Soluble tumor necrosis factor receptor 1 and 2 predict outcomes in advanced chronic kidney disease : a prospective cohort study

Background : Soluble tumor necrosis factor receptors 1 (sTNFR1) and 2 (sTNFR2) have been associated to progression of renal failure, end stage renal disease and mortality in early stages of chronic kidney disease (CKD), mostly in the context of diabetic nephropathy. The predictive value of these markers in advanced stages of CKD irrespective of the specific causes of kidney disease has not yet been defined. In this study, the relationship between sTNFR1 and sTNFR2 and the risk for adverse cardiovascular events (CVE) and all-cause mortality was investigated in a population with CKD stage 4-5, not yet on dialysis, to minimize the confounding by renal function. Patients and methods : In 131 patients, CKD stage 4-5, sTNFR1, sTNFR2 were analysed for their association to a composite endpoint of all-cause mortality or first non-fatal CVE by univariate and multivariate Cox proportional hazards models. In the multivariate models, age, gender, CRP, eGFR and significant comorbidities were included as covariates. Results : During a median follow-up of 33 months, 40 events (30.5%) occurred of which 29 deaths (22.1%) and 11 (8.4%) first non-fatal CVE. In univariate analysis, the hazard ratios (HR) of sTNFR1 and sTNFR2 for negative outcome were 1.49 (95% confidence interval (CI): 1.28-1.75) and 1.13 (95% CI: 1.06-1.20) respectively. After adjustment for clinical covariables (age, CRP, diabetes and a history of cardiovascular disease) both sTNFRs remained independently associated to outcomes (HR: sTNFR1: 1.51, 95% CI: 1.30-1.77; sTNFR2: 1.13, 95% CI: 1.06-1.20). A subanalysis of the non-diabetic patients in the study population confirmed these findings, especially for sTNFR1. Conclusion : sTNFR1 and sTNFR2 are independently associated to all-cause mortality or an increased risk for cardiovascular events in advanced CKD irrespective of the cause of kidney disease

ABO Blood Group and the Risk of Hepatocellular Carcinoma: A Case-Control Study in Patients with Chronic Hepatitis B

BACKGROUND: Studies have observed an association between the ABO blood group and risk of certain malignancies. However, no studies of the association with hepatocellular carcinoma (HCC) risk are available. We conducted this hospital-based case-control study to examine the association with HCC in patients with chronic hepatitis B (CHB). METHODS: From January 2004 to December 2008, a total of 6275 consecutive eligible patients with chronic hepatitis B virus (HBV) infection were recruited. 1105 of them were patients with HBV-related HCC and 5,170 patients were CHB without HCC. Multivariate logistic regression models were used to investigate the association between the ABO blood group and HCC risk. RESULTS: Compared with subjects with blood type O, the adjusted odds ratio (AOR) for the association of those with blood type A and HCC risk was 1.39 [95% confidence interval (CI), 1.05-1.83] after adjusting for age, sex, type 2 diabetes, cirrhosis, hepatitis B e antigen, and HBV DNA. The associations were only statistically significant [AOR (95%CI) = 1.56(1.14-2.13)] for men, for being hepatitis B e antigen positive [AOR (95%CI) = 4.92(2.83-8.57)], for those with cirrhosis [AOR (95%CI), 1.57(1.12-2.20)], and for those with HBV DNA≤10(5)copies/mL [AOR (95%CI), 1.58(1.04-2.42)]. Stratified analysis by sex indicated that compared with those with blood type O, those with blood type B also had a significantly high risk of HCC among men, whereas, those with blood type AB or B had a low risk of HCC among women. CONCLUSIONS: The ABO blood type was associated with the risk of HCC in Chinese patients with CHB. The association was gender-related

Interactions between constituent single symmetries in multiple symmetry

Item does not contain fulltextAs a rule, the discriminability of multiple symmetries from random patterns increases with the number of symmetry axes, but this number does not seem to be the only determinant. In particular, multiple symmetries with orthogonal axes seem better discriminable than multiple symmetries with nonorthogonal axes. In six experiments on imperfect two-fold symmetry, we investigated whether this is due to extra structure in the form of so-called correlation rectangles, which arise only in the case of orthogonal axes, or to the relative orientation of the axes as such. The results suggest that correlation rectangles are not perceptually relevant and that the percept of a multiple symmetry results from an orientation-dependent interaction between the constituent single symmetries. The results can be accounted for by a model involving the analysis of symmetry at all orientations, smoothing (averaging over neighboring orientations), and extraction of peaks

KANDINSKY Patterns as IQ-Test for Machine Learning

International audienceAI follows the notion of human intelligence which is unfortunately not a clearly defined term. The most common definition given by cognitive science as mental capability, includes, among others, the ability to think abstract, to reason, and to solve problems from the real world. A hot topic in current AI/machine learning research is to find out whether and to what extent algorithms are able to learn abstract thinking and reasoning similarly as humans can do – or whether the learning outcome remains on purely statistical correlation. In this paper we provide some background on testing intelligence, report some preliminary results from 271 participants of our online study on explainability, and propose to use our Kandinsky Patterns as an IQ-Test for machines. Kandinsky Patterns are mathematically describable, simple, self-contained hence controllable test data sets for the development, validation and training of explainability in AI. Kandinsky Patterns are at the same time easily distinguishable from human observers. Consequently, controlled patterns can be described by both humans and computers. The results of our study show that the majority of human explanations was made based on the properties of individual elements in an image (i.e., shape, color, size) and the appearance of individual objects (number). Comparisons of elements (e.g., more, less, bigger, smaller, etc.) were significantly less likely and the location of objects, interestingly, played almost no role in the explanation of the images. The next step is to compare these explanations with machine explanations