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Li Y, Liu Y, Liu S, Gao M, Wang W, Chen K, Huang L, Liu Y. Diabetic vascular diseases: molecular mechanisms and therapeutic strategies. Signal Transduct Target Ther 2023; 8:152. [PMID: 37037849 PMCID: PMC10086073 DOI: 10.1038/s41392-023-01400-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/19/2023] [Accepted: 02/28/2023] [Indexed: 04/12/2023] Open
Abstract
Vascular complications of diabetes pose a severe threat to human health. Prevention and treatment protocols based on a single vascular complication are no longer suitable for the long-term management of patients with diabetes. Diabetic panvascular disease (DPD) is a clinical syndrome in which vessels of various sizes, including macrovessels and microvessels in the cardiac, cerebral, renal, ophthalmic, and peripheral systems of patients with diabetes, develop atherosclerosis as a common pathology. Pathological manifestations of DPDs usually manifest macrovascular atherosclerosis, as well as microvascular endothelial function impairment, basement membrane thickening, and microthrombosis. Cardiac, cerebral, and peripheral microangiopathy coexist with microangiopathy, while renal and retinal are predominantly microangiopathic. The following associations exist between DPDs: numerous similar molecular mechanisms, and risk-predictive relationships between diseases. Aggressive glycemic control combined with early comprehensive vascular intervention is the key to prevention and treatment. In addition to the widely recommended metformin, glucagon-like peptide-1 agonist, and sodium-glucose cotransporter-2 inhibitors, for the latest molecular mechanisms, aldose reductase inhibitors, peroxisome proliferator-activated receptor-γ agonizts, glucokinases agonizts, mitochondrial energy modulators, etc. are under active development. DPDs are proposed for patients to obtain more systematic clinical care requires a comprehensive diabetes care center focusing on panvascular diseases. This would leverage the advantages of a cross-disciplinary approach to achieve better integration of the pathogenesis and therapeutic evidence. Such a strategy would confer more clinical benefits to patients and promote the comprehensive development of DPD as a discipline.
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Affiliation(s)
- Yiwen Li
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Yanfei Liu
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China
- The Second Department of Gerontology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Shiwei Liu
- Department of Nephrology and Endocrinology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China
| | - Mengqi Gao
- Department of Nephrology and Endocrinology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China
| | - Wenting Wang
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Keji Chen
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China.
| | - Luqi Huang
- China Center for Evidence-based Medicine of TCM, China Academy of Chinese Medical Sciences, Beijing, 100010, China.
| | - Yue Liu
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China.
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Wang J, Mo W, Wu Y, Xu X, Li Y, Ye J, Lai X. Combined Channel Attention and Spatial Attention Module Network for Chinese Herbal Slices Automated Recognition. Front Neurosci 2022; 16:920820. [PMID: 35769703 PMCID: PMC9234258 DOI: 10.3389/fnins.2022.920820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
Chinese Herbal Slices (CHS) are critical components of Traditional Chinese Medicine (TCM); the accurate recognition of CHS is crucial for applying to medicine, production, and education. However, existing methods to recognize the CHS are mainly performed by experienced professionals, which may not meet vast CHS market demand due to time-consuming and the limited number of professionals. Although some automated CHS recognition approaches have been proposed, the performance still needs further improvement because they are primarily based on the traditional machine learning with hand-crafted features, resulting in relatively low accuracy. Additionally, few CHS datasets are available for research aimed at practical application. To comprehensively address these problems, we propose a combined channel attention and spatial attention module network (CCSM-Net) for efficiently recognizing CHS with 2-D images. The CCSM-Net integrates channel and spatial attentions, focusing on the most important information as well as the position of the information of CHS image. Especially, pairs of max-pooling and average pooling operations are used in the CA and SA module to aggregate the channel information of the feature map. Then, a dataset of 14,196 images with 182 categories of commonly used CHS is constructed. We evaluated our framework on the constructed dataset. Experimental results show that the proposed CCSM-Net indicates promising performance and outperforms other typical deep learning algorithms, achieving a recognition rate of 99.27%, a precision of 99.33%, a recall of 99.27%, and an F1-score of 99.26% with different numbers of CHS categories.
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Affiliation(s)
- Jianqing Wang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Weitao Mo
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yan Wu
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaomei Xu
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Li
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianming Ye
- First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Xiaobo Lai
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
- First Affiliated Hospital, Gannan Medical University, Ganzhou, China
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Liu Y, Huang H, Sun Y, Li Y, Luo B, Cui J, Zhu M, Bi F, Chen K, Liu Y. Monosodium Glutamate-Induced Mouse Model With Unique Diabetic Retinal Neuropathy Features and Artificial Intelligence Techniques for Quantitative Evaluation. Front Immunol 2022; 13:862702. [PMID: 35572527 PMCID: PMC9092070 DOI: 10.3389/fimmu.2022.862702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 03/17/2022] [Indexed: 11/26/2022] Open
Abstract
Objective To establish an artificial intelligence-based method to quantitatively evaluate subtle pathological changes in retinal nerve cells and synapses in monosodium glutamate (MSG) mice and provide an effective animal model and technique for quantitative evaluation of retinal neurocytopathies. Methods ICR mice were subcutaneously injected with MSG to establish a model of metabolic syndrome. We then established a mouse model of type 1 diabetes, type 2 diabetes, and KKAy mouse model as control. The HE sections of the retina were visualized using an optical microscope. AI technology was used for quantitative evaluation of the retinal lesions in each group of rats. The surface area custom parameters of the retinal nerve fiber layer (RNFL), inner plexiform layer (IPL), inner nuclear layer (INL), and outer plexiform layer (OPL) were defined as SR, SIPL, SINL, and SOPL, respectively. Their heights were defined as HR, HIPL, HINL, and HOPL, and the number of ganglion cells was defined as A. Then, the attention-augmented fully convolutional Unet network was used to segment the retinal HE images, and AI technology to identify retinal neurocytopathies quantitatively. Results The attention-augmented fully convolutional Unet network increased PA and IOU parameters for INL, OPL, RNFL, and ganglion cells and was superior in recognizing fine structures. A quantitative AI identification of the height of each layer of the retina showed that the heights of the IPL and INL of the MSG model were significantly less than those of the control groups; the retinas of the other diabetic models did not exhibit this pathological feature. The RNFLs of type 2 diabetes were thinner, and the characteristics of retinopathy were not obvious in the other animal models. The pathological changes seen on HE images were consistent with the results of the quantitative AI evaluation. Immunohistochemistry results showed that NMDAR2A, GluR2, and NRG1 were significantly downregulated in the retina of MSG mice. Conclusions The MSG retinopathy model is closely associated with neurotransmitter abnormalities and exhibits important characteristics of retinal neurodegeneration, making it suitable for studying retinal neurocytopathies. The AI recognition technology for retinal images established in the present study can be used for the quantitative and objective evaluation of drug efficacy.
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Affiliation(s)
- Yanfei Liu
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Hui Huang
- Beijing Duan-Dian Pharmaceutical Research & Development Co., Ltd., Beijing, China
| | - Yu Sun
- North China University of Technology, Beijing, China
| | - Yiwen Li
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Binyu Luo
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Jing Cui
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Mengmeng Zhu
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Fukun Bi
- North China University of Technology, Beijing, China
| | - Keji Chen
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Yue Liu
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
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Wu D, Wang Y, Wu Y, Ding S. The Protective Effect of Genipin on Oxidative Stress Under Hypoxia and Hyperglycemia in Retinal Pigment Epithelial Cells. J BIOMATER TISS ENG 2021. [DOI: 10.1166/jbt.2021.2804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
We aimed to explore the protective effect of genipin on retinal pigment epithelial (RPE) cells under hypoxia and hyperglycemia. RPE cells were cultured under hyperglycemia and hypoxia mimicking agent DFX. The cells were then exposed to genipin (10–50 μM), genipin + phospha-tidylinositol
(3,4,5) trisphosphates (PIP3) as phosphoinositide 3-kinase (PI3K) inhibitor, and genipin+ PI3K agonist, followed by CCK-8 assay to detect the cell viability. Western blot determined PI3K/protein kinase B (AKT) pathway, and apoptosis- and anti-apoptosis-related proteins levels. MitoSOXTM Red
kit was conducted to analyze reactive oxygen species (ROS) content. Finally, confocal immunofluorescence staining assessed nuclear translocation of Nuclear factor erythroid-derived 2-like 2 (Nrf2). Hyperglycemia and hypoxia treatment induced injury in RPE cells, with nuclear translocation
of Nrf2 and ROS production. Importantly, administration of genipin alleviated the injury, up-regulated Bcl-2 expression, inhibited caspase-3 activity and nuclear translocation of Nrf2, and down-regulated the level of Bax and ROS. In addition, genipin pretreatment obviously increased PI3K and
Akt phosphorylation and promoted cell proliferation and viability. On the contrary, PI3K inhibitor inactivated PI3K/AKT and decreased cell viability while PI3K agonist showed the opposite effect. Genipin prevented oxidative stress and apoptosis induced by hyperglycemia and hypoxia through
PI3K/Akt signaling pathway.
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Affiliation(s)
- Daifeng Wu
- The Department of Ophthalmology, Fuzhou First People’s Hospital of Jiangxi Province, Fuzhou, Jiangxi, 344000, China
| | - Yulin Wang
- The Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330019, China
| | - Yueyang Wu
- School of Statistics, Shanxi University of Finance and Economics, Shanxi, Taiyuan, 030000, China
| | - Shujuan Ding
- The Department of Ophthalmology, Fuzhou First People’s Hospital of Jiangxi Province, Fuzhou, Jiangxi, 344000, China
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Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci 2021; 41:1105-1115. [PMID: 34874486 PMCID: PMC8648557 DOI: 10.1007/s11596-021-2474-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 12/01/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient's diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.
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Affiliation(s)
- Peng-ran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Lin Lu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Jia-yao Zhang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Tong-tong Huo
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Song-xiang Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Zhe-wei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
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Artificial intelligence for diabetic retinopathy screening, prediction and management. Curr Opin Ophthalmol 2020; 31:357-365. [PMID: 32740069 DOI: 10.1097/icu.0000000000000693] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
PURPOSE OF REVIEW Diabetic retinopathy is the most common specific complication of diabetes mellitus. Traditional care for patients with diabetes and diabetic retinopathy is fragmented, uncoordinated and delivered in a piecemeal nature, often in the most expensive and high-resource tertiary settings. Transformative new models incorporating digital technology are needed to address these gaps in clinical care. RECENT FINDINGS Artificial intelligence and telehealth may improve access, financial sustainability and coverage of diabetic retinopathy screening programs. They enable risk stratifying patients based on individual risk of vision-threatening diabetic retinopathy including diabetic macular edema (DME), and predicting which patients with DME best respond to antivascular endothelial growth factor therapy. SUMMARY Progress in artificial intelligence and tele-ophthalmology for diabetic retinopathy screening, including artificial intelligence applications in 'real-world settings' and cost-effectiveness studies are summarized. Furthermore, the initial research on the use of artificial intelligence models for diabetic retinopathy risk stratification and management of DME are outlined along with potential future directions. Finally, the need for artificial intelligence adoption within ophthalmology in response to coronavirus disease 2019 is discussed. Digital health solutions such as artificial intelligence and telehealth can facilitate the integration of community, primary and specialist eye care services, optimize the flow of patients within healthcare networks, and improve the efficiency of diabetic retinopathy management.
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