Retinopathy of prematurity (ROP) is a blinding eye disease in children that is characterized by the formation of neovascularization in the retina; current treatments for this disease risk retinal damage and visual impairment. This study aimed to investigate the relationship between metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), miR-106a-5p, and matrix metalloproteinase 2 (MMP-2) in an oxygen-induced retinopathy mouse model and hypoxia-induced human retinal endothelial cells and to elucidate whether MALAT1 upregulates MMP-2 signaling by inhibiting miR-106a-5p. The role of this pathway in oxygen-induced murine retinopathy and its underlying mechanism were also investigated. MALAT1 inhibited the expression of miR-106a-5p and enhanced the expression of MMP-2, which in turn caused a series of pathological changes, such as the formation of new blood vessels in the retina. In addition, knockdown of MALAT1 can downregulate the expression of MMP-2 by sponging miR-106a-5p and inhibiting cell proliferation, migration, and tube-forming ability. In conclusion, our findings suggest that MALAT1 may contribute to the occurrence and development of ROP by inhibiting miR-106a-5p and increasing the expression of MMP-2, thus providing a new perspective for the targeted therapy of ROP.
AimsTo systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness.Materials and methodsA search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm.ResultsFinally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR.ConclusionAI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023389687.
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