Abstract Objective: To identify correlation of elevated LDH & CRP levels with the outcomes of COVID-19. Methodology: The cross-sectional retrospective study consisted of 200 COVID-19 patients who presented at a private clinical in Baghdad, Iraq. It was carried out from February 2021 to February 2022. Data included age, gender and clinical presentation. Blood samples were taken for high sensitivity CRP and LDH in the serum. Results: Out of 200 patients, 50 were critical and 150 severe according to clinical features. LDH and CRP showed a significant increase (p=0.000) in critical patients. This group involved admission to the respiratory intensive care unit requiring mechanical ventilation than in patients with severe COVID-19 (760.5±6.3 vs.

Objective: Assessment of primary schools science teachers' knowledge towards health promotion in Baghdad City.
Methodology: A descriptive study was conducted at (100) primary school at Al-Rasafa, and Al-Karkh sectors in
Baghdad City, from November 29th 2007 to January 20th 2009. A probability stratified random sample of (100)
teachers who teach science subject was selected, and had at least one year of employment in the teaching field. A
questionnaire format was used which was consisted of (2) parts. The overall number of the items included in the
questionnaire were (205) items. The first part was related to the demographic data of the teachers, the second part
(six sections) was concerned with teachers' knowledge about

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

Aim: To find any association between specific ABO blood groups and FUT2 secretory status and COVID-19 in a sample of Iraqi dentists. Materials and Methods: For each participant, a questionnaire including demography, COVID-19 status, blood grouping, and RH factor, with chemo-sensitive symptoms was recorded. The saliva samples were collected and DNA was extracted from leukocytes. Sequencing of molecular detection of the FUT2 gene by real-time PCR and the data was done, whilst drawing the phylogenetic tree. Results: Out of 133, most of the dentists were female 61%, most were just under 35 years of age. The most participants in this study were predominantly with blood group O (40%), followed by B, A, and AB, with (90%) of them were RH+.

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,

<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

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T