• Tuberculosis and Respiratory Diseases
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• v.45 no.1
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• pp.128-139
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• 1998

Background: It is well known that various cytokines and growth factors secreted mainly from alveolar macrophages do the key role in the pathogenesis of IPF. But recently it has been known that structural cells like fibroblast can also release cytokines. So the phenotypic changes in fibroblasts of IPF may do a role in continuous progression of fibrosis. The aim of this study is to find out whether there is a change in the biologic properties of the lung fibroblasts of IPF. Subjects and Method: The study was done on 13 patients with IPF diagnosed by open or thoracoscopic lung biopsy and 7 control patients who underwent resectional surgery for lung cancer. Lung fibroblast cell lines (FB) were established by explant culture technique from the biopsy or resected specimen Result: Basal proliferation of the fibroblast of IPF(IFB) measured by BrdU uptake tended to be highter than control fibroblast(NFB) (0.212 0.107 vs $0.319{\pm}0.143$, p=0.0922), also there was no significant difference in proliferation after the stimulation with PDGF or 10% serum. On the contrary, the degree of inhibition in proliferation by PGE2 was significantly lower($33.0{\pm}13.1%$) in IFB than control($46.7{\pm}10.0%$, p=0.0429). The IFB secreted significantly higher amount of MCP-l($l574{\pm}1283$ pg/ml) spontaneously than NFB($243{\pm}100$ pg/ml) and also after the stimulation with TGF-$\beta$($3.23{\pm}1.31$ ng/ml vs $0.552{\pm}0.236$ ng/ml, p=0.0012). Similarly IL-8 and IL-6 seretion of IFB was significantly higher than NFB at basal state and with TGF-$beta$ stimulation. But after the maximal stimulation with IL-1,8, no significant difference in cytokine secretion was found between IFB and NFB. Conclusion : Above data suggest that the fibroblasts of IPF were phenotypically changed and these change may do a role in the pathogenesis of IPF.

• Korean Journal of Agricultural Science
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• v.42 no.2
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• pp.141-149
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• 2015

The data of Duroc swine species that were born from 2000 to 2014 excluding missing ones collected by Korea National Institute of Animal Science were used in the present study. After removing missing data we used 9756 of productions data and 1728 of reproductive reference of breeding research to study the level of inbreeding and to investigate the impact on the reproductive traits, production traits. The correlation of reproductive traits and inbreeding coefficient are -0.07, -0.08 for total number pigs born, number of pigs born alive respectively and birth weight per litter is -0.10, number of pigs born alive per litter to 21days is -0.06 and body weight per litter to 21days is -0.09. The correlation coefficients of the inbreeding coefficients of reproductive traits are shown within 10% with negative correlation (P < 0.05). Days of 90kg and Backfat in the correlation coefficient and inbreeding coefficient production traits were not observed significant correlations, Average daily gain was investigated by the positive correlation of 0.05. According to the above results, the inbreeding level gave a negative effect on the improvement of the breed traits, investigating a relatively high compared to a negative effect on other traits. But overall correlation degree is less than 10% was observed. This inbreeding coefficient has not been clearly observed due to degeneration of the average inbreeding coefficients of these generations was maintained within 10% of the population. The scale of the experimental group was about 150 degree pig husbandry is very small compared to the advanced countries. However, the level of inbreeding in the population group with the appropriate mating combinations is maintained below 10% of population is thought to be small and can minimize the effects of inbreeding degeneration. further testing utilizing this selection is constantly considered to be necessary.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• (The) Research of the performance art and culture
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• no.43
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• pp.47-78
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• 2021

This study was written to examine the acceptance of pansori in movies based on pansori, and to explore changes in modern society's perception and expectations of pansori. A pansori is getting the love of the upper and lower castes in the late Joseon period, but loses the status at the time of the Japanese colonial rule and Korean War. In response, the country designated pansori as an important intangible cultural asset in 1964 to protect the disappearance of pansori. Until the 1980s, however, pansori did not gain popularity by itself. After the 2000s, Pansori tried to breathe in with the contemporary public due to the socio-cultural demand to globalize our culture. And now Pansori is one of the most popular cultures in the world today, as the pop band Feel the Rhythm of KOREA shows. The changing public perception of pansori and its status in modern society can also be seen in the mass media called movies. This study explored the process of this change with six films based on pansori, from "Seopyeonje" directed by Lim Kwon-taek in 1993 to the film "The Singer" in 2020. First, the films "Seopyeonje" and "Hwimori" were produced in the 1990s. Both of these films show the reality of pansori, which has fallen out of public interest due to the crisis of transmission in the early and mid-20th century. And in the midst of that, he captured the scene of a singer struggling fiercely for the artistic completion of Pansori itself. Next, look at the film "Lineage of the Voice" in 2008 and "DURESORI: The Voice of East" in 2012. These two films depict the growth of children who perform art, featuring contemporary children who play pansori and Korean traditional music. Pansori in these films is no longer an old piece of music, nor is it a sublime art that is completed in harsh training. It is only naturally treated as one of the contemporary arts. Finally, "The Sound of a Flower" in 2015 and "The Singer" in 2020. The two films constructed a story from Pansori's history based on the time background of the film during the late Joseon Dynasty, when Pansori was loved the most by the people. This reflects the atmosphere of the times when traditions are used as the subject of cultural content, and shows the changed public perception of pansori and the status of pansori.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• The korean journal of orthodontics
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• v.29 no.4 s.75
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• pp.467-481
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• 1999

Cleft lip and/or palate is one of the most common congenital craniofacial anomalies. According to previous epidemiologic studies, incidence of cleft lip and/or palate has been increasing nowadays. However, there is no report about epidemiologic study of cleft lip and/or palate patients who visited dept. of orthodontics in Korea. So the purpose of this study was to provide the epidemiological characteristics and important basic clinical data for the diagnosis and the treatment of the cleft lip and/or palate patients. With the orthodontic and cleft charts, diagnostic models and X-ray films from 250 patients with cleft lip and/or palate who visited Dept. of Orthodontics, Seoul National University Dental Hospital during the last 11 years, the authors investigated patient's visiting yew, types of cleft, patient's gender, and Angle's classification of malocclusion, and surgery timing. The results were as follows ; 1. The number of cleft patients who visited Dept. of Orthodontics, SNUDH increased during 1988-1990 and then it declined until 1992. From 1993 to 1996, it showed a stationary trend. After 1997 it showed an overwhelmingly increasing trend. 2. In the cleft type, the ratio of cleft lip cleft lip and alveolus cleft palate : cleft lip and palate was 7.6:19.2:9.6:63.6. In cleft position, unilateral clefts were more than bilateral ones (cleft lip 79:21, cleft lip and alveolus 77:23, cleft lip and palate 75.5:24.5). In cleft side, left clefts were mote than right clefts (cleft lip 53.3:46.7 cleft lip and alveolus 59.5:40.5, cleft lip and palate 59.2:40.8). 3. In gender ratio, males were more than females in cleft lip (57.9:42.1), cleft lip and alveolus (68.8:31.2) and cleft lip and palate (76.1:23.9). But in cleft Palate females were more than males as 41.7: 58.3. 4. In the age groups, 7-12 year group was the most abundant as $52\%$, and then 0-6 year group ($20.4\%$), 13-18 year group ($17.2\%$), more than 18 yew group ($10.4\%$) were followed as descending order. 5. Most of the cleft lip repair surgeries were operated in 0-3 month ($60.3\%$) and 4-6 month ($17.9\%$). 6. The cleft palate repair surgeries were done in 1-2 year ($31.7\%$), 0-1 year ($25.6\%$), 2-3 year ($12.1\%$), more than 5 year ($11.6\%$) as descending order. 7. The lip scar revision surgeries were done before admission at elementary school in $60\%$. (4-6 you ($27.5\%$), 6-8 year ($19.6\%$), more than 10 year ($19.6\%$), 2-4 year ($13.7\%$) as descending order) 8. The rhinoplasties were done before admission at elementary school in $51.7\%$. (0-2 year ($7.1\%$), 2-4 year ($14.3\%$), 4-6 year ($21.4\%$), 6-8 year ($14.3\%$)). 9. The pharyngeal flap were done at 6 Y (72.5 months) after birth on average and there was even distribution of surgery timing. 10. In relationship between Angle's classification of malocclusion and cleft types, Class I was most abundant and Class III, Class II were followed as descending order in cleft lip group. But Class III was most abundant and Class I, Class II were followed as descending order in cleft lip and alveolus group, cleft palate group, and cleft lip and Palate group. The percentage of frequency in Class III malocclusion was overwhelmingly higher in cleft lip and palate group than any other groups. 11. Because the frequency of class III malocclusion was most prevalent in all age groups, anterior crossbite was the most common chief complaint of cleft patients.

• Tuberculosis and Respiratory Diseases
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• v.51 no.4
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• pp.303-314
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• 2001

Background : Matrix metalioproteinase(MMP)-2 and MMP-9 have been known to play an important role in cell migration and the tissue remodeling process by type IV collagen lysis, a major component of the basement membrane. Intra-alveolar fibrosis, secondary to an injury to the basement membrane of the alveolar epithelial lining, is a major process in the pathogenesis of idiopathic pulmonary fibrosis(IPF). Therefore, MMP-2 and MMP-9 was hypothesized to play an important role in IPF pathogenesis. As a result, their level may reflect the activity or prognosis. Method : Forty one progressive IPF patients(age $59.82{\pm}1.73$ years, M:F=23:18), 16 patients with stable IPF for more than one year without therapy(age : $63.6{\pm}2.8$ years, M:F=13:3), and 7 normal controls were enrolled in this study. The MMP-2 and MMP-9 levels in the BAL fluid and alveolar macrophage conditioned media(AM-CM) were measured by zymography and the TIMP-1 level was measured by ELISA. Results : 1) The MMP-2 level in BALF was highest in the progressive IPF group ($1.36{\pm}0.28$) followed by the stable group ($0.46{\pm}0.13$) and the controls ($0.08{\pm}0.09$), which was statistically significant. The MMP-9 level of the IPF ($0.31{\pm}0.058$) and the stable group ($0.22{\pm}0.078$) were higher than that of the control group ($0.002{\pm}0.004$). In the AM-CM, only MMP-9 was detected, which was significantly higher in IPF group ($0.80{\pm}0.1O$) than in the control group($0.23{\pm}0.081$). The TIMP-1 level was also higher in both the IPF ($36.34{\pm}8.62\;{\mu}g/ml$) and stable group ($20.83{\pm}8.53\;{\mu}g/ml$) compared to the control group ($2.80{\pm}1.05\;{\mu}g/ml$) (p<0.05). 3) There was a correlation between the MMP-2 level in the BALF with the total cell number(r=0.298) and neutrophils(r=0.357) (p<0.05), and the MMP-9 level with the number of neutrophils (r=0.407) and lymphocytes (r=0.574)(p<0.05). The TIMP-1 level correlated with the total number of cell (r=0.338, p<0.05) and neutrophils(r=0.449, p=0.059). Conclusion : Both MMP and TIMP appear to play an important role in IPF pathogenesis, and their level may reflect the disease activity.