Missing airways, ventilation defects and conductive airway physiology in asthma
Bibliographic record
Abstract
While airway obstruction, either by excessive luminal narrowing or mucus plugs, is recognized as a characteristic feature of various lung diseases, its measurement by physiological testing or lung imaging has been challenging. Currently, combined application of multiple imaging modalities allows for the quantification of obstructed airways and their link to ventilation defects. In asthma, computed tomography derived total airway count (TAC) and hyperpolarized gas magnetic resonance imaging derived ventilation defect percent (VDP) were shown to be interrelated [1]. In parallel, realistic models of asthma patients’ airway trees have pointed to the pivotal role of the asymmetrical distribution of compliant lung units in generating ventilation heterogeneity [2, 3]. In particular, the fact that the volumes of compliant lung units subtended by the bronchial tree are not perfectly symmetrically distributed at each bronchial bifurcation (coined here as “bronchial asymmetry”), introduces ventilatory flow asynchronies that produce positive phase III slopes. Such bronchial asymmetry was shown to be directly linked to conductive ventilation heterogeneity (Scond) as derived from phase III slope analysis of N2 multiple breath washout (MBW) curves [3]. Even a normal lung shows some degree of bronchial asymmetry (amongst others due to the shape of the thoracic cavity imposing the physical boundaries for lung growth) and also a non zero Scond. However in asthma, bronchial asymmetry can be greatly enhanced by the superimposed effects of narrowing of bronchi subtending the compliant units. In extreme case of complete bronchial obstruction, this dramatically affects the asymmetrical distribution of the compliant units communicating with the mouth where Scond is measured. In fact, the impact of bronchial asymmetry is a likely mechanistic explanation for the observation that in asthma, Scond is a major determinant of airway hyperresponsiveness, independent of airway inflammation [4]. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Rachel Eddy reports grants from Michael Smith Health Research BC, Canadian Respiratory Research Network, Natural Sciences and Engineering Research Council Canada; consulting fees from VIDA Diagnostics Inc.; lecture honoraria from Thorasys Thoracic Medical Systems Inc.; travel support from Canadian Institutes of Health Research – Institute of Circulatory and Respiratory Health; leadership role as Xenon MRI Clinical Trials Consortium Steering Committee Member; outside the submitted work. Conflict of interest: Grace Parraga reports grants from Canada Foundation for Innovation, Natural Sciences and Engineering Research Council Canada; lecture honoraria from GSK; advisory board participation with Polarean, CIHR; leadership role as Xenon MRI Clinical Trials Consortium Steering Committee Member; outside the submitted work. Conflict of interest: All other authors have nothing to disclose.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".