• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.124-134
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• 2019

An analytical method was developed for the determination of broflanilide and its metabolites in agricultural products. Sample preparation was conducted using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method and LC-MS/MS (liquid chromatograph-tandem mass spectrometer). The analytes were extracted with acetonitrile and cleaned up using d-SPE (dispersive solid phase extraction) sorbents such as anhydrous magnesium sulfate, primary secondary amine (PSA) and octadecyl ($C_{18}$). The limit of detection (LOD) and quantification (LOQ) were 0.004 and 0.01 mg/kg, respectively. The recovery results for broflanilide, DM-8007 and S(PFP-OH)-8007 ranged between 90.7 to 113.7%, 88.2 to 109.7% and 79.8 to 97.8% at different concentration levels (LOQ, 10LOQ, 50LOQ) with relative standard deviation (RSD) less than 8.8%. The inter-laboratory study recovery results for broflanilide and DM-8007 and S (PFP-OH)-8007 ranged between 86.3 to 109.1%, 87.8 to 109.7% and 78.8 to 102.1%, and RSD values were also below 21%. All values were consistent with the criteria ranges requested in the Codex guidelines (CAC/GL 40-1993, 2003) and the Food and Drug Safety Evaluation guidelines (2016). Therefore, the proposed analytical method was accurate, effective and sensitive for broflanilide determination in agricultural commodities.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Radiation Oncology Journal
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• v.15 no.1
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• pp.27-36
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• 1997

Purpose : This study was tried to evaluate the Potential benefits of concurrent chemoradiation therapy (low dose daily cisplatin combined with split course radiation therapy) compared with conventional radiation therapy alone in stage III non-small cell lung cancer. The end points of analyses were response rate. overall survival, survival without locoregional failure, survival without distant metastasis, prognostic factors affecting survival and treatment related toxicities. Materials and Methods : Between April 1992 and March 1994, 32 patients who had stage III non-small cell lung cancer were treated with concurrent chemoradiation therapy. Radiation therapy for 2 weeks (300 cGy given 10 times up to 3000 cGy) followed by a 3 weeks rest period and then radiation therapy for 2 more weeks (250 cGy given 10 times up to 2500 cGy) was combined with $6mg/m^2$ of cisplatin. Follow-up period ranged from 13 months to 48 months with median of 24 months. Historical control group consisted of 32 patients who had stage III non-small cell lung cancer were received conventionally fractionated (daily 170-200 cGy) radiation therapy alone. Total radiation dose ranged from 5580 cGy to 7000 cGy with median of 5940 cGy. Follow-up Period ranged from 36 months to 105 months with median of 62 months. Result : Complete reponse rate was higher in chemoradiation therapy (CRT) group than radiation therapy (RT) group (18.8% vs. 6.3%, CRT group showed lower in-field failure rate compared with RT group(25% vs. 47%. The overall survival rate had no significant differences in between CRT group and RT group (17.5% vs. 9.4% at 2 years). The survival without locoregional failure (16.5% vs. 5.3% at 2 years) and survival without distant metastasis (17% vs. 4.6% at 2 years) also had no significant differences. In subgroup analyses for Patients with good performance status (Karnofsky performance scale 80), CRT group showed significantly higher overall survival rate compared with RT group (62.5% vs. 15.6% at 2 years). The prognostic factors affecting survival rate were performance status and pathologic subtype (squamous cell cancer vs. nonsquamous cell cancer) in CRT group. In RT alone group, performance status and stage (IIIa vs IIIb) were identified as a Prognostic factors. RTOG/EORTC grade 2-3 nausea and vomiting(22% vs 6% and bone marrow toxicities (25% vs. 15.6% were significantly higher in CRT group compared with RT alone group. The incidence of RTOG/EORTC grade 3-4 pulmonary toxicity had no significant differences in between CRT group and RT group (16% vs. 6%. The incidence of WHO grade 3-4 pulmonary fibrosis also had no significant differences in both group (38% vs. 25%. In analyses for relationship of field size and Pulmonary toxicity, the Patients who treated with field size beyond 200cm2 had significantly higher rates of pulmonary toxicities. Conclusion : The CRT group showed significantly higher local control rate than RT group. There were no significant differences of survival rate in between two groups. The subgroup of patients who had good performance status showed higher overall survival rate in CRT group than RT group. In spite of higher incidence of acute toxicities with concurrent chemoradiation therapy, the survival gain in subgroup of patients with good performance status were encouraging. CRT group showed higher rate of early death within 1 year, higher 2 year survival rate compared with RT group Therefore, to evaluate the accurate effect on survival of concurrent chemoradiation therapy, systematic follow-up for long term survivors are needed.

• Korean Journal of Veterinary Research
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• v.9 no.1
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• pp.23-62
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• 1969

Throughout the studies the following experimental results were obtained and are summarized: 1. Multiplication of agents in primary cell cultures of both GF classical and CR-64 acute strain of Marek's disease infected chicken kidneys was accompanied by the formation of distinct transformed cell foci. This characteristic nature of cell transformation was passaged regularly by addition of dispersed cell from infected cultures to normal chicken kidney cell cultures, and also transferred was the nature of cell transformation to normal chick-embryo liver and neuroglial cell cultures. No cytopathic changes were noticed in inoculated chick-embryo fibroblast cultures. 2. The same cytopathic effects were noticed in normal kidney cell monolayers after the inoculation of whole blood and huffy coat cells derived from both forms of Marek's disease infected chickens. In these cases, however, the number of transformed cell foci appearing was far less than that of uninoculated monolayers prepared directly from the kidneys of Marek's disease infected chickens. 3. The change in cell culture IS regarded as a specific cell transformation focus induced by an oncogenic virus rather than it plaque in slowly progressing cytopathic effect by non-oncogenic viruses, and it is quite similar to RSV focus in chick-embryo fibroblasts in many respects. 4. The infective agent (cell transformable) were extremely cell-associated and could not be separated in an infective state from cells under the experimental conditions. 5. The focus assay of these agents was valid as shown by the high degree of linear correlation (r=0.97 and 0.99) between the relative infected cell concentration (in inoculum) and the transformed cell foci counted. 6. No differences were observed between the GF classical strain and the CR-64 acute strain of Marek's disease as far as cell culture behavior. 7. Characterization of the isolates by physical and chemical treatments, development of internuclear inclusions in Infected cells, and nucleic acid typing by differential stainings and cytochemical treatments indicated that the natures of these cell transformation agents closely resemble to those described fer the group B herpes viruses. 8. Susceptible chicks inoculated with infected kidney tissue culture cells developed specific lesions of Marek's disease, and in a case of prolonged observation after inoculation (5 weeks) the birds developed clinical symptoms and gross lesions of Marek's disease. Kidney cell cultures prepared from those inoculated birds and sacrificed showed a superior recovery of cell transformation property by formation of distinct foci. 9. Electron microscopic study of infected kidney culture cells (GF agent) by negative staining technique revealed virus particles furnishing the properties of herpes viruses. The particle was measured about $100m{\mu}$ and, so far, no herpes virus envelop has been seen from these preparations. 10. No relationship of both isolates to avian leukosis/sarcoma group viruses and PPLO was observed.

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 1995.11a
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• pp.1-48
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• 1995

식생활은 인간 생활의 주체이고 먹는다는 것은 그 수단이다. 그중 중요한 하나의 명제는 인간이 놓여진 여러 환경에서 어떻게 건강을 유지하고 그 개체가 소유하고 있는 능력을 최대치까지 생리적으로 성장 발전시킴과 동시에 최대한 수명을 연장시키기 위한 식물 섭취방법을 마이크로 레벨까지 해명하는데 있다. 인간은 일생동안 엄청난 양의 음식물을 먹는다(70세 수명일 경우 200만 파운드 즉 체중의 1,400배). 그러나 먹기는 먹되 자신의 건강과 장수를 위하여 어떤 음식을 어떻게 선택하여 어떻게 먹어야 하는 문제가 매우 중요하다. 최근 우리나라도 국민 소득이 늘면서 식생활은 서구화 경향으로 기우는 듯하다. 공해를 비롯한 수입식품 등 여러 가지 문제점이 제기됨에 따라 자연식과 건강식을 주장하는 소리가 높이 일고 있다. 그중에는 축산 식품이 콜레스테롤 함량이 다른 식품에 비하여 높게 함유하고 있다는 것으로 심혈관질환의 주범인양 무차별 강조하는 나머지 육식공포 내지는 계란 등의 혐오감 마저 불러 일으키는 경향까지 있는 듯하다. 따라서 본논고에서는 축산식품중의 콜레스테롤 함량수준이 과연 성인병의 주범인지 아니면 다른 지방산과 관련해서 올바르게 평가하고 그 문제점과 대책을 개관해 보고 요약하면 다음과 같다. 1. 사람은 유사이래 본능적으로 주변의 식물이나 동물의 고기를 먹고 성장하여 자손을 증식시키고 어느 사이에 늙으면 죽음을 맞이 하는 싸이클을 반복하면서 기나긴 세월동안 진화를 하여 오늘날의 인간으로서의 자태를 이루었다. 유인원과 같은 인류의 선조들은 수렵을 통해 육식을 많이 하였을 것이므로 인간은 원래 육식동물이 아닐까? 구석기시대의 유물을 보면 많은 뼈가 출토되고 “얄타미라”나 “라스코” 동굴벽화가 선명하게 묘사되고 있다. 2. 우리나라 선조 승구족의 일파가 백두산을 비롯한 만주 송화강 유역에 유입되면서 수렵과 목축을 주요 식품획득의 수단으로 식품문화권을 형성하면서 남하하여 한반도 민족의 조상인 맥족(貊族)으로 맥적(貊炙)이라고 하는 요리(오늘날의 불고기)를 먹었다는 기록이 있다. 3. 인간의 수명을 1900년대로 거슬러 올라가서 뉴질랜드가 세계최장수국(호주는 2위)로서 평균수명은 남자 58세, 여자 69세인 반면 일본과 한국은 당시 남자 36세, 여자 37세이던 것이 일본은 1989년에 이르러 세계 최장수국으로 등장했으나 1990년 당시 뉴질랜드${\cdot}$호주 등은 목축 및 밀(小麥) 생산국가였기 때문이라는 것과 일본은 오늘날 합리적인 식생활 국가라는 것을 간과해서는 안된다. 4. 우리나라 10대 사망원인중 (1994년도) 뇌혈관질환이 1위, 교통사고 2위, 암이 3위 순위로서 연령별로는 10~30대의 불의의 사고(교통사고), 40~60대는 암, 70대 이상은 뇌혈관질환이 가장 많다. 구미${\cdot}$일 7개국 정상국가들은 심질환 사망이 가장 높다. 5. 식생활의 변화에 있어서 우리나라는 주식으로 섭취해 왔던 곡류는 70년 대비 94년에는 0.7배 감소된 반면 육류 5배, 계란 2.4배, 우유는 무려 29.3배 증가되었다. 식생활 패턴이 서구화 경향으로 바뀌는 것 같다. 6. 71년도 우리나라의 지질섭취량은 국민 1인당 1일 평균 13.1g에 섭취에너지의 5.7%수준이었으나 92년도에는 34.5g으로서 총에너지 섭취량의 16.6%에 달하고 총섭취 지방질중 동물성 섭취 비율은 47%를 차지 한다. 국민 평균 혈청콜레스테롤 농도는 80년에 비해 88년에는 11%가 증가되었고 80년에 210mg/dl 이상 되는 콜레스테롤 혈증인 사람의 비율이 5%에서 88년에는 23%로 크게 증가했다. 7. 세계 정상국가들의 단백질 즉 축산식품의 섭취는 우리나라보다 적게는 2배, 많게는 6~7배 더 섭취하고 90년도 우리나라의 지질섭취량은 일본의 1/3수준에 불과하다. 8. 콜레스테롤은 인체를 비롯한 모든 동물체에 필수적으로 분포하고 있는 것으로 체내 존재하고 있는 총량은 90~150g, 이중 혈청콜레스테롤은 4%(6g)를 차지하고 있음에도 불구하고 이 아주 적은 콜레스테롤에 일희일비(一喜一悲) 논쟁은 60~70년 끄러오고 있다. 9. 콜레스테롤의 생체내 기능으로서는 (1) 세포벽의 지지물질 (2) 신경세포 보호막물질 (3) 담즙산의 합성 (4) 비타민 D의 합성 (5) 임신시에 반듯이 필요한 분자 (6) 기타 여러 가지 기능을 수행하는 것으로 필수적인 물질이다. 10. 우리가 식이를 통해서 섭취 콜레스테롤을 550mg정도를 섭취한다고 하더라도 이 정도의 양은 배설 소모되는 양과 거의 맞먹는 양이다. 피부와 땀샘에서 소실되는 양만도 100~300mg에 달하기 때문에 미국농무성에서 섭취량을 300mg로 제한하는 것은 무의미하다. 11. 콜레스테롤 운반체로서의 지단백질은 그 밀도가 낮은 것으로부터 킬로미크론(chylomicron), 초저밀도 지단백질(VLDL), 저밀도 지단백질(LDL) 및 고밀도 지단백질(HDL)으로 나누는데 LDL은 혈청콜레스테롤 중 약 70%, HDL은 약20%를 함유한다. 12. 혈중 콜레스테롤 수준에 영향을 미치는 요인을 열거하여 보면 다음과 같다. 1) 음식을 통해서 섭취되는 콜레스테롤 중 단지 10~40%정도가 흡수되고, 체내에서 합성되는 콜레스테롤이 증가할수록 식이콜레스테롤은 실제 혈청콜레스테롤 수준에 거의 영향을 미치지 않으므로 식이중함량에 대하여 공포를 느끼고 기피할 필요가 없다. 2) 고도불포화지방산, 단가불포화지방산, 포화지방산의 비 즉 P/M/S의 비가 균형되도록 권장한다. 3) 동맥경화를 비롯한 성인병의 원인이 되는 혈전증에는 EPA의 양을 높여줌으로서 성인병을 예방할 수 있다. 4) 오메가6지방산 아라키도닉산과 오메가3지방산인 EPA로 유도되는 에이코사에노이드 또는 프로스타노이드는 오메가6와 3지방산을 전구체로 하여 생합성되는 중요한 생리활성 물질이다. 5) 사람은 일반적으로 20세에서 60세까지 나이를 먹어감에 따라 혈중 콜레스테롤 수준이 증가하고 60세 이후부터는 일정한 수준을 유지하며 심장보호성 HDL-콜레스테롤은 감소하는 반면에 죽상경화성 LDL콜레스테롤은 증가한다. 6) 높은 HDL콜레스테롤 수준이 심장병 발생 위험요인을 감소시키는 기능을 갖고 있기 때문에 좋은 HDL이라 부르고, LDL은 나쁜 콜레스테롤이라 부르기도 하는데, 이것은 유전적 요인보다도 환경적 요인이 보다 큰 영향을 미친다. 7) 이것은 생활 형태와 영양섭취상태를 포함해서 개인적 생활패턴에 영향을 받는다. 8) 많은 실험에서 혈중 콜레스테롤 상승은 노년의 가령(加齡)에 적응하기 위한 자연적 또는 생리적인 세포의 생화학적이고 대사적인 기능을 위해 필수적일 수 있다는 것을 간과해서는 안될 것이다. 이 점으로 미루어 노년의 여성들을 위한 콜레스테롤 농도를 200mg/dl이 가장 알맞은 양이 아닌 듯하다. 9) 스트레스는 두가지 모양으로 유발되는데 해로은 스트레스(negative), 이로운 스트레스(positive)로서 긴장완화는 혈중 콜레스테롤 농도를 10% 떨어진다. 10) 자주 운동을 하는 사람들은 혈중 HDL콜레스테롤치가 운동을 하지 않는 사람보다 높다. 육체적인 운동의 정도와 혈중 HDL콜레스테롤 농도와는 정비례한다. 11) 흡연은 지방을 흡착시키므로 혈전증의 원이이 되며 혈관속의 HDL농도를 감소시킨다. 12) 에너지의 과잉섭취에 의한 체중 증가느 일반적으로 지단백질대사에 영향을 미치고, 간에서는 콜레스테롤 과잉 생산과 더불어 VLDL콜레스테롤의 LDL콜레스테롤 혈증을 나타냄으로 운동과 더불어 비만이 되지 않도록 하여야 한다. 13. 콜레스테롤 함량에 대한 조절기술 1) 식품의 우열을 평가할 때 단순히 동물성 또는 식물성 식품으로 분류해서 총괄적으로 논한다는 것은 지양되어야 한다. 이것은 그 식품에 함유하고 있는 지방산의 종류에 따라서 다르기 때문이다. 2) 인체의 원할한 기능 유지를 위해서는 P /M /S비율 뿐만 아니라 섭취 지방질의 오메가6 /오메가3계 지방산의 비율이 모두 적절한 범위에 있어야 하며 한두가지 지방산만이 과량일 때는 또 다른 불균형을 일으킬 수 있다는 점을 알아야 한다. 3) 닭고기는 오메가6지방산 함량을 높이기 위하여 사료중에 등푸른 생선이나 어분이나 어유를 첨가하여 닭고기는 첨가수준에 따라 증가됨을 알 수 있다. 4) 오늘날 계란내의 지방산 조성을 변화시켜 난황내의 오메가 3계열 지방산 함량을 증가시킨 계란의 개발이 활발해졌다. 14. 계란 콜레스테롤에 대한 소비자들의 부정적 인식을 불식시키고자 계란의 클레스테롤 함량을 낮추는 과제가 등장하면서 그 기술개발이 여러모로 시도되고 있으나 아직 실용 단계에 이르지 못했다. 15. 계란의 콜레스테롤 문제에 대한 대책으로서 난황의 크기를 감소시키는 방법에 대한 연구도 필요하다. 16. 계란 중 콜레스테롤 함량 분석치는 표현 방식에 따라서 소비자들을 혼란시킬 가능성이 있다. 또한 과거에는 비색법으로 분석했으나 오늘날은 효소법으로 분석하면 분석치에 상당한 차이가 있다. 17. 소비자의 요구를 만족시키고 버터 소비를 촉진시키기 위해 콜레스테롤을 감소시키는 물리적${\cdot}$생물학적 방식이 제안되어 있으나 현장적용이 가능한 것은 아직 없다. 18. 우리나라에서 이미 시판되고 있는 DHA우유가 선보였고 무콜레스테롤 버터의 경우 트란스(trans)형 지방산에 관해서는 논란의 여지가 많을 것이다. 끝으로 국가 목표의 하나는 복지사회 건설에 있고 복지국가 실현에는 국민 기본 욕망의 하나인 식생활 합리화가 선행되어야 한다. 소득이 늘고 국가가 발전해감에 따라 영양식${\cdot}$건강식 및 기호식을 추구하게 됨을 매우 당연한 추세라 하겠다. 우리의 식생활이 날로 향상되어 지난날의 당질 위주에서 점차 축산물쪽으로 질적 개선이 이루어진다는 것은 고무적임에 틀림없다. 이 축산물을 통한 풍요로운 식의 문화를 창출하면서 건강과 장수 그리고 후손에 이르기까지 번영하고 국가 경쟁력 강화에 심혈을 기우려야 할 때이다.

• Tuberculosis and Respiratory Diseases
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• v.41 no.4
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• pp.379-388
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• 1994

Background : In sleep apnea syndrome, arterial oxygen saturation($SaO_2$) decreases at a variable rate and to a variable degree for a given apneic period from patient to patient, and various kinds of cardiac arrythmia are known to occur. Factors supposed to affect arterial oxygen desaturation during apnea are duration of apnea, lung voulume at which apnea occurs, and oxygen consumption rate of the subject. The lung serves as preferential oxygen source during apnea, and there have been many reports related with the influence of lung volume on $SaO_2$ during apnea, but there are few, if any, studies about the influence of oxygen consumption rate of an individual on $SaO_2$ during breath holding or about the profile of arterial oxygen resaturation after breathing resumed. Methods : To investigate the changes of $SaO_2$ and heart rate(HR) during breath holding(BH) and rebreathing(RB) and to evaluate the physiologic factors responsible for the changes, lung volume measurements, and arterial blood gas analyses were performed in 17 healthy subjects. Nasal airflow by thermistor, $SaO_2$ by pulse oxymeter and ECG tracing were recorded on Polygraph(TA 4000, Gould, U.S.A.) during voluntary BH & RB at total lung capacity(TLC), at functional residual capacity(FRC) and at residual volume(RV), respectively, for the study subjects. Each subject's basal metabolic rate(BMR) was assumed on Harris-Benedict equation. Results: The time needed for $SaO_2$ to drop 2% from the basal level during breath holding(T2%) were $70.1{\pm}14.2$ sec(mean${\pm}$standard deviation) at TLC, $44.0{\pm}11.6$ sec at FRC, and $33.2{\pm}11.1$ sec at RV(TLC vs. FRC, p<0.05; FRC vs. RV, p<0.05). On rebreathing after $SaO_2$ decreased 2%, further decrement in $SaO_2$ was observed and it was significantly greater at RV($4.3{\pm}2.1%$) than at TLC($1.4{\pm}1.0%$)(p<0.05) or at FRC($1.9{\pm}1.4%$)(p<0.05). The time required for $SaO_2$ to return to the basal level after RB(Tr) at TLC was not significantly different from those at FRC or at RV. T2% had no significant correlation either with lung volumes or with BMR respectively. On the other hand, T2% had significant correlation with TLC/BMR(r=0.693, p<0.01) and FRC/BMR (r=0.615, p<0.025) but not with RV/BMR(r=0.227, p>0.05). The differences between maximal and minimal HR(${\Delta}HR$) during the BH-RB manuever were $27.5{\pm}9.2/min$ at TLC, $26.4{\pm}14.0/min$ at RV, and $19.1{\pm}6.0/min$ at FRC which was significantly smaller than those at TLC(p<0.05) or at RV(p<0.05). The mean difference of 5 p-p intervals before and after RB were $0.8{\pm}0.10$ sec and $0.72{\pm}0.09$ sec at TLC(p<0.001), $0.82{\pm}0.11$ sec and $0.73{\pm}0.09$ sec at FRC(p<0.025), and $0.77{\pm}0.09$ sec and $0.72{\pm}0.09$ sec at RV(p<0.05). Conclusion Healthy subjects showed arterial desaturation of various rates and extent during breath holding at different lung volumes. When breath held at lung volume greater than FRC, the rate of arterial desaturation significantly correlated with lung volume/basal metabolic rate, but when breath held at RV, the rate of arterial desaturation did not correlate linearly with RV/BMR. Sinus arrythmias occurred during breath holding and rebreathing manuever irrespective of the size of the lung volume at which breath holding started, and the amount of change was smallest when breath held at FRC and the change in vagal tone induced by alteration in respiratory movement might be the major responsible factor for the sinus arrythmia.

• Radiation Oncology Journal
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• v.17 no.2
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• pp.100-107
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• 1999

Purpose: This is to evaluate the acute complication, resection rate, and tumor down-staging after pre-operative concurrent chemoradiotherapy for stage IIIA (N2) non-small cell lung cancer. Materials and Methods Fifteen patients with non-small cell lung cancer were enrolled in this study from May 1997 to June 1998 in Samsung Medical Center. The median age of the patients was 61 (range, 45~67) years and male to female ratio was 12:3. Pathologic types were squamous cell carcinoma (11) and adenocarcinoma (4). Pre-operative clinical tumor stages were cT1 in 2 patients, cT2 in T2, and cT3 in 1 and all were N2. Ten patients were proved to be N2 with mediastinoscopic biopsy and five had clinically evident mediastinal Iymph node metastases on the chest CT scans. Pre-operative radiation therapy field included the primary tumor, the ipsilateral hilum, and the mediastinum. Total radiation dose was 45 Gy over 5 weeks with daily dose of 1.8 Gy. Pre-operative concurrent chemotherapy consisted of two cycles of intravenous cis-Platin (100 mg/m$^{2}$) on day 1 and oral Etoposide (50 mg/m$^{2}$/day) on days 1 through 14 with 4 weeks' interval. Surgery was followed after the pre-operative re-evaluation including chest CT scan in 3 weeks of the completion of the concurrent chemoradiotherapy if there was no evidence of disease progression. Results : Full dose radiation therapy was administered to all the 15 patients. Planned two cycles of chemotherapy was completed in 11 patients and one cycle was given to four. One treatment related death of acute respiratory distress syndrome occurred In 15 days of surgery. Hospital admission was required in three patients including one with radiation pneumonitis and two with neutropenic fever. Hematologic complications and other acute complications including esophagitis were tolerable. Resection rate was 92.3% (12/l3) in 13 patients excluding two patients who refused surgery. Pleural seeding was found in one patient after thoracotomy and tumor resection was not feasible. Post-operative tumor stagings were pT0 in 3 patients, pTl in 6, and pT2 in 3. Lymph node status findings were pN0 in 8 patients, pN1 in 1, and pN2 in 3. Pathologic tumor down-staging was 61.5% (8/13) including complete response in three patients ($23.7%). Tumor stage was unchanged in four patients (30.8%) and progression was in one (7.7%). Conclusions : Pre-operative concurrent chemoradiotherapy for Stage IIIA (N2) non-small cell lung cancer demonstrated satisfactory results with no increased severe acute complications. This treatment shceme deserves more patinet accrual with long-term follow-up.