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In this paper, we consider a status updating system where updates are generated at a constant rate at $K$ sources and sent to the corresponding recipients through a noise-free broadcast channel. We assume that perfect channel state information (CSI) is available at the transmitter before each transmission, and the transmitter is able to utilize the CSI to precode the updates. Our object is to design optimal precoding schemes to minimize the summed average age of information (AoI) at the recipients. Under various assumptions on the size of each update $B$ , the number of transmit antennas $M$ , and the number of receive antennas $N$ at each user, this paper identifies the corresponding age-optimal precoding and transmission scheduling strategies. Specifically, for the case when $N=1$ , a round-robin based updating scheme is shown to be optimal. For the two-user systems with $N>B$ or $M\notin [N:2N]$ , framed updating schemes are proven to be optimal. For other cases in the two-user systems, a framed alternating updating scheme is proven to be 2-optimal. [ABSTRACT FROM AUTHOR]

Consider an energy harvesting (EH) sensor that continuously monitors a system and sends time-stamped status update to a destination. The sensor harvests energy from nature and uses it to power its updating operations. The destination keeps track of the system status through the successfully received updates. With the recently introduced information freshness metric “Age of Information” (AoI), our objective is to design optimal online status updating policy to minimize the long-term average AoI at the destination, subject to the energy causality constraint at the sensor. Due to the noisy channel between the sensor and the destination, each transmitted update may be erased with a fixed probability, and the AoI at the destination will be reset to zero only when an update is successfully received. We first consider status updating without feedback available to the sensor and show that the Best-effort Uniform updating (BU) policy is optimal in a broadly defined class of online policies. We then investigate status updating with perfect feedback to the sensor and prove similar optimality of the Best-effort Uniform updating with Retransmission (BUR) policy. In order to prove the optimality of the proposed policies, for each case, we first identify a lower bound on the long-term average AoI among a broad class of online policies, and then construct a sequence of virtual policies to approach the lower bound asymptotically. Since those virtual policies are sub-optimal to the original policy, the original policy is thus optimal. [ABSTRACT FROM AUTHOR]

Introduction: Diabetic nephropathy (DN) remains a major cause of end-stage renal disease. The development of novel biomarkers and early diagnosis of DN are of great clinical importance. The goal of this study was to identify hub genes with diagnostic potential for DN by weighted gene co-expression network analysis (WGCNA). Methods: Gene Expression Omnibus database was searched for microarray data including distinct types of CKD. Gene co-expression network was constructed, and modules specific for DN were identified by WGCNA. Gene ontology (GO) analysis was performed, and the hub genes were screened out within the selected gene modules. In addition, cross-validation was performed in an independent dataset and in samples of renal biopsies with DN and other types of glomerular diseases. Results: Dataset GSE99339 was selected, and a total of 179 microdissected glomeruli samples were analyzed, including DN, normal control, and 7 groups of other glomerular diseases. Twenty-three modules of the total 10,947 genes were grouped by WGCNA, and a module was specifically correlated with DN (r = 0.54, p = 9e−15). GO analysis showed that module genes were mainly enriched in the accumulation of extracellular matrix (ECM). LUM, ELN, FBLN1, MMP2, FBLN5, and FMOD were identified as hub genes. Cross verification showed LUM and FMOD were higher in the DN group and were negatively correlated with estimated glomerular filtration rate (eGFR). In renal biopsies, expression levels of LUM and FMOD were higher in DN than IgA nephropathy, membranous nephropathy, and normal controls. Conclusion: By using WGCNA approach, we identified LUM and FMOD related to ECM accumulation and were specific for DN. These 2 genes may represent potential candidate diagnostic biomarkers of DN. [ABSTRACT FROM AUTHOR]

Dear Editor Diabetes mellitus (DM) has become a significant chronic condition that seriously affects human health.[1] Nowadays, China has become the country with the largest number of DM patients worldwide, of which more than 90% are type 2 diabetes mellitus (T2DM).[2] The increasing prevalence of DM exacerbates the incidence of end-stage renal disease (ESRD).[3] T2DM-related ESRD not only reduces survival rate and health-related quality of life but also places a significant cost on patients as well as society.[[4], [6]] To identify clinical predictive factors and develop prediction models for ESRD risk in T2DM patients, we used the study population extracted from the China Renal Data System, a database containing the information of more than seven million patients attended at 19 hospitals in the Chinese mainland, as previously described.[7] ESRD, including an eGFR of 15 mL/min/1.73 m SP 2 sp or less, or the commencement of dialysis or kidney transplantation due to ESRD, was classified as the outcome. These prediction models were further provided as simple bedside tools, including a risk score and a nomogram, which could be extensively applied to assess T2DM patients' ESRD risk in clinical practice, to aid clinical decision-making and sensible resource allocation. Using a randomized approach, 55 824 patients with T2DM from 10 medical centers were included in the derivation cohort, and 25 745 patients from seven additional medical institutions were included for external validation. [Extracted from the article]

Objective. Lupus nephritis (LN) is a major and severe complication of systemic lupus erythematosus (SLE). Neutrophil gelatinase-associated lipocalin (NGAL), as a promising next-generation biomarker in clinical nephrology, has received extensive attention. However, its diagnostic performance in LN has high variability. Therefore, we performed an updated meta-analysis to further evaluate the diagnostic accuracy of urinary NGAL (uNGAL). Materials and Methods. PubMed, Embase, and Cochrane Library were searched from inception to October 27, 2019. Meta-analysis was performed with a bivariate random effects model. Additionally, the summary receiver operating characteristic (SROC) curves were established. The sources of heterogeneity were explored by meta-regression, subgroup analysis, and sensitivity analysis. Publication bias was assessed using the Deeks test. Results. 19 articles consisting of 21 eligible studies were included. In diagnosing LN, the estimates (95% confidence interval (CI)) were as follows: sensitivity, 0.84 (0.71-0.91); specificity, 0.91 (0.70-0.98); and the SROC-AUC value, 0.92 (0.90-0.94). In identifying active LN, the estimates were as follows: sensitivity, 0.72 (0.56-0.84); specificity, 0.71 (0.51-0.84); and the AUC value, 0.77 (0.74-0.81). With respect to predicting renal flare, the estimates were as follows: sensitivity, 0.80 (0.57-0.92); specificity, 0.67 (0.58-0.75); and the AUC value, 0.74 (0.70-0.78). For the studies to distinguish proliferative LN, the estimates were as follows: sensitivity, 0.87 (0.66-0.97), and specificity, 0.69 (0.39-0.91). Deeks' funnel plot suggested that there was no significant publication bias. Conclusions. Our meta-analysis indicates that uNGAL was a useful biomarker for diagnosis, estimation of activity, and prediction of renal flare of LN. In addition, the usefulness of uNGAL to distinguish pathological types of LN needs to be further investigated. [ABSTRACT FROM AUTHOR]