Xiaodong Chen, Nanyang Technological University, Singapore, Singapore João Conde, Universidade Nova de Lisboa, Lisbon, Portugal Wenguo Cui, Shanghai Jiao Tong University, Shanghai, China Chunhai Fan, Shanghai Jiao Tong University, Shanghai, China Qinghua He, Nanchang University, Nanchang, China Aleksandar Ivanov, Imperial College London, London, UK Ali Khademhosseini, University of California-Los Angeles, Los Angeles, USA Luke Lee, University of California, Berkeley, USA Chwee Teck Lim, National University of Singapore, Singapore, Singapore Yuehe Lin, Washington State University, Pullman, USA Qingjun Liu, Zhejiang University, Hangzhou, China Zhe Liu, Tianjin University, Tianjin, China Niren Murthy, University of California, Berkeley, USA Je-Kyun Park, Korea Advanced Institute of Science and Technology, Daejeon, Korea Kanyi Pu, Nanyang Technological University, Singapore, Singapore Jianhua Qin, Dalian Institute of Chemical Physics, CAS, Dalian, China Milica Radisic, University of Toronto, Toronto, Canada Hélder A. Santos, University of Helsinki, Helsinki, Finland Amy Shen, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan Patrick M Sluss, Massachusetts General Hospital/Harvard Medical School ⋅ Pathology, Boston, USA Joseph Wang, University California-San Diego, La Jolla, USA Jingjuan Xu, Nanjing University, Nanjing, China Yingwei Yang, Jilin University, Changchun, China Radek Zbořil, Palacky University in Olomouc, Olomouc, Czech Republic Xiao-Bing Zhang, Hunan University, Changsha, China Lei Zheng, Southern Medical University, Guangzhou, China
Artificial intelligence (AI) has reformed the healthcare system with its compelling capabilities of processing biomedical data for disease diagnosis, prediction, and individualized management. The eye, as a non‐invasive observation window for many systemic diseases, can be used to detect the signs of chronic kidney diseases, and other diseases like hypertension and type 2 diabetes mellitus, based on specific manifestations of retinal images. Recent advances using AI technology have posed a great potential of using retinal images for rapid mass screening and prognosis prediction of kidney diseases. Herein, we outlined the key applications of AI in ophthalmology and the detection of systemic diseases based on retinal imaging, especially the current progress of retinal image‐based AI models for the detection and prediction of kidney diseases. We hope to shed light on the current opportunities and future challenges in this field to provide suggestions for further improvement and applications.
Abstract Angiotensin converting enzyme 2 (ACE2) played a critical role in regulating renin‐angiotensin‐aldosterone system (RAAS). In this research, 68Ga‐cyc‐DX600 was synthesized as PET tracer of ACE2 imaging. ACE2 positron emission tomography/magnetic resonance (PET/MR) was preliminary administered on twelve healthy volunteers, and the images were normalized and registered to establish the standard model of ACE2 PET. In diseased conditions, 68Ga‐cyc‐DX600 PET and 18F‐FDG PET were compared for COVID‐19 (one in acute phase and three in post‐COVID), anemia (n = 1) and malignancies (n = 2) to evaluate the diagnostic efficiency. 68Ga‐cyc‐DX600 PET was of a definite ACE2 dependence. For the tracer uptake of ACE2 PET/MR of female and male, differences existed in salivary glands, upper respiratory tract and kidneys, meanwhile, age, and body mass index (BMI) were also the confounding factors. RAAS‐related tissue and organs were of the relatively higher tracer uptake, such as SUVmean of cardiac chamber (3.786 ± 1.495), liver (5.342 ± 2.267), spleen (4.465 ± 2.508), and kidney (4.906 ± 1.619 for female and 8.431 ± 5.179 for male). For COVID‐19, ACE2 PET revealed ACE2 fluctuations, particularly in the susceptible organs, including liver, spleen and testis. In the case of anemia, the activated local RAS in the bone marrow was of diffuse high tracer uptake. ACE2 PET of malignancies added supplementary information to FDG PET. 68Ga‐cyc‐DX600‐based ACE2 PET models were established for visually monitoring of whole‐body ACE2 expression. The feasibility of ACE2 PET in supervising disease was primarily proved in COVID‐19, anemia and malignancies as providing a comprehensive view on the disease process and functional recovery.
Abstract Single‐atom catalysts (SACs) have attracted extensive interest owing to their maximized atomic utilization, low cost as well as outstanding catalytic activity, selectivity, and stability for diverse applications. Due to their excellent performance in electrocatalysis, SACs can be applied to electrochemical sensors, which have been a predominant tool employed in biosensing. In very recent studies, SAC‐based electrochemical biosensors have demonstrated enhanced sensing performances in biomarker detection and in vivo analysis. However, a comprehensive review of SAC‐based electrochemical biosensors has not been reported yet. Herein, we present a summary of the synthesis methods of SACs with their application in electrochemical sensor establishment and electrochemical characterization methods in electrochemical sensing. Biomedical applications utilizing SAC‐based electrochemical biosensors are introduced. Finally, the existing challenges and future prospects of SACs in the field of electrochemical biosensing are discussed.
