Liquid biopsy has emerged as a transformative,minimally invasive strategy for disease detection and longitudinal monitoring through the analysis of circulating molecular components,including cell-free DNA(cfDNA),cell-...Liquid biopsy has emerged as a transformative,minimally invasive strategy for disease detection and longitudinal monitoring through the analysis of circulating molecular components,including cell-free DNA(cfDNA),cell-free RNA,extracellular vesicles,and circulating proteins in bodily fluids.Its central objective is to enable a continuum of precision clinical interventions,from early detection and diagnosis to therapeutic guidance and disease surveillance,thereby improving patient outcomes(Figure 1).展开更多
To address the issues of low accuracy,false positives,and missed detections in conventional blood cell detection methods,we propose TBV-YOLO,an improved blood cell detection algorithm based on the YOLOv7 network.This ...To address the issues of low accuracy,false positives,and missed detections in conventional blood cell detection methods,we propose TBV-YOLO,an improved blood cell detection algorithm based on the YOLOv7 network.This algorithm incorporates our lightweight feature extraction module,BP-ELAN.By employing bi-level routing attention(BRA)and task-specific context decoupling(TSCODE),TBV-YOLO enhances the extraction of key local features and introduces richer semantic information,thereby improving the detection accuracy for dense and small targets.Additionally,the use of partial convolution(PConv)and the VoVGSCSP lightweight feature fusion module reduces computational complexity,further lightening the network model.Experimental results on the blood cell count and detection(BCCD)dataset demonstrate that the proposed model achieves a mean average precision at intersection-over-union(IoU)of 0.5(m AP@0.5)of 93.9%,precision of 86.2%,and recall of 94.1%,representing improvements of 2.6%,1.8%,and 5.7%over YOLOv7,respectively,with a model parameter size of only 8.8M.展开更多
目的验证5种小而密低密度脂蛋白胆固醇(sdLDL‑C)试剂的分析性能及检测结果一致性,为临床实验室方法学选择和标准化提供参考。方法在Beckman AU 5800全自动生化分析仪上对国内已获国家药品监督管理局医疗器械注册证的5种sdLDL‑C试剂(标为...目的验证5种小而密低密度脂蛋白胆固醇(sdLDL‑C)试剂的分析性能及检测结果一致性,为临床实验室方法学选择和标准化提供参考。方法在Beckman AU 5800全自动生化分析仪上对国内已获国家药品监督管理局医疗器械注册证的5种sdLDL‑C试剂(标为A、B、C、D、E)进行验证。参考国家卫生行业标准及美国临床和实验室标准协会文件的要求分别对5种sdLDL‑C试剂进行精密度、线性、参考区间验证及方法学比对。精密度采用高低浓度混合血清及第三方质控物。线性范围经梯度混合血清样本验证。从北京协和医院体检人群临床检测剩余样本中选取70例表观健康人,对参考区间进行验证。选取2022年11月至2023年1月临床检测剩余血清246例,用于5种试剂比对。结果5种sdLDL‑C试剂的重复性为1.1%~5.5%,实验室内不精密度为3.2%~7.0%。除试剂C外,其余4种试剂在说明书声称的分析测量范围内均呈线性(r≥0.990),且检测值与理论值的偏差均在15%以内。A~E各试剂结果落在各自提供参考区间的比例依次为100%(70/70)、94.3%(66/70)、92.9%(65/70)、98.6%(69/70)、80.0%(56/70)。5种试剂与其测定均值的相关系数r分别在0.933~0.981之间,平均绝对百分偏倚在8.3%~32.3%之间,与均值比较的平均偏倚在‑0.12~0.21 mmol/L之间。结论应用于自动生化分析仪的5种sdLDL‑C酶法试剂,具有较高的精密度,但一致性仍需提高。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.82330073 and 82402731).
摘要Liquid biopsy has emerged as a transformative,minimally invasive strategy for disease detection and longitudinal monitoring through the analysis of circulating molecular components,including cell-free DNA(cfDNA),cell-free RNA,extracellular vesicles,and circulating proteins in bodily fluids.Its central objective is to enable a continuum of precision clinical interventions,from early detection and diagnosis to therapeutic guidance and disease surveillance,thereby improving patient outcomes(Figure 1).
基金supported by the National Science and Technology Major Project of China(No.12372020)。
摘要To address the issues of low accuracy,false positives,and missed detections in conventional blood cell detection methods,we propose TBV-YOLO,an improved blood cell detection algorithm based on the YOLOv7 network.This algorithm incorporates our lightweight feature extraction module,BP-ELAN.By employing bi-level routing attention(BRA)and task-specific context decoupling(TSCODE),TBV-YOLO enhances the extraction of key local features and introduces richer semantic information,thereby improving the detection accuracy for dense and small targets.Additionally,the use of partial convolution(PConv)and the VoVGSCSP lightweight feature fusion module reduces computational complexity,further lightening the network model.Experimental results on the blood cell count and detection(BCCD)dataset demonstrate that the proposed model achieves a mean average precision at intersection-over-union(IoU)of 0.5(m AP@0.5)of 93.9%,precision of 86.2%,and recall of 94.1%,representing improvements of 2.6%,1.8%,and 5.7%over YOLOv7,respectively,with a model parameter size of only 8.8M.
摘要目的验证5种小而密低密度脂蛋白胆固醇(sdLDL‑C)试剂的分析性能及检测结果一致性,为临床实验室方法学选择和标准化提供参考。方法在Beckman AU 5800全自动生化分析仪上对国内已获国家药品监督管理局医疗器械注册证的5种sdLDL‑C试剂(标为A、B、C、D、E)进行验证。参考国家卫生行业标准及美国临床和实验室标准协会文件的要求分别对5种sdLDL‑C试剂进行精密度、线性、参考区间验证及方法学比对。精密度采用高低浓度混合血清及第三方质控物。线性范围经梯度混合血清样本验证。从北京协和医院体检人群临床检测剩余样本中选取70例表观健康人,对参考区间进行验证。选取2022年11月至2023年1月临床检测剩余血清246例,用于5种试剂比对。结果5种sdLDL‑C试剂的重复性为1.1%~5.5%,实验室内不精密度为3.2%~7.0%。除试剂C外,其余4种试剂在说明书声称的分析测量范围内均呈线性(r≥0.990),且检测值与理论值的偏差均在15%以内。A~E各试剂结果落在各自提供参考区间的比例依次为100%(70/70)、94.3%(66/70)、92.9%(65/70)、98.6%(69/70)、80.0%(56/70)。5种试剂与其测定均值的相关系数r分别在0.933~0.981之间,平均绝对百分偏倚在8.3%~32.3%之间,与均值比较的平均偏倚在‑0.12~0.21 mmol/L之间。结论应用于自动生化分析仪的5种sdLDL‑C酶法试剂,具有较高的精密度,但一致性仍需提高。