Dynamics Of Host Cells Research Articles

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

Background and ObjectiveRespiratory 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. MethodsBased 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. ResultsThe 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. ConclusionsIts visualization process could explain the symptoms of the disease in the nose and contribute to the prevention and targeted treatment of respiratory diseases.

Prediction of transport, deposition, and resultant immune response of nasal spray vaccine droplets using a CFPD-HCD model in a 6-year-old upper airway geometry to potentially prevent COVID-19.

This study focuses on the transport, deposition, and triggered immune response of intranasal vaccine droplets to the angiotensin-converting-enzyme-2-rich region, i.e., the olfactory region (OR), in the nasal cavity of a 6-year-old female to possibly prevent corona virus disease 19 (COVID-19). To investigate how administration strategy can influence nasal vaccine efficiency, a validated multi-scale model, i.e., computational fluid-particle dynamics (CFPD) and host-cell dynamics (HCD) model, was employed. Droplet deposition fraction, size change, residence time, and the area percentage of OR covered by the vaccine droplets, and triggered immune system response were predicted with different spray cone angles, initial droplet velocities, and compositions. Numerical results indicate that droplet initial velocity and composition have negligible influences on the vaccine delivery efficiency to OR. In contrast, the spray cone angle can significantly impact the vaccine delivery efficiency. The triggered immunity was not significantly influenced by the administration investigated in this study due to the low percentage of OR area covered by the droplets. To enhance the effectiveness of the intranasal vaccine to prevent COVID-19 infection, it is necessary to optimize the vaccine formulation and administration strategy so that the vaccine droplets can cover more epithelial cells in OR to minimize the number of available receptors for SARS-CoV-2.

Parameter Optimization of a Viral Dynamics Model in the Mucus Layer of the Human Nasal Cavity-Nasopharynx Based on Computational Fluid-Particle and Host-Cell Dynamics

Respiratory diseases, such as COVID-19 (coronavirus disease 2019) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), have posed a threat to human health. For infection control and a better understanding of the pathogenesis, this study mainly focused on elucidating the virus dynamics in the mucus layer of the human nasal cavity-nasopharynx, using coupled computational fluid-particle dynamics (CFPD) and host-cell dynamics (HCD) analyses. To reproduce virus transportation in the mucus layer by mucociliary motion, a three-dimensional-shell model was created using the data obtained from computed tomography (CT) of the human upper airway. By considering the mucus milieu, the target-cell-limited model was coupled with the convection-diffusion term to develop the HCD model. Parameter optimization has been shown to have a great impact on the accuracy of model prediction; therefore, this study proposes a method that divides the geometric model into multiple regions and uses Monolix for nonlinear mixed effects modeling for pharmacometrics. The results showed that data from human inoculation challenge trials could be used to estimate the corresponding parameters. The models developed and used with optimized parameters can provide relatively accurate predictions of virus dynamics, which could contribute to the prevention and treatment of respiratory diseases.

SARS-CoV-2 Dynamics in the Mucus Layer of the Human Upper Respiratory Tract Based on Host–Cell Dynamics

A thorough understanding of the inhalation dynamics of infectious aerosols indoors and infection dynamics within the host by inhaled viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in the assessment and control of infection risks indoors. Here, by combining computational fluid–particle dynamics (CFPD) and host–cell dynamics (HCD), SARS-CoV-2 infection dynamics in the mucus layer of the human upper airway were studied. To reproduce the diffusive and convective transport of the virus in the nasal cavity–nasopharynx by mucociliary motion, a three-dimensional (3D)-shell model with a mucus layer was developed. The initial virus concentrations for HCD calculation were estimated based on the deposition distribution of droplets with representative sizes analyzed by CFPD. To develop a new HCD model, the target-cell-limited model was integrated with the convection–diffusion equation. Additionally, the sensitivity of the infection rate β to the infection dynamics was systematically investigated. The results showed that the time series of SARS-CoV-2 concentration in the mucus layer strongly depended on diffusion, convection, and β. Although the SARS-CoV-2 dynamics obtained here have not been verified by corresponding clinical data, they can preliminarily reveal its transmission mode in the upper airway, which will contribute to the prevention and treatment of coronavirus disease 2019.

Infection Dynamics of SARS-CoV-2 in Mucus Layer of the Human Nasal Cavity - Nasopharynx

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the worldwide spread of coronavirus disease-2019 (COVID-19) since its emergence in 2019. Virus replication and infection dynamics after its deposition on the respiratory tissues require detailed studies for infection control. This study focused primarily on SARS-CoV-2 dynamics in the mucus layer of the nasal cavity and nasopharynx, based on coupled computational fluid-particle dynamics (CFPD) and host-cell dynamics (HCD) analyses. Considering the mucus milieu, we coupled the target-cell limited model with the convection-diffusion term to develop an improved HCD model. The infection dynamics in the mucus layer were predicted by a combination of the mucus flow field, droplet deposition distribution, and HCD. The effect of infection rate, β, was investigated as the main parameter of HCD. The results showed that the time series of SARS-CoV-2 concentration distribution in the mucus layer strongly depended on diffusion, convection, and virus production. β affected the viral load peak, its arrival time, and duration. Although the SARS-CoV-2 dynamics in the mucus layer obtained in this study have not been verified by appropriate clinical data, it can serve as a preliminary study on the virus transmission mode in the upper respiratory tract.

Effects of Initial Conditions and Parameters on the Prediction of SARS-CoV-2 Viral Load in the Upper Respiratory Tract Based on Host-Cell Dynamics

Effects of Initial Conditions and Parameters on the Prediction of SARS-CoV-2 Viral Load in the Upper Respiratory Tract Based on Host-Cell Dynamics

Lung aerosol dynamics of airborne influenza A virus-laden droplets and the resultant immune system responses: An in silico study

Influenza A Virus (IAV) replications start from the deposition of inhaled virus-laden droplets on the epithelial cells in the pulmonary tracts. In order to understand the local deposition patterns and within-host dynamics of infectious aerosols, accurate information of high-resolution imaging capabilities, as well as real-time flow cytometry analysis, are required for tracking infected cells, virus agents, and immune system responses. However, clinical and animal studies are in deficit to meet the above-mentioned demands, due to their limited operational flexibility and imaging resolution. Therefore, this study developed an experimentally validated multiscale numerical model, i.e., the Computational Fluid-Particle Dynamics (CFPD) plus Host Cell Dynamics (HCD) model, to predict the transport and deposition of the low-strain IAV-laden droplets, as well as the resultant regional immune system responses. The hygroscopic growth and shrinkage of IAV-laden droplets were also accurately modeled. The subject-specific respiratory system was discretized using polyhedron-core meshes. By recreating both mouth and nasal breathing scenarios, simulations of isotonic IAV-laden droplets with three different compositions were achieved. It is the first time that parametric analysis has been performed to investigate how different exposure conditions can influence the IAV aerodynamics in the lung and the subsequent immune system responses. Numerical results show a higher viral accretion followed by a faster immune system response in the supraglottic region when IAV-laden droplets with the higher NaCl concentration were inhaled. Consequently, more severe symptoms and longer recovery are expected at the pharynx. Furthermore, local deposition patterns of IAV-laden droplets and post-deposition infection dynamics provide direct quantitative evidence to enhance the fundamental understanding of the underlying mechanisms for upper airway and lower airway infections.