Astragaloside-IV (AS-IV) (C 41 H 68 O 14 ) is a high-purity natural product extracted from Astragalus , which has demonstrated biological activities. However, the effect of AS-IV on retinal pigment epithelial (RPE) cells in diabetic retinopathy (DR) remains unclear. In this study, high glucose (HG) was shown to promote ARPE-19 RPE cell death, increase the contents of reactive oxygen species (ROS) and oxidized glutathione (GSSG), and enhance lipid peroxidation density of mitochondrial membrane. In contrast, AS-IV decreased glutathione (GSH) content, mitochondria size and ridge. Addition of iron death inhibitor Ferrostatin-1 (Fer-1) to RPE cells decreased cell dead rate, thus indicating that HG-induced mitochondrial damage occurred due to ferroptosis. AS-IV alleviated HG-induced RPE cell damage. Furthermore, HG decreased levels of silent information regulator 1 (Sirt1) and nuclear factor (erythroid-derived 2)-like 2 (Nrf2) in the nucleus of RPE cells; AS-IV could alleviate these effects and increased expression of glutathione peroxidase 4 (GPX4), glutamate cysteine ligase (GCLM) and glutamate cysteine ligase catalytic subunit (GCLC), which are Nrf2 downstream genes. Mechanistically, AS-IV was shown to alleviate the effects of HG by increasing mir-138-5p expression in RPE cells and promoting expression of Sirt1 and Nrf2 in the nucleus. Transfection of mir-138-5p agonist inhibited the regulatory effects of AS-IV on Sirt1 and Nrf2, accompanied by decreased GPX4, GCLM and GCLC levels, and restoration of ferroptosis-related changes. Collectively, HG increased ferroptosis rate in RPE cells. In addition, AS-IV inhibited miR-138-5p expression, subsequently increasing Sirt1/Nrf2 activity and cellular antioxidant capacity to alleviate ferroptosis, resulting decreased cell death, which potentially inhibits the DR pathological process.

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.

Background: The strong but complex genetic background suggests that inherent and intrinsic rather than exogenous factors have a key role in immunopathogenesis of psoriasis. It is reasonable to speculate that the dysfunctional activity of psoriatic T cells may partly originate from the abnormal haematopoietic cells.

PurposeTo apply deep learning (DL) techniques to develop an automatic intelligent classification system identifying the specific types of myopic maculopathy (MM) based on macular optical coherence tomography (OCT) images using transfer learning (TL).MethodIn this retrospective study, a total of 3,945 macular OCT images from 2,866 myopic patients were recruited from the ophthalmic outpatients of three hospitals. After culling out 545 images with poor quality, a dataset containing 3,400 macular OCT images was manually classified according to the ATN system, containing four types of MM with high OCT diagnostic values. Two DL classification algorithms were trained to identify the targeted lesion categories: Algorithm A was trained from scratch, and algorithm B using the TL approach initiated from the classification algorithm developed in our previous study. After comparing the training process, the algorithm with better performance was tested and validated. The performance of the classification algorithm in the test and validation sets was evaluated using metrics including sensitivity, specificity, accuracy, quadratic-weighted kappa score, and the area under the receiver operating characteristic curve (AUC). Moreover, the human-machine comparison was conducted. To better evaluate the algorithm and clarify the optimization direction, the dimensionality reduction analysis and heat map analysis were also used to visually analyze the algorithm.ResultsAlgorithm B showed better performance in the training process. In the test set, the algorithm B achieved relatively robust performance with macro AUC, accuracy, and quadratic-weighted kappa of 0.986, 96.04% (95% CI: 0.951, 0.969), and 0.940 (95% CI: 0.909–0.971), respectively. In the external validation set, the performance of algorithm B was slightly inferior to that in the test set. In human-machine comparison test, the algorithm indicators were inferior to the retinal specialists but were the same as the ordinary ophthalmologists. In addition, dimensionality reduction visualization and heatmap visualization analysis showed excellent performance of the algorithm.ConclusionOur macular OCT image classification algorithm developed using the TL approach exhibited excellent performance. The automatic diagnosis system for macular OCT images of MM based on DL showed potential application prospects.