Worldwide, hundreds of millions of people have been infected with COVID-19 since December 2019; however, about 20% or less developed severe symptoms. The main aim of the current study was to  assess  the  relationship  between  the  severity of Covid-19 and different clinical and laboratory parameters. A total number of 466 Arabs have willingly joined this prospective cohort. Out of the total number, 297 subjects (63.7%) had negative COVID-19 tests, and thus, they were recruited as controls, while 169 subjects (36.3%) who tested positive for COVID-19 were enrolled as cases. Out of the total number of COVID-19 patients, 127 (75.15%) presented with mild symptoms, and 42 (24.85%) had severe symptoms. The age range for the partic

One of the most serious health disasters in recent memory is the COVID-19 epidemic. Several restriction rules have been forced to reduce the virus spreading. Masks that are properly fitted can help prevent the virus from spreading from the person wearing the mask to others. Masks alone will not protect against COVID-19; they must be used in conjunction with physical separation and avoidance of direct contact. The fast spread of this disease, as well as the growing usage of prevention methods, underscore the critical need for a shift in biometrics-based authentication schemes. Biometrics systems are affected differently depending on whether are used as one of the preventive techniques based on COVID-19 pandemic rules. This study provides an

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

Thrombosis is a common clinical feature associated with morbidity and mortality in coronavirus disease-2019 (COVID-19) patients. Cytokine storm in COVID-19 increases patients' systemic inflammation, which can cause multiple health consequences. In this work, we aimed to indicate the effect of Pfizer-BioNTech vaccination on the modulation of monocyte chemoattractant protein-3 (MCP-3), matrix metalloproteinase 1 (MMP-1), and tumor necrosis factor-alpha (TNF-α) levels, and other systemic inflammatory biomarkers that associates with COVID-19 severity in patients who suffers from thrombosis consequences. For this purpose, ninety people were collected from Ibn Al-Nafees Hospital and divided into three groups each of which contained 30 people, 15

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

Objectives: This study explored knowledge, attitude, and practice of infection control by dental students at College of Dentistry/ University of Baghdad, Iraq. Material and Methods: Three hundred dental students participated in this study. A self administrated questionnaire with 21 close ended questions related to use of personal protective equipments, infection control awareness, vaccination status, percutaneous exposures, and attitude towards treatment of patients with hepatitis B (HBV)/ or human immunodeficiency virus (HIV) was distributed to dental students. Data were analyzed using Statistical Package for Social Sciences (SPSS) version 21. Fisher exact and Chi-square test were used with significance level set to 0.05. Results: The

In this paper, a compartmental differential epidemic model of COVID-19 pandemic transmission is constructed and analyzed that accounts for the effects of media coverage. The model can be categorized into eight distinct divisions: susceptible individuals, exposed individuals, quarantine class, infected individuals, isolated class, infectious material in the environment, media coverage, and recovered individuals. The qualitative analysis of the model indicates that the disease-free equilibrium point is asymptotically stable when the basic reproduction number R0 is less than one. Conversely, the endemic equilibrium is globally asymptotically stable when R0 is bigger than one. In addition, a sensitivity analysis is conducted to determine which

Coronavirus diseases 2021 (COVID-19) on going situation in Iraq is characterized in this paper. The pandemic handling by the government and the difficulties of public health measures enforcement in Iraq. Estimation of the COVID-19 data set was performed. Iraq is endangered to the pandemic, like the rest of the world besides sharing borders with hotspot neighbouring country Iran. The government of Iraq launched proactive measures in an attempt to prevent the viral spread. Nevertheless, reports of new cases keep escalating leaving the public health officials racing to take more firm constriction to face the pandemic. The paper bring forth the current COVID-19 scenario in Iraq, the government measures towards the public health challenges, and

In this paper, the deterministic and the stochastic models are proposed to study the interaction of the Coronavirus (COVID-19) with host cells inside the human body. In the deterministic model, the value of the basic reproduction number   determines the persistence or extinction of the COVID-19. If   , one infected cell will transmit the virus to less than one cell, as a result,  the person carrying the Coronavirus will get rid of the disease .If   the infected cell  will be able to infect  all  cells that contain ACE receptors. The stochastic model proves that if  are sufficiently large then maybe  give  us ultimate disease extinction although ,  and this  facts also proved by computer simulation.

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial