πŸ‘€ Xueyi Wang

πŸ” Search πŸ“‹ Browse 🏷️ Tags ❀️ Favourites βž• Add 🧬 Extraction
4397
Articles
2763
Name variants
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
Xiying Ding, Yongxing Zhang, Yang Chen +5 more Β· 2025 Β· Journal of shoulder and elbow surgery Β· Elsevier Β· added 2026-04-24
Rotator cuff tear is the most common tendon injury. Currently, arthroscopic rotator cuff repair (ARCR) is the primary method for diagnosing and treating rotator cuff tear. One of the major complicatio Show more
Rotator cuff tear is the most common tendon injury. Currently, arthroscopic rotator cuff repair (ARCR) is the primary method for diagnosing and treating rotator cuff tear. One of the major complications following ARCR is retear. This study aims to evaluate the correlation between systemic lipid metabolism and retear occurrence after ARCR through a retrospective analysis of postoperative patients. This retrospective study reviewed consecutive patients of a single surgeon who underwent ARCR from January 2021 to January 2022. Eligibility for inclusion required complete sequential follow-up data, encompassing preoperative laboratory tests and a series of postoperative magnetic resonance imaging (MRI) evaluations at 1, 2, 3, and 6 months. Exclusion criteria included patients with incomplete laboratory tests, a history of tumors, prior shoulder surgeries, isolated subscapularis tendon tears, the rotator cuff related muscles are not clearly or completely displayed in MRI, absence of follow-up MRI, or those under treatment with lipid-lowering medications. Logistic regression analysis was employed to identify preoperative factors associated with retear, with statistical significance adjudged at P < .05. From the initial cohort of 400 patients who underwent ARCR during the study period, 202 met both inclusion and exclusion criteria. These patients were subsequently divided into a training group (n = 122) and a test group (n = 80), maintaining a ratio of 6:4. Statistical analysis revealed significant risk factors for post-ARCR retear including high body mass index (>27.1; odds ratio (OR): 5.994, 95% confidential interval (CI): 1.762-13.980; P = .042), subscapularis muscle fatty infiltration of Grades 3 and 4 (OR: 8.509, 95%CI: 3.811-17.702; P = .009), serum apolipoprotein B (ApoB) levels exceeding 1.4 g/L (OR: 9.658, 95%CI: 3.520-21.753; P = .028), and an ApoB/A1 ratio greater than 1.8 (OR: 5.098, 95%CI: 1.787-10.496; P = .016). Conversely, the serum high-density lipoprotein level above 1.2 mmol/L (OR: -3.342, 95%CI: -7.466 to 0.659; P = .039) served as a protective factor. The model incorporating these 5 factors predicted retear with a sensitivity of 78.3% and specificity of 98.0% (area under the curve = 0.924, accuracy = 90.3%). Moreover, a new model comprising 3 lipid metabolism-related factors including high-density lipoprotein, ApoB and the ApoB/A1 ratio showed a sensitivity of 80.5% and specificity of 83.2% (area under the curve = 0.866, accuracy = 85.8%) for predicting retear after ARCR. A predictive model utilizing key systemic lipid metabolism markers including HDL, ApoB, and the ApoB/A1 ratio, demonstrates effective forecasting of retear incidence following ARCR. Show less
no PDF DOI: 10.1016/j.jse.2024.12.031
APOB
Peiwei Xu, Min Nian, Jie Xiang +8 more Β· 2025 Β· Environmental science & technology Β· ACS Publications Β· added 2026-04-24
Per- and polyfluoroalkyl substances (PFAS) pose potential health risks to lipid metabolism, but the effects of emerging PFAS alternatives, particularly in children, remain unclear. This cross-sectiona Show more
Per- and polyfluoroalkyl substances (PFAS) pose potential health risks to lipid metabolism, but the effects of emerging PFAS alternatives, particularly in children, remain unclear. This cross-sectional study investigated the association between emerging PFAS exposure and lipid levels in 294 Chinese children aged 7-10 years, analyzing blood samples for 14 PFAS and lipid profiles, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB). Exposure to 6:2 Cl-PFESA, PFO4DA, and PFO5DoDA was associated with higher TC, TG, and LDL levels, with PFO4DA increasing the TC by 1.7% and PFO5DoDA increasing the TG by 10.7%. Weighted quantile sum (WQS) regression showed mixed PFAS exposure positively associated with TG (0.08, 95% CI: 0.007, 0.153). PFO4DA had the highest weight for TC (0.468), TG (0.327), LDL (0.57), ApoA1 (0.243), and ApoB (0.466), while PFMOAA had the highest weight for HDL (0.332). Bayesian Kernel Machine Regression (BKMR) analysis confirmed positive associations between the PFAS mixture and TC, TG, LDL, and ApoA1. Mediation analysis revealed that mtDNAcn significantly mediated PFAS exposure's effect on TG levels, explaining 27.2-74.2% of the total effect. These findings highlight the need for regulatory action to address the emerging PFAS risks. Show less
no PDF DOI: 10.1021/acs.est.4c13095
APOB
Ze-Yuan Yin, Shi-Min He, Xin-Yuan Zhang +16 more Β· 2025 Β· Acta pharmacologica Sinica Β· Nature Β· added 2026-04-24
Ovarian cancer presents a significant treatment challenge due to its insidious nature and high malignancy. As autophagy is a vital cellular process for maintaining homeostasis, targeting the autophagi Show more
Ovarian cancer presents a significant treatment challenge due to its insidious nature and high malignancy. As autophagy is a vital cellular process for maintaining homeostasis, targeting the autophagic pathway has emerged as an avenue for cancer therapy. In the present study, we identify apolipoprotein B100 (ApoB100), a key modulator of lipid metabolism, as a potential prognostic biomarker of ovarian cancer. ApoB100 functioned as a tumor suppressor in ovarian cancer, and the knockdown of ApoB100 promoted ovarian cancer progression in vivo. Moreover, ApoB100 blocked autophagic flux, which was dependent on interfering with the lipid accumulation/endoplasmic reticulum (ER) stress axis. The effects of LFG-500, a novel synthetic flavonoid, on ApoB100 induction were confirmed using proteomics and lipidomics analyses. Herein, LFG-500 induced lipid accumulation and ER stress and subsequently blocked autophagy by upregulating ApoB100. Moreover, data from in vivo experiments further demonstrated that ApoB100, as well as the induction of the lipid/ER stress axis and subsequent blockade of autophagy, were responsible for the anti-tumor effects of LFG-500 on ovarian cancer. Hence, our findings support that ApoB100 is a feasible target of ovarian cancer associated with lipid-regulated autophagy and provide evidence for using LFG-500 for ovarian cancer treatment. Show less
no PDF DOI: 10.1038/s41401-024-01470-x
APOB
Yu-Hang Wang, Chang-Ping Li, Jing-Xian Wang +6 more Β· 2025 Β· Reviews in cardiovascular medicine Β· added 2026-04-24
Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limi Show more
Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limited. In this observational study, 1111 PMI patients (≀55 years) at Tianjin Chest Hospital from 2017 to 2022 were selected and divided according to their SYNTAX scores into a low-risk group (≀22) and medium-high-risk group (>22). These groups were further randomly assigned to a training or test set in a ratio of 7:3. Lasso-logistic was initially used to screen out target factors. Subsequently, Lasso-logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. After comparing prediction performance, the best model was chosen to build a prediction system for coronary artery severity in PMI patients. Glycosylated hemoglobin (HbA1c), angina, apolipoprotein B (ApoB), total bile acid (TBA), B-type natriuretic peptide (BNP), D-dimer, and fibrinogen (Fg) were associated with the severity of lesions. In the test set, the area under the curve (AUC) of Lasso-logistic, RF, KNN, SVM, and XGBoost were 0.792, 0.775, 0.739, 0.656, and 0.800, respectively. XGBoost showed the best prediction performance according to the AUC, accuracy, F1 score, and Brier score. In addition, we used decision curve analysis (DCA) to assess the clinical validity of the XGBoost prediction model. Finally, an online calculator based on the XGBoost was established to measure the severity of coronary artery lesions in PMI patients. In summary, we established a novel and convenient prediction system for the severity of lesions in PMI patients. This system can swiftly identify PMI patients who also have severe coronary artery lesions before the coronary intervention, thus offering valuable guidance for clinical decision-making. Show less
πŸ“„ PDF DOI: 10.31083/RCM26102
APOB
Zixiang Ye, Enmin Xie, Zhangyu Lin +5 more Β· 2025 Β· Nutrition journal Β· BioMed Central Β· added 2026-04-24
This study aims to evaluate the relationship between apolipoproteins (ApoA1, ApoB, and the ApoB/A1 ratio) and the incidence of major adverse cardiovascular events (MACE) in patients with coronary arte Show more
This study aims to evaluate the relationship between apolipoproteins (ApoA1, ApoB, and the ApoB/A1 ratio) and the incidence of major adverse cardiovascular events (MACE) in patients with coronary artery disease (CAD) and impaired kidney function, assessing their potential role in secondary prevention. A prospective cohort of 1,640 patients with impaired kidney function who underwent percutaneous coronary intervention in China was analyzed. Patients were categorized based on the measurements of ApoA1, ApoB, and ApoB/A1 ratio. MACE, defined as a composite of all-cause mortality, cardiovascular death, nonfatal myocardial infarctions, strokes, and unplanned revascularizations, was tracked post-procedure, with statistical analyses including Kaplan-Meier survival curves and Cox regression models to identify associations with apolipoproteins. Subgroup analyses according to kidney function were conducted. During a median follow-up of 3.1Β years, 324 MACE events were observed. Multivariable Cox regression analyses illustrated higher levels of ApoB and the ApoB/A1 ratio were significantly associated with increased MACE incidence (adjusted HR [95%CI] 1.668[1.044-2.666]; adjusted HR [95%CI] 2.231[1.409-3.533], respectively), while lower ApoA1 levels correlated with a higher risk (adjusted HR [95%CI] 0.505[0.326-0.782]). ROC curve analyses indicated comparable predictive performances to traditional risk factors like LDL cholesterol. Subgroup analysis revealed that the above association was not statistically significant in the moderate-to-severe renal impairment CAD patients (eGFR < 45Β mL/min/1.73 m Our findings illustrate that apolipoproteins, specifically ApoA1 and ApoB, along with their ratio, are significant predictors of major adverse cardiovascular events in CAD patients with impaired kidney function. These results emphasize the need for incorporating apolipoprotein measurements in secondary prevention strategies for this high-risk population. Show less
πŸ“„ PDF DOI: 10.1186/s12937-025-01078-9
APOB
Yuting Li, Mingrui Wang, Na Zhang +3 more Β· 2025 Β· Ginekologia polska Β· added 2026-04-24
This study investigates the relationship between serum homocysteine, blood lipids, and perinatal outcomes in patients with diet-controlled gestational diabetes mellitus (GDM) and those with normal glu Show more
This study investigates the relationship between serum homocysteine, blood lipids, and perinatal outcomes in patients with diet-controlled gestational diabetes mellitus (GDM) and those with normal glucose tolerance (NGT). A prospective cohort of 150 diet-controlled GDM patients and 150 pregnant women with NGT, all delivering at our hospital, were selected based on predefined criteria. Data on demographics, physical parameters, and perinatal outcomes were compiled. Blood samples for fasting plasma glucose (FPG), homocysteine (Hcy), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), and apolipoprotein A1 (apoA1) were collected before delivery. GDM patients exhibited higher levels of FPG, Hcy, and the apoB/apoA1 ratio, but lower HDL-C and apoA1 levels compared to the NGT group. Adverse outcomes such as macrosomia, premature rupture of membranes, and postpartum hemorrhage were more prevalent in the GDM group. In GDM patients, neonatal birth weight positively correlated with FPG and TG levels. Stratified Hcy analysis in GDM showed no significant differences in perinatal outcomes. However, the third quartile of the apoB/apoA1 ratio had a lower incidence of macrosomia compared to the first quartile, and the second quartile showed a higher incidence of birth asphyxia. GDM patients demonstrated increased levels of Hcy, FPG, and the apoB/apoA1 ratio, correlating with more adverse perinatal outcomes than healthy pregnant individuals. The relationships between Hcy, lipids, and these outcomes remain inconclusive, highlighting the need for further research. Show less
no PDF DOI: 10.5603/gpl.101475
APOB
Ping Huang, Yong Zhao, Haiyan Wei +8 more Β· 2025 Β· International journal of chronic obstructive pulmonary disease Β· added 2026-04-24
In preliminary research and literature review, we identified a potential link between chronic obstructive pulmonary disease (COPD) and lipid metabolism. Therefore, this study employed Mendelian random Show more
In preliminary research and literature review, we identified a potential link between chronic obstructive pulmonary disease (COPD) and lipid metabolism. Therefore, this study employed Mendelian randomization (MR) analysis to investigate the potential causal connection between blood lipids and COPD. A genome-wide association study (GWAS) on COPD was conducted, encompassing a total of 112,583 European participants from the MRC-IEU. Additionally, extensive UK Biobank data pertaining to blood lipid profiles within European cohorts included measurements for low-density lipoprotein cholesterol (LDL-C) with 440,546 individuals, high-density lipoprotein cholesterol (HDL-C) with 403,943 individuals, triglycerides (TG) with 441,016 individuals, total cholesterol (TC) with 187,365 individuals, apolipoprotein A-I (apoA-I) with 393,193 individuals, and apolipoprotein B (apoB) with 439,214 individuals. Then, MR analyses were performed for lipids and COPD, respectively. The primary analytical technique employed was the inverse-variance weighted (IVW) approach, which included a 95% confidence interval (CI) to calculate the odds ratio (OR). Additionally, a sensitivity analysis was conducted to assess the dependability of the MR analysis outcomes. MR analysis was primarily based on IVW, unveiled a causal link between COPD and LDL-C (OR=0.994, 95% CI (0.989, 0.999), P=0.019), TG (OR=1.005, 95% CI (1.002, 1.009), P=0.006), and apoA-I (OR=0.995, 95% CI (0.992, 0.999), P=0.008), in addition, no causal link was found with HDL-C, TC, apoB. Sensitivity analysis demonstrated the robustness of these causal relationships. However, through multivariate MR(MVMR) and multiple testing correction, LDL-C and TG had no causal effect on the outcome. ApoA-I remained a protective factor for the risk of COPD (OR=0.994, 95% CI (0.990-0.999), P=0.008). Through MR analysis, this study offers evidence of a causal link between apoA-I with COPD. This further substantiates the potential role of lipid metabolism in COPD, and has significant clinical implications for the prevention and management of COPD. Show less
πŸ“„ PDF DOI: 10.2147/COPD.S476833
APOB
Shuqi Cao, Xia Fu, Wenjing Li +3 more Β· 2025 Β· Parkinsonism & related disorders Β· Elsevier Β· added 2026-04-24
Evidence have indicated relation between apolipoproteins and neurodegenerative disorders (NDDs). However, previous studies have produced inconsistent results, and a comprehensive analysis of apolipopr Show more
Evidence have indicated relation between apolipoproteins and neurodegenerative disorders (NDDs). However, previous studies have produced inconsistent results, and a comprehensive analysis of apolipoproteins in NDDs is currently lacking. Using Cox proportional hazards regression analysis based on data from UK Biobank, we examined the association between baseline serum levels of apolipoprotein A (ApoA) and apolipoprotein B (ApoB) and risk of Parkinson's disease (PD), Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and multiple sclerosis. Elevated baseline levels of serum ApoA (HRΒ =Β 0.84, 95Β % CI: 0.71-0.99, PΒ =Β 0.047) and ApoB (HRΒ =Β 0.67, 95Β % CI: 0.57-0.78, PΒ =Β 3.18E-07) were associated with a reduced risk of incident PD. Subgroup analyses suggested the protective effect of serum ApoA was more significant for older participants and those with lower alcohol consumption, while higher serum ApoB was a more significant protective factor in males and those without stroke. No significant associations were found between apolipoproteins and other NDDs. Increased baseline levels of serum ApoA and ApoB are linked to a lower risk of PD. These findings enhance understanding of the role of apolipoproteins in PD, and have implications for the development of therapeutic strategies in clinical trials. Show less
no PDF DOI: 10.1016/j.parkreldis.2025.107266
APOB
Yong Tan, Zixiong Zhang, Jinru Yang +8 more Β· 2025 Β· Ecotoxicology and environmental safety Β· Elsevier Β· added 2026-04-24
At present, there is no consensus on the relationship between selenium (Se) exposure and human serum lipid metabolism. The etiological role of high-Se exposure in lipid markers, dyslipidemia, and nona Show more
At present, there is no consensus on the relationship between selenium (Se) exposure and human serum lipid metabolism. The etiological role of high-Se exposure in lipid markers, dyslipidemia, and nonalcoholic fatty liver (NAFLD) remains unclear. We used serum untargeted metabolomics analysis to evaluate whether high-Se exposure is cross-sectionally associated with lipid metabolism in adults from high-Se exposure area (nβ€―=β€―112) and control area (nβ€―=β€―101) in Hubei Province, China. An untargeted liquid chromatography/mass spectrometry (LC/MS)-based metabolomic analysis identified 144 differential pathways and yielded 204 differentially abundant metabolites, including 32 lipid metabolites associated with lipids profiles. To further explore the correlation between Se exposure and serum lipid metabolism, we measured serum levels of lipid profiles among all the people, including serum cholesterol (CHOL), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein B (APOB). The average serum Se level of the high-Se exposure group was 537.18β€―ΞΌg/L, significantly higher than 72.98β€―ΞΌg/L in the control group (pβ€―<β€―0.0001). The measurement levels of serum TG, LDL-C, HDL-C, and APOB in the high-Se exposure group were 1.03 (0.76, 1.34) mmol/L, 2.25β€―Β±β€―0.48β€―mmol/L, 1.12β€―Β±β€―0.24β€―mmol/L, and 0.77β€―Β±β€―0.15β€―g/L, respectively, while the control group were 1.13 (0.84, 1.80) mmol/L, 2.56β€―Β±β€―0.61β€―mmol/L, 1.02β€―Β±β€―0.22β€―mmol/L, and 0.83β€―Β±β€―0.16β€―g/L, respectively (all p values <0.05). Correlation analysis showed a significant negative correlation between serum Se and CHOL (rβ€―=β€―-0.201, pβ€―<β€―0.01), serum Se is also associated with metabolomics markers, the negative correlation includes glyceric acid and ect., the positive correlation includes phosphorylcholine and ect. Our study suggests that high-Se exposure is negatively associated with serum lipid profiles and decreases the risk of high-TC and HDL-C dyslipidemia. Show less
no PDF DOI: 10.1016/j.ecoenv.2025.117677
APOB
Xiangyong Kong, Yanchen Cai, Yuwei Li +1 more Β· 2025 Β· Health information science and systems Β· Springer Β· added 2026-04-24
Atherosclerotic cardiovascular disease (ASCVD) is a major threat to human life and health, and dyslipidemia with elevated low-density lipoprotein cholesterol (LDL-C) is an important risk factor, and i Show more
Atherosclerotic cardiovascular disease (ASCVD) is a major threat to human life and health, and dyslipidemia with elevated low-density lipoprotein cholesterol (LDL-C) is an important risk factor, and in the optimal LDL-C scenario, apolipoprotein B (ApoB) has a more predictive value of ASCVD risk. The study is a genome-wide association study (GWAS) based on a European population. A large GWAS dataset for atherosclerotic cardiovascular diseases was targeted, including coronary heart disease (CHD), ischemic stroke (IS), large-artery atherosclerotic stroke (ISL), small-vessel stroke (ISS), and myocardial infarction (MI). Univariate two-sample mendelian randomization (MR) analyses of ApoB and the above cardiovascular diseases were performed separately, and the association was assessed mainly using the inverse variance weighted (IVW) method, with confidence intervals for the superiority ratios set at 95%. In addition, the experiment was supplemented using MR-Egger, weighted model and weighted median (WM). Based on the results of univariate two-sample mendelian randomisation analysis, it was shown that there was a causal relationship between ApoB and CHD (OR = 1.710, 95% CI 1.529-1.912, P = 0.010), ISL (OR = 1.430, 95% CI 1.231-1.661, P = 2.714E-06), ISS (OR = 1.221, 95% CI 1.062-1.405, P = 0.005) were causally related to each other and the disease prevalence ratio was positively correlated with ApoB concentration. This MR analysis demonstrated a causal relationship between ApoB and CHD, ISL, ISS, but not with the risk of developing IS and MI, which further validated the relationship between ApoB and the risk of ASCVD, and contributed to a better understanding of the genetic impact of ApoB on ASCVD, and to a certain extent, could improve the management of ApoB and reduce the prevalence of ASCVD. Show less
no PDF DOI: 10.1007/s13755-024-00323-5
APOB
Mengke Yan, Xin Cong, Hui Wang +7 more Β· 2025 Β· Poultry science Β· Elsevier Β· added 2026-04-24
Aging-related lipid metabolic disorder is related to oxidative stress. Selenium (Se)-enriched Cardamine violifolia (SEC) is known for its excellent antioxidant function. The objective of this study wa Show more
Aging-related lipid metabolic disorder is related to oxidative stress. Selenium (Se)-enriched Cardamine violifolia (SEC) is known for its excellent antioxidant function. The objective of this study was to evaluate the effects of SEC on antioxidant capacity and lipid metabolism in the liver of aged laying hens. A total of 450 sixty-five-wk-old Roman laying hens were randomly divided into 5 treatments: a basal diet (without Se supplementation, CON) and basal diets supplemented with 0.3 mg/kg Se from sodium selenite (SS), 0.3 mg/kg Se from Se-enriched yeast (SEY), 0.3 mg/kg Se from SEC (SEC), or 0.3 mg/kg Se from SEC and 0.3 mg/kg Se from SEY (SEC + SEY). The experiment lasted for 8 wk. The results showed that dietary SEC + SEY supplementation decreased (P < 0.05) triglyceride (in the plasma and liver) and total cholesterol levels (in the plasma), and increased (P < 0.05) HDL-C concentration in plasma compared to CON diet. Compared with CON diet, SEC and/or SEY supplementation decreased (P < 0.05) the mRNA expression of hepatic ACC, FAS and HMGCR, and increased (P < 0.05) PPARΞ±, VTG-II, Apo-VLDL II and ApoB expression. Dietary SEC + SEY and SEY supplementation increased (P < 0.05) Se content in egg yolk and breast muscle compared to CON diet. Dietary SEC, SEY or SEC + SEY supplementation increased (P < 0.05) the activity of antioxidant enzymes (GSH-PX, T-AOC and T-SOD) in the plasma and liver and decreased (P < 0.05) MDA content in the plasma compared to CON diet. Dietary Se supplementation promoted (P < 0.05) mRNA expression of Nrf2 in the liver. In contrast, dietary SEY and SEC supplementation resulted in a decrease (P < 0.05) of hepatic Keap1 mRNA expression compared to CON diet. Dietary SEC + SEY and/or SEC supplementation increased (P < 0.05) mRNA expression of Selenof, GPX1 and GPX4 in the liver compared with CON diet. In conclusion, dietary SEC (0.3 mg/kg Se) or SEC (0.3 mg/kg Se) + SEY (0.3 mg/kg Se) improved the antioxidant capacity and the lipid metabolism in the liver of aged laying hens, which might be associated with regulating Nrf2/Keap1 signaling pathway. Show less
πŸ“„ PDF DOI: 10.1016/j.psj.2024.104620
APOB
Lathan Liou, Judit GarcΓ­a-GonzΓ‘lez, Hei Man Wu +5 more Β· 2025 Β· Arteriosclerosis, thrombosis, and vascular biology Β· added 2026-04-24
Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alt Show more
Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alternative preventions and treatments. However, so far, there have been no systematic efforts to predict CAD subtypes using clinical and genetic factors. Here, we trained and applied statistical models incorporating clinical and genetic factors to predict CAD subtypes in 26β€…036 patients with CAD in the UK Biobank. We performed external validation of the UK Biobank models in the US-based All of Us cohort (8598 patients with CAD). Subtypes were defined as high versus normal LDL (low-density lipoprotein) levels, high versus normal Lpa (lipoprotein A) levels, ST-segment-elevation myocardial infarction versus non-ST-segment-elevation myocardial infarction, occlusive versus nonocclusive CAD, and stable versus unstable CAD. Clinical predictors included levels of ApoA, ApoB, HDL (high-density lipoprotein), triglycerides, and CRP (C-reactive protein). Genetic predictors were genome-wide and pathway-based polygenic risk scores (PRSs). Results showed that both clinical-only and genetic-only models can predict CAD subtypes, while combining clinical and genetic factors leads to greater predictive accuracy. Pathway-based PRSs had higher discriminatory power than genome-wide PRSs for the Lpa and LDL subtypes and provided insights into their etiologies. The 10-pathway PRS most predictive of the LDL subtype involved cholesterol metabolism. Pathway PRS models had poor generalizability to the All of Us cohort. In summary, we present the first systematic demonstration that CAD subtypes can be distinguished by clinical and genomic risk factors, which could have important implications for stratified cardiovascular medicine. Show less
no PDF DOI: 10.1161/ATVBAHA.124.321846
APOB
Chenxi Li, Xuhui Yang, Yan Zhong +4 more Β· 2025 Β· Translational oncology Β· Elsevier Β· added 2026-04-24
The relationship between serum lipids and prognosis of pancreatic cancer has not been confirmed. Our purpose in the study was to investigate the associations between serum lipids level and prognosis i Show more
The relationship between serum lipids and prognosis of pancreatic cancer has not been confirmed. Our purpose in the study was to investigate the associations between serum lipids level and prognosis in patients with pancreatic cancer. A retrospective study was performed on 286 pancreatic cancer patients who admitted to our hospital from January 1, 2017 to December 31, 2021. Serum lipids level were recorded. Clinical-pathological characteristics, oncologic outcomes, progression free survival (PFS) and overall survival (OS) were collected. The prognostic significance was determined by Kaplan-Meier analysis and Cox proportional hazards regression model. Regarding serum lipids level, compared to normal apolipoprotein B/ apolipoprotein A (ApoB/ApoA1), high ApoB/ApoA1 level indicated a shorter OS (HR:2.028, 95% CI: 1.174-2.504, P = 0.011) and a shorter PFS (HR:1.800, 95% CI: 1.076-3.009, P = 0.025). Other serum lipid molecules were not associated with PFS and OS. ApoB/ApoA1 might be an independent prognostic factor of pancreatic cancer. Show less
πŸ“„ PDF DOI: 10.1016/j.tranon.2024.102208
APOB
Jian Du, Zhiqi Dai, Cuiguang Li +3 more Β· 2025 Β· Journal of animal physiology and animal nutrition Β· Blackwell Publishing Β· added 2026-04-24
The benefits of plant essential oils (EO) on the health of animals have been frequently reported, but their alteration of lipid metabolism in obese pigs has yet to be explored. This study aimed to ass Show more
The benefits of plant essential oils (EO) on the health of animals have been frequently reported, but their alteration of lipid metabolism in obese pigs has yet to be explored. This study aimed to assess the impact of EO blends (oregano, cinnamon and lemon oils) on growth performance, meat physicochemical parameters, intestinal health and lipid metabolism in the small intestine of weaned Bamei (a kind of obese-type pig) piglets. One hundred and forty-four male 60-day-old weaned Bamei piglets were randomly assigned to three groups of six replicates each: CON (basal diet), T1 (basal diet + 250 mg/kg EO), and T2 (basal diet + 500 mg/kg EO) over 28 days. The results showed that T1 trended to improve the average daily gain and feed intake to body gain ratio (p < 0.1), reduced water loss (p < 0.05), and increased the redness of meat (p < 0.05) compared to the CON. In addition, a significant change in the proportion of C17:0 and C20:1 was observed in the meat of T1 (p < 0.05). Improved intestinal health was evidenced by the reduced crypt depth, improved villi-to-crypt length ratio, and better superoxide dismutase activity in T1 (p < 0.05). Further study on intestinal lipid metabolism showed that duodenal lipase activity and the mRNA expression levels of lipid transport-related genes in the jejunum (FABPs, APOA1, APOB and ACSL3) were significantly reduced, alongside diminished serum lipid metabolites (Total protein and triglyceride) in the groups fed with EO (p < 0.05). In short, EO supplementation especially at 250 mg/kg improved intestinal health and inhibited lipid metabolism, which had a positive effect on the overall performance of Bamei piglets. This new evidence contributes to understanding the early regulatory role of EO in obese pigs and their potential to alleviate adolescent obesity. Show less
no PDF DOI: 10.1111/jpn.14074
APOB
Lin Liu, Yidan Liu, Yu Tian +7 more Β· 2025 Β· Reproductive sciences (Thousand Oaks, Calif.) Β· Springer Β· added 2026-04-24
Recurrent implantation failure (RIF) is a complex and poorly understood clinical disorder characterized by failure to conceive after repeated embryo transfers. Endometrial receptivity (ER) is a prereq Show more
Recurrent implantation failure (RIF) is a complex and poorly understood clinical disorder characterized by failure to conceive after repeated embryo transfers. Endometrial receptivity (ER) is a prerequisite for implantation, and ER disorders are associated with RIF. However, little is known regarding the molecular mechanisms underlying ER in RIF. In the present study, RNA sequencing data from the mid-secretory endometrium of patients with and without RIF were analyzed to explore the potential long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) involved in RIF. The analysis revealed 213 and 1485 differentially expressed mRNAs and lncRNAs, respectively (fold change β‰₯ 2 and p < 0.05). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that these genes were mostly involved in processes related to immunity or inflammation. 5 key genes (TTR, ALB, TF, AFP, and CFTR) and a key module including 14 hub genes (AFP, ALB, APOA1, APOA2, APOB, APOH, FABP1, FGA, FGG, GC, ITIH2, SERPIND1, TF and TTR) were identified in the protein-protein interaction (PPI) network. The 5 key genes were used to further explore the lncRNA-miRNA-mRNA regulatory network. Finally, the drug ML-193 based on the 14 hub genes was identifed through the CMap. After ML-193 treatment, endometrial cell proliferation was increased, the hub genes were mostly down-regulated, and the ER marker HOXA10 was up-regulated. These results offer insights into the regulatory mechanisms of lncRNAs and mRNAs and suggest ML-193 as a therapeutic agent for RIF by enhancing ER. Show less
πŸ“„ PDF DOI: 10.1007/s43032-024-01630-8
APOB
Yue Zhang, Yang Tian, Shaobo Zheng +8 more Β· 2025 Β· Medicine Β· added 2026-04-24
Gastric cancer (GC) exhibits marked heterogeneity, patients with identical stage receive divergent outcomes. Metabolic reprogramming and aging are pivotal in reshaping the tumor microenvironment. Howe Show more
Gastric cancer (GC) exhibits marked heterogeneity, patients with identical stage receive divergent outcomes. Metabolic reprogramming and aging are pivotal in reshaping the tumor microenvironment. However, their interplay in GC prognosis remains unexplored. We analyzed RNA-seq and clinical data from The Cancer Genome Atlas Program and Gene Expression Omnibus databases. Using univariate Cox, LASSO, and multivariate Cox regression, we identified candidate genes and constructed a prognostic signature. Immune contexture, genomic alterations and drug sensitivity were compared between high- and low-risk group. The metabolic and aging related risk score, comprising 4 genes (GNAI1, GSTA1, APOC3, and LOX), was developed. Validation across multiple cohorts confirmed its robust prognostic performance. The model also effectively stratified patients into distinct risk subgroups with differential immune profiles and responses to immunotherapy. Notably, high-risk patients showed reduced sensitivity to common chemotherapeutic agents but may benefit from targeting the PI3K/mTOR pathway. Metabolic and aging related risk score serves as a promising tool for individualized risk assessment and therapeutic guidance in GC, warranting further clinical validation. Show less
πŸ“„ PDF DOI: 10.1097/MD.0000000000046616
APOC3
Yan Hu, Chao Quan, Yuanyuan Zhou +6 more Β· 2025 Β· PloS one Β· PLOS Β· added 2026-04-24
The differential diagnosis between Tuberculosis (TB) and Non-tuberculous Mycobacteria (NTM) has historically been constrained by the inadequate sensitivity and specificity of current diagnostic method Show more
The differential diagnosis between Tuberculosis (TB) and Non-tuberculous Mycobacteria (NTM) has historically been constrained by the inadequate sensitivity and specificity of current diagnostic methods. Furthermore, distinguishing between Active Tuberculosis (ATB) and Latent Tuberculosis Infection (LTBI) poses significant challenges. This study aims to develop a molecular differentiation system for ATB, LTBI, and NTM by integrating plasma proteomics with multi-dimensional analytical techniques, while also exploring key biomarkers associated with disease progression and treatment response. Using label-free quantitative technology, we conducted a plasma proteomics analysis across five groups: ATB, LTBI, NTM, Cured Patients (CPs), and Healthy Donors (HD). Differentially Expressed Proteins (DEPs) were identified through screening (FC > 1.5 or <0.67, P < 0.05), followed by Gene Ontology/KEGG pathway enrichment, STRING interaction network, and Mfuzz dynamic clustering analysis to systematically elucidate molecular characteristics. Experimental data were validated through a multidimensional quality control system (Pearson correlation coefficient, peptide distribution, molecular weight distribution, etc.). Enzyme-linked immunosorbent assay (ELISA) was employed to detect the plasma expression levels of target proteins across the groups and to facilitate comparisons. This study identified 1,338 non-redundant proteins across five cohorts. Comparative analysis revealed 142 DEPs across the three comparative groups (ATB, LTBI, and NTM), which were primarily localized in the extracellular domain. Key findings include: 27 DEPs in the ATB-LTBI group, primarily enriched in inflammatory responses (such as A2M, IL-1R2) and epithelial barrier functions (TGM3, KRT3); 69 DEPs in the ATB-NTM group, characterized by significant changes in immunoglobulin light chains (IGLV2-11) and innate immune effector molecules (S100A8); 46 DEPs in the NTM-LTBI group, closely related to lipid metabolism (APOC3) and extracellular matrix remodeling (FN1). KEGG pathway analysis revealed that DEPs in the ATB-LTBI group were enriched in nitrogen metabolism pathways, those in the ATB-NTM group were associated with thyroid hormone synthesis, and the NTM-LTBI group was involved in phagosome function. Dynamic clustering results showed six treatment response modules: Cluster 1/2 (riboflavin metabolism, complement coagulation pathway) were activated post-treatment, Cluster 3/4 (proteasome, cardiac signaling pathway) exhibited partial reversal in expression, and Cluster 5/6 (platelet activation, cytoskeleton) showed delayed regression. Research confirmed 10 differential proteins between the ATB-CPs and ATB-HD groups, including S100A8, LTA4H, and DEFA1B, which constitute a molecular fingerprint specific to ATB. ELISA validation confirmed significantly elevated S100A8 and GPX3 in ATB group, while NTM group showed higher FGB and lower ATRN levels. This study systematically reveals the plasma proteomic characteristics under infection statuses caused by different mycobacteria. A discrimination framework for ATB/LTBI/NTM was constructed based on disease-specific differential proteins, overcoming the limitations of traditional diagnostic techniques in distinguishing infection states. Through dynamic analysis of six temporal therapeutic modules, the reprogramming patterns of the host protein network during tuberculosis treatment were elucidated. This research lays a multidimensional molecular foundation for the precise typing, personalized treatment, and prognostic evaluation of mycobacterial infections. Show less
πŸ“„ PDF DOI: 10.1371/journal.pone.0339558
APOC3
Xinqiao Chu, Yaning Biao, Hongzheng Li +9 more Β· 2025 Β· Lipids in health and disease Β· BioMed Central Β· added 2026-04-24
Lipid metabolism may be linked to chronic gastritis, but its causal role remains unclear. While current research emphasizes inflammation, mucosal changes, immune regulation, genetics, and the gut micr Show more
Lipid metabolism may be linked to chronic gastritis, but its causal role remains unclear. While current research emphasizes inflammation, mucosal changes, immune regulation, genetics, and the gut microbiota, the contribution of lipid metabolism is understudied. This study aims to evaluate the impact of serum lipids and the mechanistic roles of lipid-lowering drug targets in chronic gastritis. We conducted a cross-sectional study using data from real world. Multivariable logistic regression was performed to assess the association between serum lipid profiles and gastritis. Mendelian randomization (MR) analyses based on genome-wide association study (GWAS) datasets were performed to detect the causal relationship of serum lipids, plasma lipid species, and lipid-lowering drug targets. Experimental validation was conducted using high-fat diet (HFD)-fed mice and chemically induced CAG rat models. Four thousand sixty one person, including 1,023 patients with chronic atrophic gastritis (CAG), 1,742 with non-atrophic gastritis (NAG), and 1,296 as healthy population were included in the analysis. Through covariates adjustment, TC, ApoA1, and HDL-C showed to be associated with an increased risk of chronic gastritis, whereas TG exhibited a protective effect. MR analysis confirmed a significant inverse causal relationship between TG and gastritis (OR = 0.889, 95% CI: 0.825-0.958). Ten plasma lipid species and lipid-lowering gene targets, including LPL and APOC3, were identified as causally associated with disease risk. Mediation analysis revealed six plasma lipid species as potential intermediaries linking genetic variation to gastritis. In vivo experiments demonstrated progressive hepatic steatosis and mild gastric mucosal changes in HFD-fed mice. Immunohistochemical analysis further revealed a significant reduction in LPL and APOC3 expression in gastric tissue (P < 0.05). In the CAG rat model, histological analysis revealed hepatocyte disarray, edema, and gastric mucosal atrophy. Elevated levels of TNF-Ξ±, IL-6, IL-1Ξ² and decreased levels of GAS-17 and PG I/II were also observed (P < 0.05). Western blot analyses further confirmed the downregulation of LPL and APOC3 expression in gastric tissue (P < 0.05). This study provides genetic and experimental evidence, supporting a causal role of lipid metabolism in chronic gastritis. LPL and APOC3 are implicated in its pathogenesis, highlighting potential lipid-targeted strategies for prevention and treatment. Show less
πŸ“„ PDF DOI: 10.1186/s12944-025-02782-5
APOC3
Maaike Kockx, Jeffrey Wang, Natasha J Howard +4 more Β· 2025 Β· Journal of clinical lipidology Β· Elsevier Β· added 2026-04-24
Indigenous Australians have an increased risk of type 2 diabetes mellitus (T2DM) and premature cardiovascular disease. Subpopulations of high-density lipoprotein (HDL) have been associated with increa Show more
Indigenous Australians have an increased risk of type 2 diabetes mellitus (T2DM) and premature cardiovascular disease. Subpopulations of high-density lipoprotein (HDL) have been associated with increased cardiovascular risk, but HDL composition, size, or function have not been studied in Indigenous Australians. The study consisted of 86 non-Indigenous participants, 43 of whom had T2DM, and 75 Indigenous participants, 36 of whom had T2DM. HDL lipid and apolipoprotein content were determined using enzymatic assays and enzyme-linked immunosorbent assays, respectively, and HDL size and distribution were investigated using nuclear magnetic resonance spectroscopy. Transporter-independent, ATP-binding cassette transporter (ABC)A1- and ABCG1-specific cholesterol efflux capacity (CEC) were determined using cell lines stably expressing human ABCA1 or ABCG1. Indigenous participants had significantly lower concentrations of large (10.3-12.0 nm), small (7.4-7.8 nm), and total HDL particles, which persisted after adjustment for serum triglyceride (TG), body mass index (BMI), and T2DM. HDL from Indigenous Australians was also highly enriched in TG, apolipoprotein (apo) E, and apoCIII (all P < .001). Transporter-independent and ABCG1-mediated CEC were not different between the populations. ABCA1-specific CEC per HDL particle was higher in Indigenous than in non-Indigenous subjects (P < .001), and persisted after adjustment for TG, BMI, and T2DM. Multivariable analysis identified that ABCA1-specific CEC was independently and positively associated with HDL-apoCIII and HDL-apoE levels. Indigenous Australians demonstrate significant compositional, size, and functional changes in circulating HDL, which is only partially explained by BMI, hypertriglyceridemia, or T2DM. Remodeled HDL may serve as a biomarker of increased cardiovascular risk in Indigenous Australians. Show less
no PDF DOI: 10.1016/j.jacl.2025.08.006
APOC3
Xin Huang, Qihang Li, Ping Guo +3 more Β· 2025 Β· Journal of lipid research Β· Elsevier Β· added 2026-04-24
Patients with dyslipidemia are at higher risk for inflammatory bowel disease (IBD), yet the impact of lipid-lowering medications on IBD remains unclear. This study investigates the causal relationship Show more
Patients with dyslipidemia are at higher risk for inflammatory bowel disease (IBD), yet the impact of lipid-lowering medications on IBD remains unclear. This study investigates the causal relationship between lipid-lowering drug target and IBD, with a focus on the roles of gut microbiota and inflammatory cytokines. Genetic variants associated with lipid-lowering drug targets were extracted from the Global Lipids Genetics Consortium, whereas summary statistics for IBD, Crohn's disease (CD), and ulcerative colitis were sourced from the International Inflammatory Bowel Disease Genetics Consortium. Drug-target Mendelian randomization analysis revealed that inhibiting angiopoietin-like protein 3 increased the risk of IBD and CD, whereas inhibition of apolipoprotein C-III (APOC3) heightened the risk of CD. Conversely, enhancement of LPL and LDL receptor reduced the risk of IBD and CD. Mediation analysis demonstrated that gut microbiota and inflammatory cytokines partially mediated these effects, with specific pathways such as Lachnospiraceae FCS020 (17.26%) for APOC3 and Clostridium sensu stricto 1 (20.12%) for LPL accounting for significant portions of the effects. These findings suggest that lipid-lowering drugs targeting angiopoietin-like protein 3 and APOC3 may increase the risk of IBD, whereas those targeting LPL and LDL receptor may reduce the risk. The results highlight potential for repurposing lipid-lowering drugs for IBD prevention and warrant future clinical trials to explore these targets further. Show less
πŸ“„ PDF DOI: 10.1016/j.jlr.2025.100871
APOC3
Xin Hu, Xin Jia, Xiaoming Wang Β· 2025 Β· Cardiology in review Β· added 2026-04-24
Dyslipidemia is a major risk factor for atherosclerotic cardiovascular diseases (ASCVD), and effective lipid-lowering therapies are critical for reducing ASCVD risk. This review aims to provide an upd Show more
Dyslipidemia is a major risk factor for atherosclerotic cardiovascular diseases (ASCVD), and effective lipid-lowering therapies are critical for reducing ASCVD risk. This review aims to provide an updated overview of the latest advancements in lipid-lowering therapies, focusing on emerging therapeutic targets and innovative biotechnological approaches that have shown promise in clinical research. Recent years have witnessed significant progress in lipid-lowering therapies beyond traditional statins and ezetimibe. Novel therapeutic targets, such as PCSK9 inhibitors, angiopoietin-like 3 protein inhibitors, APOC3 inhibitors, and omega-3 fatty acids, have demonstrated potent lipid-lowering efficacy. Additionally, advancements in biotechnology have led to the development of innovative agents, including monoclonal antibodies, antisense oligonucleotides (ASOs), small interfering RNA, and cholesterol vaccines, all of which have shown encouraging results in clinical trials. These therapies offer new mechanisms of action and improved efficacy in managing dyslipidemia and reducing ASCVD risk. This article comprehensively reviews the latest clinical research on emerging lipid-lowering targets and cutting-edge therapies, emphasizing their mechanisms, efficacy, and potential impact on dyslipidemia and ASCVD management. The rapid evolution of these therapies highlights a transformative era in cardiovascular disease prevention and treatment, offering hope for improved patient outcomes. Show less
no PDF DOI: 10.1097/CRD.0000000000001020
APOC3
Yu Liao, Mingchao Wang, Fuli Qin +2 more Β· 2025 Β· Frontiers in pharmacology Β· Frontiers Β· added 2026-04-24
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically Show more
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically identify Cpn's therapeutic targets and pathways in Western diet (WD)-induced obesity using integrated network pharmacology, transcriptomics, and experimental validation. A Western diet (WD)-induced mice model was used to evaluate the effectiveness of Cpn in ameliorating obesity. A network pharmacology analysis was then employed to identify the potential anti-obesity targets of Cpn. GO functional enrichment and KEGG pathway analysis were performed to elucidate the potential functions of the identified targets, followed by constructing a protein-protein interaction network to screen the core targets. Meanwhile, quantitative transcriptomics was conducted to validate and broaden the network pharmacology findings. Finally, molecular docking and quantitative real-time PCR assay were used for the core target validation. Cpn treatment effectively alleviated obesity-related symptoms in WD-induced mice. The metabolic pathway, insulin signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, lipid and atherosclerosis pathway, and core targets including CPS1, HRAS, MAPK14, PAH, ALDOB, AKT1, GSK3B, HSP90AA1, BHMT2, EGFR, CASP3, MAT1A, APOM, APOA2, APOC3, and APOA1 are involved in regulating the therapeutic effect of Cpn. This study comprehensively uncovers the potential mechanism of Cpn against obesity based on network pharmacology and quantitative transcriptomics, which provides evidence for revealing the pathogenesis of obesity, suggesting that Cpn is a possible lead compound for anti-obesity treatment. Show less
πŸ“„ PDF DOI: 10.3389/fphar.2025.1571480
APOC3
Jia Pan, Xue Wang, Youjin Zhang +6 more Β· 2025 Β· Journal of cellular and molecular medicine Β· Blackwell Publishing Β· added 2026-04-24
Apolipoprotein C3 (APOC3) and angiopoietin-like protein 8 (ANGPTL8) genes are related to lipid metabolism. The relationships between single nucleotide polymorphisms (SNPs) in the APOC3 and ANGPTL8 gen Show more
Apolipoprotein C3 (APOC3) and angiopoietin-like protein 8 (ANGPTL8) genes are related to lipid metabolism. The relationships between single nucleotide polymorphisms (SNPs) in the APOC3 and ANGPTL8 genes with metabolic dysfunction-associated steatotic liver disease (MASLD) remain controversial. This study aimed to investigate the associations between specific SNPs in the APOC3 and ANGPTL8 genes and MASLD risk, with a particular focus on the mediating role of triglycerides (TG). A total of 440 participants were enrolled and categorised into MASLD and control groups. Genotyping of APOC3 SNPs (rs5128, rs2854116 and rs2854117) and ANGPTL8 SNP (rs2278426) was conducted using polymerase chain reaction-restriction fragment length polymorphism or Sanger sequencing methods. Multivariate logistic regression was employed to estimate the associations between these SNPs and MASLD risk, and mediation analysis was performed to assess the potential mediating effect of TG. We found that APOC3 SNPs were associated with MASLD risk, with increased odds ratios (ORs) indicating a higher risk of MASLD: rs5128 CG + GG genotype (OR = 1.8, 95% CI = 1.1-2.8), rs2854116 TC + CC genotype (OR = 1.9, 95% CI = 1.1-3.1) and rs2854117 CT + TT genotype (OR = 1.9, 95% CI = 1.2-3.2). No association was found between ANGPTL8 rs2278426 and MASLD (p > 0.05). Mediation analysis revealed that TG significantly mediated these relationships, accounting for 80.25% of the effect for rs5128, 64.61% for rs2854116 and 62.59% for rs2854117. In summary, polymorphisms in APOC3 (rs5128, rs2854116 and rs2854117) were associated with MASLD risk, with TG serving as a potential mediating factor. In contrast, ANGPTL8 rs2278426 polymorphism did not show any association with MASLD. Show less
πŸ“„ PDF DOI: 10.1111/jcmm.70542
APOC3
Binyan Yu, Yanan Yang, Yijian Li +3 more Β· 2025 Β· Reproduction in domestic animals = Zuchthygiene Β· Blackwell Publishing Β· added 2026-04-24
The Tibetan sheep is a typical hypoxia-tolerant mammal, which lives on the plateau, at an altitude of between 2500 and 5000 m above sea level; the study of its hypoxic adaptation mechanism provides a Show more
The Tibetan sheep is a typical hypoxia-tolerant mammal, which lives on the plateau, at an altitude of between 2500 and 5000 m above sea level; the study of its hypoxic adaptation mechanism provides a reference for exploring the hypoxic adaptation mechanism of other animals. To grope for the genetic mechanism of adaptation to the hypoxic environment at the transcriptional level in Tibetan sheep testicular tissue, and to identify candidate genes and key pathways related to sheep adaptation, histological observation of testicular tissues from two sheep breeds was carried out using haematoxylin-eosin (HE) conventional staining. A total of 103 differentially expressed genes (DEGs) were authenticated in high altitude Tibetan sheep (ZYH) and low altitude Tibetan sheep (ZYM) by RNA sequencing technology (RNA-Seq), which included 50 up-regulated genes and 53 down-regulated genes. Functional analyses revealed several terms and pathways that were closely related to testis adaptation to the plateau. Several genes (including GGT5, AGTR2, EDN1, LPAR3, CYP2C19, IGFBP3, APOC3 and PKC1) were remarkably enriched in several pathways and terms, which may impact the Plateau adaptability of sheep by adjusting its reproductive activity and sexual maturation, and protecting Sertoli cells, various spermatocytes, and spermatogenesis processes. The results make a reasonable case for a better understanding of the molecular mechanisms of adaptation to altitude in sheep. Show less
no PDF DOI: 10.1111/rda.70037
APOC3
Bo Wang, Li Qiang, Geng Zhang +6 more Β· 2025 Β· Medicine Β· added 2026-04-24
Acute-on-chronic liver failure (ACLF) is the major cause of mortality in patients infected with the hepatitis B virus (HBV); however, early determination of the prognosis of patients with HBV-ACLF is Show more
Acute-on-chronic liver failure (ACLF) is the major cause of mortality in patients infected with the hepatitis B virus (HBV); however, early determination of the prognosis of patients with HBV-ACLF is insensitive or limited. This study aimed to analyze differentially expressed proteins in the plasma of patients with HBV-ACLF using data-independent acquisition mass spectrometry to provide a reference for short-term prognosis. Fifty HBV-ACLF patients and 15 healthy controls were enrolled in this study. Of these, 10 patients with HBV-ACLF and 5 healthy volunteers participated in data-independent acquisition-based proteomics and the potential core proteins were screened out via bioinformatics. Apolipoprotein C3 (APOC3) was selected and quantified by enzyme linked immunosorbent assays in all patients. And the area under the curve (AUC) was calculated to evaluate the value of APOC3 in the diagnosis and prognosis of patients with HBV-ACLF. A total of 247 differentially expressed proteins were identified in the serum of patients in the HBV-ACLF and normal control groups. A total of 148 proteins were upregulated and 99 proteins were downregulated in the HBV-ACLF group compared with those in the normal group. The expression level of APOC3 was 1.65β€…Β±β€…0.44 mg/mL in patients with HBV-ACLF, which was obviously lower than the normal controls (2.04β€…Β±β€…0.22 mg/mL) (Pβ€…<β€….001) (AUC was 0.766, with a sensitivity of 62%, and specificity of 93.3%). The expression level of APOC3 was 1.38β€…Β±β€…0.44 mg/mL in the non-survival group, which was obviously lower than the survival group (1.83β€…Β±β€…0.35 mg/mL) (Pβ€…<β€….0001) (AUC was 0.780, with a sensitivity of 50%, and specificity of 96.7%). APOC3 is associated with short-term prognosis of patients with HBV-ACLF and can be used as a potential prognostic biomarker in patients with HBV-ACLF. Show less
πŸ“„ PDF DOI: 10.1097/MD.0000000000041503
APOC3
Qingcong Zheng, Rongjie Lin, Du Wang +2 more Β· 2025 Β· BMC musculoskeletal disorders Β· BioMed Central Β· added 2026-04-24
It remains controversial whether lipids affect osteoporosis (OP) or bone mineral density (BMD), and causality has not been established. This study aimed to investigate the genetic associations between Show more
It remains controversial whether lipids affect osteoporosis (OP) or bone mineral density (BMD), and causality has not been established. This study aimed to investigate the genetic associations between lipids, novel non-statin lipid-lowering drug target genes, and OP and BMD. Mendelian randomization (MR) method was used to explore the genetic associations between 179 lipid species and OP, BMD. Drug-target MR analysis was used to explore the causal associations between angiopoietin-like protein 3 (ANGPTL3) and apolipoprotein C3 (APOC3) inhibitors on BMD. The IVW results with Bonferroni correction indicated that triglyceride (TG) (51:3) (OR = 1.0029; 95% CI: 1.0014-1.0045; P = 0.0002) and TG (56:6) (OR = 1.0021; 95% CI: 1.0008-1.0033; P = 0.0011) were associated with an increased risk of OP; TG (51:2) (OR = 0.9543; 95% CI: 0.9148-0.9954; P = 0.0298) was associated with decreased BMD; and ANGPTL3 inhibitor (OR = 1.1342; 95% CI: 1.0393-1.2290; P = 0.0093) and APOC3 inhibitor (OR = 1.0506; 95% CI: 1.0155-1.0857; P = 0.0058) was associated with increased BMD. MR analysis indicated causal associations between genetically predicted TGs and OP and BMD. Drug-target MR analysis showed that ANGPTL3 and APOC3 have the potential to serve as novel non-statin lipid-lowering drug targets to treat or prevent OP. Show less
πŸ“„ PDF DOI: 10.1186/s12891-024-08160-z
APOC3
Lei Wu, Zhong Zhuang, Wenqian Jia +7 more Β· 2025 Β· Poultry science Β· Elsevier Β· added 2026-04-24
Residual feed intake (RFI) has recently gained attention as a key indicator of feed efficiency in poultry. In this study, 800 slow-growing ducks with similar initial body weights were reared in an exp Show more
Residual feed intake (RFI) has recently gained attention as a key indicator of feed efficiency in poultry. In this study, 800 slow-growing ducks with similar initial body weights were reared in an experimental facility until they were culled at 42 d of age. Thirty high RFI (HRFI) and 30 low RFI (LRFI) birds were selected to evaluate their growth performance, carcass characteristics, and muscle development. Transcriptome and weighted gene co-expression correlation network analyses of pectoral muscles were conducted on six LRFI and six HRFI ducks. The results revealed that selecting for LRFI significantly reduced feed consumption (P < 0.05) and improved feed efficiency without affecting the growth performance, slaughter rate, or meat quality of ducks (P > 0.05). Moreover, compared with HRFI ducks, LRFI ducks had a lower pectoral muscle fat content (P < 0.05), larger muscle fiber diameter and area (P < 0.05), and lower muscle fiber density (P < 0.05). There were significant differences in gene expression between LRFI and HRFI ducks, with 102 upregulated and 258 downregulated genes, which were enriched in the PPAR signaling pathway, adipocytokine signaling pathway, actin cytoskeleton regulation, ECM-receptor interaction, and focal adhesion. The expression of genes associated with fat and energy metabolism, including ACSL6, PCK1, APOC3, HMGCS2, PRKAG3, and G6PC1, was downregulated in LRFI ducks, and weighted gene co-expression correlation network analysis identified PRKAG3 as a hub gene. Our findings indicate that reduced mitochondrial energy metabolism may contribute to the RFI of slow-growing ducks, with PRKAG3 playing a pivotal role in this biological process. These findings provide novel insights into the molecular changes underlying RFI variation in slow-growing ducks. Show less
πŸ“„ PDF DOI: 10.1016/j.psj.2024.104613
APOC3
Guotong Sun, Yaowen Xu, Xiuwen Liang +2 more Β· 2025 Β· International immunopharmacology Β· Elsevier Β· added 2026-04-24
The etiology of hyperlipidemia is complex, and our understanding of its underlying mechanisms is limited. Effective therapeutic strategies for hyperlipidemia remain elusive. This study aimed to confir Show more
The etiology of hyperlipidemia is complex, and our understanding of its underlying mechanisms is limited. Effective therapeutic strategies for hyperlipidemia remain elusive. This study aimed to confirm the effect of curcumin on hyperlipidemia treatment and elucidate the precise mechanism. A high-fat diet-induced hyperlipidemia model using C57BL/6J mice and HaCaT cells was established. Co-immunoprecipitation and immunofluorescence were performed to detect protein interactions, and immunoprecipitation coupled with Western blotting was used to assess protein succinylation. 40Β ΞΌM of curcumin administration promoted cell viability, increased the levels of glutathione peroxidase, glutathione, catalase, and superoxide dismutase, while reducing reactive oxygen species activity and the levels of triglycerides and malondialdehyde. Additionally, curcumin attenuated the development of hyperlipidemia in vivo. Mechanistically, 100Β mg/kg of curcumin promoted O-GlcNAcylation and increased the expression of O-linked N-acetylglucosamine transferase in HaCaT cells. Furthermore, apolipoprotein C3 was identified as a substrate of O-linked N-acetylglucosamine transferase, and O-GlcNAcylation of apolipoprotein C3 enhanced its stability. Rescue experiments further verified that curcumin exerts its effects by regulating apolipoprotein C3 expression. In conclusion, these findings provide novel insights into the treatment of hyperlipidemia. Show less
no PDF DOI: 10.1016/j.intimp.2024.113647
APOC3
Baiyi Lu, Fan Xiao, Qinjun Zhang +8 more Β· 2025 Β· iMetaOmics Β· Wiley Β· added 2026-04-24
Foam cells derived from macrophages and smooth muscle cells (SMCs) play a pivotal role in the progression of atherosclerosis. While phytosterols (PS) have demonstrated cholesterol-lowering and anti-in Show more
Foam cells derived from macrophages and smooth muscle cells (SMCs) play a pivotal role in the progression of atherosclerosis. While phytosterols (PS) have demonstrated cholesterol-lowering and anti-inflammatory properties, their impact on foam cells remains elusive. Here, we investigated the effects of PS on foam cell formation, inflammatory responses, and lipid metabolism using both single-cell RNA sequencing (scRNA-seq) and functional assays. scRNA-seq of aortic tissue from Show less
πŸ“„ PDF DOI: 10.1002/imo2.70056
APOE
Yi Li, Zhu Ni, Xiao-Yong Xia +7 more Β· 2025 Β· Frontiers in molecular biosciences Β· Frontiers Β· added 2026-04-24
Metabolic disorders and neurocognitive diseases frequently co-occur, yet the specific mechanisms driving this comorbidity remain elusive. While epidemiological associations are well-documented, the ca Show more
Metabolic disorders and neurocognitive diseases frequently co-occur, yet the specific mechanisms driving this comorbidity remain elusive. While epidemiological associations are well-documented, the causal links between these conditions are complex and incompletely understood, necessitating a systems-level investigation into their shared biological architecture. This study integrates large-scale human genetics with experimental Network-informed Mendelian randomization identified bidirectional causalities, including a 14% elevated dementia risk from type 2 diabetes and protective effects of obesity against parental Alzheimer's disease (AD). The study identified a signature encompassing key lipid metabolism hubs This multi-modal investigation provides a robust framework that converges on a high-confidence, 13-gene signature of lipid dysregulation as a central mechanistic interface, offering a powerful set of prioritized targets for future functional validation and therapeutic development at the metabolic-neurocognitive nexus. Show less
πŸ“„ PDF DOI: 10.3389/fmolb.2025.1712198
APOE