BackgroundThe objective of the present study was to investigate whether the analysis of magnesium (Mg), high-sensitivity C-reactive protein (hsCRP), and ischemia-modified albumin (IMA) concentrations can be used as a non-invasive and convenient method for diagnosing obstructive sleep apnea syndrome (OSAS).Material/MethodsAfter polysomnography, venous blood was collected from 33 patients with OSAS and 30 control individuals. Serum levels of Mg, hsCRP, and IMA were investigated. The relationship between these factors and apnea–hypopnea index (AHI) was analyzed using the Pearson correlation coefficient. The role of the factors was determined using a receiver operating characteristic (ROC) curve and multivariate logistic regression analysis.ResultsThe levels of hsCRP and IMA were significantly higher in patients with OSAS than in control subjects, while the levels of Mg were lower (P<0.05 for all). A significant correlation was noted between serum IMA (r=0.614; P<0.001) and hsCRP (r=0.453; P<0.001) levels and the AHI. The ROC showed that serum Mg (AUC=0.74(0.62–0.85)), hsCRP (AUC=0.77(0.65–0.87)), and IMA (AUC=0.78(0.66–0.87)) levels could be used as markers to diagnose OSAS. Moreover, our new model, MIh, which is obtained by multivariate analysis, yielded an AUC value of 0.93 (0.83–0.98). Continuous positive airway pressure (CPAP) treatment reversed the changes in the serum levels of Mg, hsCRP, and IMA.ConclusionsPatients with OSAS show reduced serum Mg levels and elevated serum hsCRP and IMA levels. These observed alterations can be reversed by CPAP treatment. A novel model, named MIh, may be a promising tool for OSAS diagnosis.

With the rapid development of distributed resources such as wind turbines and photovoltaics in the power system, intermittency and volatility will directly impact the security and stability of the power grid and power transactions. This paper combines the principal component analysis method and hierarchical agglomerative clustering algorithm to analyze the output characteristics of distributed resources. According to the Davies-Bouldin index to evaluate the clustering results, the typical characteristic output curves of distributed resources are obtained. Then the typical curve is used to represent the distributed resources for aggregation. Based on daily peak-valley difference and power fluctuation index, the comprehensive results are judged and the optimal solution is selected. Finally, an example with eight distributed resources is given to verify the effectiveness of the proposed method.

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