Publications by authors named "Walaa Ismail"

Attributable burden of disease estimates reported population-wide do not reflect social disparities in exposures and outcomes. This makes one of the influential scientific tools in public health decision-making insensitive to the distribution of health impacts between socioeconomic groups. Our aim was to use the often-overlooked distributive property of the population attributable fraction (PAF) to quantitatively partition the population burden attributed to know risk factors into subgroups defined by their socioeconomic position (SEP).

View Article and Find Full Text PDF
Article Synopsis
  • The study investigates the nutritional value of underutilized cereals, specifically 14 cultivars of chia and quinoa from Germany, Austria, and Egypt, and compares them to common cereals like wheat and oat.
  • Using gas chromatography-mass spectrometry (GC-MS), the research quantified 114 metabolites, revealing that quinoa and chia are rich in essential amino acids and unsaturated fatty acids, particularly omega-6 and omega-3.
  • The findings suggest that quinoa and chia have superior nutritional profiles compared to wheat and oat, making them potential cost-effective alternatives to animal proteins and suitable for inclusion in infant formulas.
View Article and Find Full Text PDF

Chronic kidney disease (CKD) refers to impairment of the kidneys that may worsen over time. Early detection of CKD is crucial for saving millions of lives. As a result, several studies are currently focused on developing computer-aided systems to detect CKD in its early stages.

View Article and Find Full Text PDF

Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of time- series data when used for Human Activity Recognition (HAR). The manual design of such neural architectures is an error-prone and time-consuming process. The search for optimal CNN architectures is considered a revolution in the design of neural networks.

View Article and Find Full Text PDF
Article Synopsis
  • This paper presents a new framework that combines convolutional neural networks (CNN) and genetic algorithms (GA) to quickly and accurately detect COVID-19 cases using chest X-ray images and multi-access edge computing technology.
  • The framework aims to address challenges like heavy hospital workloads and delays in traditional RT-PCR testing, which can hinder timely treatment for patients.
  • The model introduces an innovative CNN architecture optimized by GA to enhance performance, facilitating access for users with 5G devices to utilize this automatic COVID-19 detection tool.
View Article and Find Full Text PDF

The outbreak of coronavirus disease-2019, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a worldwide emerging crisis. Polyphenols are a class of herbal metabolites with a broad-spectrum antiviral activity. However, most polyphenols encounter limited efficacy due to their poor solubility and degradation in neutral and basic environments.

View Article and Find Full Text PDF

Objectives: This study aims to evaluate transcranial Doppler abnormalities in children with sickle cell disease (SCD) in a specialized children's hospital in El-Obeid.

Materials And Methods: This is a cross-sectional study done on 119 patients (2-18 years of age were included) who attended the sickle cell clinic in a specialized children's hospital in El-Obeid from December 2016 to February 2017; when patients do not have recent stroke symptoms, blood flow velocities were measured in both proximal internal carotid and middle cerebral arteries (MCAs) using non-imaging Doppler method, and time average mean velocities were recorded along with hemoglobin concentration and of the patients.

Results: None of the study population had MCA velocity higher than 200 cm/s; also no high conditional velocity (170-199 cm/s) was recorded, so no patient was at high risk to develop stroke.

View Article and Find Full Text PDF

The understanding of various health-oriented vital sign data generated from body sensor networks (BSNs) and discovery of the associations between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where occupants' health status is continuously monitored remotely, it is essential to provide the required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach for mining the periodic patterns obtained from BSN data.

View Article and Find Full Text PDF