Background:Post-transplant lymphoproliferative disorder(PTLD)is a lethal complication after pediatric liver transplantation,but information regarding risk factors for the development of PTLD remains unclear.This study...Background:Post-transplant lymphoproliferative disorder(PTLD)is a lethal complication after pediatric liver transplantation,but information regarding risk factors for the development of PTLD remains unclear.This study was to identify characteristics and risk factors of PTLD.Methods:A total of 705 pediatric patients who underwent liver transplantation between January 2017 and October 2018 were studied.Impact of clinical characteristics and Epstein-Barr virus(EBV)infection on the development of PTLD was evaluated.In addition,ImmuKnow assay was adopted in partial patients to analyze the immune status.Results:Twenty-five(3.5%)patients suffered from PLTD with a median time of 6 months(3–14 months)after transplantation.Extremely high tacrolimus(TAC)level was found in 2 fatal cases at PTLD onset.EBV infection was found in 468(66.4%)patients.A higher peak EBV DNA loads(>9590 copies/mL)within 3 months was a significant indicator for the onset of PTLD.In addition,the ImmuKnow assay demonstrated that overall immune response was significantly lower in patients with EBV infection and PTLD(P<0.0001).The cumulative incidence of PTLD was also higher in patients with lower ATP value(≤187 ng/mL,P<0.05).Conclusions:A careful monitoring of EBV DNA loads and tacrolimus concentration might be supportive in prevention of PTLD in pediatric patients after liver transplantation.In addition,application of the ImmuKnow assay may provide guidance in reducing immunosuppressive agents in treatment of PTLD.展开更多
Background Papillary thyroid carcinoma （PTC） represents one of the most frequent endocrine malignancies. Several factors have been found to be involved in determining the outcome of treatment for patients with PTC. ...Background Papillary thyroid carcinoma （PTC） represents one of the most frequent endocrine malignancies. Several factors have been found to be involved in determining the outcome of treatment for patients with PTC. Large tumor size, diagnosis at an early age, extra-thyroidal invasion, aggressive histological variants, and distant metastases are the most important determinants of a poor outcome. BRAF^V600E mutation has been found to be a major genetic alteration in PTC. This study aimed to evaluate progression in patients with multifocal and solitary PTC. Methods We performed a retrospective study to analyze 368 patients with PTC who underwent surgery, including 282 patients with solitary PTC and 86 patients with multifocal PTC. The status ofBRAF^V600E mutation in all tumor foci from multifocal PTC was detected. Results Our study suggested that multifocal PTC was more related to lymph node metastasis and vascular invasion than solitary PTC. However, the distant metastasis rate and 10-year survival rate showed no difference between these two groups. The number of tumor loci did not affect progression of disease in multifocal PTC patients. Lymph node metastasis in multifocal PTC patients was associated with larger tumors, diagnosis at early stage, and extra-thyroidal invasion. Conclusion The status of BRAF^V600Emutation was more frequent in multifocal PTC patients with lymph node metastasis and diagnosis at later age.展开更多
为探究黄土高原植物群落生物多样性与生态系统功能的关系,本研究以黄土丘陵区不同环境条件下稳定的自然植物群落为对象,采用3个物种多样性指数(Shannon-Wiener指数、Simpson优势度指数、Pielou均匀度指数)和4个功能多样性指数(FRic功能...为探究黄土高原植物群落生物多样性与生态系统功能的关系,本研究以黄土丘陵区不同环境条件下稳定的自然植物群落为对象,采用3个物种多样性指数(Shannon-Wiener指数、Simpson优势度指数、Pielou均匀度指数)和4个功能多样性指数(FRic功能丰富度、FDiv功能趋异指数、FEve功能均匀度和FDis功能离散度),选取地上生物量(Aboveground biomass,AGB)、土壤全氮(Soil total nitrogen,STN)、土壤有机碳(Soil organic carbon,SOC)和土壤全磷(Soil total phosphorus,STP)作为生态系统功能指标,运用冗余分析和全因子回归的方法对影响生态系统功能的因素进行分析。结果表明:Shannon指数、STP随降雨量和气温递增呈现先递增后递减的趋势,FRic呈递减趋势,FDiv,AGB,SOC,STN呈递增趋势(P<0.05);年均降雨量Pa对生态系统功能的贡献值达到21.5%,功能多样性指数(FDiv,FDis,FEve)对生态系统功能的贡献值要高于物种多样性(Shannon指数),且显著影响AGB和STP(P<0.05)。综上所述,在黄土丘陵区降雨量主要影响着生态系统功能,功能多样性对生态系统功能的影响程度比物种多样性更大。本研究可为黄土高原生物多样性和生态系统功能的恢复提供理论依据。展开更多
AIM:To evaluate the application of an intelligent diagnostic model for pterygium.METHODS:For intelligent diagnosis of pterygium,the attention mechanisms—SENet,ECANet,CBAM,and Self-Attention—were fused with the light...AIM:To evaluate the application of an intelligent diagnostic model for pterygium.METHODS:For intelligent diagnosis of pterygium,the attention mechanisms—SENet,ECANet,CBAM,and Self-Attention—were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model.The study used 1220 images of three types of anterior ocular segments of the pterygium provided by the Eye Hospital of Nanjing Medical University.Conventional classification models—VGG16,ResNet50,MobileNetV2,and EfficientNetB7—were trained on the same dataset for comparison.To evaluate model performance in terms of accuracy,Kappa value,test time,sensitivity,specificity,the area under curve(AUC),and visual heat map,470 test images of the anterior segment of the pterygium were used.RESULTS:The accuracy of the MobileNetV2+Self-Attention model with 281 MB in model size was 92.77%,and the Kappa value of the model was 88.92%.The testing time using the model was 9ms/image in the server and 138ms/image in the local computer.The sensitivity,specificity,and AUC for the diagnosis of pterygium using normal anterior segment images were 99.47%,100%,and 100%,respectively;using anterior segment images in the observation period were 88.30%,95.32%,and 96.70%,respectively;and using the anterior segment images in the surgery period were 88.18%,94.44%,and 97.30%,respectively.CONCLUSION:The developed model is lightweight and can be used not only for detection but also for assessing the severity of pterygium.展开更多
Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potent...Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potential in assisting clinicians with pterygium diagnosis.This paper provides an overview of AI-assisted pterygium diagnosis,including the AI techniques used such as machine learning,deep learning,and computer vision.Furthermore,recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection,classification and segmentation were summarized.The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed.The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis,which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.展开更多
基金supported by grants from Shanghai Munici-pal Hospital Three-year-Project for Clinical Skills’ Promotion and Innovation(16CR1003A)Shanghai Jiaotong University School of Medicine(DLY201606)National Natural Science Foundation of China(81670602)
文摘Background:Post-transplant lymphoproliferative disorder(PTLD)is a lethal complication after pediatric liver transplantation,but information regarding risk factors for the development of PTLD remains unclear.This study was to identify characteristics and risk factors of PTLD.Methods:A total of 705 pediatric patients who underwent liver transplantation between January 2017 and October 2018 were studied.Impact of clinical characteristics and Epstein-Barr virus(EBV)infection on the development of PTLD was evaluated.In addition,ImmuKnow assay was adopted in partial patients to analyze the immune status.Results:Twenty-five(3.5%)patients suffered from PLTD with a median time of 6 months(3–14 months)after transplantation.Extremely high tacrolimus(TAC)level was found in 2 fatal cases at PTLD onset.EBV infection was found in 468(66.4%)patients.A higher peak EBV DNA loads(>9590 copies/mL)within 3 months was a significant indicator for the onset of PTLD.In addition,the ImmuKnow assay demonstrated that overall immune response was significantly lower in patients with EBV infection and PTLD(P<0.0001).The cumulative incidence of PTLD was also higher in patients with lower ATP value(≤187 ng/mL,P<0.05).Conclusions:A careful monitoring of EBV DNA loads and tacrolimus concentration might be supportive in prevention of PTLD in pediatric patients after liver transplantation.In addition,application of the ImmuKnow assay may provide guidance in reducing immunosuppressive agents in treatment of PTLD.
文摘Background Papillary thyroid carcinoma （PTC） represents one of the most frequent endocrine malignancies. Several factors have been found to be involved in determining the outcome of treatment for patients with PTC. Large tumor size, diagnosis at an early age, extra-thyroidal invasion, aggressive histological variants, and distant metastases are the most important determinants of a poor outcome. BRAF^V600E mutation has been found to be a major genetic alteration in PTC. This study aimed to evaluate progression in patients with multifocal and solitary PTC. Methods We performed a retrospective study to analyze 368 patients with PTC who underwent surgery, including 282 patients with solitary PTC and 86 patients with multifocal PTC. The status ofBRAF^V600E mutation in all tumor foci from multifocal PTC was detected. Results Our study suggested that multifocal PTC was more related to lymph node metastasis and vascular invasion than solitary PTC. However, the distant metastasis rate and 10-year survival rate showed no difference between these two groups. The number of tumor loci did not affect progression of disease in multifocal PTC patients. Lymph node metastasis in multifocal PTC patients was associated with larger tumors, diagnosis at early stage, and extra-thyroidal invasion. Conclusion The status of BRAF^V600Emutation was more frequent in multifocal PTC patients with lymph node metastasis and diagnosis at later age.
文摘为探究黄土高原植物群落生物多样性与生态系统功能的关系,本研究以黄土丘陵区不同环境条件下稳定的自然植物群落为对象,采用3个物种多样性指数(Shannon-Wiener指数、Simpson优势度指数、Pielou均匀度指数)和4个功能多样性指数(FRic功能丰富度、FDiv功能趋异指数、FEve功能均匀度和FDis功能离散度),选取地上生物量(Aboveground biomass,AGB)、土壤全氮(Soil total nitrogen,STN)、土壤有机碳(Soil organic carbon,SOC)和土壤全磷(Soil total phosphorus,STP)作为生态系统功能指标,运用冗余分析和全因子回归的方法对影响生态系统功能的因素进行分析。结果表明:Shannon指数、STP随降雨量和气温递增呈现先递增后递减的趋势,FRic呈递减趋势,FDiv,AGB,SOC,STN呈递增趋势(P<0.05);年均降雨量Pa对生态系统功能的贡献值达到21.5%,功能多样性指数(FDiv,FDis,FEve)对生态系统功能的贡献值要高于物种多样性(Shannon指数),且显著影响AGB和STP(P<0.05)。综上所述,在黄土丘陵区降雨量主要影响着生态系统功能,功能多样性对生态系统功能的影响程度比物种多样性更大。本研究可为黄土高原生物多样性和生态系统功能的恢复提供理论依据。
基金Supported by the National Natural Science Foundation of China(No.61906066)Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202147191)+2 种基金Huzhou University Graduate Research Innovation Project(No.2020KYCX21)Sanming Project of Medicine in Shenzhen(SZSM202311012)Shenzhen Science and Technology Program(No.JCYJ20220530153604010).
文摘AIM:To evaluate the application of an intelligent diagnostic model for pterygium.METHODS:For intelligent diagnosis of pterygium,the attention mechanisms—SENet,ECANet,CBAM,and Self-Attention—were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model.The study used 1220 images of three types of anterior ocular segments of the pterygium provided by the Eye Hospital of Nanjing Medical University.Conventional classification models—VGG16,ResNet50,MobileNetV2,and EfficientNetB7—were trained on the same dataset for comparison.To evaluate model performance in terms of accuracy,Kappa value,test time,sensitivity,specificity,the area under curve(AUC),and visual heat map,470 test images of the anterior segment of the pterygium were used.RESULTS:The accuracy of the MobileNetV2+Self-Attention model with 281 MB in model size was 92.77%,and the Kappa value of the model was 88.92%.The testing time using the model was 9ms/image in the server and 138ms/image in the local computer.The sensitivity,specificity,and AUC for the diagnosis of pterygium using normal anterior segment images were 99.47%,100%,and 100%,respectively;using anterior segment images in the observation period were 88.30%,95.32%,and 96.70%,respectively;and using the anterior segment images in the surgery period were 88.18%,94.44%,and 97.30%,respectively.CONCLUSION:The developed model is lightweight and can be used not only for detection but also for assessing the severity of pterygium.
基金Supported by National Natural Science Foundation of China(No.61906066)Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202250196)+4 种基金Zhejiang Provincial Philosophy and Social Science Planning Project(No.21NDJC021Z)Natural Science Foundation of Ningbo City(No.202003N4072)Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014)Sanming Project of Medicine in Shenzhen(No.SZSM202011015)Shenzhen Fundamental Research Program(No.JCYJ20220818103207015).
文摘Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potential in assisting clinicians with pterygium diagnosis.This paper provides an overview of AI-assisted pterygium diagnosis,including the AI techniques used such as machine learning,deep learning,and computer vision.Furthermore,recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection,classification and segmentation were summarized.The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed.The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis,which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.
文摘土壤盐渍化严重影响大豆品质与产量,筛选耐盐大豆资源对开展盐碱地综合利用意义重大。为建立大豆苗期耐盐鉴定评价体系,设置淡水和NaCl含量为0.9%~1.8%的10个等差梯度,以蛭石为培养基质,大豆2片真叶始现时开始盐处理。结果表明, 1.2%盐处理16 d时,不同大豆种质资源耐盐等级四分位差值最大,是大豆苗期耐盐鉴定评价的最适条件。利用大豆苗期耐盐鉴定评价体系对来自国内外的504份大豆种质资源进行苗期耐盐性鉴定评价,耐盐等级为1级、2级、3级、4级、5级的大豆资源依次为46份、146份、157份、79份、76份。利用GmSALT3基因的分子标记对1级耐盐资源进行检测,其中40份(86.96%)大豆材料扩增结果与GmSALT3基因的分子标记结果相符合。为分析大豆苗期鉴定过程中盐胁迫浓度的变化趋势,确立了土壤含盐量(Y,%)与电导率(X,mScm^(–1))的回归方程:Y=0.278X–0.0618,预测精准度在95%以上。测定统计了从盐处理开始至调查结束时的培养基质含盐量变化趋势,培养基质含盐量基本维持在13 mS cm^(–1)左右。本研究为大豆苗期规模化耐盐性鉴定和培育耐盐新种质提供了技术体系和基础材料。
