Ultraviolet (UV) radiation and reactive oxygen species (ROS) impair the physiological functions of retinal pigment epithelium (RPE) cells by inducing cell apoptosis, which is the main cause of age-related macular degeneration (AMD). The mechanism by which UV/ROS induces RPE cell death is not fully addressed. Here, we observed the activation of a ceramide-endoplasmic reticulum (ER) stress-AMP activated protein kinase (AMPK) signaling axis in UV and hydrogen peroxide (H₂O₂)-treated RPE cells. UV and H₂O₂ induced an early ceramide production, profound ER stress and AMPK activation. Pharmacological inhibitors against ER stress (salubrinal), ceramide production (fumonisin B1) and AMPK activation (compound C) suppressed UV- and H₂O₂-induced RPE cell apoptosis. Conversely, cell permeable short-chain C6 ceramide and AMPK activator AICAR (5-amino-1-β-D-ribofuranosyl-imidazole-4-carboxamide) mimicked UV and H₂O₂’s effects and promoted RPE cell apoptosis. Together, these results suggest that UV/H₂O₂ activates the ceramide-ER stress-AMPK signaling axis to promote RPE cell apoptosis.

Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.

High myopia is a global ocular disease and one of the most common causes of blindness. Fundus images can be obtained in a noninvasive manner and can be used to monitor and follow up on many fundus diseases, such as high myopia. In this paper, we proposed a computer-aided diagnosis algorithm using deep convolutional neural networks (DCNNs) to grade the risk of high myopia. The input images were automatically classified into three categories: normal fundus images were labeled class 0, low-risk high-myopia images were labeled class 1, and high-risk high-myopia images were labeled class 2. We conducted model training on 758 clinical fundus images collected locally, and the average accuracy reached 98.15% according to the results of fivefold cross-validation. An additional 100 fundus images were used to evaluate the performance of DCNNs, with ophthalmologists performing external validation. The experimental results showed that DCNNs outperformed human experts with an area under the curve (AUC) of 0.9968 for the recognition of low-risk high myopia and 0.9964 for the recognition of high-risk high myopia. In this study, we were able to accurately and automatically perform high myopia classification solely using fundus images. This has great practical significance in terms of improving early diagnosis, prevention, and treatment in clinical practice.

In recent years, an increasing number of people have myopia in China, especially the younger generation. Common myopia may develop into high myopia. High myopia causes visual impairment and blindness. Parapapillary atrophy (PPA) is a typical retinal pathology related to high myopia, which is also a basic clue for diagnosing high myopia. Therefore, accurate segmentation of the PPA is essential for high myopia diagnosis and treatment. In this study, we propose an optimized Unet (OT-Unet) to solve this important task. OT-Unet uses one of the pre-trained models: Visual Geometry Group (VGG), ResNet, and Res2Net, as a backbone and is combined with edge attention, parallel partial decoder, and reverse attention modules to improve the segmentation accuracy. In general, using the pre-trained models can improve the accuracy with fewer samples. The edge attention module extracts contour information, the parallel partial decoder module combines the multi-scale features, and the reverse attention module integrates high- and low-level features. We also propose an augmented loss function to increase the weight of complex pixels to enable the network to segment more complex lesion areas. Based on a dataset containing 360 images (Including 26 pictures provided by PALM), the proposed OT-Unet achieves a high AUC (Area Under Curve) of 0.9235, indicating a significant improvement over the original Unet (0.7917).

Background To study the effectiveness and safety of aqueous humor PCR viral detection in the diagnosis of PSS. Methods 20 patients (case group) with PSS who suffered from recurrences and poor intraocular pressure (IOP) control by use of drugs underwent anterior chamber puncture. The concentration of different viruses in aqueous humor were detected by PCR. 20 patients (control group) with POAG were given the same treatment and we compared the difference of positive rate of the viruses between two groups. Finally, the effectiveness and safety of aqueous humor PCR viral detection in the diagnosis of PSS were observed. Besides, the advantage of PCR viral detection technology in treatment was evaluated. Results No complications and long-term sequelae related to anterior chamber puncture were found. The total positive rate of aqueous humor viral detection was 30% (6/20) in case group ,and was found none in control group (0/20) (P = 0.027). Then, we divided the case group into two groups. Six virus positive patients were given local and systemic antiviral treatment for 3 months and then interrupted the treatment. Fourteen patients left were given no antiviral treatment. All PSS patients were observed for 9 months from the first time of treatment. There is significant difference between two groups in recurrence rate (P = 0.013). Conclusions Viral infection is a risk factor of PSS. The detection of virus in aqueous humor by PCR is effective and safe for diagnosis of PSS, and helpful for targeted treatment on virus-positive patients.