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.
ObjectiveTo understand the research status, academic hotspots, and development trends in the interdisciplinary field of fundus diseases and artificial intelligence (AI). MethodsThe SCI-Expanded database from the Web of Science core collection, provided by the Institute for Scientific Information in the United States, was used as the data source to retrieve literature related to the intersection of retinal diseases and AI from January 1, 2014, to December 31, 2024. Bibliometric analysis tools, Origin Pro 2024 and CiteSpace 6.4.R1, were employed to analyze data on countries/regions, institutions, journals, authors, and keywords. ResultsA total of 2 103 papers related to the intersection of retinal diseases and AI were identified. The number of publications increased significantly starting in 2018, with an average annual increase of approximately 56.8 papers. Among the countries/regions that published papers, China had the highest number of publications (587), while the United Kingdom exhibited the highest intermediary centrality, with a value of 0.28. The core journal in this field was Ophthalmology, with an impact factor of 13.2. With respect to authors, the Ophthalmic Hospital of the University of Vienna in Austria had the highest number of publications (50). Keyword clustering revealed that research efforts focused on three main areas: AI-assisted diagnosis of retinal diseases (#1, #3-#5, #7), analysis of retinal images (#0, #6, #8, #9), and practical applications of retinal disease research (#2). The most frequently occurring keywords were "diabetic retinopathy", "deep learning”, and "AI", with 984, 749, and 471 occurrences, respectively. The analysis of emergent words shows that "retinal image" and "risk factor" are the persistent hotspots, while the field of mathematics is the key technical support. ConclusionsFrom 2014 to 2024, there was a growing trend in literature related to the intersection of retinal diseases and AI. China had the highest number of publications, but its intermediary centrality was relatively low. Research activities focused primarily on AI-assisted diagnosis, image analysis, and practical applications of retinal disease knowledge.
Tear fluid, as an important ocular surface fluid, can effectively reflect both ocular and systemic metabolic states through its compositional changes, making it an ideal source for discovering disease biomarkers. Current tear collection methods mainly include the Schirmer strip test and microcapillary collection, while detection technologies encompass enzyme-linked immunosorbent assay, protein chip technology, mass spectrometry, Olink targeted proteomics, and bead-based multiplex assays. Studies have shown that various biomarkers in tear fluid—such as proteins, cytokines, and chemokines that are closely associated with the pathophysiological processes of fundus diseases including diabetic retinopathy, retinal vein occlusion, age-related macular degeneration, retinopathy of prematurity, and uveitis, demonstrating potential as indicators for early diagnosis, disease assessment, and therapeutic monitoring. As a non-invasive and convenient detection tool, tear analysis shows broad application prospects in the diagnosis and treatment of fundus diseases. However, further optimization of collection and detection techniques, along with large-scale clinical studies to validate the clinical utility of tear biomarkers, is still needed to promote their standardization and widespread adoption in clinical practice.
