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1.
Maturitas ; 42(1): 85-9, 2002 May 20.
Article in English | MEDLINE | ID: mdl-12020984

ABSTRACT

Recent reports of osteoporosis in congenital estrogen deficiency in humans from estrogen resistance or aromatase deficiency have called attention to the importance of estrogen in males. It is the purpose of the present study to evaluate the effects of low- dose estrogen on glucose, lipid and bone metabolism in males with hypogonadism. Nine Thai males with primary or secondary hypogonadism were included in the study. Testosterone was discontinued at least 8 weeks before the study. The subjects received 0.3 mg of conjugated equine estrogen (CEE) daily for 4 weeks. Serum C-terminal telopeptide of type 1 collagen (CTX), total cholesterol (TC), LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), triglyceride (TG) and parameters related to insulin sensitivity were measured at baseline and 4 weeks after treatment. Insulin sensitivity was assessed by frequent intravenous glucose tolerance test. The mean age of subjects was 35.77 years (22-70 years). Insulin sensitivity index (SI) did not change significantly after the administration of CEE (P=0.09). Likewise, no change in acute insulin response (AIR(glucose)) was detected. However, glucose effectiveness (SG) significantly decreased after CEE (P<0.05). No significant change in serum TC, LDL-C, HDL-C or TG was detected. In regard to bone turnover, serum CTX significantly decreased after CEE administration (P<0.05). We concluded that low-dose estrogen administration in hypogonadal males for 4 weeks causes a decrease in bone turnover and an increase in glucose effectiveness. No effect on serum lipid concentrations or insulin sensitivity and secretion was detected.


Subject(s)
Bone Resorption/blood , Estrogen Replacement Therapy , Estrogens, Conjugated (USP)/pharmacology , Hypogonadism/blood , Insulin/blood , Administration, Oral , Adult , Aged , Blood Glucose/drug effects , Cholesterol/blood , Cholesterol, HDL/blood , Cholesterol, HDL/drug effects , Cholesterol, LDL/blood , Cholesterol, LDL/drug effects , Estrogens, Conjugated (USP)/administration & dosage , Glucose Tolerance Test , Humans , Male , Middle Aged , Peptide Fragments/blood , Peptide Fragments/drug effects , Procollagen/blood , Procollagen/drug effects , Triglycerides/blood
2.
J Med Assoc Thai ; 84(3): 332-8, 2001 Mar.
Article in English | MEDLINE | ID: mdl-11460933

ABSTRACT

In the present study we developed and assessed the performance of a simple prediction rule and a neural network model to predict beta-cell reserve in young adults with diabetes. Eighty three young adults with diabetes were included in the study. All were less than 40 years old and without apparent secondary causes of diabetes. The subjects were randomly allocated to 2 groups; group 1 (n = 59) for developing a prediction rule and training a neural network, group 2 (n = 24) for validation purpose. The prediction rule was developed by using stepwise logistic regression. Using stepwise logistic regression and modification of the derived equation, the patient would be insulin deficient if 3(waist circumference in cm) + 4(age at diagnosis) < 340 in the absence of previous diabetic ketoacidosis (DKA) or < 400 in the presence of previous DKA. When tested in the validation set, the prediction rule had positive and negative predictive values of 86.7 per cent and 77.8 per cent respectively with 83.3 per cent accuracy while the ANN model had a positive predictive value of 88.2 per cent and a negative predictive value of 100 per cent with 91.7 per cent accuracy. When testing the performance of the prediction rule and the ANN model compared to the assessment of 23 internists in a subgroup of 9 diabetics whose age at onset was less than 30 years and without a history of DKA, the ANN had the highest ability to predict beta-cell reserve (accuracy = 88.9), followed by the prediction rule (accuracy = 77.8%) and assessments by internists (accuracy = 60.9%). We concluded that beta-cell reserve in young adults with diabetes mellitus could be predicted by a simple prediction rule or a neural network model. The prediction rule and the neural network model can be helpful clinically in patients with mixed clinical features of type 1 and type 2 diabetes.


Subject(s)
Diabetes Mellitus/diagnosis , Islets of Langerhans , Neural Networks, Computer , Adolescent , Adult , Diabetes Mellitus/physiopathology , Humans , Logistic Models , Predictive Value of Tests
3.
J Med Assoc Thai ; 81(4): 265-71, 1998 Apr.
Article in English | MEDLINE | ID: mdl-9623020

ABSTRACT

A prospective randomized, double-blind, controlled study of cefoperazone/sulbactam (cefoperazone 25 mg/kg/day) + co-trimoxazole (trimethoprim 8 mg/kg/day) vs ceftazidime (100 mg/kg/day) + co-trimoxazole (trimethoprim 8 mg/kg/day) in the treatment of severe melioidosis was conducted at Srinagarind Hospital, Khon Kaen University, Khon Kaen, Thailand, from July 1995 to September 1996. A total of 84 patients were enrolled in the study. Forty of them (48%) had culture-proven melioidosis and were randomly assigned to one of the two treatment groups, each group with 20 patients. Two cases (one in each treatment group) were excluded from the final analysis due to incomplete data. There was no significant difference in the mortality rate between the two groups-16 per cent (3/19) in the cefoperazone/sulbactam group vs 21 per cent (4/19) in the ceftazidime group (p > 0.05). Bacteriological responses of successfully treated patients were similar in both groups, and both treatment regimens were well tolerated. Cefoperazone/sulbactam + co-trimoxazole can therefore be used as an alternative treatment for severe melioidosis. However, to further support this conclusion, a study with a larger patient population is needed.


Subject(s)
Anti-Bacterial Agents/therapeutic use , Anti-Infective Agents/therapeutic use , Cefoperazone/therapeutic use , Ceftazidime/therapeutic use , Drug Therapy, Combination/therapeutic use , Melioidosis/drug therapy , Sulbactam/therapeutic use , Trimethoprim, Sulfamethoxazole Drug Combination/therapeutic use , Adult , Chi-Square Distribution , Double-Blind Method , Female , Humans , Male , Middle Aged , Prospective Studies , Statistics, Nonparametric
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