目的系统评价益生菌治疗骨质疏松的效果。方法计算机检索Cochrane Library、Web of Science、PubMed、Embase、维普网、中国知网、万方数据知识服务平台,检索时限为2023年8月15日,纳入文献为益生菌治疗骨质疏松的随机对照实验。由2位研...目的系统评价益生菌治疗骨质疏松的效果。方法计算机检索Cochrane Library、Web of Science、PubMed、Embase、维普网、中国知网、万方数据知识服务平台,检索时限为2023年8月15日,纳入文献为益生菌治疗骨质疏松的随机对照实验。由2位研究人员独立筛选文献、提取数据,并评价纳入文献的偏倚风险。采用Stata.14和Revman.5.4软件分析益生菌治疗对患者骨密度、血钙、维生素D、甲状旁腺激素、骨钙素、骨碱性磷酸酶及不良反应的影响。结果最终纳入8篇文献,包括744例研究对象,Meta分析结果显示,在传统药物治疗的基础上加用益生菌,可以增加患者的髋关节骨密度[WMD 0.05(0.01,0.10)]g/cm^(3),增加患者血液中的钙离子浓度[WMD 0.26(0.02,0.50)]mmol/L,增加血液骨钙素的浓度[WMD 1.84(0.60,3.07)]ng/mL,也可以降低骨特异性碱性磷酸酶浓度[SMD-1.06(-2.06,-0.07)],并且可以降低恶心和腹泻的发生率,但对维生素D及甲状旁腺激素的改变,差异无统计学意义(P>0.05)。结论在常规治疗骨质疏松的基础上同时加用益生菌辅助治疗,或可进一步改善患者的骨密度水平,同时降低胃肠道的不良反应,更好的治疗骨质疏松,有利于患者的预后。展开更多
None-Line-of-Sight(NLOS)signals denote Global Navigation Satellite System(GNSS)signals received indirectly from satellites and could result in unacceptable positioning errors.To meet the high mission-critical transpor...None-Line-of-Sight(NLOS)signals denote Global Navigation Satellite System(GNSS)signals received indirectly from satellites and could result in unacceptable positioning errors.To meet the high mission-critical transportation and logistics demand,NLOS signals received in the built environment should be detected,corrected,and excluded.This paper proposes a cost-efective NLOS impact mitigation approach using only GNSS receivers.By exploiting more signal Quality Indicators(QIs),such as the standard deviation of pseudorange,Carrier-to-Noise Ratio(C/N0),elevation and azimuth angle,this paper compares machine-learning-based classifcation algorithms to detect and exclude NLOS signals in the pre-processing step.The probability of the presence of NLOS is predicted using regression algorithms.With a pre-defned threshold,the signals can be classifed as Line-of-Sight(LOS)or NLOS.The probability of the occurrence of NLOS is also used for signal subset selection and specifcation of a novel weighting scheme.The novel weighting scheme consists of both C/N0 and elevation angle and NLOS probability.Experimental results show that the best LOS/NLOS classifcation algorithm is the random forest.The best QI set for NLOS classifcation is the frst three QIs mentioned above and the diference of azimuth angle.The classifcation accuracy obtained from this proposed algorithm can reach 93.430%,with 2.810%false positives.The proposed signal classifer and weighting scheme improved the positioning accuracy by 69.000%and 40.700%in the horizontal direction,79.361%and 75.322%in the vertical direction,and 75.963%and 67.824%in the 3D direction.展开更多
文摘None-Line-of-Sight(NLOS)signals denote Global Navigation Satellite System(GNSS)signals received indirectly from satellites and could result in unacceptable positioning errors.To meet the high mission-critical transportation and logistics demand,NLOS signals received in the built environment should be detected,corrected,and excluded.This paper proposes a cost-efective NLOS impact mitigation approach using only GNSS receivers.By exploiting more signal Quality Indicators(QIs),such as the standard deviation of pseudorange,Carrier-to-Noise Ratio(C/N0),elevation and azimuth angle,this paper compares machine-learning-based classifcation algorithms to detect and exclude NLOS signals in the pre-processing step.The probability of the presence of NLOS is predicted using regression algorithms.With a pre-defned threshold,the signals can be classifed as Line-of-Sight(LOS)or NLOS.The probability of the occurrence of NLOS is also used for signal subset selection and specifcation of a novel weighting scheme.The novel weighting scheme consists of both C/N0 and elevation angle and NLOS probability.Experimental results show that the best LOS/NLOS classifcation algorithm is the random forest.The best QI set for NLOS classifcation is the frst three QIs mentioned above and the diference of azimuth angle.The classifcation accuracy obtained from this proposed algorithm can reach 93.430%,with 2.810%false positives.The proposed signal classifer and weighting scheme improved the positioning accuracy by 69.000%and 40.700%in the horizontal direction,79.361%and 75.322%in the vertical direction,and 75.963%and 67.824%in the 3D direction.
