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Your search keyword '"Xu, Huiting"' showing total 426 results
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426 results on '"Xu, Huiting"'

1. First-line cadonilimab plus chemotherapy in HER2-negative advanced gastric or gastroesophageal junction adenocarcinoma: a randomized, double-blind, phase 3 trial

Author

Shen, Lin, Zhang, Yanqiao, Li, Ziyu, Zhang, Xiaotian, Gao, Xiangyu, Liu, Bo, Wang, Yusheng, Ba, Yi, Li, Ning, Zhang, Ruixing, Zhang, Jingdong, Chen, Ye, Chen, Jian, Huang, Mingzhu, Fu, Yang, Liu, Mulin, Liu, Zheng, Zhao, Jun, Li, Wei, Wei, Jia, Li, Changzheng, Xu, Nong, Guo, Zengqing, Cao, Bangwei, Liu, Lian, Nie, Peng, Wan, Lixin, Sheng, Lili, Liu, Zhenyang, He, Yifu, Gu, Kangsheng, Wu, Guowu, Wang, Weibo, Zhang, Futong, Qiu, Wensheng, Guo, Jun, Ying, Jieer, Pan, Hongming, Xu, Huiting, Yuan, Yuan, Bai, Yuansong, Wang, Zhenghua, Xu, Jiye, Zhao, Xuehong, Liu, Hao, Zhang, Xizhi, Dai, Wenxiang, Xu, Hongyan, Liu, Ming, Xie, Lin, Tang, Yong, Jin, Jianying, Qu, Xiujuan, Fang, Xuefeng, Huang, Mingwei, Chen, Hao, Zheng, Zhendong, Wang, Ying, Wang, Daqing, Li, Xiaoqin, Yu, Guohua, Liu, Haiyan, Zhou, Yongjian, Zhong, Diansheng, Zeng, Shan, Kang, Mafei, Wang, Meiqing, Gao, Yong, Li, Wenxin, Wang, Zejun, Zhang, Minghui, Zhang, Jinghua, Li, Qingshan, Sun, Shujuan, Zang, Aimin, Lin, Lizhu, Xie, Ming, Zhuang, Zhixiang, Zhang, Tao, Yao, Zhifang, Lu, Dongmei, Liu, Wei, Hu, Mingxiu, Wang, Zhongmin Maxwell, Li, Baiyong, Xia, Michelle, Zhang, Jiajia, Ying, Xiangji, Pardoll, Drew M., and Ji, Jiafu

Published
2025
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3. Characteristics and patient-reported outcomes of long-term cancer survivors after apatinib-based therapy: an online survey

Author

Zhang, Tingting, Meng, Chao, He, Wei, Xu, Tao, Yang, Yi, Tu, Chongqi, Zhang, Ling, Sun, Xiaofeng, Zhu, Chunrong, Dang, Xueyi, Wang, Ke, Chen, Chuan, Yan, Xiong, Xu, Huiting, Huang, Le, Jiang, Enlai, Xia, Feng, Zhou, Xinming, Zhou, Shunkai, Zang, Weidong, Li, Xifeng, Zhang, Jin, Zheng, Jiaping, Xin, Jianjun, Huang, Bin, Zhu, Guopei, Zhu, Jiexiang, and Liang, Jun

Published
2024
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5. Towards a Better Model with Dual Transformer for Drug Response Prediction

Author

Li, Kun, Wu, Jia, Du, Bo, Petoukhov, Sergey V., Xu, Huiting, Xiao, Zheman, and Hu, Wenbin

Subjects
Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

GNN-based methods have achieved excellent results as a mainstream task in drug response prediction tasks in recent years. Traditional GNN methods use only the atoms in a drug molecule as nodes to obtain the representation of the molecular graph through node information passing, whereas the method using the transformer can only extract information about the nodes. However, the covalent bonding and chirality of a drug molecule have a great influence on the pharmacological properties of the molecule, and these information are implied in the chemical bonds formed by the edges between the atoms. In addition, CNN methods for modelling cell lines genomics sequences can only perceive local rather than global information about the sequence. In order to solve the above problems, we propose the decoupled dual transformer structure with edge embedded for drug respond prediction (TransEDRP), which is used for the representation of cell line genomics and drug respectively. For the drug branch, we encoded the chemical bond information within the molecule as the embedding of the edge in the molecular graph, extracted the global structural and biochemical information of the drug molecule using graph transformer. For the branch of cell lines genomics, we use the multi-headed attention mechanism to globally represent the genomics sequence. Finally, the drug and genomics branches are fused to predict IC50 values through the transformer layer and the fully connected layer, which two branches are different modalities. Extensive experiments have shown that our method is better than the current mainstream approach in all evaluation indicators., Comment: 28 pages, 4 figures, 5 tables

Published
2022

50. Pharmacokinetics of infigratinib and its active metabolites in Chinese patients with advanced gastric cancer harboring FGFR2 gene amplification.

Author

Yuan, Jiajia, Shen, Lin, Liu, Tian Shu, Xu, HuiTing, Yang, Jianwei, Wei, Jia, Jiang, Haiping, Deng, Yanhong, Pan, Hongming, Wang, Yusheng, Zhang, Xiaotian, Peng, Zhi, Qi, Changsong, Zhang, Lingli, Hsu, Peiwen, Song, Lin, Mu, Lei, Sun, Qiao, Gong, Jifang, and Lyu, Cheng

Subjects
ESOPHAGOGASTRIC junction, CHINESE people, GENE amplification, CANCER patients, PROTEIN-tyrosine kinase inhibitors
Abstract

Infigratinib, an FGFR1‐3 selective oral tyrosine kinase inhibitor, has shown clinical activity in cancers with FGFR alterations. The pharmacokinetics (PK) of infigratinib and its major metabolites have been characterized in global populations. This study examined the PK profile of infigratinib and its metabolites in Chinese patients. In this phase II, open‐label, single‐arm study in China, patients with advanced gastric cancer (GC) or gastroesophageal junction adenocarcinoma (GEJ) harboring FGFR2 gene amplification received 125 mg infigratinib orally once daily in a "3 weeks on, 1 week off" schedule for 28‐day cycles. Plasma PK parameters were calculated with a non‐compartmental model. Data were available from 21 patients (19 GC and two GEJ). After a single dose, peak infigratinib plasma concentration was reached at a median time of 3.1 h, with geometric mean Cmax of 85.9 ng/mL and AUC0‐t of 637 h*ng/mL. After 21‐day dosing, geometric mean infigratinib Cmax,ss of 204 ng/mL was reached at a median of 4.0 h; geometric mean AUC0‐24,ss was 3060 h*ng/mL. The geometric mean Rac,Cmax (%CV) and Rac,AUC0‐24 (%CV) of infigratinib was 2.5 (113.8) and 5.1 (138.2), respectively. A steady state of infigratinib was reached after continuous dosing for 15 days. The metabolites accounting for >10% of infigratinib were BHS697 and CQM157. The PK profiles of infigratinib and its metabolites in Chinese patients with GC or GEJ were largely consistent with known PK profiles of infigratinib from global populations. [ABSTRACT FROM AUTHOR]

Published
2024
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