Laminar fluid ejection device enables high yield and preservation of mRNA and SaRNA LNP formulations
Bibliographic record
Abstract
The development of messenger RNA (mRNA) and self-amplifying RNA (saRNA) vaccines has revolutionized modern vaccinology, particularly with the success of lipid nanoparticle (LNP)-based SARS-CoV-2 vaccines. Intranasal administration offers a promising approach for respiratory vaccines, providing mucosal immunity at the primary entry site of pathogens. However, the impact of different aerosolization delivery systems on RNA-LNP stability, recovery volume and functionality is not well understood. In this study, we compare the effects of three intranasal administration devices- a commercial Nebulizer, a commercial Spray, and a Laminar Fluid Ejection (LFE) Device developed by Rocket Science Health- on LNP physicochemical properties, RNA encapsulation efficiency, and functional protein expression level. Our findings demonstrate that high shear forces in the commercial nebulizer delivery system significantly increase LNP particle size (85 nm to 300 nm) and polydispersity index (PDI), leading to RNA degradation and reduced encapsulation efficiency (100-39%). Conversely, the LFE Device preserved LNP structural integrity, achieving the highest RNA encapsulation efficiency (94% for mRNA, 102% for saRNA) and superior functional protein expression (3-fold higher luciferase activity compared to the CM Nebulizer). These results highlight the importance of selecting an appropriate delivery system to optimize RNA-LNP delivery and retention in intranasal applications. Our study supports the LFE Device as a viable candidate for effective RNA-LNP-based mucosal vaccine administration, with potential applications in next-generation RNA therapeutics.
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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) | 0.001 | 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".