Metabolic syndrome is a major health issue in the western world. An elevated pro-inflammatory state is often found in patients with metabolic diseases such as type 2 diabetes and obesity. Atherosclerosis is one such clinical manifestation of pro-inflammatory state associated with the vasculature. The exact mechanism by which metabolic stress induces this pro-inflammatory status and promotes atherogenesis remained elusive until the discovery of the inflammasome protein complex. This complex is composed of pro-caspase-1 and pathogen sensors. Activation of inflammasome requires the transcriptional upregulation of inflammasome components and the post-translational assembly. Three models of inflammasome assembly have been proposed: 1) the ion channel model; 2) the reactive oxygen species (ROS) model; and 3) the lysosome model. In either case, inflammasome activation triggers the auto-activation of pro-caspase-1 into its mature form. Caspase-1, which was first discovered as the IL-1β converting enzyme, is known to be a major player in inflammatory and cell death pathways. Many endogenous metabolic ligands have been experimentally shown to activate inflammasome, and thus initiate the subsequent inflammation process. Further understanding of the distinct molecular mechanism by which metabolic ligands activates inflammasome could lead to developing novel therapeutic interventions for atherosclerosis and other clinical problems related to metabolic diseases. Keywordsinflammasomes; Caspase-1; ROS; Vascular Inflammation; Interleukin-1 beta; Atherosclerosis; Review INSTRODUCTIONMetabolic syndrome is a combination of medical disorders such as hypertension, insulin resistence, hyperlipidemia and central obesity that, when occurring together, significantly increase the risk for coronary artery disease (CAD), stroke, and type 2 diabetes (T2D) (1). For instance, it is the metabolic stresses on the vasculature that lead to endothelial cell activation, endothelial dysfunction, and local vascular inflammation resulting in a pathogenic process called atherosclerosis, a leading cause of CAD and stroke. In turn, these Send correspondence to: Xiao-Feng Yang, MD, PhD, FAHA, Department of Pharmacology and Cardiovascular Research Center, Temple University School of Medicine, 3500 North Broad Street, MERB 1059, Philadelphia, PA 19140, Tel: 215-707-5985, Fax: 215-707-7068, xfyang@temple.edu. NIH Public Access Author ManuscriptFront Biosci. Author manuscript; available in PMC 2013 July 01.Published in final edited form as:Front Biosci. ; 18: 638-649. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript cardiovascular diseases are the leading causes of morbidity and mortality in metabolic syndrome patients.Atherosclerosis (also known as arteriosclerotic vascular disease or ASVD) is a condition commonly referred to as a hardening or furring of the arteries. Hyperlipidemia is a major risk factor for atherosclerosis, and correction of dyslipidemia is the mainstay treatment for symptomatic cardiovascular disease. ...
The retinal pigment epithelium (RPE) is clinically involved in diverse ocular inflammatory diseases. Because perturbed RPE cells produce a variety of inflammatory substances, RPE cells may play an integral part in these diseases. Interleukin-1 (IL-1) and granulocyte-macrophage colony-stimulating factor (GM-CSF) are pleiotropic cytokines with the ability to trigger numerous inflammatory responses. This report shows that cultured human RPE cells synthesize interleukin-1 beta (IL-1 beta) and GM-CSF in response to the potentially inflammatory cytokine, IL-1 alpha, but not to E. coli endotoxin. Control RPE cells made little or no mRNA or protein for either IL-1 beta or GM-CSF. Upon stimulation of the cells by IL-1 alpha, both IL-1 beta and GM-CSF mRNAs were readily apparent by 3 hours, persisted for over 24 hours, and were translated into immunologically detectable proteins. GM-CSF protein was secreted into the culture medium, whereas IL-1 beta protein remained cell associated. The IL-1 alpha-induced mRNA and protein production were inhibited by dexamethasone. These observations provide additional evidence that RPE cells are capable of playing a pivotal role during ocular inflammation.
LncRNA play important roles in regulation of host immune and inflammation responses in defending bacterial infection. Clostridium perfringens (C. perfringens) type C is one of primary bacteria leading to piglet diarrhea and other intestinal inflammatory diseases. For the differences of host immune capacity, individuals usually show resistance and susceptibility to bacterial infection. However, whether and how lncRNAs involved in modulating host immune resistance have not been reported. We have investigated the expression patterns of ileum lncRNAs of 7-day-old piglets infected by C. perfringens type C through RNA sequencing. A total of 16 lncRNAs and 126 mRNAs were significantly differentially expressed in resistance (IR) and susceptibility (IS) groups. Many lncRNAs and mRNAs were identified to regulate resistance and susceptibility of piglets through immune related pathways. Five lncRNAs may have potential function on regulating the expressions of cytokines, these lncRNAs and cytokines work together to co-regulated piglet immune response to C. perfringens, affecting host resistance and susceptibility. These results provide valuable information for understanding the functions of lncRNA and mRNA in affecting piglet diarrhea resistance of defensing to C. perfringens type C, these lncRNAs and mRNAs may be used as the important biomarkers for decreasing C. perfringens spread and diseases in human and piglets.
Due to the rapid development of network communication technology and the significant increase in network terminal equipment, the application of new network architecture software-defined networking (SDN) combined with reinforcement learning in network traffic scheduling has become an important focus of research. Because of network traffic transmission variability and complexity, the traditional reinforcement-learning algorithms in SDN face problems such as slow convergence rates and unbalanced loads. The problems seriously affect network performance, resulting in network link congestion and the low efficiency of inter-stream bandwidth allocation. This paper proposes an automatic load-balancing architecture based on reinforcement learning (ALBRL) in SDN. In this architecture, we design a load-balancing optimization model in high-load traffic scenarios and adapt the improved Deep Deterministic Policy Gradient (DDPG) algorithm to find a near-optimal path between network hosts. The proposed ALBRL uses the sampling method of updating the experience pool with the SumTree structure to improve the random extraction strategy of the empirical-playback mechanism in DDPG. It extracts a more meaningful experience for network updating with greater probability, which can effectively improve the convergence rate. The experiment results show that the proposed ALBRL has a faster training speed than existing reinforcement-learning algorithms and significantly improves network throughput.
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