Background: Enavogliflozin is a novel sodium-glucose cotransporter-2 inhibitor currently under clinical development. This study evaluated the efficacy and safety of enavogliflozin as an add-on to metformin in Korean patients with type 2 diabetes mellitus (T2DM) against dapagliflozin. Methods: In this multicenter, double-blind, randomized, phase 3 study, 200 patients were randomized to receive enavogliflozin 0.3 mg/day (n=101) or dapagliflozin 10 mg/day (n=99) in addition to ongoing metformin therapy for 24 weeks. The primary objective of the study was to prove the non-inferiority of enavogliflozin to dapagliflozin in glycosylated hemoglobin (HbA1c) change at week 24 (non-inferiority margin of 0.35%) (Clinical trial registration number: NCT04634500). Results: Adjusted mean change of HbA1c at week 24 was -0.80% with enavogliflozin and -0.75% with dapagliflozin (difference, -0.04%; 95% confidence interval, -0.21% to 0.12%). Percentages of patients achieving HbA1c <7.0% were 61% and 62%, respectively. Adjusted mean change of fasting plasma glucose at week 24 was -32.53 and -29.14 mg/dL. An increase in urine glucosecreatinine ratio (60.48 vs. 44.94, P<0.0001) and decrease in homeostasis model assessment of insulin resistance (-1.85 vs. -1.31, P=0.0041) were significantly greater with enavogliflozin than dapagliflozin at week 24. Beneficial effects of enavogliflozin on body weight (-3.77 kg vs. -3.58 kg) and blood pressure (systolic/diastolic, -5.93/-5.41 mm Hg vs. -6.57/-4.26 mm Hg) were comparable with those of dapagliflozin, and both drugs were safe and well-tolerated. Conclusion: Enavogliflozin added to metformin significantly improved glycemic control in patients with T2DM and was noninferior to dapagliflozin 10 mg, suggesting enavogliflozin as a viable treatment option for patients with inadequate glycemic control on metformin alone.
A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.
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