Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus.

Background and objective: Type 2 Diabetes Mellitus (T2DM) with insulin resistance (IR) is prone to damage the vascular endothelial, leading to the formation of vulnerable carotid plaques and increasing ischemic stroke (IS) risk. The purpose of this study is to develop a nomogram model based on carotid ultrasound radiomics for predicting IS risk in T2DM patients. Methods: 198 T2DM patients were enrolled and separated into study and control groups based on IS history. After manually delineating carotid plaque region of interest (ROI) from images, radiomics features were identified and selected using the least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (RS). A combinatorial logistic machine learning model and nomograms were created using RS and clinical features like the triglyceride-glucose index. The three models were assessed using area under curve (AUC) and decision curve analysis (DCA). Results: Patients were divided into the training set and the testing set by the ratio of 0.7. 4 radiomics features were selected. RS and clinical variables were all statically significant in the training set and were used to create a combination model and a prediction nomogram. The combination model (radiomics + clinical nomogram) had the largest AUC in both the training set and the testing set (0.898 and 0.857), and DCA analysis showed that it had a higher overall net benefit compared to the other models. Conclusions: This study created a carotid ultrasound radiomics machine-learning-based IS risk nomogram for T2DM patients with carotid plaques. Its diagnostic performance and clinical prediction capabilities enable accurate, convenient, and customized medical care.

Single­cell RNA sequencing reveals inflammatory retinal microglia in experimental autoimmune uveitis.

Autoimmune uveitis (AU) is a kind of immune-mediated disease resulting in irreversible ocular damage and even permanent vision loss. However, the precise mechanism underlying dynamic immune changes contributing to disease initiation and progression of AU remains unclear. Here, we induced an experimental AU (EAU) model with IRBP651-670 and found that day[D]14 was the inflammatory summit with remarking clinical and histopathological manifestations and the activation of retinal microglia exhibited a time-dependent pattern in the EAU course. We conducted single-cell RNA sequencing of retinal immune cells in EAU mice at four time points and found microglia constituting the largest proportion, especially on D14. A novel inflammatory subtype (Cd74high Ccl5high) of retinal microglia was identified at the disease peak that was closely associated with modulating immune responses. In vitro experiments indicated that inflammatory stimuli induced proinflammatory microglia with the upregulation of CD74 and CCL5, and CD74 overexpression in microglia elicited their proinflammatory phenotype via nuclear factor-kappa B signaling that could be attenuated by the treatment of neutralizing CCL5 antibody to a certain extent. In-vivo blockade of Cd74 and Ccl5 effectively alleviated retinal microglial activation and disease phenotype of EAU. Therefore, we propose targeting CD74 and CCL5 of retinal microglia as promising strategies for AU treatment.