With the intensification of population aging and the popularity of electronic products, the incidence of retinal diseases continues to rise, and their complex pathogenesis seriously restricts the development of effective treatment strategies. Zebrafish has become an important model animal for ophthalmic research, especially for retinal disease research, due to its unique biological advantages. This article aims to systematically review the current application status and prospects of zebrafish models in various retinal disease research, broaden future research ideas, and provide new perspectives for the prevention and treatment of retinal diseases. This article first elaborates on the unique advantages of zebrafish as a model animal, including easy feeding, transparent embryos, rapid development of the visual system, high homology with human genes, and strong retinal regeneration ability. Subsequently, we reviewed the research and application progress of zebrafish models, focusing on various hereditary and non-hereditary retinal diseases, including diabetic retinopathy, retinopathy of prematurity, retinitis pigmentosa, rod-cone cell dystrophy, Leber congenital amaurosis, congenital static night blindness, choroidal deletion, and Budd Beeder syndrome. Studies have confirmed that a large number of zebrafish models simulating the pathological characteristics of human retinal diseases have been constructed successfully using genetic techniques such as CRISPR/Cas9 gene editing, TALEN targeted modification, chemical induction, and microinjection. These models not only effectively reproduce the clinical phenotype of retinal diseases but also play an irreplaceable role in elucidating the functions of pathogenic genes, revealing signal pathway disorders, and analyzing the mechanisms of cell death and regeneration. Additionally, the zebrafish model has shown great potential in drug screening and efficacy evaluation. These studies indicate that the zebrafish model is an ideal tool for in-depth analysis of the pathogenesis of retinal diseases, promoting precision medicine and new drug development, and has broad application prospects in the future.
Mast cell (MC) play a crucial role in non-allergic fundus diseases, including uveitis, diabetic retinopathy, and age-related macular degeneration. MCs can profoundly influence the pathological processes of these diseases by regulating inflammatory responses, promoting angiogenesis, and facilitating tissue remodeling through the degranulation and release of mediators such as histamine, cytokines, and enzymes. The application of MC-associated inhibitors has been shown to effectively mitigate or inhibit the progression of these pathologies, offering a promising strategy for treating ocular diseases. Understanding the current state of MC research in fundus diseases will enhance our insight into their role in the pathophysiological mechanisms of these conditions and encourage further research aimed at providing more effective treatment options for patients.
Choroidal vascularity index （CVI）, as a new biological parameter to quantitatively evaluate the state of choroidal vessels, has shown great potential in the diagnosis and treatment of ophthalmic diseases in recent years. CVI primarily calculated from images obtained via optical coherence tomography (OCT) and OCT angiography, demonstrates enhanced accuracy, stability, and clinical value with the advancement of three-dimensional imaging and artificial intelligence technologies. Compared with two-dimensional CVI, three-dimensional CVI comprehensively reflects the spatial distribution and structural changes of choroidal blood vessels by constructing three-dimensional choroidal models through ultra-widefield scanning. In various ophthalmic diseases, including age-related macular degeneration, central serous chorioretinopathy, diabetic retinopathy, and pathological myopia, CVI exhibits characteristic changes that not only contribute to understanding disease pathogenesis but also serve as indicators for early screening, individualized treatment, and efficacy monitoring. The application of artificial intelligence and deep learning technology improves the efficiency of automated CVI analysis, while integration with multimodal imaging further optimizes disease evaluation. Future efforts should focus on establishing standardized measurement protocols and quality control systems to promote its broader application and development in ophthalmology.