Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes

Aims/hypothesis: As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts. Methods: We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case–control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30–75 ml min−1 [1.73 m]−2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function. Results: Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets. Conclusions/interpretation: Serum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30–75 ml min−1 [1.73 m]−2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers

An optimum distributed energy balanced algorithm for wireless sensor networks

Lifetime of sensor networks based on finding energy efficient paths is a crucial topic in wireless sensor networks research. This paper aims to implement an optimal algorithm for wireless sensor networks (WSNs) to solve the most convenient problems within this area: energy efficiency, energy balance, and routing path. This metric is based on a decentralized manner as in the distributed energy balance routing algorithm (DEBR) [1]. We will demonstrate the effectiveness of our proposed method by comparing four existing efficient routing algorithms. Results are shown and discussion is held to verify the great effects on network lifetime and network energy balance

Characteristic Aspects of Uranium(VI) Adsorption Utilizing Nano-Silica/Chitosan from Wastewater Solution

A new nano-silica/chitosan (SiO2/CS) sorbent was created using a wet process to eliminate uranium(VI) from its solution. Measurements using BET, XRD, EDX, SEM, and FTIR were utilized to analyze the production of SiO2/CS. The adsorption progressions were carried out by pH, SiO2/CS dose, temperature, sorbing time, and U(VI) concentration measurements. The optimal condition for U(VI) sorption (165 mg/g) was found to be pH 3.5, 60 mg SiO2/CS, for 50 min of sorbing time, and 200 mg/L U(VI). Both the second-order sorption kinetics and Langmuir adsorption model were observed to be obeyed by the ability of SiO2/CS to eradicate U(VI). Thermodynamically, the sorption strategy was a spontaneous reaction and exothermic. According to the findings, SiO2/CS had the potential to serve as an effectual sorbent for U(VI) displacement

Using the motion of S2 to constrain vector clouds around SgrA

International audienceThe dark compact object at the centre of the Milky Way is well established to be a supermassive black hole with mass $M_{\bullet} \sim 4.3 \cdot 10^6 \, M_{\odot}$, but the nature of its environment is still under debate. In this work, we used astrometric and spectroscopic measurements of the motion of the star S2, one of the closest stars to the massive black hole, to determine an upper limit on an extended mass composed of a massive vector field around Sagittarius A*. For a vector with effective mass $10^{-19} \, \rm eV \lesssim m_s \lesssim 10^{-18} \, \rm eV$, our Markov Chain Monte Carlo analysis shows no evidence for such a cloud, placing an upper bound $M_{\rm cloud} \lesssim 0.1\% M_{\bullet}$ at $3\sigma$ confidence level. We show that dynamical friction exerted by the medium on S2 motion plays no role in the analysis performed in this and previous works, and can be neglected thus