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The COVID-19 pandemic has been going on for more than a year. COVID-19 causes asymptomatic infection, severe infection, until death. Diagnosis of COVID-19 infection is made by blood tests, antigen swabs, PCR, as well as radiology and CT-scan examinations. Severe COVID-19 infection shows similar symptoms to acute radiation injury. The paper aims to describe the similarity of severe infection COVID-19 and syndrome of acute radiation injury. This paper is a literature review. Literature was searched in the Science Direct and PubMed databases. The keywords were severe COVID-19 infection, acute radiation syndrome, similarity. The similarities between the pathophysiology of severe COVID-19 infection and acute radiation injury are cytokine storms. Symptoms of severe COVID-19 infection are acute respiratory distress syndrome (ARDS), hypoxia, and damage to the cardiovascular system. Researchers are trying to look for symptoms similar to this severe COVID-19 infection so they can find better prevention and therapeutic measures. Study of biological radiation has shown complex organ failure after exposure to high doses of radiation. This condition is called the acute radiation syndrome (ARS). Severe inflammation accompanied by cytokine storms causes severe symptoms of COVID-19 or ARS infection. ARS is diagnosed with a variety of biomarkers of radiation exposure. The similarity of symptoms and pathophysiology may be used to plan better treatment for patients with severe COVID-19 infection in the future. The treatment would be the same. [ABSTRACT FROM AUTHOR]

The first confirmed detection of COVID-19 was in December, 2019 with symptomatic fever, cough, chest pain, bilateral pneumonia, and acute respiratory distress syndrome [1]. It was observed among the RT-PCR tested patients in Wuhan, Hubei Province, China. Later due to community transmission worldwide, COVID-19 was designated as a global pandemic by the WHO in February, 2020. This virus builds mutation as it transmits forming variants of concern. The first appeared Alpha variant popped up in the United Kingdom in September 2020. While in India, Delta variant is spreading rapidly for which the developed vaccines could offer protection against serious hospitalization. [2] There has been numerous researches to build a measurable (statistical/ machine learning) model which can possibly predict the course of this pandemic to some extent. Time-Series forecasting is a widely deployed method adopted in studying breeding of pathogens, their resultant infections and possible epidemics. The Schalekamp et al[3], 2020 developed a risk model to forecast deaths using clinical reports. The Malki et al.,2020 [4] research formed a machine learning model using weather elements to evaluate transmission. In other parts of the scientific community, Hao, Xu, Hu, Wang generated a model [5] using Long Short-Term Memory (LSTM), Support Vector Machines (SVM) and Elman Neural Network. Ji et al., 2020[6] presented papers which generated powerful estimation techniques by means of Multivariate COX Regression. Nonetheless, the modified model built using susceptible-exposed-infectious-recovered (SIER) and Runge-Kutta methods is appraised to be remarkable.[7] All of these evaluate the effectiveness of policies and guidelines of a country. The objective is to detect such trends in COVID-19 cases, deaths and vaccinations and further analyze them using publicly available dataset distributed by Our World in Data [8] where each regional data is collected, merged and uploaded in comma-separated value (CSV) file. This dataset covers figures reported by governments and ministries of health from countries around the world from December 13, 2020 to the present day. [ABSTRACT FROM AUTHOR]

SARS-Cov-2, a new strain of coronavirus that has never been found in humans before, has become one of the biggest public health issues in the 21st century. By March 2021, more than 100 million people around the world had been diagnosed with the disease. Confirmed cases of COVID-19 have ranged from mild symptoms to severe or even fatal. In the early stage of the disease, fever, fatigue, dry cough is the main clinical manifestations. About half of the patients developed dyspnea more than one week later, and the severe cases rapidly progressed to acute respiratory distress syndrome. Thus, the development of the COVID-19 vaccine is important because of the impact and damage caused by the virus worldwide. This paper will first introduce some information related to the vaccine, such as the safety and efficacy of the vaccine. Then, It will illustrate data on some COVID-19 vaccines that are on the market or still in development with comparison and analysis. [ABSTRACT FROM AUTHOR]

Introduction. High frequency oscillatory ventilation (HFOV) is an alternative mode of mechanical ventilation. HFOV has been shown to provide adequate ventilation and oxygenation in acute respiratory distress syndrome (ARDS) patients and may represent an effective lung-protective ventilation in patients where conventional ventilation is failing. The aim of this study is to evaluate effects of continuous distending pressure (CDP) on variables that contribute to the oxygenation in healthy and ARDS lung model pigs. Methods. In order to simulate a lung disease, lung injury was induced by lavage with normal saline with detergent in three pigs. HFOV ventilation was applied before and after the lung lavage. CDP was stepwise increased by 2 cmH2O, until the maximum CDP (before the lung lavage 32 cmH2O and after the lung lavage 42 cmH2O) and then it was stepwise decreased by 2 cmH2O to the initial value. In this paper we analyzed the following parameters acquired during our experiments: partial pressure of oxygen in arterial blood (PaO2), cardiac output (CO) and mixed venous blood oxygen saturation (SvO2). In order to find how both PaO2 and CO affected SvO2 during the increase of CDP before and after lavage, a nonlinear regression fitting of the response in SvO2 on the predictors (PaO2 and CO) was implemented. Results. Before the lavage, with increasing of CDP, PaO2 remained constant, CO strongly decreased and SvO2 slightly decreased. After the lavage, with increasing of CDP, PaO2 strongly increased, CO decreased and SvO2 increased. So, development of SvO2 followed the PaO2 and CO trends. Changes in PaO2 and CO occur at decisive CDP step and it was much higher after the lung lavage compared to the healthy lungs. The implemented nonlinear model gives a good goodness of fitting in all three pigs. The values of PaO2 and CO estimated coefficients changed at the same decisive step of CDP identified by the trends. Also the algorithm identified a CDP step much higher after the lung lavage. Conclusions. The novelty of this study consists of the implementing of a model that allows to predict how PaO2 and CO affect SvO2. It is possible to identify a certain level of CDP (higher in ARDS model pigs) at which the contribution of PaO2 and CO to SvO2 course changes their weights. Above this value, PaO2 plays a major role in SvO2 developments. This is in concordance with the clinical experience that HFOV is suitable for patient with more severe lung diseases when much higher CDP levels are required to assure an adequate oxygenation. [ABSTRACT FROM AUTHOR]

A new "coronavirus disease-2019 (COVID-19)" has spread quickly as an acute respiratory distress syndrome (ARDS) among individuals worldwide. Moreover, the number of COVID-19 test kits available in hospitals is limited compared to the growing number of cases every day. Therefore, It is necessary to introduce an automatic detection system as a fast alternative diagnostic method to prevent the spread of COVID-19 among humans. The purpose of this study is to propose an automated method using a machine learning method (Convolutional Neural Network (SimpNet model)) for the identification of COVID-19 pneumonia-infected patients using chest computed tomography (CT) images. Threshold and mathematical morphology were used to segment lung tissue as a region of interest (ROI). The Convolutional Neural Network (CNN) based on multi - Image augmentation technique was applied as a deep feature extraction technique and to identify CT samples with Covid 19 and Non-Covid 19. Specificity, Sensitivity, Accuracy, F1- score, Area Under Curve (AUC) were used as criteria to estimate the classification's efficiency. The highest classification accuracy was achieved as 98.67%. [ABSTRACT FROM AUTHOR]

A finite deformation constitutive model for lung parenchyma—including stiffness contributions from dense soft tissue, surface tension, and internal air pressure—is constructed for dynamic loading protocols and eventual injury assessments. The material is approximated as a homogeneous isotropic, nonlinear viscoelastic solid. Internal energy depends on finite strain measures, entropy, and internal state variables. The equilibrium response in the hyperelastic limit follows from a strain energy function depending on strain attributes stemming from a Gram-Schmidt decomposition of deformation. For rapid loading, a composite closed-cell model is invoked to estimate an isentropic bulk modulus of the effective continuum that depends strongly on initial airway pressure and stress-free mass density. Viscoelasticity is addressed by internal state variables in a thermodynamically consistent manner. Stiffness degradation and injury mechanisms can be tracked by additional internal variables. Solutions for planar shock compression are reported, verifying wave speed predictions in the low amplitude limit, exhibiting moderate nonlinearity in shock velocity versus particle velocity profiles, and demonstrating strong influence of initial air pressure on Hugoniot responses. [ABSTRACT FROM AUTHOR]