• Tuberculosis and Respiratory Diseases
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• v.42 no.1
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• pp.58-66
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• 1995

Background: Acute interstitial pneumonia is a relatively rare form of interstitial pneumonia, since the vast majority of interstitial pneumonia have a more chronic course. It corresponds to the lesion described by Hamman and Rich, as Hamman-Rich disease in 1944. Another name in the clinical literature is accelerated interstitial pneumonia, idiopathic acute respiratory distress syndrome (idiopathic ARDS), and the organizing stage of diffuse alveolar damage. Acute interstitial pneumonia differs from chronic interstitial pneumonia by clinical and pathologic features. Clinically, this disease is characterized by a sudden onset and a rapid course, and reversible disease. Method and Purpose: Five cases of pathologically proven acute interstitial pneumonia were retrospectively studied to define the clinical, radiologic, and pathologic features. Results: 1) The five cases ranged in age from 31 to 77 years old. The onset of illness was acute in all patients, it began with viral-like prodrome 6~40 days prior to shortness of breath, and respiratory failure eventually developed in all patients. In 2 cases, generalized skin rash was accompanied with flu-like symptoms. Etiologic agent could not be identified in any case. 2) All patients had leukocytosis and severe hypoxemia. Pulmonary function test of 3 available cases shows restrictive ventilatory defect, and one survived patient(case 5) has a complete improvement of pulmonary function after dismissal. 3) Diffuse bilateral chest infiltrates were present radiologically. Theses were the ground-glass, consolidation, and reticular densities without honeycomb fibrosis in all patients. The pathologic abnormalities were the presence of increased numbers of macrophages and the formation of hyaline membranes within alveolar spaces. There was also interstitial thickening with edema, proliferation of immature fibroblast, and hyperplasia of type II pneumocyte. In the survived patient(case5), pathologic findings were relatively early stage of acute interstitial pneumonia, such as hyaline membrane with mild interstitial fibrosis. 4) Of the 5 patients, four patients died of respiratory failure 14~90 days after onset of first symptom, and one survived and recovered in symptoms, chest X ray, and pulmonary function test Conclusion: These results emphasize that acute interstitial pneumonia is clinically, radiologically, and pathologically distinct form of interstitial pneumonia and should be separated from the group of chronic interstitial pneumonia. Further studies will be needed to evaluate the pathogenesis and the treatment of acute interstitial pneumonia.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.3 no.1
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• pp.28-30
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• 1978

Communication actions as a social band or Human community stick (fast) to human being ceaselessly w without stopping everywhere. All of comm. actions can be kept up and developed by the education of its own. Comm. actions have to include a character of social process, and so for it the social science should t to be some essential part of it. Therefore, Comm. education have to be schemed for achieving with a point of view of synthetical s science including technical and social factor. However, recentry Comm. education be suffered to lose of itowns essential attribute and individual i independence becausing to reduce social weight recklessly in their education It is a prindiple that Comm. science is an integrate science being composed of human, social and t technical subdepartments and so comm. education have to obey for Comm. constuctional theory, i international and social claim. Originally in Korea a educational idea and genealogy forming by the comm. scientific theory has I inherited on orthodoxy. But in 1961, communication college that is only the orthodox model of Comm. education, was f forced to close by some reckless policy and then the national administration for the Comm. education h have been weakened, and so recently it’s education became to degenerate as out of genealogy or n nonsystem alike some scattering Family. On the other side, today comm. science make to it’s modern scientific factor and to keep its l integrate level, therefore, all of educational provisions and administration for the telecomm. should t to be supplement to be fit for their plural chatacters. Comm. education have to occupy an individual educational system through the comm. theory, and t then it can be coexisted with neighbour scientific field equally and can include, connect coordinate o or effect its inference in each subfactor organically. Finally, educational system for telecommunication should to be requested as preeedence that i independent field including pluralism must be formed and sufficient autonomy be guarenteed, and s so Comm. education must be to restored its orthodox genealogy and be recovered individual system a and seIfrestraint field, and then it can be accomplished its own duty for nation and society.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of Dental Rehabilitation and Applied Science
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• v.24 no.2
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• pp.129-146
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• 2008

The aim of this study was to compare and analyze wear resistance of acrylic resin tooth in denture opposing to different types of restoration materials. Also, it aimed to compare and analyze the hardness of three various types of resin artificial teeth when using five different types of denture detergents. In this study three types of artificial teeth were used. As ordinary acrylic resin tooth $Trubyte^{(R)}Biotone^{(R)}$ (Dentsply, U.S.A.) was used, and as high hardness resin tooth Endura $Posterio^{(R)}$ (Shofu, Japan) and Physio $Duracross^{(R)}$ (Nissin, Japan) were used. To compare wear resistance, gold alloy, dental porcelain, and composite resin were used as opposing restorations. In addition, with three types of resin tooth stated above, five types of denture detergents, which are $Yuhanrox^{(R)}$ (Yuhanclorox, Korea), $Polident^{(R)}$ (Yuhan Co., Korea), $Cidex^{(R)}OPA$ (Johnson & Johnson Medical Co., Korea), $Hexamedin^{(R)}$ (Bukwang Pharm Co., Korea) and Daihan sterile $water^{(R)}$ (Daihan Pharm Co., Korea) were used to compare and analyze the effects denture detergents have on the surface hardness. The results of this study were as follow : 1. When composite resin and dental porcelain were used as the opposing restorations, $Trubyte^{(R)}$ $Biotone^{(R)}$, Endura $Posterio^{(R)}$, Physio $Duracross^{(R)}$ in ascending order showed decrease of cusp height with significant difference (p<0.05). 2. When gold alloy was used as opposing restoration, there was decrease in the cusp height in order of $Trubyte^{(R)}Biotone^{(R)}$, Endura $Posterio^{(R)}$, Physio $Duracross^{(R)}$ and $Trubyte^{(R)}$ $Biotone^{(R)}$ especially showed significant decrease in high cusp height as compared to two other types of artificial tooth (p<0.05). 3. When composite resin, gold alloy and dental porcelain were used as opposing restorations, $Trubyte^{(R)}$ $Biotone^{(R)}$, Endura $Posterio^{(R)}$, Physio $Duracross^{(R)}$ in order decreased greatly in weight and these three materials showed significant difference (p<0.05). 4. Comparing the experiment values, decrease in strength had significant difference in all resin artificial teeth before and after the denture detergents were used (p<0.05). 5. When resin artificial teeth were subsided in $Yuhanrox^{(R)}$, $Cidex^{(R)}OPA$, and $Hexamedin^{(R)}$, $Trubyte^{(R)}$ $Biotone^{(R)}$, Endura $Posterio^{(R)}$, Physio $Duracross^{(R)}$ in order showed decrease in hardness, and $Trubyte^{(R)}$ $Biotone^{(R)}$ especially showed significant high decrease hardness as to other two types of artificial teeth (p<0.05). Therefore, Physio $Duracross^{(R)}$ seems to have superior wear resistance, and together with Endura $Posterio^{(R)}$, it presents excellent hardness as compared to $Trubyte^{(R)}$ $Biotone^{(R)}$. However, since this study is fragmentary, it should be given careful consideration and more study need to be done before making a definitive conclusion.

• Journal of the korean academy of Pediatric Dentistry
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• v.31 no.3
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• pp.516-526
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• 2004

Polymerization shrinkage of photoinitiation type composite resin cause several clinical problems. The purpose of this study was to evaluate the shrinkage strain stress, linear polymerization shrinkage, compressive strength and microhardness of recently developed composite resins. The composite resins were divided into four groups according to the contents of matrix and filler type. Group I : $Denfil^{TM}$(Vericom, Korea) with conventional matrix, Group II : $Charmfil^{(R)}$(Dentkist, Korea) with microfiller and nanofller mixture, Group III : $Filtek^{TM}$ Z250(3M-ESPE, USA) TEGDMA replaced by UDMA and Bis-EMA(6) in the matrix, and Group IV : $Filtek^{TM}$ Supreme(3M-ESPE, USA) using pure nanofiller. Preparation of acrylic molds were followed by filling and curing with light gun. Strain gauges were attached to each sample and the leads were connected to a strainmeter. With strainmeter shrinkage strain stress and linear polymerization shrinkage was measured for 10 minutes. The data detected at 1 minute and 10 minutes were analysed statistically with ONE-way ANOVA test. To evaluate the mechanical properties of tested materials, compressive hardness test and microhardness test were also rendered. The results can be summarized as follows : 1. Filling materials in acrylic molds showed initial temporary expansion in the early phase of polymerization. This was followed by contraction with the rapid increase in strain stress during the first 1 minute and gradually decreased during post-gel shrinkage phase. After 1 minute, there's no statistical differences of strain stress between groups. The highest strain stress was found in group IV and followed by group III, I, II at 10 minutes-measurement(p>.05). In regression analysis of strain stress, group III showed minimal inclination and followed by group II, I, IV during 1 minute. 2. In linear polymerization shrinkage test, the composite resins in every group showed initial increase of shrinkage velocity during the first 1 minute, followed by gradually decrease of shrinkage velocity. After 1 minute, group IV and group III showed statistical difference(p<.05). After 10 minutes, there were statistical differences between group IV and group I, III(p<.05) and between group II and group III(p<.05). In regression analysis of linear polymerization shrinkage, group II showed minimal inclination and followed by group IV, III, I during 1 minute. 3. In compressive strength test, group III showed the highest strength and followed by group II, IV, I. There were statistical differences between group III and group IV, I(p<.05). 4. In microhardness test, upper surfaces showed higher value than lower surfaces in every group(p<.05).

• Tuberculosis and Respiratory Diseases
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• v.46 no.2
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• pp.204-214
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• 1999

Background: Resting pulmonary function tests(PFTs) are routinely used in the evaluation of pulmonary impairment/disability. But the significance of the cardiopulmonary exercise test(CPX) in the evaluation of pulmonary impairment is controvertible. Many experts believe that dyspnea, though a necessary part of the assessment, is not a reliable predictor of impairment. Nevertheless, oxygen requirements of an organism at rest are different from at activity or exercising, and a clear relationship between resting PFTs and exercise tolerance has not been established in patients with chronic pulmonary disease. As well, the relationship between resting PFTs and dyspnea is complex. To investigate the relationship of dyspnea, resting PFTs, and CPX, we evaluated the patients of stabilized chronic pulmonary disease with clinical dyspnea rating(baseline dyspnea index, BDI), resting PFTs, and CPX. Method: The 50 patients were divided into two groups: non-severe and severe group on basis of results of resting PFTs(by criteria of ATS), CPX(by criteria of ATS or Ortega), and dyspnea rating(by focal score of BDI). Groups were compared with respect to pulmonary function, indices of CPX, and dyspnea rating. Results: 1. According to the criteria of pulmonary impairment with resting PFTs, $VO_2$max, and focal score of BDI were significantly low in the severe group(p<0.01). According to the criteria of $VO_2$max(ml/kg/min) and $VO_2$max(%), the parameters of resting PFTs, except $FEV_1$ were not significantly different between non-severe and severe(p>0.05). According to focal score($FEV_1$(%), FVC(%), MW(%), $FEV_1/FVC$, and $VO_2$max were significantly lower in the severe group(p<0.01). However, in the more severe dyspneic group(focal score<5), only $VO_2$max(ml/kg/min) and $VO_2$max(%) were low(p<0.01). $FEV_1$(%) was correlated with $VO_2$max(%)(r=0.52;p<0.01), but not predictive of exercise performance. The focal score had the correlation with max WR(%) (r=0.55;p<0.01). Sensitivity and specificity analysis were utilized to compare the different criteria used to evaluate the severity of pulmonary impairment, revealed that the classification would be different according to the criteria used. And focal score for dyspnea showed similar sensitivity and specificity. Conclusion : According to these result, resting PFTs were not superior to rating of dyspnea in prediction of exercise performance in patients with chronic pulmonary diseases and less correlative with focal score for dyspnea than $VO_2$max and max WR. Therefore, if not contraindicated, CPX would be considered to evaluate the severity of pulmonary impairment in patients with chronic pulmonary diseases, including with severe resting PFTs. Current criteria used to evaluate the severity of impairment were insufficient in considering the degree of dyspnea, so new criteria, including the severity of dyspnea, may be necessary.

• Tuberculosis and Respiratory Diseases
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• v.47 no.5
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• pp.642-649
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• 1999

Background: It is very important to determine an accurate staging of the non-small cell lung cancer(NSCLC) for an assessment of operability and it's prognosis. However, it is difficult to evaluate tumor involvement of mediastinal lymph nodes accurately utilizing noninvasive imaging modalities. PET is one of the sensitive and specific imaging modality. Unfortunately PET is limited use because of prohibitive cost involved with it's operation. Recently hybrid SPECT/PET(single photon emission computed tomography/positron emission tomography) camera based PET imaging was introduced with relatively low cost. We evaluated the usefulness of coincidence detection(CoDe) PET in the detection of metastasis to the mediastinal lymph nodes in patients with NSCLC. Methods: Twenty one patients with NSCLC were evaluated by CT or MRI and they were considered operable. CoDe PET was performed in all 21 patients prior to surgery. Tomographic slices of axial, coronal and sagittal planes were visually analysed. At surgery, mediastinal lymph nodes were removed and histological diagnosis was performed. CoDe PET findings were correlated with histological findings. Results: Twenty of 21 primary tumor masses were detected by the CoDe PET. Thirteen of 21 patients was correctly diagnosed mediastinal lymph node metastasis by the CoDe PET. Pathological N0 was 14 cases and the specificity of N0 of CoDe PET was 64.3%. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of N1 node was 83.3%, 73.3%, 55.6%, 91.7%, and 76.2% respectively. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of N2 node was 60.0%, 87.5%, 60.0%,87.5%, and 90.0% respectively. There were 3 false negative cases but the size of the 3 nodes were less than 1cm. The size of true positive nodes were 1.1cm, 1.0cm, 0.5cm respectively. There were 1 false positive among the 12 lymph nodes which were larger than 1cm. False positive cases consisted of 1 tuberculosis case, 1 pneumoconiosis case and 1 anthracosis case. Conclusion: CoDe PET has relatively high negative predictive value in the enlarged lymph node in staging of mediastinal nodes in patients with NSCLC. Therefore CoDe PET is useful in ruling out metastasis of enlarged N3 nodes. However, further study is needed including more number of patients in the future.

• Tuberculosis and Respiratory Diseases
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• v.46 no.5
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• pp.697-708
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• 1999

Background : Leukotriene (LT) $C_4$, $D_4$, and $E_4$, the main components of slow-reacting substance of anaphylaxis (SRS-A), have been suggested to play an important role in bronchial asthma such as antigen-induced bronchoconstriction, airway hyperreactivity, and pulmonary eosinophil accumulation. The purpose of this study was to evaluate the effects of treatment with the cysteinyl-LTs (cys-LTs) antagonist, pranlukast on allergen-induced guinea pig asthma model. Methods : Guinea pigs of treatment and placebo groups were sensitized by subcutaneous injection of ovalbumin(OVA) and challenged by inhalation of aerosolized OVA (1% weight/volume OVA). Normal control group did not sensitize with OVA. Oral ingestion of pranlukast and normal saline to the treatment and placebo groups was performed. In the treatment and placebo groups, airway resistance was measured before and after oral ingestion. Serum $LTC_4$ and eosinophilic infiltration of the bronchiolar and peribronchiolar tissues were measured after ingestion in the treatment and placebo groups. Results : Allergen-induced airway constriction developed in 20 (8 in treatment group, 12 in placebo group) among 35 guinea pigs. Airway resistance was significantly decreased at 3 and 6 minutes after OVA challenge in the pranlukast treatment group. In the placebo group, there was no difference of airway resistance between before and after saline ingestion. Serum $LTC_4$ levels showed 348.4 pg/ml in the treatment group, 373.9 pg/ml in the placebo group, and 364.4 pg/ml in the control group. There were no statistically significant difference between treatment and placebo group (p=0.232), and treatment and control group (p=0.501). Eosinophilic infiltrations in the peribronchiolar region per one-microscopic field ($\times$400 high power fields) demonstrated 7.06 in the treatment group, 19.2 in the placebo group, and 4.50 in the control group. There was significant decrement of eosinophilic infiltration in the treatment group which was compared with placebo group (p=0.001). Conclusion : These results demonstrate that pranlukast, a cys-LTs receptor antagonist, can attenuate allergen induced early-phase bronchoconstriction and eosinophilic infiltration in the bronchiolar tissues.