πŸ‘€ Mengru 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, Yiwei Wang, Xueyun Wang, Yijun Wang, Haomin Wang, Meng C Wang, Mengxiao Wang, Huan-You Wang, Jingheng Wang, Carol A Wang, Benjamin H Wang, Penglong Wang, Pei-Wen Wang, Jian-Long Wang, Wang Wang, Jinhui Wang, Yuanqing Wang, Jacob E Wang, Jian-Xiong Wang, Wenyu Wang, Chengze Wang, Hongmei Wang, Fengqiang Wang, Zijun Wang, Shaochun Wang, Qinwen Wang, Ruicheng Wang, Aixian Wang, Yanling Wang, Lu-Lu Wang, Linyuan Wang, Yeming Wang, Ye Wang, Tian-Yi Wang, Zhichao Wang, Dangfeng Wang, Jiucun Wang, Guo-Liang Wang, Guandi Wang, Zhuo-Xin Wang, Aili Wang, Fengliang Wang, Yingzi Wang, Lirong Wang, Xuekai Wang, Wei-En Wang, Jing-Xian Wang, Hesuiyuan Wang, Yuexin Wang, Suzhen Wang, Luping Wang, Xiuyu Wang, Zicheng Wang, Jiliang Wang, Rikang Wang, Xue Wang, Shudan Wang, Chun Wang, Hongxin Wang, Chenglong Wang, Junxiao Wang, Zhiqing Wang, Shawn Wang, Shunran Wang, Tiantian Wang, Youhua Wang, Xiao-Hui Wang, Qing-Yan Wang, Hanying Wang, Qiuping Wang, Yongzhong Wang, Jin-Xia Wang, Xiao-Tong Wang, Shun Wang, Xiaoqun Wang, Ching-Jen Wang, Xin Wang, Hanbin Wang, Yingwen Wang, Jia Bei Wang, Xiaodan Wang, Wenhan Wang, Jia-Yu Wang, Xiaozhi Wang, Xinkun Wang, Jinhao Wang, KeShan Wang, Shengdong Wang, Jinzhu Wang, Lihui Wang, Bicheng Wang, Chao-Jun Wang, Shaoyi Wang, Yajing Wang, Qing-Bin Wang, Feiyan Wang, Geng Wang, Chen Wang, Zhimin Wang, Cenxuan Wang, Wenjun Wang, Chuan-Chao Wang, Zexin Wang, Shu-Huei Wang, Yonggang Wang, Zhaoyu Wang, Xiaochuan Wang, Chuan-Hui Wang, Junshuang Wang, X F Wang, Li-Ting Wang, Chenxin Wang, Qiao-Ping Wang, Jingqi Wang, Xiongjun Wang, Shuang-Shuang Wang, Xu Wang, Houchun Wang, Yaodong Wang, Lujuan Wang, Jilin Wang, Peichang Wang, Keyun Wang, Ruixuan Wang, Zhangying Wang, Lianyong Wang, Dongyu Wang, Xinghui Wang, Binghan Wang, Guanduo Wang, Xian-e Wang, Guimin Wang, Xiaomeng Wang, Yuh-Hwa Wang, Jinru Wang, Mingyu Wang, Binbin Wang, Chaokui Wang, Linhui Wang, Youzhi Wang, Zhenqian Wang, Jialiang Wang, Sufang Wang, Haiyan Wang, Yankun Wang, Yingbo Wang, Zilong Wang, Xiao-Qun Wang, Lin-Fa Wang, Wenhao Wang, P Wang, Rui-Hong Wang, Xiao-jian WANG, Pei Chang Wang, Zhengkun Wang, Vivian Wang, Ying Wang, Zihuan Wang, Peiwen Wang, Chao Wang, Da-Zhi Wang, He-Tong Wang, Mofei Wang, Zezhou Wang, Liyong Wang, Bruce Wang, Hao-Tian Wang, Jin-Juan Wang, Yucheng Wang, Yong-Gang Wang, Saili Wang, Xiuwen Wang, Ruiquan Wang, Xinmei Wang, Zhezhi Wang, Xiao-Jie Wang, H Y Wang, Li-Dong Wang, Duanyang Wang, Kaiting Wang, Yikang Wang, Yichen Wang, Ting-Chen Wang, Meixia Wang, ZhenXue Wang, Juan Wang, Shouling Wang, Lan Wang, Li Chun Wang, Xingxin Wang, Ruibing Wang, Xue-Ying Wang, Bi-Dar Wang, Jiayang Wang, Suxia Wang, Yumin Wang, Qing Jun Wang, Xinbo Wang, Youli Wang, Yi-Ni Wang, Xinran Wang, Lixian Wang, Kan Wang, Ruiming Wang, Qing-Yuan Wang, Kai-Kun Wang, Yaoxian Wang, Qing-Jin Wang, Junmei Wang, Xin Wei Wang, J P Wang, Xufei Wang, Yuqin Wang, Handong Wang, Li-San Wang, Guoling Wang, Wenrui Wang, Zhongwei Wang, Shi-Han Wang, Ruoxi Wang, Huiping Wang, Mu Wang, Weihong Wang, Minzhou Wang, Yakun Wang, Da-Cheng Wang, Pengjie Wang, Qihua Wang, Ji-Nuo Wang, Deshou Wang, Xiaowen Wang, Yaochun Wang, Qihao Wang, Ruiying Wang, Tiange Wang, Xi Wang, Yindan Wang, Lixin Wang, Zhaofeng Wang, Guixin Wang, Erming Wang, Haoyu Wang, Kexin Wang, Yiqiao Wang, Qi-Qi Wang, Shuiyun Wang, Xi-Rui Wang, Cai-Hong Wang, Zhizheng Wang, Mingxun Wang, Liangli Wang, Theodore Wang, Alexander Wang, Huayang Wang, Yinyin Wang, Shuzhong Wang, Tingting Wang, Jiao Wang, Wenxian Wang, Jianghua Wang, Furong Wang, Shijun Wang, Le Wang, Guihua Wang, Xiaokun Wang, Xia Wang, Jiabei Wang, Guoying Wang, Zeyuan Wang, Jue Wang, Jin-E Wang, Jingru Wang, Chun-Li Wang, Xiaole Wang, Ermao Wang, Lanlan Wang, Ye-Ran Wang, Hao Wang, Xv Wang, Shikang Wang, Yufei Wang, Siyi Wang, Xiujuan Wang, Qinyun Wang, Xiangwei Wang, Jian-Hong Wang, David Q-H Wang, Chunjuan Wang, Weiyan Wang, Jia-Liang Wang, Yanxing Wang, Sheri Wang, Chenwei Wang, Haoping Wang, Sheng-Quan Wang, Xiangrong Wang, Xiao-Yi Wang, Huan Wang, Zhitao Wang, Xinyan Wang, J Wang, Kaixi Wang, Huihua Wang, Renwei Wang, Xiaoliang Wang, Xiao-Lin Wang, Tian-Lu Wang, Jiou Wang, Weiqin Wang, Jiamin Wang, Dennis Wang, Ji-Yao Wang, Pingping Wang, Jinyang Wang, Chen-Cen Wang, Chien-Wei Wang, Daolong Wang, Rong-Tsorng Wang, Yuwei Wang, Guo-Ping Wang, Zhentang Wang, F Wang, Xueju Wang, Saisai Wang, Zhehai Wang, Y B Wang, Xiao Wang, Guobing Wang, Kangmei Wang, Chunguo Wang, Longcai Wang, Haina Wang, Chih-Hsien Wang, Yuli Wang, Ling-Ling Wang, Zhangshun Wang, Xue-Lian Wang, Jianxin Wang, Da-Yan Wang, Xianghua Wang, Peng Wang, Yu Qin Wang, Zhao-Jun Wang, Rui-Rui Wang, Xingyue Wang, Man Wang, Daozhong Wang, Tian-Li Wang, Luhui Wang, Gaopin Wang, Mengze Wang, Jizheng Wang, Hong-Yan Wang, Dongying Wang, Wenkai Wang, Stephani Wang, Dan-Dan Wang, Yicheng Wang, Yusheng Wang, Junwen Wang, Gao Wang, Ruo-Nan Wang, Yifan Wang, Jueqiong Wang, Xuewei Wang, Jianning Wang, Yonglun Wang, Shiwen Wang, Lifang Wang, Fuyan Wang, Jian-Bin Wang, Chonglong Wang, Haiwei Wang, Yike Wang, Chunxia Wang, Kaijuan Wang, Minglei Wang, Jingxiao Wang, Luting Wang, David Wang, Ben Wang, Ji-zheng Wang, Yuncong Wang, Lei P Wang, Tingye Wang, Wenke Wang, Ping Wang, Min Wang, Qiang-Sheng Wang, Xuejing Wang, Zhanju Wang, Xixi Wang, Xiaodong Wang, Chaomeng Wang, Yanong Wang, Xinghao Wang, Jiaming Wang, Siyuan Wang, Jiu Wang, Ruizhi Wang, Qing Mei Wang, Wenyi Wang, Yiqing Wang, Cai Ren Wang, Lianchun Wang, Xing-Ping Wang, Xiaoman Wang, Yanjin Wang, Xueqin Wang, Chenliang Wang, Zhenshan Wang, Junhong Wang, Guiping Wang, Xianrong Wang, Xumeng Wang, Dajia Wang, Huang Wang, Huie Wang, Weiwen Wang, Ruiwen Wang, Qing Wang, Haohao Wang, Bao-Long Wang, P Jeremy Wang, Chengqiang Wang, Suli Wang, Lingyan Wang, Chi Wang, Meng Wang, Luwen Wang, Quan Wang, Yan-Jun Wang, Sen Wang, Ruining Wang, Xiaozhen Wang, Zhiping Wang, Xue-Yao Wang, Yuming Wang, Jingjing Wang, Jiazheng Wang, Yunong Wang, Chongze Wang, Rufang Wang, Qiuning Wang, Tiannan Wang, Liqing Wang, Wencheng Wang, Xuefeng Wang, Yongli Wang, Xinwen Wang, Runzhi Wang, Chaojie Wang, Wentao Wang, Zhifeng Wang, Yanan Wang, Mengqi Wang, Limin Wang, Donglin Wang, Shujin Wang, Chengbin Wang, Qiu-Xia Wang, Zhengxuan Wang, Yancun Wang, Yuhuan Wang, Wei Wang, G-W Wang, Bangmao Wang, Kejia Wang, Jinjin Wang, Qifei Wang, Guobin Wang, Chun-Lin Wang, Jing-Shi Wang, Jiheng Wang, Huajing Wang, Yanlin Wang, Chuansheng Wang, Cailian Wang, Beilan Wang, Luofu Wang, Yangpeng Wang, Jieqi Wang, Weilin Wang, Xiaoxuan Wang, Yangyufan Wang, Xiao-Fei Wang, Chen-Ma Wang, Yun Yong Wang, Shizhi Wang, B Wang, Yuling Wang, Yi-Yi Wang, Fanwen Wang, Aiyun Wang, Jian Wang, Chengyu Wang, Jing-Huan Wang, Ning Wang, Yichuan Wang, L F Wang, Chau-Jong Wang, Xin-Yang Wang, Yunzhe Wang, Xuewen Wang, Sheng-Ping Wang, Bi Wang, Qiuting Wang, Yan-Jiang Wang, Dongshi Wang, Yingna Wang, Jingyue Wang, Hongshan Wang, Chunjiong Wang, Hong-Yang Wang, Yingmei Wang, Danfeng Wang, Zhongyi Wang, Teng Wang, Chih-Hao Wang, Mingchao Wang, Yi-Chuan Wang, Chuning Wang, Shihao Wang, Ming-Wei Wang, Menglu Wang, Zhulun Wang, Wuji Wang, Dao-Xin Wang, Han Wang, Jincheng Wang, Thomas T Y Wang, Qingyun Wang, Guoliang Wang, Jihong Wang, Hong-Qin Wang, G Wang, Hsei-Wei Wang, Linfang Wang, Xiao Ling Wang, Ganyu Wang, Zhengdong Wang, Cuizhe Wang, Hongyu Wang, Tieqiao Wang, Lijuan Wang, Jingchun Wang, Youzhao Wang, Zijian Wang, Ziheng Wang, Xingyu Wang, Shuning Wang, Shaokun Wang, Zhifu Wang, Xinqi Wang, Jinqiu Wang, ZhongXia Wang, Yanyun Wang, Dadong Wang, Xingjie Wang, Yiting Wang, Zhongli Wang, Junyu Wang, Jianding Wang, Meng-Wei Wang, Yingge Wang, Zhenchang Wang, Qun Wang, Jin-Xing Wang, Lijun Wang, Shuqing Wang, Fu-Yan Wang, Sheng-Nan Wang, Feijie Wang, Qiuyan Wang, Ying-Wei Wang, Shitao Wang, Meng-hong Wang, Zhengyang Wang, Jinghong Wang, Zhiying Wang, Pei Wang, Weixue Wang, Shiyue Wang, Xiaohong Wang, Daiwei Wang, Jinghua Wang, S X Wang, Jian-Yong Wang, Zeying Wang, Can Wang, Kehan Wang, Yunzhang Wang, Jinping Wang, Chenchen Wang, Chun-Ting Wang, Yujiao Wang, Xinxin Wang, Ji Wang, Sui Wang, Wenqiang Wang, Yingwei Wang, Shuzhen Wang, Daixi Wang, Yanming Wang, Lin-Yu Wang, Hongyin Wang, Zhongqun Wang, Er-Jin Wang, Yi Wang, Ziyi Wang, Lianghai Wang, Zhendan Wang, Xiao-Ming Wang, Chengyan Wang, Hui Miao Wang, Jingyi Wang, Ranran Wang, Banghui Wang, Huilun Wang, Ai-Ting Wang, Wenxuan Wang, Yuan-Hung Wang, Zixuan Wang, Hailing Wang, Xuan-Ying Wang, Jiqiu Wang, Yalong Wang, Xiaogang Wang, Shu-qiang Wang, Yun-Jin Wang, Zijie Wang, Tianlin Wang, Mingqiang Wang, Lufang Wang, Jin'e Wang, Xiru Wang, Cuili Wang, GuoYou Wang, Zhizhong Wang, Haifei Wang, Guorong Wang, Xinyue Wang, Pei-Juan Wang, Jiangong Wang, Yingte Wang, Huajin Wang, Ruibo Wang, Kejian Wang, Cheng-Cheng Wang, Xusheng Wang, Shu-Na Wang, Panliang Wang, Mingxi Wang, Shenqi Wang, Zifeng Wang, Chaozhan Wang, Xiuyuan Hugh Wang, Yuping Wang, Xujing Wang, Kai Wang, Hongbing Wang, Sheng-Yang Wang, Jianfei Wang, Hang Wang, Jing-Jing Wang, Weizhi Wang, Jixuan Wang, De-He Wang, P L Wang, Ningjian Wang, Chunyi Wang, Isabel Z Wang, Yong Wang, Yiming Wang, Mingzhi Wang, Jiying Wang, Qian-Wen Wang, Shusen Wang, Xiaoting Wang, Baogui Wang, Mingsong Wang, Zixia Wang, Demin Wang, Shiyuan Wang, Qiuli Wang, C Wang, Dongliang Wang, Weixiao Wang, Yinsheng Wang, Chunmei Wang, Huaili Wang, Xuelian Wang, Yongjun Wang, Zhi-Qin Wang, Jiaying Wang, Yulong Wang, Ren Wang, Jingnan Wang, Qishan Wang, Zeneng Wang, Guangsuo Wang, Chijia Wang, Huiqun Wang, Hongcai Wang, Donghao Wang, Xing-Jin Wang, Zongji Wang, Shenao Wang, Jiaqian Wang, Xiaoying Wang, Yilin Wang, Hangzhou Wang, Wenchao Wang, Jieyu Wang, Li-E Wang, Xuezhen Wang, Liuyang Wang, Zhiqian Wang, Fang-Tao Wang, Qiong Wang, Meng-Meng Wang, Youji Wang, Jiafeng Wang, Xiaojing Wang, William Wang, Junmin Wang, Laijian Wang, Xuexiang Wang, Huiyan Wang, T Y Wang, Zhaofu Wang, Wen-mei Wang, Yalin Wang, Xinshuai Wang, Daqi Wang, Zhen Wang, Shi-Cheng Wang, Anni Wang, Chunhong Wang, Hai-Long Wang, Pan Wang, Charles C N Wang, Pengxiang Wang, Xianzong Wang, Xike Wang, Qianliang Wang, Chunyan Wang, Xuan Wang, Xiaofen Wang, Zhi-Jian Wang, Feng-Sheng Wang, Xiangru Wang, R Wang, Yi-Shu Wang, Jia-Lin Wang, Yonghong Wang, Lintao Wang, Pai Wang, Yanfei Wang, Xuanwen Wang, Lei-Lei Wang, Chenxuan Wang, James Wang, Xinhui Wang, Shengqi Wang, Yueshen Wang, Shan-Shan Wang, Dingting Wang, Zhige Wang, Jingfeng Wang, Yongqing Wang, Chenyang Wang, Ziliang Wang, Bao Wang, Xueyan Wang, Liping Wang, Xingde Wang, Weijun Wang, Sibo Wang, Yaoling Wang, Donghong Wang, Chenyu Wang, Justin Wang, Baolong Wang, Yiqi Wang, Fengyong Wang, Lichao Wang, Yachen Wang, Quanren Wang, Shiyu Wang, Boyu Wang, Aimin Wang, Zhenghui Wang, Hengjiao Wang, Xiaoxin X Wang, Weimin Wang, Mutian Wang, Zhuo-Hui Wang, Xingye Wang, Zou Wang, Yu-Wen Wang, Shaoli Wang, Xin-Ming Wang, Weirong Wang, Kangli Wang, Yaoxing Wang, Xuejie Wang, Qifeng Wang, Xiaoxin Wang, Yinghui Wang, Jianzhang Wang, Tom J Wang, Yaqiong Wang, Zongwei Wang, Yun-Hui Wang, Haiyun Wang, Zhiyou Wang, Lijin Wang, Jifei Wang, Haiyong Wang, Xiao-Xia Wang, Shyi-Gang P Wang, Chih-Yang Wang, Zhixin Wang, Jun-Jun Wang, Tianjing Wang, Zhixia Wang, Chuanhai Wang, Zhijie Wang, Silu Wang, Jianguo Wang, Ming-Hsi Wang, Liling Wang, Yanting Wang, Haolong Wang, Xue-Lei Wang, Ru Wang, Qinglin Wang, Christina Wang, Mimi Wang, Menghui Wang, Wenju Wang, Junhua Wang, S S Wang, Fangyong Wang, Lifen Wang, Zhenbin Wang, Yapeng Wang, Shaoshen Wang, B R Wang, Sugai Wang, Hequn Wang, Songlin Wang, Wenjie Wang, Xiang-Dong Wang, Ting-Hua Wang, Mingliang Wang, Chengniu Wang, Guoxiang Wang, E Wang, Xiaochun Wang, Xueting Wang, Ming-Jie Wang, Zhaojing Wang, Dongxu Wang, Yirui Wang, Jiatao Wang, Jing-Min Wang, Shih-Wei Wang, Zhengchun Wang, Chaoxian Wang, Zehua Wang, Qiyu Wang, Shuye Wang, Baojun Wang, Qing Kenneth Wang, Xichun Wang, Jianliu Wang, Junping Wang, Yudong Wang, Mingzhu Wang, Kangning Wang, Wei-Ting Wang, Hongfang Wang, Chengwen Wang, Changduo Wang, Jinkang Wang, Junya Wang, Fengge Wang, Jianping Wang, Chang Wang, Zhifang Wang, Deli Wang, Linghua Wang, Shitian Wang, Lingling Wang, Zhihua Wang, Jun-Ling Wang, Keyi Wang, Lingbing Wang, Peijia Wang, Ruizhe Wang, X O Wang, Wanyi Wang, Ganggang Wang, Pei-Hua Wang, Kaiyue Wang, Xiaojiao Wang, Xun Wang, Shiyang Wang, Ya-Ping Wang, Yirong Wang, Lixing Wang, Danyang Wang, Xiaotang Wang, Taian Wang, Ming Wang, Xiangcheng Wang, Xuemei Wang, Zhixiong Wang, Mengying Wang, Li-Yong Wang, Xinchao Wang, Jianlong Wang, Jinjie Wang, Nan Wang, Weidong Wang, Mei-Gui Wang, L-S Wang, Wuqing Wang, Z Wang, Ya-Zhou Wang, Xincheng Wang, Jing-Wen Wang, Jinyue Wang, Hongyun Wang, Huaizhi Wang, Yan-Zi Wang, Danling Wang, Dongqin Wang, Hongzhuang Wang, Chung-Teng Wang, Yan-Chun Wang, Shi-Xin Wang, Muxuan Wang, Yujie Wang, Yunbing Wang, Yahui Wang, Zhihong Wang, Xiaoshan Wang, Tienju Wang, Chiou-Miin Wang, Yuqian Wang, Shengyuan Wang, Yumei Wang, Ningyuan Wang, Minjie Wang, Zhenda Wang, Qing-Dong Wang, Horng-Dar Wang, Siqi Wang, Kaihong Wang, Hong-Kai Wang, Meiling Wang, Jiaxing Wang, Xueyi Wang, Zhuozhong Wang, Anlai Wang, Julie Wang, Jin-Bao Wang, Keke Wang, Zhang Wang, Yintao Wang, Yong-Bo Wang, Bing Wang, Dalu Wang, Minxian Wang, Zulong Wang, Gao T Wang, Gang Wang, Sophie H Wang, Xinquan Wang, Yi-Ting Wang, Honglian Wang, Ruyue Wang, Jia-Qiang Wang, Seungwon Wang, Shusheng Wang, Yanbin Wang, Chang-Yun Wang, Le-Xin Wang, Juling Wang, Haohui Wang, Chuanyue Wang, Tianqin Wang, Danqing Wang, Keyan Wang, Yeou-Lih Wang, Qinglu Wang, Sun Wang, Rui-Min Wang, Yong-Tang Wang, Xianwei Wang, Lixia Wang, Tong Wang, Xiaonan Wang, Feida Wang, Jiaxuan Wang, Mingrui Wang, Zixiang Wang, Y Z Wang, Yuliang Wang, Ming-Chih Wang, J J Wang, Huina Wang, Jingang Wang, Jinyun Wang, Min-sheng Wang, Wanyao Wang, Ziqiu Wang, Guo-Quan Wang, Xueping Wang, Qixue Wang, Hechuan Wang, Shang Wang, Chaohan Wang, M H Wang, L Z Wang, Jianhui Wang, Xifeng Wang, Xiaorong Wang, Yinong Wang, Zhixiu Wang, Jiaxi Wang, Jiahui Wang, Xiaofei Wang, Feifei Wang, Kesheng Wang, Rong-Chun Wang, Zhi-Xin Wang, Chaoyu Wang, Yongkuan Wang, Zuoyan Wang, Hsueh-Chun Wang, Xixiang Wang, Guanrou Wang, Songsong Wang, Hongyuan Wang, Yubing Wang, Xuliang Wang, Wen-Ying Wang, Xinglei Wang, Dao-Wen Wang, Yun Wang, Ze Wang, Jiyan Wang, Zai Wang, Guan Wang, Chih-Chun Wang, Yiqin Wang, X S Wang, Hongzhan Wang, Exing Wang, Shu-Jin Wang, Shangyu Wang, Shouzhi Wang, Yunduan Wang, Jiyong Wang, Dongdong Wang, Qingzhong Wang, Zi-Qi Wang, Renyuan Wang, Siyu Wang, Donghui Wang, Ming-Yuan Wang, Juxiang Wang, Muxiao Wang, Fu Wang, Fei Wang, Qiuyu Wang, Ertao Wang, Zhi Xiao Wang, Zunxian Wang, Hui-Nan Wang, Rongping Wang, Won-Jing Wang, Leiming Wang, Pu Wang, Shen-Nien Wang, Xiaona Wang, Meng-Ying Wang, Wen-Jie Wang, Jiaxin Wang, RuNan Wang, Jiemei Wang, Ningli Wang, Zhong-Hui Wang, Hong Wang, Hui-Yu Wang, Ziqian Wang, Xinzhou Wang, Zhoufeng Wang, Weiguang Wang, Zusen Wang, Jiajia Wang, Bin Wang, Shu-Xia Wang, Yu'e Wang, Laidi Wang, Xiao-Li Wang, Lu Wang, Zhugang Wang, Maojie Wang, Ganglin Wang, Xinyu Wang, Junlin Wang, Dong Wang, Yao Wang, Ya-Jie Wang, Zhiwu Wang, DongWei Wang, Hongdan Wang, Yanxia Wang, Maiqiu Wang, Guansong Wang, Qingtong Wang, Yingcheng Wang, Wenjuan Wang, Liying Wang, Xiaolong Wang, Weihao Wang, Qiushi Wang, Yingfei Wang, Haoyang Wang, Li-Li Wang, Yanbing Wang, Yingchun Wang, Guangming Wang, Kaiyuan Wang, Shiqi Wang, Qi-En Wang, Song Wang, Jing-Hao Wang, Lynn Yuning Wang, Zekun Wang, Rui-Ping Wang, Yining E Wang, Yuzhou Wang, Liu Wang, Maochun Wang, Cindy Wang, Qian-Liang Wang, Duo-Ping Wang, Linlin Wang, Taishu Wang, Xiang Wang, Qirui Wang, Baoming Wang, Liting Wang, Jiapan Wang, Lingda Wang, Xietong Wang, Jia-Mei Wang, Liwei Wang, Shaozheng Wang, Q Wang, Timothy C Wang, Mengyue Wang, Xing Wang, Yahong Wang, Yuyong Wang, Yujiong Wang, Guangliang Wang, Ya-Qin Wang, Yezhou Wang, Hongjian Wang, Su-Hua Wang, Qian-fei Wang, Meng-Dan Wang, Yuchen Wang, Hongpin Wang, Pengfei Wang, Ge Wang, Meijun Wang, Yan-Ming Wang, Haichao Wang, Tzung-Dau Wang, Runci Wang, Yan-Yi Wang, Cheng-Jie Wang, Chen-Yu Wang, Cong Wang, Yaxuan Wang, Y H Wang, Yongjie Wang, Yuntai Wang, Ranjing Wang, Yiru Wang, Anxiang Wang, Q Z Wang, Shimiao Wang, Guoping Wang, Junke Wang, Xingyun Wang, Zhengyi Wang, Shi-Qi Wang, Yanfeng Wang, Danxin Wang, Chaodong Wang, Zhiqi Wang, Chunyu Wang, Lijia Wang, Chunlong Wang, Haiping Wang, Qingfa Wang, Yu-Fan Wang, Baihan Wang, Chunxue Wang, Liewei Wang, Xinyi Wang, Fu-Zhen Wang, Qing-Mei Wang, Sheng Wang, Yi-Tao Wang, Dawei Wang, Xiaoyu Wang, Ziling Wang, Zhonglin Wang, Rurong Wang, Qingchun Wang, Qiang Wang, Suiyan Wang, Xu-Hong Wang, Jie Jin Wang, Chenyao Wang, Fei-Yan Wang, Shi Wang, Zhiyong Wang, Jieda Wang, Xiaoqi Wang, Linshu Wang, Ruxuan Wang, Qian Wang, Qianxu Wang, Fangjie Wang, Zhaoxia Wang, Jeremy R Wang, Mingmei Wang, Jingkang Wang, Jen-Chun Wang, Changyuan Wang, Chenglin Wang, Meng-Ru Wang, Tianpeng Wang, Zhongfang Wang, Xuedong Wang, Zhuoying Wang, Bingyu Wang, Xuelai Wang, Weilong Wang, Mengge Wang, Qin Wang, Da-Li Wang, Xuanyi Wang, Hongjuan Wang, Zhi-Hua Wang, Hong-Wei Wang, Yulai Wang, Gongming Wang, Yongni Wang, Mengya Wang, Yadong Wang, Chenghao Wang, Hongbo Wang, Kaiming Wang, Haonan Wang, Guanyun Wang, Yilu Wang, Quanxi Wang, Weiyuan Wang, Xiujun Wang, Liang-Yan Wang, Jianshe Wang, Yingxiong Wang, Cunchuan Wang, Jing-Zhai Wang, Yuelong Wang, Yuqi Wang, Xiaorui Wang, Qianjin Wang, Huijun Wang, Xiaobo Wang, Guoqian Wang, Luhong Wang, Kaining Wang, Chaohui Wang, Yanhong Wang, J-Y Wang, Qi-Bing Wang, Xiaohu Wang, Jiayan Wang, Cui-Shan Wang, Lulu Wang, Yong-Jie Wang, Shixuan Wang, Yuanyuan Wang, Jianying Wang, Haizhen Wang, Shuiliang Wang, Qianbao Wang, Jung-Pan Wang, Rixiang Wang, A Wang, Hanbing Wang, Caiqin Wang, Peigeng Wang, Yuan Wang, Yuzhuo Wang, Yubo Wang, Xianding Wang, Qiaoqi Wang, Cuiling Wang, Ai-Ling Wang, Hailong Wang, Yihao Wang, Lan-Wan Wang, Haihe Wang, S Wang, Sha Wang, Xiaoli Wang, David Q H Wang, Jianfang Wang, Yuting Wang, Jinhuan Wang, Kaixu Wang, Hongwei Wang, Yi-Wen Wang, Yizhe Wang, Shengyu Wang, Yanmei Wang, Huimin Wang, Youjie Wang, Kunhua Wang, Chongjian Wang, Ziyun Wang, Tianhui Wang, Huiying Wang, Yue-Nan Wang, Peiyin Wang, Hongbin Wang, Hong Yi Wang, Xinjun Wang, Yian Wang, Liyi Wang, Yunce Wang, Yi-Xuan Wang, Yitao Wang, Jiali Wang, Junqin Wang, Yuebing Wang, Yiping Wang, Yunpeng Wang, Yuxing Wang, Shuqi Wang, Ziyu Wang, Hongjie Wang, Xiaoyan Wang, Lianshui Wang, Xiaolu Wang, Wenya Wang, Fan Wang, Jinhua Wang, Sidan Wang, Lixiang Wang, Y L Wang, Xue-Rui Wang, Kai-Wen Wang, Zhongyu Wang, Xiaoyang Wang, Hongyang Wang, Rencheng Wang, Yinxiong Wang, Yuanli Wang, Zhuqing Wang, Y-H Wang, Yuhui Wang, Xitian Wang, Weizhen Wang, Qi Wang, Qiyuan Wang, Changlong Wang, Yatao Wang, Tengfei Wang, Yehan Wang
articles
Wei Jia, Huimin Wang, Ting Feng +5 more Β· 2025 Β· Foods (Basel, Switzerland) Β· MDPI Β· added 2026-04-24
FVPB1, a novel heteropolysaccharide, was extracted from the
πŸ“„ PDF DOI: 10.3390/foods14193452
APOB
Fangfang Wang, Dong You, Xiaoye Niu +4 more Β· 2025 Β· Cardiovascular diabetology Β· BioMed Central Β· added 2026-04-24
Plozasiran (VSA001, ARO-APOC3) is an RNA interference therapy that targets Apolipoprotein C3 (APOC3), a key regulator of lipoprotein metabolism. The study aimed at assessing the safety, tolerability, Show more
Plozasiran (VSA001, ARO-APOC3) is an RNA interference therapy that targets Apolipoprotein C3 (APOC3), a key regulator of lipoprotein metabolism. The study aimed at assessing the safety, tolerability, pharmacokinetics (PK), and pharmacodynamic (PD) profiles of plozasiran in Chinese healthy volunteers (HVs). In this double-blind, placebo-controlled, phase I clinical study, a total of 24 Chinese adult HVs received single subcutaneous (SC) injection of 25Β mg, 50Β mg plozasiran or placebo on day 1. Safety, tolerability, PK and PD profiles were accessed during a follow-up period of 85 days. Eighteen HVs received plozasiran (25Β mg: n = 9; 50Β mg: n = 9) and 6 HVs received placebo. Plozasiran was well tolerated in Chinese HVs. No death, no severe adverse events or treatment-emergent adverse events (TEAEs) leading to discontinuation were observed. TEAEs were reported in 9 of 18 HVs from plozasiran group and in 1 of 6 HVs from placebo group. All TEAEs were transient and recovered autonomously, except for 2 subjects with 4 TEAEs from plozasiran group needed concomitant medications. After SC injection, plozasiran was rapidly absorbed and quickly eliminated in the plasma. Maximum geomean serum concentration was 102 ng/mL (CV%:36.4%) and 216 ng/mL (58.1%) for 25Β mg and 50Β mg group, respectively. The median T Plozasiran at 25 and 50Β mg was well tolerated with acceptable safety profile in Chinese HVs. Safety, PK and PD profiles observed in the present study were consistent with the data reported from clinical studies conducted outside China. Show less
πŸ“„ PDF DOI: 10.1186/s12933-025-02929-9
APOB
Huixia Wang, Wenli Li, Yijia Tao +3 more Β· 2025 Β· BMC veterinary research Β· BioMed Central Β· added 2026-04-24
Neonatal piglets possess lysosome-rich foetal-type enterocytes that facilitate uptake and intracellular processing of maternally provided nutrients. However, the role of lysosomes in early-life growth Show more
Neonatal piglets possess lysosome-rich foetal-type enterocytes that facilitate uptake and intracellular processing of maternally provided nutrients. However, the role of lysosomes in early-life growth and intestinal maturation remains unclear. Therefore, this study was conducted to determine the role of lysosomes in the development of neonatal intestine in piglets. For 1-day-old neonatal piglets, a total of 12 piglets (Duroc Γ— (Landrace Γ— Large Yorkshire)) were divided into 2 groups using a split-litter design. To initiate malfunction in lysosomes, newborn piglets were subjected to oral gavage with imipramine (25Β mg/kg bodyweight) once daily for 7 days. For 21-day-old piglets, a total of 12 piglets were divided into two groups, and each group received the same treatment as described above. Piglets receiving imipramine demonstrated significantly stunted growth at 7 days of age, but not at 27 days. By postnatal day 7, the foetal-type enterocytes of untreated piglets were restricted in the mid to upper ileal villus and contained several large lysosomal vacuoles. In contrast, marked changes in ileal morphological and histological structure were observed following imipramine treatment, as evidenced by reduced degree of vacuolation, decreased lysosomal count, as well as pronounced mitochondrial swelling; however, no vacuolated enterocytes were found in 27-day-old piglets. Furthermore, signaling pathways associated with lipid transport and metabolism were significantly enriched, and the related hub genes were identified by bioinformatic analysis after imipramine administration. These findings were further confirmed by biochemical analysis demonstrating that serum levels of total cholesterol (TC) and apolipoprotein A1 (ApoA1) were significantly increased while serum ApoB was decreased in 7-day-old piglets receiving imipramine treatment. Additionally, there was an opposite trend in levels of ApoA1and ApoB in ileal mucosa compared to serum. These results demonstrate that lysosome dysfunction induced by imipramine resulted in significant growth retardation, pronounced morphological and ultrastructural alterations in ileal enterocytes, along with disrupted lipid metabolism in early postnatal piglets; however, no such effect was observed in 27-day-old piglets. These findings enhance understanding of lysosomal functions and intestinal maturation in neonatal piglets. Show less
πŸ“„ PDF DOI: 10.1186/s12917-025-05063-6
APOB
Hanyu Wang, Robert Clarke, Christiana Kartsonaki +12 more Β· 2025 Β· European heart journal open Β· Oxford University Press Β· added 2026-04-24
Little is known about the importance of blood lipids for risk of myocardial infarction (MI) in Chinese vs. European populations. We compared the associations with MI of apolioprotein B (ApoB) vs. low- Show more
Little is known about the importance of blood lipids for risk of myocardial infarction (MI) in Chinese vs. European populations. We compared the associations with MI of apolioprotein B (ApoB) vs. low-density lipoprotein cholesterol (LDL-C) and remnant-cholesterol (remnant-C) vs. triglycerides in the China Kadoorie Biobank (CKB) and UK Biobank (UKB). Plasma levels of LDL-C, high-density lipoprotein-cholesterol (HDL-C), apolipoprotein B (ApoB), apolipoprotein A1 (ApoA1), non-HDL-C, remnant-C, LDL-C/ApoB, and HDL-C/ApoA1 ratios were measured in a nested case-control study of MI (948 cases, 6101 controls) in CKB and a prospective study (5344 cases in 279 989 participants) in UKB. Associations of lipids with MI were assessed using logistic regression in CKB and Cox regression in UKB after adjustment for confounders and correction for regression dilution. The mean levels of LDL-C were about 30% lower in CKB than in UKB [2.3 (0.6) vs. 3.7 (0.8) mmol/L], but mean levels of HDL-C were comparable [1.3 (0.3) vs. 1.5 (0.4) mmol/L], as were those for triglycerides [1.8 (1.1) vs. 1.7 (1.1) mmol/L]. While the rate ratios (RRs) of MI for 1 SD higher usual levels of LDL-C in Chinese were about half those in Europeans (1.27; 1.13-1.44 vs. 1.55; 1.49-1.61), the corresponding RRs for ApoB or non-HDL with MI were comparable between Chinese and Europeans. The findings reinforce current guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) in China that advocate initiation of statin treatment in individuals at high-risk of ASCVD rather than high levels of LDL-C. Show less
πŸ“„ PDF DOI: 10.1093/ehjopen/oeaf119
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Lili Zhou, Wei Cheng, Dan Luo +10 more Β· 2025 Β· Frontiers in cell and developmental biology Β· Frontiers Β· added 2026-04-24
Cholesterol is an essential molecule for tumor cell growth and proliferation, and dysregulated cholesterol metabolism has been widely implicated in cancer pathogenesis. However, the specific role and Show more
Cholesterol is an essential molecule for tumor cell growth and proliferation, and dysregulated cholesterol metabolism has been widely implicated in cancer pathogenesis. However, the specific role and underlying molecular mechanisms of cholesterol metabolism alterations in diffuse large B-cell lymphoma (DLBCL) remain poorly understood. We retrospectively analyzed clinical data from 200 DLBCL patients and 185 healthy controls, focusing on lipid and lipoprotein levels, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and apolipoprotein E (ApoE). Univariate and multivariate Cox proportional hazard models were used to evaluate the prognostic value of these markers, and Kaplan-Meier analysis assessed their associations with overall survival (OS). Bioinformatics analysis predicted associations between lipid markers and cholesterol metabolism. Cellular experiments further investigated the expression of cholesterol metabolism-related proteins and the effect of the cholesterol-depleting agent Methyl-Ξ²-cyclodextrin (MΞ²CD) on DLBCL cells. We confirmed significant alterations in metabolic markers (such as TC and ApoA1) between the healthy control group and patients, which were significantly associated with patient prognosis and overall OS. Bioinformatics analysis revealed a strong correlation between these markers and elevated CD36 expression. In addition, DLBCL cells exhibited increased expression of cholesterol uptake and synthesis proteins (CD36, SREBP2, and HMGCR) and decreased expression of efflux proteins (APOA1, NR1H2 and ABCG1), consistent with cholesterol metabolic reprogramming. Treatment with MΞ²CD disrupted CD36 expression and cholesterol metabolism, leading to reduced DLBCL cell survival. These findings underscore the pivotal role of cholesterol metabolic reprogramming in DLBCL progression. CD36 and related metabolic markers represent promising therapeutic targets, opening novel avenues for the treatment of this malignancy. Show less
πŸ“„ PDF DOI: 10.3389/fcell.2025.1585521
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Baichao Shi, Yu Wang, Rong Luo +6 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· added 2026-04-24
This study aims to evaluate the association between mean arterial pressure (MAP) and anthropometric, metabolic, and endocrine parameters in Chinese infertile women with polycystic ovary syndrome (PCOS Show more
This study aims to evaluate the association between mean arterial pressure (MAP) and anthropometric, metabolic, and endocrine parameters in Chinese infertile women with polycystic ovary syndrome (PCOS). A total of 1,000 PCOS subjects were enrolled in the clinical trial project of Acupuncture and Clomiphene in the treatment of PCOS infertility patients (PCOSAct). Of these, 998 patients were selected for this study. Linear trends and regression analyses were conducted to evaluate the association between MAP and anthropometric, metabolic, and endocrine parameters. Logistic regression was employed to estimate the association between MAP and risk of insulin resistance (IR), nonalcoholic fatty liver disease (NAFLD) and hyperlipidemia. The receiver operating characteristics (ROC) curve was used to determine the predictive value of the MAP for IR, NAFLD and hyperlipidemia. Linear trends revealed that the MAP was positively associated with age, height, body weight, body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), systolic blood pressure (SBP) and diastolic blood pressure (DBP), hirsutism score, and acanthosis nigricans score, fasting blood glucose (FBG), fasting insulin (FINS), the homeostatic model assessment for insulin resistance (HOMA-IR), low-density lipoprotein (LDL), triglycerides (TG), total cholesterol (TC), apolipoprotein B (ApoB), ApoB/apolipoprotein A1 (ApoA1) ratio, total testosterone (TT), and free androgen index (FAI), as well as the prevalence of IR, metabolic syndrome (MetS), NAFLD, and hyperlipidemia. Conversely, MAP was negatively correlated with the quantitative insulin sensitivity check index (QUICKI), high-density lipoprotein (HDL), sex hormone-binding globulin (SHBG), luteinizing hormone (LH), the LH/follicle stimulating hormone (FSH) ratio, and anti-MΓΌllerian hormone (AMH). After adjusting for age and BMI, a significant linear relationship was observed between MAP and WC, WHR, hirsutism score, FBG, LDL, TG, TC, ApoB, and ApoB/ApoA1 ratio. Logistic regression analysis demonstrated that participants in the highest quartile (Q4) of MAP had no significantly higher odds ratios (OR) for IR, NAFLD and hyperlipidemia after adjusting for confounding factors. The ROC curve analysis indicated that the AUC Elevated MAP is associated with dysregulation of glucose and lipid metabolism and alterations in endocrine hormone levels. It may thus serve as a promising screening approach for IR-related conditions in patients with PCOS. Show less
πŸ“„ PDF DOI: 10.3389/fendo.2025.1594813
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Alexa Canchola, Keyuan Li, Kunpeng Chen +12 more Β· 2025 Β· ACS nano Β· ACS Publications Β· added 2026-04-24
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokine Show more
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. Yet systematic analyses are hampered by the absence of standardized, richly annotated data sets. Here, we introduce the Protein Corona Database (PC-DB), which compiles data from 83 studies (2000-2024) and integrates 817 NP formulations with quantitative profiles of 2497 adsorbed proteins. The PC-DB exposes pronounced heterogeneity in NP materials (metal 28.8%, silica 22.8%, lipid-based 14.8%), surface modifications, sizes (1-1400 nm), and ΞΆ-potentials (-70 to +70 mV). Subsequent meta-analysis shows that silica, polystyrene, and lipid-based NPs smaller than 100 nm with moderately negative to neutral ΞΆ-potentials preferentially bind the lipoproteins APOE and APOB-100, which are linked to receptor-mediated uptake and enhanced delivery efficiency. In contrast, metal and metal-oxide NPs carrying highly negative surface charge enrich complement component C3, indicating a greater likelihood of immune recognition and clearance. Interpretable machine learning models (LightGBM and XGBoost; ROC-AUC > 0.85) confirm NP size, ΞΆ-potential, and incubation time as the most influential predictors of protein adsorption. These results delineate how physicochemical parameters dictate PC composition and illustrate the power of predictive modeling to guide rational NP design. Show less
πŸ“„ PDF DOI: 10.1021/acsnano.5c08608
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Yuanyuan Wang, Dachuan Guo, Youzhi Wang +2 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· added 2026-04-24
[This corrects the article DOI: 10.3389/fendo.2025.1542190.].
πŸ“„ PDF DOI: 10.3389/fendo.2025.1699149
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Yuanyuan Wang, Dachuan Guo, Youzhi Wang +2 more Β· 2025 Β· Frontiers in endocrinology Β· Frontiers Β· added 2026-04-24
Low-density lipoprotein cholesterol (LDL-C) has now been the primary target for lipid-lowering therapy in the European and US guidelines for the management of dyslipidemia, with increasing interest in Show more
Low-density lipoprotein cholesterol (LDL-C) has now been the primary target for lipid-lowering therapy in the European and US guidelines for the management of dyslipidemia, with increasing interest in apolipoprotein B (ApoB) as a secondary target. The relationship between ApoB and the severity of acute myocardial infarction as well as residual risk still needs to be further determined. Coronary atherosclerosis occurs as a result of a complex set of factors, and there is a strong relationship between insulin resistance and cardiovascular disease. In contrast, there are limited studies on the relationship between TyG index (triglyceride glucose index), an indicator of insulin resistance, and cardiovascular disease. The purpose of this study was to investigate the value of ApoB and TyG index in assessing the severity of myocardial infarction and predicting prognosis. This study included 712 participants with acute myocardial infarction for a 5-year follow-up. Spearman correlation analysis and generalized linear model analysis were used to assess the correlation between ApoB and the severity of coronary atherosclerosis. Risk regression analysis was used to assess the correlation between ApoB and residual risk in patients with acute myocardial infarction, and the C-statistic, net reclassification index (NRI), and integrated discriminant improvement index (IDI) were further calculated to assess the predictive value of ApoB for residual risk after myocardial infarction. Categorizing apoB, LDL-C, and TyG indices according to tertiles, higher levels of ApoB were significantly associated with the severity of coronary artery stenosis in patients with acute myocardial infarction ( ApoB is an independent risk factor for major adverse cardiovascular events (MACE) following myocardial infarction. Elevated ApoB levels are more advantageous than elevated LDL-C levels in assessing the severity of coronary artery stenosis in myocardial infarction patients and predicting residual risk after myocardial infarction. Therefore, in patients with acute myocardial infarction, ApoB can be considered to guide further intensive treatment. However, the TyG index did not demonstrate a significant advantage in predicting cardiovascular residual risk in this study. Show less
πŸ“„ PDF DOI: 10.3389/fendo.2025.1542190
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Xuan Bai, Dingzi Zhou, Jing Luo +14 more Β· 2025 Β· Medicine Β· added 2026-04-24
Lipid metabolism abnormalities and inflammation have been implicated in gallstone disease (GSD) development, but the causal relationships and potential mediation effects among lipid metabolites, infla Show more
Lipid metabolism abnormalities and inflammation have been implicated in gallstone disease (GSD) development, but the causal relationships and potential mediation effects among lipid metabolites, inflammatory factors, and GSD remain unclear. The aim of this study is to explore the causal relationships among these 3 factors. This study employed 2-sample Mendelian Randomization (TSMR) and 2-step MR to investigate the causal relationships and potential mediation effects among 91 inflammatory factors, 6 lipid metabolism-related molecules (HDL-C, LDL-C, TG, total cholesterol, ApoA1, and ApoB), and GSD. We opted for 4 distinct MR analysis methods including inverse variance weighted method, weighted median method, MR-Egger regression method and MR-PRESSO analysis. Sensitivity analyses included MR-Egger intercept tests, Cochran's Q statistic, Steiger tests, and leave-one-out analyses. Product of coefficients method was used to estimate mediation proportion. TSMR analysis revealed that every 1-unit increase in low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB), the risk of GSD decreased by 16.5%, 10.2%, 8.4%, and 13.1%, respectively. Inflammatory factors such as Natural killer cell receptor 2B4 (CD244), Macrophage colony-stimulating factor 1 (CSF-1), and interleukin-18 receptor 1 (IL-18R1) were identified as risk factors for GSD, while Fibroblast growth factor 19 levels (FGF19), Interleukin-1-alpha levels (IL-1Ξ±), and Interleukin-8 levels (IL-8) were found to be protective. Mediation analysis through 2-step MR identified potential pathways involving ApoA1--IL-8--GSD (Pβ€…=β€….084) and IL-1Ξ±--ApoB--GSD (Pβ€…=β€….117). This study provides robust evidence of causal links between specific lipid metabolites and GSD, as well as suggestive causal associations for several inflammatory factors. However, mediation analysis did not support significant roles for lipids or inflammatory factors as mediators in GSD pathogenesis. Future research could be further pursued in areas such as drug target intervention and mechanistic studies. Show less
no PDF DOI: 10.1097/MD.0000000000044704
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Chao Fu, Yan Gong, Xiangyang Gao +8 more Β· 2025 Β· BMC gastroenterology Β· BioMed Central Β· added 2026-04-24
πŸ“„ PDF DOI: 10.1186/s12876-025-04130-4
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Mengying Yang, Xiaoman Liu, Qianqian Li +2 more Β· 2025 Β· Therapeutic advances in endocrinology and metabolism Β· SAGE Publications Β· added 2026-04-24
Metabolic-associated fatty liver disease (MAFLD) is closely associated with insulin resistance (IR) and systemic inflammation. Apolipoprotein A1 (ApoA1) and Apolipoprotein B (ApoB), as notable non-tra Show more
Metabolic-associated fatty liver disease (MAFLD) is closely associated with insulin resistance (IR) and systemic inflammation. Apolipoprotein A1 (ApoA1) and Apolipoprotein B (ApoB), as notable non-traditional lipid markers, have demonstrated distinct advantages in identifying risks related to metabolic syndrome and coronary atherosclerosis, yet its association with MAFLD and the mediating roles of IR/inflammation remain unclear. This retrospective investigation involved 1061 participants, categorized into a non-MAFLD group ( The MAFLD group exhibited markedly elevated levels of neutrophils/lymphocytes, neutrophils/platelets, systemic immune inflammation index, systemic inflammation response index, pan-immune-inflammation value and triglyceride-glucose index (TyG), TyG body mass index (TyGBMI), and metabolic score for insulin resistance (METS-IR) compared to the non-MAFLD group. Logistic regression analysis revealed that ApoB/ApoA1, TyG, TyGBMI, and METS-IR were markedly linked to MAFLD risk. Spearman's correlation analysis identified substantial positive links between ApoB/ApoA1 and TyG ( Our findings clarify the complex interrelationships between ApoB/ApoA1, MAFLD risk, inflammation, and IR, and for the first time, demonstrate that IR may act as a key potential mediator in the link between ApoB/ApoA1 and MAFLD, rather than systemic inflammation. This suggests that IR may serve a more prominent role than chronic systemic inflammation in the association between lipid metabolism and MAFLD risk, and intervening in IR may be more effective than anti-inflammatory therapy in blocking the progression from lipid metabolism disorders to MAFLD. Show less
πŸ“„ PDF DOI: 10.1177/20420188251378318
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S Y Huang, F Y Song, X O Wang +6 more Β· 2025 Β· Zhonghua er ke za zhi = Chinese journal of pediatrics Β· added 2026-04-24
no PDF DOI: 10.3760/cma.j.cn112140-20250531-00465
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Fengfeng Deng, Jianqi Sun, Lixia Liu +6 more Β· 2025 Β· Cardiovascular & hematological agents in medicinal chemistry Β· Bentham Science Β· added 2026-04-24
Pulmonary Hypertension (PH) is a significant contributor to cardiac mortality in Dilated Cardiomyopathy (DCM) patients. Inflammatory processes and oxidative stress play pivotal roles in the advancemen Show more
Pulmonary Hypertension (PH) is a significant contributor to cardiac mortality in Dilated Cardiomyopathy (DCM) patients. Inflammatory processes and oxidative stress play pivotal roles in the advancement of Pulmonary Hypertension (PH). The Monocyte-to-High-- Density-Lipoprotein Cholesterol Ratio (MHR), a newly identified biomarker indicative of inflammatory and oxidative stress, has not been extensively researched in the context of pulmonary hypertension, especially within the scope of dilated cardiomyopathy. Given the reason mentioned above, our research explores the correlation between the MHR and the severity of PH in patients suffering from DCM. In this study, we conducted a retrospective review of medical data from 107 individuals diagnosed with non-ischemic DCM, evaluating their clinical profiles, biochemical indicators, MHR, and echocardiographic parameters. We analyzed the relationships between Pulmonary Arterial Systolic Pressure (PASP) and the Ejection Fraction of the Left Ventricle (LVEF). Utilizing logistic regression analysis, we determined the predictors of PH. Findings indicated that the DCM-PH group exhibited a significantly larger male population and elevated New York Heart Association (NYHA) classification scores (both with p-values <0.001 and 0.01, respectively) compared to the DCM-only group. A positive association was observed between the PASP and parameters, such as the Dimensions of the Left Atrium (LAD) and Left Ventricle in Systole (LVDs), Monocyte (M) levels, Direct Bilirubin (DB), and MHR. Conversely, an inverse relationship was noted with serum lipid profiles, including Total Cholesterol (TC), HDL Cholesterol (HDL-c), and apolipoprotein A1. LVEF demonstrated positive linkage with the same lipid profiles and the Left Ventricular Posterior Wall Thickness (LVPWT) yet showed negative correlations with the NYHA classification, Red Blood Cell Distribution Width Standard Deviation (RDW-SD), Total Bilirubin (TB), Direct Bilirubin (DB), and dimensions of the left ventricle in diastole and systole, as well as MHR. Through logistic regression analysis, several factors were recognized as significant predictors for the severity of PH within the DCM cohort, with weight (OR1.20, CI 1.022-1.409, p=0.026), RDW-SD (OR1.988, CI 1.015-3.895, p=0.045), LVPW (OR3.577, CI 1.307-9.792, p=0.013), LVDd (OR1.333, CI 1.058-1.680, p=0.015), MHR (OR3.575, CI 1.502-8.506, p=0.032), and TB (OR1.416, CI 1.014-1.979, p=0.041) showing positive associations, while apoB (OR0.001 CI0.001-0.824, p=0.045) exhibiting negative associations, all with p-values <0.05. Higher MHR and LVD correlate with increased PASP and reduced LVEF in DCMPH patients. MHR and LVPW are independent predictors of PH severity, indicating their potential as novel severity markers in DCM-related PH. Show less
no PDF DOI: 10.2174/0118715257294388250326034612
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Deguang Yang, Ning Gu, Li Pan +8 more Β· 2025 Β· Kardiologia polska Β· added 2026-04-24
The role of lipid markers in acute coronary syndrome remains incompletely understood, particularly for novel indices such as the Castelli risk indices (CRI-I, CRI-II) and cholesterol index (CHOINDEX). Show more
The role of lipid markers in acute coronary syndrome remains incompletely understood, particularly for novel indices such as the Castelli risk indices (CRI-I, CRI-II) and cholesterol index (CHOINDEX). This study aims to elucidate the relationship between novel lipid markers and plaque rupture. In this single-center retrospective study, 649 patients with acute coronary syndrome undergoing optical coherence tomography were stratified into plaque rupture (n = 130) and non-rupture (n = 519) groups. Lipid indices included the following: CRI-I - total cholesterol/high-density lipoprotein cholesterol (HDL-C), CRI-II - low-density lipoprotein cholesterol (LDL-C)/HDL-C, and CHOINDEX - LDL-C/HDL-C. Multivariable logistic regression identified independent predictors of plaque rupture. Model performance was assessed using area under the curve and integrated discrimination improvement. The plaque rupture group had higher proportions of males (89.2% vs. 80%; P = 0.01) and smokers (57.7% vs. 44.9%; P = 0.009), with elevated LDL-C mean 3.14 vs. 2.83 mmol/l), apolipoprotein B (APOB; 1.03 vs. 0.85 g/l), CRI-I (4.75 vs. 3.91), CRI-II (3.11 vs. 2.45), and CHOINDEX (1.97 vs. 1.65; all P <0.01). Multivariable analysis identified CRI-I (odds ratio [OR], 1.57), CRI-II (OR, 2.09), CHOINDEX (OR, 0.40), and APOB (OR, 5.50) as independent predictors. The combined model (traditional factors + novel indices) showed superior discrimination (area under the curve = 0.775 vs. 0.622; integrated discrimination improvement = 0.059; P <0.001). The combined assessment of CRI-II, CRI-I, CHOINDEX, and APOB, in conjunction with traditional cardiovascular risk factors, exhibits robust diagnostic efficacy for plaque rupture. Show less
no PDF DOI: 10.33963/v.phj.107865
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Menghan Lv, Xuan Wang, Xiayue He +4 more Β· 2025 Β· Frontiers in psychiatry Β· Frontiers Β· added 2026-04-24
Obesity and dysregulated cytokine levels are prevalent in schizophrenia patients undergoing antipsychotic treatment. While cytokines are implicated in obesity, their relationship with psychopathology Show more
Obesity and dysregulated cytokine levels are prevalent in schizophrenia patients undergoing antipsychotic treatment. While cytokines are implicated in obesity, their relationship with psychopathology in schizophrenia remains underexplored. This study investigated associations between body mass index (BMI), cytokine levels, and clinical symptoms in chronic schizophrenia patients. In this cross-sectional study,201chronic schizophrenia patients (Chinese Han population) were stratified into high BMI (BMIβ‰₯25kg/m A significant negative correlation was observed between BMI and IL-2( Higher BMI in chronic schizophrenia is associated with reduced IL-2 levels, attenuated negative symptoms, and adverse lipid profiles. TNF-Ξ± may modulate psychopathology severity. These findings highlight complex interactions between metabolic dysregulation, immune markers, and clinical manifestations in schizophrenia. Show less
πŸ“„ PDF DOI: 10.3389/fpsyt.2025.1574041
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Yuxing Wang, Ming Yu, Song Yang +8 more Β· 2025 Β· Cardiovascular therapeutics Β· added 2026-04-24
πŸ“„ PDF DOI: 10.1155/cdr/5528174
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Wenhui Wu, Chengcheng Wang, Tao Zhang +12 more Β· 2025 Β· Journal of ethnopharmacology Β· Elsevier Β· added 2026-04-24
In Traditional Chinese Medicine (TCM), dampness is a pathogenic factor arising from impaired production and transportation of bodily fluids. While Fuling Zexie decoction (FLZXD) has demonstrated thera Show more
In Traditional Chinese Medicine (TCM), dampness is a pathogenic factor arising from impaired production and transportation of bodily fluids. While Fuling Zexie decoction (FLZXD) has demonstrated therapeutic efficacy in dampness constitution (DC) treatment, the material basis underlying its constitutional modulatory effects remains unclear. This study proposes objective indicators for the differentiation and therapeutic evaluation of DC and elucidates the material basis of FLZXD in DC treatment. Serum exosome proteomic profiling was conducted across two independent cohorts to identify DC-related indicators and assess the therapeutic efficacy of FLZXD in DC-associated hyperlipidemia (DC-hyperlipidemia). The bioactive compounds of FLZXD were prioritized through a comprehensive analysis of patent documentation and network pharmacology, with subsequent validation of DC-related targets using enzyme-linked immunosorbent assay (ELISA). Proteomic analysis of serum exosomes revealed signatures that differentiate individuals with a balanced constitution (BC) from those with DC. The differentially expressed proteins (DEPs) were enriched predominantly in pathways related to the complement cascade and cardiovascular diseases. FLZXD demonstrated therapeutic efficacy against DC-hyperlipidemia, as evidenced by the reversal of DEPs expression following treatment, which was supported by the patentable findings and network pharmacology analysis. Through experimental validation and pharmacological evidence, the active herbs of FLZXD (Fuling, Zexie and Baizhu, collectively referred to as FZB) were identified, and a total of 73 putative therapeutic targets involved in the dampness-resolving effects of FZB were revealed. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment further confirmed that FLZXD exerts its anti-dampness effects primarily through regulation of the complement and coagulation cascades. Among eight candidate indicators specifically associated with DC, four proteins were validated via ELISA, indicating potential utility for the differentiation of DC. The sensitivity (%), specificity (%), fold change (FC), p-value, and area under the curve (AUC) for each indicator were as follows: apolipoprotein B-100 (APOB) (100.00, 80.00, 0.63, 0.0051, 0.94), complement factor H-related protein 1 (CFHR1) (90.00, 100.00, 0.55, 0.0001, 0.98), alpha-1-acid glycoprotein 1 (ORM1) (100.00, 80.00, 0.71, 0.0043, 0.92), and pigment epithelium-derived factor (SERPINF1) (90.00, 70.00, 0.66, 0.0002, 0.87). The integrative approach, combining proteomic profiling, network pharmacology analysis, and clinical validation, establishes an integrative approach for research on TCM constitutions. This approach provides (1) molecular insights into the differentiation of DC, (2) a foundation for mechanism-based, targeted therapeutic strategies, and (3) enhanced patient stratification to support personalized treatment approaches. Show less
no PDF DOI: 10.1016/j.jep.2025.120353
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Jing Gan, Yuncong Wang, Zhuoran Shi +13 more Β· 2025 Β· NPJ precision oncology Β· Nature Β· added 2026-04-24
Increasing evidence underscores the driving role of coding and non-coding variants in cancer development. Analyzing gene sets in biological processes offers deeper insights into the molecular mechanis Show more
Increasing evidence underscores the driving role of coding and non-coding variants in cancer development. Analyzing gene sets in biological processes offers deeper insights into the molecular mechanisms of carcinogenesis. Here, we developed geMER to identify candidate driver genes genome-wide by detecting mutation enrichment regions within coding and non-coding elements. We subsequently designed a pipeline to identify a core driver gene set (CDGS) that broadly promotes carcinogenesis across multiple cancers. CDGS comprising 25 genes for 25 cancers displayed instability in DNA aberrations. Variants within the TTN enrichment region may influence the folding of the I-set domain by altering local polarity or side-chain chemistry properties of amino acids, potentially disrupting its antigen-binding capacity in LUAD. Multi-omics analysis revealed that APOB emerged as a candidate oncogene in LIHC, whose genetic alterations within the enrichment region may activate key TFs, upregulate DNA methylation levels, modulate critical histone modifications, and enhance transcriptional activity in the HepG2 and A549 cell lines compared to Panc1. Additionally, CDGS mutation status was an independent prognostic factor for the pan-cancer cohort. High-risk patients tended to develop an immunosuppressive microenvironment and demonstrated a higher likelihood of responding to ICI therapy. Finally, we provided a user-friendly web interface to explore candidate driver genes using geMER ( http://bio-bigdata.hrbmu.edu.cn/geMER/ ). Show less
πŸ“„ PDF DOI: 10.1038/s41698-025-01060-y
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Hongliang Du, Zhenze Wang, Mengyi Qi +4 more Β· 2025 Β· Cancer cell international Β· BioMed Central Β· added 2026-04-24
Oral squamous cell carcinoma (OSCC) is among the most common malignant tumors in the oral and maxillofacial regions, characterized by high drug resistance and poor treatment outcomes. This underscores Show more
Oral squamous cell carcinoma (OSCC) is among the most common malignant tumors in the oral and maxillofacial regions, characterized by high drug resistance and poor treatment outcomes. This underscores the urgent need to identify novel biomarkers for OSCC. Differentially expressed messenger RNAs (mRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) (DE-mRNAs, DE-miRNAs, and DE-lncRNAs) between primary and control groups, as well as metastatic and primary groups, were identified using whole transcriptome sequencing data. Candidate OSCC genes were derived from DE-mRNAs. Potential biomarkers were then identified using five algorithms from CytoHubba. Biomarkers were validated via univariate Cox regression and Kaplan-Meier (K-M) survival analysis. Additional analyses included subcellular localization, mutation analysis, and Gene Set Enrichment Analysis (GSEA). Key drugs for OSCC treatment were also identified. Quantitative real time polymerase chain reaction (qRT-PCR) and immunohistochemistry were employed to verify the expression levels of key biomarkers. A total of 304 candidate genes were identified, with 29 potential biomarkers selected by five algorithms. ANPEP, APOB, GLP1R, and SI exhibited significant survival differences in the K-M curves, establishing them as OSCC biomarkers. These biomarkers were predominantly localized in the cytoplasm, with SI and APOB showing the highest mutation susceptibility. Enrichment analysis revealed that the 'interferon-gamma response'biological function was co-enriched by ANPEP, APOB, and SI. Furthermore, BIBW2992 (afatinib) and PF.02341066 (crizotinib) were most strongly correlated with the biomarkers, suggesting their potential as key drugs for OSCC treatment. Additionally, the findings were validated by qRT-PCR and immunohistochemical analyses, and the results were consistent with the RNA-seq data. ANPEP, APOB, GLP1R, and SI were identified as potential OSCC biomarkers, offering valuable insights for further research and therapeutic development. Show less
πŸ“„ PDF DOI: 10.1186/s12935-025-03913-9
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Guoping Wu, Zhe Dong, Zhongcai Li +12 more Β· 2025 Β· Schizophrenia (Heidelberg, Germany) Β· Nature Β· added 2026-04-24
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause Show more
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause of their life expectancy being 15-20 years shorter than that of the general population. Identifying comorbidity patterns and uncovering differences in immune and metabolic function are crucial steps toward improving prevention and management strategies. A retrospective cross-sectional study was conducted using electronic medical records of inpatients discharged between 2015 and 2024 from a municipal psychiatric hospital in China. The study included patients diagnosed with Schizophrenia, Schizotypal, and Delusional Disorders (SSDs) (ICD-10: F20-F29). Comorbidity patterns were identified through latent class analysis (LCA) based on the 20 most common comorbid conditions among SSD patients. To investigate differences in peripheral blood metabolic and immune function, linear regression or generalized linear models were applied to 44 laboratory test indicators collected during the acute episode. The Benjamini-Hochberg method was used for p-value correction, and the false discovery rate (FDR) was calculated, with statistical significance set at FDR < 0.05. Among 3,697 inpatients with SSDs, four distinct comorbidity clusters were identified: SSDs only (Class 1), High-Risk Metabolic Multisystem Disorders (Class 2, n = 39), Low-Risk Metabolic Multisystem Disorders (Class 3, n = 573), and Sleep Disorders (Class 4, n = 205). Compared to Class 1, Class 2 exhibited significantly elevated levels of apolipoprotein A (ApoA; β = 90.62), apolipoprotein B (ApoB; β = 0.181), mean platelet volume (MPV; β = 0.994), red cell distribution width-coefficient of variation (RDW-CV; β = 1.182), antistreptolysin O (ASO; β = 276.80), and absolute lymphocyte count (ALC; β = 0.306), along with reduced apolipoprotein AI (ApoAI; β = -0.173) and hematocrit (HCT; β = -35.13). Class 3 showed moderate increases in low-density lipoprotein cholesterol (LDL-C; β = 0.113), MPV (β = 0.267), white blood cell count (WBC; β = 0.476), and absolute neutrophil count (ANC; β = 0.272), with decreased HCT (β = -9.81). Class 4 was characterized by elevated aggregate index of systemic inflammation (AISI; β = 81.07), neutrophil-to-lymphocyte ratio (NLR; β = 0.465), and systemic inflammation response index (SIRI; β = 0.346), indicating a heightened inflammatory state. The comorbidity patterns of patients with SCZ can be distinctly classified. During the acute episode, those with comorbid metabolic disorders exhibit a higher risk of cardiovascular diseases and immune system abnormalities, while patients with comorbid sleep disorders present a pronounced systemic inflammatory state and immune dysfunction. This study provides a basis for the chronic disease management and anti-inflammatory treatment, while also offering objective biomarker insights for transdiagnostic research. Show less
πŸ“„ PDF DOI: 10.1038/s41537-025-00646-6
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Chi Chen, Yimeng Gu, Junfei Xu +9 more Β· 2025 Β· Scientific reports Β· Nature Β· added 2026-04-24
Apolipoprotein B (apoB) can be measured directly and accurately, and better predicts atherogenic risk than conventional lipid profiles. We aimed to investigate whether total and regional (trunk or leg Show more
Apolipoprotein B (apoB) can be measured directly and accurately, and better predicts atherogenic risk than conventional lipid profiles. We aimed to investigate whether total and regional (trunk or leg) fat deposits are associated with apoB levels in general US adults. 4585 participants were enrolled from the US National Health and Nutritional Surveys from 2011 to 2016. Overall and regional body fat were measured using dual-energy X-ray absorptiometry. The associations of total and regional fat with apoB levels were evaluated using linear regression models. Following adjustment for demographic, lifestyle, and clinical risk factors, whole-body fat percentage was positively associated with apoB levels. Additionally, percent trunk fat was positively associated (highest vs. lowest tertile beta = 17.73 for men and 14.89 for women, respectively), whereas percent leg fat was inversely associated (highest vs. lowest tertile beta = - 4.84 for men and - 6.55 for women, respectively) with apoB levels in both sexes. The association for trunk fat and leg fat remained significant after further adjustment for body mass index or waist circumference. Higher percent trunk fat combined with lower percent leg fat was associated with particularly higher apoB. In conclusion, among general US adults, both elevated trunk fat and reduced leg fat are associated with higher levels of apoB. Further research is required to elucidate the underlying pathophysiological mechanisms. Show less
πŸ“„ PDF DOI: 10.1038/s41598-025-10502-3
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Guangyu Gao, Tianci Yao, Chengyun Liu +10 more Β· 2025 Β· Food science & nutrition Β· Wiley Β· added 2026-04-24
Phytosterols have been recommended as a lifestyle intervention for early lipid management-which has a significant impact on frailty. However, their effect on frailty remains unclear. Studies have show Show more
Phytosterols have been recommended as a lifestyle intervention for early lipid management-which has a significant impact on frailty. However, their effect on frailty remains unclear. Studies have shown that genetic proxied total blood phytosterol affects the development of cardiovascular disease through non-HDL-c and apolipoprotein B mediation, which makes phytosterol an underlying risk factor for frailty. The aim of this Mendelian randomization (MR) study was to investigate the genetic associations between phytosterols and frailty. We used univariate Mendelian randomization (UVMR) to assess the causal effects of blood phytosterols on the Frailty Index (FI) and Fried Frailty Score (FFS). We also employed multivariate Mendelian randomization (MVMR) and Two-step MR (TSMR) to evaluate the mediating role of blood lipids in the relationship between blood phytosterols and FI. We used the product of coefficients method to calculate the mediating effect. The inverse-variance weighted method was used as the primary analysis. Genetically proxied higher levels of blood total sitosterol were significantly associated with a higher risk of Frailty Index (OR = 1.035, 95% CI = 1.009-1.061, Show less
πŸ“„ PDF DOI: 10.1002/fsn3.70616
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Yuhang Wang, Shuang Shi, Xinghua Wei +3 more Β· 2025 Β· Diabetes, metabolic syndrome and obesity : targets and therapy Β· added 2026-04-24
The concurrent rise of childhood obesity and hyperuricemia presents a serious public health concern. These conditions interact through complex metabolic mechanisms and significantly increase long-term Show more
The concurrent rise of childhood obesity and hyperuricemia presents a serious public health concern. These conditions interact through complex metabolic mechanisms and significantly increase long-term risks of cardiometabolic diseases. Machine learning (ML) offers an effective framework for constructing efficient risk prediction models in pediatric populations. This study aimed to develop and evaluate two ML models-Random Forest (RF) and Support Vector Classification (SVC)-to predict the risk of childhood obesity and hyperuricemia by integrating clinical and biochemical variables. A total of 101 children were enrolled, including 60 with obesity and 41 with obesity plus hyperuricemia. Data preprocessing involved recursive feature elimination (RFE), ROSE-based oversampling, and feature standardization. Both RF and SVC models were trained and evaluated using area under the ROC curve (AUC), precision-recall curves, and calibration curves. SHAP (Shapley Additive Explanations) analysis was conducted to interpret feature contributions. Both models demonstrated strong predictive performance, with AUCs reaching 0.96. The SVC model achieved slightly higher average precision and recall, making it more suitable for community- or school-based screening of high-risk children. In contrast, the RF model exhibited superior calibration, suggesting its greater utility in clinical decision-making where probabilistic risk estimation guides personalized follow-up or intervention planning. SHAP analysis identified glomerular filtration rate (GFR), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein B (ApoB) as key predictors, some exhibiting nonlinear associations with disease risk. RF and SVC models offer reliable tools for early risk prediction of obesity and hyperuricemia in children, each tailored to distinct clinical scenarios. These findings support early identification and targeted intervention. Future studies will explore the integration of metabolomic data and ensemble approaches to further enhance model performance and clinical applicability. Show less
πŸ“„ PDF DOI: 10.2147/DMSO.S519284
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Xiao Wang, Ke Yang, Xiao-Wei Chen Β· 2025 Β· Journal of cellular physiology Β· Wiley Β· added 2026-04-24
Products encoded by approximately 30% of the mammalian genome exit the endoplasmic reticulum via the coat complex II (COPII) system en route to their functional destination. Among these cargoes, APOB- Show more
Products encoded by approximately 30% of the mammalian genome exit the endoplasmic reticulum via the coat complex II (COPII) system en route to their functional destination. Among these cargoes, APOB-containing lipoproteins stand out as abundant and bulky secretory particles with profound implications for human health and diseases. Recent insights into the specialized intracellular itinerary of lipoprotein metabolism and transport not only shed light on longstanding questions of lipid dynamics, but also highlight challenges faced by the COPII machinery in accommodating these complex, unconventional cargoes. Emerging evidence supports that tightly-regulated COPII condensation enables maximal capacity of cargo transport, providing a potential solution tailored for efficient lipoprotein delivery without affecting general protein secretion. This distinction suggests that targeting COPII condensation may provide new therapeutic strategies for lipid-associated diseases. Indeed, recent studies have identified manganese as a key modulator of this process, offering novel insights into its physiological relevance and potential translations. Show less
no PDF DOI: 10.1002/jcp.70061
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Bin Feng, Yi Wang, Jingjie Xu +3 more Β· 2025 Β· Lipids in health and disease Β· BioMed Central Β· added 2026-04-24
This study aimed to (1) evaluate small dense low-density lipoprotein cholesterol (sdLDL-C) dynamics from prediabetes to type 2 diabetes mellitus (T2DM) with complications, (2) validate existing sdLDL- Show more
This study aimed to (1) evaluate small dense low-density lipoprotein cholesterol (sdLDL-C) dynamics from prediabetes to type 2 diabetes mellitus (T2DM) with complications, (2) validate existing sdLDL-C estimation formulas (Sampson’s, Srisawasdi’s, Han’s) in Chinese populations, and (3) develop a population-specific formula for enhanced accuracy. A multicenter study recruited 1,944 participants (216 controls, 70 with prediabetes, 212 with newly diagnosed T2DM, 164 with treated T2DM, and 1,286 in validation cohorts). Lipid profiles, including sdLDL-C (measured via enzymatic assays), were analyzed. Formula performance was assessed using spearman correlation, intraclass correlation coefficients (ICC), and multivariable linear regression. A novel formula was derived via multivariable regression. Atherogenic lipid triad manifestations emerged early: sdLDL-C was significantly elevated in participants with prediabetes (1.07 [0.73, 1.40] vs. 0.57 [0.44, 0.72] mmol/L in controls, P < 0.05) and further increased in those with T2DM, correlating strongly with triglycerides (TG; sdLDL-C elevation begins in prediabetes, highlighting its value for early atherosclerotic cardiovascular disease (ASCVD) risk assessment. Current formulas show population-specific limitations, whereas the new model provides improved accuracy for Chinese T2DM patients, enabling cost-effective sdLDL-C estimation and personalized lipid management. The online version contains supplementary material available at 10.1186/s12944-025-02636-0. Show less
πŸ“„ PDF DOI: 10.1186/s12944-025-02636-0
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Neng Wang, Yu Zheng, Shuai Tao +1 more Β· 2025 Β· BMC gastroenterology Β· BioMed Central Β· added 2026-04-24
This study aimed to elucidate the correlations among dyslipidemia, immune function, and clinical outcomes in patients with acute-on-chronic liver failure (ACLF), with particular emphasis on the clinic Show more
This study aimed to elucidate the correlations among dyslipidemia, immune function, and clinical outcomes in patients with acute-on-chronic liver failure (ACLF), with particular emphasis on the clinical significance of lipid metabolism and cellular immune parameters in hepatitis B virus-associated ACLF (HBV-ACLF). A retrospective analysis was conducted on 803 patients with HBV-ACLF admitted to the Shanghai Public Health Clinical Center from January 2014 to January 2024. Patients were stratified into deceased (n = 414) and survival (n = 389) groups based on clinical outcomes. Clinical baseline data, lipid metabolic indices, and cellular immune parameters were collected. The Spearman correlation coefficient was utilized to assess the correlation between lipid metabolic indices and cellular immune parameters, and a multivariate Cox proportional hazards model was applied to analyze risk factors for mortality. Compared to the survival group, lipid metabolism indices in the deceased group were significantly reduced (P < 0.05). Lipid metabolism indices, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (APOA1), apolipoprotein B (APOB), total cholesterol (TC), and triglycerides (TG), demonstrated significant negative correlations with the severity of liver failure (P < 0.05). Correlation analysis with lymphocyte subset counts revealed positive correlations between low-density lipoprotein, TG, TC, APOB, and CD3 + T cells, CD4 + T cells, CD8 + T cells, and CD45 + T cells (P < 0.05). APOA1 and HDL-C were positively correlated with B cells and NK cells (P < 0.05). TG and APOB showed significant negative correlations with the CD4/CD8 ratio (P < 0.05). Multivariate Cox analysis identified age, creatinine, total bilirubin, international normalized ratio (INR), hepatic encephalopathy, and hepatorenal syndrome as independent risk factors affecting the short-term prognosis of HBV-ACLF, while sodium, APOA1, and APOB were identified as independent protective factors for ACLF (HR = 0.984, 95% CI: 0.974-0.995, P < 0.001, HR = 0.267,95% CI: 0.120-0.596, P = 0.001, HR = 0.486, 95% CI: 0.282-0.838, P = 0.010). Patients with HBV-ACLF exhibit decreased levels of TC, TG, LDL-C, HDL-C, APOA1, and APOB. These alterations in serum lipid profiles are associated with immune dysfunction and disease progression in HBV-ACLF. Notably, APOA1 and APOB serve as protective factors against 90-day mortality in hospitalized ACLF patients. Further investigation is warranted to elucidate the relationship between lipid metabolism disturbances and peripheral immunity in ACLF. Show less
πŸ“„ PDF DOI: 10.1186/s12876-025-04004-9
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Zhiming Zhao, Wei Lu, Changwei Li +2 more Β· 2025 Β· American journal of physiology. Endocrinology and metabolism Β· added 2026-04-24
Kelch-like protein 12 (KLHL12) has been shown to regulate coat complex II (COPII)-mediated endoplasmic reticulum (ER)-to-Golgi trafficking of large cargos carrying procollagen or apolipoprotein B-100 Show more
Kelch-like protein 12 (KLHL12) has been shown to regulate coat complex II (COPII)-mediated endoplasmic reticulum (ER)-to-Golgi trafficking of large cargos carrying procollagen or apolipoprotein B-100 containing very-low-density lipoprotein (VLDL). It is known that lipid absorption and chylomicron metabolism in enterocytes are dependent on apolipoprotein B-48 (ApoB48) and COPII-mediated trafficking. This study aimed to investigate whether KLHL12 in the intestine regulates dietary lipid absorption, chylomicron assembly, and metabolic phenotypes in mice. We generated Show less
πŸ“„ PDF DOI: 10.1152/ajpendo.00219.2025
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Chunli Shao, Shu Zhang, Zhifeng Cheng +17 more Β· 2025 Β· Atherosclerosis Β· Elsevier Β· added 2026-04-24
Several protein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to significantly reduce low-density lipoprotein cholesterol (LDL-C) levels in statin-intolerant patients, but none Show more
Several protein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to significantly reduce low-density lipoprotein cholesterol (LDL-C) levels in statin-intolerant patients, but none have been verified in Chinese patients. This study aimed to evaluate the efficacy and safety of ongericimab, a novel PCSK9 monoclonal antibody, in Chinese statin-intolerant patients with primary hypercholesterolemia or mixed dyslipidemia. This was a randomized, multicenter, double-blind, placebo-controlled phase 3 study designed to enroll 120 statin-intolerant adult patients. Eligible patients were randomly assigned in a 2:1 ratio to receive ongericimab 150Β mg or placebo subcutaneously every 2 weeks for 12 weeks in the double-blind treatment period, followed by 40 weeks of ongericimab treatment during the open-label period. The primary endpoint was a percentage change in LDL-C from baseline to week 12. The key secondary endpoints included percentage change from baseline to week 12 in non-high density lipoprotein cholesterol (non-HDL-C), apolipoprotein B (ApoB), total cholesterol (TC), and lipoprotein(a) [Lp(a)]. From February 6, 2023, to September 23, 2024, a total of 139 patients were enrolled. The least-squares (LS) mean difference between ongericimab and placebo groups in LDL-C from baseline to week 12 was -66.2Β % (95Β % CI: 74.2Β %, -58.2Β %; pΒ <Β 0.0001), with reductions sustained up to week 52. Ongericimab also significantly reduced levels of non-HDL-C, ApoB, TC, and Lp(a). The overall incidence of treatment-emergent adverse events was comparable between the ongericimab and placebo groups. Ongericimab significantly reduced LDL-C as well as other atherogenic lipid levels and was well tolerated in Chinese statin-intolerant patients with primary hypercholesterolemia or mixed dyslipidemia. http://www. gov; Unique Identifier: NCT05621070. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.120408
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Yayu Wang, Yue Chang, Pei Zhang +4 more Β· 2025 Β· Lipids in health and disease Β· BioMed Central Β· added 2026-04-24
Duchenne muscular dystrophy (DMD) is a serious, progressive neuromuscular condition that predominantly impacts male individuals, marked by progressive muscle weakness resulting from mutations in the d Show more
Duchenne muscular dystrophy (DMD) is a serious, progressive neuromuscular condition that predominantly impacts male individuals, marked by progressive muscle weakness resulting from mutations in the dystrophin gene (DMD) encoding dystrophin. DMD is a primary muscle disorder that often presents with secondary abnormalities in lipid metabolism and decreased bone mineral density. Although disturbances in circulating lipid profiles and skeletal health have been observed in individuals with DMD, their relationship remains underexplored.This study aimed to investigate the potential association between lipid metabolic disturbances and spinal bone mineral density in patients with DMD by combining clinical lipid levels and bone density with transcriptomic pathway analysis of DMD muscle tissue. Retrospective analysis was performed on 219 genetically confirmed DMD patients and 99 age-matched healthy controls. Healthy controls with a family history of genetic disorders were excluded. Clinical data included lipid profiles (triglycerides [TGs], remnant cholesterol [RC]); bone mineral density of the lumbar spine was evaluated using Dual-energy X-ray absorptiometry (DXA); and corticosteroid use, including treatment status, dose, and duration. Patients were stratified by corticosteroid exposure. Restricted cubic splines and multivariable regression models were applied to explore potential relationships between lipid parameters and bone mineral density. Bioinformatic analyses were performed on RNA sequencing data from muscle biopsy samples from patients with DMD (GSE38417 dataset) and an independent validation cohort (GSE6011 dataset), focusing on pathways related to lipid metabolism and osteoclast differentiation. Patients with DMD had higher TG, RC, and apolipoprotein B (ApoB) levels and lower high-density lipoprotein cholesterol (HDL-C) levels than healthy controls (P < 0.05). Elevated TG and RC levels were associated with reduced spine bone mineral density, independent of corticosteroid use. The bioinformatic analyses identified key pathways, including sphingolipid metabolism and osteoclast differentiation, as well as hub genes such as FCGR2B, C1QA, which are involved in lipid regulation and bone remodeling. Lipid abnormalities, particularly elevated TG and RC levels, were significantly associated with lower bone mineral density in patients with DMD. These findings suggest that lipid abnormalities are involved in bone health impairment in DMD, warranting further studies to confirm the association. Show less
πŸ“„ PDF DOI: 10.1186/s12944-025-02628-0
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