👤 Xueyi Wang

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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, Di 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, 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
Chunxiao Dang, Xiaofeng Wang, Pengfei Liu +2 more · 2024 · International journal of women's health · added 2026-04-24
Observational studies have investigated the association between lipid-lowering drugs and breast cancer (BC) and endometrial cancer (EC), but some controversy remains. This paper aims to explore the ca Show more
Observational studies have investigated the association between lipid-lowering drugs and breast cancer (BC) and endometrial cancer (EC), but some controversy remains. This paper aims to explore the causal relationship between genetic proxies for lipid-lowering drugs and breast and endometrial cancers using drug-target Mendelian randomization (MR). Analyses were mainly performed using inverse variance weighted (IVW), heterogeneity and horizontal pleiotropy tests, and sensitivity analysis to assess the robustness of the results and causal relationship. HMGCR, APOB, and NPC1L1 increased the risk of breast cancer, LPL increased the risk of endometrial cancer, and APOC3 decreased the risk of breast and endometrial cancer. No heterogeneity or horizontal pleiotropy was detected, and nor was there any evidence of an association between other lipid-lowering drugs and breast and endometrial cancer. Our study demonstrated genetically that HMGCR inhibition, APOB inhibition, and NPC1L1 inhibition decrease the risk of breast cancer, LPL agonist increases the risk of endometrial cancer, and APOC3 inhibition decreases the risk of breast cancer and endometrial cancer, and these findings provide genetic insights into the potential risks of lipid-lowering drug therapy. Show less
📄 PDF DOI: 10.2147/IJWH.S468733
APOB
Chang Su, Juan Tian, Xueqing He +3 more · 2024 · ImmunoTargets and therapy · added 2026-04-24
Dyslipidemia has been implicated in the pathogenesis of several diseases, including thyroid dysfunction and immune disorders. However, whether circulating lipids and long-term use of lipid-lowering dr Show more
Dyslipidemia has been implicated in the pathogenesis of several diseases, including thyroid dysfunction and immune disorders. However, whether circulating lipids and long-term use of lipid-lowering drugs influence the development of autoimmune thyroid disease (AITD) remains unclear. This study aims to evaluate the effects of lipid-lowering drugs on AITD and explore their potential mechanisms. Two-sample and two-step Mendelian randomization (MR) studies were performed to assess the causal relationships between circulating lipids (LDL-C, TC, TG, and ApoB) and seven lipid-lowering drug targets ( There was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD ( There was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD. Lipid-lowering drug target gene inhibitors reduced the AITD risk by modulating inflammatory factors. Show less
📄 PDF DOI: 10.2147/ITT.S487319
APOB
Dong-Juan Xu, Yi-Lei Shen, Meng-Meng Hu +6 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Parkinson's disease (PD) and Alzheimer's disease (AD) are common neurodegenerative diseases with multifaceted etiology. Nutritional and metabolic abnormalities are frequently implicated in PD and AD. Show more
Parkinson's disease (PD) and Alzheimer's disease (AD) are common neurodegenerative diseases with multifaceted etiology. Nutritional and metabolic abnormalities are frequently implicated in PD and AD. In this observational study, we analyzed a series of nutritional markers, and aimed to understand their association with AD and PD risk. A total of 424 PD patients, 314 AD patients, and 388 healthy controls were included. Nutritional markers including Hemoglobin A1c, vitamin B12, folate, apolipoprotein B (ApoB), apolipoprotein A1 (ApoA1), low-density lipoprotein (LDL), high-density lipoprotein, triglyceride, total cholesterol (TC), uric acid and homocysteine (HCY) were measured. Significance for odds ratios examining was Multifactor risk analysis showed that ApoB, LDL, and TC reduce PD risk, while HCY increase PD risk. ApoA1 and HCY are protective and risk factors for AD, respectively. The cross-sectional study demonstrates that HCY and lipid metabolism markers are associated with PD and AD risks. Our findings support the involvement of one-carbon metabolism and lipid metabolism disturbance in PD and AD. Show less
📄 PDF DOI: 10.1016/j.heliyon.2024.e40191
APOB
Qinghan Meng, Haina Ma, Nannan Tian +12 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Type 2 diabetes (T2DM) is a significant risk factor for coronary heart disease (CHD). This study aimed to assess the variations in biomarkers associated with CHD in T2DM patients across different age Show more
Type 2 diabetes (T2DM) is a significant risk factor for coronary heart disease (CHD). This study aimed to assess the variations in biomarkers associated with CHD in T2DM patients across different age groups in the Han Chinese population. A strict selection process was employed, involving three groups: a control group (n = 300) with no medical history, a new-onset T2DM group (n = 300), and a new-onset T2DM + CHD group (n = 300). Participants in each group were further categorized based on age: Group 1 (<60 years), Group 2 (60-75 years), and Group 3 (>75 years). Fasting glucose, hemoglobin A1c (HbA1c), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), ApoB/ApoA1 ratio, lipoprotein(a) [Lp(a)], high-sensitivity C-reactive protein (hsCRP), and homocysteine (HCY) levels were analyzed in all groups. Both T2DM and T2DM + CHD groups exhibited elevated levels of TG, TC, LDL-C, ApoB, ApoB/ApoA1, Lp(a), hsCRP, and HCY, alongside decreased levels of HDL-C and ApoA1 in comparison to the control group. Notably, when comparing the T2DM to the T2DM + CHD groups, significant increases were noted in ApoB, Lp(a), and hsCRP levels in the T2DM + CHD group, whereas other biomarkers did not show significant differences. Across all age groups, the patterns remained consistent, with the T2DM and T2DM + CHD groups showing elevated levels of TG, TC, LDL-C, ApoB, ApoB/ApoA1, Lp(a), hsCRP, and HCY, and decreased levels of HDL-C and ApoA1 compared to their respective age-matched control groups. Furthermore, within each age category, significant increases in ApoB, Lp(a), and hsCRP were specifically observed with advancing age in the T2DM + CHD group, with Lp(a) and hsCRP levels showing particularly notable elevations, underscoring their potential as significant indicators of CHD risk in the T2DM population. Lp(a) and hsCRP may serve as valuable risk biomarkers for the development of CHD in T2DM patients. Understanding the variations in these biomarkers across different age groups can assist in risk assessment and the development of personalized management strategies for CHD in T2DM patients. Show less
📄 PDF DOI: 10.1016/j.heliyon.2024.e40074
APOB
Song Yang, Kun He, Weikang Zhang +10 more · 2024 · International journal of biological macromolecules · Elsevier · added 2026-04-24
Adult tethered cord syndrome (ATCS) has a hidden onset and delayed clinical symptoms. The purpose of this study is to identify hub proteins in the cerebrospinal fluid of ATCS patients through bioinfor Show more
Adult tethered cord syndrome (ATCS) has a hidden onset and delayed clinical symptoms. The purpose of this study is to identify hub proteins in the cerebrospinal fluid of ATCS patients through bioinformatics analysis, and to find significant heterogeneity in these proteins between ATCS patients and non ATCS patients (control group). Firstly, differential genes were screened based on proteomic results. Compared with the control group, 18 differentially expressed proteins were upregulated and 18 differentially expressed proteins were downregulated in the cerebrospinal fluid of ATCS patients. Then, GO, KEGG, and GESA functional enrichment analysis showed that ATCS patients were active in biological processes such as coagulation, inflammatory response, and regulation of humoral immune response, suggesting the possibility of spinal cord injury. In addition, protein network interaction analysis indicates that APOB, APOC3, FGA, and FGG are defined as hub proteins. The correlation between ATCS patients and immune characteristics was analyzed using the CIBERSORT algorithm, which may have generated a unique immune microenvironment. Finally, Western blotting was used to experimentally validate APOB, APOC3, FGA, and FGG. The results showed that APOB, APOC3, FGA, and FGG were upregulated in the cerebrospinal fluid of ATCS patients and had an important impact on the repair and functional maintenance of spinal cord injury. They can be used as key proteins for early and accurate diagnosis and treatment of spinal cord thrombosis syndrome, and suggest that the spinal cord of ATCS patients may be damaged, which can serve as potential therapeutic targets. Show less
no PDF DOI: 10.1016/j.ijbiomac.2024.137534
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Yuhui Huang, Xuehui Sun, Qingxia Huang +13 more · 2024 · Translational psychiatry · Nature · added 2026-04-24
The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites a Show more
The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors. In the Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired groups according to their performance in two objective cognitive tests. A two-step strategy of cross-sectional discovery followed by prospective validation was applied. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately-to-severely impaired) during the 2-year follow-up with 204 cognitively normal controls by age and sex at a 1:3 ratio, and conducted conditional logistic regression. We identified 28 out of 78 metabolites cross-sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were prospectively associated with 28%, 28%, 29%, and 33% lower risk of developing cognitive impairment, respectively. Incorporating 13 metabolite biomarkers selected through Lasso regression into the traditional risk factors-based prediction model substantially improved the ability to predict incident cognitive impairment (AUROC: 0.839 vs. 0.703, P < 0.001; AUPRC: 0.705 vs. 0.405, P < 0.001). This study identified specific plasma metabolites related to cognitive impairment. Incorporation of specific metabolites substantially improved the prediction performance for cognitive impairment. Show less
📄 PDF DOI: 10.1038/s41398-024-03147-9
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Haomin Huang, Lamei Li, Anni Yang +5 more · 2024 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Coronary artery disease (CAD) remains the primary cause of death worldwide, and familial hypercholesterolemia (FH) is a common disease that leads to CAD. This study aimed to explore the difference in Show more
Coronary artery disease (CAD) remains the primary cause of death worldwide, and familial hypercholesterolemia (FH) is a common disease that leads to CAD. This study aimed to explore the difference in CAD risk between FH and non-FH patients with high low-density lipoprotein cholesterol (LDL-C) levels. Individuals (≥18 years) who underwent coronary angiography (CAG) from June 2016 to September 2020 were consecutively enrolled. Participants with LDL-C levels ≥4.0 mmol/L were ultimately included in this study. For all participants, next-generation sequencing was performed with expanded gene panels including 11 genes (LDLR, APOB, PCSK9, LDLRAP1, ABCG5, ABCG8, LIPA, LPA, APOBR, LRPAP1, and STAP1). A total of 223 individuals were included in this study. According to the CAG findings, 199 CAD patients and 24 non-CAD patients were included. The proportions of FH genes, regardless of whether 3 major genes or all 11 genes were sequenced, were not significantly different between the CAD and non-CAD groups ( FH mutation did not increase the rate of CAD in individuals with an MLDL-C level ≥4.0 mmol/L. However, among CAD patients (MLDL-C level ≥4.0 mmol/L) with almost normal renal function (≥87.4 ml/min/1.73 m Show less
📄 PDF DOI: 10.3389/fcvm.2024.1434392
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Hui Zhang, Yiming Liu, Li Feng +9 more · 2024 · BMC gastroenterology · BioMed Central · added 2026-04-24
This study explored the correlation between peripheral blood lipid levels and clinicopathological parameters in patients with advanced gastric cancer (GC), focusing on changes in lipid levels during d Show more
This study explored the correlation between peripheral blood lipid levels and clinicopathological parameters in patients with advanced gastric cancer (GC), focusing on changes in lipid levels during disease progression. Pathological features and serum lipid profiles of 179 patients with stage III-IV gastric adenocarcinoma were analyzed. Lipid parameters examined included total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), apolipoprotein AI (Apo AI), apolipoprotein B (Apo B), lipoprotein(a) (Lp(a)), among others. The total cholesterol-lymphocyte score (TL score) and BMI were also calculated. The association between lipid parameters and clinicopathological characteristics such as age, gender, family history, and metastasis sites was assessed. In GC patients, females had higher TG levels than males. Patients with peritoneal metastasis had significantly lower levels of TC, LDL-C, Apo B, and B/A ratio. Those with lung metastasis exhibited higher LDL-C levels and lower levels of VLDL-C. No significant associations were found between lipid levels and metastasis to distant lymph nodes, liver, or bone. Female patients with ovarian metastasis had significantly lower VLDL-C levels. Multivariate analysis revealed low TC as an independent risk factor for peritoneal metastasis, high LDL-C and low VLDL-C levels for lung metastasis, and younger age and low VLDL-C for ovarian metastasis. Specific blood lipid levels are significantly associated with metastatic sites in advanced gastric cancer. Lipid profiles could serve as potential biomarkers for predicting metastatic sites in GC patients. Show less
📄 PDF DOI: 10.1186/s12876-024-03479-2
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Chia-Hsuan Cheng, Hiromi Yatsuda, Han-Hsiang Chen +3 more · 2024 · Sensors (Basel, Switzerland) · MDPI · added 2026-04-24
Cardiovascular disease (CVD) represents the leading cause of death worldwide. For individuals at elevated risk for cardiovascular disease, early detection and monitoring of lipid status is imperative. Show more
Cardiovascular disease (CVD) represents the leading cause of death worldwide. For individuals at elevated risk for cardiovascular disease, early detection and monitoring of lipid status is imperative. The majority of lipid measurements conducted in hospital settings employ optical detection, which necessitates the use of relatively large-sized detection machines. It is, therefore, necessary to develop point-of-care testing (POCT) for lipoprotein in order to monitor CVD. To enhance the management and surveillance of CVD, this study sought to develop a POCT approach for apolipoprotein B (ApoB) utilizing a shear horizontal surface acoustic wave (SH-SAW) platform to assess the risk of heart disease. The platform employs a reflective SH-SAW sensor to reduce the sensor size and enhance the phase-shifted signals. In this study, the platform was utilized to monitor the impact of a weekly almond and oat milk or statins intervention on alterations in CVD risk. The SH-SAW ApoB test exhibited a linear range of 0 to 212 mg/dL, and a coefficient correlation (R) of 0.9912. Following a four-week intervention period, both the almond and oat milk intervention (-23.3%, Show less
📄 PDF DOI: 10.3390/s24206517
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Weiyong Xu, Zhenchang Wang, Huaqing Yao +2 more · 2024 · International journal of general medicine · added 2026-04-24
To investigate the distribution of arteriosclerotic vessels of arteriosclerosis, differential serum lipid profiles, and differences in the proportion of dyslipidaemia between patients with single-site Show more
To investigate the distribution of arteriosclerotic vessels of arteriosclerosis, differential serum lipid profiles, and differences in the proportion of dyslipidaemia between patients with single-site arteriosclerosis and multi-site arteriosclerosis (significant hardening of ≥2 arteries). The data of 6581 single-site arteriosclerosis patients and 5940 multi-site arteriosclerosis patients were extracted from the hospital medical record system. Serum total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein (Apo) A1, ApoB concentrations and C-reactive protein (CRP) between patients with single-site arteriosclerosis and multi-site arteriosclerosis were collected and analyzed. The most diseased arteries were coronary arteries (n=7099, 33.7%), limb arteries (n=6546, 31.1%), and carotid arteries (n=5279, 25.1%). TC, LDL-C, TC/HDL-C, and LDL-C/HDL-C levels were higher and CRP level was lower in multi-site arteriosclerosis patients than those in single-site arteriosclerosis patients. The TC, LDL-C levels in non-elderly (<65 years old) female patients were higher and TG/HDL-C, TC/HDL-C, LDL-C/HDL-C levels were lower than those in non-elderly male patients, while the TG, TC, LDL-C, and TG/HDL-C levels in elderly (≥65 years old) female patients were higher and LDL-C/HDL-C level was lower than those in elderly male patients. The proportion of dyslipidemia in descending order was as follows: low HDL-C (31.9%), elevated TG (16.9%), elevated TC (9.0%), and elevated LDL-C (4.2%). The levels of TC, LDL-C, TC/HDL-C, and LDL-C/HDL-C in patients with peripheral arteriosclerosis were higher than those in patients with cardio-cerebrovascular arteriosclerosis. There were differences in serum lipid levels in patients with arteriosclerosis with different age, gender and distribution of arteriosclerotic vessels. Show less
📄 PDF DOI: 10.2147/IJGM.S483324
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Yufeng Wang, Linbo Guan, Xinghui Liu +6 more · 2024 · The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians · Taylor & Francis · added 2026-04-24
Gestational diabetes mellitus (GDM) is associated with metabolic abnormalities such as an altered serum lipid profile. This study investigated the influence of polymorphisms in the lipid metabolism-re Show more
Gestational diabetes mellitus (GDM) is associated with metabolic abnormalities such as an altered serum lipid profile. This study investigated the influence of polymorphisms in the lipid metabolism-related cholesteryl ester transfer protein gene ( This prospective case-control study included 665 women with GDM and 1,044 women with uncomplicated pregnancies. The PCR-restriction fragment length polymorphism method was used to genotype rs708272 and rs1800775 single nucleotide polymorphisms (SNPs). Lipid and glucose metabolic parameters were assessed. Genetic associations with related traits were analyzed. Genotype distributions of rs708272 and rs1800775 in patients with GDM were similar to those in normal controls. However, the two In patients with GDM, the rs708272 polymorphism was associated with atherogenic lipid levels (TG, TC, LDL-C, and ApoB), whereas the rs708272 and rs1800775 polymorphisms were associated with glucose metabolism and insulin resistance parameters, which were influenced by the body mass index. These results suggest that genetic associations with atherogenic metabolic factors may increase the risk of adverse outcomes in mothers with GDM and their offspring. Show less
no PDF DOI: 10.1080/14767058.2024.2415375
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X N Zhang, Q T Meng, H W Zhang +5 more · 2024 · Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] · added 2026-04-24
no PDF DOI: 10.3760/cma.j.cn112150-20240708-00546
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Haizhen Wang, Cyrus Nikain, Konstantinos I Fortounas +15 more · 2024 · Molecular metabolism · Elsevier · added 2026-04-24
Triglycerides (TGs) associate with apolipoprotein B100 (apoB100) to form very low density lipoproteins (VLDLs) in the liver. The repertoire of factors that facilitate this association is incompletely Show more
Triglycerides (TGs) associate with apolipoprotein B100 (apoB100) to form very low density lipoproteins (VLDLs) in the liver. The repertoire of factors that facilitate this association is incompletely understood. FITM2, an integral endoplasmic reticulum (ER) protein, was originally discovered as a factor participating in cytosolic lipid droplet (LD) biogenesis in tissues that do not form VLDL. We hypothesized that in the liver, in addition to promoting cytosolic LD formation, FITM2 would also transfer TG from its site of synthesis in the ER membrane to nascent VLDL particles within the ER lumen. Experiments were conducted using a rat hepatic cell line (McArdle-RH7777, or McA cells), an established model of mammalian lipoprotein metabolism, and mice. FITM2 expression was reduced using siRNA in cells and by liver specific cre-recombinase mediated deletion of the Fitm2 gene in mice. Effects of FITM2 deficiency on VLDL assembly and secretion in vitro and in vivo were measured by multiple methods, including density gradient ultracentrifugation, chromatography, mass spectrometry, stimulated Raman scattering (SRS) microscopy, sub-cellular fractionation, immunoprecipitation, immunofluorescence, and electron microscopy. 1) FITM2-deficient hepatic cells in vitro and in vivo secrete TG-depleted VLDL particles, but the number of particles is unchanged compared to controls; 2) FITM2 deficiency in mice on a high fat diet (HFD) results in decreased plasma TG levels. The number of apoB100-containing lipoproteins remains similar, but shift from VLDL to low density lipoprotein (LDL) density; 3) Both in vitro and in vivo, when TG synthesis is stimulated and FITM2 is deficient, TG accumulates in the ER, and despite its availability this pool is unable to fully lipidate apoB100 particles; 4) FITM2 deficiency disrupts ER morphology and results in ER stress. The results suggest that FITM2 contributes to VLDL lipidation, especially when newly synthesized hepatic TG is in abundance. In addition to its fundamental importance in VLDL assembly, the results also suggest that under dysmetabolic conditions, FITM2 may be an important factor in the partitioning of TG between cytosolic LDs and VLDL particles. Show less
📄 PDF DOI: 10.1016/j.molmet.2024.102048
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Wang-Dong Xu, You-Yue Chen, Xiang Wang +2 more · 2024 · Seminars in arthritis and rheumatism · Elsevier · added 2026-04-24
The aim of this study is to develop and validate a nomogram that can assist clinicians in identifying female systemic lupus erythematosus (SLE) patients of reproductive age complicated with interstiti Show more
The aim of this study is to develop and validate a nomogram that can assist clinicians in identifying female systemic lupus erythematosus (SLE) patients of reproductive age complicated with interstitial lung disease (ILD). Clinical, laboratory data of SLE patients were first collected. Meteorological data were then gathered according to the geographical locations of the SLE patients. Diagnostic results, univariate logistic regression, elastic net regression, and multivariate logistic regression were used to screen for risk factors for female SLE patients of reproductive age complicated with ILD. A nomogram was constructed using these risk factors and was internally and externally validated through methods such as calculating the concordance index, plotting calibration curves, drawing receiver operating characteristic curves, and clinical decision curves. A total of 4798 SLE patients were included in this study, with 2488 patients in the development set and 2310 patients in the external validation set. The patients in the development set were randomly divided into a training set (N = 1742) and an internal testing set (N = 746) at a ratio of 7:3. Eight independent risk factors for ILD were identified, including APOB, APOA1, ALP, PLT, HCT, EOS-R, LYM-R, and age. The nomogram model was developed, and the areas under the receiver operating characteristic curve was 0.811 (0.748, 0.875), 0.820 (0.727,0.913), and 0.889 (0.869, 0.909) for the three sets, respectively. We established a nomogram model using easily accessible clinical and laboratory data to predict the probability of female SLE patients of reproductive age developing ILD. Show less
no PDF DOI: 10.1016/j.semarthrit.2024.152556
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Erin O Jacob, Adam D McIntyre, Jian Wang +1 more · 2024 · The Journal of international medical research · SAGE Publications · added 2026-04-24
To investigate the relationship between plasma lipoprotein (a) (Lp[a]) and lipid profiles in patients with severe hypertriglyceridaemia (HTG). This case-control study undertook a retrospective chart r Show more
To investigate the relationship between plasma lipoprotein (a) (Lp[a]) and lipid profiles in patients with severe hypertriglyceridaemia (HTG). This case-control study undertook a retrospective chart review of patients from the Lipid Genetics Clinic at London Health Sciences Centre in Ontario, Canada. Plasma Lp(a) was compared between patients with severe HTG and healthy normolipidaemic control subjects. Severe HTG was defined by plasma triglycerides (TG) ≥ 10 mmol/l. Pairwise correlations between Lp(a), TG, apolipoprotein B (apo B) and non-high-density lipoprotein cholesterol (non-HDL-C) were evaluated. This study reviewed 4400 patients and identified 154 patients with severe HTG, which were compared with 272 control subjects. The median Lp(a) was significantly lower in patients with severe HTG compared with control subjects (5.0 versus 10.2 mg/dl, respectively). No correlation was observed between Lp(a) and TG or non-HDL-C. Lp(a) and apo B were modestly correlated in patients with severe HTG ( Patients with severe HTG have lower plasma Lp(a) than normolipidaemic control subjects. The basis for this relationship is not immediately apparent but is hypothesis-generating and warrants further investigation. Show less
📄 PDF DOI: 10.1177/03000605241289294
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Dong Liu, Jin Zhang, Xiaoyu Zhang +9 more · 2024 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
In recent years, the position of PCSK9 inhibitors as adjuvant therapy to statins in guidelines has further improved. However, there remained a dearth of direct comparative studies among different PCSK Show more
In recent years, the position of PCSK9 inhibitors as adjuvant therapy to statins in guidelines has further improved. However, there remained a dearth of direct comparative studies among different PCSK9 inhibitors. Therefore, this study aimed to conduct a network meta-analysis to evaluate the efficacy and safety of different PCSK9 inhibitors combined with statins. A comprehensive literature search was conducted from the study's inception to 12 November 2023, encompassing multiple online databases including PubMed, Embase, Cochrane Central, Web of Science, and ClinicalTrials.gov to obtain relevant randomized controlled trials. Frequentist network meta-analysis was employed to compare the efficacy and safety of different PCSK9 inhibitors. The efficacy endpoints were low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (ApoB), and lipoprotein (a) (Lp(a)). The safety endpoints were any adverse events (AE), severe adverse events (SAE), AE leading to treatment discontinuation, and injection-site reaction. Compared with placebo and ezetimibe, all PCSK9 inhibitors demonstrated significant reductions in LDL-C levels. Notably, evolocumab exhibited the most pronounced effect with a treatment difference of -63.67% (-68.47% to -58.87%) compared with placebo. Regarding dosage selection for evolocumab, the regimen of 140 mg Q2W (-69.13%, -74.55% to -63.72%) was superior to 420 mg QM (-61.51%, -65.97% to -57.05%). Based on rankings and Compared with placebo and ezetimibe, PCSK9 inhibitors can significantly reduce LDL-C, ApoB, and Lp(a) when combined with statins to treat hypercholesterolemia. Furthermore, PCSK9 inhibitors and ezetimibe exhibit similar safety profiles. [PROSPERO], identifier [CRD42023490506]. Show less
📄 PDF DOI: 10.3389/fcvm.2024.1454918
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Di Feng, Xiao Wang, Jiahui Song +8 more · 2024 · Human reproduction (Oxford, England) · Oxford University Press · added 2026-04-24
Is there a relationship between serum uric acid and fructose levels in polycystic ovary syndrome (PCOS)? Elevated serum uric acid levels in women with PCOS positively correlate with serum fructose lev Show more
Is there a relationship between serum uric acid and fructose levels in polycystic ovary syndrome (PCOS)? Elevated serum uric acid levels in women with PCOS positively correlate with serum fructose levels, and elevated serum fructose levels are an independent risk factor for hyperuricemia in women with PCOS. Our previous study suggested a link between elevated serum fructose levels and PCOS. Fructose is unique as it generates uric acid during metabolism, and high uric acid levels are associated with metabolic disorders and an increased risk of anovulation. However, the relationship between serum uric acid and fructose levels in women with PCOS remains unclear. In a case-control study of 774 women (482 controls and 292 patients with PCOS) between May and October 2020 at the Shengjing Hospital of China Medical University, the relationship between uric acid and fructose levels in women with PCOS was examined. Participants were divided into subgroups based on various factors, including BMI, insulin resistance, dyslipidemia, metabolic syndrome, and hyperuricemia. Serum uric acid concentrations were measured using enzymatic assays, and serum fructose levels were determined using a fluorescent enzyme immunoassay. Dietary fructose data were collected through a validated food-frequency questionnaire of 81 food items. We applied restricted cubic splines to a flexibly model and visualized the linear/nonlinear relationships between serum uric acid and fructose levels in PCOS. Multivariate logistic analysis was executed to assess the association between serum fructose levels and hyperuricemia in PCOS. Human granulosa cell and oocyte mRNA profile sequencing data were downloaded for mapping uric acid and fructose metabolism genes in PCOS. Further downstream analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and protein-protein interactions were then carried out on the differentially expressed genes (DEGs). The correlation between uric acid and fructose metabolism genes was calculated using the Pearson correlation coefficient. The GeneCards database was used to identify DEGs related to uric acid and fructose metabolism in PCOS, and then several DEGs were confirmed by quantitative real-time PCR. Both serum fructose and uric acid levels were significantly increased in women with PCOS compared with the control women (P  <  0.001), and there was no statistically significant difference in dietary fructose intake between PCOS and controls, regardless of metabolic status. There was a positive linear correlation between serum uric acid and fructose levels in women with PCOS (Poverall < 0.001, Pnon-linear = 0.30). In contrast, no correlation was found in control women (Poverall = 0.712, Pnon-linear = 0.43). Additionally, a non-linear association was observed in the obese subgroup of patients with PCOS (Poverall < 0.001, Pnon-linear = 0.02). Serum uric acid levels were linearly and positively associated with serum fructose levels in patients with PCOS with insulin resistance, dyslipidemia, and metabolic syndrome. Furthermore, even after adjusting for confounding factors, elevated serum fructose levels were an independent risk factor for hyperuricemia in patients with PCOS (P  =  0.001; OR, 1.380; 95% CI, 1.207-1.577). There were 28 uric acid and 25 fructose metabolism genes which showed a significant correlation in PCOS. Seven upregulated genes (CAT, CRP, CCL2, TNF, MMP9, GCG, and APOB) related to uric acid and fructose metabolism in PCOS ovarian granulosa cells were ultimately successfully validated using quantitative real-time PCR. Due to limited conditions, more possible covariates (such as smoking and ethnicity) were not included, and the underlying molecular mechanism between fructose and uric acid levels in women with PCOS remains to be further investigated. The results of this study and our previous research indicate that the high uric acid status of PCOS may be mediated by fructose metabolism disorders, highlighting the importance of analyzing fructose metabolism, and especially its metabolic byproduct uric acid, during the clinical diagnosis of PCOS. These results suggest the adverse effects of high uric acid in PCOS, and the importance of taking early interventions regarding uric acid levels to reduce the occurrence and development of further clinical signs, such as metabolic disorders in women with PCOS. This work was supported by: the National Natural Science Foundation of China (No. 82371647, No. 82071607, and No. 32100691); LiaoNing Revitalization Talents Program (No. XLYC1907071); Fok Ying Tung Education Foundation (No. 151039); and Outstanding Scientific Fund of Shengjing Hospital (No. 202003). No competing interests were declared. N/A. Show less
no PDF DOI: 10.1093/humrep/deae219
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Yongjiang Cheng, Jingyan Ye, Junyuan Huang +1 more · 2024 · PeerJ · added 2026-04-24
Cholestasis is characterized by the accumulation of bile in the liver or biliary system due to obstruction or impaired flow, necessitating lipid profiling to assess lipid metabolism abnormalities. Int Show more
Cholestasis is characterized by the accumulation of bile in the liver or biliary system due to obstruction or impaired flow, necessitating lipid profiling to assess lipid metabolism abnormalities. Intrahepatic cholestasis, being the most significant type of cholestasis, further complicates the assessment of lipid abnormalities. However, the accuracy of low-density lipoprotein cholesterol (LDL-C) measurement in intrahepatic cholestasis patients remains uncertain. This study aimed to evaluate the consistency of the homogeneous assay and the Friedewald formula in detecting LDL-C levels and identify factors influencing LDL-C test results in intrahepatic patients with cholestasis. Retrospective analysis of laboratory data was conducted on intrahepatic cholestatic patients. Correlations between LDL-C values obtained using the homogeneous method (LDL-C(D)) and the Friedewald formula (LDL-C(F)), as well as associations between high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A1 (ApoA1), LDL-C(D) and LDL-C(F), and apolipoprotein B (ApoB), were analyzed. Logistic regression analyses were employed to identify diagnostic indicators for inaccurate LDL-C measurements in intrahepatic cholestatic patients. Compared to patients with intrahepatic cholestasis without jaundice, the correlation between LDL-C(F) and LDL-C(D) was weaker in those with jaundice. Additionally, HDL-C exhibited a strong correlation with ApoA1 in both jaundice and non-jaundice cholestasis cases. Elevated non-HDL-C to APOB ratio (NH-C/B Ratio) levels (>4.5) were identified as a reliable predictor of inaccurate LDL-C measurements in patients with chronic intrahepatic cholestasis accompanied by jaundice. LDL-C measurement reliability is moderately weaker in patients with intrahepatic cholestasis accompanied by jaundice. Elevated levels of the NH-C/B ratio serve as a significant predictor of inaccurate LDL-C measurements in this chronic patient population, highlighting its clinical relevance for diagnostic assessments. Show less
📄 PDF DOI: 10.7717/peerj.18224
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DanDan Zhang, Jing Wu, Guoqiang Ren +5 more · 2024 · Brain and behavior · Wiley · added 2026-04-24
To explore the diagnostic value of serum apolipoprotein B100 (Apo B100) combined with hippocampal volume in Alzheimer's disease (AD). A total of 59 AD patients and 59 healthy subjects were selected. T Show more
To explore the diagnostic value of serum apolipoprotein B100 (Apo B100) combined with hippocampal volume in Alzheimer's disease (AD). A total of 59 AD patients and 59 healthy subjects were selected. The Mini-Mental State Examination (MMSE) was used for neuropsychological assessment. Blood glucose and serum lipid levels were detected by biochemical analyzer. Polymerase chain reaction (PCR) was used to detect apolipoprotein E (Apo E) ε3/ε4 genotypes in the plasma. Hippocampal volume was calculated using Slicer software. Independent-sample t test or Mann-Whitney U test were used to compare the levels of various indicators between the two groups. Spearman's correlation analysis was used to analyze the correlation between each level. The receiver operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) was calculated to compare the diagnostic efficacy of individual and combined detection of serum Apo B100 levels and hippocampal volume in AD. Compared with the healthy control group, the levels of serum total cholesterol (TC), low-density lipoprotein (LDL), Apo B100, and plasma Apo E ε3/ε4 were higher in the AD group, and serum high-density lipoprotein (HDL) level was lower in the AD group (both p < 0.05). The hippocampal volume in the AD group was lower than in the control group (p < 0.01). The serum Apo B100 level was negatively correlated with MMSE score (r = -0.646), whereas hippocampal volume was positively correlated with MMSE score (r = 0.630). ROC curve analysis showed that the AUC of the combined serum Apo B100 level and hippocampal volume for AD was higher than that of either alone (AUC = 0.821, p < 0.01). Serum Apo B100 level is elevated, and the hippocampal volume is reduced in AD patients. The combined detection of the two has a higher diagnostic efficiency for AD than other alone and has the potential to become an important indicator for the diagnosis of AD in the future. Show less
📄 PDF DOI: 10.1002/brb3.70066
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Longfei Wang, Qiong Cheng · 2024 · Cancer control : journal of the Moffitt Cancer Center · SAGE Publications · added 2026-04-24
APOBEC-1 complementation factor (A1CF) and Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-1 (APOBEC-1) constitute the minimal proteins necessary for the editing of apolipoprotein B (apoB) Show more
APOBEC-1 complementation factor (A1CF) and Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-1 (APOBEC-1) constitute the minimal proteins necessary for the editing of apolipoprotein B (apoB) mRNA in vitro. Unlike APOBEC-1 and apoB mRNA, the ubiquitous expression of A1CF in human tissues suggests its unique biological significance, with various factors such as protein kinase C, thyroid hormones, and insulin regulating the activity and expression of A1CF. Nevertheless, few studies have provided an overview of this topic. We conducted a literature review to describe the molecular mechanisms of A1CF and its relevance to human diseases. In the PubMed database, we used the keywords 'A1CF' and 'APOBEC-1 complementation factor' to collect peer-reviewed articles published in English from 2000 to 2023. Two authors independently reviewed the articles and reached the consensus. After reviewing 127 articles, a total of 61 articles that met the inclusion criteria were included in the present review. Studies revealed that A1CF is involved in epigenetic regulation of reproductive cells affecting embryonic development, and that it is closely associated with the occurrence of gout due to its editing properties on apoB. A1CF can also affect the process of epithelial-mesenchymal transition in renal tubular epithelial cells and promote liver regeneration by controlling the stability of IL-6 mRNA, but no influence on cardiac function was found. Furthermore, increasing evidence suggests that A1CF may promote the occurrence and development of breast cancer, lung cancer, renal cell carcinoma, hepatocellular carcinoma, endometrial cancer, and glioma. This review clarifies the association between A1CF and other complementary factors and their impact on the development of human diseases, aiming to provide guidance for further research on A1CF, which can help treat human diseases and promote health. Show less
📄 PDF DOI: 10.1177/10732748241284952
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Xuan Wang, Lu Lyu, Wei Li +6 more · 2024 · Diabetes & metabolic syndrome · Elsevier · added 2026-04-24
This investigation aimed to evaluate the efficacy and safety of rosuvastatin in treating moderate to severe metabolic associated fatty liver disease (MAFLD). This prospective, open-label, randomized s Show more
This investigation aimed to evaluate the efficacy and safety of rosuvastatin in treating moderate to severe metabolic associated fatty liver disease (MAFLD). This prospective, open-label, randomized study included non-diabetic participants with metabolic syndrome and intrahepatocellular lipid (IHCL) levels >10 %, as determined by proton magnetic resonance spectroscopy ( Thirty-two participants completed the study. Rosuvastatin resulted in a significant absolute (△IHCL: 7.61 ± 4.51 vs. 1.54 ± 5.33, p = 0.002) and relative reduction in IHCL (△IHCL%: -42.28 ± 24.90 % vs. -8.91 ± 31.93 %, p = 0.003) compared to the control. Reduction in IHCL correlated significantly with decreases in low-density lipoprotein cholesterol (LDL-C) (r = 0.574, p < 0.01), apolipoprotein B (ApoB) (r = 0.660, p < 0.001), and free fatty acids (FFA) (r = 0.563, p = 0.005). No significant safety differences were observed between groups. Rosuvastatin significantly reduced hepatic steatosis in individuals with moderate to severe MAFLD and metabolic syndrome over 52 weeks, while maintaining a favorable safety profile. Show less
no PDF DOI: 10.1016/j.dsx.2024.103126
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Jiayu Wang, Lisi Xu, Xuemei Chen +10 more · 2024 · Journal of Alzheimer's disease : JAD · added 2026-04-24
Apolipoproteins and cortical morphology are closely associated with memory complaints, and both may contribute to the development of Alzheimer's disease. To examine whether apolipoprotein B (ApoB), ap Show more
Apolipoproteins and cortical morphology are closely associated with memory complaints, and both may contribute to the development of Alzheimer's disease. To examine whether apolipoprotein B (ApoB), apolipoprotein A-1 (ApoA1), and their ratio (ApoB/ApoA1) are associated with cortical morphology in patients with memory complaints. Ninety-seven patients underwent neuropsychological testing, measurements of ApoB, ApoA1, ApoB/ApoA1, plasma Alzheimer's biomarker, apolipoprotein E (ApoE) genotyping, and 3T structural magnetic resonance imaging (sMRI) scans. Based on sMRI scanning locations, patients were categorized into the University of Electronic Science and Technology (UESTC) and the Fourth People's Hospital of Chengdu (FPHC). The Computational Anatomy Toolbox within Statistical Parametric Mapping was used to calculate each patient's cortical morphology index based on sMRI data. The cortical morphology index and apolipoproteins were also analyzed. Significant positive correlations were found between ApoB and sulcal depth in the lateral occipital cortex among the UESTC, the FPHC, and the total sample groups, and negative correlations were observed between sulcal depth in the lateral occipital cortex and the scores of the Shape Trails Test Part A and B. In the FPHC group, the scores of the Montreal Cognitive Assessment Basic, delayed recall of the Auditory Verbal Learning Test, Animal Fluency Test and Boston Naming Test were positively correlated with the sulcal depth. ApoB is associated with the sulcal depth in the lateral occipital cortex, potentially relating to speed/executive function in individuals with memory complaints. Show less
no PDF DOI: 10.3233/JAD-230863
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Jiajie Mei, Xiaodan Fu, Zhenzhu Liu +9 more · 2024 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
Rapid progression of non-target lesions (NTLs) leads to a high incidence of NTL related cardiac events post-PCI, which accounting half of the recurrent cardiac events. It is important to identify the Show more
Rapid progression of non-target lesions (NTLs) leads to a high incidence of NTL related cardiac events post-PCI, which accounting half of the recurrent cardiac events. It is important to identify the risk factors and establish an accurate clinical prediction model for the rapid progression of NTLs post-PCI. PCSK9 inhibitors lower LDL-c levels significantly, also show the anti-inflammation effect, and may have the potential to reduce the rapid progression of NTLs post-PCI. We tried to test this hypothesis and explore the potential mechanisms. This retrospective study included 1250 patients who underwent the first PCI and underwent repeat coronary angiography for recurrence of chest pain within 24 months. General characteristics, laboratory tests and inflammatory factors(IL-10, IL-6, IL-8, IL-1β, sIL-2R, and TNF-α) were collected. Machine learning (LASSO regression) was mainly employed to select the important characteristic risk factors for the rapid progression of NTLs post-PCI and build prediction models. Finally, mediator analysis was employed to explore the potential mechanisms by which PCSK9 inhibitors reduce the rapid progression of NTLs post-PCI. There were more diabetes, less beta-blockers and PCSK9 inhibitors application, higher HbA1c, LDL-c, ApoB, TG, TC, uric acid, hs-CRP, TNF-α, IL-6, IL-8, and sIL-2R in NTL progressed group. LDL-c, hs-CRP, IL-8, and sIL-2R were characteristic risk factors for the rapid progression of NTLs post-PCI, combining LDL-c, hs-CRP, IL-8, and sIL-2R builds the optimal model for predicting the rapid progression of NTLs post-PCI (AUC = 0.632). LDL-c had a clear and incomplete mediating effect (95% CI, mediating effect: 51.56%) in the reduction of the progression of NTLs by PCSK9 inhibitors, and there was a possible mediating effect of IL-8 (90% CI), and sIL-2R (90% CI). LDL-c, hs-CRP, IL-8, and sIL-2R may be the key characteristic risk factors for the rapid progression of NTLs post-PCI, and combining these parameters might predict the rapid progression of NTLs post-PCI. The application of PCSK9 inhibitors had a negative correlation with the rapid progression of NTLs. In addition to the significant LDL-c-lowering, PCSK9 inhibitors may reduce the rapid progression of NTLs by reducing local inflammation of plaque. ChiCTR2200058529; Date of registration: 2022-04-10. Show less
📄 PDF DOI: 10.1186/s12872-024-04186-2
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Zhenqian Wang, Jiaying Zhang, Feng Jiao +3 more · 2024 · Journal of clinical lipidology · Elsevier · added 2026-04-24
Blood lipid levels were associated with chronic kidney disease (CKD) in patients with type 2 diabetes (T2D), but the genetic basis and causal nature remain unclear. This study aimed to investigate the Show more
Blood lipid levels were associated with chronic kidney disease (CKD) in patients with type 2 diabetes (T2D), but the genetic basis and causal nature remain unclear. This study aimed to investigate the relationships of lipids and their fractions with CKD in patients with T2D. Our prospective analysis involved 8,607 White participants with T2D but no CKD at baseline from the UK Biobank. Five common lipid traits were included as exposures. Weighted genetic risk scores (GRSs) for these lipid traits were developed. The causal associations between lipid traits, as well as lipid fractions, and CKD were explored using linear or nonlinear Mendelian randomization (MR). The 10-year predicted probabilities of CKD were evaluated via integrating MR and Cox models. Higher GRS of apolipoprotein B (ApoB) was associated with an increased CKD risk (hazard ratio (HR) [95% confidence interval (CI)]:1.07[1.02,1.13] per SD; P = 0.008) after adjusting for potential confounders. Linear MR indicated a positive association between genetically predicted ApoB levels and CKD (HR [95% CI]:1.53 [1.12,2.09]; P = 0.008), but no evidence of associations was found between other lipid traits and CKD in T2D. Regarding 12 ApoB- containing lipid fractions, a significant causal association was found between medium very-low-density lipoprotein particles and CKD (HR[95% CI]:1.16[1.02,1.32];P = 0.020). Nonlinear MR did not support nonlinearity in these causal associations. The 10-year probability curve showed that ApoB level was positively associated with the risk of CKD in patients with T2D. Lower ApoB levels were causally associated with a reduced risk of CKD in patients with T2D, positioning ApoB as a potential therapeutic target for CKD prevention in this population. Show less
no PDF DOI: 10.1016/j.jacl.2024.07.004
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Xiaohui Zhu, Dongmei Jiang, Hongjie Zhang +3 more · 2024 · Diabetes, metabolic syndrome and obesity : targets and therapy · added 2026-04-24
The study aimed to explore the correlation between retinal nerve fiber layer thickness (RNFLT) with blood biochemical indicators and cognitive dysfunction in patients with type 2 diabetes mellitus (T2 Show more
The study aimed to explore the correlation between retinal nerve fiber layer thickness (RNFLT) with blood biochemical indicators and cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM) and the possible mechanism, thereby providing more theoretical basis for the occurrence and prevention of diabetes related complications. Eighty T2DM patients treated in our hospital from March 2022 to September 2022 were selected as the study subjects, and the clinical data of the patients were retrospectively analyzed. All patients underwent fundus fluorescein angiography (FFA) to analyze the changes in retinal blood vessels. Patients who met the inclusion criteria were divided as the diabetic retinopathy (DR) group (n=46) and simple diabetes group (n=34). The RNFLT, blood biochemical indexes and changes in cognitive functions of the patients were detected. The correlation between RNFLT with blood biochemical indexes and cognitive dysfunction was analyzed. Compared with the simple diabetes group, patients in the DR group had much lower mean, nasal, inferior and superior thicknesses ( DR patients had significantly reduced RNFLT, elevated levels of blood glucose related indicators, and cognitive dysfunction. There existed a correlation between RNFLT and FBG, HbA1c, HOMA-IR index, TMT-A, TMT-B and MMSE. Show less
📄 PDF DOI: 10.2147/DMSO.S470297
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Xinyue Ming, Shirui Chen, Huijuan Li +3 more · 2024 · Cellular signalling · Elsevier · added 2026-04-24
This study aimed to investigate the effects of hepatic microRNA-122 (miR-122) on Sortilin-mediated apolipoprotein B100 (apoB-100) secretion, and on aortic lipid deposition and atherosclerosis (AS) les Show more
This study aimed to investigate the effects of hepatic microRNA-122 (miR-122) on Sortilin-mediated apolipoprotein B100 (apoB-100) secretion, and on aortic lipid deposition and atherosclerosis (AS) lesions and to clarify the antiatherosclerotic mechanism of 6-methylcoumarin (6-MC) via the modulation of miR-122. Bioinformatics analysis revealed that miR-122 was putatively overexpressed in a liver-specific manner and was downregulated in steatotic livers. miR-122 was shown to suppress the expression of Sortilin by complementarily pairing to the 3'-untranslated region (3'-UTR) of Sortilin mRNA via bioinformatics and dual-luciferase reporter assays, impeding Sortilin-mediated apoB-100 secretion from HepG2 cells. Administration of 6-MC significantly upregulated hepatocellular miR-122 levels, reducing Sortilin expression and apoB-100 secretion in HepG2 cells. The miR-122 mimic vigorously enhanced 6-MC-depressed Sortilin expression, while miR-122 inhibitor repealed the inhibitory effect of 6-MC on Sortilin expression to some extent in HepG2 cells. After internal intervention with the miR-122 precursor, and 6-MC supplementation alone or in combination with the miR-122 sponge led to the reduction in blood triglyceride (TG) levels, low-density lipoprotein-cholesterol (LDL-C) and apoB-100 and a reduction in aortic lipid deposition and AS lesions in apolipoprotein E-deficient (ApoE Show less
no PDF DOI: 10.1016/j.cellsig.2024.111384
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Enmin Ding, Fuchang Deng, Jianlong Fang +23 more · 2024 · Environmental health perspectives · added 2026-04-24
Environmental contaminants (ECs) are increasingly recognized as crucial drivers of dyslipidemia and cardiovascular disease (CVD), but the comprehensive impact spectrum and interlinking mechanisms rema Show more
Environmental contaminants (ECs) are increasingly recognized as crucial drivers of dyslipidemia and cardiovascular disease (CVD), but the comprehensive impact spectrum and interlinking mechanisms remain uncertain. We aimed to systematically evaluate the association between exposure to 80 ECs across seven divergent categories and markers of dyslipidemia and investigate their underpinning biomolecular mechanisms via an unbiased integrative approach of internal chemical exposome and multi-omics. A longitudinal study involving 76 healthy older adults was conducted in Jinan, China, and participants were followed five times from 10 September 2018 to 19 January 2019 in 1-month intervals. A broad spectrum of seven chemical categories covering the prototypes and metabolites of 102 ECs in serum or urine as well as six serum dyslipidemia markers [total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, apolipoprotein (Apo)A1, ApoB, and ApoE4] were measured. Multi-omics, including the blood transcriptome, serum/urine metabolome, and serum lipidome, were profiled concurrently. Exposome-wide association study and the deletion/substitution/addition algorithms were applied to explore the associations between 80 EC exposures detection frequency Eight main ECs [1-naphthalene, 1-pyrene, 2-fluorene, dibutyl phosphate, tri-phenyl phosphate, mono-(2-ethyl-5-hydroxyhexyl) phthalate, chromium, and vanadium] were significantly associated with most dyslipidemia markers. Multi-omics indicated that the associations were mediated by endogenous biomolecules and pathways, primarily pertinent to CVD, inflammation, and metabolism. Clinical measures of cytokines and electrocardiograms further cross-validated the association of these exogenous ECs with systemic inflammation and cardiac function, demonstrating their potential mechanisms in driving dyslipidemia pathogenesis. It is imperative to prioritize mitigating exposure to these ECs in the primary prevention and control of the dyslipidemia epidemic. https://doi.org/10.1289/EHP13864. Show less
📄 PDF DOI: 10.1289/EHP13864
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Joel T Rämö, Sean J Jurgens, Shinwan Kany +8 more · 2024 · Circulation · added 2026-04-24
Despite a proposed causal role for LDL-C (low-density lipoprotein cholesterol) in aortic stenosis (AS), randomized controlled trials of lipid-lowering therapy failed to prevent severe AS. We aimed to Show more
Despite a proposed causal role for LDL-C (low-density lipoprotein cholesterol) in aortic stenosis (AS), randomized controlled trials of lipid-lowering therapy failed to prevent severe AS. We aimed to assess the impact on AS and peak velocity across the aortic valve conferred by lifelong alterations in LDL-C levels mediated by protein-disrupting variants in 3 clinically significant genes for LDL (low-density lipoprotein) metabolism ( We used sequencing data and electronic health records from UK Biobank (UKB) and All of Us and magnetic resonance imaging data from UKB. We identified predicted protein-disrupting variants with the Loss Of Function Transcript Effect Estimator (LOFTEE) and AlphaMissense algorithms and evaluated their associations with LDL-C and peak velocity across the aortic valve (UK Biobank), as well as diagnosed AS and aortic valve replacement (UK Biobank and All of Us). We included 421 049 unrelated participants (5621 with AS) in UKB and 195 519 unrelated participants (1087 with AS) in All of Us. Carriers of protein-disrupting variants in Rare genetic variants that confer lifelong higher or lower LDL-C levels are associated with substantially increased and decreased risk of AS, respectively. Early and sustained lipid-lowering therapy may slow or prevent AS development. Show less
📄 PDF DOI: 10.1161/CIRCULATIONAHA.124.070982
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Giada Di Nunzio, Sanna Hellberg, Yuyang Zhang +18 more · 2024 · Nature cardiovascular research · Nature · added 2026-04-24
Apolipoprotein-B (APOB)-containing lipoproteins cause atherosclerosis. Whether the vasculature is the initially responding site or if atherogenic dyslipidemia affects other organs simultaneously is un Show more
Apolipoprotein-B (APOB)-containing lipoproteins cause atherosclerosis. Whether the vasculature is the initially responding site or if atherogenic dyslipidemia affects other organs simultaneously is unknown. Here we show that the liver responds to a dyslipidemic insult based on inducible models of familial hypercholesterolemia and APOB tracing. An acute transition to atherogenic APOB lipoprotein levels resulted in uptake by Kupffer cells and rapid accumulation of triglycerides and cholesterol in the liver. Bulk and single-cell RNA sequencing revealed a Kupffer-cell-specific transcriptional program that was not activated by a high-fat diet alone or detected in standard liver function or pathological assays, even in the presence of fulminant atherosclerosis. Depletion of Kupffer cells altered the dynamic of plasma and liver lipid concentrations, indicating that these liver macrophages help restrain and buffer atherogenic lipoproteins while simultaneously secreting atherosclerosis-modulating factors into plasma. Our results place Kupffer cells as key sentinels in organizing systemic responses to lipoproteins at the initiation of atherosclerosis. Show less
📄 PDF DOI: 10.1038/s44161-024-00448-6
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Zhehan Yang, Junpan Chen, Minghao Wen +6 more · 2024 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Aberrant lipid metabolism is intricately linked to the development of endometrial cancer, and statin lipid-lowering medications are regarded as promising adjunctive therapies for future management of Show more
Aberrant lipid metabolism is intricately linked to the development of endometrial cancer, and statin lipid-lowering medications are regarded as promising adjunctive therapies for future management of this malignancy. This study employed Mendelian randomization (MR) to explore the causal association between lipid traits and endometrial cancer while assessing the potential impact of drug targets on lower lipids on endometrial cancer. Two-sample Mendelian randomization was employed to probe the causal association between lipid traits and endometrial carcinoma. Drug-target Mendelian randomization was also utilized to identify potential drug-target genes for managing endometrial carcinoma. In instances where lipid-mediated effects through particular drug targets were notable, the impacts of these drug targets on endometrial carcinoma risk factors were investigated to bolster the findings. No causal association between genetically predicted lipid traits (LDL-C, TG, TC, and HDL-C) and EC was found in two-sample Mendelian randomization. In drug target Mendelian randomization, genetic modeling of apolipoprotein B (APOB) (OR [95%CI]=0.31, [0.16-0.60]; The results of our MR study revealed no causal association between genetically predicted lipid traits (LDL-C, TG, TC, and HDL-C) and EC. Among the six lipid-lowering drug targets, we observed a significant association between lower predicted APOB levels and higher CETP levels with an increased risk of endometrioid carcinoma. These findings provide novel insights into the importance of lipid regulation in individuals with endometrial carcinoma, warranting further clinical validation and mechanistic investigations. Show less
📄 PDF DOI: 10.3389/fendo.2024.1446457
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