What Causes Uneven Aerosol Deposition in the Bronchoconstricted Lung? A Quantitative Imaging Study
Bibliographic record
Abstract
Abstract Background: A previous PET-CT imaging study of 14 bronchoconstricted asthmatic subjects showed that peripheral aerosol deposition was highly variable among subjects and lobes. The aim of this work was to identify and quantify factors responsible for this variability. Methods: A theoretical framework was formulated to integrate four factors affecting aerosol deposition: differences in ventilation, in how air vs. aerosol distribute at each bifurcation, in the fraction of aerosol escaping feeding airways, and in the fraction of aerosol reaching the periphery that is exhaled. These factors were quantified in 12 of the subjects using PET-CT measurements of relative specific deposition sD* , relative specific ventilation sV* (measured with dynamic PET or estimated as change in expansion between two static HRCTs), average lobar expansion F VOL , and breathing frequency measured during aerosol inhalation f N . Results: The fraction of the variance of sD* explained by sV* (0.38), by bifurcation effects (0.38), and by differences in deposition along feeding airways (0.31) were similar in magnitude. We could not directly estimate the contribution of aerosol that was exhaled. Differences in expansion did not explain any fraction of the variability in sD* among lobes. The dependence of sD* on sV* was high in subjects breathing with low f N , but weakened among those breathing faster. Finally, sD* / sV* showed positive dependence on F VOL among low f N subjects, while the dependence was negative among high f N subjects. Conclusion: The theoretical framework allowed us to analyze experimentally measured aerosol deposition imaging data. When considering bronchoconstricted asthmatic subjects, a dynamic measurement of ventilation is required to evaluate its effect on aerosol transport. The mechanisms behind the identified effects of f N and F VOL on aerosol deposition need further study and may have important implications for aerosol therapy in subjects with heterogeneous ventilation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".