πŸ‘€ Xueyi Wang

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Also published as: A Wang, Ai-Ling Wang, Ai-Ting Wang, Aihua Wang, Aijun Wang, Aili Wang, Aimin Wang, Aiting Wang, Aixian Wang, Aiyun Wang, Aizhong Wang, Alexander Wang, Alice Wang, Allen Wang, Anlai Wang, Anli Wang, Annette Wang, Anni Wang, Anqi Wang, Anthony Z Wang, Anxiang Wang, Anxin Wang, Ao Wang, Aoli Wang, B R Wang, B Wang, Baihan Wang, Baisong Wang, Baitao Wang, Bangchen Wang, Banghui Wang, Bangmao Wang, Bangshing Wang, Bao Wang, Bao-Long Wang, Baocheng Wang, Baofeng Wang, Baogui Wang, Baojun Wang, Baoli Wang, Baolong Wang, Baoming Wang, Baosen Wang, Baowei Wang, Baoying Wang, Baoyun Wang, Bei Bei Wang, Bei Wang, Beibei Wang, Beilan Wang, Beilei Wang, Ben Wang, Benjamin H Wang, Benzhong Wang, Bi Wang, Bi-Dar Wang, Biao Wang, Bicheng Wang, Bijue Wang, Bin Wang, Bin-Xue Wang, Binbin Wang, Bing Qing Wang, Bing Wang, Binghai Wang, Binghan Wang, Bingjie Wang, Binglong Wang, Bingnan Wang, Bingyan Wang, Bingyu Wang, Binquan Wang, Biqi Wang, Bo Wang, Bochu Wang, Boyu Wang, Bruce Wang, C Wang, C Z Wang, Cai Ren Wang, Cai-Hong Wang, Cai-Yun Wang, Cailian Wang, Caiqin Wang, Caixia Wang, Caiyan Wang, Can Wang, Cangyu Wang, Carol A Wang, Catherine Ruiyi Wang, Cenxuan Wang, Chan Wang, Chang Wang, Chang-Yun Wang, Changduo Wang, Changjing Wang, Changliang Wang, Changlong Wang, Changqian Wang, Changtu Wang, Changwei Wang, Changying Wang, Changyu Wang, Changyuan Wang, Changzhen Wang, Chao Wang, Chao-Jun Wang, Chao-Yung Wang, Chaodong Wang, Chaofan Wang, Chaohan Wang, Chaohui Wang, Chaojie Wang, Chaokui Wang, Chaomeng Wang, Chaoqun Wang, Chaoxian Wang, Chaoyi Wang, Chaoyu Wang, Chaozhan Wang, Charles C N Wang, Chau-Jong Wang, Chen Wang, Chen-Cen Wang, Chen-Ma Wang, Chen-Yu Wang, Chenchen Wang, Chenfei Wang, Cheng An Wang, Cheng Wang, Cheng-Cheng Wang, Cheng-Jie Wang, Cheng-zhang Wang, Chengbin Wang, Chengcheng Wang, Chenggang Wang, Chenghao Wang, Chenghua Wang, Chengjian Wang, Chengjun Wang, Chenglin Wang, Chenglong Wang, Chengniu Wang, Chengqiang Wang, Chengshuo Wang, Chenguang Wang, Chengwen Wang, Chengyan Wang, Chengyu Wang, Chengze Wang, Chenji Wang, Chenliang Wang, Chenwei Wang, Chenxi Wang, Chenxin Wang, Chenxuan Wang, Chenyang Wang, Chenyao Wang, Chenyin Wang, Chenyu Wang, Chenzi Wang, Chi Chiu Wang, Chi Wang, Chi-Ping Wang, Chia-Chuan Wang, Chia-Lin Wang, Chien-Hsun Wang, Chien-Wei Wang, Chih-Chun Wang, Chih-Hao Wang, Chih-Hsien Wang, Chih-Liang Wang, Chih-Yang Wang, Chih-Yuan Wang, Chijia Wang, Ching C Wang, Ching-Jen Wang, Chiou-Miin Wang, Chong Wang, Chongjian Wang, Chonglong Wang, Chongmin Wang, Chongze Wang, Christina Wang, Christine Wang, Chu Wang, Chuan Wang, Chuan-Chao Wang, Chuan-Hui Wang, Chuan-Jiang Wang, Chuan-Wen Wang, Chuang Wang, Chuanhai Wang, Chuansen Wang, Chuansheng Wang, Chuanxin Wang, Chuanyue Wang, Chuduan Wang, Chun Wang, Chun-Chieh Wang, Chun-Juan Wang, Chun-Li Wang, Chun-Lin Wang, Chun-Ting Wang, Chun-Xia Wang, Chung-Hsi Wang, Chung-Hsing Wang, Chung-Teng Wang, Chunguo Wang, Chunhong Wang, Chuning Wang, Chunjiong Wang, Chunjuan Wang, Chunle Wang, Chunli Wang, Chunlong Wang, Chunmei Wang, Chunsheng Wang, Chunting Wang, Chunxia Wang, Chunxue Wang, Chunyan Wang, Chunyang Wang, Chunyi Wang, Chunyu Wang, Chuyao Wang, Cindy Wang, Ciyang Wang, Cong Wang, Congcong Wang, Congrong Wang, Congrui Wang, Cui Wang, Cui-Fang Wang, Cui-Shan Wang, Cuili Wang, Cuiling Wang, Cuizhe Wang, Cun-Yu Wang, Cunchuan Wang, Cunyi Wang, D Wang, Da Wang, Da-Cheng Wang, Da-Li Wang, Da-Yan Wang, Da-Zhi Wang, Dadong Wang, Dai Wang, Daijun Wang, Daiwei Wang, Daixi Wang, Dajia Wang, Dake Wang, Dali Wang, Dalong Wang, Dalu Wang, Dan Wang, Dan-Dan Wang, Danan Wang, Dandan Wang, Danfeng Wang, Dang Wang, Dangfeng Wang, Danling Wang, Danqing Wang, Danxin Wang, Danyang Wang, Dao Wen Wang, Dao-Wen Wang, Dao-Xin Wang, Daolong Wang, Daoping Wang, Daozhong Wang, Dapeng Wang, Daping Wang, Daqi Wang, Daqing Wang, David Q H Wang, David Q-H Wang, David Wang, Dawei Wang, Dayan Wang, Dayong Wang, Dazhi Wang, De-He Wang, Dedong Wang, Dehao Wang, Deli Wang, Delin Wang, Delong Wang, Demin Wang, Deming Wang, Dengbin Wang, Dennis Qing Wang, Dennis Wang, Deqi Wang, Deshou Wang, Dezhong Wang, Di Wang, Dinghui Wang, Dingting Wang, Dingxiang Wang, Dong D Wang, Dong Hao Wang, Dong Wang, Dong-Dong Wang, Dong-Jie Wang, Dong-Mei Wang, DongWei Wang, Dongdong Wang, Donggen Wang, Donghao Wang, Donghong Wang, Donghui Wang, Dongliang Wang, Donglin Wang, Dongmei Wang, Dongqin Wang, Dongshi Wang, Dongxia Wang, Dongxu Wang, Dongyan Wang, Dongyang Wang, Dongyi Wang, Dongying Wang, Dongyu Wang, Doudou Wang, Du Wang, Duan Wang, Duanyang Wang, Duo-Ping Wang, E Wang, Edward Wang, En-bo Wang, En-hua Wang, Endi Wang, Enhua Wang, Er-Jin Wang, Erfei Wang, Erika Y Wang, Ermao Wang, Erming Wang, Ertao Wang, Eryao Wang, Eunice S Wang, Exing Wang, F Wang, Fa-Kai Wang, Fan Wang, Fanchang Wang, Fang Wang, Fang-Tao Wang, Fangfang Wang, Fangjie Wang, Fangjun Wang, Fangyan Wang, Fangyong Wang, Fangyu Wang, Fanhua Wang, Fanwen Wang, Fanxiong Wang, Fei Wang, Fei-Fei Wang, Fei-Yan Wang, Feida Wang, Feifei Wang, Feijie Wang, Feimiao Wang, Feixiang Wang, Feiyan Wang, Fen Wang, Feng Wang, Feng-Sheng Wang, Fengchong Wang, Fengge Wang, Fenghua Wang, Fengliang Wang, Fenglin Wang, Fengling Wang, Fengqiang Wang, Fengyang Wang, Fengying Wang, Fengyong Wang, Fengyun Wang, Fengzhen Wang, Fengzhong Wang, Fu Wang, Fu-Sheng Wang, Fu-Yan Wang, Fu-Zhen Wang, Fubao Wang, Fubing Wang, Fudi Wang, Fuhua Wang, Fuqiang Wang, Furong Wang, Fuwen Wang, Fuxin Wang, Fuyan Wang, G Q Wang, G Wang, G-W Wang, Gan Wang, Gang Wang, Ganggang Wang, Ganglin Wang, Gangyang Wang, Ganyu Wang, Gao T Wang, Gao Wang, Gaofu Wang, Gaopin Wang, Gavin Wang, Ge Wang, Geng Wang, Genghao Wang, Gengsheng Wang, Gongming Wang, Guan Wang, Guan-song Wang, Guandi Wang, Guanduo Wang, Guang Wang, Guang-Jie Wang, Guang-Rui Wang, Guangdi Wang, Guanghua Wang, Guanghui Wang, Guangliang Wang, Guangming Wang, Guangsuo Wang, Guangwen Wang, Guangyan Wang, Guangzhi Wang, Guanrou Wang, Guanru Wang, Guansong Wang, Guanyun Wang, Gui-Qi Wang, Guibin Wang, Guihu Wang, Guihua Wang, Guimin Wang, Guiping Wang, Guiqun Wang, Guixin Wang, Guixue Wang, Guiying Wang, Guo-Du Wang, Guo-Hua Wang, Guo-Liang Wang, Guo-Ping Wang, Guo-Quan Wang, Guo-hong Wang, GuoYou Wang, Guobin Wang, Guobing Wang, Guodong Wang, Guohang Wang, Guohao Wang, Guoliang Wang, Guoling Wang, Guoping Wang, Guoqian Wang, Guoqiang Wang, Guoqing Wang, Guorong Wang, Guowen Wang, Guoxiang Wang, Guoxiu Wang, Guoyi Wang, Guoying Wang, Guozheng Wang, H J Wang, H Wang, H X Wang, H Y Wang, H-Y Wang, Hai Bo Wang, Hai Wang, Hai Yang Wang, Hai-Feng Wang, Hai-Jun Wang, Hai-Long Wang, Haibin Wang, Haibing Wang, Haibo Wang, Haichao Wang, Haichuan Wang, Haifei Wang, Haifeng Wang, Haihe Wang, Haihong Wang, Haihua Wang, Haijiao Wang, Haijing Wang, Haijiu Wang, Haikun Wang, Hailei Wang, Hailin Wang, Hailing Wang, Hailong Wang, Haimeng Wang, Haina Wang, Haining Wang, Haiping Wang, Hairong Wang, Haitao Wang, Haiwei Wang, Haixia Wang, Haixin Wang, Haixing Wang, Haiyan Wang, Haiying Wang, Haiyong Wang, Haiyun Wang, Haizhen Wang, Han Wang, Hanbin Wang, Hanbing Wang, Hanchao Wang, Handong Wang, Hang Wang, Hangzhou Wang, Hanmin Wang, Hanping Wang, Hanqi Wang, Hanying Wang, Hanyu Wang, Hanzhi Wang, Hao Wang, Hao-Ching Wang, Hao-Hua Wang, Hao-Tian Wang, Hao-Yu Wang, Haobin Wang, Haochen Wang, Haohao Wang, Haohui Wang, Haojie Wang, Haolong Wang, Haomin Wang, Haoming Wang, Haonan Wang, Haoping Wang, Haoqi Wang, Haoran Wang, Haowei Wang, Haoxin Wang, Haoyang Wang, Haoyu Wang, Haozhou Wang, He Wang, He-Cheng Wang, He-Ling Wang, He-Ping Wang, He-Tong Wang, Hebo Wang, Hechuan Wang, Heling Wang, Hemei Wang, Heming Wang, Heng Wang, Heng-Cai Wang, Hengjiao Wang, Hengjun Wang, Hequn Wang, Hesuiyuan Wang, Heyong Wang, Hezhi Wang, Hong Wang, Hong Yi Wang, Hong-Gang Wang, Hong-Hui Wang, Hong-Kai Wang, Hong-Qin Wang, Hong-Wei Wang, Hong-Xia Wang, Hong-Yan Wang, Hong-Yang Wang, Hong-Ying Wang, Hongbin Wang, Hongbing Wang, Hongbo Wang, Hongcai Wang, Hongda Wang, Hongdan Wang, Hongfang Wang, Hongjia Wang, Hongjian Wang, Hongjie Wang, Hongjuan Wang, Hongkun Wang, Honglei Wang, Hongli Wang, Honglian Wang, Honglun Wang, Hongmei Wang, Hongpin Wang, Hongqian Wang, Hongshan Wang, Hongsheng Wang, Hongtao Wang, Hongwei Wang, Hongxia Wang, Hongxin Wang, Hongyan Wang, Hongyang Wang, Hongyi Wang, Hongyin Wang, Hongying Wang, Hongyu Wang, Hongyuan Wang, Hongyue Wang, Hongyun Wang, Hongze Wang, Hongzhan Wang, Hongzhuang Wang, Horng-Dar Wang, Houchun Wang, Hsei-Wei Wang, Hsueh-Chun Wang, Hu WANG, Hua Wang, Hua-Qin Wang, Hua-Wei Wang, Huabo Wang, Huafei Wang, Huai-Zhou Wang, Huaibing Wang, Huaili Wang, Huaizhi Wang, Huajin Wang, Huajing Wang, Hualin Wang, Hualing Wang, Huan Wang, Huan-You Wang, Huang Wang, Huanhuan Wang, Huanyu Wang, Huaquan Wang, Huating Wang, Huawei Wang, Huaxiang Wang, Huayang Wang, Huei Wang, Hui Miao Wang, Hui Wang, Hui-Hui Wang, Hui-Li Wang, Hui-Nan Wang, Hui-Yu Wang, HuiYue Wang, Huie Wang, Huiguo Wang, Huihua Wang, Huihui Wang, Huijie Wang, Huijun Wang, Huilun Wang, Huimei Wang, Huimin Wang, Huina Wang, Huiping Wang, Huiquan Wang, Huiqun Wang, Huishan Wang, Huiting Wang, Huiwen Wang, Huixia Wang, Huiyan Wang, Huiyang Wang, Huiyao Wang, Huiying Wang, Huiyu Wang, Huizhen Wang, Huizhi Wang, Huming Wang, I-Ching Wang, Iris X Wang, Isabel Z Wang, J J Wang, J P Wang, J Q Wang, J Wang, J Z Wang, J-Y Wang, Jacob E Wang, James Wang, Jeffrey Wang, Jen-Chun Wang, Jen-Chywan Wang, Jennifer E Wang, Jennifer T Wang, Jennifer X Wang, Jenny Y Wang, Jeremy R Wang, Jeremy Wang, Ji M Wang, Ji Wang, Ji-Nuo Wang, Ji-Yang Wang, Ji-Yao Wang, Ji-zheng Wang, Jia Bei Wang, Jia Bin Wang, Jia Wang, Jia-Liang Wang, Jia-Lin Wang, Jia-Mei Wang, Jia-Peng Wang, Jia-Qi Wang, Jia-Qiang Wang, Jia-Ying Wang, Jia-Yu Wang, Jiabei Wang, Jiabo Wang, Jiafeng Wang, Jiafu Wang, Jiahao Wang, Jiahui Wang, Jiajia Wang, Jiakun Wang, Jiale Wang, Jiali Wang, Jialiang Wang, Jialin Wang, Jialing Wang, Jiamin Wang, Jiaming Wang, Jian Wang, Jian'an Wang, Jian-Bin Wang, Jian-Guo Wang, Jian-Hong Wang, Jian-Long Wang, Jian-Wei Wang, Jian-Xiong Wang, Jian-Yong Wang, Jian-Zhi Wang, Jian-chun Wang, Jianan Wang, Jianbing Wang, Jianbo Wang, Jianding Wang, Jianfang Wang, Jianfei Wang, Jiang Wang, Jiangbin Wang, Jiangbo Wang, Jianghua Wang, Jianghui Wang, Jiangong Wang, Jianguo Wang, Jianhao Wang, Jianhua Wang, Jianhui Wang, Jiani Wang, Jianjiao Wang, Jianjie Wang, Jianjun Wang, Jianle Wang, Jianli Wang, Jianlin Wang, Jianliu Wang, Jianlong Wang, Jianmei Wang, Jianmin Wang, Jianning Wang, Jianping Wang, Jianqin Wang, Jianqing Wang, Jianqun Wang, Jianru Wang, Jianshe Wang, Jianshu Wang, Jiantao Wang, Jianwei Wang, Jianwu Wang, Jianxiang Wang, Jianxin Wang, Jianye Wang, Jianying Wang, Jianyong Wang, Jianyu Wang, Jianzhang Wang, Jianzhi Wang, Jiao Wang, Jiaojiao Wang, Jiapan Wang, Jiaping Wang, Jiaqi Wang, Jiaqian Wang, Jiatao Wang, Jiawei Wang, Jiawen Wang, Jiaxi Wang, Jiaxin Wang, Jiaxing Wang, Jiaxuan Wang, Jiayan Wang, Jiayang Wang, Jiayi Wang, Jiaying Wang, Jiayu Wang, Jiazheng Wang, Jiazhi Wang, Jie Jin Wang, Jie Wang, Jieda Wang, Jieh-Neng Wang, Jiemei Wang, Jieqi Wang, Jieyan Wang, Jieyu Wang, Jifei Wang, Jiheng Wang, Jihong Wang, Jiliang Wang, Jilin Wang, Jin Wang, Jin'e Wang, Jin-Bao Wang, Jin-Cheng Wang, Jin-Da Wang, Jin-E Wang, Jin-Juan Wang, Jin-Liang Wang, Jin-Xia Wang, Jin-Xing Wang, Jincheng Wang, Jindan Wang, Jinfei Wang, Jinfeng Wang, Jinfu Wang, Jing J Wang, Jing Wang, Jing-Hao Wang, Jing-Huan Wang, Jing-Jing Wang, Jing-Long Wang, Jing-Min Wang, Jing-Shi Wang, Jing-Wen Wang, Jing-Xian Wang, Jing-Yi Wang, Jing-Zhai Wang, Jingang Wang, Jingchun Wang, Jingfan Wang, Jingfeng Wang, Jingheng Wang, Jinghong Wang, Jinghua Wang, Jinghuan Wang, Jingjing Wang, Jingkang Wang, Jinglin Wang, Jingmin Wang, Jingnan Wang, Jingqi Wang, Jingru Wang, Jingtong Wang, Jingwei Wang, Jingwen Wang, Jingxiao Wang, Jingyang Wang, Jingyi Wang, Jingying Wang, Jingyu Wang, Jingyue Wang, Jingyun Wang, Jingzhou Wang, Jinhai Wang, Jinhao Wang, Jinhe Wang, Jinhua Wang, Jinhuan Wang, Jinhui Wang, Jinjie Wang, Jinjin Wang, Jinkang Wang, Jinling Wang, Jinlong Wang, Jinmeng Wang, Jinning Wang, Jinping Wang, Jinqiu Wang, Jinrong Wang, Jinru Wang, Jinsong Wang, Jintao Wang, Jinxia Wang, Jinxiang Wang, Jinyang Wang, Jinyu Wang, Jinyue Wang, Jinyun Wang, Jinzhu Wang, Jiou Wang, Jipeng Wang, Jiqing Wang, Jiqiu Wang, Jisheng Wang, Jiu Wang, Jiucun Wang, Jiun-Ling Wang, Jiwen Wang, Jixuan Wang, Jiyan Wang, Jiying Wang, Jiyong Wang, Jizheng Wang, John Wang, Jou-Kou Wang, Joy Wang, Ju Wang, Juan Wang, Jue Wang, Jueqiong Wang, Jufeng Wang, Julie Wang, Juling Wang, Jun Kit Wang, Jun Wang, Jun Yi Wang, Jun-Feng Wang, Jun-Jie Wang, Jun-Jun Wang, Jun-Ling Wang, Jun-Sheng Wang, Jun-Sing Wang, Jun-Zhuo Wang, Jundong Wang, Junfeng Wang, Jung-Pan Wang, Junhong Wang, Junhua Wang, Junhui Wang, Junjiang Wang, Junjie Wang, Junjun Wang, Junkai Wang, Junke Wang, Junli Wang, Junlin Wang, Junling Wang, Junmei Wang, Junmin Wang, Junpeng Wang, Junping Wang, Junqin Wang, Junqing Wang, Junrui Wang, Junsheng Wang, Junshi Wang, Junshuang Wang, Junwen Wang, Junxiao Wang, Junya Wang, Junying Wang, Junyu Wang, Justin Wang, Jutao Wang, Juxiang Wang, K Wang, Kai Wang, Kai-Kun Wang, Kai-Wen Wang, Kaicen Wang, Kaihao Wang, Kaihe Wang, Kaihong Wang, Kaijie Wang, Kaijuan Wang, Kailu Wang, Kaiming Wang, Kaining Wang, Kaiting Wang, Kaixi Wang, Kaixu Wang, Kaiyan Wang, Kaiyuan Wang, Kaiyue Wang, Kan Wang, Kangli Wang, Kangling Wang, Kangmei Wang, Kangning Wang, Ke Wang, Ke-Feng Wang, KeShan Wang, Kehan Wang, Kehao Wang, Kejia Wang, Kejian Wang, Kejun Wang, Keke Wang, Keming Wang, Kenan Wang, Keqing Wang, Kesheng Wang, Kexin Wang, Keyan Wang, Keyi Wang, Keyun Wang, Kongyan Wang, Kuan Hong Wang, Kui Wang, Kun Wang, Kunhua Wang, Kunpeng Wang, Kunzheng Wang, L F Wang, L M Wang, L Wang, L Z Wang, L-S Wang, Laidi Wang, Laijian Wang, Laiyuan Wang, Lan Wang, Lan-Wan Wang, Lan-lan Wang, Lanlan Wang, Larry Wang, Le Wang, Le-Xin Wang, Ledan Wang, Lee-Kai Wang, Lei P Wang, Lei Wang, Lei-Lei Wang, Leiming Wang, Leishen Wang, Leli Wang, Leran Wang, Lexin Wang, Leying Wang, Li Chun Wang, Li Dong Wang, Li Wang, Li-Dong Wang, Li-E Wang, Li-Juan Wang, Li-Li Wang, Li-Na Wang, Li-San Wang, Li-Ting Wang, Li-Xin Wang, Li-Yong Wang, LiLi Wang, Lian Wang, Lianchun Wang, Liang Wang, Liang-Yan Wang, Liangfu Wang, Lianghai Wang, Liangli Wang, Liangliang Wang, Liangxu Wang, Lianshui Wang, Lianyong Wang, Libo Wang, Lichan Wang, Lichao Wang, Liewei Wang, Lifang Wang, Lifei Wang, Lifen Wang, Lifeng Wang, Ligang Wang, Lihong Wang, Lihua Wang, Lihui Wang, Lijia Wang, Lijin Wang, Lijing Wang, Lijuan Wang, Lijun Wang, Liling Wang, Lily Wang, Limeng Wang, Limin Wang, Liming Wang, Lin Wang, Lin-Fa Wang, Lin-Yu Wang, Lina Wang, Linfang Wang, Ling Jie Wang, Ling Wang, Ling-Ling Wang, Lingbing Wang, Lingda Wang, Linghua Wang, Linghuan Wang, Lingli Wang, Lingling Wang, Lingyan Wang, Lingzhi Wang, Linhua Wang, Linhui Wang, Linjie Wang, Linli Wang, Linlin Wang, Linping Wang, Linshu Wang, Linshuang Wang, Lintao Wang, Linxuan Wang, Linying Wang, Linyuan Wang, Liping Wang, Liqing Wang, Liqun Wang, Lirong Wang, Litao Wang, Liting Wang, Liu Wang, Liusong Wang, Liuyang Wang, Liwei Wang, Lixia Wang, Lixian Wang, Lixiang Wang, Lixin Wang, Lixing Wang, Lixiu Wang, Liyan Wang, Liyi Wang, Liying Wang, Liyong Wang, Liyuan Wang, Liyun Wang, Long Wang, Longcai Wang, Longfei Wang, Longsheng Wang, Longxiang Wang, Lou-Pin Wang, Lu Wang, Lu-Lu Wang, Lueli Wang, Lufang Wang, Luhong Wang, Luhui Wang, Lujuan Wang, Lulu Wang, Luofu Wang, Luping Wang, Luting Wang, Luwen Wang, Luxiang Wang, Luya Wang, Luyao Wang, Luyun Wang, Lynn Yuning Wang, M H Wang, M Wang, M Y Wang, M-J Wang, Maiqiu Wang, Man Wang, Mangju Wang, Manli Wang, Mao-Xin Wang, Maochun Wang, Maojie Wang, Maoju Wang, Mark Wang, Mei Wang, Mei-Gui Wang, Mei-Xia Wang, Meiding Wang, Meihui Wang, Meijun Wang, Meiling Wang, Meixia Wang, Melissa T Wang, Meng C Wang, Meng Wang, Meng Yu Wang, Meng-Dan Wang, Meng-Lan Wang, Meng-Meng Wang, Meng-Ru Wang, Meng-Wei Wang, Meng-Ying Wang, Meng-hong Wang, Mengge Wang, Menghan Wang, Menghui Wang, Mengjiao Wang, Mengjing Wang, Mengjun Wang, Menglong Wang, Menglu Wang, Mengmeng Wang, Mengqi Wang, Mengru Wang, Mengshi Wang, Mengwen Wang, Mengxiao Wang, Mengya Wang, Mengyao Wang, Mengying Wang, Mengyuan Wang, Mengyue Wang, Mengyun Wang, Mengze Wang, Mengzhao Wang, Mengzhi Wang, Mian Wang, Miao Wang, Mimi Wang, Min Wang, Min-sheng Wang, Ming Wang, Ming-Chih Wang, Ming-Hsi Wang, Ming-Jie Wang, Ming-Wei Wang, Ming-Yang Wang, Ming-Yuan Wang, Mingchao Wang, Mingda Wang, Minghua Wang, Minghuan Wang, Minghui Wang, Mingji Wang, Mingjin Wang, Minglei Wang, Mingliang Wang, Mingmei Wang, Mingming Wang, Mingqiang Wang, Mingrui Wang, Mingsong Wang, Mingxi Wang, Mingxia Wang, Mingxun Wang, Mingya Wang, Mingyang Wang, Mingyi Wang, Mingyu Wang, Mingzhi Wang, Mingzhu Wang, Minjie Wang, Minjun Wang, Minmin Wang, Minxian Wang, Minxiu Wang, Minzhou Wang, Miranda C Wang, Mo Wang, Mofei Wang, Monica Wang, Mu Wang, Mutian Wang, Muxiao Wang, Muxuan Wang, N Wang, Na Wang, Nan Wang, Nana Wang, Nanbu Wang, Nannan Wang, Nanping Wang, Neng Wang, Ni Wang, Niansong Wang, Ning Wang, Ningjian Wang, Ningli Wang, Ningyuan Wang, Nuan Wang, Oliver Wang, Ouchen Wang, P Jeremy Wang, P L Wang, P N Wang, P Wang, Pai Wang, Pan Wang, Pan-Pan Wang, Panfeng Wang, Panliang Wang, Pei Chang Wang, Pei Wang, Pei-Hua Wang, Pei-Jian Wang, Pei-Juan Wang, Pei-Wen Wang, Pei-Yu Wang, Peichang Wang, Peigeng Wang, Peihe Wang, Peijia Wang, Peijuan Wang, Peijun Wang, Peilin Wang, Peipei Wang, Peirong Wang, Peiwen Wang, Peixi Wang, Peiyao Wang, Peiyin Wang, Peng Wang, Peng-Cheng Wang, Pengbo Wang, Pengchao Wang, Pengfei Wang, Pengjie Wang, Pengju Wang, Penglai Wang, Penglong Wang, Pengpu Wang, Pengtao Wang, Pengxiang Wang, Pengyu Wang, Pin Wang, Ping Wang, Pingchuan Wang, Pingfeng Wang, Pingping Wang, Pintian Wang, Po-Jen Wang, Pu Wang, Q Wang, Q Z Wang, Qi Wang, Qi-Bing Wang, Qi-En Wang, Qi-Jia Wang, Qi-Qi Wang, Qian Wang, Qian-Liang Wang, Qian-Wen Wang, Qian-Zhu Wang, Qian-fei Wang, Qianbao Wang, Qiang Wang, Qiang-Sheng Wang, Qiangcheng Wang, Qianghu Wang, Qiangqiang Wang, Qianjin Wang, Qianliang Wang, Qianqian Wang, Qianrong Wang, Qianru Wang, Qianwen Wang, Qianxu Wang, Qiao Wang, Qiao-Ping Wang, Qiaohong Wang, Qiaoqi Wang, Qiaoqiao Wang, Qifan Wang, Qifei Wang, Qifeng Wang, Qigui Wang, Qihao Wang, Qihua Wang, Qijia Wang, Qiming Wang, Qin Wang, Qing Jun Wang, Qing K Wang, Qing Kenneth Wang, Qing Mei Wang, Qing Wang, Qing-Bin Wang, Qing-Dong Wang, Qing-Jin Wang, Qing-Liang Wang, Qing-Mei Wang, Qing-Yan Wang, Qing-Yuan Wang, Qing-Yun Wang, QingDong Wang, Qingchun Wang, Qingfa Wang, Qingfeng Wang, Qinghang Wang, Qingliang Wang, Qinglin Wang, Qinglu Wang, Qingming Wang, Qingping Wang, Qingqing Wang, Qingshi Wang, Qingshui Wang, Qingsong Wang, Qingtong Wang, Qingyong Wang, Qingyu Wang, Qingyuan Wang, Qingyun Wang, Qingzhong Wang, Qinqin Wang, Qinrong Wang, Qintao Wang, Qinwen Wang, Qinyun Wang, Qiong Wang, Qiqi Wang, Qirui Wang, Qishan Wang, Qiu-Ling Wang, Qiu-Xia Wang, Qiuhong Wang, Qiuli Wang, Qiuling Wang, Qiuning Wang, Qiuping Wang, Qiushi Wang, Qiuting Wang, Qiuyan Wang, Qiuyu Wang, Qiwei Wang, Qixue Wang, Qiyu Wang, Qiyuan Wang, Quan Wang, Quan-Ming Wang, Quanli Wang, Quanren Wang, Quanxi Wang, Qun Wang, Qunxian Wang, Qunzhi Wang, R Wang, Ran Wang, Ranjing Wang, Ranran Wang, Re-Hua Wang, Ren Wang, Rencheng Wang, Renjun Wang, Renqian Wang, Renwei Wang, Renxi Wang, Renxiao Wang, Renyuan Wang, Rihua Wang, Rikang Wang, Rixiang Wang, Robert Yl Wang, Rong Wang, Rong-Chun Wang, Rong-Rong Wang, Rong-Tsorng Wang, RongRong Wang, Rongjia Wang, Rongping Wang, Rongyun Wang, Ru Wang, RuNan Wang, Ruey-Yun Wang, Rufang Wang, Ruhan Wang, Rui Wang, Rui-Hong Wang, Rui-Min Wang, Rui-Ping Wang, Rui-Rui Wang, Ruibin Wang, Ruibing Wang, Ruibo Wang, Ruicheng Wang, Ruifang Wang, Ruijing Wang, Ruimeng Wang, Ruimin Wang, Ruiming Wang, Ruinan Wang, Ruining Wang, Ruiquan Wang, Ruiwen Wang, Ruixian Wang, Ruixin Wang, Ruixuan Wang, Ruixue Wang, Ruiying Wang, Ruizhe Wang, Ruizhi Wang, Rujie Wang, Ruling Wang, Ruming Wang, Runci Wang, Runuo Wang, Runze Wang, Runzhi Wang, Ruo-Nan Wang, Ruo-Ran Wang, Ruonan Wang, Ruosu Wang, Ruoxi Wang, Rurong Wang, Ruting Wang, Ruxin Wang, Ruxuan Wang, Ruyue Wang, S L Wang, S S Wang, S Wang, S X Wang, Sa A Wang, Sa Wang, Saifei Wang, Saili Wang, Sainan Wang, Saisai Wang, Sangui Wang, Sanwang Wang, Sasa Wang, Sen Wang, Seok Mui Wang, Seungwon Wang, Sha Wang, Shan Wang, Shan-Shan Wang, Shang Wang, Shangyu Wang, Shanshan Wang, Shao-Kang Wang, Shaochun Wang, Shaohsu Wang, Shaokun Wang, Shaoli Wang, Shaolian Wang, Shaoshen Wang, Shaowei Wang, Shaoyi Wang, Shaoying Wang, Shaoyu Wang, Shaozheng Wang, Shasha Wang, Shau-Chun Wang, Shawn Wang, Shen Wang, Shen-Nien Wang, Shenao Wang, Sheng Wang, Sheng-Min Wang, Sheng-Nan Wang, Sheng-Ping Wang, Sheng-Quan Wang, Sheng-Yang Wang, Shengdong Wang, Shengjie Wang, Shengli Wang, Shengqi Wang, Shengya Wang, Shengyao Wang, Shengyu Wang, Shengyuan Wang, Shenqi Wang, Sheri Wang, Shi Wang, Shi-Cheng Wang, Shi-Han Wang, Shi-Qi Wang, Shi-Xin Wang, Shi-Yao Wang, Shibin Wang, Shichao Wang, Shicung Wang, Shidong Wang, Shifa Wang, Shifeng Wang, Shih-Wei Wang, Shihan Wang, Shihao Wang, Shihua Wang, Shijie Wang, Shijin Wang, Shijun Wang, Shikang Wang, Shimiao Wang, Shiqi Wang, Shiqiang Wang, Shitao Wang, Shitian Wang, Shiwen Wang, Shixin Wang, Shixuan Wang, Shiyang Wang, Shiyao Wang, Shiyin Wang, Shiyu Wang, Shiyuan Wang, Shiyue Wang, Shizhi Wang, Shouli Wang, Shouling Wang, Shouzhi Wang, Shu Wang, Shu-Huei Wang, Shu-Jin Wang, Shu-Ling Wang, Shu-Na Wang, Shu-Song Wang, Shu-Xia Wang, Shu-qiang Wang, Shuai Wang, Shuaiqin Wang, Shuang Wang, Shuang-Shuang Wang, Shuang-Xi Wang, Shuangyuan Wang, Shubao Wang, Shudan Wang, Shuge Wang, Shuguang Wang, Shuhe Wang, Shuiliang Wang, Shuiyun Wang, Shujin Wang, Shukang Wang, Shukui Wang, Shun Wang, Shuning Wang, Shunjun Wang, Shunran Wang, Shuo Wang, Shuping Wang, Shuqi Wang, Shuqing Wang, Shuren Wang, Shusen Wang, Shusheng Wang, Shushu Wang, Shuu-Jiun Wang, Shuwei Wang, Shuxia Wang, Shuxin Wang, Shuya Wang, Shuye Wang, Shuyue Wang, Shuzhe Wang, Shuzhen Wang, Shuzhong Wang, Shyi-Gang P Wang, Si Wang, Sibo Wang, Sidan Wang, Sihua Wang, Sijia Wang, Silas L Wang, Silu Wang, Simeng Wang, Siqi Wang, Siqing Wang, Siwei Wang, Siyang Wang, Siyi Wang, Siying Wang, Siyu Wang, Siyuan Wang, Siyue Wang, Song Wang, Songjiao Wang, Songlin Wang, Songping Wang, Songsong Wang, Songtao Wang, Sophie H Wang, Stephani Wang, Su'e Wang, Su-Guo Wang, Su-Hua Wang, Sufang Wang, Sugai Wang, Sui Wang, Suiyan Wang, Sujie Wang, Sujuan Wang, Suli Wang, Sun Wang, Supeng Perry Wang, Suxia Wang, Suyun Wang, Suzhen Wang, T Q Wang, T Wang, T Y Wang, Taian Wang, Taicheng Wang, Taishu Wang, Tammy C Wang, Tao Wang, Taoxia Wang, Teng Wang, Tengfei Wang, Theodore Wang, Thomas T Y Wang, Tian Wang, Tian-Li Wang, Tian-Lu Wang, Tian-Tian Wang, Tian-Yi Wang, Tiancheng Wang, Tiange Wang, Tianhao Wang, Tianhu Wang, Tianhui Wang, Tianjing Wang, Tianjun Wang, Tianlin Wang, Tiannan Wang, Tianpeng Wang, Tianqi Wang, Tianqin Wang, Tianqing Wang, Tiansheng Wang, Tiansong Wang, Tiantian Wang, Tianyi Wang, Tianying Wang, Tianyuan Wang, Tielin Wang, Tienju Wang, Tieqiao Wang, Timothy C Wang, Ting Chen Wang, Ting Wang, Ting-Chen Wang, Ting-Hua Wang, Ting-Ting Wang, Tingting Wang, Tingye Wang, Tingyu Wang, Tom J Wang, Tong Wang, Tong-Hong Wang, Tongsong Wang, Tongtong Wang, Tongxia Wang, Tongxin Wang, Tongyao Wang, Tony Wang, Tzung-Dau Wang, Victoria Wang, Vivian Wang, W Wang, Wanbing Wang, Wanchun Wang, Wang Wang, Wangxia Wang, Wanliang Wang, Wanxia Wang, Wanyao Wang, Wanyi Wang, Wanyu Wang, Wayseen Wang, Wei Wang, Wei-En Wang, Wei-Feng Wang, Wei-Lien Wang, Wei-Qi Wang, Wei-Ting Wang, Wei-Wei Wang, Weicheng Wang, Weiding Wang, Weidong Wang, Weifan Wang, Weiguang Wang, Weihao Wang, Weihong Wang, Weihua Wang, Weijian Wang, Weijie Wang, Weijun Wang, Weilin Wang, Weiling Wang, Weilong Wang, Weimin Wang, Weina Wang, Weining Wang, Weipeng Wang, Weiqin Wang, Weiqing Wang, Weirong Wang, Weiwei Wang, Weiwen Wang, Weixiao Wang, Weixue Wang, Weiyan Wang, Weiyu Wang, Weiyuan Wang, Weizhen Wang, Weizhi Wang, Weizhong Wang, Wen Wang, Wen-Chang Wang, Wen-Der Wang, Wen-Fei Wang, Wen-Jie Wang, Wen-Jun Wang, Wen-Qing Wang, Wen-Xuan Wang, Wen-Yan Wang, Wen-Ying Wang, Wen-Yong Wang, Wen-mei Wang, Wenbin Wang, Wenbo Wang, Wence Wang, Wenchao Wang, Wencheng Wang, Wendong Wang, Wenfei Wang, Wengong Wang, Wenhan Wang, Wenhao Wang, Wenhe Wang, Wenhui Wang, Wenjie Wang, Wenjing Wang, Wenju Wang, Wenjuan Wang, Wenjun Wang, Wenkai Wang, Wenkang Wang, Wenke Wang, Wenming Wang, Wenqi Wang, Wenqiang Wang, Wenqing Wang, Wenran Wang, Wenrui Wang, Wentao Wang, Wentian Wang, Wenting Wang, Wenwen Wang, Wenxia Wang, Wenxian Wang, Wenxiang Wang, Wenxiu Wang, Wenxuan Wang, Wenya Wang, Wenyan Wang, Wenyi Wang, Wenying Wang, Wenyu Wang, Wenyuan Wang, Wenzhou Wang, William Wang, Won-Jing Wang, Wu-Wei Wang, Wuji Wang, Wuqing Wang, Wusan Wang, X E Wang, X F Wang, X O Wang, X S Wang, X Wang, X-T Wang, Xi Wang, Xi-Hong Wang, Xi-Rui Wang, Xia Wang, Xian Wang, Xian-e Wang, Xianding Wang, Xianfeng Wang, Xiang Wang, Xiang-Dong Wang, Xiangcheng Wang, Xiangding Wang, Xiangdong Wang, Xiangguo Wang, Xianghua Wang, Xiangkun Wang, Xiangrong Wang, Xiangru Wang, Xiangwei Wang, Xiangyu Wang, Xianna Wang, Xianqiang Wang, Xianrong Wang, Xianshi Wang, Xianshu Wang, Xiansong Wang, Xiantao Wang, Xianwei Wang, Xianxing Wang, Xianze Wang, Xianzhe Wang, Xianzong Wang, Xiao Ling Wang, Xiao Qun Wang, Xiao Wang, Xiao-Ai Wang, Xiao-Fei Wang, Xiao-Hui Wang, Xiao-Jie Wang, Xiao-Juan Wang, Xiao-Lan Wang, Xiao-Li Wang, Xiao-Lin Wang, Xiao-Ming Wang, Xiao-Pei Wang, Xiao-Qian Wang, Xiao-Qun Wang, Xiao-Tong Wang, Xiao-Xia Wang, Xiao-Yi Wang, Xiao-Yun Wang, Xiao-jian WANG, Xiao-liang Wang, Xiaobin Wang, Xiaobo Wang, Xiaochen Wang, Xiaochuan Wang, Xiaochun Wang, Xiaodan Wang, Xiaoding Wang, Xiaodong Wang, Xiaofang Wang, Xiaofei Wang, Xiaofen Wang, Xiaofeng Wang, Xiaogang Wang, Xiaohong Wang, Xiaohu Wang, Xiaohua Wang, Xiaohui Wang, Xiaojia Wang, Xiaojian Wang, Xiaojiao Wang, Xiaojie Wang, Xiaojing Wang, Xiaojuan Wang, Xiaojun Wang, Xiaokun Wang, Xiaole Wang, Xiaoli Wang, Xiaoliang Wang, Xiaolin Wang, Xiaoling Wang, Xiaolong Wang, Xiaolu Wang, Xiaolun Wang, Xiaoman Wang, Xiaomei Wang, Xiaomeng Wang, Xiaomin Wang, Xiaoming Wang, Xiaona Wang, Xiaonan Wang, Xiaoning Wang, Xiaoqi Wang, Xiaoqian Wang, Xiaoqin Wang, Xiaoqing Wang, Xiaoqiu Wang, Xiaoqun Wang, Xiaorong Wang, Xiaorui Wang, Xiaoshan Wang, Xiaosong Wang, Xiaotang Wang, Xiaoting Wang, Xiaotong Wang, Xiaowei Wang, Xiaowen Wang, Xiaowu Wang, Xiaoxia Wang, Xiaoxiao Wang, Xiaoxin Wang, Xiaoxin X Wang, Xiaoxuan Wang, Xiaoya Wang, Xiaoyan Wang, Xiaoyang Wang, Xiaoye Wang, Xiaoying Wang, Xiaoyu Wang, Xiaozhen Wang, Xiaozhi Wang, Xiaozhong Wang, Xiaozhu Wang, Xichun Wang, Xidi Wang, Xietong Wang, Xifeng Wang, Xifu Wang, Xijun Wang, Xike Wang, Xin Wang, Xin Wei Wang, Xin-Hua Wang, Xin-Liang Wang, Xin-Ming Wang, Xin-Peng Wang, Xin-Qun Wang, Xin-Shang Wang, Xin-Xin Wang, Xin-Yang Wang, Xin-Yue Wang, Xinbo Wang, Xinchang Wang, Xinchao Wang, Xinchen Wang, Xincheng Wang, Xinchun Wang, Xindi Wang, Xindong Wang, Xing Wang, Xing-Huan Wang, Xing-Jin Wang, Xing-Jun Wang, Xing-Lei Wang, Xing-Ping Wang, Xing-Quan Wang, Xingbang Wang, Xingchen Wang, Xingde Wang, Xingguo Wang, Xinghao Wang, Xinghui Wang, Xingjie Wang, Xingjin Wang, Xinglei Wang, Xinglong Wang, Xingqin Wang, Xinguo Wang, Xingxin Wang, Xingxing Wang, Xingye Wang, Xingyu Wang, Xingyue Wang, Xingyun Wang, Xinhui Wang, Xinjing Wang, Xinjun Wang, Xinke Wang, Xinkun Wang, Xinli Wang, Xinlin Wang, Xinlong Wang, Xinmei Wang, Xinqi Wang, Xinquan Wang, Xinran Wang, Xinrong Wang, Xinru Wang, Xinrui Wang, Xinshuai Wang, Xintong Wang, Xinwen Wang, Xinxin Wang, Xinyan Wang, Xinyang Wang, Xinye Wang, Xinyi Wang, Xinying Wang, Xinyu Wang, Xinyue Wang, Xinzhou Wang, Xiong Wang, Xiongjun Wang, Xiru Wang, Xitian Wang, Xiu-Lian Wang, Xiu-Ping Wang, Xiufen Wang, Xiujuan Wang, Xiujun Wang, Xiurong Wang, Xiuwen Wang, Xiuyu Wang, Xiuyuan Hugh Wang, Xixi Wang, Xixiang Wang, Xiyan Wang, Xiyue Wang, Xizhi Wang, Xu Wang, Xu-Hong Wang, Xuan Wang, Xuan-Ren Wang, Xuan-Ying Wang, Xuanwen Wang, Xuanyi Wang, Xubo Wang, Xudong Wang, Xue Wang, Xue-Feng Wang, Xue-Hua Wang, Xue-Lei Wang, Xue-Lian Wang, Xue-Rui Wang, Xue-Yao Wang, Xue-Ying Wang, Xuebin Wang, Xueding Wang, Xuedong Wang, Xuefei Wang, Xuefeng Wang, Xueguo Wang, Xuehao Wang, Xuejie Wang, Xuejing Wang, Xueju Wang, Xuejun Wang, Xuekai Wang, Xuelai Wang, Xuelian Wang, Xuelin Wang, Xuemei Wang, Xuemin Wang, Xueping Wang, Xueqian Wang, Xueqin Wang, Xuesong Wang, Xueting Wang, Xuewei Wang, Xuewen Wang, Xuexiang Wang, Xueyan Wang, Xueying Wang, Xueyun Wang, Xuezhen Wang, Xuezheng Wang, Xufei Wang, Xujing Wang, Xuliang Wang, Xumeng Wang, Xun Wang, Xuping Wang, Xuqiao Wang, Xuru Wang, Xusheng Wang, Xv Wang, Y Alan Wang, Y B Wang, Y H Wang, Y L Wang, Y P Wang, Y Wang, Y Y Wang, Y Z Wang, Y-H Wang, Y-S Wang, Ya Qi Wang, Ya Wang, Ya Xing Wang, Ya-Han Wang, Ya-Jie Wang, Ya-Long Wang, Ya-Nan Wang, Ya-Ping Wang, Ya-Qin Wang, Ya-Zhou Wang, Yachen Wang, Yachun Wang, Yadong Wang, Yafang Wang, Yafen Wang, Yahong Wang, Yahui Wang, Yajie Wang, Yajing Wang, Yajun Wang, Yake Wang, Yakun Wang, Yali Wang, Yalin Wang, Yaling Wang, Yalong Wang, Yan Ming Wang, Yan Wang, Yan-Chao Wang, Yan-Chun Wang, Yan-Feng Wang, Yan-Ge Wang, Yan-Jiang Wang, Yan-Jun Wang, Yan-Ming Wang, Yan-Yang Wang, Yan-Yi Wang, Yan-Zi Wang, Yana Wang, Yanan Wang, Yanbin Wang, Yanbing Wang, Yanchun Wang, Yancun Wang, Yanfang Wang, Yanfei Wang, Yanfeng Wang, Yang Wang, Yang-Yang Wang, Yange Wang, Yanggan Wang, Yangpeng Wang, Yangyang Wang, Yangyufan Wang, Yanhai Wang, Yanhong Wang, Yanhua Wang, Yanhui Wang, Yani Wang, Yanjin Wang, Yanjun Wang, Yankun Wang, Yanlei Wang, Yanli Wang, Yanliang Wang, Yanlin Wang, Yanling Wang, Yanmei Wang, Yanming Wang, Yanni Wang, Yanong Wang, Yanping Wang, Yanqing Wang, Yanru Wang, Yanting Wang, Yanwen Wang, Yanxia Wang, Yanxing Wang, Yanyang Wang, Yanyun Wang, Yanzhe Wang, Yanzhu Wang, Yao Wang, Yaobin Wang, Yaochun Wang, Yaodong Wang, Yaohe Wang, Yaokun Wang, Yaoling Wang, Yaolou Wang, Yaoxian Wang, Yaoxing Wang, Yaozhi Wang, Yapeng Wang, Yaping Wang, Yaqi Wang, Yaqian Wang, Yaqiong Wang, Yaru Wang, Yatao Wang, Yating Wang, Yawei Wang, Yaxian Wang, Yaxin Wang, Yaxiong Wang, Yaxuan Wang, Yayu Wang, Yazhou Wang, Ye Wang, Ye-Ran Wang, Yefu Wang, Yeh-Han Wang, Yehan Wang, Yeming Wang, Yen-Feng Wang, Yen-Sheng Wang, Yeou-Lih Wang, Yeqi Wang, Yezhou Wang, Yi Fan Wang, Yi Lei Wang, Yi Wang, Yi-Cheng Wang, Yi-Chuan Wang, Yi-Ming Wang, Yi-Ni Wang, Yi-Ning Wang, Yi-Shan Wang, Yi-Shiuan Wang, Yi-Shu Wang, Yi-Tao Wang, Yi-Ting Wang, Yi-Wen Wang, Yi-Xin Wang, Yi-Xuan Wang, Yi-Yi Wang, Yi-Ying Wang, Yi-Zhen Wang, Yi-sheng Wang, YiLi Wang, Yian Wang, Yibin Wang, Yibing Wang, Yichen Wang, Yicheng Wang, Yichuan Wang, Yifan Wang, Yifei Wang, Yigang Wang, Yige Wang, Yihan Wang, Yihao Wang, Yihe Wang, Yijin Wang, Yijing Wang, Yijun Wang, Yikang Wang, Yike Wang, Yilin Wang, Yilu Wang, Yimeng Wang, Yiming Wang, Yin Wang, Yin-Hu Wang, Yinan Wang, Yinbo Wang, Yindan Wang, Ying Wang, Ying-Piao Wang, Ying-Wei Wang, Ying-Zi Wang, Yingbo Wang, Yingcheng Wang, Yingchun Wang, Yingfei Wang, Yingge Wang, Yinggui Wang, Yinghui Wang, Yingjie Wang, Yingmei Wang, Yingna Wang, Yingping Wang, Yingqiao Wang, Yingtai Wang, Yingte Wang, Yingwei Wang, Yingwen Wang, Yingxiong Wang, Yingxue Wang, Yingyi Wang, Yingying Wang, Yingzi Wang, Yinhuai Wang, Yining E Wang, Yinong Wang, Yinsheng Wang, Yintao Wang, Yinuo Wang, Yinxiong Wang, Yinyin Wang, Yiou Wang, Yipeng Wang, Yiping Wang, Yiqi Wang, Yiqiao Wang, Yiqin Wang, Yiqing Wang, Yiquan Wang, Yirong Wang, Yiru Wang, Yirui Wang, Yishan Wang, Yishu Wang, Yitao Wang, Yiting Wang, Yiwei Wang, Yiwen Wang, Yixi Wang, Yixian Wang, Yixuan Wang, Yiyan Wang, Yiyi Wang, Yiying Wang, Yizhe Wang, Yong Wang, Yong-Bo Wang, Yong-Gang Wang, Yong-Jie Wang, Yong-Jun Wang, Yong-Tang Wang, Yongbin Wang, Yongdi Wang, Yongfei Wang, Yongfeng Wang, Yonggang Wang, Yonghong Wang, Yongjie Wang, Yongjun Wang, Yongkang Wang, Yongkuan Wang, Yongli Wang, Yongliang Wang, Yonglun Wang, Yongmei Wang, Yongming Wang, Yongni Wang, Yongqiang Wang, Yongqing Wang, Yongrui Wang, Yongsheng Wang, Yongxiang Wang, Yongyi Wang, Yongzhong Wang, You Wang, Youhua Wang, Youji Wang, Youjie Wang, Youli Wang, Youzhao Wang, Youzhi Wang, Yu Qin Wang, Yu Tian Wang, Yu Wang, Yu'e Wang, Yu-Chen Wang, Yu-Fan Wang, Yu-Fen Wang, Yu-Hang Wang, Yu-Hui Wang, Yu-Ping Wang, Yu-Ting Wang, Yu-Wei Wang, Yu-Wen Wang, Yu-Ying Wang, Yu-Zhe Wang, Yu-Zhuo Wang, Yuan Wang, Yuan-Hung Wang, Yuanbo Wang, Yuanfan Wang, Yuanjiang Wang, Yuanli Wang, Yuanqiang Wang, Yuanqing Wang, Yuanyong Wang, Yuanyuan Wang, Yuanzhen Wang, Yubing Wang, Yubo Wang, Yuchen Wang, Yucheng Wang, Yuchuan Wang, Yudong Wang, Yue Wang, Yue-Min Wang, Yue-Nan Wang, YueJiao Wang, Yuebing Wang, Yuecong Wang, Yuegang Wang, Yuehan Wang, Yuehong Wang, Yuehu Wang, Yuehua Wang, Yuelong Wang, Yuemiao Wang, Yueshen Wang, Yueting Wang, Yuewei Wang, Yuexiang Wang, Yuexin Wang, Yueying Wang, Yueze Wang, Yufei Wang, Yufeng Wang, Yugang Wang, Yuh-Hwa Wang, Yuhan Wang, Yuhang Wang, Yuhua Wang, Yuhuai Wang, Yuhuan Wang, Yuhui Wang, Yujia Wang, Yujiao Wang, Yujie Wang, Yujiong Wang, Yulai Wang, Yulei Wang, Yuli Wang, Yuliang Wang, Yulin Wang, Yuling Wang, Yulong Wang, Yumei Wang, Yumeng Wang, Yumin Wang, Yuming Wang, Yun Wang, Yun Yong Wang, Yun-Hui Wang, Yun-Jin Wang, Yun-Xing Wang, Yunbing Wang, Yunce Wang, Yunchao Wang, Yuncong Wang, Yunduan Wang, Yunfang Wang, Yunfei Wang, Yunhan Wang, Yunhe Wang, Yunong Wang, Yunpeng Wang, Yunqiong Wang, Yuntai Wang, Yunzhang Wang, Yunzhe Wang, Yunzhi Wang, Yupeng Wang, Yuping Wang, Yuqi Wang, Yuqian Wang, Yuqiang Wang, Yuqin Wang, Yusha Wang, Yushe Wang, Yusheng Wang, Yutao Wang, Yuting Wang, Yuwei Wang, Yuwen Wang, Yuxiang Wang, Yuxing Wang, Yuxuan Wang, Yuxue Wang, Yuyan Wang, Yuyang Wang, Yuyin Wang, Yuying Wang, Yuyong Wang, Yuzhong Wang, Yuzhou Wang, Yuzhuo Wang, Z P Wang, Z Wang, Z-Y Wang, Zai Wang, Zaihua Wang, Ze Wang, Zechen Wang, Zehao Wang, Zehua Wang, Zekun Wang, Zelin Wang, Zeneng Wang, Zengtao Wang, Zeping Wang, Zexin Wang, Zeying Wang, Zeyu Wang, Zeyuan Wang, Zezhou Wang, Zhan Wang, Zhang Wang, Zhanggui Wang, Zhangshun Wang, Zhangying Wang, Zhanju Wang, Zhao Wang, Zhao-Jun Wang, Zhaobo Wang, Zhaofeng Wang, Zhaofu Wang, Zhaohai Wang, Zhaohui Wang, Zhaojing Wang, Zhaojun Wang, Zhaoming Wang, Zhaoqing Wang, Zhaosong Wang, Zhaotong Wang, Zhaoxi Wang, Zhaoxia Wang, Zhaoyu Wang, Zhe Wang, Zhehai Wang, Zhehao Wang, Zhen Wang, ZhenXue Wang, Zhenbin Wang, Zhenchang Wang, Zhenda Wang, Zhendan Wang, Zhendong Wang, Zheng Wang, Zhengbing Wang, Zhengchun Wang, Zhengdong Wang, Zhenghui Wang, Zhengkun Wang, Zhenglong Wang, Zhenguo Wang, Zhengwei Wang, Zhengxuan Wang, Zhengyang Wang, Zhengyi Wang, Zhengyu Wang, Zhenhua Wang, Zhenning Wang, Zhenqian Wang, Zhenshan Wang, Zhentang Wang, Zhenwei Wang, Zhenxi Wang, Zhenyu Wang, Zhenze Wang, Zhenzhen Wang, Zheyi Wang, Zheyue Wang, Zhezhi Wang, Zhi Wang, Zhi Xiao Wang, Zhi-Gang Wang, Zhi-Guo Wang, Zhi-Hao Wang, Zhi-Hong Wang, Zhi-Hua Wang, Zhi-Jian Wang, Zhi-Long Wang, Zhi-Qin Wang, Zhi-Wei Wang, Zhi-Xiao Wang, Zhi-Xin Wang, Zhibo Wang, Zhichao Wang, Zhicheng Wang, Zhicun Wang, Zhidong Wang, Zhifang Wang, Zhifeng Wang, Zhifu Wang, Zhigang Wang, Zhige Wang, Zhiguo Wang, Zhihao Wang, Zhihong Wang, Zhihua Wang, Zhihui Wang, Zhiji Wang, Zhijian Wang, Zhijie Wang, Zhijun Wang, Zhilun Wang, Zhimei Wang, Zhimin Wang, Zhipeng Wang, Zhiping Wang, Zhiqi Wang, Zhiqian Wang, Zhiqiang Wang, Zhiqing Wang, Zhiren Wang, Zhiruo Wang, Zhisheng Wang, Zhitao Wang, Zhiting Wang, Zhiwu Wang, Zhixia Wang, Zhixiang Wang, Zhixiao Wang, Zhixin Wang, Zhixing Wang, Zhixiong Wang, Zhixiu Wang, Zhiying Wang, Zhiyong Wang, Zhiyou Wang, Zhiyu Wang, Zhiyuan Wang, Zhizheng Wang, Zhizhong Wang, Zhong Wang, Zhong-Hao Wang, Zhong-Hui Wang, Zhong-Ping Wang, Zhong-Yu Wang, ZhongXia Wang, Zhongfang Wang, Zhongjing Wang, Zhongli Wang, Zhonglin Wang, Zhongqun Wang, Zhongsu Wang, Zhongwei Wang, Zhongyi Wang, Zhongyu Wang, Zhongyuan Wang, Zhongzhi Wang, Zhou Wang, Zhou-Ping Wang, Zhoufeng Wang, Zhouguang Wang, Zhuangzhuang Wang, Zhugang Wang, Zhulin Wang, Zhulun Wang, Zhuo Wang, Zhuo-Hui Wang, Zhuo-Jue Wang, Zhuo-Xin Wang, Zhuowei Wang, Zhuoying Wang, Zhuozhong Wang, Zhuqing Wang, Zi Wang, Zi Xuan Wang, Zi-Hao Wang, Zi-Qi Wang, Zi-Yi Wang, Zicheng Wang, Zifeng Wang, Zihan Wang, Ziheng Wang, Zihua Wang, Zihuan Wang, Zijian Wang, Zijie Wang, Zijue Wang, Zijun Wang, Zikang Wang, Zikun Wang, Ziliang Wang, Zilin Wang, Ziling Wang, Zilong Wang, Zining Wang, Ziping Wang, Ziqi Wang, Ziqian Wang, Ziqiang Wang, Ziqing Wang, Ziqiu Wang, Zitao Wang, Ziwei Wang, Zixi Wang, Zixia Wang, Zixian Wang, Zixiang Wang, Zixu Wang, Zixuan Wang, Ziyi Wang, Ziying Wang, Ziyu Wang, Ziyun Wang, Zongbao Wang, Zonggui Wang, Zongji Wang, Zongkui Wang, Zongqi Wang, Zongwei Wang, Zou Wang, Zulong Wang, Zumin Wang, Zun Wang, Zunxian Wang, Zuo Wang, Zuoheng Wang, Zuoyan Wang, Zusen Wang
articles
Chun-Hao Han, Xiao-Yu Zhao, Chuan-Wen Wang +5 more Β· 2025 Β· Frontiers in veterinary science Β· Frontiers Β· added 2026-04-24
The quality of eggshells holds substantial economic significance and serves as a critical selection criterion in poultry breeding. Eggshell translucency significantly impairs their aesthetic quality, Show more
The quality of eggshells holds substantial economic significance and serves as a critical selection criterion in poultry breeding. Eggshell translucency significantly impairs their aesthetic quality, which is structurally attributed to the thinning of the eggshell membrane or reduced tensile strength. In this study, 836 dwarf white hens were selected, with 45 hens each assigned to the opaque group and the translucent group. Grading for eggshell translucency was conducted at 75, 80, and 85β€―weeks of age. Based on the results from these three gradings, 35 hens that consistently produced translucent eggs and 35 hens that consistently produced opaque eggs were reclassified into the translucent group and the opaque group, respectively. The thickness of the eggshell membrane, latitudinal and longitudinal tensile force and length, and other indicators related to eggshell membrane quality were measured. Correlation analysis was performed using RNA-seq genomics and DIA proteomics based on the relationships among these indicators. Transcriptome analysis revealed 179 significantly differentially expressed genes, indicating that the causes of translucent eggshells are associated with metabolism, signal transduction, the immune system, molecular binding, transport, and catabolism. Seven potential candidate genes, including Show less
πŸ“„ PDF DOI: 10.3389/fvets.2025.1583291
APOA4
Ella D'Amico, Tyler J McNeill, Adam M Khay +6 more Β· 2025 Β· The journals of gerontology. Series A, Biological sciences and medical sciences Β· Oxford University Press Β· added 2026-04-24
Despite the growing burden of knee osteoarthritis on aging populations, our mechanistic understanding of this disease remains lacking. Though knee osteoarthritis is a whole joint disease, the impact o Show more
Despite the growing burden of knee osteoarthritis on aging populations, our mechanistic understanding of this disease remains lacking. Though knee osteoarthritis is a whole joint disease, the impact of intra-articular structures such as the infrapatellar fat pad (IFP) on cartilage health is unclear. This study investigated the effect of age on paracrine communication between the IFP and chondrocytes. To isolate the effects of the IFP secretome on chondrocytes, aged chondrocytes from male and female mice were incubated with conditioned media from sex-matched young IFPs, aged IFPs, or control media. Extracellular matrix protein expression increased in both male and female chondrocytes exposed to young, but not aged, conditioned media relative to control media. The effect of the young IFP was not concomitant with changes in extracellular matrix degradation proteins, ADAMTS4 or MMP13. To identify factors mediating the effects of the IFP on chondrocytes that are altered with aging, we performed mass spectrometry of young and aged conditioned media and transcriptomics of aged chondrocytes treated with young or aged conditioned media. We then integrated the 2 datasets using network analyses. From the conditioned media, 2 secreted proteins, Mfge8 and Apoa4, were significantly changed with aging. In silico perturbation of the corresponding receptors of these IFP-secreted factors identified multiple enriched pathways in chondrocytes, including negative regulation of nitric oxide synthase activity. Overall, the data suggest that young IFPs release paracrine factors that promote extracellular matrix production in chondrocytes, potentially via regulation of nitric oxide levels, but that this effect is diminished with aging. Show less
no PDF DOI: 10.1093/gerona/glaf072
APOA4
Yihong Gan, Yilin Zhang, Jingqun Liu +10 more Β· 2025 Β· International immunopharmacology Β· Elsevier Β· added 2026-04-24
Cardiovascular diseases from abnormal lipid metabolism significantly increase mortality in systemic lupus erythematosus (SLE). The causal link between dyslipidemia and SLE is unclear. Lipid metabolism Show more
Cardiovascular diseases from abnormal lipid metabolism significantly increase mortality in systemic lupus erythematosus (SLE). The causal link between dyslipidemia and SLE is unclear. Lipid metabolism in patients with SLE was evaluated based on clinical data from 511 patients with SLE and 706 healthy individuals. Bidirectional Mendelian randomization (MR) was employed to assess causal links between 179 plasma lipid metabolites, lipid-lowering drug targets, and SLE risk. Genetic instruments from GWAS and eQTL data were used to evaluate CETP and APOA4 effects. Peripheral blood CETP and apolipoprotein levels in SLE patients were validated via ELISA. SLE patients exhibited reduced HDL-C (PΒ <Β 0.0001), APOA1 (PΒ <Β 0.0001), and APOA4 (PΒ <Β 0.0001), alongside elevated triglycerides (TG, PΒ <Β 0.0001), APOC3, APOD, and APOF. MR identified three lipid metabolites-PC(18:2β‚‚β‚€:4), TG(56:6), and TG(58:7)-as causal factors for SLE (PΒ <Β 2.79E-5). CETP inhibition significantly reduced SLE risk via HDL-C modulation (ORΒ =Β 0.72, PΒ =Β 3.38E-08) and influenced LDL-C, TG, and apolipoproteins. Clinical validation confirmed elevated CETP and reduced APOA4 in SLE, correlating with disease activity. APOA4 activation showed protective effects, while PCSK9 inhibition lacked relevance. Bidirectional Mendelian randomization analyses confirmed dyslipidemia as a causal antecedent to SLE, with no evidence of reverse causation. A variety of MR analyses and clinical validation indicated that targeting HDL-C regulation offers significant advantages for managing dyslipidemia in patients with SLE, with CETP identified as the optimal pharmacological target. Show less
no PDF DOI: 10.1016/j.intimp.2025.114736
APOA4
Qiting Fang, Zhonghua Liu, Kaixi Wang Β· 2025 Β· Journal of agricultural and food chemistry Β· ACS Publications Β· added 2026-04-24
Selenium (Se) foliar fertilizers enhance crop nutrition and address human selenium deficiency, while improper application may lead to excessive intake and residue accumulation. Our study comprehensive Show more
Selenium (Se) foliar fertilizers enhance crop nutrition and address human selenium deficiency, while improper application may lead to excessive intake and residue accumulation. Our study comprehensively assessed the toxicity and function of novel selenium nanoparticles and traditional sodium selenite fertilizers across cell, zebrafish, and murine models. Both fertilizers enhanced antioxidant pathways at low doses, but selenium nanoparticles exhibited stronger antioxidant and ferroptosis-modulating effects with lower toxicity at a high dose. Sodium selenite increased total and lipid ROS production, leading to decreased viability of cells and increased distortion and mortality of zebrafish. In mice, sodium selenite induced hepatic toxicity and decreased GPX4. Transcriptome analysis revealed that sodium selenite downregulated c-JUN and APOA4, weakening the antioxidant defense, whereas selenium nanoparticles promoted ferroptosis resistance through FGF21. These findings suggest selenium nanoparticles as a safer alternative for Se biofortification, mitigating health risks while supporting food security and environmental sustainability. Show less
no PDF DOI: 10.1021/acs.jafc.5c02034
APOA4
Ingoo Lee, Zachary S Wallace, Yuqi Wang +4 more Β· 2025 Β· bioRxiv : the preprint server for biology Β· Cold Spring Harbor Laboratory Β· added 2026-04-24
Genome-wide association studies have linked millions of genetic variants to biomedical phenotypes, but their utility has been limited by lack of mechanistic understanding and widespread epistatic inte Show more
Genome-wide association studies have linked millions of genetic variants to biomedical phenotypes, but their utility has been limited by lack of mechanistic understanding and widespread epistatic interactions. Recently, Transformer models have emerged as a powerful machine learning architecture with potential to address these and other challenges. Accordingly, here we introduce the Genotype-to-Phenotype Transformer (G2PT), a framework for modeling hierarchical information flow among variants, genes, multigenic systems, and phenotypes. As proof-of-concept, we use G2PT to model the genetics of TG/HDL (triglycerides to high-density lipoprotein cholesterol), an indicator of metabolic health. G2PT predicts this trait via attention to 1,395 variants underlying at least 20 systems, including immune response and cholesterol transport, with accuracy exceeding state-of-the-art. It implicates 40 epistatic interactions, including epistasis between Show less
no PDF DOI: 10.1101/2024.10.23.619940
APOA4
Bin Jia, Tingting Wang, Liangxuan Pan +6 more Β· 2025 Β· Clinical proteomics Β· BioMed Central Β· added 2026-04-24
Pulmonary nodule with diameters ranging 8-30Β mm has a high occurrence rate, and distinguishing benign from malignant nodules can greatly improve the patient outcome of lung cancer. However, sensitive Show more
Pulmonary nodule with diameters ranging 8-30Β mm has a high occurrence rate, and distinguishing benign from malignant nodules can greatly improve the patient outcome of lung cancer. However, sensitive and specific liquid-biopsy methods have yet to achieve satisfactory clinical goals. We enrolled three cohorts and a total of 185 patients diagnosed with benign (BE) and malignant (MA) pulmonary nodules. Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified plasma proteome from these patients. We then performed logistic regression analysis to classify benign from malignant nodules, using cohort 1 as discovery data set and cohort 2 and 3 as independent validation data sets. We also developed a targeted multi-reaction monitoring (MRM) method to measure the concentration of the selected six peptide markers in plasma samples. We quantified a total of 451 plasma proteins, with 15 up-regulated and 5 down-regulated proteins from patients diagnosed as having malignant nodules. Logistic regression identified a six-protein panel comprised of APOA4, CD14, PFN1, APOB, PLA2G7, and IGFBP2 that classifies benign and malignant nodules with improved accuracy. In cohort 1, the area under curve (AUC) of the training and testing reached 0.87 and 0.91, respectively. We achieved a sensitivity of 100%, specificity of 40%, positive predictive value (PPV) of 62.5%, and negative predictive value (NPV) of 100%. In two independent cohorts, the 6-biomarker panel showed a sensitivity, specificity, PPV, and NPV of 96.2%, 35%, 65.8%, and 87.5% respectively in cohort 2, and 91.4%, 54.2%, 74.4%, and 81.3% respectively in cohort 3. We performed a targeted LC-MS/MS method to quantify plasma concentration of the six peptides and applied logistic regression to classify benign and malignant nodules with AUC of the training and testing reached 0.758 and 0.751, respectively. Our study identified a panel of plasma protein biomarkers for distinguishing benign from malignant pulmonary nodules that worth further development into a clinically valuable assay. Show less
πŸ“„ PDF DOI: 10.1186/s12014-025-09532-w
APOA4
Ruiquan Wang, Hongqi Zhao Β· 2025 Β· The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology Β· added 2026-04-24
Accurate diagnosis of Crohn's disease (CD) is paramount due to its resemblance to other inflammatory bowel diseases. Early and precise diagnosis plays a pivotal role in tailoring personalized treatmen Show more
Accurate diagnosis of Crohn's disease (CD) is paramount due to its resemblance to other inflammatory bowel diseases. Early and precise diagnosis plays a pivotal role in tailoring personalized treatments, thereby enhancing the quality of life for CD patients. Differential gene expression analysis was conducted to identify genes from the mRNA expression profiles of CD samples, followed by pathway enrichment analysis. The immune cell infiltration levels of each CD patient sample were assessed. Using weighted gene co-expression network analysis, key gene modules linked to CD were found. Hub gene identification was made easier by the construction of protein-protein interaction networks. Next, utilizing the Least Absolute Shrinkage and Selection Operator on the hub genes in the training set, a diagnostic model was created. The accuracy of the model was then confirmed using a different validation set. Our analysis revealed 651 differentially expressed genes, enriched in leukocyte chemotaxis and inflammation-related pathways. Immunization results showed a higher abundance of T cells CD4 memory resting, macrophages M2, and plasma cells in CD patients. Weighted gene co-expression network analysis linked the turquoise module with macrophages M2. Eight hub genes (APOA1, APOA4, CYP2C8, CYP2C9, CYP2J2, EPHX2, HSD3B1, and LPL) formed the diagnostic model, exhibiting excellent diagnostic performance with area under curve values of 0.94 (training set) and 0.941 (validation set). The CD diagnostic model, based on hub genes, shows exceptional diagnostic accuracy, providing a valuable reference for CD diagnosis. Show less
πŸ“„ PDF DOI: 10.5152/tjg.2025.23605
APOA4
Song Luo, Xiaorui Wang, Bo Ma +12 more Β· 2025 Β· Biomolecules & biomedicine Β· added 2026-04-24
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the selective death of motor neurons in the spinal cord, brainstem, and motor cortex. This study investigates the ef Show more
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the selective death of motor neurons in the spinal cord, brainstem, and motor cortex. This study investigates the effects of simvastatin on the G93A-copper/zinc superoxide dismutase (G93ASOD1) transgenic mouse model of ALS. The experiment included three groups: C57BL/6 wild-type mice, C57BL/6J SOD1G93A mice treated with PBS (SOD1G93A + PBS), and C57BL/6J SOD1G93A mice treated with simvastatin (SOD1G93A + simvastatin). The primary endpoints were survival rates, body weight changes, performance in pole climbing and suspension tests, and neurological deficit scores. Pathological changes were assessed using hematoxylin and eosin staining, transmission electron microscopy, Nissl staining, and Masson staining. Proteomic and metabolomic analyses were performed to identify differentially expressed proteins (DEPs) and metabolites. Quantitative real-time polymerase chain reaction and western blotting were used to measure gene expression. Although there were no significant differences in survival rates, body weight, pole climbing, and suspension test performance, or neurological deficit scores between the SOD1G93A + simvastatin and SOD1G93A + PBS groups, simvastatin treatment improved axonal organization within the spinal cord, increased the number of neurons, and reduced cytoplasmic swelling and gastrocnemius fibrosis. A total of 47 DEPs and 13 differential metabolites were identified between the SOD1G93A + PBS and SOD1G93A + simvastatin groups. Notably, the expression levels of Apoa4 and Alb were elevated in the SOD1G93A + simvastatin group compared to the SOD1G93A + PBS group. Our results suggest that simvastatin may have potential therapeutic effects in ALS, likely involving the modulation of Apoa4 and Alb expression. Show less
πŸ“„ PDF DOI: 10.17305/bb.2024.11218
APOA4
Yuchun Fu, Ping Xia, Cheng Chen +4 more Β· 2025 Β· Talanta Β· Elsevier Β· added 2026-04-24
The lack of standardized objective approaches hinders the accurate diagnosis and treatment of depression. Herein, a novel electrochemical platform was created utilizing cost-effective and rapid 3D pri Show more
The lack of standardized objective approaches hinders the accurate diagnosis and treatment of depression. Herein, a novel electrochemical platform was created utilizing cost-effective and rapid 3D printing technology to overcome the constraints of conventional diagnostic methods. This method allows for highly sensitive detection of Apolipoprotein A4 (Apo-A4), an important biomarker for depression, using dual-signal outputs. The electrode material utilized in this setup consisted of a combination of carbon black/polylactic acid (CB/PLA) and ferrocene-chitosan-gold nanoparticles (Fc-CS-AuNPs). On the other hand, the signal label was composed of gold nanoparticles-thionine-secondary antibody (AuNPs-Thi-Ab Show less
no PDF DOI: 10.1016/j.talanta.2024.127235
APOA4
Sijing Shi, Kaikai Lu, Yijun Tao +6 more Β· 2025 Β· MedComm Β· Wiley Β· added 2026-04-24
πŸ“„ PDF DOI: 10.1002/mco2.70555
APOA5
Chenjie Li, Dongjie Yang, Xiaowen Wang +4 more Β· 2025 Β· Journal of molecular medicine (Berlin, Germany) Β· Springer Β· added 2026-04-24
Apolipoprotein A5 (ApoA5) and Cell Death-Inducing DNA Fragmentation Factor-like Effector C (CIDEC) are involved in hepatic lipid metabolism and implicated in metabolic dysfunction-associated steatotic Show more
Apolipoprotein A5 (ApoA5) and Cell Death-Inducing DNA Fragmentation Factor-like Effector C (CIDEC) are involved in hepatic lipid metabolism and implicated in metabolic dysfunction-associated steatotic liver disease (MASLD). This study explores the role of the ApoA5-CIDEC interaction in regulating hepatic lipid metabolism, inflammation and fibrosis in MASLD. C57BL/6Β J mice were used to evaluate hepatic steatosis, liver function, and fibrosis under different ApoA5 expression conditions. Co-immunoprecipitation and immunofluorescence confirmed ApoA5-CIDEC interaction on lipid droplets (LDs). HepG2 cells were used to assess the effects of ApoA5 and CIDEC on triglycerides (TG), free fatty acids (FFAs), fatty acid beta-oxidation (FAO), and de novo lipogenesis (DNL). Key lipid metabolism and inflammatory markers, including fatty acid-binding protein 4 (FABP4), were analyzed. ApoA5-overexpression in mice improved hepatic steatosis, function, and fibrosis, reducing TG, FFAs, DNL, ApoB secretion, and pro-inflammatory cytokine secretion (IL-6, IL-1Ξ², TNF-Ξ±), while enhancing FAO in HepG2 cells. ApoA5-knockdown led to opposite effects. ApoA5 and CIDEC co-localized with LDs, interacting with FABP4 to jointly regulate lipid metabolism and inflammation. The effects of ApoA5 were mediated through reduced CIDEC expression. ApoA5 regulates hepatic lipid metabolism, inflammation, and fibrosis through its interaction with CIDEC. Targeting the ApoA5-CIDEC axis may provide a novel therapeutic approach for treating MASLD. KEY MESSAGES: ApoA5 reduces hepatic fibrosis and inflammatory cytokine secretion. ApoA5 interacts and co-localizes with CIDEC on lipid droplets. ApoA5-CIDEC interaction regulates lipid metabolism and inflammatory cytokine secretion in hepatocytes. ApoA5-CIDEC axis regulates FABP4 expression. Targeting the ApoA5-CIDEC axis offers therapeutic potential for MASLD. Show less
πŸ“„ PDF DOI: 10.1007/s00109-025-02619-9
APOA5
Bilal Bashir, Natalie Forrester, Paul Downie +22 more Β· 2025 Β· Genetics in medicine open Β· Elsevier Β· added 2026-04-24
Familial chylomicronemia syndrome (FCS) is a rare autosomal recessive disorder. This study aimed to analyze the genotype distribution of FCS-causing genes in the United Kingdom. Data were anonymously Show more
Familial chylomicronemia syndrome (FCS) is a rare autosomal recessive disorder. This study aimed to analyze the genotype distribution of FCS-causing genes in the United Kingdom. Data were anonymously collated from 2 genetic testing laboratories providing national genetic diagnosis services for severe hypertriglyceridemia in the United Kingdom. As of December 2023, 880 individuals underwent genetic testing for FCS. The mean (SD) age at the time of genetic testing was 42.5 (15.3) years. After genotyping, 12.9% of the individuals ( The genetic architecture of FCS in the United Kingdom is complex, with a substantial proportion affected by non- Show less
πŸ“„ PDF DOI: 10.1016/j.gimo.2025.103445
APOA5
Yaozhong Liu, Huilun Wang, Minzhi Yu +19 more Β· 2025 Β· Circulation Β· added 2026-04-24
Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease with no effective pharmacological treatments. The causal role of triglycerides (TGs) in AAA development remains unclear and contr Show more
Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease with no effective pharmacological treatments. The causal role of triglycerides (TGs) in AAA development remains unclear and controversial. Mendelian randomization was applied to assess causal relationships between lipoproteins, circulating proteins, metabolites, and the risk of AAA. To test the hypothesis that elevated plasma TG levels accelerate AAA development, we used Mendelian randomization analyses integrating genetic, proteomic, and metabolomic data identified causal relationships between elevated TG-rich lipoproteins, TG metabolism-related proteins/metabolites, and AAA risk. In the angiotensin II infusion AAA model, most These findings identify hypertriglyceridemia as a key contributor to AAA pathogenesis and suggest that targeting TG-rich lipoproteins may be a promising therapeutic strategy for AAA. Show less
πŸ“„ PDF DOI: 10.1161/CIRCULATIONAHA.125.074737
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Lingyan Li, Xingjie Wu, Qianqian Guo +9 more Β· 2025 Β· Journal of pharmaceutical analysis Β· Elsevier Β· added 2026-04-24
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, over Show more
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, overshadowing its influence on the biological effects of formulations. In this study, we reevaluated the delivery effect of CH using widely applied lipid microspheres (LM) as a model DDS. We conducted comprehensive investigations into the impact of CH on the distribution, cell uptake, and protein corona (PC) of LM at sites of cardiovascular inflammatory injury. The results demonstrated that moderate CH promoted the accumulation of LM at inflamed cardiac and vascular sites without exacerbating damage while partially mitigating pathological damage. Then, the slow cellular uptake rate observed for CH@LM contributed to a prolonged duration of drug efficacy. Network pharmacology and molecular docking analyses revealed that CH depended on LM and exerted its biological effects by modulating peroxisome proliferator-activated receptor gamma (PPAR-Ξ³) expression in vascular endothelial cells and estrogen receptor alpha (ERΞ±) protein levels in myocardial cells, thereby enhancing LM uptake at cardiovascular inflammation sites. Proteomics analysis unveiled a serum adsorption pattern for CH@LM under inflammatory conditions showing significant adsorption with CH metabolism-related apolipoprotein family members such as apolipoprotein A-V (Apoa5); this may be a major contributing factor to their prolonged circulation Show less
πŸ“„ PDF DOI: 10.1016/j.jpha.2024.101182
APOA5
Zehua Huang, Li Wen, Chunlan Huang +12 more Β· 2025 Β· Chinese medical journal Β· added 2026-04-24
no PDF DOI: 10.1097/CM9.0000000000003663
APOA5
Yaozhong Liu, Huilun Wang, Minzhi Yu +19 more Β· 2025 Β· medRxiv : the preprint server for health sciences Β· Cold Spring Harbor Laboratory Β· added 2026-04-24
Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease without effective medications. This study integrated genetic, proteomic, and metabolomic data to identify causation between incre Show more
Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease without effective medications. This study integrated genetic, proteomic, and metabolomic data to identify causation between increased triglyceride (TG)-rich lipoproteins and AAA risk. Three hypertriglyceridemia mouse models were employed to test the hypothesis that increased plasma TG concentrations accelerate AAA development and rupture. In the angiotensin II-infusion AAA model, most Show less
no PDF DOI: 10.1101/2024.08.07.24311621
APOA5
Sanaz Lordfard, Jian Wang, Adam D McIntyre +2 more Β· 2025 Β· CJC open Β· Elsevier Β· added 2026-04-24
Heterozygous familial hypercholesterolemia (HeFH) is the most prevalent inherited dyslipidemia, and it predisposes individuals to premature atherosclerotic cardiovascular disease. Genetic testing can Show more
Heterozygous familial hypercholesterolemia (HeFH) is the most prevalent inherited dyslipidemia, and it predisposes individuals to premature atherosclerotic cardiovascular disease. Genetic testing can provide a definitive diagnosis. The spectrum of causal DNA variants in Ontario patients with hypercholesterolemia is not fully defined. In Southwestern Ontario patients with a clinical diagnosis of HeFH, we performed targeted next-generation DNA sequencing and bioinformatic analysis to determine the qualitative and quantitative spectrum of pathogenic and likely pathogenic (P/LP) variants. We observed 101 unique P/LP variants in 254 patients, of which 6 were novel This study provides a comprehensive overview of the clinical and genetic spectrum of HeFH in Southwestern Ontario. The P/LP variant diversity reflects historical colonization and later migration patterns both from across the world and interprovincially from Quebec. Show less
πŸ“„ PDF DOI: 10.1016/j.cjco.2025.09.003
APOB
Yuxuan Tao, Chenglong Yao, Runjia Liu +4 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· added 2026-04-24
Chronic heart failure (CHF) is frequently complicated by depression, which worsens prognosis but remains underdiagnosed due to symptom overlap and a lack of objective screening tools. Although biomark Show more
Chronic heart failure (CHF) is frequently complicated by depression, which worsens prognosis but remains underdiagnosed due to symptom overlap and a lack of objective screening tools. Although biomarkers reflecting lipid metabolism, insulin resistance, inflammation, and neuro-immuno-endocrine imbalance have been implicated in both CHF and depression, their predictive value for psychiatric outcomes in CHF patients is unclear. This study aimed to develop and validate interpretable machine learning (ML) models for predicting depression risk in CHF patients via the use of clinical and biomarker data. We retrospectively enrolled 3, 110 CHF patients admitted between January 2015 and December 2024 at Guang'anmen Hospital. Demographic, clinical, and laboratory indicators, including apolipoprotein B (ApoB), the triglyceride-glucose (TyG) index, and a novel glycated TyG (gTyG) index, were collected. Logistic regression and restricted cubic spline analyses were used to assess dose-response associations between biomarkers and depression. Eight ML algorithms were trained and evaluated, with model interpretability assessed via SHapley Additive exPlanation (SHAP). Among the 3, 110 patients, 37.3% had comorbid depression. Elevated ApoB and gTyG indices were strongly associated with depression risk in both the unadjusted and fully adjusted models (ApoB Q4 vs. Q1: OR 5.41, 95% CI 3.72-7.87; gTyG Q4 vs. Q1: OR 2.88, 95% CI 1.88-4.41; both P < 0.001), demonstrating clear nonlinear dose-response relationships. The TyG index was associated with depression in the crude analyses but lost significance after adjustment. Among the ML models, the RF model achieved the best performance (AUC 0.933 in training, accuracy 0.814, sensitivity 0.939). SHAP analysis revealed that the ApoB and gTyG indices were the most influential predictors. A user-friendly web application was developed for individualized risk prediction. This study demonstrated that the ApoB and gTyG index are robust biomarkers for predicting depression risk in CHF patients. The RF model provided the highest predictive accuracy and interpretability, highlighting its potential utility for early risk stratification and targeted intervention. The incorporation of these biomarkers into routine clinical practice may facilitate timely identification and management of depression in CHF patients, ultimately improving patient outcomes. Show less
πŸ“„ PDF DOI: 10.3389/fendo.2025.1737713
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Chao Zhao, Nuan Wang, Di Shi +3 more Β· 2025 Β· Lipids Β· Wiley Β· added 2026-04-24
Ischemic stroke is frequently associated with symptomatic intracranial atherosclerotic stenosis (sICAS), is a leading cause of global disability and mortality. Current guidelines recommend dual antipl Show more
Ischemic stroke is frequently associated with symptomatic intracranial atherosclerotic stenosis (sICAS), is a leading cause of global disability and mortality. Current guidelines recommend dual antiplatelet and intensive statin therapies. Proprotein convertase subtilisin 9/kexin type 9 (PCSK9) inhibitors have emerged as a potent lipid-lowering therapy, potentially influenced by genetic variations, particularly in the CYP2C19 gene. This study at Xuzhou Central Hospital from January 2021 to December 2023 included 151 patients divided into a statin group (n = 73) and a PCSK9 inhibitor (PCSK9i) group (n = 78). It evaluated lipid profiles, inflammatory markers, neurological function, and clinical outcomes over a 180-day follow-up period, with additional analysis stratified by CYP2C19 genotype. The PCSK9i group demonstrated significant improvements in lipid parameters compared to the statin group, including greater reductions in low-density lipoprotein cholesterol (LDL-C) (p = 0.008), total cholesterol (TC) (p < 0.001), and triacylglycerols (TAG) (p = 0.041), along with apolipoprotein A1 (ApoA1) and apolipoprotein B (ApoB) (both p < 0.001). Inflammatory markers, particularly interleukin-6 (IL-6), significantly reduced in the PCSK9i group (p < 0.001). In the PCSK9i group, CYP2C19 rapid metabolizers achieved greater reductions in LDL-C (p = 0.021), ApoB (p = 0.003), and IL-6 levels (p = 0.041) compared to slow metabolizers. Post-treatment modified Rankin Scale (mRS) scores were significantly lower in rapid metabolizers compared to slow metabolizers (p = 0.018), though clinical events occurred infrequently in both subgroups. This study demonstrates that PCSK9 inhibitor therapy combined with statins provides enhanced lipid-lowering and anti-inflammatory effects compared to statin monotherapy in sICAS patients. While the CYP2C19 genotype may influence specific treatment responses, particularly lipid parameters, its impact on clinical outcomes requires further investigation. Show less
no PDF DOI: 10.1002/lipd.70018
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Yao-Fei Wei, Yi-Shu Wang, Jia-Yin Song +3 more Β· 2025 Β· Frontiers in immunology Β· Frontiers Β· added 2026-04-24
The contribution of circulating group 3 innate lymphoid cells (ILC3s) to lipid dysregulation has remained poorly defined, and the mechanisms through which washed microbiota transplantation (WMT) impro Show more
The contribution of circulating group 3 innate lymphoid cells (ILC3s) to lipid dysregulation has remained poorly defined, and the mechanisms through which washed microbiota transplantation (WMT) improves lipid metabolism require further clarification. Peripheral ILC subsets and plasma IL-22 were assessed in hyperlipidemia patients and healthy controls. The lipid-lowering effects of WMT were evaluated in a prospective cohort without lipid-lowering medications. Gut microbial and plasma metabolite profiles before and after WMT were analyzed. A hyperlipidemic mouse model was used to determine whether healthy microbiota promote hepatic ILC3 homing via integrin Ξ±4. Hyperlipidemia was characterized by reduced circulating ILC3s, integrin Ξ±4 Hyperlipidemia is associated with depletion of circulating ILC3s and reduced IL-22. Restoration of ILC3 subsets and enhancement of integrin Ξ±4-dependent hepatic homing are achieved after WMT, accompanying improvements in lipid metabolism. Show less
πŸ“„ PDF DOI: 10.3389/fimmu.2025.1688070
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Lili Qiao, Jiameng Miao, Weixuan Du +5 more Β· 2025 Β· Frontiers in clinical diabetes and healthcare Β· Frontiers Β· added 2026-04-24
Diabetes mellitus and dyslipidemia are major risk factors for atherosclerosis. Hypoechoic plaques, which indicate vulnerable or unstable plaques, may rupture and lead to ischemic stroke, cognitive imp Show more
Diabetes mellitus and dyslipidemia are major risk factors for atherosclerosis. Hypoechoic plaques, which indicate vulnerable or unstable plaques, may rupture and lead to ischemic stroke, cognitive impairment, increased adverse cardiac events, and even death. This study aimed to investigate the correlation between plasma lipid levels and the characteristics of atherosclerotic plaques in adult patients with type 2 diabetes mellitus. A retrospective analysis was conducted on adult patients with type 2 mellitus who were hospitalized in the Department of Endocrinology at Affiliated Hospital of Hebei University between January 2017 and December 2021.Patients were categorized into two groups based on arterial ultrasound results. Statistical analyses were performed to compare plasma lipid levels and plaque characteristics across the groups. 1) Statistically significant differences were observed among the two groups in terms of gender, hypertension, age, duration of diabetes mellitus, plaque location, triglycerides (TG),total cholesterol (TC), Apolipoprotein A1 (Apo A1),very-low-density lipoprotein (VLDL), VLDL/apolipoprotein B(ApoB), high-density lipoprotein cholesterol (HDL)/ApoA1 ( In clinical practice, the characteristics of atherosclerotic plaques and lipid profiles should be jointly evaluated to guide targeted treatment and effectively reduce the risk of atherosclerotic cardiovascular disease. Show less
πŸ“„ PDF DOI: 10.3389/fcdhc.2025.1688715
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Jia-Xuan Zhang, Zhi-Qiang Huang, Jian-Ming Yang +2 more Β· 2025 Β· Neuropsychiatric disease and treatment Β· added 2026-04-24
To assess the predictive ability of baseline serum apolipoprotein B (ApoB) and the ratio of ApoB to apolipoprotein A1 (ApoB/ApoA1 ratio) for dyslipidemia risk in patients receiving second-generation a Show more
To assess the predictive ability of baseline serum apolipoprotein B (ApoB) and the ratio of ApoB to apolipoprotein A1 (ApoB/ApoA1 ratio) for dyslipidemia risk in patients receiving second-generation antipsychotics (SGAs). Medical records of patients hospitalized between March 2019 and March 2025 were retrospectively reviewed. The optimal cut-off points for baseline serum ApoB levels and the ApoB/ApoA1 ratio were identified using a maximally selected log-rank statistic analysis. Multivariable Cox proportional hazards models estimated hazard ratios (HRs) with 95% confidence intervals (95% CIs). The Kaplan-Meier method with Log rank testing was used to compare the cumulative incidence of dyslipidemia between groups defined by these cut-off points. Of 311 enrolled patients, 33 (10.6%) lacking baseline ApoA1 measurements were excluded from ApoB/ApoA1 ratio analyses. The optimal cut-off points were 0.70 g/L for baseline ApoB and 0.45 for the ApoB/ApoA1 ratio. Multivariable Cox proportional hazards models, fully adjusted for covariates, demonstrated significantly elevated dyslipidemia risk for patients exceeding these thresholds vs low-risk groups: adjusted HR 2.98 (95% CI: 2.05-4.32, p < 0.001) for high ApoB and 3.17 (95% CI: 1.62-6.22, p = 0.001) for high ApoB/ApoA1 ratio. Continuous analysis showed each 0.1 g/L ApoB increase conferred a 34% higher risk (adjusted HR 1.34, 95% CI: 1.21-1.48, p < 0.001), while each 0.1-unit ApoB/ApoA1 ratio increase conferred a 20% higher risk (adjusted HR 1.20, 95% CI: 1.10-1.30, p < 0.001). Kaplan-Meier curves confirmed significantly higher cumulative dyslipidemia incidence in high vs low groups for both markers (Log rank test, both p < 0.001). Baseline serum ApoB levels and the ApoB/ApoA1 ratio are valuable risk markers for dyslipidemia in patients treated with SGAs. Show less
πŸ“„ PDF DOI: 10.2147/NDT.S564450
APOB
Ya-Ting Chen, Jing Sui, Yu Yang +16 more Β· 2025 Β· BMC medicine Β· BioMed Central Β· added 2026-04-24
Pentadecanoic acid (PEA), an odd-chain fatty acid derived from diet by the gut microbiome, has garnered increasing attention for its systemic health-promoting properties. Its potential role in bladder Show more
Pentadecanoic acid (PEA), an odd-chain fatty acid derived from diet by the gut microbiome, has garnered increasing attention for its systemic health-promoting properties. Its potential role in bladder cancer (BC) occurrence and invasion, however, remains unclear. Large-scale cohorts' analyses were performed to assess the association between dietary PEA and BC occurrence and invasion. In vitro and in vivo experiments, including EJ and T24 BC cell assays and a BBN-induced mouse model, were conducted to experimentally assess the impact of PEA on BC. Serum proteomics, gut microbiome, and targeted fecal lipidomics analyses were employed to explore the underlying mechanisms. Dietary PEA was negatively associated with BC occurrence and invasion in cohort analyses. PEA suppressed EJ and T24 BC cell migration, invasion, and proliferation, while inhibiting BC development in a BBN-induced mouse model. In vivo serum proteomics identified differentially expressed lipid-related proteins (e.g., Apoe and Apob) following PEA treatment, implicating its modulation of lipid metabolism pathways. Considering the essential role of the gut-bladder axis, the gut microbiome analysis exhibited that PEA markedly altered bacteria (e.g., g_Alistipes) and fungi (e.g., o_Erysiphales, g_Teberdinia, and g_Gibberella), with concomitant lipid metabolism changes. Furthermore, targeted fecal lipidomics demonstrated the shifts in key lipids, such as phosphatidylethanolamines (PE) involved in essential lipid clusters, suggesting regulation by gut microbiome linked to BC development. Collectively, our findings demonstrate that PEA mitigates BC by reshaping the gut microbiome and modulating lipid metabolism, providing new insights into its molecular and therapeutic potential. Show less
πŸ“„ PDF DOI: 10.1186/s12916-025-04554-5
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Yifan Cui, Yuqian Wang, Xiaoxia Wang +4 more Β· 2025 Β· International journal of genomics Β· added 2026-04-24
Due to the growth in the global consumption of assisted reproductive technology (ART), it is possible that long-term health impacts on offspring have come into focus. ART has offered a welcome solutio Show more
Due to the growth in the global consumption of assisted reproductive technology (ART), it is possible that long-term health impacts on offspring have come into focus. ART has offered a welcome solution to infertility, but the fear has been on its effect on the metabolic health of children born on their behalf. Past studies indicate that ART-conceived individuals can have characteristic metabolic profiles relative to their naturally conceived (NC) peers and are therefore potentially predisposed to changes in lipid and glucose handling. Physiopathological glycolipid metabolism, a hallmark of cardiometabolic health, is believed to be modulated not only by environmental and other external factors but also by intracellular regulation proteins, including sterol regulatory element-binding protein (SREBP) and miR-33, although there is little evidence on the effects of ART on these regulatory pathways in early childhood. This paper sought to compare the glycolipid metabolic profile of the kids who are in preschool age and who were conceived through ART and kids who were NC. The second aim was to study the expression of SREBP-1/2 and miR-33 in peripheral blood and the possible nature of the role of these players in regulating early-life metabolism. A total of 220 children aged between 3 and 6 years were recruited of which complete data has been obtained from 206 children out of 98 that were conceived via in vitro fertilization/intracytoplasmic sperm injection (ICSI) (ART group) and 108 that were conceived naturally (NC group). Anthropometric measures-such as body weight, height, and waist circumference-to determine physical growth and obesity status were taken. Biochemical variables, triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), fasting serum insulin (FINS), and homeostatic model assessment of insulin resistance (HOMA-IR) were determined. A centrifugal column was used to obtain peripheral blood RNA, and relative gene expression levels of SREBP-1, SREBP-2, miR-33a, and miR-33b were measured by qPCR. Compared with the IVF group, children in the ICSI group had significantly lower weight, height, and waist circumference ( Our data suggest that although children born by means of ART are otherwise normal in their glycolipid metabolism, they are more prone to overweight and obesity and have different biochemical and molecular characteristics than NC children. The upregulation of miR-33b, SREBP-1, and SREBP-2 observed indicates that ART can play a role in regulating the process of glycolipid metabolism during early childhood at a molecular level. Such alterations might not present the form of a blatant metabolic condition at this age but may consist of initial symptoms of future troublesome metabolic health. Prolonged follow-up of the ART offspring and additional mechanistic work are desirable to be able to determine whether these early changes are the underlying reasons behind higher metabolic risk as adults. Show less
πŸ“„ PDF DOI: 10.1155/ijog/2271298
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Ling-Xia Ha, Jin-Juan Wang, Ying-Ying Yuan +2 more Β· 2025 Β· International journal of women's health Β· added 2026-04-24
Women diagnosed with PCOS exhibit a high prevalence of obstructive sleep apnea (OSA). This study aims to assess risk factors of OSA among patients with PCOS. This retrospective study included 126 pati Show more
Women diagnosed with PCOS exhibit a high prevalence of obstructive sleep apnea (OSA). This study aims to assess risk factors of OSA among patients with PCOS. This retrospective study included 126 patients with PCOS who were categorized into an OSA group (n = 30) and a non-OSA group (n = 96) according to the apnea-hypopnea index (AHI). A control group comprised 72 patients without PCOS who presented during the same period for infertility due to fallopian tube, pelvic, or male factors. Patients with PCOS A multivariate logistic regression model was used to analyze independent risk factors for OSA in the PCOS group. Patients with PCOS had significantly higher AHI values and elevated values for various physical indicators, including body mass index (BMI) and neck, waist, and hip circumferences; prolactin (PRL); fasting plasma glucose (FPG); insulin (FINS); triglycerides (TG); homeostasis model assessment of insulin resistance (HOMA-IR); 2-hour postprandial glucose (2-hPG) and insulin (2-hINS); AHI; and oxygen desaturation index (ODI). Conversely, levels of high-density lipoprotein cholesterol (HDL-C) and lowest oxygen saturation (LSaO OSA in PCOS patients is linked to metabolic indicators. High neck circumference and BMI levels were independent risk factors, highlighting the need for OSA in routine PCOS screening, particularly in the context of metabolic dysregulation. Show less
πŸ“„ PDF DOI: 10.2147/IJWH.S543184
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Yale Tang, Chao Wang, Luxuan Li +5 more Β· 2025 Β· Biomolecules Β· MDPI Β· added 2026-04-24
This study aimed to investigate whether knockout of the
πŸ“„ PDF DOI: 10.3390/biom15101454
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Bingbing Fan, Yuqing Ye, Zihan Wang +4 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· added 2026-04-24
Gout is a chronic inflammatory condition increasingly recognized as a risk factor for cardiovascular events (CVE). Early identification of high-risk individuals is crucial for targeted prevention and Show more
Gout is a chronic inflammatory condition increasingly recognized as a risk factor for cardiovascular events (CVE). Early identification of high-risk individuals is crucial for targeted prevention and management. However, conventional risk stratification approaches often fall short in accuracy and clinical utility. This study aimed to develop and validate a robust, interpretable machine learning (ML)-based model for predicting CVE in patients with gout. This retrospective cohort study included 686 hospitalized gout patients at Xiyuan Hospital (Beijing, China) between January 1, 2013, and December 31, 2023. We applied Synthetic Minority Oversampling Technique (SMOTE) combined with random undersampling of the majority class. Then, patients were randomly divided into training (70%) and testing (30%) sets. A comprehensive set of clinical and biochemical variables (n = 39) was collected. Feature selection was performed using Boruta algorithms and Lasso to identify the most predictive variables. Multiple ML algorithms-including Decision Tree Learner, LightGBM Learner, K Nearest Neighbors Learner, CatBoost Learner, Gradient Boosting Desicion Tree Learner-were implemented to construct predictive models. SHAP values were used to assess model interpretability, and robustness was evaluated through 10-fold bootstrap resampling with enhanced standard error estimation. Of the 686 patients, 263 experienced cardiovascular events during follow-up (incidence rate: 38.3%). A logistic regression model was constructed based on eight variables selected using the Boruta feature selection algorithm: sex, age, PLT, EOS, LYM, CO2, GLU and APO-B. Among the five models evaluated, the CatBoost classifier achieved the best performance, with the highest area under the ROC curve (AUC) of 0.976 and the recall of 0.971. Furthermore, SHAP (SHapley Additive exPlanations) values were employed to provide both global and individual-level interpretability of the CatBoost model. To assess the model's generalization performance, bootstrap resampling was performed 10 times. Based on these results, the standard error was improved using machine learning-based enhancement methods, thereby optimizing the model's robustness and predictive stability. The logistic regression analysis revealed that age (OR=1.351, p<0.001), CO2 (OR=0.603, p=0.004), eosinophil count (OR=2.128, p=0.001), and platelet count (OR=0.961, p<0.001) were significantly associated with the outcome, indicating their potential roles as independent predictors. Notably, while APO_B (p=0.138) and sex (p=0.132) showed no significant association, glucose levels (OR=2.1, p=0.066) exhibited a marginal trend toward significance, warranting further investigation. This tool may support clinicians in identifying high-risk individuals, enabling early interventions and optimized management strategies. This study has several limitations. First, the analysis was based on a single-center dataset, which may limit the generalizability of the findings. External validation in multi-center and prospective cohorts, along with an expanded sample size, is warranted to confirm these results. Second, key confounding factors such as medication use, lifestyle habits, and gout flare frequency were not included in the analysis; future studies should incorporate these variables to provide a more comprehensive assessment. Show less
πŸ“„ PDF DOI: 10.3389/fendo.2025.1599028
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Jinglu Yu, Zimeng Pan, Miao Sun +6 more Β· 2025 Β· Expert review of molecular diagnostics Β· Taylor & Francis Β· added 2026-04-24
To construct a nomogram for predicting metabolic syndrome (MetS) in women with polycystic ovary syndrome. In this retrospective study, we analyzed clinical and biochemical data from 859 Chinese women Show more
To construct a nomogram for predicting metabolic syndrome (MetS) in women with polycystic ovary syndrome. In this retrospective study, we analyzed clinical and biochemical data from 859 Chinese women diagnosed with PCOS. Univariable logistic regression and forward stepwise logistic regression were employed to identify independent predictors of MetS. A predictive nomogram was developed that integrates age, acne status, body mass index (BMI), fasting insulin levels (FINS), and the ApoB/ApoA ratio. The model's discriminative performance, calibration accuracy, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration curves accompanied by Brier scores, Hosmer - Lemeshow tests, decision curve analysis (DCA), and clinical impact curves (CIC). Internal validation was conducted through bootstrap resampling over 1,000 iterations. The nomogram exhibited strong discriminative capability with an AUC of 0.874 (95% CI: 0.850-0.899), surpassing BMI alone which had an AUC of 0.824 ( The proposed nomogram accurately predicts MetS risk in PCOS patients, supporting early identification and individualized management. Show less
no PDF DOI: 10.1080/14737159.2025.2579046
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Duanlu Hou, Yuanyuan Wang, Shuang Zhai +3 more Β· 2025 Β· BMC neurology Β· BioMed Central Β· added 2026-04-24
The clinical significance and contribution of the lipid profile in atherosclerosis are well established. However, further investigation is needed in stroke patients, particularly regarding apolipoprot Show more
The clinical significance and contribution of the lipid profile in atherosclerosis are well established. However, further investigation is needed in stroke patients, particularly regarding apolipoprotein B100 (ApoB100), a novel non-traditional lipid component in the lipid profile. To explore lipid parameters and their impact on stroke outcomes in patients with and without thrombolysis. We prospectively enrolled patients with acute ischemic stroke (AIS) at a single center, including those who did and did not receive thrombolysis. Participants were stratified into improvement (favorable outcome at 2 weeks) and non-improvement groups. Demographic, laboratory, imaging, and clinical scale data were compared between groups. Random forest analyses were used to evaluate the predictive value and importance of individual lipid measures: triglycerides, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), ApoB100, and lipoprotein(a), which better describe the internal characteristics of the profile. Complete data were available for 262 AIS patients, 165 of whom received thrombolysis. Plasma ApoB100 levels were significantly lower in the thrombolysis group (p < 0.001) and decreased ApoB100 levels were independently associated with 2-week stroke improvement (p = 0.009, OR = 0.89, 95% CI: 0.84-0.93). Random-forest feature-importance plots revealed that HDL and ApoB100 (each contributing > 15%) were the strongest lipid predictors of a favorable outcome, outperforming the other lipid variables. We found that thrombolysis is associated with ApoB100 decrease and a decrease in ApoB100 can predict the 2-week functional improvement in stroke. HDL and ApoB100 emerge as more important determinants of favorable AIS outcomes in this machine-learning analysis. These findings warrant external validation in multi-center trials. ChiCTR1800018315, 11/09/2018. Show less
πŸ“„ PDF DOI: 10.1186/s12883-025-04444-6
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Anna Tilp, Dimitris Nasias, Andrew L Carley +10 more Β· 2025 Β· Arteriosclerosis, thrombosis, and vascular biology Β· added 2026-04-24
Movement of circulating lipids into tissues and arteries requires transfer across the endothelial cell (EC) barrier. This process allows the heart to obtain fatty acids, its chief source of energy, an Show more
Movement of circulating lipids into tissues and arteries requires transfer across the endothelial cell (EC) barrier. This process allows the heart to obtain fatty acids, its chief source of energy, and apoB-containing lipoproteins to cross the arterial endothelial barrier, leading to cholesterol accumulation in the subendothelial space. Multiple studies have established elevated postprandial TRLs (triglyceride-rich lipoproteins) as an independent risk factor for cardiovascular disease. We explored how chylomicrons affect ECs and transfer their fatty acids across the EC barrier. We had reported that media from chylomicron-treated ECs lead to lipid droplet 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 content. We also studied the EV-induced transcription changes in macrophages and ECs and whether knockdown of SR-BI (scavenger receptor-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 lipid droplet accumulation. The EVs contained little free fatty acids and triglycerides, 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. Fatty acids 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
πŸ“„ PDF DOI: 10.1161/ATVBAHA.125.322712
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