Smooth Muscle Mechanics: Implications for Airway Hyperresponsiveness
Bibliographic record
Abstract
Human bronchial smooth muscle preparations from 10 freshly dissected pneumonectomy samples were evaluated for their mechanical characteristics and compared with results obtained for similar samples obtained from porcine trachea. Length-tension relationships of in vitro smooth muscle were evaluated for passive stretching as well as active isometric force generation and isotonic shortening using electrical field stimulation. At the length (Lmax) producing maximal force (Pmax) resting tension was very high (60.0 +/- 8.8% Pmax) compared with porcine trachealis (5.2 +/- 2.3% Pmax). Maximum shortening was 25.0 +/- 9.0% at a length of 75% Lmax with suboptimal shortening occurring at Lmax (12.0 +/- 3.4%) for the human bronchus, whereas optimal shortening of porcine trachealis (71.4 +/- 3.6%) occurred at Lmax. Morphometric evaluation revealed threefold less muscle per cross-sectional area of tissue for human (8.7 +/- 1.5%) versus porcine (24.8 +/- 1.9%) preparations. We conclude that the high passive tension and the decreased maximum shortening are produced by a relatively large load which must be overcome for the muscle to shorten, presumably provided by the greater connective tissue elastic component present in the airway. We suggest that a decrease in airway wall elastance would increase smooth muscle shortening, thereby leading to excessive responses to contractile agonists as seen in vivo in asthma.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".