Sustained Delivery and Pharmacodynamics of an Integrin Antagonist for Ocular Angiogenesis

Ocular angiogenesis, or the formation of new blood vessels in the eye, is the leading cause of blindness in a variety of clinical conditions. Success in elucidation of several key steps in angiogenesis cascade has opened a door for anti-angiogenesis therapies. Development of novel therapeutic agents provides effective treatment for ocular disorders. However, treatment of many posterior segment diseases like age-related macular degeneration (AMD) and diabetic retinopathy (DRP) is far from satisfactory due to the limited availability of novel therapeutic drugs and the low efficiency of traditional drug delivery methods. In the present study, we investigated the anti-angiogenic properties of a novel small integrin antagonist, EMD478761, and developed sustained release systems to locally and continuously deliver this compound. In part I, sustained delivery implants were designed and investigation of their anti-angiogenic efficacy, including inhibition and regression, was performed using in vivo chick chorioallantoic membrane (CAM) assay. In part II, laser-induced choroidal neovascularization (CNV) rat model was employed to further examine the angiogenic inhibitory effect of EMD478761 from a sustained release microimplant. And in part III, the pharmacodynamics of EMD478761 was studied to reveal the mechanisms by which EMD478761 inhibited angiogenesis. Results from in vivo CAM assay and CNV rat model demonstrated that EMD478761 inhibited and regressed basic fibroblast growth factor (bFGF)-induced angiogenesis, and suppressed laser-induced CNV via sustained release implants. The pharmacodynamics of this drug was studied to better understand the mechanisms of the drug's action mode in preventing neovascularization. In vitro, EMD478761 inhibited human umbilical vein endothelial cell (HUVEC) proliferation, caused HUVEC detachment in vitronectin-coated surfaces in a time- and dose-dependent manner, and disrupted endothelial cell tube formation on Matrigel. In addition, EMD478761 induced HUVEC apoptosis on vitronectin via caspase-3 activation pathway. In vivo, EMD478761 induced endothelial cell apoptosis within CNV lesions as demonstrated by terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay. In addition, EMD478761 increased the integrin αvβ3 internalization in HUVECs, while it did not affect integrin αvβ3 expression levels after 12 hours treatment. Taken together, these findings demonstrate that sustained delivery of EMD478761 may provide an effective antiangiogenic approach for the treatment of ocular angiogenesis

An automatic deep learning-based workflow for glioblastoma survival prediction using pre-operative multimodal MR images

We proposed a fully automatic workflow for glioblastoma (GBM) survival prediction using deep learning (DL) methods. 285 glioma (210 GBM, 75 low-grade glioma) patients were included. 163 of the GBM patients had overall survival (OS) data. Every patient had four pre-operative MR scans and manually drawn tumor contours. For automatic tumor segmentation, a 3D convolutional neural network (CNN) was trained and validated using 122 glioma patients. The trained model was applied to the remaining 163 GBM patients to generate tumor contours. The handcrafted and DL-based radiomic features were extracted from auto-contours using explicitly designed algorithms and a pre-trained CNN respectively. 163 GBM patients were randomly split into training (n=122) and testing (n=41) sets for survival analysis. Cox regression models with regularization techniques were trained to construct the handcrafted and DL-based signatures. The prognostic power of the two signatures was evaluated and compared. The 3D CNN achieved an average Dice coefficient of 0.85 across 163 GBM patients for tumor segmentation. The handcrafted signature achieved a C-index of 0.64 (95% CI: 0.55-0.73), while the DL-based signature achieved a C-index of 0.67 (95% CI: 0.57-0.77). Unlike the handcrafted signature, the DL-based signature successfully stratified testing patients into two prognostically distinct groups (p-value<0.01, HR=2.80, 95% CI: 1.26-6.24). The proposed 3D CNN generated accurate GBM tumor contours from four MR images. The DL-based signature resulted in better GBM survival prediction, in terms of higher C-index and significant patient stratification, than the handcrafted signature. The proposed automatic radiomic workflow demonstrated the potential of improving patient stratification and survival prediction in GBM patients

A novel predictive model for the recurrence of pediatric alopecia areata by bioinformatics analysis and a single-center prospective study

BackgroundAlopecia areata (AA) is a disease featured by recurrent, non-scarring hair loss with a variety of clinical manifestations. The outcome of AA patients varies greatly. When they progress to the subtypes of alopecia totalis (AT) or alopecia universalis (AU), the outcome is unfavorable. Therefore, identifying clinically available biomarkers that predict the risk of AA recurrence could improve the prognosis for AA patients.MethodsIn this study, we conducted weighted gene co-expression network analysis (WGCNA) and functional annotation analysis to identify key genes that correlated to the severity of AA. Then, 80 AA children were enrolled at the Department of Dermatology, Wuhan Children’s Hospital between January 2020 to December 2020. Clinical information and serum samples were collected before and after treatment. And the serum level of proteins coded by key genes were quantitatively detected by ELISA. Moreover, 40 serum samples of healthy children from the Department of Health Care, Wuhan Children’s Hospital were used for healthy control.ResultsWe identified four key genes that significantly increased (CD8A, PRF1, and XCL1) or decreased (BMP2) in AA tissues, especially in the subtypes of AT and AU. Then, the serum levels of these markers in different groups of AA patients were detected to validate the results of bioinformatics analysis. Similarly, the serum levels of these markers were found remarkedly correlated with the Severity of Alopecia Tool (SALT) score. Finally, a prediction model that combined multiple markers was established by conducting a logistic regression analysis.ConclusionIn the present study, we construct a novel model based on serum levels of BMP2, CD8A, PRF1, and XCL1, which served as a potential non-invasive prognostic biomarker for forecasting the recurrence of AA patients with high accuracy

Progress in the Study of the Left Atrial Function Index in Cardiovascular Disease: A Literature Review

Some studies have shown that left ventricular structure and function play an important role in the risk stratification and prognosis of cardiovascular disease. The clinical application of left atrial function in cardiovascular disease has gradually attracted attention in the cardiovascular field. There are many traditional methods to evaluate left atrial function. Left atrial function related indexes measured by echocardiography has been identified as a powerful predictor of cardiovascular disease in recent years, but they have some limitations. The left atrial function index has been found to evaluate left atrial function more effectively than traditional parameters. Furthermore, it is a valuable predictor of the risk stratification and prognosis in patients with clinical cardiovascular disease such as heart failure, atrial fibrillation, hypertension, and coronary heart disease. </p