Aim: This study was to observe the changes of the gene expression of microRNA-155 and 127 in the neonatal rats with Acute Lung Injury (ALI). Methods: Eighty neonatal SD rats were randomly divided into experimental groups (intraperitoneal injection with LPS, n=60) and control (intraperitoneal injection with NS, n=20). Neonatal rats in experimental groups (LPS 1 mg, 2 mg and 3 mg group, n=20) were received corresponding dose of LPS (1, 2 and 3 mg/kg, diluted with saline to 0.2 ml), while neonatal rats in control group were injected 0.2 ml saline respectively. Then we killed rats at 1 h, 6 h, 12 h and 24 h respectively in each group. The lung general pathological changes were observed. The expression of microRNA-155 and 127 were detected by semi-quantitative reverse transcription-polymerase chain reaction. Tumor Necrosis Factor-α (TNF-α) and Interleukelin-6 (IL-6) in Bronchoalveolar Lavage Fluid (BALF) were detected by enzyme-linked immunosorbent assay. The expression of TNF-α and IL-6 protein in lung tissue were detected by western blotting. Results: The expression of microRNA-155 was up-regulated in the tissues and BALF in a dose and timedependent manner and microRNA-127 down-regulated. The difference were statistically significant when compared with the control group (all P<0.05). The TNF-α and IL-6 level of BALF rats with ALI in LPS groups were increased compared with the control group in a dose-dependent manner (all P<0.05). A similar trend had been seen in TNF-α and IL-6 protein level of lung tissues, the difference was statistically significant. Conclusion: The expression of microRNA-155 and 127 are associated with severity of the ALI in a dose and time-dependent manner, and they might be considered as potential biomarkers for early diagnosis of ALI.

In order to solve the problem of low abnormal diagnosis rate of self-powered power supply system, an improved grey wolf optimization-support vector machine (GWO-SVM) algorithm combined with maximal information coefficient (MIC) are proposed. First, the feature sets of 11 kinds of monitoring data are optimized and selected based on MIC for self-powered power supply system. By eliminating redundant variables and insensitive variables, feature variable sets with great influence on abnormal diagnosis are selected. Second, by upgrading the selection method of control parameter σ from linear to nonlinear, an improved GWO-SVM algorithm that can take into account both global and local search capabilities is proposed. Furthermore, the optimal feature set which has great influence on abnormal diagnosis is selected as the input of the proposed algorithm, and then the abnormal diagnosis method combining the improved GWO-SVM with MIC is constructed for self-powered power supply system. The specific algorithm flow and step are given. Finally, compared with other algorithm, the simulation experiments show that the GWO-SVM method has a higher accuracy and a higher recall rate for the abnormal diagnosis in the self-powered power supply system.