40 blood sample were taken from children infected with kala-azar at age less than ten years, who were admitted to El-Eskan child hospital, and central health laboratory in Baghdad. At the same time 15 sample collected from healthy child for comparing. During research estimated immunoglobulins level ( IgG, IgM, IgA) and (IFAT) IFAT test recorded a higher indicate antibodies level witch was synergistic with infected severity, the stander level of antibodies arrange between 1/16, 1/32 and 1/64 that means it graduate from the lowest one 1/16 into the highest one 1/64 within groups. During infectious the IgG ,IgM level recorded the higher level (2012.9 mg/dl) and (154.3 mg/dl) compared to the control group (874.3 mg/dl),

<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: The altered status of some essential trace elements observed in diabetes could have deleterious influences on the health of the diabetics. Objectives: To estimate and study the potential role of serum Selenium in type 1, type 2 diabetics and healthy subjects; and its relation with lipid profile and glycemic index. Methods: A case control designed study was carried out at the National Diabetes Center (NDC) / Al-Mustansiria University; on a total of 94 participants formed of 32 type 1 diabetics, 32 type 2 diabetics and 30 healthy control participants. Data collected about age, sex and BMI; also, blood samples examined for FPG, HbA1C, serum total cholesterol, HDL cholesterol, non-HDL cholesterol, serum triglyceride and sera were

Persuasion is an indispensable skill in everyday life; that is why, it has aroused researchers’ interest. This study aims to investigate the most frequently used persuasive strategies in texting WHO COVID-19 Virtual Press Conferences and explore how these strategies are employed to achieve persuasive messages.To this end, a text of WHO COVID-19 Virtual Press Conferences has been chosen randomly to be analyzed based on Dillard and Shen’s (2013) “Persuasive strategies in Health Campaigns”. A qualitative method has been adopted in analyzing the selected data to investigate the credibility and validity of the persuasive strategies used in such a domain. Findings have shown that most of the persuasive appeals based on the adopted mode

Drug consultation is an important part of pharmaceutical care. mobile phone call or text message can serve as an easy, effective, and implementable alternative to improving medication adherence and clinical outcomes by providing the information needed significantly for people with chronic illnesses like diabetes and hypertension particularly during pandemics like COVID-19 pandemic.

Mycobacterium tuberculosis is the cause of the major world health issue, tuberculosis (TB). The cytokine, tumor necrosis factor alpha (TNF-α) has been implicated in protection against TB in the early stages of the disease. TNF-α is an effective cytokine in the killing of intracellular M. tuberculosis. This study inducted to investigate whether there is any relationship between levels of TNF-α in sera of TB patients and their recovery, and is there any difference in the level of this cytokine in sera of female and male TB patients. This study included 29 patients with pulmonary TB (18 female and 11 male), their ages ranging from 37 to 59 years. All of them received first line TB therapy. They were consulted at Pasture Center during Septem

       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