Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is caused coronavirus disease
2019 (COVID-19) affecting people worldwide. The angiotensin converting enzyme2 (ACE2) represents a
receptor of SARS-CoV-2 on the infected host cell. Apelin or its receptor agonists suppress the production of
angiotensin-converting enzyme (ACE) and angiotensin II (Ang-II) and is characterized by a protective effect
against SARS-CoV-2.
Objective: The study aims to assess the serum level of Elabela biomarker as an early detector for Acute
Kidney Injury (AKI) in patients with COVID-19.
Cases and Methods: This is a case-control study which included 45 hospitalized adult patients in multiple
centers (pu

Background: Early surgery during the active phase of infective endocarditis was considered to carry high morbidity and mortality due to technical difficulties on an inflamed tissues.

Objectives: Is to focus a light on the increasing indications for surgery during the active phase of infective endocarditis which lead to significant drop in the hospital mortality.  

Patients and Methods: Eighteen patients with bacterial endocarditis and valvular dysfunction were admitted to Iraqi center for heart diseases during the period from January 2008 –April 2013. All of them were fully investigated and adequately prepared for open heart surgery.

Result:

The transformation of a physical system to mathematical base is very important due to analysis of the systems behavior. In this paper an electric power system is considered, we design mathematical model for the determination of the increase in operational cost of transmission line from Haditha Dam substation to Qa'im substation . We derived relations which the approximate distance for VARS transmission must satisfy with considering minimum losses in the system. MATLAB computer programming is used to obtain the numerical results. The developed mathematical model and the numerical results could be useful to electric power systems engineers

Background: The global threat of COVID-19 outbreak and on the 11 March 2020, WHO acknowledged that the virus would likely spread to all countries across the globe and declared the coronavirus outbreak a pandemic which is the fifth pandemic since 20 century and this has brought human lives to a sudden and complete lockdown and the confirmed cases of this disease and deaths continue to rise in spite of people around the world are taking important actions to mitigate and decrease transmission and save lives. Objectives: To assess the effect of exercise and physical activity on the immunity against COVID-19. Methods: Collected electronic databases including (Medline, EMBASE, Google Scholar, PubMed and Web of Science) were searched with

     This paper considers the maximum number of weekly cases and deaths caused by the COVID-19 pandemic in Iraq from its outbreak in February 2020 until the first of July 2022. Some probability distributions were fitted to the data. Maximum likelihood estimates were obtained and the goodness of fit tests were performed. Results revealed that the maximum weekly cases were best fitted by the Dagum distribution, which was accepted by three goodness of fit tests. The generalized Pareto distribution best fitted the maximum weekly deaths, which was also accepted by the goodness of fit tests. The statistical analysis was carried out using the Easy-Fit software and Microsoft Excel 2019.

<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

This paper presents a novel idea as it investigates the rescue effect of the prey with fluctuation effect for the first time to propose a modified predator-prey model that forms a non-autonomous model. However, the approximation method is utilized to convert the non-autonomous model to an autonomous one by simplifying the mathematical analysis and following the dynamical behaviors. Some theoretical properties of the proposed autonomous model like the boundedness, stability, and Kolmogorov conditions are studied. This paper's analytical results demonstrate that the dynamic behaviors are globally stable and that the rescue effect improves the likelihood of coexistence compared to when there is no rescue impact. Furthermore, numerical simul

      Coronavirus is considered the first virus to sweep the world in the twenty-first century, it appeared by the end of 2019. It started in the Chinese city of Wuhan and began to spread in different regions around the world too quickly and uncontrollable due to the lack of medical examinations and their inefficiency. So, the process of detecting the disease needs an accurate and quickly detection techniques and tools. The X-Ray images are good and quick in diagnosing the disease, but an automatic and accurate diagnosis is needed. Therefore, this paper presents an automated methodology based on deep learning in diagnosing COVID-19. In this paper, the proposed system is using a convolutional neural network, which is considered one o

     By the time we conducted the current study,- COVID-19 epidemic has already become a global challenge, paralyzing socio-economic activity dramatically.

 Hence   , this study aimed to identify the most valuable prognostic indicators for COVID19 patients' early and accurate diagnosis by comparing laboratory biomarkers like C -reactive protein between non-severe and severe groups of patients. Depending on clinical symptoms, ---337 COVID-19 patients were enrolled at the Basra City Hospital from March 29 to April 29,2020   were classified into severe and non severe groups.

        A total of 337 patients were diagnosed with C