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The rates of morbidity and mortality of hepatocellular carcinoma (HCC) have not lessened because of difficulty in treating tumor metastasis. Mongolian Saussurea involucrata (SIE) possesses various anticancer activities, including apoptosis and cell cycle arrest. However, detailed effects and molecular mechanisms of SIE on metastasis are unclear. Thus, the present study was undertaken to investigate antimetastatic effects on HCC cells as well as possible mechanisms. Effects of SIE on the growth, adhesion, migration, aggregation and invasion of the SK-Hep1 human HCC cell line were investigated. SIE inhibited cell growth of metastatic cells in dose-and time-dependent manners. Incubation of SK-Hep1 cells with 200-400µg/mL of SIE significantly inhibited cell adhesion to gelatin-coated substrate. In the migration (wound healing) and aggregation assays, SIE treated cells showed lower levels than untreated cells. Invasion assays revealed that SIE treatment inhibited cell invasion capacity of HCC cells substantially. Quantitative real time PCR showed inhibitory effects of SIE on MMP-2/-9 and MT1-MMP mRNA levels, and stimulatory effects on TIMP-1, an inhibitor of MMPs. The present study not only demonstrated that invasion and motility of cancer cells were inhibited by SIE, but also indicated that such effects were likely associated with the decrease in MMP-2/-9 expression of SK-Hep1 cells. From these results, it was suggested that SIE could be used as potential anti-tumor agent.

e Both interleukin-17A (IL-17A) and IL-17F are proinflammatory cytokines that have an important role in intestinal homeostasis via receptor signaling. These cytokines have been characterized in chickens, but very little is known about their receptors and their functional activity. We provide here the first description of the sequence analysis, bioactivity, and comparative expression analysis of chicken IL-17RA (chIL-17RA) in chickens infected with Salmonella and Eimeria, two major infectious agents of gastrointestinal diseases of poultry of economic importance. A full-length chIL-17RA cDNA with a 2,568-bp coding region was identified from chicken thymus cDNA. chIL-17RA shares ca. 46% identity with mammalian homologues and 29.2 to 31.5% identity with its piscine counterparts. chIL-17RA transcript expression was relatively high in the thymus and in the chicken macrophage cell line HD11. The chIL-17RA-specific small interfering RNA inhibits interleukin-6 (IL-6), IL-8, and IL-1␤ mRNA expression in chicken embryo fibroblast cells (but not in DF-1 cells) stimulated with chIL-17A or chIL-17F. Interaction between chIL-17RA and chIL-17A was confirmed by coimmunoprecipitation. Downregulation of chIL-17RA occurred in concanavalin A-or lipopolysaccharide-activated splenic lymphocytes but not in poly(I·C)-activated splenic lymphocytes. In Salmonella-and Eimeria-infected chickens, the expression levels of the chIL-17RA transcript were downregulated in intestinal tissues from chickens infected with two Eimeria species, E. tenella or E. maxima, that preferentially infect the cecum and jejunum, respectively. However, chIL-17RA expression was generally unchanged in Salmonella infection. These results suggest that chIL-17RA has an important role in mucosal immunity to intestinal intracellular parasite infections such as Eimeria infection.

This experiment was conducted to investigate leptin mRNA expression, adipocyte size, and their relationship in several adipose tissues of fattening steers. Subcutaneous, perirenal, intermuscular and intramuscular adipose tissues were collected from three crossbred steers (Japanese Black cattle X Holstein) aged 21 months. The mRNA level and adipocyte diameter were determined in these adipose tissues. The intramuscular adipose tissue had a lower leptin mRNA level than the intermuscular and perirenal adipose tissues ( P < 0.05). Leptin mRNA level was lower in the subcutaneous depot than in the intermuscular depot ( P < 0.05). Adipocyte diameter was larger in the intermuscular adipose tissue than in the subcutaneous and intramuscular adipose tissues ( P < 0.05). Leptin mRNA level was positively correlated with adipocyte diameter ( r 2 = 0.81, P < 0.05). These results suggest that the cattle have fat depot-specific differences in leptin gene expression, which are a result of a difference in adipocyte size.

Adipose tissue development and function play a critical role in the regulation of energy balance, lipid metabolism, and the pathophysiology of metabolic syndromes. Although the effect of zinc ascorbate supplementation in diabetes or glycemic control is known in humans, the underlying mechanism is not well described. Here, we investigated the effect of a zinc-chelated vitamin C (ZnC) compound on the adipogenic differentiation of 3T3-L1 preadipocytes. Treatment with ZnC for 8 d significantly promoted adipogenesis, which was characterized by increased glycerol-3-phosphate dehydrogenase activity and intracellular lipid accumulation in 3T3-L1 cells. Meanwhile, ZnC induced a pronounced up-regulation of the expression of glucose transporter type 4 (GLUT4) and the adipocyte-specific gene adipocyte protein 2 (aP2). Analysis of mRNA and protein levels further showed that ZnC increased the sequential expression of peroxisome proliferator-activated receptor gamma (PPARγ) and CCAAT/enhancer-binding protein alpha (C/EBPα), the key transcription factors of adipogenesis. These results indicate that ZnC could promote adipogenesis through PPARγ and C/EBPα, which act synergistically for the expression of aP2 and GLUT4, leading to the generation of insulin-responsive adipocytes and can thereby be useful as a novel therapeutic agent for the management of diabetes and related metabolic disorders.

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

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