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您的检索式：作者名="Chenyang Bu"
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| 1 | Detection of telomerase activity in biopsy samples for predicting prognosis in cirrhotic patients with hepatocellular carcinoma after laparoscopic radiofrequency ablation therapy显示文摘Objective: To explore the role of telomerase activity detected in biopsy samples for evaluating the efficacy of lapa- roscopic radiofrequency ablation (RFA) therapy in patients with hepatocellular carcinoma (HCC) and liver cirrhosis. Methods: From August 2001 to October 2004, 34 cirrhotic patients with HCC were treated by laparoscopic RFA under general anesthe- sia. A total of 34 tumors, with a mean maximum tumor diameter of 4.0 ± 1.0 cm, were all located on the liver surface or adja- cent to the gallbladder. Laparoscopic ultrasound-guided core biopsy for liver lesions was performed before and immediately after RFA therapy. In these biopsy samples, telomerase activity was detected by the ELISA-based telomeric repeat amplifica- tion protocol (ELISA-TRAP) assay, and pathological examination was routinely performed. Results: Laparoscopic RFA was successfully performed in all the 34 patients. A complete tumor necrosis was achieved in all patients on the contrast-enhanced helical CT scanning one month after laparoscopic RFA. The positive rates of telomerase activity and histopathologic diagnosis in biopsy samples were 91.2% (31/34) and 100% (34/34) respectively before RFA, and 26.5% (9/34) and 0% respectively after RFA. During a median follow-up period of 35 months (range, 18–51 months), the rates of local tumor recurrence at the ablation sites in post-RFA telomerase-positive and negative patients were 88.9% (8/9) and 4% (1/25) respectively (P < 0.01), and the rates of distant recurrence within the livers were 0% (0/9) and 12% (3/25) respectively (P > 0.05). Conclusion: For cirrhotic patients with HCC treated by laparoscopic RFA, detection of telomerase activity in biopsy samples may be useful for evaluating the therapeutic efficacy of RFA and predicting postoperative local tumor recurrence. | Ruifang Fan Fulu Chai Zhipeng Han Chenyang Wang Xianling Guo Xinxin Bu Lixin Wei | 2007 | The Chinese-German Journal of Clinical Oncology2007，6，3： | 1 |
| 2 | Tumor Necrosis Factor-alpha Promotes Tumor Growth by Inducing Vascular Endothelial Growth Factor显示文摘 | Yingying Jing Nannan Ma Tingting Fan Chenyang Wang Xinxin Bu Guocheng Jiang Rong Li Lu Gao Ding Li Mengchao Wu Lixin Wei | 2011 | Cancer Investigation2011，，7： | 1 |
| 3 | HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets显示文摘Spreadsheets contain a lot of valuable data and have many practical applications.The key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell pairs.Most existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell pairs.In this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of spreadsheets.For improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location information.Our experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines. | Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng | 2023 | Data Intelligence2023，5，3： | 0 |
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