Andrographolide promotes pancreatic duct cells differentiation into insulin-producing cells by targeting PDX-1.

Regeneration of β-cells by differentiation of pancreatic progenitor cells has the potential to fundamentally solve the problems of the loss of β-cell function and mass during disease progression in both type 1 or 2 diabetes. Therefore, discovery of novel differentiation inducers to promote islet regeneration is of great significance. Pancreatic and duodenal homeobox1 (PDX-1) is a key transcription factor that promotes the development and maturation of pancreatic β-cells. To screen potential novel small molecules for enhancing differentiation of PNAC-1 cells, a human pancreatic ductal cell lines into insulin-producing cells (IPCs), we developed a high-throughput screening method through fusing the PDX-1 promoter region with a luciferase reporter gene. We screened and identified that andrographolide named C1037 stimulates PDX-1 expression in both mRNA and protein level and significantly promotes PANC-1 cells differentiation into IPCs as compared with that of control cells. The therapeutic effect of C037 in Streptozotocin induced diabetic mouse model through differentiation of pancreatic ductal cells into insulin positive islets was also observed. Our study provides a novel method to screen compounds regulating the differentiation of pancreatic progenitor cells having the potential of enhancing islet regeneration for diabetes therapy

Andrographolide Promotes Pancreatic Duct Cells Differentiation into Insulin-Producing Cells by Targeting PDX-1

Abstract(#br)Regeneration of β-cells by differentiation of pancreatic progenitor cells has the potential to fundamentally solve the problems of the loss of β-cell function and mass during disease progression in both type 1 or 2 diabetes. Therefore, discovery of novel differentiation inducers to promote islet regeneration is of great significance. Pancreatic and duodenal homeobox1 (PDX-1) is a key transcription factor that promotes the development and maturation of pancreatic β-cells. To screen for potential novel small molecules for enhancing differentiation of PNAC-1 cells, a human pancreatic ductal cell lines into insulin-producing cells (IPCs), we developed a high-throughput screening method through fusing the PDX-1 promoter region with a luciferase reporter gene. We screened and identified that andrographolide named C1037 stimulates PDX-1 expression in both mRNA and protein level and significantly promotes PANC-1 cells differentiation into IPCs as compared with that of control cells. The therapeutic effect of C037 in Streptozotocin induced diabetic mouse model through differentiation of CK19 positive pancreatic ductal cells into insulin positive islets was also observed. Our study provides a novel method to screen compounds regulating the differentiation of pancreatic progenitor cells having the potential of enhancing islet regeneration for diabetes therapy

Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI

Abstract Background Schizophrenia is a clinical syndrome, and its causes have not been well determined. The objective of this study was to investigate the alteration of brain functional connectivity between schizophrenia and healthy control, and present a practical solution for accurately identifying schizophrenia at single-subject level. Methods 24 schizophrenia patients and 21 matched healthy subjects were recruited to undergo the resting-state functional magnetic resonance imaging (rs-fMRI) scanning. First, we constructed the brain network by calculating the Pearson correlation coefficient between each pair of the brain regions. Then, this study proposed a novel non-negative discriminant functional connectivity selection method, i.e. non-negative elastic-net based method (N2EN), to extract the alteration of brain functional connectivity between schizophrenia and healthy control. It ranks the significance of the connectivity with a uniform criterion by introducing the non-negative constraint. Finally, kernel discriminant analysis (KDA) is exploited to classify the subjects with the selected discriminant brain connectivity features. Results The proposed method is applied into schizophrenia classification, and achieves the sensitivity, specificity and accuracy of 100, 90.48 and 95.56%, respectively. Our findings also indicate the alteration of functional network can be used as the biomarks for guiding the schizophrenia diagnosis. The regions of cuneus, superior frontal gyrus, medial, paracentral lobule, calcarine fissure, surrounding cortex, etc. are highly relevant to schizophrenia. Conclusions This study provides a method for accurately identifying schizophrenia, which outperforms several state-of-the-art methods, including conventional brain network classification, multi-threshold brain network based classification, frequent sub-graph based brain network classification and support vector machine. Our investigation suggested that the selected discriminant resting-state functional connectivities are meaningful features for classifying schizophrenia and healthy control

Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification

Brain network analysis can help reveal the pathological basis of neurological disorders and facilitate automated diagnosis of brain diseases, by exploring connectivity patterns in the human brain. Effectively representing the brain network has always been the fundamental task of computeraided brain network analysis. Previous studies typically utilize human-engineered features to represent brain connectivity networks, but these features may not be well coordinated with subsequent classifiers. Besides, brain networks are often equipped with multiple hubs (i.e., nodes occupying a central position in the overall organization of a network), providing essential clues to describe connectivity patterns. However, existing studies often fail to explore such hubs from brain connectivity networks. To address these two issues, we propose a Connectivity Network analysis method with discriminative Hub Detection (CNHD) for brain disease diagnosis using functional magnetic resonance imaging (fMRI) data. Specifically, we incorporate both feature extraction of brain networks and network-based classification into a unified model, while discriminative hubs can be automatically identified from data via ℓ1-norm and ℓ2,1-norm regularizers. The proposed CNHD method is evaluated on three real-world schizophrenia datasets with fMRI scans. Experimental results demonstrate that our method not only outperforms several state-of-the-art approaches in disease diagnosis, but also is effective in automatically identifying disease-related network hubs in the human brain

The complete chloroplast genome of Sauvagesia rhodoleuca, an endangered species endemic to China

Sauvagesia rhodoleuca is an endangered and national key protected species of China, with limited natural distribution in Guangdong and Guangxi, Southern China. Here we reported the first complete chloroplast genome of S. rhodoleuca using genome skimming approach. The chloroplast genome is 157,300 bp in length, with a large single-copy region (LSC) of 86,021 bp and a small single-copy region (SSC) of 18,137 bp separated by a pair of inverted repeats (IRs) of 26,571 bp. It encodes 112 unique genes, including 80 protein-coding genes, 28 transfer RNA genes, and four ribosomal RNA genes. Phylogenetic analysis results strongly supported that S. rhodoleuca was closely related to Medusagyne oppositifolia

Dual Attention Multi-Instance Deep Learning for Alzheimer’s Disease Diagnosis with Structural MRI

Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagnosis, which could reflect the variations of brain. However, due to the local brain atrophy, only a few regions in sMRI scans have obvious structural changes, which are highly correlative with pathological features. Hence, the key challenge of sMRI-based brain disease diagnosis is to enhance the identification of discriminative features. To address this issue, we propose a dual attention multi-instance deep learning network (DA-MIDL) for the early diagnosis of Alzheimer’s disease (AD) and its prodromal stage mild cognitive impairment (MCI). Specifically, DA-MIDL consists of three primary components: 1) the Patch-Nets with spatial attention blocks for extracting discriminative features within each sMRI patch whilst enhancing the features of abnormally changed micro-structures in the cerebrum, 2) an attention multi-instance learning (MIL) pooling operation for balancing the relative contribution of each patch and yield a global different weighted representation for the whole brain structure, and 3) an attention-aware global classifier for further learning the integral features and making the AD-related classification decisions. Our proposed DA-MIDL model is evaluated on the baseline sMRI scans of 1689 subjects from two independent datasets (i.e., ADNI and AIBL). The experimental results show that our DA-MIDL model can identify discriminative pathological locations and achieve better classification performance in terms of accuracy and generalizability, compared with several state-of-the-art methods

Brain Age Prediction Based on Resting-State Functional MRI Using Similarity Metric Convolutional Neural Network

Brain age prediction is important for understanding brain development and aging. Currently, researchers can predict brain age using resting-state functional MRI (rs-fMRI) data. However, there are differences in brain age development among different subjects, and the same subject also has different development at different ages. So far, how to accurately estimate brain age using rs-fMRI efficiently remains a challenging problem. Therefore, a brain age prediction model with the similarity metric convolutional neural network is proposed in this paper. Specifically, this paper first introduces a siamese convolutional neural network, which includes convolution, batch normalization, and pooling steps simultaneously learns the features of two groups of rs-fMRI, and designs a similarity measurement network. Subsequently, fMRI images of two groups of different subjects are input into the network, and a similarity metirc module is designed to calculate the similarity between the two groups of images. Then the network is optimized by a loss function, and finally, the average value of the three groups of sample labels with the greatest similarity is taken. The absolute mean error and correlation coefficients obtained from the model are 5.337 and 0.6279, respectively. Experimental results show that this method has low mean absolute error and high correlation coefficient on the longitudinal imaging data set of Southwest University