Congenital toxoplasmosis (CT) and congenital rubella (CR) infections are well-known causes potentially leading to devastating consequences. This report aims to address the prevalence of each of these infections among suspected infants in a pediatric hospital in Baghdad.

The study sample includes 120 blood samples of suspected infants consulting Al-Alwyia pediatric teaching hospital over one year. This report represents an extension of a previous article published in Al-Kindy College Medical Journal KCMJ about cytomegalovirus infection (CMV). Using the enzyme-linked immunosorbent assay (ELISA) method, the results show that 5.8% and 5% were positive for specific IgM antibodies for rubella and toxoplasmosis respectively. Results also

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The aim of this study was to establish the existence and interaction of TMPRSS2 – ERG gene fusion status with clinicopathological features of prostate cancer patients. This research consisted of 123 embedded formalin-fixed tissues obtained from the prostate tumor patients. The above gene fusion is detected through the technique of fluorescent in situ hybridization (FISH) by means of a triple color probe. Seven samples have not been scored due to technical difficulties and 46 patients have fusion (39.6%), while the remaining (70) have not been seen with fusion. Of the 46 fusion-positive, 17 (36%) were caused by ERG-translocation, of the other 29 (63%) were caused by the interstitial segment deletion between the two genes due to the

Background: Cytomegalovirus (CMV) virus is a recognized important cause of congenital CMV infection which carries a significant risk for symptomatic disease and developmental defects in newborns. Its prevalence varies from place to other and time to time. This study is conducted to estimate its prevalence in Baghdad among infants suspected of having a congenital infection and to study the associated findings.   

 Subjects and Methods: The study was carried out in Al-Alwyia pediatrics teaching hospital. Data were collected, and blood samples were taken for infants suspected to have intrauterine infections over a period of one year, from 1 October 2019 to 1 October 2020.  Immunoglobulin M (IgM) tests for CMV w

This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota