Diabetic retinopathy (DR) is a leading cause of acquired blindness among adults.High glucose (HG) induces oxidative injury and apoptosis in retinal ganglion cells (RGCs), serving as a primary pathological mechanism of DR. MIND4-17 activates nuclear-factor-E2-related factor 2 (Nrf2) signaling via modifying one cysteine (C151) residue of Kelch-like ECH-associated protein 1 (Keap1). The current study tested its effect in HG-treated primary murine RGCs. We show that MIND4-17 disrupted Keap1-Nrf2 association, leading to Nrf2 protein stabilization and nuclear translocation, causing subsequent expression of key Nrf2 target genes, including heme oxygenase-1 and NAD(P)H quinone oxidoreductase 1. Functional studies showed that MIND4-17 pretreatment significantly inhibited HG-induced cytotoxicity and apoptosis in primary murine RGCs. Reactive oxygen species production and oxidative injury in HG-treated murine RGCs were attenuated by MIND4-17. Nrf2 silencing (by targeted small interfering RNA) or knockout (by CRISPR/Cas9 method) abolished MIND4-17-induced RGC cytoprotection against HG. Additionally, Keap1 knockout or silencing mimicked and abolished MIND4-17-induced activity in RGCs.In vivo, MIND4-17 intravitreal injection activated Nrf2 signaling and attenuated retinal dysfunction by light damage in mice. We conclude that MIND4-17 activates Nrf2 signaling to protect murine RGCs from HG-induced oxidative injury.
The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence each other. Numerous systemic diseases lead to special clinical manifestations and complications in the eyes. Typical diseases include diabetic retinopathy, hypertensive retinopathy, thyroid associated ophthalmopathy, optic neuromyelitis, and Behcet’s disease. Systemic disease-related ophthalmopathy is usually a chronic disease, and the analysis of imaging markers is helpful for a comprehensive diagnosis of these diseases. Recently, artificial intelligence (AI) technology based on deep learning has rapidly developed, leading to numerous achievements and arousing widespread concern. Presently, AI technology has made significant progress in research on imaging markers of systemic disease-related ophthalmopathy; however, there are also many limitations and challenges. This article reviews the research achievements, limitations, and future prospects of AI image analysis technology in systemic disease-related ophthalmopathy.
This study is aimed at developing an intelligent algorithm based on deep learning and discussing its application for the classification and diagnosis of retinal vein occlusions (RVO) using fundus images. A total of 501 fundus images of healthy eyes and patients with RVO were used for model training and testing to investigate an intelligent diagnosis system. The images were first classified into four categories by fundus disease specialists: (i) healthy fundus (group 0), (ii) branch RVO (BRVO) (group 1), (iii) central RVO (CRVO) (group 2), and (iv) macular branch RVO (MBRVO) (group 3), before being diagnosed using the ResNet18 network model. Intelligent diagnoses were compared with clinical diagnoses. The specificity of the intelligent diagnosis system under each attention mechanism was 100% in group 0 and also revealed a high sensitivity of over 95%,
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score of over 97%, and an accuracy of over 97% in this group. For the other three groups, the specificities of diagnosis ranged from 0.45 to 0.91 with different attention mechanisms, in which the ResNet18+coordinate attention (CA) model had the highest specificities of 0.91, 0.88, and 0.83 for groups 1, 2, and 3, respectively. It also provided a high accuracy of over 94% with a coordinate attention mechanism in all four groups. The intelligent diagnosis and classifier system developed herein based on deep learning can determine the presence of RVO and classify disease according to the site of occlusion. This proposed system is expected to provide a new tool for RVO diagnosis and screening and will help solve the current challenges due to the shortage of medical resources.
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