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| http://dx.doi.org/10.3389/fpls.2020.00744 | DOI Listing |
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Bolstered bone regeneration by multiscale customized magnesium scaffolds with hierarchical structures and tempered degradation.
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Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
Addressing irregular bone defects is a formidable clinical challenge, as traditional scaffolds frequently fail to meet the complex requirements of bone regeneration, resulting in suboptimal healing. This study introduces a novel 3D-printed magnesium scaffold with hierarchical structure (macro-, meso-, and nano-scales) and tempered degradation (microscale), intricately customized at multiple scales to bolster bone regeneration according to patient-specific needs. For the hierarchical structure, at the macroscale, it can feature anatomic geometries for seamless integration with the bone defect; The mesoscale pores are devised with optimized curvature and size, providing an adequate mechanical response as well as promoting cellular proliferation and vascularization, essential for natural bone mimicry; The nanoscale textured surface is enriched with a layered double hydroxide membrane, augmenting bioactivity and osteointegration.
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Research and Development Center for Precision Medicine, University of Tsukuba, Ibaraki, Japan.
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From the Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Taoyuan Rd No. 89, Nanshan District, Shenzhen 518000, Guangdong, China (H.H., Z.D., Y.Q.); Medical AI Laboratory and Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China (J.M., R.L., B.H.); Department of Medical Imaging, People's Hospital of Longhua, Shenzhen, Guangdong, China (X.P., Y.Z.); and Department of Radiology, Shenzhen People's Hospital, Shenzhen, Guangdong, China (D.Z., G.H.).
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1.
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