The clinical and immunological characteristics of the SLE patients in our study place our population in the middle of the spectrum between other Asian and Caucasian populations.

OBJECTIVES: Many studies indicate that microRNAs (miRNAs) could be potential biomarkers for various diseases. The purpose of this study was to investigate the clinical value of serum exosomal miRNAs in systemic lupus erythematosus (SLE). METHODS: Serum exosomes were isolated from 38 patients with SLE and 18 healthy controls (HCs). The expression of miR-21, miR-146a and miR-155 within exosomes was examined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Using receiver operating characteristic (ROC) curves, we evaluated the diagnostic value of exosomal miRNAs. RESULTS: Exosomal miR-21 and miR-155 were upregulated ( p <0.01), whereas miR-146a expression ( p <0.05) was downregulated in patients with SLE, compared to that in HCs. The expression of miR-21 ( p <0.01) and miR-155 ( p <0.05) was higher in SLE patients with lupus nephritis (LN) than in those without LN (non-LN). The analysis of ROC curves revealed that the expression of miR-21 and miR-155 showed a potential diagnostic value for LN. Furthermore, miR-21 (R=0.44, p <0.05) and miR-155 (R=0.33, p <0.05) were positively correlated with proteinuria. The expression of miR-21 was negatively associated with anti-SSA/Ro antibodies (R=−0.38, p <0.05), and that of miR-146a was negatively associated with anti-dsDNA antibodies (R=−0.39, p <0.05). CONCLUSIONS: These findings suggested that exosomal miR-21 and miR-155 expression levels may serve as potential biomarkers for the diagnosis of SLE and LN.

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Traffic congestion is a major concern in many cities around the world. Previous work mainly focuses on the prediction of congestion and analysis of traffic flows, while the congestion correlation between road segments has not been studied yet. In this paper, we propose a three-phase framework to study the congestion correlation between road segments from multiple real world data. In the first phase, we extract congestion information on each road segment from GPS trajectories of over 10,000 taxis, define congestion correlation and propose a corresponding mining algorithm to find out all the existing correlations. In the second phase, we extract various features on each pair of road segments from road network and POI data. In the last phase, the results of the first two phases are input into several classifiers to predict congestion correlation. We further analyze the important features and evaluate the results of the trained classifiers. We found some important patterns that lead to a high/low congestion correlation, and they can facilitate building various transportation applications. The proposed techniques in our framework are general, and can be applied to other pairwise correlation analysis.

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