目的探讨孕妇首次产前检查减少不必要的血清AST检测,以实现孕妇产前检查AST的分级检测,降低国家医疗费用支出。方法 从我科实验室信息系统(laboratory information system,LIS)中检索以患者类别为“孕期保健”的ALT和AST(试剂中不含有5...目的探讨孕妇首次产前检查减少不必要的血清AST检测,以实现孕妇产前检查AST的分级检测,降低国家医疗费用支出。方法 从我科实验室信息系统(laboratory information system,LIS)中检索以患者类别为“孕期保健”的ALT和AST(试剂中不含有5-磷酸吡哆醛)检测数据。以卫生行业标准(WS/T404.1-2012)女性AST参考区间上限为标准,即当AST>35U·L^-1判定为阳性。利用ALT受试者工作特征(ROC)曲线判断AST是否需要检测。结果 AST与ALT呈正相关[r=0.88(95%CI:0.87~0.89),P<0.01]。ALT诊断AST阳性的ROC曲线下面积(AUC)为0.970(95%CI:0.961~0.977),最佳Cut-Off值分别为32U·L^-1,灵敏度为91.2%(95%CI:84.8%~95.5%),特异度为93.7%(95%CI:92.5%~94.8%),有87.4%的AST不需要检测,此时漏检率为0.6%,漏检AST水平中位数为43.0 U·L^-1。结论 孕妇首次产前检查没必要与ALT同步检测AST,可实行孕妇首次产前检查肝功能的分级检测。展开更多
In the background of“double carbon,”vigorously developing new energy is particularly important.Wind power is an important clean energy source.In the field of new energy,wind power scale is also expanding.With the wi...In the background of“double carbon,”vigorously developing new energy is particularly important.Wind power is an important clean energy source.In the field of new energy,wind power scale is also expanding.With the wind turbine,the probability of large-scale blade damage is also increasing.Because the large wind turbine blade crack detection cost is high and because of the poor working environment,this paper proposes a wind turbine blade surface defect detection method based on UAV acquisition images and digital image pro-cessing.The application of weighted averages to achieve grayscale processing,followed by median filtering to achieve image noise reduction,and an improved histogram equalization algorithm is proposed and used for the characteristics of the UAV acquisition images,which enhances the image by limiting the contrast adaptive his-togram equalization algorithm to make the details at the target area and defects more clear and complete,and improves the detection efficiency.The detection of the blade surface is achieved by separating and extracting the feature information from the defects through image foreground segmentation,threshold processing,and framing by the connected domain.The validity and accuracy of the proposed method in leaf detection were verified by experiments.展开更多
文摘In the background of“double carbon,”vigorously developing new energy is particularly important.Wind power is an important clean energy source.In the field of new energy,wind power scale is also expanding.With the wind turbine,the probability of large-scale blade damage is also increasing.Because the large wind turbine blade crack detection cost is high and because of the poor working environment,this paper proposes a wind turbine blade surface defect detection method based on UAV acquisition images and digital image pro-cessing.The application of weighted averages to achieve grayscale processing,followed by median filtering to achieve image noise reduction,and an improved histogram equalization algorithm is proposed and used for the characteristics of the UAV acquisition images,which enhances the image by limiting the contrast adaptive his-togram equalization algorithm to make the details at the target area and defects more clear and complete,and improves the detection efficiency.The detection of the blade surface is achieved by separating and extracting the feature information from the defects through image foreground segmentation,threshold processing,and framing by the connected domain.The validity and accuracy of the proposed method in leaf detection were verified by experiments.
