Hexa Histidine–Tagged Recombinant Human Cytoglobin Deactivates Hepatic Stellate Cells and Inhibits Liver Fibrosis by Scavenging Reactive Oxygen Species

BACKGROUND & AIMS: Anti-fibrotic therapy remains an unmet medical need in human chronic liver disease. We report the anti-fibrotic properties of cytoglobin (CYGB), a respiratory protein expressed in hepatic stellate cells (HSCs), the main cell type involved in liver fibrosis. APPROACH & RESULTS: Cygb-deficient mice which had bile duct ligation (BDL)-induced liver cholestasis or choline-deficient L-amino acid-defined (CDAA) diet-induced steatohepatitis significantly exacerbated liver damage, fibrosis and reactive oxygen species (ROS) formation. All these manifestations were attenuated in Cygb-overexpressing mice. We produced 6His-tagged recombinant human CYGB (His-CYGB), traced its bio-distribution and assessed its function in HSCs or in mice with advanced liver cirrhosis using thioacetamide (TAA) or 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC). In cultured HSCs, extracellular His-CYGB was endocytosed and accumulated in endosomes via clathrin-mediated pathway. His-CYGB significantly impeded ROS formation spontaneously or in the presence of ROS inducers in HSCs, thus leading to the attenuation of collagen type I alpha 1 production and alpha-smooth muscle actin expression. Replacement the iron centre of the heme group with cobalt nullified the effect of His-CYGB. In addition, His-CYGB induced interferon-β secretion by HSCs which partly contributed to its anti-fibrotic function. Momelotinib incompletely reversed the effect of His-CYGB. Intravenously injected His-CYGB markedly suppressed liver inflammation, fibrosis and oxidative cell damage in TAA- or DDC-administered mice without adverse effects. RNA-seq analysis revealed the downregulation of inflammation and fibrosis-related genes and the upregulation of antioxidant genes in both cell culture and liver tissues. The injected His-CYGB predominantly localised to HSCs but not to macrophages, suggesting specific targeting effects. His-CYGB exhibited no toxicity in humanised liver chimeric PXB mice. CONCLUSIONS: His-CYGB could have anti-fibrotic clinical applications for human chronic liver diseases

Cancer cells produce liver metastasis via gap formation in sinusoidal endothelial cells through proinflammatory paracrine mechanisms

Intracellular gap (iGap) formation in liver sinusoidal endothelial cells (LSECs) is caused by the destruction of fenestrae and appears under pathological conditions; nevertheless, their role in metastasis of cancer cells to the liver remained unexplored. We elucidated that hepatotoxin-damaged and fibrotic livers gave rise to LSECs-iGap formation, which was positively correlated with increased numbers of metastatic liver foci after intrasplenic injection of Hepa1-6 cells. Hepa1-6 cells induced interleukin-23-dependent tumor necrosis factor-α (TNF-α) secretion by LSECs and triggered LSECs-iGap formation, toward which their processes protruded to transmigrate into the liver parenchyma. TNF-α triggered depolymerization of F-actin and induced matrix metalloproteinase 9 (MMP9), intracellular adhesion molecule 1, and CXCL expression in LSECs. Blocking MMP9 activity by doxycycline or an MMP2/9 inhibitor eliminated LSECs-iGap formation and attenuated liver metastasis of Hepa1-6 cells. Overall, this study revealed that cancer cells induced LSEC-iGap formation via proinflammatory paracrine mechanisms and proposed MMP9 as a favorable target for blocking cancer cell metastasis to the liver

Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning

This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts. This mechanism enables RSMA to flexibly manage interference and thus enhance energy and spectral efficiency. Although possessing outstanding advantages, optimizing power allocation in RSMA is very challenging under the uncertainty of the communication channel and the transmitter has limited knowledge of the channel information. To solve the problem, we first develop a Markov Decision Process framework to model the dynamic of the communication channel. The deep reinforcement algorithm is then proposed to find the optimal power allocation policy for the transmitter without requiring any prior information of the channel. The simulation results show that the proposed scheme can outperform baseline schemes in terms of average sum-rate under different power and QoS requirements

IRDRC: An Intelligent Real-Time Dual-Functional Radar-Communication System for Automotive Vehicles

© 2012 IEEE. This letter introduces an intelligent Real-time Dual-functional Radar-Communication (iRDRC) system for autonomous vehicles (AVs). This system enables an AV to perform both radar and data communications functions to maximize bandwidth utilization as well as significantly enhance safety. In particular, the data communications function allows the AV to transmit data, e.g., of current traffic, to edge computing systems and the radar function is used to enhance the reliability and reduce the collision risks of the AV, e.g., under bad weather conditions. The problem of the iRDRC is to decide when to use the communication mode or the radar mode to maximize the data throughput while minimizing the miss detection probability of unexpected events given the uncertainty of surrounding environment. To solve the problem, we develop a deep reinforcement learning algorithm that allows the AV to quickly obtain the optimal policy without requiring any prior information about the environment. Simulation results show that the proposed scheme outperforms baseline schemes in terms of data throughput, miss detection probability, and convergence rate

Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications

Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches. With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation

Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization

© 2016, Springer-Verlag Berlin Heidelberg. The main objective of this study is to produce a landslide susceptibility map for the Lao Cai area (Vietnam) using a new hybrid intelligent method based on least squares support vector machines (LSSVM) and artificial bee colony (ABC) optimization, namely LSSVM-BC. LSSVM and ABC are state-of-the-art soft computing techniques that have been rarely utilized in landslide susceptibility assessment. LSSVM is adopted to develop landslide prediction model whereas ABC was used to optimize the prediction model by identifying an appropriate set of the LSSVM hyper-parameters. To establish the hybrid intelligent method, a GIS database with ten landslide-influencing factors and 340 landslide locations that occurred mainly during the last 20-years was constructed. These historical landslide locations were collected from the existing inventories that sourced from (i) five landslide projects carried out in this study areas before and (ii) interpretations of SPOT satellite images with resolution of 2.5 m. The study area was geographically split into two different parts, with landslides located in the first part was used for building models whereas the other landslides in the second part was used for the model validation. Performance of the LSSVM-BC model was assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Result shows that the prediction power of the model is good with the area under the curve (AUC) = 0.900. Experiments have pointed out the prediction power of the LSSVM-BC is better than that obtained from the popular support vector machines. Therefore, the proposed model is a promising tool for spatial prediction of landslides at the study area. The landslide susceptibility map is useful for landuse planning for the Lao Cai area

Hexa Histidine–Tagged Recombinant Human Cytoglobin Deactivates Hepatic Stellate Cells and Inhibits Liver Fibrosis by Scavenging Reactive Oxygen Species

BACKGROUND & AIMS: Anti-fibrotic therapy remains an unmet medical need in human chronic liver disease. We report the anti-fibrotic properties of cytoglobin (CYGB), a respiratory protein expressed in hepatic stellate cells (HSCs), the main cell type involved in liver fibrosis. APPROACH & RESULTS: Cygb-deficient mice which had bile duct ligation (BDL)-induced liver cholestasis or choline-deficient L-amino acid-defined (CDAA) diet-induced steatohepatitis significantly exacerbated liver damage, fibrosis and reactive oxygen species (ROS) formation. All these manifestations were attenuated in Cygb-overexpressing mice. We produced 6His-tagged recombinant human CYGB (His-CYGB), traced its bio-distribution and assessed its function in HSCs or in mice with advanced liver cirrhosis using thioacetamide (TAA) or 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC). In cultured HSCs, extracellular His-CYGB was endocytosed and accumulated in endosomes via clathrin-mediated pathway. His-CYGB significantly impeded ROS formation spontaneously or in the presence of ROS inducers in HSCs, thus leading to the attenuation of collagen type I alpha 1 production and alpha-smooth muscle actin expression. Replacement the iron centre of the heme group with cobalt nullified the effect of His-CYGB. In addition, His-CYGB induced interferon-β secretion by HSCs which partly contributed to its anti-fibrotic function. Momelotinib incompletely reversed the effect of His-CYGB. Intravenously injected His-CYGB markedly suppressed liver inflammation, fibrosis and oxidative cell damage in TAA- or DDC-administered mice without adverse effects. RNA-seq analysis revealed the downregulation of inflammation and fibrosis-related genes and the upregulation of antioxidant genes in both cell culture and liver tissues. The injected His-CYGB predominantly localised to HSCs but not to macrophages, suggesting specific targeting effects. His-CYGB exhibited no toxicity in humanised liver chimeric PXB mice. CONCLUSIONS: His-CYGB could have anti-fibrotic clinical applications for human chronic liver diseases