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Also published as: Junli Wang, Xindi Wang, Junpeng Wang, Tingyu Wang, Guoqiang Wang, Yuxuan Wang, Hanzhi Wang, Zhi-Long Wang, Shanshan Wang, Wenfei Wang, Dengbin Wang, Yen-Sheng Wang, Chuanxin Wang, Zeyu Wang, Beibei Wang, Taicheng Wang, Xingguo Wang, Z P Wang, Yue-Min Wang, Chenghua Wang, Xianqiang Wang, Congrong Wang, Yanhai Wang, Du Wang, Xianzhe Wang, Zuoheng Wang, Yongyi Wang, Zhihui Wang, Yanhua Wang, Limeng Wang, H J Wang, Pei-Jian Wang, Yana Wang, Congrui Wang, Larry Wang, Yu-Zhuo Wang, Sihua Wang, Wanchun Wang, Jialin Wang, Xinying Wang, Shuguang Wang, Yinhuai Wang, Xiaobin Wang, Yuying Wang, Hebo Wang, Leli Wang, Jiayu Wang, Zhaojun Wang, Hai Wang, Si Wang, Re-Hua Wang, Xuping Wang, Bo Wang, Shubao Wang, Songjiao Wang, Hongjia Wang, Victoria Wang, Ling Wang, Jianjie Wang, Haining Wang, Dali Wang, Ji-Yang Wang, Cheng Wang, Weifan Wang, Yuanqiang Wang, Zhixiao Wang, Yaxian Wang, Zhigang Wang, Haochen Wang, Jia-Ying Wang, Shichao Wang, Ruosu Wang, N Wang, Haixing Wang, Guiqun Wang, Zhiting Wang, Dan Wang, Wangxia Wang, Jing-Long Wang, Yaqian Wang, Yafang Wang, Xing-Jun Wang, Dapeng Wang, Zhongyuan Wang, Junsheng Wang, Zhaohai Wang, He-Ping Wang, Minmin Wang, Wenzhou Wang, Zhaohui Wang, Yanfang Wang, Pengtao Wang, Leran Wang, Qianwen Wang, Hongkun Wang, Sa Wang, Y Alan Wang, Liyan Wang, Jou-Kou Wang, Mingda Wang, Chenfei Wang, Yuehan Wang, Simeng Wang, Yuhua Wang, Ruibin Wang, Haibo Wang, Ni Wang, Guoxiu Wang, Zhuangzhuang Wang, Yajie Wang, Zhixiang Wang, Sangui Wang, Xiantao Wang, Yan-Yang Wang, Mengjun Wang, Ruling Wang, Peihe Wang, Miao Wang, Zaihua Wang, Jun-Jie Wang, Mengyao Wang, Zhiyu Wang, Changzhen Wang, Xijun Wang, Chengjian Wang, Yiyi Wang, Mo Wang, Xiaolun Wang, Danan Wang, Fanchang Wang, Zilin Wang, Fanhua Wang, Supeng Perry Wang, Gavin Wang, Yi-Ying Wang, Yani Wang, Zhuowei Wang, Weiwei Wang, Haifeng Wang, Yi-Shiuan Wang, Yan-Chao Wang, Xiaotong Wang, Jia-Qi Wang, Yongliang Wang, Yongming Wang, Fengchong Wang, Jianyong Wang, Zeping Wang, Huaquan Wang, Xiaojia Wang, Tao Wang, Tianjun Wang, Siying Wang, Zhenze Wang, Zhijian Wang, Li Wang, Heming Wang, Jingtong Wang, Xuefei Wang, Yingqiao Wang, Xiao Qun Wang, Chun-Chieh Wang, Shuang-Xi Wang, Laiyuan Wang, Zhaoming Wang, Yinggui Wang, Qi-Jia Wang, Wen-Yan Wang, Mingming Wang, Peipei Wang, Chien-Hsun Wang, Qiuhong Wang, Monica Wang, Lexin Wang, Xiufen Wang, Yuehua Wang, Pingfeng Wang, Caiyan Wang, Weijie Wang, Yigang Wang, Jieyan Wang, Huiquan Wang, Chunsheng Wang, Yunhe Wang, Changtu Wang, Qingliang Wang, Guanghua Wang, Yongbin Wang, Zhaobo Wang, Minghui Wang, Junshi Wang, Jingyu Wang, Longsheng Wang, Fen Wang, Xianshu Wang, Jianwu Wang, Jun-Zhuo Wang, Zhixing Wang, Lei Wang, Yiyan Wang, Jinglin Wang, Jinhe Wang, Enhua Wang, Yuecong Wang, Xueying Wang, Jennifer T Wang, Xin-Hua Wang, Shijie Wang, Chun-Xia Wang, Yuanjiang Wang, Xiaojun Wang, Shunjun Wang, Chun-Juan Wang, M Wang, Jinfei Wang, Jinghuan Wang, Xuru Wang, Xiao-Lan Wang, Yu-Chen Wang, Zhi-Guo Wang, Luya Wang, Shuwei Wang, Pingchuan Wang, Qifan Wang, Xing-Quan Wang, Weiding Wang, Xuebin Wang, Yaling Wang, Chenyin Wang, Allen Wang, Liyuan Wang, Rong-Rong Wang, Wusan Wang, Wayseen Wang, Qianru Wang, Yi-Xin Wang, Hailin Wang, Yu-Hang Wang, Xuesong Wang, Haojie Wang, Wanxia Wang, Mengwen Wang, Hanping Wang, Yuhang Wang, Lueli Wang, Xinchang Wang, Oliver Wang, Shuge Wang, Jianhao Wang, Chong Wang, Kui Wang, Litao Wang, Zining Wang, Ming-Yang Wang, Hongxia Wang, Mingyi Wang, Hai Bo Wang, Bingnan Wang, Hongqian Wang, Jisheng Wang, Jiakun Wang, Maoju Wang, Xiaoqiu Wang, Dongyi Wang, Hai Yang Wang, Pengju Wang, Xiaofeng Wang, Huming Wang, Jian'an Wang, Qianrong Wang, Xiaowei Wang, Xiangkun Wang, Da Wang, Hongying Wang, Changying Wang, Changyu Wang, Xiaoqin Wang, Zhenxi Wang, Qiaoqiao Wang, Yu Tian Wang, Yupeng Wang, Xinli Wang, YueJiao Wang, Jian-chun Wang, Pengchao Wang, Xiao-Juan Wang, Siqing Wang, C Z Wang, Pengbo Wang, Baoli Wang, Yu-Zhe Wang, Gui-Qi Wang, Dazhi Wang, Yanwen Wang, Xingqin Wang, Shijin Wang, Wenming Wang, Fanxiong Wang, Tiansong Wang, Shuzhe Wang, Jie Wang, Jinling Wang, Yunfang Wang, Luyao Wang, Cun-Yu Wang, Zikang Wang, Quan-Ming Wang, Yingying Wang, Chia-Chuan Wang, Xintong Wang, Jufeng Wang, Xuejun Wang, Xiao-Qian Wang, Yijin Wang, Meng Yu Wang, Tianyi Wang, Chia-Lin Wang, Zhuo-Jue Wang, Yaohe Wang, Rong Wang, Hao-Hua Wang, Yong-Jun Wang, Xubo Wang, Dalong Wang, Yan-Ge Wang, Erika Y Wang, Ruixian Wang, Jin-Liang Wang, Shicung Wang, Saifei Wang, Jintao Wang, Zhenzhen Wang, Jiawei Wang, Beilei Wang, Huabo Wang, Huiyu Wang, Hongtao Wang, Chengjun Wang, Guo-Du Wang, Taoxia Wang, Zitao Wang, Jingwen Wang, Yibin Wang, Long Wang, Xinjing Wang, Qunzhi Wang, Liangliang Wang, Bangchen Wang, Yu-Fen Wang, Shibin Wang, Congcong Wang, Xiong Wang, Zhiren Wang, Xiaozhu Wang, Hong-Xia Wang, Qingyong Wang, Tianying Wang, Tammy C Wang, Huijie Wang, Tiansheng Wang, Mengzhao Wang, Jianshu Wang, Xinlong Wang, Benzhong Wang, Zhipeng Wang, Kaijie Wang, Xiaomin Wang, Peijun Wang, Zhiqiang Wang, Jundong Wang, Zheng Wang, Yueze Wang, Sujuan Wang, Qing-Yun Wang, Xiaoqing Wang, Zongqi Wang, Zhicun Wang, Fudi Wang, Seok Mui Wang, Wanbing Wang, Kejun Wang, Nanping Wang, Mingyang Wang, Wenxia Wang, Yaru Wang, Zikun Wang, Shidong Wang, Bei Bei Wang, Yu-Hui Wang, Rui Wang, Yige Wang, Tongxin Wang, Xiaohua Wang, Changjing Wang, Xingjin Wang, Bingjie Wang, Shaoyu Wang, Hui-Hui Wang, Zhenyu Wang, Baoying Wang, Yang-Yang Wang, Shi-Yao Wang, Lifei Wang, Fangfang Wang, Zhimei Wang, Kunpeng Wang, Binglong Wang, Daijun Wang, Qinghang Wang, Zi Wang, Shushu Wang, QingDong Wang, Qing K Wang, Fuhua Wang, Yanni Wang, Jianle Wang, Wenyan Wang, Jinning Wang, Ziqi Wang, Wei-Qi Wang, Yaolou Wang, Haoming Wang, Jian-Wei Wang, Tian Wang, Peixi Wang, Iris X Wang, Tongxia Wang, Mei-Xia Wang, Haiying Wang, Tielin Wang, Hongze Wang, Chung-Hsi Wang, Peiyao Wang, Linli Wang, Guanru Wang, Yuzhong Wang, Yunhan Wang, Jianan Wang, Menglong Wang, Yingxue Wang, Jiayi Wang, Dingxiang Wang, Ting Wang, Fenglin Wang, Jianqun Wang, Ran Wang, Kuan Hong Wang, Liusong Wang, Wen-Der Wang, Yixuan Wang, Feng Wang, Kaicen Wang, Eryao Wang, Yulei Wang, Huaibing Wang, Zhongzhi Wang, Jinrong Wang, Sujie Wang, Xiaozhong Wang, Xiao-Pei Wang, Li-Na Wang, H X Wang, Linjie Wang, Zhaosong Wang, Yafen Wang, Chuan-Wen Wang, Xiaoning Wang, Li-Xin Wang, Silas L Wang, Baocheng Wang, Hongyi Wang, Zhi-Xiao Wang, Shengjie Wang, Zhi-Hao Wang, Yaokun Wang, Shao-Kang Wang, Qunxian Wang, Jianghui Wang, Zhao Wang, Jianzhi Wang, Ruijing Wang, Ling Jie Wang, Qingshi Wang, Jianye Wang, Yuqiang Wang, Kangling Wang, Anxin Wang, Shengli Wang, Zhulin Wang, Hua-Wei Wang, Yiwen Wang, Yang Wang, Hanqi Wang, Changwei Wang, Honglei Wang, Yi Lei Wang, Wenkang Wang, Junjie Wang, Yazhou Wang, Peng-Cheng Wang, Chenzi Wang, Anqi Wang, Yuemiao Wang, Xuelin Wang, Rujie Wang, Dongyan Wang, Yuxue Wang, Wengong Wang, Qigui Wang, Junqing Wang, Ruhan Wang, Xinye Wang, Huihui Wang, Gengsheng Wang, Mark Wang, Zhidong Wang, Mengmeng Wang, Yuwen Wang, Liang Wang, Huaxiang Wang, Fangjun Wang, Huixia Wang, Haijiao Wang, Hong-Hui Wang, Yi-Shan Wang, Yunchao Wang, Junjun Wang, Binghai Wang, Xinguo Wang, Jun-Sing Wang, Lingzhi Wang, Yuexiang Wang, Hong-Gang Wang, Yen-Feng Wang, Xidi Wang, Jiawen Wang, Liangfu Wang, Lifeng Wang, Shihan Wang, Wentian Wang, Sa A Wang, Lee-Kai Wang, Yu-Wei Wang, Zumin Wang, Shau-Chun Wang, Jianjiao Wang, Tian-Tian Wang, Jiantao Wang, Edward Wang, Jianbo Wang, Qingfeng Wang, Wenran Wang, Xiaolin Wang, Fenghua Wang, Rongjia Wang, Shiqiang Wang, Caixia Wang, Guihu Wang, Xindong Wang, Wenxiu Wang, Xueguo Wang, YiLi Wang, Aizhong Wang, Qiqi Wang, Chengcheng Wang, D Wang, L Wang, Jianhua Wang, Qiuling Wang, Shaolian Wang, Wen-Qing Wang, Wenqing Wang, Yuchuan Wang, Guangdi Wang, Yiquan Wang, Huimei Wang, Genghao Wang, Zun Wang, Miranda C Wang, Annette Wang, Chi-Ping Wang, Hanmin Wang, Zhaoxi Wang, Shifeng Wang, Runze Wang, Mangju Wang, Junjiang Wang, Dong D Wang, Xiu-Ping Wang, Haijiu Wang, Linghuan Wang, Yiying Wang, Renqian Wang, Nana Wang, Xiangdong Wang, Shiyin Wang, Chaoyi Wang, Menghan Wang, Shuyue Wang, Yongmei Wang, Nanbu Wang, Lihua Wang, Hongyue Wang, Jianli Wang, Chunli Wang, Minghua Wang, Junkai Wang, Chenguang Wang, Siyue Wang, Jun Wang, Shu-Song Wang, Bingyan Wang, Qingping Wang, Zhong-Yu Wang, Fei-Fei Wang, Jennifer E Wang, Z-Y Wang, Dongxia Wang, Dang Wang, Zi-Hao Wang, Rihua Wang, Jutao Wang, Yanzhe Wang, Guohao Wang, Liming Wang, Yishu Wang, Xuemin Wang, Xianfeng Wang, Zixu Wang, Jingfan Wang, Guang-Jie Wang, Guixue Wang, Jiaojiao Wang, Yaxin Wang, Haibing Wang, Weizhong Wang, Hairong Wang, Hai-Jun Wang, Mingji Wang, Yongrui Wang, Huizhi Wang, Longfei Wang, Chongmin Wang, Jingyang Wang, Zhong-Ping Wang, Huanhuan Wang, Baisong Wang, Xiaohui Wang, Fengyang Wang, Wanliang Wang, Ziqiang Wang, Chuan Wang, Jeffrey Wang, Ying-Zi Wang, Ziwei Wang, Xian Wang, Hanyu Wang, Qiming Wang, Dedong Wang, Fengying Wang, Xiaoya Wang, Zhenhua Wang, Yanchun Wang, Keming Wang, Zi-Yi Wang, Dezhong Wang, Jingying Wang, Shouli Wang, Lan-lan Wang, Weiyu Wang, Yuhuai Wang, Jun Yi Wang, Wenying Wang, Xue-Feng Wang, Xing-Lei Wang, Yuehong Wang, Pengyu Wang, Yihe Wang, Guodong Wang, Weijian Wang, Wu-Wei Wang, Y Wang, Ruonan Wang, Jianbing Wang, Mian Wang, Dennis Qing Wang, Nannan Wang, Zuo Wang, Christine Wang, Ruixin Wang, Yaxiong Wang, Siwei Wang, Yuanzhen Wang, Wen-Chang Wang, Haijing Wang, X Wang, Melissa T Wang, Haixia Wang, Qianghu Wang, Hongsheng Wang, Xiurong Wang, Shaowei Wang, Shuo Wang, Zengtao Wang, Yun-Xing Wang, Songtao Wang, Mei Wang, Mengyun Wang, Qingming Wang, Ke-Feng Wang, Zhihao Wang, Haoqi Wang, X E Wang, Xin-Shang Wang, Dongmei Wang, Lingli Wang, Huai-Zhou Wang, Hua Wang, Kunzheng Wang, Mao-Xin Wang, Jingzhou Wang, Jiaqi Wang, Xingbang Wang, Wence Wang, Yongdi Wang, Xin-Qun Wang, Guoyi Wang, Jian-Guo Wang, Jiafu Wang, Pin Wang, Libo Wang, Junling Wang, J Z Wang, Haozhou Wang, Jing Wang, Hezhi Wang, T Q Wang, Xi-Hong Wang, Yuanfan Wang, Endi Wang, Hua-Qin Wang, Jeremy Wang, Songping Wang, Suyun Wang, Jiqing Wang, Shu-Ling Wang, Jennifer X Wang, Lily Wang, Yin-Hu Wang, Jen-Chywan Wang, Qingqing Wang, Shuangyuan Wang, Haihong Wang, Luyun Wang, Yake Wang, Ya-Nan Wang, Weicheng Wang, Jianxiang Wang, Zihua Wang, Lin Wang, Fu-Sheng Wang, Zongbao Wang, Tong-Hong Wang, Xianze Wang, Ting-Ting Wang, Haibin Wang, Xin-Yue Wang, Zhi-Gang Wang, Ziying Wang, Shukang Wang, Wen-Jun Wang, Delin Wang, Yating Wang, Xuehao Wang, Yefu Wang, Yi-Ning Wang, Cheng-zhang Wang, Jing J Wang, Xinglong Wang, Yanqing Wang, Tongyao Wang, Dongyang Wang, Deqi Wang, Qiao Wang, Alice Wang, Yunzhi Wang, Dayong Wang, Renxi Wang, Yeh-Han Wang, Mingya Wang, Longxiang Wang, Hualin Wang, Hailei Wang, Ao Wang, Wanyu Wang, Jiale Wang, Qiangcheng Wang, Huishan Wang, Yunqiong Wang, Xudong Wang, Xifu Wang, Wen-Xuan Wang, Dao Wen Wang, Zhi-Wei Wang, Xingchen Wang, Yanyang Wang, Yutao Wang, Huizhen Wang, Hu WANG, Y P Wang, Wen Wang, Qingsong Wang, Baofeng Wang, Ruo-Ran Wang, Yaobin Wang, Changliang Wang, Pintian Wang, Dai Wang, Su-Guo Wang, Ruting Wang, Fengzhen Wang, Qinrong Wang, HuiYue Wang, Baosen Wang, Shuhe Wang, Yifei Wang, Jiun-Ling Wang, Junhui Wang, Guangzhi Wang, Qijia Wang, Yushe Wang, Jinlong Wang, Zhouguang Wang, Huiyao Wang, Shuxin Wang, Yingyi Wang, Jing-Yi Wang, Yongxiang Wang, Zhi Wang, Dehao Wang, Yi-sheng Wang, Jiazhi Wang, Yunfei Wang, Mingjin Wang, Yaozhi Wang, Jinyu Wang, Jinmeng Wang, LiLi Wang, Shuai Wang, Yan Wang, Jun Kit Wang, Cui Wang, Zhan Wang, Dong-Jie Wang, Yangyang Wang, Xiangguo Wang, Runuo Wang, Ruimin Wang, Pengpu Wang, Nuan Wang, Guangyan Wang, Xin-Liang Wang, Minxiu Wang, Ruifang Wang, Hui Wang, Hongda Wang, Xiyan Wang, Jinxia Wang, Xinchen Wang, Haihua Wang, Delong Wang, Yayu Wang, Xue-Hua Wang, Xin-Peng Wang, Changqian Wang, Bei Wang, Ya-Han Wang, Chih-Liang Wang, P N Wang, Xiaoqian Wang, Xianshi Wang, Zhiruo Wang, Xueding Wang, Renxiao Wang, Yi-Ming Wang, Tianqi Wang, Ledan Wang, Rongyun Wang, Gan Wang, Qinqin Wang, Yuxiang Wang, Feimiao Wang, Mengyuan Wang, Chaofan Wang, Linshuang Wang, Yanhui Wang, Zhenglong Wang, Zongkui Wang, Zhenwei Wang, Xiyue Wang, Yi Fan Wang, Xiao-Ai Wang, Po-Jen Wang, Xinyang Wang, Linying Wang, Fa-Kai Wang, Yimeng Wang, Dong-Mei Wang, Anli Wang, Hui-Li Wang, Jianqing Wang, Honglun Wang, Wei-Feng Wang, Kaihao Wang, Jialing Wang, Shuren Wang, Cui-Fang Wang, Wenqi Wang, Peilin Wang, Wen-Fei Wang, Guang-Rui Wang, T Wang, Weiqing Wang, Ciyang Wang, Biao Wang, Kaihe Wang, Jieh-Neng Wang, Tony Wang, Yuehu Wang, Zhicheng Wang, Tongtong Wang, Zi Xuan Wang, Yingtai Wang, Xin-Xin Wang, Chu Wang, Tianhao Wang, Shukui Wang, Ching C Wang, Yulin Wang, Chunyang Wang, Yeqi Wang, Yinbo Wang, Kongyan Wang, Weiling Wang, Linxuan Wang, Shengya Wang, Yaqi Wang, Huating Wang, Aiting Wang, Ya Xing Wang, Daoping Wang, Shasha Wang, Wei-Lien Wang, Quanli Wang, Yanru Wang, L M Wang, Bijue Wang, H Wang, Jipeng Wang, Xiaoxia Wang, Shuu-Jiun Wang, Baitao Wang, Haimeng Wang, Chung-Hsing Wang, Weining Wang, M Y Wang, Wenwen Wang, Zhongsu Wang, Xiaochen Wang, Ligang Wang, Shaohsu Wang, Bing Qing Wang, Jiangbin Wang, Yajun Wang, Chunting Wang, Hemei Wang, En-hua Wang, H-Y Wang, Zixi Wang, Wenjing Wang, Haikun Wang, Ruxin Wang, Jianru Wang, Yongqiang Wang, Ouchen Wang, Jianyu Wang, Shen Wang, Yixi Wang, Zhi-Hong Wang, Li Dong Wang, Zhou-Ping Wang, Wen-Yong Wang, Meng-Lan Wang, Xiaojie Wang, Leying Wang, Yi-Zhen Wang, Y Y Wang, Jianlin Wang, Guoqing Wang, Jiani Wang, Guan-song Wang, You Wang, Xiangding Wang, Ke Wang, Wendong Wang, Yue Wang, Zhe Wang, K Wang, Zhuo Wang, Su'e Wang, Cangyu Wang, Erfei Wang, Xiaoming Wang, Aijun Wang, Xiaoye Wang, Jun-Sheng Wang, Wenxiang Wang, Yanjun Wang, Qiangqiang Wang, Yachun Wang, Haitao Wang, Tiancheng Wang, Gangyang Wang, Jianmin Wang, Jiabo Wang, Yijing Wang, Mengzhi Wang, Yinuo Wang, Zhou Wang, Guiying Wang, Xuezheng Wang, Shan Wang, Aoli Wang, Fuqiang Wang, Yawei Wang, Xianxing Wang, Ya-Long Wang, Yuyang Wang, Dong Hao Wang, Y-S Wang, Zelin Wang, Liqun Wang, Cunyi Wang, Qian-Zhu Wang, Yinan Wang, Panfeng Wang, Guangwen Wang, J Q Wang, Guang Wang, Yu-Ping Wang, John Wang, Jiaping Wang, Zhisheng Wang, Xuan-Ren Wang, Xiaowu Wang, Zhengyu Wang, Baowei Wang, Zhijun Wang, Zhong-Hao Wang, Fengzhong Wang, Jin-Da Wang, Zhaoqing Wang, Yuanbo Wang, Haixin Wang, Yaping Wang, Lixiu Wang, Mingxia Wang, Neng Wang, Guozheng Wang, Yan-Feng Wang, Huafei Wang, Yuhan Wang, Xingxing Wang, Wenhe Wang, Xing-Huan Wang, Xiansong Wang, Yishan Wang, Ruming Wang, Ya Qi Wang, Yueying Wang, Chunle Wang, Shihua Wang, W Wang, Hengjun Wang, Meihui Wang, Huanyu Wang, Ruinan Wang, Qiwei Wang, Zhong Wang, Shiyao Wang, Jian-Zhi Wang, Ruimeng Wang, Jinxiang Wang, Jinsong Wang, Bin-Xue Wang, Fuwen Wang, Yiou Wang, Shifa Wang, Yin Wang, Yanzhu Wang, Jia Bin Wang, Siyang Wang, Zhanggui Wang, Yueting Wang, Qingyu Wang, Qianqian Wang, Xiu-Lian Wang, Fengling Wang, Chenxi Wang, Cheng An Wang, Yipeng Wang, Weipeng Wang, Zechen Wang, Shuaiqin Wang, Xueqian Wang, Chan Wang, Guohang Wang, Cai-Yun Wang, Jiang Wang, Huei Wang, Yufeng Wang, Heng Wang, Qing-Liang Wang, Chuang Wang, Xiaofang Wang, Hao-Ching Wang, Junying Wang, Jianwei Wang, Jinhai Wang, Hanchao Wang, Penglai Wang, I-Ching Wang, S L Wang, Tianhu Wang, Sheng-Min Wang, Pan-Pan Wang, Duan Wang, Xuqiao Wang, Minghuan Wang, Wei-Wei Wang, Xiaojian Wang, Shuping Wang, Jinfu Wang, Biqi Wang, Zhenguo Wang, Fangyan Wang, Sainan Wang, Peijuan Wang, Pei-Yu Wang, Yuyan Wang, Fuxin Wang, Ji M Wang, Yange Wang, Yali Wang, Wenhui Wang, Leishen Wang, Lichan Wang, Xianna Wang, Wenbin Wang, Kenan Wang, Chih-Yuan Wang, Yanlei Wang, Ju Wang, Yanliang Wang, Keqing Wang, Bangshing Wang, Dayan Wang, Yongsheng Wang, Dinghui Wang, Zheyue Wang, Xinke Wang, Daqing Wang, Yan Ming Wang, He-Ling Wang, Shengyao Wang, Jiwen Wang, Xizhi Wang, Luxiang Wang, Dandan Wang, RongRong Wang, Heng-Cai Wang, Jindan Wang, Xiaoding Wang, Yumeng Wang, Heling Wang, Xiao-Yun Wang, Meiding Wang, Zhilun Wang, Guo-hong Wang, Na Wang, Yanli Wang, Fubing Wang, Feixiang Wang, Zhiyuan Wang, Yi-Cheng Wang, Zhengwei Wang, Wenyuan Wang, Yu-Ying Wang, Jianqin Wang, Sijia Wang, Chuansen Wang, Huawei Wang, Kaiyan Wang, Qingyuan Wang, Yujia Wang, Lian Wang, Junrui Wang, Chao-Yung Wang, Zehao Wang, Ruixue Wang, Minjun Wang, Jin Wang, Xiaoxiao Wang, Jun-Feng Wang, Binquan Wang, Shuxia Wang, Donggen Wang, Deming Wang, Chenggang Wang, Chuduan Wang, Haichuan Wang, Catherine Ruiyi Wang, Hai-Feng Wang, Anthony Z Wang, Guanghui Wang, Jiahao Wang, Xiaosong Wang, Zijue Wang, Wenbo Wang, M-J Wang, Yu Wang, Yingping Wang, Zhengbing Wang, G Q Wang, Mengjing Wang, Zhendong Wang, Kailu Wang, Jinfeng Wang, Zhiguo Wang, Yusha Wang, Jianmei Wang, Kun Wang, Lihong Wang, Haoxin Wang, Haowei Wang, Ziqing Wang, Aihua Wang, Yuanyong Wang, Sanwang Wang, Doudou Wang, Hao-Yu Wang, Peirong Wang, Wenting Wang, Yibing Wang, He Wang, Jia-Peng Wang, Shixin Wang, En-bo Wang, Dong-Dong Wang, Hualing Wang, Hongyan Wang, Shaoying Wang, Yingjie Wang, Tianqing Wang, Guo-Hua Wang, Yongfei Wang, Lijing Wang, Hongli Wang, Zixian Wang, Niansong Wang, Liangxu Wang, Xinrong Wang, X-T Wang, Zhenning Wang, Dake Wang, Yu-Ting Wang, Zonggui Wang, Daping Wang, Joy Wang, Chenji Wang, Jingmin Wang, Yuyin Wang, Jin-Cheng Wang, Jiangbo Wang, Huiyang Wang, Chi Chiu Wang, He-Cheng Wang, Zhongjing Wang, Weina Wang, Qiaohong Wang, Qintao Wang, Jenny Y Wang, Zheyi Wang, Robert Yl Wang, Zhaotong Wang, Ya Wang, Fangyu Wang, Haobin Wang, Tianyuan Wang, Xinrui Wang, Zhehao Wang, Yihan Wang, Chuan-Jiang Wang, Jianjun Wang, Yongfeng Wang, Gaofu Wang, Ying-Piao Wang, Jingwei Wang, Mengjiao Wang, Chuyao Wang, Yanping Wang, Xinchun Wang, Shu Wang, Guibin Wang, Hong-Ying Wang, Linping Wang, Yugang Wang, Xinru Wang, Fengyun Wang, Heyong Wang, Ziping Wang, Yuegang Wang, Xiangyu Wang, Haoran Wang, Xiaomei Wang, Fang Wang, Lina Wang, Guowen Wang, Liyun Wang, Qingshui Wang, Baoyun Wang, Li-Juan Wang, Tongsong Wang, Jingyun Wang, Huiguo Wang, Zhibo Wang, Lou-Pin Wang, Renjun Wang, Huiting Wang, Junfeng Wang, Zihan Wang, Linhua Wang, Zhiji Wang, Fubao Wang, Eunice S Wang, Xiaojuan Wang, Yuewei Wang, Shuang Wang, Ruey-Yun Wang, Xiaoling Wang, Weihua Wang, Yanggan Wang, Jia Wang, Chaoqun Wang, Xiao-liang Wang, Manli Wang, Yongkang Wang, Huiwen Wang, Ting Chen Wang, Yixian Wang, Xinlin Wang, Shuya Wang, Bochu Wang, Kehao Wang, Sasa Wang, Mengshi Wang, Qiu-Ling Wang, Chengshuo Wang, Mengru Wang, Yiwei Wang, Xueyun Wang, Yijun Wang, Haomin Wang, Meng C Wang, Mengxiao Wang, Huan-You Wang, Jingheng Wang, Carol A Wang, Benjamin H Wang, Penglong Wang, Pei-Wen Wang, Jian-Long Wang, Wang Wang, Jinhui Wang, Yuanqing Wang, Jacob E Wang, Jian-Xiong Wang, Wenyu Wang, Chengze Wang, Hongmei Wang, Fengqiang Wang, Zijun Wang, Shaochun Wang, Qinwen Wang, Ruicheng Wang, Aixian Wang, Yanling Wang, Lu-Lu Wang, Linyuan Wang, Yeming Wang, Ye Wang, Tian-Yi Wang, Zhichao Wang, Dangfeng Wang, Jiucun Wang, Guo-Liang Wang, Guandi Wang, Zhuo-Xin Wang, Aili Wang, Fengliang Wang, Yingzi Wang, Lirong Wang, Xuekai Wang, Wei-En Wang, Jing-Xian Wang, Hesuiyuan Wang, Yuexin Wang, Suzhen Wang, Luping Wang, Xiuyu Wang, Zicheng Wang, Jiliang Wang, Rikang Wang, Xue Wang, Shudan Wang, Chun Wang, Hongxin Wang, Chenglong Wang, Junxiao Wang, Zhiqing Wang, Shawn Wang, Shunran Wang, Tiantian Wang, Youhua Wang, Xiao-Hui Wang, Qing-Yan Wang, Hanying Wang, Qiuping Wang, Yongzhong Wang, Jin-Xia Wang, Xiao-Tong Wang, Shun Wang, Xiaoqun Wang, Ching-Jen Wang, Xin Wang, Hanbin Wang, Yingwen Wang, Jia Bei Wang, Xiaodan Wang, Wenhan Wang, Jia-Yu Wang, Xiaozhi Wang, Xinkun Wang, Jinhao Wang, KeShan Wang, Shengdong Wang, Jinzhu Wang, Lihui Wang, Bicheng Wang, Chao-Jun Wang, Shaoyi Wang, Yajing Wang, Qing-Bin Wang, Feiyan Wang, Geng Wang, Chen Wang, Zhimin Wang, Cenxuan Wang, Wenjun Wang, Chuan-Chao Wang, Zexin Wang, Shu-Huei Wang, Yonggang Wang, Zhaoyu Wang, Xiaochuan Wang, Chuan-Hui Wang, Junshuang Wang, X F Wang, Li-Ting Wang, Chenxin Wang, Qiao-Ping Wang, Jingqi Wang, Xiongjun Wang, Shuang-Shuang Wang, Xu Wang, Houchun Wang, Yaodong Wang, Lujuan Wang, Jilin Wang, Peichang Wang, Keyun Wang, Ruixuan Wang, Zhangying Wang, Lianyong Wang, Dongyu Wang, Xinghui Wang, Binghan Wang, Guanduo Wang, Xian-e Wang, Guimin Wang, Xiaomeng Wang, Yuh-Hwa Wang, Jinru Wang, Mingyu Wang, Binbin Wang, Chaokui Wang, Linhui Wang, Youzhi Wang, Zhenqian Wang, Jialiang Wang, Sufang Wang, Haiyan Wang, Yankun Wang, Yingbo Wang, Zilong Wang, Xiao-Qun Wang, Lin-Fa Wang, Wenhao Wang, P Wang, Rui-Hong Wang, Xiao-jian WANG, Pei Chang Wang, Zhengkun Wang, Vivian Wang, Ying Wang, Zihuan Wang, Peiwen Wang, Chao Wang, Da-Zhi Wang, He-Tong Wang, Mofei Wang, Zezhou Wang, Liyong Wang, Bruce Wang, Hao-Tian Wang, Jin-Juan Wang, Yucheng Wang, Yong-Gang Wang, Saili Wang, Xiuwen Wang, Ruiquan Wang, Xinmei Wang, Zhezhi Wang, Xiao-Jie Wang, H Y Wang, Li-Dong Wang, Duanyang Wang, Kaiting Wang, Yikang Wang, Yichen Wang, Ting-Chen Wang, Meixia Wang, ZhenXue Wang, Juan Wang, Shouling Wang, Lan Wang, Li Chun Wang, Xingxin Wang, Ruibing Wang, Xue-Ying Wang, Bi-Dar Wang, Jiayang Wang, Suxia Wang, Yumin Wang, Qing Jun Wang, Xinbo Wang, Youli Wang, Yi-Ni Wang, Xinran Wang, Lixian Wang, Kan Wang, Ruiming Wang, Qing-Yuan Wang, Kai-Kun Wang, Yaoxian Wang, Qing-Jin Wang, Junmei Wang, Xin Wei Wang, J P Wang, Xufei Wang, Yuqin Wang, Handong Wang, Li-San Wang, Guoling Wang, Wenrui Wang, Zhongwei Wang, Shi-Han Wang, Ruoxi Wang, Huiping Wang, Mu Wang, Weihong Wang, Minzhou Wang, Yakun Wang, Da-Cheng Wang, Pengjie Wang, Qihua Wang, Ji-Nuo Wang, Deshou Wang, Xiaowen Wang, Yaochun Wang, Qihao Wang, Ruiying Wang, Tiange Wang, Xi Wang, Yindan Wang, Lixin Wang, Zhaofeng Wang, Guixin Wang, Erming Wang, Haoyu Wang, Kexin Wang, Yiqiao Wang, Qi-Qi Wang, Shuiyun Wang, Xi-Rui Wang, Cai-Hong Wang, Zhizheng Wang, Mingxun Wang, Liangli Wang, Theodore Wang, Alexander Wang, Huayang Wang, Yinyin Wang, Shuzhong Wang, Tingting Wang, Jiao Wang, Wenxian Wang, Jianghua Wang, Furong Wang, Shijun Wang, Le Wang, Guihua Wang, Xiaokun Wang, Xia Wang, Jiabei Wang, Guoying Wang, Zeyuan Wang, Jue Wang, Jin-E Wang, Jingru Wang, Chun-Li Wang, Xiaole Wang, Ermao Wang, Lanlan Wang, Ye-Ran Wang, Hao Wang, Xv Wang, Shikang Wang, Yufei Wang, Siyi Wang, Xiujuan Wang, Qinyun Wang, Xiangwei Wang, Jian-Hong Wang, David Q-H Wang, Chunjuan Wang, Weiyan Wang, Jia-Liang Wang, Yanxing Wang, Sheri Wang, Chenwei Wang, Haoping Wang, Sheng-Quan Wang, Xiangrong Wang, Xiao-Yi Wang, Huan Wang, Zhitao Wang, Xinyan Wang, J Wang, Kaixi Wang, Huihua Wang, Renwei Wang, Xiaoliang Wang, Xiao-Lin Wang, Tian-Lu Wang, Jiou Wang, Weiqin Wang, Jiamin Wang, Dennis Wang, Ji-Yao Wang, Pingping Wang, Jinyang Wang, Chen-Cen Wang, Chien-Wei Wang, Daolong Wang, Rong-Tsorng Wang, Yuwei Wang, Guo-Ping Wang, Zhentang Wang, F Wang, Xueju Wang, Saisai Wang, Zhehai Wang, Y B Wang, Xiao Wang, Guobing Wang, Kangmei Wang, Chunguo Wang, Longcai Wang, Haina Wang, Chih-Hsien Wang, Yuli Wang, Ling-Ling Wang, Zhangshun Wang, Xue-Lian Wang, Jianxin Wang, Da-Yan Wang, Xianghua Wang, Peng Wang, Yu Qin Wang, Zhao-Jun Wang, Rui-Rui Wang, Xingyue Wang, Man Wang, Daozhong Wang, Tian-Li Wang, Luhui Wang, Gaopin Wang, Mengze Wang, Jizheng Wang, Hong-Yan Wang, Dongying Wang, Wenkai Wang, Stephani Wang, Dan-Dan Wang, Yicheng Wang, Yusheng Wang, Junwen Wang, Gao Wang, Ruo-Nan Wang, Yifan Wang, Jueqiong Wang, Xuewei Wang, Jianning Wang, Yonglun Wang, Shiwen Wang, Lifang Wang, Fuyan Wang, Jian-Bin Wang, Chonglong Wang, Haiwei Wang, Yike Wang, Chunxia Wang, Kaijuan Wang, Minglei Wang, Jingxiao Wang, Luting Wang, David Wang, Ben Wang, Ji-zheng Wang, Yuncong Wang, Lei P Wang, Tingye Wang, Wenke Wang, Ping Wang, Min Wang, Qiang-Sheng Wang, Xuejing Wang, Zhanju Wang, Xixi Wang, Xiaodong Wang, Chaomeng Wang, Yanong Wang, Xinghao Wang, Jiaming Wang, Siyuan Wang, Jiu Wang, Ruizhi Wang, Qing Mei Wang, Wenyi Wang, Yiqing Wang, Cai Ren Wang, Lianchun Wang, Xing-Ping Wang, Xiaoman Wang, Yanjin Wang, Xueqin Wang, Chenliang Wang, Zhenshan Wang, Junhong Wang, Guiping Wang, Xianrong Wang, Xumeng Wang, Dajia Wang, Huang Wang, Huie Wang, Weiwen Wang, Ruiwen Wang, Qing Wang, Haohao Wang, Bao-Long Wang, P Jeremy Wang, Chengqiang Wang, Suli Wang, Lingyan Wang, Chi Wang, Meng Wang, Luwen Wang, Quan Wang, Yan-Jun Wang, Sen Wang, Ruining Wang, Xiaozhen Wang, Zhiping Wang, Xue-Yao Wang, Yuming Wang, Jingjing Wang, Jiazheng Wang, Yunong Wang, Chongze Wang, Rufang Wang, Qiuning Wang, Tiannan Wang, Liqing Wang, Wencheng Wang, Xuefeng Wang, Yongli Wang, Xinwen Wang, Runzhi Wang, Chaojie Wang, Wentao Wang, Zhifeng Wang, Yanan Wang, Mengqi Wang, Limin Wang, Donglin Wang, Shujin Wang, Chengbin Wang, Qiu-Xia Wang, Zhengxuan Wang, Yancun Wang, Yuhuan Wang, Wei Wang, G-W Wang, Bangmao Wang, Kejia Wang, Jinjin Wang, Qifei Wang, Guobin Wang, Chun-Lin Wang, Jing-Shi Wang, Jiheng Wang, Huajing Wang, Yanlin Wang, Chuansheng Wang, Cailian Wang, Beilan Wang, Luofu Wang, Yangpeng Wang, Jieqi Wang, Weilin Wang, Xiaoxuan Wang, Yangyufan Wang, Xiao-Fei Wang, Chen-Ma Wang, Yun Yong Wang, Shizhi Wang, B Wang, Yuling Wang, Yi-Yi Wang, Fanwen Wang, Aiyun Wang, Jian Wang, Chengyu Wang, Jing-Huan Wang, Ning Wang, Yichuan Wang, L F Wang, Chau-Jong Wang, Xin-Yang Wang, Yunzhe Wang, Xuewen Wang, Sheng-Ping Wang, Bi Wang, Qiuting Wang, Yan-Jiang Wang, Dongshi Wang, Yingna Wang, Jingyue Wang, Hongshan Wang, Chunjiong Wang, Hong-Yang Wang, Yingmei Wang, Danfeng Wang, Zhongyi Wang, Teng Wang, Chih-Hao Wang, Mingchao Wang, Yi-Chuan Wang, Chuning Wang, Shihao Wang, Ming-Wei Wang, Menglu Wang, Zhulun Wang, Wuji Wang, Dao-Xin Wang, Han Wang, Jincheng Wang, Thomas T Y Wang, Qingyun Wang, Guoliang Wang, Jihong Wang, Hong-Qin Wang, G Wang, Hsei-Wei Wang, Linfang Wang, Xiao Ling Wang, Ganyu Wang, Zhengdong Wang, Cuizhe Wang, Hongyu Wang, Tieqiao Wang, Lijuan Wang, Jingchun Wang, Youzhao Wang, Zijian Wang, Ziheng Wang, Xingyu Wang, Shuning Wang, Shaokun Wang, Zhifu Wang, Xinqi Wang, Jinqiu Wang, ZhongXia Wang, Yanyun Wang, Dadong Wang, Xingjie Wang, Yiting Wang, Zhongli Wang, Junyu Wang, Jianding Wang, Meng-Wei Wang, Yingge Wang, Zhenchang Wang, Qun Wang, Jin-Xing Wang, Lijun Wang, Shuqing Wang, Fu-Yan Wang, Sheng-Nan Wang, Feijie Wang, Qiuyan Wang, Ying-Wei Wang, Shitao Wang, Meng-hong Wang, Zhengyang Wang, Jinghong Wang, Zhiying Wang, Pei Wang, Weixue Wang, Shiyue Wang, Xiaohong Wang, Daiwei Wang, Jinghua Wang, S X Wang, Jian-Yong Wang, Zeying Wang, Can Wang, Kehan Wang, Yunzhang Wang, Jinping Wang, Chenchen Wang, Chun-Ting Wang, Yujiao Wang, Xinxin Wang, Ji Wang, Sui Wang, Wenqiang Wang, Yingwei Wang, Shuzhen Wang, Daixi Wang, Yanming Wang, Lin-Yu Wang, Hongyin Wang, Zhongqun Wang, Er-Jin Wang, Yi Wang, Ziyi Wang, Lianghai Wang, Zhendan Wang, Xiao-Ming Wang, Chengyan Wang, Hui Miao Wang, Jingyi Wang, Ranran Wang, Banghui Wang, Huilun Wang, Ai-Ting Wang, Wenxuan Wang, Yuan-Hung Wang, Zixuan Wang, Hailing Wang, Xuan-Ying Wang, Jiqiu Wang, Yalong Wang, Xiaogang Wang, Shu-qiang Wang, Yun-Jin Wang, Zijie Wang, Tianlin Wang, Mingqiang Wang, Lufang Wang, Jin'e Wang, Xiru Wang, Cuili Wang, GuoYou Wang, Zhizhong Wang, Haifei Wang, Guorong Wang, Xinyue Wang, Pei-Juan Wang, Jiangong Wang, Yingte Wang, Huajin Wang, Ruibo Wang, Kejian Wang, Cheng-Cheng Wang, Xusheng Wang, Shu-Na Wang, Panliang Wang, Mingxi Wang, Shenqi Wang, Zifeng Wang, Chaozhan Wang, Xiuyuan Hugh Wang, Yuping Wang, Xujing Wang, Kai Wang, Hongbing Wang, Sheng-Yang Wang, Jianfei Wang, Hang Wang, Jing-Jing Wang, Weizhi Wang, Jixuan Wang, De-He Wang, P L Wang, Ningjian Wang, Chunyi Wang, Isabel Z Wang, Yong Wang, Yiming Wang, Mingzhi Wang, Jiying Wang, Qian-Wen Wang, Shusen Wang, Xiaoting Wang, Baogui Wang, Mingsong Wang, Zixia Wang, Demin Wang, Shiyuan Wang, Qiuli Wang, C Wang, Dongliang Wang, Weixiao Wang, Yinsheng Wang, Chunmei Wang, Huaili Wang, Xuelian Wang, Yongjun Wang, Zhi-Qin Wang, Jiaying Wang, Yulong Wang, Ren Wang, Jingnan Wang, Qishan Wang, Zeneng Wang, Guangsuo Wang, Chijia Wang, Huiqun Wang, Hongcai Wang, Donghao Wang, Xing-Jin Wang, Zongji Wang, Shenao Wang, Jiaqian Wang, Xiaoying Wang, Yilin Wang, Hangzhou Wang, Wenchao Wang, Jieyu Wang, Li-E Wang, Xuezhen Wang, Liuyang Wang, Zhiqian Wang, Fang-Tao Wang, Qiong Wang, Meng-Meng Wang, Youji Wang, Jiafeng Wang, Xiaojing Wang, William Wang, Junmin Wang, Laijian Wang, Xuexiang Wang, Huiyan Wang, T Y Wang, Zhaofu Wang, Wen-mei Wang, Yalin Wang, Xinshuai Wang, Daqi Wang, Zhen Wang, Shi-Cheng Wang, Anni Wang, Chunhong Wang, Hai-Long Wang, Pan Wang, Charles C N Wang, Pengxiang Wang, Xianzong Wang, Xike Wang, Qianliang Wang, Chunyan Wang, Xuan Wang, Xiaofen Wang, Zhi-Jian Wang, Feng-Sheng Wang, Xiangru Wang, R Wang, Yi-Shu Wang, Jia-Lin Wang, Yonghong Wang, Lintao Wang, Pai Wang, Yanfei Wang, Xuanwen Wang, Lei-Lei Wang, Chenxuan Wang, James Wang, Xinhui Wang, Shengqi Wang, Yueshen Wang, Shan-Shan Wang, Dingting Wang, Zhige Wang, Jingfeng Wang, Yongqing Wang, Chenyang Wang, Ziliang Wang, Bao Wang, Xueyan Wang, Liping Wang, Xingde Wang, Weijun Wang, Sibo Wang, Yaoling Wang, Donghong Wang, Chenyu Wang, Justin Wang, Baolong Wang, Yiqi Wang, Fengyong Wang, Lichao Wang, Yachen Wang, Quanren Wang, Shiyu Wang, Boyu Wang, Aimin Wang, Zhenghui Wang, Hengjiao Wang, Xiaoxin X Wang, Weimin Wang, Mutian Wang, Zhuo-Hui Wang, Xingye Wang, Zou Wang, Yu-Wen Wang, Shaoli Wang, Xin-Ming Wang, Weirong Wang, Kangli Wang, Yaoxing Wang, Xuejie Wang, Qifeng Wang, Xiaoxin Wang, Yinghui Wang, Jianzhang Wang, Tom J Wang, Yaqiong Wang, Zongwei Wang, Yun-Hui Wang, Haiyun Wang, Zhiyou Wang, Lijin Wang, Jifei Wang, Haiyong Wang, Xiao-Xia Wang, Shyi-Gang P Wang, Chih-Yang Wang, Zhixin Wang, Jun-Jun Wang, Tianjing Wang, Zhixia Wang, Chuanhai Wang, Zhijie Wang, Silu Wang, Jianguo Wang, Ming-Hsi Wang, Liling Wang, Yanting Wang, Haolong Wang, Xue-Lei Wang, Ru Wang, Qinglin Wang, Christina Wang, Mimi Wang, Menghui Wang, Wenju Wang, Junhua Wang, S S Wang, Fangyong Wang, Lifen Wang, Zhenbin Wang, Yapeng Wang, Shaoshen Wang, B R Wang, Sugai Wang, Hequn Wang, Songlin Wang, Wenjie Wang, Xiang-Dong Wang, Ting-Hua Wang, Mingliang Wang, Chengniu Wang, Guoxiang Wang, E Wang, Xiaochun Wang, Xueting Wang, Ming-Jie Wang, Zhaojing Wang, Dongxu Wang, Yirui Wang, Jiatao Wang, Jing-Min Wang, Shih-Wei Wang, Zhengchun Wang, Chaoxian Wang, Zehua Wang, Qiyu Wang, Shuye Wang, Baojun Wang, Qing Kenneth Wang, Xichun Wang, Jianliu Wang, Junping Wang, Yudong Wang, Mingzhu Wang, Kangning Wang, Wei-Ting Wang, Hongfang Wang, Chengwen Wang, Changduo Wang, Jinkang Wang, Junya Wang, Fengge Wang, Jianping Wang, Chang Wang, Zhifang Wang, Deli Wang, Linghua Wang, Shitian Wang, Lingling Wang, Zhihua Wang, Jun-Ling Wang, Keyi Wang, Lingbing Wang, Peijia Wang, Ruizhe Wang, X O Wang, Wanyi Wang, Ganggang Wang, Pei-Hua Wang, Kaiyue Wang, Xiaojiao Wang, Xun Wang, Shiyang Wang, Ya-Ping Wang, Yirong Wang, Lixing Wang, Danyang Wang, Xiaotang Wang, Taian Wang, Ming Wang, Xiangcheng Wang, Xuemei Wang, Zhixiong Wang, Mengying Wang, Li-Yong Wang, Xinchao Wang, Jianlong Wang, Jinjie Wang, Nan Wang, Weidong Wang, Mei-Gui Wang, L-S Wang, Wuqing Wang, Z Wang, Ya-Zhou Wang, Xincheng Wang, Jing-Wen Wang, Jinyue Wang, Hongyun Wang, Huaizhi Wang, Yan-Zi Wang, Danling Wang, Dongqin Wang, Hongzhuang Wang, Chung-Teng Wang, Yan-Chun Wang, Shi-Xin Wang, Muxuan Wang, Yujie Wang, Yunbing Wang, Yahui Wang, Zhihong Wang, Xiaoshan Wang, Tienju Wang, Chiou-Miin Wang, Yuqian Wang, Shengyuan Wang, Yumei Wang, Ningyuan Wang, Minjie Wang, Zhenda Wang, Qing-Dong Wang, Horng-Dar Wang, Siqi Wang, Kaihong Wang, Hong-Kai Wang, Meiling Wang, Jiaxing Wang, Xueyi Wang, Zhuozhong Wang, Anlai Wang, Julie Wang, Jin-Bao Wang, Keke Wang, Zhang Wang, Yintao Wang, Yong-Bo Wang, Bing Wang, Dalu Wang, Minxian Wang, Zulong Wang, Gao T Wang, Gang Wang, Sophie H Wang, Xinquan Wang, Yi-Ting Wang, Honglian Wang, Ruyue Wang, Jia-Qiang Wang, Seungwon Wang, Shusheng Wang, Yanbin Wang, Chang-Yun Wang, Le-Xin Wang, Juling Wang, Haohui Wang, Chuanyue Wang, Tianqin Wang, Danqing Wang, Keyan Wang, Yeou-Lih Wang, Qinglu Wang, Sun Wang, Rui-Min Wang, Yong-Tang Wang, Xianwei Wang, Lixia Wang, Tong Wang, Xiaonan Wang, Feida Wang, Jiaxuan Wang, Mingrui Wang, Zixiang Wang, Y Z Wang, Yuliang Wang, Ming-Chih Wang, J J Wang, Huina Wang, Jingang Wang, Jinyun Wang, Min-sheng Wang, Wanyao Wang, Ziqiu Wang, Guo-Quan Wang, Xueping Wang, Qixue Wang, Hechuan Wang, Shang Wang, Chaohan Wang, M H Wang, L Z Wang, Jianhui Wang, Xifeng Wang, Xiaorong Wang, Yinong Wang, Zhixiu Wang, Jiaxi Wang, Jiahui Wang, Xiaofei Wang, Feifei Wang, Kesheng Wang, Rong-Chun Wang, Zhi-Xin Wang, Chaoyu Wang, Yongkuan Wang, Zuoyan Wang, Hsueh-Chun Wang, Xixiang Wang, Guanrou Wang, Songsong Wang, Hongyuan Wang, Yubing Wang, Xuliang Wang, Wen-Ying Wang, Xinglei Wang, Dao-Wen Wang, Yun Wang, Ze Wang, Jiyan Wang, Zai Wang, Guan Wang, Chih-Chun Wang, Yiqin Wang, X S Wang, Hongzhan Wang, Exing Wang, Shu-Jin Wang, Shangyu Wang, Shouzhi Wang, Yunduan Wang, Jiyong Wang, Dongdong Wang, Qingzhong Wang, Zi-Qi Wang, Renyuan Wang, Siyu Wang, Donghui Wang, Ming-Yuan Wang, Juxiang Wang, Muxiao Wang, Fu Wang, Fei Wang, Qiuyu Wang, Ertao Wang, Zhi Xiao Wang, Zunxian Wang, Hui-Nan Wang, Rongping Wang, Won-Jing Wang, Leiming Wang, Pu Wang, Shen-Nien Wang, Xiaona Wang, Meng-Ying Wang, Wen-Jie Wang, Jiaxin Wang, RuNan Wang, Jiemei Wang, Ningli Wang, Zhong-Hui Wang, Hong Wang, Hui-Yu Wang, Ziqian Wang, Xinzhou Wang, Zhoufeng Wang, Weiguang Wang, Zusen Wang, Jiajia Wang, Bin Wang, Shu-Xia Wang, Yu'e Wang, Laidi Wang, Xiao-Li Wang, Lu Wang, Zhugang Wang, Maojie Wang, Ganglin Wang, Xinyu Wang, Junlin Wang, Dong Wang, Yao Wang, Ya-Jie Wang, Zhiwu Wang, DongWei Wang, Hongdan Wang, Yanxia Wang, Maiqiu Wang, Guansong Wang, Qingtong Wang, Yingcheng Wang, Wenjuan Wang, Liying Wang, Xiaolong Wang, Weihao Wang, Qiushi Wang, Yingfei Wang, Haoyang Wang, Li-Li Wang, Yanbing Wang, Yingchun Wang, Guangming Wang, Kaiyuan Wang, Shiqi Wang, Qi-En Wang, Song Wang, Jing-Hao Wang, Lynn Yuning Wang, Zekun Wang, Rui-Ping Wang, Yining E Wang, Yuzhou Wang, Liu Wang, Maochun Wang, Cindy Wang, Qian-Liang Wang, Duo-Ping Wang, Linlin Wang, Taishu Wang, Xiang Wang, Qirui Wang, Baoming Wang, Liting Wang, Jiapan Wang, Lingda Wang, Xietong Wang, Jia-Mei Wang, Liwei Wang, Shaozheng Wang, Q Wang, Timothy C Wang, Mengyue Wang, Xing Wang, Yahong Wang, Yuyong Wang, Yujiong Wang, Guangliang Wang, Ya-Qin Wang, Yezhou Wang, Hongjian Wang, Su-Hua Wang, Qian-fei Wang, Meng-Dan Wang, Yuchen Wang, Hongpin Wang, Pengfei Wang, Ge Wang, Meijun Wang, Yan-Ming Wang, Haichao Wang, Tzung-Dau Wang, Runci Wang, Yan-Yi Wang, Cheng-Jie Wang, Chen-Yu Wang, Cong Wang, Yaxuan Wang, Y H Wang, Yongjie Wang, Yuntai Wang, Ranjing Wang, Yiru Wang, Anxiang Wang, Q Z Wang, Shimiao Wang, Guoping Wang, Junke Wang, Xingyun Wang, Zhengyi Wang, Shi-Qi Wang, Yanfeng Wang, Danxin Wang, Chaodong Wang, Zhiqi Wang, Chunyu Wang, Lijia Wang, Chunlong Wang, Haiping Wang, Qingfa Wang, Yu-Fan Wang, Baihan Wang, Chunxue Wang, Liewei Wang, Xinyi Wang, Fu-Zhen Wang, Qing-Mei Wang, Sheng Wang, Yi-Tao Wang, Dawei Wang, Xiaoyu Wang, Ziling Wang, Zhonglin Wang, Rurong Wang, Qingchun Wang, Qiang Wang, Suiyan Wang, Xu-Hong Wang, Jie Jin Wang, Chenyao Wang, Fei-Yan Wang, Shi Wang, Zhiyong Wang, Jieda Wang, Xiaoqi Wang, Linshu Wang, Ruxuan Wang, Qian Wang, Qianxu Wang, Fangjie Wang, Zhaoxia Wang, Jeremy R Wang, Mingmei Wang, Jingkang Wang, Jen-Chun Wang, Changyuan Wang, Chenglin Wang, Meng-Ru Wang, Tianpeng Wang, Zhongfang Wang, Xuedong Wang, Zhuoying Wang, Bingyu Wang, Xuelai Wang, Weilong Wang, Mengge Wang, Qin Wang, Da-Li Wang, Xuanyi Wang, Hongjuan Wang, Zhi-Hua Wang, Hong-Wei Wang, Yulai Wang, Gongming Wang, Yongni Wang, Mengya Wang, Yadong Wang, Chenghao Wang, Hongbo Wang, Kaiming Wang, Haonan Wang, Guanyun Wang, Yilu Wang, Quanxi Wang, Weiyuan Wang, Xiujun Wang, Liang-Yan Wang, Jianshe Wang, Yingxiong Wang, Cunchuan Wang, Jing-Zhai Wang, Yuelong Wang, Yuqi Wang, Xiaorui Wang, Qianjin Wang, Huijun Wang, Xiaobo Wang, Guoqian Wang, Luhong Wang, Kaining Wang, Chaohui Wang, Yanhong Wang, J-Y Wang, Qi-Bing Wang, Xiaohu Wang, Jiayan Wang, Cui-Shan Wang, Lulu Wang, Yong-Jie Wang, Shixuan Wang, Yuanyuan Wang, Jianying Wang, Haizhen Wang, Shuiliang Wang, Qianbao Wang, Jung-Pan Wang, Rixiang Wang, A Wang, Hanbing Wang, Caiqin Wang, Peigeng Wang, Yuan Wang, Yuzhuo Wang, Yubo Wang, Xianding Wang, Qiaoqi Wang, Cuiling Wang, Ai-Ling Wang, Hailong Wang, Yihao Wang, Lan-Wan Wang, Haihe Wang, S Wang, Sha Wang, Xiaoli Wang, David Q H Wang, Jianfang Wang, Yuting Wang, Jinhuan Wang, Kaixu Wang, Hongwei Wang, Yi-Wen Wang, Yizhe Wang, Shengyu Wang, Yanmei Wang, Huimin Wang, Youjie Wang, Kunhua Wang, Chongjian Wang, Ziyun Wang, Tianhui Wang, Huiying Wang, Yue-Nan Wang, Peiyin Wang, Hongbin Wang, Hong Yi Wang, Xinjun Wang, Yian Wang, Liyi Wang, Yunce Wang, Yi-Xuan Wang, Yitao Wang, Jiali Wang, Junqin Wang, Yuebing Wang, Yiping Wang, Yunpeng Wang, Yuxing Wang, Shuqi Wang, Ziyu Wang, Hongjie Wang, Xiaoyan Wang, Lianshui Wang, Xiaolu Wang, Wenya Wang, Fan Wang, Jinhua Wang, Sidan Wang, Lixiang Wang, Y L Wang, Xue-Rui Wang, Kai-Wen Wang, Zhongyu Wang, Xiaoyang Wang, Hongyang Wang, Rencheng Wang, Yinxiong Wang, Yuanli Wang, Zhuqing Wang, Y-H Wang, Yuhui Wang, Xitian Wang, Weizhen Wang, Qi Wang, Qiyuan Wang, Changlong Wang, Yatao Wang, Tengfei Wang, Yehan Wang
articles
Xiying Ding, Yongxing Zhang, Yang Chen +5 more Β· 2025 Β· Journal of shoulder and elbow surgery Β· Elsevier Β· added 2026-04-24
Rotator cuff tear is the most common tendon injury. Currently, arthroscopic rotator cuff repair (ARCR) is the primary method for diagnosing and treating rotator cuff tear. One of the major complicatio Show more
Rotator cuff tear is the most common tendon injury. Currently, arthroscopic rotator cuff repair (ARCR) is the primary method for diagnosing and treating rotator cuff tear. One of the major complications following ARCR is retear. This study aims to evaluate the correlation between systemic lipid metabolism and retear occurrence after ARCR through a retrospective analysis of postoperative patients. This retrospective study reviewed consecutive patients of a single surgeon who underwent ARCR from January 2021 to January 2022. Eligibility for inclusion required complete sequential follow-up data, encompassing preoperative laboratory tests and a series of postoperative magnetic resonance imaging (MRI) evaluations at 1, 2, 3, and 6 months. Exclusion criteria included patients with incomplete laboratory tests, a history of tumors, prior shoulder surgeries, isolated subscapularis tendon tears, the rotator cuff related muscles are not clearly or completely displayed in MRI, absence of follow-up MRI, or those under treatment with lipid-lowering medications. Logistic regression analysis was employed to identify preoperative factors associated with retear, with statistical significance adjudged at P < .05. From the initial cohort of 400 patients who underwent ARCR during the study period, 202 met both inclusion and exclusion criteria. These patients were subsequently divided into a training group (n = 122) and a test group (n = 80), maintaining a ratio of 6:4. Statistical analysis revealed significant risk factors for post-ARCR retear including high body mass index (>27.1; odds ratio (OR): 5.994, 95% confidential interval (CI): 1.762-13.980; P = .042), subscapularis muscle fatty infiltration of Grades 3 and 4 (OR: 8.509, 95%CI: 3.811-17.702; P = .009), serum apolipoprotein B (ApoB) levels exceeding 1.4 g/L (OR: 9.658, 95%CI: 3.520-21.753; P = .028), and an ApoB/A1 ratio greater than 1.8 (OR: 5.098, 95%CI: 1.787-10.496; P = .016). Conversely, the serum high-density lipoprotein level above 1.2 mmol/L (OR: -3.342, 95%CI: -7.466 to 0.659; P = .039) served as a protective factor. The model incorporating these 5 factors predicted retear with a sensitivity of 78.3% and specificity of 98.0% (area under the curve = 0.924, accuracy = 90.3%). Moreover, a new model comprising 3 lipid metabolism-related factors including high-density lipoprotein, ApoB and the ApoB/A1 ratio showed a sensitivity of 80.5% and specificity of 83.2% (area under the curve = 0.866, accuracy = 85.8%) for predicting retear after ARCR. A predictive model utilizing key systemic lipid metabolism markers including HDL, ApoB, and the ApoB/A1 ratio, demonstrates effective forecasting of retear incidence following ARCR. Show less
no PDF DOI: 10.1016/j.jse.2024.12.031
APOB
Peiwei Xu, Min Nian, Jie Xiang +8 more Β· 2025 Β· Environmental science & technology Β· ACS Publications Β· added 2026-04-24
Per- and polyfluoroalkyl substances (PFAS) pose potential health risks to lipid metabolism, but the effects of emerging PFAS alternatives, particularly in children, remain unclear. This cross-sectiona Show more
Per- and polyfluoroalkyl substances (PFAS) pose potential health risks to lipid metabolism, but the effects of emerging PFAS alternatives, particularly in children, remain unclear. This cross-sectional study investigated the association between emerging PFAS exposure and lipid levels in 294 Chinese children aged 7-10 years, analyzing blood samples for 14 PFAS and lipid profiles, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB). Exposure to 6:2 Cl-PFESA, PFO4DA, and PFO5DoDA was associated with higher TC, TG, and LDL levels, with PFO4DA increasing the TC by 1.7% and PFO5DoDA increasing the TG by 10.7%. Weighted quantile sum (WQS) regression showed mixed PFAS exposure positively associated with TG (0.08, 95% CI: 0.007, 0.153). PFO4DA had the highest weight for TC (0.468), TG (0.327), LDL (0.57), ApoA1 (0.243), and ApoB (0.466), while PFMOAA had the highest weight for HDL (0.332). Bayesian Kernel Machine Regression (BKMR) analysis confirmed positive associations between the PFAS mixture and TC, TG, LDL, and ApoA1. Mediation analysis revealed that mtDNAcn significantly mediated PFAS exposure's effect on TG levels, explaining 27.2-74.2% of the total effect. These findings highlight the need for regulatory action to address the emerging PFAS risks. Show less
no PDF DOI: 10.1021/acs.est.4c13095
APOB
Ze-Yuan Yin, Shi-Min He, Xin-Yuan Zhang +16 more Β· 2025 Β· Acta pharmacologica Sinica Β· Nature Β· added 2026-04-24
Ovarian cancer presents a significant treatment challenge due to its insidious nature and high malignancy. As autophagy is a vital cellular process for maintaining homeostasis, targeting the autophagi Show more
Ovarian cancer presents a significant treatment challenge due to its insidious nature and high malignancy. As autophagy is a vital cellular process for maintaining homeostasis, targeting the autophagic pathway has emerged as an avenue for cancer therapy. In the present study, we identify apolipoprotein B100 (ApoB100), a key modulator of lipid metabolism, as a potential prognostic biomarker of ovarian cancer. ApoB100 functioned as a tumor suppressor in ovarian cancer, and the knockdown of ApoB100 promoted ovarian cancer progression in vivo. Moreover, ApoB100 blocked autophagic flux, which was dependent on interfering with the lipid accumulation/endoplasmic reticulum (ER) stress axis. The effects of LFG-500, a novel synthetic flavonoid, on ApoB100 induction were confirmed using proteomics and lipidomics analyses. Herein, LFG-500 induced lipid accumulation and ER stress and subsequently blocked autophagy by upregulating ApoB100. Moreover, data from in vivo experiments further demonstrated that ApoB100, as well as the induction of the lipid/ER stress axis and subsequent blockade of autophagy, were responsible for the anti-tumor effects of LFG-500 on ovarian cancer. Hence, our findings support that ApoB100 is a feasible target of ovarian cancer associated with lipid-regulated autophagy and provide evidence for using LFG-500 for ovarian cancer treatment. Show less
no PDF DOI: 10.1038/s41401-024-01470-x
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Yu-Hang Wang, Chang-Ping Li, Jing-Xian Wang +6 more Β· 2025 Β· Reviews in cardiovascular medicine Β· added 2026-04-24
Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limi Show more
Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limited. In this observational study, 1111 PMI patients (≀55 years) at Tianjin Chest Hospital from 2017 to 2022 were selected and divided according to their SYNTAX scores into a low-risk group (≀22) and medium-high-risk group (>22). These groups were further randomly assigned to a training or test set in a ratio of 7:3. Lasso-logistic was initially used to screen out target factors. Subsequently, Lasso-logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. After comparing prediction performance, the best model was chosen to build a prediction system for coronary artery severity in PMI patients. Glycosylated hemoglobin (HbA1c), angina, apolipoprotein B (ApoB), total bile acid (TBA), B-type natriuretic peptide (BNP), D-dimer, and fibrinogen (Fg) were associated with the severity of lesions. In the test set, the area under the curve (AUC) of Lasso-logistic, RF, KNN, SVM, and XGBoost were 0.792, 0.775, 0.739, 0.656, and 0.800, respectively. XGBoost showed the best prediction performance according to the AUC, accuracy, F1 score, and Brier score. In addition, we used decision curve analysis (DCA) to assess the clinical validity of the XGBoost prediction model. Finally, an online calculator based on the XGBoost was established to measure the severity of coronary artery lesions in PMI patients. In summary, we established a novel and convenient prediction system for the severity of lesions in PMI patients. This system can swiftly identify PMI patients who also have severe coronary artery lesions before the coronary intervention, thus offering valuable guidance for clinical decision-making. Show less
πŸ“„ PDF DOI: 10.31083/RCM26102
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Zixiang Ye, Enmin Xie, Zhangyu Lin +5 more Β· 2025 Β· Nutrition journal Β· BioMed Central Β· added 2026-04-24
This study aims to evaluate the relationship between apolipoproteins (ApoA1, ApoB, and the ApoB/A1 ratio) and the incidence of major adverse cardiovascular events (MACE) in patients with coronary arte Show more
This study aims to evaluate the relationship between apolipoproteins (ApoA1, ApoB, and the ApoB/A1 ratio) and the incidence of major adverse cardiovascular events (MACE) in patients with coronary artery disease (CAD) and impaired kidney function, assessing their potential role in secondary prevention. A prospective cohort of 1,640 patients with impaired kidney function who underwent percutaneous coronary intervention in China was analyzed. Patients were categorized based on the measurements of ApoA1, ApoB, and ApoB/A1 ratio. MACE, defined as a composite of all-cause mortality, cardiovascular death, nonfatal myocardial infarctions, strokes, and unplanned revascularizations, was tracked post-procedure, with statistical analyses including Kaplan-Meier survival curves and Cox regression models to identify associations with apolipoproteins. Subgroup analyses according to kidney function were conducted. During a median follow-up of 3.1Β years, 324 MACE events were observed. Multivariable Cox regression analyses illustrated higher levels of ApoB and the ApoB/A1 ratio were significantly associated with increased MACE incidence (adjusted HR [95%CI] 1.668[1.044-2.666]; adjusted HR [95%CI] 2.231[1.409-3.533], respectively), while lower ApoA1 levels correlated with a higher risk (adjusted HR [95%CI] 0.505[0.326-0.782]). ROC curve analyses indicated comparable predictive performances to traditional risk factors like LDL cholesterol. Subgroup analysis revealed that the above association was not statistically significant in the moderate-to-severe renal impairment CAD patients (eGFR < 45Β mL/min/1.73 m Our findings illustrate that apolipoproteins, specifically ApoA1 and ApoB, along with their ratio, are significant predictors of major adverse cardiovascular events in CAD patients with impaired kidney function. These results emphasize the need for incorporating apolipoprotein measurements in secondary prevention strategies for this high-risk population. Show less
πŸ“„ PDF DOI: 10.1186/s12937-025-01078-9
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Yuting Li, Mingrui Wang, Na Zhang +3 more Β· 2025 Β· Ginekologia polska Β· added 2026-04-24
This study investigates the relationship between serum homocysteine, blood lipids, and perinatal outcomes in patients with diet-controlled gestational diabetes mellitus (GDM) and those with normal glu Show more
This study investigates the relationship between serum homocysteine, blood lipids, and perinatal outcomes in patients with diet-controlled gestational diabetes mellitus (GDM) and those with normal glucose tolerance (NGT). A prospective cohort of 150 diet-controlled GDM patients and 150 pregnant women with NGT, all delivering at our hospital, were selected based on predefined criteria. Data on demographics, physical parameters, and perinatal outcomes were compiled. Blood samples for fasting plasma glucose (FPG), homocysteine (Hcy), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), and apolipoprotein A1 (apoA1) were collected before delivery. GDM patients exhibited higher levels of FPG, Hcy, and the apoB/apoA1 ratio, but lower HDL-C and apoA1 levels compared to the NGT group. Adverse outcomes such as macrosomia, premature rupture of membranes, and postpartum hemorrhage were more prevalent in the GDM group. In GDM patients, neonatal birth weight positively correlated with FPG and TG levels. Stratified Hcy analysis in GDM showed no significant differences in perinatal outcomes. However, the third quartile of the apoB/apoA1 ratio had a lower incidence of macrosomia compared to the first quartile, and the second quartile showed a higher incidence of birth asphyxia. GDM patients demonstrated increased levels of Hcy, FPG, and the apoB/apoA1 ratio, correlating with more adverse perinatal outcomes than healthy pregnant individuals. The relationships between Hcy, lipids, and these outcomes remain inconclusive, highlighting the need for further research. Show less
no PDF DOI: 10.5603/gpl.101475
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Ping Huang, Yong Zhao, Haiyan Wei +8 more Β· 2025 Β· International journal of chronic obstructive pulmonary disease Β· added 2026-04-24
In preliminary research and literature review, we identified a potential link between chronic obstructive pulmonary disease (COPD) and lipid metabolism. Therefore, this study employed Mendelian random Show more
In preliminary research and literature review, we identified a potential link between chronic obstructive pulmonary disease (COPD) and lipid metabolism. Therefore, this study employed Mendelian randomization (MR) analysis to investigate the potential causal connection between blood lipids and COPD. A genome-wide association study (GWAS) on COPD was conducted, encompassing a total of 112,583 European participants from the MRC-IEU. Additionally, extensive UK Biobank data pertaining to blood lipid profiles within European cohorts included measurements for low-density lipoprotein cholesterol (LDL-C) with 440,546 individuals, high-density lipoprotein cholesterol (HDL-C) with 403,943 individuals, triglycerides (TG) with 441,016 individuals, total cholesterol (TC) with 187,365 individuals, apolipoprotein A-I (apoA-I) with 393,193 individuals, and apolipoprotein B (apoB) with 439,214 individuals. Then, MR analyses were performed for lipids and COPD, respectively. The primary analytical technique employed was the inverse-variance weighted (IVW) approach, which included a 95% confidence interval (CI) to calculate the odds ratio (OR). Additionally, a sensitivity analysis was conducted to assess the dependability of the MR analysis outcomes. MR analysis was primarily based on IVW, unveiled a causal link between COPD and LDL-C (OR=0.994, 95% CI (0.989, 0.999), P=0.019), TG (OR=1.005, 95% CI (1.002, 1.009), P=0.006), and apoA-I (OR=0.995, 95% CI (0.992, 0.999), P=0.008), in addition, no causal link was found with HDL-C, TC, apoB. Sensitivity analysis demonstrated the robustness of these causal relationships. However, through multivariate MR(MVMR) and multiple testing correction, LDL-C and TG had no causal effect on the outcome. ApoA-I remained a protective factor for the risk of COPD (OR=0.994, 95% CI (0.990-0.999), P=0.008). Through MR analysis, this study offers evidence of a causal link between apoA-I with COPD. This further substantiates the potential role of lipid metabolism in COPD, and has significant clinical implications for the prevention and management of COPD. Show less
πŸ“„ PDF DOI: 10.2147/COPD.S476833
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Shuqi Cao, Xia Fu, Wenjing Li +3 more Β· 2025 Β· Parkinsonism & related disorders Β· Elsevier Β· added 2026-04-24
Evidence have indicated relation between apolipoproteins and neurodegenerative disorders (NDDs). However, previous studies have produced inconsistent results, and a comprehensive analysis of apolipopr Show more
Evidence have indicated relation between apolipoproteins and neurodegenerative disorders (NDDs). However, previous studies have produced inconsistent results, and a comprehensive analysis of apolipoproteins in NDDs is currently lacking. Using Cox proportional hazards regression analysis based on data from UK Biobank, we examined the association between baseline serum levels of apolipoprotein A (ApoA) and apolipoprotein B (ApoB) and risk of Parkinson's disease (PD), Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and multiple sclerosis. Elevated baseline levels of serum ApoA (HRΒ =Β 0.84, 95Β % CI: 0.71-0.99, PΒ =Β 0.047) and ApoB (HRΒ =Β 0.67, 95Β % CI: 0.57-0.78, PΒ =Β 3.18E-07) were associated with a reduced risk of incident PD. Subgroup analyses suggested the protective effect of serum ApoA was more significant for older participants and those with lower alcohol consumption, while higher serum ApoB was a more significant protective factor in males and those without stroke. No significant associations were found between apolipoproteins and other NDDs. Increased baseline levels of serum ApoA and ApoB are linked to a lower risk of PD. These findings enhance understanding of the role of apolipoproteins in PD, and have implications for the development of therapeutic strategies in clinical trials. Show less
no PDF DOI: 10.1016/j.parkreldis.2025.107266
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Yong Tan, Zixiong Zhang, Jinru Yang +8 more Β· 2025 Β· Ecotoxicology and environmental safety Β· Elsevier Β· added 2026-04-24
At present, there is no consensus on the relationship between selenium (Se) exposure and human serum lipid metabolism. The etiological role of high-Se exposure in lipid markers, dyslipidemia, and nona Show more
At present, there is no consensus on the relationship between selenium (Se) exposure and human serum lipid metabolism. The etiological role of high-Se exposure in lipid markers, dyslipidemia, and nonalcoholic fatty liver (NAFLD) remains unclear. We used serum untargeted metabolomics analysis to evaluate whether high-Se exposure is cross-sectionally associated with lipid metabolism in adults from high-Se exposure area (nβ€―=β€―112) and control area (nβ€―=β€―101) in Hubei Province, China. An untargeted liquid chromatography/mass spectrometry (LC/MS)-based metabolomic analysis identified 144 differential pathways and yielded 204 differentially abundant metabolites, including 32 lipid metabolites associated with lipids profiles. To further explore the correlation between Se exposure and serum lipid metabolism, we measured serum levels of lipid profiles among all the people, including serum cholesterol (CHOL), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein B (APOB). The average serum Se level of the high-Se exposure group was 537.18β€―ΞΌg/L, significantly higher than 72.98β€―ΞΌg/L in the control group (pβ€―<β€―0.0001). The measurement levels of serum TG, LDL-C, HDL-C, and APOB in the high-Se exposure group were 1.03 (0.76, 1.34) mmol/L, 2.25β€―Β±β€―0.48β€―mmol/L, 1.12β€―Β±β€―0.24β€―mmol/L, and 0.77β€―Β±β€―0.15β€―g/L, respectively, while the control group were 1.13 (0.84, 1.80) mmol/L, 2.56β€―Β±β€―0.61β€―mmol/L, 1.02β€―Β±β€―0.22β€―mmol/L, and 0.83β€―Β±β€―0.16β€―g/L, respectively (all p values <0.05). Correlation analysis showed a significant negative correlation between serum Se and CHOL (rβ€―=β€―-0.201, pβ€―<β€―0.01), serum Se is also associated with metabolomics markers, the negative correlation includes glyceric acid and ect., the positive correlation includes phosphorylcholine and ect. Our study suggests that high-Se exposure is negatively associated with serum lipid profiles and decreases the risk of high-TC and HDL-C dyslipidemia. Show less
no PDF DOI: 10.1016/j.ecoenv.2025.117677
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Xiangyong Kong, Yanchen Cai, Yuwei Li +1 more Β· 2025 Β· Health information science and systems Β· Springer Β· added 2026-04-24
Atherosclerotic cardiovascular disease (ASCVD) is a major threat to human life and health, and dyslipidemia with elevated low-density lipoprotein cholesterol (LDL-C) is an important risk factor, and i Show more
Atherosclerotic cardiovascular disease (ASCVD) is a major threat to human life and health, and dyslipidemia with elevated low-density lipoprotein cholesterol (LDL-C) is an important risk factor, and in the optimal LDL-C scenario, apolipoprotein B (ApoB) has a more predictive value of ASCVD risk. The study is a genome-wide association study (GWAS) based on a European population. A large GWAS dataset for atherosclerotic cardiovascular diseases was targeted, including coronary heart disease (CHD), ischemic stroke (IS), large-artery atherosclerotic stroke (ISL), small-vessel stroke (ISS), and myocardial infarction (MI). Univariate two-sample mendelian randomization (MR) analyses of ApoB and the above cardiovascular diseases were performed separately, and the association was assessed mainly using the inverse variance weighted (IVW) method, with confidence intervals for the superiority ratios set at 95%. In addition, the experiment was supplemented using MR-Egger, weighted model and weighted median (WM). Based on the results of univariate two-sample mendelian randomisation analysis, it was shown that there was a causal relationship between ApoB and CHD (OR = 1.710, 95% CI 1.529-1.912, P = 0.010), ISL (OR = 1.430, 95% CI 1.231-1.661, P = 2.714E-06), ISS (OR = 1.221, 95% CI 1.062-1.405, P = 0.005) were causally related to each other and the disease prevalence ratio was positively correlated with ApoB concentration. This MR analysis demonstrated a causal relationship between ApoB and CHD, ISL, ISS, but not with the risk of developing IS and MI, which further validated the relationship between ApoB and the risk of ASCVD, and contributed to a better understanding of the genetic impact of ApoB on ASCVD, and to a certain extent, could improve the management of ApoB and reduce the prevalence of ASCVD. Show less
no PDF DOI: 10.1007/s13755-024-00323-5
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Mengke Yan, Xin Cong, Hui Wang +7 more Β· 2025 Β· Poultry science Β· Elsevier Β· added 2026-04-24
Aging-related lipid metabolic disorder is related to oxidative stress. Selenium (Se)-enriched Cardamine violifolia (SEC) is known for its excellent antioxidant function. The objective of this study wa Show more
Aging-related lipid metabolic disorder is related to oxidative stress. Selenium (Se)-enriched Cardamine violifolia (SEC) is known for its excellent antioxidant function. The objective of this study was to evaluate the effects of SEC on antioxidant capacity and lipid metabolism in the liver of aged laying hens. A total of 450 sixty-five-wk-old Roman laying hens were randomly divided into 5 treatments: a basal diet (without Se supplementation, CON) and basal diets supplemented with 0.3 mg/kg Se from sodium selenite (SS), 0.3 mg/kg Se from Se-enriched yeast (SEY), 0.3 mg/kg Se from SEC (SEC), or 0.3 mg/kg Se from SEC and 0.3 mg/kg Se from SEY (SEC + SEY). The experiment lasted for 8 wk. The results showed that dietary SEC + SEY supplementation decreased (P < 0.05) triglyceride (in the plasma and liver) and total cholesterol levels (in the plasma), and increased (P < 0.05) HDL-C concentration in plasma compared to CON diet. Compared with CON diet, SEC and/or SEY supplementation decreased (P < 0.05) the mRNA expression of hepatic ACC, FAS and HMGCR, and increased (P < 0.05) PPARΞ±, VTG-II, Apo-VLDL II and ApoB expression. Dietary SEC + SEY and SEY supplementation increased (P < 0.05) Se content in egg yolk and breast muscle compared to CON diet. Dietary SEC, SEY or SEC + SEY supplementation increased (P < 0.05) the activity of antioxidant enzymes (GSH-PX, T-AOC and T-SOD) in the plasma and liver and decreased (P < 0.05) MDA content in the plasma compared to CON diet. Dietary Se supplementation promoted (P < 0.05) mRNA expression of Nrf2 in the liver. In contrast, dietary SEY and SEC supplementation resulted in a decrease (P < 0.05) of hepatic Keap1 mRNA expression compared to CON diet. Dietary SEC + SEY and/or SEC supplementation increased (P < 0.05) mRNA expression of Selenof, GPX1 and GPX4 in the liver compared with CON diet. In conclusion, dietary SEC (0.3 mg/kg Se) or SEC (0.3 mg/kg Se) + SEY (0.3 mg/kg Se) improved the antioxidant capacity and the lipid metabolism in the liver of aged laying hens, which might be associated with regulating Nrf2/Keap1 signaling pathway. Show less
πŸ“„ PDF DOI: 10.1016/j.psj.2024.104620
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Lathan Liou, Judit GarcΓ­a-GonzΓ‘lez, Hei Man Wu +5 more Β· 2025 Β· Arteriosclerosis, thrombosis, and vascular biology Β· added 2026-04-24
Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alt Show more
Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alternative preventions and treatments. However, so far, there have been no systematic efforts to predict CAD subtypes using clinical and genetic factors. Here, we trained and applied statistical models incorporating clinical and genetic factors to predict CAD subtypes in 26β€…036 patients with CAD in the UK Biobank. We performed external validation of the UK Biobank models in the US-based All of Us cohort (8598 patients with CAD). Subtypes were defined as high versus normal LDL (low-density lipoprotein) levels, high versus normal Lpa (lipoprotein A) levels, ST-segment-elevation myocardial infarction versus non-ST-segment-elevation myocardial infarction, occlusive versus nonocclusive CAD, and stable versus unstable CAD. Clinical predictors included levels of ApoA, ApoB, HDL (high-density lipoprotein), triglycerides, and CRP (C-reactive protein). Genetic predictors were genome-wide and pathway-based polygenic risk scores (PRSs). Results showed that both clinical-only and genetic-only models can predict CAD subtypes, while combining clinical and genetic factors leads to greater predictive accuracy. Pathway-based PRSs had higher discriminatory power than genome-wide PRSs for the Lpa and LDL subtypes and provided insights into their etiologies. The 10-pathway PRS most predictive of the LDL subtype involved cholesterol metabolism. Pathway PRS models had poor generalizability to the All of Us cohort. In summary, we present the first systematic demonstration that CAD subtypes can be distinguished by clinical and genomic risk factors, which could have important implications for stratified cardiovascular medicine. Show less
no PDF DOI: 10.1161/ATVBAHA.124.321846
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Chenxi Li, Xuhui Yang, Yan Zhong +4 more Β· 2025 Β· Translational oncology Β· Elsevier Β· added 2026-04-24
The relationship between serum lipids and prognosis of pancreatic cancer has not been confirmed. Our purpose in the study was to investigate the associations between serum lipids level and prognosis i Show more
The relationship between serum lipids and prognosis of pancreatic cancer has not been confirmed. Our purpose in the study was to investigate the associations between serum lipids level and prognosis in patients with pancreatic cancer. A retrospective study was performed on 286 pancreatic cancer patients who admitted to our hospital from January 1, 2017 to December 31, 2021. Serum lipids level were recorded. Clinical-pathological characteristics, oncologic outcomes, progression free survival (PFS) and overall survival (OS) were collected. The prognostic significance was determined by Kaplan-Meier analysis and Cox proportional hazards regression model. Regarding serum lipids level, compared to normal apolipoprotein B/ apolipoprotein A (ApoB/ApoA1), high ApoB/ApoA1 level indicated a shorter OS (HR:2.028, 95% CI: 1.174-2.504, P = 0.011) and a shorter PFS (HR:1.800, 95% CI: 1.076-3.009, P = 0.025). Other serum lipid molecules were not associated with PFS and OS. ApoB/ApoA1 might be an independent prognostic factor of pancreatic cancer. Show less
πŸ“„ PDF DOI: 10.1016/j.tranon.2024.102208
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Jian Du, Zhiqi Dai, Cuiguang Li +3 more Β· 2025 Β· Journal of animal physiology and animal nutrition Β· Blackwell Publishing Β· added 2026-04-24
The benefits of plant essential oils (EO) on the health of animals have been frequently reported, but their alteration of lipid metabolism in obese pigs has yet to be explored. This study aimed to ass Show more
The benefits of plant essential oils (EO) on the health of animals have been frequently reported, but their alteration of lipid metabolism in obese pigs has yet to be explored. This study aimed to assess the impact of EO blends (oregano, cinnamon and lemon oils) on growth performance, meat physicochemical parameters, intestinal health and lipid metabolism in the small intestine of weaned Bamei (a kind of obese-type pig) piglets. One hundred and forty-four male 60-day-old weaned Bamei piglets were randomly assigned to three groups of six replicates each: CON (basal diet), T1 (basal diet + 250 mg/kg EO), and T2 (basal diet + 500 mg/kg EO) over 28 days. The results showed that T1 trended to improve the average daily gain and feed intake to body gain ratio (p < 0.1), reduced water loss (p < 0.05), and increased the redness of meat (p < 0.05) compared to the CON. In addition, a significant change in the proportion of C17:0 and C20:1 was observed in the meat of T1 (p < 0.05). Improved intestinal health was evidenced by the reduced crypt depth, improved villi-to-crypt length ratio, and better superoxide dismutase activity in T1 (p < 0.05). Further study on intestinal lipid metabolism showed that duodenal lipase activity and the mRNA expression levels of lipid transport-related genes in the jejunum (FABPs, APOA1, APOB and ACSL3) were significantly reduced, alongside diminished serum lipid metabolites (Total protein and triglyceride) in the groups fed with EO (p < 0.05). In short, EO supplementation especially at 250 mg/kg improved intestinal health and inhibited lipid metabolism, which had a positive effect on the overall performance of Bamei piglets. This new evidence contributes to understanding the early regulatory role of EO in obese pigs and their potential to alleviate adolescent obesity. Show less
no PDF DOI: 10.1111/jpn.14074
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Lin Liu, Yidan Liu, Yu Tian +7 more Β· 2025 Β· Reproductive sciences (Thousand Oaks, Calif.) Β· Springer Β· added 2026-04-24
Recurrent implantation failure (RIF) is a complex and poorly understood clinical disorder characterized by failure to conceive after repeated embryo transfers. Endometrial receptivity (ER) is a prereq Show more
Recurrent implantation failure (RIF) is a complex and poorly understood clinical disorder characterized by failure to conceive after repeated embryo transfers. Endometrial receptivity (ER) is a prerequisite for implantation, and ER disorders are associated with RIF. However, little is known regarding the molecular mechanisms underlying ER in RIF. In the present study, RNA sequencing data from the mid-secretory endometrium of patients with and without RIF were analyzed to explore the potential long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) involved in RIF. The analysis revealed 213 and 1485 differentially expressed mRNAs and lncRNAs, respectively (fold change β‰₯ 2 and p < 0.05). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that these genes were mostly involved in processes related to immunity or inflammation. 5 key genes (TTR, ALB, TF, AFP, and CFTR) and a key module including 14 hub genes (AFP, ALB, APOA1, APOA2, APOB, APOH, FABP1, FGA, FGG, GC, ITIH2, SERPIND1, TF and TTR) were identified in the protein-protein interaction (PPI) network. The 5 key genes were used to further explore the lncRNA-miRNA-mRNA regulatory network. Finally, the drug ML-193 based on the 14 hub genes was identifed through the CMap. After ML-193 treatment, endometrial cell proliferation was increased, the hub genes were mostly down-regulated, and the ER marker HOXA10 was up-regulated. These results offer insights into the regulatory mechanisms of lncRNAs and mRNAs and suggest ML-193 as a therapeutic agent for RIF by enhancing ER. Show less
πŸ“„ PDF DOI: 10.1007/s43032-024-01630-8
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Yue Zhang, Yang Tian, Shaobo Zheng +8 more Β· 2025 Β· Medicine Β· added 2026-04-24
Gastric cancer (GC) exhibits marked heterogeneity, patients with identical stage receive divergent outcomes. Metabolic reprogramming and aging are pivotal in reshaping the tumor microenvironment. Howe Show more
Gastric cancer (GC) exhibits marked heterogeneity, patients with identical stage receive divergent outcomes. Metabolic reprogramming and aging are pivotal in reshaping the tumor microenvironment. However, their interplay in GC prognosis remains unexplored. We analyzed RNA-seq and clinical data from The Cancer Genome Atlas Program and Gene Expression Omnibus databases. Using univariate Cox, LASSO, and multivariate Cox regression, we identified candidate genes and constructed a prognostic signature. Immune contexture, genomic alterations and drug sensitivity were compared between high- and low-risk group. The metabolic and aging related risk score, comprising 4 genes (GNAI1, GSTA1, APOC3, and LOX), was developed. Validation across multiple cohorts confirmed its robust prognostic performance. The model also effectively stratified patients into distinct risk subgroups with differential immune profiles and responses to immunotherapy. Notably, high-risk patients showed reduced sensitivity to common chemotherapeutic agents but may benefit from targeting the PI3K/mTOR pathway. Metabolic and aging related risk score serves as a promising tool for individualized risk assessment and therapeutic guidance in GC, warranting further clinical validation. Show less
πŸ“„ PDF DOI: 10.1097/MD.0000000000046616
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Yan Hu, Chao Quan, Yuanyuan Zhou +6 more Β· 2025 Β· PloS one Β· PLOS Β· added 2026-04-24
The differential diagnosis between Tuberculosis (TB) and Non-tuberculous Mycobacteria (NTM) has historically been constrained by the inadequate sensitivity and specificity of current diagnostic method Show more
The differential diagnosis between Tuberculosis (TB) and Non-tuberculous Mycobacteria (NTM) has historically been constrained by the inadequate sensitivity and specificity of current diagnostic methods. Furthermore, distinguishing between Active Tuberculosis (ATB) and Latent Tuberculosis Infection (LTBI) poses significant challenges. This study aims to develop a molecular differentiation system for ATB, LTBI, and NTM by integrating plasma proteomics with multi-dimensional analytical techniques, while also exploring key biomarkers associated with disease progression and treatment response. Using label-free quantitative technology, we conducted a plasma proteomics analysis across five groups: ATB, LTBI, NTM, Cured Patients (CPs), and Healthy Donors (HD). Differentially Expressed Proteins (DEPs) were identified through screening (FC > 1.5 or <0.67, P < 0.05), followed by Gene Ontology/KEGG pathway enrichment, STRING interaction network, and Mfuzz dynamic clustering analysis to systematically elucidate molecular characteristics. Experimental data were validated through a multidimensional quality control system (Pearson correlation coefficient, peptide distribution, molecular weight distribution, etc.). Enzyme-linked immunosorbent assay (ELISA) was employed to detect the plasma expression levels of target proteins across the groups and to facilitate comparisons. This study identified 1,338 non-redundant proteins across five cohorts. Comparative analysis revealed 142 DEPs across the three comparative groups (ATB, LTBI, and NTM), which were primarily localized in the extracellular domain. Key findings include: 27 DEPs in the ATB-LTBI group, primarily enriched in inflammatory responses (such as A2M, IL-1R2) and epithelial barrier functions (TGM3, KRT3); 69 DEPs in the ATB-NTM group, characterized by significant changes in immunoglobulin light chains (IGLV2-11) and innate immune effector molecules (S100A8); 46 DEPs in the NTM-LTBI group, closely related to lipid metabolism (APOC3) and extracellular matrix remodeling (FN1). KEGG pathway analysis revealed that DEPs in the ATB-LTBI group were enriched in nitrogen metabolism pathways, those in the ATB-NTM group were associated with thyroid hormone synthesis, and the NTM-LTBI group was involved in phagosome function. Dynamic clustering results showed six treatment response modules: Cluster 1/2 (riboflavin metabolism, complement coagulation pathway) were activated post-treatment, Cluster 3/4 (proteasome, cardiac signaling pathway) exhibited partial reversal in expression, and Cluster 5/6 (platelet activation, cytoskeleton) showed delayed regression. Research confirmed 10 differential proteins between the ATB-CPs and ATB-HD groups, including S100A8, LTA4H, and DEFA1B, which constitute a molecular fingerprint specific to ATB. ELISA validation confirmed significantly elevated S100A8 and GPX3 in ATB group, while NTM group showed higher FGB and lower ATRN levels. This study systematically reveals the plasma proteomic characteristics under infection statuses caused by different mycobacteria. A discrimination framework for ATB/LTBI/NTM was constructed based on disease-specific differential proteins, overcoming the limitations of traditional diagnostic techniques in distinguishing infection states. Through dynamic analysis of six temporal therapeutic modules, the reprogramming patterns of the host protein network during tuberculosis treatment were elucidated. This research lays a multidimensional molecular foundation for the precise typing, personalized treatment, and prognostic evaluation of mycobacterial infections. Show less
πŸ“„ PDF DOI: 10.1371/journal.pone.0339558
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Xinqiao Chu, Yaning Biao, Hongzheng Li +9 more Β· 2025 Β· Lipids in health and disease Β· BioMed Central Β· added 2026-04-24
Lipid metabolism may be linked to chronic gastritis, but its causal role remains unclear. While current research emphasizes inflammation, mucosal changes, immune regulation, genetics, and the gut micr Show more
Lipid metabolism may be linked to chronic gastritis, but its causal role remains unclear. While current research emphasizes inflammation, mucosal changes, immune regulation, genetics, and the gut microbiota, the contribution of lipid metabolism is understudied. This study aims to evaluate the impact of serum lipids and the mechanistic roles of lipid-lowering drug targets in chronic gastritis. We conducted a cross-sectional study using data from real world. Multivariable logistic regression was performed to assess the association between serum lipid profiles and gastritis. Mendelian randomization (MR) analyses based on genome-wide association study (GWAS) datasets were performed to detect the causal relationship of serum lipids, plasma lipid species, and lipid-lowering drug targets. Experimental validation was conducted using high-fat diet (HFD)-fed mice and chemically induced CAG rat models. Four thousand sixty one person, including 1,023 patients with chronic atrophic gastritis (CAG), 1,742 with non-atrophic gastritis (NAG), and 1,296 as healthy population were included in the analysis. Through covariates adjustment, TC, ApoA1, and HDL-C showed to be associated with an increased risk of chronic gastritis, whereas TG exhibited a protective effect. MR analysis confirmed a significant inverse causal relationship between TG and gastritis (OR = 0.889, 95% CI: 0.825-0.958). Ten plasma lipid species and lipid-lowering gene targets, including LPL and APOC3, were identified as causally associated with disease risk. Mediation analysis revealed six plasma lipid species as potential intermediaries linking genetic variation to gastritis. In vivo experiments demonstrated progressive hepatic steatosis and mild gastric mucosal changes in HFD-fed mice. Immunohistochemical analysis further revealed a significant reduction in LPL and APOC3 expression in gastric tissue (P < 0.05). In the CAG rat model, histological analysis revealed hepatocyte disarray, edema, and gastric mucosal atrophy. Elevated levels of TNF-Ξ±, IL-6, IL-1Ξ² and decreased levels of GAS-17 and PG I/II were also observed (P < 0.05). Western blot analyses further confirmed the downregulation of LPL and APOC3 expression in gastric tissue (P < 0.05). This study provides genetic and experimental evidence, supporting a causal role of lipid metabolism in chronic gastritis. LPL and APOC3 are implicated in its pathogenesis, highlighting potential lipid-targeted strategies for prevention and treatment. Show less
πŸ“„ PDF DOI: 10.1186/s12944-025-02782-5
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Maaike Kockx, Jeffrey Wang, Natasha J Howard +4 more Β· 2025 Β· Journal of clinical lipidology Β· Elsevier Β· added 2026-04-24
Indigenous Australians have an increased risk of type 2 diabetes mellitus (T2DM) and premature cardiovascular disease. Subpopulations of high-density lipoprotein (HDL) have been associated with increa Show more
Indigenous Australians have an increased risk of type 2 diabetes mellitus (T2DM) and premature cardiovascular disease. Subpopulations of high-density lipoprotein (HDL) have been associated with increased cardiovascular risk, but HDL composition, size, or function have not been studied in Indigenous Australians. The study consisted of 86 non-Indigenous participants, 43 of whom had T2DM, and 75 Indigenous participants, 36 of whom had T2DM. HDL lipid and apolipoprotein content were determined using enzymatic assays and enzyme-linked immunosorbent assays, respectively, and HDL size and distribution were investigated using nuclear magnetic resonance spectroscopy. Transporter-independent, ATP-binding cassette transporter (ABC)A1- and ABCG1-specific cholesterol efflux capacity (CEC) were determined using cell lines stably expressing human ABCA1 or ABCG1. Indigenous participants had significantly lower concentrations of large (10.3-12.0 nm), small (7.4-7.8 nm), and total HDL particles, which persisted after adjustment for serum triglyceride (TG), body mass index (BMI), and T2DM. HDL from Indigenous Australians was also highly enriched in TG, apolipoprotein (apo) E, and apoCIII (all P < .001). Transporter-independent and ABCG1-mediated CEC were not different between the populations. ABCA1-specific CEC per HDL particle was higher in Indigenous than in non-Indigenous subjects (P < .001), and persisted after adjustment for TG, BMI, and T2DM. Multivariable analysis identified that ABCA1-specific CEC was independently and positively associated with HDL-apoCIII and HDL-apoE levels. Indigenous Australians demonstrate significant compositional, size, and functional changes in circulating HDL, which is only partially explained by BMI, hypertriglyceridemia, or T2DM. Remodeled HDL may serve as a biomarker of increased cardiovascular risk in Indigenous Australians. Show less
no PDF DOI: 10.1016/j.jacl.2025.08.006
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Xin Huang, Qihang Li, Ping Guo +3 more Β· 2025 Β· Journal of lipid research Β· Elsevier Β· added 2026-04-24
Patients with dyslipidemia are at higher risk for inflammatory bowel disease (IBD), yet the impact of lipid-lowering medications on IBD remains unclear. This study investigates the causal relationship Show more
Patients with dyslipidemia are at higher risk for inflammatory bowel disease (IBD), yet the impact of lipid-lowering medications on IBD remains unclear. This study investigates the causal relationship between lipid-lowering drug target and IBD, with a focus on the roles of gut microbiota and inflammatory cytokines. Genetic variants associated with lipid-lowering drug targets were extracted from the Global Lipids Genetics Consortium, whereas summary statistics for IBD, Crohn's disease (CD), and ulcerative colitis were sourced from the International Inflammatory Bowel Disease Genetics Consortium. Drug-target Mendelian randomization analysis revealed that inhibiting angiopoietin-like protein 3 increased the risk of IBD and CD, whereas inhibition of apolipoprotein C-III (APOC3) heightened the risk of CD. Conversely, enhancement of LPL and LDL receptor reduced the risk of IBD and CD. Mediation analysis demonstrated that gut microbiota and inflammatory cytokines partially mediated these effects, with specific pathways such as Lachnospiraceae FCS020 (17.26%) for APOC3 and Clostridium sensu stricto 1 (20.12%) for LPL accounting for significant portions of the effects. These findings suggest that lipid-lowering drugs targeting angiopoietin-like protein 3 and APOC3 may increase the risk of IBD, whereas those targeting LPL and LDL receptor may reduce the risk. The results highlight potential for repurposing lipid-lowering drugs for IBD prevention and warrant future clinical trials to explore these targets further. Show less
πŸ“„ PDF DOI: 10.1016/j.jlr.2025.100871
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Xin Hu, Xin Jia, Xiaoming Wang Β· 2025 Β· Cardiology in review Β· added 2026-04-24
Dyslipidemia is a major risk factor for atherosclerotic cardiovascular diseases (ASCVD), and effective lipid-lowering therapies are critical for reducing ASCVD risk. This review aims to provide an upd Show more
Dyslipidemia is a major risk factor for atherosclerotic cardiovascular diseases (ASCVD), and effective lipid-lowering therapies are critical for reducing ASCVD risk. This review aims to provide an updated overview of the latest advancements in lipid-lowering therapies, focusing on emerging therapeutic targets and innovative biotechnological approaches that have shown promise in clinical research. Recent years have witnessed significant progress in lipid-lowering therapies beyond traditional statins and ezetimibe. Novel therapeutic targets, such as PCSK9 inhibitors, angiopoietin-like 3 protein inhibitors, APOC3 inhibitors, and omega-3 fatty acids, have demonstrated potent lipid-lowering efficacy. Additionally, advancements in biotechnology have led to the development of innovative agents, including monoclonal antibodies, antisense oligonucleotides (ASOs), small interfering RNA, and cholesterol vaccines, all of which have shown encouraging results in clinical trials. These therapies offer new mechanisms of action and improved efficacy in managing dyslipidemia and reducing ASCVD risk. This article comprehensively reviews the latest clinical research on emerging lipid-lowering targets and cutting-edge therapies, emphasizing their mechanisms, efficacy, and potential impact on dyslipidemia and ASCVD management. The rapid evolution of these therapies highlights a transformative era in cardiovascular disease prevention and treatment, offering hope for improved patient outcomes. Show less
no PDF DOI: 10.1097/CRD.0000000000001020
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Yu Liao, Mingchao Wang, Fuli Qin +2 more Β· 2025 Β· Frontiers in pharmacology Β· Frontiers Β· added 2026-04-24
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically Show more
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically identify Cpn's therapeutic targets and pathways in Western diet (WD)-induced obesity using integrated network pharmacology, transcriptomics, and experimental validation. A Western diet (WD)-induced mice model was used to evaluate the effectiveness of Cpn in ameliorating obesity. A network pharmacology analysis was then employed to identify the potential anti-obesity targets of Cpn. GO functional enrichment and KEGG pathway analysis were performed to elucidate the potential functions of the identified targets, followed by constructing a protein-protein interaction network to screen the core targets. Meanwhile, quantitative transcriptomics was conducted to validate and broaden the network pharmacology findings. Finally, molecular docking and quantitative real-time PCR assay were used for the core target validation. Cpn treatment effectively alleviated obesity-related symptoms in WD-induced mice. The metabolic pathway, insulin signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, lipid and atherosclerosis pathway, and core targets including CPS1, HRAS, MAPK14, PAH, ALDOB, AKT1, GSK3B, HSP90AA1, BHMT2, EGFR, CASP3, MAT1A, APOM, APOA2, APOC3, and APOA1 are involved in regulating the therapeutic effect of Cpn. This study comprehensively uncovers the potential mechanism of Cpn against obesity based on network pharmacology and quantitative transcriptomics, which provides evidence for revealing the pathogenesis of obesity, suggesting that Cpn is a possible lead compound for anti-obesity treatment. Show less
πŸ“„ PDF DOI: 10.3389/fphar.2025.1571480
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Jia Pan, Xue Wang, Youjin Zhang +6 more Β· 2025 Β· Journal of cellular and molecular medicine Β· Blackwell Publishing Β· added 2026-04-24
Apolipoprotein C3 (APOC3) and angiopoietin-like protein 8 (ANGPTL8) genes are related to lipid metabolism. The relationships between single nucleotide polymorphisms (SNPs) in the APOC3 and ANGPTL8 gen Show more
Apolipoprotein C3 (APOC3) and angiopoietin-like protein 8 (ANGPTL8) genes are related to lipid metabolism. The relationships between single nucleotide polymorphisms (SNPs) in the APOC3 and ANGPTL8 genes with metabolic dysfunction-associated steatotic liver disease (MASLD) remain controversial. This study aimed to investigate the associations between specific SNPs in the APOC3 and ANGPTL8 genes and MASLD risk, with a particular focus on the mediating role of triglycerides (TG). A total of 440 participants were enrolled and categorised into MASLD and control groups. Genotyping of APOC3 SNPs (rs5128, rs2854116 and rs2854117) and ANGPTL8 SNP (rs2278426) was conducted using polymerase chain reaction-restriction fragment length polymorphism or Sanger sequencing methods. Multivariate logistic regression was employed to estimate the associations between these SNPs and MASLD risk, and mediation analysis was performed to assess the potential mediating effect of TG. We found that APOC3 SNPs were associated with MASLD risk, with increased odds ratios (ORs) indicating a higher risk of MASLD: rs5128 CG + GG genotype (OR = 1.8, 95% CI = 1.1-2.8), rs2854116 TC + CC genotype (OR = 1.9, 95% CI = 1.1-3.1) and rs2854117 CT + TT genotype (OR = 1.9, 95% CI = 1.2-3.2). No association was found between ANGPTL8 rs2278426 and MASLD (p > 0.05). Mediation analysis revealed that TG significantly mediated these relationships, accounting for 80.25% of the effect for rs5128, 64.61% for rs2854116 and 62.59% for rs2854117. In summary, polymorphisms in APOC3 (rs5128, rs2854116 and rs2854117) were associated with MASLD risk, with TG serving as a potential mediating factor. In contrast, ANGPTL8 rs2278426 polymorphism did not show any association with MASLD. Show less
πŸ“„ PDF DOI: 10.1111/jcmm.70542
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Binyan Yu, Yanan Yang, Yijian Li +3 more Β· 2025 Β· Reproduction in domestic animals = Zuchthygiene Β· Blackwell Publishing Β· added 2026-04-24
The Tibetan sheep is a typical hypoxia-tolerant mammal, which lives on the plateau, at an altitude of between 2500 and 5000 m above sea level; the study of its hypoxic adaptation mechanism provides a Show more
The Tibetan sheep is a typical hypoxia-tolerant mammal, which lives on the plateau, at an altitude of between 2500 and 5000 m above sea level; the study of its hypoxic adaptation mechanism provides a reference for exploring the hypoxic adaptation mechanism of other animals. To grope for the genetic mechanism of adaptation to the hypoxic environment at the transcriptional level in Tibetan sheep testicular tissue, and to identify candidate genes and key pathways related to sheep adaptation, histological observation of testicular tissues from two sheep breeds was carried out using haematoxylin-eosin (HE) conventional staining. A total of 103 differentially expressed genes (DEGs) were authenticated in high altitude Tibetan sheep (ZYH) and low altitude Tibetan sheep (ZYM) by RNA sequencing technology (RNA-Seq), which included 50 up-regulated genes and 53 down-regulated genes. Functional analyses revealed several terms and pathways that were closely related to testis adaptation to the plateau. Several genes (including GGT5, AGTR2, EDN1, LPAR3, CYP2C19, IGFBP3, APOC3 and PKC1) were remarkably enriched in several pathways and terms, which may impact the Plateau adaptability of sheep by adjusting its reproductive activity and sexual maturation, and protecting Sertoli cells, various spermatocytes, and spermatogenesis processes. The results make a reasonable case for a better understanding of the molecular mechanisms of adaptation to altitude in sheep. Show less
no PDF DOI: 10.1111/rda.70037
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Bo Wang, Li Qiang, Geng Zhang +6 more Β· 2025 Β· Medicine Β· added 2026-04-24
Acute-on-chronic liver failure (ACLF) is the major cause of mortality in patients infected with the hepatitis B virus (HBV); however, early determination of the prognosis of patients with HBV-ACLF is Show more
Acute-on-chronic liver failure (ACLF) is the major cause of mortality in patients infected with the hepatitis B virus (HBV); however, early determination of the prognosis of patients with HBV-ACLF is insensitive or limited. This study aimed to analyze differentially expressed proteins in the plasma of patients with HBV-ACLF using data-independent acquisition mass spectrometry to provide a reference for short-term prognosis. Fifty HBV-ACLF patients and 15 healthy controls were enrolled in this study. Of these, 10 patients with HBV-ACLF and 5 healthy volunteers participated in data-independent acquisition-based proteomics and the potential core proteins were screened out via bioinformatics. Apolipoprotein C3 (APOC3) was selected and quantified by enzyme linked immunosorbent assays in all patients. And the area under the curve (AUC) was calculated to evaluate the value of APOC3 in the diagnosis and prognosis of patients with HBV-ACLF. A total of 247 differentially expressed proteins were identified in the serum of patients in the HBV-ACLF and normal control groups. A total of 148 proteins were upregulated and 99 proteins were downregulated in the HBV-ACLF group compared with those in the normal group. The expression level of APOC3 was 1.65β€…Β±β€…0.44 mg/mL in patients with HBV-ACLF, which was obviously lower than the normal controls (2.04β€…Β±β€…0.22 mg/mL) (Pβ€…<β€….001) (AUC was 0.766, with a sensitivity of 62%, and specificity of 93.3%). The expression level of APOC3 was 1.38β€…Β±β€…0.44 mg/mL in the non-survival group, which was obviously lower than the survival group (1.83β€…Β±β€…0.35 mg/mL) (Pβ€…<β€….0001) (AUC was 0.780, with a sensitivity of 50%, and specificity of 96.7%). APOC3 is associated with short-term prognosis of patients with HBV-ACLF and can be used as a potential prognostic biomarker in patients with HBV-ACLF. Show less
πŸ“„ PDF DOI: 10.1097/MD.0000000000041503
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Qingcong Zheng, Rongjie Lin, Du Wang +2 more Β· 2025 Β· BMC musculoskeletal disorders Β· BioMed Central Β· added 2026-04-24
It remains controversial whether lipids affect osteoporosis (OP) or bone mineral density (BMD), and causality has not been established. This study aimed to investigate the genetic associations between Show more
It remains controversial whether lipids affect osteoporosis (OP) or bone mineral density (BMD), and causality has not been established. This study aimed to investigate the genetic associations between lipids, novel non-statin lipid-lowering drug target genes, and OP and BMD. Mendelian randomization (MR) method was used to explore the genetic associations between 179 lipid species and OP, BMD. Drug-target MR analysis was used to explore the causal associations between angiopoietin-like protein 3 (ANGPTL3) and apolipoprotein C3 (APOC3) inhibitors on BMD. The IVW results with Bonferroni correction indicated that triglyceride (TG) (51:3) (OR = 1.0029; 95% CI: 1.0014-1.0045; P = 0.0002) and TG (56:6) (OR = 1.0021; 95% CI: 1.0008-1.0033; P = 0.0011) were associated with an increased risk of OP; TG (51:2) (OR = 0.9543; 95% CI: 0.9148-0.9954; P = 0.0298) was associated with decreased BMD; and ANGPTL3 inhibitor (OR = 1.1342; 95% CI: 1.0393-1.2290; P = 0.0093) and APOC3 inhibitor (OR = 1.0506; 95% CI: 1.0155-1.0857; P = 0.0058) was associated with increased BMD. MR analysis indicated causal associations between genetically predicted TGs and OP and BMD. Drug-target MR analysis showed that ANGPTL3 and APOC3 have the potential to serve as novel non-statin lipid-lowering drug targets to treat or prevent OP. Show less
πŸ“„ PDF DOI: 10.1186/s12891-024-08160-z
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Lei Wu, Zhong Zhuang, Wenqian Jia +7 more Β· 2025 Β· Poultry science Β· Elsevier Β· added 2026-04-24
Residual feed intake (RFI) has recently gained attention as a key indicator of feed efficiency in poultry. In this study, 800 slow-growing ducks with similar initial body weights were reared in an exp Show more
Residual feed intake (RFI) has recently gained attention as a key indicator of feed efficiency in poultry. In this study, 800 slow-growing ducks with similar initial body weights were reared in an experimental facility until they were culled at 42 d of age. Thirty high RFI (HRFI) and 30 low RFI (LRFI) birds were selected to evaluate their growth performance, carcass characteristics, and muscle development. Transcriptome and weighted gene co-expression correlation network analyses of pectoral muscles were conducted on six LRFI and six HRFI ducks. The results revealed that selecting for LRFI significantly reduced feed consumption (P < 0.05) and improved feed efficiency without affecting the growth performance, slaughter rate, or meat quality of ducks (P > 0.05). Moreover, compared with HRFI ducks, LRFI ducks had a lower pectoral muscle fat content (P < 0.05), larger muscle fiber diameter and area (P < 0.05), and lower muscle fiber density (P < 0.05). There were significant differences in gene expression between LRFI and HRFI ducks, with 102 upregulated and 258 downregulated genes, which were enriched in the PPAR signaling pathway, adipocytokine signaling pathway, actin cytoskeleton regulation, ECM-receptor interaction, and focal adhesion. The expression of genes associated with fat and energy metabolism, including ACSL6, PCK1, APOC3, HMGCS2, PRKAG3, and G6PC1, was downregulated in LRFI ducks, and weighted gene co-expression correlation network analysis identified PRKAG3 as a hub gene. Our findings indicate that reduced mitochondrial energy metabolism may contribute to the RFI of slow-growing ducks, with PRKAG3 playing a pivotal role in this biological process. These findings provide novel insights into the molecular changes underlying RFI variation in slow-growing ducks. Show less
πŸ“„ PDF DOI: 10.1016/j.psj.2024.104613
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Guotong Sun, Yaowen Xu, Xiuwen Liang +2 more Β· 2025 Β· International immunopharmacology Β· Elsevier Β· added 2026-04-24
The etiology of hyperlipidemia is complex, and our understanding of its underlying mechanisms is limited. Effective therapeutic strategies for hyperlipidemia remain elusive. This study aimed to confir Show more
The etiology of hyperlipidemia is complex, and our understanding of its underlying mechanisms is limited. Effective therapeutic strategies for hyperlipidemia remain elusive. This study aimed to confirm the effect of curcumin on hyperlipidemia treatment and elucidate the precise mechanism. A high-fat diet-induced hyperlipidemia model using C57BL/6J mice and HaCaT cells was established. Co-immunoprecipitation and immunofluorescence were performed to detect protein interactions, and immunoprecipitation coupled with Western blotting was used to assess protein succinylation. 40Β ΞΌM of curcumin administration promoted cell viability, increased the levels of glutathione peroxidase, glutathione, catalase, and superoxide dismutase, while reducing reactive oxygen species activity and the levels of triglycerides and malondialdehyde. Additionally, curcumin attenuated the development of hyperlipidemia in vivo. Mechanistically, 100Β mg/kg of curcumin promoted O-GlcNAcylation and increased the expression of O-linked N-acetylglucosamine transferase in HaCaT cells. Furthermore, apolipoprotein C3 was identified as a substrate of O-linked N-acetylglucosamine transferase, and O-GlcNAcylation of apolipoprotein C3 enhanced its stability. Rescue experiments further verified that curcumin exerts its effects by regulating apolipoprotein C3 expression. In conclusion, these findings provide novel insights into the treatment of hyperlipidemia. Show less
no PDF DOI: 10.1016/j.intimp.2024.113647
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Baiyi Lu, Fan Xiao, Qinjun Zhang +8 more Β· 2025 Β· iMetaOmics Β· Wiley Β· added 2026-04-24
Foam cells derived from macrophages and smooth muscle cells (SMCs) play a pivotal role in the progression of atherosclerosis. While phytosterols (PS) have demonstrated cholesterol-lowering and anti-in Show more
Foam cells derived from macrophages and smooth muscle cells (SMCs) play a pivotal role in the progression of atherosclerosis. While phytosterols (PS) have demonstrated cholesterol-lowering and anti-inflammatory properties, their impact on foam cells remains elusive. Here, we investigated the effects of PS on foam cell formation, inflammatory responses, and lipid metabolism using both single-cell RNA sequencing (scRNA-seq) and functional assays. scRNA-seq of aortic tissue from Show less
πŸ“„ PDF DOI: 10.1002/imo2.70056
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Yi Li, Zhu Ni, Xiao-Yong Xia +7 more Β· 2025 Β· Frontiers in molecular biosciences Β· Frontiers Β· added 2026-04-24
Metabolic disorders and neurocognitive diseases frequently co-occur, yet the specific mechanisms driving this comorbidity remain elusive. While epidemiological associations are well-documented, the ca Show more
Metabolic disorders and neurocognitive diseases frequently co-occur, yet the specific mechanisms driving this comorbidity remain elusive. While epidemiological associations are well-documented, the causal links between these conditions are complex and incompletely understood, necessitating a systems-level investigation into their shared biological architecture. This study integrates large-scale human genetics with experimental Network-informed Mendelian randomization identified bidirectional causalities, including a 14% elevated dementia risk from type 2 diabetes and protective effects of obesity against parental Alzheimer's disease (AD). The study identified a signature encompassing key lipid metabolism hubs This multi-modal investigation provides a robust framework that converges on a high-confidence, 13-gene signature of lipid dysregulation as a central mechanistic interface, offering a powerful set of prioritized targets for future functional validation and therapeutic development at the metabolic-neurocognitive nexus. Show less
πŸ“„ PDF DOI: 10.3389/fmolb.2025.1712198
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