• Childhood Kidney Diseases
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• v.7 no.2
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• pp.125-132
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• 2003

Purpose : Lipoprotein(a) is a genetically determined risk factor for atherosclerotic vascular disease and is elevated in patients with renal disease. Especially the patients with nephrotic syndrome exhibit excessively high Lp(a) plasma concentrations. Also the patients with end-stage renal disease have elevated Lp(a) levels. But the mechanism underlying this elevation is unclear. Thus, in this study, by measuring the level of serum Lp(a) in common renal diseases in children, we hoped to see whether there would be a change in Lp(a) in renal diseases other than nephrotic syndrome. Then, we figured out its implications, and looked for the factors that affect the Lp(a) concentrations. Methods : A total of 75 patients(34 patients with hematuria of unknown etiology, 10 with hematuria and hypercalciuria, 8 with IgA nephropathy, 8 with poststreptococcal glomerulone phritis, 3 with $Henoch-Sch\"{o}nlein$ nephritis, 7 with urinary tract infection, and 5 with or- thostatic proteinuria) were studied. The control group included 20 patients without renal and liver disease. Serum Lp(a), total protein, and albumin levels, 24-hour urine protein and calcium excretions, creatinine clearance and the number of RBCs and WBCs in the urinary sediment were evaluated. Data analysis was peformed using the Student t-test and a P-value less than 0.05 was considered to be statistically significant. Results : LP(a) was not correlated with 24-hour urine calcium and creatinine. Lp(a) level had a positive correlation with proteinuria and negative correlation with serum albumin and serum protein. Among the common renal diseases in children, Lp(a) was elevated only in orthostatic proteinuria (P<0.05). Conclusion : Lp(a) is correlated with proteinuria, serum protein, and serum albumin, but not with any kind of specific renal disease. Afterward, Lp(a) needs to be assessed in patients with orthostatic proteinuria and its possible role as a prognostic factor could be confirmed.

• Journal of fish pathology
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• v.26 no.3
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• pp.255-263
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• 2013

The innate immune response is fundamental defense response of vertebrates and invertebrates. Especially, the innate immune response important for larvae that lack of resistance to infectious diseases in the early stages. Galectin is one of the kinds of lectin and presents in the fish mucous that involves innate immune response. Galectin have been studied from various fishing species, but expression analysis of galectin is still unclear during early developmental stage in olive flounder. In this study, we investigated gene expression of galectin-1 from various developmental stage and tissues. We excised several tissues including the muscle, fin, eye, gill, brain, stomach, intestine, kidney, spleen and liver from adult olive flounder and confirmed gene expression of galectin-1 using RT-PCR and quantitative real-time PCR. Expression of galectin-1 was significantly higher in muscle, stomach and intestinal tissue than other tissue in adult fish (5 and 29 months). Also, galectin-1 gene was detected from 0 DAH and gradually increased to 35 DAH and since then decreased after stomach development period. Induction of galectin-1 during the early developmental stage suggest that muscle, fin and eye tissue is formed and begins the secretion of galectin this period. In addition, increased expression levels at 35 DAH suggest that due to complete formation of stomach and intestine, increase of secretion and activation of enzyme. This study shows that expression of galectin-1 during early developmental stages and adult period in olive flounder and can be expect that galectin-1 play essental role in the innate immune system throughout the whole life time. Galectin-1 is primary barrier such as skin and digestive tissue against pathogen infection, also digestive tract developmental period is important for pathogen invasion can be expected that it will serve. Mass mortality due to the disease in seed production is continuing damage, therefore these result will be meaningful about infectious disease during early developmental stages as a basic data for the study.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.69-76
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• 2013

The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.