Biogenic synthesis of titanium nanoparticles by Streptomyces rubrolavendulae for sustainable management of Icerya aegyptiaca (Douglas).

Biosynthesized nanoparticles have a variety of applications, and microorganisms are considered one of the most ideal sources for the synthesis of green nanoparticles. Icerya aegyptiaca (Douglas) is a pest that has many generations per year and can affect 123 plant species from 49 families by absorbing sap from bark, forming honeydew, causing sooty mold, and attracting invasive ant species, leading to significant agricultural losses. The purpose of this work was to synthesize titanium dioxide nanoparticles (TiO-NPs) from marine actinobacteria and evaluate their insecticidal effects on Icerya aegyptiaca (Hemiptera: Monophlebidae), in addition to explaining their effects on protein electrophoresis analysis of SDS‒PAGE proteins from control and treated insects after 24, 72 and 120 h of exposure.

Prevalence and antibiogram of shigatoxinproducing in camel meat and offal.

Background: Camels are important animals in Egypt and other Arab countries on the basis of their economic value and ethnic culture. is implicated in several gastrointestinal infections and outbreaks worldwide, especially in developing countries. It causes infections that might lead to death.

Heavy metal contents in salted fish retailed in Egypt: Dietary intakes and health risk assessment.

Background: In Egypt, salted fish is considered a typically processed fish, including salted sardine, salted mullet (feseikh), keeled mullet (sahlia), and herrings. High-quality protein, polyunsaturated fatty acids, vital amino acids, and trace minerals such as magnesium and calcium are all abundant in fish. However, eating salted fish can expose people to toxins found in the environment, such as heavy metals.

Active Learning for Handling Missing Data.

Recently, the massive growth of IoT devices and Internet data, which are widely used in many applications, including industry and healthcare, has dramatically increased the amount of free unlabeled data collected. However, this unlabeled data is useless if we want to learn supervised machine learning models. The expensive and time-consuming cost of labeling makes the problem even more challenging.

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital.

Purpose: Since the declaration of COVID-19 as a pandemic, a wide between-country variation was observed regarding in-hospital mortality and its predictors. Given the scarcity of local research and the need to prioritize the provision of care, this study was conducted aiming to measure the incidence of in-hospital COVID-19 mortality and to develop a simple and clinically applicable model for its prediction.

Methods: COVID-19-confirmed patients admitted to the designated isolation areas of Ain-Shams University Hospitals (April 2020-February 2021) were included in this retrospective cohort study (n = 3663).