Experimental tracking and numerical mapping of novel coronavirus micro-droplet deposition through nasal inhalation in the human respiratory system

Development of multi-generation lower respiratory tract model and insights into the transport and deposition characteristics of inhalable particles

Airborne particles can spread quickly and enter human respiratory system via inhalation, causing chronic diseases, even cancer. Although recent studies have informed of toxicity of various pollutants, understanding the transport and deposition characteristics of particles in lower respiratory tract is still challenging. The current study proposes a novel model to simulate flow field change from the entrance of lower respiratory tract to pulmonary acinus, while studying particle transport and deposition characteristics. This model for lower respiratory tract with several bronchial extensions containing virtual pulmonary acinus is calculated using computational fluid dynamics and dynamics mesh. The results showed that in the first 10 generations of the lower respiratory tract, vortices and gravity interfered with particles' trajectory, affecting particle deposition distribution. For the first to the tenth-generation respiratory tract, coarse particles were deposited throughout almost the whole respiratory tract model. In contrast, ultrafine particles did not deposit in the higher-generation respiratory tract. The particle enrichment ability of various lobes was uneven with three particle deposition fraction variation patterns. Virtual pulmonary acinus influenced particle deposition and distribution because of vortex ring's trapped ability during expansion and contraction. This new attempt to build a virtual pulmonary acinus model to simulate particle deposition effects in human respiratory system may provide a reference for studying the toxicities of inhalable particles in the exposed human body.

Relaxation and creep response of the alveolar lung to diagnosis and treatments for respiratory and lung disorders

BACKGROUND

The lung Extracellular Matrix (ECM) contains a considerable part of the parenchymal cells. It contains three essential components: elastin and collagen within a proteoglycan (PG) viscoelastic network. Elastin provides the lung's elasticity property, a necessity for normal breathing, while collagen prepares structural support and strength, and PGs give stability and cushioning within tissue loading. Bacterial and viral respiratory diseases are dependent on changes in the ECM ingredients, which result in an alteration of the lung tissue strength.

PURPOSE

In the present study, this variation was investigated by changing the volume ratio of the ECM ingredients in the viscoelastic model.

RESULTS

As a result, the relaxation curves continuously declined by reducing the volume ratios of elastin, collagen, and PGs; subsequently, the lung stiffness decreased. Also, the Standard Linear Solid (SLS) model-based results demonstrated excellent accordance with empirical data with only minor deviations. The resting relaxation modulus and the creep modulus for the ECM tissue were 51 kPa and approximately 0.02 kPa, respectively, and the maximum total modulus of elasticity reached 121 kPa.

CONCLUSIONS

Moreover, this model demonstrates individual alveolar mechanical behaviours and adds another pathway to the generalized Kelvin-Voigt and Maxwell models in predicting the progress of lung diseases.

Visual prediction and parameter optimization of viral dynamics in the mucus milieu of the upper airway based on CFPD-HCD analysis

BACKGROUND AND OBJECTIVE

Respiratory diseases caused by viruses are a major human health problem. To better control the infection and understand the pathogenesis of these diseases, this paper studied SARS-CoV-2, a novel coronavirus outbreak, as an example.

METHODS

Based on coupled computational fluid and particle dynamics (CFPD) and host-cell dynamics (HCD) analyses, we studied the viral dynamics in the mucus layer of the human nasal cavity-nasopharynx. To reproduce the effect of mucociliary movement on the diffusive and convective transport of viruses in the mucus layer, a 3D-shell model was constructed using CT data of the upper respiratory tract (URT) of volunteers. Considering the mucus environment, the HCD model was established by coupling the target cell-limited model with the convection-diffusion term. Parameter optimization of the HCD model is the key problem in the simulation. Therefore, this study focused on the parameter optimization of the viral dynamics model, divided the geometric model into multiple compartments, and used Monolix to perform the nonlinear mixed effects (NLME) of pharmacometrics to discuss the influence of factors such as the number of mucus layers, number of compartments, diffusion rate, and mucus flow velocity on the prediction results.

RESULTS

The findings showed that sufficient experimental data can be used to estimate the corresponding parameters of the HCD model. The optimized convection-diffusion case with a two-layer multi-compartment low-velocity model could accurately predict the viral dynamics.

CONCLUSIONS

Its visualization process could explain the symptoms of the disease in the nose and contribute to the prevention and targeted treatment of respiratory diseases.

A mathematical model for biomechanical behavior of the aortic arch

The aortic arch plays a significant role in homeostatic mechanisms to retain blood pressure at stable balance in the cardiovascular system. Therefore, the objective is to estimate and identify cardiovascular illness imposed by the abnormal blood hemodynamic domain. In this regard, hemodynamic forces are monitored by the baroreflex of the artery wall. Therefore, these receptors quickly detect the abnormal stress magnitudes in the aortic arterial wall. The present study presents a 3D aortic arch model extracted by a Computerized tomography scan. Also, the numerical solution was carried out by ANSYS 2020 R1 in view of Fluid-Structure Interaction After that, we found wall shear stress (WSS), pressure, and velocity in the fluid domain. Also, the normal stress was analyzed to determine the aortic arch baroreflex location in the solid range. In this regard, higher WSS values are measured at the supra-aortic branches going out the aortic arch that reached 42.5 Pa. Also, higher normal stress happened at the aortic root and the supra-aortic branches and reached approximately 200 kPa at peak systole.

Investigation of the Upper Respiratory Tract of a Male Smoker with Laryngeal Cancer by Inhaling Air Associated with Various Physical Activity Levels

Smokers are at a higher risk of laryngeal cancer, which is a type of head and neck cancer in which cancer cells proliferate and can metastasize to other tissues after a tumor has formed. Cigarette smoke greatly reduces the inhaled air quality and can also lead to laryngeal cancer. In this study, the upper airway of a 70-year-old smoker with laryngeal cancer was reconstructed by taking a CT scan using Mimics software. To solve the governing equations, computational fluid dynamics (CFD) with a pressure base approach was used with the help of Ansys 2021 R1 software. As a result, the maximum turbulence intensity occurred in the larynx. At 13 L/min, 55 L/min, and 100 L/min, the maximum turbulence intensity was 1.1, 3.5, and 6.1, respectively. The turbulence intensity in the respiratory system is crucial because it demonstrates the ability to transfer energy. The maximum wall shear stress (WSS) also occurred in the larynx. At 13 L/min, 55 L/min, and 100 L/min, the maximum WSS was 0.62 Pa, 5.4 Pa, and 12.4 Pa, respectively. The WSS index cannot be calculated in vivo and should be calculated in vitro. Excessive WSS in the epiglottis is inappropriate and can lead to an airway obstruction. Furthermore, real mathematical modeling outcomes provide an approach for future prevention, treatment, and management planning by forecasting the zones prone to an acceleration of disease progression. In this regard, accurate computational modeling leads to pre-visualization in surgical planning to define the best reformative techniques to determine the most probable patient condition consequences.

Biomedical and biophysical limits to mathematical modeling of pulmonary system mechanics: a scoping review on aerosol and drug delivery

Undoubtedly, the construction of the biomechanical geometry systems with the help of computer tomography (CT) and magnetic resonance imaging (MRI) has made a significant advancement in studying in vitro numerical models as accurately as possible. However, some simplifying assumptions in the computational studies of the respiratory system have caused errors and deviations from the in vivo actual state. The most important of these hypotheses is how to generate volume from the point cloud exported from CT or MRI images, not paying attention to the wall thickness and its effect in computational fluid dynamic method, statistical logic of aerosol trap in software; and most importantly, the viscoelastic effect of respiratory tract wall in living tissue pointed in the fluid–structure interaction method. So that applying the viscoelastic dynamic mesh effect in the form of the moving deforming mesh can be very effective in achieving more appropriate response quality. Also, changing the volume fraction of the pulmonary extracellular matrix constituents leads to changes in elastic modulus (storage modulus) and the viscous modulus (loss modulus) of lung tissue. Therefore, in the biomedical computational methods where the model wall is considered flexible, the viscoelastic properties of the texture must be considered correctly.

Effect of swirling flow and particle-release pattern on drug delivery to human tracheobronchial airways

The present study aims to investigate the effect of swirling flow on particle deposition in a realistic human airway. A computational fluid dynamic (CFD) model was utilized for the simulation of oral inhalation and particle transport patterns, considering the k-ω turbulence model. Lagrangian particle tracking was used to track the particles' trajectories. A normal breathing condition (30 L/min) was applied, and two-micron particles were injected into the mouth, considering swirling flow to the oral inhalation airflow. Different cases were considered for releasing the particles, which evaluated the impacts of various parameters on the deposition efficiency (DE), including the swirl intensity, injection location and pattern of the particle. The work's novelty is applying several injection locations and diameters simultaneously. The results show that the swirling flow enhances the particle deposition efficiency (20-40%) versus no-swirl flow, especially in the mouth. However, releasing particles inside the mouth, or injecting them randomly with a smaller injection diameter (d_inj) reduced DE in swirling flow condition, about 50 to 80%. Injecting particles inside the mouth can decrease DE by about 20%, and releasing particles with smaller d_inj leads to 50% less DE in swirling flow. In conclusion, it is indicated that the airflow condition is an important parameter for a reliable drug delivery, and it is more beneficial to keep the inflow uniform and avoid swirling flow.