Visualization of local deposition of nebulized aerosols in a human upper respiratory tract model

In Silico Quantification of Intersubject Variability on Aerosol Deposition in the Oral Airway

The extrathoracic oral airway is not only a major mechanical barrier for pharmaceutical aerosols to reach the lung but also a major source of variability in lung deposition. Using computational fluid dynamics, deposition of 1−30 µm particles was predicted in 11 CT-based models of the oral airways of adults. Simulations were performed for mouth breathing during both inspiration and expiration at two steady-state flow rates representative of resting/nebulizer use (18 L/min) and of dry powder inhaler (DPI) use (45 L/min). Consistent with previous in vitro studies, there was a large intersubject variability in oral deposition. For an optimal size distribution of 1−5 µm for pharmaceutical aerosols, our data suggest that >75% of the inhaled aerosol is delivered to the intrathoracic lungs in most subjects when using a nebulizer but only in about half the subjects when using a DPI. There was no significant difference in oral deposition efficiency between inspiration and expiration, unlike subregional deposition, which shows significantly different patterns between the two breathing phases. These results highlight the need for incorporating a morphological variation of the upper airway in predictive models of aerosol deposition for accurate predictions of particle dosimetry in the intrathoracic region of the lung.

Smartphone-based holographic measurement of polydisperse suspended particulate matter with various mass concentration ratios

Real-time monitoring of suspended particulate matter (PM) has become essential in daily life due to the adverse effects of long-term exposure to PMs on human health and ecosystems. However, conventional techniques for measuring micro-scale particulates commonly require expensive instruments. In this study, a smartphone-based device is developed for real-time monitoring of suspended PMs by integrating a smartphone-based digital holographic microscopy (S-DHM) and deep learning algorithms. The proposed S-DHM-based PM monitoring device is composed of affordable commercial optical components and a smartphone. Overall procedures including digital image processing, deep learning training, and correction process are optimized to minimize the prediction error and computational cost. The proposed device can rapidly measure the mass concentrations of coarse and fine PMs from holographic speckle patterns of suspended polydisperse PMs in water with measurement errors of 22.8 ± 18.1% and 13.5 ± 9.8%, respectively. With further advances in data acquisition and deep learning training, this study would contribute to the development of hand-held devices for monitoring polydisperse non-spherical pollutants suspended in various media.

TRANSITION AND LAMINAR FLOWS IN A REALISTIC GEOMETRY OF HUMAN UPPER AIRWAY

In this study, a realistic respiratory airway model extending from oral to the end of the trachea including all the key details of the passage was produced. A series of CT scan images were used to generate the topological data of airway cross-sections that were used to generate the computational model, as well as the three-dimensional (3D) printed model of the passage for experimental study. The airflow velocity field and pressure drop in the airway for different breathing rates of 5, 7.5, 10, and 12.5[Formula: see text]L/min were investigated numerically (by laminar and transition models) and experimentally. The velocity distributions, pressure variation, and streamlines along the oral–trachea airway model were studied. The maximum pressure drop was shown to occur in the narrowest part of the larynx region. It was also concluded that the laryngeal jet could significantly influence the airway flow patterns in the trachea. A comparison between the numerical results and experimental data showed that the transition [Formula: see text]–kl–[Formula: see text] model can give better predictions of pressure losses, especially for flow rates higher than 10[Formula: see text]L/min. The simulation results for the velocity profiles in the trachea were also compared with the available particle image velocimetry (PIV) data and earlier simulations. Despite inter-personal variability and difference in the flow regime, the qualitative agreement was found.

In Vitro Evaluation of Nasal Aerosol Depositions: An Insight for Direct Nose to Brain Drug Delivery

The nasal cavity is an attractive route for both local and systemic drug delivery and holds great potential for access to the brain via the olfactory region, an area where the blood–brain barrier (BBB) is effectively absent. However, the olfactory region is located at the roof of the nasal cavity and only represents ~5–7% of the epithelial surface area, presenting significant challenges for the deposition of drug molecules for nose to brain drug delivery (NTBDD). Aerosolized particles have the potential to be directed to the olfactory region, but their specific deposition within this area is confounded by a complex combination of factors, which include the properties of the formulation, the delivery device and how it is used, and differences in inter-patient physiology. In this review, an in-depth examination of these different factors is provided in relation to both in vitro and in vivo studies and how advances in the fabrication of nasal cast models and analysis of aerosol deposition can be utilized to predict in vivo outcomes more accurately. The challenges faced in assessing the nasal deposition of aerosolized particles within the paediatric population are specifically considered, representing an unmet need for nasal and NTBDD to treat CNS disorders.

Accurate real-time monitoring of high particulate matter concentration based on holographic speckles and deep learning

Accurate real-time monitoring of particulate matter (PM) has emerged as a global issue due to the hazardous effects of PM on public health and industry. However, conventional PM monitoring techniques are usually cumbersome and require expensive equipments. In this study, Holo-SpeckleNet is proposed as a fast and accurate PM concentration measurement technique with high throughput using a deep learning based holographic speckle pattern analysis. Speckle pattern datasets of PMs for a wide range of PM concentrations were acquired by using a digital in-line holography microscopy system. Deep autoencoder and regression algorithms were trained with the captured speckle pattern datasets to directly measure PM concentration from speckle pattern images without any air intake device and time-consuming post image processing. The proposed technique was applied to predict various PM concentrations using the test datasets, optimize hyperparameters, and compare its performance with a convolutional neural network (CNN) algorithm. As a result, high PM concentration values can be measured over air quality index of 150, above which human exposure is unhealthy. In addition, the proposed technique exhibits higher measurement accuracy and less overfitting than the CNN with a relative error of 7.46 ± 3.92%. It can be applied to design a compact air quality monitoring device for highly accurate and real-time measurement of PM concentrations under hazardous environment, such as factories or construction sites.

A 3D printed human upper respiratory tract model for particulate deposition profiling

Pulmonary route is the main route of drug delivery for patients with asthma and chronic obstructive pulmonary diseases, offering several advantages over the oral route. Determining the amount of drug deposited onto various parts of the respiratory tract allows for a good correlation to clinical efficacy of inhalation drug devices. However, current in vitro cascade impactors measure only the aerodynamic particle size distribution, which does not truly represent the in vivo deposition pattern in human respiratory tract. In this study, a human upper respiratory tract model was fabricated using a 3D printer and subsequently characterized for its dimensional accuracy, surface finishing and air leaking. The effects of using a spacer and/or various airflow rates were also investigated. To assess this in vitro model, the deposition pattern of a model drug, namely, salbutamol sulphate, was tested. The resultant deposition pattern of salbutamol sulphate from a metered dose inhaler at 15 L per minute with the spacer, showed no significant difference from that of a published radiological in vivo study performed in adult humans. In addition, it was also found that the deposition pattern of salbutamol at 35 L per minute was comparable to the results of another published study in human. This in vitro model, showing reasonable in vitro-in vivo correlation, may provide opportunities for personalized medicine in special populations or disease states.

An experimental study of respiratory aerosol transport in phantom lung bronchioles

The transport and deposition of micrometer-sized particles in the lung is the primary mechanism for the spread of aerosol borne diseases such as corona virus disease-19 (COVID-19). Considering the current situation, modeling the transport and deposition of drops in human lung bronchioles is of utmost importance to determine their consequences on human health. The current study reports experimental observations on deposition in micro-capillaries, representing distal lung bronchioles, over a wide range of Re that imitates the particle dynamics in the entire lung. The experiment investigated deposition in tubes of diameter ranging from 0.3 mm to 2 mm and over a wide range of Reynolds number (10-2 ⩽ Re ⩽ 103). The range of the tube diameter and Re used in this study is motivated by the dimensions of lung airways and typical breathing flow rates. The aerosol fluid was loaded with boron doped carbon quantum dots as fluorophores. An aerosol plume was generated from this mixture fluid using an ultrasonic nebulizer, producing droplets with 6.5 µm as a mean diameter and over a narrow distribution of sizes. The amount of aerosol deposited on the tube walls was measured using a spectrofluorometer. The experimental results show that dimensionless deposition (δ) varies inversely with the bronchiole aspect ratio (L ¯ ), with the effect of the Reynolds number (Re) being significant only at lowL ¯ . δ also increased with increasing dimensionless bronchiole diameter (D ¯ ), but it is invariant with the particle size based Reynolds number. We show that δ L ¯ ∼ R e - 2 for 10-2 ⩽ Re ⩽ 1, which is typical of a diffusion dominated regime. For Re ⩾ 1, in the impaction dominated regime, δ L ¯ is shown to be independent of Re. We also show a crossover regime where sedimentation becomes important. The experimental results conclude that lower breathing frequency and higher breath hold time could significantly increase the chances of getting infected with COVID-19 in crowded places.

Numerical investigation of aerosol transport in a classroom with relevance to COVID-19

The present study investigates aerosol transport and surface deposition in a realistic classroom environment using computational fluid-particle dynamics simulations. Effects of particle size, aerosol source location, glass barriers, and windows are explored. While aerosol transport in air exhibits some stochasticity, it is found that a significant fraction (24%-50%) of particles smaller than 15 µm exit the system within 15 min through the air conditioning system. Particles larger than 20 µm almost entirely deposit on the ground, desks, and nearby surfaces in the room. Source location strongly influences the trajectory and deposition distribution of the exhaled aerosol particles and affects the effectiveness of mitigation measures such as glass barriers. Glass barriers are found to reduce the aerosol transmission of 1 µm particles from the source individual to others separated by at least 2.4 m by ∼92%. By opening windows, the particle exit fraction can be increased by ∼38% compared to the case with closed windows and reduces aerosol deposition on people in the room. On average, ∼69% of 1 µm particles exit the system when the windows are open.

Investigation of airflow at different activity conditions in a realistic model of human upper respiratory tract

In the present study, the turbulent flows inside a realistic model of the upper respiratory tract were investigated numerically and experimentally. The airway model included the geometrical details of the oral cavity to the end of the trachea that was based on a series of CT-scan images. The topological data of the respiratory tract were used for generating the computational model as well as the 3D-printed model that was used in the experimental pressure drop measurement. Different airflow rates of 30, 45, and 60 L/min, which correspond to the light, semi-light, and heavy activity breathing conditions, were investigated numerically using turbulence and transition models, as well as experimentally. Simulation results for airflow properties, including velocity vectors, pressure drops, streamlines, eddy viscosity, and turbulent kinetic energy contours in the oral-trachea airway model, were presented. The simulated pressure drop was compared with the experimental data, and reasonable agreement was found. The obtained results showed that the maximum pressure drop occurs in the narrowest part of the larynx region. A comparison between the numerical results and experimental data showed that the transition (γ-Reθ) SST model predicts higher pressure losses, especially at higher breathing rates. Formations of the secondary flows in the oropharynx and trachea regions were also observed. In addition, the simulation results showed that in the trachea region, the secondary flow structures dissipated faster for the flow rate of 60 L/min compared to the lower breathing rates of 30 and 45 L/min.

Particle transport and deposition correlation with near-wall flow characteristic under inspiratory airflow in lung airways

Exposure of lung airways to detrimental suspended aerosols in the environment increases the vulnerability of the respiratory and cardiovascular systems. In addition, recent developments in therapeutic inhalation devices magnify the importance of particle transport. In this manuscript, particle transport and deposition patterns in the upper tracheobronchial (TB) tree were studied where the inertial forces are considerable for microparticles. Wall shear stress divergence (WSSdiv) is proposed as a wall-based parameter that can predict particle deposition patterns. WSSdiv is proportional to near-wall normal velocity and can quantify the strength of flow towards and away from the wall. Computational fluid dynamics (CFD) simulations were performed to quantify airflow velocity and WSS vectors for steady inhalation in one case-control and unsteady inhalation in six subject-specific airway trees. Turbulent flow simulation was performed for the steady case using large eddy simulation to study the effect of turbulence. Magnetic resonance velocimetry (MRV) measurements were used to validate the case-control CFD simulation. Inertial particle transport was modeled by solving the Maxey-Riley equation in a Lagrangian framework. Deposition percentage (DP) was quantified for the case-control model over five particle sizes. DP was found to be proportional to particle size in agreement with previous studies in the literature. A normalized deposition concentration (DC) was defined to characterize localized deposition. A relatively strong correlation (Pearson value > 0.7) was found between DC and positive WSSdiv for physiologically relevant Stokes (St) numbers. Additionally, a regional analysis was performed after dividing the lungs into smaller areas. A spatial integral of positive WSSdiv over each division was shown to maintain a very strong correlation (Pearson value > 0.9) with cumulative spatial DC or regional dosimetry. The conclusions were generalized to a larger population in which two healthy and four asthmatic patients were investigated. This study shows that WSSdiv could be used to predict the qualitative surface deposition and relative regional dosimetry without the need to solve a particle transport problem.

Ventilation Modulation and Nanoparticle Deposition in Respiratory and Olfactory Regions of Rabbit Nose

The rabbit nose's ability to filter out inhaled agents is directly related to its defense to infectious diseases. The knowledge of the rabbit nose anatomy is essential to appreciate its functions in ventilation regulation, aerosol filtration and olfaction. The objective of this study is to numerically simulate the inhalation and deposition of nanoparticles in a New Zealand white (NZW) rabbit nose model with an emphasis on the structure-function relation under normal and sniffing conditions. To simulate the sniffing scenario, the original nose model was modified to generate new models with enlarged nostrils or vestibules based on video images of a rabbit sniffing. Ventilations into the maxilloturbinate and olfactory region were quantified with varying nostril openings, and deposition rates of inhaled aerosols ranging from 0.5 nm to 1000 nm were characterized on the total, sub-regional and local basis. Results showed that particles which deposited in the olfactory region came from a specific area in the nostril. The spiral vestibule played an essential role in regulating flow resistance and flow partition into different parts of the nose. Increased olfactory doses were persistently predicted in models with expanded nostrils or vestibule. Particles in the range of 5-50 nm are more sensitive to the geometry variation than other nanoparticles. It was also observed that exhaled aerosols occupy only the central region of the nostril, which minimized the mixing with the aerosols close to the nostril wall, and potentially allowed the undisruptive sampling of odorants. The results of this study shed new light on the ventilation regulation and inhalation dosimetry in the rabbit nose, which can be further implemented to studies of infectious diseases and immunology in rabbits.

Radiation Dosimetry of Inhaled Radioactive Aerosols: CFPD and MCNP Transport Simulations of Radionuclides in the Lung

Despite extensive efforts in studying radioactive aerosols, including the transmission of radionuclides in different chemical matrices throughout the body, the internal organ-specific radiation dose due to inhaled radioactive aerosols has largely relied on experimental deposition data and simplified human phantoms. Computational fluid-particle dynamics (CFPD) has proven to be a reliable tool in characterizing aerosol transport in the upper airways, while Monte Carlo based radiation codes allow accurate simulation of radiation transport. The objective of this study is to numerically assess the radiation dosimetry due to particles decaying in the respiratory tract from environmental radioactive exposures by coupling CFPD with Monte Carlo N-Particle code, version 6 (MCNP6). A physiologically realistic mouth-lung model extending to the bifurcation generation G9 was used to simulate airflow and particle transport within the respiratory tract. Polydisperse aerosols with different distributions were considered, and deposition distribution of the inhaled aerosols on the internal airway walls was quantified. The deposition mapping of radioactive aerosols was then registered to the respiratory tract of an image-based whole-body adult male model (VIP-Man) to simulate radiation transport and energy deposition. Computer codes were developed for geometry visualization, spatial normalization, and source card definition in MCNP6. Spatial distributions of internal radiation dosimetry were compared for different radionuclides (¹³¹I, ^134,137Cs, ⁹⁰Sr-⁹⁰Y, ¹⁰³Ru and ^239,240Pu) in terms of the radiation fluence, energy deposition density, and dose per decay.