Diffuse alveolar hemorrhage (DAH) is an uncommon complication in patients with systemic lupus erythematosus (SLE), and mortality remains high. In recent years, cases of DAH due to SLE treated with extracorporeal membrane oxygenation (ECMO) have rarely been reported. The authors present a case of a 43-year-old woman with SLE who had rapidly aggravating dyspnea and hemoptysis. She was diagnosed as having DAH with refractory respiratory failure and was successfully managed with veno-venous ECMO. We propose ECMO as a useful salvage therapy in patients with alveolar hemorrhage secondary to SLE who are failing conventional ventilatory support.
Adult ; Dyspnea ; Extracorporeal Membrane Oxygenation* ; Female ; Hemoptysis ; Hemorrhage* ; Humans ; Lupus Erythematosus, Systemic* ; Mortality ; Respiratory Distress Syndrome, Adult ; Respiratory Insufficiency ; Salvage Therapy

No abstract available.
Angiomyoma* ; Vulva*

No abstract available.
Angiomyoma* ; Vulva*

No abstract available.
Angiography ; Coronary Angiography* ; Coronary Vessels ; Echocardiography* ; Mucocutaneous Lymph Node Syndrome*

OBJECTIVES: To elucidate an association of the fragile X syndrome with autism, Southern blot analysis was performed in 66 autistic children aged from 2 years to 11 years. METHODS: Subjects were 66 autistic children with of autistic disorder diagnosed by DSM-IV criteria and Childhood Autism Rating Scale-Korean version. Genomic DNA was extracted from peripheral blood and DNA was used to detect a FMR (Fragile Mental Retardation)-1 gene. Xho/PstI probes and two restriction enzymes (EcoRI, EagI)were used for Southern blot analysis. RESULTS: There were one boy with a methylated mosaic pattern and 3 boys and 2 girls with an unmethylated premutation band. But there was no full mutation pattern. CONCLUSION: Although the possibility of the relationship between autistic disorder and FMR-1 gene has been suggested, the results from this study do not provide any definite association of FMR-1 gene with autism in autistic children.
Autistic Disorder ; Blotting, Southern ; Child* ; Diagnostic and Statistical Manual of Mental Disorders ; DNA ; Female ; Fragile X Syndrome ; Humans ; Male ; Molecular Biology*

No abstract available.
Sciatic Nerve*

We present a rare case of synchronous ileal inflammatory fibroid polyp and Meckel’s diverticulum detected during laparoscopic surgery for adult intussusception. A 48-year-old woman presented with sudden onset of severe abdominal pain. Abdominal computed tomography revealed a segment of ileocecal intussusception. Thus, laparoscopic exploration was performed, which revealed an ileal mass with an outpouching closed luminal structure in the distal ileum. Two abnormal structures were resected via mini-laparotomy, and the patient was discharged without postoperative complications. Histopathological examination confirmed an ileal inflammatory fibroid polyp and Meckel’s diverticulum with ectopic pancreatic tissue.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis has been reported in Graves' disease patients treated with antithyroid drugs (ATDs), especially propylthiouracil. ATD-induced ANCA-associated vasculitis usually involved the kidneys followed by the respiratory organs and skin. The treatment of ANCA-associated vasculitis induced by ATDs is to stop ATD therapy immediately, which often leads to an overall good prognosis. We report a case of ANCA-associated vasculitis in the peripheral nerves of the lower extremities in a 66-year-old woman who was treated with methimazole (MMI) for Graves' disease. To our knowledge, this is the third case of peripheral nervous system (PNS) involvement of ATD-induced vasculitis and the first case of PNS vasculitis associated with MMI.
Aged ; Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis ; Antibodies, Antineutrophil Cytoplasmic ; Antithyroid Agents ; Female ; Graves Disease ; Humans ; Kidney ; Lower Extremity ; Methimazole ; Peripheral Nerves ; Peripheral Nervous System ; Prognosis ; Propylthiouracil ; Skin ; Vasculitis

OBJECTIVE: To compare maternal characteristics and pregnancy outcomes in a group of women with gestational diabetes mellitus(GDM) diagnosed in our hospital with those of women without this disorder. MATERIALS AND METHOD: This is a retrospective study of 402 gestational diabetic women with singleton cephalic presenting pregnancies delivered at Ilsin Christian Hospital during the period January 1, 1997, through December 31, 1999. National Diabetes Data Group thresholds were used to diagnose gestational diabetes. Women in this group were compared with a nondiabetic control group(n=430) randomly selected and effects of confounding variables were analyzed using stratified analysis. RESULTS: Prevalence of GDM was 2.36%. Women with gestational diabetes were significantly older, heavier, of greater parity and more often had the following risk factors for GDM. Hypertension, cesarean delivery, macrosomia, and large for gestational age(LGA) rate were significantly increased. The adequate treatment group diagnosed before 32wks and received glucose control was compared with a inadequate treatment group. And there was no significant difference in maternal characteristics and pregnancy outcomes between the two groups. Within the adequate treatment group, three groups were subdivided by birth weight as SGA(small for gestational age), AGA(appropriate for gestational age) and LGA. The group with LGA had no difference in age, parity, pregnancy-induced hypertension, 2hr mean postprandial glucose level except body mass index and insulin treatment rate. After adjusting these two factors with stratified analysis, there was no general association between birth weight and glucose level(p=0.342). CONCLUSIONS: Maternal characteristics of gestational diabetes were significantly different compared with those of nondiabetic women. Pregnancy outcomes of gestational diabetic women were not improved by our conventional management and more intensified but acceptable and compliable treatment should be tried.
Birth Weight ; Body Mass Index ; Confounding Factors (Epidemiology) ; Diabetes, Gestational* ; Female ; Glucose ; Humans ; Hypertension ; Hypertension, Pregnancy-Induced ; Insulin ; Parity ; Pregnancy ; Pregnancy Outcome* ; Pregnancy* ; Prevalence ; Retrospective Studies ; Risk Factors

OBJECTIVES: The primary objective of this study is to compare model performance of machine learning methods with that of a previous study in which a nonlinear mixed effects model was created using NONMEM(R) for the pharmacokinetic and pharmacodynamic data for propofol. The secondary objective was to evaluate if a pharmacodynamic model describing the relationship between the dose of propofol and bispectral index (BIS) outperform that describing the relationship between a pharmacokinetic model derived-predicted concentrations of propofol and BIS. METHODS: Data were collected during a study involving the infusion of propofol into healthy volunteers. Pharmacokinetic and pharmacodynamic models were constructed using artificial neural networks (ANNs), support vector machines (SVMs), and multi-method ensembles and were compared with the nonlinear mixed effects method as implemented by NONMEM(R). Model performance was assessed by goodness-of-fit statistics, paired t-tests between predicted and observed values for each model and scatterplots. RESULTS: In pharmacokinetic analysis, ensemble I, the mean of ANN and NONMEM(R) predictions, achieved minimal error and the highest correlation coefficient. SVM produced the highest error and the lowest correlation coefficient. In pharmacodynamic analysis, ANN exhibited the best performance. An ANNModel describing the relationship between the dose of propofol and BIS was not inferior to an ANN model describing the relationship between predicted concentrations of propofol derived from an ANN pharmacokinetic model and BIS. CONCLUSIONS: In pharmacokinetic analysis, ensemble combined with ANN achieved slightly better performance than NONMEM(R). The relationship between the dose of propofol and BIS can be predicted without considering pharmacokinetics of propofol.
Machine Learning ; Propofol ; Support Vector Machine