Generation of liposome aerosols with the Aeroneb Pro and the AeroProbe nebulizers
Bibliographic record
Abstract
BACKGROUND: Inhalation of therapeutic aerosols is a long-established means of drug delivery to the lungs or to the systemic circulation. In addition to solutions, suspensions, and particulates, liposomal formulations are being developed for aerosol administration. In this report, we investigated the membrane integrity of liposomes encapsulating the fluorescent model compound, calcein, after nebulization using two novel aerosolization devices, the Aeroneb Pro vibrating-mesh nebulizer (Aerogen, Dangan, Ireland) and the AeroProbe intracorporeal nebulizing catheter (Trudell Medical Corporation, London, Ontario, Canada). MATERIALS AND METHODS: The influence of lipid composition and lamellarity on the stability of the vesicles was investigated by measuring changes in median diameter, zeta-potential, and calcein retention. RESULTS: Both nebulizers were able to successfully aerosolise 1.5 mL of liposome suspension in a short period of time. The diameter and zeta-potential of the liposomes was preserved upon nebulization, and the calcein retention was above 70% in all cases. CONCLUSIONS: It can, hence, be concluded that both systems, the Aeroneb Pro and the AeroProbe, are well suited for the pulmonary delivery of liposomal formulations, with the AeroProbe having the additional advantage of allowing targeted delivery into the select regions of the lungs with a high degree of efficiency and control.
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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 | 0.000 |
| 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 | 0.001 | 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".