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

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

A pilot numerical study of odorant transport to the olfactory region during sensory evaluations following ISO 16000-28

We numerically analyzed odorant transportation from a sniffing device (funnel) to the olfactory region of the nasal cavity during breathing. We followed the procedure for the perceived air quality evaluations described in the ISO 16000-28 standard, and used acetone defined as the standard test substance for pi-scale evaluations in this standard; we also used ammonia and acetic acid, as acetone also emitted by humans. We modelled two breathing conditions: normal breathing (through nose) and sniffing. We evaluated olfactory receptor access under these breathing conditions. The acetone absorption flux to the olfactory epithelial tissues was analyzed using a computer-simulated person with a numerical respiratory tract model and a physiologically based pharmacokinetic model that was used to validate the prediction accuracy. The absorption flux and sensible/latent heat flux to the olfactory epithelial tissue were analyzed quantitatively. We also analyzed the impact of flow through the ortho- and retro-nasal pathways on the absorption flux to the olfactory region. The transient inhalation/exhalation airflow profile, breathing, and sniffing conditions had a significant impact on the absorption flux to the olfactory region of the nasal cavity. We observed two peaks of odorant absorption flux in one breath one during inhalation and one during exhalation. For example, 0.5μg/(m2s) of peak acetone absorption flux in the olfactory region during inhalation and 0.1μg/(m2s) during exhalation was observed