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

Article information: COVID-19 has roused the scientic community, prompting calls for immediate solutions to avoid the infection or at least reduce the virus's spread. Despite the availability of several licensed vaccinations to boost human immunity against the disease, various mutated strains of the virus continue to emerge, posing a danger to the vaccine's ecacy against new mutations. As a result, the importance of the early detection of COVID-19 infection becomes evident. Cough is a prevalent symptom in all COVID-19 mutations. Unfortunately, coughing can be a symptom of various of diseases, including pneumonia and inuenza. Thus, identifying the coughing behavior might help clinicians diagnose the COVID-19 infection earlier and distinguish

Numerous blood biomarkers are altered in COVID-19 patients; however, no early biochemical markers are currently being used in clinical practice to predict COVID-19 severity. COVID-19, the most recent pandemic, is caused by the SRS-CoV-2 coronavirus.  The study was aimed to identify patient groups with a high and low risk of developing COVID-19 using a cluster analysis of several biomarkers. 137 women with confirmed SARS CoV-2 RNA testing were collected and analyzed for biochemical profiles. Two-dimensional automated hierarchy clustering of all biomarkers was applied, and patients were sorted into classes. Biochemistry marker variations (Ferritin, lactate dehydrogenase LDH, D-dimer, and C- reactive protein CRP) have split COVID-19 patien

Many studies dealt with the consequences of  SARS CoV-2  (which cause COVID-19 infection) on the nervous system especially sensory nerves where the virus causes loss of taste and smell as it’s known, and may affect auditory nerves and be the expected cause of some hearing problems. A case-control analytic study was performed on a connivance sample of society of university students from a medical faculty. Each participant filled out a questionnaire contains demographic data and general, auditory and respiratory health condition, in addition to vaccination status. In the other side, the audio- examinations were performed on the study sample including Pure Tone Audiometry (PTA) and tympanometry. Two statistical methods; chi-square and t

Factor analysis is distinguished by its ability to shorten and arrange many variables in a small number of linear components. In this research, we will study the essential variables that affect the Coronavirus disease 2019 (COVID-19), which is supposed to contribute to the diagnosis of each patient group based on linear measurements of the disease and determine the method of treatment with application data for (600) patients registered in General AL-KARAMA Hospital in Baghdad from 1/4/2020 to 15/7/2020. The explanation of the variances from the total variance of each factor separately was obtained with six elements, which together explained 69.266% of the measure's variability. The most important variable are cough, idleness, fever, headach

The most common cause of upper respiratory tract infection is coronavirus, which has a crown appearance due to the existence of spikes on its envelope. D-dimer levels in the plasma have been considered a prognostic factor for COVID-19 patients.

      The aim of the present study is to compare the biochemical action of the three vaccines taken in Iraq: Pfizer Biontech, AstraZeneca Oxford and Sinopharm based on biochemical parameters. Seventy COVID-19 Iraqi patients ( males and females ) were participated in the present study and classified into 7 groups : Gc :  COVID-19 patients ( without vaccine ) , Gp₁: COVID-19 patients took one dose of Pfizer Biontech, Gp₂ : COVID-19 patients took two doses of Pfizer Biontech, Ga₁ : patients took one dose of AstraZeneca Oxford vaccine , Ga₂: patients took two doses of AstraZeneca Oxford vaccine , Gs₁ : patients took one dose of Sinopharm vaccine and Gs₂:

The covid-19 global pandemic has influenced the day-to-day lives of people across the world. One consequence of this has been significant distortion to the subjective speed at which people feel like time is passing. To date, temporal distortions during covid-19 have mainly been studied in Europe. The current study therefore sought to explore experiences of the passage of time in Iraq. An online questionnaire was used to explore the passage of time during the day, week and the 11 months since the first period of covid-19 restrictions were imposed in Iraq. The questionnaire also measured affective and demographic factors, and task-load. The results showed that distortions to the passage of time were widespread in Iraq. Participants co

The Coronavirus Disease (COVID-19) has recently emerged as a human pathogen caused by SARS-CoV-2 virus was first reported from Wuhan, China, on 31 December 2019. Upon study, it has been used molecular docking to binding affinity between COVID-19 protease enzyme and flavonoids with evaluations based on docking scores calculated by AutoDock Vina. Results showed that naringin suppressed COVID-19 protease, as it has the highest binding value than other flavonoids including quercetin, hesperetin, garcina and naringenin. An important finding in this study is that naringin with neighboring poly hydroxyl groups can serve as inhibitors of COVID-19 protease bind to the S pocket of protein, it is shown that residues His163, Glu166, Asn142, His41and