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The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, developing an innovative tool that accurately predicts transport behaviour and reduces computational time is essential. This study aims to develop a novel and innovative machine learning (ML) model to predict particle deposition to the lower airways. The first-ever study uses ML techniques to explore the pulmonary aerosol TD in a digital 17-generation airway model. The ML model uses the computational data for a 17-generation airway model and four standard ML regression models are used to save the computational cost. Random forest (RF), k-nearest neighbour (k-NN), multi-layer perceptron (MLP) and Gaussian process regression (GPR) techniques are used to develop the ML models. The MLP regression model displays more accurate estimates than other ML models. Finally, a prediction model is developed, and the results are significantly closer to the measured values. The prediction model predicts the deposition efficiency (DE) for different particle sizes and flow rates. A comprehensive lobe-specific DE is also predicted for various flow rates. This first-ever aerosol transport prediction model can accurately predict the DE in different regions of the airways in a couple of minutes. This innovative approach and accurate prediction will improve the literature and knowledge of the field. [ABSTRACT FROM AUTHOR]

Airway stenosis is a global respiratory health problem that is caused by airway injury, endotracheal intubation, malignant tumor, lung aging, or autoimmune diseases. A precise understanding of the airflow dynamics and pharmaceutical aerosol transport through the multi-stenosis airways is vital for targeted drug delivery, and is missing from the literature. The object of this study primarily relates to behaviors and nanoparticle transport through the multi-stenosis sections of the trachea and upper airways. The combination of a CT-based mouth–throat model and Weibel's model was adopted in the ANSYS FLUENT solver for the numerical simulation of the Euler–Lagrange (E-L) method. Comprehensive grid refinement and validation were performed. The results from this study indicated that, for all flow rates, a higher velocity was usually found in the stenosis section. The maximum velocity was found in the stenosis section having a 75% reduction, followed by the stenosis section having a 50% reduction. Increasing flow rate resulted in higher wall shear stress, especially in stenosis sections. The highest pressure was found in the mouth–throat section for all flow rates. The lowest pressure was usually found in stenosis sections, especially in the third generation. Particle escape rate was dependent on flow rate and inversely dependent on particle size. The overall deposition efficiency was observed to be significantly higher in the mouth–throat and stenosis sections compared to other areas. However, this was proven to be only the case for a particle size of 1 nm. Moreover, smaller nanoparticles were usually trapped in the mouth–throat section, whereas larger nanoparticle sizes escaped through the lower airways from the left side of the lung; this accounted for approximately 50% of the total injected particles, and 36% escaped from the right side. The findings of this study can improve the comprehensive understanding of airflow patterns and nanoparticle deposition. This would be beneficial in work with polydisperse particle deposition for treatment of comprehensive stenosis with specific drugs under various disease conditions. [ABSTRACT FROM AUTHOR]

The efficient generation of high concentrations of fine-particle, pure surfactant aerosols provides the possibility of new, rapid, and effective treatment modalities for Acute Respiratory Distress Syndrome (ARDS). SUPRAER-CATM is a patented technology by Kaer BiotherapeuticsTM, which is a new class of efficient aerosol drug generation and delivery system using Compressor Air (CA). SUPRAER-CA is capable of aerosolizing relatively viscous solutions or suspensions of proteins and surfactants and of delivering them as pure fine particle dry aerosols. In this Computational Fluid Dynamics (CFD) study, we select a number of sites within the upper 17 generations of the human respiratory tract for calculation of the deposition of dry pulmonary surfactant aerosol particles. We predict the percentage of inhaled dry pulmonary surfactant aerosol arriving from the respiratory bronchioles to the terminal alveolar sacs. The dry pulmonary surfactant aerosols, with a Mass Median Aerodynamic Diameter (MMAD) of 2.6 µm and standard deviation of 1.9 µm, are injected into the respiratory tract at a dry surfactant aerosol flow rate of 163 mg/min to be used in the CFD study at an air inhalation flow rate of 44 L/min. This CFD study in the upper 17th generation of a male adult lung has shown computationally that the penetration fraction (PF) is approximately 25% for the inhaled surfactant aerosols. In conclusion, an ARDS patient might receive approximately one gram of inspired dry surfactant aerosol during an administration period of one hour as a possible means of further inflating partly collapsed alveoli. [ABSTRACT FROM AUTHOR]

The recent outbreak of the SARS CoV-2 virus has had a significant effect on human respiratory health around the world. The contagious disease infected a large proportion of the world population, resulting in long-term health issues and an excessive mortality rate. The SARS CoV-2 virus can spread as small aerosols and enters the respiratory systems through the oral (nose or mouth) airway. The SARS CoV-2 particle transport to the mouth–throat and upper airways is analyzed by the available literature. Due to the tiny size, the virus can travel to the terminal airways of the respiratory system and form a severe health hazard. There is a gap in the understanding of the SARS CoV-2 particle transport to the terminal airways. The present study investigated the SARS CoV-2 virus particle transport and deposition to the terminal airways in a complex 17-generation lung model. This first-ever study demonstrates how far SARS CoV-2 particles can travel in the respiratory system. ANSYS Fluent solver was used to simulate the virus particle transport during sleep and light and heavy activity conditions. Numerical results demonstrate that a higher percentage of the virus particles are trapped at the upper airways when sleeping and in a light activity condition. More virus particles have lung contact in the right lung than the left lung. A comprehensive lobe specific deposition and deposition concentration study was performed. The results of this study provide a precise knowledge of the SARs CoV-2 particle transport to the lower branches and could help the lung health risk assessment system. [ABSTRACT FROM AUTHOR]

Understanding transportation and deposition (TD) of aerosol particles in the human respiratory system can help clinical treatment of lung diseases using medicines. The lung airway diameters and the breathing capacity of human lungs normally increase with age until the age of 30. Many studies have analyzed the particle TD in the human lung airways. However, the knowledge of the nanoparticle TD in airways of infants and children with varying inhalation flow rates is still limited in the literature. This study investigates nanoparticle (5 nm ≤ dp ≤ 500 nm) TD in the lungs of infants, children, and adults. The inhalation air flow rates corresponding to three ages are considered as Q in = 3.22   L / min (infant), 8.09   L / min (Child), and Q in = 14   L / min (adult). It is found that less particles are deposited in upper lung airways (G0–G3) than in lower airways (G12–G15) in the lungs of all the three age groups. The results suggest that the particle deposition efficiency in lung airways increases with the decrease of particle size due to the Brownian diffusion mechanism. About 3% of 500 nm particles are deposited in airways G12–G15 for the three age groups. As the particle size is decreased to 5 nm, the deposition rate in G12–G15 is increased to over 95%. The present findings can help medical therapy by individually simulating the distribution of drug-aerosol for the patient-specific lung. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two phase breathing dynamics has been studied in realistic lower airways. • Flow analysis is carried out and deposition/distribution patterns are depicted. • Majority of particles enter right lung while deposition is higher in left lung. • Distribution of particles to upper lobes reduces due to inertial impaction. • Right upper, left lower and right lower lobes are regions with highest deposition. Targeted drug delivery is an advanced method discussed in the literature for optimized treatment of diseases. However, the data for a precise understanding of pharmaceutical aerosol transport to the desired positions in the airways is not sufficient in the literature. Hence, in this work the transport and deposition of particles have been studied numerically in a realistic model of the respiratory system. The model was reconstructed based on CT-scan images of a healthy 28-year-old male and the commercial code ANSYS Fluent was used for analysis. After validation, distribution and deposition patterns of particles have been presented along with analysis of flow field dynamics. It was found that majority of particles enter the right lung while deposition is higher in the left lung and that the left lower lobe, left upper lobe and right lower lobe have the highest rate of lobar deposition. It was also observed that inertial impaction plays the dominant role in deposition of larger diameter particles at higher flow rates at the upper airways. The present findings improve our insight toward regional distribution and deposition of particles and assists in more accurate prediction of particle transport for drug delivery. [ABSTRACT FROM AUTHOR]