A Novel Intranasal Spray Device for the Administration of Nanoparticles to Rodents
Bibliographic record
Abstract
Experimental intranasal (IN) delivery of nanoparticle (NP) drug carriers is typically performed using a pipette with or without anesthesia, a technique that may be a poor simulation of practical IN administration of drug-loaded NPs in humans. Existing IN spray devices suffer from drawbacks in terms of variability in dose-control and spray duration as well as the application of nonuniform pressure fields when a NP-formulated drug is aerosolized. Furthermore, existing spray devices require large volumes that may not be available or may be prohibitively expensive to prepare. In response, we have developed a novel pneumatically driven IN spray device for the administration of NPs, which is capable of administering extremely small quantities (50–100 μl) of NP suspension in a fine spray that disperses the NPs uniformly onto the tissue. This device was validated using haloperidol-loaded Solanum tuberosum lectin (STL)-functionalized, poly(ethylene glycol)–block-poly(d,l-lactic-co-glycolic acid) (PEG–PLGA) NPs targeted for delivery to the brain for schizophrenia treatment. A pneumatic pressure of 100 kPa was found to be optimal to produce a spray that effectively aerosolizes NP suspensions and delivers them evenly to the olfactory epithelium. IN administration of STL-functionalized NPs using the IN spray device increased brain tissue haloperidol concentrations by a factor of 1.2–1.5× compared to STL-functionalized NPs administered IN with a pipette. Such improved delivery enables the use of lower drug doses and thus offers both fewer local side-effects and lower costs without compromising therapeutic efficacy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".