Purpose: Diabetic retinopathy (DR) is the leading cause of blindness among working age adults. Circular RNAs (circRNAs) are a kind of noncoding RNAs that are involved in the development of some diseases. Here, we aimed to determine the possible role of circRNAs in the pathogenesis of DR by determining the expression profile of circRNAs in the serum of DR patients. Methods: Nineteen subjects with type 2 diabetes mellitus with proliferative DR (T2DR), 15 subjects with type 2 diabetes mellitus without DR (T2DM), and 21 age-matched nondiabetic control subjects were included in the study. Expression profiles in the serum samples from 5 subjects of each group were studied by circular microarray and validated by quantitative real-time polymerase chain re- action (qRT-PCR) in another 40 subjects. Bioinformatic software was used to predict the microRNA response elements. Results: Thirty circRNAs were significantly upregulated in the serum of T2DR patients compared with the serum from both T2DM and control patients. Further, the altered expression of 7 circRNAs (hsa_circRNA_063981, hsa_circRNA_ 404457, hsa_circRNA_100750, hsa_circRNA_406918, hsa_ circRNA_104387, hsa_circRNA_103410, and hsa_circRNA_ 100192) were verified by qRT-PCR. Conclusion: This study suggested a potential role of circRNAs in the pathogenesis of DR and provides novel molecular targets for clinical therapy.

Background: Pathologic myopia (PM) associated with myopic maculopathy (MM) and “Plus” lesions is a major cause of irreversible visual impairment worldwide. Therefore, we aimed to develop a series of deep learning algorithms and artificial intelligence (AI)–models for automatic PM identification, MM classification, and “Plus” lesion detection based on retinal fundus images.Materials and Methods: Consecutive 37,659 retinal fundus images from 32,419 patients were collected. After excluding 5,649 ungradable images, a total dataset of 32,010 color retinal fundus images was manually graded for training and cross-validation according to the META-PM classification. We also retrospectively recruited 1,000 images from 732 patients from the three other hospitals in Zhejiang Province, serving as the external validation dataset. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and quadratic-weighted kappa score were calculated to evaluate the classification algorithms. The precision, recall, and F1-score were calculated to evaluate the object detection algorithms. The performance of all the algorithms was compared with the experts’ performance. To better understand the algorithms and clarify the direction of optimization, misclassification and visualization heatmap analyses were performed.Results: In five-fold cross-validation, algorithm I achieved robust performance, with accuracy = 97.36% (95% CI: 0.9697, 0.9775), AUC = 0.995 (95% CI: 0.9933, 0.9967), sensitivity = 93.92% (95% CI: 0.9333, 0.9451), and specificity = 98.19% (95% CI: 0.9787, 0.9852). The macro-AUC, accuracy, and quadratic-weighted kappa were 0.979, 96.74% (95% CI: 0.963, 0.9718), and 0.988 (95% CI: 0.986, 0.990) for algorithm II. Algorithm III achieved an accuracy of 0.9703 to 0.9941 for classifying the “Plus” lesions and an F1-score of 0.6855 to 0.8890 for detecting and localizing lesions. The performance metrics in external validation dataset were comparable to those of the experts and were slightly inferior to those of cross-validation.Conclusion: Our algorithms and AI-models were confirmed to achieve robust performance in real-world conditions. The application of our algorithms and AI-models has promise for facilitating clinical diagnosis and healthcare screening for PM on a large scale.

Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic prevalence. It is desirable to develop the automatically intelligent system to facilitate these time- and labor- consuming tasks. In this study, we designed a series of deep learning systems to detect PM and myopic macular lesions according to a recent international photographic classification system (META-PM) classification based on color fundus images. Notably, our systems recorded robust performance both in the test and external validation dataset. The performance was comparable to the general ophthalmologist and retinal specialist. With the extensive adoption of this technology, effective mass screening for myopic population will become feasible on a national scale.

Previous studies demonstrated that dysregulation of G protein‐coupled receptor 120 (GPR120) plays a protective role in osteoarthritis (OA). However, the mechanism underlying how GPR120 is downregulated remains largely unknown. In the present study, we evaluated whether GPR120 is regulated by microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). Our results show that GPR120 was negatively regulated by miR‐15b‐5p through targeting 3′ untranslated region (3′UTR), and that miR‐15b‐5p was negatively regulated by LINC00662. Further luciferase assay shows that LINC00662‐miR‐15b‐5p signaling pathway contributed the regulation of GPR120 expression. Functionally, the decreased of LINC00662 caused increased miR‐15b‐5p, thereby leading to decreased GPR120. The decreased GPR120 then contributes to increased expression of inflammatory factors including tumor necrosis factor α (TNF‐α), interleukin (IL)‐6 and IL‐8, cell apoptosis, and decreased apoptosis‐related protein levels including cleaved caspase‐3, cleaved caspase‐9, and Bax in cultured rat chondrocytes. In summary, the present study shows that LINC00662‐miR‐15b‐5p signaling pathway is involved in the regulation of GPR120, thereby contributing to arthritis.