• Journal of Life Science
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• v.22 no.10
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• pp.1407-1414
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• 2012

Hormone replacement therapy (HRT) is an effective regimen that has been found to prevent these diseases in postmenopausal women. However, HRT is accompanied by an increased risk of unfavorable outcomes. This study was conducted to evaluate the effects of Eisenia Bicyclis extract on lipids in ovariectomized rats. Fifty 7-week-old female Sprague-Dawley rats were randomly assigned to four groups: sham-operated rats (SHAM), ovariectomized rats (OVX-CON), and ovariectomized rats that were treated with Eisenia bicyclis extracts. The extract-treated diets were fed to the rats for 6 weeks after operation. Antioxidant effects were measured by DPPH free radical scavenging activity. Antioxidant activity of the ethanol extract increased in a dose-dependent manner and was about 55.9% in a concentration of 100 ${\mu}g/ml$. We measured the total cholesterol content, triglyceride content, HDL-cholesterol content, LDL-cholesterol content, atherosclerotic index, cardiac risk factor in serum, and anti-platelet aggregation and blood rheology. The total cholesterol and triglyceride concentration in serum increased for the OVX-control group, but supplementation with the E. bicyclis extract caused these factors to decrease. Notably, the serum LDL-cholesterol concentration in the OVX-EB200 group was significantly lower than the OVX-CON group. In addition, the blood passage times in rats that received the E. bicyclis extract were more rapid than the times in the untreated group (OVX-CON). Microscopic evaluation revealed that whole blood passed more smoothly through the microchannels in rats in the E. bicyclis extract supplement groups. Our results clarified the effects of E. bicyclis extract on serum lipid content in ovariectomized rats, and consequently we expect positive effects from providing E. bicyclis extract to postmenopausal women with cardiovascular disease.

• Journal of Chest Surgery
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• v.40 no.1 s.270
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• pp.32-36
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• 2007

Background: Although acute renal failure (ARF) after coronary artery bypass graft (CABG) is relatively rare, but devastating complication with high mortality. Our study aims to evaluate the effectiveness of early application of CRRT in patients with ARF which developed after on-pump CABG. Material and Method: Two hundred and eighty seven patients underwent isolated on-pump CABG between May 2002 and Feb. 2006 at our institution, of whom 15 (5.2%) needed CRRT (11 patients for postoperatively developed ARF and the remaining 4 patients with preexisting dialysis-dependent chronic renal failure (CRF) for postoperative hemodynamic and metabolic control). Criteria for early application of CRRT were as follows; decreased urine output less than 0.5cc/h/kg for 2 consecutive hours and elevated serum creatinine level greater than 2.0 mg/dL. Result: The incidence of ARF requiring CRRT after on-pump CABG was 3.9% (11/283) and the overall hospital mortality of patient with CRRT was 33.3% (5/15). Of 5 deaths, 4 were patients with postoperatively developed ARF, and 1 was a patient with pre-existing dialysis-dependent CRF patient. The mean time between the operation and the initiation of CRRT was $25.8{\pm}5.8$ hours and the mean duration of CRRT was $62.1{\pm}41.2$ hours. Of the 7 survivors who were not on dialysis-dependent preoperatively, 6 patients fully recovered renal function during hospital stay and 1 patient required permanent renal supportive treatment after discharge from hospital. Conclusion: Early application of CRRT could maintain stable postoperative hemodynamic status and make outcomes better than those of previous reports in patients with ARF which developed after on-pump CABG.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.4940-4950
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• 2011

cis-(3R,5R)-Atorvastatin Ca (1) used for hyperlipidemia have four stereomers. However, It is very difficult to prepare stereoselective stereomers. In this paper, the reduction of 3,5-diketo atorvastatin ester (3) was performed using $Me_4NHB(OAc)_3$ in acetic acid as a reductant and showed excellent stereoselectivity in the double reduction of 3,5-diketo atorvastatin ester (3). As a result, reduction of compound 3 by $Me_4NHB(OAc)_3$ was purely obtained with cis-(3R,5R)-atorvastatin ester (4) of 1.5% and trans-(3R,5S)-atorvastatin ester (5) of 98.5%. Also, cis-(3R,5R)-atorvastatin Ca (1) and trans-(3R,5S)-atorvastatin Ca (7) were used to determine efficacy in the treatment of liver damage and hyperlipidemia induced by a high-fat diet in rats and to study the performance of the January 2010 experient was conducted. As a result, total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-c), low-density lipoprotein-cholesterol (LDL-c), and triglyceride (TG) levels of compound 1 and 7 groups were $93.0{\pm}0.5$, $43.5{\pm}0.8$, $40.4{\pm}1.4$, $45.6{\pm}0.9\;mg/d{\ell}$ and $110.0{\pm}0.7$, $33.3{\pm}0.6$, $65.8{\pm}1.9$, $54.8{\pm}1.2\;mg/d{\ell}$, respectively. Atherogenic index (AI) and cardiac risk factor (CRF) in compound 1 and 7 were $1.14{\pm}0.05$, $2.14{\pm}0.05$ and $2.31{\pm}0.06$, $3.31{\pm}0.06$, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were $51.9{\pm}4.6$, $16.0{\pm}2.1\;IU/{\ell}$ and $75.8{\pm}4.4$, $35.1{\pm}9.7\;IU/{\ell}$. Taken together, while compound 1 treat against high-fat diet-induced hyperlipidemia by attenuating hepatic lipid depots and reducing oxidative stress, compound 7 group had a low curative effect on hyperlipidemia induced by a high-fat diet in rats. These findings suggest that new method about synthesis of stereoselective stereomers and indicate that it may consider using in a clinical trial.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.161-177
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• 2019

In this paper, we study the performance improvement of the answer extraction in Question-Answering system by using sentence dependency parsing result. The Question-Answering (QA) system consists of query analysis, which is a method of analyzing the user's query, and answer extraction, which is a method to extract appropriate answers in the document. And various studies have been conducted on two methods. In order to improve the performance of answer extraction, it is necessary to accurately reflect the grammatical information of sentences. In Korean, because word order structure is free and omission of sentence components is frequent, dependency parsing is a good way to analyze Korean syntax. Therefore, in this study, we improved the performance of the answer extraction by adding the features generated by dependency parsing analysis to the inputs of the answer extraction model (Bidirectional LSTM-CRF). The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. In this study, we compared the performance of the answer extraction model when inputting basic word features generated without the dependency parsing and the performance of the model when inputting the addition of the Eojeol tag feature and dependency graph embedding feature. Since dependency parsing is performed on a basic unit of an Eojeol, which is a component of sentences separated by a space, the tag information of the Eojeol can be obtained as a result of the dependency parsing. The Eojeol tag feature means the tag information of the Eojeol. The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. From the dependency parsing result, a graph is generated from the Eojeol to the node, the dependency between the Eojeol to the edge, and the Eojeol tag to the node label. In this process, an undirected graph is generated or a directed graph is generated according to whether or not the dependency relation direction is considered. To obtain the embedding of the graph, we used Graph2Vec, which is a method of finding the embedding of the graph by the subgraphs constituting a graph. We can specify the maximum path length between nodes in the process of finding subgraphs of a graph. If the maximum path length between nodes is 1, graph embedding is generated only by direct dependency between Eojeol, and graph embedding is generated including indirect dependencies as the maximum path length between nodes becomes larger. In the experiment, the maximum path length between nodes is adjusted differently from 1 to 3 depending on whether direction of dependency is considered or not, and the performance of answer extraction is measured. Experimental results show that both Eojeol tag feature and dependency graph embedding feature improve the performance of answer extraction. In particular, considering the direction of the dependency relation and extracting the dependency graph generated with the maximum path length of 1 in the subgraph extraction process in Graph2Vec as the input of the model, the highest answer extraction performance was shown. As a result of these experiments, we concluded that it is better to take into account the direction of dependence and to consider only the direct connection rather than the indirect dependence between the words. The significance of this study is as follows. First, we improved the performance of answer extraction by adding features using dependency parsing results, taking into account the characteristics of Korean, which is free of word order structure and omission of sentence components. Second, we generated feature of dependency parsing result by learning - based graph embedding method without defining the pattern of dependency between Eojeol. Future research directions are as follows. In this study, the features generated as a result of the dependency parsing are applied only to the answer extraction model in order to grasp the meaning. However, in the future, if the performance is confirmed by applying the features to various natural language processing models such as sentiment analysis or name entity recognition, the validity of the features can be verified more accurately.

• Journal of Life Science
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• v.19 no.4
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• pp.508-513
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• 2009

Pine needles have long been used as a traditional health-promoting medicinal food in Korea. To investigate the effects of pine (pinus densiflora) needle extracts on blood flow and serum lipid improvement were assessed in vivo. 8 week-old Sprague Dawley strain rats were divided into four groups of seven rats each; CON, 0.5% CHOL, HOT water and Sub-supercritical group. Serum total cholesterol and triglyceride contents were lower in the CON group than the 0.5% CHOL group. Three weeks of feeding hot water and sub-supercritical extract resulted in a decrease in serum triglyceride and total cholesterol level. The level of HDL-cholesterol in the 0.5% CHOL group was significantly (p<0.05) reduced compared to the CON group, but it had a tendency to increase with pine needle extract supplementation. Blood passage time of the pine needle extracts supplemented group was higher than the 0.5% CHOL group. Microscopic observation showed that whole blood passed smoothly through the micro channels in pine needle extracts supplemented groups. The platelet aggregation ability of the groups treated with pine needle extracts was less than that of the 0.5% CHOL group. All these results suggest that pine needle extracts might improve blood homeostasis mediated via antiplatelet activities.

• Korean Journal of Biological Psychiatry
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• v.8 no.1
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• pp.20-36
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• 2001

The debate about whether depressive disorders should be divided into categories or arrayed along a continuum has gone for decade, without resolution. In our review, there is more evidence consistent with the spectrum concept than there is with the idea that depressive disorders constitute discrete clusters marked by relatively discontinuous boundaries. First, "depression spectrum", "is there a common genetic factors in bipolar and unipolar affective disorder", "threshold model of depression" and "bipolar spectrum disorder" are reviewed. And, a new subtype of depression is so called SeCA depression that is a stressor-precipitated, cortisol-induced, serotonin-related, anxiety/aggression-driven depression. SeCA depression is discussed. But, there is with the idea that depressive disorders constitute discrete subtypes marked by relatively discontinuous boundaries. This subtypes of depressive disorder were reviewed from a variety of theoretical frames of reference. The following issues are discussed ; Dexamethasone suppression test(DST), TRH stimulation test, MHPG, Temperament Character Inventory(TCI), and heart rate variability(HRV).

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.233-240
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• 2018

The aim of this study was to investigate the cardiometabolic risk factors (CRF) of community dwelling women based on a combination of body mass index (BMI) and waist circumference (WC). This cross-sectional study was based on 1,447 subjects between 30 and 60 years of age. Subjects were categorized into 4 groups by BMI and WC [group 1, BMI<$25kg/m^2$ and WC<85 cm; group 2, BMI<$25kg/m^2$ and WC>85 cm; group 3, BMI>$25kg/m^2$ and WC<85 cm; and group 4 (BMI>$25kg/m^2$ and WC>85 cm. Logistic regression analyses showed that subjects in group 2 had 1.75 times increased risk of clustering of 2 or more CRFs compared with subjects in group 1 (p<0.001). In conclusion, early detection of people with normal weight but high waist circumference may prevent them from getting worse by implementation of lifestyle intervention, consisting of regular exercise and healthy eating. In addition, further studies on appropriate exercise contents for them should be examined.

• The Journal of Korean Medicine
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• v.27 no.3 s.67
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• pp.107-131
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• 2006

Background and Aims: Dansamtongmek-tang (DSTMT) and Dansamsengmek-san (DSSMS) have been used for many years as therapeutic agents for the acute stage of cerebrovascular disease, hypertension and hyperlipidemia in Oriental medicine, but the effects of DSTMT and DSSMS on hyperlipidemia and safety for cell damage are not yet well-known. This study was done to investigate the effects of DSTMT and DSSMS on hyperlipidemia. Methods: In vivo test: after administering DSTMT and DSSMS to SHR and ICR occurred hyperlipidemia for 3 weeks, we analyzed body weight, cholesterol levels. TG, HDL-chol, LDL-chol, LDH in plasma, brain, liver and kidney tissue, and DNA by RT-PCR. In vitro test: after administering DSTMT and DSSMS to human hepatocellular carcinoma in hypoxia, we observed cell cohesion by light microscope, analyzed the inflow of Ca2+ by confocal laser scanning microscope and DNA by RT-PCR. Results: DSTMT significantly decreased the levels of triglyceride and increased the levels of HDL-cholesterol in SHR, and significantly decreased the levels of LDL-cholesterol and body weight and increased the levels of HDL-cholesterol in ICR. DSSMS significantly decreased body weight, total cholesterol levels, LDL-cholesterol, LDH and cardiac risk factor (CRE) in SHR and significantly decreased the levels of total cholesterol, triglyceride, LDL-cholesterol, LDH and CRF in ICR. DSTMT had an effect on protecting cells from damage by inhibiting production of p53 mRNA, and in DSSMS, by inhibiting production of p53 mRNA and p21 mRNA after hypoxia. DSTMT effectively blocked off Ca2+ at low density, but DSSMS effectively blocked off Ca2+ at high density. Both DSTMT and DSSMS had an effect on inhibiting lipid metabolism by blocking off production of apo B mRNA. Conclusions: These results suggest that DSTMT and DSSMS might be usefully applied for treatment of hyperlipidemia and suppression of brain damage.