phase of contraction. Specifically, we examined the roles of Rho-kinase and protein kinase C (PKC) in force maintenance. We found that for the same degree of initial force inhibition, Rho-kinase substantially reduced the muscle's ability to sustain force under static conditions, whereas inhibition of PKC had a minimal effect on sustaining force. Under oscillatory strain, Rho-kinase inhibition caused further decline in force, but again, PKC inhibition had a minimal effect. We also found that Rho-kinase inhibition led to a decrease in the myosin filament mass in the muscle cells, suggesting that one of the functions of Rho-kinase is to stabilize myosin filaments. The results also suggest that dissolution of myosin filaments may be one of the mechanisms underlying the phenomenon of fluidization. These findings can shed light on the mechanism underlying deep inspiration induced bronchodilation.
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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 | 0.001 |
| 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".