👤 Riping Wu

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Also published as: Jiake Wu, Ming-Jiuan Wu, Yijian Wu, Siying Wu, Fong-Li Wu, Chih-Chung Wu, Jin'en Wu, Zixiang Wu, D P Wu, Zhongwei Wu, Haiping Wu, Geyan Wu, Qi-Zhu Wu, Jianjin Wu, Su Wu, Shwu-Yuan Wu, Xiaodi Wu, Changxin Wu, Kuen-Phon Wu, Guofeng Wu, Zhiping Wu, Xiaojun Wu, Qibing Wu, Cheng-Hsin Wu, Junhua Wu, Xiaoting Wu, Wenze Wu, Yandi Wu, Hong Wu, Zhong Wu, An-Chih Wu, Jianhui Wu, Xiaoke Wu, Zhenguo Wu, Jason H Y Wu, Selena Meiyun Wu, Yi-Mi Wu, Bing-Bing Wu, M Wu, Hui-Mei Wu, Danni Wu, Minqing Wu, Sijie Wu, Geng-ze Wu, Kun Wu, Cheng-Hua Wu, Zhaoyang Wu, Shaofei Wu, Qihan Wu, Kunling Wu, R Ryanne Wu, Hao Wu, Mingxuan Wu, Pei Wu, Wendy Wu, Yukang Wu, Douglas C Wu, Jingtao Wu, Guizhen Wu, Zhangjie Wu, Lili Wu, Jianwu Wu, Min-Jiao Wu, Biaoliang Wu, Huan Wu, Shengxi Wu, Fei-Fei Wu, Peih-Shan Wu, Guoqing Wu, Yu-Yuan Wu, Pei-Yu Wu, Jing Wu, Geting Wu, Lun-Gang Wu, Dongzhe Wu, G Wu, Junlong Wu, Jia-Jun Wu, Jiangyue Wu, Muzhou Wu, Junzhu Wu, Ray-Chin Wu, Jian-Qiu Wu, T Wu, Jianxiong Wu, Liping Wu, Haiwei Wu, Guoping Wu, Yong-Hao Wu, Jin-hua Wu, Yi Wu, Chongming Wu, You Wu, Xudong Wu, Qunzheng Wu, Liqiang Wu, Cuiling Wu, Kunfang Wu, Bian Wu, Limeng Wu, Jason Wu, Shuying Wu, Zhibing Wu, Caihong Wu, Naqiong Wu, Joseph C Wu, Huating Wu, Tianhao Wu, Zhi-Hong Wu, Congying Wu, Gaojun Wu, Chiao-En Wu, Dongping Wu, Li Wu, Yihang Wu, Haixia Wu, Shaoxuan Wu, Gen Wu, Fanchang Wu, Xiaorong Wu, Mei Wu, Mingjie Wu, Jiahao Wu, Jiapei Wu, Lingqian Wu, Jia Wu, Fangge Wu, Yanhui Wu, Sen-Chao Wu, Zhiqiang Wu, Shugeng Wu, Sarah Wu, Dongmei Wu, Xuanqin Wu, Caiwen Wu, Junjing Wu, Jiangdong Wu, Guihua Wu, Meini Wu, Yingbiao Wu, Rui Wu, Hua-Yu Wu, Bifeng Wu, Jingwan Wu, Lingling Wu, Junzheng Wu, Xinmiao Wu, Yi-Fang Wu, Yuyi Wu, Qinglin Wu, Yixuan Wu, Leilei Wu, Bin Wu, Tianqi Wu, Hui-Chen Wu, Shiya Wu, Jian Wu, Sijun Wu, Cong Wu, Yiwen Wu, Feng Wu, Xi-Ze Wu, Qiuji Wu, Alexander T H Wu, Semon Wu, Qinan Wu, Lai Man Natalie Wu, Zhuokai Wu, Ran Wu, Panyun Wu, Kui Wu, Yumei Wu, Xinrui Wu, Yueling Wu, Biwei Wu, Xing Wu, Jiayi Wu, Hua Wu, Yuen-Jung Wu, Bingjie Wu, Xiaoliang Wu, Matthew A Wu, Jin Wu, Juanjuan Wu, Qiuhong Wu, Hongfu Wu, Xiaoming Wu, Ming-Sian Wu, Ronghua Wu, Junduo Wu, Dandan Wu, Ming-Shiang Wu, Yuliang Wu, Ying-Ying Wu, Chaoling Wu, Guang-Liang Wu, De Wu, Yihua Wu, Yuanyuan Wu, Tsung-Jui Wu, Yulian Wu, Han Wu, Lipeng Wu, Zhihao Wu, Jiexi Wu, Anna H Wu, Huazhen Wu, Qiu Wu, Yaqin Wu, Shengru Wu, Chieh-Lin Stanley Wu, Xiaoqian Wu, Xiahui Wu, Jianli Wu, Yun-Wen Wu, Jian-Yi Wu, Qiuya Wu, Tsai-Kun Wu, Xinyin Wu, Guoyao Wu, Zhenfeng Wu, Guoli Wu, Bill X Wu, J W Wu, Zujun Wu, Jianliang Wu, Yuanshun Wu, Ling-Ying Wu, Zeng-An Wu, Jianrong Wu, Xue Wu, Ke Wu, Cheng-Yang Wu, Mengxue Wu, Jinghong Wu, Rongrong Wu, Ruolan Wu, Rong Wu, Kevin Zl Wu, Run Wu, Xiaohong Wu, Zaihao Wu, Chaowei Wu, Yu-Ke Wu, Xinjing Wu, Anyue Wu, Yun Wu, Meili Wu, Shu Wu, Wanxia Wu, Xuan Wu, Yi-No Wu, Chao-Liang Wu, Chengwei Wu, Y-W Wu, Pensee Wu, Zhao-Bo Wu, Guangxian Wu, Xiao Wu, Juanli Wu, Xinlei Wu, Changjie Wu, Sai Wu, Jiawei Wu, Yujuan Wu, Haoze Wu, Renlv Wu, Xiaoyang Wu, Yipeng Wu, Yuh-Lin Wu, Yu'e Wu, Dan-Chun Wu, An-Hua Wu, Meng-Chao Wu, Yuanhao Wu, Jer-Yuarn Wu, Qian-Yan Wu, Guangyan Wu, Huisheng Wu, Shuting Wu, Huijuan Wu, Long-Jun Wu, Alice Ying-Jung Wu, Xiru Wu, Zhenfang Wu, Lidi Wu, Yetong Wu, Disheng Wu, Linmei Wu, Huiwen Wu, Zhenzhou Wu, Yuhong Wu, Liang Wu, Liyan Wu, Kuan-Li Wu, Pei-Ting Wu, Xiao-Jin Wu, Lifeng Wu, Terence Wu, Shujuan Wu, Gang Wu, Xue-Mei Wu, Szu-Hsien Wu, Yan-ling Wu, Xiaokang Wu, Lingyan Wu, Yih-Jer Wu, Xinghua Wu, Chunfu Wu, Yingxia Wu, Rongling Wu, Xifeng Wu, Jinhua Wu, Sihan Wu, Ming-Yue Wu, Shiyang Wu, K D Wu, Jinmei Wu, Luyan Wu, Shin-Long Wu, Shuai Wu, Zhipeng Wu, Guangzhen Wu, Zhixiang Wu, Longting Wu, Zhengsheng Wu, Xiaoqiong Wu, Yaoxing Wu, Yuqin Wu, Yudan Wu, Zoe Wu, Hongting Wu, Chi-Jen Wu, R Wu, Zhongqiu Wu, Meina Wu, Dengying Wu, Anke Wu, Cheng-Jang Wu, Hsi-Chin Wu, Shufang Wu, Yongjiang Wu, Yuan-de Wu, Sihui Wu, Qi Wu, Fenfang Wu, Wenhui Wu, K S Wu, Nana Wu, Jianzhi Wu, Lin-Han Wu, Zhen Wu, Jinjun Wu, Chen-Lu Wu, Jing-Fang Wu, Haiyan Wu, Yihui Wu, Qiqing Wu, Dai-Chao Wu, Zhengzhi Wu, Zhenyan Wu, Wen-Jeng Wu, Sean M Wu, Guanming Wu, Yongqun Wu, Hei-Man Wu, Su-Hui Wu, Diana H Wu, Ben J Wu, Pingxian Wu, Chew-Wun Wu, Yillin Wu, Xiaobing Wu, Jiang-Bo Wu, Jerry Wu, Siming Wu, Zijun Wu, Daqing Wu, Yu-Hsuan Wu, Lichao Wu, Zhimin Wu, Qijing Wu, Daxian Wu, Zhaoyi Wu, Z Wu, Tong Wu, Cheng-Chun Wu, Tracy Wu, Shusheng Wu, D Wu, Ting-Ting Wu, Xiao-Yan Wu, Lan Wu, J Wu, Changchen Wu, Qi-Fang Wu, Changwei Wu, Liangyan Wu, Liufeng Wu, Kan Wu, Eugenia Wu, Mingming Wu, Xiaolong Wu, Chunru Wu, Zhaofei Wu, Shenhao Wu, Li-Peng Wu, Yuna Wu, Minna Wu, Justin Che-Yuen Wu, Buling Wu, Wutian Wu, Chengyu Wu, Yuwei Wu, Guixin Wu, Haijing Wu, Hei Man Wu, Qiuchen Wu, Xiao-Hui Wu, Junfei Wu, Xiaofeng Wu, Wenda Wu, Linyu Wu, Yung-Fu Wu, Mengbo Wu, Zhenling Wu, Maoqing Wu, Zuping Wu, Julian Wu, Chun-Chieh Wu, Binbin Wu, Xiaohui Wu, Qian Wu, Xinchun Wu, Shuisheng Wu, Linxiang Wu, Xueqing Wu, Bo Wu, Moxin Wu, Xiao-Cheng Wu, Anzhou Wu, Shuyi Wu, Jiahui Wu, Meiqin Wu, Shihao Wu, Jer-Yuan Wu, Wen-Shu Wu, Wudelehu Wu, Ruonan Wu, Song Wu, Yulin Wu, De-Fu Wu, Hongyu Wu, Yurong Wu, Zixuan Wu, Shih-Ying Wu, Chih-Hsing Wu, Chengrong Wu, Yinghao Wu, Yuanzhao Wu, Wenjie Wu, Baochuan Wu, Ziliang Wu, Liuting Wu, Chia-Ling Wu, Y Q Wu, Man Wu, Na Wu, Wutain Wu, Chenyang Wu, Jinyu Wu, Selwin K Wu, Ping Wu, Lorna Wu, D I Wu, Yi-Cheng Wu, Jianzhong Wu, Xiaoyun Wu, Zhourui Wu, Li-Jun Wu, Xinhe Wu, Zhi-Wei Wu, Yinan Wu, Xinyan Wu, Xin Wu, Ting-Feng Wu, Yawei Wu, Shixin Wu, Hong-Mei Wu, Xiaojin Wu, Yiqun Wu, Tsung-Teh Wu, Jiarui Wu, Qi-Nian Wu, Ju Wu, Kai-Yue Wu, Pengjie Wu, Xi-Chen Wu, Zhe Wu, Shaoping Wu, Zhou Wu, Han-Jie Wu, Haijiang Wu, Weijie Wu, Xiaojie Wu, Hongfei Wu, Yi-Ying Wu, Zhentian Wu, Ze Wu, Kai-Hong Wu, Yuting Wu, Minyao Wu, Xueyan Wu, Feifei Wu, Shinan Wu, Yonghui Wu, Haoxuan Wu, Yanzhi Wu, Yiyi Wu, Dong Wu, Guohao Wu, Shibo Wu, Wenjing Wu, Wenqian Wu, Tian Wu, Tiantian Wu, Hai-Yan Wu, Chong Wu, Hongxian Wu, Daoyuan Wu, Zongfu Wu, Ling Wu, Yuxiang Wu, Xilong Wu, Yuyu Wu, Huijian Wu, Zong-Jia Wu, Fengming Wu, Guorong Wu, Chuanhong Wu, Choufei Wu, Chi-Chung Wu, Junfang Wu, Xingwei Wu, Ling-Fei Wu, Xiaoqing Wu, Xinyang Wu, Xiaomin Wu, Yili Wu, Hong-Fu Wu, Shao-Ming Wu, Thomas D Wu, Lizhen Wu, Yuanming Wu, Hsien-Ming Wu, Jian Hui Wu, Litong Wu, Yuxian Wu, Weihua Wu, Lei Wu, C Wu, Wei Wu, Yu-E Wu, Qiulian Wu, Mei-Hwan Wu, Yuexiu Wu, Shaoze Wu, Zilong Wu, Chi-Hao Wu, Baojin Wu, Chao Wu, Yao Wu, Ya Wu, Do-Bo Wu, Wenjun Wu, Zhongren Wu, Nini Wu, Michael C Wu, Ning Wu, Jie Wu, Ming J Wu, Yi-Syuan Wu, Zhenzhen Wu, Limei Wu, Tianwen Wu, Wen-Chieh Wu, Yunhua Wu, Junfeng Wu, Shunan Wu, Junqi Wu, Jianing Wu, Honglin Wu, Maureen Wu, Yexiang Wu, Yan-Hua Wu, Mengjun Wu, Y H Wu, Mingxing Wu, Liuying Wu, Suhua Wu, Xiaomeng Wu, Shyh-Jong Wu, Tung-Ho Wu, Wenxian Wu, Hongliang Wu, Ed Xuekui Wu, Xuekun Wu, Wenqiang Wu, Chuang Wu, Jingyi Wu, Duojiao Wu, Xueyuan Wu, Ji-Zhou Wu, Lianqian Wu, Gaige Wu, Qing-Qian Wu, Haihu Wu, Xiushan Wu, Xueyao Wu, Tingchun Wu, Yafei Wu, Lingxi Wu, R-J Wu, Weidong Wu, Re-Wen Wu, Zhidan Wu, Peiyao Wu, Xuemei Wu, Chen Wu, Yiting Wu, Kerui Wu, Lihong Wu, Shiqi Wu, Liren Wu, Xiuhua Wu, Beili Wu, Yongqi Wu, Ruihong Wu, Huini Wu, Lingyun Wu, Guang-Long Wu, Po-Chang Wu, Wenxue Wu, Qinghua Wu, Ru-Zi Wu, Wenlin Wu, Changjing Wu, Xiexing Wu, J Y Wu, Jianping Wu, Guanggeng Wu, W J Wu, Zhichong Wu, Di Wu, Shaoyu Wu, Xiaotong Wu, Junyong Wu, Hui Wu, Shengde Wu, Hongyan Wu, Mengyuan Wu, Yutong Wu, Zheming Wu, Yiping Wu, Guiping Wu, Wen-Hui Wu, Dapeng Wu, Bing Wu, Wen-Sheng Wu, Yunpeng Wu, Li-Ling Wu, Xiao-Yuan Wu, Qiu-Li Wu, Baiyan Wu, Xiao-Ye Wu, Ying Wu, Da-Hua Wu, Hsing-Chieh Wu, Hui-Xuan Wu, Chieh-Jen Wu, Pengning Wu, Sichen Wu, S F Wu, Mengying Wu, Jia-En Wu, Ming-Der Wu, Weida Wu, Qi-Jun Wu, Guo-Chao Wu, Zhenyong Wu, Qi-Biao Wu, Yangfeng Wu, Lijie Wu, Zhiye Wu, Jihui Wu, Qianqian Wu, JieQian Wu, Zhengliang L Wu, Jingyun Wu, Xiaoman Wu, Ruohao Wu, Zhengfeng Wu, Yiyang Wu, Xiao-Jun Wu, Lizi Wu, Qiang Wu, J-Z Wu, Guangjie Wu, Pengfei Wu, Jundong Wu, Jianying Wu, Beier Wu, Meng-Ling Wu, Jamie L Y Wu, Lingxiang Wu, Xilin Wu, Keija Wu, Yanhua Wu, An-Li Wu, Chengbiao Wu, Yi-Ming Wu, Huanghui Wu, Dong-Feng Wu, Kunsheng Wu, Zhengcan Wu, Yuxin Wu, Kun-Rong Wu, Dong-Fang Wu, Guanxian Wu, Sensen Wu, Guifen Wu, Yifeng Wu, Pin Wu, Tzu-Chun Wu, Qingping Wu, R M Wu, Mian Wu, S J Wu, Haisu Wu, Senquan Wu, Jingjing Wu, Cheng Wu, Meng Wu, Geping Wu, Yumin Wu, Yu Wu, Xia Wu, William Ka Kei Wu, Xian-Run Wu, Juan Wu, Pei-Ei Wu, Meng-Hsun Wu, Yingying Wu, S M Wu, Xiangwei Wu, Guangrun Wu, Liuxin Wu, Yangyu Wu, Jia-Hui Wu, Jin-Zhen Wu, Shaohuan Wu, S L Wu, Yanli Wu, June K Wu, Haishan Wu, H Wu, Zhou-Ming Wu, Deqing Wu, Dong-Bo Wu, Tao Wu, Binxin Wu, Yalan Wu, Xiangxin Wu, Xueji Wu, Hongxi Wu, Zhonghui Wu, Jiaxi Wu, Tianzhi Wu, Meiqi Wu, Yan-Jun Wu, Weiwei Wu, Lijuan Wu, Tingqin Wu, Jianming Wu, P L Wu, Yih-Ru Wu, Lanlan Wu, Jianjun Wu, An-Xin Wu, Jianguang Wu, Xingjie Wu, Jianzhang Wu, Xianan Wu, Wei-Ping Wu, Haoan Wu, Fang-Tzu Wu, Zhongjun Wu, Wenwen Wu, Xi Wu, Teng Wu, Xiaoling Wu, Mengjuan Wu, Wen Wu, Yifan Wu, Yang Wu, Qianhu Wu, Shenyue Wu, Wu-Tian Wu, Qianwen Wu, Ye Wu, Lixing Wu, Gui-Qin Wu, Grace F Wu, Xing-Ping Wu, Ming Wu, Lisha Wu, Yanchuan Wu, Siqi Wu, Yuming Wu, Yuan Wu, I H Wu, Yu-Ting Wu, Hailong Wu, Minghua Wu, B Wu, Zhenlong Wu, Fang Wu, Guanzhong Wu, Liqun Wu, Guifu Wu, Chris Y Wu, Zhikang Wu, Qi-Yong Wu, Qingshi Wu, Zhao-Yang Wu, Man-Jing Wu, Chih-Ching Wu, Jun Wu, Jinhui Wu, Jincheng Wu, Linhong Wu, Hung-Tsung Wu, Tangchun Wu, Xinglong Wu, Zhen-Yang Wu, Ma Wu, Yin Wu, Jiu-Lin Wu, Dongyan Wu, Yong Wu, Yan Wu, Weizhen Wu, Changyu Wu, Fanggeng Wu, Dishan Wu, Yue Wu, Yi-Long Wu, Ge-ru Wu, Jinqiao Wu, Jing-Wen Wu, Zhongyang Wu, Lifang Wu, Songfen Wu, Sheng-Li Wu, Jia-Wei Wu, Yihan Wu, Kebang Wu, Wenyong Wu, Cai-Qin Wu, Yilong Wu, Hsiu-Chuan Wu, Yanan Wu, Xueqian Wu, Yen-Wen Wu, Paul W Wu, Xing-De Wu, Ying-Ting Wu, Yucan Wu, Mingfu Wu, Na-Qiong Wu, Linzhi Wu, Jinze Wu, Xuhan Wu, H J Wu, Ruize Wu, Dirong Wu, Chung-Yi Wu, Yaohong Wu, Jianyi Wu, Jugang Wu, Jiao Wu, Liang-Huan Wu, Xueling Wu, Ruying Wu, Gen Sheng Wu, Zhaoyuan Wu, Shiwen Wu, Andong Wu, Yu-Ling Wu, Hsan-Au Wu, Jia-Qi Wu, Yanting Wu, Xihai Wu, Lulu Wu, Xuxian Wu, Xiaomei Wu, Jingyue Wu, Shuihua Wu, Ren Wu, S Wu, Yupeng Wu, Haoming Wu, Samuel M Wu, Fan Wu, Yuesheng Wu, Tiange Wu, Yihe Wu, Jiayu Wu, Chia-Lung Wu, Shuang Wu, Shengnan Wu, Yaojiong Wu, Y Wu, Y Y Wu, Zhuoze Wu, Zimu Wu, Depei Wu, Yi-Hua Wu, Haiyun Wu, Yanyan Wu, Min Wu, Wenjuan Wu, Jinfeng Wu, Guangxi Wu, Junjie Wu, Yawen Wu, Pinglian Wu, Hui-Hui Wu, Xunwei Wu, Xuefeng Wu, Depeng Wu, Constance Wu, Dianqing Wu, Qibiao Wu, Hao-Tian Wu, Nan Wu, Hanyu Wu, Xiaojiang Wu, Cheng-Jun Wu, San-pin Wu, Xiaofan Wu, Xiwei Wu, Shi-Xin Wu, Shao-Guo Wu, Sunyi Wu, Yueheng Wu, Chengqian Wu, Kuixian Wu, Xin-Xi Wu, Guanyi Wu, Qiuxia Wu, Danhong Wu, He Wu, Zhong-Jun Wu, Siyi Wu, Xiangsheng Wu, Lanxiang Wu, Kaili Wu, Liting Wu, Ping-Hsun Wu, Zheng Wu, Wen-Ling Wu, Jiang-Nan Wu, Huanlin Wu, Yongfei Wu, Catherine A Wu, Leslie Wu, Shuo Wu, Peng-Fei Wu, Cho-Kai Wu, Meng-Han Wu, Hon-Yen Wu, Anguo Wu, Yuguang Philip Wu, Hai-Yin Wu, Yicheng Wu, Xiaolang Wu, Qing Wu, Yujie Wu, Haomin Wu, V C Wu, Xingdong Wu, Hengyu Wu, Jiang Wu, Chengxi Wu, Xiaoli Wu, Junyi Wu, William K K Wu, Ling-qian Wu, Chun Wu, Lesley Wu, Niting Wu, Jiayuan Wu, Xueying Wu, S-F Wu, Yingning Wu, David Wu, Mei-Na Wu, Joshua L Wu, Jin-Shang Wu, Guanzhao Wu, Jianqiang Wu, Runda Wu, Li-Hsien Wu, Rongjie Wu, June-Hsieh Wu, Huazhang Wu, Huanwen Wu, Xiu-Zhi Wu, Xianfeng Wu, Yanran Wu, Weibin Wu, Xuanshuang Wu, Yan Yan Wu, G X Wu, Runpei Wu, Jiaqi Wu, Chien-Ting Wu, Li-Na Wu, Qinfeng Wu, Chia-Chang Wu, Yueming Wu, Siyu Wu, Renhai Wu, Baojian Wu, Yi-Xia Wu, Renrong Wu, Wei-Yin Wu, C-H Wu, Chuan-Ling Wu, Xinran Wu, Fengying Wu, Qiuliang Wu, Guanhui Wu, Jinjie Wu, Wei-Chi Wu, Wei-Xun Wu, Meng-Na Wu, Lin Wu, Wan-Fu Wu, Jiajing Wu, Colin Chih-Chien Wu, Yajie Wu, Qiaowei Wu, Yaru Wu, Xiaoping Wu, Xue-Yan Wu, Mengchao Wu, Weijun Wu, Boquan Wu, Chunyan Wu, Zelai Wu, Pei-Wen Wu, Guojun Wu, Yichen Wu, Ming-Tao Wu, Hsueh-Erh Wu, Guang-Bo Wu, Zhi-Yong Wu, Chia-Zhen Wu, Kay L H Wu, Yong-Hong Wu, Anping Wu, Jiahang Wu, Xiaobin Wu, Ching-Yi Wu, Linzhen Wu, Xiaoxing Wu, Haidong Wu, Zhen-Qi Wu, Mark N Wu, Jianmin Wu, Xianpei Wu, Guanrong Wu, An-Dong Wu, Yanchun Wu, Dongsheng Wu, Ren-Chin Wu, Yuchen Wu, Mengna Wu, Lijun Wu, Zhuanbin Wu, Yanjing Wu, Lun Wu, Haodi Wu, Si-Jia Wu, Yongfa Wu, Hai-Ping Wu, Ximei Wu, Wenyu Wu, Xiangping Wu, L-F Wu, Yixia Wu, Yiran Wu, Haiying Wu, Yanhong Wu, Xiayin Wu, Yushun Wu, Yali Wu, Qitian Wu, Qin Wu, Xiaofu Wu, Jiamei Wu, Xiaoyong Wu, Qiong Wu, Xiaoying Wu, Wujun Wu, N Wu, Peiyi Wu, Yongmei Wu, Xiaojing Wu, Yizhou Wu, Dan Wu, Wen-Qiang Wu, Anshi Wu, Junqing Wu, Xiao-Yang Wu, Zhaoxia Wu, Liyang Wu, Hongke Wu, Mengqiu Wu, Haibin Wu, Peng Wu, Ding Lan Wu, Lecheng Wu, Yingzhi Wu, Kejia Wu, Anyi Wu, Junshu Wu, Jianxin Wu, Deguang Wu, Jiaxuan Wu, W Wu, Justin C Y Wu, Jiong Wu, Yu-Chih Wu, Qinglan Wu, Xinyi Wu, Diana Wu, Zhongluan Wu, Xuefen Wu, Yanqiong Wu, Shengming Wu, Jian-Lin Wu, Donglin Wu, Daren Wu, Lintao Wu, Xiaodong Wu, Chang-Jiun Wu, Chunshuai Wu, Irene X Y Wu, Yaping Wu, Xiping Wu, Yangna Wu, Zongheng Wu, Chia-Chen Wu, Wenyi Wu, Yansheng Wu, Shaojun Wu, Aimin Wu, Caisheng Wu, Xu Wu, Zhongchan Wu, Fei Wu, Yaohua Wu, Yibo Wu, Qinyi Wu, Zhengyu Wu, Yadi Wu, Hang Wu, L Wu, Mingjun Wu, Yuetong Wu, Wen-Juan Wu, Guangming Wu, Lingzhi Wu, Tingting Wu, Yuanbing Wu, Zhong-Yan Wu, Zhuzhu Wu, Cuiyan Wu, Baoqin Wu, Colin O Wu, Shuyan Wu, Hongmei Wu, Guangsen Wu, Xiaolin Wu, An Guo Wu, Kailang Wu, Chien-Sheng Wu, Chun-Hua Wu, Wenqi Wu, Jemma X Wu, Quanhui Wu, Qing-Wu Wu, Yanxiang Wu, Jiajin Wu, Qiao Wu, Yuan Kai 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