Fibroblast growth factor 21 inhibited ischemic arrhythmias via targeting miR-143/EGR1 axis.

Ventricular arrhythmia is the most common cause of sudden cardiac death in patients with myocardial infarction (MI). Fibroblast growth factor 21 (FGF21) has been shown to play an important role in cardiovascular and metabolic diseases. However, the effects of FGF21 on ventricular arrhythmias following MI have not been addressed yet. The present study was conducted to investigate the pharmacological action of FGF21 on ventricular arrhythmias after MI. Adult male mice were administrated with or without recombinant human basic FGF21 (rhbFGF21), and the susceptibility to arrhythmias was assessed by programmed electrical stimulation and optical mapping techniques. Here, we found that rhbFGF21 administration reduced the occurrence of ventricular tachycardia (VT), improved epicardial conduction velocity and shorted action potential duration at 90% (APD90) in infarcted mouse hearts. Mechanistically, FGF21 may improve cardiac electrophysiological remodeling as characterized by the decrease of INa and IK1 current density in border zone of infarcted mouse hearts. Consistently, in vitro study also demonstrated that FGF21 may rescue oxidant stress-induced dysfunction of INa and IK1 currents in cultured ventricular myocytes. We further found that oxidant stress-induced down-regulation of early growth response protein 1 (EGR1) contributed to INa and IK1 reduction in post-infarcted hearts, and FGF21 may recruit EGR1 into the SCN5A and KCNJ2 promoter regions to up-regulate NaV1.5 and Kir2.1 expression at transcriptional level. Moreover, miR-143 was identified as upstream of EGR1 and mediated FGF21-induced EGR1 up-regulation in cardiomyocytes. Collectively, rhbFGF21 administration effectively suppressed ventricular arrhythmias in post-infarcted hearts by regulating miR-143-EGR1-NaV1.5/Kir2.1 axis, which provides novel therapeutic strategies for ischemic arrhythmias in clinics.

Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis.

Background Annualized Relapse Rate (ARR) is one of the most important indicators of disease progression in patients with Multiple Sclerosis (MS). However, imaging markers that can effectively predict ARR are currently unavailable. In this study, we developed a deep learning-based method for the automated extraction of radiomics features from Positron Emission Computed Tomography (PET) and Magnetic Resonance (MR) images to predict ARR in patients with MS. Methods Twenty-five patients with a definite diagnosis of Relapsing-Remitting MS (RRMS) were enrolled in this study. We designed a multi-branch fully convolutional neural network to segment lesions from PET/MR images. After that, radiomics features were extracted from the obtained lesion volume of interest. Three feature selection methods were used to retain features highly correlated with ARR. We combined four classifiers with different feature selection methods to form twelve models for ARR classification. Finally, the model with the best performance was chosen. Results Our network achieved precise automatic lesion segmentation with a Dice Similarity Coefficient (DSC) of 0.81 and a precision of 0.86. Radiomics features from lesions filtered by Recursive Feature Elimination (RFE) achieved the best performance in the Support Vector Machines (SVM) classifier. The classification model performance was best when radiomics from both PET and MR were combined to predict ARR, with high accuracy at 0.88 and Area Under the ROC curves (AUC) at 0.96, which outperformed MR or PET-based model and clinical indicators-based model. Conclusion Our automatic segmentation masks can replace manual ones with excellent performance. Furthermore, the deep learning and PET/MR radiomics-based model in our research is an effective tool in assisting ARR classification of MS patients.

Optical Waveguide and Photoluminescent Polarization in Organic Cocrystal Polymorphs.

Organic cocrystals whose unique polymorphic feature can provide a feasible way to investigate and understand the relationship between luminescence properties and aggregate structures have attracted increasing attention in the area of organic optoelectronics. Herein, we prepare polymorphic cocrystals (C1, C2) by using 9,10-bis((E)-2-(pyridin-3-yl)vinyl)anthracene (BP3VA) as chromophore and 1,3,5-trifluoro-2,4,6-triiodobenzene (FIB) as conformer. Both C1 and C2 stack with segregated stacking form, but different intermolecular interactions promote the formation of sheet cocrystals C1 and needle cocrystals C2. C1 exhibits anisotropic optical waveguide property and photoluminescent polarization, while C2 only exhibits the quasi-one-dimensional optical waveguide property. The different optical properties originate from the varieties of molecular packing modes and directions of the optical transition dipole in the two polymorphic cocrystals, which can be clarified through the structure analysis and theoretical calculation. The study can provide a deep understanding of the structure-property relationship of cocrystals and benefit the rational design of novel functional materials.

Using the Baidu Search Index to Predict the Incidence of HIV/AIDS in China.

Based on a panel of 30 provinces and a timeframe from January 2009 to December 2013, we estimate the association between monthly human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) incidence and the relevant Internet search query volumes in Baidu, the most widely used search engine among the Chinese. The pooled mean group (PMG) model show that the Baidu search index (BSI) positively predicts the increase in HIV/AIDS incidence, with a 1% increase in BSI associated with a 2.1% increase in HIV/AIDS incidence on average. This study proposes a promising method to estimate and forecast the incidence of HIV/AIDS, a type of infectious disease that is culturally sensitive and highly unevenly distributed in China; the method can be taken as a complement to a traditional HIV/AIDS surveillance system.