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3D scanner's potential as a novel tool for lymphedema measurement in mouse hindlimb models.

Sci Rep

January 2025

Department of Plastic and Reconstructive Surgery, Faculty of Medicine, Hokkaido University, Sapporo, Japan.

Lymphedema is characterized by persistent swelling due to impaired lymphatic function and presents significant challenges in both research and clinical settings. Traditional contact-based measurement techniques such as paw thickness and circumferential measurements using calipers or silk thread are useful but limited by observer variability and measurement accuracy. Non-contact methods, including various imaging techniques, offer improvements but often at higher cost and complexity.

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Virtual staining from bright-field microscopy for label-free quantitative analysis of plant cell structures.

Plant Mol Biol

January 2025

Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-Ku, Kumamoto, 860-8555, Japan.

The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry.

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Predicting and optimising outcome for biliary atresia.

Semin Pediatr Surg

January 2025

Dept of Paediatric Surgery, Kings College Hospital, London SE59RS, England, United Kingdom. Electronic address:

Biliary atresia (BA) remains a disease of significant morbidity and mortality world-wide. Early and accurate diagnosis facilitates early intervention and improves outcomes. The gold standard in diagnosing BA is a liver biopsy followed by cholangiography, usually performed intra-operatively.

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The detection of rare or deviant stimuli shares common brain circuits involved in temporal processing and salience, critical for cognitive control. Disruption in these processes may contribute to the mechanisms of the disease and explain cognitive deficits observed in psychosis and related disorders. We designed a neuroimaging study, using oddball task-based functional sequences (fMRI) and diffusion tensor imaging (DTI), comparing healthy controls (HC, n = 14, 7 females) and patients with stable psychosis (PSY, n = 20, 10 females).

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Objectives: We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.

Methods: The MRI features were extracted for molecular subgroups by a novel multi-parameter convolutional neural network (CNN) called Bi-ResNet-MB. Then, MR features were used to establish a prognosis model based on XGBoost.

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