• The Journal of Dong Guk Oriental Medicine
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• v.8 no.2
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• pp.115-129
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• 2000

The cause that the increase of animality fat intakes, under exercise, fatness, adding the stress, advanced age etc., the occurrence rate of the circulation system disease has been increased. And the thrombosis importantly came to the front as the risk factor of these circulation system's disease. Nowadays, the ischemic disease has especially discussed, for example the angina or myocardial infarction, originated in thrombosis that came from the platelet aggregation. In the western medicine, as the cure and prevention, using the aspirin or ticlopidine for platelet aggregation suppressant. But in the , the curing method must be used properly according to the pectoralgia or heartache's kind, state, grade. The platelet do not attache to the normal hemangioendothelial cell. But when it stimulated by endothelium peronia and so on, it attache to the injury endothelium or rise aggregation between the platelet. On this time, it secrete the platelet aggregation inducer as like ADP, thromboxane A2 from the inside of platelet. So it has first defensive function through the aggregation augment that prevent the celerity consumption of blood. But the activation of abnormal platelet occur the platelet grume and thrombogenesis. So it bring up the occlusive angiosis, so to speak, cardiovascular disease, cerebrovascular disease, arterial sclerosis. In oriental medicine, the thrombosis in the category of blood stasis and this blood stasis present the generalise or local blood circulation disturbance that generated by all kinds of pathological fact or blood stream retention accompanying with a series of syndrome. As the syndrome, stabbing pain fixed at certain region, squamous and dry skin, fullness and pain of the chest and hypochondrium, firmness and fullness of the lower abdomen, black stool, dark purple tongue or with ecchymoses and petechiae etc.. has been created. And it becomes the pathopoiesis cause that the convulsion and palpitation, severe palpitatiion, tympanites, the symtom complex with a mass or swelling in the abdomen, insanity, stricken by wind etc.. Moreover, it used the drugs for invigorating blood circulation and eliminating blood stasis or drugs for removing blood stasis for all kinds of syndrome through the blood stasis. And the drugs for activating the blood circulation, such as Salviae Radix, Angelicae Sinensis Radix, Persicae Semen, Achyranthis Radix, Cnidii Rhizoma, Carthami Flos are used for that. And it is used to the herbs of insects that has strong effect about the disintergrating blood stasis such as Hirudo, Scolopendrae Corpus, Buthus, Lumbricus etc.. On this study, It used Geijibokryunghwan(GBH) and the consisting herbs to investigate the influence of platelet aggregation about drugs that used to improvement various symptoms created by the thrombosis in oriental medicine. GBH formula has as formula recorded in the , action of 'eleminating the evil and not impairment of healthy energy' and 'promoting the flow of QI and cold and heat, so used for the expel blood stasis herbs from the ancient. Therefore we investigated the restraint effect of GBH and the consisting herbs about the platelet agregation induced to the ADP, AA or collagen. The conclusion is following. 1. When it added the aggregation inducer after that it added GBH and individual consisting herbs in the PRP, GBH showed the (+) inhibition effect on the platelet aggregation and it showed the (+) inhibition effect in the individual consisting herbs as like Paeoniae Radix and Moutan Cortex Radicis. 2. It showed the (+), (+,++) inhibition effect on the platelet aggregation in Paeoniae Radix Hoelen, Paeoniae Radix Moutan Cortex Radicis, Hoelen Moutan Cortex Radicis etc. 3. In the aggregation inhibition activating on the difference of density, GBH showed strong inhibition effect to the aggregation state induced to collagen, and it showed the inhibition effect in the individual consisting herbs as like Paeoniae Radix and Moutan Cortex Radicis about the aggregation induced by the collagen. 4. It showed the strong inhibition effect about the aggregation induced by the collagen in Paeoniae Radix Hoelen, Paeoniae Radix Moutan Cortex Radicis, Hoelen Moutan Cortex Radicis etc Like this, as confirm GBH and the individual consisting herb's inhibition effect of platelet aggregation, We considerated that GBH and the individual consisting herbs have practical applicational value of clinical trial in the thrombosis caused by platelet aggregation.

• Tuberculosis and Respiratory Diseases
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• v.49 no.5
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• pp.601-613
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• 2000

Background : Idiopathic pulmonary fibrosis (IPF) is a fatal progressive fibrous disease of the lung of unknown etiology. Recently it has been classified into several distinct entities on the basis of pathologic and clinical characteristics, ie : usual interstitial pneumonia (UIP), desquamative interstitial pneumonia (DIP), acute interstitial pneumonia (AIP), bronchiolitis obliterans with organizing pneumonia (BOOP), and nonspecific interstitial pneumonia (NSIP). IPF is now applied only for UIP, which has the worst prognosis. The previous reports of 3-5 year median survival appears to be overoptimistic because other types with better prognosis like NSIP or BOOP might have been included. Therefore, this study was performed to determine the clinical course and the prognostic factors of UIP as diagnosed by surgical lung biopsy. Methods : The subjects were 72 UIP patients (age $58.2{\pm}11.6$ years, M : F=45 : 27, median follow up period : 18.1 months (0.7-103.6) diagnosed by surgical lung biopsy at the Asan Medical Center (68 patients) and the Paik Hospital in Seoul (4 patients). Clinical scores (level of dyspnea : 1-20 points), radiologic score (honeycombing : HC score 0-5 points, ground glass : GG score 0-5 points), and physiologic scores (FVC : 1-12 points, $FEV_1$ : 0-3 points, TLC : 0-10 points, $D_{LCO)$ : 0-5 points, $AaDO_2$ : 0-10 points) were summed into a total CRP score. Results : 1) The one year survival rate was 78.3%, while the rate for three year survival was 58.1%, and the median survival period was 42.5months. 2) Short term (1 year) prognosis : The patients who died within one year of diagnosis (14 patients) had the higher initial total CRP score ($28.6{\pm}8.3$ vs. $16.6{\pm}9.7$) than those who lived longer than one year (46 patients). The difference in the total CRP score was attributed to the symptom score ($8.4{\pm}2.1$ vs. $5.7{\pm}3.9$) and the physiologic score ($15.7{\pm}7.1$ vs. $6.7{\pm}5.7$) including FVC, $D_{LCO)$ and $AaDO_2$. 3) Long-term (3year) prognosis : The total CRP score ($12.2{\pm}6.7$ vs. $28.7{\pm}7.9$ : including symptom score, FVC, $D_{LCO)$ and $AaDO_2$) at the time of diagnosis were also different for the long-term survivors and those who lived less than 3 years. 4) Cox regression analysis showed $D_{LCO)$ (${\geq}$60%) (Hazard ratio : 4.56, 95% CI : 2.30-16.04) was the independent prognostic factors of UIP (P<0.05). Conclusion : These results suggest that $D_{LCO)$ at the time of diagnosis seem to be a prognostic markers of biopsy-proven UIP.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.