Developing a machine learning model with enhanced performance for predicting COVID-19 from patients presenting to the emergency room with acute respiratory symptoms.

Artificial Intelligence is playing a crucial role in healthcare by enhancing decision-making and data analysis, particularly during the COVID-19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elderly and those with underlying health issues like chronic diseases. This study aimed to develop a machine learning model to improve the prediction of COVID-19 in patients with acute respiratory symptoms.

Assessment of vitamin D status and associated risk factors in high-altitude populations affected by multiple sclerosis: A case-control study.

Several studies have suggested a correlation between serum vitamin D (VitD) level and multiple sclerosis (MS). MS has a known latitudinal distribution pattern, with greater incidence, prevalence, and mortality rates at higher latitudes. This study aims to assess levels of VitD and serum potassium in subjects with MS and the impact of gender and age as disease risk factors.

Understanding the effects of COVID-19 on patients with diabetic nephropathy: a systematic review.

Background: Diabetic nephropathy is one of the consequences of diabetes mellitus that causes a continuous decline in the eGFR. After the COVID-19 pandemic, studies have shown that patients with diabetic nephropathy who had contracted COVID-19 have higher rates of morbidity and disease progression. The aim of this study was to systematically review the literature to determine and understand the effects and complications of SARS-CoV-2 on patients with diabetic nephropathy.

Adenoid Hypertrophy and Orthodontic Complications: An Assessment of Parental Knowledge in Saudi Arabia.

Background Adenoid hypertrophy is a common condition that can cause upper airway obstruction in children and adolescents, leading to various complications, including dental and maxillofacial abnormalities. However, parents may have limited knowledge of the orthodontic complications associated with this condition. Objective This study aimed to assess the knowledge and attitude of parents toward the orthodontic complications of untreated adenoid hypertrophy and to promote their awareness about this problem.

Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations.

Objectives: To develop a Deep Learning Artificial Intelligence (AI) model that automatically localizes the position of radiographic stent gutta percha (GP) markers in cone beam computed tomography (CBCT) images to identify proposed implant sites within the images, and to test the performance of the newly developed AI model.

Materials And Methods: Thirty-four CBCT datasets were used for initial model training, validation and testing. The CBCT datasets were those of patients who had a CBCT examination performed wearing a radiographic stent for implant treatment planning.

Review on Different Vesicular Drug Delivery Systems (VDDSs) and Their Applications.

Background: Colloidal dispersions, also known as vesicular drug delivery systems (VDDSs), are highly ordered assemblies composed of one or more concentric bilayers formed by the self-assembly of amphiphilic building blocks in the presence of water.

Objective: VDDSs are important to target the entrapped drugs at specific sites inside the body, control the drug release, enhance the drug bioavailability, and reduce undesired side effects.

Methods: There are different types of VDDSs suitable for the entrapment of both hydrophilic and lipophilic drugs.

Food neophobia and pickiness among children and associations with socioenvironmental and cognitive factors.

Food neophobia and picky eating are common behaviors exhibited among children that may impact their nutritional health. Due to the limited data available regarding food neophobia and pickiness in Saudi Arabia, this study aimed to estimate the prevalence of picky eating and food neophobia among Saudi children and to examine the association of socioenvironmental and cognitive factors with food neophobia and pickiness. The study included 216 healthy children with no known allergy, aged 3-7 years old, recruited from six schools in Jeddah, Saudi Arabia.