Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.

Introduction: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.

Methods: A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging.

Predicting Mortality in Patients Hospitalized With Acute Myocardial Infarction: From the National Cardiovascular Data Registry.

Background: In-hospital mortality risk prediction is an important tool for benchmarking quality and patient prognostication. Given changes in patient characteristics and treatments over time, a contemporary risk model for patients with acute myocardial infarction (MI) is needed.

Methods: Data from 313 825 acute MI hospitalizations between January 2019 and December 2020 for adults aged ≥18 years at 784 sites in the National Cardiovascular Data Registry Chest Pain-MI Registry were used to develop a risk-standardized model to predict in-hospital mortality.

Acute infectious mononucleosis generates persistent, functional EBNA-1 antibodies with high cross-reactivity to alpha crystalline beta.

Epstein-Barr Virus (EBV) infects over 95% of the world's population and is the most common cause of infectious mononucleosis (IM). Epidemiologic studies have linked EBV with certain cancers or autoimmune conditions, including multiple sclerosis (MS). Recent studies suggest that molecular mimicry between EBV proteins, particularly EBV nuclear antigen 1 (EBNA-1), and self-proteins is a plausible mechanism through which EBV infection may contribute to the development of autoimmune disorders.