Coronavirus disease (COVID-19) is an acute disease that affects the respiratory system which initially appeared in Wuhan, China. In Feb 2019 the sickness began to spread swiftly throughout the entire planet, causing significant health, social, and economic problems. Time series is an important statistical method used to study and analyze a particular phenomenon, identify its pattern and factors, and use it to predict future values. The main focus of the research is to shed light on the study of SARIMA, NARNN, and hybrid models, expecting that the series comprises both linear and non-linear compounds, and that the ARIMA model can deal with the linear component and the NARNN model can deal with the non-linear component. The models

This case series aims to evaluate patients affected with post COVID‐19 mucormycosis from clinical presentation to surgical and pharmacological treatment to improve the disease prognosis.

This study aims to identify maternal death cases caused by Coronavirus infection 2019 pneumonia, including disease progression, fetal consequences, and the fatality cause.

Patients and methodology: A retrospective case collection of Iraqi pregnant women in their second and third trimesters diagnosed with COVID-19 pneumonia and died due to it.

The four cases were all of a young age, had a brief complaint period, and had no comorbidities. Fever, dyspnea, and fatigue were the most common symptoms. Hypoxia was present in all cases and was the cause of mortality in three cases, with thromboembolism being a potential cause in the fourth. Prelabour membrane breakup, fetal growth restriction, and fetal death are al

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

      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:

Objective: The study the association of procalcitonin (PCT) and c-reactive protein (CRP) levels in COVID-19 patients and it's role as a guide in progress and management of those patients. Methodology: This cross-sectional study analyzed 200 CIOVID-19 patients in a single privet center in Baghdad, Iraq from January 1, 2021 to January 1, 2022. Demographic data like age, sex, and clinical symptoms were recorded. High sensitivity CRP and PCT in the serum were measured via dry fluorescence immunoassay (Lansionbio-China). Results: Out of 200 patients, 50 had moderate Covid and 150 had severe disease. Mean serum PCT levels was 0.039±0.05 ng/mL in the moderate group (range 0.011-0.067) and 0.43±0.21 ng/mL in the severe group (range 0.21

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system