Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility. Result. A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis. Conclusion. Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.
Background: The aim of this study is to evaluate the performance of three existing prediction scores which are applicable to adults for identifying nonalcoholic fatty liver disease (NAFLD) in Chinese children. Methods: We used data from routine check-up based medical records of 1845 children to validate the performance of three existing scoring systems including the hepatic steatosis index (HSI), Zhejiang University index (ZJU index), and triglyceride-glucose index (TyG index) in detection of NAFLD in children.Propensity score matching was applied to adjust for potential confounding effects in both training and validation cohorts. The area under the curve (AUC) of the receiver operating characteristic curve analysis was utilized to assess the performance of the three scoring systems.Results: Children with NAFLD had higher scores of HSI, ZJU index, and TyG index when compared with the control group (children without NAFLD). Elevated HSI, ZJU index, and TyG index scores were significantly associated with the presence of pediatric NAFLD since adjusted odds ratio and 95% CI with per interquartile range elevation of the HSI, ZJU index, and TyG index were
A three-dimensional Lattice Boltzmann two-phase model capable of dealing with large liquid and gas density ratios and with a partial wetting surface is introduced. This is based on a high density ratio model combined with a partial wetting boundary method. The predicted three-dimensional droplets at different partial wetting conditions at equilibrium are in good agreement with analytical solutions. Despite the large density ratio, the spurious velocity around the interface is not substantial, and is rather insensitive to the examined liquid and gas density and viscosity ratios. The influence of the gravitational force on the droplet shape is also examined through the variations of the Bond number, where the droplet shape migrates from spherical to flattened interface in tandem with the increase of the Bond number. The predicted interfaces under constant Bond number are also validated against measurements with good agreements.
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