ABSTRACT
Introduction. Targeted at the hemostatic system and the vascular endothelium, COVID-19 triggers the pathogenetic cascade of disorders in these systems. This cascade leads to the cerebral infarction, significant aggravation of other neurovas-cular diseases and neurological disorders, which requires an in-depth study. Objective: to identify the impact of factors selected among 21 candidate genes and metabolic markers on disease severity and the probability of death from SARS-CoV-2 infection in patients with a history of ischemic stroke (IS) and apparently healthy participants. Materials and Methods. We analyzed genetic, clinical, and laboratory findings in 85 patients with IS occurred at least one year before the study. During the first stage, participants were divided into three groups: Group 1 — 25 patients with a history of IS and COVID-19 at least one year prior to the study;Group 2 — 35 patients with IS history and no clinical manifestations or known COVID-19 history at baseline;and Group 3 — 20 apparently healthy participants as controls who had no clinical manifestations or information about a positive test for COVID-19 at baseline (November 2021). During the second stage, a new Group 4 included 25 patients with a history of IS who were treated for COVID-19 at baseline. Single venous blood tests were used to assess the levels of metabolic markers and identify genetic polymorphisms of hemostasis, immune response, endothelial function, and lipid metabolism in all study participants. Results. We identified the significant factors that determined the irreversible effects (damage) and fatal outcomes in patients with COVID-19 via the throm-bophilia genetic polymorphisms variations as follows: F13 encoding fibrin-stabilizing factor XIII — fibrinase (statistical probability of the factor influence > 90%), and SERPINE1 encoding endothelial plasminogen activator inhibitor-1 (PAI-1;statistical probability of the factor influence > 95%). High admission levels of homocysteine, interleukin-6, and activated partial thromboplastin time in patients with COVID-19 were associated with a severe disease course and fatal outcomes. Conclusion. Information about gene variations that trigger thrombosis and the adequate immune response can improve the effectiveness of specific therapy. Patients should understand their genetic profile, since this knowledge may prevent COVID-19 complications and significantly reduce the risk of a vascular catastrophe. © Dutova T. I., Banin I. N., Sazonov I. E., Peleshenko E. I., 2023.
ABSTRACT
A modelling approach is proposed to study ozone distribution and destruction in indoor spaces. The level of ozone gas concentration in the air, confined within an indoor space during an ozone-based disinfection process, was modelled. The emission and removal of ozone from the air volume were carried out using a generator located in the middle of the room. The computational fluid dynamics (CFD) model proposed accounts for ozone generation and decay kinetics, and buoyancy variations in the airflow. This framework was validated against experimental measurements at different locations in the room during the disinfection cycle. The model was then applied to a more challenging environment and demonstrated the suitability of ozone circulation as a disinfection process. The study also highlights the need for a well-controlled ozone removal process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Purpose The purpose of this paper is to devise a tool based on computational fluid dynamics (CFD) and machine learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A gated recurrent units neural network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking data sets. Design/methodology/approach A computational methodology is used for investigating how infectious particles that originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor airflow is obtained by means of an in-house parallel CFD solver, which uses a one equation Spalart-Allmaras turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted by human breath. The numerical results are used for the ML training. Findings In this work, it is shown that the developed ML model, based on the GRU-NN, can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results in this paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space. Originality/value This study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environment, potentially leading to the new design. A parametric study is carried out to evaluate the impact of system settings on time variation particles emitted by human breath within the space considered.
ABSTRACT
Purpose: A novel modelling approach is proposed to study ozone distribution and destruction in indoor spaces. The level of ozone gas concentration in the air, confined within an indoor space during an ozone-based disinfection process, is analysed. The purpose of this work is to investigate how ozone is distributed in time within an enclosed space. Design/methodology/approach: A computational methodology for predicting the space- and time-dependent ozone concentration within the room across the consecutive steps of the disinfection process (generation, dwelling and destruction modes) is proposed. The emission and removal of ozone from the air volume are possible by means of a generator located in the middle of the room. This model also accounts for ozone reactions and decay kinetics, and gravity effect on the air. Finding: This work is validated against experimental measurements at different locations in the room during the disinfection cycle. The numerical results are in good agreement with the experimental data. This comparison proves that the presented methodology is able to provide accurate predictions of the time evolution of ozone concentration at different locations of the enclosed space. Originality/value: This study introduces a novel computational methodology describing solute transport by turbulent flow for predicting the level of ozone concentration within a closed room during a COVID-19 disinfection process. A parametric study is carried out to evaluate the impact of system settings on the time variation of ozone concentration within the space considered. © 2021, Emerald Publishing Limited.