BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common types of cancers worldwide,ranking fifth among men and seventh among women,resulting in more than 7 million deaths annually.With the development of med...BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common types of cancers worldwide,ranking fifth among men and seventh among women,resulting in more than 7 million deaths annually.With the development of medical tech-nology,the 5-year survival rate of HCC patients can be increased to 70%.How-ever,HCC patients are often at increased risk of cardiovascular disease(CVD)death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients.Moreover,CVD and cancer have become major disease burdens worldwide.Thus,further research is needed to lessen the risk of CVD death in HCC patient survivors.METHODS This study was conducted on the basis of the Surveillance,Epidemiology,and End Results database and included HCC patients with a diagnosis period from 2010 to 2015.The independent risk factors were identified using the Fine-Gray model.A nomograph was constructed to predict the CVM in HCC patients.The nomograph performance was measured using Harrell’s concordance index(C-index),calibration curve,receiver operating characteristic(ROC)curve,and area under the ROC curve(AUC)value.Moreover,the net benefit was estimated via decision curve analysis(DCA).RESULTS The study included 21545 HCC patients,of whom 619 died of CVD.Age(<60)[1.981(1.573-2.496),P<0.001],marital status(married)[unmarried:1.370(1.076-1.745),P=0.011],alpha fetoprotein(normal)[0.778(0.640-0.946),P=0.012],tumor size(≤2 cm)[(2,5]cm:1.420(1.060-1.903),P=0.019;>5 cm:2.090(1.543-2.830),P<0.001],surgery(no)[0.376(0.297-0.476),P<0.001],and chemotherapy(none/unknown)[0.578(0.472-0.709),P<0.001]were independent risk factors for CVD death in HCC patients.The discrimination and calibration of the nomograph were better.The C-index values for the training and validation sets were 0.736 and 0.665,respectively.The AUC values of the ROC curves at 2,4,and 6 years were 0.702,0.725,0.740 in the training set and 0.697,0.710,0.744 in the validation set,respectively.The calibration curves showed that the predicted probab-ilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities.DCA demonstrated that the prediction model has a high net benefit.CONCLUSION Risk factors for CVD death in HCC patients were investigated for the first time.The nomograph served as an important reference tool for relevant clinical management decisions.展开更多
The liquid phase behavior of the fine-grained 5083 AI alloy obtained through thermomechanical process was investigated during the tensile tests in a temperature range of 380-570℃ and strain rate range of 4.17× 1...The liquid phase behavior of the fine-grained 5083 AI alloy obtained through thermomechanical process was investigated during the tensile tests in a temperature range of 380-570℃ and strain rate range of 4.17× 10^-4- 1.0× 10^-2 s^-1. The maximum elongation 530% of the fine-grained 5083 AI alloy was obtained at 550℃ and 4.17× 10^-4 s^-1. Fracture analysis by scanning electron microscopy （SEM） indicated that the formation of filament （formed by liquid phase） was greatly affected by the tensile temperature and strain rate. The results also showed that the optimum morphology of formed filament was obtained at 550℃ and a strain rate of 4.17× 10^-4 s^-1. The effect of liquid phase on superplastic deformation of the alloy was further discussed.展开更多
Purpose To establish a competing-risks model and compare it with traditional survival analysis,aiming to identify more precise prognostic factors for angiosarcoma.The presence of competing risks suggests that prognost...Purpose To establish a competing-risks model and compare it with traditional survival analysis,aiming to identify more precise prognostic factors for angiosarcoma.The presence of competing risks suggests that prognostic factors derived from the conventional Cox regression model may exhibit bias.Methods Patient data pertaining to angiosarcoma cases diagnosed from 2000 to 2019 were extracted from the Sur-veillance,Epidemiology,and End Results(SEER)database.Multivariate analysis employed both the Cox regression model and the Fine-Gray model,while univariate analysis utilized the cumulative incidence function and Gray’s test.Results A total of 3,905 enrolled patients diagnosed with angiosarcoma were included,out of which 2,781 suc-cumbed to their condition:1,888 fatalities resulted from angiosarcoma itself,and 893 were attributed to other causes.The Fine-Gray model,through multivariable analysis,identified SEER stage,gender,race,surgical status,chemotherapy status,radiotherapy status,and marital status as independent prognostic factors for angiosarcoma.The Cox regression model,due to the occurrence of competing-risk events,could not accurately estimate the effect values and yielded false-negative outcomes.Clearly,when analyzing clinical survival data with multiple endpoints,the competing-risks model demonstrates superior performance.Conclusion This current investigation may enhance clinicians’comprehension of angiosarcoma and furnish refer-ence data for making clinical decisions.展开更多
基金Health Technology Project of Tianjin,No.ZC20175.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common types of cancers worldwide,ranking fifth among men and seventh among women,resulting in more than 7 million deaths annually.With the development of medical tech-nology,the 5-year survival rate of HCC patients can be increased to 70%.How-ever,HCC patients are often at increased risk of cardiovascular disease(CVD)death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients.Moreover,CVD and cancer have become major disease burdens worldwide.Thus,further research is needed to lessen the risk of CVD death in HCC patient survivors.METHODS This study was conducted on the basis of the Surveillance,Epidemiology,and End Results database and included HCC patients with a diagnosis period from 2010 to 2015.The independent risk factors were identified using the Fine-Gray model.A nomograph was constructed to predict the CVM in HCC patients.The nomograph performance was measured using Harrell’s concordance index(C-index),calibration curve,receiver operating characteristic(ROC)curve,and area under the ROC curve(AUC)value.Moreover,the net benefit was estimated via decision curve analysis(DCA).RESULTS The study included 21545 HCC patients,of whom 619 died of CVD.Age(<60)[1.981(1.573-2.496),P<0.001],marital status(married)[unmarried:1.370(1.076-1.745),P=0.011],alpha fetoprotein(normal)[0.778(0.640-0.946),P=0.012],tumor size(≤2 cm)[(2,5]cm:1.420(1.060-1.903),P=0.019;>5 cm:2.090(1.543-2.830),P<0.001],surgery(no)[0.376(0.297-0.476),P<0.001],and chemotherapy(none/unknown)[0.578(0.472-0.709),P<0.001]were independent risk factors for CVD death in HCC patients.The discrimination and calibration of the nomograph were better.The C-index values for the training and validation sets were 0.736 and 0.665,respectively.The AUC values of the ROC curves at 2,4,and 6 years were 0.702,0.725,0.740 in the training set and 0.697,0.710,0.744 in the validation set,respectively.The calibration curves showed that the predicted probab-ilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities.DCA demonstrated that the prediction model has a high net benefit.CONCLUSION Risk factors for CVD death in HCC patients were investigated for the first time.The nomograph served as an important reference tool for relevant clinical management decisions.
文摘The liquid phase behavior of the fine-grained 5083 AI alloy obtained through thermomechanical process was investigated during the tensile tests in a temperature range of 380-570℃ and strain rate range of 4.17× 10^-4- 1.0× 10^-2 s^-1. The maximum elongation 530% of the fine-grained 5083 AI alloy was obtained at 550℃ and 4.17× 10^-4 s^-1. Fracture analysis by scanning electron microscopy （SEM） indicated that the formation of filament （formed by liquid phase） was greatly affected by the tensile temperature and strain rate. The results also showed that the optimum morphology of formed filament was obtained at 550℃ and a strain rate of 4.17× 10^-4 s^-1. The effect of liquid phase on superplastic deformation of the alloy was further discussed.
基金supported by Key Scientific Problems and Medical Technical Problems Research Project of China Medical Education Association[2022KTZ009]Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization[2021B1212040007].
文摘Purpose To establish a competing-risks model and compare it with traditional survival analysis,aiming to identify more precise prognostic factors for angiosarcoma.The presence of competing risks suggests that prognostic factors derived from the conventional Cox regression model may exhibit bias.Methods Patient data pertaining to angiosarcoma cases diagnosed from 2000 to 2019 were extracted from the Sur-veillance,Epidemiology,and End Results(SEER)database.Multivariate analysis employed both the Cox regression model and the Fine-Gray model,while univariate analysis utilized the cumulative incidence function and Gray’s test.Results A total of 3,905 enrolled patients diagnosed with angiosarcoma were included,out of which 2,781 suc-cumbed to their condition:1,888 fatalities resulted from angiosarcoma itself,and 893 were attributed to other causes.The Fine-Gray model,through multivariable analysis,identified SEER stage,gender,race,surgical status,chemotherapy status,radiotherapy status,and marital status as independent prognostic factors for angiosarcoma.The Cox regression model,due to the occurrence of competing-risk events,could not accurately estimate the effect values and yielded false-negative outcomes.Clearly,when analyzing clinical survival data with multiple endpoints,the competing-risks model demonstrates superior performance.Conclusion This current investigation may enhance clinicians’comprehension of angiosarcoma and furnish refer-ence data for making clinical decisions.
