Publications by authors named "Feras Al-Obeidat"

Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain.

Aim: This meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma.

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Article Synopsis
  • Asthma is a common respiratory condition in the U.S., impacting 8.7% of the population, with significant implications for public health and policy decisions.
  • The study analyzed data from 64,222 participants (20 years and older) and found notable risk factors, including higher prevalence among females, individuals aged 60 and older, and non-Hispanic whites.
  • Key risk factors associated with asthma include being female, having low income, being obese, and smoking, suggesting that public health efforts should focus on these areas for better prevention and management.
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Background: Prostate cancer (PCa) is a prevalent cancer with significant morbidity and mortality rates. In most cases, PCa remains asymptomatic until advanced disease manifests with symptoms, such as benign prostate hyperplasia. Timely detection and better management have improved overall survival in patients with PCa, and fatigue, reduced physical activity, and impaired quality of life (QoL) remain major challenges that impact daily life.

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This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression.

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In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally.

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The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today's society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe.

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