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Atherosclerosis risk is elevated in diabetic patients, but the underlying mechanism such as the involvement of macrophages remains unclear. Here, we investigated the underlying mechanism related to the pro-inflammatory activation of macrophages in the development of diabetic atherosclerosis. Bioinformatics tools were used to analyze the macrophage-related transcriptome differences in patients with atherosclerosis and diabetic mice.

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Dihydromyricetin (Dih), a naturally occurring flavonoid, has been identified to exert a protective effect against ischemia/reperfusion injury. However, the detailed mechanisms remain unclear. Here we investigated the biological role of Dih in preventing hypoxia/reoxygenation (H/R) injury in cardiomyocytes.

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18F-Sodium Fluoride PET/CT as a Tool to Assess Enthesopathies in X-Linked Hypophosphatemia.

Calcif Tissue Int

January 2025

Endocrinology Department, School of Medicine, Pontificia Universidad Católica de Chile, Av. Diagonal Paraguay 262, Cuarto Piso, Santiago, Chile.

X-linked hypophosphatemia (XLH) is a rare metabolic disorder characterized by elevated FGF23 and chronic hypophosphatemia, leading to impaired skeletal mineralization and enthesopathies that are associated with pain, stiffness, and diminished quality of life. The natural history of enthesopathies in XLH remains poorly defined, partly due to absence of a sensitive quantitative tool for assessment and monitoring. This study investigates the utility of 18F-NaF PET/CT scans in characterizing enthesopathies in XLH subjects.

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Anthropogenic disturbances degrade ecosystems, elevating the risk of emerging infectious diseases from wildlife. However, the key environmental factors for preventing tick-borne disease infection in relation to host species, landscape components, and climate conditions remain unknown. This study focuses on identifying crucial environmental factors contributing to the outbreak of severe fever with thrombocytopenia syndrome (SFTS), a tick-borne disease, in Miyazaki Prefecture, southern Japan.

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Purpose: To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.

Methods: On lateral full-spine radiographs of 295 consecutive patients, a two-staged region-based convolutional neural network (R-CNN) was trained to detect anatomical landmarks and calculate thoracic kyphosis (TK), lumbar lordosis (LL), sacral slope (SS), and sagittal vertical axis (SVA). Performance was evaluated on 65 radiographs not used for training, which were measured independently by 6 readers (3 radiologists, 3 surgeons), and the median per measurement was set as the reference standard.

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