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The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis (ARN). The potential application of artificial intelligence (AI) algorithms in these areas of clinical research has not been reported previously. The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs. A total of 149 wide-angle fundus photographs from 40 eyes of 32 ARN patients were collected, and the U-Net method was used to construct the AI algorithm. Thereby, a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time. This algorithm had an area under the receiver operating curve of 0.92, with 86% sensitivity and 88% specificity in the detection of retinal necrosis. For the purpose of retinal necrosis evaluation, necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples (
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Objective To investigate the inhibiting effect of CGP77675 (CGP),a Src inhibitor,on epithelial-mesenchymal transition (EMT) of human retinal pigment epithelial (RPE) cells induced by transformation growth factor-β1 (TGF-β1).Methods Human RPE cell line (ARPE19 cells) was cultured in vitro and divided into control group,TGF-β1 group and TGF-β1 +CGP group.Corresponding agent was added into culture medium based on grouping.The morphology of the cells were examined under the optical microscope 3 days after culture.The expressions of EMT-related genes and proteins in the cells were detected by real-time quantitative PCR and Western blot,respectively,including fibronectin 1 (FN 1),and plasminogen activation inhibitor 1 (PAI1),and the expressions of zonula occludens protein 1 (ZO1) and cytoskeleton protein filamentous actin (F-actin) were detected by immunofluorescence staining.MTT assay was employed to evaluate the cell proliferation rate.The migration distance of the cells was measured by scratch test.Results The ARPE19 cells in the control group showed an epithelial-like morphology and F-actin and ZO-1 were expressed along cell membrane.In the TGF-β1 group,the cells appeared to be fibrous-like,and the fluorescence staining of F-actin was disordered and ZO-1 was discontinuous on the cell membrane.The cells in the TGF-β1 +CGP group remained to be an epithelial-like in shape with clear and complete expressions of F-actin and ZO-1.The relative expressions of FN1 mRNA and PAI1 mRNA in the cells were 0.211 ± 0.080 and 0.116±0.073,1.000±0.001 and 1.000±0.001,0.368±0.097 and 0.362±0.048 in the control group,TGF-β1 group and TGF-β1 +CGP groups,showing significant differences among the groups (F=33.14,82.92;both at P＜0.01),with the highest expressions ofFN1 mRNA and PAI1 mRNA in the TGF-β1 group (all at P＜0.05).The relative expressions of FN1 and PAI1 proteins were 0.166±0.055 and 0.327±0.066,1.000±0.001 and 1.000± 0.001,0.143 ± 0.030 and 0.260 ± 0.077 in the control group,TGF-β1 group and TGF-β1 + CGP group,with significant differences among three groups (F=181.90,48.85;both at P＜0.01),and the expressions FN1 and PAI1 proteins were significantly higher in the TGF-β1 than those in the control group and TGF-β1 +CGP group (all at P＜0.05).The cell proliferative rate in the TGF-β1+CGP group was (79.30±3.44) ％ and (54.80±7.39) ％ at the third day and seventh day after culture,which were significantly reduced in comparison with (99.50 ± 1.00)％ and (99.10±0.50)％ in the control group as well as (95.10±4.20)％ and (92.10±4.50)％ in the TGF-β1 group (all at P＜0.05).The migration distance was disappeared in the TGF-β1 group,and the scratch width was not obviously changed in the TGF-β1 +CGP group.Conclusions Src inhibitor can inhibit EMT process of ARPE19 cells induced by TGF-β1,indicating that Src signaling pathway may play a critical role in EMT of RPE cells.
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ObjectiveTo analyze the different effects of spiral progress method and traditional method used in computer teaching in TCM colleges and universities.MethodsStudents were divided into the groups of spiral and traditional. At the end of the course, students would have a course examination.Results Students in spiral group got better scores than students in traditional group (P<0.05).Conclusion Using spiral progress method in computer teaching in TCM colleges and universities can help students develop the right thinking model and scientific learning method. It can also improve students’ ability to use computer knowledge in TCM study and research.