Routine vaccination activities, such as detection, reporting, and management of adverse events following immunization (AEFIs), are generally handled by healthcare providers (HCPs). Safe vaccines against severe acute respiratory syndrome coronavirus (SARS-CoV-2) were introduced to control the Coronavirus Disease-19 (COVID-19) pandemic. The study aimed to assess the knowledge, perceptions, and practice of HCPs in Iraq about reporting adverse events following COVID-19 vaccination, and their association with sociodemographic variables. The study was a cross-sectional study that was carried out between August and September 2021 at the COVID-19 vaccination centers in Iraq. This study used an online and paper-based questionnaire, which

The aim of this research is to shed some light on the level of

serum total sialic acid (TSA) in individuals with typhoid fever. The individuals were at age of (35-45) years old and (TSA) was measured by resorcinol reagents. The results showed significant reduction in (TSA) compared to control or normal individuals.

The prolactin hormone played role in the many autoimmune disorders. To determine the importance of high levels of prolactin in triggering rheumatoid arthritis, thirty patient's women with hyperprolactinemia aged (20-45) years old have been investigated and compared with twenty five healthy individuals. All the studied groups were carried out to measure the concentration of citrulinated peptide(CCP) by enzyme linked immunosorbent assay( ELISA), antikeratin antibodies (AKA)and antinuclear antibodies(ANA) by indirect fluorescent assay IFAT. There was a significant elevation of CCP concentration compared with control groups (P< 0.05). The percentage of antikeratin antibodies and antinuclear antibodies was (20%, 10%) respectively, and

     This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being  0.66975075, 0.470

The aim of the research is to identify the relationship between health anxiety associated with Coronavirus (Covid 19) and its relationship to health behavior among Baghdad University employees, as well as to identify the differences in health anxiety and health behavior according to the variables (gender, occupation, and age). To achieve the objectives of the research, a scale was designed to measure the health anxiety in addition to the adoption of the health behavior scale prepared by (Renner & Schwarzer, 2005). The two scales were applied to a sample of (277) academics and (206) employees, while the number of students was (667). The sample was chosen by electronic application from a number of colleges at Al-Jadiriyah Complex. Afte

The two parameters of Exponential-Rayleigh distribution were estimated using the maximum likelihood estimation method (MLE) for progressively censoring data. To find estimated values for these two scale parameters using real data for COVID-19 which was taken from the Iraqi Ministry of Health and Environment, AL-Karkh General Hospital. Then the Chi-square test was utilized to determine if the sample (data) corresponded with the Exponential-Rayleigh distribution (ER). Employing the nonlinear membership function (s-function) to find fuzzy numbers for these parameters estimators. Then utilizing the ranking function transforms the fuzzy numbers into crisp numbers. Finally, using mean square error (MSE) to compare the outcomes of the survival

     Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing