Abstract

Backgrounds: Jaundice occurs in most newborn due to increased bilirubin concentration. Jaundice is observed during the first week after birth in approximately 60% of term neonates. A high level of bilirubin is neurotoxic and may cause neonatal kernicterus, auditory neuropathy or death.

Objectives: to compare the Rh group compatibility, serum bilirubin (total and direct), serum albumin and several liver enzymes between physiological and pathological neonatal jaundice

Materials and Methods: A cross sectional study for one hundred neonates with jaundice divided into group 1: 50 newborns with physiological jaundice, Group 2: 50 newborns with

The aim of the research is the detection of heavy metals using (Inductively coupled Plasma ICP) for samples in Tigris river at intakes of water treatment plants Baghdad (Sharq dejla, Al-Wathba, Al-Wahda, and Al-Dora) and samples at Tigris banks near (Al-Adhamya, Al-Shuhda bridge and al-Jadrya).
All the recorded results were fitted with Iraqi standers No. 25 in 1967 for all samples with heavy metals (arsenic Ar, Cadmium Cd, Chromium Cr, Zinc Zn, Lead Pb, Copper Cu, Nickel Ni, Manganese Mn, Ferrous Fe) where all concentration were lower than standard values except Cadmium (0.01- 0.014) in plants intakes and (0.027- 0.048) in river samples while the standard value is (0.005).
Other tests such as chemical oxygen demand and oil &

COVID-19 is a coronavirus disease caused by the severe acute respiratory syndrome. According to the World Health Organization (WHO), coronavirus-2 (SARS-CoV-2) was responsible for 87,747,940 recorded infections and 1,891,352 confirmed deaths as of January 9, 2021. Antibodies that target the Sprotein are efficient in neutralizing the virus. Methodology: 180 samples were collected from clinical sources (Blood and Nasopharyngeal swabs) and from different ages and genders at diverse hospitals in Baghdad / IRAQ between November 5, 2021, to January 20, 2022. All samples were confirmed infected with COVID-19 disease by RT-PCR technique. Haematology analysis and blood group were done for all samples, and Enzyme-Linked Immunosorbent Assay used an Ig

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121