Objective To discuss whether mild hyperuricemia can lead to kidney damage and the protection of decreased uric acid,through observing that hyperuricemia did damage to glomerulus endothelial function and cell proliferation of vascular smooth muscle in rats. Methods Fifty-four male SD rats were divided into four groups,the control group,model group (Oxonate),allopurinol group and Oxonate+allopurinol group.Rats were administered on a low sodium diet and their systolic blood pressure (SBP) were measured each 10 days.ELISA was used to detect rat plasma markers of endothelial function damage [nitric oxide (NO),type-1 plasminogen activator inhibitor (PAI-1),endothelin 1 (ET-1)] and cell proliferation of vascular smooth muscle[plateletderived growth factor (PDGF),cycloxygenase 2 (COX2),monocyte chemotactic protein-1 (MCP-1)],and the markers of inflammatory reaction[interleukin-18 (IL-18),tumor necrosis factor α(TNF-α)].PDGF and nitric oxide synthase (NOS) levels of rats were detected by immunohistochemical method.Renal tissue pathology of rats was observed. Results Compared to the control group,the plasmic concentration of COX2,ET-1,IL-18,PAI-1,PDGF,TNF-o,MCP-1 increased,and NO decreased significantly in rats of model group (all P＜0.05),expression of NOS significantly reduced and PDGF increased (all P＜0.05).Under light microscope,vascular wall thickening,intimal proliferation and lumen slight stricture without uric acid crystals in renal tissue were found in model group,which were obviously improved by using allopurinol. Conclusion Mild hyperuricemia can do damage to endothelial function of glomerulus and lead to vascular cell proliferation,which can be improved through decreasing uric acid.

Objective To evaluate the clinico-pathologic presentations and prognosis in the very elderly patients undergoing renal biopsy.Methods The patients who underwent renal biopsy in Nephrology Center of the First Affiliated Hospital of Zhengzhou University were screened from May 2012 to March 2016.All patients were divided into observation group (aged ≥80 years) and control group (aged 65-70 years).The clinico-pathological classifications and prognosis were compared between the two groups.Results Primary glomerulopathy was the most frequent pathologic diagnosis in observation and control groups[20(60.6％) and 64(64.0％),respectively,P=0.726].Among primary glomerulopathy,membranous nephropathy was the most frequent histopathological type[10(50.0％) and 40 (62.5％)] in observation and control groups,respectively,(P =0.320).Among secondary glomerulopathy,the number of patients in observation group were 10 cases (30.3％) and were 13 cases (13.0％) in control group (t=5.194,P＜0.05),with no significant differences between the two groups in amyloid degeneration,ANCA-associated vasculitis,HBV-associated Glomerulonephritis,and nephritis of Schonlein-Henoch purpura.In the very elderly patients with nephrotic syndrome,glomerular minimal change was the most common histological type [7 (30.4％)],followed by membranous nephropathy[6 (26.1％)].Furthermore,there were no side effects of perinephric hematoma,gross hematuria,arteriovenous fistula or other complications.Conclusions The pathological types distribution of patients aged ≥ 80 versus 65-70 years is different.And the renal biopsy is relatively safe and has an important role for the very elderly patients.

Road traffic accidents (RTA) can cause a large number of casualties and property losses. Driving fatigue is one of the important factors leading to RTA. Electrophysiological signals, as a kind of information feedback for the nervous system to regulate body functions, can reflect drivers’ fatigue state. However, there is a lack of systematic reviews on the current research on electrophysiological signals as information input of machine learning methods for driving fatigue recognition. By investigating fatigue-related literature, the current paper summarized the neural regulation mechanism of fatigue, clarified that driving fatigue is caused by both psychological and physiological loads, recognized inducing factors related to driving fatigue, and summed up electrophysiological signals now in use of driving fatigue recognition, as well as their physiological mechanisms and related indicators. Machine learning algorithms are widely used in identifying driving fatigue. Based on existing studies that used electrophysiological signals as information input source and applied various machine learning algorithms to build driving fatigue identification models, this paper compared the effectiveness of various machine learning algorithms, and described the advantages and disadvantages of supervised machine learning. It is pointed out that suitable classification algorithms should be selected according to sample conditions and model eigenvalues when applied to driving fatigue recognition. In addition, a variety of electrophysiological signals as information sources can help improve the accuracy of a fatigue recognition model, but the increase of model input eigenvalues cannot. Finally, the research progress of identification methods based on electrophysiological signals provided new opportunities for identifying driving fatigue.