👤 Xueyi Wang

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Also published as: A Wang, Ai-Ling Wang, Ai-Ting Wang, Aihua Wang, Aijun Wang, Aili Wang, Aimin Wang, Aiting Wang, Aixian Wang, Aiyun Wang, Aizhong Wang, Alexander Wang, Alice Wang, Allen Wang, Anlai Wang, Anli Wang, Annette Wang, Anni Wang, Anqi Wang, Anthony Z Wang, Anxiang Wang, Anxin Wang, Ao Wang, Aoli Wang, B R Wang, B Wang, Baihan Wang, Baisong Wang, Baitao Wang, Bangchen Wang, Banghui Wang, Bangmao Wang, Bangshing Wang, Bao Wang, Bao-Long Wang, Baocheng Wang, Baofeng Wang, Baogui Wang, Baojun Wang, Baoli Wang, Baolong Wang, Baoming Wang, Baosen Wang, Baowei Wang, Baoying Wang, Baoyun Wang, Bei Bei Wang, Bei Wang, Beibei Wang, Beilan Wang, Beilei Wang, Ben Wang, Benjamin H Wang, Benzhong Wang, Bi Wang, Bi-Dar Wang, Biao Wang, Bicheng Wang, Bijue Wang, Bin Wang, Bin-Xue Wang, Binbin Wang, Bing Qing Wang, Bing Wang, Binghai Wang, Binghan Wang, Bingjie Wang, Binglong Wang, Bingnan Wang, Bingyan Wang, Bingyu Wang, Binquan Wang, Biqi Wang, Bo Wang, Bochu Wang, Boyu Wang, Bruce Wang, C Wang, C Z Wang, Cai Ren Wang, Cai-Hong Wang, Cai-Yun Wang, Cailian Wang, Caiqin Wang, Caixia Wang, Caiyan Wang, Can Wang, Cangyu Wang, Carol A Wang, Catherine Ruiyi Wang, Cenxuan Wang, Chan Wang, Chang Wang, Chang-Yun Wang, Changduo Wang, Changjing Wang, Changliang Wang, Changlong Wang, Changqian Wang, Changtu Wang, Changwei Wang, Changying Wang, Changyu Wang, Changyuan Wang, Changzhen Wang, Chao Wang, Chao-Jun Wang, Chao-Yung Wang, Chaodong Wang, Chaofan Wang, Chaohan Wang, Chaohui Wang, Chaojie Wang, Chaokui Wang, Chaomeng Wang, Chaoqun Wang, Chaoxian Wang, Chaoyi Wang, Chaoyu Wang, Chaozhan Wang, Charles C N Wang, Chau-Jong Wang, Chen Wang, Chen-Cen Wang, Chen-Ma Wang, Chen-Yu Wang, Chenchen Wang, Chenfei Wang, Cheng An Wang, Cheng Wang, Cheng-Cheng Wang, Cheng-Jie Wang, Cheng-zhang Wang, Chengbin Wang, Chengcheng Wang, Chenggang Wang, Chenghao Wang, Chenghua Wang, Chengjian Wang, Chengjun Wang, Chenglin Wang, Chenglong Wang, Chengniu Wang, Chengqiang Wang, Chengshuo Wang, Chenguang Wang, Chengwen Wang, Chengyan Wang, Chengyu Wang, Chengze Wang, Chenji Wang, Chenliang Wang, Chenwei Wang, Chenxi Wang, Chenxin Wang, Chenxuan Wang, Chenyang Wang, Chenyao Wang, Chenyin Wang, Chenyu Wang, Chenzi Wang, Chi Chiu Wang, Chi Wang, Chi-Ping Wang, Chia-Chuan Wang, Chia-Lin Wang, Chien-Hsun Wang, Chien-Wei Wang, Chih-Chun Wang, Chih-Hao Wang, Chih-Hsien Wang, Chih-Liang Wang, Chih-Yang Wang, Chih-Yuan Wang, Chijia Wang, Ching C Wang, Ching-Jen Wang, Chiou-Miin Wang, Chong Wang, Chongjian Wang, Chonglong Wang, Chongmin Wang, Chongze Wang, Christina Wang, Christine Wang, Chu Wang, Chuan Wang, Chuan-Chao Wang, Chuan-Hui Wang, Chuan-Jiang Wang, Chuan-Wen Wang, Chuang Wang, Chuanhai Wang, Chuansen Wang, Chuansheng Wang, Chuanxin Wang, Chuanyue Wang, Chuduan Wang, Chun Wang, Chun-Chieh Wang, Chun-Juan Wang, Chun-Li Wang, Chun-Lin Wang, Chun-Ting Wang, Chun-Xia Wang, Chung-Hsi Wang, Chung-Hsing Wang, Chung-Teng Wang, Chunguo Wang, Chunhong Wang, Chuning Wang, Chunjiong Wang, Chunjuan Wang, Chunle Wang, Chunli Wang, Chunlong Wang, Chunmei Wang, Chunsheng Wang, Chunting Wang, Chunxia Wang, Chunxue Wang, Chunyan Wang, Chunyang Wang, Chunyi Wang, Chunyu Wang, Chuyao Wang, Cindy Wang, Ciyang Wang, Cong Wang, Congcong Wang, Congrong Wang, Congrui Wang, Cui Wang, Cui-Fang Wang, Cui-Shan Wang, Cuili Wang, Cuiling Wang, Cuizhe Wang, Cun-Yu Wang, Cunchuan Wang, Cunyi Wang, D Wang, Da Wang, Da-Cheng Wang, Da-Li Wang, Da-Yan Wang, Da-Zhi Wang, Dadong Wang, Dai Wang, Daijun Wang, Daiwei Wang, Daixi Wang, Dajia Wang, Dake Wang, Dali Wang, Dalong Wang, Dalu Wang, Dan Wang, Dan-Dan Wang, Danan Wang, Dandan Wang, Danfeng Wang, Dang Wang, Dangfeng Wang, Danling Wang, Danqing Wang, Danxin Wang, Danyang Wang, Dao Wen Wang, Dao-Wen Wang, Dao-Xin Wang, Daolong Wang, Daoping Wang, Daozhong Wang, Dapeng Wang, Daping Wang, Daqi Wang, Daqing Wang, David Q H Wang, David Q-H Wang, David Wang, Dawei Wang, Dayan Wang, Dayong Wang, Dazhi Wang, De-He Wang, Dedong Wang, Dehao Wang, Deli Wang, Delin Wang, Delong Wang, Demin Wang, Deming Wang, Dengbin Wang, Dennis Qing Wang, Dennis Wang, Deqi Wang, Deshou Wang, Dezhong Wang, Di Wang, Dinghui Wang, Dingting Wang, Dingxiang Wang, Dong D Wang, Dong Hao Wang, Dong Wang, Dong-Dong Wang, Dong-Jie Wang, Dong-Mei Wang, DongWei Wang, Dongdong Wang, Donggen Wang, Donghao Wang, Donghong Wang, Donghui Wang, Dongliang Wang, Donglin Wang, Dongmei Wang, Dongqin Wang, Dongshi Wang, Dongxia Wang, Dongxu Wang, Dongyan Wang, Dongyang Wang, Dongyi Wang, Dongying Wang, Dongyu Wang, Doudou Wang, Du Wang, Duan Wang, Duanyang Wang, Duo-Ping Wang, E Wang, Edward Wang, En-bo Wang, En-hua Wang, Endi Wang, Enhua Wang, Er-Jin Wang, Erfei Wang, Erika Y Wang, Ermao Wang, Erming Wang, Ertao Wang, Eryao Wang, Eunice S Wang, Exing Wang, F Wang, Fa-Kai Wang, Fan Wang, Fanchang Wang, Fang Wang, Fang-Tao Wang, Fangfang Wang, Fangjie Wang, Fangjun Wang, Fangyan Wang, Fangyong Wang, Fangyu Wang, Fanhua Wang, Fanwen Wang, Fanxiong Wang, Fei Wang, Fei-Fei Wang, Fei-Yan Wang, Feida Wang, Feifei Wang, Feijie Wang, Feimiao Wang, Feixiang Wang, Feiyan Wang, Fen Wang, Feng Wang, Feng-Sheng Wang, Fengchong Wang, Fengge Wang, Fenghua Wang, Fengliang Wang, Fenglin Wang, Fengling Wang, Fengqiang Wang, Fengyang Wang, Fengying Wang, Fengyong Wang, Fengyun Wang, Fengzhen Wang, Fengzhong Wang, Fu Wang, Fu-Sheng Wang, Fu-Yan Wang, Fu-Zhen Wang, Fubao Wang, Fubing Wang, Fudi Wang, Fuhua Wang, Fuqiang Wang, Furong Wang, Fuwen Wang, Fuxin Wang, Fuyan Wang, G Q Wang, G Wang, G-W Wang, Gan Wang, Gang Wang, Ganggang Wang, Ganglin Wang, Gangyang Wang, Ganyu Wang, Gao T Wang, Gao Wang, Gaofu Wang, Gaopin Wang, Gavin Wang, Ge Wang, Geng Wang, Genghao Wang, Gengsheng Wang, Gongming Wang, Guan Wang, Guan-song Wang, Guandi Wang, Guanduo Wang, Guang Wang, Guang-Jie Wang, Guang-Rui Wang, Guangdi Wang, Guanghua Wang, Guanghui Wang, Guangliang Wang, Guangming Wang, Guangsuo Wang, Guangwen Wang, Guangyan Wang, Guangzhi Wang, Guanrou Wang, Guanru Wang, Guansong Wang, Guanyun Wang, Gui-Qi Wang, Guibin Wang, Guihu Wang, Guihua Wang, Guimin Wang, Guiping Wang, Guiqun Wang, Guixin Wang, Guixue Wang, Guiying Wang, Guo-Du Wang, Guo-Hua Wang, Guo-Liang Wang, Guo-Ping Wang, Guo-Quan Wang, Guo-hong Wang, GuoYou Wang, Guobin Wang, Guobing Wang, Guodong Wang, Guohang Wang, Guohao Wang, Guoliang Wang, Guoling Wang, Guoping Wang, Guoqian Wang, Guoqiang Wang, Guoqing Wang, Guorong Wang, Guowen Wang, Guoxiang Wang, Guoxiu Wang, Guoyi Wang, Guoying Wang, Guozheng Wang, H J Wang, H Wang, H X Wang, H Y Wang, H-Y Wang, Hai Bo Wang, Hai Wang, Hai Yang Wang, Hai-Feng Wang, Hai-Jun Wang, Hai-Long Wang, Haibin Wang, Haibing Wang, Haibo Wang, Haichao Wang, Haichuan Wang, Haifei Wang, Haifeng Wang, Haihe Wang, Haihong Wang, Haihua Wang, Haijiao Wang, Haijing Wang, Haijiu Wang, Haikun Wang, Hailei Wang, Hailin Wang, Hailing Wang, Hailong Wang, Haimeng Wang, Haina Wang, Haining Wang, Haiping Wang, Hairong Wang, Haitao Wang, Haiwei Wang, Haixia Wang, Haixin Wang, Haixing Wang, Haiyan Wang, Haiying Wang, Haiyong Wang, Haiyun Wang, Haizhen Wang, Han Wang, Hanbin Wang, Hanbing Wang, Hanchao Wang, Handong Wang, Hang Wang, Hangzhou Wang, Hanmin Wang, Hanping Wang, Hanqi Wang, Hanying Wang, Hanyu Wang, Hanzhi Wang, Hao Wang, Hao-Ching Wang, Hao-Hua Wang, Hao-Tian Wang, Hao-Yu Wang, Haobin Wang, Haochen Wang, Haohao Wang, Haohui Wang, Haojie Wang, Haolong Wang, Haomin Wang, Haoming Wang, Haonan Wang, Haoping Wang, Haoqi Wang, Haoran Wang, Haowei Wang, Haoxin Wang, Haoyang Wang, Haoyu Wang, Haozhou Wang, He Wang, He-Cheng Wang, He-Ling Wang, He-Ping Wang, He-Tong Wang, Hebo Wang, Hechuan Wang, Heling Wang, Hemei Wang, Heming Wang, Heng Wang, Heng-Cai Wang, Hengjiao Wang, Hengjun Wang, Hequn Wang, Hesuiyuan Wang, Heyong Wang, Hezhi Wang, Hong Wang, Hong Yi Wang, Hong-Gang Wang, Hong-Hui Wang, Hong-Kai Wang, Hong-Qin Wang, Hong-Wei Wang, Hong-Xia Wang, Hong-Yan Wang, Hong-Yang Wang, Hong-Ying Wang, Hongbin Wang, Hongbing Wang, Hongbo Wang, Hongcai Wang, Hongda Wang, Hongdan Wang, Hongfang Wang, Hongjia Wang, Hongjian Wang, Hongjie Wang, Hongjuan Wang, Hongkun Wang, Honglei Wang, Hongli Wang, Honglian Wang, Honglun Wang, Hongmei Wang, Hongpin Wang, Hongqian Wang, Hongshan Wang, Hongsheng Wang, Hongtao Wang, Hongwei Wang, Hongxia Wang, Hongxin Wang, Hongyan Wang, Hongyang Wang, Hongyi Wang, Hongyin Wang, Hongying Wang, Hongyu Wang, Hongyuan Wang, Hongyue Wang, Hongyun Wang, Hongze Wang, Hongzhan Wang, Hongzhuang Wang, Horng-Dar Wang, Houchun Wang, Hsei-Wei Wang, Hsueh-Chun Wang, Hu WANG, Hua Wang, Hua-Qin Wang, Hua-Wei Wang, Huabo Wang, Huafei Wang, Huai-Zhou Wang, Huaibing Wang, Huaili Wang, Huaizhi Wang, Huajin Wang, Huajing Wang, Hualin Wang, Hualing Wang, Huan Wang, Huan-You Wang, Huang Wang, Huanhuan Wang, Huanyu Wang, Huaquan Wang, Huating Wang, Huawei Wang, Huaxiang Wang, Huayang Wang, Huei Wang, Hui Miao Wang, Hui Wang, Hui-Hui Wang, Hui-Li Wang, Hui-Nan Wang, Hui-Yu Wang, HuiYue Wang, Huie Wang, Huiguo Wang, Huihua Wang, Huihui Wang, Huijie Wang, Huijun Wang, Huilun Wang, Huimei Wang, Huimin Wang, Huina Wang, Huiping Wang, Huiquan Wang, Huiqun Wang, Huishan Wang, Huiting Wang, Huiwen Wang, Huixia Wang, Huiyan Wang, Huiyang Wang, Huiyao Wang, Huiying Wang, Huiyu Wang, Huizhen Wang, Huizhi Wang, Huming Wang, I-Ching Wang, Iris X Wang, Isabel Z Wang, J J Wang, J P Wang, J Q Wang, J Wang, J Z Wang, J-Y Wang, Jacob E Wang, James Wang, Jeffrey Wang, Jen-Chun Wang, Jen-Chywan Wang, Jennifer E Wang, Jennifer T Wang, Jennifer X Wang, Jenny Y Wang, Jeremy R Wang, Jeremy Wang, Ji M Wang, Ji Wang, Ji-Nuo Wang, Ji-Yang Wang, Ji-Yao Wang, Ji-zheng Wang, Jia Bei Wang, Jia Bin Wang, Jia Wang, Jia-Liang Wang, Jia-Lin Wang, Jia-Mei Wang, Jia-Peng Wang, Jia-Qi Wang, Jia-Qiang Wang, Jia-Ying Wang, Jia-Yu Wang, Jiabei Wang, Jiabo Wang, Jiafeng Wang, Jiafu Wang, Jiahao Wang, Jiahui Wang, Jiajia Wang, Jiakun Wang, Jiale Wang, Jiali Wang, Jialiang Wang, Jialin Wang, Jialing Wang, Jiamin Wang, Jiaming Wang, Jian Wang, Jian'an Wang, Jian-Bin Wang, Jian-Guo Wang, Jian-Hong Wang, Jian-Long Wang, Jian-Wei Wang, Jian-Xiong Wang, Jian-Yong Wang, Jian-Zhi Wang, Jian-chun Wang, Jianan Wang, Jianbing Wang, Jianbo Wang, Jianding Wang, Jianfang Wang, Jianfei Wang, Jiang Wang, Jiangbin Wang, Jiangbo Wang, Jianghua Wang, Jianghui Wang, Jiangong Wang, Jianguo Wang, Jianhao Wang, Jianhua Wang, Jianhui Wang, Jiani Wang, Jianjiao Wang, Jianjie Wang, Jianjun Wang, Jianle Wang, Jianli Wang, Jianlin Wang, Jianliu Wang, Jianlong Wang, Jianmei Wang, Jianmin Wang, Jianning Wang, Jianping Wang, Jianqin Wang, Jianqing Wang, Jianqun Wang, Jianru Wang, Jianshe Wang, Jianshu Wang, Jiantao Wang, Jianwei Wang, Jianwu Wang, Jianxiang Wang, Jianxin Wang, Jianye Wang, Jianying Wang, Jianyong Wang, Jianyu Wang, Jianzhang Wang, Jianzhi Wang, Jiao Wang, Jiaojiao Wang, Jiapan Wang, Jiaping Wang, Jiaqi Wang, Jiaqian Wang, Jiatao Wang, Jiawei Wang, Jiawen Wang, Jiaxi Wang, Jiaxin Wang, Jiaxing Wang, Jiaxuan Wang, Jiayan Wang, Jiayang Wang, Jiayi Wang, Jiaying Wang, Jiayu Wang, Jiazheng Wang, Jiazhi Wang, Jie Jin Wang, Jie Wang, Jieda Wang, Jieh-Neng Wang, Jiemei Wang, Jieqi Wang, Jieyan Wang, Jieyu Wang, Jifei Wang, Jiheng Wang, Jihong Wang, Jiliang Wang, Jilin Wang, Jin Wang, Jin'e Wang, Jin-Bao Wang, Jin-Cheng Wang, Jin-Da Wang, Jin-E Wang, Jin-Juan Wang, Jin-Liang Wang, Jin-Xia Wang, Jin-Xing Wang, Jincheng Wang, Jindan Wang, Jinfei Wang, Jinfeng Wang, Jinfu Wang, Jing J Wang, Jing Wang, Jing-Hao Wang, Jing-Huan Wang, Jing-Jing Wang, Jing-Long Wang, Jing-Min Wang, Jing-Shi Wang, Jing-Wen Wang, Jing-Xian Wang, Jing-Yi Wang, Jing-Zhai Wang, Jingang Wang, Jingchun Wang, Jingfan Wang, Jingfeng Wang, Jingheng Wang, Jinghong Wang, Jinghua Wang, Jinghuan Wang, Jingjing Wang, Jingkang Wang, Jinglin Wang, Jingmin Wang, Jingnan Wang, Jingqi Wang, Jingru Wang, Jingtong Wang, Jingwei Wang, Jingwen Wang, Jingxiao Wang, Jingyang Wang, Jingyi Wang, Jingying Wang, Jingyu Wang, Jingyue Wang, Jingyun Wang, Jingzhou Wang, Jinhai Wang, Jinhao Wang, Jinhe Wang, Jinhua Wang, Jinhuan Wang, Jinhui Wang, Jinjie Wang, Jinjin Wang, Jinkang Wang, Jinling Wang, Jinlong Wang, Jinmeng Wang, Jinning Wang, Jinping Wang, Jinqiu Wang, Jinrong Wang, Jinru Wang, Jinsong Wang, Jintao Wang, Jinxia Wang, Jinxiang Wang, Jinyang Wang, Jinyu Wang, Jinyue Wang, Jinyun Wang, Jinzhu Wang, Jiou Wang, Jipeng Wang, Jiqing Wang, Jiqiu Wang, Jisheng Wang, Jiu Wang, Jiucun Wang, Jiun-Ling Wang, Jiwen Wang, Jixuan Wang, Jiyan Wang, Jiying Wang, Jiyong Wang, Jizheng Wang, John Wang, Jou-Kou Wang, Joy Wang, Ju Wang, Juan Wang, Jue Wang, Jueqiong Wang, Jufeng Wang, Julie Wang, Juling Wang, Jun Kit Wang, Jun Wang, Jun Yi Wang, Jun-Feng Wang, Jun-Jie Wang, Jun-Jun Wang, Jun-Ling Wang, Jun-Sheng Wang, Jun-Sing Wang, Jun-Zhuo Wang, Jundong Wang, Junfeng Wang, Jung-Pan Wang, Junhong Wang, Junhua Wang, Junhui Wang, Junjiang Wang, Junjie Wang, Junjun Wang, Junkai Wang, Junke Wang, Junli Wang, Junlin Wang, Junling Wang, Junmei Wang, Junmin Wang, Junpeng Wang, Junping Wang, Junqin Wang, Junqing Wang, Junrui Wang, Junsheng Wang, Junshi Wang, Junshuang Wang, Junwen Wang, Junxiao Wang, Junya Wang, Junying Wang, Junyu Wang, Justin Wang, Jutao Wang, Juxiang Wang, K Wang, Kai Wang, Kai-Kun Wang, Kai-Wen Wang, Kaicen Wang, Kaihao Wang, Kaihe Wang, Kaihong Wang, Kaijie Wang, Kaijuan Wang, Kailu Wang, Kaiming Wang, Kaining Wang, Kaiting Wang, Kaixi Wang, Kaixu Wang, Kaiyan Wang, Kaiyuan Wang, Kaiyue Wang, Kan Wang, Kangli Wang, Kangling Wang, Kangmei Wang, Kangning Wang, Ke Wang, Ke-Feng Wang, KeShan Wang, Kehan Wang, Kehao Wang, Kejia Wang, Kejian Wang, Kejun Wang, Keke Wang, Keming Wang, Kenan Wang, Keqing Wang, Kesheng Wang, Kexin Wang, Keyan Wang, Keyi Wang, Keyun Wang, Kongyan Wang, Kuan Hong Wang, Kui Wang, Kun Wang, Kunhua Wang, Kunpeng Wang, Kunzheng Wang, L F Wang, L M Wang, L Wang, L Z Wang, L-S Wang, Laidi Wang, Laijian Wang, Laiyuan Wang, Lan Wang, Lan-Wan Wang, Lan-lan Wang, Lanlan Wang, Larry Wang, Le Wang, Le-Xin Wang, Ledan Wang, Lee-Kai Wang, Lei P Wang, Lei Wang, Lei-Lei Wang, Leiming Wang, Leishen Wang, Leli Wang, Leran Wang, Lexin Wang, Leying Wang, Li Chun Wang, Li Dong Wang, Li Wang, Li-Dong Wang, Li-E Wang, Li-Juan Wang, Li-Li Wang, Li-Na Wang, Li-San Wang, Li-Ting Wang, Li-Xin Wang, Li-Yong Wang, LiLi Wang, Lian Wang, Lianchun Wang, Liang Wang, Liang-Yan Wang, Liangfu Wang, Lianghai Wang, Liangli Wang, Liangliang Wang, Liangxu Wang, Lianshui Wang, Lianyong Wang, Libo Wang, Lichan Wang, Lichao Wang, Liewei Wang, Lifang Wang, Lifei Wang, Lifen Wang, Lifeng Wang, Ligang Wang, Lihong Wang, Lihua Wang, Lihui Wang, Lijia Wang, Lijin Wang, Lijing Wang, Lijuan Wang, Lijun Wang, Liling Wang, Lily Wang, Limeng Wang, Limin Wang, Liming Wang, Lin Wang, Lin-Fa Wang, Lin-Yu Wang, Lina Wang, Linfang Wang, Ling Jie Wang, Ling Wang, Ling-Ling Wang, Lingbing Wang, Lingda Wang, Linghua Wang, Linghuan Wang, Lingli Wang, Lingling Wang, Lingyan Wang, Lingzhi Wang, Linhua Wang, Linhui Wang, Linjie Wang, Linli Wang, Linlin Wang, Linping Wang, Linshu Wang, Linshuang Wang, Lintao Wang, Linxuan Wang, Linying Wang, Linyuan Wang, Liping Wang, Liqing Wang, Liqun Wang, Lirong Wang, Litao Wang, Liting Wang, Liu Wang, Liusong Wang, Liuyang Wang, Liwei Wang, Lixia Wang, Lixian Wang, Lixiang Wang, Lixin Wang, Lixing Wang, Lixiu Wang, Liyan Wang, Liyi Wang, Liying Wang, Liyong Wang, Liyuan Wang, Liyun Wang, Long Wang, Longcai Wang, Longfei Wang, Longsheng Wang, Longxiang Wang, Lou-Pin Wang, Lu Wang, Lu-Lu Wang, Lueli Wang, Lufang Wang, Luhong Wang, Luhui Wang, Lujuan Wang, Lulu Wang, Luofu Wang, Luping Wang, Luting Wang, Luwen Wang, Luxiang Wang, Luya Wang, Luyao Wang, Luyun Wang, Lynn Yuning Wang, M H Wang, M Wang, M Y Wang, M-J Wang, Maiqiu Wang, Man Wang, Mangju Wang, Manli Wang, Mao-Xin Wang, Maochun Wang, Maojie Wang, Maoju Wang, Mark Wang, Mei Wang, Mei-Gui Wang, Mei-Xia Wang, Meiding Wang, Meihui Wang, Meijun Wang, Meiling Wang, Meixia Wang, Melissa T Wang, Meng C Wang, Meng Wang, Meng Yu Wang, Meng-Dan Wang, Meng-Lan Wang, Meng-Meng Wang, Meng-Ru Wang, Meng-Wei Wang, Meng-Ying Wang, Meng-hong Wang, Mengge Wang, Menghan Wang, Menghui Wang, Mengjiao Wang, Mengjing Wang, Mengjun Wang, Menglong Wang, Menglu Wang, Mengmeng Wang, Mengqi Wang, Mengru Wang, Mengshi Wang, Mengwen Wang, Mengxiao Wang, Mengya Wang, Mengyao Wang, Mengying Wang, Mengyuan Wang, Mengyue Wang, Mengyun Wang, Mengze Wang, Mengzhao Wang, Mengzhi Wang, Mian Wang, Miao Wang, Mimi Wang, Min Wang, Min-sheng Wang, Ming Wang, Ming-Chih Wang, Ming-Hsi Wang, Ming-Jie Wang, Ming-Wei Wang, Ming-Yang Wang, Ming-Yuan Wang, Mingchao Wang, Mingda Wang, Minghua Wang, Minghuan Wang, Minghui Wang, Mingji Wang, Mingjin Wang, Minglei Wang, Mingliang Wang, Mingmei Wang, Mingming Wang, Mingqiang Wang, Mingrui Wang, Mingsong Wang, Mingxi Wang, Mingxia Wang, Mingxun Wang, Mingya Wang, Mingyang Wang, Mingyi Wang, Mingyu Wang, Mingzhi Wang, Mingzhu Wang, Minjie Wang, Minjun Wang, Minmin Wang, Minxian Wang, Minxiu Wang, Minzhou Wang, Miranda C Wang, Mo Wang, Mofei Wang, Monica Wang, Mu Wang, Mutian Wang, Muxiao Wang, Muxuan Wang, N Wang, Na Wang, Nan Wang, Nana Wang, Nanbu Wang, Nannan Wang, Nanping Wang, Neng Wang, Ni Wang, Niansong Wang, Ning Wang, Ningjian Wang, Ningli Wang, Ningyuan Wang, Nuan Wang, Oliver Wang, Ouchen Wang, P Jeremy Wang, P L Wang, P N Wang, P Wang, Pai Wang, Pan Wang, Pan-Pan Wang, Panfeng Wang, Panliang Wang, Pei Chang Wang, Pei Wang, Pei-Hua Wang, Pei-Jian Wang, Pei-Juan Wang, Pei-Wen Wang, Pei-Yu Wang, Peichang Wang, Peigeng Wang, Peihe Wang, Peijia Wang, Peijuan Wang, Peijun Wang, Peilin Wang, Peipei Wang, Peirong Wang, Peiwen Wang, Peixi Wang, Peiyao Wang, Peiyin Wang, Peng Wang, Peng-Cheng Wang, Pengbo Wang, Pengchao Wang, Pengfei Wang, Pengjie Wang, Pengju Wang, Penglai Wang, Penglong Wang, Pengpu Wang, Pengtao Wang, Pengxiang Wang, Pengyu Wang, Pin Wang, Ping Wang, Pingchuan Wang, Pingfeng Wang, Pingping Wang, Pintian Wang, Po-Jen Wang, Pu Wang, Q Wang, Q Z Wang, Qi Wang, Qi-Bing Wang, Qi-En Wang, Qi-Jia Wang, Qi-Qi Wang, Qian Wang, Qian-Liang Wang, Qian-Wen Wang, Qian-Zhu Wang, Qian-fei Wang, Qianbao Wang, Qiang Wang, Qiang-Sheng Wang, Qiangcheng Wang, Qianghu Wang, Qiangqiang Wang, Qianjin Wang, Qianliang Wang, Qianqian Wang, Qianrong Wang, Qianru Wang, Qianwen Wang, Qianxu Wang, Qiao Wang, Qiao-Ping Wang, Qiaohong Wang, Qiaoqi Wang, Qiaoqiao Wang, Qifan Wang, Qifei Wang, Qifeng Wang, Qigui Wang, Qihao Wang, Qihua Wang, Qijia Wang, Qiming Wang, Qin Wang, Qing Jun Wang, Qing K Wang, Qing Kenneth Wang, Qing Mei Wang, Qing Wang, Qing-Bin Wang, Qing-Dong Wang, Qing-Jin Wang, Qing-Liang Wang, Qing-Mei Wang, Qing-Yan Wang, Qing-Yuan Wang, Qing-Yun Wang, QingDong Wang, Qingchun Wang, Qingfa Wang, Qingfeng Wang, Qinghang Wang, Qingliang Wang, Qinglin Wang, Qinglu Wang, Qingming Wang, Qingping Wang, Qingqing Wang, Qingshi Wang, Qingshui Wang, Qingsong Wang, Qingtong Wang, Qingyong Wang, Qingyu Wang, Qingyuan Wang, Qingyun Wang, Qingzhong Wang, Qinqin Wang, Qinrong Wang, Qintao Wang, Qinwen Wang, Qinyun Wang, Qiong Wang, Qiqi Wang, Qirui Wang, Qishan Wang, Qiu-Ling Wang, Qiu-Xia Wang, Qiuhong Wang, Qiuli Wang, Qiuling Wang, Qiuning Wang, Qiuping Wang, Qiushi Wang, Qiuting Wang, Qiuyan Wang, Qiuyu Wang, Qiwei Wang, Qixue Wang, Qiyu Wang, Qiyuan Wang, Quan Wang, Quan-Ming Wang, Quanli Wang, Quanren Wang, Quanxi Wang, Qun Wang, Qunxian Wang, Qunzhi Wang, R Wang, Ran Wang, Ranjing Wang, Ranran Wang, Re-Hua Wang, Ren Wang, Rencheng Wang, Renjun Wang, Renqian Wang, Renwei Wang, Renxi Wang, Renxiao Wang, Renyuan Wang, Rihua Wang, Rikang Wang, Rixiang Wang, Robert Yl Wang, Rong Wang, Rong-Chun Wang, Rong-Rong Wang, Rong-Tsorng Wang, RongRong Wang, Rongjia Wang, Rongping Wang, Rongyun Wang, Ru Wang, RuNan Wang, Ruey-Yun Wang, Rufang Wang, Ruhan Wang, Rui Wang, Rui-Hong Wang, Rui-Min Wang, Rui-Ping Wang, Rui-Rui Wang, Ruibin Wang, Ruibing Wang, Ruibo Wang, Ruicheng Wang, Ruifang Wang, Ruijing Wang, Ruimeng Wang, Ruimin Wang, Ruiming Wang, Ruinan Wang, Ruining Wang, Ruiquan Wang, Ruiwen Wang, Ruixian Wang, Ruixin Wang, Ruixuan Wang, Ruixue Wang, Ruiying Wang, Ruizhe Wang, Ruizhi Wang, Rujie Wang, Ruling Wang, Ruming Wang, Runci Wang, Runuo Wang, Runze Wang, Runzhi Wang, Ruo-Nan Wang, Ruo-Ran Wang, Ruonan Wang, Ruosu Wang, Ruoxi Wang, Rurong Wang, Ruting Wang, Ruxin Wang, Ruxuan Wang, Ruyue Wang, S L Wang, S S Wang, S Wang, S X Wang, Sa A Wang, Sa Wang, Saifei Wang, Saili Wang, Sainan Wang, Saisai Wang, Sangui Wang, Sanwang Wang, Sasa Wang, Sen Wang, Seok Mui Wang, Seungwon Wang, Sha Wang, Shan Wang, Shan-Shan Wang, Shang Wang, Shangyu Wang, Shanshan Wang, Shao-Kang Wang, Shaochun Wang, Shaohsu Wang, Shaokun Wang, Shaoli Wang, Shaolian Wang, Shaoshen Wang, Shaowei Wang, Shaoyi Wang, Shaoying Wang, Shaoyu Wang, Shaozheng Wang, Shasha Wang, Shau-Chun Wang, Shawn Wang, Shen Wang, Shen-Nien Wang, Shenao Wang, Sheng Wang, Sheng-Min Wang, Sheng-Nan Wang, Sheng-Ping Wang, Sheng-Quan Wang, Sheng-Yang Wang, Shengdong Wang, Shengjie Wang, Shengli Wang, Shengqi Wang, Shengya Wang, Shengyao Wang, Shengyu Wang, Shengyuan Wang, Shenqi Wang, Sheri Wang, Shi Wang, Shi-Cheng Wang, Shi-Han Wang, Shi-Qi Wang, Shi-Xin Wang, Shi-Yao Wang, Shibin Wang, Shichao Wang, Shicung Wang, Shidong Wang, Shifa Wang, Shifeng Wang, Shih-Wei Wang, Shihan Wang, Shihao Wang, Shihua Wang, Shijie Wang, Shijin Wang, Shijun Wang, Shikang Wang, Shimiao Wang, Shiqi Wang, Shiqiang Wang, Shitao Wang, Shitian Wang, Shiwen Wang, Shixin Wang, Shixuan Wang, Shiyang Wang, Shiyao Wang, Shiyin Wang, Shiyu Wang, Shiyuan Wang, Shiyue Wang, Shizhi Wang, Shouli Wang, Shouling Wang, Shouzhi Wang, Shu Wang, Shu-Huei Wang, Shu-Jin Wang, Shu-Ling Wang, Shu-Na Wang, Shu-Song Wang, Shu-Xia Wang, Shu-qiang Wang, Shuai Wang, Shuaiqin Wang, Shuang Wang, Shuang-Shuang Wang, Shuang-Xi Wang, Shuangyuan Wang, Shubao Wang, Shudan Wang, Shuge Wang, Shuguang Wang, Shuhe Wang, Shuiliang Wang, Shuiyun Wang, Shujin Wang, Shukang Wang, Shukui Wang, Shun Wang, Shuning Wang, Shunjun Wang, Shunran Wang, Shuo Wang, Shuping Wang, Shuqi Wang, Shuqing Wang, Shuren Wang, Shusen Wang, Shusheng Wang, Shushu Wang, Shuu-Jiun Wang, Shuwei Wang, Shuxia Wang, Shuxin Wang, Shuya Wang, Shuye Wang, Shuyue Wang, Shuzhe Wang, Shuzhen Wang, Shuzhong Wang, Shyi-Gang P Wang, Si Wang, Sibo Wang, Sidan Wang, Sihua Wang, Sijia Wang, Silas L Wang, Silu Wang, Simeng Wang, Siqi Wang, Siqing Wang, Siwei Wang, Siyang Wang, Siyi Wang, Siying Wang, Siyu Wang, Siyuan Wang, Siyue Wang, Song Wang, Songjiao Wang, Songlin Wang, Songping Wang, Songsong Wang, Songtao Wang, Sophie H Wang, Stephani Wang, Su'e Wang, Su-Guo Wang, Su-Hua Wang, Sufang Wang, Sugai Wang, Sui Wang, Suiyan Wang, Sujie Wang, Sujuan Wang, Suli Wang, Sun Wang, Supeng Perry Wang, Suxia Wang, Suyun Wang, Suzhen Wang, T Q Wang, T Wang, T Y Wang, Taian Wang, Taicheng Wang, Taishu Wang, Tammy C Wang, Tao Wang, Taoxia Wang, Teng Wang, Tengfei Wang, Theodore Wang, Thomas T Y Wang, Tian Wang, Tian-Li Wang, Tian-Lu Wang, Tian-Tian Wang, Tian-Yi Wang, Tiancheng Wang, Tiange Wang, Tianhao Wang, Tianhu Wang, Tianhui Wang, Tianjing Wang, Tianjun Wang, Tianlin Wang, Tiannan Wang, Tianpeng Wang, Tianqi Wang, Tianqin Wang, Tianqing Wang, Tiansheng Wang, Tiansong Wang, Tiantian Wang, Tianyi Wang, Tianying Wang, Tianyuan Wang, Tielin Wang, Tienju Wang, Tieqiao Wang, Timothy C Wang, Ting Chen Wang, Ting Wang, Ting-Chen Wang, Ting-Hua Wang, Ting-Ting Wang, Tingting Wang, Tingye Wang, Tingyu Wang, Tom J Wang, Tong Wang, Tong-Hong Wang, Tongsong Wang, Tongtong Wang, Tongxia Wang, Tongxin Wang, Tongyao Wang, Tony Wang, Tzung-Dau Wang, Victoria Wang, Vivian Wang, W Wang, Wanbing Wang, Wanchun Wang, Wang Wang, Wangxia Wang, Wanliang Wang, Wanxia Wang, Wanyao Wang, Wanyi Wang, Wanyu Wang, Wayseen Wang, Wei Wang, Wei-En Wang, Wei-Feng Wang, Wei-Lien Wang, Wei-Qi Wang, Wei-Ting Wang, Wei-Wei Wang, Weicheng Wang, Weiding Wang, Weidong Wang, Weifan Wang, Weiguang Wang, Weihao Wang, Weihong Wang, Weihua Wang, Weijian Wang, Weijie Wang, Weijun Wang, Weilin Wang, Weiling Wang, Weilong Wang, Weimin Wang, Weina Wang, Weining Wang, Weipeng Wang, Weiqin Wang, Weiqing Wang, Weirong Wang, Weiwei Wang, Weiwen Wang, Weixiao Wang, Weixue Wang, Weiyan Wang, Weiyu Wang, Weiyuan Wang, Weizhen Wang, Weizhi Wang, Weizhong Wang, Wen Wang, Wen-Chang Wang, Wen-Der Wang, Wen-Fei Wang, Wen-Jie Wang, Wen-Jun Wang, Wen-Qing Wang, Wen-Xuan Wang, Wen-Yan Wang, Wen-Ying Wang, Wen-Yong Wang, Wen-mei Wang, Wenbin Wang, Wenbo Wang, Wence Wang, Wenchao Wang, Wencheng Wang, Wendong Wang, Wenfei Wang, Wengong Wang, Wenhan Wang, Wenhao Wang, Wenhe Wang, Wenhui Wang, Wenjie Wang, Wenjing Wang, Wenju Wang, Wenjuan Wang, Wenjun Wang, Wenkai Wang, Wenkang Wang, Wenke Wang, Wenming Wang, Wenqi Wang, Wenqiang Wang, Wenqing Wang, Wenran Wang, Wenrui Wang, Wentao Wang, Wentian Wang, Wenting Wang, Wenwen Wang, Wenxia Wang, Wenxian Wang, Wenxiang Wang, Wenxiu Wang, Wenxuan Wang, Wenya Wang, Wenyan Wang, Wenyi Wang, Wenying Wang, Wenyu Wang, Wenyuan Wang, Wenzhou Wang, William Wang, Won-Jing Wang, Wu-Wei Wang, Wuji Wang, Wuqing Wang, Wusan Wang, X E Wang, X F Wang, X O Wang, X S Wang, X Wang, X-T Wang, Xi Wang, Xi-Hong Wang, Xi-Rui Wang, Xia Wang, Xian Wang, Xian-e Wang, Xianding Wang, Xianfeng Wang, Xiang Wang, Xiang-Dong Wang, Xiangcheng Wang, Xiangding Wang, Xiangdong Wang, Xiangguo Wang, Xianghua Wang, Xiangkun Wang, Xiangrong Wang, Xiangru Wang, Xiangwei Wang, Xiangyu Wang, Xianna Wang, Xianqiang Wang, Xianrong Wang, Xianshi Wang, Xianshu Wang, Xiansong Wang, Xiantao Wang, Xianwei Wang, Xianxing Wang, Xianze Wang, Xianzhe Wang, Xianzong Wang, Xiao Ling Wang, Xiao Qun Wang, Xiao Wang, Xiao-Ai Wang, Xiao-Fei Wang, Xiao-Hui Wang, Xiao-Jie Wang, Xiao-Juan Wang, Xiao-Lan Wang, Xiao-Li Wang, Xiao-Lin Wang, Xiao-Ming Wang, Xiao-Pei Wang, Xiao-Qian Wang, Xiao-Qun Wang, Xiao-Tong Wang, Xiao-Xia Wang, Xiao-Yi Wang, Xiao-Yun Wang, Xiao-jian WANG, Xiao-liang Wang, Xiaobin Wang, Xiaobo Wang, Xiaochen Wang, Xiaochuan Wang, Xiaochun Wang, Xiaodan Wang, Xiaoding Wang, Xiaodong Wang, Xiaofang Wang, Xiaofei Wang, Xiaofen Wang, Xiaofeng Wang, Xiaogang Wang, Xiaohong Wang, Xiaohu Wang, Xiaohua Wang, Xiaohui Wang, Xiaojia Wang, Xiaojian Wang, Xiaojiao Wang, Xiaojie Wang, Xiaojing Wang, Xiaojuan Wang, Xiaojun Wang, Xiaokun Wang, Xiaole Wang, Xiaoli Wang, Xiaoliang Wang, Xiaolin Wang, Xiaoling Wang, Xiaolong Wang, Xiaolu Wang, Xiaolun Wang, Xiaoman Wang, Xiaomei Wang, Xiaomeng Wang, Xiaomin Wang, Xiaoming Wang, Xiaona Wang, Xiaonan Wang, Xiaoning Wang, Xiaoqi Wang, Xiaoqian Wang, Xiaoqin Wang, Xiaoqing Wang, Xiaoqiu Wang, Xiaoqun Wang, Xiaorong Wang, Xiaorui Wang, Xiaoshan Wang, Xiaosong Wang, Xiaotang Wang, Xiaoting Wang, Xiaotong Wang, Xiaowei Wang, Xiaowen Wang, Xiaowu Wang, Xiaoxia Wang, Xiaoxiao Wang, Xiaoxin Wang, Xiaoxin X Wang, Xiaoxuan Wang, Xiaoya Wang, Xiaoyan Wang, Xiaoyang Wang, Xiaoye Wang, Xiaoying Wang, Xiaoyu Wang, Xiaozhen Wang, Xiaozhi Wang, Xiaozhong Wang, Xiaozhu Wang, Xichun Wang, Xidi Wang, Xietong Wang, Xifeng Wang, Xifu Wang, Xijun Wang, Xike Wang, Xin Wang, Xin Wei Wang, Xin-Hua Wang, Xin-Liang Wang, Xin-Ming Wang, Xin-Peng Wang, Xin-Qun Wang, Xin-Shang Wang, Xin-Xin Wang, Xin-Yang Wang, Xin-Yue Wang, Xinbo Wang, Xinchang Wang, Xinchao Wang, Xinchen Wang, Xincheng Wang, Xinchun Wang, Xindi Wang, Xindong Wang, Xing Wang, Xing-Huan Wang, Xing-Jin Wang, Xing-Jun Wang, Xing-Lei Wang, Xing-Ping Wang, Xing-Quan Wang, Xingbang Wang, Xingchen Wang, Xingde Wang, Xingguo Wang, Xinghao Wang, Xinghui Wang, Xingjie Wang, Xingjin Wang, Xinglei Wang, Xinglong Wang, Xingqin Wang, Xinguo Wang, Xingxin Wang, Xingxing Wang, Xingye Wang, Xingyu Wang, Xingyue Wang, Xingyun Wang, Xinhui Wang, Xinjing Wang, Xinjun Wang, Xinke Wang, Xinkun Wang, Xinli Wang, Xinlin Wang, Xinlong Wang, Xinmei Wang, Xinqi Wang, Xinquan Wang, Xinran Wang, Xinrong Wang, Xinru Wang, Xinrui Wang, Xinshuai Wang, Xintong Wang, Xinwen Wang, Xinxin Wang, Xinyan Wang, Xinyang Wang, Xinye Wang, Xinyi Wang, Xinying Wang, Xinyu Wang, Xinyue Wang, Xinzhou Wang, Xiong Wang, Xiongjun Wang, Xiru Wang, Xitian Wang, Xiu-Lian Wang, Xiu-Ping Wang, Xiufen Wang, Xiujuan Wang, Xiujun Wang, Xiurong Wang, Xiuwen Wang, Xiuyu Wang, Xiuyuan Hugh Wang, Xixi Wang, Xixiang Wang, Xiyan Wang, Xiyue Wang, Xizhi Wang, Xu Wang, Xu-Hong Wang, Xuan Wang, Xuan-Ren Wang, Xuan-Ying Wang, Xuanwen Wang, Xuanyi Wang, Xubo Wang, Xudong Wang, Xue Wang, Xue-Feng Wang, Xue-Hua Wang, Xue-Lei Wang, Xue-Lian Wang, Xue-Rui Wang, Xue-Yao Wang, Xue-Ying Wang, Xuebin Wang, Xueding Wang, Xuedong Wang, Xuefei Wang, Xuefeng Wang, Xueguo Wang, Xuehao Wang, Xuejie Wang, Xuejing Wang, Xueju Wang, Xuejun Wang, Xuekai Wang, Xuelai Wang, Xuelian Wang, Xuelin Wang, Xuemei Wang, Xuemin Wang, Xueping Wang, Xueqian Wang, Xueqin Wang, Xuesong Wang, Xueting Wang, Xuewei Wang, Xuewen Wang, Xuexiang Wang, Xueyan Wang, Xueying Wang, Xueyun Wang, Xuezhen Wang, Xuezheng Wang, Xufei Wang, Xujing Wang, Xuliang Wang, Xumeng Wang, Xun Wang, Xuping Wang, Xuqiao Wang, Xuru Wang, Xusheng Wang, Xv Wang, Y Alan Wang, Y B Wang, Y H Wang, Y L Wang, Y P Wang, Y Wang, Y Y Wang, Y Z Wang, Y-H Wang, Y-S Wang, Ya Qi Wang, Ya Wang, Ya Xing Wang, Ya-Han Wang, Ya-Jie Wang, Ya-Long Wang, Ya-Nan Wang, Ya-Ping Wang, Ya-Qin Wang, Ya-Zhou Wang, Yachen Wang, Yachun Wang, Yadong Wang, Yafang Wang, Yafen Wang, Yahong Wang, Yahui Wang, Yajie Wang, Yajing Wang, Yajun Wang, Yake Wang, Yakun Wang, Yali Wang, Yalin Wang, Yaling Wang, Yalong Wang, Yan Ming Wang, Yan Wang, Yan-Chao Wang, Yan-Chun Wang, Yan-Feng Wang, Yan-Ge Wang, Yan-Jiang Wang, Yan-Jun Wang, Yan-Ming Wang, Yan-Yang Wang, Yan-Yi Wang, Yan-Zi Wang, Yana Wang, Yanan Wang, Yanbin Wang, Yanbing Wang, Yanchun Wang, Yancun Wang, Yanfang Wang, Yanfei Wang, Yanfeng Wang, Yang Wang, Yang-Yang Wang, Yange Wang, Yanggan Wang, Yangpeng Wang, Yangyang Wang, Yangyufan Wang, Yanhai Wang, Yanhong Wang, Yanhua Wang, Yanhui Wang, Yani Wang, Yanjin Wang, Yanjun Wang, Yankun Wang, Yanlei Wang, Yanli Wang, Yanliang Wang, Yanlin Wang, Yanling Wang, Yanmei Wang, Yanming Wang, Yanni Wang, Yanong Wang, Yanping Wang, Yanqing Wang, Yanru Wang, Yanting Wang, Yanwen Wang, Yanxia Wang, Yanxing Wang, Yanyang Wang, Yanyun Wang, Yanzhe Wang, Yanzhu Wang, Yao Wang, Yaobin Wang, Yaochun Wang, Yaodong Wang, Yaohe Wang, Yaokun Wang, Yaoling Wang, Yaolou Wang, Yaoxian Wang, Yaoxing Wang, Yaozhi Wang, Yapeng Wang, Yaping Wang, Yaqi Wang, Yaqian Wang, Yaqiong Wang, Yaru Wang, Yatao Wang, Yating Wang, Yawei Wang, Yaxian Wang, Yaxin Wang, Yaxiong Wang, Yaxuan Wang, Yayu Wang, Yazhou Wang, Ye Wang, Ye-Ran Wang, Yefu Wang, Yeh-Han Wang, Yehan Wang, Yeming Wang, Yen-Feng Wang, Yen-Sheng Wang, Yeou-Lih Wang, Yeqi Wang, Yezhou Wang, Yi Fan Wang, Yi Lei Wang, Yi Wang, Yi-Cheng Wang, Yi-Chuan Wang, Yi-Ming Wang, Yi-Ni Wang, Yi-Ning Wang, Yi-Shan Wang, Yi-Shiuan Wang, Yi-Shu Wang, Yi-Tao Wang, Yi-Ting Wang, Yi-Wen Wang, Yi-Xin Wang, Yi-Xuan Wang, Yi-Yi Wang, Yi-Ying Wang, Yi-Zhen Wang, Yi-sheng Wang, YiLi Wang, Yian Wang, Yibin Wang, Yibing Wang, Yichen Wang, Yicheng Wang, Yichuan Wang, Yifan Wang, Yifei Wang, Yigang Wang, Yige Wang, Yihan Wang, Yihao Wang, Yihe Wang, Yijin Wang, Yijing Wang, Yijun Wang, Yikang Wang, Yike Wang, Yilin Wang, Yilu Wang, Yimeng Wang, Yiming Wang, Yin Wang, Yin-Hu Wang, Yinan Wang, Yinbo Wang, Yindan Wang, Ying Wang, Ying-Piao Wang, Ying-Wei Wang, Ying-Zi Wang, Yingbo Wang, Yingcheng Wang, Yingchun Wang, Yingfei Wang, Yingge Wang, Yinggui Wang, Yinghui Wang, Yingjie Wang, Yingmei Wang, Yingna Wang, Yingping Wang, Yingqiao Wang, Yingtai Wang, Yingte Wang, Yingwei Wang, Yingwen Wang, Yingxiong Wang, Yingxue Wang, Yingyi Wang, Yingying Wang, Yingzi Wang, Yinhuai Wang, Yining E Wang, Yinong Wang, Yinsheng Wang, Yintao Wang, Yinuo Wang, Yinxiong Wang, Yinyin Wang, Yiou Wang, Yipeng Wang, Yiping Wang, Yiqi Wang, Yiqiao Wang, Yiqin Wang, Yiqing Wang, Yiquan Wang, Yirong Wang, Yiru Wang, Yirui Wang, Yishan Wang, Yishu Wang, Yitao Wang, Yiting Wang, Yiwei Wang, Yiwen Wang, Yixi Wang, Yixian Wang, Yixuan Wang, Yiyan Wang, Yiyi Wang, Yiying Wang, Yizhe Wang, Yong Wang, Yong-Bo Wang, Yong-Gang Wang, Yong-Jie Wang, Yong-Jun Wang, Yong-Tang Wang, Yongbin Wang, Yongdi Wang, Yongfei Wang, Yongfeng Wang, Yonggang Wang, Yonghong Wang, Yongjie Wang, Yongjun Wang, Yongkang Wang, Yongkuan Wang, Yongli Wang, Yongliang Wang, Yonglun Wang, Yongmei Wang, Yongming Wang, Yongni Wang, Yongqiang Wang, Yongqing Wang, Yongrui Wang, Yongsheng Wang, Yongxiang Wang, Yongyi Wang, Yongzhong Wang, You Wang, Youhua Wang, Youji Wang, Youjie Wang, Youli Wang, Youzhao Wang, Youzhi Wang, Yu Qin Wang, Yu Tian Wang, Yu Wang, Yu'e Wang, Yu-Chen Wang, Yu-Fan Wang, Yu-Fen Wang, Yu-Hang Wang, Yu-Hui Wang, Yu-Ping Wang, Yu-Ting Wang, Yu-Wei Wang, Yu-Wen Wang, Yu-Ying Wang, Yu-Zhe Wang, Yu-Zhuo Wang, Yuan Wang, Yuan-Hung Wang, Yuanbo Wang, Yuanfan Wang, Yuanjiang Wang, Yuanli Wang, Yuanqiang Wang, Yuanqing Wang, Yuanyong Wang, Yuanyuan Wang, Yuanzhen Wang, Yubing Wang, Yubo Wang, Yuchen Wang, Yucheng Wang, Yuchuan Wang, Yudong Wang, Yue Wang, Yue-Min Wang, Yue-Nan Wang, YueJiao Wang, Yuebing Wang, Yuecong Wang, Yuegang Wang, Yuehan Wang, Yuehong Wang, Yuehu Wang, Yuehua Wang, Yuelong Wang, Yuemiao Wang, Yueshen Wang, Yueting Wang, Yuewei Wang, Yuexiang Wang, Yuexin Wang, Yueying Wang, Yueze Wang, Yufei Wang, Yufeng Wang, Yugang Wang, Yuh-Hwa Wang, Yuhan Wang, Yuhang Wang, Yuhua Wang, Yuhuai Wang, Yuhuan Wang, Yuhui Wang, Yujia Wang, Yujiao Wang, Yujie Wang, Yujiong Wang, Yulai Wang, Yulei Wang, Yuli Wang, Yuliang Wang, Yulin Wang, Yuling Wang, Yulong Wang, Yumei Wang, Yumeng Wang, Yumin Wang, Yuming Wang, Yun Wang, Yun Yong Wang, Yun-Hui Wang, Yun-Jin Wang, Yun-Xing Wang, Yunbing Wang, Yunce Wang, Yunchao Wang, Yuncong Wang, Yunduan Wang, Yunfang Wang, Yunfei Wang, Yunhan Wang, Yunhe Wang, Yunong Wang, Yunpeng Wang, Yunqiong Wang, Yuntai Wang, Yunzhang Wang, Yunzhe Wang, Yunzhi Wang, Yupeng Wang, Yuping Wang, Yuqi Wang, Yuqian Wang, Yuqiang Wang, Yuqin Wang, Yusha Wang, Yushe Wang, Yusheng Wang, Yutao Wang, Yuting Wang, Yuwei Wang, Yuwen Wang, Yuxiang Wang, Yuxing Wang, Yuxuan Wang, Yuxue Wang, Yuyan Wang, Yuyang Wang, Yuyin Wang, Yuying Wang, Yuyong Wang, Yuzhong Wang, Yuzhou Wang, Yuzhuo Wang, Z P Wang, Z Wang, Z-Y Wang, Zai Wang, Zaihua Wang, Ze Wang, Zechen Wang, Zehao Wang, Zehua Wang, Zekun Wang, Zelin Wang, Zeneng Wang, Zengtao Wang, Zeping Wang, Zexin Wang, Zeying Wang, Zeyu Wang, Zeyuan Wang, Zezhou Wang, Zhan Wang, Zhang Wang, Zhanggui Wang, Zhangshun Wang, Zhangying Wang, Zhanju Wang, Zhao Wang, Zhao-Jun Wang, Zhaobo Wang, Zhaofeng Wang, Zhaofu Wang, Zhaohai Wang, Zhaohui Wang, Zhaojing Wang, Zhaojun Wang, Zhaoming Wang, Zhaoqing Wang, Zhaosong Wang, Zhaotong Wang, Zhaoxi Wang, Zhaoxia Wang, Zhaoyu Wang, Zhe Wang, Zhehai Wang, Zhehao Wang, Zhen Wang, ZhenXue Wang, Zhenbin Wang, Zhenchang Wang, Zhenda Wang, Zhendan Wang, Zhendong Wang, Zheng Wang, Zhengbing Wang, Zhengchun Wang, Zhengdong Wang, Zhenghui Wang, Zhengkun Wang, Zhenglong Wang, Zhenguo Wang, Zhengwei Wang, Zhengxuan Wang, Zhengyang Wang, Zhengyi Wang, Zhengyu Wang, Zhenhua Wang, Zhenning Wang, Zhenqian Wang, Zhenshan Wang, Zhentang Wang, Zhenwei Wang, Zhenxi Wang, Zhenyu Wang, Zhenze Wang, Zhenzhen Wang, Zheyi Wang, Zheyue Wang, Zhezhi Wang, Zhi Wang, Zhi Xiao Wang, Zhi-Gang Wang, Zhi-Guo Wang, Zhi-Hao Wang, Zhi-Hong Wang, Zhi-Hua Wang, Zhi-Jian Wang, Zhi-Long Wang, Zhi-Qin Wang, Zhi-Wei Wang, Zhi-Xiao Wang, Zhi-Xin Wang, Zhibo Wang, Zhichao Wang, Zhicheng Wang, Zhicun Wang, Zhidong Wang, Zhifang Wang, Zhifeng Wang, Zhifu Wang, Zhigang Wang, Zhige Wang, Zhiguo Wang, Zhihao Wang, Zhihong Wang, Zhihua Wang, Zhihui Wang, Zhiji Wang, Zhijian Wang, Zhijie Wang, Zhijun Wang, Zhilun Wang, Zhimei Wang, Zhimin Wang, Zhipeng Wang, Zhiping Wang, Zhiqi Wang, Zhiqian Wang, Zhiqiang Wang, Zhiqing Wang, Zhiren Wang, Zhiruo Wang, Zhisheng Wang, Zhitao Wang, Zhiting Wang, Zhiwu Wang, Zhixia Wang, Zhixiang Wang, Zhixiao Wang, Zhixin Wang, Zhixing Wang, Zhixiong Wang, Zhixiu Wang, Zhiying Wang, Zhiyong Wang, Zhiyou Wang, Zhiyu Wang, Zhiyuan Wang, Zhizheng Wang, Zhizhong Wang, Zhong Wang, Zhong-Hao Wang, Zhong-Hui Wang, Zhong-Ping Wang, Zhong-Yu Wang, ZhongXia Wang, Zhongfang Wang, Zhongjing Wang, Zhongli Wang, Zhonglin Wang, Zhongqun Wang, Zhongsu Wang, Zhongwei Wang, Zhongyi Wang, Zhongyu Wang, Zhongyuan Wang, Zhongzhi Wang, Zhou Wang, Zhou-Ping Wang, Zhoufeng Wang, Zhouguang Wang, Zhuangzhuang Wang, Zhugang Wang, Zhulin Wang, Zhulun Wang, Zhuo Wang, Zhuo-Hui Wang, Zhuo-Jue Wang, Zhuo-Xin Wang, Zhuowei Wang, Zhuoying Wang, Zhuozhong Wang, Zhuqing Wang, Zi Wang, Zi Xuan Wang, Zi-Hao Wang, Zi-Qi Wang, Zi-Yi Wang, Zicheng Wang, Zifeng Wang, Zihan Wang, Ziheng Wang, Zihua Wang, Zihuan Wang, Zijian Wang, Zijie Wang, Zijue Wang, Zijun Wang, Zikang Wang, Zikun Wang, Ziliang Wang, Zilin Wang, Ziling Wang, Zilong Wang, Zining Wang, Ziping Wang, Ziqi Wang, Ziqian Wang, Ziqiang Wang, Ziqing Wang, Ziqiu Wang, Zitao Wang, Ziwei Wang, Zixi Wang, Zixia Wang, Zixian Wang, Zixiang Wang, Zixu Wang, Zixuan Wang, Ziyi Wang, Ziying Wang, Ziyu Wang, Ziyun Wang, Zongbao Wang, Zonggui Wang, Zongji Wang, Zongkui Wang, Zongqi Wang, Zongwei Wang, Zou Wang, Zulong Wang, Zumin Wang, Zun Wang, Zunxian Wang, Zuo Wang, Zuoheng Wang, Zuoyan Wang, Zusen Wang
articles
Zhezhe Chen, Qiongjun Zhu, Hong Xu +8 more · 2025 · Nature communications · Nature · added 2026-04-24
Many patients are suffering from atherosclerosis without typical risk factors, which can cause severe cardiovascular complications. Trimethylamine N-oxide (TMAO), derived from gut microbes, is a key u Show more
Many patients are suffering from atherosclerosis without typical risk factors, which can cause severe cardiovascular complications. Trimethylamine N-oxide (TMAO), derived from gut microbes, is a key unconventional contributor to the development of atherosclerosis. Here we present a strategy performed by orally administered nano-functionalized probiotics (PDMF@LGG) to inhibit TMAO through the gut microbiota-trimethylamine (TMA)-TMAO axis. PDMF@LGG, composed of polydopamine-coated Lacticaseibacillus rhamnosus GG and nanoparticles based on a reactive oxygen species (ROS)-responsive polymeric prodrug of fluoromethylcholine (FMC), can promote the retention of probiotics and nanoparticles in the intestine to persistently scavenge elevated ROS and release drugs. This process suppresses TMA production and absorption, lowering plasma TMAO levels. The therapeutic effects on male ApoE Show less
📄 PDF DOI: 10.1038/s41467-025-66448-7
APOE
Xianbing Bai, Hongmei Du, Xiangxuan Liu +9 more · 2025 · Molecular neurobiology · Springer · added 2026-04-24
Sleep Deprivation (SD) severely disrupts emotional regulation, predisposing individuals to mood disturbances and anxiety. However, the precise mechanisms underlying anxiety triggered by sleep loss rem Show more
Sleep Deprivation (SD) severely disrupts emotional regulation, predisposing individuals to mood disturbances and anxiety. However, the precise mechanisms underlying anxiety triggered by sleep loss remain elusive. In this study, a mouse model of chronic SD was established using a continuously running treadmill paradigm for 28 days. SD induced anxiety-like behaviors and hippocampal ApoE downregulation. Furthermore, SD downregulated the expression of the autophagy-related protein ATG5 and upregulated p62. In addition, SD inhibited AMPK phosphorylation and induced mTOR phosphorylation. Levels of pro-inflammatory cytokines, including TNF-α, IL-1β, and IL-18, were markedly increased. Immunofluorescence staining revealed a notable increase in the activation of microglia and astrocytes in the hippocampi of SD mice. Either hippocampal overexpression of ApoE via bilateral AAV injection or rapamycin treatment significantly alleviated anxiety-like behaviors, enhanced autophagy, and reduced neuroinflammation in SD mice. Thus, SD induces anxiety by suppressing autophagy level. This effect is mediated through the inhibition of ApoE-dependent AMPK phosphorylation and the concomitant promotion of mTOR phosphorylation, revealing a potential therapeutic target. Show less
no PDF DOI: 10.1007/s12035-025-05610-0
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
Anjing Liu, Roulan Jiang, Ruixi Li +16 more · 2025 · Research square · added 2026-04-24
Molecular QTL studies quantify whether genetic variants affect molecular traits, but non-linear effects including distributional patterns, variance, and interactions provide mechanistic insights beyon Show more
Molecular QTL studies quantify whether genetic variants affect molecular traits, but non-linear effects including distributional patterns, variance, and interactions provide mechanistic insights beyond mean-level associations. Methods for detecting distributional effects have been developed for eQTL analysis, yet applications have focused on method demonstrations rather than large-scale biological discovery. We comprehensively mapped quantile, variance, and interaction QTLs across 34 data-set from 22 molecular contexts in >2,300 human brain donors, revealing that 48.7% of quantile QTLs (qQTLs) exhibit context-dependent regulation invisible to linear models, with enrichment at phenotypic extremes and in cell-type-specific regulatory elements, chromatin accessibility regions, and long-range chromosomal contacts. qQTL variants explained additional trait heritability beyond linear QTLs for brain-related traits. At Alzheimer's disease (AD) risk loci, qQTL analysis revealed complex regulatory architecture including variance effects at Show less
📄 PDF DOI: 10.21203/rs.3.rs-8219833/v1
APOE
Chang Sheng, Rui Zhou, Hongcai Wang +4 more · 2025 · Journal of the American Heart Association · added 2026-04-24
Estimated pulse wave velocity (ePWV), a noninvasive marker of arterial stiffness, reflects vascular aging and has been associated with increased coronary artery disease (CAD) risk. However, the interp Show more
Estimated pulse wave velocity (ePWV), a noninvasive marker of arterial stiffness, reflects vascular aging and has been associated with increased coronary artery disease (CAD) risk. However, the interplay between ePWV and genetic factors, including polygenic risk score (PRS) and apolipoprotein E genotypes, in determining CAD susceptibility remains unclear. We analyzed data from the HRS (Health and Retirement Study), including 5856 participants (4741 White and 1115 Black individuals) without baseline CAD. ePWV was calculated, and genetic risk was assessed using PRS and apolipoprotein E genotyping. Cox proportional hazards models evaluated the associations between ePWV, genetic predisposition, and CAD incidence, with stratified analyses by race and sex. Mediation analyses explored underlying mechanisms. Elevated ePWV (≥10 m/s) was significantly associated with increased CAD risk (hazard ratio [HR], 1.50 [95% CI, 1.25-1.81], Vascular aging and genetic predisposition interact in complex ways to influence CAD risk, with notable variations across racial and sex subgroups. These findings highlight the need for personalized prevention strategies incorporating both vascular health and genetic risk profiling. Show less
📄 PDF DOI: 10.1161/JAHA.125.042610
APOE
Wenhuang Guo, Jinyun Wang, Zaoshang Chang +6 more · 2025 · Scientific reports · Nature · added 2026-04-24
Regular exercise training has been shown to significantly decrease atherosclerosis (AS) related mortality and hospitalization rates. Recent research has identified that circulating exosome-derived mic Show more
Regular exercise training has been shown to significantly decrease atherosclerosis (AS) related mortality and hospitalization rates. Recent research has identified that circulating exosome-derived microRNAs (miRNAs) are closely related to the progression of AS through intercellular communication. But the role of exosome-derived miRNAs in exercise-mediated protection remains to be explored. This study proposes that exercise may ameliorate vascular dysfunction and plaque formation associated with AS by modulating the expression profile of exosomal miRNAs. In this study, ApoE Show less
📄 PDF DOI: 10.1038/s41598-025-30174-3
APOE
Chenming Liu, Sutong Xu, Hongkai Yao +7 more · 2025 · Journal of neuroinflammation · BioMed Central · added 2026-04-24
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders characterized by β-amyloid (Aβ) deposition, neurofibrillary tangles, neuronal loss, and neuroinflammation. It represen Show more
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders characterized by β-amyloid (Aβ) deposition, neurofibrillary tangles, neuronal loss, and neuroinflammation. It represents a growing global health crisis. Although astrocytes contribute to neuroinflammatory cascades, their molecular regulators in AD progression remains elusive. Here, through single-cell transcriptomic analysis, we identified SerpinA3N as a disease-progressive modulator upregulated in AD astrocytes, with expression levels correlating with pathological severity. Astrocytic SerpinA3N knockdown in AD mice rescued cognitive deficits across multiple behavioral tests, and concurrently attenuated neuroinflammatory responses, as evidenced by decreased astrocytic/microglial activation and reduced cytotoxic substance release. Moreover, histopathological analyses demonstrated decreased neuronal loss and Aβ deposition following SerpinA3N knockdown. Mechanistically, we elucidated that SerpinA3N cooperated with APOE to exacerbate AD pathology through NFκB signaling activation. Our study uncovers a novel astrocyte-mediated pathogenic cascade driving AD progression and establishes SerpinA3N as a promising therapeutic target for neuroinflammation modulation in AD. Show less
📄 PDF DOI: 10.1186/s12974-025-03644-8
APOE
Qijun Wo, Jiafeng Shou, Jun Shi +4 more · 2025 · PloS one · PLOS · added 2026-04-24
Prostate cancer (PCa) remains a leading cause of cancer-related mortality in men, with challenges in diagnosis and treatment due to tumor heterogeneity. This study identifies palmitoylation-related si Show more
Prostate cancer (PCa) remains a leading cause of cancer-related mortality in men, with challenges in diagnosis and treatment due to tumor heterogeneity. This study identifies palmitoylation-related signature genes as potential diagnostic and therapeutic targets. Integrating GEO datasets, six differentially expressed genes (DEGs) linked to palmitoylation were identified. Machine learning algorithms (LASSO, RF, SVM) selected three core genes: TRPM4, LAMB3, and APOE. A diagnostic model based on these genes achieved an AUC of 0.929, demonstrating robust accuracy in distinguishing PCa from normal tissues. Functional analysis revealed roles in lipid metabolism and immune modulation, with ssGSEA highlighting correlations between key genes and immune cell infiltration. Experimental validation showed that LAMB3 overexpression suppressed PCa cell proliferation, migration, and invasion, while knockdown enhanced these processes. Molecular docking identified diethylstilbestrol as a potential therapeutic agent targeting LAMB3 and APOE. These findings emphasize the clinical relevance of palmitoylation-related genes in PCa diagnosis and therapy, offering novel biomarkers and insights for personalized treatment strategies. Show less
📄 PDF DOI: 10.1371/journal.pone.0338407
APOE
Tao Geng, Mengwei Feng, Kaiyan Wang +11 more · 2025 · FASEB journal : official publication of the Federation of American Societies for Experimental Biology · added 2026-04-24
The uptake of modified lipoproteins by macrophages to form foam cells is a crucial step in atherosclerosis (AS) development. N7-methylguanosine (m7G) is frequently methylated internally in eukaryotic Show more
The uptake of modified lipoproteins by macrophages to form foam cells is a crucial step in atherosclerosis (AS) development. N7-methylguanosine (m7G) is frequently methylated internally in eukaryotic RNA transcripts and plays a crucial role in various processes. This study aimed to investigate the m7G RNA methylation profile in AS. We employed high-throughput sequencing to analyze the m7G methylome in foam cells induced by ox-LDL, using an in vitro AS model. Then, m7G-seq, RNA-seq, bioinformatic analysis, cell biological analyses, followed by qRT-PCR were performed. Additionally, the roles of SCARB2 and RASSF8 were investigated in an in vivo AS mouse model, and cells with SCARB2/RASSF8 overexpression/knockdown. In vitro and in vivo oil red O staining confirmed the successful establishment of the atherosclerotic foam cell and mouse models. We identified 1197 m7G peaks and 430 differentially expressed mRNAs during foam cell formation. Bioinformatics analyses revealed different m7G peaks associated with the gonadotropin-releasing hormone (GnRH) signaling pathway, cytoskeleton-dependent intracellular transport, and mitochondrial organization, regulating the processes of macrophage foaminess. Moreover, 28 key differentially expressed methylated genes were identified. m7G methyltransferases (WDR4, METTL1, WBSCR22) were upregulated in the AS cell model, and m7G modification genes (SCARB2 and RASSF8) associated with pathological processes were confirmed. Immunofluorescence staining showed that RASSF8 and SCARB2 were both expressed in AS mice plaque tissues. Finally, RASSF8/SCARB2 overexpression could promote apoptosis and lipid accumulation of ox-LDL-induced RAW264.7 cells. An m7G transcriptome-wide map of AS in vitro was created, and the differentially m7G methylated genes SCARB2 and RASSF8 may be crucial in macrophage foaminess. Our findings offer novel insights into the underlying mechanisms and potential treatments for AS. Show less
no PDF DOI: 10.1096/fj.202501027RR
APOE
Zhiwang Zhang, Fan Yang, Wei Wang +7 more · 2025 · Molecular biomedicine · BioMed Central · added 2026-04-24
Mitochondria play an essential role in regulating various physiological functions including bioenergetics, calcium homeostasis, redox signaling, and lipid metabolism and also are involved in the patho Show more
Mitochondria play an essential role in regulating various physiological functions including bioenergetics, calcium homeostasis, redox signaling, and lipid metabolism and also are involved in the pathogenesis of cardiovascular diseases. However, the relationship between mitochondrial calcium homeostasis in vascular smooth muscle cells (VSMCs) and atherosclerosis remains poorly understood. Here, we demonstrate that cholesterol induces mitochondrial calcium overload and lipid accumulation in VSMCs, which is resulted from dysregulation of mitochondrial calcium uniporter (MCU), as evidenced by genetic and pharmacologic inhibition of MCU. Furthermore, MCU inhibitors alleviate Western diet-induced atherosclerosis in ApoE-/- mice. Mechanistically, high-fat and high-cholesterol diets induce the contact between mitochondria and the endoplasmic reticulum (ER) in VSMCs as indicated by transmission electron microscopy, proximity ligation assay and immunofluorescence staining, which increases the formation of mitochondria-associated membranes (MAMs), leading to Ca2 + release from the ER into the mitochondria and thus elevating Ca2 + in the mitochondria. Using mitochondrial calcium uptake 1 (MICU1) mutant and Ca2 + detection assay, we confirmed that this increased Ca2 + binds to MICU1, a blocker of MCU, to impair its ability to block MCU, thus enabling the MCU to remain open and resulting in mitochondrial calcium overload. Further, mitochondrial calcium overload dysregulates fatty acid β-oxidation by modulating medium-chain acyl-CoA dehydrogenase (ACADM), thereby leading to lipid deposition. The inhibition of MCU alleviates the pathological changes elecited by cholesterol. Our findings unveil the previously unrecognized role of MAM-MICU1-MCU axis in cholesterol-induced mitochondrial calcium overload and atherosclerosis, indicating that MCU represents a promising therapeutic target for the treatment of atherosclerosis. Show less
📄 PDF DOI: 10.1186/s43556-025-00384-2
APOE
Olav M Andersen, Matthijs W J de Waal, Giulia Monti +103 more · 2025 · Molecular neurodegeneration · BioMed Central · added 2026-04-24
Olav M Andersen, Matthijs W J de Waal, Giulia Monti, Niccolo Tesi, Anne Mette G Jensen, Christa de Geus, Rosalina van Spaendonk, Maartje Vogel, Shahzad Ahmad, Najaf Amin, Philippe Amouyel, Gary W Beecham, Céline Bellenguez, Claudine Berr, Joshua C Bis, Anne Boland, Paola Bossù, Femke Bouwman, Jose Bras, Camille Charbonnier, Jordi Clarimon, Carlos Cruchaga, Antonio Daniele, Jean-François Dartigues, Stéphanie Debette, Jean-François Deleuze, Nicola Denning, Anita L Destefano, Oriol Dols-Icardo, Cornelia M Van Duijn, Lindsay A Farrer, Maria Victoria Fernández, Wiesje M van der Flier, Nick C Fox, Daniela Galimberti, Emmanuelle Genin, Johan J P Gille, Benjamin Grenier-Boley, Detelina Grozeva, Yann Le Guen, Rita Guerreiro, Jonathan L Haines, Clive Holmes, Holger Hummerich, M Arfan Ikram, M Kamran Ikram, Amit Kawalia, Robert Kraaij, Jean-Charles Lambert, Marc Lathrop, Afina W Lemstra, Alberto Lleó, Richard M Myers, Marcel M A M Mannens, Rachel Marshall, Eden R Martin, Carlo Masullo, Richard Mayeux, Simon Mead, Patrizia Mecocci, Alun Meggy, Merel O Mol, Benedetta Nacmias, Adam C Naj, Valerio Napolioni, J Nicholas Cochran, Gaël Nicolas, Florence Pasquier, Pau Pastor, Margaret A Pericak-Vance, Yolande A L Pijnenburg, Fabrizio Piras, Olivier Quenez, Alfredo Ramirez, Rachel Raybould, Richard Redon, Marcel J T Reinders, Anne-Claire Richard, Steffi G Riedel-Heller, Fernando Rivadeneira, Jeroen G J van Rooij, Stéphane Rousseau, Natalie S Ryan, Pascual Sanchez-Juan, Gerard D Schellenberg, Philip Scheltens, Jonathan M Schott, Sudha Seshadri, Daoud Sie, Rebecca Sims, Erik A Sistermans, Sandro Sorbi, John C Van Swieten, Betty Tijms, André G Uitterlinden, Pieter Jelle Visser, Michael Wagner, David Wallon, Li-San Wang, Julie Williams, Jennifer S Yokoyama, Aline Zarea, Sven J van der Lee, Johan G Olsen, Marc Hulsman, Henne Holstege Show less
Protein truncating variants (PTVs) in To identify high-priority missense variants (HPVs), we applied ‘domain mapping of disease mutations’ for the 637 unique coding In this sample, PTVs and HPVs assoc Show more
Protein truncating variants (PTVs) in To identify high-priority missense variants (HPVs), we applied ‘domain mapping of disease mutations’ for the 637 unique coding In this sample, PTVs and HPVs associated with respectively a 35- and 10-fold increased risk of early onset AD and 17- and 6-fold increased risk of overall AD. The median age at onset (AAO) of PTV- and HPV-carriers was 62 and 64 years, and Our results justify a debate on whether HPV carriers should be considered for clinical counseling. The online version contains supplementary material available at 10.1186/s13024-025-00907-z. Show less
📄 PDF DOI: 10.1186/s13024-025-00907-z
APOE
Chih-Hsien Wang, Yu-Chen Chou, Hsin-Yun Li +7 more · 2025 · Mikrochimica acta · Springer · added 2026-04-24
Relying on a single biomarker in biomedical analysis is often insufficient for accurate disease or pathogen determination. A recent trend is using simultaneous multiplex detection of multiple biomarke Show more
Relying on a single biomarker in biomedical analysis is often insufficient for accurate disease or pathogen determination. A recent trend is using simultaneous multiplex detection of multiple biomarkers to improve diagnostic accuracy and throughput. To enable multiplex detection, we developed a series of surface-enhanced Raman scattering (SERS) nanoprobes, referred to as nanoaggregate-embedded beads (NAEBs). These NAEBs were synthesized using three distinct Raman reporter molecules: Safranin O, ethyl violet, and cresyl violet acetate. By integrating the NAEBs with magnetic nanoparticles and a simple capillary magnetofluidic device, we developed a rapid and simultaneous multiplex detection platform for genetic analysis of an aquacultural pathogen Vibrio parahaemolyticus (VP) for pirA, pirB, and ompA and genotyping of Alzheimer's disease's risk factor biomarker Apoliproprotein E (ApoE). For VP detection, a limit of detection (LOD) as low as ~ 10 Show less
no PDF DOI: 10.1007/s00604-025-07724-7
APOE
Hongxin Cheng, Qing Zhang, Wen Zhong +6 more · 2025 · Signal transduction and targeted therapy · Nature · added 2026-04-24
Atherosclerosis serves as the core pathological basis of cardiovascular, cerebrovascular, and peripheral arterial diseases, posing a serious threat to human health. However, current mainstream treatme Show more
Atherosclerosis serves as the core pathological basis of cardiovascular, cerebrovascular, and peripheral arterial diseases, posing a serious threat to human health. However, current mainstream treatments such as statin drugs and stent implantation are associated with significant side effects or limited efficacy, highlighting the urgent need for new therapeutic strategies. Pulsed electromagnetic fields (PEMFs), due to their noninvasive nature and anti-inflammatory properties, show potential in the treatment of atherosclerosis. This study utilized ApoE-/- mice, ApoE-/-NLRP3-/- knockout mice, human umbilical vein endothelial cells (HUVECs), human aortic endothelial cells (HAECs), and human plasma samples for experiments, revealing significant endothelial cell (EC) inflammation and pyroptosis during the progression of atherosclerosis. PEMFs were found to effectively inhibit the activation of the NLRP3 inflammasome, reduce plaque formation, and delay the progression of atherosclerosis. Proteomic analysis of plasma from atherosclerosis patients further indicated elevated expression levels of proteins related to inflammation and pyroptosis, with particularly notable changes in membrane proteins. Mechanistic studies demonstrated that PEMFs improve mitochondrial dysfunction in ECs by regulating membrane tension and the mechanosensitive tension-mediated transient receptor potential vanilloid 4 (TRPV4) channels, thereby reducing pyroptosis. This discovery not only reveals a novel mechanobiological pathway but also provides a solid theoretical foundation for the development of PEMF-based therapies for atherosclerosis. Schematic diagram of the mechanism by which PEMFs treat atherosclerosis (created in BioRender). Wei, B. (2025) https://BioRender.com/undefined ). Show less
📄 PDF DOI: 10.1038/s41392-025-02479-2
APOE
Guofu Zhong, Qingqing Liu, Qing Zhang +11 more · 2025 · Phytomedicine : international journal of phytotherapy and phytopharmacology · Elsevier · added 2026-04-24
Sparstolonin B (SSNB) and Curcumin (Cur), from a pair of compatible herbs, were previously identified as anti-inflammation and T helper 17 (Th17) modulation reagents. However, their compatible roles i Show more
Sparstolonin B (SSNB) and Curcumin (Cur), from a pair of compatible herbs, were previously identified as anti-inflammation and T helper 17 (Th17) modulation reagents. However, their compatible roles in atherosclerosis (AS) and underlying mechanisms remain uninvestigated. In vivo, the apoE The gene-disease interaction and hub gene network reveals Th17-associated genes in the pathogenesis of atherosclerosis. In vitro, SSNB and Cur reduced oxLDL-induced BMDC activation by downregulating CD36. SSNB showed stronger inhibition to inflammatory activation of DC, while Cur more intensively suppressed co-stimulatory molecules. For the Th17/Treg bias in co-culture of BMDC and CD4 Our findings reveal, for the first time, that SSNB and Cur alleviate AS by modulating Th17-stromal cell interactions, with the IL-17RA-TAK1-NF-κB pathway as a related mediator. Notably, SSNB and Cur exhibit distinct anti-atherogenic roles. SSNB primarily targets TLR4/CD36 to inhibit DC activation, Th17 differentiation, VSMC inflammation and mainly inhibited TAK1 phosphorylation, while Cur more significant inhibited macrophage inflammation, and more directly inhibited NF-κB P65 phosphorylation. This study will be valuable for developing novel and precise adjuvant therapies for AS. Show less
no PDF DOI: 10.1016/j.phymed.2025.157578
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
Zi Wang, Yinan Wang, Ruosen Yuan +8 more · 2025 · Molecular biomedicine · BioMed Central · added 2026-04-24
Phenotypic switching of vascular smooth muscle cells (VSMCs) from a contractile toward a synthetic phenotype plays a critical role in atherosclerosis. Although the redox-sensitive sentrin/Small Ubiqui Show more
Phenotypic switching of vascular smooth muscle cells (VSMCs) from a contractile toward a synthetic phenotype plays a critical role in atherosclerosis. Although the redox-sensitive sentrin/Small Ubiquitin-like Modifier (SUMO)-specific protease 3 (SENP3), which preferentially deconjugates SUMO2/3, has been linked to oxidative stress, its role in atherosclerosis remains poorly defined. In this study, we demonstrate that SENP3 is significantly upregulated in human and mouse atherosclerotic lesions and in VSMCs exposed to pro-atherogenic stimuli. Using smooth muscle-specific Senp3 knockout mice (ApoE Show less
📄 PDF DOI: 10.1186/s43556-025-00365-5
APOE
Liugui Chen, Suyu Yang, Di Wang +1 more · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
Glaucoma is a neurodegenerative disease characterized by the progressive loss of retinal ganglion cell and optic nerve damage. Recent studies have highlighted the pivotal role of microglia in the onse Show more
Glaucoma is a neurodegenerative disease characterized by the progressive loss of retinal ganglion cell and optic nerve damage. Recent studies have highlighted the pivotal role of microglia in the onset and progression of glaucoma. This review aims to elucidate the key mechanisms of microglial activation in glaucoma and assess its potential as a therapeutic target for novel treatment strategies. Microglia activation in glaucoma is multifactorial, driven by biomechanical, metabolic, and inflammatory signals. Activated microglia contribute to both neuroinflammatory injury and neuroprotective responses. Their interaction with other kinds of cell establishes a dynamic inflammatory signaling network that exacerbates retinal ganglion cell loss. Furthermore, emerging evidence suggests that key targets in microglial activation, such as APOE, LGALS3, CX3CR1, etc. play critical roles in disease progression, revealing promising targets for therapeutic intervention. Microglia act as central regulators of the retinal immune microenvironment in glaucoma. Their dual role in neurotoxicity and neuroprotection is shaped by complex interactions with other kinds of cell. Targeting microglial activation state and restoring metabolic homeostasis represent promising strategies for the development of pressure-independent treatments for glaucoma. Show less
📄 PDF DOI: 10.3389/fimmu.2025.1685495
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
Lifang Chen, Wei Zhang, Huan Chen +11 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
Histone deacetylase 3 (HDAC3) is an epigenetic modifying enzyme closely linked to the development of atherosclerosis. Endothelial inflammation is a critical factor in atherosclerosis. However, the rol Show more
Histone deacetylase 3 (HDAC3) is an epigenetic modifying enzyme closely linked to the development of atherosclerosis. Endothelial inflammation is a critical factor in atherosclerosis. However, the role of HDAC3 in mediating epigenetic modifications and regulating endothelial inflammation in atherosclerosis remains unclear. This study aims to investigate the impact of HDAC3 on endothelial inflammation and its contribution to atherosclerosis. Firstly, single-cell transcriptomic analysis identified elevated expression of HDAC3 and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) in inflammatory endothelial cells of atherosclerotic plaques in symptomatic patients. Endothelial-specific knockout HDAC3 in an apolipoprotein E knockout (ApoE Show less
📄 PDF DOI: 10.1038/s41418-025-01620-6
APOE
Hao Xu, Junjie Ma, Nanjun Li +6 more · 2025 · NPJ precision oncology · Nature · added 2026-04-24
Thyroid cancer, the most common endocrine malignancy, is characterized by a unique and complex tumor microenvironment (TME). To unravel the high tumor heterogeneity and molecular mechanisms driving ca Show more
Thyroid cancer, the most common endocrine malignancy, is characterized by a unique and complex tumor microenvironment (TME). To unravel the high tumor heterogeneity and molecular mechanisms driving cancer progression, we performed single-cell RNA sequencing (scRNA-seq) analysis, enabling a comprehensive exploration of cellular diversity and molecular dynamics at single-cell resolution. We employed Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and subsequent identification of cellular clusters. Differential gene expression analysis across subclusters was conducted using the FindAllMarkers function, while the DoHeatmap function was utilized to visualize the distribution of differentially expressed genes. The AUCell algorithm was applied to evaluate pathway enrichment within specific cell subtypes. To decipher cellular communication networks, we integrated the CellChat and NicheNet algorithms, which revealed intricate intercellular signaling interactions. Finally, multiplex immunohistochemistry (mIHC) was performed to validate key cellular interactions identified in silico. By analyzing 405,077 single cells from 50 thyroid cancer samples (including papillary, anaplastic, and metastatic tumors) and 14 normal thyroid tissues, we identified four major cellular subpopulations through unbiased clustering based on gene expression patterns and representative cellular markers. The TME was found to encompass diverse immune, endothelial, and mesenchymal cell subtypes, including novel populations such as CD4 + HSPA1A + T cells. Functional pathway enrichment analysis highlighted the roles of abundant cell types in tumor progression. Cell-cell communication analysis uncovered potential immunotherapeutic targets and revealed critical crosstalk among hub niche cells, including APOE+ macrophages, EMT-like cancer-associated fibroblasts (CAFs), and RBP7+ endothelial cells. These findings were further validated by multiplex immunohistochemistry, confirming the spatial organization and interactions of these cell populations within the TME. Our study provides a comprehensive single-cell transcriptomic atlas of thyroid cancer, offering profound insights into tumor heterogeneity, the functional roles of key niche cells, and potential biomarkers for anticancer therapy. These findings not only enhance our understanding of thyroid cancer biology but also pave the way for the development of novel therapeutic strategies targeting the TME. Show less
📄 PDF DOI: 10.1038/s41698-025-00924-7
APOE
Yuemei Zhang, Yuxin Cao, Yongxin Sun +12 more · 2025 · Proceedings of the National Academy of Sciences of the United States of America · National Academy of Sciences · added 2026-04-24
The activation of blood monocytes and the infiltration of monocyte-derived macrophages into the vessel walls are the central part of atherosclerosis. However, the mechanisms underlying the processes r Show more
The activation of blood monocytes and the infiltration of monocyte-derived macrophages into the vessel walls are the central part of atherosclerosis. However, the mechanisms underlying the processes remain unclear. Here, we report that G-protein signaling modulator 1 (GPSM1) plays a critical role in atherogenesis. We found that GPSM1 expression in lesional macrophages was increased during atherosclerosis development both in mice and humans. Myeloid-specific GPSM1 ablation protects mice against atherosclerosis and reduces aortic inflammation in both Show less
no PDF DOI: 10.1073/pnas.2517531122
APOE
Xinxin Wang, Ryan Christ, Erica Young +8 more · 2025 · medRxiv : the preprint server for health sciences · Cold Spring Harbor Laboratory · added 2026-04-24
A key methodological challenge for genome-wide association studies is how to leverage haplotype diversity and allelic heterogeneity to improve trait association power, especially in noncoding regions Show more
A key methodological challenge for genome-wide association studies is how to leverage haplotype diversity and allelic heterogeneity to improve trait association power, especially in noncoding regions where it is difficult to predict variant impacts and define functional units for variant aggregation. Genealogy-based association methods have the potential to bridge this gap by testing combinations of common and rare haplotypes based purely on their ancestral relationships. In parallel work, we have developed an efficient local ancestry inference engine and a novel statistical method (LOCATER) for combining signals present on different branches of a locus specific haplotype tree. Here, we developed a genome-wide LOCATER analysis pipeline and applied it to a genome sequencing study of 6,795 Finnish individuals with 101 cardiometabolic traits and 18.9 million autosomal variants. We identify 351 significant trait associations at 47 distinct genomic loci and find that LOCATER boosts single marker test (SMT) association signal at 5 loci by combining independent signals from distinct alleles. LOCATER successfully recovers known quantitative trait loci not found by SMT, including Show less
no PDF DOI: 10.1101/2024.11.04.24316696
APOE
Qi Chen, Yuan-Shu Peng, Qian Zhong +11 more · 2025 · Journal of nanobiotechnology · BioMed Central · added 2026-04-24
Atherosclerosis (AS) is a chronic inflammatory disorder characterized by foam cell formation and persistent inflammation as central pathological drivers. Although colchicine (Col) exhibits potent anti Show more
Atherosclerosis (AS) is a chronic inflammatory disorder characterized by foam cell formation and persistent inflammation as central pathological drivers. Although colchicine (Col) exhibits potent anti-inflammatory activities, its clinical application is limited by a narrow therapeutic window. In the present study, we developed phosphatidylserine-exposing nanovesicles (Col@PSVs) that leverage the innate phagocytic capacity of macrophage-derived foam cells by presenting surface "eat-me" signals, thereby enabling targeted immune modulation. The synergistic collaboration between Col and PSVs allows low-dose Col to retain robust anti-inflammatory efficacy while mitigating dose-dependent toxicity. Mechanistically, Col@PSVs potently suppress CCR7-mediated NF-κB signaling activation in foam cells, leading to a marked downregulation of pro-inflammatory cytokine and disruption of inflammatory cascades. In ApoE Show less
📄 PDF DOI: 10.1186/s12951-025-03840-x
APOE
Jing Liu, Junshuang Wang, Shuang Lv +7 more · 2025 · PloS one · PLOS · added 2026-04-24
Radiation-induced brain injury (RIBI) is a significant complication following radiotherapy for brain tumors, leading to neurocognitive deficits and other neurological impairments. This study aims to i Show more
Radiation-induced brain injury (RIBI) is a significant complication following radiotherapy for brain tumors, leading to neurocognitive deficits and other neurological impairments. This study aims to identify potential biomarkers and therapeutic targets for RIBI by utilizing advanced proteomic techniques to explore the molecular mechanisms underlying RIBI. A rat model of RIBI was established and subjected to whole-brain irradiation (30 Gy). Tandem mass tagging (TMT)-based quantitative proteomics, combined with high-resolution mass spectrometry, was used to identify differentially expressed proteins (DEPs) in the brain tissues of irradiated rats. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to identify the biological processes and pathways involved. Protein-protein interaction (PPI) networks were constructed to identify key hub proteins. A total of 35 DEPs were identified, including PHLDA3, APOE and CPE. GO enrichment analysis revealed that the DEPs were mainly involved in lipid transport, cell adhesion, and metabolic processes. KEGG analysis highlighted the enrichment of pathways related to metabolism, tight junctions, and PPAR signaling. APOE was identified as a key hub protein through PPI network analysis, indicating its potential role in RIBI pathophysiology. Immunohistochemistry further validated the increased expression of PHLDA3, APOE, and CPE in the brain tissue of irradiated rats. This study provides valuable insights into the molecular mechanisms of RIBI by identifying key proteins and their associated pathways. The findings suggest that these proteins, particularly APOE and PHLDA3, could serve as potential biomarkers and therapeutic targets for clinical intervention in RIBI. These results not only enhance our understanding of RIBI's molecular pathology but also open new avenues for the development of targeted therapies to mitigate radiation-induced neurotoxicity. Show less
📄 PDF DOI: 10.1371/journal.pone.0337608
APOE
Shaokun Wang, Jingchun Han, Nan Gao +2 more · 2025 · Brain research bulletin · Elsevier · added 2026-04-24
Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is a common complication after carbon monoxide poisoning. This study focused on the role and mechanism of Axin-1 regulating ferrop Show more
Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is a common complication after carbon monoxide poisoning. This study focused on the role and mechanism of Axin-1 regulating ferroptosis in DEACMP. Nissl staining, immunohistochemistry, immunofluorescence and Prussian blue were used to evaluate the histopathology and iron distribution of DEACMP rats. The N6-methyladenosine (m The expression of Axin-1 in DEACMP rats was increased, and its up-regulation was related to IGF2BP2-mediated m IGF2BP2-mediated m Show less
no PDF DOI: 10.1016/j.brainresbull.2025.111624
AXIN1
Mei Lu, Xiaohui Li, Lin Ma +4 more · 2025 · IUBMB life · Wiley · added 2026-04-24
Muscle wasting, characterized by loss of muscle mass and strength, severely impacts patient quality of life and is associated with numerous chronic diseases and aging. The molecular mechanisms are com Show more
Muscle wasting, characterized by loss of muscle mass and strength, severely impacts patient quality of life and is associated with numerous chronic diseases and aging. The molecular mechanisms are complex, involving protein synthesis/degradation imbalance. Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) and ubiquitin-specific peptidase 7 (USP7) have diverse cellular roles, but their coordinated function in skeletal muscle homeostasis remains poorly understood. DYRK1A overexpression in vivo induced muscle atrophy phenotypes, including reduced muscle mass, grip strength, fiber cross-sectional area (CSA), altered fiber type composition, and neuromuscular junction integrity, accompanied by elevated atrophy markers: muscle atrophy F-box protein (Atrogin-1), muscle ring finger 1 (MuRF-1), myostatin and suppressed myogenic markers: myoblast determination protein 1 (MyoD), myogenin (MyoG), myocyte enhancer factor 2C (Mef2c), myogenic factor 5 (Myf5). Conversely, pharmacological inhibition of DYRK1A with Harmine ameliorated these atrophy phenotypes in transgenic DYRK1A overexpressing (TgD) mice. In vivo, USP7 deficiency resulted in similar muscle wasting phenotypes. In vitro, DYRK1A overexpression or USP7 overexpression inhibited C2C12 myoblast proliferation and differentiation, effects rescued by Wnt3a treatment or USP7 knockdown, respectively. Mechanistically, DYRK1A activity suppressed active β-catenin levels. USP7 was found to interact with and deubiquitinate axis inhibition protein 1 (Axin1), leading to its stabilization. Knockdown of USP7 increased Axin1 ubiquitination and degradation, thereby promoting β-catenin signaling and myogenesis, counteracting the effects of DYRK1A. Our findings reveal a novel signaling axis where DYRK1A and USP7 cooperatively suppress Wnt/β-catenin signaling to promote muscle wasting. DYRK1A likely acts upstream, potentially phosphorylating pathway components, whereas USP7 stabilizes the β-catenin destruction complex scaffold protein Axin1 through deubiquitination. This coordinated action inhibits myogenesis and activates atrophy pathways. Targeting DYRK1A or USP7 could represent promising therapeutic strategies for muscle wasting disorders. Show less
no PDF DOI: 10.1002/iub.70061
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Jieyan Wang, Qi Cheng, Fangyu Luo +2 more · 2025 · Medicine · added 2026-04-24
Growing evidence have indicated the bidirectional relationships between various inflammatory cytokines and prostate cancer (PCa), but the causality between genetic susceptibility to inflammatory cytok Show more
Growing evidence have indicated the bidirectional relationships between various inflammatory cytokines and prostate cancer (PCa), but the causality between genetic susceptibility to inflammatory cytokines and PCa was still in initial exploratory phase. This bidirectional Mendelian randomization (MR) research was manipulated to draw causative inferences and the effect of direction between 91 inflammatory cytokines and PCa. Genetic data of PCa were originated from a publicly accessible genome-wide association study with 3269 individuals and 459,664 controls, and inflammatory cytokines summarized by a protein quantitative trait locus study were embodied 14,824 participants. We considered inverse variance weighted as a primarily statistical approach, and utilized MR-Egger regression, weighted median, MR-PRESSO, and simulation extrapolation method to enhance the accuracy of the ultimate outcome. In sensitivity analysis, MR-Egger method and Cochran Q statistic of inverse variance weighted were employed to access the heterogeneity. The results suggested a causal relationship between fms-related tyrosine kinase 3 ligand (Flt3L), recombinant monocyte chemotactic protein (MCP) 2, MCP4, and the incidence of PCa (odds ratio [OR]: 1.0016, 95% confidence interval [CI]: 1.0000-1.0032, P = .045; OR: 0.9979, 95% CI: 0.9958-1.0000, P = .045; OR: 1.0012, 95% CI: 1.0001-1.0023, P = .031). In addition, reverse analysis showed that PCa was correlated with the elevated level of adenosine deaminase, axin-1, C-X-C motif chemokine ligand 6, Flt3L, interleukin (IL)-24, and IL-33 (Beta: 1.7661, 95% CI: 0.2092-3.3229, P = .026; Beta: 1.9185, 95% CI: 0.1548-3.6822, P = .033; Beta: 1.9681, 95% CI: 0.4207-3.5155, P = .013; Beta: 1.6589, 95% CI: 0.0733-3.2446, P = .040; Beta: 2.2091, 95% CI: 0.4682-3.9500, P = .013; Beta: 1.8438, 95% CI: 0.0815-3.6061, P = 040). This study highlighted the causality between several inflammatory factors and the setting of PCa. Specifically, the results suggested that Flt3L and MCP4 may be risk factors for PCa, whereas MCP2 may be a favorable factor for PCa. Conversely, adenosine deaminase, axin-1, C-X-C motif chemokine ligand 6, IL-24, IL-33, and Flt3L were involved in the downstream of PCa progression. Show less
📄 PDF DOI: 10.1097/MD.0000000000044180
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Xian Chen, Hui Wang, Qianqian Li +4 more · 2025 · Discover oncology · Springer · added 2026-04-24
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether c Show more
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether causal relationships exist among these associations remains unclear, as traditional observational studies are susceptible to confounding factors. To evaluate causal relationships between kidney cancer, kidney fibrosis, and inflammatory factors using Mendelian randomization, and explore tumor microenvironment heterogeneity through single-cell analysis. Based on large-scale GWAS data, bidirectional Mendelian randomization analysis was performed to assess causal relationships between kidney cancer and kidney fibrosis, using MR Egger, inverse variance weighted (IVW), and weighted mode methods. Causal associations between kidney cancer and inflammatory factors including Axin-1, C-C motif chemokine 28, and interleukin-10 receptor subunit were analyzed. Single-cell RNA sequencing data from the GEO database (GSM4819725) was integrated for tumor microenvironment analysis. Bidirectional Mendelian randomization analysis revealed no significant causal relationship between kidney cancer and kidney fibrosis [kidney cancer→kidney fibrosis: IVW OR=0.992(95%CI: 0.913-1.077, P=0.842); kidney fibrosis→kidney cancer: IVW OR=0.922(95%CI: 0.824-1.030, P=0.151)]. However, significant positive causal associations were identified between kidney cancer and multiple inflammatory factors: Axin-1 levels [OR=1.448(95%CI: 1.107-1.894, P=0.007)], C-C motif chemokine 28 [OR=1.287(95%CI: 1.076-1.540, P=0.006)], and interleukin-10 receptor subunit [OR=1.135(95%CI: 1.032-1.248, P=0.009)]. Sensitivity analyses confirmed the robustness of results. Single-cell analysis revealed cellular heterogeneity in the tumor microenvironment, including various cell types such as immune cells, T cells, and NK cells, with pseudotime analysis demonstrating cell differentiation trajectories and dynamic gene expression changes. Mendelian randomization analysis provides genetic evidence for causal relationships between kidney cancer and inflammatory factors, while excluding direct causal associations between kidney cancer and kidney fibrosis. Show less
📄 PDF DOI: 10.1007/s12672-025-03343-z
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