The unresolved COVID‐19 pandemic considerably impacts the health services in Iraq and worldwide. Consecutive waves of mutated virus increased virus spread and further constrained health systems. Although molecular identification of the virus by polymerase chain reaction is the only recommended method in diagnosing COVID‐19 infection, radiological, biochemical, and hematological studies are substantially important in risk stratification, patient follow‐up, and outcome prediction.

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

In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

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

Background: In December 2019, an episode of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARSCoV2) was reported in Wuhan, China and has spread around the world, increasing the number of contagions. Cytomegalovirus (CMV) and Epstein-Barr virus (EBV) are common herpesviruses that can cause persistent latent infections and affect the developing immune system.The study was conducted to explore the prevalence and reactivation of CMV and EBV antibodies in COVID-19 patients group in comparison to healthy group and to investigate the association between the presence of these viruses with each of severity of disease and oral hygiene. Materials and Methods: Eighty Five subjects were participated in this case control study (5

SUMMARY. The objectives of the present study were to assess the possible predictors of COVID-19 severity and duration of hospitalization and to identify the possible correlation between patient parameters, disease severity and duration of hospitalization. The study included retrospective medical record extraction of previous coron avirus COVID-19 patients in Basra hospitals, Iraq from March 1st and May 31st, 2020. The information of the participants was investigated anonymously. All the patients’ characteristics, treatments, vital signs and laboratory tests (hematological, renal and liver function tests) were collected. The analysis was conducted using the SPSS (version 22, USA). Spearman correlation was used to measure the relations

Georeferencing process is one of the most important prerequisites for various geomatics applications; for example, photogrammetry, laser scan analysis, remotely sensing, spatial and descriptive data collection, and others. Georeferencing mostly involves the transformation of coordinates obtained from images that are inhomogeneous due to accuracy differences. The georeferencing depends on image resolution and accuracy level of measurements of reference points ground coordinates.  Accordingly, this study discusses the subject of coordinate’s transformation from the image to the global coordinates system (WGS84) to find a suitable method that provides more accurate results. In this study, the Artificial Neural Network (ANN) method wa

Abstract

Objective(s): To assess the job satisfaction during of covid-19 among the nurses in respiratory isolation units of coronavirus disease.

Methodology: A descriptive cross-sectional design was carried out in four hospitals at isolation units of coronavirus disease from the period (21th December, 2021 to 27th January, 2022). A non-probability (convenience) sampling method consists of (300) nurse was selected convenience based on the study criteria. The tool used to measure the job satisfaction is Job satisfaction scale for clinical nursing (JSS-CN). This tool consists of two parts, the first part is for demographic information and consists of 8 items, and the second

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

The aim of our study is to solve a nonlinear epidemic model, which is the COVID-19 epidemic model in Iraq, through the application of initial value problems in the current study. The model has been presented as a system of ordinary differential equations that has parameters that change with time. Two numerical simulation methods are proposed to solve this model as suitable methods for solving systems whose coefficients change over time. These methods are the Mean Monte Carlo Runge-Kutta method (MMC_RK) and the Mean Latin Hypercube Runge-Kutta method (MLH_RK). The results of numerical simulation methods are compared with the results of the numerical Runge-Kutta 4th order method (RK4) from 2021 to 2025 using the absolute error, which prove