Background: coronavirus 19 is a beta-coronavirus, enveloped and roughly spherical with approximately 60 to 140 nm in diameter with positive-sense single-stranded RNA genome.

Objectives: Measurement of interleukin 6 (IL6) level in a group of patients with confirmed Covid19 infection and its correlation with many hematological and  biochemical parameters , mainly lymphocyte , neutrophil  count and their ratio , platelet count , serum ferritin , C reactive protein as well as D-dimer level

Subjects and Methods: This study was conducted on 60 PCR positive patients variably affected by COVID-19 , cases collected sequentially from June till November 20

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

Background: Age progression is regarded as a critical risk factor in morbidity and mortality because of a weakened immune system. Although various studies have dealt with electrolyte imbalance in COVID-19 patients, the outcomes of these studies were partially understood. Objective: The current study aims to determine some biochemical parameters in old Iraqi COVID-19 patients and highlight the outcomes according to the aging role in the development of COVID-19 by suggesting new mechanisms. Materials and methods: forty COVID-19 patients were enrolled in the current study and divided into two groups: Gm includes (20) men, and Gf includes  (20) women. The parameters (Na+, K+, Cl-, LDH, and Hb ) were determined in sera of patients and c

<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

Background: COVID-19 has caused a considerable number of hospital admissions in China since December 2019. Many COVID-19 patients experience signs of acute respiratory distress syndrome, and some are even in danger of dying. Objective: to measure the serum levels of D-dimer, Neutrophil-Lymphocyte count ratio (NLR), and neopterin in patients hospitalized with severe COVID-19 in Baghdad, Iraq. And to determine the cut-off values (critical values) of these markers for the distinction between the severe patients diagnosed with COVID‐19 and the controls. Materials and methods: In this case-control study, we collect blood from 89 subjects, 45 were severe patients hospitalized in many Baghdad medical centers who were diagnosed with COVID

The aim of our study is to solve a nonlinear epidemic model, which is the COVID-19 epidemic model in Iraq, through the application of initial value problems in the current study. The model has been presented as a system of ordinary differential equations that has parameters that change with time. Two numerical simulation methods are proposed to solve this model as suitable methods for solving systems whose coefficients change over time. These methods are the Mean Monte Carlo Runge-Kutta method (MMC_RK) and the Mean Latin Hypercube Runge-Kutta method (MLH_RK). The results of numerical simulation methods are compared with the results of the numerical Runge-Kutta 4th order method (RK4) from 2021 to 2025 using the absolute error, which prove

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.