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HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs.

Autor: Ho CS; Department of Physical Medicine and Rehabilitation, Taoyuan Chang Gung Memorial Hospital, Taoyuan, Taiwan; Comprehensive Sports Medicine Center, Taoyuan Chang Gung Memorial Hospital, Taoyuan, Taiwan; Master of Science Degree Program in Innovation for Smart Medicine, Chang Gung University, Taoyuan, Taiwan; College of Management, Chang Gung University, Taoyuan, Taiwan., Fan TY; Department of Research and Development, Chang Gung Medical Technology Co., Ltd., No. 11-5, Wenhua 2nd Road., Ltd., Guishan District., Taoyuan City 333, Taiwan., Kuo CF; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan., Yen TY; Department of Physical Medicine and Rehabilitation, Taoyuan Chang Gung Memorial Hospital, Taoyuan, Taiwan., Chang SY; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan., Pei YC; Master of Science Degree Program in Innovation for Smart Medicine, Chang Gung University, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Center of Vascularized Tissue Allograft, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan. Electronic address: yspeii@gmail.com., Chen YP; Master of Science Degree Program in Innovation for Smart Medicine, Chang Gung University, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan. Electronic address: yuepengc@gmail.com.

Publikováno v: Bone [Bone] 2024 Nov 03; Vol. 190, pp. 117317. Date of Electronic Publication: 2024 Nov 03.

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Autor: Yen TY; Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; School of Medicine, Chang Gung University, No. 259, Wenhua 1st Road, Guishan District, Taoyuan City 33302, Taiwan., Ho CS; Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; School of Medicine, Chang Gung University, No. 259, Wenhua 1st Road, Guishan District, Taoyuan City 33302, Taiwan., Pei YC; Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; School of Medicine, Chang Gung University, No. 259, Wenhua 1st Road, Guishan District, Taoyuan City 33302, Taiwan; Center of Vascularized Tissue Allograft, Gung Memorial Hospital at Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan., Fan TY; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; Technology R&D Department, Chang Gung Medical Technology Co., Ltd., Taoyuan City 333, Taiwan., Chang SY; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan., Kuo CF; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333, Taiwan; Department of Internal Medicine, College of Medicine, Chang Gung University, Taoyuan City 333, Taiwan; Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, UK., Chen YP; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; Master of Science Degree Program in Innovation for Smart Medicine, Chang Gung University, No. 259, Wenhua 1st Road, Guishan District, Taoyuan City 33302, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333, Taiwan. Electronic address: yuepengc@gmail.com.

Publikováno v: Bone [Bone] 2024 Jul; Vol. 184, pp. 117107. Date of Electronic Publication: 2024 Apr 25.

Sugar transporters spatially organize microbiota colonization along the longitudinal root axis of Arabidopsis.

Autor: Loo EP; Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Molecular Physiology, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany. Electronic address: loo@hhu.de., Durán P; Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany., Pang TY; Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Computer Science and Department of Biology, 40225 Düsseldorf, Germany; Heinrich Heine University Düsseldorf, Medical Faculty and University Hospital Düsseldorf, Division of Cardiology, Pulmonology and Vascular Medicine, 40225 Düsseldorf, Germany., Westhoff P; Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Plant Metabolism and Metabolomics Laboratory, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany., Deng C; Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Molecular Physiology, 40225 Düsseldorf, Germany., Durán C; Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany., Lercher M; Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Computer Science and Department of Biology, 40225 Düsseldorf, Germany; Heinrich Heine University Düsseldorf, Medical Faculty and University Hospital Düsseldorf, Division of Cardiology, Pulmonology and Vascular Medicine, 40225 Düsseldorf, Germany., Garrido-Oter R; Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany; Earlham Institute, Norwich NR4 7UZ, UK., Frommer WB; Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Institute for Molecular Physiology, 40225 Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, 40225 Düsseldorf, Germany; Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, 464-8601 Nagoya, Japan. Electronic address: frommew@hhu.de.

Publikováno v: Cell host & microbe [Cell Host Microbe] 2024 Apr 10; Vol. 32 (4), pp. 543-556.e6. Date of Electronic Publication: 2024 Mar 12.