Inflammation is an important protective response mechanism that occurs against microbial invasion or injury. However, excessive inflammation may lead to cause of morbidity and mortality in diseases. The activated macrophages plays a vital role in inflammatory response by stimulation of lipopolysaccharide (LPS) and tumor necrosis factor-α (TNF-α). This activation further damages the host by inducing certain pro-inflammatory mediators such as nitric oxide (NO), interleukin-1β (IL-1β), interleukin- 6 (IL-6), TNF-α, inducible nitrous oxide (iNOS) and cyclooxygenase (COX-2). Flavonoids are bioactive compounds with potential effects as anti-cancer, anti-inflammation, anti-viral and anti-bacterial activities. Polymethoxyflavones (PMFs) are unique to citrus plants which are of specific interest owing to their biological effects that includes lipoprotein metabolism and anti-inflammatory activity. Sinensetin is one of the PMFs having five methoxy groups on the basic benzo-γ-pyrone skeleton with a carbonyl group at the C4 position. Sinensetin have been known for exerting various pharmacological activities including anti-angiogenesis, anti-diabetic and anti-inflammatory activities. However, there are no studies focused on the anti-inflammatory effects of sinensetin on skeletal muscle cells. In the present study, we investigated the antiinflammatory effect of flavonoids isolated from Sinensetin on the production of pro-inflammatory mediators mediated by nuclear factor-kappa B (NF-κB) by inhibition of signal transduction in LPS - induced L6 skeletal muscle cells.

The use of non-therapeutic antibiotics as animal feed additives has raised public health concerns due to the increasing resistance of pathogens to antibiotics. It is therefore required to develop safe and effective alternative feed additives to replace non-therapeutic antibiotics. The aim of this study was to assess the effects of the multiherbal compound, KIOM-C, on growth performance and immune response of growing-to-finishing pigs under farm conditions. The experimental trials were performed in a Korean commercial swine growing-to-finishing complex, and a total of 70-day-old 160 pigs were selected. Eighty pigs were treated with KIOM-C at the level of 2 kg/tonne until slaughter age (KT group), while another 80 pigs were not treated with KIOM-C (NT group). All animals were vaccinated against foot-and-mouth disease (FMD) at 60 and 110 days of age. During the trial period, average daily weight gain (ADWG), average daily feed intake (ADFI), feed conversion ratio (FCR), survival rates, and average slaughter ages were measured. The serum concentrations of tumor necrosis factor-a (TNF-α), interferon-γ (IFN-γ), and IgA were also evaluated. In order to evaluate specific humoral immune responses, the foot-and-mouth disease virus (FMDV) serotype O-specific antibody was measured. The ADWG, ADFI, and FCR of the KT group were significantly greater than those of the NT group (p<0.05). Serum concentrations of IgA in the KT group was statistically higher than the NT group. The antibody levels of the KT group against FMDV serotype O was higher than the NT group, and 86.67% of the KT group tested positive for anti-FMDV antibodies. Overall, these findings suggest that KIOM-C improves growth performance and immune response of pigs under growing-to-finishing farm conditions, and implies that the herbal compound may be used as a suitable alternative feed additive.

The incidence of stomach cancer has been found to be gradually decreasing; however, it remains one of the most frequently occurring malignant cancers in Korea. According to statistics of 2017, stomach cancer is the top cancer in men and the fourth most important cancer in women, necessitating methods for its early detection and treatment. Considerable research in the field of bioinformatics has been conducted in cancer studies, and bioinformatics approaches might help develop methods and models for its early prediction. We aimed to develop a classification method based on deep learning and demonstrate its application to gene expression data obtained from patients with stomach cancer. Data of 60,483 genes from 334 patients with stomach cancer in The Cancer Genome Atlas were evaluated by principal component analysis, heatmaps, and the convolutional neural network (CNN) algorithm. We combined the RNA-seq gene expression data with clinical data, searched candidate genes, and analyzed them using the CNN deep learning algorithm. We performed learning using the sample type and vital status of patients with stomach cancer and verified the results. We obtained an accuracy of 95.96% for sample type and 50.51% for vital status. Despite overfitting owing to the limited number of patients, relatively accurate results for sample type were obtained. This approach can be used to predict the prognosis of stomach cancer, which has many types and underlying causes.