👤 Shih-Ying Wu

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Also published as: Aimin Wu, Alexander T H Wu, Alice Ying-Jung Wu, An Guo Wu, An-Chih Wu, An-Dong Wu, An-Hua Wu, An-Li Wu, An-Xin Wu, Andong Wu, Anguo Wu, Anke Wu, Anna H Wu, Anping Wu, Anshi Wu, Anyi Wu, Anyue Wu, Anzhou Wu, B Wu, Baiyan Wu, Baochuan Wu, Baojian Wu, Baojin Wu, Baoqin Wu, Beier Wu, Beili Wu, Ben J Wu, Bian Wu, Biaoliang Wu, Bifeng Wu, Bill X Wu, Bin Wu, Binbin Wu, Bing Wu, Bing-Bing Wu, Bingjie Wu, Binxin Wu, Biwei Wu, Bo Wu, Boquan Wu, Buling Wu, C Wu, C-H Wu, Cai-Qin Wu, Caihong Wu, Caisheng Wu, Caiwen Wu, Catherine A Wu, Chang-Jiun Wu, Changchen Wu, Changjie Wu, Changjing Wu, Changwei Wu, Changxin Wu, Changyu Wu, Chao Wu, Chao-Liang Wu, Chaoling Wu, Chaowei Wu, Chen Wu, Chen-Lu Wu, Cheng Wu, Cheng-Chun Wu, Cheng-Hsin Wu, Cheng-Hua Wu, Cheng-Jang Wu, Cheng-Jun Wu, Cheng-Yang Wu, Chengbiao Wu, Chengqian Wu, Chengrong Wu, Chengwei Wu, Chengxi Wu, Chengyu Wu, Chenyang Wu, Chew-Wun Wu, Chi-Chung Wu, Chi-Hao Wu, Chi-Jen Wu, Chia-Chang Wu, Chia-Chen Wu, Chia-Ling Wu, Chia-Lung Wu, Chia-Zhen Wu, Chiao-En Wu, Chieh-Jen Wu, Chieh-Lin Stanley Wu, Chien-Sheng Wu, Chien-Ting Wu, Chih-Ching Wu, Chih-Chung Wu, Chih-Hsing Wu, Ching-Yi Wu, Cho-Kai Wu, Chong Wu, Chongming Wu, Choufei Wu, Chris Y Wu, Chuan-Ling Wu, Chuang Wu, Chuanhong Wu, Chun Wu, Chun-Chieh Wu, Chun-Hua Wu, Chunfu Wu, Chung-Yi Wu, Chunru Wu, Chunshuai Wu, Chunyan Wu, Colin Chih-Chien Wu, Colin O Wu, Cong Wu, Congying Wu, Constance Wu, Cuiling Wu, Cuiyan Wu, D I Wu, D P Wu, D Wu, Da-Hua Wu, Dai-Chao Wu, Dan Wu, Dan-Chun Wu, Dandan Wu, Danhong Wu, Danni Wu, Daoyuan Wu, Dapeng Wu, Daqing Wu, Daren Wu, David Wu, Daxian Wu, De Wu, De-Fu Wu, Deguang Wu, Dengying Wu, Depei Wu, Depeng Wu, Deqing Wu, Di Wu, Diana H Wu, Diana Wu, Dianqing Wu, Ding Lan Wu, Dirong Wu, Dishan Wu, Disheng Wu, Do-Bo Wu, Dong Wu, Dong-Bo Wu, Dong-Fang Wu, Dong-Feng Wu, Donglin Wu, Dongmei Wu, Dongping Wu, Dongsheng Wu, Dongyan Wu, Dongzhe Wu, Douglas C Wu, Duojiao Wu, Ed Xuekui Wu, Eugenia Wu, Fan Wu, Fanchang Wu, Fang Wu, Fang-Tzu Wu, Fangge Wu, Fanggeng Wu, Fei Wu, Fei-Fei Wu, Feifei Wu, Fenfang Wu, Feng Wu, Fengming Wu, Fengying Wu, Fong-Li Wu, G Wu, G X Wu, Gaige Wu, Gang Wu, Gaojun Wu, Ge-ru Wu, Gen Sheng Wu, Gen Wu, Geng-ze Wu, Geping Wu, Geting Wu, Geyan Wu, Grace F Wu, Guang-Bo Wu, Guang-Liang Wu, Guang-Long Wu, Guanggeng Wu, Guangjie Wu, Guangming Wu, Guangrun Wu, Guangsen Wu, Guangxi Wu, Guangxian Wu, Guangyan Wu, Guangzhen Wu, Guanhui Wu, Guanming Wu, Guanrong Wu, Guanxian Wu, Guanyi Wu, Guanzhao Wu, Guanzhong Wu, Gui-Qin Wu, Guifen Wu, Guifu Wu, Guihua Wu, Guiping Wu, Guixin Wu, Guizhen Wu, Guo-Chao Wu, Guofeng Wu, Guohao Wu, Guojun Wu, Guoli Wu, Guoping Wu, Guoqing Wu, Guorong Wu, Guoyao Wu, H J Wu, H Wu, Hai-Ping Wu, Hai-Yan Wu, Hai-Yin Wu, Haibin Wu, Haidong Wu, Haihu Wu, Haijiang Wu, Haijing Wu, Hailong Wu, Haiping Wu, Haishan Wu, Haisu Wu, Haiwei Wu, Haixia Wu, Haiyan Wu, Haiying Wu, Haiyun Wu, Han Wu, Han-Jie Wu, Hang Wu, Hanyu Wu, Hao Wu, Hao-Tian Wu, Haoan Wu, Haodi Wu, Haomin Wu, Haoming Wu, Haoxuan Wu, Haoze Wu, He Wu, Hei Man Wu, Hei-Man Wu, Hengyu Wu, Hon-Yen Wu, Hong Wu, Hong-Fu Wu, Hong-Mei Wu, Hongfei Wu, Hongfu Wu, Hongke Wu, Hongliang Wu, Honglin Wu, Hongmei Wu, Hongting Wu, Hongxi Wu, Hongxian Wu, Hongyan Wu, Hongyu Wu, Hsan-Au Wu, Hsi-Chin Wu, Hsien-Ming Wu, Hsing-Chieh Wu, Hsiu-Chuan Wu, Hsueh-Erh Wu, Hua Wu, Hua-Yu Wu, Huan Wu, Huanghui Wu, Huanlin Wu, Huanwen Wu, Huating Wu, Huazhang Wu, Huazhen Wu, Hui Wu, Hui-Chen Wu, Hui-Hui Wu, Hui-Mei Wu, Hui-Xuan Wu, Huijian Wu, Huijuan Wu, Huini Wu, Huisheng Wu, Huiwen Wu, Hung-Tsung Wu, I H Wu, Irene X Y Wu, J W Wu, J Wu, J Y Wu, J-Z Wu, Jamie L Y Wu, Jason H Y Wu, Jason Wu, Jemma X Wu, Jer-Yuan Wu, Jer-Yuarn Wu, Jerry Wu, Ji-Zhou Wu, Jia Wu, Jia-En Wu, Jia-Hui Wu, Jia-Jun Wu, Jia-Qi Wu, Jia-Wei Wu, Jiahang Wu, Jiahao Wu, Jiahui Wu, Jiajin Wu, Jiajing Wu, Jiake Wu, Jiamei Wu, Jian Hui Wu, Jian Wu, Jian-Lin Wu, Jian-Qiu Wu, Jian-Yi Wu, Jiang Wu, Jiang-Bo Wu, Jiang-Nan Wu, Jiangdong Wu, Jianguang Wu, Jiangyue Wu, Jianhui Wu, Jianing Wu, Jianjin Wu, Jianjun Wu, Jianli Wu, Jianliang Wu, Jianmin Wu, Jianming Wu, Jianping Wu, Jianqiang Wu, Jianrong Wu, Jianwu Wu, Jianxin Wu, Jianxiong Wu, Jianyi Wu, Jianying Wu, Jianzhang Wu, Jianzhi Wu, Jianzhong Wu, Jiao Wu, Jiapei Wu, Jiaqi Wu, Jiarui Wu, Jiawei Wu, Jiaxi Wu, Jiaxuan Wu, Jiayi Wu, Jiayu Wu, Jiayuan Wu, Jie Wu, JieQian Wu, Jiexi Wu, Jihui Wu, Jin Wu, Jin'en Wu, Jin-Shang Wu, Jin-Zhen Wu, Jin-hua Wu, Jincheng Wu, Jinfeng Wu, Jing Wu, Jing-Fang Wu, Jing-Wen Wu, Jinghong Wu, Jingjing Wu, Jingtao Wu, Jingwan Wu, Jingyi Wu, Jingyue Wu, Jingyun Wu, Jinhua Wu, Jinhui Wu, Jinjie Wu, Jinjun Wu, Jinmei Wu, Jinqiao Wu, Jinyu Wu, Jinze Wu, Jiong Wu, Jiu-Lin Wu, Joseph C Wu, Joshua L Wu, Ju Wu, Juan Wu, Juanjuan Wu, Juanli Wu, Jugang Wu, Julian Wu, Jun Wu, Jundong Wu, Junduo Wu, June K Wu, June-Hsieh Wu, Junfang Wu, Junfei Wu, Junfeng Wu, Junhua Wu, Junjie Wu, Junjing Wu, Junlong Wu, Junqi Wu, Junqing Wu, Junshu Wu, Junyi Wu, Junyong Wu, Junzheng Wu, Junzhu Wu, Justin C Y Wu, Justin Che-Yuen Wu, K D Wu, K S Wu, Kai-Hong Wu, Kai-Yue Wu, Kailang Wu, Kaili Wu, Kan Wu, Kay L H Wu, Ke Wu, Kebang Wu, Keija Wu, Kejia Wu, Kerui Wu, Kevin Zl Wu, Kuan-Li Wu, Kuen-Phon Wu, Kui Wu, Kuixian Wu, Kun Wu, Kun-Rong Wu, Kunfang Wu, Kunling Wu, Kunsheng Wu, L Wu, L-F Wu, Lai Man Natalie Wu, Lan Wu, Lanlan Wu, Lanxiang Wu, Lecheng Wu, Lei Wu, Leilei Wu, Lesley Wu, Leslie Wu, Li Wu, Li-Hsien Wu, Li-Jun Wu, Li-Ling Wu, Li-Na Wu, Li-Peng Wu, Liang Wu, Liang-Huan Wu, Liangyan Wu, Lianqian Wu, Lichao Wu, Lidi Wu, Lifang Wu, Lifeng Wu, Lihong Wu, Lijie Wu, Lijuan Wu, Lijun Wu, Lili Wu, Limei Wu, Limeng Wu, Lin Wu, Lin-Han Wu, Ling Wu, Ling-Fei Wu, Ling-Ying Wu, Ling-qian Wu, Lingling Wu, Lingqian Wu, Lingxi Wu, Lingxiang Wu, Lingyan Wu, Lingyun Wu, Lingzhi Wu, Linhong Wu, Linmei Wu, Lintao Wu, Linxiang Wu, Linyu Wu, Linzhen Wu, Linzhi Wu, Lipeng Wu, Liping Wu, Liqiang Wu, Liqun Wu, Liren Wu, Lisha Wu, Liting Wu, Litong Wu, Liufeng Wu, Liuting Wu, Liuxin Wu, Liuying Wu, Lixing Wu, Liyan Wu, Liyang Wu, Lizhen Wu, Lizi Wu, Long-Jun Wu, Longting Wu, Lorna Wu, Lulu Wu, Lun Wu, Lun-Gang Wu, Luyan Wu, M Wu, Ma Wu, Man Wu, Man-Jing Wu, Maoqing Wu, Mark N Wu, Matthew A Wu, Maureen Wu, Mei Wu, Mei-Hwan Wu, Mei-Na Wu, Meili Wu, Meina Wu, Meini Wu, Meiqi Wu, Meiqin Wu, Meng Wu, Meng-Chao Wu, Meng-Han Wu, Meng-Hsun Wu, Meng-Ling Wu, Meng-Na Wu, Mengbo Wu, Mengchao Wu, Mengjuan Wu, Mengjun Wu, Mengna Wu, Mengqiu Wu, Mengxue Wu, Mengying Wu, Mengyuan Wu, Mian Wu, Michael C Wu, Min Wu, Min-Jiao Wu, Ming J Wu, Ming Wu, Ming-Der Wu, Ming-Jiuan Wu, Ming-Shiang Wu, Ming-Sian Wu, Ming-Tao Wu, Ming-Yue Wu, Mingfu Wu, Minghua Wu, Mingjie Wu, Mingjun Wu, Mingming Wu, Mingxing Wu, Mingxuan Wu, Minna Wu, Minqing Wu, Minyao Wu, Moxin Wu, Muzhou Wu, N Wu, Na Wu, Na-Qiong Wu, Nan Wu, Nana Wu, Naqiong Wu, Ning Wu, Nini Wu, Niting Wu, P L Wu, Panyun Wu, Paul W Wu, Pei Wu, Pei-Ei Wu, Pei-Ting Wu, Pei-Wen Wu, Pei-Yu Wu, Peih-Shan Wu, Peiyao Wu, Peiyi Wu, Peng Wu, Peng-Fei Wu, Pengfei Wu, Pengjie Wu, Pengning Wu, Pensee Wu, Pin Wu, Ping Wu, Ping-Hsun Wu, Pinglian Wu, Pingxian Wu, Po-Chang Wu, Qi Wu, Qi-Biao Wu, Qi-Fang Wu, Qi-Jun Wu, Qi-Nian Wu, Qi-Yong Wu, Qi-Zhu Wu, Qian Wu, Qian-Yan Wu, Qiang Wu, Qianhu Wu, Qianqian Wu, Qianwen Wu, Qiao Wu, Qiaowei Wu, Qibiao Wu, Qibing Wu, Qihan Wu, Qijing Wu, Qin Wu, Qinan Wu, Qinfeng Wu, Qing Wu, Qing-Qian Wu, Qing-Wu Wu, Qinghua Wu, Qinglan Wu, Qinglin Wu, Qingping Wu, Qingshi Wu, Qinyi Wu, Qiong Wu, Qiqing Wu, Qitian Wu, Qiu Wu, Qiu-Li Wu, Qiuchen Wu, Qiuhong Wu, Qiuji Wu, Qiulian Wu, Qiuliang Wu, Qiuxia Wu, Qiuya Wu, Quanhui Wu, Qunzheng Wu, R M Wu, R Ryanne Wu, R Wu, R-J Wu, Ran Wu, Ray-Chin Wu, Re-Wen Wu, Ren Wu, Ren-Chin Wu, Renhai Wu, Renlv Wu, Renrong Wu, Riping Wu, Rong Wu, Ronghua Wu, Rongjie Wu, Rongling Wu, Rongrong Wu, Ru-Zi Wu, Rui Wu, Ruihong Wu, Ruize Wu, Run Wu, Runda Wu, Runpei Wu, Ruohao Wu, Ruolan Wu, Ruonan Wu, Ruying Wu, S F Wu, S J Wu, S L Wu, S M Wu, S Wu, S-F Wu, Sai Wu, Samuel M Wu, San-pin Wu, Sarah Wu, Sean M Wu, Selena Meiyun Wu, Selwin K Wu, Semon Wu, Sen-Chao Wu, Senquan Wu, Sensen Wu, Shao-Guo Wu, Shao-Ming Wu, Shaofei Wu, Shaohuan Wu, Shaojun Wu, Shaoping Wu, Shaoxuan Wu, Shaoyu Wu, Shaoze Wu, Sheng-Li Wu, Shengde Wu, Shengming Wu, Shengnan Wu, Shengru Wu, Shengxi Wu, Shenhao Wu, Shenyue Wu, Shi-Xin Wu, Shibo Wu, Shihao Wu, Shin-Long Wu, Shinan Wu, Shiqi Wu, Shiwen Wu, Shixin Wu, Shiya Wu, Shiyang Wu, Shu Wu, Shuai Wu, Shuang Wu, Shufang Wu, Shugeng Wu, Shuihua Wu, Shuisheng Wu, Shujuan Wu, Shunan Wu, Shuo Wu, Shusheng Wu, Shuting Wu, Shuyan Wu, Shuyi Wu, Shuying Wu, Shwu-Yuan Wu, Shyh-Jong Wu, Si-Jia Wu, Sichen Wu, Sihan Wu, Sihui Wu, Sijie Wu, Sijun Wu, Siming Wu, Siqi Wu, Siyi Wu, Siying Wu, Siyu Wu, Song Wu, Songfen Wu, Su Wu, Su-Hui Wu, Suhua Wu, Sunyi Wu, Szu-Hsien Wu, T Wu, Tangchun Wu, Tao Wu, Teng Wu, Terence Wu, Thomas D Wu, Tian Wu, Tiange Wu, Tianhao Wu, Tianqi Wu, Tiantian Wu, Tianwen Wu, Tianzhi Wu, Ting-Feng Wu, Ting-Ting Wu, Tingchun Wu, Tingqin Wu, Tingting Wu, Tong Wu, Tracy Wu, Tsai-Kun Wu, Tsung-Jui Wu, Tsung-Teh Wu, Tung-Ho Wu, Tzu-Chun Wu, V C Wu, W J Wu, W Wu, Wan-Fu Wu, Wanxia Wu, Wei Wu, Wei-Chi Wu, Wei-Ping Wu, Wei-Xun Wu, Wei-Yin Wu, Weibin Wu, Weida Wu, Weidong Wu, Weihua Wu, Weijie Wu, Weijun Wu, Weiwei Wu, Weizhen Wu, Wen Wu, Wen-Chieh Wu, Wen-Hui Wu, Wen-Jeng Wu, Wen-Juan Wu, Wen-Ling Wu, Wen-Qiang Wu, Wen-Sheng Wu, Wen-Shu Wu, Wenda Wu, Wendy Wu, Wenhui Wu, Wenjie Wu, Wenjing Wu, Wenjuan Wu, Wenjun Wu, Wenlin Wu, Wenqi Wu, Wenqian Wu, Wenqiang Wu, Wenwen Wu, Wenxian Wu, Wenxue Wu, Wenyi Wu, Wenyong Wu, Wenyu Wu, Wenze Wu, William K K Wu, William Ka Kei Wu, Wu-Tian Wu, Wudelehu Wu, Wujun Wu, Wutain Wu, Wutian Wu, Xi Wu, Xi-Chen Wu, Xi-Ze Wu, Xia Wu, Xiahui Wu, Xian-Run Wu, Xianan Wu, Xianfeng Wu, Xiangping Wu, Xiangsheng Wu, Xiangwei Wu, Xiangxin Wu, Xianpei Wu, Xiao Wu, Xiao-Cheng Wu, Xiao-Hui Wu, Xiao-Jin Wu, Xiao-Jun Wu, Xiao-Yan Wu, Xiao-Yang Wu, Xiao-Ye Wu, Xiao-Yuan Wu, Xiaobin Wu, Xiaobing Wu, Xiaodi Wu, Xiaodong Wu, Xiaofan Wu, Xiaofeng Wu, Xiaofu Wu, Xiaohong Wu, Xiaohui Wu, Xiaojiang Wu, Xiaojie Wu, Xiaojin Wu, Xiaojing Wu, Xiaojun Wu, Xiaokang Wu, Xiaoke Wu, Xiaolang Wu, Xiaoli Wu, Xiaoliang Wu, Xiaolin Wu, Xiaoling Wu, Xiaolong Wu, Xiaoman Wu, Xiaomei Wu, Xiaomeng Wu, Xiaomin Wu, Xiaoming Wu, Xiaoping Wu, Xiaoqian Wu, Xiaoqing Wu, Xiaoqiong Wu, Xiaorong Wu, Xiaoting Wu, Xiaotong Wu, Xiaoxing Wu, Xiaoyang Wu, Xiaoying Wu, Xiaoyong Wu, Xiaoyun Wu, Xiayin Wu, Xiexing Wu, Xifeng Wu, Xihai Wu, Xilin Wu, Xilong Wu, Ximei Wu, Xin Wu, Xin-Xi Wu, Xinchun Wu, Xing Wu, Xing-De Wu, Xing-Ping Wu, Xingdong Wu, Xinghua Wu, Xingjie Wu, Xinglong Wu, Xingwei Wu, Xinhe Wu, Xinjing Wu, Xinlei Wu, Xinmiao Wu, Xinran Wu, Xinrui Wu, Xinyan Wu, Xinyang Wu, Xinyi Wu, Xinyin Wu, Xiping Wu, Xiru Wu, Xiu-Zhi Wu, Xiuhua Wu, Xiushan Wu, Xiwei Wu, Xu Wu, Xuan Wu, Xuanqin Wu, Xuanshuang Wu, Xudong Wu, Xue Wu, Xue-Mei Wu, Xue-Yan Wu, Xuefen Wu, Xuefeng Wu, Xueji Wu, Xuekun Wu, Xueling Wu, Xuemei Wu, Xueqian Wu, Xueqing Wu, Xueyan Wu, Xueyao Wu, Xueying Wu, Xueyuan Wu, Xuhan Wu, Xunwei Wu, Xuxian Wu, Y H Wu, Y Q Wu, Y Wu, Y Y Wu, Y-W Wu, Ya Wu, Yadi Wu, Yafei Wu, Yajie Wu, Yalan Wu, Yali Wu, Yan Wu, Yan Yan Wu, Yan-Hua Wu, Yan-Jun Wu, Yan-ling Wu, Yanan Wu, Yanchuan Wu, Yanchun Wu, Yandi Wu, Yang Wu, Yangfeng Wu, Yangna Wu, Yangyu Wu, Yanhong Wu, Yanhua Wu, Yanhui Wu, Yanjing Wu, Yanli Wu, Yanqiong Wu, Yanran Wu, Yansheng Wu, Yanting Wu, Yanxiang Wu, Yanyan Wu, Yanzhi Wu, Yao Wu, Yaohong Wu, Yaohua Wu, Yaojiong Wu, Yaoxing Wu, Yaping Wu, Yaqin Wu, Yaru Wu, Yawei Wu, Yawen Wu, Ye Wu, Yen-Wen Wu, Yetong Wu, Yexiang Wu, Yi Wu, Yi-Cheng Wu, Yi-Fang Wu, Yi-Hua Wu, Yi-Long Wu, Yi-Mi Wu, Yi-Ming Wu, Yi-No Wu, Yi-Syuan Wu, Yi-Xia Wu, Yi-Ying Wu, Yibo Wu, Yichen Wu, Yicheng Wu, Yifan Wu, Yifeng Wu, Yih-Jer Wu, Yih-Ru Wu, Yihan Wu, Yihang Wu, Yihe Wu, Yihua Wu, Yihui Wu, Yijian Wu, Yili Wu, Yillin Wu, Yilong Wu, Yin Wu, Yinan Wu, Ying Wu, Ying-Ting Wu, Ying-Ying Wu, Yingbiao Wu, Yinghao Wu, Yingning Wu, Yingxia Wu, Yingying Wu, Yingzhi Wu, Yipeng Wu, Yiping Wu, Yiqun Wu, Yiran Wu, Yiting Wu, Yiwen Wu, Yixia Wu, Yixuan Wu, Yiyang Wu, Yiyi Wu, Yizhou Wu, Yong Wu, Yong-Hao Wu, Yong-Hong Wu, Yongfa Wu, Yongfei Wu, Yonghui Wu, Yongjiang Wu, Yongmei Wu, Yongqi Wu, Yongqun Wu, You Wu, Yu Wu, Yu'e Wu, Yu-Chih Wu, Yu-E Wu, Yu-Hsuan Wu, Yu-Ke Wu, Yu-Ling Wu, Yu-Ting Wu, Yu-Yuan Wu, Yuan Kai Wu, Yuan Wu, Yuan-de Wu, Yuanbing Wu, Yuanhao Wu, Yuanming Wu, Yuanshun Wu, Yuanyuan Wu, Yuanzhao Wu, Yucan Wu, Yuchen Wu, Yudan Wu, Yue Wu, Yueheng Wu, Yueling Wu, Yueming Wu, Yuen-Jung Wu, Yuesheng Wu, Yuetong Wu, Yuexiu Wu, Yuguang Philip Wu, Yuh-Lin Wu, Yuhong Wu, Yujie Wu, Yujuan Wu, Yukang Wu, Yulian Wu, Yuliang Wu, Yulin Wu, Yumei Wu, Yumin Wu, Yuming Wu, Yun Wu, Yun-Wen Wu, Yuna Wu, Yung-Fu Wu, Yunhua Wu, Yunpeng Wu, Yupeng Wu, Yuqin Wu, Yurong Wu, Yushun Wu, Yuting Wu, Yutong Wu, Yuwei Wu, Yuxian Wu, Yuxiang Wu, Yuxin Wu, Yuyi Wu, Yuyu Wu, Z Wu, Zaihao Wu, Ze Wu, Zelai Wu, Zeng-An Wu, Zhangjie Wu, Zhao-Bo Wu, Zhao-Yang Wu, Zhaofei Wu, Zhaoxia Wu, Zhaoyang Wu, Zhaoyi Wu, Zhaoyuan Wu, Zhe Wu, Zheming Wu, Zhen Wu, Zhen-Qi Wu, Zhen-Yang Wu, Zhenfang Wu, Zhenfeng Wu, Zheng Wu, Zhengcan Wu, Zhengfeng Wu, Zhengliang L Wu, Zhengsheng Wu, Zhenguo Wu, Zhengyu Wu, Zhengzhi Wu, Zhenling Wu, Zhenlong Wu, Zhentian Wu, Zhenyan Wu, Zhenyong Wu, Zhenzhen Wu, Zhenzhou Wu, Zhi-Hong Wu, Zhi-Wei Wu, Zhi-Yong Wu, Zhibing Wu, Zhichong Wu, Zhidan Wu, Zhihao Wu, Zhikang Wu, Zhimin Wu, Zhipeng Wu, Zhiping Wu, Zhiqiang Wu, Zhixiang Wu, Zhiye Wu, Zhong Wu, Zhong-Jun Wu, Zhong-Yan Wu, Zhongchan Wu, Zhonghui Wu, Zhongjun Wu, Zhongluan Wu, Zhongqiu Wu, Zhongren Wu, Zhongwei Wu, Zhongyang Wu, Zhou Wu, Zhou-Ming Wu, Zhourui Wu, Zhuanbin Wu, Zhuokai Wu, Zhuoze Wu, Zhuzhu Wu, Zijun Wu, Ziliang Wu, Zilong Wu, Zimu Wu, Zixiang Wu, Zixuan Wu, Zoe Wu, Zong-Jia Wu, Zongfu Wu, Zongheng Wu, Zujun Wu, Zuping Wu
articles
Chengxi Wu, Yaoyao Li, Yuting Liu +7 more · 2025 · International journal of nanomedicine · added 2026-04-24
In the microenvironment of atherosclerosis (AS), low-density lipoprotein (LDL) accumulates in injured endothelial areas and undergoes oxidation, thereby generating oxidized LDL (ox-LDL). The formation Show more
In the microenvironment of atherosclerosis (AS), low-density lipoprotein (LDL) accumulates in injured endothelial areas and undergoes oxidation, thereby generating oxidized LDL (ox-LDL). The formation of ox-LDL, in turn, not only amplifies endothelial cell (EC) dysfunction but also triggers macrophage polarization into the pro-inflammatory M1 phenotype. This cascade results in increased inflammatory cytokine secretion and exacerbated lipid accumulation. Therefore, a dual-targeting strategy aimed at both ECs and macrophages to inhibit the vicious circle between inflammation and lipids is a promising avenue for AS treatment. Simvastatin (SIM)-loaded nanomicelles (PLA-PEG/SIM) were prepared using the thin-film hydration method. Then, platelet membrane (PM) was coated the nanomicelles via sonication to obtain PM@PLA-PEG/SIM dual-targeting biomimetic nanoparticles. The morphological features of the nanoparticles were assessed by transmission electron microscopy (TEM). Cytotoxicity was evaluated using the CCK-8 assay and live/dead cell staining. Their targeting ability toward ECs and macrophages was assessed by flow cytometry and confocal laser scanning microscopy (CLSM). The biosafety, targeting ability, and therapeutic efficacy of PM@PLA-PEG/SIM against AS were further validated in ApoE PM@PLA-PEG/SIM effectively reduced the drug toxicity of SIM, exhibiting good biocompatibility. In vitro, cell experiment results showed that the nanoparticles inhibited foam cell formation, decreased interleukin-6 (IL-6) expression, and increased interleukin-4 (IL-4) and interleukin-10 (IL-10) expression by promoting macrophage repolarization. In vivo, results indicated that the formulation demonstrated excellent plaque-targeting ability. More importantly, the plaque area and lipid levels in the PM@PLA-PEG/SIM group were lowest, and plaques were most stable, showing its best therapeutic efficiency. PM@PLA-PEG/SIM alleviated progression of AS by co-targeting ECs and macrophages to inhibit the vicious cycle between inflammation and lipids. Our study provides a new strategy for the treatment of the disease by the co-targeting biomimetic nanoparticle. Show less
📄 PDF DOI: 10.2147/IJN.S558039
APOE
Ni Wang, Yanan Xu, Jiahui Li +7 more · 2025 · Journal of microbiology and biotechnology · added 2026-04-24
As a chronic lipid driven arterial disease, dyslipidemia is one of the most critical risk factors for atherosclerosis (AS). The gut microbiota plays an important role in regulating host lipid metaboli Show more
As a chronic lipid driven arterial disease, dyslipidemia is one of the most critical risk factors for atherosclerosis (AS). The gut microbiota plays an important role in regulating host lipid metabolism disorders. Studies have shown that the herb "Gualou-Xiebai" (GLXB) can effectively regulate the blood lipid levels of ApoE Show less
📄 PDF DOI: 10.4014/jmb.2510.10023
APOE
Boyang Zeng, Cong Ma, Shuaishuai Zhang +18 more · 2025 · Lipids in health and disease · BioMed Central · added 2026-04-24
Current evidence suggests that apolipoprotein E (APOE) is associated with lipid metabolism, cardiovascular diseases, and neurodegenerative disorders. However, the physiological pathways of APOE-mediat Show more
Current evidence suggests that apolipoprotein E (APOE) is associated with lipid metabolism, cardiovascular diseases, and neurodegenerative disorders. However, the physiological pathways of APOE-mediated inflammation remain incompletely elucidated, and a specific inflammatory marker that captures the pro-inflammatory activity of the APOE ε4 allele remains elusive. As a composite peripheral blood biomarker, Systemic immune-inflammation index (SII) is a novel marker of inflammation. This study aimed to investigate the association between APOE alleles and Systemic Immune-Inflammation Index. A total of 13,926 participants (9,098 males and 4,828 females) were recruited from The People’s Liberation Army General Hospital (November 2017 to July 2019). APOE alleles (ε2, ε3, and ε4) were determined by genotyping rs429358 and rs7412 SNPs. SII was calculated as (platelet count × neutrophil count)/lymphocyte count. Multivariable linear regression models (adjusted for demographics, lifestyle, and clinical covariates) and subgroup analyses were performed to assess the APOE-SII associations, with ε3 as the reference. The frequencies of APOE alleles ɛ3, ɛ2, and ɛ4 were70.7%, 13.8%, and 15.5% respectively in 13,926 Chinese patients. The mean SII was lower in ɛ2 carriers than in ɛ3 (373.74*10⁹/L vs. 403.53*10⁹/L, APOE contributes to elevated disease risk by inducing a state of chronic low-grade inflammation, resulting from modulation of both adaptive and innate immune responses. Show less
📄 PDF DOI: 10.1186/s12944-025-02842-w
APOE
Ke Tang, Ya Han, Dongqing Sun +11 more · 2025 · Genome medicine · BioMed Central · added 2026-04-24
Metabolic reprogramming is a hallmark of cancer; however, the mechanisms driving metabolic heterogeneity across diverse cell types in the tumor microenvironment remain poorly understood. Most existing Show more
Metabolic reprogramming is a hallmark of cancer; however, the mechanisms driving metabolic heterogeneity across diverse cell types in the tumor microenvironment remain poorly understood. Most existing methods predict metabolic states at the pathway level but rarely map reaction-level alterations to their upstream regulators, thereby constraining both interpretability and translational relevance. We developed MetroSCREEN, a reference-guided computational framework that infers reaction-level metabolic flux propensity and nominates upstream regulators from bulk and single-cell transcriptomes. MetroSCREEN uses a fast enrichment-based procedure to quantify reaction-level metabolic activity. To characterize metabolic regulons, it integrates intrinsic gene-regulatory signals with extrinsic cell-cell interaction cues, then applies a robust multi-evidence ranking scheme to combine these information sources, and finally employs a constraint-based causal discovery module to infer regulatory directionality. MetroSCREEN accurately predicts reaction-level metabolic activities and their upstream regulators, as demonstrated using paired transcriptomic-metabolomic datasets from the cancer cell lines. We further validated predicted regulators with in-house single-cell CRISPR screens in PC9 cells targeting metabolic regulators. Applying MetroSCREEN to a pan-cancer single-cell atlas of more than 700,000 fibroblasts and myeloid cells across 36 cancer types, we identified ZNF281 and STAT1 as key regulators of collagen metabolism, which is elevated in extracellular-matrix-associated fibroblasts and macrophages at tumor margins. By contrast, APOE and KLF7 regulate sphingolipid metabolism and antigen presentation in macrophages. Leveraging extensive tumor profiles, MetroSCREEN also delineates metabolic subtypes and regulators associated with patient survival and response to immunotherapy. MetroSCREEN is a robust and scalable approach for characterizing metabolic heterogeneity and pinpointing metabolic regulators at single-cell resolution, unveiling novel antitumor targets for future metabolic interventions. The source codes of MetroSCREEN is available at the Github site https://github.com/wanglabtongji/MetroSCREEN . Show less
📄 PDF DOI: 10.1186/s13073-025-01572-z
APOE
Shuai Huang, Jiawei Wu, Ling He · 2025 · Frontiers in neuroscience · Frontiers · added 2026-04-24
Apolipoprotein E (APOE) epsilon4 (ε4) is a major genetic risk factor for late-onset Alzheimer's disease (AD), with women exhibiting heightened vulnerability to APOE ε4-associated cognitive impairment. Show more
Apolipoprotein E (APOE) epsilon4 (ε4) is a major genetic risk factor for late-onset Alzheimer's disease (AD), with women exhibiting heightened vulnerability to APOE ε4-associated cognitive impairment. Despite recognition of this sexual dimorphism, the underlying biological mechanisms remain incompletely understood. We performed weighted gene co-expression network analysis (WGCNA) on RNA-seq data from the Mayo Clinic cohort ( Four co-expression modules ( we reveal a female-specific APOE ε4-driven molecular network linking endothelial dysfunction to tau pathology. These hub genes provide potential biomarkers, while vincamine represents a targeted prevention and therapeutic candidate for high-risk APOE ε4-positive women. Show less
📄 PDF DOI: 10.3389/fnins.2025.1683204
APOE
Tonnar Castellano, Ting Chen Wang, Emma Nolan +30 more · 2025 · Alzheimer's & dementia : the journal of the Alzheimer's Association · Wiley · added 2026-04-24
New methods estimate amyloid positivity onset age (EAOA) from amyloid positron emission tomography (PET). We explore the genetics of EAOA to identify molecular factors underlying the earliest Alzheime Show more
New methods estimate amyloid positivity onset age (EAOA) from amyloid positron emission tomography (PET). We explore the genetics of EAOA to identify molecular factors underlying the earliest Alzheimer's disease (AD) changes. Harmonized amyloid PET data from 4216 participants were used in genome-wide survival, tissue-specific gene expression, and genetic covariance analyses of EAOA. Variants in apolipoprotein E (APOE), ABCA7, and RASGEF1C associated with earlier EAOA. APOE ε4/ε4 and ε3/ε4 converted 6.3 and 5 years earlier than ε3/ε3, respectively. ε2 was protective against earlier EAOA. rs4147929, an expression quantitative trait locus for ABCA7, associated with a 4 year earlier EAOA. This variant was associated with lower brain expression of ABCA7, which was associated with increased amyloid pathology at autopsy. Multiple immune-related diseases shared genetic covariance with EAOA. APOE, ABCA7, and RASGEF1C associated with earlier EAOA, with supporting evidence from tissue-specific expression analyses, offering insights into intervenable targets at early stages of AD. Novel methods estimate how long ago a patient converted to amyloid positivity. Estimating this amyloid clock allows us to determine the onset of the earliest Alzheimer's disease changes. We evaluated what genes influence when someone converts to amyloid positivity. Apolipoprotein E (APOE), ABCA7, and RASGEF1C associated with earlier age of amyloid positivity. Genetic results were supported by tissue-specific expression analyses. Show less
📄 PDF DOI: 10.1002/alz.71006
APOE
Li Zhu, Jun Gao, Zijian Liu +2 more · 2025 · Nutrients · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/nu17233713
APOE
Lachlan Cribb, Margarita Moreno Betancur, Julia Sarant +13 more · 2025 · medRxiv : the preprint server for health sciences · Cold Spring Harbor Laboratory · added 2026-04-24
Promising evidence indicates that treating hearing loss with hearing aids (HAs) could reduce dementia risk. We extend this evidence by investigating the effect of HAs on plasma biomarkers of Alzheimer Show more
Promising evidence indicates that treating hearing loss with hearing aids (HAs) could reduce dementia risk. We extend this evidence by investigating the effect of HAs on plasma biomarkers of Alzheimer's disease and related dementias (ADRD). We emulated two target trials using observational data from Australian participants of the ASPREE study. Eligible participants had self-reported hearing problems, no past HA use, and were dementia-free. HA prescriptions and frequency of HA use were measured by questionnaire. Phosphorylated-tau181 (pTau181), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and amyloid-β (Aβ) 42/40 were measured after approximately 6-8 years. We estimated the effect of new HA prescription (first target trial) and the frequency of HA use (second target trial) using targeted maximum likelihood estimation, with multiple imputation for missing data. Across imputed datasets, a median of 2842 eligible individuals were included (mean age 75 years, 48% female), with a median of 735 receiving a new HA prescription. Among survivors, the estimated mean differences comparing HA prescription and no HA prescription were 1.8 pg/mL (95% CI: -0.6, 4.1), 0.1 pg/mL (-7.8, 8.0), -2.2 pg/mL (-14.5, 10.1), and -0.7 (-2.6, 1.2) for the concentrations of pTau181, NfL, GFAP, and (Aβ42 × 1000)/Aβ40, respectively. Mean differences did not differ substantially across levels of potential baseline effect modifiers, including APOE-ε4 genotype and cognition. In community-dwelling older people with hearing loss and no dementia, we found minimal effects of HA prescription and frequency of HA use on plasma ADRD biomarkers after a 7-year follow-up. Show less
📄 PDF DOI: 10.1101/2025.11.19.25340558
APOE
Jiayao Hu, Hao Liu, Yizhou Wu +5 more · 2025 · Journal of nanobiotechnology · BioMed Central · added 2026-04-24
The active ingredients of Traditional Chinese Medicine with diverse structures exhibited anti-inflammatory and lipid lowering functions, demonstrating significant therapeutic effects in inflammatory d Show more
The active ingredients of Traditional Chinese Medicine with diverse structures exhibited anti-inflammatory and lipid lowering functions, demonstrating significant therapeutic effects in inflammatory diseases of atherosclerosis. We incorporate Astaxanthin (AST) and Dihydroartemisinin (DHA) into PLGA NPs to synthesized HA@PLGA@AST/DHA NPs (HPAD NPs) for alleviating atherosclerosis. In vitro assay indicated that the designed HPAD NPs promoted cholesterol efflux of macrophages by enhancing selective lipophagy, which is benefit to lipid antigen degradation. Meanwhile, HPAD NPs regulated T-cell differentiation and crucially induced macrophages from pro-inflammatory M1 type to anti-inflammatory M2 type. In vivo study demonstrated that HPAD NPs decreased the necrotic core dimension and improved plaque stability in ApoE Show less
📄 PDF DOI: 10.1186/s12951-025-03830-z
APOE
Yue Wang, Wenxin Zhao, Leli Zhang +5 more · 2025 · Redox biology · Elsevier · added 2026-04-24
Rupture of vulnerable atherosclerotic plaques is a major cause of acute cardiovascular events. Vascular smooth muscle cell (VSMC) senescence promotes plaque vulnerability by impairing fibrous cap inte Show more
Rupture of vulnerable atherosclerotic plaques is a major cause of acute cardiovascular events. Vascular smooth muscle cell (VSMC) senescence promotes plaque vulnerability by impairing fibrous cap integrity. Although melatonin exhibits atheroprotective potential, its capacity to stabilize plaques by targeting VSMC senescence along with the underlying mechanisms, remains unclear. In this study, a vulnerable plaque model was established in ApoE Show less
📄 PDF DOI: 10.1016/j.redox.2025.103939
APOE
Xiaoguang Li, Ning Dou, Linshan Zhong +5 more · 2025 · BME frontiers · added 2026-04-24
📄 PDF DOI: 10.34133/bmef.0203
APOE
Shiqi Wu, Hening Li, Pintian Wang +1 more · 2025 · Medicine · added 2026-04-24
Natural killer (NK) cells are an integral component of the tumor microenvironment, and their role in immune checkpoint inhibitors (ICI) therapy has garnered increasing attention. However, comprehensiv Show more
Natural killer (NK) cells are an integral component of the tumor microenvironment, and their role in immune checkpoint inhibitors (ICI) therapy has garnered increasing attention. However, comprehensive studies on NK cells across cancers, especially their impact on immunotherapy response, remain limited. We used machine learning algorithms to establish a pan-cancer natural killer cell immunotherapy predictive model (NKCIPM) by combining single-cell RNA sequencing data from 164 samples across 6 cancer types and bulk RNA-seq data from different tumor samples. Tumor immune cell infiltration analysis, drug sensitivity analysis, and cell-cell communication were also further conducted. An upregulation of NK cell proportions post-immunotherapy and the identification of 188 NK cell differentially expressed genes were observed through single-cell RNA sequencing analysis. By integrating bulk RNA-seq data and applying machine learning algorithms, 7 key hub genes were identified, ultimately leading to the construction of NKCIPM, with APOE emerging as the most influential hub gene. Further analysis using the CIBERSORT algorithm revealed that the signature genes within this model were significantly associated with immune cell infiltration and response to ICI. Additionally, therapeutic evaluation of CHEK1 and CHEK2 targets demonstrated potential significance in the communication between B cells, NK cells, and mast cells within the context of ICI therapy. In summary, the NKCIPM model offers a valuable tool for predicting immunotherapy outcomes and informing clinical decision-making, highlighting the potential of NK cell signature genes as therapeutic targets. Show less
📄 PDF DOI: 10.1097/MD.0000000000045753
APOE
Chen Yao, Geng Wang, Quanhui Wu +6 more · 2025 · Medicine · added 2026-04-24
Aortic dissection (AD) involves complex interactions among amino acid, glucose, and lipid metabolism, exacerbating aortic inflammation and extracellular matrix (ECM) degradation, coupled with smooth m Show more
Aortic dissection (AD) involves complex interactions among amino acid, glucose, and lipid metabolism, exacerbating aortic inflammation and extracellular matrix (ECM) degradation, coupled with smooth muscle cell (SMC) dysfunction (phenotypic alteration, aging, apoptosis). To explore AD pathogenesis, we integrated single-cell RNA sequencing (scRNA-seq), metabolomics, machine learning, and Mendelian randomization to investigate SMC changes and gene-metabolite interactions. ScRNA-seq data (GSE213740, GSE155468) were analyzed for cell clustering and pseudo-time trajectories via Seurat and Monocle2. Metabolomics (9 samples: 6 AD, 3 controls) and machine learning validated key genes/metabolites, with Mendelian randomization assessing causal links. Nine cell subsets and 2000 variable genes were identified, with SMCs central to AD via cholesterol metabolism. APOE and PLTP were key genes; metabolomics highlighted cholesterol esters (CEs) and triglycerides (TGs) as critical metabolites. Machine learning confirmed APOE/PLTP's high predictive accuracy (AUC: 0.796-0.989). Mendelian randomization linked elevated CEs and TGs to increased AD risk (IVW: P = .04 and P = .02, respectively). This study establishes a gene-metabolite network where APOE and PLTP regulate CEs/TGs, influencing SMC function and AD progression, offering potential therapeutic targets. Show less
📄 PDF DOI: 10.1097/MD.0000000000045846
APOE
Tsung-Jui Wu, Yi-Cheng Wang, Chia-Wen Lu +2 more · 2025 · Antioxidants (Basel, Switzerland) · MDPI · added 2026-04-24
Vascular calcification (VC) is a multifactorial pathological deposition of calcium in the vasculature and is associated with severe cardiovascular outcomes, particularly in patients with chronic kidne Show more
Vascular calcification (VC) is a multifactorial pathological deposition of calcium in the vasculature and is associated with severe cardiovascular outcomes, particularly in patients with chronic kidney disease (CKD). Various vitamin K analogs have been found to influence the development of VC. We utilized a high-phosphate-induced VC model in mouse vascular smooth muscle cells (VSMCs) and developed an in vivo VC model using ApoE Show less
📄 PDF DOI: 10.3390/antiox14111328
APOE
Hangfei Liang, Fanghong Zheng, Jincheng Wu +5 more · 2025 · Cell death & disease · Nature · added 2026-04-24
Axin1 plays a critical role in regulating the Wnt/β-catenin signaling pathway and cancer progression, and its polymerization is indispensable for the assembly of the β-catenin destruction complex. How Show more
Axin1 plays a critical role in regulating the Wnt/β-catenin signaling pathway and cancer progression, and its polymerization is indispensable for the assembly of the β-catenin destruction complex. However, the mechanisms that control Axin1 polymerization are limited. Here, we reveal that TRIM15 interferes with the polymerization of Axin1, thereby promoting Wnt activation and colorectal cancer growth. Mechanistically, TRIM15 strongly interacts with Axin1 through its coiled-coil domain to disrupt the polymerization among Axin1 molecules. Manipulation of TRIM15 expression dramatically weakens Wnt signaling, cell proliferation, and tumor growth. Furthermore, conditional genetic ablation of Trim15 in mice inhibits tumor formation in both AOM/DSS-induced and Apc Show less
📄 PDF DOI: 10.1038/s41419-025-08400-7
AXIN1
Junkang Zhao, Jiannan Han, Xiuying Fan +7 more · 2025 · Mediators of inflammation · added 2026-04-24
Evidence is accumulating that links gut microbiota, a crucial component of the immune environment, to Sjogren's syndrome (SS). The mechanisms underlying the influence of gut microbiota on the onset an Show more
Evidence is accumulating that links gut microbiota, a crucial component of the immune environment, to Sjogren's syndrome (SS). The mechanisms underlying the influence of gut microbiota on the onset and development of SS are still not completely understood. To this end, we applied a Mendelian randomization (MR) framework to investigate whether inflammatory cytokines mediate the association of gut microbiota with SS. Our MR analysis leveraged publicly available GWAS data, including information on 211 gut microbiota taxa sourced from the MiBioGen consortium (18,340 participants), summary statistics for 91 inflammatory cytokines obtained from a study of 14,824 individuals, and genetic data for SS derived from the UK Biobank (407,746 participants). To investigate causal associations between gut microbiota and SS, we primarily employed the inverse variance weighted method, supported by additional techniques such as MR-Egger, simple mode, weighted median, and weighted mode for validation. The potential mediating effect of inflammatory cytokines in the gut microbiota-SS relationship was investigated using both mediation MR and multivariable MR (MVMR) analyses. MR analysis identified five microbiota taxa causally associated with SS. Particularly, class Gammaproteobacteria (OR = 3.468, 95% CI = 1.139-10.557, The findings suggest that certain gut microbiota is sociated with an increased risk of SS, mediated by specific inflammatory cytokines. Show less
📄 PDF DOI: 10.1155/mi/1951493
AXIN1
Jian Wu · 2025 · Urolithiasis · Springer · added 2026-04-24
Previous observational studies have highlighted a significant link between dyslipidemia and kidney stones. However, whether plasma lipid composition directly influences kidney stone formation and the Show more
Previous observational studies have highlighted a significant link between dyslipidemia and kidney stones. However, whether plasma lipid composition directly influences kidney stone formation and the extent to which inflammatory proteins mediate this relationship remain uncertain. This study utilizes genetic variation data from the recent genome-wide association studies to analyze 179 plasma lipids and 91 inflammatory proteins in relation to kidney stones. By applying a two-sample Mendelian randomization (MR) approach, we systematically investigated the potential causal effects of plasma lipids on kidney stones and assessed the mediating role of inflammatory proteins through a two-stage MR analysis. The findings revealed that specific phosphatidylcholines (PC) (including PC(14:0₁₈:1), PC(16:0₂₀:2), PC(16:1₁₈:0), and PC(18:0₁₈:3)) exhibited positive causal associations with kidney stone risk, while sterol esters (27:1/18:0) demonstrated stone risk-reducing effects. Among inflammatory proteins, monocyte chemoattractant protein 2 and tumor necrosis factor ligand superfamily member 14 (TNFSF14) were associated with increased kidney stone risk, whereas Axin-1, macrophage colony-stimulating factor 1, C-X-C motif chemokine 10, interleukin-5, and urokinase-type plasminogen activator (uPA) correlated with reduced risk. Further mediation analysis revealed that TNFSF14 and uPA may serve as mediators in the relationship between the plasma lipidome and kidney stone formation. This study provides insights into the mechanisms by which plasma lipid metabolism influences kidney stone development through inflammatory regulatory networks. These findings lay a theoretical foundation for lipidomics- and inflammation-based biomarker risk prediction, as well as targeted intervention strategies for kidney stone prevention. Show less
📄 PDF DOI: 10.1007/s00240-025-01905-y
AXIN1
Jinyu Bai, Xueli Qiu, Huajian Shan +10 more · 2025 · Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · Oxford University Press · added 2026-04-24
The Wnt/β-catenin signaling pathway is a classical pathway that regulates bone metabolism. The G protein inhibitory α subunits 1 and 3 (Gαi1/3) can couple with multiple growth factor/cytokine receptor Show more
The Wnt/β-catenin signaling pathway is a classical pathway that regulates bone metabolism. The G protein inhibitory α subunits 1 and 3 (Gαi1/3) can couple with multiple growth factor/cytokine receptors and act as universal adaptor proteins to mediate the activation of key downstream signaling pathways. However, it remains unclear whether and how Gαi1/3 proteins mediate Wnt/β-catenin signal transduction. In this study, we utilized single-cell sequencing analysis and employed viral transfection and gene editing techniques to alter the expression of Gαi1/3 in mouse embryonic osteoblast precursor cells. We examined the relationship between Gαi1/3 expression and the Wnt/β-catenin signaling pathway. Immunoprecipitation and confocal experiments were conducted to further explore the mechanisms by which Gαi1/3 exerts its functions. Osteogenic-related protein levels were detected by Western blotting, and the effects of Gαi1/3 proteins on osteogenic function were examined through alkaline phosphatase and Alizarin red staining. Additionally, micro-CT was used to compare bone mass in mice with different levels of Gαi1/3 expression, showing the relationship between Gαi1/3 and bone formation. Our findings indicate that Gαi1/3 proteins are significantly inversely correlated with age. Gαi1/3, rather than Gαi2, mediates the Wnt/β-catenin signaling pathway and promotes osteogenesis. Mechanistically, Gαi1/3 interacts with Axin1 and recruits it to the cell membrane, leading to inactivation of the β-catenin degradation complex. This results in β-catenin accumulation and nuclear translocation, where it activates the transcription of osteogenic genes. In vivo experiments further confirm that knockdown of Gαi1/3 significantly inhibits bone formation in mice. Our study identified Gαi1/3 as key regulatory proteins in Wnt/β-catenin signaling-mediated osteogenesis, and further elucidated its molecular mechanism in bone formation, which may provide a new therapeutic target for osteoporosis. Show less
no PDF DOI: 10.1093/jbmr/zjaf143
AXIN1
Li Niu, Yubo Li, Hao Wu +7 more · 2025 · Journal of Alzheimer's disease reports · SAGE Publications · added 2026-04-24
Neuroinflammation represents a central pathological mechanism in Alzheimer's disease (AD). Lipopolysaccharide (LPS) is a potent inducer of neuroinflammation and demonstrates elevated circulating level Show more
Neuroinflammation represents a central pathological mechanism in Alzheimer's disease (AD). Lipopolysaccharide (LPS) is a potent inducer of neuroinflammation and demonstrates elevated circulating levels in AD patients. This study aims to investigate the genetic association between serum LPS activity level, inflammatory proteins and AD. A two-sample mendelian randomization (MR) analysis was performed to explore the causal effect of serum LPS activity level and 91 inflammatory proteins on AD, including 1, 260, 136 sporadic AD and 2, 838, 825 familial AD patients, respectively. Meta-analysis was conducted on multiple datasets to determine statistically significant results that was initially observed in one dataset. Serum LPS activity level is a risk factor for early onset sporadic AD with OR = 1.392, 95% CI: 1.038-1.869. In most other sporadic AD datasets, LPS shows a trend of increasing the risk of AD onset. After meta-analysis in 10 independent datasets, no association between LPS and sporadic AD was observed. In most familial AD datasets, LPS level demonstrated a trend of decreasing AD risk in MR analysis, however, meta-analysis of the combined 8 datasets showed no statistically significant difference. Two inflammatory proteins, AXIN1 and IL-1 alpha, were identified as significant risk factors for sporadic AD. This study suggested that serum LPS activity level may present a risk effect in early onset sporadic AD. Two inflammatory proteins AXIN1 and IL-1 alpha were associated with the risk of sporadic AD. These findings provide a new perspective for the early diagnosis and treatment of sporadic and familial AD. Show less
📄 PDF DOI: 10.1177/25424823251385589
AXIN1
Hongyan Qian, Min Tang, Tianqi Wu +7 more · 2025 · Laboratory investigation; a journal of technical methods and pathology · Elsevier · added 2026-04-24
Cervical cancer (CC) remains a major global health challenge, with radiotherapy resistance (RR) representing a critical impediment to treatment efficacy. This study investigated the underlying mechani Show more
Cervical cancer (CC) remains a major global health challenge, with radiotherapy resistance (RR) representing a critical impediment to treatment efficacy. This study investigated the underlying mechanisms of replication stress (RS) in RR and identified potential therapeutic targets for CC. A comprehensive bioinformatics workflow was applied to analyze the expression profiles and prognostic significance of RS-related differentially expressed genes (RSRDs) in patients with RR. The prognostic utility of an RS-based risk score model was subsequently evaluated in the context of the tumor microenvironment, somatic mutation landscape, etc. The clinical relevance of the identified hub RSRDs was validated through immunohistochemistry, univariate and multivariate Cox regression analyses, and a prognostic nomogram using data from a real-world patient cohort. Functional assays conducted both in vitro and in vivo further confirmed the role of the key RSRD. Thus, enrichment analysis of the 124 common differentially expressed genes showed RS-related biological processes were enriched. The RS risk score model, constructed using 2 hub RSRDs (AXIN1 and C-terminal binding protein 1) identified through Least Absolute Shrinkage and Selection Operator (LASSO) regression, showed strong diagnostic and prognostic performance. Enrichment analysis showed the risk score model influenced CC prognosis by tumor microenvironment and mutation, etc. Immunohistochemistry analysis of tissue microarrays explored a significant downregulation of AXIN1 in RR samples. AXIN1 was also an independent prognosis biomarker for CC patients, particularly among patients receiving radiotherapy. Knockdown of AXIN1 significantly inhibited the radiosensitivity in CC cell lines, and in vivo experiments showed AXIN1 knockdown led to increased tumor volume following radiotherapy. Molecular docking analysis illustrated JQ1 may promote AXIN1 expression. This study is the first to identify AXIN1 as a replication stress-associated gene with prognostic value in CC, specifically in the context of radiotherapy. These findings may support personalized treatment strategies and provide a foundation for future investigations into RS-targeted therapies in CC. Show less
no PDF DOI: 10.1016/j.labinv.2025.104244
AXIN1
Joseph K McKenna, Yalan Wu, Praveen Sonkusre +3 more · 2025 · PLoS genetics · PLOS · added 2026-04-24
WNT/β-catenin signaling is mediated by the transcriptional coactivator β-catenin (CTNNB1). CTNNB1 abundance is regulated by phosphorylation and proteasomal degradation, promoted by a destruction compl Show more
WNT/β-catenin signaling is mediated by the transcriptional coactivator β-catenin (CTNNB1). CTNNB1 abundance is regulated by phosphorylation and proteasomal degradation, promoted by a destruction complex composed of the scaffold proteins APC and AXIN1 or AXIN2, and the kinases casein kinase 1α (CSNK1A1) and GSK3A or GSK3B. Loss of CSNK1A1 increases CTNNB1 abundance, resulting in hyperactive WNT signaling. Previously, we demonstrated that the HECT domain E3 ubiquitin ligase HUWE1 is necessary for hyperactive WNT signaling in HAP1 haploid human cells lacking CSNK1A1. Here, we investigated the mechanism underlying this requirement. In HAP1 cells lacking CSNK1A1, GSK3A/GSK3B still phosphorylated a fraction of CTNNB1, promoting its degradation. HUWE1 loss enhanced GSK3A/GSK3B-dependent CTNNB1 phosphorylation, further reducing CTNNB1 abundance. However, the reduction in CTNNB1 caused by HUWE1 loss was smaller than the reduction in WNT target gene transcription. To test whether the reduction in WNT signaling caused by HUWE1 loss resulted from reduced CTNNB1 alone, we engineered the endogenous CTNNB1 locus in HAP1 cells to encode a CTNNB1 variant insensitive to destruction complex-mediated phosphorylation and degradation. HUWE1 loss in these cells did not change CTNNB1 abundance but still reduced WNT signaling, demonstrating that another mechanism was at play. Genetic interaction and overexpression analyses revealed that the reduction in WNT signaling caused by HUWE1 loss required not only GSK3A or GSK3B, but also APC and AXIN1. Therefore, in HAP1 cells lacking CSNK1A1, a residual destruction complex containing APC, AXIN1 and GSK3A or GSK3B downregulates WNT signaling by phosphorylating and targeting CTNNB1 for degradation, and HUWE1 enhances WNT signaling by antagonizing this activity. Regulation of WNT signaling by HUWE1 also requires its ubiquitin ligase activity. We conclude that HUWE1 enhances WNT/CTNNB1 signaling through two mechanisms, one that antagonizes destruction complex-mediated CTNNB1 degradation and another that is independent of changes in CTNNB1 abundance. Coordinated regulation of CTNNB1 abundance and a second signaling step by HUWE1 would be an efficient way to control WNT signaling output, enabling sensitive and robust activation of the pathway. Show less
📄 PDF DOI: 10.1371/journal.pgen.1011677
AXIN1
Yixi Wang, Zhuokai Wu, Yiheng Zheng +3 more · 2025 · Journal of Alzheimer's disease : JAD · SAGE Publications · added 2026-04-24
BackgroundPrevious studies with limited sample sizes have indicated a link between mitochondrial traits, inflammatory proteins, and Alzheimer's disease. The exact causality and their mediation relatio Show more
BackgroundPrevious studies with limited sample sizes have indicated a link between mitochondrial traits, inflammatory proteins, and Alzheimer's disease. The exact causality and their mediation relationships remain unclear.ObjectiveOur study aimed to delve into the genetic underpinnings of mitochondrial function and circulating inflammatory proteins in the pathogenesis of Alzheimer's disease.MethodsWe leveraged aggregated data from the largest genome-wide association study, including 69 mitochondrial traits, 91 circulating inflammatory proteins, and Alzheimer's disease. Bidirectional mendelian randomization (MR) analyses were performed to investigate their primary causal relationships. Thereafter a two-step MR mediation analysis was utilized to clarify the modulating effects of inflammatory proteins on mitochondria and Alzheimer's disease.ResultsOur study identified mitochondrial phenylalanine-tRNA ligase and 4-hydroxy-2-oxoglutarate aldolase as risk factors for Alzheimer's disease, and serine protease HtrA2 and carbonic anhydrase 5A as protective factors against Alzheimer's disease. Four inflammatory proteins (T-cell surface glycoprotein CD5, C-X-C motif chemokine 11, TGF-α, and TNF-related apoptosis-inducing ligand) played protective roles against Alzheimer's disease. Axin-1 and IL-6 increased the risk of Alzheimer's disease. Furthermore, T-cell surface glycoprotein CD5 was found to be a significant mediator between mitochondrial serine protease HTRA2 and Alzheimer's disease with the two-step MR method, accounting for 10.83% of the total effect.ConclusionsOur study emphasized mitochondrial HtrA2-T cell CD5 as a negative axis in Alzheimer's disease, offering novel perspectives on its etiology, pathogenesis, and treatment. Show less
no PDF DOI: 10.1177/13872877251329517
AXIN1
Chengfang Tang, Chu Tang, Xuanchi Zhu +9 more · 2025 · British journal of pharmacology · Blackwell Publishing · added 2026-04-24
As a highly heterogeneous cancer, hepatocellular carcinoma (HCC) shows different response rates to the multi-kinase inhibitor lenvatinib. Thus, it is important to explore genetic biomarkers for precis Show more
As a highly heterogeneous cancer, hepatocellular carcinoma (HCC) shows different response rates to the multi-kinase inhibitor lenvatinib. Thus, it is important to explore genetic biomarkers for precision lenvatinib therapy in HCC. The effect and mechanism of AXIN1 mutation on HCC were revealed by cell proliferation assay, long-term clone formation assay, sphere formation assay and small molecule inhibitor library screening. A new therapeutic strategy targeting HCC with AXIN1 mutation was evaluated in humanized models (patient-derived xenograft [PDX] and patient-derived organoid [PDO]). Based on The Cancer Genome Atlas (TCGA) data, we screened 6 most frequently lost tumour suppressor genes in HCC (TP53, ARID1A, AXIN1, CDKN2A, ARID2 and PTEN) and identified AXIN1 as the most crucial gene for lenvatinib sensitivity. Further study showed that AXIN1-knockout HCC cells had a more malignant phenotype and lower sensitivity to lenvatinib in vitro and in vivo. Mechanistically, the WNT pathway and its target gene c-Myc were activated when AXIN1 was missing, and the expression of tumour suppressor p15 was inhibited by transcription co-repressors c-Myc and Miz-1, resulting in the exacerbation of the resistant phenotype. Screening of a library of epigenetic-related enzyme inhibitors showed that a KDM5B inhibitor up-regulated p15 expression, leading to increased sensitivity to lenvatinib in vitro and in vivo. AXIN1-deficient patients have a lower response to lenvatinib, which may be associated with suppression of p15 mediated by WNT pathway activation. KDM5B inhibitors can restore p15 levels, resulting in efficient killing of resistant cells in HCC. Show less
no PDF DOI: 10.1111/bph.17413
AXIN1
Jin Li, Jiawen Wang, Yaodong Li +7 more · 2025 · Biology · MDPI · added 2026-04-24
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia, with current therapies offering only limited symptomatic relief and lacking disease-modifying ef Show more
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia, with current therapies offering only limited symptomatic relief and lacking disease-modifying efficacy. Addressing this critical therapeutic gap, natural multi-target compounds like mulberroside A (MsA)-a bioactive glycoside from Show less
📄 PDF DOI: 10.3390/biology14091114
BACE1
Nan Wang, Xin-Zhu Li, Xiao-Wen Jiang +10 more · 2025 · Molecular neurobiology · Springer · added 2026-04-24
no PDF DOI: 10.1007/s12035-025-05265-x
BACE1
Nan Wang, Wenjie Liu, Lijun Zhou +11 more · 2025 · ACS omega · ACS Publications · added 2026-04-24
[This retracts the article DOI: 10.1021/acsomega.2c03368.].
📄 PDF DOI: 10.1021/acsomega.5c06137
BACE1
Ruoping Yanzhang, Zhaojie Yang, Xiangping Li +5 more · 2025 · Discover oncology · Springer · added 2026-04-24
Osteosarcoma (OS) is an invasive and lethal malignancy showing a low 5 year survival rate, underscoring the need for identifying new therapeutic targets and their inhibitors to enhance prevention and Show more
Osteosarcoma (OS) is an invasive and lethal malignancy showing a low 5 year survival rate, underscoring the need for identifying new therapeutic targets and their inhibitors to enhance prevention and treatment strategies. In this study, in vitro experiments including CCK-8 assay, anchorage-independent growth assays, and plate cloning assays were used to detect the anti-proliferation ability of natural compound tangeretin towards OS cells. An integrated approach was performed including WGCNA and network pharmacology to identify the key genes of tangeretin for the treatment of OS. Multigene diagnostic model, reverse transcription quantitative polymerase chain reaction (RT-qPCR) analysis along with molecular docking analysis were further conducted to validate the reliability of the targets obtained by bioinformatics methods. Single-cell and gene enrichment analyses were chosen to explore the mechanism of tangeretin in OS. Hub genes identified by the bioinformatics strategy included ABCC1, AKR1C3, BACE1, and CA12. RT-qPCR validation and molecular docking analysis confirmed that ABCC1 and BACE1 were the most likely potential targets. A multigene diagnostic model for OS demonstrated moderate accuracy of the hub genes. Single-cell sequencing results indicated that these two hub targets were closely related to OS and provided more potential mechanisms for targeting OS. Our research highlights the therapeutic potential of the natural compound tangeretin and its antineoplastic mechanisms in OS. It offers new insights into the molecular mechanisms of tangeretin, paving the way for the development of effective OS treatments. Show less
📄 PDF DOI: 10.1007/s12672-025-03221-8
BACE1
Lei Xia, Junjie Li, Yayan Pang +12 more · 2025 · Science advances · Science · added 2026-04-24
β-Site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1) is the rate-limiting enzyme for amyloid-β (Aβ) generation and is considered promising drug target for Alzheimer's disease (AD). The co- Show more
β-Site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1) is the rate-limiting enzyme for amyloid-β (Aβ) generation and is considered promising drug target for Alzheimer's disease (AD). The co-chaperone BAG3 (Bcl-2-associated athanogene 3) plays an important role in maintaining intracellular protein homeostasis by regulating heat shock protein 70 (HSP70). Here, we reported that BAG3 expression was significantly elevated in AD. It interacted with and stabilized BACE1 by delaying its degradation through ubiquitin-proteasome and autophagy-lysosomal pathways. BAG3 Show less
📄 PDF DOI: 10.1126/sciadv.adt7981
BACE1
Yun Zhang, Huaqiu Chen, Yijia Feng +14 more · 2025 · Nature aging · Nature · added 2026-04-24
Individuals with type 2 diabetes mellitus have an increased risk of developing Alzheimer's disease (AD). GLP-1 receptor agonists (GLP-1RAs) are used for glycemic control in diabetes and show potential Show more
Individuals with type 2 diabetes mellitus have an increased risk of developing Alzheimer's disease (AD). GLP-1 receptor agonists (GLP-1RAs) are used for glycemic control in diabetes and show potential neuroprotective properties, but their effects on AD and the underlying mechanisms are not well understood. Here we demonstrate that GLP-1RAs can alleviate AD-related phenotypes by activating 5' AMP-activated protein kinase (AMPK) signaling. We found that plasma GLP-1 levels were decreased in AD model mice and negatively correlated with amyloid-beta (Aβ) load in patients with AD. Enhancing GLP-1 signaling through GLP-1RAs increased CaMKK2-AMPK signaling, which subsequently reduced BACE1-mediated cleavage of amyloid precursor protein (APP) and Aβ generation. GLP-1RAs also increased AMPK activity in microglia, inhibiting neuroinflammation and promoting Aβ phagocytosis. Consequently, GLP-1RAs inhibited plaque formation and improved memory deficits in AD model mice. Our findings indicate that AMPK activation mediates the effects of GLP-1RAs on AD, highlighting the therapeutic potential of GLP-1RAs for the treatment of AD. Show less
📄 PDF DOI: 10.1038/s43587-025-00869-3
BACE1
Nan Wang, Xin-Zhu Li, Xiao-Wen Jiang +10 more · 2025 · Molecular neurobiology · Springer · added 2026-04-24
Alzheimer's disease (AD) is a multifactorial neuropathology characterized by the accumulation of amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs) and cholinergic system dysfunction. At presen Show more
Alzheimer's disease (AD) is a multifactorial neuropathology characterized by the accumulation of amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs) and cholinergic system dysfunction. At present, there is no effective treatment strategy for AD. Our previous research showed that ZJQ-3F acts as an inhibitor of AChE/BACE1/GSK3β, and showed good blood-brain barrier permeability, appropriate bioavailability and oral safety. In order to further study, the protective effect of ZJQ-3F on APP/PS1/Tau transgenic mice was determined. APP/PS1/Tau transgenic mice model of AD was treated with ZJQ-3F from the age of 8 to 12 months, and then behavioral tests was conducted. Western blot, immunohistochemistry and immunofluorescence staining were used to evaluate the level of tau protein, Aβ plaques and synaptic function. Our results revealed that administration of ZJQ-3F could improve the cognitive function of APP/PS1/Tau transgenic mice. In addition, compared with APP/PS1/Tau mice, the protein expression levels of tau protein phosphorylation site at Ser396, Thr212 and Thr181 in the cortex and hippocampus of ZJQ-3F treated mice was significantly decreased. Moreover, the results showed that ZJQ-3F significantly reduced the deposition of Aβ in the cortex and hippocampus. Furthermore, the results indicated that the protein expression levels of PSD95, SYP and SYT in the cortex and hippocampus were increased markedly after ZJQ-3F was given. Our studies suggest that the chronic administration of ZJQ-3F can improve learning and memory ability, reduce tau protein phosphorylation, reduce Aβ deposition and improve synaptic dysfunction in APP/PS1/Tau transgenic model of AD, indicating that ZJQ-3F can be used as a multi-target inhibitor to slow down the progress of AD. Show less
📄 PDF DOI: 10.1007/s12035-025-04982-7
BACE1