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This study was done to evaluate clinical usefulness of cystatin C levels of serum and urine in predicting renal impairment in normoalbuminuric patients with type 2 diabetes and to evaluate the association between albuminuria and serum/urine cystatin C. Type 2 diabetic patients (n = 332) with normoalbuminuria (n = 210), microalbuminuria (n = 83) and macroalbuminuria (n = 42) were enrolled. Creatinine, urinary albumin levels, serum/urine cystatin C and estimated glomerular filtration rate (eGFR by MDRD [Modification of Diet in Renal Disease] and CKD-EPI [Chronic Kidney Disease Epidemiology Collaboration] equations) were determined. The cystatin C levels of serum and urine increased with increasing degree of albuminuria, reaching higher levels in macroalbuminuric patients (P < 0.001). In multiple regression analysis, serum cystatin C was affected by C-reactive protein (CRP), sex, albumin-creatinine ratio (ACR) and eGFR. Urine cystatin C was affected by triglyceride, age, eGFR and ACR. In multivariate logistic analysis, cystatin C levels of serum and urine were identified as independent factors associated with eGFR < 60 mL/min/1.73 m2 estimated by MDRD equation in patients with normoalbuminuria. On the other hand, eGFR < 60 mL/min/1.73 m2 estimated by CKD-EPI equation was independently associated with low level of high-density lipoprotein in normoalbuminuric patients. The cystatin C levels of serum and urine could be useful markers for renal dysfunction in type 2 diabetic patients with normoalbuminuria.

SnO recently has attracted particular attention as a powerful buffer layer for organic optoelectronic devices due to its outstanding properties such as high electron mobility, suitable band alignment, and high optical transparency. Here, we report on facile low-temperature solution-processed SnO nanoparticles (NPs) in applications for a cathode buffer layer (CBL) of inverted organic solar cells (iOSCs). The conduction band energy of SnO NPs estimated by ultraviolet photoelectron spectroscopy was 4.01 eV, a salient feature that is necessary for an appropriate CBL. Using SnO NPs as CBL derived from a 0.1 M precursor concentration, P3HT:PCBM-based iOSCs showed the best power conversion efficiency (PCE) of 2.9%. The iOSC devices using SnO NPs as CBL revealed excellent long-term device stabilities, and the PCE was retained at ∼95% of its initial value after 10 weeks in ambient air. These solution-processed SnO NPs are considered to be suitable for the low-cost, high throughput roll-to-roll process on a flexible substrate for optoelectronic devices.

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This paper presents an effective optimization scheme for the measurement-based load modeling based on the sensitivity analysis of composite load model parameters. Each parameter of load model has different effects on its dynamic response. Moreover, some parameters are insensitive to the change of others. To estimate the dynamic interactions between parameters, their sensitivity is analyzed by using the eigenvalues of Hessian matrix used in the optimization algorithm. Also, the linear dependence between two load model parameters is then identified by examining the condition number of Jacobian matrix. With this parameter analysis, the performance of optimization process for measurement-based composite load modeling is improved by reducing the number of necessary parameters to consider. The performance of proposed method is verified with the practical data measured at a feeder in a real substation.