Nebulized therapies for childhood pulmonary hypertension: An in vitro model
Bibliographic record
Abstract
OBJECTIVES: Sildenafil, tezosentan, and prostacyclin reduce pulmonary vascular pressures in pulmonary hypertension, but have potential to vasodilate the systemic circulation. Nebulized vasodilators allow targeted drug delivery, high local drug concentrations, less systemic hypotension, and better matching of the lung's ventilation and perfusion. We aimed to estimate pulmonary deposition of these drugs from commonly employed nebulizers using in vitro techniques and to create a mathematical model to predict inspired mass of aerosol. DESIGN: Lung deposition was estimated by characterization of drug output and particle size distribution (PSD) of nebulizers using helium-neon laser diffraction techniques. A mathematical model for each device was created to estimate pulmonary deposition using patients' breathing patterns and was verified with a mechanical-breathing model. RESULTS: Total output and PSD were similar for the Hudson Updraft II and Whisperjet nebulizers, consisting of half the nebulizer's charge, with (1/4) of particles < or = 5 microm, in the respirable fraction (RF). Drug output increased with inspiratory flow for the Pari LC Star. Differences were noted in device performance, depending on the drug tested. Estimated pulmonary deposition (mean, 95% CI) was 8.1 (7.2, 9.0)% of the initial drug charge for the Hudson Updraft II, 6.4 (5.8, 7.0)% for the Whisperjet, and 33.0 (28.3, 37.9)% for the Pari LC Star. A mechanical model was consistent with our mathematical model. CONCLUSIONS: All drugs could be nebulized, but expected pulmonary deposition varied depending on the nebulizer and drug.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".