AIM: To establish a clinical scoring model to predict risk of acute-on-chronic liver failure(ACLF) in chronic hepatitis B(CHB) patients.METHODS: This was a retrospective study of 1457 patients hospitalized for CHB bet...AIM: To establish a clinical scoring model to predict risk of acute-on-chronic liver failure(ACLF) in chronic hepatitis B(CHB) patients.METHODS: This was a retrospective study of 1457 patients hospitalized for CHB between October 2008 and October 2013 at the Beijing Ditan Hospital, Capital Medical University, China. The patients were divided into two groups: severe acute exacerbation(SAE) group(n = 382) and non-SAE group(n = 1075). The SAE group was classified as the high-risk group based on the higher incidence of ACLF in this group than in the non-SAE group(13.6% vs 0.4%). Two-thirds of SAE patients were randomly assigned to risk-model derivation and the other one-third to model validation. Univariate risk factors associated with the outcome were entered into a multivariate logistic regression model for screening independent risk factors. Each variable was assigned an integer value based on the regression coefficients, and the final score was the sum of these values in the derivation set. Model discrimination and calibration were assessed using area under the receiver operating characteristic curve and the Hosmer-Lemeshow test. RESULTS: The risk prediction scoring model includedthe following four factors: age ≥ 40 years, total bilirubin ≥ 171 μmol/L, prothrombin activity 40%-60%, and hepatitis B virus DNA > 107 copies/m L. The sum risk score ranged from 0 to 7; 0-3 identified patients with lower risk of ACLF, whereas 4-7 identified patients with higher risk. The Kaplan-Meier analysis showed the cumulative risk for ACLF and ACLF-related death in the two risk groups(0-3 and 4-7 scores) of the primary cohort over 56 d, and log-rank test revealed a significant difference(2.0% vs 33.8% and 0.8% vs 9.4%, respectively; both P < 0.0001). In the derivation and validation data sets, the model had good discrimination(C index = 0.857, 95% confidence interval: 0.800-0.913 and C index = 0.889, 95% confidence interval: 0.820-0.957, respectively) and calibration demonstrated by the Hosmer-Lemeshow test(χ2 = 4.516, P = 0.808 and χ2 = 1.959, P = 0.923, respectively).CONCLUSION: Using the scoring model, clinicians can easily identify patients(total score ≥ 4) at high risk of ACLF and ACLF-related death early during SAE.展开更多
AIM:To test the efficacy and safety of Profermin in inducing remission in patients with active ulcerative colitis(UC).METHODS:The study included 39 patients with mild to moderate UC defined as a Simple Clinical Coliti...AIM:To test the efficacy and safety of Profermin in inducing remission in patients with active ulcerative colitis(UC).METHODS:The study included 39 patients with mild to moderate UC defined as a Simple Clinical Colitis Activity Index(SCCAI)>4 and<12(median:7.5),who were treated open-label with Profermintwice daily for 24 wk.Daily SCCAI was reported observer blinded via the Internet.RESULTS:In an intention to treat(ITT)analysis,the mean reduction in SCCAI score was 56.5%.Of the 39 patients,24(62%)reached the primary endpoint,which was proportion of patients with≥50%reduction in SCCAI.Our secondary endpoint,the proportion of patients in remission defined as SCCAI≤2.5,was in ITT analysis reached in 18 of the 39 patients(46%).In a repeated-measure regression analysis,the estimated mean reduction in score was 5.0 points(95%CI:4.1-5.9,P<0.001)and the estimated mean time taken to obtain half the reduction in score was 28 d(95%CI:26-30).There were no serious adverse events(AEs)or withdrawals due to AEs.Profermin was generally well tolerated.CONCLUSION:Profermin is safe and may be effective in inducing remission of active UC.展开更多
Climate normal calculation over Oman is a key challenge due to scattered and inconsistent ground observations.Based on the available observations record to calculate the normal,the study derives the Provisional Climat...Climate normal calculation over Oman is a key challenge due to scattered and inconsistent ground observations.Based on the available observations record to calculate the normal,the study derives the Provisional Climate Normal.The data from selected stations in this project have been tested and investigated by using the quality check and missing data evaluation.It is found that ERA-5 is the best reanalysis data in temperature as the correlation coefficient is higher compared to ERA-5 land and MERRA-2 which explains the reason for choosing ERA-5 data to calculate the regression model to fulfil the missing data.By using MODIS data,the Urban Heat Island Index(UHI)shows that Sohar and Muscat stations have the greatest UHI effect and Rustaq has the negative UHI trend.Regarding the interpolation,the project used different interpolation methods to calculate the provisional climate normal for Oman,showing that different variogram fitting models must be used for different months for Kriging interpolation.The final results show that all the interpolation methods struggle to predict the air temperature during summertime due to a complex spatial contrast and distribution,except GWR which shows a well-predicted dataset by which it can be said that the GWR is the best performing method for the temperature over Oman.展开更多
To the Editor:Coronary computed tomography angiography(CCTA)has been increasingly,widely performed for diagnosing coronary artery,disease,lAnatomical diagnosis,that is,stenosis grading,is stillthe main diagnostic inde...To the Editor:Coronary computed tomography angiography(CCTA)has been increasingly,widely performed for diagnosing coronary artery,disease,lAnatomical diagnosis,that is,stenosis grading,is stillthe main diagnostic index provided'by most CCTA tests.Post-processing and interpretation of stenosis are 2 essential'steps that need to be performed bycardiovascular imaging professionals from scan completion to diagnosis conclusion,which is repetitive and time-consuming,taking an average of 30 minutes each case in China and becoming the bottleneck and gradually creating an imbalance between supply and demand.In ine with the rapid development of artificial intelligence(Al)technology in recent years,it has been expected to solve these specific problems.We developed an AI system for automating post-processing and diagnostic reporting of CCTA data using deep learning algorithms to establishanew1-clickworkflowforeverydayuse,namely,CCTA-AI(Figure 1).To further assess its capabilities,this study intends to answer 2 following questions:To what extent can it improve the efficiency of post-processing?To what extent can CCTA-AI detect and calculate coronary artery stenosis due to each atherosclerotic plaque?展开更多
Aim:We conducted a pilot study that combines immunotherapy(cyclic interleukin-2 interferon-beta sequence)and hormone therapy(HT)to overcome endocrine resistance in metastatic breast cancer.Methods:The final results of...Aim:We conducted a pilot study that combines immunotherapy(cyclic interleukin-2 interferon-beta sequence)and hormone therapy(HT)to overcome endocrine resistance in metastatic breast cancer.Methods:The final results of a 2:1 control-case retrospective observational study are here shown following 22 additional months of postoperative follow-up and 6 further controls.There were 95 controls and 42 cases in total.The 95 controls were ER+/HER2-metastatic breast cancer patients who underwent first-line HT with aromatase inhibitors(AIs)or fulvestrant.Twenty-eight of them(28.9%)also received biological drugs including cyclin kinase inhibitors(CKIs).The 42 cases were ER+metastatic breast cancer patients who received interferon beta-interleukin-2 immunotherapy in addition to first-line HT.Selective estrogen receptor modulators/down-regulators(SERMs/SERDs)were used for HT in 39(92.9%)of them and AIs in the remaining 3.Results:Median progression-free survival(PFS)and overall survival(OS)were significantly longer in the 42 studied patients who received hormone immunotherapy(HIT)than in the 95 controls(median time 33 vs.18 months,P=0.002,and 81 vs.62 months,P=0.019).In the analysis adjusted for disease-free interval(DFI),hormone receptor,HER2 status,visceral involvement,AIs,and biological therapy,the PFS and OS hazard ratio(HR)further increased in favor of the 42 cases(P=0.004 and P=0.044 respectively).In the same ER+/HER2-metastatic breast cancer patients treated with both AIs and CKIs,a median PFS ranging from 25.3 to 28.18 months and a median OS of 37.5 months were observed.Conclusions:This study strongly suggests multi-center randomized clinical trials should be performed to enter our proposed immunotherapy into clinical practice.展开更多
Background:Distinguishing multiple primary lung cancer(MPLC)from intrapulmonary metastasis(IPM)is critical for their disparate treatment strategy and prognosis.This study aimed to establish a non-invasive model to mak...Background:Distinguishing multiple primary lung cancer(MPLC)from intrapulmonary metastasis(IPM)is critical for their disparate treatment strategy and prognosis.This study aimed to establish a non-invasive model to make the differentiation pre-operatively.Methods:We retrospectively studied 168 patients with multiple lung cancers(307 pairs of lesions)including 118 cases for modeling and internal validation,and 50 cases for independent external validation.Radiomic features on computed tomography(CT)were extracted to calculate the absolute deviation of paired lesions.Features were then selected by correlation coefficients and random forest classifier 5-fold cross-validation,based on which the lesion pair relation estimation(PRE)model was developed.A major voting strategy was used to decide diagnosis for cases with multiple pairs of lesions.Cases from another institute were included as the external validation set for the PRE model to compete with two experienced clinicians.Results:Seven radiomic features were selected for the PRE model construction.With major voting strategy,the mean area under receiver operating characteristic curve(AUC),accuracy,sensitivity,and specificity of the training versus internal validation versus external validation cohort to distinguish MPLC were 0.983 versus 0.844 versus 0.793,0.942 versus 0.846 versus 0.760,0.905 versus 0.728 versus 0.727,and 0.962 versus 0.910 versus 0.769,respectively.AUCs of the two clinicians were 0.619 and 0.580.Conclusions:The CT radiomic feature-based lesion PRE model is potentially an accurate diagnostic tool for the differentiation of MPLC and IPM,which could help with clinical decision making.展开更多
基金Supported by Grants from National Natural Science Foundation of China,No.81273743,No.81473641and 215 Program,No.2013-2-11
文摘AIM: To establish a clinical scoring model to predict risk of acute-on-chronic liver failure(ACLF) in chronic hepatitis B(CHB) patients.METHODS: This was a retrospective study of 1457 patients hospitalized for CHB between October 2008 and October 2013 at the Beijing Ditan Hospital, Capital Medical University, China. The patients were divided into two groups: severe acute exacerbation(SAE) group(n = 382) and non-SAE group(n = 1075). The SAE group was classified as the high-risk group based on the higher incidence of ACLF in this group than in the non-SAE group(13.6% vs 0.4%). Two-thirds of SAE patients were randomly assigned to risk-model derivation and the other one-third to model validation. Univariate risk factors associated with the outcome were entered into a multivariate logistic regression model for screening independent risk factors. Each variable was assigned an integer value based on the regression coefficients, and the final score was the sum of these values in the derivation set. Model discrimination and calibration were assessed using area under the receiver operating characteristic curve and the Hosmer-Lemeshow test. RESULTS: The risk prediction scoring model includedthe following four factors: age ≥ 40 years, total bilirubin ≥ 171 μmol/L, prothrombin activity 40%-60%, and hepatitis B virus DNA > 107 copies/m L. The sum risk score ranged from 0 to 7; 0-3 identified patients with lower risk of ACLF, whereas 4-7 identified patients with higher risk. The Kaplan-Meier analysis showed the cumulative risk for ACLF and ACLF-related death in the two risk groups(0-3 and 4-7 scores) of the primary cohort over 56 d, and log-rank test revealed a significant difference(2.0% vs 33.8% and 0.8% vs 9.4%, respectively; both P < 0.0001). In the derivation and validation data sets, the model had good discrimination(C index = 0.857, 95% confidence interval: 0.800-0.913 and C index = 0.889, 95% confidence interval: 0.820-0.957, respectively) and calibration demonstrated by the Hosmer-Lemeshow test(χ2 = 4.516, P = 0.808 and χ2 = 1.959, P = 0.923, respectively).CONCLUSION: Using the scoring model, clinicians can easily identify patients(total score ≥ 4) at high risk of ACLF and ACLF-related death early during SAE.
基金Supported by Danish Innovation Law Grant,J.nr.3414-06-01530from the Danish Food Industry Agency under the Ministry of Food,Agriculture and FisheriesNordisk Rebalance,who developed and manufactured Profermin,and partly financed the study
文摘AIM:To test the efficacy and safety of Profermin in inducing remission in patients with active ulcerative colitis(UC).METHODS:The study included 39 patients with mild to moderate UC defined as a Simple Clinical Colitis Activity Index(SCCAI)>4 and<12(median:7.5),who were treated open-label with Profermintwice daily for 24 wk.Daily SCCAI was reported observer blinded via the Internet.RESULTS:In an intention to treat(ITT)analysis,the mean reduction in SCCAI score was 56.5%.Of the 39 patients,24(62%)reached the primary endpoint,which was proportion of patients with≥50%reduction in SCCAI.Our secondary endpoint,the proportion of patients in remission defined as SCCAI≤2.5,was in ITT analysis reached in 18 of the 39 patients(46%).In a repeated-measure regression analysis,the estimated mean reduction in score was 5.0 points(95%CI:4.1-5.9,P<0.001)and the estimated mean time taken to obtain half the reduction in score was 28 d(95%CI:26-30).There were no serious adverse events(AEs)or withdrawals due to AEs.Profermin was generally well tolerated.CONCLUSION:Profermin is safe and may be effective in inducing remission of active UC.
文摘Climate normal calculation over Oman is a key challenge due to scattered and inconsistent ground observations.Based on the available observations record to calculate the normal,the study derives the Provisional Climate Normal.The data from selected stations in this project have been tested and investigated by using the quality check and missing data evaluation.It is found that ERA-5 is the best reanalysis data in temperature as the correlation coefficient is higher compared to ERA-5 land and MERRA-2 which explains the reason for choosing ERA-5 data to calculate the regression model to fulfil the missing data.By using MODIS data,the Urban Heat Island Index(UHI)shows that Sohar and Muscat stations have the greatest UHI effect and Rustaq has the negative UHI trend.Regarding the interpolation,the project used different interpolation methods to calculate the provisional climate normal for Oman,showing that different variogram fitting models must be used for different months for Kriging interpolation.The final results show that all the interpolation methods struggle to predict the air temperature during summertime due to a complex spatial contrast and distribution,except GWR which shows a well-predicted dataset by which it can be said that the GWR is the best performing method for the temperature over Oman.
基金National Key Research and Development Program of China(No.2019YFE0107800)the Beijing Municipal Science and Technology Commission(No.Z201100005620009)。
文摘To the Editor:Coronary computed tomography angiography(CCTA)has been increasingly,widely performed for diagnosing coronary artery,disease,lAnatomical diagnosis,that is,stenosis grading,is stillthe main diagnostic index provided'by most CCTA tests.Post-processing and interpretation of stenosis are 2 essential'steps that need to be performed bycardiovascular imaging professionals from scan completion to diagnosis conclusion,which is repetitive and time-consuming,taking an average of 30 minutes each case in China and becoming the bottleneck and gradually creating an imbalance between supply and demand.In ine with the rapid development of artificial intelligence(Al)technology in recent years,it has been expected to solve these specific problems.We developed an AI system for automating post-processing and diagnostic reporting of CCTA data using deep learning algorithms to establishanew1-clickworkflowforeverydayuse,namely,CCTA-AI(Figure 1).To further assess its capabilities,this study intends to answer 2 following questions:To what extent can it improve the efficiency of post-processing?To what extent can CCTA-AI detect and calculate coronary artery stenosis due to each atherosclerotic plaque?
文摘Aim:We conducted a pilot study that combines immunotherapy(cyclic interleukin-2 interferon-beta sequence)and hormone therapy(HT)to overcome endocrine resistance in metastatic breast cancer.Methods:The final results of a 2:1 control-case retrospective observational study are here shown following 22 additional months of postoperative follow-up and 6 further controls.There were 95 controls and 42 cases in total.The 95 controls were ER+/HER2-metastatic breast cancer patients who underwent first-line HT with aromatase inhibitors(AIs)or fulvestrant.Twenty-eight of them(28.9%)also received biological drugs including cyclin kinase inhibitors(CKIs).The 42 cases were ER+metastatic breast cancer patients who received interferon beta-interleukin-2 immunotherapy in addition to first-line HT.Selective estrogen receptor modulators/down-regulators(SERMs/SERDs)were used for HT in 39(92.9%)of them and AIs in the remaining 3.Results:Median progression-free survival(PFS)and overall survival(OS)were significantly longer in the 42 studied patients who received hormone immunotherapy(HIT)than in the 95 controls(median time 33 vs.18 months,P=0.002,and 81 vs.62 months,P=0.019).In the analysis adjusted for disease-free interval(DFI),hormone receptor,HER2 status,visceral involvement,AIs,and biological therapy,the PFS and OS hazard ratio(HR)further increased in favor of the 42 cases(P=0.004 and P=0.044 respectively).In the same ER+/HER2-metastatic breast cancer patients treated with both AIs and CKIs,a median PFS ranging from 25.3 to 28.18 months and a median OS of 37.5 months were observed.Conclusions:This study strongly suggests multi-center randomized clinical trials should be performed to enter our proposed immunotherapy into clinical practice.
基金supported by Grants from the National Natural Science Foundation of China(No.82102109)by Grants from Development Center for Medical Science&Technology National Health Commission of China(No.WA2020RW10)by Grants from Shanghai Municipal Commission of Health and Family Planning Program(No.20184Y0037).
文摘Background:Distinguishing multiple primary lung cancer(MPLC)from intrapulmonary metastasis(IPM)is critical for their disparate treatment strategy and prognosis.This study aimed to establish a non-invasive model to make the differentiation pre-operatively.Methods:We retrospectively studied 168 patients with multiple lung cancers(307 pairs of lesions)including 118 cases for modeling and internal validation,and 50 cases for independent external validation.Radiomic features on computed tomography(CT)were extracted to calculate the absolute deviation of paired lesions.Features were then selected by correlation coefficients and random forest classifier 5-fold cross-validation,based on which the lesion pair relation estimation(PRE)model was developed.A major voting strategy was used to decide diagnosis for cases with multiple pairs of lesions.Cases from another institute were included as the external validation set for the PRE model to compete with two experienced clinicians.Results:Seven radiomic features were selected for the PRE model construction.With major voting strategy,the mean area under receiver operating characteristic curve(AUC),accuracy,sensitivity,and specificity of the training versus internal validation versus external validation cohort to distinguish MPLC were 0.983 versus 0.844 versus 0.793,0.942 versus 0.846 versus 0.760,0.905 versus 0.728 versus 0.727,and 0.962 versus 0.910 versus 0.769,respectively.AUCs of the two clinicians were 0.619 and 0.580.Conclusions:The CT radiomic feature-based lesion PRE model is potentially an accurate diagnostic tool for the differentiation of MPLC and IPM,which could help with clinical decision making.
