Objective: The study aimed to 1) measure the prevalence of depression and anxiety among Iraqi pharmacy and medical students at a number of universities in Baghdad using Hospital Anxiety and Depression Scale (HADS) and 2) investigate the association between various sociodemographic factors and students’ HADS scores. Methods: This study was based on a cross-sectional descriptive design in four universities in Baghdad, Iraq. Depression and anxiety were screened using an Arabic version of the HADS. An online survey was administered via Qualtrics to convenience samples of students at four colleges of pharmacy and a college of medicine between March and June 2018. Multiple linear regression was used to identify factors associated

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt

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

By March 2020, a pandemic had been emerged Corona Virus Infection in 2019 (COVID-19), which was triggered through the sensitive pulmonary syndrome (SARS disease corona virus- 2 (SARS COV-2). Overall precise path physiology of SARS COV-2 still unknown, as does the involvement of every element of the acute or adaptable immunity systems. Additionally, evidence from additional corona virus groups, including SARS COV as well as the Middle East pulmonary disease, besides that, fresh discoveries might help researchers fully comprehend SARS CoV-2. Toll-like receptors (TLRs) serve a critical part in both detection of viral particles as well as the stimulation of the body's immune response. When TLR systems are activated, pro-inflammatory cy

الناصر، عامر عبد الرزاق عبد المحسن والكبيسي، صلاح الدين عواد كريم. 2018. إمكانية تبني الحوسبة السحابية الهجينة في الجامعات العراقية : دراسة تحليلية باستخدام أنموذج القبول التكنولوجي. مجلة الإدا

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

<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

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,