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Record W4400932665 · doi:10.1016/j.jconrel.2024.07.034

Understanding the role of swirling flow in dry powder inhalers: Implications for design considerations and pulmonary delivery

2024· review· en· W4400932665 on OpenAlexaff
Jiale Chen, Yuqing Ye, Qingliang Yang, Ziyi Fan, Yuanyuan Shao, Xiaoyang Wei, Kaiqi Shi, Jie Dong, Ying Ma, Jesse Zhu

Bibliographic record

VenueJournal of Controlled Release · 2024
Typereview
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsWestern University
Fundersnot available
KeywordsDry-powder inhalerMouthpieceDrug deliveryComputer scienceFlow (mathematics)MedicineFlow propertiesNanotechnologyRisk analysis (engineering)Materials scienceInhalerMechanicsAsthmaPhysics

Abstract

fetched live from OpenAlex

Dry powder inhalers (DPIs) are widely employed to treat respiratory diseases, offering numerous advantages such as high dose capacity and stable formulations. However, they usually face challenges in achieving sufficient pulmonary drug delivery and minimizing excessive oropharyngeal deposition. This review provides a new viewpoint to address these challenges by focusing on the role of swirling flow, a crucial yet under-researched aspect that induces strong turbulence. In the review, we comprehensively discuss both key classic designs (tangential inlet, swirling chamber, grid mesh, and mouthpiece) and innovative designs in inhalers, exploring how the induced swirling flow initiates powder dispersion and promotes delivery efficiency. Valuable design considerations to effectively coordinate inhalers with formulations and patients are also provided. It is highlighted that the delicate manipulation of swirling flow is essential to maximize benefits. By emphasizing the role of swirling flow and its potential application, this review offers promising insights for advancing DPI technology and optimizing therapeutic outcomes in inhaled therapy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.124
GPT teacher head0.341
Teacher spread0.217 · 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 designOther design
Domainnot available
GenreReview

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

Citations12
Published2024
Admission routes1
Has abstractyes

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