2021
DOI: 10.1016/j.ijpharm.2021.120307
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A 3D printed human upper respiratory tract model for particulate deposition profiling

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Cited by 22 publications
(7 citation statements)
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“…It compares the effect of aerosol bursts and momentum on drug deposition. About %55 of the particles injected from the conventional pMDI nozzle deposited in the MT, and the remaining particles passed through the trachea, in agreement with in vitro results (Biswas et al 2017;Kaviratna et al 2019;Lim et al 2021) and CFD modelling studies (Kleinstreuer et al 2007; Walenga and Longest 2016) with similar MT geometries. Less than %2 and %3 deposited in the mouth and trachea region, respectively.…”
Section: Regional Depositionsupporting
confidence: 81%
“…It compares the effect of aerosol bursts and momentum on drug deposition. About %55 of the particles injected from the conventional pMDI nozzle deposited in the MT, and the remaining particles passed through the trachea, in agreement with in vitro results (Biswas et al 2017;Kaviratna et al 2019;Lim et al 2021) and CFD modelling studies (Kleinstreuer et al 2007; Walenga and Longest 2016) with similar MT geometries. Less than %2 and %3 deposited in the mouth and trachea region, respectively.…”
Section: Regional Depositionsupporting
confidence: 81%
“…For lung deposition, cascade impactors are known to have at best modest correlation with in vivo lung deposition data due to differences in “mouth-throat” geometry and inhalation profiles [ 132 ]. IVIVC could be improved by employing realistic “mouth-throats” connected to cascade impactors or 3D-printed lung models and simulated breathing profiles [ 117 , 133 ], yet the selection of suitable anatomical “mouth-throats” and representative inhalation profiles remains to be controversial. Alternatively, in silico modeling by computational fluid dynamics (CFD) is a trendy strategy in predicting in vivo deposition, though they are more technically demanding compared to in vitro methods [ 134 ].…”
Section: Challenges and Knowledge Gaps For Clinical Translationmentioning
confidence: 99%
“…we observed whether the solenoid pressed the driver, the upper computer software could adequately receive the monitoring results in real time, and whether the receiving function worked normally; the test results were analyzed and are summarized in Table 6. The interval time was calculated using Equation (14). ∆t = STA × 65535 + VAL + t e (14) where STA denotes the number of overflows, VAL denotes the current channel value of the timer, and t e denotes the driving time of the push-pull solenoid, which was set to approximately 9 ms for this study.…”
Section: Inspiration-press Synchronization Testmentioning
confidence: 99%
“…The interval time was calculated using Equation (14). ∆t = STA × 65535 + VAL + t e (14) where STA denotes the number of overflows, VAL denotes the current channel value of the timer, and t e denotes the driving time of the push-pull solenoid, which was set to approximately 9 ms for this study.…”
Section: Inspiration-press Synchronization Testmentioning
confidence: 99%
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