This article examines and compares the performance evaluation indicator systems of British and American military hospitals,including their hospital system architecture,indicator frameworks,specific indicators,and evaluation methods.To enhance the comparability of indicators,they were categorized into military attributes,service quality efficiency,economic indicators,and growth and development indicators.Based on the comparative analysis of British and American military hospital performance indicator systems,the following suggestions are proposed for the performance evaluation system of public hospitals in China:(1)increasing the indicator diversity and improving the structure of indicators;(2)optimizing indicator reliability throughout the entire process to enhance the scientific ground of the indicator system;and(3)integrating data assets to facilitate joint application of big data.

Objective:To analyze the electroencephalogram (EEG) features of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI), and to combine the characteristics for classification and prediction.Methods:One hundred and thirty-five patients attending the Department of Neurology at the General Hospital of Tianjin Medical University were enrolled, including 34 patients with AD, 67 patients with MCI, and 34 healthy control (HC). The electroencephalogram signals of these patients in the resting state were collected and preprocessed. Relative power spectral density features and sample entropy features on a multi-band scale were extracted to compare the whole-brain differences in electroencephalogram features among the 3 groups of subjects, and then subdivided into brain regions and individual leads for in-depth analysis. The above two features were fused to classify and predict AD, MCI, and HC by support vector machine (SVM).Results:The frontal regions had higher δ relative power spectral densities than the other regions, and the occipital and temporal regions showed relatively lower distributions. θ-Band relative power spectral densities had a more even distribution of sizes across brain regions. α-Band relative power spectral densities were concentrated in the occipital lobe, while β-band relative power spectral densities were mainly concentrated in the parietal and temporal lobes. Except for the central lobe, the δ-band relative power spectral densities of the AD group were higher than those of the MCI group ( P < 0.05) and HC group ( P < 0.01) in all brain regions and the whole brain. θ-band relative power spectral densities of the AD group were higher than those of the MCI gourp ( P < 0.001) and HC group ( P < 0.001) in the whole brain and in all brain regions. α-Band relative power spectral densities of the AD group were lower than those of the other groups only in the temporal lobe (all P < 0.05). The relative power spectral density of the β-band in the AD group was higher than that of the other groups in the whole brain and in all brain regions ( P < 0.05, 0.01, 0.001). The difference in the relative power spectral density of the δ-band in the C3 lead in the central lobe of the AD and HC groups was statistically significant ( P < 0.05). The relative power spectral density of the γ-band in the temporal lobe was higher than that in the other regions of the AD group, the MCI group, and the HC group. The relative power spectral density of the γ-band in the T3 lead in the AD group was significantly lower than that in the T4 lead. The average entropy of samples in the whole brain and in each brain region was lower than that in the HC group in the AD and MCI groups (all P < 0.05). The entropy of the samples at lead C3 in the AD group was lower than that in the MCI group ( P < 0.05). The differences between the relative power spectral density, sample entropy, and the actual data classification evaluation indexes (accuracy rate, precision rate, recall rate, and F1 score) that fused the two features, and the rearranged data were all statistically significant (all P < 0.001). When the relative power spectral density feature and the sample entropy feature were fused in the classification features, the best classification prediction was achieved, with an accuracy rate of 80%, a precision rate of 78%, a recall rate of 78%, and the F1 score of 79%. Conclusions:Relative power spectral density and sample entropy analysis can reveal the abnormalities of electroencephalogram activities of AD and MCI patients from different perspectives (linear and nonlinear), and the combination of these two features in classification prediction can improve the classification effect.

Objective To explore the effect of T140 on SDF-1 and MMPs levels through targeted blocking SDF-1/CXCR4 signaling pathway , and to investigate the function of T140 to prevent from cartilage degeneration. Methods Thirty-six 9-month-old male healthy Hartley guinea pigs were divided into three groups: experimental group(group A,n = 12),experimental control group(group B,n = 12) and blank control group (group C,n = 12). In the 2nd,4th,6th,8th,10th,12th week, the levels of SDF-1 in serum were quantified with ELISA. In the 12th week, mRNA levels of MMP-3,MMP-9 and MMP-13 in articular cartilages were measured with RT-PCR. Results The serum levels of SDF-1 of the group A decreased gradually but increased in group B and C. Group A had a statistical significance compared with group B and C at the same time point (P< 0.05).The mRNA levels of MMPs in group A were lower than group B and C (P < 0.05). Conclusion T140 could block the SDF-1/CRCR4 signaling pathway and decrease the secretion of SDF-1 and mRNA expression levels of MMPs and reduce the cartilage degeneration.