Following chemotherapy for acute myelomoncytic leukemia, an 3-year-old boy developed several painful erythematous indurated patches at previous sampling sites and at the site where an arm board and adhesive tape were used in securing an intravenous infusion set. The lesions rapidly progressed to ulcers with central black eschars. Biopsies and cultures demonstrated a fungus, Aspergillus flavs, as the etiologic agent, without evidence of systemic dissemination. Local care, including s irgical debridement, were performed. The patient also received treatment with intravenous; amphotericin B followed by oral itraconazole. Six months later, the skin lesions had healed, leaving some scar and deformities.
Adhesives ; Amphotericin B ; Arm ; Aspergillosis* ; Aspergillus ; Biopsy ; Child* ; Child, Preschool ; Cicatrix ; Congenital Abnormalities ; Debridement ; Drug Therapy ; Fungi ; Humans ; Infusions, Intravenous ; Itraconazole ; Leukemia* ; Male ; Skin ; Ulcer

As a family respond to any stressful situation as a whole system, cancer diagnosis of a child, as a serious life event, could be emotional shock to destroy homeostasis of the family system. A family has a resilient capacity to adjust and adapt to stressful events. Previous studies have been focused on family stress and adaptation, but little attention has been given to family value as one of resilient factors. The data for model testing were collected from July 18, 2000 to August 30, 2000 and the analysis included 309 parents of children who are diagnosed as cancer, 18 or less years of age, and treated either hospitalized or at the outpatient clinics. The data analysis utilized SAS 6.12 and LISREL 8 for descriptive statistics, correlation, cluster analysis, factor analysis, and LISREL. The study findings are as follows. 1) Monthly income ( gamma =-0.28, t=-5.81) was the most important factor to explain family strain along with family support ( gamma =-0.11, t=-2.43), severity of children's illness ( gamma =0.26, t=5.22), and family stressor ( gamma =0.22, t=4.62). All of these factors together explained 40% of variance in family strain. 2) Among general family value, the relationship with the parents ( gamma =0.28, t=4.89) and relationship with the children ( gamma =0.20, t=3.60) showed positive effects to family value for cancer children, while relationship with the spouse ( gamma =-0.19, t=-3.22) and the age of the cancer children ( gamma =-0.11, t=-2.21) showed negative effects. These predictors together explained 22% of variance in family value for cancer children. 3)Family hardiness was explained mostly by family strain ( gamma =-0.53, t=-8.65) along with direct negative effects of family persistency and indirect negative effects of severity of children's illness, family stressor, relationship with the spouse, and the children's age. Family value for cancer children was the most important predictor with positive effect ( gamma =0.44, t=6.76) along with indirect effects of monthly income, relationship with the parents, relationship with the children, support from family and significant others, and confidence with the health professionals. 51% of variance in family hardiness was explained by all of these predictors. 4)The most important predictor for family adaptation was family stressor ( gamma =-0.50, t=-6.85) with direct and indirect negative effects along with the severity of children's illness ( gamma =-0.27, t=-5.21). However, family value for cancer children showed compromised total effect ( gamma =-0.13, t=-1.99) with negative direct effects ( gamma =-0.28, t=-3.43) and positive indirect effects ( gamma =0.14, t=3.01). Similarly, confidence with the health professionals also showed compromised total effect ( gamma =0.09, t=1.99) with positive direct effects and negative indirect effects. Family hardiness showed the biggest positive direct effects while other factors such as monthly income, family stressor, family persistence, support of family and significant others, relationship with the parents, relationship with the children, and relationship with the spouse, and children's age showed indirect effects only. 39% of variance in family adaptation was explained by all of these predictors.
Ambulatory Care Facilities ; Child* ; Diagnosis ; Health Occupations ; Homeostasis ; Humans ; Life Change Events ; Parents ; Shock ; Spouses ; Statistics as Topic ; Child Health

As a family respond to any stressful situation as a whole system, cancer diagnosis of a child, as a serious life event, could be emotional shock to destroy homeostasis of the family system. A family has a resilient capacity to adjust and adapt to stressful events. Previous studies have been focused on family stress and adaptation, but little attention has been given to family value as one of resilient factors. The data for model testing were collected from July 18, 2000 to August 30, 2000 and the analysis included 309 parents of children who are diagnosed as cancer, 18 or less years of age, and treated either hospitalized or at the outpatient clinics. The data analysis utilized SAS 6.12 and LISREL 8 for descriptive statistics, correlation, cluster analysis, factor analysis, and LISREL. The study findings are as follows. 1) Monthly income ( gamma =-0.28, t=-5.81) was the most important factor to explain family strain along with family support ( gamma =-0.11, t=-2.43), severity of children's illness ( gamma =0.26, t=5.22), and family stressor ( gamma =0.22, t=4.62). All of these factors together explained 40% of variance in family strain. 2) Among general family value, the relationship with the parents ( gamma =0.28, t=4.89) and relationship with the children ( gamma =0.20, t=3.60) showed positive effects to family value for cancer children, while relationship with the spouse ( gamma =-0.19, t=-3.22) and the age of the cancer children ( gamma =-0.11, t=-2.21) showed negative effects. These predictors together explained 22% of variance in family value for cancer children. 3)Family hardiness was explained mostly by family strain ( gamma =-0.53, t=-8.65) along with direct negative effects of family persistency and indirect negative effects of severity of children's illness, family stressor, relationship with the spouse, and the children's age. Family value for cancer children was the most important predictor with positive effect ( gamma =0.44, t=6.76) along with indirect effects of monthly income, relationship with the parents, relationship with the children, support from family and significant others, and confidence with the health professionals. 51% of variance in family hardiness was explained by all of these predictors. 4)The most important predictor for family adaptation was family stressor ( gamma =-0.50, t=-6.85) with direct and indirect negative effects along with the severity of children's illness ( gamma =-0.27, t=-5.21). However, family value for cancer children showed compromised total effect ( gamma =-0.13, t=-1.99) with negative direct effects ( gamma =-0.28, t=-3.43) and positive indirect effects ( gamma =0.14, t=3.01). Similarly, confidence with the health professionals also showed compromised total effect ( gamma =0.09, t=1.99) with positive direct effects and negative indirect effects. Family hardiness showed the biggest positive direct effects while other factors such as monthly income, family stressor, family persistence, support of family and significant others, relationship with the parents, relationship with the children, and relationship with the spouse, and children's age showed indirect effects only. 39% of variance in family adaptation was explained by all of these predictors.
Ambulatory Care Facilities ; Child* ; Diagnosis ; Health Occupations ; Homeostasis ; Humans ; Life Change Events ; Parents ; Shock ; Spouses ; Statistics as Topic ; Child Health

BACKGROUND: Ferritin is known to be associated with insulin resistance (IR) and oxidative stress; however, recent studies have shown that there is an association between ferritin and anti-oxidative status. To date, the biphasic response of ferritin to oxidative stress has not been fully evaluated. Thus, we investigated the association between ferritin and IR and anti-oxidative status in obese and non-obese women. METHODS: We evaluated the homeostasis model assessment of insulin resistance (HOMA-IR) and total anti-oxidant status (TAS) in a total of 111 healthy women between the ages of 32 and 68 years. RESULTS: In all of the study subjects, ferritin levels were positively correlated with age (r = 0.38, P < 0.001), body mass index (r = 0.24, P = 0.01), TAS (r = 0.38, P < 0.001) and HOMA-IR (r = 0.20, P = 0.04). In the subgroup analysis, ferritin levels were correlated with age (r = 0.39, P < 0.001) and TAS (r = 0.43, P < 0.001) in the non-obese group and with insulin (r = 0.50, P = 0.02) and HOMA-IR (r = 0.52, P = 0.01) levels in the obese group. On stepwise multiple linear regression analysis, ferritin was found to be independently associated with TAS (B = 177.16, P < 0.0001) in the non-obese group and independently associated with HOMA-IR (B = 30.36, P = 0.01) in the obese group. CONCLUSION: Our findings suggest ferritin is associated with IR in obese women and with anti-oxidative status in non-obese women. Further studies are warranted to elucidate the precise role of ferritin in obesity.
Body Mass Index ; Female ; Ferritins ; Homeostasis ; Humans ; Insulin ; Insulin Resistance ; Linear Models ; Obesity ; Oxidative Stress

BACKGROUND: The aims of this study were to compare adipokine concentrations of pregnant women in the 24th~28th weeks of gestation to those of non-pregnant women. We compared the concentrations of adipokines in women with gestational diabetes mellitus (GDM), gestational impaired glucose tolerance (GIGT) and normal glucose tolerance (NGT). We also investigated the role of adipokines in the development of gestational glucose intolerance. METHODS: We surveyed 129 pregnant women who underwent a 100 g oral glucose tolerance test (OGTT) during the 24th~28th weeks of gestation. Participants were classified into three groups: (1) NGT (n = 40), (2) GIGT (n = 45), and (3) GDM (n = 44). Pregnant subjects with NGT were matched to non-pregnant controls for BMI and age (n = 41). RESULTS: Pregnant women with NGT exhibited significantly decreased adiponectin levels and elevated leptin levels compared to non-pregnant controls. Mean plasma resistin levels were significantly higher in women with GDM and GIGT than in women with NGT. Resistin and fasting glucose were significant predictors for the development of gestational glucose intolerance. CONCLUSION: Plasma adiponectin levels were decreased and leptin levels were increased in pregnant subjects with NGT compared to BMI and age matched non-pregnant controls. Women with GDM and GIGT exhibit significantly elevated concentrations of resistin compared with women with NGT. Increased resistin levels were also associated with the development of gestational glucose intolerance. Resistin may play an important role on the development of gestational glucose intolerance in Korean women.
Adipokines ; Adiponectin ; Diabetes, Gestational ; Fasting ; Female ; Glucose ; Glucose Intolerance ; Glucose Tolerance Test ; Humans ; Leptin ; Plasma ; Pregnancy ; Pregnant Women ; Resistin

BACKGROUND: Serum gamma-GT is one of the biliary enzymes with the only enzymatic activity capable of cleaving extracellular glutathione, thus originating precursor amino acids for the intracellular resynthesis of glutathione. Several population-based studies have shown a strong cross-sectional association between serum gamma-GT concentration and cardiovascular risk factors. And prospective studies showed that serum gamma-GT concentration had a prognostic impact on cardiovascular mortality. But, the mechanism by which gamma-GT is associated with cardiovascular disease is not elucidated. We hypothesized that there was an association between serum gamma-GT and ferritin, a marker of oxidative stress. In this study, we investigated the relationship between serum gamma-GT and serum ferritin. METHODS: By reviewing the medical records of 288 healthy adults, we determined the serum levels of gamma-GT and ferritin according to age, body mass index, systolic blood pressure, diastolic blood pressure, triglycerides, total cholesterol, LDL-cholesterol, fasting blood sugar, hs-CRP, serum ferritin, AST, ALT, uric acid and smoking history. We studied the relationship between the variables by Pearson correlation coefficients and multiple stepwise regression analysis. Mean values of serum gamma-GT according to the smoking history were compared using t-test. RESULTS: Serum gamma-GT correlated positively with serum ferritin (r=0.42; P<0.001). BMI, triglycerides, total cholesterol, fasting blood sugar, AST and ALT also showed statistically significant correlation. Smokers showed significantly higher serum gamma-GT. Serum gamma-GT correlated with serum ferritin, ALT, current smoking and triglycerides by multiple stepwise regression analysis. CONCLUSION: Serum gamma-GT correlated positively with serum ferritin.
Adult* ; Amino Acids ; Blood Glucose ; Blood Pressure ; Body Mass Index ; Cardiovascular Diseases ; Cholesterol ; Fasting ; Ferritins* ; gamma-Glutamyltransferase* ; Glutathione ; Humans ; Medical Records ; Mortality ; Oxidative Stress ; Risk Factors ; Smoke ; Smoking ; Triglycerides ; Uric Acid

BACKGROUND: Osteoprotegerin (OPG) is a glycoprotein that acts as a decoy receptor to receptor-activated RANKL (receptor-activated NF-kappa B ligand) and inhibits the differentiation of osteoclasts. OPG knock-out mice showed severe osteoporosis and aortic calcification and high serum OPG levels have been shown to predict future cardiovascular mortality in old Caucasian females. We measured serum OPG levels in coronary artery disease patients, compared serum OPG levels among different groups according to the number of stenotic vessels and observed the correlation with aortic calcification and cardiovascular risk factors. METHODS: One hundred subjects were enrolled in which coronary angiograms were performed due to chest pain in Kangbuk Samsung Hospital from April to August, 2003 (59 males, 41 females, mean age 56.9 +/- 11.9 yrs). Blood pressure, body mass index, fasting blood glucose, total cholesterol, triglycerides, low-density lipoprotein (LDL) cholesterol and high-density lipoprotein (HDL) cholesterol levels were measured in every subject. Cardiac echocardiograms were checked in 82 subjects and left ventricular mass indices (LV mass index) were calculated. Serum OPG levels were measured with enzyme-linked immunosorbent assay (ELISA) method. The presence of calcifications in aortic knob was checked in simple chest X-ray. RESULTS: Subjects were divided in 4 groups according to the number of stenotic vessels (significant stenosis>or=50%); 45 subjects in normal group, 30 in 1-vessel disease group, 15 in 2-vessel disease group and 10 in 3-vessel disease group. Mean value for age was significantly different among groups (p<0.01). Mean serum HDL-cholesterol level of normal group was higher than that of 1-vessel disease or 2-vessel disease group (p<0.05). Serum OPG levels increased significantly as the number of stenotic vessels increased and in post-hoc analysis, mean serum OPG levels were higher in 3-vessel disease group than normal or 1-vessel disease groups (p<0.05). Age, LV mass index and number of stenotic vessels showed significantly positive correlation with serum OPG levels, although only number of stenotic vessels showed persistently significant correlation after adjustment for age. There were no differences of serum OPG levels according to the presence of fasting hyperglycemia or aortic calcifications. CONCLUSION: Serum OPG levels increased as the number of stenotic coronary arteries increased and showed positive relationships with age, LV mass index. OPG seems to be elevated as a compensatory mechanism to the progression of atherosclerosis in humans.
Animals ; Atherosclerosis ; Blood Glucose ; Blood Pressure ; Body Mass Index ; Chest Pain ; Cholesterol ; Coronary Artery Disease* ; Coronary Vessels* ; Enzyme-Linked Immunosorbent Assay ; Fasting ; Female ; Glycoproteins ; Humans ; Hyperglycemia ; Lipoproteins ; Male ; Mice ; Mice, Knockout ; Mortality ; NF-kappa B ; Osteoclasts ; Osteoporosis ; Osteoprotegerin* ; Risk Factors* ; Thorax ; Triglycerides