The relationship between fatigue levels and psychosocial adjustment in elderly individuals with chronic obstructive pulmonary disease: A descriptive study.

Background: Chronic Obstructive Pulmonary Disease (COPD) is associated with physical limitations and significant social, psychological, and behavioral challenges. This study investigates the relationship between fatigue levels and psychosocial adjustment in COPD patients, considering their sociodemographic characteristics.

Methods: A descriptive study was conducted with 160 COPD patients hospitalized in the Pulmonology Department of a university hospital.

Evaluation of balance in children with hemophilia and the effect of balance on quality of life.

Background: Repeated joint bleeds are reported to decrease static balance in children with hemophilia (CwH).

Research Question: Is dynamic balance affected in CwH? Does dynamic balance affect the quality of life in these patients?

Methods: This cross-sectional study included thirty male children aged 7-18 years diagnosed with hemophilia, along with thirty healthy male children as controls. Dynamic balance was evaluated using the limits of stability (LOS) test and a fall risk test, both conducted via the Biodex Balance System.

Correction: Investigation of RhoA, ROCK1, and ROCK2 Gene Expressions in Autism Spectrum Disorders.

[This corrects the article DOI: 10.7759/cureus.74810.

Investigation of RhoA, ROCK1, and ROCK2 Gene Expressions in Autism Spectrum Disorders.

Objective: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that emerges in early childhood and is characterized by difficulties in social communication, repetitive behaviors, and restricted interests. The Ras homolog (Rho)/Rho-kinase signaling pathway plays a critical role in maintaining synaptic structure and function, as it regulates the actin cytoskeleton. This study aims to investigate the expression of the Ras homolog (Rho) family member A (), Rho-kinase 1 (), and Rho-kinase 2 () genes within this pathway in relation to ASD.

Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions.