Tissue elastance influences airway smooth muscle shortening: comparison of mechanical properties among different species
Bibliographic record
Abstract
We have observed striking differences in the mechanical properties of airway smooth muscle preparations among different species. In this study, we provide a novel analysis on the influence of tissue elastance on smooth muscle shortening using previously published data from our laboratory. We have found that isolated human airways exhibit substantial passive tension in contrast to airways from the dog and pig, which exhibit little passive tension (<5% of maximal active force versus approximately 60% for human bronchi). In the dog and pig, airway preparations shorten up to 70% from Lmax (the length at which maximal active force occurs), whereas human airways shorten by only approximately 12% from Lmax. Isolated airways from the rabbit exhibit relatively low passive tension (approximately 22% Fmax) and shorten by 60% from Lmax. Morphologic evaluation of airway cross sections revealed that 25-35% of the airway wall is muscle in canine, porcine, and rabbit airways in contrast to approximately 9% in human airway preparations. We postulate that the large passive tension needed to stretch the muscle to Lmax reflects the high connective tissue content surrounding the smooth muscle, which limits shortening during smooth muscle contraction by imposing an elastic load, as well as by causing radial constraint.
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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 | 0.001 | 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 | 0.000 | 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".