Saran R, Robinson B, Abbott KC, et al. US Renal Data System 2018 Annual Data Report: epidemiology of kidney disease in the United States. Am J Kidney Dis. 2019;73(3)(suppl 1):Svii-Sxxii, S1-S772.

Erratum Regarding “US Renal Data System 2017 Annual Data Report: Epidemiology of Kidney Disease in the United States” (Am J Kidney Dis. 2018;71[3][suppl 1]:Svii,S1-S676) American Journal of Kidney Diseases Vol. 71 Issue 4 In Brief The March 2018 supplement to AJKD, the US Renal Data System 2017 Annual Data Report, contained an error in the author list. Nicole Bhave, MD, was erroneously omitted as an author when the supplement was initially published. Her name should have appeared between the fourth author, Lawrence Y.C. Agodoa, MD, and the fifth (now sixth) author, Jennifer Bragg-Gresham, PhD. The author list has been corrected in the HTML and PDF versions of the supplement as of March 7, 2018. Full-Text PDF

Extensive medical research showed that patients, with high protein concentration in urine, have various kinds of kidney diseases, referred to as proteinuria. Urinary protein biomarkers are useful for diagnosis of many health conditions – kidney and cardio vascular diseases, cancers, diabetes, infections. This review focuses on the instrumental quantification (electrophoresis, chromatography, immunoassays, mass spectrometry, fluorescence spectroscopy, the infrared spectroscopy, and Raman spectroscopy) of proteins (the most of all albumin) in human urine matrix. Different techniques provide unique information on what constituents of the urine are. Due to complex nature of urine, a separation step by electrophoresis or chromatography are often used for proteomics study of urine. Mass spectrometry is a powerful tool for the discovery and the analysis of biomarkers in urine, however, costs of the analysis are high, especially for quantitative analysis. Immunoassays, which often come with fluorescence detection, are major qualitative and quantitative tools in clinical analysis. While Infrared and Raman spectroscopies do not give extensive information about urine, they could become important tools for the routine clinical diagnostics of kidney problems, due to rapidness and low-cost. Thus, it is important to review all the applicable techniques and methods related to urine analysis. In this review, a brief overview of each technique's principle is introduced. Where applicable, research papers about protein determination in urine are summarized with the main figures of merits, such as the limit of detection, the detectable range, recovery and accuracy, when available.

Neutrophil-to-lymphocyte ratio (NLR) is a novel inflammation index that has been shown to independently predict poor clinical outcomes. We aimed to evaluate the role of NLR in the prediction of long-term mortality in patients with stable liver cirrhosis. This is a retrospective observational cohort study in which 145 stable cirrhotic patients without infection, hepatocellular carcinoma, and ongoing steroid therapy were enrolled between January 2009 and December 2011. The primary end point was survival during follow-up. NLR along with Child-Turcotte-Pugh (CTP), Model for End-Stage Liver Disease (MELD) scores, and Charlson comorbidity index were assessed for the prediction of mortality. There were 86 men and 59 women, mean age 58.9±13.4 years. The etiologies of liver cirrhosis included viral hepatitis (n=73), cryptogenic (50), alcoholic (12), and other (10). The mean follow-up duration was 27.8±6.8 months, during which 40 patients died. The mean NLRs were 2.08±0.99 and 4.39±3.0 in surviving and nonsurviving patients, respectively (P<0.001). Kaplan-Meier survival analysis was carried out according to the median NLR above and below 2.72. Patients with NLR of at least 2.72 had a significantly lower survival (log rank, P<0.001). NLR was found to be an independent predictor of mortality in all Cox Regression models (odds ratio 1.2; 95% confidence interval 1.2-1.3; P<0.001). Receiver operating characteristic analysis showed that cut-off values of 4.22, 3.07, and 2.96 for NLR predicted 12, 24, and 36-month mortality, respectively (AUC: 0.806, P=0.0029; 0.841, P<0.0001 and 0.783, P<0.0001, respectively). NLR is a predictor of mortality independent of CTP and MELD scores in patients with liver cirrhosis. NLR could predict mortality in the subgroup of patients with low MELD scores as well.