Regional Ventilation and Aerosol Deposition with Helium-Oxygen in Bronchoconstricted Asthmatic Lungs
Bibliographic record
Abstract
BACKGROUND: Theoretical models suggest that He-O2 as carrier gas may lead to more homogeneous ventilation and aerosol deposition than air. However, these effects have not been clinically consistent and it is unclear why subjects may or may not respond to the therapy. Here we present 3D-imaging data of aerosol deposition and ventilation distributions from subjects with asthma inhaling He-O2 as carrier gas. The data are compared with those that we previously obtained from a similar group of subjects inhaling air. METHODS: Subjects with mild-to-moderate asthma were bronchoconstricted with methacholine and imaged with PET-CT while inhaling aerosol carried with He-O2. Mean-normalized-values of lobar specific ventilation sV* and deposition sD* were derived and the factors affecting the distribution of sD* were evaluated along with the effects of breathing frequency (f) and regional expansion (FVOL). RESULTS: Lobar distributions of sD* and sV* with He-O2 were not statistically different from those previously measured with air. However, with He-O2 there was a larger number of lobes having sV* and sD* closer to unity and, in those subjects with uneven deposition distributions, the correlation of sD* with sV* was on average higher (p < 0.05) in He-O2 (0.84 ± 0.8) compared with air (0.55 ± 0.28). In contrast with air, where the frequency of breathing during nebulization was associated with the degree of sD*-sV* correlation, with He-O2 there was no association. Also, the modulation of f on the correlation between FVOL and sD*/sV* in air, was not observed in He-O2. CONCLUSION: There were no differences in the inter-lobar heterogeneity of sD* or sV* in this group of mild asthmatic subjects breathing He-O2 compared with patients previously breathing air. Future studies, using these personalized 3D data sets as input to CFD models, are needed to understand if, and for whom, breathing He-O2 during aerosol inhalation may be beneficial.
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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 | 0.000 |
| 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".