Epidemiological and Clinical Insights into : A Six-Year Study on Age, Antibiotics, and Specimens.

Background: This six-year retrospective study provides an in-depth analysis of the epidemiological and clinical patterns associated with () infections, focusing on age distribution, antibiotic resistance profiles, and specimen types.

Aim: The research examines the incidence and characteristics of non-Multi-Drug Resistant (non-MDR) and Multi-Drug Resistant (MDR) strains by reviewing patient records from January 2016 to December 2022.

Methods: Through a statistical analysis, the study highlights the incidence rates across diverse age groups and explores the impact of antibiotic treatment regimens on infection outcomes.

Bloodstream infections caused by Staphylococcus aureus in patients admitted to King Abdulaziz University Hospital, Jeddah, Saudi Arabia.

Objective: Currently, there is a limited amount of published data on the incidence of bloodstream infections (BSI) caused by both methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-susceptible Staphylococcus aureus (MSSA) in most parts of the Arabian Peninsula. Thus, it is extremely important to have information concerning the distribution and prevalence of MRSA and MSSA to better handle and manage future epidemics.  This study aimed to investigate the correlation between MRSA and/or MSSA with BSI at King Abdulaziz University Hospital (KAUH), Jeddah, Saudi Arabia.

Electronic cigarettes use and parental factors among children and adolescents, Jeddah. A cross-sectional study.

E-cigarettes are gaining popularity worldwide, necessitating their control. This study investigated the impact of parental factors on E-cigarette use among children-adolescents in Jeddah, Saudi Arabia. A cross-sectional survey involving 1,044 parents of children aged 10- to 21 was conducted in malls.

A Review of Artificial Intelligence-Based Dyslexia Detection Techniques.

Problem: Dyslexia is a learning disorder affecting an individual's ability to recognize words and understand concepts. It remains underdiagnosed due to its complexity and heterogeneity. The use of traditional assessment techniques, including subjective evaluation and standardized tests, increases the likelihood of delayed or incorrect diagnosis.

A Feature-Fusion Technique-Based Alzheimer's Disease Classification Using Magnetic Resonance Imaging.

Background: Early identification of Alzheimer's disease (AD) is essential for optimal treatment and management. Deep learning (DL) technologies, including convolutional neural networks (CNNs) and vision transformers (ViTs) can provide promising outcomes in AD diagnosis. However, these technologies lack model interpretability and demand substantial computational resources, causing challenges in the resource-constrained environment.