Investigating distributions of inhaled aerosols in the lungs of post-COVID-19 clusters through a unified imaging and modeling approach
Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved..
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:195 |
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| Enthalten in: |
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences - 195(2024) vom: 01. Apr., Seite 106724 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Xuan [VerfasserIn] |
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| Links: |
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| Themen: |
Clusters |
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| Anmerkungen: |
Date Completed 11.03.2024 Date Revised 02.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ejps.2024.106724 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Date Revised 02.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND: 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 | ||
| 520 | |a METHODS: 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 | ||
| 520 | |a RESULTS: 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 | ||
| 520 | |a CONCLUSIONS: 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Clusters | |
| 650 | 4 | |a Computational fluid dynamics | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Long COVID | |
| 650 | 4 | |a PASC | |
| 700 | 1 | |a Li, Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Rajaraman, Prathish K |e verfasserin |4 aut | |
| 700 | 1 | |a Comellas, Alejandro P |e verfasserin |4 aut | |
| 700 | 1 | |a Hoffman, Eric A |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Ching-Long |e verfasserin |4 aut | |
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