OBJECTIVE:To analyze the effects of the mutations in the b3-adrenoceptor (b3-AR) gene and/or uncoupling protein3 (UCP3) gene promoter on body fat distribution and glycemic control after mild weight reduction in overweight-obese subjects with coronary artery disease (CAD) or metabolic syndrome. DESIGN: Clinical intervention study of the À300 kcal/day mild weight reduction program for 12 weeks. SUBJECTS: A total of 224 overweight-obese subjects with CAD or metabolic disorder, subdivided into the following four categories: (1) wild type (TT-CC, n ¼ 73); (2) only UCP3 promoter variant (TT-CT/TT, n ¼ 90); (3) only b3-AR variant (TA/AA-CC, n ¼ 29); (4) both variants (TA/AA-CT/TT, n ¼ 32). MEASUREMENT: Body mass index (BMI), blood pressure, calorie intakes, body fat distribution, serum glucose, insulin, free fatty acids, C-peptide and lipids before and after weight reduction. RESULTS: After 12 weeks, all subjects lost approximately 5% of their initial body weight. Despite similar weight reduction, the highest decreases in abdominal adipose tissue at both L1 and L4 levels were observed in the 'wild-type' group (Po0.001) and the second highest in 'only UPC3 promoter variant' group (Po0.001). On the other hand, both variant-carriers had the smallest reduction only in visceral fat area at the L4 level. All subjects except both variant-carriers showed significant reductions in the fasting levels of glucose and FFA. The response areas of glucose (Po0.01) and insulin (Po0.05) were reduced largest in the 'wild-type' group and second largest in the 'UCP3 promoter variant' group. CONCLUSION: All the four groups showed similar weight reduction after À300 kcal/d for 12 weeks. However, the beneficial effects on body fat distribution and glycemic control were greatest in the 'wild-type' group and smallest in 'both variants' group. In addition, these effects were less beneficial in carriers with b3-AR gene variant than with UCP3 gene promoter variant.

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We compared the haemodynamic effects of nicardipine and sodium nitroprusside after coronary artery bypass graft surgery. When post-surgery systolic blood pressure reached > 150 mmHg, patients were randomly given nicardipine (N group, n = 26) or sodium nitroprusside (S group, n = 21). The drugs were infused at a rate of 2 microg/kg per min for 10 min. If the target blood pressure (120-140 mmHg) was not achieved, the infusion rate was increased by 1 microg/kg per min every 10 min. Cardiac and stroke volume indices had increased significantly in the N group after 10 min and in both groups after 60 min. The infusion duration and total dose of drug were significantly lower in the N group compared with the S group. Nicardipine infusion controlled post-operative hypertension more rapidly and was superior to sodium nitroprusside in maintaining left ventricular performance immediately after drug infusion.

With the increase of the adult orthodontic population, there is a need for an accurate and evidence-based prediction of the posttreatment face in 3 dimensions (3D). The objectives of this study are 1) to develop a 3D postorthodontic face prediction method based on a deep learning network using the patient-specific factors and orthodontic treatment conditions and 2) to validate the accuracy and clinical usability of the proposed method. Paired sets ( n = 268) of pretreatment (T1) and posttreatment (T2) cone-beam computed tomography (CBCT) of adult patients were trained with a conditional generative adversarial network to generate 3D posttreatment facial data based on the patient’s gender, age, and the changes of upper (ΔU1) and lower incisor position (ΔL1) as input. The accuracy was calculated with prediction error and mean absolute distances between real T2 (T2) and predicted T2 (PT2) near 6 perioral landmark regions, as well as percentage of prediction error less than 2 mm using test sets ( n = 44). For qualitative evaluation, an online survey was conducted with experienced orthodontists as panels ( n = 56). Overall, PT2 indicated similar 3D changes to the T2 face, with the most apparent changes simulated in the perioral regions. The mean prediction error was 1.2 ± 1.01 mm with 80.8% accuracy. More than 50% of the experienced orthodontists were unable to distinguish between real and predicted images. In this study, we proposed a valid 3D postorthodontic face prediction method by applying a deep learning algorithm trained with CBCT data sets.