Hantaviruses infect their reservoir hosts and humans, but the infection only causes disease in humans. In Asia and Europe (the Old World), the hantaviruses usually cause haemorrhagic fever with renal syndrome (HFRS). This article summarizes the current understanding of hantavirus epidemiology, as well as the clinical manifestations, pathogenesis, renal pathology, diagnosis, treatment, and prevention of HFRS. Moreover, the spatiotemporal distribution of HFRS was analysed based on the latest data obtained from the Chinese Centre for Disease Control and Prevention, for the period January 2004 to April 2015, to provide valuable information for the practical application of more effective HFRS control and prevention strategies in China.

Background: This study aimed to analyze the scientific outputs of diabetic kidney disease (DKD) research and explore its hotspots and frontiers from 2000 to 2017, using bibliometric methods. Methods: Articles in DKD research between 2000 and 2017 were retrieved from the Web of Science Core Collection (WoSCC). We used the VOSviewer 1.6.8 and CiteSpace 5.2 to analyze publication years, journals, countries, institutions, authors, references, and keywords. Keywords with citation bursts were used to analyze the research hotspots and emerging trends. Results: We identified 27,577 publications in DKD research from 2000 to 2017. The annual publication number increased with time. Nephrology Dialysis Transplantation published the highest number of articles. The United States was the most influential country with most publications and collaborations with other countries. Harvard University was the leading institute. Parving had the most cited publications. Keywords analysis indicated that the renin–angiotensin system inhibition used to be the most prevalent research topic, while recent research hotspots were podocyte, inflammation, and biomarker. The biomarkers for DKD screening, diagnosis, and prognosis could be a research frontier. Conclusions: The number of DKD related publications rapidly increased over the past 2 decades. Our study revealed the structure, hotspots, and evolution trends of DKD research. Further studies and more collaborations are needed.

Hemorrhagic fever with renal syndrome (HFRS) caused by hantaviruses is a serious public health problem in China, accounting for 90% of HFRS cases reported globally. In this study, we applied geographical information system (GIS), spatial autocorrelation analyses and a seasonal autoregressive-integrated moving average (SARIMA) model to describe and predict HFRS epidemic with the objective of monitoring and forecasting HFRS in mainland China. Chinese HFRS data from 2004 to 2016 were obtained from National Infectious Diseases Reporting System (NIDRS) database and Chinese Centre for Disease Control and Prevention (CDC). GIS maps were produced to detect the spatial distribution of HFRS cases. The Moran's I was adopted in spatial global autocorrelation analysis to identify the integral spatiotemporal pattern of HFRS outbreaks, while the local Moran's Ii was performed to identify ‘hotspot’ regions of HFRS at province level. A fittest SARIMA model was developed to forecast HFRS incidence in the year 2016, which was selected by Akaike information criterion and Ljung–Box test. During 2004–2015, a total of 165 710 HFRS cases were reported with the average annual incidence at province level ranged from 0 to 13.05 per 100 000 persons. Global Moran's I analysis showed that the HFRS outbreaks presented spatially clustered distribution, with the degree of cluster gradually decreasing from 2004 to 2009, then turned out to be randomly distributed and reached lowest point in 2012. Local Moran's Ii identified that four provinces in northeast China contributed to a ‘high–high’ cluster as a traditional epidemic centre, and Shaanxi became another HFRS ‘hotspot’ region since 2011. The monthly incidence of HFRS decreased sharply from 2004 to 2009 in mainland China, then increased markedly from 2010 to 2012, and decreased again since 2013, with obvious seasonal fluctuations. The SARIMA ((0,1,3) × (1,0,1)12) model was the most fittest forecasting model for the dataset of HFRS in mainland China. The spatiotemporal distribution of HFRS in mainland China varied in recent years; together with the SARIMA forecasting model, this study provided several potential decision supportive tools for the control and risk-management plan of HFRS in China.

Background: The directly measured glomerular filtrate rate (mGFR) is the gold standard for kidney function, but it is invasive and costly. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations have been widely used to estimate GFR, however, the comparative accuracy of estimated GFR (eGFR) using creatinine and cystatin C in CKD-EPI equations remains unclear. We performed this meta-analysis to assess the bias and accuracy of eGFR using equations of CKD-EPI crea , CKD-EPI cys , and CKD-EPI crea/cys in adult populations relevant to primary health care.Methods: Pubmed, Web of Science, EMBASE, and the Cochrane Library were searched from inception until December 2019 for related studies.Results: A total of 35 studies with 23,667 participants, which reported the data on the bias, and/or P30, and/ or R were included. The difference in the bias of eGFR using CKD-EPI cys was 4.84 mL/min/1.73 m 2 (95% CI, 1.88~7.80) lower than using CKD-EPI crea , and 1.50 mL/min/1.73 m 2 (95% CI, 0.05~2.95) lower than using CKD-EPI crea/cys . These gaps increased in subgroups of low mGFR (<60 mL/min/1.73 m 2 ). CKD-EPI crea/cys eGFR achieved the highest accuracy, 7.50% higher than CKD-EPI crea (95% CI, 4.81~10.18), and 3.21% higher than CKD-EPI cys (95% CI, -0.43~6.85); and the best correlation with mGFR, with Fisher's z transformed R of 1.20 (95% CI, 0.89-1.50).Conclusions: CKD-EPI crea/cys and CKD-EPI cys gave less bias and more accurate estimates of mGFR than CKD-EPI crea . More variables and coefficients could be added in CKD-EPI equations to achieve less bias and more accuracy in future research.