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가족력을 동반한 von Hippel-Lindau Disease 1예
김병욱,민준기,전두수,채현석,한석원,정인식,선희식,박영섭,최창락 대한내과학회 1993 대한내과학회지 Vol.44 No.1
저자들은 23세 여자환자 및 그가족이 상염색체 우성으로 유전되는 가계도를 보이며, 소뇌 혈관종, 췌장 낭종 및 신장 낭종을 동반하는 von Hippel-Lindau disease 1예를 경험하였기에 문헌 고찰과 함께 보고하는 바이다. Von Hippel-Lindau disease is a rare disorder with multipe organ involvement including cerebellum, retina and visceral organs such as pancreas, kidney and epidydimis. Recently we experienced a case of von Hippel-Lindau disease in a family confirmed by operation for cerebellar tumor. A 23-year-old girl was referred from a local clinic for frequent hiccups and pancreatic cysts. Abdominal ultra-sound and computed tomogram revealed varying sized multiple pancreatic and renal cysts. Brain CT and MRI showed hypervascular cerebellar tumor, but there was no evidence of retinal angioma on fundoscopy and and no pheochromocytoma on the laboratory findings and radiologic studies. Her chromosomal study was normal. Her family members including her father and sister were also diagnosed to have multiple pancreatic cysts by ultrasound, however no more studies were available.
Kim, Sang Geon,Kim, Young Mi,Choi, Jong Young,Han, Joon-Yeol,Jang, Jeong Won,Cho, Se-Hyun,Um, Soon Ho,Chon, Chae Yoon,Lee, Dong Hoo,Jang, Ja-June,Yu, Eunsil,Lee, Young Sok Pharmaceutical Society of Great Britain 2011 Journal of pharmacy and pharmacology Vol.63 No.5
<P>Oltipraz, a cancer chemopreventive agent, has an anticirrhotic effect in animals. A phase II trial was designed to investigate the preliminary efficacy of oltipraz therapy in liver fibrosis or cirrhosis.</P>
Preparation and Stability Evaluation of Docetaxel-Loaded Oral Liposome
Chon, Chong-Run,Kim, Hyun-Mi,Lee, Pung-Sok,Oh, Eui-Chaul,Lee, Ma-Se The Korean Society of Pharmaceutical Sciences and 2010 Journal of Pharmaceutical Investigation Vol.40 No.2
Docetaxel-loaded liposomes were prepared by emulsion-solvent evaporation method, then coated with chitosan at room temperature and lyophilized. This system was designed in order to improve solubility and stability of docetaxel in the GI tract for oral drug delivery. The solubilizing effect of some frequently used solubilizers and/or liposome was determined. Among the results docetaxel-loaded liposomes prepared with 0.5% TPGS as a solubilizer showed 100-fold higher solubility than docetaxel. In a stability test, mean particle size of different liposome formulations was measured by a particle size analyzer in simulated gastric fluid (SGF) and in simulated intestinal fluid (SIF). The particle size of uncoated liposomes was significantly increased compared with that of chitosan-coated liposomes in SGF, however, there was no significant difference between coated and uncoated liposome in SIF. It is evident that chitosan-coated liposomes were more stable in GI conditions. The release characteristics of docetaxel-loaded liposomes were also investigated in three buffer solutions (pH 1.2, 4.0, 6.8). Docetaxel release did not occur in pH 1.2 for 4 hrs. However, in pH 4.0 and 6.8 conditions, docetaxel was gradually released over 24 hrs as a sustained release. It seems that aggregation and precipitation of particles by electrostatic interaction might protect docetaxel from being released. In Conclusion, the results from this study show that the chitosan-coated liposomes may be useful in enhancing solubility and GI stability of docetaxel.
Extreme Learning Machine 기반 퍼지 패턴 분류기 설계
안태천(Tae-Chon Ahn),노석범(Sok-Beom Roh),황국연(Kuk-Yeon Hwang),王繼紅(Jihong Wang),김용수(Yong Soo Kim) 한국지능시스템학회 2015 한국지능시스템학회논문지 Vol.25 No.5
본 논문에서는 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 노이즈에 강한 특성을 보이는 퍼지 집합 이론을 이용한 새로운 패턴 분류기를 제안 한다. 기존 인공 신경망에 비해 학습속도가 매우 빠르며, 모델의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 학습 알고리즘을 퍼지 패턴 분류기에 적용하여 퍼지 패턴 분류기의 학습 속도와 패턴 분류 일반화 성능을 개선 한다. 제안된 퍼지 패턴 분류기의 학습 속도와 일반화 성능을 평가하기 위하여, 다양한 머신 러닝 데이터 집합을 사용한다. In this paper, we introduce a new pattern classifier which is based on the learning algorithm of Extreme Learning Machine the sort of artificial neural networks and fuzzy set theory which is well known as being robust to noise. The learning algorithm used in Extreme Learning Machine is faster than the conventional artificial neural networks. The key advantage of Extreme Learning Machine is the generalization ability for regression problem and classification problem. In order to evaluate the classification ability of the proposed pattern classifier, we make experiments with several machine learning data sets.