Brain functional connectivity changes on fMRI in patients with chronic pelvic pain treated with the Neuro Emotional Technique: a randomised controlled trial.

Background: Chronic pelvic pain is a substantial clinical challenge that profoundly impacts quality of life for many women. The Neuro Emotional Technique (NET) is a novel mind-body intervention designed to attenuate emotional arousal of distressing thoughts and pain. This study evaluated functional connectivity changes in key areas of the brain in patients with chronic pelvic pain receiving the NET intervention.

Mutational Landscape of Recurrent/Metastatic Head and Neck Squamous Cell Carcinoma and Association with Immune Checkpoint Inhibitor Outcome.

Purpose: Understanding the mutational landscape of recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) is important in identifying biomarkers to determine which patients may benefit from immune checkpoint inhibitors (ICIs).

Patients And Methods: The HAWK (NCT02207530), CONDOR (NCT02319044), and EAGLE (NCT02369874) studies evaluated R/M HNSCC treatment with durvalumab or durvalumab-tremelimumab. Tumor tissue samples pooled from HAWK/CONDOR (n=153) and plasma cell-free DNA samples from EAGLE (n=285) were analyzed to identify somatic alterations and association with survival.

Comparative Evaluation of Music and Movement Therapy and Visual Pedagogy as Interventional Tools in Promoting Oral Health in Children With Autism Spectrum Disorder: A Randomized Controlled Trial.

Aim: To compare the effectiveness of music and movement therapy (M&MT) and visual pedagogy (VP) as interventional tools in promoting oral health in children with Autism spectrum disorder (ASD).

Materials And Methods: Seventy-two children with ASD aged 7-15 years were randomized into two groups (N = 36), Group I received M&MT and Group II received VP. Plaque and gingival indices were recorded at baseline and the end of first, second, and third months.

Graph Neural Network Learning on the Pediatric Structural Connectome.

Purpose: Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), specifically graph convolutional networks (GCNs), have gained popularity lately for their effectiveness in learning on graph data, achieving strong performance in adult sex classification tasks, their application to pediatric populations remains unexplored. We seek to characterize the capacity for GNN models to learn connectomic patterns on pediatric data through an exploration of training techniques and architectural design choices.