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Your search keyword '"Dong, Zhixia"' showing total 11 results
Start Over Author "Dong, Zhixia" Publisher elsevier b.v.
11 results on '"Dong, Zhixia"'

5. A deep learning model based on magnifying endoscopy with narrow-band imaging to evaluate intestinal metaplasia grading and OLGIM staging: A multicenter study.

Author

Niu, Wenlu, Liu, Leheng, Dong, Zhixia, Bu, Xiongzhu, Yao, Fanghao, Wang, Jing, Wu, Xiaowan, Chen, Congying, Mao, Tiancheng, Wu, Yulun, Yuan, Lin, Wan, Xinjian, and Zhou, Hui

Abstract

Patients with stage III or IV of operative link for gastric intestinal metaplasia assessment (OLGIM) are at a higher risk of gastric cancer (GC). We aimed to construct a deep learning (DL) model based on magnifying endoscopy with narrow-band imaging (ME-NBI) to evaluate OLGIM staging. This study included 4473 ME-NBI images obtained from 803 patients at three endoscopy centres. The endoscopic expert marked intestinal metaplasia (IM) regions on endoscopic images of the target biopsy sites. Faster Region-Convolutional Neural Network model was used to grade IM lesions and predict OLGIM staging. The diagnostic performance of the model for IM grading in internal and external validation sets, as measured by the area under the curve (AUC), was 0.872 and 0.803, respectively. The accuracy of this model in predicting the high-risk stage of OLGIM was 84.0%, which was not statistically different from that of three junior (71.3%, p = 0.148) and three senior endoscopists (75.3%, p = 0.317) specially trained in endoscopic images corresponding to pathological IM grade, but higher than that of three untrained junior endoscopists (64.0%, p = 0.023). This DL model can assist endoscopists in predicting OLGIM staging using ME-NBI without biopsy, thereby facilitating screening high-risk patients for GC. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Enhancing gastrointestinal submucosal tumor recognition in endoscopic ultrasonography: A novel multi-attribute guided contextual attention network.

Author

Zheng, Hangbin, Dong, Zhixia, Liu, Tianyuan, Zheng, Hanyao, Wan, Xinjian, and Bao, Jinsong

Subjects
*ENDOSCOPIC ultrasonography, *GASTROINTESTINAL tumors, *GASTROINTESTINAL stromal tumors, *ULTRASONIC imaging, *FEATURE extraction, *DEEP learning
Abstract

Endoscopic ultrasonography (EUS) is a valuable imaging modality for diagnosing gastrointestinal submucosal tumors (SMTs). However, inherent content variations in EUS images due to gastrointestinal tract mobility and handheld ultrasound instability challenge the saliency of SMTs' visual features. The presence of fine-grained inter-class and large intra-class differences further complicates EUS-based diagnosis. To address these issues, this paper presents a novel Multi-Attribute Guided Contextual Attention Network (MAG-CA-Net) for interpretable SMT recognition in EUS. Inspired by endoscopists' clinical diagnosis expertise, our framework initially localizes abnormal areas based on echo attributes and subsequently determines tumor categories using contextual semantics. Experimental results demonstrate the effectiveness of MAG-CA-Net, exhibiting improved recognition recall and precision rates for gastrointestinal stromal tumor, leiomyoma, and pancreatic rest. Specifically, the MAG network facilitates abnormal area localization, while the CA mechanism guides the model to focus on the most discriminative tumor-context-associated regions. The proposed method achieved an average classification accuracy of 93.16%, an average precision of 93.17%, a weighted recall of 93.16%, and an average F1-score of 93.15 % for the three disease categories. The proposed approach provides crucial guidelines for data collection standards and model development in the clinical diagnosis process of SMTs under EUS. Its interpretability analysis enhances the credibility of clinical physicians towards assisted diagnostic methods based on deep learning. The source code will be publicly available at https://gitee.com/HangbinZheng/mag-ca-net. • Hierarchical identification strategy combining clinical knowledge and data-driven models for submucosal tumor detection. • Contextual attention mechanism enhances tumor recognition using gastrointestinal layered features. • Improved adaptability to endoscopic ultrasound image variation and fine-grained feature extraction compared to state-of-the-art models. • Visual interpretability close to experienced endoscopists enhances method's interpretability. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Viral sequence evolution in Chinese genotype 1b chronic hepatitis C patients experiencing unsuccessful interferon treatment

Author

Xiang, Xiaogang, Lu, Jie, Dong, Zhixia, Zhou, Huijuan, Tao, Wanyin, Guo, Qing, Zhou, Xiaqiu, Bao, Shisan, Xie, Qing, and Zhong, Jin

Subjects
*BIOLOGICAL evolution, *CHINESE people, *HEPATITIS C treatment, *INTERFERONS, *POLYMERASE chain reaction, *TRANSCRIPTION factors, *GENETIC mutation, *LIVER cancer, *RIBAVIRIN
Abstract

Abstract: The efficiencies of IFN-α based therapy in chronic genotype 1b HCV patients are still unsatisfied to date. The mechanisms underlining treatment failure remain unclear and controversial. To investigate HCV sequence evolution in unsuccessfully treated genotype 1b patients before, during and after the therapy, full-length open-reading-frame of HCV genomes at week 0, week 48 and year 5 in one breakthrough and one nonresponse patients were amplified by reverse transcription (RT)-nested-PCR and sequenced. Mutations were scored and analyzed according to their locations in the HCV genome. HCV sequences in the breakthrough patient displayed significantly more mutations during the one-year therapy than that in the nonresponse patient, with p7 and NS2 encoding regions having the highest mutation rates. Most of the mutations selected during the therapy phase in the breakthrough patient were maintained and few new mutations arose in the four-year post-therapy phase, suggesting these mutations might not compromise viral fitness. Altogether our data suggest that mutations occurred during the therapy phase in the breakthrough patient are likely driven by the action of interferon and ribavirin, and these mutations may have important effects on the responses to interferon based therapy in genotype 1b HCV patients. [Copyright &y& Elsevier]

Published
2011
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10. “Fatty” or “steatotic”: position statement from a linguistic perspective by the Chinese-speaking community

Author

Miao, Lei, Ye, Shu-Mian, Fan, Jian-Gao, Seto, Wai-Kay, Yu, Hon Ho, Yu, Ming-Lung, Kao, Jia-Horng, Boon-Bee Goh, George, Young, Dan Yock, Wong, Yu Jun, Chan, Wah-Kheong, Yang, Wah, Jia, Jidong, Lau, George, Wei, Lai, Shi, Junping, Zhang, Huijie, Bi, Yan, Pik-Shan Kong, Alice, Pan, Calvin Q., Zheng, Ming-Hua, Liang, Huiqing, Yang, Ling, Li, Xinhua, Zeng, Qing-Lei, Gao, Rong, Hu, Songhao, Yan, Bi, Jin, Xiaozhi, Li, Gang, Chen, En-Qiang, Hu, Dandan, Fan, Xiaotang, Hu, Peng, Chang, Xiangrong, Jin, Yihui, Cai, Yijing, Chen, Liangmiao, Wen, Qianjun, Sun, Jian, Xu, Hexiang, Li, Junfeng, Yang, Yongping, Huang, Ang, Zhang, Dongmei, Tan, Lin, Li, Dongdong, Zhu, Yueyong, Cai, Chenxi, Gu, Xuemei, Shen, Jilong, Zhong, Jianhong, Li, Lu, Li, Zhenzhen, Ma, Chiye, Liu, Yaming, Zhang, Yimin, Zhao, Lei, Han, Juqiang, Chen, Tao, Zhang, Qiang, Yang, Song, Zhang, Le, Chen, Lanlan, Feng, Gong, Wang, Qixia, Hao, Kunyan, Lu, Qinghua, Mao, Yimin, Zhong, Yandan, Wang, Ningjian, Xin, Yongning, Yu, Yongtao, Qi, Xingshun, Wang, Ke, He, Yingli, Du, Mulong, Zou, Zhengsheng, Xia, Mingfeng, Zhao, Suxian, Zhao, Jingjie, Xie, Wen, Zhang, Yao, Ji, Mao, Richeng, Du, Qingwei, Chen, Haitao, Song, Yongfeng, Wang, Cunchuan, Lu, Yan, Song, Yu, Zhang, Chi, Shi, Li, Mak, Lungyi, Chen, Li, Xu, Liang, Yuan, Hai-Yang, Hong, Liang, Hai, Li, Wu, Xiaoning, Yang, Naibin, Li, Jing-Wei, Jiejin, Zou, Zhuolin, Zheng, Wen, Zhao, Jian, Zhang, Xiang, Huang, Chen-Xiao, Yao, Ying, Yuan, Bao-Hong, Huang, Shanshan, Min, Lian, Chai, Jin, Hong, Wandong, Miao, Kai-Wen, Xiao, Tie, Chen, Shun-Ping, Ye, Feng, Song, Yuhu, Zhang, Jinshun, Zhou, Xiao-Dong, Wang, Mingwei, Dai, Kai, Lou, Jianjun, Duan, Xu, Yu, Hongyan, Jin, Xi, Fu, Liyun, Zhang, Yanliang, Ye, Junzhao, Liu, Feng, Chen, Qin-Fen, Zhou, Yong-Hai, Duan, Xiaohua, Zhang, Qun, Zhang, Faming, Cao, Zhujun, Li, Yingxu, Sun, Dan-Qin, Hu, Ai-Rong, Liu, Fenghua, Chen, Yuanwen, Zhang, Dianbao, Gao, Feng, Ye, Hua, Rao, Huiying, Luo, Kaizhong, Dai, Zhijuan, Wang, Chia-Chi, Tang, Shanhong, Hua, Jing, Deng, Cunliang, Zhou, Ling, Fan, Yu-Chen, Wu, Mingyue, Lu, Hongyan, Zhang, Xiaoxun, Zhang, Huai, Ni, Yan, Kei Ng, Stephen Ka, Li, Chunming, Liu, Chang, Zhang, Xia, Shi, Yu, Yan, Hongmei, Xu, Jinghang, Zhou, Yu-Jie, Cheng, Yuan, Bai, Honglian, Hu, Xiang, Gao, Yufeng, Lin, Biaoyang, Gu, Guangxiang, Chen, Jin, Hu, Xiaoli, Yuan, Xiwei, Wang, Jie, Chen, Qiang, Yiling, Li, Zhu, Xiao Jia, Chen, Xu, Zhu, Yongfen, Liu, Xiaolin, Wang, Bing, Cai, Mingyan, Chen, Enguang, Chen, Jun, Chen, Jingshe, Deng, Hong, Chen, Xiaoxin, Chen, Yingxiao, Cheng, Xinran, Chen, Fei, Ding, Yang, Dong, Zhixia, Ding, Yanhua, Qingxian, Cai, Deng, Zerun, Cai, Tingchen, Chen, Yaxi, Chen, Zhongwei, Chen, Xing, Huang, Jiaofeng, Huang, Mingxing, Fu, Lei, Jin, Jianhong, Geng, Bin, Chen, Yu, Chen, Ruicong, Jin, Weimin, Li, Dongliang, Jin, Xianghong, Li, Jian-Jun, Zhang, Jie, Matsiyit, Alimjan, Wang, Guiqi, Gao, Tian, Zhang, Shu, Yan, Wenmao, Liu, Jie, Chen, Peng, Hu, Hao, Li, Ming, Yuan, Ping Ge, Chen, Yi, Dong, Zhiyong, Li, Xiaopeng, Lin, Su, Li, Jie, Li Ang, Xujing, Liu, Xin, Liu, Shousheng, Li, Min-Dian, Qian, Hui, Qi, Minghua, Peng, Liang, Luo, Fei, Dang, Shuangsuo, Mao, Xianhua, Sheng, Qiyue, Lyu, Jiaojian, Liu, Chenghai, Qi, Kemin, Ma, Honglei, Lu, Zhonghua, Pan, Qiong, Miao, Qing, Li, Xiaosong, Lin, Huapeng, Shui, Guanghou, Qu, Shen, Fei, Wang, Liu, Chang-Hai, Xia, Fan, Wang, Dan, Pan, Ziyan, Hu, Fangzheng, Xu, Long, Xiong, Qing-Fang, Yang, Rui-Xu, Wang, Qi, Chen, Ligang, W Ang, Danny, Ren, Wanhua, Tong, Xiaofei, You, Ningning, Xing, Yanqing, Sun, Chao, Yu, Zhuo, Shuangxu, Xu, Honghai, Sun, Yi, Zhang, Taotao, Wu, Wei, Zhang, Yingmei, Ye, Qing, Zhang, Zhongheng, Yan, Jie, Zhou, Bengjie, Liu, Weiqiang, Li, Yongguo, Zhao, Lili, Lei, Siyi, Zhu, Guangqi, Ouyang, Huang, Zhou, Yaoyao, Yin, Jianhui, Xia, Yongsheng, He, Qiancheng, Zhang, Xiaoyong, Yang, Qiao, Yao, Libin, Pan, Xiazhen, Wang, Xiaodong, Li, Yangyang, Zhu, Shenghao, Zhao, Xinyan, Chen, Sui-Dan, Zhu, Jiansheng, Zeng, Jing, Tang, Liangjie, Hu, Kunpeng, Yang, Wanshui, Huang, Bingyuan, Zhuang, Chengle, Xun, Yunhao, Zhou, Jianghua, Xu, Wenjing, Wu, Bian, Zhang, Xuewu, He, Yong, Mei, Zubing, Xia, Zefeng, Lu, Bin Feng, Li, Qiang, Li, Jia, Yan, Xuebing, Wen, Zhengrong, Liu, Wenyue, Xu, Dongsheng, Chen, Huiting, Wang, Jing, Song, Juan, Peng, Jie, Chen, Jionghuang, Li, Shuchen, Zheng, Yongping, Zhi-Zhi, Xing, Tang, Jieting, Liu, Chuan, Chen, Chao, Guicheng, Wu, Ye, Quanzhong, Ka, Li, Zhou, Yuping, Jia, Xiaoli, Zou, Ziyuan, Zu, Fuqiang, Cai, Yongqian, Chen, Yunzhi, Chu, Jinguo, Yan, Bing, Wang, Tie, Pan, Qiuwei, Xie, Lingling, Zeng, Xufen, Liu, Bingrong, Su, Minghua, Mu, Yibing, Zeng, Menghua, Guo, Yuntong, Yang, Yongfeng, Zhang, Xiaoguan, Wu, Shike, Pan, Jin-Shui, Cao, Li, Feng, Wenhuan, Yubin, Yang, Wang, Na, Lu, Xiaolan, Lu, Guanhua, Xiong, Jianbo, Zhuang, Jianbin, Shi, Guojun, Zhu, Yanfei, Ying, Xing, Qiao, Zengpei, Zhang, Rui, Li, Yuting, Lei, Yuanli, Xixi, Wu, Tian, Na, Lian, Liyou, Zhang, Binbin, Xiaozhu, Huang, Yan, Chen, Wenying, Liu, Kun, Zhang, Ruinan, Lai, Qintao, Wang, Fudi, Wen, Caiyun, Zhang, Xinlei, Wu, Lili, Liang, Yaqin, Jie, You, Xinzhejin, Zeng, Qiqiang, Zhu, Qiang, Chao, Zheng, Shou, Lan, Jin, Wei-Lin, Ye, Chenhui, Han, Yu, Xie, Gangqiao, Zhao, Jing, Ye, Chunyan, Wang, Hua, Song, Lintao, Feng, Juan, Huang, Yubei, Su, Wen, Bai, Juli, Wong, Vincent, Wang, Huifeng, Ming, Wai-Kit, Yu, Yue-Cheng, Jin, Yan, Zhao, Yan, Gao, Lilian, Liangwang, Chen, Hanbin, Ruifangwang, Tang, Yuhan, Chen, Gang, Liu, Dabin, Cai, Xiaobo, Xue, Feng, Yang, Qinhe, Sun, Guangyong, Zhu, Chunxia, Huang, Zhifeng, Zhou, Hongwen, Xiao, Xiao, Hou, Xin, He, Jie, Ji, Dong, Xiao, Huanming, Chi, Xiaoling, Zou, Huaibin, Shi, Yiwen, Fan, Xingliang, Hu, Xiaoyu, Huang, Zhouqin, Cao, Haixia, Jiang, Jingjing, Zhao, Qiang, Chen, Wei, Li, Shi Bo, Zhang, Fan, Chen, Zhiyun, Liu, Jinfeng, Li, Shibo, Liu, Jing, Li, Li, Li, Ruyu, Kun, Ya, Xiao, ErHui, Wang, Tingyao, Wang, Chunjiong, Aili, Aikebaier, Liu, Xiaoxia, Ding, Ran, Zhu, Chonggui, Zeng, Xin, Wu, Miao, Li, Zhen, Yang, Tao, Qin, Yunfei, Sun, Lihua, Xu, Ying, Fu, Xianghui, and Li, Yongyin

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11. Sodium butyrate alleviates cholesterol gallstones by regulating bile acid metabolism.

Author

Ye, Xin, Shen, Shuang, Xu, Zhengjie, Zhuang, Qian, Xu, Jingxian, Wang, Jingjing, Dong, Zhixia, and Wan, Xinjian

Subjects
*BUTYRATES, *FARNESOID X receptor, *BILE acids, *SODIUM butyrate, *SHORT-chain fatty acids, *GALLSTONES, *BILE salts
Abstract

Cholesterol overloading and bile acid metabolic disorders play an important role in the onset of cholesterol gallstone (CGS). Short-chain fatty acids (SCFAs) can regulate bile acid metabolism by modulating the gut microbiota. However, the role and mechanism by which sodium butyrate (NaB) targets bile acids to attenuate CGS are still unknown. In this study, continuous administration of 12 mg/day for 8 weeks was decreased the incidence of gallstones induced by lithogenic diet (LD) from 100% to 25%. NaB modulated SCFAs and improved the gut microbiota. The remodeling of the gut microbiota changed the bile acid compositions and decreased cecal tauro-α-muricholic acid (T-α-MCA) and tauro-β-muricholic acid (T-β-MCA) which are effective farnesoid X receptor (FXR) antagonists. The quantitative real-time PCR examination showed that NaB significantly increased levels of ileal Fxr, fibroblast growth factor-15 (Fgf-15) and small heterodimer partner (Shp) mRNA and subsequently inhibited bile acid synthesis. In addition, NaB enhanced bile acid excretion by increasing the levels of hepatic multidrug resistance protein 2 (Mdr2) and bile salt export pump (Bsep) mRNA, and it enhanced bile acid reabsorption in the intestine by increasing the levels of ileal bile acid transporter (Ibat) mRNA. In addition, NaB reduced the absorption of cholesterol in the intestine and inhibited the excretion of cholesterol in the liver, which reduced the cholesterol concentration in serum and bile. Furthermore, the protective effects of NaB administration were abolished by FXR antagonists. Taken together, our results suggest that NaB mitigates CGS by modulating the gut microbiota to regulate the FXR-FGF-15/SHP signaling pathway. [ABSTRACT FROM AUTHOR]

Published
2021
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