Journal article
Investigating distributions of inhaled aerosols in the lungs of post-COVID-19 clusters through a unified imaging and modeling approach
European journal of pharmaceutical sciences, Vol.195, 106724
04/01/2024
DOI: 10.1016/j.ejps.2024.106724
PMCID: PMC10948263
PMID: 38340875
Abstract
Recent studies, based on clinical data, have identified sex and age as significant factors associated with an increased risk of long COVID. These two factors align with the two post-COVID-19 clusters identified by a deep learning algorithm in computed tomography (CT) lung scans: Cluster 1 (C1), comprising predominantly females with small airway diseases, and Cluster 2 (C2), characterized by older individuals with fibrotic-like patterns. This study aims to assess the distributions of inhaled aerosols in these clusters.
140 COVID survivors examined around 112 days post-diagnosis, along with 105 uninfected, non-smoking healthy controls, were studied. Their demographic data and CT scans at full inspiration and expiration were analyzed using a combined imaging and modeling approach. A subject-specific CT-based computational model analysis was utilized to predict airway resistance and particle deposition among C1 and C2 subjects. The cluster-specific structure and function relationships were explored.
In C1 subjects, distinctive features included airway narrowing, a reduced homothety ratio of daughter over parent branch diameter, and increased airway resistance. Airway resistance was concentrated in the distal region, with a higher fraction of particle deposition in the proximal airways. On the other hand, C2 subjects exhibited airway dilation, an increased homothety ratio, reduced airway resistance, and a shift of resistance concentration towards the proximal region, allowing for deeper particle penetration into the lungs.
This study revealed unique mechanistic phenotypes of airway resistance and particle deposition in the two post-COVID-19 clusters. The implications of these findings for inhaled drug delivery effectiveness and susceptibility to air pollutants were explored.
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Details
- Title: Subtitle
- Investigating distributions of inhaled aerosols in the lungs of post-COVID-19 clusters through a unified imaging and modeling approach
- Creators
- Xuan Zhang - University of IowaFrank Li - University of IowaPrathish K. Rajaraman - University of IowaAlejandro P. Comellas - University of IowaEric A. Hoffman - University of IowaChing-Long Lin - IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA
- Resource Type
- Journal article
- Publication Details
- European journal of pharmaceutical sciences, Vol.195, 106724
- DOI
- 10.1016/j.ejps.2024.106724
- PMID
- 38340875
- PMCID
- PMC10948263
- NLM abbreviation
- Eur J Pharm Sci
- ISSN
- 0928-0987
- eISSN
- 1879-0720
- Publisher
- Elsevier B.V
- Grant note
- DOI: 10.13039/100000138, name: US Department of Education; DOI: 10.13039/100000002, name: National Institutes of Health
- Language
- English
- Electronic publication date
- 02/08/2024
- Date published
- 04/01/2024
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Pulmonary, Critical Care, and Occupational Medicine; ICTS; IIHR--Hydroscience and Engineering; Mechanical Engineering; Internal Medicine
- Record Identifier
- 9984559813202771
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