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Record W4366448895 · doi:10.21203/rs.3.rs-2823997/v1

The Impact of Actuator Nozzle and Surroundings Condition on Drug Delivery using Pressurized-Metered Dose Inhalers

2023· preprint· en· W4366448895 on OpenAlexafffund
Mahsa Jahed, Janusz A. Koziński, Leila Pakzad

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaVirginia Commonwealth University
KeywordsNozzleAerosolAirflowActuatorDeposition (geology)Computational fluid dynamicsSpray nozzleDrug deliveryMaterials scienceMechanicsEnvironmental scienceAerospace engineeringMechanical engineeringMeteorologyEngineeringNanotechnologyPhysicsGeologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract The most commonly used method to deliver aerosolized drugs to the lung is with pressurized metered-dose inhalers (pMDIs). The spray actuator is a critical component of a pMDI, since it controls the atomization process by forming aerosol plumes and determining droplet size distribution. Through computational fluid dynamics (CFD) simulations, this study investigated the effect of two different nozzle types (single conventional and twin nozzles) on drug deposition in the mouth-throat (MT) region. We also studied the behavior of aerosol plumes in both an open-air environment and the MT geometry. Our study revealed that spray aerosol generated in an unconfined, open-air environment with no airflow behaves distinctly from spray introduced into the MT geometry in the presence of airflow. In addition, the actuator structure significantly impacts the device's efficacy. In the real MT airway, we found that the twin nozzle increases drug deposition in the MT region and its higher aerosol velocity negatively affects its efficiency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.122
GPT teacher head0.443
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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