Replacement of diseased retinal pigment epithelium (RPE) cells with healthy RPE cells by transplantation is one option to treat several retinal degenerative diseases including age-related macular degeneration, which are caused by RPE loss and dysfunction. A cellular scaffold as a carrier for transplanted cells, may hold immense promise for facilitating cell migration and promoting the integration of RPE cells into the host environment. Scaffolds can be prepared from a variety of natural and synthetic materials. Strategies, such as surface modification and structure adjustment, can improve the biomimetic properties of the scaffolds, optimize cell attachment and cellular function following transplantation and lay a foundation of clinical application in the future.
Single cell RNA sequencing technique provides a strong technical support for the analysis of cell heterogeneity in biological tissues, and has been widely used in biomedical research. In recent years, considerable scRNA-seq data have been accumulated in the research of ocular fundus diseases. The ocular fundus is abundant for the network of vessel and neuron, which leads to the complicated pathogenesis of fundus diseases. Through single cell RNA sequencing technique, the expression of thousands of genes of certain cell types or even subtypes can be obtained in the disease environment. Single cell RNA sequencing technique accurately reveals the pathogenic cell types and pathogenic mechanisms of ocular fundus diseases such as neovascular retinopathy, which provides a theoretical basis for the birth of new diagnosis and treatment targets. The construction of multi-omics single-cell database of ocular fundus diseases will enable high-quality data to be further explored and provide an analysis platform for ophthalmic researchers.
ObjectiveTo apply the multi-modal deep learning model to automatically classify the ultra-widefield fluorescein angiography (UWFA) images of diabetic retinopathy (DR). MethodsA retrospective study. From 2015 to 2020, 798 images of 297 DR patients with 399 eyes who were admitted to Eye Center of Renmin Hospital of Wuhan University and were examined by UWFA were used as the training set and test set of the model. Among them, 119, 171, and 109 eyes had no retinopathy, non-proliferative DR (NPDR), and proliferative DR (PDR), respectively. Localization and assessment of fluorescein leakage and non-perfusion regions in early and late orthotopic images of UWFA in DR-affected eyes by jointly optimizing CycleGAN and a convolutional neural network (CNN) classifier, an image-level supervised deep learning model. The abnormal images with lesions were converted into normal images with lesions removed using the improved CycleGAN, and the difference images containing the lesion areas were obtained; the difference images were classified by the CNN classifier to obtain the prediction results. A five-fold cross-test was used to evaluate the classification accuracy of the model. Quantitative analysis of the marker area displayed by the differential images was performed to observe the correlation between the ischemia index and leakage index and the severity of DR. ResultsThe generated fake normal image basically removed all the lesion areas while retaining the normal vascular structure; the difference images intuitively revealed the distribution of biomarkers; the heat icon showed the leakage area, and the location was basically the same as the lesion area in the original image. The results of the five-fold cross-check showed that the average classification accuracy of the model was 0.983. Further quantitative analysis of the marker area showed that the ischemia index and leakage index were significantly positively correlated with the severity of DR (β=6.088, 10.850; P＜0.001). ConclusionThe constructed multimodal joint optimization model can accurately classify NPDR and PDR and precisely locate potential biomarkers.