Heterogeneity of airway wall dimensions in humans: a critical determinant of lung function in asthmatics and nonasthmatics
Bibliographic record
Abstract
Airway remodeling, a key feature of asthma, alters every layer of the airway wall but most strikingly the airway smooth muscle (ASM) layer. Airway remodeling in asthmatics contributes to fixed airflow obstruction and can amplify airway narrowing caused by ASM activation. Previous modeling studies have shown that the increase in ASM mass has the largest effect on increasing maximal airway narrowing. Simulated heterogeneity in the dimensions and properties of the airway wall can further amplify airway narrowing. Using measurements made on histological sections from donor lungs, we show for the first time that there is profound heterogeneity of ASM area and wall area in both nonasthmatics and asthmatics. Using a mathematical model, we found that this heterogeneity, together with changes in the mean values, contributes to an increased baseline resistance and elastance in asthmatics as well as a leftward shift in the responsiveness of the airways to a simulated agonist in both nonasthmatics and asthmatics. The ability of heterogeneous wall dimensions to shift the dose-response curve is largely due to an increased susceptibility for the small airways to close. This research confirms that heterogeneity of airway wall dimensions can contribute to exaggerated airway narrowing and provides an actual assessment of the magnitude of these effects.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".