The aim of the present study is to describe epidemiologic and clinical presentation, clinical complications and outcomes of patients diagnosed with influenza A infection (H1N1) during a one-year period. We retrospectively investigated 300 patients with influenza-like clinical presentation during the period January 2015 - January 2016 in King Khalid Hospital, Saudi Arabia. Fifty-four patients out of 300 (18%) were diagnosed with H1N1 virus infection; their age ranged from 7 months to 85 years, with a mean age of 25 years. Among them, 34 (63%) were males and 20 (37%) were females, with a M:F ratio of 1.70. The findings of this study show the great spread of influenza A outside the main holy cities of Saudi Arabia, and underline the absolute need for strict prevention strategies including vaccinations, public awareness and hygiene measures.
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http://dx.doi.org/10.4081/idr.2017.6930 | DOI Listing |
J Atten Disord
January 2025
Johns Hopkins Aramco Healthcare, Clinical Psychology and Counseling Services Unit, Saudi Arabia.
Objective: This study investigated the psychometric properties of the Arabic version of the Adult Self-Report Scale-5 (the ASRS-5-AR) within a large sample of adults residing in Saudi Arabia.
Methods: This cross-sectional study applied the ASRS-5-AR to a random sample of 4,299 Saudi and non-Saudi adults, aged 19 to 66 years (31.16 ± 9.
SAGE Open Med
January 2025
College of Medicine King Khalid University, Abha, Saudi Arabia.
Background: The coronavirus disease 2019 (COVID-19) pandemic has affected millions of people worldwide, and although it is primarily a respiratory illness, gastrointestinal symptoms have been reported in a significant proportion of patients.
Aim: Prevalence of gastrointestinal symptoms after recovery from COVID-19.
Methodology: A community-based cross-sectional study was conducted in the Aseer region of Saudi Arabia.
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFFront Artif Intell
January 2025
RV University, Bengaluru, India.
Introduction: Cyber situational awareness is critical for detecting and mitigating cybersecurity threats in real-time. This study introduces a comprehensive methodology that integrates the Isolation Forest and autoencoder algorithms, Structured Threat Information Expression (STIX) implementation, and ontology development to enhance cybersecurity threat detection and intelligence. The Isolation Forest algorithm excels in anomaly detection in high-dimensional datasets, while autoencoders provide nonlinear detection capabilities and adaptive feature learning.
View Article and Find Full Text PDFNanoscale Adv
December 2024
Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University Taif 21944 Saudi Arabia.
Mesoporous materials have garnered significant interest because of their porous structure, large surface area and ease of surface functionalization to incorporate the functional groups of choice. Herein, chiral mesoporous silica nanoparticles (CMSNPs) were prepared using quaternary amino silane as the template, tetramethyl orthosilicate as the silica source and proline and cellulose as chiral selector. The developed CMSNPs were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental analysis, Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction analysis, BET surface area analysis and BJH pore size/volume analysis.
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