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

Neuroradiology

January 2025

Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.

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.

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Introduction: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment but can give rise to immune-related adverse events such as ICI-related diabetes mellitus (DM).

Case Presentation: We herein present the case of a 59-year-old Japanese man with malignant melanoma who developed ICI-related DM after 18 months of nivolumab treatment. He experienced marked hyperglycemia and diabetic ketoacidosis without a personal or family history of diabetes.

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Background: Orthodontic-orthognathic treatment is the standard of care for moderate and/or severe skeletal class III (SCIII) malocclusion. Following orthognathic surgery, morphological changes in the temporomandibular joint structures (TMJ) may contribute to condylar resorption (CR).

Objectives: This systematic review aimed to identify the morphological signs of condylar resorption (changes in the condylar head, position, neck, disk, and joint space) following orthognathic surgery in patients with SCIII compared with those with skeletal class II (SCII) malocclusion.

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As the clinical applicability of peripheral nerve stimulation (PNS) expands, the need for PNS-specific safety criteria becomes pressing. This study addresses this need, utilizing a novel machine learning and computational bio-electromagnetics modeling platform to establish a safety criterion that captures the effects of fields and currents induced on axons. Our approach is comprised of three steps: experimentation, model creation, and predictive simulation.

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