Background: The study was designed for the assessment of the knowledge of medical students regarding pandemics. In the current designed study, the level of awareness was checked and the majority of students were found aware of SARS-CoV and SARS-Cov2 (Covid-19).

Objective: To assess the awareness of SARS-CoV and SARS-Cov2 (Covid-19) among medical students of Pakistan.

Subjects and Methods: A cross-sectional survey was carried out in different universities of Pakistan from May to August 2020. A self-constructed questionnaire by Pursuing the clinical and community administration of COVID-19 given by the National Health Commission of the People's Republic of China was used am

       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

      The current study objective is to synthesize activated carbon (AC) from compressed wood using the ZnCl2 activating agent and to assess the ciprofloxacin (CIP) elimination efficiency in simulated wastewater. The produced AC was characterized using multiple techniques, including SEM, BET, FTIR, AFM, and XRD. The adsorbent demonstrates high adsorption performance, achieving 91% removal of CIP within 5 hours at an initial pollutant concentration of 100 mg/L with an AC dose of 2 g/L. Experimental data correspond to the Freundlich isotherm model (R² = 0.995) as well as the Langmuir competitive fitting (R² = 0.99), while the root mean square error (RMSE) equation best fits the Langmuir model. Moreover, the pseudo-second-order

Abstract:

Background: Retinol binding protein 4 (RBP4), an adipokine that participate in a lipid metabolism or insulin resistance through a complex regulatory network. Recently, RBP4 was reported to be associated with many cardiovascular diseases (CVDs) risk factors in patients of type 2 diabetes mellitus (T2DM). This study aims to study the correlation of  serum RBP4 with some markers of glycemic control, dyslipidemia, hypertension and obesity in T2DM Iraqi patients.

Subjects and Methods: one hundred fifty participants were enrolled in this coss-sectional study, 120 of participants were T2DM patients and 30 were apparently healthy individuals to serve as control gro

News headlines are key elements in spreading news. They are unique texts written in a special language which enables readers understand the overall nature and importance of the topic. However, this special language causes difficulty for readers in understanding the headline. To illuminate this difficulty, it is argued that a pragmatic analysis from a speech act theory perspective is a plausible tool for a headline analysis. The main objective of the study is to pragmatically analyze the most frequently employed types of speech acts in the news headlines covering COVID-19 in Aljazeera English website. To this end, Bach and Harnish's (1979) Taxonomy of Speech Acts has been adopted to analyze the data. Thirty headlines have been collected f

A total of 33 Iraq male positive for Toxoplasmosis and Iraq male negative for Toxoplasmosis (controls) were studies to Evaluation of some biochemical and immunological parameters changes.The parameters included lipid profile such as (Cholesterol(C), Triglycerides(TG), High-Density Lipoprotein (HDL), Low-Density Lipoprotein (LDL) and very Low-Density Lipoprotein (VLDL) and complement component C3 and C4. The results revealed significant decrease in the total cholesterol, Triglycerides, LDL and non-significant in vLDL (129.96±1.63, 130.69± 2.80, 87.19±1.97, 29.24± 0.83 mg/dl respectively) and non-significant increase in HDL(24.22 ±0.62) mg/dl compared with control group(152.07± 1.63, 156.48± 6.55, 99.26 ±1.39, 31.49± 1.30 and 21.31±

<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