Impact of normal, obstructive, and restrictive breathing patterns on aerosol drug delivery with jet and mesh nebulizers in simulated spontaneously breathing adults
Bibliographic record
Abstract
Background Aerosol drug delivery is widely used in treating respiratory conditions, but the patient’s breathing pattern can significantly influence its effectiveness. This study investigates the impact of normal, obstructive, and restrictive breathing patterns on aerosol drug delivery with jet and mesh nebulizers in a simulated model of spontaneously breathing adults. Methods A spontaneously breathing adult was simulated using a teaching manikin (Nasco Healthcare) connected to a breathing simulator (QuickLung Breather; IngMar Medical Inc). A collecting filter (CareFusion) was placed distal to the bifurcation of the mainstem bronchi and connected to the breathing simulator. Albuterol sulfate (2.5 mg/3 mL) was delivered with jet (MistyMax 10) and mesh nebulizers (Aerogen Ultra). Each experiment was conducted in triplicate (n = 3), comparing drug delivery across six breathing patterns: (1) normal, (2) moderate obstruction, (3) severe obstruction, (4) moderate restriction, (5) severe restriction, and (6) combined obstruction and restriction. Data analysis included the Friedman ANOVA, uncorrected Dunn’s test, and paired t-tests with Holm-Sidak’s multiple comparison test (GraphPad Prism 10.3), with statistical significance set at p < 0.05. Results Our findings indicate that obstructive, restrictive, and combined breathing patterns significantly reduce aerosol deposition with jet and mesh nebulizers compared to normal breathing ( p < 0.05). Aerosol delivery with the mesh nebulizer was up to 3-fold more than the jet nebulizer regardless of the breathing pattern tested in this study ( p < 0.05). Conclusions This study highlights the necessity for tailored aerosol therapy strategies to optimize drug delivery in patients with different respiratory conditions..
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".