Hapln2 in neurological diseases and its potential as therapeutic target

Hyaluronan and proteoglycan link protein 2 (Hapln2) is important for the binding of chondroitin sulfate proteoglycans to hyaluronan. Hapln2 deficiency leads to the abnormal expression of extracellular matrix (ECM) proteins and dysfunctional neuronal conductivity, demonstrating the vital role of Hapln2 in these processes. Studies have revealed that Hapln2 promotes the aggregation of α-synuclein, thereby contributing to neurodegeneration in Parkinson’s disease (PD), and it was recently suggested to be in intracellular neurofibrillary tangles (NFTs). Additionally, the expression levels of Hapln2 showed lower in the anterior temporal lobes of individuals with schizophrenia than those of healthy subjects. Together, these studies implicate the involvement of Hapln2 in the pathological processes of neurological diseases. A better understanding of the function of Hapln2 in the central nervous system (CNS) will provide new insights into the molecular mechanisms of these diseases and help to establish promising therapeutic strategies. Herein, we review the recent progress in defining the role of Hapln2 in brain physiology and pathology

Combination of Diabetes Risk Factors and Hepatic Steatosis in Chinese: The Cardiometabolic Risk in Chinese (CRC) Study

Aims Hepatic steatosis has been related to insulin resistance and increased diabetes risk. We assessed whether combination of diabetes risk factors, evaluated by the Finnish Diabetes Risk Score, was associated with risk of hepatic steatosis in an apparently healthy Chinese population. Research Design and Methods The study samples were from a community-based health examination survey in central China. In total 1,780 men and women (18–64 y) were included in the final analyses. Hepatic steatosis was diagnosed by ultrasonography. We created combination of diabetes risk factors score on basis of age, Body Mass Index, waist circumference, physical activity at least 4 h a week, daily consumption of fruits, berries or vegetables, history of antihypertensive drug treatment, history of high blood glucose. The total risk score is a simple sum of the individual weights, and values range from 0 to 20. Results: Hepatic steatosis was present 18% in the total population. In multivariate models, the odds ratios of hepatic steatosis were 1.20 (95%CI 1.15–1.25) in men and 1.25 (95%CI 1.14–1.37) in women by each unit increase in the combination of diabetes risk factors score, after adjustment for blood pressure, liver enzymes, plasma lipids, and fasting glucose. The area under the receiver operating characteristic curve for hepatic steatosis was 0.78 (95%CI 0.76–0.80), 0.76 in men (95%CI 0.74–0.78) and 0.83 (95%CI 0.79–0.87) in women. Conclusions: Our data suggest that combination of major diabetes risk factors was significantly related to risk of hepatic steatosis in Chinese adults

Neck circumference and early stage atherosclerosis: the cardiometabolic risk in Chinese (CRC) study

Background: Neck circumference (NC) has been previously related to cardiometabolic risk factors. In this study we examined the association between NC and early stage atherosclerosis in Chinese adults. Methods: The study samples were from a community-based health examination survey in central China. In total 2,318 men and women (18-64 y) were included in the final analyses. Carotid radial pulse wave velocity (crPWV), carotid femoral PWV (cfPWV), carotid artery dorsalis pedis PWV (cdPWV) and NC were measured. Results: After adjustment for age, sex, lipids, glucose, blood pressure, heart rate, body mass index (BMI), high NC was significantly associated with an increasing trend of cfPWV, cdPWV and crPWV (P = 0.001, 0.049, and 0.038; respectively). In addition, we found significant interaction between hypertension status and NC level in relation to cfPWV, adjusted for age, sex, BMI, fasting glucose, lipids and heart rate(P for interaction = 0.034). The associations between NC and cfPWV were significant (P = 0.02) among those with hypertension, but not significant among those without hypertension. Conclusions: Our data showed that high NC was associated with an increased risk of early stage atherosclerosis in Chinese adults, independent of other metabolic risk factors. Hypertension might modify the association between NC and cfPWV

Profound Presentation of Retinopathy in a Patient with Sickle Cell trait and Diabetes Mellitus

This is a Photo Essay and does not have an abstract

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning

Objective: Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation. Approach: The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network. Main Results: Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-based T2 mapping and comparable results to conventional methods were obtained in the human brain. Significance: As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.Comment: 18 pages, 8 figure