This research aims to investigate the approaches adopted by Iraqi newspapers in addressing the COVID-19 pandemic crisis. Employing a descriptive methodology and survey technique, the study conducts content analysis on articles published in three prominent newspapers: Al-Sabah, Al-Mada, and Tareeq Al-Shaab. A multi-stage sampling method was employed, encompassing 260 issues of the aforementioned newspapers. Data collection involved the use of a content analysis questionnaire, with the "How it was said?" method utilized to determine analysis categories.
The results showed that Al-Sabah newspaper adopted a positive approach in addressing COVID-19-related topics, while Al-Mada newspaper remained neutral, and Tare

BACKGROUND: Coronavirus current pandemic (COVID-19) is the striking subject worldwide hitting countries in an unexplained non-universal pattern. Bacillus Calmette–Guérin (BCG) vaccine was an adopted recent justification depending on its non-specific immune activation properties. Still the problem of post-vaccine short duration of protection needs to be solved. The same protective mechanism was identified in active or latent tuberculosis (TB). For each single patient of active TB, there are about nine cases of asymptomatic latent TB apparently normal individuals living within the community without restrictions carrying benefits of immune activation and involved in re-infection cycles in an excellent example of repeated immunity tr

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

Physicians are likely to expend significant labor and time while manually calculating blood smears. Automatic computer-based methods for classifying acute lymphoblastic leukemia have trouble correctly lighting stained white blood cell microscopy images and accurately separating cells that touch or overlap. Additionally, incorporating machine learning techniques into medical services is very hard because doctors can deal with rough guesses as long as the results aren't too bad, but they can't use these calculations for actual medical care. Enabling a A deep network having knowledge of the accuracy of its own predictions is a fascinating and crucial issue. Most instances segmentation frameworks weigh the mask quality during the instance

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons.

Background: Coronavirus, which causes respiratory illness, has been a public health issue in recent decades. Because the clinical symptoms of infection are not always specific, it is difficult to expose all suspects to qualitative testing in order to confirm or rule out infection as a test. Methods: According to the scientific studies and investigations, seventy-three results of scientific articles and research  were obtained using PubMed, Medline, Research gate and Google Scholar. The research keywords used were COVID-19, coronavirus, blood parameters, and saliva. Results: This review provides a report on the changes in the blood and saliva tests of those who are infected with the COVID-19.COVID-19 is a systemic infection that has