Understanding regional aerosol deposition in pediatric airways during oral breathing: Insights from computational modeling
Bibliographic record
Abstract
While there has been considerable investigation into the deposition of inhaled aerosols in the airways of adults, less is known about where aerosols deposit in the lungs of children. Clinical investigation into aerosol deposition in children is complicated by ethical concerns surrounding ionizing radiation studies in children. To meet the need for non-clinical methods of estimating regional deposition in pediatric airways, multiple in silico models were developed to represent the lungs of girls and boys aged 6, 8, 10 and 12 years. The models were symmetric and used a single-path deterministic approach to calculate aerosol deposition in the airways. Regional deposition estimates were provided for children using a fixed set of controlled breathing patterns before characterizing regional deposition during typical tidal breathing in each age group. Deposition patterns were found to be strongly influenced by inhalation flow rate and aerodynamic particle size. Differences between boys and girls in the fraction of inhaled aerosol depositing were minimal during fixed breathing patterns, with higher deposition in all regions of the younger age groups. However, when breathing patterns were adjusted to represent typical tidal breathing in each age group, age differences in the regional fraction of particles depositing became negligible. Moreover, peak deposition fractions in both the conducting and peripheral airways occurred within a narrow range of aerodynamic particle diameters between 2.4 and 2.6 μm, a smaller size range than for adults. During exposure over a fixed period of time, age-related differences in minute ventilation resulted in a larger aerosol dose depositing in the intrathoracic airways of older children. Such differences suggest that to achieve comparative dosing in this age range, older children should inhale aerosols for a shorter time. These findings provide an improved understanding of regional deposition in pediatric airways and will assist in optimizing regional drug delivery to children. • Deposition patterns were influenced by particle size and inhalation flow rate. • No differences in deposition due to sex; influence of age during fixed breathing. • During a typical tidal breath, age-related differences in deposition evened out. • Deposited dose was higher in older children over multiple breaths. • Peak deposition in intra-thoracic regions was at smaller particle sizes than adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".