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
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
激素性股骨头坏死(steroid-induced osteonecrosis of the femoral head,SIONFH)是由于糖皮质激素使用不当或过度而引起的髋关节疾病,发病机制尚未统一,临床疗效亦不佳。当前,没有效果明确的药物可以延缓疾病进程,而中医药治疗SIONFH在...激素性股骨头坏死(steroid-induced osteonecrosis of the femoral head,SIONFH)是由于糖皮质激素使用不当或过度而引起的髋关节疾病,发病机制尚未统一,临床疗效亦不佳。当前,没有效果明确的药物可以延缓疾病进程,而中医药治疗SIONFH在临床上取得一定疗效。即便如此,仍未能完整的从分子生物及细胞生物学角度阐明中药治疗SIONFH的作用机制。转化生长因子-β(TGF-β)/骨形态发生蛋白(BMP)/Smad信号通路的转导是防治SIONFH的研究热点之一,故该文阐明了该信号通路的转导机制以及与SIONFH的联系,检索了基于该通路治疗SIONFH的全部中药及复方并阐述其影响机制。基于中医对SIONFH的认识,现临床上使用补肝肾强筋骨以及活血祛瘀通络类的方药治疗SIONFH,且具有良好的疗效。中药通过调控该通路,可刺激骨髓间充质干细胞成骨分化,降低破骨细胞含量,减少脂肪生成,改善微循环,抗氧化损伤,促进股骨头内血管新生,从而促进股骨头损伤的修复。现基于TGF-β/BMP/Smad信号通路对中医药治疗SIONFH的研究进展做一综述,期许为中医药治疗SIONFH提供理论依据及参考。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 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.
文摘激素性股骨头坏死(steroid-induced osteonecrosis of the femoral head,SIONFH)是由于糖皮质激素使用不当或过度而引起的髋关节疾病,发病机制尚未统一,临床疗效亦不佳。当前,没有效果明确的药物可以延缓疾病进程,而中医药治疗SIONFH在临床上取得一定疗效。即便如此,仍未能完整的从分子生物及细胞生物学角度阐明中药治疗SIONFH的作用机制。转化生长因子-β(TGF-β)/骨形态发生蛋白(BMP)/Smad信号通路的转导是防治SIONFH的研究热点之一,故该文阐明了该信号通路的转导机制以及与SIONFH的联系,检索了基于该通路治疗SIONFH的全部中药及复方并阐述其影响机制。基于中医对SIONFH的认识,现临床上使用补肝肾强筋骨以及活血祛瘀通络类的方药治疗SIONFH,且具有良好的疗效。中药通过调控该通路,可刺激骨髓间充质干细胞成骨分化,降低破骨细胞含量,减少脂肪生成,改善微循环,抗氧化损伤,促进股骨头内血管新生,从而促进股骨头损伤的修复。现基于TGF-β/BMP/Smad信号通路对中医药治疗SIONFH的研究进展做一综述,期许为中医药治疗SIONFH提供理论依据及参考。