Aerosol deposition in 3D models of the upper airways and trachea of rhesus macaques
Little is known about aerosol deposition in macaques, variability in deposition between animals, or how deposition in macaques and humans compare. This is despite the use of macaques in assessments of toxic aerosols that are often translated to estimates of human exposure. We used three dimensional...
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Veröffentlicht in: | Aerosol science and technology 2020-08, Vol.54 (8), p.983-991 |
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Sprache: | eng |
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Zusammenfassung: | Little is known about aerosol deposition in macaques, variability in deposition between animals, or how deposition in macaques and humans compare. This is despite the use of macaques in assessments of toxic aerosols that are often translated to estimates of human exposure. We used three dimensional (3D) physical models of the upper airways and trachea (UAT) of Rhesus macaques to begin to fill in this information gap. Models of the UAT of five, living rhesus macaques were produced from CT scans, using 3D printing technology. Models were exposed to a polydisperse aerosol containing 0.54 to 9.65 micron particles, during constant flowrates of 2, 4, and 6 liters per min. Percent deposition in UAT models was quantified using an Aerodynamic Particle Sizer and was compared to in vivo upper airway deposition in ten, adult human subjects. Deposition in the UAT models increased as Stokes number increased. Deposition also varied significantly between models, but intermodel variability was reduced when plotted as a function of Stokes number. Using Stokes number, deposition in four of the five UAT models overlapped with each other and also overlapped with human upper airway deposition. These models could be used to explore the relationship between factors that affect toxic aerosol deposition in the UAT in vitro and pathology following toxic aerosol exposure in Rhesus macaques in vivo. Results from those experiments could also be applicable to humans because of deposition similarities.
Copyright © 2020 American Association for Aerosol Research |
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ISSN: | 0278-6826 1521-7388 |
DOI: | 10.1080/02786826.2020.1757031 |