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    题名 作者 年代 出处 被引量
1Prevalence of congenital heart disease at live birth: an accurate assessment by echocardiographic screening显示文摘Qu‐ming Zhao Xiao‐jing Ma Bing Jia Guo‐ying Huang 2013Acta Paediatr2013,,4:1
2Splenic hilar lymph node metastasis independently predicts poor survival for patients with gastric cancers in the upper and/or the middle third of the stomach显示文摘Guo‐lianZhu ZheSun Zhen‐ningWang Ying‐yingXu YanXu ZhiZhu Hui‐mianXu 2012J. Surg. Oncol2012,,8:1
3Heat shock protein genes ( hsp20 , hsp75 and hsp90 ) from Pieris rapae : Molecular cloning and transcription in response to parasitization by Pteromalus puparum显示文摘Jia‐Ying Zhu Guo‐Xing Wu Gong‐Yin Ye Cui Hu 2012Insect Science2012,,2:1
4Rapamycin combined with donor immature dendritic cells promotes liver allograft survival in association with CD4+CD25+Foxp3+ regulatory T cell expansion显示文摘Guo‐Ying Wang Yang Yang Hua Li Jian Zhang Min‐Ru Li Qi Zhang Gui‐Hua Chen 2012Hepatology Research2012,,2:1
5Identification of xanthones from Swertia punicea using high‐performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry显示文摘Xin‐Gang Du Wei Wang Qing‐Ying Zhang Jun Cheng Bharathi Avula Ikhlas A. Khan De‐An Guo 2012Rapid Commun Mass Spectrom2012,,24:1
6Effect of Xiongbing compound on the pharmacokinetics and brain targeting of tetramethylpyrazine显示文摘Li‐Sheng Wang Zong‐Feng Shi Ying‐Feng Zhang Qi Guo Yu‐Wei Huang Li‐Ling Zhou 2012Journal of Pharmacy and Pharmacology2012,,11:1
7Effect of low temperature on chlorophyll biosynthesis in albinism line of wheat ( Triticum aestivum ) FA85显示文摘Xiao‐Gang Liu Hong Xu Jing‐Ya Zhang Guang‐Wang Liang Ying‐Tuan Liu Ai‐Guang Guo 2012Physiologia Plantarum2012,,3:1
8miR‐125b regulates side population in breast cancer and confers a chemoresistant phenotype显示文摘Hong‐Jiang Wang Ying‐Qiu Guo Guang Tan Lei Dong Lei Cheng Ke‐Jun Li Zhong‐Yu Wang Hai‐Feng Luo 2013J Cell Biochem2013,,10:1
9A Novel Oncogenic Role of Inositol Phosphatase SHIP2 in ER‐Negative Breast Cancer Stem Cells: Involvement of JNK/Vimentin Activation显示文摘Chiung‐Hui Fu Ruey‐Jen Lin John Yu Wen‐Wei Chang Guo‐Shiou Liao Wen‐Ying Chang Ling‐Ming Tseng Yi‐Fang Tsai Jyh‐Cherng Yu Alice L. Yu 2014Stem Cells2014,,8:1
10Proteasome inhibitor interacts synergistically with autophagy inhibitor to suppress proliferation and induce apoptosis in hepatocellular carcinoma显示文摘Bo Hui Ying‐Hong Shi Zhen‐Bin Ding Jian Zhou Cheng‐Yu Gu Yuan‐Fei Peng Hua Yang Wei‐Ren Liu Guo‐Ming Shi Jia Fan 2012Cancer2012,,22:1
11Pooled analysis of the performance of liquid‐based cytology in population‐based cervical cancer screening studies in China显示文摘Qin‐Jing Pan Shang‐ying Hu Xun Zhang Pu‐wa Ci Wen‐hua Zhang Hui‐qin Guo Jian Cao Fang‐hui Zhao Alice Lytwyn You‐lin Qiao 2013Cancer Cytopathology2013,,9:1
12Cas12a-based on-site, rapid detection of genetically modified crops显示文摘Genetically modified(GM)crops that impart herbicide resistance,improved tolerance to biotic and abiotic stresses,and enhanced yield or nutritional quality have been developed and grown worldwide(ISAAA Brief 55-2019:International Servicefor theAcquisition ofAgri-biotech Applications(ISAAA),2019).In response topublic concern on genetically modified organisms(GMOs),DNA-based and protein-based methods have been developed to detect GMOs infood/feed samples(Fraitureetal.,2015).These methods,however,require expensive equipment and technical expertise and are unsuitable for field diagnosis(DNA-based methods)or cannot detect non-coding sequences such as promoters or terminators(protein-based methods).Zhiqiang Duan Xiaoliang Yang Xingkun Ji Ying Chen Xiaomu Niu Anping Guo Jian‐Kang Zhu Feng Li Zhaobo Lang Hui Zhao 2022Journal of Integrative Plant Biology2022,64,10:0
13Expression of FOXJ1 in hepatocellular carcinoma: Correlation with patients’ prognosis and tumor cell proliferation显示文摘Hong‐Wei Chen Xiao‐Dong Huang Hong‐Chen Li Song He Run‐Zhou Ni Cui‐Hua Chen Chen Peng Gang Wu Gui‐Hua Wang Ying‐Ying Wang Yun‐Hong Zhao Yi‐Xin Zhang Ai‐Guo Shen Hui‐Min Wang 2013Mol. Carcinog2013,,8:1
14Development and validation of a nomogram to predict cardiac death after radiotherapy for esophageal cancer显示文摘Background:Patients frequently die from cardiac causes after radiotherapy for esophageal cancer.Early detection of cardiac death risk in these patients is crucial to improve clinical decision‐making and prognosis.Thus,we modeled the risk of cardiac death after irradiation for esophageal cancer.Methods:A retrospective analysis of 37,599 esophageal cancer cases treated with radiotherapy in the SEER database between 2000 and 2018 was performed.The selected cases were randomly assigned to the model development group(n=26,320)and model validation group(n=11,279)at a ratio of 7:3.We identified the risk factors most commonly associated with cardiac death by least absolute shrinkage and selection operator regression analysis(LASSO).The endpoints for model development and validation were 5‐and 10‐year survival rates.The net clinical benefit of the models was evaluated by decision curve analysis(DCA)and concordance index(C‐index).The performance of the models was further assessed by creating a receiver operating characteristic curve(ROC)and calculating the area under the curve(AUC).Kaplan‐Meier(K‐M)survival analysis was performed on the probability of death.Patients were classified according to death probability thresholds.Five‐and ten‐year survival rates for the two groups were shown using K‐M curves.Results:The major risk factors for cardiac death were age,surgery,year of diagnosis,sequence of surgery and radiotherapy,chemotherapy and a number of tumors,which were used to create the nomogram.The C‐indexes of the nomograms were 0.708 and 0.679 for the development and validation groups,respectively.DCA showed the good net clinical benefit of nomograms in predicting 5‐and 10‐year risk of cardiac death.The model exhibited moderate predictive power for 5‐and 10‐year cardiac mortality(AUC:0.833 and 0.854,respectively),and for the development and validation cohorts(AUC:0.76 and 0.813,respectively).Conclusions:Our nomogram may assist clinicians in making clinical decisions about patients undergoing radiotherapy for esophageal cancer based on early detection of cardiac death risk.Xinfang Lv Xue Wu Kai Liu Xinke Zhao Chenliang Pan Jing Zhao Juan Chang Huan Guo Xiang Gao Xiaodong Zhi Chunzhen Ren Qilin Chen Hugang Jiang Chunling Wang Ying‐Dong Li 2023Cancer Innovation2023,2,5:0
15Tolerance and acceptance of hepatic venous pressure gradient measurement in cirrhosis(CHESS1904):An international multicenter study显示文摘Aim:To determine the tolerance and acceptance of hepatic venous pressure gradient(HVPG)measurements in patients with liver cirrhosis.Methods:This prospective international multicenter study included 271 patients with cirrhosis who were scheduled to undergo HVPG measurement between October 2019 and June 2020.Data related to the tolerance and acceptance of HVPG measurements were collected using descriptive questionnaires.Results:HVPG measurements were technically successful in all 271 patients,with 141(52.0%)undergoing HVPG measurement alone.The complication rate was 0.4%.Postoperative pain was significantly lower than preoperative expected pain(p<0.001)and intraoperative pain(p<0.001),and intraoperative pain was also significantly lower than preoperative expected pain(p=0.036).No,mild,moderate,severe,and intolerable discomfort scores were reported by 36.9%,44.6%,11.1%,6.3%,and 0.4%of these patients,respectively,during HVPG measurement and by 54.6%32.5%,11.4%,1.5%,and 0%,respectively,after HVPG measurement.Of these patients,39.5%had little understanding and 10%had no understanding of the value of HVPG measurement,with 35.1%and 4.1%regarding HVPG measurements as being of little or no help,respectively.Most patients reported that they would definitely(15.5%),probably(46.9%),or possibly(29.9%)choose to undergo additional HVPG measurements again,and 62.7%regarded the cost of the procedure as acceptable.Conclusion:HVPG measurement was safe and well‐tolerated in patients with cirrhosis,but patient education and communication are warranted to improve the acceptance of this procedure.Jun‐Hui Sun He Zhao Haijun Zhang Lei Li NecatiÖrmeci Zi‐Niu Yu Xun Li Shuangxi Li Xujun Yang Huaping Wei Xiaoliang Zhu Zhengcong Zhang Yajin Wang Zhongwei Zhao Jianting Mao Qiaohong Wu Xiaole Sun Huiling Xiang Kefeng Jia Chao Yang Wei Wu Xiuqing Lin Haixin Yao Changzeng Zuo Jitao Wang Bo Zhang Chunqing Zhang Xiaoling Wu Guangchuan Wang Shengjuan Yao Ruihang Wang Li Zhou Hui Huan Qingli Tu Xue Pu Feng Zhang Qin Yin Linpeng Zhang Ying Guo Jian Wang Kohei Kotani Sawako Uchida‐Kobayashi Norifumi Kawada He Zhu Li Li Wei Wang Guo Zhang Lei Yu Xudong Cui Qingliang Zhu Hailong Zhang Xiaoli Hu Rafael OXimenes Adriano Gonçalves de Araújo Giulliano Gardenghi Yubao Zheng Zebin Wu Mingsheng Huang Xiaoyong Chen Jun Wu Feng Xie Yang Bo Shengjuan Hu Linke Ma Xiao Li Xiaolong Qi 2022Portal Hypertension & Cirrhosis2022,1,1:0
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