RESUMO
Administering incorrect doses of conventional anesthetic agents through the pulmonary route can cause potential health risks such as blood coagulation, platelet dysfunction, and deteriorating organ function. As an alternative, xenon can minimize the impact on the cardiovascular system and provide the neuroprotective effect, hemodynamic stability, and fast recovery. However, the inhalation pattern still needs to be carefully monitored and controlled to avoid health risks caused by over administering xenon to patients during unconsciousness. Thus, high-resolution lung absorption and whole-body translocation data are critically needed to fully understand how to administer the gas and coordinate with the patient to accurately control the dose. Clinical studies are not able to provide accurate dosimetry data due to their limited operational flexibility and imaging resolution. Therefore, a computational fluid dynamics (CFD) model was employed in this study to simulate the transport and absorption of the inhaled xenon which is connected with a physiologically based pharmacokinetic (PBPK) model to predict the translocation into the systemic regions. To study the effects of different breathing patterns on xenon transport dynamics in the human body, a realistic breathing waveform and two steady-state flow rates with inhalation durations of 2 and 1.5 seconds were selected. For the realistic breathing cycle, the inhalation-exhalation periods are defined for a human at rest and the other two cases have a fixed volumetric flow rate of 15 L/min. As the two latter cases only simulate the inspiratory phase, a 1-second holding time was applied to represent the missing periods of the full breathing time. Simulations were performed in a subject-specific human upper airway configuration from mouth to G6. Numerical results show that with the accurate lung uptake predictions obtained from the CFD model, the hybrid CFD-PBPK model with TRANSIT compartments generates more precise and breath-specific trends compared to simple PBPK models. Numerical results demonstrate that breathing pattern can significantly influence the xenon uptake in the human body, which can be utilized as a critical factor to be coordinated by clinicians to achieve the optimized xenon dose. Furthermore, parametric analyses were performed for the influence of breathing patterns on local airflow distributions, gas species translocations, and lung elimination mechanisms followed by species diffusion into the systemic regions.
RESUMO
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 epidemiological computational 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 accurately modeled. The subject-specific respiratory system was discretized by generating the new polyhedral-core mesh. By simulating both mouth and nasal breathing scenarios, the inhalations of isotonic IAV-laden droplets with three different compositions were achieved. It is the first time that parametric analysis was performed using the multiscale model on how different exposure conditions can influence the virus 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 droplets with the higher salt concentration were inhaled. Consequently, more severe symptoms and longer recovery are expected at the pharynx. Furthermore, local deposition maps of IAV-laden droplets and post-deposition infection dynamics provide informative and direct evidence which significantly enhance the fundamental understanding of the underlying mechanisms for upper airway and lower airway infections.
RESUMO
Recently, toxicants such as formaldehyde and acrolein were detected in electronic cigarette (EC) aerosols. It is imperative to conduct research and provide sufficient quantitative evidence to address the associated potential health risks. However, it is still a lack of informative data, i.e., high-resolution local dosimetry of inhaled aerosols in lung airways and other systemic regions, due to the limited imaging resolutions, restricted operational flexibilities, and invasive nature of experimental and clinical studies. In this study, an experimentally validated multiscale numerical model, i.e., Computational Fluid-Particle Dynamics (CFPD) model combined with a Physiologically Based Toxicokinetic (PBTK) model is developed to predict the systemic translocation of nicotine and acrolein in the human body after the deposition in the respiratory system. In-silico parametric analysis is performed for puff topography influence on the deposition and translocation of nicotine and acrolein in human respiratory systems and the systemic region. Results indicate that the puff volume and holding time can contribute to the variations of the nicotine and acrolein plasma concentration due to enhanced aerosol deposition in the lung. The change in the holding time has resulted in significant difference in the chemical translocation which was neglected in a large group of experimental studies. The capability of simulating multiple puffs of the new CFPD-PBTK model paves the way to a valuable computational simulation tool for assessing the chronic health effects of inhaled EC toxicants.
