👤 Xueyi Wang

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Also published as: A Wang, Ai-Ling Wang, Ai-Ting Wang, Aihua Wang, Aijun Wang, Aili Wang, Aimin Wang, Aiting Wang, Aixian Wang, Aiyun Wang, Aizhong Wang, Alexander Wang, Alice Wang, Allen Wang, Anlai Wang, Anli Wang, Annette Wang, Anni Wang, Anqi Wang, Anthony Z Wang, Anxiang Wang, Anxin Wang, Ao Wang, Aoli Wang, B R Wang, B Wang, Baihan Wang, Baisong Wang, Baitao Wang, Bangchen Wang, Banghui Wang, Bangmao Wang, Bangshing Wang, Bao Wang, Bao-Long Wang, Baocheng Wang, Baofeng Wang, Baogui Wang, Baojun Wang, Baoli Wang, Baolong Wang, Baoming Wang, Baosen Wang, Baowei Wang, Baoying Wang, Baoyun Wang, Bei Bei Wang, Bei Wang, Beibei Wang, Beilan Wang, Beilei Wang, Ben Wang, Benjamin H Wang, Benzhong Wang, Bi Wang, Bi-Dar Wang, Biao Wang, Bicheng Wang, Bijue Wang, Bin Wang, Bin-Xue Wang, Binbin Wang, Bing Qing Wang, Bing Wang, Binghai Wang, Binghan Wang, Bingjie Wang, Binglong Wang, Bingnan Wang, Bingyan Wang, Bingyu Wang, Binquan Wang, Biqi Wang, Bo Wang, Bochu Wang, Boyu Wang, Bruce Wang, C Wang, C Z Wang, Cai Ren Wang, Cai-Hong Wang, Cai-Yun Wang, Cailian Wang, Caiqin Wang, Caixia Wang, Caiyan Wang, Can Wang, Cangyu Wang, Carol A Wang, Catherine Ruiyi Wang, Cenxuan Wang, Chan Wang, Chang Wang, Chang-Yun Wang, Changduo Wang, Changjing Wang, Changliang Wang, Changlong Wang, Changqian Wang, Changtu Wang, Changwei Wang, Changying Wang, Changyu Wang, Changyuan Wang, Changzhen Wang, Chao Wang, Chao-Jun Wang, Chao-Yung Wang, Chaodong Wang, Chaofan Wang, Chaohan Wang, Chaohui Wang, Chaojie Wang, Chaokui Wang, Chaomeng Wang, Chaoqun Wang, Chaoxian Wang, Chaoyi Wang, Chaoyu Wang, Chaozhan Wang, Charles C N Wang, Chau-Jong Wang, Chen Wang, Chen-Cen Wang, Chen-Ma Wang, Chen-Yu Wang, Chenchen Wang, Chenfei Wang, Cheng An Wang, Cheng Wang, Cheng-Cheng Wang, Cheng-Jie Wang, Cheng-zhang Wang, Chengbin Wang, Chengcheng Wang, Chenggang Wang, Chenghao Wang, Chenghua Wang, Chengjian Wang, Chengjun Wang, Chenglin Wang, Chenglong Wang, Chengniu Wang, Chengqiang Wang, Chengshuo Wang, Chenguang Wang, Chengwen Wang, Chengyan Wang, Chengyu Wang, Chengze Wang, Chenji Wang, Chenliang Wang, Chenwei Wang, Chenxi Wang, Chenxin Wang, Chenxuan Wang, Chenyang Wang, Chenyao Wang, Chenyin Wang, Chenyu Wang, Chenzi Wang, Chi Chiu Wang, Chi Wang, Chi-Ping Wang, Chia-Chuan Wang, Chia-Lin Wang, Chien-Hsun Wang, Chien-Wei Wang, Chih-Chun Wang, Chih-Hao Wang, Chih-Hsien Wang, Chih-Liang Wang, Chih-Yang Wang, Chih-Yuan Wang, Chijia Wang, Ching C Wang, Ching-Jen Wang, Chiou-Miin Wang, Chong Wang, Chongjian Wang, Chonglong Wang, Chongmin Wang, Chongze Wang, Christina Wang, Christine Wang, Chu Wang, Chuan Wang, Chuan-Chao Wang, Chuan-Hui Wang, Chuan-Jiang Wang, Chuan-Wen Wang, Chuang Wang, Chuanhai Wang, Chuansen Wang, Chuansheng Wang, Chuanxin Wang, Chuanyue Wang, Chuduan Wang, Chun Wang, Chun-Chieh Wang, Chun-Juan Wang, Chun-Li Wang, Chun-Lin Wang, Chun-Ting Wang, Chun-Xia Wang, Chung-Hsi Wang, Chung-Hsing Wang, Chung-Teng Wang, Chunguo Wang, Chunhong Wang, Chuning Wang, Chunjiong Wang, Chunjuan Wang, Chunle Wang, Chunli Wang, Chunlong Wang, Chunmei Wang, Chunsheng Wang, Chunting Wang, Chunxia Wang, Chunxue Wang, Chunyan Wang, Chunyang Wang, Chunyi Wang, Chunyu Wang, Chuyao Wang, Cindy Wang, Ciyang Wang, Cong Wang, Congcong Wang, Congrong Wang, Congrui Wang, Cui Wang, Cui-Fang Wang, Cui-Shan Wang, Cuili Wang, Cuiling Wang, Cuizhe Wang, Cun-Yu Wang, Cunchuan Wang, Cunyi Wang, D Wang, Da Wang, Da-Cheng Wang, Da-Li Wang, Da-Yan Wang, Da-Zhi Wang, Dadong Wang, Dai Wang, Daijun Wang, Daiwei Wang, Daixi Wang, Dajia Wang, Dake Wang, Dali Wang, Dalong Wang, Dalu Wang, Dan Wang, Dan-Dan Wang, Danan Wang, Dandan Wang, Danfeng Wang, Dang Wang, Dangfeng Wang, Danling Wang, Danqing Wang, Danxin Wang, Danyang Wang, Dao Wen Wang, Dao-Wen Wang, Dao-Xin Wang, Daolong Wang, Daoping Wang, Daozhong Wang, Dapeng Wang, Daping Wang, Daqi Wang, Daqing Wang, David Q H Wang, David Q-H Wang, David Wang, Dawei Wang, Dayan Wang, Dayong Wang, Dazhi Wang, De-He Wang, Dedong Wang, Dehao Wang, Deli Wang, Delin Wang, Delong Wang, Demin Wang, Deming Wang, Dengbin Wang, Dennis Qing Wang, Dennis Wang, Deqi Wang, Deshou Wang, Dezhong Wang, Di Wang, Dinghui Wang, Dingting Wang, Dingxiang Wang, Dong D Wang, Dong Hao Wang, Dong Wang, Dong-Dong Wang, Dong-Jie Wang, Dong-Mei Wang, DongWei Wang, Dongdong Wang, Donggen Wang, Donghao Wang, Donghong Wang, Donghui Wang, Dongliang Wang, Donglin Wang, Dongmei Wang, Dongqin Wang, Dongshi Wang, Dongxia Wang, Dongxu Wang, Dongyan Wang, Dongyang Wang, Dongyi Wang, Dongying Wang, Dongyu Wang, Doudou Wang, Du Wang, Duan Wang, Duanyang Wang, Duo-Ping Wang, E Wang, Edward Wang, En-bo Wang, En-hua Wang, Endi Wang, Enhua Wang, Er-Jin Wang, Erfei Wang, Erika Y Wang, Ermao Wang, Erming Wang, Ertao Wang, Eryao Wang, Eunice S Wang, Exing Wang, F Wang, Fa-Kai Wang, Fan Wang, Fanchang Wang, Fang Wang, Fang-Tao Wang, Fangfang Wang, Fangjie Wang, Fangjun Wang, Fangyan Wang, Fangyong Wang, Fangyu Wang, Fanhua Wang, Fanwen Wang, Fanxiong Wang, Fei Wang, Fei-Fei Wang, Fei-Yan Wang, Feida Wang, Feifei Wang, Feijie Wang, Feimiao Wang, Feixiang Wang, Feiyan Wang, Fen Wang, Feng Wang, Feng-Sheng Wang, Fengchong Wang, Fengge Wang, Fenghua Wang, Fengliang Wang, Fenglin Wang, Fengling Wang, Fengqiang Wang, Fengyang Wang, Fengying Wang, Fengyong Wang, Fengyun Wang, Fengzhen Wang, Fengzhong Wang, Fu Wang, Fu-Sheng Wang, Fu-Yan Wang, Fu-Zhen Wang, Fubao Wang, Fubing Wang, Fudi Wang, Fuhua Wang, Fuqiang Wang, Furong Wang, Fuwen Wang, Fuxin Wang, Fuyan Wang, G Q Wang, G Wang, G-W Wang, Gan Wang, Gang Wang, Ganggang Wang, Ganglin Wang, Gangyang Wang, Ganyu Wang, Gao T Wang, Gao Wang, Gaofu Wang, Gaopin Wang, Gavin Wang, Ge Wang, Geng Wang, Genghao Wang, Gengsheng Wang, Gongming Wang, Guan Wang, Guan-song Wang, Guandi Wang, Guanduo Wang, Guang Wang, Guang-Jie Wang, Guang-Rui Wang, Guangdi Wang, Guanghua Wang, Guanghui Wang, Guangliang Wang, Guangming Wang, Guangsuo Wang, Guangwen Wang, Guangyan Wang, Guangzhi Wang, Guanrou Wang, Guanru Wang, Guansong Wang, Guanyun Wang, Gui-Qi Wang, Guibin Wang, Guihu Wang, Guihua Wang, Guimin Wang, Guiping Wang, Guiqun Wang, Guixin Wang, Guixue Wang, Guiying Wang, Guo-Du Wang, Guo-Hua Wang, Guo-Liang Wang, Guo-Ping Wang, Guo-Quan Wang, Guo-hong Wang, GuoYou Wang, Guobin Wang, Guobing Wang, Guodong Wang, Guohang Wang, Guohao Wang, Guoliang Wang, Guoling Wang, Guoping Wang, Guoqian Wang, Guoqiang Wang, Guoqing Wang, Guorong Wang, Guowen Wang, Guoxiang Wang, Guoxiu Wang, Guoyi Wang, Guoying Wang, Guozheng Wang, H J Wang, H Wang, H X Wang, H Y Wang, H-Y Wang, Hai Bo Wang, Hai Wang, Hai Yang Wang, Hai-Feng Wang, Hai-Jun Wang, Hai-Long Wang, Haibin Wang, Haibing Wang, Haibo Wang, Haichao Wang, Haichuan Wang, Haifei Wang, Haifeng Wang, Haihe Wang, Haihong Wang, Haihua Wang, Haijiao Wang, Haijing Wang, Haijiu Wang, Haikun Wang, Hailei Wang, Hailin Wang, Hailing Wang, Hailong Wang, Haimeng Wang, Haina Wang, Haining Wang, Haiping Wang, Hairong Wang, Haitao Wang, Haiwei Wang, Haixia Wang, Haixin Wang, Haixing Wang, Haiyan Wang, Haiying Wang, Haiyong Wang, Haiyun Wang, Haizhen Wang, Han Wang, Hanbin Wang, Hanbing Wang, Hanchao Wang, Handong Wang, Hang Wang, Hangzhou Wang, Hanmin Wang, Hanping Wang, Hanqi Wang, Hanying Wang, Hanyu Wang, Hanzhi Wang, Hao Wang, Hao-Ching Wang, Hao-Hua Wang, Hao-Tian Wang, Hao-Yu Wang, Haobin Wang, Haochen Wang, Haohao Wang, Haohui Wang, Haojie Wang, Haolong Wang, Haomin Wang, Haoming Wang, Haonan Wang, Haoping Wang, Haoqi Wang, Haoran Wang, Haowei Wang, Haoxin Wang, Haoyang Wang, Haoyu Wang, Haozhou Wang, He Wang, He-Cheng Wang, He-Ling Wang, He-Ping Wang, He-Tong Wang, Hebo Wang, Hechuan Wang, Heling Wang, Hemei Wang, Heming Wang, Heng Wang, Heng-Cai Wang, Hengjiao Wang, Hengjun Wang, Hequn Wang, Hesuiyuan Wang, Heyong Wang, Hezhi Wang, Hong Wang, Hong Yi Wang, Hong-Gang Wang, Hong-Hui Wang, Hong-Kai Wang, Hong-Qin Wang, Hong-Wei Wang, Hong-Xia Wang, Hong-Yan Wang, Hong-Yang Wang, Hong-Ying Wang, Hongbin Wang, Hongbing Wang, Hongbo Wang, Hongcai Wang, Hongda Wang, Hongdan Wang, Hongfang Wang, Hongjia Wang, Hongjian Wang, Hongjie Wang, Hongjuan Wang, Hongkun Wang, Honglei Wang, Hongli Wang, Honglian Wang, Honglun Wang, Hongmei Wang, Hongpin Wang, Hongqian Wang, Hongshan Wang, Hongsheng Wang, Hongtao Wang, Hongwei Wang, Hongxia Wang, Hongxin Wang, Hongyan Wang, Hongyang Wang, Hongyi Wang, Hongyin Wang, Hongying Wang, Hongyu Wang, Hongyuan Wang, Hongyue Wang, Hongyun Wang, Hongze Wang, Hongzhan Wang, Hongzhuang Wang, Horng-Dar Wang, Houchun Wang, Hsei-Wei Wang, Hsueh-Chun Wang, Hu WANG, Hua Wang, Hua-Qin Wang, Hua-Wei Wang, Huabo Wang, Huafei Wang, Huai-Zhou Wang, Huaibing Wang, Huaili Wang, Huaizhi Wang, Huajin Wang, Huajing Wang, Hualin Wang, Hualing Wang, Huan Wang, Huan-You Wang, Huang Wang, Huanhuan Wang, Huanyu Wang, Huaquan Wang, Huating Wang, Huawei Wang, Huaxiang Wang, Huayang Wang, Huei Wang, Hui Miao Wang, Hui Wang, Hui-Hui Wang, Hui-Li Wang, Hui-Nan Wang, Hui-Yu Wang, HuiYue Wang, Huie Wang, Huiguo Wang, Huihua Wang, Huihui Wang, Huijie Wang, Huijun Wang, Huilun Wang, Huimei Wang, Huimin Wang, Huina Wang, Huiping Wang, Huiquan Wang, Huiqun Wang, Huishan Wang, Huiting Wang, Huiwen Wang, Huixia Wang, Huiyan Wang, Huiyang Wang, Huiyao Wang, Huiying Wang, Huiyu Wang, Huizhen Wang, Huizhi Wang, Huming Wang, I-Ching Wang, Iris X Wang, Isabel Z Wang, J J Wang, J P Wang, J Q Wang, J Wang, J Z Wang, J-Y Wang, Jacob E Wang, James Wang, Jeffrey Wang, Jen-Chun Wang, Jen-Chywan Wang, Jennifer E Wang, Jennifer T Wang, Jennifer X Wang, Jenny Y Wang, Jeremy R Wang, Jeremy Wang, Ji M Wang, Ji Wang, Ji-Nuo Wang, Ji-Yang Wang, Ji-Yao Wang, Ji-zheng Wang, Jia Bei Wang, Jia Bin Wang, Jia Wang, Jia-Liang Wang, Jia-Lin Wang, Jia-Mei Wang, Jia-Peng Wang, Jia-Qi Wang, Jia-Qiang Wang, Jia-Ying Wang, Jia-Yu Wang, Jiabei Wang, Jiabo Wang, Jiafeng Wang, Jiafu Wang, Jiahao Wang, Jiahui Wang, Jiajia Wang, Jiakun Wang, Jiale Wang, Jiali Wang, Jialiang Wang, Jialin Wang, Jialing Wang, Jiamin Wang, Jiaming Wang, Jian Wang, Jian'an Wang, Jian-Bin Wang, Jian-Guo Wang, Jian-Hong Wang, Jian-Long Wang, Jian-Wei Wang, Jian-Xiong Wang, Jian-Yong Wang, Jian-Zhi Wang, Jian-chun Wang, Jianan Wang, Jianbing Wang, Jianbo Wang, Jianding Wang, Jianfang Wang, Jianfei Wang, Jiang Wang, Jiangbin Wang, Jiangbo Wang, Jianghua Wang, Jianghui Wang, Jiangong Wang, Jianguo Wang, Jianhao Wang, Jianhua Wang, Jianhui Wang, Jiani Wang, Jianjiao Wang, Jianjie Wang, Jianjun Wang, Jianle Wang, Jianli Wang, Jianlin Wang, Jianliu Wang, Jianlong Wang, Jianmei Wang, Jianmin Wang, Jianning Wang, Jianping Wang, Jianqin Wang, Jianqing Wang, Jianqun Wang, Jianru Wang, Jianshe Wang, Jianshu Wang, Jiantao Wang, Jianwei Wang, Jianwu Wang, Jianxiang Wang, Jianxin Wang, Jianye Wang, Jianying Wang, Jianyong Wang, Jianyu Wang, Jianzhang Wang, Jianzhi Wang, Jiao Wang, Jiaojiao Wang, Jiapan Wang, Jiaping Wang, Jiaqi Wang, Jiaqian Wang, Jiatao Wang, Jiawei Wang, Jiawen Wang, Jiaxi Wang, Jiaxin Wang, Jiaxing Wang, Jiaxuan Wang, Jiayan Wang, Jiayang Wang, Jiayi Wang, Jiaying Wang, Jiayu Wang, Jiazheng Wang, Jiazhi Wang, Jie Jin Wang, Jie Wang, Jieda Wang, Jieh-Neng Wang, Jiemei Wang, Jieqi Wang, Jieyan Wang, Jieyu Wang, Jifei Wang, Jiheng Wang, Jihong Wang, Jiliang Wang, Jilin Wang, Jin Wang, Jin'e Wang, Jin-Bao Wang, Jin-Cheng Wang, Jin-Da Wang, Jin-E Wang, Jin-Juan Wang, Jin-Liang Wang, Jin-Xia Wang, Jin-Xing Wang, Jincheng Wang, Jindan Wang, Jinfei Wang, Jinfeng Wang, Jinfu Wang, Jing J Wang, Jing Wang, Jing-Hao Wang, Jing-Huan Wang, Jing-Jing Wang, Jing-Long Wang, Jing-Min Wang, Jing-Shi Wang, Jing-Wen Wang, Jing-Xian Wang, Jing-Yi Wang, Jing-Zhai Wang, Jingang Wang, Jingchun Wang, Jingfan Wang, Jingfeng Wang, Jingheng Wang, Jinghong Wang, Jinghua Wang, Jinghuan Wang, Jingjing Wang, Jingkang Wang, Jinglin Wang, Jingmin Wang, Jingnan Wang, Jingqi Wang, Jingru Wang, Jingtong Wang, Jingwei Wang, Jingwen Wang, Jingxiao Wang, Jingyang Wang, Jingyi Wang, Jingying Wang, Jingyu Wang, Jingyue Wang, Jingyun Wang, Jingzhou Wang, Jinhai Wang, Jinhao Wang, Jinhe Wang, Jinhua Wang, Jinhuan Wang, Jinhui Wang, Jinjie Wang, Jinjin Wang, Jinkang Wang, Jinling Wang, Jinlong Wang, Jinmeng Wang, Jinning Wang, Jinping Wang, Jinqiu Wang, Jinrong Wang, Jinru Wang, Jinsong Wang, Jintao Wang, Jinxia Wang, Jinxiang Wang, Jinyang Wang, Jinyu Wang, Jinyue Wang, Jinyun Wang, Jinzhu Wang, Jiou Wang, Jipeng Wang, Jiqing Wang, Jiqiu Wang, Jisheng Wang, Jiu Wang, Jiucun Wang, Jiun-Ling Wang, Jiwen Wang, Jixuan Wang, Jiyan Wang, Jiying Wang, Jiyong Wang, Jizheng Wang, John Wang, Jou-Kou Wang, Joy Wang, Ju Wang, Juan Wang, Jue Wang, Jueqiong Wang, Jufeng Wang, Julie Wang, Juling Wang, Jun Kit Wang, Jun Wang, Jun Yi Wang, Jun-Feng Wang, Jun-Jie Wang, Jun-Jun Wang, Jun-Ling Wang, Jun-Sheng Wang, Jun-Sing Wang, Jun-Zhuo Wang, Jundong Wang, Junfeng Wang, Jung-Pan Wang, Junhong Wang, Junhua Wang, Junhui Wang, Junjiang Wang, Junjie Wang, Junjun Wang, Junkai Wang, Junke Wang, Junli Wang, Junlin Wang, Junling Wang, Junmei Wang, Junmin Wang, Junpeng Wang, Junping Wang, Junqin Wang, Junqing Wang, Junrui Wang, Junsheng Wang, Junshi Wang, Junshuang Wang, Junwen Wang, Junxiao Wang, Junya Wang, Junying Wang, Junyu Wang, Justin Wang, Jutao Wang, Juxiang Wang, K Wang, Kai Wang, Kai-Kun Wang, Kai-Wen Wang, Kaicen Wang, Kaihao Wang, Kaihe Wang, Kaihong Wang, Kaijie Wang, Kaijuan Wang, Kailu Wang, Kaiming Wang, Kaining Wang, Kaiting Wang, Kaixi Wang, Kaixu Wang, Kaiyan Wang, Kaiyuan Wang, Kaiyue Wang, Kan Wang, Kangli Wang, Kangling Wang, Kangmei Wang, Kangning Wang, Ke Wang, Ke-Feng Wang, KeShan Wang, Kehan Wang, Kehao Wang, Kejia Wang, Kejian Wang, Kejun Wang, Keke Wang, Keming Wang, Kenan Wang, Keqing Wang, Kesheng Wang, Kexin Wang, Keyan Wang, Keyi Wang, Keyun Wang, Kongyan Wang, Kuan Hong Wang, Kui Wang, Kun Wang, Kunhua Wang, Kunpeng Wang, Kunzheng Wang, L F Wang, L M Wang, L Wang, L Z Wang, L-S Wang, Laidi Wang, Laijian Wang, Laiyuan Wang, Lan Wang, Lan-Wan Wang, Lan-lan Wang, Lanlan Wang, Larry Wang, Le Wang, Le-Xin Wang, Ledan Wang, Lee-Kai Wang, Lei P Wang, Lei Wang, Lei-Lei Wang, Leiming Wang, Leishen Wang, Leli Wang, Leran Wang, Lexin Wang, Leying Wang, Li Chun Wang, Li Dong Wang, Li Wang, Li-Dong Wang, Li-E Wang, Li-Juan Wang, Li-Li Wang, Li-Na Wang, Li-San Wang, Li-Ting Wang, Li-Xin Wang, Li-Yong Wang, LiLi Wang, Lian Wang, Lianchun Wang, Liang Wang, Liang-Yan Wang, Liangfu Wang, Lianghai Wang, Liangli Wang, Liangliang Wang, Liangxu Wang, Lianshui Wang, Lianyong Wang, Libo Wang, Lichan Wang, Lichao Wang, Liewei Wang, Lifang Wang, Lifei Wang, Lifen Wang, Lifeng Wang, Ligang Wang, Lihong Wang, Lihua Wang, Lihui Wang, Lijia Wang, Lijin Wang, Lijing Wang, Lijuan Wang, Lijun Wang, Liling Wang, Lily Wang, Limeng Wang, Limin Wang, Liming Wang, Lin Wang, Lin-Fa Wang, Lin-Yu Wang, Lina Wang, Linfang Wang, Ling Jie Wang, Ling Wang, Ling-Ling Wang, Lingbing Wang, Lingda Wang, Linghua Wang, Linghuan Wang, Lingli Wang, Lingling Wang, Lingyan Wang, Lingzhi Wang, Linhua Wang, Linhui Wang, Linjie Wang, Linli Wang, Linlin Wang, Linping Wang, Linshu Wang, Linshuang Wang, Lintao Wang, Linxuan Wang, Linying Wang, Linyuan Wang, Liping Wang, Liqing Wang, Liqun Wang, Lirong Wang, Litao Wang, Liting Wang, Liu Wang, Liusong Wang, Liuyang Wang, Liwei Wang, Lixia Wang, Lixian Wang, Lixiang Wang, Lixin Wang, Lixing Wang, Lixiu Wang, Liyan Wang, Liyi Wang, Liying Wang, Liyong Wang, Liyuan Wang, Liyun Wang, Long Wang, Longcai Wang, Longfei Wang, Longsheng Wang, Longxiang Wang, Lou-Pin Wang, Lu Wang, Lu-Lu Wang, Lueli Wang, Lufang Wang, Luhong Wang, Luhui Wang, Lujuan Wang, Lulu Wang, Luofu Wang, Luping Wang, Luting Wang, Luwen Wang, Luxiang Wang, Luya Wang, Luyao Wang, Luyun Wang, Lynn Yuning Wang, M H Wang, M Wang, M Y Wang, M-J Wang, Maiqiu Wang, Man Wang, Mangju Wang, Manli Wang, Mao-Xin Wang, Maochun Wang, Maojie Wang, Maoju Wang, Mark Wang, Mei Wang, Mei-Gui Wang, Mei-Xia Wang, Meiding Wang, Meihui Wang, Meijun Wang, Meiling Wang, Meixia Wang, Melissa T Wang, Meng C Wang, Meng Wang, Meng Yu Wang, Meng-Dan Wang, Meng-Lan Wang, Meng-Meng Wang, Meng-Ru Wang, Meng-Wei Wang, Meng-Ying Wang, Meng-hong Wang, Mengge Wang, Menghan Wang, Menghui Wang, Mengjiao Wang, Mengjing Wang, Mengjun Wang, Menglong Wang, Menglu Wang, Mengmeng Wang, Mengqi Wang, Mengru Wang, Mengshi Wang, Mengwen Wang, Mengxiao Wang, Mengya Wang, Mengyao Wang, Mengying Wang, Mengyuan Wang, Mengyue Wang, Mengyun Wang, Mengze Wang, Mengzhao Wang, Mengzhi Wang, Mian Wang, Miao Wang, Mimi Wang, Min Wang, Min-sheng Wang, Ming Wang, Ming-Chih Wang, Ming-Hsi Wang, Ming-Jie Wang, Ming-Wei Wang, Ming-Yang Wang, Ming-Yuan Wang, Mingchao Wang, Mingda Wang, Minghua Wang, Minghuan Wang, Minghui Wang, Mingji Wang, Mingjin Wang, Minglei Wang, Mingliang Wang, Mingmei Wang, Mingming Wang, Mingqiang Wang, Mingrui Wang, Mingsong Wang, Mingxi Wang, Mingxia Wang, Mingxun Wang, Mingya Wang, Mingyang Wang, Mingyi Wang, Mingyu Wang, Mingzhi Wang, Mingzhu Wang, Minjie Wang, Minjun Wang, Minmin Wang, Minxian Wang, Minxiu Wang, Minzhou Wang, Miranda C Wang, Mo Wang, Mofei Wang, Monica Wang, Mu Wang, Mutian Wang, Muxiao Wang, Muxuan Wang, N Wang, Na Wang, Nan Wang, Nana Wang, Nanbu Wang, Nannan Wang, Nanping Wang, Neng Wang, Ni Wang, Niansong Wang, Ning Wang, Ningjian Wang, Ningli Wang, Ningyuan Wang, Nuan Wang, Oliver Wang, Ouchen Wang, P Jeremy Wang, P L Wang, P N Wang, P Wang, Pai Wang, Pan Wang, Pan-Pan Wang, Panfeng Wang, Panliang Wang, Pei Chang Wang, Pei Wang, Pei-Hua Wang, Pei-Jian Wang, Pei-Juan Wang, Pei-Wen Wang, Pei-Yu Wang, Peichang Wang, Peigeng Wang, Peihe Wang, Peijia Wang, Peijuan Wang, Peijun Wang, Peilin Wang, Peipei Wang, Peirong Wang, Peiwen Wang, Peixi Wang, Peiyao Wang, Peiyin Wang, Peng Wang, Peng-Cheng Wang, Pengbo Wang, Pengchao Wang, Pengfei Wang, Pengjie Wang, Pengju Wang, Penglai Wang, Penglong Wang, Pengpu Wang, Pengtao Wang, Pengxiang Wang, Pengyu Wang, Pin Wang, Ping Wang, Pingchuan Wang, Pingfeng Wang, Pingping Wang, Pintian Wang, Po-Jen Wang, Pu Wang, Q Wang, Q Z Wang, Qi Wang, Qi-Bing Wang, Qi-En Wang, Qi-Jia Wang, Qi-Qi Wang, Qian Wang, Qian-Liang Wang, Qian-Wen Wang, Qian-Zhu Wang, Qian-fei Wang, Qianbao Wang, Qiang Wang, Qiang-Sheng Wang, Qiangcheng Wang, Qianghu Wang, Qiangqiang Wang, Qianjin Wang, Qianliang Wang, Qianqian Wang, Qianrong Wang, Qianru Wang, Qianwen Wang, Qianxu Wang, Qiao Wang, Qiao-Ping Wang, Qiaohong Wang, Qiaoqi Wang, Qiaoqiao Wang, Qifan Wang, Qifei Wang, Qifeng Wang, Qigui Wang, Qihao Wang, Qihua Wang, Qijia Wang, Qiming Wang, Qin Wang, Qing Jun Wang, Qing K Wang, Qing Kenneth Wang, Qing Mei Wang, Qing Wang, Qing-Bin Wang, Qing-Dong Wang, Qing-Jin Wang, Qing-Liang Wang, Qing-Mei Wang, Qing-Yan Wang, Qing-Yuan Wang, Qing-Yun Wang, QingDong Wang, Qingchun Wang, Qingfa Wang, Qingfeng Wang, Qinghang Wang, Qingliang Wang, Qinglin Wang, Qinglu Wang, Qingming Wang, Qingping Wang, Qingqing Wang, Qingshi Wang, Qingshui Wang, Qingsong Wang, Qingtong Wang, Qingyong Wang, Qingyu Wang, Qingyuan Wang, Qingyun Wang, Qingzhong Wang, Qinqin Wang, Qinrong Wang, Qintao Wang, Qinwen Wang, Qinyun Wang, Qiong Wang, Qiqi Wang, Qirui Wang, Qishan Wang, Qiu-Ling Wang, Qiu-Xia Wang, Qiuhong Wang, Qiuli Wang, Qiuling Wang, Qiuning Wang, Qiuping Wang, Qiushi Wang, Qiuting Wang, Qiuyan Wang, Qiuyu Wang, Qiwei Wang, Qixue Wang, Qiyu Wang, Qiyuan Wang, Quan Wang, Quan-Ming Wang, Quanli Wang, Quanren Wang, Quanxi Wang, Qun Wang, Qunxian Wang, Qunzhi Wang, R Wang, Ran Wang, Ranjing Wang, Ranran Wang, Re-Hua Wang, Ren Wang, Rencheng Wang, Renjun Wang, Renqian Wang, Renwei Wang, Renxi Wang, Renxiao Wang, Renyuan Wang, Rihua Wang, Rikang Wang, Rixiang Wang, Robert Yl Wang, Rong Wang, Rong-Chun Wang, Rong-Rong Wang, Rong-Tsorng Wang, RongRong Wang, Rongjia Wang, Rongping Wang, Rongyun Wang, Ru Wang, RuNan Wang, Ruey-Yun Wang, Rufang Wang, Ruhan Wang, Rui Wang, Rui-Hong Wang, Rui-Min Wang, Rui-Ping Wang, Rui-Rui Wang, Ruibin Wang, Ruibing Wang, Ruibo Wang, Ruicheng Wang, Ruifang Wang, Ruijing Wang, Ruimeng Wang, Ruimin Wang, Ruiming Wang, Ruinan Wang, Ruining Wang, Ruiquan Wang, Ruiwen Wang, Ruixian Wang, Ruixin Wang, Ruixuan Wang, Ruixue Wang, Ruiying Wang, Ruizhe Wang, Ruizhi Wang, Rujie Wang, Ruling Wang, Ruming Wang, Runci Wang, Runuo Wang, Runze Wang, Runzhi Wang, Ruo-Nan Wang, Ruo-Ran Wang, Ruonan Wang, Ruosu Wang, Ruoxi Wang, Rurong Wang, Ruting Wang, Ruxin Wang, Ruxuan Wang, Ruyue Wang, S L Wang, S S Wang, S Wang, S X Wang, Sa A Wang, Sa Wang, Saifei Wang, Saili Wang, Sainan Wang, Saisai Wang, Sangui Wang, Sanwang Wang, Sasa Wang, Sen Wang, Seok Mui Wang, Seungwon Wang, Sha Wang, Shan Wang, Shan-Shan Wang, Shang Wang, Shangyu Wang, Shanshan Wang, Shao-Kang Wang, Shaochun Wang, Shaohsu Wang, Shaokun Wang, Shaoli Wang, Shaolian Wang, Shaoshen Wang, Shaowei Wang, Shaoyi Wang, Shaoying Wang, Shaoyu Wang, Shaozheng Wang, Shasha Wang, Shau-Chun Wang, Shawn Wang, Shen Wang, Shen-Nien Wang, Shenao Wang, Sheng Wang, Sheng-Min Wang, Sheng-Nan Wang, Sheng-Ping Wang, Sheng-Quan Wang, Sheng-Yang Wang, Shengdong Wang, Shengjie Wang, Shengli Wang, Shengqi Wang, Shengya Wang, Shengyao Wang, Shengyu Wang, Shengyuan Wang, Shenqi Wang, Sheri Wang, Shi Wang, Shi-Cheng Wang, Shi-Han Wang, Shi-Qi Wang, Shi-Xin Wang, Shi-Yao Wang, Shibin Wang, Shichao Wang, Shicung Wang, Shidong Wang, Shifa Wang, Shifeng Wang, Shih-Wei Wang, Shihan Wang, Shihao Wang, Shihua Wang, Shijie Wang, Shijin Wang, Shijun Wang, Shikang Wang, Shimiao Wang, Shiqi Wang, Shiqiang Wang, Shitao Wang, Shitian Wang, Shiwen Wang, Shixin Wang, Shixuan Wang, Shiyang Wang, Shiyao Wang, Shiyin Wang, Shiyu Wang, Shiyuan Wang, Shiyue Wang, Shizhi Wang, Shouli Wang, Shouling Wang, Shouzhi Wang, Shu Wang, Shu-Huei Wang, Shu-Jin Wang, Shu-Ling Wang, Shu-Na Wang, Shu-Song Wang, Shu-Xia Wang, Shu-qiang Wang, Shuai Wang, Shuaiqin Wang, Shuang Wang, Shuang-Shuang Wang, Shuang-Xi Wang, Shuangyuan Wang, Shubao Wang, Shudan Wang, Shuge Wang, Shuguang Wang, Shuhe Wang, Shuiliang Wang, Shuiyun Wang, Shujin Wang, Shukang Wang, Shukui Wang, Shun Wang, Shuning Wang, Shunjun Wang, Shunran Wang, Shuo Wang, Shuping Wang, Shuqi Wang, Shuqing Wang, Shuren Wang, Shusen Wang, Shusheng Wang, Shushu Wang, Shuu-Jiun Wang, Shuwei Wang, Shuxia Wang, Shuxin Wang, Shuya Wang, Shuye Wang, Shuyue Wang, Shuzhe Wang, Shuzhen Wang, Shuzhong Wang, Shyi-Gang P Wang, Si Wang, Sibo Wang, Sidan Wang, Sihua Wang, Sijia Wang, Silas L Wang, Silu Wang, Simeng Wang, Siqi Wang, Siqing Wang, Siwei Wang, Siyang Wang, Siyi Wang, Siying Wang, Siyu Wang, Siyuan Wang, Siyue Wang, Song Wang, Songjiao Wang, Songlin Wang, Songping Wang, Songsong Wang, Songtao Wang, Sophie H Wang, Stephani Wang, Su'e Wang, Su-Guo Wang, Su-Hua Wang, Sufang Wang, Sugai Wang, Sui Wang, Suiyan Wang, Sujie Wang, Sujuan Wang, Suli Wang, Sun Wang, Supeng Perry Wang, Suxia Wang, Suyun Wang, Suzhen Wang, T Q Wang, T Wang, T Y Wang, Taian Wang, Taicheng Wang, Taishu Wang, Tammy C Wang, Tao Wang, Taoxia Wang, Teng Wang, Tengfei Wang, Theodore Wang, Thomas T Y Wang, Tian Wang, Tian-Li Wang, Tian-Lu Wang, Tian-Tian Wang, Tian-Yi Wang, Tiancheng Wang, Tiange Wang, Tianhao Wang, Tianhu Wang, Tianhui Wang, Tianjing Wang, Tianjun Wang, Tianlin Wang, Tiannan Wang, Tianpeng Wang, Tianqi Wang, Tianqin Wang, Tianqing Wang, Tiansheng Wang, Tiansong Wang, Tiantian Wang, Tianyi Wang, Tianying Wang, Tianyuan Wang, Tielin Wang, Tienju Wang, Tieqiao Wang, Timothy C Wang, Ting Chen Wang, Ting Wang, Ting-Chen Wang, Ting-Hua Wang, Ting-Ting Wang, Tingting Wang, Tingye Wang, Tingyu Wang, Tom J Wang, Tong Wang, Tong-Hong Wang, Tongsong Wang, Tongtong Wang, Tongxia Wang, Tongxin Wang, Tongyao Wang, Tony Wang, Tzung-Dau Wang, Victoria Wang, Vivian Wang, W Wang, Wanbing Wang, Wanchun Wang, Wang Wang, Wangxia Wang, Wanliang Wang, Wanxia Wang, Wanyao Wang, Wanyi Wang, Wanyu Wang, Wayseen Wang, Wei Wang, Wei-En Wang, Wei-Feng Wang, Wei-Lien Wang, Wei-Qi Wang, Wei-Ting Wang, Wei-Wei Wang, Weicheng Wang, Weiding Wang, Weidong Wang, Weifan Wang, Weiguang Wang, Weihao Wang, Weihong Wang, Weihua Wang, Weijian Wang, Weijie Wang, Weijun Wang, Weilin Wang, Weiling Wang, 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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, 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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
Hua Liu, Jinrong Wang, Wenming Wang +2 more · 2025 · Journal of inflammation research · added 2026-04-24
Traumatic brain injury (TBI) is a leading cause of disability and death worldwide, involving complex pathophysiological responses such as metabolic disturbance and systemic inflammation. This study ai Show more
Traumatic brain injury (TBI) is a leading cause of disability and death worldwide, involving complex pathophysiological responses such as metabolic disturbance and systemic inflammation. This study aimed to evaluate the prognostic value of selected metabolic and inflammatory biomarkers in predicting short- and medium-term mortality in patients with moderate-to-severe TBI. We conducted a retrospective cohort study of patients with TBI admitted between March 29, 2018, and July 31, 2023. Clinical data, including a panel of metabolic (eg, triglyceride-glucose index [TYG], APOB/A1 ratio) and inflammatory biomarkers (eg, neutrophil-to-platelet ratio [NPR]), were collected within 24 hours of admission. Mortality was assessed at 14 days, 30 days, and hospital discharge. Multivariate Cox regression models and ROC curve analysis were used to assess prognostic associations and model performance. A total of 2555 patients were enrolled, of whom 579 (22.67%) underwent surgical treatment. Multivariate Cox proportional hazards regression analysis revealed that the triglyceride-glucose index (TYG) was an independent predictor of short-term mortality in TBI patients, while the neutrophil-to-platelet ratio (NPR) and apolipoprotein B/A1 (APOB/A1) ratio were independent predictors of both short- and mid-term mortality. In addition, surgical treatment was associated with an increased risk of mid-term mortality, while tracheostomy significantly reduced mortality risk across all time points. Receiver operating characteristic (ROC) curve analysis showed that the regression model incorporating inflammatory markers had the highest areas under the curve (AUCs) of 0.904, 0.897, and 0.897, demonstrating superior performance in predicting short- and mid-term mortality. Additionally, in the subgroup analysis of non-operation patients, TYG and NPR had a more significant impact on mortality risk. Metabolic and inflammatory biomarkers, including TYG, NPR, and APOB/A1 ratio, provide valuable prognostic information in patients with TBI. These markers may assist clinicians in early risk stratification and personalized treatment planning. Show less
📄 PDF DOI: 10.2147/JIR.S519606
APOB
Hui Wang, Sensen Wu, Dikang Pan +6 more · 2025 · Nutrition & diabetes · Nature · added 2026-04-24
This study aimed to investigate the role of Apolipoprotein B (Apo B) in diabetic nephropathy (DN) from epidemiological and genetic perspectives. We employed weighted multivariable-adjusted logistic re Show more
This study aimed to investigate the role of Apolipoprotein B (Apo B) in diabetic nephropathy (DN) from epidemiological and genetic perspectives. We employed weighted multivariable-adjusted logistic regression to assess the relationship between ApoB and DN risk, utilizing data from the National Health and Nutrition Examination Survey spanning 2007-2016. Then, we used restricted cubic splines (RCS) to flexibly model and visualize the relation of predicted ApoB levels with DN risk. Subsequently, a bidirectional two-sample Mendelian randomization study using genome-wide association study summary statistics was performed. The primary Inverse Variance Weighted method, along with supplementary MR approaches, was employed to verify the causal link between ApoB and DN. Sensitivity analyses were conducted to confirm the robustness of the results. Our observational study enrolled 2242 participants with diabetes mellitus from NHANES. The multivariable logistic regression model indicated that elevated ApoB levels (>1.2 g/L), compared to low levels (<0.8 g/L), were significantly associated with DN risk (P < 0.05). The RCS model revealed a positive linear association with the risk of DN when ApoB levels exceeded 1.12 g/L (OR = 1.29, 95% CI: 1.07-1.57, P = 0.008). However, the MR IVW method did not reveal a direct causal effect of DN on ApoB (OR: 0.976; 95% CI: 0.950-1.004; P = 0.095), nor a direct causal effect of ApoB on DN (OR: 0.837; 95% CI: 0.950-1.078; P = 0.428). The evidence from observational studies indicates a positive correlation between ApoB levels exceeding 1.12 g/L and the onset of DN. However, the causal effects of ApoB on DN and vice versa were not supported by the MR analysis. Show less
📄 PDF DOI: 10.1038/s41387-025-00370-1
APOB
Chunyu Yang, Xin Chai, Yachen Wang +8 more · 2025 · Cardiovascular diabetology · BioMed Central · added 2026-04-24
Existing evidence suggests that elevated 1-hour post-load plasma glucose (1-h PG ≥ 8.6 mmol/L) during an oral glucose tolerance test (OGTT) is associated with atherogenic lipid parameters which are li Show more
Existing evidence suggests that elevated 1-hour post-load plasma glucose (1-h PG ≥ 8.6 mmol/L) during an oral glucose tolerance test (OGTT) is associated with atherogenic lipid parameters which are linked to an increased risk of cardiovascular disease (CVD). However, it remains unclear whether normal glucose tolerance (NGT) individuals with elevated 1-h PG (NGT-1hPG-high) should still be considered low-risk. Therefore, this study aims to demonstrate comprehensive lipid characteristics in individuals with different glycemic status stratified by 1-h PG, with a particular focus on those with NGT-1hPG-high. This cross-sectional study included individuals aged 25-55 years with high-risk of diabetes from the Daqing Diabetes Prevention Study II (Daqing DPS-II). Individuals were categorized into different glycemic status based on the World Health Organization's 1999 criteria and the International Diabetes Federation's 2024 position statement on 1-h PG. Traditional (TC, TG, HDL-C, LDL-C) and non-traditional lipid parameters [ApoA-1, ApoB, sdLDL-C, Lp(a), non-HDL-C, remnant cholesterol (RC), ApoB/ApoA-1, LDL-C/ApoB] were measured. Dyslipidemia was defined according to the 2023 Chinese Guidelines for Lipid Management. The China-PAR equation was used to estimate 10-year CVD risk. Spearman's correlation coefficients were calculated to evaluate the correlation between lipid parameters and 10-year CVD risk. Logistic and multiple linear regression models were performed to assess the association between 1-h PG and dyslipidemia as well as lipid parameters adjusting for covariates. Among 2 469 individuals, 22.7% had NGT with normal 1-h PG (NGT-1hPG-normal), 19.9% had NGT-1hPG-high, 2.6% had prediabetes with normal 1-h PG (PDM-1hPG-normal), 34.2% had prediabetes with elevated 1-h PG (PDM-1hPG-high), and 20.6% had newly diagnosed diabetes. The prevalence of dyslipidemia did not significantly differ between NGT-1hPG-high and PDM-1hPG-high (OR = 1.13, 95%CI: 0.88-1.44, P > 0.05). Higher 1-h PG levels were consistently associated with an atherogenic lipid profile, characterized by increased TC, TG, LDL-C, ApoB, sdLDL-C, non-HDL-C, RC and ApoB/ApoA-1, along with decreased ApoA-1, HDL-C and LDL-C/ApoB (all P < 0.05). Among lipid parameters, TG, sdLDL-C, RC, ApoB/ApoA-1, LDL-C/ApoB and HDL-C showed the strongest correlation with 10-year CVD risk, with Spearman's correlation coefficients of 0.41, 0.38, 0.35, 0.31, - 0.37 and - 0.36, respectively. In the NGT-1hPG-high, TG, sdLDL-C, and ApoB/ApoA-1 levels were significantly higher, while HDL-C and LDL-C/ApoB levels were significantly lower compared to counterparts with NGT-1hPG-normal (all P < 0.05). Moreover, except for TG and RC (both P < 0.01), the majority of lipid parameter levels in NGT-1hPG-high did not significantly differ from those in PDM (all P > 0.05). NGT-1hPG-high exhibited a similar atherogenic lipid profile to that observed in PDM. 1-h PG could serve as a potential indicator for the early identification of at-risk individuals who may otherwise go undetected among NGT population. Show less
📄 PDF DOI: 10.1186/s12933-025-02722-8
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Xiang Lian, Xiaoyan Li, Kexin Wang +3 more · 2025 · Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics · added 2026-04-24
To investigate the gene detection results of 2 patients with familial hypercholesterolemia (FH) caused by complex heterozygous variation, and to clarify the relationship between clinical manifestation Show more
To investigate the gene detection results of 2 patients with familial hypercholesterolemia (FH) caused by complex heterozygous variation, and to clarify the relationship between clinical manifestations and gene variation. Two patients (patient 1 and 2) with FH who visited Beijing Anzhen Hospital Affiliated to Capital Medical University in 2018 were selected as research subjects. A retrospective study method was used to collect clinical and family history data of the two patients. And 2 mL of peripheral venous blood from each of the two patients was collected, and genomic DNA extraction was performed on the blood samples. Sanger sequencing was used to validate the variant sites of the two patients detected by whole-exome sequencing (WES). Pathogenicity of variants was classified based on the American College of Medical Genetics and Genomics (ACMG) Standards and Guidelines for the Classification of Genetic Variants (hereinafter referred to as the "ACMG Guidelines"), and the impact of variant was analyzed using multiple bioinformatics tools including SIFT, PolyPhen-2, and SWISS-MODEL. This study has been approved by Beijing Anzhen Hospital Affiliated to Capital Medical University (Ethics No. 2024215X). Patient 1 initially presented with early-onset coronary heart disease, with initial lipid levels of serum total cholesterol (TC) 9.86 mmol/L (normal reference value: 3.10~5.20 mmol/L) and serum low-density lipoprotein cholesterol (LDL-C) 8.37 mmol/L (normal reference value: 1.27~3.12 mmol/L) on admission. Patient 1 initially underwent treatment with rosuvastatin combined with ezetimibe for one month, but the lipid-lowering effect was not significant. The lipid-lowering therapy was then adjusted to atorvastatin combined with ezetimibe and probucol. After one year of treatment, the patient developed paroxysmal chest pain symptoms. A follow-up lipid profile showed a serum TC level of 4.50 mmol/L and a LDL-C level of 3.55 mmol/L. The lipid-lowering regimen was continued, and the serum LDL-C levels were maintained between 2.65 and 3.66 mmol/L. Patient 2 was found to have an abnormally high blood lipid level and carotid artery hardening during physical examination, with an initial blood lipid level of serum TC 11.82 mmol/L and serum LDL-C 9.63 mmol/L. After receiving rosuvastatain therapy, the lipid-lowering effect was significant. WES revealed that patient 1 carried the heterozygous variants c.1871₁₈₇₃del(p.Ile624del) and c.1747C>T (p.His583Tyr) in the LDLR gene (NM₀₀₀₅₂₇.4), while patient 2 carried the heterozygous variants c.1747C>T (p.His583Tyr) in the LDLR gene and c.6936₆₉₃₇inv (p.Ile2313Val) in the APOB gene (NM₀₀₀₃₈₄₎. According to the ACMG Guidelines, the LDLR gene c.1747C>T (p.His583Tyr) was classified as a pathogenic variant (PS3+PM1+PM2_supporting+PM5+PP2+PP3), and c.1871₁₈₇₃del (p.Ile624del) was classified as a pathogenic variant (PS3+PS4+PM2_supporting+PM1+PM4); the APOB gene c.6936₆₉₃₇inv (p.Ile2313Val) was classified as a variant of uncertain clinical significance (PM2_supporting BP4). Patients 1 and 2 in this study were patients with complex heterozygous variant FH, and their genotypic differences may be related to the differences in clinical serum LDL-C levels and the efficacy of hypolipidemic agents. Show less
no PDF DOI: 10.3760/cma.j.cn511374-20241026-00562
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Xiaobing Luo, Hongying Cai, Xiaofeng Wang +4 more · 2025 · Scientific reports · Nature · added 2026-04-24
Crystals or stones within the gallbladder wall in patients with gallbladder stones (GBS) have been occasionally reported, but their clinical features and aetiology remain unclear. This retrospective s Show more
Crystals or stones within the gallbladder wall in patients with gallbladder stones (GBS) have been occasionally reported, but their clinical features and aetiology remain unclear. This retrospective study analysed 323 consecutive patients with GBS who underwent rigid choledochoscopic gallbladder-preserving cholecystolithotomy to determine the detection rate, clinical features, and potential risk factors of gallbladder intramural stones (IS). IS were found in 24.1% (78/323) of patients, characterised by distinct cholangioscopic findings, including stone shadows, yellow floating bands, or a combination of both within the gallbladder wall. Compared to patients without IS, those with IS had a higher prevalence of Clonorchis sinensis (C. sinensis) eggs (60.3% vs. 40.8%, P < 0.05) and elevated serum cholesterol, LDL cholesterol, and Apo-B levels (P < 0.05). However, stone composition and C. sinensis egg detection rates did not differ between intraluminal stones and IS within the same patient (P > 0.05). Logistic regression analysis revealed that IS were associated with C. sinensis infection and elevated Apo-B levels. In conclusion, IS share homology with intraluminal stones in the same patient with GBS and exhibit unique appearances in rigid choledochoscopy. For patients with GBS and IS, elevated serum Apo-B levels and C. sinensis infection were independent risk factors. Show less
📄 PDF DOI: 10.1038/s41598-025-00721-z
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Xiao-Yan Shi, Ya-Kun Liu, Yan Chen +3 more · 2025 · Pediatric obesity · Blackwell Publishing · added 2026-04-24
Metabolic dysfunction-associated steatotic liver disease (MASLD) has become a prevalent liver condition in children and teenagers with obesity. Unfortunately, there is no standardized treatment. To ex Show more
Metabolic dysfunction-associated steatotic liver disease (MASLD) has become a prevalent liver condition in children and teenagers with obesity. Unfortunately, there is no standardized treatment. To examine the connection between apolipoprotein B (apoB), apolipoprotein A1 (apoA1), and the apoB/apoA1 ratio with the occurrence of MASLD in this population. A retrospective study was made on children and adolescents with obesity in a children's hospital between the period 2020 and 2022. Anthropometric data, ultrasound results, and blood biochemistry were analysed to assess the connection between apoB, apoA1, and the presence of MASLD. Of the 916 participants included, 313 were diagnosed with MASLD. The level of serum apoB reflected a substantial dose-response correlation with the odds of having MASLD. When apoB levels exceeded the 50th percentile, the risk increased significantly, and at the 95th percentile, the odds were 4.83 times higher than at the 50th percentile (95% CI: 2.02-11.56). The ratio of apoB/apoA1 at the 95th percentile was connected to a 2.41-fold higher prevalence compared to the 50th percentile (95% CI: 1.33-4.37). No significant correlation was found between the levels of apoA1 and MASLD prevalence. Elevated levels of apoB and the apoB/apoA1 ratio have been strongly connected to increased MASLD prevalence in children and adolescents with obesity; hence, signifying their potential usefulness as biomarkers for early detection and intervention. Show less
no PDF DOI: 10.1111/ijpo.70017
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Q Zang, F Li, Y Ju +6 more · 2025 · Scandinavian journal of rheumatology · Taylor & Francis · added 2026-04-24
Recent studies suggest that dyslipidaemia may play a critical role in the progression of cardiovascular disease in Takayasu arteritis (TA), although the exact relationship between dyslipidaemia and TA Show more
Recent studies suggest that dyslipidaemia may play a critical role in the progression of cardiovascular disease in Takayasu arteritis (TA), although the exact relationship between dyslipidaemia and TA disease activity remains unclear, which is the focus of this study. We evaluated dyslipidaemia and atherosclerosis in a cohort of untreated female patients. Fifty untreated female patients with TA (median age 30 years) and 98 healthy controls matched for age and body mass index (median age 30 years) were assessed for lipid profiles [total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), ApoB, ApoE, lipoprotein(a)], inflammatory markers [C-reactive protein (CRP), erythrocyte sedimentation rate (ESR)], and atherosclerotic plaque frequency. TA patients exhibited significantly higher levels of TG and the non-HDL-C/HDL-C ratio than the control group, whereas TC, HDL-C, LDL-C, and ApoA1 levels were significantly lower. Pearson's correlation analysis indicated a positive correlation between CRP and ApoB, as well as the non-HDL-C/HDL-C ratio, and negative correlations with TG, HDL-C, and ApoA1. Atherosclerotic plaques were detected in 14.3% of the TA patients. Multivariate regression analysis revealed that the presence of atherosclerotic plaques was associated only with age, independent of inflammatory markers and lipoprotein levels. The results of this study indicate that untreated female TA patients exhibit a markedly dysregulated serum lipid profile. Atherosclerosis in early TA was not related to lipids or markers of inflammation. Show less
no PDF DOI: 10.1080/03009742.2025.2488096
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Jun Wang, Kefen Zhang, Xiuming Tang +2 more · 2025 · Journal of cancer research and therapeutics · added 2026-04-24
Currently, understanding of the nonlinear relationship between age and hepatocellular carcinoma (HCC) prognosis is insufficient. Thus, this study aimed to analyze the relationship between age at HCC d Show more
Currently, understanding of the nonlinear relationship between age and hepatocellular carcinoma (HCC) prognosis is insufficient. Thus, this study aimed to analyze the relationship between age at HCC diagnosis and overall survival (OS) and identify possible influencing mechanisms. Clinical data from the TCGA public database were analyzed. Restricted cubic spline and segmented logistic regression were employed to explore the nonlinear relationship between age at diagnosis and mortality risk following hepatectomy. Furthermore, bioinformatics methods were employed to understand the possible mechanisms of this nonlinear relationship at the genetic level. The results indicated a nonlinear relationship between age at diagnosis and OS, with the age of 60 years identified as a critical point. Segmented regression showed that age ≥60 years is an unfavorable prognostic factor. The "DNA mismatch repair" pathway was considerably enriched in patients aged <60 years. However, the gene mutation rate of "APOB," "MUC16," "ALB," and "PCLO" and the median tumor mutation burden were relatively more evident in patients aged ≥60 years. MGEA12 was more highly expressed in tumor tissues than in normal ones, particularly in patients aged ≥60 years. The survival rate of the high-expression group was lower than that of the low-expression group. At the mRNA level, the MGEA12 expression in Huh-7 and SUN449 was higher than that in the HSC-LX2 cell line. A nonlinear relationship was found between age at HCC diagnosis and OS, with the age of 60 years being the critical point. MGEA12 may affect the prognosis of elderly people. Show less
no PDF DOI: 10.4103/jcrt.jcrt_1690_24
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Ting Tang, Junjie Hao, Qingyan Yang +2 more · 2025 · Endocrine · Springer · added 2026-04-24
This study investigated the relationship between lipoprotein profiles and sarcopenia in patients with type 2 diabetes mellitus (T2DM). The objective is to provide a solid theoretical foundation and tr Show more
This study investigated the relationship between lipoprotein profiles and sarcopenia in patients with type 2 diabetes mellitus (T2DM). The objective is to provide a solid theoretical foundation and treatment strategies for clinical prevention and management of diabetes, particularly in individuals with concurrent sarcopenia. In this study, we selected inpatients aged over 60 years diagnosed with T2DM who were admitted to the Department of Geriatrics at Qinghai University Affiliated Hospital from July 2023 to June 2024 as research subjects. We collected general patient data, including gender, age, ethnicity, height, weight, and calculated body mass index (BMI). Key indices measured included glycated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoproteins A and B (ApoA and ApoB), phospholipids, lipoprotein(a) [Lp(a)], very low-density lipoprotein (VLDL), and free fatty acids (FFA). Additionally, we assessed limb skeletal muscle mass, grip strength, walking speed, and calculated the appendicular skeletal muscle mass index (ASMI). Based on Asian diagnostic criteria for sarcopenia, patients were categorized into a non-sarcopenic group or a group with T2DM combined with sarcopenia. Baseline laboratory data along with ASMI measurements, grip strength assessments, and walking speeds were statistically analyzed for both groups. Compared with T2DM patients without sarcopenia, the levels of HbA1c, Lp(a), FFA, serum albumin, TC, TG, HDL-C, ApoA and VLDL in type 2 diabetic patients with sarcopenia were statistically significant (all P < 0.05). When multivariate adjustments were made for these clinical features, age (OR = 1.18, 95%CI: 1.11-1.25, P < 0.001), BMI (OR = 0.81, 95%CI: 0.72-0.92, P < 0.001), ApoA (OR = 0.03, 95%CI: 0.00-0.90, P = 0.043), Lp(a) > = 15.5 mg/dL (OR = 3.14, 95%CI: 1.51-6.54, P = 0.002) and FFA > = 0.48 g/L (OR = 4.11, 95%CI: 1.97-8.57, P < 0.001) were independent predictors of diabetes mellitus with sarcopenia. ROC curve analysis showed that free fatty acids (AUC = 0.721, 95%CI: 0.660-0.782, P < 0.001) in T2DM with sarcopenia has good predictive value judgment. Age, BMI, ApoA, Lp(a), and FFA were independent predictors of T2DM with sarcopenia. Serum free fatty acids have a good predictive value in the judgment of T2DM complicated with sarcopenia. Show less
📄 PDF DOI: 10.1007/s12020-025-04226-7
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Da Luo, Elias Björnson, Xiaoying Wang +7 more · 2025 · International journal of cardiology · Elsevier · added 2026-04-24
The per-particle pathogenicity of very-low-density lipoprotein (VLDL) and lipoprotein(a) [Lp(a)] with risk of valvular heart diseases (VHD) other than aortic stenosis compared with low-density lipopro Show more
The per-particle pathogenicity of very-low-density lipoprotein (VLDL) and lipoprotein(a) [Lp(a)] with risk of valvular heart diseases (VHD) other than aortic stenosis compared with low-density lipoprotein (LDL) remains unclear. Single-nucleotide polymorphism specific clusters associated with LDL cholesterol (LDL-C), VLDL cholesterol (VLDL-C) and Lp(a) were identified. The relationships of genetically predicted variation in apolipoprotein B (apoB) in these lipoproteins with risk of VHD and its major types (aortic stenosis, aortic regurgitation, and mitral regurgitation) were evaluated to determine the comparative pathogenicity by Mendelian randomization (MR) analyses. The VHD odds ratio (OR) per 1 g/L higher apoB was 1.09 [95 % confidence interval (CI) 1.04-1.15] in LDL vs. 1.45 (95 % CI 1.25-1.69) in VLDL vs. 2.71 (95 % CI 1.92-3.82) in Lp(a) based on the cluster-based MR analyses. The polygenic scores for each lipoprotein weighted by apoB similarly showed a greater OR of VHD per 1 g/L apoB in VLDL [1.20 (95 % CI 1.06-1.37)] and in Lp(a) [2.54, (95 % CI 1.95-3.32)] compared with that in LDL [1.05 (95 % CI 1.01-1.08)]. Multivariable MR analyses further revealed the strong effects of VLDL-C and Lp(a) on VHD risk independent of LDL-C. In addition, significant associations between Lp(a) and all three major types of VHD were observed, while LDL and VLDL had no impact on aortic and mitral regurgitation. VLDL and Lp(a) appear to have significantly greater per-particle pathogenicity in VHD compared to LDL. The distinct impacts of lipoproteins on different VHD subtypes suggest the inadequacy of just focusing on LDL-lowering treatment for valve disorders. Show less
no PDF DOI: 10.1016/j.ijcard.2025.133218
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Xuliang Luo, Yan Guo, Xuelian Li +6 more · 2025 · BMC genomics · BioMed Central · added 2026-04-24
Aromatase, encoded by Cyp19a1, is the rate limiting enzyme in biosynthesis of estrogens, and excessive aromatase can reduce the semen quality in roosters. Seminal plasma extracellular vesicles (SPEV) Show more
Aromatase, encoded by Cyp19a1, is the rate limiting enzyme in biosynthesis of estrogens, and excessive aromatase can reduce the semen quality in roosters. Seminal plasma extracellular vesicles (SPEV) are nanoscale vesicles that carry and transmit signaling molecules, thereby affecting semen quality. Currently it is still unclear whether SPEV are involved in the process of that aromatase affects the quality semen in chicken. To clarify this issue, lentivirus carrying Cyp19a1 (LV-CYP19A1) for over-expression of aromatase was constructed and injected to testis of 35-week-old roosters. Semen quality and seminal plasma hormone were measured, and SPEV were also extracted and proteome sequencing was performed after treatment of LV-CYP19A1. The results indicated that semen volume, fertility, sperm motility, testosterone (T) levels were significantly decreased, and estradiol (E Our results reveal that aromatase can down-regulate the protein expression related to regulation of ATP synthesis and metabolism, and sperm motility in SPEV, thereby reducing semen quality in roosters. Show less
📄 PDF DOI: 10.1186/s12864-025-11500-5
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Chunbo Zhuang, Fangfang Cui, Jin Chen +3 more · 2025 · Biochimica et biophysica acta. Molecular basis of disease · Elsevier · added 2026-04-24
Excessive hepatic lipid accumulation is the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), yet its underlying mechanisms still not fully understood. In this study, we id Show more
Excessive hepatic lipid accumulation is the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), yet its underlying mechanisms still not fully understood. In this study, we identified RNA binding motif protein 39 (Rbm39) as a key modulator of hepatic lipid homeostasis during MASLD progression. To establish in vivo MASLD model, mice were fed either a high-fat diet (HFD) or a Gubra-Amylin NASH (GAN) diet. We employed adeno-associated virus to manipulate Rbm39 expression levels to assess its role in MASLD. Transcriptome analysis was conducted to pinpoint the genes targeted by Rbm39. Western blot, RT-PCR, dual-luciferase reporter gene assays, and alternative splicing analysis were utilized to delve into the molecular mechanisms. Our results showed that Rbm39 expression was notably decreased in the livers of MASLD mice. Knockdown of hepatic Rbm39 aggravated HFD-induced hepatic steatosis and GAN diet-induced MASH, along with a notable decrease in serum lipid levels. Conversely, overexpression of Rbm39 attenuated MASLD development and progression. RNA sequencing data analysis indicated that Rbm39 regulated the expression of apolipoprotein B (Apob) and fatty acid-binding protein 4 (Fabp4), both of which are crucial for lipid transport. Mechanistically, Rbm39 enhanced the transcription of Apob by upregulating hepatocyte nuclear factor 4α (Hnf4α), while it suppressed Fabp4 transcription by regulating alternative splicing of hypoxia inducible factor-1α (Hif-1α). These findings highlight the pivotal role of Rbm39 in maintaining hepatic lipid homeostasis and suggest its potential as a therapeutic target for MASLD. Show less
no PDF DOI: 10.1016/j.bbadis.2025.167815
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Pengfei Xie, Weinan Xie, Zhaobo Wang +8 more · 2025 · Diabetology & metabolic syndrome · BioMed Central · added 2026-04-24
Patients with diabetic nephropathy (DN) often present with lipid profile abnormalities. While associations between these parameters and DN have been suggested, confounding factors obscure causal relat Show more
Patients with diabetic nephropathy (DN) often present with lipid profile abnormalities. While associations between these parameters and DN have been suggested, confounding factors obscure causal relationships. This study employed bidirectional Mendelian randomization (MR) to explore these links. Using genome-wide association study (GWAS) data, the primary analysis used the inverse-variance weighted (IVW) method, which was supported by MR-Egger regression and a weighted median estimator (WME). Sensitivity analyses, including heterogeneity, pleiotropy tests, leave-one-out, and reverse causality analyses, were conducted. The IVW model revealed the following: (1) causal relationships between triglycerides (TG) (OR: 1.5807, 95% CI: 1.2578-1.9865, P = 0.0001), high-density lipoprotein cholesterol (HDL-C) (OR: 0.7342, 95% CI: 0.5729-0.9409, P = 0.0146), and apolipoprotein A1 (ApoA1) (OR: 0.6506, 95% CI: 0.5190-0.8156, P = 0.0002) and DN; (2) causal relationships between TG (OR: 1.0607, 95% CI: 1.0143-1.1093, P = 0.0098), HDL-C (OR: 0.9453, 95% CI: 0.9053-1.9871, P = 0.0109), and apolipoprotein B (ApoB) (OR: 1.0672, 95% CI: 0.0070-1.1310, P = 0.0280) and the urinary albumin-creatinine ratio (UACR); (3) no causal relationship between total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), ApoB and DN, or between TC, LDL-C, ApoA1 and UACR; (4) none of the results showed reverse causality. TG is a risk factor for DN and UACR; HDL-C is protective for both; ApoA1 protects against DN; and ApoB is a risk factor for UACR. To further explore the underlying mechanisms between TG, HDL-C, ApoA1, ApoB, and their associations with DN and UACR, and to provide reference for the selection of lipid management and treatment strategies for clinical DN patients. This study demonstrated that causal relationships between TG, HDL-C, and ApoA1 with DN and between TG, HDL-C, and ApoB with the UACR. Show less
📄 PDF DOI: 10.1186/s13098-025-01641-8
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Anqi Wang, Hui Ren, Yanyan Zhang +2 more · 2025 · Poultry science · Elsevier · added 2026-04-24
Fatty liver hemorrhagic syndrome (FLHS) is a common nutritional and metabolic disease in laying hens, leading to a rapid decline in egg production. This study aims to evaluate the antioxidant effects Show more
Fatty liver hemorrhagic syndrome (FLHS) is a common nutritional and metabolic disease in laying hens, leading to a rapid decline in egg production. This study aims to evaluate the antioxidant effects of dietary supplementation with Pueraria Lobatae Radix polysaccharide (PLRP) on laying hens with FLHS induced by a high-energy low-protein (HELP) diet. A total of 72 thirty-seven-wk-old Hy-Line Brown laying hens were divided into 4 groups: basal diet (CON), HELP diet (HELP), HELP + 100 mg/kg PLRP (HELP-Low), and HELP + 300 mg/kg PLRP (HELP-High), with 6 replicates of 3 hens each. After 4 weeks on the HELP diet, PLRP was added to the diet of the HELP-Low and HELP-High groups for 8 weeks. The results demonstrated that PLRP supplementation significantly improved laying rate compared to the HELP group, with the HELP-Low and HELP-High groups exhibiting respective increases of 23.81% and 28.57% (P < 0.01). PLRP also promoted follicular development, increasing the number of stratified, primary, and secondary follicles and improving the ovarian index. Biochemical analysis revealed enhanced antioxidant activity, with increased levels of T-AOC, T-SOD, and GSH-Px and reduced MDA in the liver and ovaries of PLRP-treated hens (P < 0.05). At the molecular level, PLRP upregulated mRNA expression of ER-α, ER-β, MTTP, APOB, APOVLDL-II, and VTG-II in the liver, as well as VLDLR, LHR, and FSHR in the ovaries, facilitating yolk precursor biosynthesis and follicular development (P < 0.05). It indicated that PLRP supplementation mitigates oxidative stress and enhances yolk precursor synthesis, thereby improving egg production in FLHS-affected hens. PLRP shows promise as an effective feed additive for preventing and alleviating FLHS in laying hens. Future studies will investigate the regulatory effects of PLRP on gut microbiota composition and its potential interactions with FLHS in laying hens. Show less
📄 PDF DOI: 10.1016/j.psj.2025.105062
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Song Liu, Xingjin Wang, Jiaqiang Hu +2 more · 2025 · Diabetes, obesity & metabolism · Blackwell Publishing · added 2026-04-24
To evaluate the efficacy and safety of siRNA drugs that lower Lp(a) in patients with dyslipidaemia. A network meta-analysis and systematic review were conducted to compare siRNA drugs targeting Lp(a), Show more
To evaluate the efficacy and safety of siRNA drugs that lower Lp(a) in patients with dyslipidaemia. A network meta-analysis and systematic review were conducted to compare siRNA drugs targeting Lp(a), based on relevant randomized controlled trials (RCTs). A comprehensive search was performed in PubMed, Embase, Web of Science and the Cochrane Library (up to October 24, 2024). RCTs with an intervention duration of at least 12 weeks were included. Eligible studies compared siRNA drugs that reduce Lp(a), including both Lp(a)-targeted and non-targeted agents, with placebo or other siRNA drugs that reduce Lp(a). The primary outcomes were the percentage reduction and absolute reduction in Lp(a), percentage reduction in low-density lipoprotein cholesterol (LDL-C), percentage reduction in apolipoprotein B (apo(B)), adverse events and serious adverse events, including injection-site reactions. The risk of bias was assessed using the Cochrane Risk of Bias Tool (ROB2), and a random-effects network meta-analysis was performed using the frequentist approach. Confidence in effect estimates was evaluated using the Confidence In Network Meta-Analysis (CINeMA) framework. A total of 14 trials involving 5646 participants were included. Lp(a)-targeted siRNA agents, particularly Olpasiran, demonstrated strong efficacy in significantly reducing Lp(a) levels, with the greatest percentage reduction in Lp(a) (mean difference [MD]: -92.06%; 95% CI: -102.43% to -81.69%; P-score: 0.98). Olpasiran also showed the greatest absolute reduction in Lp(a) (MD: -250.70 nmol/L; 95% confidence interval [CI]: -279.89 to -221.50; P-score: 0.99). Certain non-Lp(a)-targeted siRNA agents, such as inclisiran and zodasiran, also showed modest reductions in Lp(a) levels, reducing Lp(a) by approximately 15%. Lp(a)-targeted siRNA agents reduced LDL-C by more than 20% and decreased apo(B) by approximately 15%. In terms of safety, most drugs exhibited favourable safety profiles with no significant differences compared to placebo. However, zerlasiran raised concerns regarding injection-site reactions and other adverse events when compared to placebo. Lp(a)-targeted siRNA agents have shown robust effectiveness in substantially reducing Lp(a) levels, including both percentage and absolute reductions, with moderate improvements in LDL-C and apo(B) concentrations. Non-Lp(a)-targeted siRNA agents also demonstrate modest reductions in Lp(a) levels. The safety profile is generally favourable, but zerlasiran and inclisiran may increase the incidence of injection-site reactions. Show less
no PDF DOI: 10.1111/dom.16355
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Wenxiu Wang, Rui Li, Zimin Song +4 more · 2025 · JAMA cardiology · added 2026-04-24
Despite substantial progress in low-density lipoprotein cholesterol (LDL-C)-lowering strategies, residual cardiovascular risk remains. Apolipoprotein C3 (APOC3) has emerged as a novel target for lower Show more
Despite substantial progress in low-density lipoprotein cholesterol (LDL-C)-lowering strategies, residual cardiovascular risk remains. Apolipoprotein C3 (APOC3) has emerged as a novel target for lowering triglycerides. Multiple clinical trials of small-interfering RNA therapeutics targeting APOC3 are currently underway. To investigate whether genetically predicted lower APOC3 is associated with a reduction in cardiovascular risk and if the combined exposure to APOC3 and LDL-C-lowering variants is associated with a reduction in the risk of coronary heart disease (CHD). This was a population-based genetic association study with 2 × 2 factorial mendelian randomization. Included were participants of European ancestry in the UK Biobank. Data were analyzed from November 2023 to July 2024. Genetic scores were constructed to mimic the effects of APOC3, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), and proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors. Plasma lipid and lipoprotein levels, CHD, and type 2 diabetes (T2D). This study included 401 548 UK Biobank participants (mean [SD] age, 56.9 [8.0] years; 216 901 female [54.0%]). Genetically predicted lower APOC3 was associated with a lower risk of CHD (odds ratio [OR], 0.96; 95% CI, 0.93-0.98) and T2D (0.97; 95% CI, 0.95-0.99). Genetically lower APOC3 and PCSK9 were associated with a similar magnitude of risk reduction in CHD per 10-mg/dL decrease in apolipoprotein B (ApoB) level (APOC3: 0.70; 95% CI, 0.59-0.83; PCSK9: 0.71; 95% CI, 0.65-0.77). Combined exposure to genetically lower APOC3 and PCSK9 was associated with an additive lower risk of CHD (APOC3: 0.96; 95% CI, 0.92-0.99; PCSK9: 0.93; 95% CI, 0.90-0.97; combined: 0.90; 95% CI, 0.86-0.93). Genetically lower HMGCR was also associated with a lower risk of CHD, and the risk was further reduced when combined with APOC3 (0.93; 95% CI, 0.90-0.97). Genetically predicted lower APOC3 was associated with a reduced risk of CHD that is comparable with that associated with lower PCSK9 per unit decrease in ApoB. Combined exposure to APOC3 and LDL-C-lowering variants was associated with an additive reduction in CHD risk. Future studies are warranted to investigate the therapeutic potential of these combined therapies, particularly among high-risk patients who cannot achieve therapeutic targets with existing lipid-lowering therapies. Show less
no PDF DOI: 10.1001/jamacardio.2025.0195
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Ruibing Li, Jinyang Wang, Jianan Wang +7 more · 2025 · Journal of inflammation research · added 2026-04-24
Neuromyelitis optica spectrum disorder (NMOSD) is a group of immune-mediated disorders that often lead to severe disability. The diagnosis and monitoring of NMOSD can be challenging, particularly in s Show more
Neuromyelitis optica spectrum disorder (NMOSD) is a group of immune-mediated disorders that often lead to severe disability. The diagnosis and monitoring of NMOSD can be challenging, particularly in seronegative cases, highlighting the need for reliable biomarkers to enhance clinical management. This study aimed to identify serum lipid biomarkers for the diagnosis and monitoring of NMOSD and to assess their potential to improve clinical decision-making. We conducted a comprehensive serum proteomic analysis in a discovery cohort of NMOSD patients and controls to identify lipid-related proteins associated with NMOSD. Subsequently, we validated the candidate biomarkers in the retrospective cohort and developed diagnostic models using a random forest algorithm. The association between these lipid biomarkers and disease activity was further evaluated in longitudinal analysis. Our analysis identified a panel of serum lipid-related biomarkers that demonstrated significant differences between NMOSD patients and controls. The diagnostic models achieved the impressive accuracy of 72% for the full NMOSD spectrum, 72% for AQP4-IgG+ NMOSD, and 68% for double seronegative NMOSD. Importantly, these biomarkers showed a correlation with disease activity, with levels changing from relapse to remission. Additionally, a combination of these lipid biomarkers was found to predict relapse with the AUC of 0.861. A user-friendly smartphone application was developed to facilitate the straightforward "input-index, output-answer" screening process, enhancing both clinical decision-making and patient care. The diagnostic model based on the serum lipid-related indexes (TC, TG, LDL, HDL, ApoA1, and ApoB) may be the useful tool for NMOSD in diagnosis and monitoring of disease stage, thereby improving the treatment outcome for patients. Future studies should focus on integrating these biomarkers into routine clinical practice to realize their full potential in enhancing NMOSD management. Show less
📄 PDF DOI: 10.2147/JIR.S496018
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Anna Tilp, Dimitrios Nasias, Andrew Carley +10 more · 2025 · bioRxiv : the preprint server for biology · Cold Spring Harbor Laboratory · added 2026-04-24
Movement of circulating lipids into tissues and arteries requires transfer across the endothelial cell barrier. This process allows the heart to obtain fatty acids (FAs), its chief source of energy an Show more
Movement of circulating lipids into tissues and arteries requires transfer across the endothelial cell barrier. This process allows the heart to obtain fatty acids (FAs), its chief source of energy and apolipoprotein B (apoB)-containing lipoproteins to cross the arterial endothelial barrier leading to cholesterol accumulation in the subendothelial space. Multiple studies have established elevated postprandial triglyceride-rich lipoproteins (TRLs) as an independent risk factor for cardiovascular disease (CVD). We explored how chylomicrons affect ECs and transfer their FAs across the EC barrier. We had reported that media from chylomicron-treated ECs leads to lipid droplet (LD) formation in macrophages. To determine the responsible component of this media, we assessed whether removing the extracellular vesicles (EVs) would obviate this effect. EVs from control and treated cells were then characterized by protein, lipid and microRNA (miR) content. We also studied the EV-induced transcription changes in macrophages and ECs and whether knockdown of scavenger receptor-BI (SR-BI) altered these responses. In addition, using chylomicrons labeled with [ Chylomicron treatment of ECs led to an inflammatory response that included production of EVs that drove macrophage LD accumulation. The EVs contained little free fatty acids and triglyceride, but abundant phospholipids and diacylglycerols. In concert with this, [ EC chylomicron metabolism produces EVs that increase macrophage inflammation and create LDs. Media containing these EVs also increases EC inflammation, illustrating an autocrine inflammatory process. FAs within chylomicron triglycerides are converted to phospholipids within EVs. Thus, EC uptake of chylomicrons constitutes an important pathway for vascular inflammation and tissue lipid acquisition. Show less
no PDF DOI: 10.1101/2025.02.28.640926
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Haoyu Wang, Tian Tu, Lijun Yin +2 more · 2025 · BMC cancer · BioMed Central · added 2026-04-24
Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers in Show more
Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers intricately linked to OC's molecular pathogenesis represents a critical avenue for enhancing patient outcomes and survival prospects. In this comprehensive study, we embarked on a bioinformatics journey, leveraging the vast repository of single nucleotide polymorphism (SNP) data from OC patients available within the TCGA database. Our overarching goal was to unearth the genetic underpinnings of OC, shedding light on potential prognostic markers that could significantly impact clinical decision-making and patient care. Our meticulous analysis led to the discovery of five mutated genes-APOB, BRCA1, COL6A3, LRP1, and LRP1B-engaged in the intricate world of lipid metabolism. These genes, previously unexplored in the context of OC, emerged as prominent figures in our investigation, showcasing their potential roles in OC progression. The intricate interplay between lipid metabolism and cancer development has garnered considerable attention in recent years, and our findings underscore the relevance of these genes in the context of OC. To fortify our discoveries, we delved into the realm of survival analysis, a pivotal component of our investigation. The results yielded compelling evidence of significant correlations between patient survival and the expression levels of the aforementioned genes. This critical insight underscores the potential utility of these genes as prognostic markers, illuminating a path toward more personalized and effective approaches to patient care. Our study represents a multifaceted approach to unraveling the complex molecular pathogenesis of OC. By harnessing the power of high-throughput data mining, we uncovered genetic insights that may reshape our understanding of this formidable disease. We complemented these findings with advanced techniques such as RT-qPCR and Western blot, further dissecting the intricacies of OC's molecular landscape. This holistic approach not only deepens our understanding but also provides essential bioinformatics information that holds promise in assessing patient prognosis. In summary, our study represents a significant stride in the quest to decode the molecular intricacies of ovarian cancer. Our findings spotlight the potential prognostic significance of APOB, BRCA1, COL6A3, LRP1, and LRP1B, inviting further exploration into their roles in OC progression. Ultimately, our research carries the potential to shape the future of OC management, offering a glimpse into a more personalized and effective approach to patient care. Show less
📄 PDF DOI: 10.1186/s12885-025-13841-6
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Yu Cui, Yanzhu Chen, Mengting Hu +7 more · 2025 · Computational biology and chemistry · Elsevier · added 2026-04-24
The gut microbiota plays a crucial role in human health, but its impact on lipid metabolism remains unclear. Understanding the causal relationship between gut bacteria and lipid profiles is essential Show more
The gut microbiota plays a crucial role in human health, but its impact on lipid metabolism remains unclear. Understanding the causal relationship between gut bacteria and lipid profiles is essential for developing strategies to prevent and treat dyslipidemia and cardiovascular diseases. This study aimed to assess this relationship using two-sample Mendelian randomization (MR). Data for both exposure and outcomes were obtained from the IEU-GWAS database, with lipid profile data sourced from a publication. Genome-wide significant single nucleotide polymorphisms (SNPs), which were independent of outcome factors but correlated with exposure variables, were identified as instrumental variables. Several MR methods, including weighted analysis, maximum likelihood, inverse variance weighting (IVW), MR-Egger, and weighted median, were applied. Colocalization analysis further validated the findings. The analysis revealed microbial groups with causal relationships to ApoA1, ApoB, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, and triglycerides. Reverse MR and colocalization analysis provided additional confirmation of these results. This study offers new evidence of the causal link between gut microbiota and lipid profiles, providing insights for improving lipid profiles and reducing cardiovascular disease risk. Show less
no PDF DOI: 10.1016/j.compbiolchem.2025.108422
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Zhaoyuan Sun, Jinzhi Liu, Aihua Wang +1 more · 2025 · Scientific reports · Nature · added 2026-04-24
Small and dense LDL cholesterol (sdLDL-C) and apolipoprotein B (ApoB) have important roles in promoting the development of atherosclerosis and are highly correlated with the degree of atherosclerosis. Show more
Small and dense LDL cholesterol (sdLDL-C) and apolipoprotein B (ApoB) have important roles in promoting the development of atherosclerosis and are highly correlated with the degree of atherosclerosis. Several studies have found differences in anterior and posterior circulation strokes and in the mechanisms of their atherosclerosis, but little research has been done on the relationship of sdLDL-C and ApoB to atherosclerotic stenosis in anterior and posterior circulation strokes. We analyzed the correlation between sdLDL-C and ApoB and the degree of arterial stenosis in patients with posterior circulation stroke. We included 230 anterior circulation stroke (ACS) patients and 170 posterior circulation stroke (PCS) patients. Blood specimens were collected at admission, serum ApoB and sdLDL-C concentrations were measured, and the degree of arterial stenosis was determined on the basis of vascular imaging. We analyzed the predictive value of ApoB and sdLDL-C for the degree of cerebral artery stenosis in patients with PCS. For patients with nonmild stenosis, sdLDL-C and ApoB levels were higher in the PCS group than in the ACS group (P < 0.05). SdLDL-C (P < 0.001) and ApoB (P < 0.05) were independent risk factors for increased intracranial artery stenosis in the posterior circulation group. Binary logistic regression analysis showed that sdLDL-C (P < 0.05) and ApoB (P < 0.05) were independent risk factors for non-mild stenosis of the intracranial arteries in patients with PCS after correction for confounders. In the posterior circulation group, there was an interaction between the effects of sdLDL and ApoB on intracranial artery stenosis, P < 0.05. Plotting the ROC curve showed that the AUC of the combined detection of sdLDL-C and ApoB was 0.791, which was better than that of the single index. We built nomogram model, the DCA curves, calibration curves, NRI index, and IDI index of both the modeling and validation groups indicated that the diagnostic efficacy and clinical benefit of the combined sdLDL-C and ApoB assay were greater than those of single-indicator assays for cerebral artery stenosis in posterior circulation stroke. Risk factors contributing to the increased degree of intracranial arterial stenosis in ACS and PCS vary somewhat. SdLDL-C and ApoB may be of value in clinical decision making as predictors of cerebral arterial stenosis in patients with PCS. Show less
📄 PDF DOI: 10.1038/s41598-025-93074-6
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Tongxue Zhang, Yajing Li, Xiaoyu Liu +8 more · 2025 · Kardiologiia · added 2026-04-24
Aim    Aortic aneurysm is characterized by localized expansion and damage to the vessel wall. While apolipoprotein B (ApoB) has been linked to atherosclerosis, its causal relationship with aortic aneu Show more
Aim    Aortic aneurysm is characterized by localized expansion and damage to the vessel wall. While apolipoprotein B (ApoB) has been linked to atherosclerosis, its causal relationship with aortic aneurysm remains unclear. This study used a Mendelian randomization (MR) approach to explore the causal relationships between ApoB, aortic aneurysm, and potential mediators.Material and methods    Single nucleotide polymorphism (SNP) data related to ApoB, apolipoprotein A1 (ApoA1), triglycerides, frailty index, and aortic aneurysm were obtained from large-scale genome-wide association studies. MR analysis was conducted to evaluate causal relationships, using inverse variance weighting (IVW) as the primary statistical method. Additionally, we assessed whether the frailty index mediates the relationship between ApoB and aortic aneurysm.Results    Univariate MR analysis revealed that ApoB is significantly associated with aortic aneurysm (IVW odds ratio (OR) = 1.443, 95 % confidence interval (CI) = 1.273-1.637, p < 0.001). Multivariable MR (MVMR) analysis, adjusted for ApoA1 and triglycerides, confirmed these results. In mediation analysis, the frailty index was found to partially mediate the effect of ApoB on aortic aneurysm (mediation contribution: 20.1 %-23.1 %). The ORs for ApoB and the frailty index with respect to aortic aneurysm were 1.325 (95 % CI = 1.168-1.505) and 4.188 (95 % CI = 1.859-9.435), respectively.Conclusion    ApoB has a causal relationship with aortic aneurysm, with the frailty index acting as a partial mediator in this pathway. Show less
no PDF DOI: 10.18087/cardio.2025.2.n2796
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Jingjing Guo, Haifan Qiu, Jianping Wang +3 more · 2025 · Frontiers in medicine · Frontiers · added 2026-04-24
To establish the reference interval for the serum lipid index in pregnant women and to explore the relationship between lipid metabolism levels and pregnancy outcomes. Data were derived from 446 pregn Show more
To establish the reference interval for the serum lipid index in pregnant women and to explore the relationship between lipid metabolism levels and pregnancy outcomes. Data were derived from 446 pregnancy women and 317 healthy non-pregnant women. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein (a) [Lp(a)], and hypersensitive C-reactive protein (hs-CRP) were measured in both groups. The mean and standard deviation of each index were calculated to establish the reference range of normal serum lipid levels in pregnant women in mid-to-late pregnancy. The associations between serum lipid levels and perinatal outcomes were assessed statistically. There were no significant differences in age, pregnancy, or parity between the adverse outcome and normal delivery groups, but the caesarean section rate was significantly higher in the adverse outcome group. The levels of hs-CRP, TG, TC, HDL-C, LDL-C, and ApoA1 were significantly higher in the adverse outcome group. Elevated hs-CRP, TG, and HDL-C levels were risk factors for adverse pregnancy outcomes. According to the receiver operating characteristic curve, the optimal threshold of the combined diagnosis of these three indicators to predict adverse pregnancy outcomes was 0.534, and the area under the curve was 0.822. The establishment of lipid reference intervals in the second and third trimesters of pregnancy can effectively evaluate lipid metabolism in pregnant women, and the measurement of lipid metabolism in pregnant women is helpful in predicting adverse pregnancy outcomes. Show less
📄 PDF DOI: 10.3389/fmed.2025.1530525
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Pengfei Zhang, Wenting Wang, Qian Xu +5 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. Show more
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. This study explores the genetic causal associations of different circulating lipids and lipid-lowering drug targets with coronary artery calcification (CAC) and abdominal aortic artery calcification (AAC). We obtained single-nucleotide polymorphisms (SNPs) and expression quantitative trait loci (eQTLs) associated with seven circulating lipids and 13 lipid-lowering drug targets from publicly available genome-wide association studies and eQTL databases. Causal associations were investigated by univariable, multivariable, drug-target, and summary data-based Mendelian randomization (MR) analyses. Potential mediation effects of metabolic risk factors were evaluated. MR analysis revealed that genetic proxies for low-density lipoprotein cholesterol (LDL-C), triglycerides (TC) and Lipoprotein (a) (Lp(a)) were causally associated with CAC severity, and apolipoprotein B (apoB) level was causally associated with AAC severity. A significant association was detected between hepatic Lipoprotein(A) (LPA) gene expression and CAC severity. Colocalisation analysis supported the hypothesis that the association between LPA expression and CAC quantity is driven by different causal variant sites within the ±1 Mb flanking region of LPA. Serum calcium and phosphorus had causal associations with CAC severity. Inhibitors targeting LPA might represent CAC drug candidates. Moreover, T2DM, hypercalcemia, and hyperphosphatemia are positively causally associated with CAC severity, while chronic kidney disease and estimated glomerular filtration rate are not. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.119136
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Juan Zhou, Shanshan Wang, Qiang Wang +11 more · 2025 · Food & function · Royal Society of Chemistry · added 2026-04-24
Central obesity poses a significant health threat. Lutein-rich fruits and vegetables may help manage obesity. Limited evidence suggests that lutein exerts health effects by inhibiting advanced glycati Show more
Central obesity poses a significant health threat. Lutein-rich fruits and vegetables may help manage obesity. Limited evidence suggests that lutein exerts health effects by inhibiting advanced glycation end products (AGEs), but data on its effects in centrally obese individuals are sparse. Thus, we aimed to investigate the effects of lutein supplementation in subjects with central obesity. A double-blind, randomized controlled trial was conducted involving patients with central obesity. Anthropometric indices, dietary intake, metabolic parameters, carotenoid and AGEs levels were compared between those receiving a 32-week intervention of 10 mg d Show less
no PDF DOI: 10.1039/d4fo05578k
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Shuai Wang, Hanshen Zhou, Kaili Cai +4 more · 2025 · World journal of surgical oncology · BioMed Central · added 2026-04-24
To explore the risk factors of post pancreatectomy diabetes mellitus (PPTDM)in pancreatic ductal carcinoma (PDAC) patients and the value of perioperative fasting blood glucose (FBG) level expression o Show more
To explore the risk factors of post pancreatectomy diabetes mellitus (PPTDM)in pancreatic ductal carcinoma (PDAC) patients and the value of perioperative fasting blood glucose (FBG) level expression on the long-term survival after surgery. Between December 2015 and December 2019, a cohort of 509 patients diagnosed with PDAC and undergoing resection at our hospital was analyzed. They were stratified into two groups, Control group (Control) and study group (PPTDM), depending on the onset of postoperative diabetes mellitus. We analyzed the survival rates at 6 months, 12 months and 24 months post-operation in the two groups. We use univariate and logostic multivariate regressions to analyze the risk factors for PPTDM. ROC curve analysis was conducted to assess the diagnostic significance of perioperative FBG levels regarding patients' long-term survival rates. The Kaplan-Meier method was employed to assess the impact of both preoperative and postoperative FBG levels on the survival rates within 24 months for each patient group. The comparison of general clinical data between the two groups shows marginal differences without statistical significance(P > 0.05); Patients in PPTDM group had significantly higher BMI, preoperative jaundice proportion, larger tumor diameter, higher TNM stage and higher proportion of distal pancreatectomy (DP), with P values of 0.023, 0.010, 0.040, 0.012 and 0.005, respectively. The levels of preoperative FBG and postoperative FBG in PPTDM patients exhibited statistically significant elevation compared to the control group (P < 0.05). There were no significant differences in surgery-related indicators between the two groups in operative time, number of dissected positive lymph nodes, total number of dissected lymph nodes, intraoperative blood loss and other related data (P > 0.05). Hospitalization duration of PPTDM patients was longer than control group (P = 0.047). PPTDM group had significantly higher expression concentrations of BUN, Cr, TG, LDL and Apo-B factors (P = 0.023, 0.024, 0.013, 0.045 and 0.017). 17 patients (5.03%) died in the PPTDM group and 4 patients (2.35%) in control group which had significantly difference (P = 0.020). In univariate and logostic multivariate regression analysis indicated tumor size, jaundice, BUN, Cr, TG, LDL, Apo-B concentrations and DP approach were significantly correlated to the risk for PPTDM (P < 0.05). ROC curve analysis results showed combining of preoperative and postoperation FBG showed the highest diagnostic efficacy, followed by postoperation FBG and preoperative FBG. The AUC areas of the three groups were 0.745, 0.623 and 0.588, respectively, and the critical values of the three groups were 9.81/9.95 mmol/L, 10.18 mmol/L and 10.23 mmol/L, respectively, with statistical significance (P < 0.05). Results were considered statistically significant if the p-value was less than 0.05. PPTDM stands as a significant postoperative complication following pancreatic cancer surgery, characterized by a high incidence and severity. Several risk factors have garnered considerable attention among clinical surgeon. PPTDM may be an influential factor in postoperative prognosis of pancreatic cancer. The expression levels of preoperative and postoperative blood glucose hold diagnostic value for the long-term prognosis of pancreatic cancer patients. Early regulation and intervention by surgeons concerning perioperative FBG could potentially mitigate the risk of PPTDM. Show less
📄 PDF DOI: 10.1186/s12957-025-03705-5
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Ling-Ling Wang, Zi-Xiang Xu, Bo-Qian Sun +3 more · 2025 · Angiology · SAGE Publications · added 2026-04-24
Lipid ratio is a balance between atherogenesis and antiatherogenesis. it is an important predictive marker of carotid plaque. The lipid ratios, which include non-high-density lipoprotein cholesterol ( Show more
Lipid ratio is a balance between atherogenesis and antiatherogenesis. it is an important predictive marker of carotid plaque. The lipid ratios, which include non-high-density lipoprotein cholesterol (non-HDL-C)/high-density lipoprotein cholesterol (HDL-C), remnant cholesterol (RC)/HDL-C, apolipoprotein B (ApoB)/apolipoprotein A1 (ApoA1), low-density lipoprotein cholesterol (LDL-C)/HDL-C, ApoB/HDL-C, total cholesterol (TC)/HDL-C, triglycerides (TG)/HDL-C, were included and analyzed. Sex differences in the relationship between lipid ratios and carotid plaque were discussed. The risk of carotid plaque was found to be significantly associated with the Non-HDL-C /HDL-C, RC/HDL-C, ApoB/ApoA1, LDL-C /HDL-C, ApoB/HDL-C, TC/HDL-C in females but not in males. The ApoB/HDL risk presented the highest relationship with carotid plaque in females only. The predictive value of the aforementioned lipid ratios for carotid plaque was observed in females only. Show less
no PDF DOI: 10.1177/00033197251316624
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Robert S Rosenson, J Antonio G López, Daniel Gaudet +14 more · 2025 · JAMA cardiology · added 2026-04-24
Lipoprotein(a) (Lp[a]) is thought to be the major carrier of oxidized phospholipids (OxPL). OxPL are believed to be a potent driver of inflammation and atherosclerosis. Olpasiran, a small interfering Show more
Lipoprotein(a) (Lp[a]) is thought to be the major carrier of oxidized phospholipids (OxPL). OxPL are believed to be a potent driver of inflammation and atherosclerosis. Olpasiran, a small interfering RNA, blocks Lp(a) production by inducing degradation of apolipoprotein(a) messenger RNA. Olpasiran's effects on OxPL and systemic markers of inflammation are not well described. To assess the effects of olpasiran on OxPL, high-sensitivity interleukin 6 (hs-IL-6), and hs-C-reactive protein (hs-CRP) in the OCEAN(a)-DOSE randomized clinical trial. OCEAN(a)-DOSE was an international, multicenter, placebo-controlled, phase 2, dose-finding randomized clinical trial conducted between July 2020 and November 2022. A total of 281 patients with atherosclerotic cardiovascular disease and Lp(a) levels greater than 150 nmol/L were included. Participants were randomized to receive 1 of 4 active subcutaneous doses of olpasiran vs placebo: (1) 10 mg, administered every 12 weeks (Q12W); (2) 75 mg, Q12W; (3) 225 mg, Q12W; or (4) 225 mg, administered every 24 weeks (Q24W). OxPL on apolipoprotein B (OxPL-apoB), hs-CRP, and hs-IL-6 were assessed at baseline, week 36, and week 48 in 272 patients. The primary outcome was placebo-adjusted change in OxPL-apoB from baseline to week 36. Among 272 participants, median (IQR) age was 62 years (56-69), and 86 participants (31.6%) were female. Baseline median (IQR) Lp(a) concentration was 260.3 nmol/L (198.1-352.4) and median (IQR) OxPL-apoB concentration was 26.5 nmol/L (19.7-33.9). The placebo-adjusted mean percentage change in OxPL-apoB from baseline to week 36 was -51.6% (95% CI, -64.9% to -38.2%) for the 10-mg Q12W dose, -89.7% (95% CI, -103.0% to -76.4%) for the 75-mg Q12W dose, -92.3% (95% CI, -105.6% to -78.9%) for the 225-mg Q12W dose, and -93.7% (95% CI, -107.1% to -80.3%) for the Q24W dose (P < .001 for all). These effects were maintained to week 48 (-50.8%, -100.2%, -104.7%, and -85.8%, respectively; P < .001 for all). There was a strong correlation between percentage reduction in Lp(a) and OxPL-apoB for patients treated with olpasiran (r = 0.79; P < .001). Olpasiran did not significantly impact hs-CRP or hs-IL-6 compared with placebo to weeks 36 or 48 (P > .05). In the OCEAN(a)-DOSE multicenter randomized clinical trial, olpasiran led to a significant and sustained reduction in OxPL-apoB but no significant effects on hs-CRP or hs-IL-6. Show less
no PDF DOI: 10.1001/jamacardio.2024.5433
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Zeyu Wang, Zixiao Yin, Guangyong Sun +2 more · 2025 · Lipids in health and disease · BioMed Central · added 2026-04-24
The liver‒brain axis is critical in neurodegenerative diseases (NDs), with lipid metabolism influencing neuroinflammation and microglial function. A systematic investigation of the genetic relationshi Show more
The liver‒brain axis is critical in neurodegenerative diseases (NDs), with lipid metabolism influencing neuroinflammation and microglial function. A systematic investigation of the genetic relationship between lipid metabolism abnormalities and ND, namely, Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS), is lacking. To assess potential causal links between ND and six lipid parameters, two-sample Mendelian randomization (MR) was used. Large-scale European ancestry GWAS data for lipid parameters and ND (AD, ALS, PD, and MS) were used. Genetic variants demonstrating significant correlations (P < 5 × 10 MR via the inverse-variance weighted method revealed causal effects of cholesterol (CHOL, OR = 1.10, 95% CI: 1.03-1.18, P = 4.23 × 10⁻ Higher CHOL and LDLC levels were associated with increased ALS risk, suggesting a potential causal link, and supporting the liver‒brain axis hypothesis in ND. Current genetic evidence does not support a significant role for lipid metabolism in PD and MS etiology, suggesting the relationship between lipid metabolism and other NDs may be more complex and warrants further investigation. Show less
📄 PDF DOI: 10.1186/s12944-025-02455-3
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Yuanlong Hu, Xinhai Cui, Mengkai Lu +11 more · 2025 · Mayo Clinic proceedings · Elsevier · added 2026-04-24
To investigate the causal relationship between various lipid-modifying drugs and new-onset diabetes, as well as the mediators contributing to this relationship. Mediation Mendelian randomization was p Show more
To investigate the causal relationship between various lipid-modifying drugs and new-onset diabetes, as well as the mediators contributing to this relationship. Mediation Mendelian randomization was performed to investigate the causal effect of lipid-modifying drug targets on type 2 diabetes (T2D) outcomes and the proportion of this association that is mediated through ectopic fat accumulation traits. Specific sets of variants in or near genes that encode 11 lipid-modifying drug targets (LDLR, HMGCR, NPC1L1, PCSK9, APOB, ABCG5/ABCG8, LPL, PPARA, ANGPTL3, APOC3, and CETP; for expansion of gene symbols, use search tool at www.genenames.org) were extracted. Random effects inverse variance weighted were performed to evaluate the causal effects among outcomes. Mediation analyses were performed to identify the mediators of the association between lipid-modifying drugs and T2D. The study was conducted from November 10, 2023, to April 2, 2024 RESULTS: The genetic mimicry of HMGCR and APOB inhibition was associated with an increased T2D risk, whereas the genetic mimicry of LPL enhancement was linked to a lower T2D risk. Gluteofemoral adipose tissue volume was a mediator for explaining 9.52% (P=.002), 16.90% (P=.03), and 10.50% (P=.003) of the total effect of HMGCR, APOB, and LPL on T2D susceptibility, respectively. Liver fat was a mediator for explaining 21.12% (P=.005), 12.28% (P=.03), and 9.84% (P=.005) of the total effect of HMGCR, APOB, and LPL on T2D susceptibility, respectively. Our findings support the hypothesis that liver fat and gluteofemoral adipose tissue play a mediating role in the prodiabetic effects of HMGCR and APOB inhibition, as well as in the antidiabetic effects of LPL enhancement. Show less
no PDF DOI: 10.1016/j.mayocp.2024.10.018
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