1.A case of ateriosclerotic aneurysm of the deep femoral artery
Chang Soon CHO ; Sang Oh LEE ; Byoung Yoon RYU ; Hong Ki KIM ; Chang Sig CHOI
Journal of the Korean Society for Vascular Surgery 1991;7(1):7-11
No abstract available.
Aneurysm
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Femoral Artery
2.Enzyme Analysis in Patients with Renal Stones Who Were Treated by Repeated Extracorporeal Shock Wave Lithotripsy.
Soon Sig OH ; Gyung Woo JUNG ; Jin Han YOON
Korean Journal of Urology 1994;35(3):254-260
Among the patients who underwent extracorporeal shock wave lithotripsy (Modulith SL 20, Karl Storz, Germany) for renal stones between December 1991 and July 1992, 33 patients were selected for study. Of 33 patients 23 had 1 session and 10 patients had 2 session after 48 hours. We measured 24 hours urinary N-acetyl-beta-D-glucosaminidase(NAG), lactate dehydrogenase (LDH), r-glutamyl transpeptidase (r-GTP) activity, urea, creatinine and protein and serum alkaline phosphatase (ALP), glutamic oxaloacetic transaminase(GOT), glutamic pyruvic transaminase (GPT), lactate dehydrogenase (LDH), r-glutamyl transpeptidase( r-GTP) activity, urea nitrogen(BUN), creatinine, albumin and globulin before and after extracorporeal shock wave lithotripsy(ESWL) for measurement as a marker of renal damage and extent of renal damage between one session group and two session group. Serum LDH and 24 hours urinary NAG activity is significantly increased after treatment and was still high on the 3rd day. Statistic significance was not found between 1 session group and 2 session group for serum LDH and 24 hours urinary NAG activity on the 1st and 3rd day post-ESWL. These results suggest that repeated ESWL after one session for renal stone did not significantly damage the kidney.
Alanine Transaminase
;
Alkaline Phosphatase
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Creatinine
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Humans
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Kidney
;
L-Lactate Dehydrogenase
;
Lithotripsy*
;
Shock*
;
Urea
3.A Case of Bleeding Duodenal Varices in a Patient with Idiopathic Portal Hypertension.
Seung Chan SONG ; Dong Hyun SOHN ; Gwang Ho MUN ; Woo Kyoon RHO ; Hee Sig MUN ; Dong Soo HAN ; Joo Hyun SOHN ; Yong Chul JUN ; Oh Young LEE ; Byung Chul YOON ; Ho Soon CHOI ; Joon Soo HAHM ; Min Ho LEE ; Choon Suhk KEE ; Kyung Nam PARK
Korean Journal of Gastrointestinal Endoscopy 1998;18(2):244-248
Bleeding duodenal varices are a rare complication in patients with portal hypertension. Cirrhosis followed by portal vein obstruction and splenic vein obstruction are the most common causes. Although the prognosis of bleeding duodenal varices is usually poor, an awareness of its characteristic presentation may enable diagnostic and therapeutic proce- dures to be performed rapidly with an increased likelihood of a reaching successful out- come. In this study, we report a case of bleeding duodenal varices in a 23-year-old woman with idiopathic portal hypertension who was also suffering with recurrent melena. Panendoscopy identified prominant tortuous varices with central erosion in the 3rd portion of the duodenum and no esophageal and gastric varices. The varices were successfully treated by distal splenorenal shunt.
Duodenum
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Esophageal and Gastric Varices
;
Female
;
Fibrosis
;
Hemorrhage*
;
Humans
;
Hypertension, Portal*
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Melena
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Portal Vein
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Prognosis
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Splenic Vein
;
Splenorenal Shunt, Surgical
;
Varicose Veins*
;
Young Adult
4.Prediction of Gestational Age at Birth using an Artificial Neural Networks in Singleton Preterm Birth
Jee Yun LEE ; Soo Jung JO ; Eun Jin JUNG ; Kwang Sig LEE ; Seung Woo KIM ; Ho Yeon KIM ; Geum Joon CHO ; Soon Cheol HONG ; Min Jeong OH ; Hai Joong KIM ; Ki Hoon AHN
Journal of the Korean Society of Maternal and Child Health 2018;22(3):151-161
PURPOSE: The objective of the present study was to predict the gestational age at preterm birth using artificial neural networks for singleton pregnancy. METHODS: Artificial neural networks (ANNs) were used as a tool for the prediction of gestational age at birth. ANNs trained using obstetrical data of 125 cases, including 56 preterm and 69 non-preterm deliveries. Using a 36-variable obstetrical input set, gestational weeks at delivery were predicted by 89 cases of training sets, 18 cases of validating sets, and 18 cases of testing sets (total: 125 cases). After training, we validated the model by another 12 cases containing data of preterm deliveries. RESULTS: To define the accuracy of the developed model, we confirmed the correlation coefficient (R) and mean square error of the model. For validating sets, the correlation coefficient was 0.839, but R of testing sets was 0.892, and R of total 125 cases was 0.959. The neural networks were well trained, and the model predictions were relatively good. Furthermore, the model was validated with another dataset of 12 cases, and the correlation coefficient was 0.709. The error days were 11.58±13.73. CONCLUSION: In the present study, we trained the ANNs and developed the predictive model for gestational age at delivery. Although the prediction for gestational age at birth in singleton preterm birth was feasible, further studies with larger data, including detailed risk variables of preterm birth and other obstetrical outcomes, are needed.
Dataset
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Gestational Age
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Parturition
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Pregnancy
;
Premature Birth