BACKGROUND The treatment of hepatocellular carcinoma(HCC)≥10 cm remains a challenge.AIM To consolidate the role of surgical resection for HCC larger than 10 cm.METHODS Eligible HCC patients were identified from the C...BACKGROUND The treatment of hepatocellular carcinoma(HCC)≥10 cm remains a challenge.AIM To consolidate the role of surgical resection for HCC larger than 10 cm.METHODS Eligible HCC patients were identified from the Chang Gung Research Database,the largest multi-institution database,which collected medical records of all patients from Chang Gung Memorial Foundation.The surgical outcome of HCC≥10 cm(L-HCC)was compared to that of HCC<10 cm(S-HCC)(model 1).The survival of L-HCC after either liver resection or transarterial chemoembolization(TACE)was also analyzed(model 2).The long-term risks of all-cause mortality and recurrence were assessed to consolidate the role of surgery for L-HCC.RESULTS From January 2004 to July 2015,a total of 32403 HCC patients were identified from the Chang Gung Research Database.Among 3985 patients who received liver resection,3559(89.3%)had S-HCC,and 426 had L-HCC.The L-HCC patients had a worse disease-free survival(0.27 for L-HCC vs 0.40 for S-HCC)and overall survival(0.18 for L-HCC vs 0.45 for S-HCC)than the S-HCC after liver resection(both P<0.001).However,the surgical and long-term outcome of resected L-HCC had improved dramatically in the recent decades.After adjusting for covariates,surgery could provide a better outcome for L-HCC than TACE(adjusted hazard ratio of all-cause mortality:0.46,95%confidence interval:0.38-0.56 for surgery).Subgroup analysis stratified by different stages showed similar trend of survival benefit among L-HCC patients receiving surgery.CONCLUSION Our study demonstrated an improving surgical outcome for HCC larger than 10 cm.Under selected conditions,surgery is better than TACE in terms of disease control and survival and should be performed.Due to inferior survival,a subclassification within T1 stage should be considered.Future studies are mandatory to confirm our findings.展开更多
Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly c...Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly correlated with clinicopathological characteristics and prognostic outcomes of patients with breast cancer remains elusive.This study systematically investigated the clinical significance of ZWINT expression in breast cancer through integrated molecular subtyping and survival analysis.Methods We systematically characterized the spatial expression pattern of ZWINT across various breast cancer subtypes and assessed its prognostic significance using an integrated bioinformatics approach that involved multi-omics analysis.The approach included the Breast Cancer Gene-Expression Miner v5.1(bc-GenExMiner v5.1),TNMplot,MuTarget,PrognoScan database,and Database for Annotation,Visualization,and Integrated Discovery(DAVID).Results Our analysis revealed consistent upregulation of ZWINT mRNA and protein expression across distinct clinicopathological subtypes of breast cancer.ZWINT overexpression demonstrated significant co-occurrence with truncating mutations in cadherin 1(CDH1)and tumor protein p53(TP53),suggesting potential functional crosstalk in tumor progression pathways.The overexpression of ZWINT correlated with adverse clinical outcomes,showing 48%increased mortality risk(overall survival:HR 1.48,95%CI 1.23–1.79),66%higher recurrence probability(relapse-free survival:1.66,95%CI 1.50–1.84),and 63%elevated metastasis risk(distant metastasis-free survival:HR 1.63,95%CI 1.39–1.90).Multivariate Cox regression incorporating TNM staging and molecular subtypes confirmed ZWINT as an independent prognostic determinant(P<0.001,Harrell’s C-index=0.7827),which was validated through bootstrap resampling(1000 iterations).Conclusion ZWINT may serve as a potential biomarker for prognosis and a possible therapeutic target alongside TP53/CDH1 in breast cancer.展开更多
油中溶解气体分析（dissolved gas analysis,DGA）是现场电力变压器故障诊断最常用的方法。然而,油中溶解气体含量较容易受到变压器结构、容量、故障位置以及故障程度等因素的影响,从而降低了变压器故障诊断的可靠性。为了提升变压器故...油中溶解气体分析（dissolved gas analysis,DGA）是现场电力变压器故障诊断最常用的方法。然而,油中溶解气体含量较容易受到变压器结构、容量、故障位置以及故障程度等因素的影响,从而降低了变压器故障诊断的可靠性。为了提升变压器故障诊断正确率,该文提出了基于支持向量机（support vector machie,SVM）和遗传算法（geneti calgorithm,GA）优选的DGA新特征参量。首先,以28个DGA比值为输入,建立了基于SVM的变压器故障诊断模型;其次,采用GA同时对SVM参数和DGA比值进行优化,得到9个优选DGA比值作为变压器故障诊断用新特征参量。对IEC TC 10故障数据库的诊断结果表明：DGA新特征参量的故障诊断正确率为84%,较常用的DGA含量和IEC比值的诊断正确率提高10%~25%;并且无论采用哪种特征参量,支持向量机的诊断结果均优于神经网络诊断模型。最后,采用DGA新特征参量对国内117组变压器的故障诊断正确率达到了87.18%,再次验证了该方法的有效性。展开更多
The nonlinear activation functions in the deep CNN(Convolutional Neural Network)based on fluid dynamics are presented.We propose two types of activation functions by applying the so-called parametric softsign to the n...The nonlinear activation functions in the deep CNN(Convolutional Neural Network)based on fluid dynamics are presented.We propose two types of activation functions by applying the so-called parametric softsign to the negative region.We use significantly the well-known TensorFlow as the deep learning framework.The CNN architecture consists of three convolutional layers with the max-pooling and one fullyconnected softmax layer.The CNN approaches are applied to three benchmark datasets,namely,MNIST,CIFAR-10,and CIFAR-100.Numerical results demonstrate the workability and the validity of the present approach through comparison with other numerical performances.展开更多
文摘BACKGROUND The treatment of hepatocellular carcinoma(HCC)≥10 cm remains a challenge.AIM To consolidate the role of surgical resection for HCC larger than 10 cm.METHODS Eligible HCC patients were identified from the Chang Gung Research Database,the largest multi-institution database,which collected medical records of all patients from Chang Gung Memorial Foundation.The surgical outcome of HCC≥10 cm(L-HCC)was compared to that of HCC<10 cm(S-HCC)(model 1).The survival of L-HCC after either liver resection or transarterial chemoembolization(TACE)was also analyzed(model 2).The long-term risks of all-cause mortality and recurrence were assessed to consolidate the role of surgery for L-HCC.RESULTS From January 2004 to July 2015,a total of 32403 HCC patients were identified from the Chang Gung Research Database.Among 3985 patients who received liver resection,3559(89.3%)had S-HCC,and 426 had L-HCC.The L-HCC patients had a worse disease-free survival(0.27 for L-HCC vs 0.40 for S-HCC)and overall survival(0.18 for L-HCC vs 0.45 for S-HCC)than the S-HCC after liver resection(both P<0.001).However,the surgical and long-term outcome of resected L-HCC had improved dramatically in the recent decades.After adjusting for covariates,surgery could provide a better outcome for L-HCC than TACE(adjusted hazard ratio of all-cause mortality:0.46,95%confidence interval:0.38-0.56 for surgery).Subgroup analysis stratified by different stages showed similar trend of survival benefit among L-HCC patients receiving surgery.CONCLUSION Our study demonstrated an improving surgical outcome for HCC larger than 10 cm.Under selected conditions,surgery is better than TACE in terms of disease control and survival and should be performed.Due to inferior survival,a subclassification within T1 stage should be considered.Future studies are mandatory to confirm our findings.
基金supported by the Research Project of Maternal and Child Health Hospital of Hubei Province(No.2023SFYM008)Key Project of Hubei Provincial Natural Science Foundation(No.JCZRLH202500304).
文摘Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly correlated with clinicopathological characteristics and prognostic outcomes of patients with breast cancer remains elusive.This study systematically investigated the clinical significance of ZWINT expression in breast cancer through integrated molecular subtyping and survival analysis.Methods We systematically characterized the spatial expression pattern of ZWINT across various breast cancer subtypes and assessed its prognostic significance using an integrated bioinformatics approach that involved multi-omics analysis.The approach included the Breast Cancer Gene-Expression Miner v5.1(bc-GenExMiner v5.1),TNMplot,MuTarget,PrognoScan database,and Database for Annotation,Visualization,and Integrated Discovery(DAVID).Results Our analysis revealed consistent upregulation of ZWINT mRNA and protein expression across distinct clinicopathological subtypes of breast cancer.ZWINT overexpression demonstrated significant co-occurrence with truncating mutations in cadherin 1(CDH1)and tumor protein p53(TP53),suggesting potential functional crosstalk in tumor progression pathways.The overexpression of ZWINT correlated with adverse clinical outcomes,showing 48%increased mortality risk(overall survival:HR 1.48,95%CI 1.23–1.79),66%higher recurrence probability(relapse-free survival:1.66,95%CI 1.50–1.84),and 63%elevated metastasis risk(distant metastasis-free survival:HR 1.63,95%CI 1.39–1.90).Multivariate Cox regression incorporating TNM staging and molecular subtypes confirmed ZWINT as an independent prognostic determinant(P<0.001,Harrell’s C-index=0.7827),which was validated through bootstrap resampling(1000 iterations).Conclusion ZWINT may serve as a potential biomarker for prognosis and a possible therapeutic target alongside TP53/CDH1 in breast cancer.
文摘The nonlinear activation functions in the deep CNN(Convolutional Neural Network)based on fluid dynamics are presented.We propose two types of activation functions by applying the so-called parametric softsign to the negative region.We use significantly the well-known TensorFlow as the deep learning framework.The CNN architecture consists of three convolutional layers with the max-pooling and one fullyconnected softmax layer.The CNN approaches are applied to three benchmark datasets,namely,MNIST,CIFAR-10,and CIFAR-100.Numerical results demonstrate the workability and the validity of the present approach through comparison with other numerical performances.
