In Silico Analysis and Experimental Validation of Active Compounds from Cichorium intybus L. Ameliorating Liver Injury

This study aimed at investigating the possible mechanisms of hepatic protective activity of Cichorium intybus L. (chicory) in acute liver injury. Pathological observation, reactive oxygen species (ROS) detection and measurements of biochemical indexes on mouse models proved hepatic protective effect of Cichorium intybus L. Identification of active compounds in Cichorium intybus L. was executed through several methods including ultra performance liquid chromatography/time of flight mass spectrometry (UPLC-TOF-MS). Similarity ensemble approach (SEA) docking, molecular modeling, molecular docking, and molecular dynamics (MD) simulation were applied in this study to explore possible mechanisms of the hepato-protective potential of Cichorium intybus L. We then analyzed the chemical composition of Cichorium intybus L., and found their key targets. Furthermore, in vitro cytological examination and western blot were used for validating the efficacy of the selected compounds. In silico analysis and western blot together demonstrated that selected compound 10 in Cichorium intybus L. targeted Akt-1 in hepatocytes. Besides, compound 13 targeted both caspase-1 and Akt-1. These small compounds may ameliorate liver injury by acting on their targets, which are related to apoptosis or autophagy. The conclusions above may shed light on the complex molecular mechanisms of Cichorium intybus L. acting on hepatocytes and ameliorating liver injury.     

A practical reliability assessment approach and its application for pile-stabilized slopes using FORM and support vector machine

Bulletin of Engineering Geology and the Environment - Many uncertainties exist in pile-stabilized slopes which make their design substantially complicated. In this paper, a first-order reliability...     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.