Background and Purpose: Coronavirus has posed an unfamiliar threat to the world. Despite such circumstances, Malaysians continue to stay optimistic by keeping abreast with updates and mostly by seeking refuge in hopeful and consoling messages shared by fellow citizens. This study identified Facebook postings with positive messages, posted by Malaysians during the Movement Control Order (MCO) implemented by the Malaysian government as a form of prosocial behaviour.   Methodology: Through an analytic framework consisting of Positive Discourse Analysis and Critical Discourse Analysis, 15 Facebook postings related to COVID-19 were selected and identified as positive discourse, which were coded and categorised using a thematic analysi

Early and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN,

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: 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