• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 1998.05a
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• pp.37-54
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• 1998

This study was conducted to investigate the effects of mugwort extracts on the blood ethanol concentration, liver function and low level of cadmuim(Cd) in rats. The effects of mugwort extracts on the blood ethanol concentration was studied in Sprague-Dawley rats (10 weeks old) administered p.o. with 25% ethanol (5g/1kg body weight) and then injected with mugwort extracts (at the 2% levels of daily feed consumption compared with the concentration of catechins level in mugwort extracts) in caudal vein. SD rats were divided into five groups : control group (CON-E, only ethanol and 0.85% saline sol'n treated instead of each extracts), water extracts of mugwort treated to the control (MDW-E), ethanol extracts of mugwort treated to the control (POH-E). And then rat plasma of each time (0hr, 1hr, 2hr, 3hr) was investigated ethanol concentration by gas chromatography. Another rats were measured at the time of 0 and 5hr for the test of GOD(Glutamic Oxaloacetic Transaminase) and GPT(Glutamic Pyruvic Transaminase). Components of each extracts were analyzed by using high performance liquid chromatography. The effects of mugwort extracts on the liver function were studied in culture of rat hepatocyte composed of three groups : Control group and two groups treated with each extracts (1% & 2% MDW, 1% & 2% MOH). Condition of rat hepatocytes cultured for 36hr at $37^{\circ}C$(5% $CO_2$ incubator), number of cells, GOT and GPT activity were investigated. The results obtained were summarized as follows ; 1. Catechins level of mugwort extracts was $8{\sim}10mg/100g(MDW)$, $3{\sim}4mg/100g(MOH)$ 2. The contents of (-)-Epigallocatechin was high in MDW 3. The effects of mugwort extracts on the blood ethanol concentration were as follows; 1) The order in ethanol degradation efficiency was MDW-E > MOH-E > CON-E. 2) Ethanol concentration significantly decreased (p<0.05) in MDW-E and MOH-E. 4. The effects of mugwort extracts on the liver function were as follows; (rat hepatocytes cultured for 36hr at $37^{\circ}C$) 1) Cells condition of MDW-L was better than other groups. 2) The order in number of cells (rat hepatocytes) was 2% MDW-L >1% MDW-L >1% MOH-L > Con-L > 2% MOH-L 5. Cd treatment increased concentrations of hepatic GSH level, and decreased GOT activity in plasma. Therefore, this results suggest that the effects of mugwort extracts may an important rols in degradation ethanol and recovery liver function in body. Also, Mugwort extracts may modify the toxicities of Cd in Cd-treated rats and play an important roles in preventing the liver from various toxicants including Cd in Cd treated rats.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.