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Record W4327923964 · doi:10.1016/j.heliyon.2023.e14673

Nebulization of pharmacological solutions with an innovative medical device based on microvaporization

2023· article· en· W4327923964 on OpenAlexaff
Bruno Brandimarte, L Businco, Francesco Cappello, Roberto Fiore, Giuseppe Bastone, Gianfranco Gualdi, Saadi Sollaku, Emanuele Casciani, Federica Tortorella, Pasquale Longo, Eleonora Centanini, Silva Pavaci, Federica Sangiuolo, Maria Patrizia Patrizi, Shane Miersch, Sachdev S. Sidhu, Virgilio Sacchini, Giuseppe Novelli

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteFondazione Roma
KeywordsVaporizationAerosolMaterials scienceProcess engineeringThermal energyEnvironmental scienceComputer scienceChemistryEngineeringThermodynamicsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The currently available nebulization devices have a slow aerosol flow and produce vapor with large microdrops. Improved devices that achieve higher airflow and produce smaller microdrops are needed to improve the clinical care of patients. To address this critical need, we developed a novel system for the molecular vaporization of liquids. This device vaporizes an active pharmacological substance dissolved in water, alcohol, or a mixture of water and alcohol using two energy sources at the same time: high-frequency ultrasound and thermal induction. Application of energy to a solution contained in the device's tank allows, within tens of seconds, for the vaporization of the solution itself, with the generation of a vapor consisting of microdrops of very small diameter (0.2-0.3 μm). In this article, we illustrate the technology used, the main verification tests performed, and the primary fields of application for this device. In particular, the advantages of both the aerosol delivery system and the administration system are highlighted.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.049
GPT teacher head0.335
Teacher spread0.286 · 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 teacher head, 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

Citations2
Published2023
Admission routes1
Has abstractyes

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