Effects of age, sex and education on verbal fluency tasks in a normal Saudi sample.

Introduction: This study's purpose is to describe the performance of healthy community dwelling Saudi Arabians on fluency tasks and explore the effects of age, sex and education.

Methods: Arabic-speaking Saudi Arabians > 18 were chosen through convenience sampling. Included were healthy community members whose first language is Arabic.

Did the National Lockdown in Saudi Arabia Reduce Lower Respiratory Illnesses in Children?

This study aims to explore the effect of lockdown and early precautionary measures implemented in Saudi Arabia on number of pediatric hospitalizations due to lower respiratory illnesses (bronchiolitis, asthma, and pneumonia). This is a retrospective cross-sectional study aims to review patients from four major hospitals in Saudi Arabia. All pediatric hospitalizations secondary to asthma, bronchiolitis, and pneumonia during the months of the lockdown (March, April, and May) in 2020 were documented.

Normative and Equated Data of the Original and Basic Versions of the Montreal Cognitive Assessment among Community Dwelling Saudi Arabians.

Introduction: Currently, there are standard and basic versions of the MoCA, the latter designed for those with lower educational achievements. Community-based normative data on these versions of the MoCA from Arabic populations are deficient, and there is little data demonstrating how both scales perform in comparison. We aim to obtain normative performances from both versions and equate the measures of both scales.

Patient awareness about the indications and complications of sleeve gastrectomy.

Objectives: To assess patients' awareness of the indications and complications of sleeve gastrectomy in King Khalid University Hospital, Riyadh, Saudi Arabia.

Methods: The cross-sectional study conducted from December 2017 to May 2018 in KKUH, Riyadh, Saudi Arabia included all patients aged 18 years and older. Data collection was conducted through self-administered questionnaires.

Estimating optimal window size for analysis of low-coverage next-generation sequence data.

Motivation: Current high-throughput sequencing has greatly transformed genome sequence analysis. In the context of very low-coverage sequencing (<0.1×), performing 'binning' or 'windowing' on mapped short sequences ('reads') is critical to extract genomic information of interest for further evaluation, such as copy-number alteration analysis.