Association between serum uric acid level and systemic lupus erythematosus kidney outcome: An observational study in Southern Chinese population and a meta-analysis.

OBJECTIVE: The study aimed to explore the effect of serum uric acid (SUA) level on the progression of kidney function in systemic lupus erythematosus (SLE) patients. METHODS: A total of 123 biopsy-proven lupus nephritis (LN) patients were included in this retrospective observational study. Cox proportional hazard regression analyses as well as restricted cubic spline analyses were performed to identify predictors of renal outcome in LN patients. We also performed a systematic review and meta-analysis for SUA and overall kidney outcomes in SLE patients. RESULTS: Based on the laboratory tests at renal biopsy, 72 (58.5%) of the 123 patients had hyperuricemia. The median (IQR) follow-up duration was 3.67 years (1.79-6.63 years), and a total of 110 (89.4%) patients experienced progression of LN. Increased serum uric acid level, whether analyzed as continuous or categorical variable, was associated with higher risk of LN progression in Cox proportional hazard regression model (hazard ratio [HR]: 1.003, 95% confidence interval [CI]: 1.001-1.005; HR: 1.780, 95% CI: 1.201-2.639, respectively). This relationship maintained in women (HR: 1.947, 95% CI: 1.234-3.074) but not men (HR: 2.189, 95% CI: 0.802-5.977). The meta-analysis showed a similar result that both continuous and categorical SUA were positively associated with the risk of kidney function progression in LN (weighted mean difference [WMD]: 1.73, 95% CI: 0.97-2.49; odds ratio [OR]: 1.55, 95% CI: 1.20-2.01, respectively). CONCLUSIONS: Our study found overall and especially in women that higher SUA in LN patients were associated with increased risk of renal progression. Meta-analysis yielded consistent results. Future studies are required to establish if uric acid can be used as a biomarker for risk assessment and/or as a novel therapeutic target in SLE.

General Paradigm of Edge-Based Internet of Things Data Mining for Geohazard Prevention.

Geological hazards (geohazards) are geological processes or phenomena formed under external-induced factors causing losses to human life and property. Geohazards are sudden, cause great harm, and have broad ranges of influence, which bring considerable challenges to geohazard prevention. Monitoring and early warning are the most common strategies to prevent geohazards. With the development of the internet of things (IoT), IoT-based monitoring devices provide rich and fine data, making geohazard monitoring and early warning more accurate and effective. IoT-based monitoring data can be transmitted to a cloud center for processing to provide credible data references for geohazard early warning. However, the massive numbers of IoT devices occupy most resources of the cloud center, which increases the data processing delay. Moreover, limited bandwidth restricts the transmission of large amounts of geohazard monitoring data. Thus, in some cases, cloud computing is not able to meet the real-time requirements of geohazard early warning. Edge computing technology processes data closer to the data source than to the cloud center, which provides the opportunity for the rapid processing of monitoring data. This article presents the general paradigm of edge-based IoT data mining for geohazard prevention, especially monitoring and early warning. The paradigm mainly includes data acquisition, data mining and analysis, and data interpretation. Moreover, a real case is used to illustrate the details of the presented general paradigm. Finally, this article discusses several key problems for the general paradigm of edge-based IoT data mining for geohazard prevention.