Laminar Fluid Ejection for Olfactory Drug Delivery: A Proof of Concept Study
Bibliographic record
Abstract
Focal intranasal drug delivery to the olfactory cleft is a promising avenue for pharmaceuticals targeting the brain. However, traditional nasal sprays often fail to deliver enough medication to this specific area. We present a laminar fluid ejection (LFE) method for precise delivery of medications to the olfactory cleft. Using a 3D-printed model of the nasal passages, we determined the precise velocity and angle of insertion needed to deposit fluid at the olfactory cleft. Then, we conducted three proof-of-concept in-vivo imaging studies to confirm olfactory delivery in humans. First, we used Technetium-99 (a radiolabeled tracer) and methylene blue (a laboratory-made dye) to visualize olfactory deposition. Both tracers showed successful deposition. In a separate study, we used functional MRI (fMRI), to compare our LFE method with a conventional nasal spray while delivering insulin. From the fMRI results, we qualitatively observed focal decreases in brain activity in prefrontal cortex following insulin delivery. Overall, these preliminary results suggest that LFE offers a targeted approach to olfactory drug delivery, opening opportunities for access to the brain.Clinical and Translational Impact Statement - Focal deposition at the olfactory cleft is a promising target for delivering medication to the brain. We present in-human tests of a laminar fluid ejection method for intranasal drug delivery and demonstrate improvements over conventional nasal spray.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".