Aerosol drug delivery in pediatric airways: in vitro and CFD insights into tongue position and inhalation patterns using soft mist inhalers
Bibliographic record
Abstract
Respiratory diseases such as asthma have a significant impact on children worldwide, underscoring the need for accurate assessments of aerosol drug delivery. This study integrates computational fluid dynamics (CFD) and in vitro experiments to evaluate drug deposition from a soft mist inhaler (SMI) in a pediatric mouth-throat (MT) airway. Large eddy simulation (LES) and the discrete phase model (DPM) were employed in ANSYS Fluent to investigate the effects of various inhalation profiles and tongue positions on droplet behaviour. The numerical results closely matched in vitro data obtained from a next-generation impactor, with a root mean square error (RMSE) of 0.061. We found that the deposition of aerosol medications in pediatric patients was over twice that of adults at an inhalation flow rate of 30 l/min under normal tongue posture. Lowering the tongue position reduced deposition within the mouth and on the device's mouthpiece, while increasing deposition in the throat and at the outlet. Higher flow rates enhanced the retention of small droplets (0.1-2 μm) and broadened the deposition sites. A predictive correlation for mouth deposition was established for Stokes numbers greater than 0.02. Simulating realistic asthma profiles, along with 2-step and 3-step pulsatile inhalation patterns, enhanced the retention of small droplets and decreased the deposition of larger droplets (ranging from 5 to 60 µm). These conditions contributed to reduced mouth deposition and increased drug loss to the mouthpiece. Notably, pulsatile profiles increased tongue deposition, whereas the asthma profile enhanced deposition on the palate wall.
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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) | 0.000 | 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 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".