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Record W2914664947 · doi:10.1186/s12890-019-0807-9

Aerosol drug delivery to the lungs during nasal high flow therapy: an in vitro study

2019· article· en· W2914664947 on OpenAlexaff
Martin Wallin, Patricia A. Tang, Rachel Yoon Kyung Chang, Mingshi Yang, Warren H. Finlay, Hak‐Kim Chan

Bibliographic record

VenueBMC Pulmonary Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDrugDrug deliveryPharmacologyIntensive care medicineNanotechnology

Abstract

fetched live from OpenAlex

BACKGROUND: Aerosol delivery through a nasal high flow (NHF) system is attractive for clinicians as it allows for simultaneous administration of oxygen and inhalable drugs. However, delivering a fine particle fraction (FPF, particle wt. fraction < 5.0 μm) of drugs into the lungs has been very challenging, with highest value of only 8%. Here, we aim to develop an efficient nose-to-lung delivery system capable of delivering improved quantities (FPF > 16%) of dry powder aerosols to the lungs via an NHF system. METHODS: We evaluated the FPF of spray-dried mannitol with leucine with a next generation impactor connected to a nasopharyngeal outlet of an adult nasal airway replica. In addition, we investigated the influence of different dispersion (20-30 L/min) and inspiratory (20-40 L/min) flow rates, on FPF. RESULTS: We found an FPF of 32% with dispersion flow rate at 25 L/min and inspiratory flow rate at 40 L/min. The lowest FPF (21%) obtained was at the dispersion flow rate at 30 L/min and inspiratory flow rate at 30 L/min. A higher inspiratory flow rate was generally associated with a higher FPF. The nasal cannula accounted for most loss of aerosols. CONCLUSIONS: In conclusion, delivering a third of inhalable powder to the lungs is possible in vitro through an NHF system using a low dispersion airflow and a highly dispersible powder. Our results may lay the foundation for clinical evaluation of powder aerosol delivery to the lungs during NHF therapy in humans.

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)0.001
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 score0.770
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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