Disease-State Dependent Associations Between Intrinsic Brain Function and Symptoms of Fatigue, Depression, and Anxiety in Crohn's Disease.

Background: Extraintestinal symptoms (EIS) in inflammatory bowel diseases, including fatigue, depression and anxiety, are highly prevalent, but poorly understood. Alterations of brain function may contribute to EIS, but their association with disease activity is unclear. This study analyzed intrinsic neural activity (INA) of individuals with Crohn's disease (CD) in different disease states and examined the relationship between INA and EIS.

Digestive cancers: mechanisms, therapeutics and management.

Cancers of the digestive system are major contributors to global cancer-associated morbidity and mortality, accounting for 35% of annual cases of cancer deaths. The etiologies, molecular features, and therapeutic management of these cancer entities are highly heterogeneous and complex. Over the last decade, genomic and functional studies have provided unprecedented insights into the biology of digestive cancers, identifying genetic drivers of tumor progression and key interaction points of tumor cells with the immune system.

Symptom Reporting in Patients with Primary Biliary Cholangitis: Higher Burden of Symptoms detection using an interactive App.

Introduction: Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease causing bile duct destruction and inflammation, impacting patient quality of life (QoL) due to variable symptoms. Digital symptom-tracker apps may improve patient care through enhanced monitoring. This feasibility study reassessed symptom burden in PBC patients using a tailored symptom-tracker app, focusing on its usability, effectiveness, and impact on management and QoL.

Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis.

Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly changing regulatory, research, and market environment presents challenges around selecting and evaluating the most suitable solution. To support the clinical adoption of AI auto-segmentation systems, Selection Criteria recommendations were developed to enable a holistic evaluation of vendors, considering not only raw performance but associated risks uniquely related to the clinical deployment of AI.