Background: DNA methylation is one of the important gene expression mechanisms of the cell. When cytosine of CpG dinucleotide in promotor is hypomethylated, expression of some genes that is controlled by this promoter is altered. In this study, the author investigated the effect of DNA demethylating agent, 5-aza-2'-deoxycytidine (ADC), on the expressions of cancer antigen genes, MHC and B7 in 4 lung cancer cell lines, NCIH1703, NCIH522, MRC-5, and A549. Material and Method: After treatment of cell lines, NCIH1703, NCIH522, MRC-5 and A549 with ADC (1 uM) for 48 hours, RT-PCR was performed by using the primers of MAGE, GAGE, NY-ESO-1, PSMA, CEA, and SCC antigen gene. In order to find the optimal ADC treatment condition for induction of cancer antigen, we studied the effect of ADC treatment time and dose on the cancer antigen gene expression. To know the effect of ADC on the expression of MHC or B7 and cell growth, cells were treated with 1 uM of ADC for 72 hours for FACS analysis or cells were treated with 0.2, 1 or 5 uM of ADC for 96 hours for cell counting. Result: After treatment of ADC (1 uM) for 48 hours, the expressions of MAGE, GAGE, NY-ESO-1, and PSMA genes increased in some cell lines. Among 6 MAGE isotypes tested, and gene expression of MAGE-1, -2, -3, -4 and -6 could be induced by ADC treatment. However, CEA gene expression did not change and SCC gene expression was decreased by ADC treatment. Gene expression was generally induced 24 - 28 hours after ADC treatment and expression of MAGE, GAGE, and NY-ESO-1 was maintained at least 14 days after ADC ADC teatment, and expression of MAGE, GAGE, and NY-ESO-1 was maintained at least 14 days after ADC teatment in ADC-Free medium. Most gene expression could be induced at 0.2 uM of ADC, but gene expression increased dependently on ADC treatment dose. The expression of MHC and B7 was not increased by ADC treatment in all four cell lines, and the growth rate of 4 cell lines decreased significantly with the increase of ADC concentrations. Conclusion: Treatment of lung cancer cell lines with ADC increases the gene expression MAGE, GAGE and NY-ESO-1 that are capable of induction of cytotoxic T lymphocyte response. We suggest that treatment with 1 uM of ADC for 48 hours and then culturing in ADC-free medium is optimal condition for induction of cancer antigen. However, ADC has no effect on MHC and B7 induction, additional modification for increase of expression of MHC, B7 and cytokine will be needed for production of efficient cancer cell vaccine.
Chemical determination were made to analyze the concentration of nutrients including proximate composition, vitamins, minerals, amino acids and fatty acids in three kinds of locally produced infantile formula milk(DFM) and one fermented milk(FM). An attempt was also made to compare the analytical values of nutrients against guaranteed values. The results obtained are summarized as follows; 1. The analyzed value of protein and fat content in DFM-A.B.C was slightly lower than that of guaranteed values and a reverse trend was observed in the case of total carbohydrate. There were no differences in proximate composition among the three kinds of DFM. It was found that the FM contained less protein and fat than the fresh milk, but it seemed that a considerable amount of sugar was added to FM. 2. The analyzed values of vitamin
The applicability of a well-type autotrophic sulfur-oxidizing reactive barrier (L
It has been reported that degranulation of mast cells in rats, rabbits and dog was observed after dosing xylazine hydrochloride(Xh) which has been widely used as sedative, analgesic and muscular relaxant. Therefore, this experiment was conducted to examine the relations between Xh and histamine release and to identify the action of
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used