Age-related macular degeneration (AMD) represents a significant cause of visual impairment and blindness in individuals over 65 years old. In recent years, gene therapy has emerged as a research hotspot for wet AMD, with adeno-associated virus (AAV) vectors being widely utilized due to their non-pathogenic nature, low immunogenicity, broad tissue tropism, and capacity for sustained transgene expression. Several related studies have progressed to clinical trial stages. Although challenges persist, including immunogenicity concerns, limited vector capacity, and potential long-term adverse effects, the continuous advancement of research strategies and technologies holds promise. Future developments may employ AAV delivery systems to achieve gene supplementation, gene editing, or gene silencing of angiogenesis-related signaling molecules, thereby providing novel therapeutic approaches for wet AMD.
Diabetic retinopathy (DR) is a major cause of visual impairment among working-age populations. In recent years, artificial intelligence (AI) has demonstrated significant application value in DR diagnosis, leveraging core advantages such as high efficiency and low error rates. Currently, the technical system of AI in DR image diagnosis mainly includes links like image preprocessing, feature extraction, diverse algorithmic models, and dataset construction. In practical applications, AI models can achieve automated screening and grading diagnosis of DR images, enhance diagnostic efficiency by integrating multimodal technologies, and have been successfully applied to mobile devices; meanwhile, the development of explainable AI has further boosted the credibility of AI models. Currently, this field still faces challenges, including insufficient data quality and scale, limited model interpretability, inadequate clinical validation, ethical and privacy risks, and a lack of unified technical standards. In the future, with continuous technological breakthroughs and the establishment of standardized evaluation systems, the reliability and accessibility of AI in DR diagnosis will be further enhanced.
Age-related macular degeneration (AMD) is one of the leading causes of irreversible visual impairment worldwide. At present, there is no effective treatment for advanced subretinal fibrosis in atrophic AMD and exudative AMD. Epithelial-mesenchymal transition of retinal pigment epithelial cells is a key pathological link connecting early damage and late atrophy and fibrosis of AMD. Microenvironmental factors such as oxidative stress and hypoxia trigger epithelial-mesenchymal transition, and a complex molecular regulatory network is formed by core signaling pathways such as transcription factors, transforming growth factor-β/SMAD and Wnt/β-catenin, epigenetic modification and autophagy mechanisms. Emerging therapeutic strategies such as targeted inhibition of oxidative stress, blocking of key signaling pathways and intervention based on non-coding RNA provide theoretical basis and new ideas for blocking the pathological process of AMD and clinical precision treatment. Future studies need to further clarify the specific molecular mechanisms of different stages of AMD in order to develop more accurate combined treatment options to effectively block the pathological process and save the patient's vision.