Rho-Kinase Inhibition of Active Force and Passive Tension in Airway Smooth Muscle: A Strategy for Treating Airway Hyperresponsiveness in Asthma
Bibliographic record
Abstract
Rho-kinase inhibitors have been identified as a class of potential drugs for treating asthma because of their ability to reduce airway inflammation and active force in airway smooth muscle (ASM). Past research has revealed that, besides the effect on the ASM's force generation, rho-kinase (ROCK) also regulates actin filament formation and filament network architecture and integrity, thus affecting ASM's cytoskeletal stiffness. The present review is not a comprehensive examination of the roles played by ROCK in regulating ASM function but is specifically focused on passive tension, which is partially determined by the cytoskeletal stiffness of ASM. Understanding the molecular basis for maintaining active force and passive tension in ASM by ROCK will allow us to determine the suitability of ROCK inhibitors and its downstream enzymes as a class of drugs in treating airway hyperresponsiveness seen in asthma. Because clinical trials using ROCK inhibitors in the treatment of asthma have yet to be conducted, the present review focuses on the in vitro effects of ROCK inhibitors on ASM's mechanical properties which include active force generation, relaxation, and passive stiffness. The review provides justification for future clinical trials in the treatment of asthma using ROCK inhibitors alone and in combination with other pharmacological and mechanical interventions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".