Application of Sigma metrics in assessing the clinical performance of verified versus non-verified reagents for routine biochemical analytes

Introduction: Sigma metrics analysis is considered an objective method to evaluate the performance of a new measurement system. This study was designed to assess the analytical performance of verified versus non-verified reagents for routine biochemical analytes in terms of Sigma metrics. Materials and methods: The coefficient of variation (CV) was calculated according to the mean and standard deviation (SD) derived from the internal quality control for 20 consecutive days. The data were measured on an Architect c16000 analyser with reagents from four manufacturers. Commercial reference materials were used to estimate the bias. Total allowable error (TEa) was based on the CLIA 1988 guidelines. Sigma metrics were calculated in terms of CV, percent bias and TEa. Normalized method decisions charts were built by plotting the normalized bias (biasa: bias%/ TEa) on the Y-axis and the normalized imprecision (CVa: mean CV%/TEa) on the X-axis. Results: The reagents were compared between different manufacturers in terms of the Sigma metrics for relevant analytes. Abbott and Leadman’s verified reagents provided better Sigma metrics for the alanine aminotransferase assay than non-verified reagents (Mindray and Zybio). All reagents performed well for the aspartate aminotransferase and uric acid assays with a sigma of 5 or higher. Abbott achieved the best performance for the urea assay, evidenced by the sigma of 2.83 higher than all reagents, which were below 1-sigma. Conclusion: Sigma metrics analysis system is helpful for clarifying the performance of candidate non-verified reagents in clinical laboratory. Our study suggests that the quality of non-verified reagents should be assessed strictly

Hybrid features for skeleton-based action recognition based on network fusion

© 2020 John Wiley & Sons, Ltd. In recent years, the topic of skeleton-based human action recognition has attracted significant attention from researchers and practitioners in graphics, vision, animation, and virtual environments. The most fundamental issue is how to learn an effective and accurate representation from spatiotemporal action sequences towards improved performance, and this article aims to address the aforementioned challenge. In particular, we design a novel method of hybrid features' extraction based on the construction of multistream networks and their organic fusion. First, we train a convolution neural networks (CNN) model to learn CNN-based features with the raw skeleton coordinates and their temporal differences serving as input signals. The attention mechanism is injected into the CNN model to weigh more effective and important information. Then, we employ long short-term memory (LSTM) to obtain long-term temporal features from action sequences. Finally, we generate the hybrid features by fusing the CNN and LSTM networks, and we classify action types with the hybrid features. The extensive experiments are performed on several large-scale publically available databases, and promising results demonstrate the efficacy and effectiveness of our proposed framework

A review of urinary angiotensin converting enzyme 2 in diabetes and diabetic nephropathy

Urinary angiotensin converting enzyme 2 (ACE2) is significantly increased in diabetes and diabetic nephropathy. While studies on its clinical significance are still underway, its urinary expression, association with metabolic and renal parameters has been in the recent past considerably studied. The recent studies have demystified urine ACE2 in many ways and suggested the roles it could play in the management of diabetic nephropathy. In all studies the expression of urinary ACE2 was determined by enzyme activity assay and/with the quantification of ACE2 protein and mRNA by methods whose reliability are yet to be evaluated. This review summarizes recent findings on expression of urinary ACE2, examines its relationship with clinical parameters and highlights possible applications in management of diabetic nephropathy