Objective To evaluate the sensitivity and specificity of body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) measurements in diagnosing abdominal visceral obesity. Methods BMI, WC, and WHR wer...Objective To evaluate the sensitivity and specificity of body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) measurements in diagnosing abdominal visceral obesity. Methods BMI, WC, and WHR were assessed in 690 Chinese adults (305 men and 385 women) and compared with magnetic resonance imaging (MRI) measurements of abdominal visceral adipose tissue (VA). Receiver operating characteristic (ROC) curves were generated and used to determine the threshold point for each anthropometric parameter. Results 1) MRI showed that 61.7% of overweight/obese individuals (BMI≥25 kg/m2) and 14.2% of normal weight (BMI<25 kg/m2) individuals had abdominal visceral obesity (VA≥100 cm2). 2) VA was positively correlated with each anthropometric variable, of which WC showed the highest correlation (r=0.73-0.77, P<0.001). 3) The best cut-off points for assessing abdominal visceral obesity were as followed: BMI of 26 kg/m2, WC of 90 cm, and WHR of 0.93, with WC being the most sensitive and specific factor. 4) Among subjects with BMI≥28 kg/m2 or WC≥95 cm, 95% of men and 90% of women appeared to have abdominal visceral obesity. Conclusion Measurements of BMI, WC, and WHR can be used in the prediction of abdominal visceral obesity, of which WC was the one with better accuracy.展开更多
Objective: To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29 201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and...Objective: To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29 201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and the endemic values of cutaneous leishmaniasis were determined by retrospective clusters derived from spatiotemporal permutation modeling on a time-series design. The accuracy of the values was assessed using receiver operating characteristic(ROC) curve. SPSS version 22, Arc GIS, and ITSM 2002 software tools were used for analysis. Results: Nine statistically significant retrospective clusters(P<0.05) resulted in finding seven significant and accurate endemic values(P<0.1). These valid endemic scores were generalized to the other 18 cities based on 6 different climates in the province. Conclusions: Retrospectively detected clusters with the help of ROC curve analysis could help determine cutaneous leishmaniasis endemic values which are essential for future prediction and prevention policies in the area.展开更多
Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecologic...Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to climate.Due to a lack of time frame in existing works,conser-vation adoption affects the performance of existing works.The proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its extinction.The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species.However,the frame-work demonstrates the huge difference in the estimated significance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate change.The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.展开更多
接收者操作特性(Receiver operating characteristics,ROC)曲线下面积(Area under the ROC curve,AUC)常被用于度量分类器在整个类先验分布上的总体分类性能.原始Boosting算法优化分类精度,但在AUC度量下并非最优.提出了一种AUC优化Boos...接收者操作特性(Receiver operating characteristics,ROC)曲线下面积(Area under the ROC curve,AUC)常被用于度量分类器在整个类先验分布上的总体分类性能.原始Boosting算法优化分类精度,但在AUC度量下并非最优.提出了一种AUC优化Boosting改进算法,通过在原始Boosting迭代中引入数据重平衡操作,实现弱学习算法优化目标从精度向AUC的迁移.实验结果表明,较之原始Boosting算法,新算法在AUC度量下能获得更好性能.展开更多
文摘Objective To evaluate the sensitivity and specificity of body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) measurements in diagnosing abdominal visceral obesity. Methods BMI, WC, and WHR were assessed in 690 Chinese adults (305 men and 385 women) and compared with magnetic resonance imaging (MRI) measurements of abdominal visceral adipose tissue (VA). Receiver operating characteristic (ROC) curves were generated and used to determine the threshold point for each anthropometric parameter. Results 1) MRI showed that 61.7% of overweight/obese individuals (BMI≥25 kg/m2) and 14.2% of normal weight (BMI<25 kg/m2) individuals had abdominal visceral obesity (VA≥100 cm2). 2) VA was positively correlated with each anthropometric variable, of which WC showed the highest correlation (r=0.73-0.77, P<0.001). 3) The best cut-off points for assessing abdominal visceral obesity were as followed: BMI of 26 kg/m2, WC of 90 cm, and WHR of 0.93, with WC being the most sensitive and specific factor. 4) Among subjects with BMI≥28 kg/m2 or WC≥95 cm, 95% of men and 90% of women appeared to have abdominal visceral obesity. Conclusion Measurements of BMI, WC, and WHR can be used in the prediction of abdominal visceral obesity, of which WC was the one with better accuracy.
文摘Objective: To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29 201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and the endemic values of cutaneous leishmaniasis were determined by retrospective clusters derived from spatiotemporal permutation modeling on a time-series design. The accuracy of the values was assessed using receiver operating characteristic(ROC) curve. SPSS version 22, Arc GIS, and ITSM 2002 software tools were used for analysis. Results: Nine statistically significant retrospective clusters(P<0.05) resulted in finding seven significant and accurate endemic values(P<0.1). These valid endemic scores were generalized to the other 18 cities based on 6 different climates in the province. Conclusions: Retrospectively detected clusters with the help of ROC curve analysis could help determine cutaneous leishmaniasis endemic values which are essential for future prediction and prevention policies in the area.
文摘Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to climate.Due to a lack of time frame in existing works,conser-vation adoption affects the performance of existing works.The proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its extinction.The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species.However,the frame-work demonstrates the huge difference in the estimated significance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate change.The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.
文摘目的探讨肾小管及肾小球相关标志物在2型糖尿病(type 2 diabetes mellitus,T2DM)患者不同肾损伤阶段的诊断价值。方法选取于2018年4月1日至2019年10月31日入住首都医科大学附属北京同仁医院内分泌科的T2DM患者272例,完善临床生化指标及尿蛋白四项:尿微量白蛋白/肌酐(urinary albumin to creatinine ratio,ACR)、α1-微球蛋白/肌酐(urinary α1-microglobulin to creatinine ratio,UA1CR)、免疫球蛋白G/肌酐(urinary immunoglobulin G to creatinine ratio,UIGG)、转铁蛋白/肌酐(urinary transferrin to creatinine ratio,UTRF);进行眼底照相、核医学99mTc-EC检测肾有效血浆流量(effective renal plasma flow,ERPF)和99mTc-DTPA检测肾小球滤过率(glomerular filtration rate,GFR)。根据ACR和眼底检查结果分为4组:正常蛋白尿无糖尿病视网膜病变(diabetic retinopathy,DR)132例,即对照组(ACR≤30 mg/g);正常蛋白尿合并DR 32例,为糖尿病肾病(diabetic kidney disease,DKD)前期组;微量蛋白尿组78例(30<ACR≤300 mg/g)和大量蛋白尿组30例(ACR>300 mg/g)。比较四组间尿蛋白四项和ERPF、GFR的水平,通过受试者工作特征(receiver operating characteristic,ROC)曲线评价上述各指标在不同肾损伤阶段的诊断价值。结果尿蛋白四项和ERPF、GFR的水平在不同组间差异有统计学意义(P<0.05)。在尿蛋白正常组中,DR组中肾小管功能标志物UA1CR较对照组明显升高(P<0.01);肾小球功能标志物ACR、UTRF和GFR在两组间差异无统计学意义(P>0.05),DR组UIGG较对照组升高(P<0.01)。在微量蛋白尿组和大量蛋白尿组,尿蛋白四项随肾损伤程度增加而增加,而ERPF和GFR随肾损伤程度增加而降低。ROC曲线分析显示,在尿蛋白排出正常的T2DM患者中合并DR组中肾小管功能标志物UA1CR和ERPF的曲线下面积(area under the curve,AUC)分别为68.2%(P<0.01)和60.5%(P<0.05),而肾小球功能标志物ACR和GFR的AUC均小于60%,差异无统计学意义(P>0.05)。尿蛋白四项及GFR在微量和大量蛋白尿组的AUC均大于60%(P<0.05),ERPF在大量蛋白尿组AUC为67.2%(P<0.05)。结论T2DM极早期微血管改变即ACR正常仅有DR时,肾小管标志物UA1CR先于肾小球标志物ACR和GFR发生变化。肾损伤早期,肾小管标志物诊断效能优于肾小球;肾损伤后期,肾小球标志物诊断效能优于肾小管。提示DKD肾小管功能的改变可能早于肾小球。
文摘接收者操作特性(Receiver operating characteristics,ROC)曲线下面积(Area under the ROC curve,AUC)常被用于度量分类器在整个类先验分布上的总体分类性能.原始Boosting算法优化分类精度,但在AUC度量下并非最优.提出了一种AUC优化Boosting改进算法,通过在原始Boosting迭代中引入数据重平衡操作,实现弱学习算法优化目标从精度向AUC的迁移.实验结果表明,较之原始Boosting算法,新算法在AUC度量下能获得更好性能.
