In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,...In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and severity.However,manual urine sediment examination is labor-intensive,time-consuming,and subjective.Traditional machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments.Deep learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image datasets.Unfortunately,only a limited number of publicly available urine sediment datasets are currently available.To alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert guidance.Two main challenges are associated with our dataset:a large number of small objects and the occlusion between these small objects.Our manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this dataset.Specifically,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and effort.We propose an improved lightweight one-stage object detection algorithm called Discriminatory-YOLO.The proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the image.The local context attention module captures context information beyond the object region,while the global background suppression module emphasizes objects in uninformative backgrounds.We comprehensively evaluate our method on the UriSed2K dataset,which includes seven categories of urine sediments,such as erythrocytes(red blood cells),leukocytes(white blood cells),epithelial cells,crystals,mycetes,broken erythrocytes,and broken leukocytes,achieving the best average precision(AP)of 95.3%while taking only 10 ms per image.The source code and dataset are available at https://github.com/binghuiwu98/discriminatoryyolov5.展开更多
The collision cross-sections(CCS)measurement using ion mobility spectrometry(IMS)in combination with mass spectrometry(MS)offers a great opportunity to increase confidence in metabolite identification.However,owing to...The collision cross-sections(CCS)measurement using ion mobility spectrometry(IMS)in combination with mass spectrometry(MS)offers a great opportunity to increase confidence in metabolite identification.However,owing to the lack of sensitivity and resolution,IMS has an analytical challenge in studying the CCS values of very low-molecular-weight metabolites(VLMs250 Da).Here,we describe an analytical method using ultrahigh-performance liquid chromatography(UPLC)coupled to a traveling wave ion mobility-quadrupole-time-of-flight mass spectrometer optimized for the measurement of VLMs in human urine samples.The experimental CCS values,along with mass spectral properties,were reported for the 174 metabolites.The experimental data included the mass-to-charge ratio(m/z),retention time(RT),tandem MS(MS/MS)spectra,and CCS values.Among the studied metabolites,263 traveling wave ion mobility spectrometry(TWIMS)-derived CCS values(TWCCSN2)were reported for the first time,and more than 70%of these were CCS values of VLMs.The TWCCSN2 values were highly repeatable,with inter-day variations of<1%relative standard deviation(RSD).The developed method revealed excellent TWCCSN2 accuracy with a CCS difference(DCCS)within±2%of the reported drift tube IMS(DTIMS)and TWIMS CCS values.The complexity of the urine matrix did not affect the precision of the method,as evidenced by DCCS within±1.92%.According to the Metabolomics Standards Initiative,55 urinary metabolites were identified with a confidence level of 1.Among these 55 metabolites,53(96%)were VLMs.The larger number of confirmed compounds found in this study was a result of the addition of TWCCSN2 values,which clearly increased metabolite identification confidence.展开更多
Background:The effective management of bladder cancer(BCa)depends on the early diagnosis and surveillance.Previous studies have explored numerous urinary molecules as potential biomarkers of BCa.However,the molecular ...Background:The effective management of bladder cancer(BCa)depends on the early diagnosis and surveillance.Previous studies have explored numerous urinary molecules as potential biomarkers of BCa.However,the molecular functions and cell-of-origin profiles of these biomarkers are yet to be elucidated.In this study,we aimed to provide a comprehensive overview of the landscape of urinary biomarker genes for BCa.Methods:We conducted an exhaustive literature search in PubMed,through which 555 biomarker genes were identified.We then analyzed the BCa single-cell atlas to infer the cellular origin of these BCa urine biomarker genes and performed functional enrichment analysis to gain insights into the functional molecular implications of these biomarkers.Results:These genes are involved in tumor proliferation,angiogenesis,cellmigration,and cell death and are predominantly expressed in epithelial and stromal cells.Interestingly,our analysis ofmultiomics tumor data revealed a discordance between tissue and urine in terms of differential methylation and RNA expression,suggesting that biomarker discovery for liquid biopsies should ideally begin with the analysis of bodily fluids rather than relying interest and that test strategies incorporating multiple molecular markers represent an ongoing trend.Conclusions:Collectively,our study has built a landscape of BCa urine biomarker genes,uncovered molecular insights into these biomarkers,and revealed the bibliometric trends in this field,which will contribute to the discovery of novel biomarkers in the future.展开更多
建立一种基于美国官方分析化学师协会(Association of Official Analytical Chemists,AOAC)方法检测黑果枸杞及其制品中花青素含量的改进pH示差法。考察了黑果枸杞及其制品中花青素的最佳提取和检测条件,通过液相色谱-三重四级杆串联质...建立一种基于美国官方分析化学师协会(Association of Official Analytical Chemists,AOAC)方法检测黑果枸杞及其制品中花青素含量的改进pH示差法。考察了黑果枸杞及其制品中花青素的最佳提取和检测条件,通过液相色谱-三重四级杆串联质谱法鉴别出黑果枸杞中花青素的具体化学结构,并计算出混合花青素的平均摩尔质量。通过分光光度法测得混合花青素的平均摩尔消光系数,对改进后的pH示差法进行方法学验证和花青素的含量测定。结果显示,最佳提取和检测条件如下:黑果枸杞花青素提取溶剂为盐酸-80%(体积分数)乙醇(3∶97,体积比),料液比为1∶100(g∶mL),提取温度为50℃,提取时间为30 min,缓冲溶液稀释5倍后静置平衡20 min。液相色谱-三重四级杆串联质谱法鉴别黑果枸杞中主要以矮牵牛素类花青素为主(占97.96%),黑果枸杞特有的混合花青素平均摩尔质量为912.7 g/mol,平均摩尔消光系数为29591 L/(mol·cm)。pH示差法改进后能够满足方法学验证要求,固体样品和液体样品最低检出限分别为28.2 mg/100 g、0.282 mg/100 mL。方法改进后花青素提取增长率均大于20%,静置平衡20 min后单次检测结果精密度小于0.3%。以矮牵牛素类花青素代替矢车菊素-3-O-葡萄糖苷计算花青素含量平均提高了2.41倍,能真实地反映黑果枸杞及其制品中花青素的含量。展开更多
为满足不同种类食品对大豆分离蛋白(soybean protein isolate,SPI)不同功能性的需求,本研究利用红外光谱快速采集70组不同pH值处理后SPI的数据,探讨pH值变化对SPI结构含量的影响。使用均值中心化、多元散射校正、标准正态变量变换和归...为满足不同种类食品对大豆分离蛋白(soybean protein isolate,SPI)不同功能性的需求,本研究利用红外光谱快速采集70组不同pH值处理后SPI的数据,探讨pH值变化对SPI结构含量的影响。使用均值中心化、多元散射校正、标准正态变量变换和归一化算法对红外光谱数据进行预处理,基于二维相关红外光谱提取特征波段,再利用偏最小二乘(partial least square,PLS)法和算术优化算法-随机森林(arithmetic optimization algorithm-random forests,AOA-RF)建立不同pH值条件下SPI结构及含量的预测模型。结果表明,经均值中心化和多元散射校正结合处理后,α-螺旋、β-折叠、β-转角和无规卷曲模型的相对标准偏差分别为1.29%、1.60%、1.37%、7.28%,两者结合对光谱数据的预处理效果最佳。预测α-螺旋和β-折叠含量最优模型为AOA-RF(特征波段),校正集决定系数为0.9350和0.9266,预测集决定系数为0.8568和0.8701;预测β-转角和无规卷曲含量最优模型为PLS(特征波段),校正集决定系数为0.9154和0.8817,预测集决定系数为0.8913和0.7843。本研究结果可为工业生产过程中产品质量快速检测和工艺条件控制提供理论支撑。展开更多
基金This work was partially supported by the National Natural Science Foundation of China(Grant Nos.61906168,U20A20171)Zhejiang Provincial Natural Science Foundation of China(Grant Nos.LY23F020023,LY21F020027)Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects(Grant Nos.2022SDSJ01).
文摘In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and severity.However,manual urine sediment examination is labor-intensive,time-consuming,and subjective.Traditional machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments.Deep learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image datasets.Unfortunately,only a limited number of publicly available urine sediment datasets are currently available.To alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert guidance.Two main challenges are associated with our dataset:a large number of small objects and the occlusion between these small objects.Our manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this dataset.Specifically,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and effort.We propose an improved lightweight one-stage object detection algorithm called Discriminatory-YOLO.The proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the image.The local context attention module captures context information beyond the object region,while the global background suppression module emphasizes objects in uninformative backgrounds.We comprehensively evaluate our method on the UriSed2K dataset,which includes seven categories of urine sediments,such as erythrocytes(red blood cells),leukocytes(white blood cells),epithelial cells,crystals,mycetes,broken erythrocytes,and broken leukocytes,achieving the best average precision(AP)of 95.3%while taking only 10 ms per image.The source code and dataset are available at https://github.com/binghuiwu98/discriminatoryyolov5.
基金supported by the Postdoctoral Fellowship Program(Grant No.:(IO)R016320001)by Mahidol University,Thailand.supported by Mahidol University,Thailand(to Associate Professor Sakda Khoomrung)funding support from the National Science,Research and Innovation Fund(NSRF)via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation,Thailand(Grant No.:B36G660007).
文摘The collision cross-sections(CCS)measurement using ion mobility spectrometry(IMS)in combination with mass spectrometry(MS)offers a great opportunity to increase confidence in metabolite identification.However,owing to the lack of sensitivity and resolution,IMS has an analytical challenge in studying the CCS values of very low-molecular-weight metabolites(VLMs250 Da).Here,we describe an analytical method using ultrahigh-performance liquid chromatography(UPLC)coupled to a traveling wave ion mobility-quadrupole-time-of-flight mass spectrometer optimized for the measurement of VLMs in human urine samples.The experimental CCS values,along with mass spectral properties,were reported for the 174 metabolites.The experimental data included the mass-to-charge ratio(m/z),retention time(RT),tandem MS(MS/MS)spectra,and CCS values.Among the studied metabolites,263 traveling wave ion mobility spectrometry(TWIMS)-derived CCS values(TWCCSN2)were reported for the first time,and more than 70%of these were CCS values of VLMs.The TWCCSN2 values were highly repeatable,with inter-day variations of<1%relative standard deviation(RSD).The developed method revealed excellent TWCCSN2 accuracy with a CCS difference(DCCS)within±2%of the reported drift tube IMS(DTIMS)and TWIMS CCS values.The complexity of the urine matrix did not affect the precision of the method,as evidenced by DCCS within±1.92%.According to the Metabolomics Standards Initiative,55 urinary metabolites were identified with a confidence level of 1.Among these 55 metabolites,53(96%)were VLMs.The larger number of confirmed compounds found in this study was a result of the addition of TWCCSN2 values,which clearly increased metabolite identification confidence.
基金supported by the Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University(FBW,grant ZNJC202210)the CAMS Innovation Fund for Medical Sciences(FBW,grant 2022-I2M-C&T-B-118)+1 种基金the National Natural Science Foundation of China(XYM,grant 82303057)the Natural Science Foundation of Hubei Province of China(XYM,grant 2023 AFB521).
文摘Background:The effective management of bladder cancer(BCa)depends on the early diagnosis and surveillance.Previous studies have explored numerous urinary molecules as potential biomarkers of BCa.However,the molecular functions and cell-of-origin profiles of these biomarkers are yet to be elucidated.In this study,we aimed to provide a comprehensive overview of the landscape of urinary biomarker genes for BCa.Methods:We conducted an exhaustive literature search in PubMed,through which 555 biomarker genes were identified.We then analyzed the BCa single-cell atlas to infer the cellular origin of these BCa urine biomarker genes and performed functional enrichment analysis to gain insights into the functional molecular implications of these biomarkers.Results:These genes are involved in tumor proliferation,angiogenesis,cellmigration,and cell death and are predominantly expressed in epithelial and stromal cells.Interestingly,our analysis ofmultiomics tumor data revealed a discordance between tissue and urine in terms of differential methylation and RNA expression,suggesting that biomarker discovery for liquid biopsies should ideally begin with the analysis of bodily fluids rather than relying interest and that test strategies incorporating multiple molecular markers represent an ongoing trend.Conclusions:Collectively,our study has built a landscape of BCa urine biomarker genes,uncovered molecular insights into these biomarkers,and revealed the bibliometric trends in this field,which will contribute to the discovery of novel biomarkers in the future.
文摘为满足不同种类食品对大豆分离蛋白(soybean protein isolate,SPI)不同功能性的需求,本研究利用红外光谱快速采集70组不同pH值处理后SPI的数据,探讨pH值变化对SPI结构含量的影响。使用均值中心化、多元散射校正、标准正态变量变换和归一化算法对红外光谱数据进行预处理,基于二维相关红外光谱提取特征波段,再利用偏最小二乘(partial least square,PLS)法和算术优化算法-随机森林(arithmetic optimization algorithm-random forests,AOA-RF)建立不同pH值条件下SPI结构及含量的预测模型。结果表明,经均值中心化和多元散射校正结合处理后,α-螺旋、β-折叠、β-转角和无规卷曲模型的相对标准偏差分别为1.29%、1.60%、1.37%、7.28%,两者结合对光谱数据的预处理效果最佳。预测α-螺旋和β-折叠含量最优模型为AOA-RF(特征波段),校正集决定系数为0.9350和0.9266,预测集决定系数为0.8568和0.8701;预测β-转角和无规卷曲含量最优模型为PLS(特征波段),校正集决定系数为0.9154和0.8817,预测集决定系数为0.8913和0.7843。本研究结果可为工业生产过程中产品质量快速检测和工艺条件控制提供理论支撑。
