👤 Zhongjing Wang

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Also published as: A Wang, Ai-Ling Wang, Ai-Ting Wang, Aihua Wang, Aijun Wang, Aili Wang, Aimin Wang, Aiting Wang, Aixian Wang, Aiyun Wang, Aizhong Wang, Alexander Wang, Alice Wang, Allen Wang, Anlai Wang, Anli Wang, Annette Wang, Anni Wang, Anqi Wang, Anthony Z Wang, Anxiang Wang, Anxin Wang, Ao Wang, Aoli Wang, B R Wang, B Wang, Baihan Wang, Baisong Wang, Baitao Wang, Bangchen Wang, Banghui Wang, Bangmao Wang, Bangshing Wang, Bao Wang, Bao-Long Wang, Baocheng Wang, Baofeng Wang, Baogui Wang, Baojun Wang, Baoli Wang, Baolong Wang, Baoming Wang, Baosen Wang, Baowei Wang, Baoying Wang, Baoyun Wang, Bei Bei Wang, Bei Wang, Beibei Wang, Beilan Wang, Beilei Wang, Ben Wang, Benjamin H Wang, Benzhong Wang, Bi Wang, Bi-Dar Wang, Biao Wang, Bicheng Wang, Bijue Wang, Bin Wang, Bin-Xue Wang, Binbin Wang, Bing Qing Wang, Bing Wang, Binghai Wang, Binghan Wang, Bingjie Wang, Binglong Wang, Bingnan Wang, Bingyan Wang, Bingyu Wang, Binquan Wang, Biqi Wang, Bo Wang, Bochu Wang, Boyu Wang, Bruce Wang, C Wang, C Z Wang, Cai Ren Wang, Cai-Hong Wang, Cai-Yun Wang, Cailian Wang, Caiqin Wang, Caixia Wang, Caiyan Wang, Can Wang, Cangyu Wang, Carol A Wang, Catherine Ruiyi Wang, Cenxuan Wang, Chan Wang, Chang Wang, Chang-Yun Wang, Changduo Wang, Changjing Wang, Changliang Wang, Changlong Wang, Changqian Wang, Changtu Wang, Changwei Wang, Changying Wang, Changyu Wang, Changyuan Wang, Changzhen Wang, Chao Wang, Chao-Jun Wang, Chao-Yung Wang, Chaodong Wang, Chaofan Wang, Chaohan Wang, Chaohui Wang, Chaojie Wang, Chaokui Wang, Chaomeng Wang, Chaoqun Wang, Chaoxian Wang, Chaoyi Wang, Chaoyu Wang, Chaozhan Wang, Charles C N Wang, Chau-Jong Wang, Chen Wang, Chen-Cen Wang, Chen-Ma Wang, Chen-Yu Wang, Chenchen Wang, Chenfei Wang, Cheng An Wang, Cheng Wang, Cheng-Cheng Wang, Cheng-Jie Wang, Cheng-zhang Wang, Chengbin Wang, Chengcheng Wang, Chenggang Wang, Chenghao Wang, Chenghua Wang, Chengjian Wang, Chengjun Wang, Chenglin Wang, Chenglong Wang, Chengniu Wang, Chengqiang Wang, Chengshuo Wang, Chenguang Wang, Chengwen Wang, Chengyan Wang, Chengyu Wang, Chengze Wang, Chenji Wang, Chenliang Wang, Chenwei Wang, Chenxi Wang, Chenxin Wang, Chenxuan Wang, Chenyang Wang, Chenyao Wang, Chenyin Wang, Chenyu Wang, Chenzi Wang, Chi Chiu Wang, Chi Wang, Chi-Ping Wang, Chia-Chuan Wang, Chia-Lin Wang, Chien-Hsun Wang, Chien-Wei Wang, Chih-Chun Wang, Chih-Hao Wang, Chih-Hsien Wang, Chih-Liang Wang, Chih-Yang Wang, Chih-Yuan Wang, Chijia Wang, Ching C Wang, Ching-Jen Wang, Chiou-Miin Wang, Chong Wang, Chongjian Wang, Chonglong Wang, Chongmin Wang, Chongze Wang, Christina Wang, Christine Wang, Chu Wang, Chuan Wang, Chuan-Chao Wang, Chuan-Hui Wang, Chuan-Jiang Wang, Chuan-Wen Wang, Chuang Wang, Chuanhai Wang, Chuansen Wang, Chuansheng Wang, Chuanxin Wang, Chuanyue Wang, Chuduan Wang, Chun Wang, Chun-Chieh Wang, Chun-Juan Wang, Chun-Li Wang, Chun-Lin Wang, Chun-Ting Wang, Chun-Xia Wang, Chung-Hsi Wang, Chung-Hsing Wang, Chung-Teng Wang, Chunguo Wang, Chunhong Wang, Chuning Wang, Chunjiong Wang, Chunjuan Wang, Chunle Wang, Chunli Wang, Chunlong Wang, Chunmei Wang, Chunsheng Wang, Chunting Wang, Chunxia Wang, Chunxue Wang, Chunyan Wang, Chunyang Wang, Chunyi Wang, Chunyu Wang, Chuyao Wang, Cindy Wang, Ciyang Wang, Cong Wang, Congcong Wang, Congrong Wang, Congrui Wang, Cui Wang, Cui-Fang Wang, Cui-Shan Wang, Cuili Wang, Cuiling Wang, Cuizhe Wang, Cun-Yu Wang, Cunchuan Wang, Cunyi Wang, D Wang, Da Wang, Da-Cheng Wang, Da-Li Wang, Da-Yan Wang, Da-Zhi Wang, Dadong Wang, Dai Wang, Daijun Wang, Daiwei Wang, Daixi Wang, Dajia Wang, Dake Wang, Dali Wang, Dalong Wang, Dalu Wang, Dan Wang, Dan-Dan Wang, Danan Wang, Dandan Wang, Danfeng Wang, Dang Wang, Dangfeng Wang, Danling Wang, Danqing Wang, Danxin Wang, Danyang Wang, Dao Wen Wang, Dao-Wen Wang, Dao-Xin Wang, Daolong Wang, Daoping Wang, Daozhong Wang, Dapeng Wang, Daping Wang, Daqi Wang, Daqing Wang, David Q H Wang, David Q-H Wang, David Wang, Dawei Wang, Dayan Wang, Dayong Wang, Dazhi Wang, De-He Wang, Dedong Wang, Dehao Wang, Deli Wang, Delin Wang, Delong Wang, Demin Wang, Deming Wang, Dengbin Wang, Dennis Qing Wang, Dennis Wang, Deqi Wang, Deshou Wang, Dezhong Wang, Di Wang, Dinghui Wang, Dingting Wang, Dingxiang Wang, Dong D Wang, Dong Hao Wang, Dong Wang, Dong-Dong Wang, Dong-Jie Wang, Dong-Mei Wang, DongWei Wang, Dongdong Wang, Donggen Wang, Donghao Wang, Donghong Wang, Donghui Wang, Dongliang Wang, Donglin Wang, Dongmei Wang, Dongqin Wang, Dongshi Wang, Dongxia Wang, Dongxu Wang, Dongyan Wang, Dongyang Wang, Dongyi Wang, Dongying Wang, Dongyu Wang, Doudou Wang, Du Wang, Duan Wang, Duanyang Wang, Duo-Ping Wang, E Wang, Edward Wang, En-bo Wang, En-hua Wang, Endi Wang, Enhua Wang, Er-Jin Wang, Erfei Wang, Erika Y Wang, Ermao Wang, Erming Wang, Ertao Wang, Eryao Wang, Eunice S Wang, Exing Wang, F Wang, Fa-Kai Wang, Fan Wang, Fanchang Wang, Fang Wang, Fang-Tao Wang, Fangfang Wang, Fangjie Wang, Fangjun Wang, Fangyan Wang, Fangyong Wang, Fangyu Wang, Fanhua Wang, Fanwen Wang, Fanxiong Wang, Fei Wang, Fei-Fei Wang, Fei-Yan Wang, Feida Wang, Feifei Wang, Feijie Wang, Feimiao Wang, Feixiang Wang, Feiyan Wang, Fen Wang, Feng Wang, Feng-Sheng Wang, Fengchong Wang, Fengge Wang, Fenghua Wang, Fengliang Wang, Fenglin Wang, Fengling Wang, Fengqiang Wang, Fengyang Wang, Fengying Wang, Fengyong Wang, Fengyun Wang, Fengzhen Wang, Fengzhong Wang, Fu Wang, Fu-Sheng Wang, Fu-Yan Wang, Fu-Zhen Wang, Fubao Wang, Fubing Wang, Fudi Wang, Fuhua Wang, Fuqiang Wang, Furong Wang, Fuwen Wang, Fuxin Wang, Fuyan Wang, G Q Wang, G Wang, G-W Wang, Gan Wang, Gang Wang, Ganggang Wang, Ganglin Wang, Gangyang Wang, Ganyu Wang, Gao T Wang, Gao Wang, Gaofu Wang, Gaopin Wang, Gavin Wang, Ge Wang, Geng Wang, Genghao Wang, Gengsheng Wang, Gongming Wang, Guan Wang, Guan-song Wang, Guandi Wang, Guanduo Wang, Guang Wang, Guang-Jie Wang, Guang-Rui Wang, Guangdi Wang, Guanghua Wang, Guanghui Wang, Guangliang Wang, Guangming Wang, Guangsuo Wang, Guangwen Wang, Guangyan Wang, Guangzhi Wang, Guanrou Wang, Guanru Wang, Guansong Wang, Guanyun Wang, Gui-Qi Wang, Guibin Wang, Guihu Wang, Guihua Wang, Guimin Wang, Guiping Wang, Guiqun Wang, Guixin Wang, Guixue Wang, Guiying Wang, Guo-Du Wang, Guo-Hua Wang, Guo-Liang Wang, Guo-Ping Wang, Guo-Quan Wang, Guo-hong Wang, GuoYou Wang, Guobin Wang, Guobing Wang, Guodong Wang, Guohang Wang, Guohao Wang, Guoliang Wang, Guoling Wang, Guoping Wang, Guoqian Wang, Guoqiang Wang, Guoqing Wang, Guorong Wang, Guowen Wang, Guoxiang Wang, Guoxiu Wang, Guoyi Wang, Guoying Wang, Guozheng Wang, H J Wang, H Wang, H X Wang, H Y Wang, H-Y Wang, Hai Bo Wang, Hai Wang, Hai Yang Wang, Hai-Feng Wang, Hai-Jun Wang, Hai-Long Wang, Haibin Wang, Haibing Wang, Haibo Wang, Haichao Wang, Haichuan Wang, Haifei Wang, Haifeng Wang, Haihe Wang, Haihong Wang, Haihua Wang, Haijiao Wang, Haijing Wang, Haijiu Wang, Haikun Wang, Hailei Wang, Hailin Wang, Hailing Wang, Hailong Wang, Haimeng Wang, Haina Wang, Haining Wang, Haiping Wang, Hairong Wang, Haitao Wang, Haiwei Wang, Haixia Wang, Haixin Wang, Haixing Wang, Haiyan Wang, Haiying Wang, Haiyong Wang, Haiyun Wang, Haizhen Wang, Han Wang, Hanbin Wang, Hanbing Wang, Hanchao Wang, Handong Wang, Hang Wang, Hangzhou Wang, Hanmin Wang, Hanping Wang, Hanqi Wang, Hanying Wang, Hanyu Wang, Hanzhi Wang, Hao Wang, Hao-Ching Wang, Hao-Hua Wang, Hao-Tian Wang, Hao-Yu Wang, Haobin Wang, Haochen Wang, Haohao Wang, Haohui Wang, Haojie Wang, Haolong Wang, Haomin Wang, Haoming Wang, Haonan Wang, Haoping Wang, Haoqi Wang, Haoran Wang, Haowei Wang, Haoxin Wang, Haoyang Wang, Haoyu Wang, Haozhou Wang, He Wang, He-Cheng Wang, He-Ling Wang, He-Ping Wang, He-Tong Wang, Hebo Wang, Hechuan Wang, Heling Wang, Hemei Wang, Heming Wang, Heng Wang, Heng-Cai Wang, Hengjiao Wang, Hengjun Wang, Hequn Wang, Hesuiyuan Wang, Heyong Wang, Hezhi Wang, Hong Wang, Hong Yi Wang, Hong-Gang Wang, Hong-Hui Wang, Hong-Kai Wang, Hong-Qin Wang, Hong-Wei Wang, Hong-Xia Wang, Hong-Yan Wang, Hong-Yang Wang, Hong-Ying Wang, Hongbin Wang, Hongbing Wang, Hongbo Wang, Hongcai Wang, Hongda Wang, Hongdan Wang, Hongfang Wang, Hongjia Wang, Hongjian Wang, Hongjie Wang, Hongjuan Wang, Hongkun Wang, Honglei Wang, Hongli Wang, Honglian Wang, Honglun Wang, Hongmei Wang, Hongpin Wang, Hongqian Wang, Hongshan Wang, Hongsheng Wang, Hongtao Wang, Hongwei Wang, Hongxia Wang, Hongxin Wang, Hongyan Wang, Hongyang Wang, Hongyi Wang, Hongyin Wang, Hongying Wang, Hongyu Wang, Hongyuan Wang, Hongyue Wang, Hongyun Wang, Hongze Wang, Hongzhan Wang, Hongzhuang Wang, Horng-Dar Wang, Houchun Wang, Hsei-Wei Wang, Hsueh-Chun Wang, Hu WANG, Hua Wang, Hua-Qin Wang, Hua-Wei Wang, Huabo Wang, Huafei Wang, Huai-Zhou Wang, Huaibing Wang, Huaili Wang, Huaizhi Wang, Huajin Wang, Huajing Wang, Hualin Wang, Hualing Wang, Huan Wang, Huan-You Wang, Huang Wang, Huanhuan Wang, Huanyu Wang, Huaquan Wang, Huating Wang, Huawei Wang, Huaxiang Wang, Huayang Wang, Huei Wang, Hui Miao Wang, Hui Wang, Hui-Hui Wang, Hui-Li Wang, Hui-Nan Wang, Hui-Yu Wang, HuiYue Wang, Huie Wang, Huiguo Wang, Huihua Wang, Huihui Wang, Huijie Wang, Huijun Wang, Huilun Wang, Huimei Wang, Huimin Wang, Huina Wang, Huiping Wang, Huiquan Wang, Huiqun Wang, Huishan Wang, Huiting Wang, Huiwen Wang, Huixia Wang, Huiyan Wang, Huiyang Wang, Huiyao Wang, Huiying Wang, Huiyu Wang, Huizhen Wang, Huizhi Wang, Huming Wang, I-Ching Wang, Iris X Wang, Isabel Z Wang, J J Wang, J P Wang, J Q Wang, J Wang, J Z Wang, J-Y Wang, Jacob E Wang, James Wang, Jeffrey Wang, Jen-Chun Wang, Jen-Chywan Wang, Jennifer E Wang, Jennifer T Wang, Jennifer X Wang, Jenny Y Wang, Jeremy R Wang, Jeremy Wang, Ji M Wang, Ji Wang, Ji-Nuo Wang, Ji-Yang Wang, Ji-Yao Wang, Ji-zheng Wang, Jia Bei Wang, Jia Bin Wang, Jia Wang, Jia-Liang Wang, Jia-Lin Wang, Jia-Mei Wang, Jia-Peng Wang, Jia-Qi Wang, Jia-Qiang Wang, Jia-Ying Wang, Jia-Yu Wang, Jiabei Wang, Jiabo Wang, Jiafeng Wang, Jiafu Wang, Jiahao Wang, Jiahui Wang, Jiajia Wang, Jiakun Wang, Jiale Wang, Jiali Wang, Jialiang Wang, Jialin Wang, Jialing Wang, Jiamin Wang, Jiaming Wang, Jian Wang, Jian'an Wang, Jian-Bin Wang, Jian-Guo Wang, Jian-Hong Wang, Jian-Long Wang, Jian-Wei Wang, Jian-Xiong Wang, Jian-Yong Wang, Jian-Zhi Wang, Jian-chun Wang, Jianan Wang, Jianbing Wang, Jianbo Wang, Jianding Wang, Jianfang Wang, Jianfei Wang, Jiang Wang, Jiangbin Wang, Jiangbo Wang, Jianghua Wang, Jianghui Wang, Jiangong Wang, Jianguo Wang, Jianhao Wang, Jianhua Wang, Jianhui Wang, Jiani Wang, Jianjiao Wang, Jianjie Wang, Jianjun Wang, Jianle Wang, Jianli Wang, Jianlin Wang, Jianliu Wang, Jianlong Wang, Jianmei Wang, Jianmin Wang, Jianning Wang, Jianping Wang, Jianqin Wang, Jianqing Wang, Jianqun Wang, Jianru Wang, Jianshe Wang, Jianshu Wang, Jiantao Wang, Jianwei Wang, Jianwu Wang, Jianxiang Wang, Jianxin Wang, Jianye Wang, Jianying Wang, Jianyong Wang, Jianyu Wang, Jianzhang Wang, Jianzhi Wang, Jiao Wang, Jiaojiao Wang, Jiapan Wang, Jiaping Wang, Jiaqi Wang, Jiaqian Wang, Jiatao Wang, Jiawei Wang, Jiawen Wang, Jiaxi Wang, Jiaxin Wang, Jiaxing Wang, Jiaxuan Wang, Jiayan Wang, Jiayang Wang, Jiayi Wang, Jiaying Wang, Jiayu Wang, Jiazheng Wang, Jiazhi Wang, Jie Jin Wang, Jie Wang, Jieda Wang, Jieh-Neng Wang, Jiemei Wang, Jieqi Wang, Jieyan Wang, Jieyu Wang, Jifei Wang, Jiheng Wang, Jihong Wang, Jiliang Wang, Jilin Wang, Jin Wang, Jin'e Wang, Jin-Bao Wang, Jin-Cheng Wang, Jin-Da Wang, Jin-E Wang, Jin-Juan Wang, Jin-Liang Wang, Jin-Xia Wang, Jin-Xing Wang, Jincheng Wang, Jindan Wang, Jinfei Wang, Jinfeng Wang, Jinfu Wang, Jing J Wang, Jing Wang, Jing-Hao Wang, Jing-Huan Wang, Jing-Jing Wang, Jing-Long Wang, Jing-Min Wang, Jing-Shi Wang, Jing-Wen Wang, Jing-Xian Wang, Jing-Yi Wang, Jing-Zhai Wang, Jingang Wang, Jingchun Wang, Jingfan Wang, Jingfeng Wang, Jingheng Wang, Jinghong Wang, Jinghua Wang, Jinghuan Wang, Jingjing Wang, Jingkang Wang, Jinglin Wang, Jingmin Wang, Jingnan Wang, Jingqi Wang, Jingru Wang, Jingtong Wang, Jingwei Wang, Jingwen Wang, Jingxiao Wang, Jingyang Wang, Jingyi Wang, Jingying Wang, Jingyu Wang, Jingyue Wang, Jingyun Wang, Jingzhou Wang, Jinhai Wang, Jinhao Wang, Jinhe Wang, Jinhua Wang, Jinhuan Wang, Jinhui Wang, Jinjie Wang, Jinjin Wang, Jinkang Wang, Jinling Wang, Jinlong Wang, Jinmeng Wang, Jinning Wang, Jinping Wang, Jinqiu Wang, Jinrong Wang, Jinru Wang, Jinsong Wang, Jintao Wang, Jinxia Wang, Jinxiang Wang, Jinyang Wang, Jinyu Wang, Jinyue Wang, Jinyun Wang, Jinzhu Wang, Jiou Wang, Jipeng Wang, Jiqing Wang, Jiqiu Wang, Jisheng Wang, Jiu Wang, Jiucun Wang, Jiun-Ling Wang, Jiwen Wang, Jixuan Wang, Jiyan Wang, Jiying Wang, Jiyong Wang, Jizheng Wang, John Wang, Jou-Kou Wang, Joy Wang, Ju Wang, Juan Wang, Jue Wang, Jueqiong Wang, Jufeng Wang, Julie Wang, Juling Wang, Jun Kit Wang, Jun Wang, Jun Yi Wang, Jun-Feng Wang, Jun-Jie Wang, Jun-Jun Wang, Jun-Ling Wang, Jun-Sheng Wang, Jun-Sing Wang, Jun-Zhuo Wang, Jundong Wang, Junfeng Wang, Jung-Pan Wang, Junhong Wang, Junhua Wang, Junhui Wang, Junjiang Wang, Junjie Wang, Junjun Wang, Junkai Wang, Junke Wang, Junli Wang, Junlin Wang, Junling Wang, Junmei Wang, Junmin Wang, Junpeng Wang, Junping Wang, Junqin Wang, Junqing Wang, Junrui Wang, Junsheng Wang, Junshi Wang, Junshuang Wang, Junwen Wang, Junxiao Wang, Junya Wang, Junying Wang, Junyu Wang, Justin Wang, Jutao Wang, Juxiang Wang, K Wang, Kai Wang, Kai-Kun Wang, Kai-Wen Wang, Kaicen Wang, Kaihao Wang, Kaihe Wang, Kaihong Wang, Kaijie Wang, Kaijuan Wang, Kailu Wang, Kaiming Wang, Kaining Wang, Kaiting Wang, Kaixi Wang, Kaixu Wang, Kaiyan Wang, Kaiyuan Wang, Kaiyue Wang, Kan Wang, Kangli Wang, Kangling Wang, Kangmei Wang, Kangning Wang, Ke Wang, Ke-Feng Wang, KeShan Wang, Kehan Wang, Kehao Wang, Kejia Wang, Kejian Wang, Kejun Wang, Keke Wang, Keming Wang, Kenan Wang, Keqing Wang, Kesheng Wang, Kexin Wang, Keyan Wang, Keyi Wang, Keyun Wang, Kongyan Wang, Kuan Hong Wang, Kui Wang, Kun Wang, Kunhua Wang, Kunpeng Wang, Kunzheng Wang, L F Wang, L M Wang, L Wang, L Z Wang, L-S Wang, Laidi Wang, Laijian Wang, Laiyuan Wang, Lan Wang, Lan-Wan Wang, Lan-lan Wang, Lanlan Wang, Larry Wang, Le Wang, Le-Xin Wang, Ledan Wang, Lee-Kai Wang, Lei P Wang, Lei Wang, Lei-Lei Wang, Leiming Wang, Leishen Wang, Leli Wang, Leran Wang, Lexin Wang, Leying Wang, Li Chun Wang, Li Dong Wang, Li Wang, Li-Dong Wang, Li-E Wang, Li-Juan Wang, Li-Li Wang, Li-Na Wang, Li-San Wang, Li-Ting Wang, Li-Xin Wang, Li-Yong Wang, LiLi Wang, Lian Wang, Lianchun Wang, Liang Wang, Liang-Yan Wang, Liangfu Wang, Lianghai Wang, Liangli Wang, Liangliang Wang, Liangxu Wang, Lianshui Wang, Lianyong Wang, Libo Wang, Lichan Wang, Lichao Wang, Liewei Wang, Lifang Wang, Lifei Wang, Lifen Wang, Lifeng Wang, Ligang Wang, Lihong Wang, Lihua Wang, Lihui Wang, Lijia Wang, Lijin Wang, Lijing Wang, Lijuan Wang, Lijun Wang, Liling Wang, Lily Wang, Limeng Wang, Limin Wang, Liming Wang, Lin Wang, Lin-Fa Wang, Lin-Yu Wang, Lina Wang, Linfang Wang, Ling Jie Wang, Ling Wang, Ling-Ling Wang, Lingbing Wang, Lingda Wang, Linghua Wang, Linghuan Wang, Lingli Wang, Lingling Wang, Lingyan Wang, Lingzhi Wang, Linhua Wang, Linhui Wang, Linjie Wang, Linli Wang, Linlin Wang, Linping Wang, Linshu Wang, Linshuang Wang, Lintao Wang, Linxuan Wang, Linying Wang, Linyuan Wang, Liping Wang, Liqing Wang, Liqun Wang, Lirong Wang, Litao Wang, Liting Wang, Liu Wang, Liusong Wang, Liuyang Wang, Liwei Wang, Lixia Wang, Lixian Wang, Lixiang Wang, Lixin Wang, Lixing Wang, Lixiu Wang, Liyan Wang, Liyi Wang, Liying Wang, Liyong Wang, Liyuan Wang, Liyun Wang, Long Wang, Longcai Wang, Longfei Wang, Longsheng Wang, Longxiang Wang, Lou-Pin Wang, Lu Wang, Lu-Lu Wang, Lueli Wang, Lufang Wang, Luhong Wang, Luhui Wang, Lujuan Wang, Lulu Wang, Luofu Wang, Luping Wang, Luting Wang, Luwen Wang, Luxiang Wang, Luya Wang, Luyao Wang, Luyun Wang, Lynn Yuning Wang, M H Wang, M Wang, M Y Wang, M-J Wang, Maiqiu Wang, Man Wang, Mangju Wang, Manli Wang, Mao-Xin Wang, Maochun Wang, Maojie Wang, Maoju Wang, Mark Wang, Mei Wang, Mei-Gui Wang, Mei-Xia Wang, Meiding Wang, Meihui Wang, Meijun Wang, Meiling Wang, Meixia Wang, Melissa T Wang, Meng C Wang, Meng Wang, Meng Yu Wang, Meng-Dan Wang, Meng-Lan Wang, Meng-Meng Wang, Meng-Ru Wang, Meng-Wei Wang, Meng-Ying Wang, Meng-hong Wang, Mengge Wang, Menghan Wang, Menghui Wang, Mengjiao Wang, Mengjing Wang, Mengjun Wang, Menglong Wang, Menglu Wang, Mengmeng Wang, Mengqi Wang, Mengru Wang, Mengshi Wang, Mengwen Wang, Mengxiao Wang, Mengya Wang, Mengyao Wang, Mengying Wang, Mengyuan Wang, Mengyue Wang, Mengyun Wang, Mengze Wang, Mengzhao Wang, Mengzhi Wang, Mian Wang, Miao Wang, Mimi Wang, Min Wang, Min-sheng Wang, Ming Wang, Ming-Chih Wang, Ming-Hsi Wang, Ming-Jie Wang, Ming-Wei Wang, Ming-Yang Wang, Ming-Yuan Wang, Mingchao Wang, Mingda Wang, Minghua Wang, Minghuan Wang, Minghui Wang, Mingji Wang, Mingjin Wang, Minglei Wang, Mingliang Wang, Mingmei Wang, Mingming Wang, Mingqiang Wang, Mingrui Wang, Mingsong Wang, Mingxi Wang, Mingxia Wang, Mingxun Wang, Mingya Wang, Mingyang Wang, Mingyi Wang, Mingyu Wang, Mingzhi Wang, Mingzhu Wang, Minjie Wang, Minjun Wang, Minmin Wang, Minxian Wang, Minxiu Wang, Minzhou Wang, Miranda C Wang, Mo Wang, Mofei Wang, Monica Wang, Mu Wang, Mutian Wang, Muxiao Wang, Muxuan Wang, N Wang, Na Wang, Nan Wang, Nana Wang, Nanbu Wang, Nannan Wang, Nanping Wang, Neng Wang, Ni Wang, Niansong Wang, Ning Wang, Ningjian Wang, Ningli Wang, Ningyuan Wang, Nuan Wang, Oliver Wang, Ouchen Wang, P Jeremy Wang, P L Wang, P N Wang, P Wang, Pai Wang, Pan Wang, Pan-Pan Wang, Panfeng Wang, Panliang Wang, Pei Chang Wang, Pei Wang, Pei-Hua Wang, Pei-Jian Wang, Pei-Juan Wang, Pei-Wen Wang, Pei-Yu Wang, Peichang Wang, Peigeng Wang, Peihe Wang, Peijia Wang, Peijuan Wang, Peijun Wang, Peilin Wang, Peipei Wang, Peirong Wang, Peiwen Wang, Peixi Wang, Peiyao Wang, Peiyin Wang, Peng Wang, Peng-Cheng Wang, Pengbo Wang, Pengchao Wang, Pengfei Wang, Pengjie Wang, Pengju Wang, Penglai Wang, Penglong Wang, Pengpu Wang, Pengtao Wang, Pengxiang Wang, Pengyu Wang, Pin Wang, Ping Wang, Pingchuan Wang, Pingfeng Wang, Pingping Wang, Pintian Wang, Po-Jen Wang, Pu Wang, Q Wang, Q Z Wang, Qi Wang, Qi-Bing Wang, Qi-En Wang, Qi-Jia Wang, Qi-Qi Wang, Qian Wang, Qian-Liang Wang, Qian-Wen Wang, Qian-Zhu Wang, Qian-fei Wang, Qianbao Wang, Qiang Wang, Qiang-Sheng Wang, Qiangcheng Wang, Qianghu Wang, Qiangqiang Wang, Qianjin Wang, Qianliang Wang, Qianqian Wang, Qianrong Wang, Qianru Wang, Qianwen Wang, Qianxu Wang, Qiao Wang, Qiao-Ping Wang, Qiaohong Wang, Qiaoqi Wang, Qiaoqiao Wang, Qifan Wang, Qifei Wang, Qifeng Wang, Qigui Wang, Qihao Wang, Qihua Wang, Qijia Wang, Qiming Wang, Qin Wang, Qing Jun Wang, Qing K Wang, Qing Kenneth Wang, Qing Mei Wang, Qing Wang, Qing-Bin Wang, Qing-Dong Wang, Qing-Jin Wang, Qing-Liang Wang, Qing-Mei Wang, Qing-Yan Wang, Qing-Yuan Wang, Qing-Yun Wang, QingDong Wang, Qingchun Wang, Qingfa Wang, Qingfeng Wang, Qinghang Wang, Qingliang Wang, Qinglin Wang, Qinglu Wang, Qingming Wang, Qingping Wang, Qingqing Wang, Qingshi Wang, Qingshui Wang, Qingsong Wang, Qingtong Wang, Qingyong Wang, Qingyu Wang, Qingyuan Wang, Qingyun Wang, Qingzhong Wang, Qinqin Wang, Qinrong Wang, Qintao Wang, Qinwen Wang, Qinyun Wang, Qiong Wang, Qiqi Wang, Qirui Wang, Qishan Wang, Qiu-Ling Wang, Qiu-Xia Wang, Qiuhong Wang, Qiuli Wang, Qiuling Wang, Qiuning Wang, Qiuping Wang, Qiushi Wang, Qiuting Wang, Qiuyan Wang, Qiuyu Wang, Qiwei Wang, Qixue Wang, Qiyu Wang, Qiyuan Wang, Quan Wang, Quan-Ming Wang, Quanli Wang, Quanren Wang, Quanxi Wang, Qun Wang, Qunxian Wang, Qunzhi Wang, R Wang, Ran Wang, Ranjing Wang, Ranran Wang, Re-Hua Wang, Ren Wang, Rencheng Wang, Renjun Wang, Renqian Wang, Renwei Wang, Renxi Wang, Renxiao Wang, Renyuan Wang, Rihua Wang, Rikang Wang, Rixiang Wang, Robert Yl Wang, Rong Wang, Rong-Chun Wang, Rong-Rong Wang, Rong-Tsorng Wang, RongRong Wang, Rongjia Wang, Rongping Wang, Rongyun Wang, Ru Wang, RuNan Wang, Ruey-Yun Wang, Rufang Wang, Ruhan Wang, Rui Wang, Rui-Hong Wang, Rui-Min Wang, Rui-Ping Wang, Rui-Rui Wang, Ruibin Wang, Ruibing Wang, Ruibo Wang, Ruicheng Wang, Ruifang Wang, Ruijing Wang, Ruimeng Wang, Ruimin Wang, Ruiming Wang, Ruinan Wang, Ruining Wang, Ruiquan Wang, Ruiwen Wang, Ruixian Wang, Ruixin Wang, Ruixuan Wang, Ruixue Wang, Ruiying Wang, Ruizhe Wang, Ruizhi Wang, Rujie Wang, Ruling Wang, Ruming Wang, Runci Wang, Runuo Wang, Runze Wang, Runzhi Wang, Ruo-Nan Wang, Ruo-Ran Wang, Ruonan Wang, Ruosu Wang, Ruoxi Wang, Rurong Wang, Ruting Wang, Ruxin Wang, Ruxuan Wang, Ruyue Wang, S L Wang, S S Wang, S Wang, S X Wang, Sa A Wang, Sa Wang, Saifei Wang, Saili Wang, Sainan Wang, Saisai Wang, Sangui Wang, Sanwang Wang, Sasa Wang, Sen Wang, Seok Mui Wang, Seungwon Wang, Sha Wang, Shan Wang, Shan-Shan Wang, Shang Wang, Shangyu Wang, Shanshan Wang, Shao-Kang Wang, Shaochun Wang, Shaohsu Wang, Shaokun Wang, Shaoli Wang, Shaolian Wang, Shaoshen Wang, Shaowei Wang, Shaoyi Wang, Shaoying Wang, Shaoyu Wang, Shaozheng Wang, Shasha Wang, Shau-Chun Wang, Shawn Wang, Shen Wang, Shen-Nien Wang, Shenao Wang, Sheng Wang, Sheng-Min Wang, Sheng-Nan Wang, Sheng-Ping Wang, Sheng-Quan Wang, Sheng-Yang Wang, Shengdong Wang, Shengjie Wang, Shengli Wang, Shengqi Wang, Shengya Wang, Shengyao Wang, Shengyu Wang, Shengyuan Wang, Shenqi Wang, Sheri Wang, Shi Wang, Shi-Cheng Wang, Shi-Han Wang, Shi-Qi Wang, Shi-Xin Wang, Shi-Yao Wang, Shibin Wang, Shichao Wang, Shicung Wang, Shidong Wang, Shifa Wang, Shifeng Wang, Shih-Wei Wang, Shihan Wang, Shihao Wang, Shihua Wang, Shijie Wang, Shijin Wang, Shijun Wang, Shikang Wang, Shimiao Wang, Shiqi Wang, Shiqiang Wang, Shitao Wang, Shitian Wang, Shiwen Wang, Shixin Wang, Shixuan Wang, Shiyang Wang, Shiyao Wang, Shiyin Wang, Shiyu Wang, Shiyuan Wang, Shiyue Wang, Shizhi Wang, Shouli Wang, Shouling Wang, Shouzhi Wang, Shu Wang, Shu-Huei Wang, Shu-Jin Wang, Shu-Ling Wang, Shu-Na Wang, Shu-Song Wang, Shu-Xia Wang, Shu-qiang Wang, Shuai Wang, Shuaiqin Wang, Shuang Wang, Shuang-Shuang Wang, Shuang-Xi Wang, Shuangyuan Wang, Shubao Wang, Shudan Wang, Shuge Wang, Shuguang Wang, Shuhe Wang, Shuiliang Wang, Shuiyun Wang, Shujin Wang, Shukang Wang, Shukui Wang, Shun Wang, Shuning Wang, Shunjun Wang, Shunran Wang, Shuo Wang, Shuping Wang, Shuqi Wang, Shuqing Wang, Shuren Wang, Shusen Wang, Shusheng Wang, Shushu Wang, Shuu-Jiun Wang, Shuwei Wang, Shuxia Wang, Shuxin Wang, Shuya Wang, Shuye Wang, Shuyue Wang, Shuzhe Wang, Shuzhen Wang, Shuzhong Wang, Shyi-Gang P Wang, Si Wang, Sibo Wang, Sidan Wang, Sihua Wang, Sijia Wang, Silas L Wang, Silu Wang, Simeng Wang, Siqi Wang, Siqing Wang, Siwei Wang, Siyang Wang, Siyi Wang, Siying Wang, Siyu Wang, Siyuan Wang, Siyue Wang, Song Wang, Songjiao Wang, Songlin Wang, Songping Wang, Songsong Wang, Songtao Wang, Sophie H Wang, Stephani Wang, Su'e Wang, Su-Guo Wang, Su-Hua Wang, Sufang Wang, Sugai Wang, Sui Wang, Suiyan Wang, Sujie Wang, Sujuan Wang, Suli Wang, Sun Wang, Supeng Perry Wang, Suxia Wang, Suyun Wang, Suzhen Wang, T Q Wang, T Wang, T Y Wang, Taian Wang, Taicheng Wang, Taishu Wang, Tammy C Wang, Tao Wang, Taoxia Wang, Teng Wang, Tengfei Wang, Theodore Wang, Thomas T Y Wang, Tian Wang, Tian-Li Wang, Tian-Lu Wang, Tian-Tian Wang, Tian-Yi Wang, Tiancheng Wang, Tiange Wang, Tianhao Wang, Tianhu Wang, Tianhui Wang, Tianjing Wang, Tianjun Wang, Tianlin Wang, Tiannan Wang, Tianpeng Wang, Tianqi Wang, Tianqin Wang, Tianqing Wang, Tiansheng Wang, Tiansong Wang, Tiantian Wang, Tianyi Wang, Tianying Wang, Tianyuan Wang, Tielin Wang, Tienju Wang, Tieqiao Wang, Timothy C Wang, Ting Chen Wang, Ting Wang, Ting-Chen Wang, Ting-Hua Wang, Ting-Ting Wang, Tingting Wang, Tingye Wang, Tingyu Wang, Tom J Wang, Tong Wang, Tong-Hong Wang, Tongsong Wang, Tongtong Wang, Tongxia Wang, Tongxin Wang, Tongyao Wang, Tony Wang, Tzung-Dau Wang, Victoria Wang, Vivian Wang, W Wang, Wanbing Wang, Wanchun Wang, Wang Wang, Wangxia Wang, Wanliang Wang, Wanxia Wang, Wanyao Wang, Wanyi Wang, Wanyu Wang, Wayseen Wang, Wei Wang, Wei-En Wang, Wei-Feng Wang, Wei-Lien Wang, Wei-Qi Wang, Wei-Ting Wang, Wei-Wei Wang, Weicheng Wang, Weiding Wang, Weidong Wang, Weifan Wang, Weiguang Wang, Weihao Wang, Weihong Wang, Weihua Wang, Weijian Wang, Weijie Wang, Weijun Wang, Weilin Wang, Weiling Wang, Weilong Wang, Weimin Wang, Weina Wang, Weining Wang, Weipeng Wang, Weiqin Wang, Weiqing Wang, Weirong Wang, Weiwei Wang, Weiwen Wang, Weixiao Wang, Weixue Wang, Weiyan Wang, Weiyu Wang, Weiyuan Wang, Weizhen Wang, Weizhi Wang, Weizhong Wang, Wen Wang, Wen-Chang Wang, Wen-Der Wang, Wen-Fei Wang, Wen-Jie Wang, Wen-Jun Wang, Wen-Qing Wang, Wen-Xuan Wang, Wen-Yan Wang, Wen-Ying Wang, Wen-Yong Wang, Wen-mei Wang, Wenbin Wang, Wenbo Wang, Wence Wang, Wenchao Wang, Wencheng Wang, Wendong Wang, Wenfei Wang, Wengong Wang, Wenhan Wang, Wenhao Wang, Wenhe Wang, Wenhui Wang, Wenjie Wang, Wenjing Wang, Wenju Wang, Wenjuan Wang, Wenjun Wang, Wenkai Wang, Wenkang Wang, Wenke Wang, Wenming Wang, Wenqi Wang, Wenqiang Wang, Wenqing Wang, Wenran Wang, Wenrui Wang, Wentao Wang, Wentian Wang, Wenting Wang, Wenwen Wang, Wenxia Wang, Wenxian Wang, Wenxiang Wang, Wenxiu Wang, Wenxuan Wang, Wenya Wang, Wenyan Wang, Wenyi Wang, Wenying Wang, Wenyu Wang, Wenyuan Wang, Wenzhou Wang, William Wang, Won-Jing Wang, Wu-Wei Wang, Wuji Wang, Wuqing Wang, Wusan Wang, X E Wang, X F Wang, X O Wang, X S Wang, X Wang, X-T Wang, Xi Wang, Xi-Hong Wang, Xi-Rui Wang, Xia Wang, Xian Wang, Xian-e Wang, Xianding Wang, Xianfeng Wang, Xiang Wang, Xiang-Dong Wang, Xiangcheng Wang, Xiangding Wang, Xiangdong Wang, Xiangguo Wang, Xianghua Wang, Xiangkun Wang, Xiangrong Wang, Xiangru Wang, Xiangwei Wang, Xiangyu Wang, Xianna Wang, Xianqiang Wang, Xianrong Wang, Xianshi Wang, Xianshu Wang, Xiansong Wang, Xiantao Wang, Xianwei Wang, Xianxing Wang, Xianze Wang, Xianzhe Wang, Xianzong Wang, Xiao Ling Wang, Xiao Qun Wang, Xiao Wang, Xiao-Ai Wang, Xiao-Fei Wang, Xiao-Hui Wang, Xiao-Jie Wang, Xiao-Juan Wang, Xiao-Lan Wang, Xiao-Li Wang, Xiao-Lin Wang, Xiao-Ming Wang, Xiao-Pei Wang, Xiao-Qian Wang, Xiao-Qun Wang, Xiao-Tong Wang, Xiao-Xia Wang, Xiao-Yi Wang, Xiao-Yun Wang, Xiao-jian WANG, Xiao-liang Wang, Xiaobin Wang, Xiaobo Wang, Xiaochen Wang, Xiaochuan Wang, Xiaochun Wang, Xiaodan Wang, Xiaoding Wang, Xiaodong Wang, Xiaofang Wang, Xiaofei Wang, Xiaofen Wang, Xiaofeng Wang, Xiaogang Wang, Xiaohong Wang, Xiaohu Wang, Xiaohua Wang, Xiaohui Wang, Xiaojia Wang, Xiaojian Wang, Xiaojiao Wang, Xiaojie Wang, Xiaojing Wang, Xiaojuan Wang, Xiaojun Wang, Xiaokun Wang, Xiaole Wang, Xiaoli Wang, Xiaoliang Wang, Xiaolin Wang, Xiaoling Wang, Xiaolong Wang, Xiaolu Wang, Xiaolun Wang, Xiaoman Wang, Xiaomei Wang, Xiaomeng Wang, Xiaomin Wang, Xiaoming Wang, Xiaona Wang, Xiaonan Wang, Xiaoning Wang, Xiaoqi Wang, Xiaoqian Wang, Xiaoqin Wang, Xiaoqing Wang, Xiaoqiu Wang, Xiaoqun Wang, Xiaorong Wang, Xiaorui Wang, Xiaoshan Wang, Xiaosong Wang, Xiaotang Wang, Xiaoting Wang, Xiaotong Wang, Xiaowei Wang, Xiaowen Wang, Xiaowu Wang, Xiaoxia Wang, Xiaoxiao Wang, Xiaoxin Wang, Xiaoxin X Wang, Xiaoxuan Wang, Xiaoya Wang, Xiaoyan Wang, Xiaoyang Wang, Xiaoye Wang, Xiaoying Wang, Xiaoyu Wang, Xiaozhen Wang, Xiaozhi Wang, Xiaozhong Wang, Xiaozhu Wang, Xichun Wang, Xidi Wang, Xietong Wang, Xifeng Wang, Xifu Wang, Xijun Wang, Xike Wang, Xin Wang, Xin Wei Wang, Xin-Hua Wang, Xin-Liang Wang, Xin-Ming Wang, Xin-Peng Wang, Xin-Qun Wang, Xin-Shang Wang, Xin-Xin Wang, Xin-Yang Wang, Xin-Yue Wang, Xinbo Wang, Xinchang Wang, Xinchao Wang, Xinchen Wang, Xincheng Wang, Xinchun Wang, Xindi Wang, Xindong Wang, Xing Wang, Xing-Huan Wang, Xing-Jin Wang, Xing-Jun Wang, Xing-Lei Wang, Xing-Ping Wang, Xing-Quan Wang, Xingbang Wang, Xingchen Wang, Xingde Wang, Xingguo Wang, Xinghao Wang, Xinghui Wang, Xingjie Wang, Xingjin Wang, Xinglei Wang, Xinglong Wang, Xingqin Wang, Xinguo Wang, Xingxin Wang, Xingxing Wang, Xingye Wang, Xingyu Wang, Xingyue Wang, Xingyun Wang, Xinhui Wang, Xinjing Wang, Xinjun Wang, Xinke Wang, Xinkun Wang, Xinli Wang, Xinlin Wang, Xinlong Wang, Xinmei Wang, Xinqi Wang, Xinquan Wang, Xinran Wang, Xinrong Wang, Xinru Wang, Xinrui Wang, Xinshuai Wang, Xintong Wang, Xinwen Wang, Xinxin Wang, Xinyan Wang, Xinyang Wang, Xinye Wang, Xinyi Wang, Xinying Wang, Xinyu Wang, Xinyue Wang, Xinzhou Wang, Xiong Wang, Xiongjun Wang, Xiru Wang, Xitian Wang, Xiu-Lian Wang, Xiu-Ping Wang, Xiufen Wang, Xiujuan Wang, Xiujun Wang, Xiurong Wang, Xiuwen Wang, Xiuyu Wang, Xiuyuan Hugh Wang, Xixi Wang, Xixiang Wang, Xiyan Wang, Xiyue Wang, Xizhi Wang, Xu Wang, Xu-Hong Wang, Xuan Wang, Xuan-Ren Wang, Xuan-Ying Wang, Xuanwen Wang, Xuanyi Wang, Xubo Wang, Xudong Wang, Xue Wang, Xue-Feng Wang, Xue-Hua Wang, Xue-Lei Wang, Xue-Lian Wang, Xue-Rui Wang, Xue-Yao Wang, Xue-Ying Wang, Xuebin Wang, Xueding Wang, Xuedong Wang, Xuefei Wang, Xuefeng Wang, Xueguo Wang, Xuehao Wang, Xuejie Wang, Xuejing Wang, Xueju Wang, Xuejun Wang, Xuekai Wang, Xuelai Wang, Xuelian Wang, Xuelin Wang, Xuemei Wang, Xuemin Wang, Xueping Wang, Xueqian Wang, Xueqin Wang, Xuesong Wang, Xueting Wang, Xuewei Wang, Xuewen Wang, Xuexiang Wang, Xueyan Wang, Xueyi Wang, Xueying Wang, Xueyun Wang, Xuezhen Wang, Xuezheng Wang, Xufei Wang, Xujing Wang, Xuliang Wang, Xumeng Wang, Xun Wang, Xuping Wang, Xuqiao Wang, Xuru Wang, Xusheng Wang, Xv Wang, Y Alan Wang, Y B Wang, Y H Wang, Y L Wang, Y P Wang, Y Wang, Y Y Wang, Y Z Wang, Y-H Wang, Y-S Wang, Ya Qi Wang, Ya Wang, Ya Xing Wang, Ya-Han Wang, Ya-Jie Wang, Ya-Long Wang, Ya-Nan Wang, Ya-Ping Wang, Ya-Qin Wang, Ya-Zhou Wang, Yachen Wang, Yachun Wang, Yadong Wang, Yafang Wang, Yafen Wang, Yahong Wang, Yahui Wang, Yajie Wang, Yajing Wang, Yajun Wang, Yake Wang, Yakun Wang, Yali Wang, Yalin Wang, Yaling Wang, Yalong Wang, Yan Ming Wang, Yan Wang, Yan-Chao Wang, Yan-Chun Wang, Yan-Feng Wang, Yan-Ge Wang, Yan-Jiang Wang, Yan-Jun Wang, Yan-Ming Wang, Yan-Yang Wang, Yan-Yi Wang, Yan-Zi Wang, Yana Wang, Yanan Wang, Yanbin Wang, Yanbing Wang, Yanchun Wang, Yancun Wang, Yanfang Wang, Yanfei Wang, Yanfeng Wang, Yang Wang, Yang-Yang Wang, Yange Wang, Yanggan Wang, Yangpeng Wang, Yangyang Wang, Yangyufan Wang, Yanhai Wang, Yanhong Wang, Yanhua Wang, Yanhui Wang, Yani Wang, Yanjin Wang, Yanjun Wang, Yankun Wang, Yanlei Wang, Yanli Wang, Yanliang Wang, Yanlin Wang, Yanling Wang, Yanmei Wang, Yanming Wang, Yanni Wang, Yanong Wang, Yanping Wang, Yanqing Wang, Yanru Wang, Yanting Wang, Yanwen Wang, Yanxia Wang, Yanxing Wang, Yanyang Wang, Yanyun Wang, Yanzhe Wang, Yanzhu Wang, Yao Wang, Yaobin Wang, Yaochun Wang, Yaodong Wang, Yaohe Wang, Yaokun Wang, Yaoling Wang, Yaolou Wang, Yaoxian Wang, Yaoxing Wang, Yaozhi Wang, Yapeng Wang, Yaping Wang, Yaqi Wang, Yaqian Wang, Yaqiong Wang, Yaru Wang, Yatao Wang, Yating Wang, Yawei Wang, Yaxian Wang, Yaxin Wang, Yaxiong Wang, Yaxuan Wang, Yayu Wang, Yazhou Wang, Ye Wang, Ye-Ran Wang, Yefu Wang, Yeh-Han Wang, Yehan Wang, Yeming Wang, Yen-Feng Wang, Yen-Sheng Wang, Yeou-Lih Wang, Yeqi Wang, Yezhou Wang, Yi Fan Wang, Yi Lei Wang, Yi Wang, Yi-Cheng Wang, Yi-Chuan Wang, Yi-Ming Wang, Yi-Ni Wang, Yi-Ning Wang, Yi-Shan Wang, Yi-Shiuan Wang, Yi-Shu Wang, Yi-Tao Wang, Yi-Ting Wang, Yi-Wen Wang, Yi-Xin Wang, Yi-Xuan Wang, Yi-Yi Wang, Yi-Ying Wang, Yi-Zhen Wang, Yi-sheng Wang, YiLi Wang, Yian Wang, Yibin Wang, Yibing Wang, Yichen Wang, Yicheng Wang, Yichuan Wang, Yifan Wang, Yifei Wang, Yigang Wang, Yige Wang, Yihan Wang, Yihao Wang, Yihe Wang, Yijin Wang, Yijing Wang, Yijun Wang, Yikang Wang, Yike Wang, Yilin Wang, Yilu Wang, Yimeng Wang, Yiming Wang, Yin Wang, Yin-Hu Wang, Yinan Wang, Yinbo Wang, Yindan Wang, Ying Wang, Ying-Piao Wang, Ying-Wei Wang, Ying-Zi Wang, Yingbo Wang, Yingcheng Wang, Yingchun Wang, Yingfei Wang, Yingge Wang, Yinggui Wang, Yinghui Wang, Yingjie Wang, Yingmei Wang, Yingna Wang, Yingping Wang, Yingqiao Wang, Yingtai Wang, Yingte Wang, Yingwei Wang, Yingwen Wang, Yingxiong Wang, Yingxue Wang, Yingyi Wang, Yingying Wang, Yingzi Wang, Yinhuai Wang, Yining E Wang, Yinong Wang, Yinsheng Wang, Yintao Wang, Yinuo Wang, Yinxiong Wang, Yinyin Wang, Yiou Wang, Yipeng Wang, Yiping Wang, Yiqi Wang, Yiqiao Wang, Yiqin Wang, Yiqing Wang, Yiquan Wang, Yirong Wang, Yiru Wang, Yirui Wang, Yishan Wang, Yishu Wang, Yitao Wang, Yiting Wang, Yiwei Wang, Yiwen Wang, Yixi Wang, Yixian Wang, Yixuan Wang, Yiyan Wang, Yiyi Wang, Yiying Wang, Yizhe Wang, Yong Wang, Yong-Bo Wang, Yong-Gang Wang, Yong-Jie Wang, Yong-Jun Wang, Yong-Tang Wang, Yongbin Wang, Yongdi Wang, Yongfei Wang, Yongfeng Wang, Yonggang Wang, Yonghong Wang, Yongjie Wang, Yongjun Wang, Yongkang Wang, Yongkuan Wang, Yongli Wang, Yongliang Wang, Yonglun Wang, Yongmei Wang, Yongming Wang, Yongni Wang, Yongqiang Wang, Yongqing Wang, Yongrui Wang, Yongsheng Wang, Yongxiang Wang, Yongyi Wang, Yongzhong Wang, You Wang, Youhua Wang, Youji Wang, Youjie Wang, Youli Wang, Youzhao Wang, Youzhi Wang, Yu Qin Wang, Yu Tian Wang, Yu Wang, Yu'e Wang, Yu-Chen Wang, Yu-Fan Wang, Yu-Fen Wang, Yu-Hang Wang, Yu-Hui Wang, Yu-Ping Wang, Yu-Ting Wang, Yu-Wei Wang, Yu-Wen Wang, Yu-Ying Wang, Yu-Zhe Wang, Yu-Zhuo Wang, Yuan Wang, Yuan-Hung Wang, Yuanbo Wang, Yuanfan Wang, Yuanjiang Wang, Yuanli Wang, Yuanqiang Wang, Yuanqing Wang, Yuanyong Wang, Yuanyuan Wang, Yuanzhen Wang, Yubing Wang, Yubo Wang, Yuchen Wang, Yucheng Wang, Yuchuan Wang, Yudong Wang, Yue Wang, Yue-Min Wang, Yue-Nan Wang, YueJiao Wang, Yuebing Wang, Yuecong Wang, Yuegang Wang, Yuehan Wang, Yuehong Wang, Yuehu Wang, Yuehua Wang, Yuelong Wang, Yuemiao Wang, Yueshen Wang, Yueting Wang, Yuewei Wang, Yuexiang Wang, Yuexin Wang, Yueying Wang, Yueze Wang, Yufei Wang, Yufeng Wang, Yugang Wang, Yuh-Hwa Wang, Yuhan Wang, Yuhang Wang, Yuhua Wang, Yuhuai Wang, Yuhuan Wang, Yuhui Wang, Yujia Wang, Yujiao Wang, Yujie Wang, Yujiong Wang, Yulai Wang, Yulei Wang, Yuli Wang, Yuliang Wang, Yulin Wang, Yuling Wang, Yulong Wang, Yumei Wang, Yumeng Wang, Yumin Wang, Yuming Wang, Yun Wang, Yun Yong Wang, Yun-Hui Wang, Yun-Jin Wang, Yun-Xing Wang, Yunbing Wang, Yunce Wang, Yunchao Wang, Yuncong Wang, Yunduan Wang, Yunfang Wang, Yunfei Wang, Yunhan Wang, Yunhe Wang, Yunong Wang, Yunpeng Wang, Yunqiong Wang, Yuntai Wang, Yunzhang Wang, Yunzhe Wang, Yunzhi Wang, Yupeng Wang, Yuping Wang, Yuqi Wang, Yuqian Wang, Yuqiang Wang, Yuqin Wang, Yusha Wang, Yushe Wang, Yusheng Wang, Yutao Wang, Yuting Wang, Yuwei Wang, Yuwen Wang, Yuxiang Wang, Yuxing Wang, Yuxuan Wang, Yuxue Wang, Yuyan Wang, Yuyang Wang, Yuyin Wang, Yuying Wang, Yuyong Wang, Yuzhong Wang, Yuzhou Wang, Yuzhuo Wang, Z P Wang, Z Wang, Z-Y Wang, Zai Wang, Zaihua Wang, Ze Wang, Zechen Wang, Zehao Wang, Zehua Wang, Zekun Wang, Zelin Wang, Zeneng Wang, Zengtao Wang, Zeping Wang, Zexin Wang, Zeying Wang, Zeyu Wang, Zeyuan Wang, Zezhou Wang, Zhan Wang, Zhang Wang, Zhanggui Wang, Zhangshun Wang, Zhangying Wang, Zhanju Wang, Zhao Wang, Zhao-Jun Wang, Zhaobo Wang, Zhaofeng Wang, Zhaofu Wang, Zhaohai Wang, Zhaohui Wang, Zhaojing Wang, Zhaojun Wang, Zhaoming Wang, Zhaoqing Wang, Zhaosong Wang, Zhaotong Wang, Zhaoxi Wang, Zhaoxia Wang, Zhaoyu Wang, Zhe Wang, Zhehai Wang, Zhehao Wang, Zhen Wang, ZhenXue Wang, Zhenbin Wang, Zhenchang Wang, Zhenda Wang, Zhendan Wang, Zhendong Wang, Zheng Wang, Zhengbing Wang, Zhengchun Wang, Zhengdong Wang, Zhenghui Wang, Zhengkun Wang, Zhenglong Wang, Zhenguo Wang, Zhengwei Wang, Zhengxuan Wang, Zhengyang Wang, Zhengyi Wang, Zhengyu Wang, Zhenhua Wang, Zhenning Wang, Zhenqian Wang, Zhenshan Wang, Zhentang Wang, Zhenwei Wang, Zhenxi Wang, Zhenyu Wang, Zhenze Wang, Zhenzhen Wang, Zheyi Wang, Zheyue Wang, Zhezhi Wang, Zhi Wang, Zhi Xiao Wang, Zhi-Gang Wang, Zhi-Guo Wang, Zhi-Hao Wang, Zhi-Hong Wang, Zhi-Hua Wang, Zhi-Jian Wang, Zhi-Long Wang, Zhi-Qin Wang, Zhi-Wei Wang, Zhi-Xiao Wang, Zhi-Xin Wang, Zhibo Wang, Zhichao Wang, Zhicheng Wang, Zhicun Wang, Zhidong Wang, Zhifang Wang, Zhifeng Wang, Zhifu Wang, Zhigang Wang, Zhige Wang, Zhiguo Wang, Zhihao Wang, Zhihong Wang, Zhihua Wang, Zhihui Wang, Zhiji Wang, Zhijian Wang, Zhijie Wang, Zhijun Wang, Zhilun Wang, Zhimei Wang, Zhimin Wang, Zhipeng Wang, Zhiping Wang, Zhiqi Wang, Zhiqian Wang, Zhiqiang Wang, Zhiqing Wang, Zhiren Wang, Zhiruo Wang, Zhisheng Wang, Zhitao Wang, Zhiting Wang, Zhiwu Wang, Zhixia Wang, Zhixiang Wang, Zhixiao Wang, Zhixin Wang, Zhixing Wang, Zhixiong Wang, Zhixiu Wang, Zhiying Wang, Zhiyong Wang, Zhiyou Wang, Zhiyu Wang, Zhiyuan Wang, Zhizheng Wang, Zhizhong Wang, Zhong Wang, Zhong-Hao Wang, Zhong-Hui Wang, Zhong-Ping Wang, Zhong-Yu Wang, ZhongXia Wang, Zhongfang Wang, Zhongli Wang, Zhonglin Wang, Zhongqun Wang, Zhongsu Wang, Zhongwei Wang, Zhongyi Wang, Zhongyu Wang, Zhongyuan Wang, Zhongzhi Wang, Zhou Wang, Zhou-Ping Wang, Zhoufeng Wang, Zhouguang Wang, Zhuangzhuang Wang, Zhugang Wang, Zhulin Wang, Zhulun Wang, Zhuo Wang, Zhuo-Hui Wang, Zhuo-Jue Wang, Zhuo-Xin Wang, Zhuowei Wang, Zhuoying Wang, Zhuozhong Wang, Zhuqing Wang, Zi Wang, Zi Xuan Wang, Zi-Hao Wang, Zi-Qi Wang, Zi-Yi Wang, Zicheng Wang, Zifeng Wang, Zihan Wang, Ziheng Wang, Zihua Wang, Zihuan Wang, Zijian Wang, Zijie Wang, Zijue Wang, Zijun Wang, Zikang Wang, Zikun Wang, Ziliang Wang, Zilin Wang, Ziling Wang, Zilong Wang, Zining Wang, Ziping Wang, Ziqi Wang, Ziqian Wang, Ziqiang Wang, Ziqing Wang, Ziqiu Wang, Zitao Wang, Ziwei Wang, Zixi Wang, Zixia Wang, Zixian Wang, Zixiang Wang, Zixu Wang, Zixuan Wang, Ziyi Wang, Ziying Wang, Ziyu Wang, Ziyun Wang, Zongbao Wang, Zonggui Wang, Zongji Wang, Zongkui Wang, Zongqi Wang, Zongwei Wang, Zou Wang, Zulong Wang, Zumin Wang, Zun Wang, Zunxian Wang, Zuo Wang, Zuoheng Wang, Zuoyan Wang, Zusen Wang
articles
Le Zhang, Yan Xie, Shun Wang +4 more · 2025 · Genes & diseases · Elsevier · added 2026-04-24
Neuropathic pain (NP) is a chronic debilitating disease caused by nerve damage or various diseases, significantly impairs patients' quality of life. Super-enhancers (SEs) are important cis-regulatory Show more
Neuropathic pain (NP) is a chronic debilitating disease caused by nerve damage or various diseases, significantly impairs patients' quality of life. Super-enhancers (SEs) are important cis-regulatory elements, but how they affect NP remains elusive. Therefore, we aim to explore the molecular mechanism by which SEs are involved in NP progression and identify potential drug candidate targets. We first established a NP model in rats, and subsequently performed H3K27ac ChIP-Seq and RNA-Seq on their spinal cord tissues to analyze the active enhancers. By integrated analysis of ChIP-seq data and RNA-seq data, we clarified a series of SE-associated genes involved in NP progression. qPCR and double immunofluorescence staining results suggested that Show less
📄 PDF DOI: 10.1016/j.gendis.2025.101545
JMJD1C
Yiming Wang, Yifei Chen, Jianbo Yang +2 more · 2025 · Journal of physiology and biochemistry · Springer · added 2026-04-24
Macrophage is considered as a critical driving factor in the progression of atherosclerosis (AS), and epigenetic heterogeneity contributes important mechanisms in this process. Here, we identified tha Show more
Macrophage is considered as a critical driving factor in the progression of atherosclerosis (AS), and epigenetic heterogeneity contributes important mechanisms in this process. Here, we identified that a histone demethylase jumonji domain-containing protein 1 C (JMJD1C) is a promising biomarker for atherosclerotic cerebral infarction through clinical analysis. Then, AOPE Show less
no PDF DOI: 10.1007/s13105-024-01058-3
JMJD1C
Qian Chen, Saisai Wang, Juqing Zhang +12 more · 2025 · Protein & cell · Oxford University Press · added 2026-04-24
JMJD1C (Jumonji Domain Containing 1C), a member of the lysine demethylase 3 (KDM3) family, is universally required for the survival of several types of acute myeloid leukemia (AML) cells with differen Show more
JMJD1C (Jumonji Domain Containing 1C), a member of the lysine demethylase 3 (KDM3) family, is universally required for the survival of several types of acute myeloid leukemia (AML) cells with different genetic mutations, representing a therapeutic opportunity with broad application. Yet how JMJD1C regulates the leukemic programs of various AML cells is largely unexplored. Here we show that JMJD1C interacts with the master hematopoietic transcription factor RUNX1, which thereby recruits JMJD1C to the genome to facilitate a RUNX1-driven transcriptional program that supports leukemic cell survival. The underlying mechanism hinges on the long N-terminal disordered region of JMJD1C, which harbors two inseparable abilities: condensate formation and direct interaction with RUNX1. This dual capability of JMJD1C may influence enhancer-promoter contacts crucial for the expression of key leukemic genes regulated by RUNX1. Our findings demonstrate a previously unappreciated role for the non-catalytic function of JMJD1C in transcriptional regulation, underlying a mechanism shared by different types of leukemias. Show less
📄 PDF DOI: 10.1093/procel/pwae059
JMJD1C
Hailin Huang, Jia Geng, Yang Long +11 more · 2025 · Molecular genetics and genomics : MGG · Springer · added 2026-04-24
Neurodevelopmental disorders (NDDs) exhibit complex genotype-phenotype associations that frequently result in inconclusive variant interpretations, contributing to suboptimal diagnostic yields (~ 40%) Show more
Neurodevelopmental disorders (NDDs) exhibit complex genotype-phenotype associations that frequently result in inconclusive variant interpretations, contributing to suboptimal diagnostic yields (~ 40%). Koolen-de Vries syndrome (KdVS), an autosomal dominant NDD caused by KANSL1 haploinsufficiency, exemplifies this diagnostic challenge with its multisystem manifestations and lack of systematic genotype-phenotype associations. To address this gap, we constructed a comprehensive KdVS genotype-phenotype repository by systematically integrating all molecularly confirmed cases from global literature. Comprehensive phenotypic analysis revealed that core KdVS features include developmental delay/intellectual disability, characteristic craniofacial dysmorphism, hypotonia, and multisystem abnormalities. Phenotypic association analysis identified 249 significant correlations, demonstrating that KdVS clinical manifestations are highly interconnected rather than representing isolated features, such as the association between strabismus and hydrocephalus (OR = 14.26). Application of this repository to screen a Chinese rare disease cohort identified 53 KANSL1 variants. Among these, one de novo nonsense variant (NM₀₀₁₁₉₃₄₆₆.2: c.902T > G, p.Leu301Ter) was classified as pathogenic in a Chinese boy with classic KdVS features. The remaining 52 variants were categorized as variants of uncertain significance (VUS), approximately half of which were absent from gnomAD databases. Each VUS was comprehensively annotated with detailed clinical profiles to facilitate phenotype-driven reinterpretation. In conclusion, this study establishes KdVS as a highly interconnected multisystem disorder and demonstrates that deep phenotypic association analysis enhanced genetic diagnosis. This disease-specific repository approach provides a scalable framework for improving molecular diagnostics across rare NDDs. Show less
no PDF DOI: 10.1007/s00438-025-02322-x
KANSL1
Xianchang Zeng, Lingyun Wei, Lu Lv +6 more · 2025 · Frontiers in pharmacology · Frontiers · added 2026-04-24
The molecular pathogenesis of lung adenocarcinoma (LUAD) involves genomic mutations, autophagy dysregulation, and signaling pathway disruptions. Autophagy, a key cellular process, is tightly linked to Show more
The molecular pathogenesis of lung adenocarcinoma (LUAD) involves genomic mutations, autophagy dysregulation, and signaling pathway disruptions. Autophagy, a key cellular process, is tightly linked to cancer development; genes like ATG5 and ATG10 influence lung cancer progression, and epigenetic regulators modulate autophagy-related carcinogenesis. However, the role of epigenetic-autophagy genes in LUAD's tumor microenvironment is under-researched. We used the "limma"" package to identify differential epigenetic-related genes associated with altered autophagy regulation (A-ERGs) in LUAD. Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. Cell experiments, real-time PCR, and Western blot were conducted to verify the expression of KDM6B and KANSL1 and their effects on T-cell differentiation. Based on single-cell and transcriptome analyses, we screened 19 A-ERGs that were significantly differentially expressed in lung cancer tissues. These genes were primarily enriched in exhausted T cells. Subsequently, through machine learning, KDM6B and KANSL1 were identified to have excellent diagnostic performance. Single-cell level and transcriptome correlation analyses revealed that the expression of these two genes was associated with exhausted T cells. Results from In this study, we utilized bulk and single-cell transcriptomic data to uncover the potential molecular mechanisms of A-ERGs in lung cancer. We explored the characteristic distribution of these genes in the tumor immune microenvironment and identified two A-ERGs, KDM6B and KANSL1, as potential diagnostic biomarkers for lung adenocarcinoma (LUAD). Our findings offer novel strategies for targeted therapeutic interventions in LUAD. Show less
📄 PDF DOI: 10.3389/fphar.2025.1542338
KANSL1
Rui Guo, Chunhong Duan, Mehdi Zarrei +9 more · 2025 · Scientific reports · Nature · added 2026-04-24
Congenital heart disease (CHD) is the most common type of birth defects in humans. Genetic factors have been identified as an important contributor to the etiology of CHD. However, the underlying gene Show more
Congenital heart disease (CHD) is the most common type of birth defects in humans. Genetic factors have been identified as an important contributor to the etiology of CHD. However, the underlying genetic causes in most individuals remain unclear. Here, 101 individuals with CHD and their unaffected parents were included in this study. Chromosomal microarray analysis (CMA) as a first-tier clinical diagnostic tool was applied for all affected individuals, followed by trio-based whole exome sequencing (WES) of 76 probands and proband-only WES of 3 probands. We detected aneuploidies in 2 individuals (trisomy 21 and monosomy X), 21 pathogenic and likely pathogenic copy number variants (CNVs) in 19 individuals, and pathogenic and likely pathogenic SNVs/InDels in 8 individuals. The combined genetic diagnostic yield was 28.7%, including 20.8% with chromosomal abnormalities and 7.9% with sequence-level variants. Eighteen CNVs in 17 individuals were associated with 13 recurrent chromosomal microdeletion/microduplication syndromes, the most common being 22q11.2 deletion syndrome. Pathogenic/likely pathogenic sequence-level variants were identified in 8 genes, including GATA6, FLNA, KANSL1, TRAF7, KAT6A, PKD1L1, RIT1, and SMAD6. Trio sequencing facilitated the identification of pathogenic variation (55.6% were de novo missense variants). In individuals with extracardiac features, the overall detection rate was significantly higher (61.5%) than in individuals with isolated CHD (17.3%) (P = 4.6 × 10 Show less
📄 PDF DOI: 10.1038/s41598-025-06977-9
KANSL1
Hui Wang, Timothy S Chang, Beth A Dombroski +64 more · 2025 · Movement disorders : official journal of the Movement Disorder Society · Wiley · added 2026-04-24
The 17q21.31 region with various structural forms characterized by the H1/H2 haplotypes and three large copy number variations (CNVs) represents the strongest risk locus in progressive supranuclear pa Show more
The 17q21.31 region with various structural forms characterized by the H1/H2 haplotypes and three large copy number variations (CNVs) represents the strongest risk locus in progressive supranuclear palsy (PSP). To investigate the association between CNVs and structural forms on 17q.21.31 with the risk of PSP. Utilizing whole genome sequencing data from 1684 PSP cases and 2392 controls, the three large CNVs (α, β, and γ) and structural forms within 17q21.31 were identified and analyzed for their association with PSP. We found that the copy number of γ was associated with increased PSP risk (odds ratio [OR] = 1.10, P = 0.0018). From H1β1γ1 (OR = 1.21) and H1β2γ1 (OR = 1.24) to H1β1γ4 (OR = 1.57), structural forms of H1 with additional copies of γ displayed a higher risk for PSP. The frequency of the risk sub-haplotype H1c rises from 1% in individuals with two γ copies to 88% in those with eight copies. Additionally, γ duplication up-regulates expression of ARL17B, LRRC37A/LRRC37A2, and NSFP1, while down-regulating KANSL1. Single-nucleus RNA-seq of the dorsolateral prefrontal cortex analysis reveals γ duplication primarily up-regulates LRRC37A/LRRC37A2 in neuronal cells. The copy number of γ is associated with the risk of PSP after adjusting for H1/H2, indicating that the complex structure at 17q21.31 is an important consideration when evaluating the genetic risk of PSP. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. Show less
📄 PDF DOI: 10.1002/mds.30150
KANSL1
Diana M Cornejo-Sanchez, Thashi Bharadwaj, Rui Dong +4 more · 2025 · European journal of human genetics : EJHG · Nature · added 2026-04-24
Age-related (AR) hearing loss (HL) is the most prevalent sensorineural disorder in older adults. Here we demonstrate that rare-variants in well-established Mendelian HL genes play an important role in Show more
Age-related (AR) hearing loss (HL) is the most prevalent sensorineural disorder in older adults. Here we demonstrate that rare-variants in well-established Mendelian HL genes play an important role in ARHL etiology. In all we identified 32 Mendelian HL genes which are associated with ARHL. We performed single and rare-variant aggregate association analyses using exome data obtained from white-Europeans with self-reported hearing phenotypes from the UK Biobank. Our analysis revealed previously unreported associations between ARHL and rare-variants in Mendelian non-syndromic and syndromic HL genes, including MYO15A, and WFS1. Additionally, rare-variant aggregate association analyses identified associations with Mendelian HL genes i.e., ACTG1, GRHL2, KCNQ4, MYO7A, PLS1, TMPRSS3, and TNRC6B. Four novel ARHL genes were also detected: FBXO2 and PALM3, implicated in HL in mice, TWF1, associated with HL in Dalmatian dogs, and TXNDC17. In-silico analyses provided further evidence of inner ear expression of these genes in both murine and human models, supporting their relevance to ARHL. Analysis of variants with minor allele frequency >0.005 revealed additional ARHL associations with known e.g., ILDR1 and novel i.e., ABHD12, COA8, KANSL1, SERAC1, and UBE3B Mendelian non-syndromic and syndromic HL genes as well as ARHL associations with genes that have not been previously reported to be involved in HL e.g., VCL. Rare-variants in Mendelian HL genes typically exhibited higher effect sizes for ARHL compared to those in other associated genes. In conclusion, this study highlights the critical role Mendelian non-syndromic and syndromic HL genes play in the etiology of ARHL. Show less
📄 PDF DOI: 10.1038/s41431-025-01789-x
KANSL1
Fangling Huang, Su'e Wang, Zhengrong Peng +2 more · 2025 · Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · added 2026-04-24
The neurotoxicity of carbon monoxide (CO) to the central nervous system is a key pathogenesis of delayed encephalopathy after acute carbon monoxide poisoning (DEACMP). Our previous study found that re Show more
The neurotoxicity of carbon monoxide (CO) to the central nervous system is a key pathogenesis of delayed encephalopathy after acute carbon monoxide poisoning (DEACMP). Our previous study found that retinoic acid (RA) can suppress the neurotoxic effects of CO. This study further explores, in vivo and in vitro, the molecular mechanisms by which RA alleviates CO-induced central nervous system damage. A cytotoxic model was established using the mouse hippocampal neuronal cell line HT22 and primary oligodendrocytes exposed to CO, and a DEACMP animal model was established in adult Kunming mice. Cell viability and apoptosis of hippocampal neurons and oligodendrocytes were assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and Annexin V/propidium iodide (PI) double staining. The transcriptional and protein expression of each gene was detected using real-time fluorescence quantitative PCR (RT-qPCR) and Western blotting. Long noncoding RNA (lncRNA) RA at 10 and 20 μmol/L significantly reversed CO-induced apoptosis of hippocampal neurons and oligodendrocytes, downregulation of RA alleviates CO-induced apoptosis of hippocampal neurons and oligodendrocytes, thereby reducing central nervous system injury and exerting neuroprotective effects. LncRNA Show less
📄 PDF DOI: 10.11817/j.issn.1672-7347.2025.240318
LINGO1
Qi He, Lin Jiang, Feng-Lei Chao +11 more · 2025 · Experimental neurology · Elsevier · added 2026-04-24
Leucine-rich repeat and immunoglobulin-like domain-containing nogo receptor-interacting protein 1 (LINGO-1) is a neuronal system-specific transmembrane protein that is highly expressed in the brains o Show more
Leucine-rich repeat and immunoglobulin-like domain-containing nogo receptor-interacting protein 1 (LINGO-1) is a neuronal system-specific transmembrane protein that is highly expressed in the brains of patients with Alzheimer's disease (AD), and our previous findings showed that LINGO-1 antagonism can improve cognitive function and protect hippocampal GABAergic interneurons in AD model mice. However, the specific mechanism underlying these effects is not clear. In this study, an adeno-associated virus (AAV) was used to directly interfere with hippocampal LINGO-1 in vivo, and LINGO-1 antagonists, cannabinoid type 1 receptor (CB1R) agonists, and CB1R antagonists were used to treat mouse hippocampal neurons (HT22 neurons) in vitro. We found that overexpressing hippocampal LINGO-1 in normal young mice impaired spatial learning and memory and reduced hippocampal CB1R protein levels, whereas silencing hippocampal LINGO-1 in AD model mice had the opposite effect. Additionally, antagonizing LINGO-1 increased CB1R/tyrosine kinase receptor B (TrkB) signalling and rescued CB1R- rich cholecystokinin-GABAergic (CCK-GABAergic) interneurons in HT22 neurons transduced with an APP/PS1-expressing virus. Competitive inhibition of LINGO-1 and CB1R was observed, and antagonizing LINGO-1 reversed the changes in HT22 neurons caused by the inhibition of CB1R, such as the decreases in the protein levels of doublecortin (DCX), TrkB, and phosphorylated TrkB (p-TrkB). These findings provide an important scientific basis for further exploration of the mechanism by which LINGO-1 regulates cognitive function and hippocampal GABAergic interneurons in AD model mice. Show less
no PDF DOI: 10.1016/j.expneurol.2025.115319
LINGO1
Xuejin Xu, Zhen Wang · 2025 · Discover oncology · Springer · added 2026-04-24
Head and neck squamous cell carcinoma (HNSC) is a significant global health challenge. While traditional risk factors are well-established, the role of environmental pollutants in HNSC development rem Show more
Head and neck squamous cell carcinoma (HNSC) is a significant global health challenge. While traditional risk factors are well-established, the role of environmental pollutants in HNSC development remains unclear. To investigate the causal relationship between environmental pollution factors and HNSC risk using Mendelian Randomization (MR) analysis. Two-sample MR analysis was performed using genome-wide association study data from the IEU OpenGWAS project and HNSC RNA-seq data from TCGA. Environmental pollution-associated genes (MRGs) were identified and analyzed along with autophagy-related genes (ATGs) in HNSC samples. Cox proportional hazards models were used to develop a clinical prediction model. MR analysis revealed significant causal relationships between nitrogen dioxide air pollution, nitrogen oxides air pollution, PM2.5, and increased HNSC risk. Nine MRGs were identified, with four (IRF4, LINGO1, PTHLH, RSRC1) differentially expressed in HNSC. A six-factor clinical prediction model (IRF4, LINGO1, PTHLH, RSRC1, Age, USP10) showed good predictive performance for HNSC survival (C-index = 0.63, 10-year AUC = 0.761). Tumor mutation burden and immune cell infiltration analyses provided further insights into HNSC biology. This study provides evidence for causal relationships between specific air pollutants and HNSC risk, and identifies potential gene targets for further investigation. The developed clinical prediction model may aid in HNSC prognosis and personalized treatment strategies. Show less
📄 PDF DOI: 10.1007/s12672-025-02009-0
LINGO1
Sara A Wennersten, Hongxia Wang, J Lee Franklin +1 more · 2025 · Vascular pharmacology · Elsevier · added 2026-04-24
The transition of smooth muscle cells (SMCs) from a contractile to a synthetic phenotype is a key contributor to cardiovascular disease (CVD) pathologies, such as atherosclerosis and in-stent restenos Show more
The transition of smooth muscle cells (SMCs) from a contractile to a synthetic phenotype is a key contributor to cardiovascular disease (CVD) pathologies, such as atherosclerosis and in-stent restenosis. We previously reported that loss of leiomodin 1 (LMOD1), a coronary artery disease risk gene highly expressed in SMCs, promotes SMC phenotypic switching in vitro. However, the in vivo role of LMOD1 and the molecular mechanisms driving this transition remain unknown. In this study, we found that Lmod1 heterozygous mice subjected to carotid artery ligation developed larger neointimal lesions. Histopathological analyses attributed this phenotype to increased SMC proliferation. RNA sequencing studies of LMOD1-deficient SMCs revealed a significant upregulation of genes associated with increased cell proliferation, particularly those involved in the G1/S phase transition. Further analysis identified cyclin-dependent kinase 6 (CDK6) as a potential mediator of this hyperproliferative response. Notably, the knockdown of CDK6 in LMOD1-deficient cultured SMCs restored SMC proliferation to near baseline levels, indicating that the observed phenotype is reversible in vitro. Collectively, these findings indicate that LMOD1 deficiency promotes SMC proliferation by upregulating CDK6 expression and provide mechanistic insight into how reduced LMOD1 expression may contribute to increased neointimal lesion size and vascular remodeling. Show less
📄 PDF DOI: 10.1016/j.vph.2025.107555
LMOD1
Haiyan Wang, Søren Madsen, Elise J Needham +7 more · 2025 · The journals of gerontology. Series A, Biological sciences and medical sciences · Oxford University Press · added 2026-04-24
Calorie restriction (CR; calorie intake reduced by ∼20%-40% below ad libitum, AL, intake) potentiates skeletal muscle insulin sensitivity during old age by incompletely understood mechanisms. We aimed Show more
Calorie restriction (CR; calorie intake reduced by ∼20%-40% below ad libitum, AL, intake) potentiates skeletal muscle insulin sensitivity during old age by incompletely understood mechanisms. We aimed to identify CR-induced changes in muscle insulin signaling that may explain this enhanced sensitivity. We examined how CR (65% of AL intake for 8-weeks) alters muscle insulin action and signaling in aged rats (24-month old) of both sexes. We assessed insulin-stimulated glucose uptake (ISGU) in muscle together with deep phosphoproteomic profiling. CR enhanced ISGU in both sexes, with higher ISGU in females regardless of diet. We identified 590 diet-responsive phosphosites, indicating extensive CR-induced remodelling of muscle phosphorylation, particularly within structural and contractile pathways. Strikingly, 70% of these sites were sex-specific. Numerous insulin-responsive sites were identified (193 in females; 107 in males) with 60 overlapping sites. The magnitude of the insulin-effects among all significantly regulated sites correlated between sexes. S1443 phosphorylation on EH domain-binding protein 1-like protein-1 (Ehbp1l1; a potential regulator of Rab proteins that control GLUT4 glucose transporter trafficking) was insulin-responsive in both sexes but only associated to ISGU in females. Personalized phosphoproteomic analysis also identified insulin-responsive sites on Leiomodin-1 (Lmod1) that correlated with ISGU across individuals. Both Lmod1 and Ehbp1l1 have strong genetic association with glycemic traits in humans, reinforcing their translational relevance. This study revealed sex-dependent and sex-independent phosphosignaling mechanisms that associate with muscle insulin responsiveness as well as hundreds of sex-specific, CR-responsive phosphosites. These findings provide a rich resource for future research on CR and insulin sensitivity. Show less
📄 PDF DOI: 10.1093/gerona/glaf231
LMOD1
Kaiming Wang, Caihong Liu, Lei Yi +9 more · 2025 · BMC genomics · BioMed Central · added 2026-04-24
Skeletal muscle is the largest tissue in mammals, and it plays a crucial role in metabolism and homeostasis. Skeletal muscle development and regeneration consist of a series of carefully regulated cha Show more
Skeletal muscle is the largest tissue in mammals, and it plays a crucial role in metabolism and homeostasis. Skeletal muscle development and regeneration consist of a series of carefully regulated changes in gene expression. Leiomodin2 (LMOD2) gene is specifically expressed in the heart and skeletal muscle. But the physiological functions and mechanisms of LMOD2 on skeletal muscle development are unknown. In this study, we examined the expression levels of the LMOD2 in porcine tissues and C2C12 cells. LMOD2 is mainly expressed in the heart, followed by skeletal muscle. The expression level of LMOD2 gradually decreased with skeletal muscle growth, but increased after injury. LMOD2 expression levels increased gradually with C2C12 cells proliferation and differentiation. In terms of function, the muscle fiber types were altered after LMOD2 was knocked out in C2C12 cells, MyHC-I and MyHC-2b were inhibited, whereas MyHC-2a and MyHC-2x were promoted. LMOD2 knockout has different effects on LMOD family, LMOD1 expression level was promoted, while LMOD3 was inhibited. Loss of LMOD2 suppressed cell viability and PAX7 protein expression. At the transcriptome level, proliferation-related genes and muscle contraction-related genes were respectively inhibited after LMOD2 knockout. In terms of molecular networks, a series of experiments have shown that MyoG is a transcription factor for LMOD2, while miR-335-3p can negatively regulate LMOD2 expression. We screened ACTC1 as a candidate interacting protein for LMOD2 using protein prediction software and RNA-seq, and Co-IP experiments confirmed the relationship between LMOD2 and ACTC1. In vivo, Lentivirus-mediated LMOD2 knockdown reduces muscle mass. LMOD2 knockdown inhibited MyHC-I mRNA expression, but had no effect on MyHC-2b. The protein expression of MyHC-I, MyHC-2x, and MyHC-2b was suppressed after LMOD2 knockdown. Collectively, our data indicates that LMOD2 knockout inhibits myoblast proliferation and alters muscle fiber types. MyoG is a transcription factor for LMOD2, while miR-335-3p can negatively regulate LMOD2 expression. Moreover, LMOD2 and ACTC1 interact to regulate myogenic differentiation. Our study provides a new target for skeletal muscle development. Show less
📄 PDF DOI: 10.1186/s12864-025-11897-z
LMOD1
Rong Du, Ajay Kumar, Enzhi Yang +3 more · 2025 · Current issues in molecular biology · MDPI · added 2026-04-24
Glaucoma is a leading cause of irreversible blindness, normally associated with dysfunction and degeneration of the trabecular meshwork (TM) as the primary cause. Trabecular meshwork stem cells (TMSCs Show more
Glaucoma is a leading cause of irreversible blindness, normally associated with dysfunction and degeneration of the trabecular meshwork (TM) as the primary cause. Trabecular meshwork stem cells (TMSCs) have emerged as promising candidates for TM regeneration toward glaucoma therapies, yet their molecular characteristics remain poorly defined. In this study, we performed a comprehensive transcriptomic comparison of human TMSCs and human TM cells (TMCs) using RNA sequencing and microarray analyses, followed by qPCR validation. A total of 465 differentially expressed genes were identified, with 254 upregulated in TMSCs and 211 in TMCs. A functional enrichment analysis revealed that TMSCs are associated with development, immune signaling, and extracellular matrix remodeling pathways, while TMCs are enriched in structural, contractile, and adhesion-related functions. A network topology analysis identified Show less
📄 PDF DOI: 10.3390/cimb47070514
LMOD1
Jiaxin Shi, Bo Peng, Ran Xu +4 more · 2025 · Postgraduate medical journal · Oxford University Press · added 2026-04-24
Gastroesophageal reflux disease (GERD) is a chronic inflammatory gastrointestinal disease, which has no thoroughly effective or safe treatment. Elevated oxidative stress is a common consequence of chr Show more
Gastroesophageal reflux disease (GERD) is a chronic inflammatory gastrointestinal disease, which has no thoroughly effective or safe treatment. Elevated oxidative stress is a common consequence of chronic inflammatory conditions. We employed Summary-data based MR (SMR) analysis to assess the associations between gene molecular characteristics and GERD. Exposure data were the summary-level data on the levels of DNA methylation, gene expression, and protein expression, which obtained from related methylation, expression, and protein quantitative trait loci investigations (mQTL, eQTL, and pQTL). Outcome data, Genome-wide association study (GWAS) summary statistics of GERD, were extracted from the Ong's study (discovery), the Dönertaş's study (replication), and the FinnGen study (replication). Colocalization analysis was performed to determine if the detected signal pairs shared a causative genetic mutation. Oxidative stress related genes and druggable genes were imported to explore oxidative stress mechanism underlying GERD and therapeutic targets of GERD. The Drugbank database was utilized to conduct druggability evaluation. After multi-omics SMR analysis and colocalization analysis, we identified seven key genes for GERD, which were SUOX and SERPING1, DUSP13, SULT1A1, LMOD1, UBE2L6, and PSCA. SUOX was screened out to be the mediator, which suggest that GERD is related to oxidative stress. SERPING1, SULT1A1, and PSCA were selected to be the druggable genes. These findings offered strong support for the identification of GERD treatment targets in the future as well as for the study of the oxidative stress mechanism underlying GERD. Show less
no PDF DOI: 10.1093/postmj/qgae182
LMOD1
Bingjie Wu, Xiaoyue Cheng, Ruimin Zheng +10 more · 2025 · Human reproduction open · Oxford University Press · added 2026-04-24
Does preconception mental health status in either partner affect fertility and infertility, and is this association modified by socioeconomic status (SES)? Preconception mental health problems in both Show more
Does preconception mental health status in either partner affect fertility and infertility, and is this association modified by socioeconomic status (SES)? Preconception mental health problems in both partners are associated with lower couple fertility, with the synergistic impact being most pronounced among couples with low SES status. Mental health problems are rising among young adults, and fertility rates are declining. Women's preconception mental health has been linked to lower fertility, but few studies have examined the combined impact of both partners' mental health. The modifying role of SES in these associations is also poorly understood. This couple-based prospective cohort study included 966 preconception couples who sought preconception care and were followed for 12 months in the Shanghai Birth Cohort between 2013 and 2015. The couples' mental health status was evaluated at enrolment using the Center for Epidemiological Studies-Depression Scale, Zung Self-Rating Anxiety Scale, and Perceived Stress Scale. The outcomes included couple fecundability (measured by the TTP) and infertility (i.e. TTP >12 menstrual cycles). In the partner-specific model, Cox proportional hazards models and logistic regression were used to evaluate the associations between each partner's depression, anxiety, and stress levels and couples' fertility. In the couple-based model, cross-classification and quantile g-computation were first applied to identify couples' joint exposure to specific psychological conditions in relation to fertility. Latent profile analysis (LPA) was then conducted to characterize distinct latent profiles of couples' overall mental health statuses, followed by Cox proportional hazards models and logistic regression to examine the corresponding associations. Key symptoms in the couples' depression, anxiety, and stress scales were determined by elastic net regression and least absolute shrinkage and selection operator. To assess the potential effect modification of SES on the association between couples' mental health and fertility, we conducted stratified analyses by male and female partner education levels and household income. In the female partner-specific model, a 1 SD increase in depression score was associated with 10% lower fecundability (FOR = 0.90, 95% CI: 0.82, 0.99). Likewise, a 1 SD increase in the stress score was associated with 13% lower fecundability (FOR = 0.87, 95% CI: 0.79, 0.96). Male anxiety was associated with a higher risk of infertility (OR = 1.19, 95% CI: 1.01, 1.42). Stratified analyses showed that depression, anxiety, and stress were significantly associated with lower fecundability among males with an education level Show less
📄 PDF DOI: 10.1093/hropen/hoaf071
LPA
Jiali Wang, Yilin Wang, Yue Wang +3 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24
As global population aging intensifies, mental health issues in older adults are increasingly prominent, with depression being particularly prevalent and detrimental. The study investigated how substi Show more
As global population aging intensifies, mental health issues in older adults are increasingly prominent, with depression being particularly prevalent and detrimental. The study investigated how substituting sedentary behavior (SB) and sleep (SLP) with physical activity (PA) affects depression risk in this population. Meta-analysis was conducted by searching four databases: PubMed, Scopus, SPORTdiscus, and PsycINFO (via EBSCOhost platform) for relevant studies published until January 2025. Regression coefficients (β) with 95% confidence intervals (CIs) for depressive symptoms were estimated. Publication bias was assessed using funnel plots and Egger's tests, and heterogeneity was evaluated using Q tests and the I Among 18,912 participants (53.45% female, ≥60 years) across nine studies, replacing SB with MVPA significantly reduced depression (β = -0.12, 95% CI: -0.20, -0.04), subgroup analyses indicated that reallocating 10, 30 and 60 min/day of SB to MVPA ( Substituting SB and SLP with MVPA is significantly associated with a reduction in depression, whereas no significant association is observed when replaced by LPA. https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=546666, identifier CRD42024546666. Show less
📄 PDF DOI: 10.3389/fpsyg.2025.1682987
LPA
Sijia Tu, Mengyang Cai, Gang Wang +1 more · 2025 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
To investigate the associations of monocyte count, lipoprotein(a) [Lp(a)], and monocyte-to-HDL ratio (MHR) with in-stent restenosis (ISR) in coronary heart disease (CHD) patients after drug-eluting st Show more
To investigate the associations of monocyte count, lipoprotein(a) [Lp(a)], and monocyte-to-HDL ratio (MHR) with in-stent restenosis (ISR) in coronary heart disease (CHD) patients after drug-eluting stent (DES) implantation, and to develop a predictive risk model. This study enrolled 190 CHD patients who underwent DES implantation from 2019 to 2024. Based on 1-year coronary angiography, patients were divided into an ISR group ( Compared to the Non-ISR group, ISR patients had higher monocyte count, MHR, and Lp(a) levels (all Monocyte count, Lp(a), and MHR are closely linked to ISR in CHD patients post-DES. Combined assessment enhances risk prediction, offering a basis for early identification and personalized management to reduce restenosis and improve outcomes. Show less
📄 PDF DOI: 10.3389/fcvm.2025.1672158
LPA
Huatao Zheng, Dan Li, Rentao Ma +3 more · 2025 · Frontiers in public health · Frontiers · added 2026-04-24
With the aging population in China, research on preventing frailty is crucial. This study aims to investigate the independent and combined associations of the Dietary inflammatory index (DII) and phys Show more
With the aging population in China, research on preventing frailty is crucial. This study aims to investigate the independent and combined associations of the Dietary inflammatory index (DII) and physical activity (PA) with frailty among Chinese older adults. A total of 285 participants aged ≥60 years with 87 males and 186 females were recruited from Hunan Province. Daily moderate physical activity (MPA), vigorous physical activity (VPA) and light physical activity (LPA) were objectively measured using a triaxial accelerometer. A Food Frequency Questionnaire 25 (FFQ25) was used to assess the participants' dietary patterns, and DII was calculated. Six combined exposure groups were formed based on PA and DII: pro-inflammatory diet and insufficient PA group, neutral diet and insufficient PA group, anti-inflammatory diet and insufficient PA group, pro-inflammatory diet and sufficient PA group, neutral diet and sufficient PA group, and anti-inflammatory diet and sufficient PA group. Frailty was assessed using the Frailty Phenotype (FP), logistic regression analyzed the associations between dietary patterns, PA, and frailty. A total of 285 older adults participants were initially recruited, but 12 were excluded due to missing data. Consequently, 273 participants were included in the final analysis. Compared to individuals with insufficient PA, those with sufficient PA were associated with significantly lower odds of frailty (OR = 0.468, 95%CI = 0.242-0.907). Participants following an anti-inflammatory diet had significantly lower odds of frailty compared with those following a pro-inflammatory diet (OR = 0.467, 95%CI = 0.221-0.988). In the combined groups, frailty prevalence was significantly lower the group with anti-inflammatory diet and sufficient PA group (OR = 0.204, 95%CI = 0.072-0.583), compared with pro-inflammatory diet and insufficient PA group. The sensitivity analysis showed that the associations between anti-inflammatory diet and sufficient PA with frailty remained statistically significant, with the direction of the associations unchanged. These findings suggest that the results are robust. Our study indicates that adhering to an anti-inflammatory diet and maintaining sufficient PA may be associated with a lower likelihood of frailty. Achieving an adequate amount of PA and following a healthy dietary pattern may serve as potential preventive measures against frailty. Show less
📄 PDF DOI: 10.3389/fpubh.2025.1739530
LPA
Xiaojuan Li, Tiewei Li, Pengfei Xuan +2 more · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
Lysophosphatidic acid (LPA) has anti-inflammatory and protective effects in sepsis, yet clinical evidence on its correlation with sepsis progression and outcomes is limited. This study aimed to evalua Show more
Lysophosphatidic acid (LPA) has anti-inflammatory and protective effects in sepsis, yet clinical evidence on its correlation with sepsis progression and outcomes is limited. This study aimed to evaluate the association of plasma LPA levels with sepsis development, severity, and mortality. A total of 42 sepsis patients and 29 controls with common infections were included. Among the sepsis patients, 15 succumbed during hospitalization. Plasma LPA levels were measured, and clinical data were retrospectively analyzed. Plasma LPA was significantly lower in sepsis patients compared to controls, and further reduced in non-survivors. Notably, correlation analyses suggested that LPA levels were negatively correlated with neutrophil count, procalcitonin, interleukin-6, and Sequential Organ Failure Assessment (SOFA) score. Multivariate regression analysis identified LPA as an independent risk factor for sepsis onset and in-hospital mortality. Receiver operating characteristic (ROC) curve analysis revealed that LPA had a high diagnostic accuracy for sepsis (area under the ROC curve [AUC] = 0.92, 95% CI = 0.86-0.99, P < 0.001) and was a strong predictor of in-hospital mortality (AUC = 0.86, 95% CI = 0.76-0.97, P < 0.001). Reduced plasma LPA levels in sepsis patients are inversely correlated with infection/inflammation markers and SOFA scores. Together, these results suggest that LPA may serve as a potential diagnostic and prognostic biomarker for sepsis, supporting its potential as a complementary tool to enhance early risk stratification and guide bedside clinical decision-making. Show less
📄 PDF DOI: 10.3389/fimmu.2025.1725394
LPA
Liqun Wang, Honglei Li, Tianqi Qiao +3 more · 2025 · Frontiers in public health · Frontiers · added 2026-04-24
This study investigates the heterogeneity in kindergarten teachers' perceptions of organizational climate and its impact on job burnout. Guided by the AGIL model from social systems theory and the Job Show more
This study investigates the heterogeneity in kindergarten teachers' perceptions of organizational climate and its impact on job burnout. Guided by the AGIL model from social systems theory and the Job Demands-Resources (JD-R) model, it addresses the need to move beyond variable-centered approaches to understand how distinct climate profiles are associated with teacher well-being. A person-centered latent profile analysis (LPA) was employed. A sample of 1,008 kindergarten teachers from China completed measures assessing organizational climate and burnout. The analysis aimed to identify distinct climate profiles and examine their relationships with demographic variables (kindergarten type, assessment level, teaching experience) and the three dimensions of burnout (emotional exhaustion, depersonalization, reduced personal accomplishment). The LPA revealed five distinct organizational climate profiles: Controlled, Moderate, Indifferent, Positive, and Authoritative. Profile membership was significantly predicted by kindergarten assessment level and teachers' years of experience, but not by kindergarten type. Crucially, the profiles differed significantly across all burnout dimensions. Teachers in Positive climates reported the lowest burnout levels, whereas those in Controlled and Indifferent climates experienced the highest. The findings underscore the structural diversity of organizational climates in early childhood settings and their profound psychological consequences. This study validates the application of social systems theory and the JD-R model in this context, revealing how different configurations of job demands and resources shape teacher well-being. The results provide a theoretical lens for understanding educational organizations and offer practical implications for developing tailored, climate-specific intervention strategies to mitigate burnout and support sustainable professional development. Show less
📄 PDF DOI: 10.3389/fpubh.2025.1708777
LPA
Baolong Wang, Peiyou Chen, Zhihao Jia +1 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24
The purpose of this study is to explore the effect of physical activity on the executive function of 5-6-year-old children and to provide a theoretical and empirical basis for further research on impr Show more
The purpose of this study is to explore the effect of physical activity on the executive function of 5-6-year-old children and to provide a theoretical and empirical basis for further research on improvements in the executive function of children caused by physical activity. A total of 170 children (5-6 years old) from several kindergartens were selected via multistage stratified sampling. All the children wore 7-day accelerometers (ActiGraph GT3X) to measure their daily physical activities. Parents completed the preschool children's executive function questionnaire (BRIEF-P) to assess their daily executive function. (1) The total duration of physical activity (TPA) was 110.84 ± 22.52 min/day, the duration of low-intensity physical activity (LPA) was 36.23 ± 7.53 min/day, and the duration of medium- and high-intensity physical activity (MVPA) was 74.55 ± 16.77 min/day. A total of 82.6% of the children reached the recommended amount of MVPA. (2) After adjusting for body mass index (BMI), parents' highest educational background and parents' total monthly income, MVPA was negatively correlated with children's total executive function score ( Physical activity can improve the executive function of children aged 5-6 years to some extent. MVPA can improve children's executive function and subdomains, and there is a correlation between boys' physical activity and executive function. Show less
📄 PDF DOI: 10.3389/fpsyg.2025.1651806
LPA
Shoudi Hu, Zihan Shan, Xintong Shen +5 more · 2025 · BMC women's health · BioMed Central · added 2026-04-24
Perimenopause is a critical turning point in women's life cycle, and the issue of sleep disturbance during perimenopause not only affects individual health, but also has profound implications for fami Show more
Perimenopause is a critical turning point in women's life cycle, and the issue of sleep disturbance during perimenopause not only affects individual health, but also has profound implications for family functioning, socioeconomic status, and public health policies. Therefore, this study aims to explore different potential profiles of sleep quality in perimenopausal women in the community and analyze the influencing factors of different profiles. A cross-sectional study was conducted from July 2024 to December 2024, and a total of 281 perimenopausal women in the community were recruited from 4 communities in Bengbu by convenience sampling. The participants completed the pittsburgh sleep quality index (PSQI), and self-rating anxiety scale (SAS), self-rating depression scale (SDS) and simplified coping style questionnaire (SCSQ). Latent profile analysis(LPA) was employed to identify latent profiles of sleep quality of perimenopausal women in the community. The predictors of sleep quality in different latent profiles were assessed via multinomial logistic regression analysis. One-way ANOVA, chi-square test or Fisher exact test, and the Kruskal-Walis test were used to compare the PSQI scores of perimenopausal women in the community under different latent profile characteristics. The mean age of 281 perimenopausal women was 50.09 ± 5.08 years, and the prevalence of sleep disorders was 31.3%. The sleep quality of perimenopausal women in community could be divided into three different latent profiles: good sleep quality group (68.7%), falling sleep and maintenance difficulty group (24.2%), and poor sleep quality with sleep disorder group (7.1%). Taking the good sleep quality group as the reference group, drinking history (OR = 2.061), chronic disease history (OR = 2.154), spouse's health status (OR = 1.871) and anxiety (OR = 4.390) were the risk factors to predict the difficulty in falling asleep and maintaining sleep in community perimenopausal women (P < 0.05). Spouse's health status (OR = 2.139) and anxiety (OR = 19.029) were the risk factors for poor sleep quality and sleep disorders in community perimenopausal women (P < 0.05). There are three qualitatively different potential profile categories of sleep quality in perimenopausal women in the community, and drinking history, chronic disease, poor spouse health and anxiety have predictive effects on their profile categories. In the future, community nursing staff can take targeted interventions according to different categories of sleep quality in perimenopausal women to improve sleep quality and level of health promotion. Show less
📄 PDF DOI: 10.1186/s12905-025-04217-w
LPA
Jinyue Liu, Yueping Jiang, Yueyi Xing +5 more · 2025 · BMC gastroenterology · BioMed Central · added 2026-04-24
This study aimed to assess the prognostic significance of serum lipoprotein(a) [Lp(a)] levels regarding overall survival (OS) and progression-free survival (PFS) among patients diagnosed with pancreat Show more
This study aimed to assess the prognostic significance of serum lipoprotein(a) [Lp(a)] levels regarding overall survival (OS) and progression-free survival (PFS) among patients diagnosed with pancreatic cancer (PC). A retrospective cohort of 364 pathologically confirmed PC patients treated at the Affiliated Hospital of Qingdao University between January 2019 and December 2022 was analyzed. The optimal cutoff for Lp(a) was identified using X-tile software, allowing categorization into high and low Lp(a) groups. To minimize selection bias, propensity score matching (PSM) was utilized. Survival outcomes were compared using Kaplan-Meier curves and log-rank tests. Cox proportional hazards models were applied to identify independent prognostic variables affecting OS and PFS. Patients with high Lp(a) had significantly shorter OS and PFS both before and after PSM (post-PSM OS: 12.28 vs. 27.67 months, P = 0.003; PFS: 7.00 vs. 11.30 months, P = 0.002). Multivariate Cox analysis confirmed high Lp(a) as an independent predictor of poor OS [HR = 2.11 (1.17-3.81), P = 0.013] and PFS [HR = 2.14 (1.20-3.83), P = 0.010]. In the surgical subgroup (n = 215), high Lp(a) was also associated with worse OS (16.43 vs. 35.47 months, P = 0.02) and PFS (8.40 vs. 11.77 months, P = 0.036). Multivariate analysis in this subgroup showed that high Lp(a) remained an independent risk factor for OS [HR = 2.82 (1.36-5.87), P = 0.006] and PFS [HR = 2.01 (1.06-3.86), P = 0.034]. Elevated serum Lp(a) is an independent predictor of reduced OS and PFS in patients with pancreatic cancer. In contrast to conventional lipid profiles, the genetic stability of Lp(a) makes it a reliable baseline prognostic marker. Show less
📄 PDF DOI: 10.1186/s12876-025-04573-9
LPA
Caixia Deng, Jingxing Liu, Xiaoqian Wu +4 more · 2025 · Behavioral sciences (Basel, Switzerland) · MDPI · added 2026-04-24
Problematic smartphone use (PSU) has become a growing concern among young populations, raising significant issues for their physical and psychological well-being. Guided by Compensatory Internet Use T Show more
Problematic smartphone use (PSU) has become a growing concern among young populations, raising significant issues for their physical and psychological well-being. Guided by Compensatory Internet Use Theory and the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study examined the associations between different forms of childhood trauma and PSU. Participants were 2717 college students (661 males, 22.49%; Mage = 19.81 years). Two chain mediation models were tested, and latent profile analysis (LPA) was employed to capture individual differences from a person-centred perspective. LPA revealed three distinct trauma profiles: low childhood trauma, moderate childhood abuse, and high childhood abuse. Across both variable-centred and person-centred ap-proaches, rumination and social anxiety were identified as mediators linking childhood trauma to PSU. These findings advance understanding of the pathways through which childhood trauma contributes to PSU in college students. By integrating variable- and person-centred approaches, the study highlights the importance of cognitive-emotional mechanisms and provides implications for targeted prevention and intervention strategies. Show less
📄 PDF DOI: 10.3390/bs15121676
LPA
Zejun Fan, Zhenyu Li, Yiqing Jin +9 more · 2025 · Life medicine · Oxford University Press · added 2026-04-24
Recent advances in human blastoids have opened new avenues for modeling early human development and implantation. Human blastoids can be generated in large numbers, making them well-suited for high-th Show more
Recent advances in human blastoids have opened new avenues for modeling early human development and implantation. Human blastoids can be generated in large numbers, making them well-suited for high-throughput screening. However, automated methods for evaluating and characterizing blastoid morphology are lacking. We developed a deep-learning model-deepBlastoid-for automated classification of live human blastoids using only brightfield images. The model processes 273.6 images per second with an average accuracy of 87%, which is further improved to 97% by integrating a Confidence Rate metric. deepBlastoid outperformed human experts in throughput while matching accuracy in blastoid classification. We demonstrated the utility of the model in two use cases: (i) systematic assessment of the effect of lysophosphatidic acid (LPA) on blastoid formation and (ii) evaluating the impact of dimethyl sulfoxide (DMSO) on blastoid formation. The evaluation results of deepBlastoid using over 10,000 images were consistent with the known drug effects and showed subtle but significant effects that might have been overlooked in manual assessments. The publicly available deepBlastoid model enables researchers to train customized models based on their imaging and protocols, providing an efficient, automated tool for blastoid classification with broad applications in research, drug screening, and Show less
📄 PDF DOI: 10.1093/lifemedi/lnaf026
LPA
Lulu Wu, Ziqing Qi, Yue Zhang +5 more · 2025 · Frontiers in public health · Frontiers · added 2026-04-24
To identify latent profiles of demoralization among older adults with disabilities, analyze their influencing factors, and examine their associations with active aging. From February to July 2025, a c Show more
To identify latent profiles of demoralization among older adults with disabilities, analyze their influencing factors, and examine their associations with active aging. From February to July 2025, a convenience sample of 411 older adults with disabilities was recruited from a tertiary hospital in Anhui Province, China. Data were collected using a general information questionnaire, the Chinese version of the Demoralization Scale, and the Active Aging Scale. Latent profile analysis (LPA) was performed based on demoralization subscale scores. Univariate and multinominal analyses were employed to investigate the influencing factors, and the Kruskal-Wallis The prevalence of demoralization syndrome was 49.1%. LPA identified three distinct profiles: the Well-Adapted Group (53.3%), the Disheartened-Helpless Group (23.8%), and the Fully Demoralized Group (22.9%). The Kruskal-Wallis Nearly half of the older adults with disabilities experienced demoralization, with heterogeneous subgroups identified. The active aging status of demoralized subgroups requires urgent attention. These findings suggest the need for targeted interventions tailored to the characteristics of each profile to improve mental health and promote active aging in this population. Show less
📄 PDF DOI: 10.3389/fpubh.2025.1715566
LPA
Wenyi Wang, Xinyun Pan, Yan Yan +1 more · 2025 · BMC nursing · BioMed Central · added 2026-04-24
Robotic technology is transforming healthcare by delivering more precision, convenience, and efficiency, as seen in applications like blood collection robots. However, the full potential of such innov Show more
Robotic technology is transforming healthcare by delivering more precision, convenience, and efficiency, as seen in applications like blood collection robots. However, the full potential of such innovations hinges critically on patient acceptance. To systematically understand the drivers of intention to use, the concept of technological readiness-an individual's stable propensity to embrace new technologies-provides a valuable lens. Nevertheless, the demographic profile of technological readiness and its specific relationship with the intention to use nursing robots remain underexplored. This study aimed to identify profiles of technology readiness among patients, analyze the factors influencing these profiles, and investigate the relationship between different profiles and the intention to use blood collection robots. In this study, data on technology readiness and intention to use were collected from 331 patients between December 2024 and February 2025 through a cross-sectional survey. Latent profile analysis (LPA) was employed to assess population heterogeneity in patients' technology readiness, whereas logistic regression analysis was applied to identify factors associated with the intention to use blood collection robots. Potential profiling revealed three distinct technology readiness populations: conservative avoiders (47.7%), ambivalent adopters (18.5%), and active adopters (33.8%). The results of the three-profile classification are related to the intention to use blood collection robots. The results clearly show that the intention to use the blood collection robot by patients belonging to conservative avoiders (profile 1) was weaker and statistically significant than both active adopters (profile 3) and ambivalent adopters (profile 2). While profile 2 has a high intention to use, it also has a high level of insecurity and discomfort. Interestingly, the group of patients with low education levels is more likely to be attributed to active adopters. The results of the multiple regression analysis on patients' intention to use revealed significant differences in education level, experience with robots, and potential profiles. This study reveals the complex psychological characteristics of patient groups when encountering new technologies and their potential relationship with intention to use nursing robots. This suggests that clinical practice should account for patient heterogeneity from standardized procedures to a patient-centered approach. Targeted measures should be proposed to enhance the efficiency of technology implementation and improve patient experience. Show less
📄 PDF DOI: 10.1186/s12912-025-04226-y
LPA
Zijian Wang, Radek Zenkl, Latifa Greche +33 more · 2025 · Plant phenomics (Washington, D.C.) · Elsevier · added 2026-04-24
Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection an Show more
Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 ​%. However, the precision for stems with 54 ​% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies. Show less
📄 PDF DOI: 10.1016/j.plaphe.2025.100084
LPA