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Record W3142510400 · doi:10.22215/etd/2020-14229

Pressurized Metered-Dose (pMDI) Aerosol Spray Deposition in Large-and Medium-Volume Spacers

2020· dissertation· en· W3142510400 on OpenAlexaff
Nicholas Ogrodnik

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsCarleton University
Fundersnot available
KeywordsDeposition (geology)AerosolSalbutamolVolumetric flow rateMaterials scienceVolume (thermodynamics)ChemistryAsthmaMedicineMechanicsInternal medicineGeologyOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Spacers (or holding chambers) are typically used with pressurized metered-dose inhalers (pMDIs) by patients suffering from chronic lower respiratory tract diseases such as asthma and chronic obstructive pulmonary disease (COPD).Attachments such as spacers provide a number of benefits including: a reduction of the "Cold Freon effect 1 ", better coordination (between pMDI actuation and inhalation), and a reduction of the inertial impaction of the medication in the oropharyngeal tract and oropharynx.These factors improve the inhalation of medication resulting in a larger percentage of medication delivered to the lungs when using a spacer device.However, spacer devices can also cause a loss of medication delivered by the pMDI due to deposition of the medication on the walls of the spacer itself.As such, it is important to understand the mechanisms which cause this medication loss to allow for future spacer devices to be designed more efficiently.Regional deposition of the medication, salbutamol sulphate, was studied in a medium-and large-volume spacer, namely, the Volumatic™ and OptiChamber® spacer.This study was completed using both experimental and numerical analyses.Experiments were conducted at typical inspiratory flow rates ranging from 30 to 60 L/min, which were typical for medication delivery.The amount of deposition of medication in the spacer device was assessed using spectrophotometry.Computational fluid dynamics (CFD) was also used to allow the implementation of particle tracking and quantify the 1 It should be noted that chlorofluorocarbons (CFCs) are no longer used in pMDIs and, as such, Freon is no longer present in the inhalers.However, the concept of a cold plume impacting the back of the throat (which was described by the Cold Freon effect) is still present in pMDIs even with current propellants.iii deposition (and its mechanism) numerically at a flow rate of 30 L/min.Simulations used both mean flow and turbulent particle tracking, applying unsteady Reynolds-averaged Navier-Stokes (URANS) equations with a shear stress transport (SST) turbulence model.Deposition of salbutamol sulphate in the Volumatic™ and OptiChamber® spacers was found to be greater in the lower half as opposed to the upper half of the spacer due to a downward spray angle.Additionally, it was determined more deposition of medication could be expected in the distal half of the spacer as opposed to the proximal half.An increase in flow rate demonstrated a minimal increase in the medication delivered to the inline filter which was analogous to that reaching the patient.The numerical analysis demonstrated that turbulence effects are likely to cause deposition in both the Volumatic™ and OptiChamber® spacer.Results suggested that a larger flow rate does not necessarily allow for more medication to be delivered, it acts only to shift the region of the deposition.As such, it is conjectured that each spacer should have an optimal flow rate as to where the most medication will be delivered without excessive inhalation by the patient.I would like to express my sincerest gratitude to my thesis supervisor, Professor Edgar Matida, for all the support, guidance and encouragement provided throughout my studies, during both my undergraduate and graduate research.I always appreciated your patient approach and positive attitude throughout my academic journey.This gratitude is extended to my colleagues as well, providing me clarity in times of stress, always willing to lend a helping hand and to listen whenever I needed a voice of reason.At home, I would like to thank my partner, as without her encouragement, and her never-ending love and support, I would not be where I am today.Your grace and passion mean the world to me.To my parents and brother, as well, who supported my education, encouraged me to be my best and were always there with advice and a home cooked meal whenever I needed to vent.I am sure, Mom, you would be happy to hear I am finally done!To my partner's family, who were always so understanding and caring.You are a wonderful bunch, bringing so much positivity and kindness (and great food!) to my life.Lastly

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)0.000
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 score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.004

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 head0.013
GPT teacher head0.273
Teacher spread0.259 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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