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, 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Wei Jia, Huimin Wang, Ting Feng +5 more Β· 2025 Β· Foods (Basel, Switzerland) Β· MDPI Β· added 2026-04-24
FVPB1, a novel heteropolysaccharide, was extracted from the
πŸ“„ PDF DOI: 10.3390/foods14193452
APOB
Fangfang Wang, Dong You, Xiaoye Niu +4 more Β· 2025 Β· Cardiovascular diabetology Β· BioMed Central Β· added 2026-04-24
Plozasiran (VSA001, ARO-APOC3) is an RNA interference therapy that targets Apolipoprotein C3 (APOC3), a key regulator of lipoprotein metabolism. The study aimed at assessing the safety, tolerability, Show more
Plozasiran (VSA001, ARO-APOC3) is an RNA interference therapy that targets Apolipoprotein C3 (APOC3), a key regulator of lipoprotein metabolism. The study aimed at assessing the safety, tolerability, pharmacokinetics (PK), and pharmacodynamic (PD) profiles of plozasiran in Chinese healthy volunteers (HVs). In this double-blind, placebo-controlled, phase I clinical study, a total of 24 Chinese adult HVs received single subcutaneous (SC) injection of 25Β mg, 50Β mg plozasiran or placebo on day 1. Safety, tolerability, PK and PD profiles were accessed during a follow-up period of 85 days. Eighteen HVs received plozasiran (25Β mg: n = 9; 50Β mg: n = 9) and 6 HVs received placebo. Plozasiran was well tolerated in Chinese HVs. No death, no severe adverse events or treatment-emergent adverse events (TEAEs) leading to discontinuation were observed. TEAEs were reported in 9 of 18 HVs from plozasiran group and in 1 of 6 HVs from placebo group. All TEAEs were transient and recovered autonomously, except for 2 subjects with 4 TEAEs from plozasiran group needed concomitant medications. After SC injection, plozasiran was rapidly absorbed and quickly eliminated in the plasma. Maximum geomean serum concentration was 102 ng/mL (CV%:36.4%) and 216 ng/mL (58.1%) for 25Β mg and 50Β mg group, respectively. The median T Plozasiran at 25 and 50Β mg was well tolerated with acceptable safety profile in Chinese HVs. Safety, PK and PD profiles observed in the present study were consistent with the data reported from clinical studies conducted outside China. Show less
πŸ“„ PDF DOI: 10.1186/s12933-025-02929-9
APOB
Huixia Wang, Wenli Li, Yijia Tao +3 more Β· 2025 Β· BMC veterinary research Β· BioMed Central Β· added 2026-04-24
Neonatal piglets possess lysosome-rich foetal-type enterocytes that facilitate uptake and intracellular processing of maternally provided nutrients. However, the role of lysosomes in early-life growth Show more
Neonatal piglets possess lysosome-rich foetal-type enterocytes that facilitate uptake and intracellular processing of maternally provided nutrients. However, the role of lysosomes in early-life growth and intestinal maturation remains unclear. Therefore, this study was conducted to determine the role of lysosomes in the development of neonatal intestine in piglets. For 1-day-old neonatal piglets, a total of 12 piglets (Duroc Γ— (Landrace Γ— Large Yorkshire)) were divided into 2 groups using a split-litter design. To initiate malfunction in lysosomes, newborn piglets were subjected to oral gavage with imipramine (25Β mg/kg bodyweight) once daily for 7 days. For 21-day-old piglets, a total of 12 piglets were divided into two groups, and each group received the same treatment as described above. Piglets receiving imipramine demonstrated significantly stunted growth at 7 days of age, but not at 27 days. By postnatal day 7, the foetal-type enterocytes of untreated piglets were restricted in the mid to upper ileal villus and contained several large lysosomal vacuoles. In contrast, marked changes in ileal morphological and histological structure were observed following imipramine treatment, as evidenced by reduced degree of vacuolation, decreased lysosomal count, as well as pronounced mitochondrial swelling; however, no vacuolated enterocytes were found in 27-day-old piglets. Furthermore, signaling pathways associated with lipid transport and metabolism were significantly enriched, and the related hub genes were identified by bioinformatic analysis after imipramine administration. These findings were further confirmed by biochemical analysis demonstrating that serum levels of total cholesterol (TC) and apolipoprotein A1 (ApoA1) were significantly increased while serum ApoB was decreased in 7-day-old piglets receiving imipramine treatment. Additionally, there was an opposite trend in levels of ApoA1and ApoB in ileal mucosa compared to serum. These results demonstrate that lysosome dysfunction induced by imipramine resulted in significant growth retardation, pronounced morphological and ultrastructural alterations in ileal enterocytes, along with disrupted lipid metabolism in early postnatal piglets; however, no such effect was observed in 27-day-old piglets. These findings enhance understanding of lysosomal functions and intestinal maturation in neonatal piglets. Show less
πŸ“„ PDF DOI: 10.1186/s12917-025-05063-6
APOB
Hanyu Wang, Robert Clarke, Christiana Kartsonaki +12 more Β· 2025 Β· European heart journal open Β· Oxford University Press Β· added 2026-04-24
Little is known about the importance of blood lipids for risk of myocardial infarction (MI) in Chinese vs. European populations. We compared the associations with MI of apolioprotein B (ApoB) vs. low- Show more
Little is known about the importance of blood lipids for risk of myocardial infarction (MI) in Chinese vs. European populations. We compared the associations with MI of apolioprotein B (ApoB) vs. low-density lipoprotein cholesterol (LDL-C) and remnant-cholesterol (remnant-C) vs. triglycerides in the China Kadoorie Biobank (CKB) and UK Biobank (UKB). Plasma levels of LDL-C, high-density lipoprotein-cholesterol (HDL-C), apolipoprotein B (ApoB), apolipoprotein A1 (ApoA1), non-HDL-C, remnant-C, LDL-C/ApoB, and HDL-C/ApoA1 ratios were measured in a nested case-control study of MI (948 cases, 6101 controls) in CKB and a prospective study (5344 cases in 279 989 participants) in UKB. Associations of lipids with MI were assessed using logistic regression in CKB and Cox regression in UKB after adjustment for confounders and correction for regression dilution. The mean levels of LDL-C were about 30% lower in CKB than in UKB [2.3 (0.6) vs. 3.7 (0.8) mmol/L], but mean levels of HDL-C were comparable [1.3 (0.3) vs. 1.5 (0.4) mmol/L], as were those for triglycerides [1.8 (1.1) vs. 1.7 (1.1) mmol/L]. While the rate ratios (RRs) of MI for 1 SD higher usual levels of LDL-C in Chinese were about half those in Europeans (1.27; 1.13-1.44 vs. 1.55; 1.49-1.61), the corresponding RRs for ApoB or non-HDL with MI were comparable between Chinese and Europeans. The findings reinforce current guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) in China that advocate initiation of statin treatment in individuals at high-risk of ASCVD rather than high levels of LDL-C. Show less
πŸ“„ PDF DOI: 10.1093/ehjopen/oeaf119
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Lili Zhou, Wei Cheng, Dan Luo +10 more Β· 2025 Β· Frontiers in cell and developmental biology Β· Frontiers Β· added 2026-04-24
Cholesterol is an essential molecule for tumor cell growth and proliferation, and dysregulated cholesterol metabolism has been widely implicated in cancer pathogenesis. However, the specific role and Show more
Cholesterol is an essential molecule for tumor cell growth and proliferation, and dysregulated cholesterol metabolism has been widely implicated in cancer pathogenesis. However, the specific role and underlying molecular mechanisms of cholesterol metabolism alterations in diffuse large B-cell lymphoma (DLBCL) remain poorly understood. We retrospectively analyzed clinical data from 200 DLBCL patients and 185 healthy controls, focusing on lipid and lipoprotein levels, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and apolipoprotein E (ApoE). Univariate and multivariate Cox proportional hazard models were used to evaluate the prognostic value of these markers, and Kaplan-Meier analysis assessed their associations with overall survival (OS). Bioinformatics analysis predicted associations between lipid markers and cholesterol metabolism. Cellular experiments further investigated the expression of cholesterol metabolism-related proteins and the effect of the cholesterol-depleting agent Methyl-Ξ²-cyclodextrin (MΞ²CD) on DLBCL cells. We confirmed significant alterations in metabolic markers (such as TC and ApoA1) between the healthy control group and patients, which were significantly associated with patient prognosis and overall OS. Bioinformatics analysis revealed a strong correlation between these markers and elevated CD36 expression. In addition, DLBCL cells exhibited increased expression of cholesterol uptake and synthesis proteins (CD36, SREBP2, and HMGCR) and decreased expression of efflux proteins (APOA1, NR1H2 and ABCG1), consistent with cholesterol metabolic reprogramming. Treatment with MΞ²CD disrupted CD36 expression and cholesterol metabolism, leading to reduced DLBCL cell survival. These findings underscore the pivotal role of cholesterol metabolic reprogramming in DLBCL progression. CD36 and related metabolic markers represent promising therapeutic targets, opening novel avenues for the treatment of this malignancy. Show less
πŸ“„ PDF DOI: 10.3389/fcell.2025.1585521
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Baichao Shi, Yu Wang, Rong Luo +6 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· added 2026-04-24
This study aims to evaluate the association between mean arterial pressure (MAP) and anthropometric, metabolic, and endocrine parameters in Chinese infertile women with polycystic ovary syndrome (PCOS Show more
This study aims to evaluate the association between mean arterial pressure (MAP) and anthropometric, metabolic, and endocrine parameters in Chinese infertile women with polycystic ovary syndrome (PCOS). A total of 1,000 PCOS subjects were enrolled in the clinical trial project of Acupuncture and Clomiphene in the treatment of PCOS infertility patients (PCOSAct). Of these, 998 patients were selected for this study. Linear trends and regression analyses were conducted to evaluate the association between MAP and anthropometric, metabolic, and endocrine parameters. Logistic regression was employed to estimate the association between MAP and risk of insulin resistance (IR), nonalcoholic fatty liver disease (NAFLD) and hyperlipidemia. The receiver operating characteristics (ROC) curve was used to determine the predictive value of the MAP for IR, NAFLD and hyperlipidemia. Linear trends revealed that the MAP was positively associated with age, height, body weight, body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), systolic blood pressure (SBP) and diastolic blood pressure (DBP), hirsutism score, and acanthosis nigricans score, fasting blood glucose (FBG), fasting insulin (FINS), the homeostatic model assessment for insulin resistance (HOMA-IR), low-density lipoprotein (LDL), triglycerides (TG), total cholesterol (TC), apolipoprotein B (ApoB), ApoB/apolipoprotein A1 (ApoA1) ratio, total testosterone (TT), and free androgen index (FAI), as well as the prevalence of IR, metabolic syndrome (MetS), NAFLD, and hyperlipidemia. Conversely, MAP was negatively correlated with the quantitative insulin sensitivity check index (QUICKI), high-density lipoprotein (HDL), sex hormone-binding globulin (SHBG), luteinizing hormone (LH), the LH/follicle stimulating hormone (FSH) ratio, and anti-MΓΌllerian hormone (AMH). After adjusting for age and BMI, a significant linear relationship was observed between MAP and WC, WHR, hirsutism score, FBG, LDL, TG, TC, ApoB, and ApoB/ApoA1 ratio. Logistic regression analysis demonstrated that participants in the highest quartile (Q4) of MAP had no significantly higher odds ratios (OR) for IR, NAFLD and hyperlipidemia after adjusting for confounding factors. The ROC curve analysis indicated that the AUC Elevated MAP is associated with dysregulation of glucose and lipid metabolism and alterations in endocrine hormone levels. It may thus serve as a promising screening approach for IR-related conditions in patients with PCOS. Show less
πŸ“„ PDF DOI: 10.3389/fendo.2025.1594813
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Alexa Canchola, Keyuan Li, Kunpeng Chen +12 more Β· 2025 Β· ACS nano Β· ACS Publications Β· added 2026-04-24
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokine Show more
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. Yet systematic analyses are hampered by the absence of standardized, richly annotated data sets. Here, we introduce the Protein Corona Database (PC-DB), which compiles data from 83 studies (2000-2024) and integrates 817 NP formulations with quantitative profiles of 2497 adsorbed proteins. The PC-DB exposes pronounced heterogeneity in NP materials (metal 28.8%, silica 22.8%, lipid-based 14.8%), surface modifications, sizes (1-1400 nm), and ΞΆ-potentials (-70 to +70 mV). Subsequent meta-analysis shows that silica, polystyrene, and lipid-based NPs smaller than 100 nm with moderately negative to neutral ΞΆ-potentials preferentially bind the lipoproteins APOE and APOB-100, which are linked to receptor-mediated uptake and enhanced delivery efficiency. In contrast, metal and metal-oxide NPs carrying highly negative surface charge enrich complement component C3, indicating a greater likelihood of immune recognition and clearance. Interpretable machine learning models (LightGBM and XGBoost; ROC-AUC > 0.85) confirm NP size, ΞΆ-potential, and incubation time as the most influential predictors of protein adsorption. These results delineate how physicochemical parameters dictate PC composition and illustrate the power of predictive modeling to guide rational NP design. Show less
πŸ“„ PDF DOI: 10.1021/acsnano.5c08608
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Yuanyuan Wang, Dachuan Guo, Youzhi Wang +2 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· added 2026-04-24
[This corrects the article DOI: 10.3389/fendo.2025.1542190.].
πŸ“„ PDF DOI: 10.3389/fendo.2025.1699149
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Yuanyuan Wang, Dachuan Guo, Youzhi Wang +2 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· added 2026-04-24
Low-density lipoprotein cholesterol (LDL-C) has now been the primary target for lipid-lowering therapy in the European and US guidelines for the management of dyslipidemia, with increasing interest in Show more
Low-density lipoprotein cholesterol (LDL-C) has now been the primary target for lipid-lowering therapy in the European and US guidelines for the management of dyslipidemia, with increasing interest in apolipoprotein B (ApoB) as a secondary target. The relationship between ApoB and the severity of acute myocardial infarction as well as residual risk still needs to be further determined. Coronary atherosclerosis occurs as a result of a complex set of factors, and there is a strong relationship between insulin resistance and cardiovascular disease. In contrast, there are limited studies on the relationship between TyG index (triglyceride glucose index), an indicator of insulin resistance, and cardiovascular disease. The purpose of this study was to investigate the value of ApoB and TyG index in assessing the severity of myocardial infarction and predicting prognosis. This study included 712 participants with acute myocardial infarction for a 5-year follow-up. Spearman correlation analysis and generalized linear model analysis were used to assess the correlation between ApoB and the severity of coronary atherosclerosis. Risk regression analysis was used to assess the correlation between ApoB and residual risk in patients with acute myocardial infarction, and the C-statistic, net reclassification index (NRI), and integrated discriminant improvement index (IDI) were further calculated to assess the predictive value of ApoB for residual risk after myocardial infarction. Categorizing apoB, LDL-C, and TyG indices according to tertiles, higher levels of ApoB were significantly associated with the severity of coronary artery stenosis in patients with acute myocardial infarction ( ApoB is an independent risk factor for major adverse cardiovascular events (MACE) following myocardial infarction. Elevated ApoB levels are more advantageous than elevated LDL-C levels in assessing the severity of coronary artery stenosis in myocardial infarction patients and predicting residual risk after myocardial infarction. Therefore, in patients with acute myocardial infarction, ApoB can be considered to guide further intensive treatment. However, the TyG index did not demonstrate a significant advantage in predicting cardiovascular residual risk in this study. Show less
πŸ“„ PDF DOI: 10.3389/fendo.2025.1542190
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Xuan Bai, Dingzi Zhou, Jing Luo +14 more Β· 2025 Β· Medicine Β· added 2026-04-24
Lipid metabolism abnormalities and inflammation have been implicated in gallstone disease (GSD) development, but the causal relationships and potential mediation effects among lipid metabolites, infla Show more
Lipid metabolism abnormalities and inflammation have been implicated in gallstone disease (GSD) development, but the causal relationships and potential mediation effects among lipid metabolites, inflammatory factors, and GSD remain unclear. The aim of this study is to explore the causal relationships among these 3 factors. This study employed 2-sample Mendelian Randomization (TSMR) and 2-step MR to investigate the causal relationships and potential mediation effects among 91 inflammatory factors, 6 lipid metabolism-related molecules (HDL-C, LDL-C, TG, total cholesterol, ApoA1, and ApoB), and GSD. We opted for 4 distinct MR analysis methods including inverse variance weighted method, weighted median method, MR-Egger regression method and MR-PRESSO analysis. Sensitivity analyses included MR-Egger intercept tests, Cochran's Q statistic, Steiger tests, and leave-one-out analyses. Product of coefficients method was used to estimate mediation proportion. TSMR analysis revealed that every 1-unit increase in low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB), the risk of GSD decreased by 16.5%, 10.2%, 8.4%, and 13.1%, respectively. Inflammatory factors such as Natural killer cell receptor 2B4 (CD244), Macrophage colony-stimulating factor 1 (CSF-1), and interleukin-18 receptor 1 (IL-18R1) were identified as risk factors for GSD, while Fibroblast growth factor 19 levels (FGF19), Interleukin-1-alpha levels (IL-1Ξ±), and Interleukin-8 levels (IL-8) were found to be protective. Mediation analysis through 2-step MR identified potential pathways involving ApoA1--IL-8--GSD (Pβ€…=β€….084) and IL-1Ξ±--ApoB--GSD (Pβ€…=β€….117). This study provides robust evidence of causal links between specific lipid metabolites and GSD, as well as suggestive causal associations for several inflammatory factors. However, mediation analysis did not support significant roles for lipids or inflammatory factors as mediators in GSD pathogenesis. Future research could be further pursued in areas such as drug target intervention and mechanistic studies. Show less
no PDF DOI: 10.1097/MD.0000000000044704
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Chao Fu, Yan Gong, Xiangyang Gao +8 more Β· 2025 Β· BMC gastroenterology Β· BioMed Central Β· added 2026-04-24
πŸ“„ PDF DOI: 10.1186/s12876-025-04130-4
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Mengying Yang, Xiaoman Liu, Qianqian Li +2 more Β· 2025 Β· Therapeutic advances in endocrinology and metabolism Β· SAGE Publications Β· added 2026-04-24
Metabolic-associated fatty liver disease (MAFLD) is closely associated with insulin resistance (IR) and systemic inflammation. Apolipoprotein A1 (ApoA1) and Apolipoprotein B (ApoB), as notable non-tra Show more
Metabolic-associated fatty liver disease (MAFLD) is closely associated with insulin resistance (IR) and systemic inflammation. Apolipoprotein A1 (ApoA1) and Apolipoprotein B (ApoB), as notable non-traditional lipid markers, have demonstrated distinct advantages in identifying risks related to metabolic syndrome and coronary atherosclerosis, yet its association with MAFLD and the mediating roles of IR/inflammation remain unclear. This retrospective investigation involved 1061 participants, categorized into a non-MAFLD group ( The MAFLD group exhibited markedly elevated levels of neutrophils/lymphocytes, neutrophils/platelets, systemic immune inflammation index, systemic inflammation response index, pan-immune-inflammation value and triglyceride-glucose index (TyG), TyG body mass index (TyGBMI), and metabolic score for insulin resistance (METS-IR) compared to the non-MAFLD group. Logistic regression analysis revealed that ApoB/ApoA1, TyG, TyGBMI, and METS-IR were markedly linked to MAFLD risk. Spearman's correlation analysis identified substantial positive links between ApoB/ApoA1 and TyG ( Our findings clarify the complex interrelationships between ApoB/ApoA1, MAFLD risk, inflammation, and IR, and for the first time, demonstrate that IR may act as a key potential mediator in the link between ApoB/ApoA1 and MAFLD, rather than systemic inflammation. This suggests that IR may serve a more prominent role than chronic systemic inflammation in the association between lipid metabolism and MAFLD risk, and intervening in IR may be more effective than anti-inflammatory therapy in blocking the progression from lipid metabolism disorders to MAFLD. Show less
πŸ“„ PDF DOI: 10.1177/20420188251378318
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S Y Huang, F Y Song, X O Wang +6 more Β· 2025 Β· Zhonghua er ke za zhi = Chinese journal of pediatrics Β· added 2026-04-24
no PDF DOI: 10.3760/cma.j.cn112140-20250531-00465
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Fengfeng Deng, Jianqi Sun, Lixia Liu +6 more Β· 2025 Β· Cardiovascular & hematological agents in medicinal chemistry Β· Bentham Science Β· added 2026-04-24
Pulmonary Hypertension (PH) is a significant contributor to cardiac mortality in Dilated Cardiomyopathy (DCM) patients. Inflammatory processes and oxidative stress play pivotal roles in the advancemen Show more
Pulmonary Hypertension (PH) is a significant contributor to cardiac mortality in Dilated Cardiomyopathy (DCM) patients. Inflammatory processes and oxidative stress play pivotal roles in the advancement of Pulmonary Hypertension (PH). The Monocyte-to-High-- Density-Lipoprotein Cholesterol Ratio (MHR), a newly identified biomarker indicative of inflammatory and oxidative stress, has not been extensively researched in the context of pulmonary hypertension, especially within the scope of dilated cardiomyopathy. Given the reason mentioned above, our research explores the correlation between the MHR and the severity of PH in patients suffering from DCM. In this study, we conducted a retrospective review of medical data from 107 individuals diagnosed with non-ischemic DCM, evaluating their clinical profiles, biochemical indicators, MHR, and echocardiographic parameters. We analyzed the relationships between Pulmonary Arterial Systolic Pressure (PASP) and the Ejection Fraction of the Left Ventricle (LVEF). Utilizing logistic regression analysis, we determined the predictors of PH. Findings indicated that the DCM-PH group exhibited a significantly larger male population and elevated New York Heart Association (NYHA) classification scores (both with p-values <0.001 and 0.01, respectively) compared to the DCM-only group. A positive association was observed between the PASP and parameters, such as the Dimensions of the Left Atrium (LAD) and Left Ventricle in Systole (LVDs), Monocyte (M) levels, Direct Bilirubin (DB), and MHR. Conversely, an inverse relationship was noted with serum lipid profiles, including Total Cholesterol (TC), HDL Cholesterol (HDL-c), and apolipoprotein A1. LVEF demonstrated positive linkage with the same lipid profiles and the Left Ventricular Posterior Wall Thickness (LVPWT) yet showed negative correlations with the NYHA classification, Red Blood Cell Distribution Width Standard Deviation (RDW-SD), Total Bilirubin (TB), Direct Bilirubin (DB), and dimensions of the left ventricle in diastole and systole, as well as MHR. Through logistic regression analysis, several factors were recognized as significant predictors for the severity of PH within the DCM cohort, with weight (OR1.20, CI 1.022-1.409, p=0.026), RDW-SD (OR1.988, CI 1.015-3.895, p=0.045), LVPW (OR3.577, CI 1.307-9.792, p=0.013), LVDd (OR1.333, CI 1.058-1.680, p=0.015), MHR (OR3.575, CI 1.502-8.506, p=0.032), and TB (OR1.416, CI 1.014-1.979, p=0.041) showing positive associations, while apoB (OR0.001 CI0.001-0.824, p=0.045) exhibiting negative associations, all with p-values <0.05. Higher MHR and LVD correlate with increased PASP and reduced LVEF in DCMPH patients. MHR and LVPW are independent predictors of PH severity, indicating their potential as novel severity markers in DCM-related PH. Show less
no PDF DOI: 10.2174/0118715257294388250326034612
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Deguang Yang, Ning Gu, Li Pan +8 more Β· 2025 Β· Kardiologia polska Β· added 2026-04-24
The role of lipid markers in acute coronary syndrome remains incompletely understood, particularly for novel indices such as the Castelli risk indices (CRI-I, CRI-II) and cholesterol index (CHOINDEX). Show more
The role of lipid markers in acute coronary syndrome remains incompletely understood, particularly for novel indices such as the Castelli risk indices (CRI-I, CRI-II) and cholesterol index (CHOINDEX). This study aims to elucidate the relationship between novel lipid markers and plaque rupture. In this single-center retrospective study, 649 patients with acute coronary syndrome undergoing optical coherence tomography were stratified into plaque rupture (n = 130) and non-rupture (n = 519) groups. Lipid indices included the following: CRI-I - total cholesterol/high-density lipoprotein cholesterol (HDL-C), CRI-II - low-density lipoprotein cholesterol (LDL-C)/HDL-C, and CHOINDEX - LDL-C/HDL-C. Multivariable logistic regression identified independent predictors of plaque rupture. Model performance was assessed using area under the curve and integrated discrimination improvement. The plaque rupture group had higher proportions of males (89.2% vs. 80%; P = 0.01) and smokers (57.7% vs. 44.9%; P = 0.009), with elevated LDL-C mean 3.14 vs. 2.83 mmol/l), apolipoprotein B (APOB; 1.03 vs. 0.85 g/l), CRI-I (4.75 vs. 3.91), CRI-II (3.11 vs. 2.45), and CHOINDEX (1.97 vs. 1.65; all P <0.01). Multivariable analysis identified CRI-I (odds ratio [OR], 1.57), CRI-II (OR, 2.09), CHOINDEX (OR, 0.40), and APOB (OR, 5.50) as independent predictors. The combined model (traditional factors + novel indices) showed superior discrimination (area under the curve = 0.775 vs. 0.622; integrated discrimination improvement = 0.059; P <0.001). The combined assessment of CRI-II, CRI-I, CHOINDEX, and APOB, in conjunction with traditional cardiovascular risk factors, exhibits robust diagnostic efficacy for plaque rupture. Show less
no PDF DOI: 10.33963/v.phj.107865
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Menghan Lv, Xuan Wang, Xiayue He +4 more Β· 2025 Β· Frontiers in psychiatry Β· Frontiers Β· added 2026-04-24
Obesity and dysregulated cytokine levels are prevalent in schizophrenia patients undergoing antipsychotic treatment. While cytokines are implicated in obesity, their relationship with psychopathology Show more
Obesity and dysregulated cytokine levels are prevalent in schizophrenia patients undergoing antipsychotic treatment. While cytokines are implicated in obesity, their relationship with psychopathology in schizophrenia remains underexplored. This study investigated associations between body mass index (BMI), cytokine levels, and clinical symptoms in chronic schizophrenia patients. In this cross-sectional study,201chronic schizophrenia patients (Chinese Han population) were stratified into high BMI (BMIβ‰₯25kg/m A significant negative correlation was observed between BMI and IL-2( Higher BMI in chronic schizophrenia is associated with reduced IL-2 levels, attenuated negative symptoms, and adverse lipid profiles. TNF-Ξ± may modulate psychopathology severity. These findings highlight complex interactions between metabolic dysregulation, immune markers, and clinical manifestations in schizophrenia. Show less
πŸ“„ PDF DOI: 10.3389/fpsyt.2025.1574041
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Yuxing Wang, Ming Yu, Song Yang +8 more Β· 2025 Β· Cardiovascular therapeutics Β· added 2026-04-24
πŸ“„ PDF DOI: 10.1155/cdr/5528174
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Wenhui Wu, Chengcheng Wang, Tao Zhang +12 more Β· 2025 Β· Journal of ethnopharmacology Β· Elsevier Β· added 2026-04-24
In Traditional Chinese Medicine (TCM), dampness is a pathogenic factor arising from impaired production and transportation of bodily fluids. While Fuling Zexie decoction (FLZXD) has demonstrated thera Show more
In Traditional Chinese Medicine (TCM), dampness is a pathogenic factor arising from impaired production and transportation of bodily fluids. While Fuling Zexie decoction (FLZXD) has demonstrated therapeutic efficacy in dampness constitution (DC) treatment, the material basis underlying its constitutional modulatory effects remains unclear. This study proposes objective indicators for the differentiation and therapeutic evaluation of DC and elucidates the material basis of FLZXD in DC treatment. Serum exosome proteomic profiling was conducted across two independent cohorts to identify DC-related indicators and assess the therapeutic efficacy of FLZXD in DC-associated hyperlipidemia (DC-hyperlipidemia). The bioactive compounds of FLZXD were prioritized through a comprehensive analysis of patent documentation and network pharmacology, with subsequent validation of DC-related targets using enzyme-linked immunosorbent assay (ELISA). Proteomic analysis of serum exosomes revealed signatures that differentiate individuals with a balanced constitution (BC) from those with DC. The differentially expressed proteins (DEPs) were enriched predominantly in pathways related to the complement cascade and cardiovascular diseases. FLZXD demonstrated therapeutic efficacy against DC-hyperlipidemia, as evidenced by the reversal of DEPs expression following treatment, which was supported by the patentable findings and network pharmacology analysis. Through experimental validation and pharmacological evidence, the active herbs of FLZXD (Fuling, Zexie and Baizhu, collectively referred to as FZB) were identified, and a total of 73 putative therapeutic targets involved in the dampness-resolving effects of FZB were revealed. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment further confirmed that FLZXD exerts its anti-dampness effects primarily through regulation of the complement and coagulation cascades. Among eight candidate indicators specifically associated with DC, four proteins were validated via ELISA, indicating potential utility for the differentiation of DC. The sensitivity (%), specificity (%), fold change (FC), p-value, and area under the curve (AUC) for each indicator were as follows: apolipoprotein B-100 (APOB) (100.00, 80.00, 0.63, 0.0051, 0.94), complement factor H-related protein 1 (CFHR1) (90.00, 100.00, 0.55, 0.0001, 0.98), alpha-1-acid glycoprotein 1 (ORM1) (100.00, 80.00, 0.71, 0.0043, 0.92), and pigment epithelium-derived factor (SERPINF1) (90.00, 70.00, 0.66, 0.0002, 0.87). The integrative approach, combining proteomic profiling, network pharmacology analysis, and clinical validation, establishes an integrative approach for research on TCM constitutions. This approach provides (1) molecular insights into the differentiation of DC, (2) a foundation for mechanism-based, targeted therapeutic strategies, and (3) enhanced patient stratification to support personalized treatment approaches. Show less
no PDF DOI: 10.1016/j.jep.2025.120353
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Jing Gan, Yuncong Wang, Zhuoran Shi +13 more Β· 2025 Β· NPJ precision oncology Β· Nature Β· added 2026-04-24
Increasing evidence underscores the driving role of coding and non-coding variants in cancer development. Analyzing gene sets in biological processes offers deeper insights into the molecular mechanis Show more
Increasing evidence underscores the driving role of coding and non-coding variants in cancer development. Analyzing gene sets in biological processes offers deeper insights into the molecular mechanisms of carcinogenesis. Here, we developed geMER to identify candidate driver genes genome-wide by detecting mutation enrichment regions within coding and non-coding elements. We subsequently designed a pipeline to identify a core driver gene set (CDGS) that broadly promotes carcinogenesis across multiple cancers. CDGS comprising 25 genes for 25 cancers displayed instability in DNA aberrations. Variants within the TTN enrichment region may influence the folding of the I-set domain by altering local polarity or side-chain chemistry properties of amino acids, potentially disrupting its antigen-binding capacity in LUAD. Multi-omics analysis revealed that APOB emerged as a candidate oncogene in LIHC, whose genetic alterations within the enrichment region may activate key TFs, upregulate DNA methylation levels, modulate critical histone modifications, and enhance transcriptional activity in the HepG2 and A549 cell lines compared to Panc1. Additionally, CDGS mutation status was an independent prognostic factor for the pan-cancer cohort. High-risk patients tended to develop an immunosuppressive microenvironment and demonstrated a higher likelihood of responding to ICI therapy. Finally, we provided a user-friendly web interface to explore candidate driver genes using geMER ( http://bio-bigdata.hrbmu.edu.cn/geMER/ ). Show less
πŸ“„ PDF DOI: 10.1038/s41698-025-01060-y
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Hongliang Du, Zhenze Wang, Mengyi Qi +4 more Β· 2025 Β· Cancer cell international Β· BioMed Central Β· added 2026-04-24
Oral squamous cell carcinoma (OSCC) is among the most common malignant tumors in the oral and maxillofacial regions, characterized by high drug resistance and poor treatment outcomes. This underscores Show more
Oral squamous cell carcinoma (OSCC) is among the most common malignant tumors in the oral and maxillofacial regions, characterized by high drug resistance and poor treatment outcomes. This underscores the urgent need to identify novel biomarkers for OSCC. Differentially expressed messenger RNAs (mRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) (DE-mRNAs, DE-miRNAs, and DE-lncRNAs) between primary and control groups, as well as metastatic and primary groups, were identified using whole transcriptome sequencing data. Candidate OSCC genes were derived from DE-mRNAs. Potential biomarkers were then identified using five algorithms from CytoHubba. Biomarkers were validated via univariate Cox regression and Kaplan-Meier (K-M) survival analysis. Additional analyses included subcellular localization, mutation analysis, and Gene Set Enrichment Analysis (GSEA). Key drugs for OSCC treatment were also identified. Quantitative real time polymerase chain reaction (qRT-PCR) and immunohistochemistry were employed to verify the expression levels of key biomarkers. A total of 304 candidate genes were identified, with 29 potential biomarkers selected by five algorithms. ANPEP, APOB, GLP1R, and SI exhibited significant survival differences in the K-M curves, establishing them as OSCC biomarkers. These biomarkers were predominantly localized in the cytoplasm, with SI and APOB showing the highest mutation susceptibility. Enrichment analysis revealed that the 'interferon-gamma response'biological function was co-enriched by ANPEP, APOB, and SI. Furthermore, BIBW2992 (afatinib) and PF.02341066 (crizotinib) were most strongly correlated with the biomarkers, suggesting their potential as key drugs for OSCC treatment. Additionally, the findings were validated by qRT-PCR and immunohistochemical analyses, and the results were consistent with the RNA-seq data. ANPEP, APOB, GLP1R, and SI were identified as potential OSCC biomarkers, offering valuable insights for further research and therapeutic development. Show less
πŸ“„ PDF DOI: 10.1186/s12935-025-03913-9
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Guoping Wu, Zhe Dong, Zhongcai Li +12 more Β· 2025 Β· Schizophrenia (Heidelberg, Germany) Β· Nature Β· added 2026-04-24
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause Show more
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause of their life expectancy being 15-20 years shorter than that of the general population. Identifying comorbidity patterns and uncovering differences in immune and metabolic function are crucial steps toward improving prevention and management strategies. A retrospective cross-sectional study was conducted using electronic medical records of inpatients discharged between 2015 and 2024 from a municipal psychiatric hospital in China. The study included patients diagnosed with Schizophrenia, Schizotypal, and Delusional Disorders (SSDs) (ICD-10: F20-F29). Comorbidity patterns were identified through latent class analysis (LCA) based on the 20 most common comorbid conditions among SSD patients. To investigate differences in peripheral blood metabolic and immune function, linear regression or generalized linear models were applied to 44 laboratory test indicators collected during the acute episode. The Benjamini-Hochberg method was used for p-value correction, and the false discovery rate (FDR) was calculated, with statistical significance set at FDR < 0.05. Among 3,697 inpatients with SSDs, four distinct comorbidity clusters were identified: SSDs only (Class 1), High-Risk Metabolic Multisystem Disorders (Class 2, n = 39), Low-Risk Metabolic Multisystem Disorders (Class 3, n = 573), and Sleep Disorders (Class 4, n = 205). Compared to Class 1, Class 2 exhibited significantly elevated levels of apolipoprotein A (ApoA; β = 90.62), apolipoprotein B (ApoB; β = 0.181), mean platelet volume (MPV; β = 0.994), red cell distribution width-coefficient of variation (RDW-CV; β = 1.182), antistreptolysin O (ASO; β = 276.80), and absolute lymphocyte count (ALC; β = 0.306), along with reduced apolipoprotein AI (ApoAI; β = -0.173) and hematocrit (HCT; β = -35.13). Class 3 showed moderate increases in low-density lipoprotein cholesterol (LDL-C; β = 0.113), MPV (β = 0.267), white blood cell count (WBC; β = 0.476), and absolute neutrophil count (ANC; β = 0.272), with decreased HCT (β = -9.81). Class 4 was characterized by elevated aggregate index of systemic inflammation (AISI; β = 81.07), neutrophil-to-lymphocyte ratio (NLR; β = 0.465), and systemic inflammation response index (SIRI; β = 0.346), indicating a heightened inflammatory state. The comorbidity patterns of patients with SCZ can be distinctly classified. During the acute episode, those with comorbid metabolic disorders exhibit a higher risk of cardiovascular diseases and immune system abnormalities, while patients with comorbid sleep disorders present a pronounced systemic inflammatory state and immune dysfunction. This study provides a basis for the chronic disease management and anti-inflammatory treatment, while also offering objective biomarker insights for transdiagnostic research. Show less
πŸ“„ PDF DOI: 10.1038/s41537-025-00646-6
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Chi Chen, Yimeng Gu, Junfei Xu +9 more Β· 2025 Β· Scientific reports Β· Nature Β· added 2026-04-24
Apolipoprotein B (apoB) can be measured directly and accurately, and better predicts atherogenic risk than conventional lipid profiles. We aimed to investigate whether total and regional (trunk or leg Show more
Apolipoprotein B (apoB) can be measured directly and accurately, and better predicts atherogenic risk than conventional lipid profiles. We aimed to investigate whether total and regional (trunk or leg) fat deposits are associated with apoB levels in general US adults. 4585 participants were enrolled from the US National Health and Nutritional Surveys from 2011 to 2016. Overall and regional body fat were measured using dual-energy X-ray absorptiometry. The associations of total and regional fat with apoB levels were evaluated using linear regression models. Following adjustment for demographic, lifestyle, and clinical risk factors, whole-body fat percentage was positively associated with apoB levels. Additionally, percent trunk fat was positively associated (highest vs. lowest tertile beta = 17.73 for men and 14.89 for women, respectively), whereas percent leg fat was inversely associated (highest vs. lowest tertile beta = - 4.84 for men and - 6.55 for women, respectively) with apoB levels in both sexes. The association for trunk fat and leg fat remained significant after further adjustment for body mass index or waist circumference. Higher percent trunk fat combined with lower percent leg fat was associated with particularly higher apoB. In conclusion, among general US adults, both elevated trunk fat and reduced leg fat are associated with higher levels of apoB. Further research is required to elucidate the underlying pathophysiological mechanisms. Show less
πŸ“„ PDF DOI: 10.1038/s41598-025-10502-3
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Guangyu Gao, Tianci Yao, Chengyun Liu +10 more Β· 2025 Β· Food science & nutrition Β· Wiley Β· added 2026-04-24
Phytosterols have been recommended as a lifestyle intervention for early lipid management-which has a significant impact on frailty. However, their effect on frailty remains unclear. Studies have show Show more
Phytosterols have been recommended as a lifestyle intervention for early lipid management-which has a significant impact on frailty. However, their effect on frailty remains unclear. Studies have shown that genetic proxied total blood phytosterol affects the development of cardiovascular disease through non-HDL-c and apolipoprotein B mediation, which makes phytosterol an underlying risk factor for frailty. The aim of this Mendelian randomization (MR) study was to investigate the genetic associations between phytosterols and frailty. We used univariate Mendelian randomization (UVMR) to assess the causal effects of blood phytosterols on the Frailty Index (FI) and Fried Frailty Score (FFS). We also employed multivariate Mendelian randomization (MVMR) and Two-step MR (TSMR) to evaluate the mediating role of blood lipids in the relationship between blood phytosterols and FI. We used the product of coefficients method to calculate the mediating effect. The inverse-variance weighted method was used as the primary analysis. Genetically proxied higher levels of blood total sitosterol were significantly associated with a higher risk of Frailty Index (OR = 1.035, 95% CI = 1.009-1.061, Show less
πŸ“„ PDF DOI: 10.1002/fsn3.70616
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Yuhang Wang, Shuang Shi, Xinghua Wei +3 more Β· 2025 Β· Diabetes, metabolic syndrome and obesity : targets and therapy Β· added 2026-04-24
The concurrent rise of childhood obesity and hyperuricemia presents a serious public health concern. These conditions interact through complex metabolic mechanisms and significantly increase long-term Show more
The concurrent rise of childhood obesity and hyperuricemia presents a serious public health concern. These conditions interact through complex metabolic mechanisms and significantly increase long-term risks of cardiometabolic diseases. Machine learning (ML) offers an effective framework for constructing efficient risk prediction models in pediatric populations. This study aimed to develop and evaluate two ML models-Random Forest (RF) and Support Vector Classification (SVC)-to predict the risk of childhood obesity and hyperuricemia by integrating clinical and biochemical variables. A total of 101 children were enrolled, including 60 with obesity and 41 with obesity plus hyperuricemia. Data preprocessing involved recursive feature elimination (RFE), ROSE-based oversampling, and feature standardization. Both RF and SVC models were trained and evaluated using area under the ROC curve (AUC), precision-recall curves, and calibration curves. SHAP (Shapley Additive Explanations) analysis was conducted to interpret feature contributions. Both models demonstrated strong predictive performance, with AUCs reaching 0.96. The SVC model achieved slightly higher average precision and recall, making it more suitable for community- or school-based screening of high-risk children. In contrast, the RF model exhibited superior calibration, suggesting its greater utility in clinical decision-making where probabilistic risk estimation guides personalized follow-up or intervention planning. SHAP analysis identified glomerular filtration rate (GFR), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein B (ApoB) as key predictors, some exhibiting nonlinear associations with disease risk. RF and SVC models offer reliable tools for early risk prediction of obesity and hyperuricemia in children, each tailored to distinct clinical scenarios. These findings support early identification and targeted intervention. Future studies will explore the integration of metabolomic data and ensemble approaches to further enhance model performance and clinical applicability. Show less
πŸ“„ PDF DOI: 10.2147/DMSO.S519284
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Xiao Wang, Ke Yang, Xiao-Wei Chen Β· 2025 Β· Journal of cellular physiology Β· Wiley Β· added 2026-04-24
Products encoded by approximately 30% of the mammalian genome exit the endoplasmic reticulum via the coat complex II (COPII) system en route to their functional destination. Among these cargoes, APOB- Show more
Products encoded by approximately 30% of the mammalian genome exit the endoplasmic reticulum via the coat complex II (COPII) system en route to their functional destination. Among these cargoes, APOB-containing lipoproteins stand out as abundant and bulky secretory particles with profound implications for human health and diseases. Recent insights into the specialized intracellular itinerary of lipoprotein metabolism and transport not only shed light on longstanding questions of lipid dynamics, but also highlight challenges faced by the COPII machinery in accommodating these complex, unconventional cargoes. Emerging evidence supports that tightly-regulated COPII condensation enables maximal capacity of cargo transport, providing a potential solution tailored for efficient lipoprotein delivery without affecting general protein secretion. This distinction suggests that targeting COPII condensation may provide new therapeutic strategies for lipid-associated diseases. Indeed, recent studies have identified manganese as a key modulator of this process, offering novel insights into its physiological relevance and potential translations. Show less
no PDF DOI: 10.1002/jcp.70061
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Bin Feng, Yi Wang, Jingjie Xu +3 more Β· 2025 Β· Lipids in health and disease Β· BioMed Central Β· added 2026-04-24
This study aimed to (1) evaluate small dense low-density lipoprotein cholesterol (sdLDL-C) dynamics from prediabetes to type 2 diabetes mellitus (T2DM) with complications, (2) validate existing sdLDL- Show more
This study aimed to (1) evaluate small dense low-density lipoprotein cholesterol (sdLDL-C) dynamics from prediabetes to type 2 diabetes mellitus (T2DM) with complications, (2) validate existing sdLDL-C estimation formulas (Sampson’s, Srisawasdi’s, Han’s) in Chinese populations, and (3) develop a population-specific formula for enhanced accuracy. A multicenter study recruited 1,944 participants (216 controls, 70 with prediabetes, 212 with newly diagnosed T2DM, 164 with treated T2DM, and 1,286 in validation cohorts). Lipid profiles, including sdLDL-C (measured via enzymatic assays), were analyzed. Formula performance was assessed using spearman correlation, intraclass correlation coefficients (ICC), and multivariable linear regression. A novel formula was derived via multivariable regression. Atherogenic lipid triad manifestations emerged early: sdLDL-C was significantly elevated in participants with prediabetes (1.07 [0.73, 1.40] vs. 0.57 [0.44, 0.72] mmol/L in controls, P < 0.05) and further increased in those with T2DM, correlating strongly with triglycerides (TG; sdLDL-C elevation begins in prediabetes, highlighting its value for early atherosclerotic cardiovascular disease (ASCVD) risk assessment. Current formulas show population-specific limitations, whereas the new model provides improved accuracy for Chinese T2DM patients, enabling cost-effective sdLDL-C estimation and personalized lipid management. The online version contains supplementary material available at 10.1186/s12944-025-02636-0. Show less
πŸ“„ PDF DOI: 10.1186/s12944-025-02636-0
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Neng Wang, Yu Zheng, Shuai Tao +1 more Β· 2025 Β· BMC gastroenterology Β· BioMed Central Β· added 2026-04-24
This study aimed to elucidate the correlations among dyslipidemia, immune function, and clinical outcomes in patients with acute-on-chronic liver failure (ACLF), with particular emphasis on the clinic Show more
This study aimed to elucidate the correlations among dyslipidemia, immune function, and clinical outcomes in patients with acute-on-chronic liver failure (ACLF), with particular emphasis on the clinical significance of lipid metabolism and cellular immune parameters in hepatitis B virus-associated ACLF (HBV-ACLF). A retrospective analysis was conducted on 803 patients with HBV-ACLF admitted to the Shanghai Public Health Clinical Center from January 2014 to January 2024. Patients were stratified into deceased (n = 414) and survival (n = 389) groups based on clinical outcomes. Clinical baseline data, lipid metabolic indices, and cellular immune parameters were collected. The Spearman correlation coefficient was utilized to assess the correlation between lipid metabolic indices and cellular immune parameters, and a multivariate Cox proportional hazards model was applied to analyze risk factors for mortality. Compared to the survival group, lipid metabolism indices in the deceased group were significantly reduced (P < 0.05). Lipid metabolism indices, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (APOA1), apolipoprotein B (APOB), total cholesterol (TC), and triglycerides (TG), demonstrated significant negative correlations with the severity of liver failure (P < 0.05). Correlation analysis with lymphocyte subset counts revealed positive correlations between low-density lipoprotein, TG, TC, APOB, and CD3 + T cells, CD4 + T cells, CD8 + T cells, and CD45 + T cells (P < 0.05). APOA1 and HDL-C were positively correlated with B cells and NK cells (P < 0.05). TG and APOB showed significant negative correlations with the CD4/CD8 ratio (P < 0.05). Multivariate Cox analysis identified age, creatinine, total bilirubin, international normalized ratio (INR), hepatic encephalopathy, and hepatorenal syndrome as independent risk factors affecting the short-term prognosis of HBV-ACLF, while sodium, APOA1, and APOB were identified as independent protective factors for ACLF (HR = 0.984, 95% CI: 0.974-0.995, P < 0.001, HR = 0.267,95% CI: 0.120-0.596, P = 0.001, HR = 0.486, 95% CI: 0.282-0.838, P = 0.010). Patients with HBV-ACLF exhibit decreased levels of TC, TG, LDL-C, HDL-C, APOA1, and APOB. These alterations in serum lipid profiles are associated with immune dysfunction and disease progression in HBV-ACLF. Notably, APOA1 and APOB serve as protective factors against 90-day mortality in hospitalized ACLF patients. Further investigation is warranted to elucidate the relationship between lipid metabolism disturbances and peripheral immunity in ACLF. Show less
πŸ“„ PDF DOI: 10.1186/s12876-025-04004-9
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Zhiming Zhao, Wei Lu, Changwei Li +2 more Β· 2025 Β· American journal of physiology. Endocrinology and metabolism Β· added 2026-04-24
Kelch-like protein 12 (KLHL12) has been shown to regulate coat complex II (COPII)-mediated endoplasmic reticulum (ER)-to-Golgi trafficking of large cargos carrying procollagen or apolipoprotein B-100 Show more
Kelch-like protein 12 (KLHL12) has been shown to regulate coat complex II (COPII)-mediated endoplasmic reticulum (ER)-to-Golgi trafficking of large cargos carrying procollagen or apolipoprotein B-100 containing very-low-density lipoprotein (VLDL). It is known that lipid absorption and chylomicron metabolism in enterocytes are dependent on apolipoprotein B-48 (ApoB48) and COPII-mediated trafficking. This study aimed to investigate whether KLHL12 in the intestine regulates dietary lipid absorption, chylomicron assembly, and metabolic phenotypes in mice. We generated Show less
πŸ“„ PDF DOI: 10.1152/ajpendo.00219.2025
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Chunli Shao, Shu Zhang, Zhifeng Cheng +17 more Β· 2025 Β· Atherosclerosis Β· Elsevier Β· added 2026-04-24
Several protein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to significantly reduce low-density lipoprotein cholesterol (LDL-C) levels in statin-intolerant patients, but none Show more
Several protein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to significantly reduce low-density lipoprotein cholesterol (LDL-C) levels in statin-intolerant patients, but none have been verified in Chinese patients. This study aimed to evaluate the efficacy and safety of ongericimab, a novel PCSK9 monoclonal antibody, in Chinese statin-intolerant patients with primary hypercholesterolemia or mixed dyslipidemia. This was a randomized, multicenter, double-blind, placebo-controlled phase 3 study designed to enroll 120 statin-intolerant adult patients. Eligible patients were randomly assigned in a 2:1 ratio to receive ongericimab 150Β mg or placebo subcutaneously every 2 weeks for 12 weeks in the double-blind treatment period, followed by 40 weeks of ongericimab treatment during the open-label period. The primary endpoint was a percentage change in LDL-C from baseline to week 12. The key secondary endpoints included percentage change from baseline to week 12 in non-high density lipoprotein cholesterol (non-HDL-C), apolipoprotein B (ApoB), total cholesterol (TC), and lipoprotein(a) [Lp(a)]. From February 6, 2023, to September 23, 2024, a total of 139 patients were enrolled. The least-squares (LS) mean difference between ongericimab and placebo groups in LDL-C from baseline to week 12 was -66.2Β % (95Β % CI: 74.2Β %, -58.2Β %; pΒ <Β 0.0001), with reductions sustained up to week 52. Ongericimab also significantly reduced levels of non-HDL-C, ApoB, TC, and Lp(a). The overall incidence of treatment-emergent adverse events was comparable between the ongericimab and placebo groups. Ongericimab significantly reduced LDL-C as well as other atherogenic lipid levels and was well tolerated in Chinese statin-intolerant patients with primary hypercholesterolemia or mixed dyslipidemia. http://www. gov; Unique Identifier: NCT05621070. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.120408
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Yayu Wang, Yue Chang, Pei Zhang +4 more Β· 2025 Β· Lipids in health and disease Β· BioMed Central Β· added 2026-04-24
Duchenne muscular dystrophy (DMD) is a serious, progressive neuromuscular condition that predominantly impacts male individuals, marked by progressive muscle weakness resulting from mutations in the d Show more
Duchenne muscular dystrophy (DMD) is a serious, progressive neuromuscular condition that predominantly impacts male individuals, marked by progressive muscle weakness resulting from mutations in the dystrophin gene (DMD) encoding dystrophin. DMD is a primary muscle disorder that often presents with secondary abnormalities in lipid metabolism and decreased bone mineral density. Although disturbances in circulating lipid profiles and skeletal health have been observed in individuals with DMD, their relationship remains underexplored.This study aimed to investigate the potential association between lipid metabolic disturbances and spinal bone mineral density in patients with DMD by combining clinical lipid levels and bone density with transcriptomic pathway analysis of DMD muscle tissue. Retrospective analysis was performed on 219 genetically confirmed DMD patients and 99 age-matched healthy controls. Healthy controls with a family history of genetic disorders were excluded. Clinical data included lipid profiles (triglycerides [TGs], remnant cholesterol [RC]); bone mineral density of the lumbar spine was evaluated using Dual-energy X-ray absorptiometry (DXA); and corticosteroid use, including treatment status, dose, and duration. Patients were stratified by corticosteroid exposure. Restricted cubic splines and multivariable regression models were applied to explore potential relationships between lipid parameters and bone mineral density. Bioinformatic analyses were performed on RNA sequencing data from muscle biopsy samples from patients with DMD (GSE38417 dataset) and an independent validation cohort (GSE6011 dataset), focusing on pathways related to lipid metabolism and osteoclast differentiation. Patients with DMD had higher TG, RC, and apolipoprotein B (ApoB) levels and lower high-density lipoprotein cholesterol (HDL-C) levels than healthy controls (P < 0.05). Elevated TG and RC levels were associated with reduced spine bone mineral density, independent of corticosteroid use. The bioinformatic analyses identified key pathways, including sphingolipid metabolism and osteoclast differentiation, as well as hub genes such as FCGR2B, C1QA, which are involved in lipid regulation and bone remodeling. Lipid abnormalities, particularly elevated TG and RC levels, were significantly associated with lower bone mineral density in patients with DMD. These findings suggest that lipid abnormalities are involved in bone health impairment in DMD, warranting further studies to confirm the association. Show less
πŸ“„ PDF DOI: 10.1186/s12944-025-02628-0
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