BACKGROUND Signet ring cell carcinoma(SRCC)is an uncommon subtype in colorectal cancer(CRC),with a short survival time.Therefore,it is imperative to establish a useful prognostic model.As a simple visual predictive to...BACKGROUND Signet ring cell carcinoma(SRCC)is an uncommon subtype in colorectal cancer(CRC),with a short survival time.Therefore,it is imperative to establish a useful prognostic model.As a simple visual predictive tool,nomograms combining a quantification of all proven prognostic factors have been widely used for predicting the outcomes of patients with different cancers in recent years.Until now,there has been no nomogram to predict the outcome of CRC patients with SRCC.AIM To build effective nomograms for predicting overall survival(OS)and causespecific survival(CSS)of CRC patients with SRCC.METHODS Data were extracted from the Surveillance,Epidemiology,and End Results database between 2004 and 2015.Multivariate Cox regression analyses were used to identify independent variables for both OS and CSS to construct the nomograms.Performance of the nomograms was assessed by concordance index,calibration curves,and receiver operating characteristic(ROC)curves.ROC curves were also utilized to compare benefits between the nomograms and the tumor-node-metastasis(TNM)staging system.Patients were classified as high-risk,moderate-risk,and low-risk groups using the novel nomograms.Kaplan-Meier curves were plotted to compare survival differences.RESULTS In total,1230 patients were included.The concordance index of the nomograms for OS and CSS were 0.737(95%confidence interval:0.728-0.747)and 0.758(95%confidence interval:0.738-0.778),respectively.The calibration curves and ROC curves demonstrated good predictive accuracy.The 1-,3-,and 5-year area under the curve values of the nomogram for predicting OS were 0.796,0.825 and 0.819,in comparison to 0.743,0.798,and 0.803 for the TNM staging system.In addition,the 1-,3-,and 5-year area under the curve values of the nomogram for predicting CSS were 0.805,0.847 and 0.863,in comparison to 0.740,0.794,and 0.800 for the TNM staging system.Based on the novel nomograms,stratified analysis showed that the 5-year probability of survival in the high-risk,moderate-risk,and low-risk groups was 6.8%,37.7%,and 67.0%for OS(P<0.001),as well as 9.6%,38.5%,and 67.6%for CSS(P<0.001),respectively.CONCLUSION Convenient and visual nomograms were built and validated to accurately predict the OS and CSS rates for CRC patients with SRCC,which are superior to the conventional TNM staging system.展开更多
BACKGROUND Insulin is the preferred clinical treatment for hospitalized patients with type 2 diabetes mellitus(T2DM)to control blood glucose effectively.Hypoglycemia is one of the most common adverse events.Accurate p...BACKGROUND Insulin is the preferred clinical treatment for hospitalized patients with type 2 diabetes mellitus(T2DM)to control blood glucose effectively.Hypoglycemia is one of the most common adverse events.Accurate prediction of the risk of hypoglycemia is critical in reducing hypoglycemic events and related adverse events in hospitalized diabetic patients treated with insulin.AIM To develop and validate a hypoglycemia risk prediction tool for hospitalized patients with T2DM treated with insulin.METHODS This retrospective study included 802 hospitalized patients with T2DM in the Department of Endocrinology,the Third Affiliated Hospital of Sun Yat-sen University,between January 2021 and December 2021.The hypoglycemia risk prediction model was developed using logistic regression and nomogram models.The model was validated and calibrated using receiver operating characteristic curves and the Hosmer-Lemeshow goodness of fit test.RESULTS The incidence of hypoglycemia among the enrolled patients was 44.9%.The hypoglycemic risk prediction model included six predictors:Body mass index,duration of diabetes,history of hypoglycemia within 1 year,glomerular filtration rate,blood triglyceride levels,and duration of treatment.The hypoglycemia risk prediction model displayed high discrimination ability(area under the curve=0.67)and good calibration power(goodness of fit,χ^(2)=12.25,P=0.14).CONCLUSION The hypoglycemia risk prediction model for hospitalized patients with T2DM on insulin therapy displayed high reliability and discrimination ability.The model is a promising tool for clinicians to screen hospitalized patients with T2DM and an elevated risk of hypoglycemia and guide personalized interventions to prevent and treat hypoglycemia.展开更多
China Jinping Underground Laboratory(CJPL)is ideal for studying solar,geo-,and supernova neutrinos.A precise measurement of the cosmic-ray background is essential in proceeding with R&D research for these MeV-scal...China Jinping Underground Laboratory(CJPL)is ideal for studying solar,geo-,and supernova neutrinos.A precise measurement of the cosmic-ray background is essential in proceeding with R&D research for these MeV-scale neutrino experiments.Using a 1-ton prototype detector for the Jinping Neutrino Experiment(JNE),we detected 264 high-energy muon events from a 645.2-day dataset from the first phase of CJPL(CJPL-I),reconstructed their directions,and measured the cosmic-ray muon flux to be (3.53±0.22_stat.±0.07_sys.)×-10^(-10)cm^(-2).The observed angular distributions indicate the leakage of cosmic-ray muon background and agree with simulation data accounting for Jinping mountain's terrain.A survey of muon fluxes at different laboratory locations,considering both those situated under mountains and those down mine shafts,indicates that the flux at the former is generally a factor of (4±2) larger than at the latter,with the same vertical overburden.This study provides a convenient back-of-the-envelope estimation for the muon flux of an underground experiment.展开更多
文摘BACKGROUND Signet ring cell carcinoma(SRCC)is an uncommon subtype in colorectal cancer(CRC),with a short survival time.Therefore,it is imperative to establish a useful prognostic model.As a simple visual predictive tool,nomograms combining a quantification of all proven prognostic factors have been widely used for predicting the outcomes of patients with different cancers in recent years.Until now,there has been no nomogram to predict the outcome of CRC patients with SRCC.AIM To build effective nomograms for predicting overall survival(OS)and causespecific survival(CSS)of CRC patients with SRCC.METHODS Data were extracted from the Surveillance,Epidemiology,and End Results database between 2004 and 2015.Multivariate Cox regression analyses were used to identify independent variables for both OS and CSS to construct the nomograms.Performance of the nomograms was assessed by concordance index,calibration curves,and receiver operating characteristic(ROC)curves.ROC curves were also utilized to compare benefits between the nomograms and the tumor-node-metastasis(TNM)staging system.Patients were classified as high-risk,moderate-risk,and low-risk groups using the novel nomograms.Kaplan-Meier curves were plotted to compare survival differences.RESULTS In total,1230 patients were included.The concordance index of the nomograms for OS and CSS were 0.737(95%confidence interval:0.728-0.747)and 0.758(95%confidence interval:0.738-0.778),respectively.The calibration curves and ROC curves demonstrated good predictive accuracy.The 1-,3-,and 5-year area under the curve values of the nomogram for predicting OS were 0.796,0.825 and 0.819,in comparison to 0.743,0.798,and 0.803 for the TNM staging system.In addition,the 1-,3-,and 5-year area under the curve values of the nomogram for predicting CSS were 0.805,0.847 and 0.863,in comparison to 0.740,0.794,and 0.800 for the TNM staging system.Based on the novel nomograms,stratified analysis showed that the 5-year probability of survival in the high-risk,moderate-risk,and low-risk groups was 6.8%,37.7%,and 67.0%for OS(P<0.001),as well as 9.6%,38.5%,and 67.6%for CSS(P<0.001),respectively.CONCLUSION Convenient and visual nomograms were built and validated to accurately predict the OS and CSS rates for CRC patients with SRCC,which are superior to the conventional TNM staging system.
基金Supported by Medical Scientific Research Foundation of Guangdong Province of China,No.A2023183 and No.A2024530Nursing Innovation Development Research Project,No.YJYZ202304+2 种基金National Natural Science Foundation of China,No.72204277Guangdong Basic and Applied Basic Research Foundation,No.2025A15150127063rd Affiliated Hospital of Sun Yat-sen University,Clinical Research Program,No.YHJH202404.
文摘BACKGROUND Insulin is the preferred clinical treatment for hospitalized patients with type 2 diabetes mellitus(T2DM)to control blood glucose effectively.Hypoglycemia is one of the most common adverse events.Accurate prediction of the risk of hypoglycemia is critical in reducing hypoglycemic events and related adverse events in hospitalized diabetic patients treated with insulin.AIM To develop and validate a hypoglycemia risk prediction tool for hospitalized patients with T2DM treated with insulin.METHODS This retrospective study included 802 hospitalized patients with T2DM in the Department of Endocrinology,the Third Affiliated Hospital of Sun Yat-sen University,between January 2021 and December 2021.The hypoglycemia risk prediction model was developed using logistic regression and nomogram models.The model was validated and calibrated using receiver operating characteristic curves and the Hosmer-Lemeshow goodness of fit test.RESULTS The incidence of hypoglycemia among the enrolled patients was 44.9%.The hypoglycemic risk prediction model included six predictors:Body mass index,duration of diabetes,history of hypoglycemia within 1 year,glomerular filtration rate,blood triglyceride levels,and duration of treatment.The hypoglycemia risk prediction model displayed high discrimination ability(area under the curve=0.67)and good calibration power(goodness of fit,χ^(2)=12.25,P=0.14).CONCLUSION The hypoglycemia risk prediction model for hospitalized patients with T2DM on insulin therapy displayed high reliability and discrimination ability.The model is a promising tool for clinicians to screen hospitalized patients with T2DM and an elevated risk of hypoglycemia and guide personalized interventions to prevent and treat hypoglycemia.
基金Supported in part by the National Natural Science Foundation of China(11620101004,11475093)the Key Laboratory of Particle&Radiation Imaging(Tsinghua University,the CAS Center for Excellence in Particle Physics(CCEPP),and Guangdong Basic and Applied Basic Research Foundation(2019A1515012216)Portion of this work performed at Brookhaven National Laboratory is supponted in part by the United States Department of Energy(DE-SC0012704)。
文摘China Jinping Underground Laboratory(CJPL)is ideal for studying solar,geo-,and supernova neutrinos.A precise measurement of the cosmic-ray background is essential in proceeding with R&D research for these MeV-scale neutrino experiments.Using a 1-ton prototype detector for the Jinping Neutrino Experiment(JNE),we detected 264 high-energy muon events from a 645.2-day dataset from the first phase of CJPL(CJPL-I),reconstructed their directions,and measured the cosmic-ray muon flux to be (3.53±0.22_stat.±0.07_sys.)×-10^(-10)cm^(-2).The observed angular distributions indicate the leakage of cosmic-ray muon background and agree with simulation data accounting for Jinping mountain's terrain.A survey of muon fluxes at different laboratory locations,considering both those situated under mountains and those down mine shafts,indicates that the flux at the former is generally a factor of (4±2) larger than at the latter,with the same vertical overburden.This study provides a convenient back-of-the-envelope estimation for the muon flux of an underground experiment.
