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Study of BiJeung by 18 doctors - Study of II - (18인(人)의 비증(痺證) 논술(論述)에 대(對)한 연구(硏究) - 《비증전집(痺證專輯)》 에 대(對)한 연구(硏究) II -)

  • Sohn, Dong Woo;Oh, Min Suk
    • Journal of Haehwa Medicine
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    • v.9 no.1
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    • pp.595-646
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    • 2000
  • I. Introduction Bi(痺) means blocking. BiJeung is one kind of symptoms making muscles, bones and jonts feel pain, numbness or edema. For example it can be gout or SLE etc. says that Bi is combination of PungHanSeup. And many doctors said that BiJeung is caused by food, fatigue, sex, stress and change of weather. Therefore we must treat BiJeung by character of patients and characteristic of the disease. Many famous doctors studied medical science by their fathers or teachers. So the history of medical science is long. So I studied ${\ll}Bijeungjujip{\gg}$. II. Final Decision 1. JoGeumTak(趙金鐸) devided BiJeung into Pung, Han, Seup and EumHeo, HeulHeo, YangHeo, GanSinHeo by charcter or reaction of pain. And he use DaeJinGyoTang, GyegiGakYakJiMoTang, SamyoSan, etc. 2. JangPaeGyeu(張沛圭) focused on division of HanYeol(寒熱; coldness and heat) in spite of complexity of BiJeung. He also used insects for treatment. They are very useful for treatment of BiJeung because they can remove EoHyeol(瘀血). 3. SeolMaeng(薛盟) said that the actual cause of BiJeung is Seup. So he thought that BiJeung can be divided into PungSeup, SeupYeol, HanSeup. And he established 6 rules to treat BiJeung and he studied herbs. 4. JangGi(張琪) introduced 10 prescriptions and 10 rules to cure BiJeung. The 1st prescription is for OyeSa, 2nd for internal Yeol, 3rd for old BiJeung, 4th for Soothing muscles, 5th for HanSeup, 6th for regular BiJeung, 7th for functional disorder, 8th for YeolBi, 9th for joint pain and 10th for pain of lower limb. 5. GangSeYoung(江世英) used PungYeongTang(風靈湯) for the treatment of PungBi, OGyeHeukHoTang(烏桂黑虎湯) for HanBi, BangGiMokGwaTang(防己木瓜湯) for SeupBi, YeolBiTang(熱痺湯) for YeolBi, WoDaeRyeokTang(牛大力湯) for GiHei, HyeolPungGeunTang(血楓根湯) for HyeolHeo, ToJiRyongTang(土地龍湯) for the acute stage of SeupBi, OJoRyongTang(五爪龍湯) for the chronic stage of SeupBi, and so on. 6. ShiGeumMook(施今墨) devided BiJeung into four types. They are PungSeupYeol, PungHanSeup, GiHyeolSil(氣血實) and GiHyeolHeo(氣血虛). And he introduced the eight rules of the treatment(SanPun(散風), ChukHan(逐寒), GeoSeuP(, CheongYeol(淸熱), TongRak(通絡), HwalHyeol(活血), HaengGi(行氣), BoHeo(補虛)). 7. WangYiYou(王李儒) explained the acute athritis and said that it can be applicable to HaneBi(行痺). And he used GyeJiJakYakJiMoTang(桂枝芍蘂知母湯) for HanBi and YeolBiJinTongTang(熱痺鎭痛湯) for YeolBi. 8. JangJinYeo(章眞如) said that YeolBi is more common than HanBi. The sympthoms of YeolBi are severe pain, fever, dried tongue, insomnia, etc. And he devided YeolBi into SilYeol and HeoYeol. In case of SilYeol, he used GyeoJiTangHapBaekHoTang(桂枝湯合白虎湯) and in case of HeoYeol he used JaEumYangAekTang(滋陰養液湯). 9. SaHaeJu(謝海洲) introduced three important rules of treatment and four appropriate rules of treatment of BiJeung. 10. YouDoJu(劉渡舟) said that YeolBi is more common than HanBi. He used GaGamMokBanGiTang(加減木防已湯) for YeolBi, GyeJiJakYakJiMoTang or GyeJiBuJaTang(桂枝附子湯) for HanBi and WooHwangHwan(牛黃丸) for the joint pain. 11. GangYiSon(江爾遜) focused on the internal cause. The most important internal cause is JeongGiHeo(正氣虛). So he tried to treat BiJeung by means of balance of Gi and Hyeol. So he ususlly used ODuTang(烏頭湯) and SamHwangTang(三黃湯) for YeolBi, OJeokSan(五積散) for HanBi, SamBiTang(三痺湯) for the chronic BiJeung. 12. HoGeonHwa(胡建華) said that to distinguish YeolBi from Hanbi is very difficult. So he used GyeJiJakYakJiMoTang in case of mixture of HanBi and YeoBi. 13. PiBokGo(畢福高) said that the most common BiJeung is HanBi. He usually used acupuncture with medicine. He followed the theory of EumYongHwa(嚴用和)-he focused on SeonBoHuSa(先補後瀉). 14. ChoiMunBin(崔文彬) used GeoPungHwalHyeolTang(祛風活血湯) for HanBi, SanHanTongRakTang(散寒通絡湯) for TongBi(痛痺), LiSeupHwaRakTang(利濕和絡湯) for ChakBi(着痺), CheongYeolTongGyeolChukBiTang(淸熱通經逐痺湯) for YeolBi(熱痺) and GeoPungHwalHyeolTang(祛風活血湯) for PiBi(皮痺). 15. YouleokSeon(劉赤選) introduced the common principle for the treatment of BiJeung. He used HaePuneDeungTang(海風藤湯) for HaengBi(行痺), SinChakTang(腎着湯), DokHwalGiSaengTang(獨活寄生湯) for TongBi(痛痺), TongPungBang(痛風方) for ChakBi(着痺) and SangGiYiMiTangGaYeongYangGakTang(桑枝苡米湯加羚羊角骨) for YeolBi(熱痺). 16. LimHakHwa(林鶴和) said about TanTan(movement disorders or numbness) and devided TanTan into the acute stage and the chronic stage. He used acupuncture at the meridian spot like YeolGyeol(列缺), HapGok(合谷), etc. And he also used MaHwangBuJaSeSinTang(麻黃附子細辛湯) in case of the acute stage. In the chronic stage he used BangPungTang(防風湯). 17. JinBaekGeun(陳伯勤) liked to use three rules(HwaHyeol(活血), ChiDam(治痰), BoSin(補腎)) to treat BiJeung. He used JinTongSan(鎭痛散) for the purpose of HwalHyeol(活血), SoHwalRakDan(小活絡丹) for ChiDam(治痰) and DokHwalGiSaengTang(獨活寄生湯) for BoSin(補腎). 18. YimGyeHak(任繼學) focused on YangHyeolJoGi(養血調氣) if the stage of BiJeung is chronic. And in the chronic stage he insisted on not using GalHwal(羌活), DokHwal(獨活) and BangPung(防風).

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Development of the Accident Prediction Model for Enlisted Men through an Integrated Approach to Datamining and Textmining (데이터 마이닝과 텍스트 마이닝의 통합적 접근을 통한 병사 사고예측 모델 개발)

  • Yoon, Seungjin;Kim, Suhwan;Shin, Kyungshik
    • Journal of Intelligence and Information Systems
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    • v.21 no.3
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    • pp.1-17
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    • 2015
  • In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

Antimicrobial, Antioxidant and Cellular Protective Effects against Oxidative Stress of Anemarrhena asphodeloides Bunge Extract and Fraction (지모 뿌리 추출물과 분획물의 항균활성과 항산화 활성 및 세포보호 연구)

  • Lee, Yun Ju;Song, Ba Reum;Lee, Sang Lae;Shin, Hyuk Soo;Park, Soo Nam
    • Microbiology and Biotechnology Letters
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    • v.46 no.4
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    • pp.360-371
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    • 2018
  • Extracts and fractions of Anemarrhena asphodeloides Bunge were prepared and their physiological activities and components were analyzed. Antimicrobial activities of the ethyl acetate and aglycone fractions were $78{\mu}g/ml$ and $31{\mu}g/ml$, respectively, for Staphylococcus aureus and $156{\mu}g/ml$ and $125{\mu}g/ml$, respectively, for Pseudomonas aeruginosa. 1,1-Diphenyl-2-picrylhydrazyl free radical scavenging activities ($FSC_{50}$) of 50% ethanol extract, ethyl acetate fraction, and aglycone fraction of A. asphodeloides extracts were $146.2{\mu}g/ml$, $23.19{\mu}g/ml$, and $71.06{\mu}g/ml$, respectively. The total antioxidant capacity ($OSC_{50}$) in an $Fe^{3+}$-EDTA/hydrogen peroxide ($H_2O_2$) system were $17.5{\mu}g/ml$, $1.5{\mu}g/ml$, and $1.4{\mu}g/ml$, respectively. The cytoprotective effect (${\tau}_{50}$) in $^1O_2$-induced erythrocyte hemolysis was 181 min with $4{\mu}g/ml$ of the aglycone fraction. The ${\tau}_{50}$ of the aglycone fraction was approximately 4-times higher than that of (+)-${\alpha}$-tocopherol (${\tau}_{50}$, 41 min). Analysis of $H_2O_2$-induced damage of HaCaT cells revealed that the maximum cell viabilities for the 50% ethanol extract, ethyl acetate fraction, and aglycone fraction were 86.23%, 86.59%, and 89.70%, respectively. The aglycone fraction increased cell viability up to 11.53% at $1{\mu}g/ml$ compared to the positive control treated with $H_2O_2$. Analysis of ultraviolet B radiation-induced HaCaT cell damage revealed up to 41.77% decreased intracellular reactive oxygen species in the $2{\mu}g/ml$ aglycone fraction compared with the positive control treated with ultraviolet B radiation. The findings suggest that the extracts and fractions of A. asphodeloides Bunge have potential applications in the field of cosmetics as natural preservatives and antioxidants.