👤 Xueyi Wang

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Also published as: A Wang, Ai-Ling Wang, Ai-Ting Wang, Aihua Wang, Aijun Wang, Aili Wang, Aimin Wang, Aiting Wang, Aixian Wang, Aiyun Wang, Aizhong Wang, Alexander Wang, Alice Wang, Allen Wang, Anlai Wang, Anli Wang, Annette Wang, Anni Wang, Anqi Wang, Anthony Z Wang, Anxiang Wang, Anxin Wang, Ao Wang, Aoli Wang, B R Wang, B Wang, Baihan Wang, Baisong Wang, Baitao Wang, Bangchen Wang, Banghui Wang, Bangmao Wang, Bangshing Wang, Bao Wang, Bao-Long Wang, Baocheng Wang, Baofeng Wang, Baogui Wang, Baojun Wang, Baoli Wang, Baolong Wang, Baoming Wang, Baosen Wang, Baowei Wang, Baoying Wang, Baoyun Wang, Bei Bei Wang, Bei Wang, Beibei Wang, Beilan Wang, Beilei Wang, Ben Wang, Benjamin H Wang, Benzhong Wang, Bi Wang, Bi-Dar Wang, Biao Wang, Bicheng Wang, Bijue Wang, Bin Wang, Bin-Xue Wang, Binbin Wang, Bing Qing Wang, Bing Wang, Binghai Wang, Binghan Wang, Bingjie Wang, Binglong Wang, Bingnan Wang, Bingyan Wang, Bingyu Wang, Binquan Wang, Biqi Wang, Bo Wang, Bochu Wang, Boyu Wang, Bruce Wang, C Wang, C Z Wang, Cai Ren Wang, Cai-Hong Wang, Cai-Yun Wang, Cailian Wang, Caiqin Wang, Caixia Wang, Caiyan Wang, Can Wang, Cangyu Wang, Carol A Wang, Catherine Ruiyi Wang, Cenxuan Wang, Chan Wang, Chang Wang, Chang-Yun Wang, Changduo Wang, Changjing Wang, Changliang Wang, Changlong Wang, Changqian Wang, Changtu Wang, Changwei Wang, Changying Wang, Changyu Wang, Changyuan Wang, Changzhen Wang, Chao Wang, Chao-Jun Wang, Chao-Yung Wang, Chaodong Wang, Chaofan Wang, Chaohan Wang, Chaohui Wang, Chaojie Wang, Chaokui Wang, Chaomeng Wang, Chaoqun Wang, Chaoxian Wang, Chaoyi Wang, Chaoyu Wang, Chaozhan Wang, Charles C N Wang, Chau-Jong Wang, Chen Wang, Chen-Cen Wang, Chen-Ma Wang, Chen-Yu Wang, Chenchen Wang, Chenfei Wang, Cheng An Wang, Cheng Wang, Cheng-Cheng Wang, Cheng-Jie Wang, Cheng-zhang Wang, Chengbin Wang, Chengcheng Wang, Chenggang Wang, Chenghao Wang, Chenghua Wang, Chengjian Wang, Chengjun Wang, Chenglin Wang, Chenglong Wang, Chengniu Wang, Chengqiang Wang, Chengshuo Wang, Chenguang Wang, Chengwen Wang, Chengyan Wang, Chengyu Wang, Chengze Wang, Chenji Wang, Chenliang Wang, Chenwei Wang, Chenxi Wang, Chenxin Wang, Chenxuan Wang, Chenyang Wang, Chenyao Wang, Chenyin Wang, Chenyu Wang, Chenzi Wang, Chi Chiu Wang, Chi Wang, Chi-Ping Wang, Chia-Chuan Wang, Chia-Lin Wang, Chien-Hsun Wang, Chien-Wei Wang, Chih-Chun Wang, Chih-Hao Wang, Chih-Hsien Wang, Chih-Liang Wang, Chih-Yang Wang, Chih-Yuan Wang, Chijia Wang, Ching C Wang, Ching-Jen Wang, Chiou-Miin Wang, Chong Wang, Chongjian Wang, Chonglong Wang, Chongmin Wang, Chongze Wang, Christina Wang, Christine Wang, Chu Wang, Chuan Wang, Chuan-Chao Wang, Chuan-Hui Wang, Chuan-Jiang Wang, Chuan-Wen Wang, Chuang Wang, Chuanhai Wang, Chuansen Wang, Chuansheng Wang, Chuanxin Wang, Chuanyue Wang, Chuduan Wang, Chun Wang, Chun-Chieh Wang, Chun-Juan Wang, Chun-Li Wang, Chun-Lin Wang, Chun-Ting Wang, Chun-Xia Wang, Chung-Hsi Wang, Chung-Hsing Wang, Chung-Teng Wang, Chunguo Wang, Chunhong Wang, Chuning Wang, Chunjiong Wang, Chunjuan Wang, Chunle Wang, Chunli Wang, Chunlong Wang, Chunmei Wang, Chunsheng Wang, Chunting Wang, Chunxia Wang, Chunxue Wang, Chunyan Wang, Chunyang Wang, Chunyi Wang, Chunyu Wang, Chuyao Wang, Cindy Wang, Ciyang Wang, Cong Wang, Congcong Wang, Congrong Wang, Congrui Wang, Cui Wang, Cui-Fang Wang, Cui-Shan Wang, Cuili Wang, Cuiling Wang, Cuizhe Wang, Cun-Yu Wang, Cunchuan Wang, Cunyi Wang, D Wang, Da Wang, Da-Cheng Wang, Da-Li Wang, Da-Yan Wang, Da-Zhi Wang, Dadong Wang, Dai Wang, Daijun Wang, Daiwei Wang, Daixi Wang, Dajia Wang, Dake Wang, Dali Wang, Dalong Wang, Dalu Wang, Dan Wang, Dan-Dan Wang, Danan Wang, Dandan Wang, Danfeng Wang, Dang Wang, Dangfeng Wang, Danling Wang, Danqing Wang, Danxin Wang, Danyang Wang, Dao Wen Wang, Dao-Wen Wang, Dao-Xin Wang, Daolong Wang, Daoping Wang, Daozhong Wang, Dapeng Wang, Daping Wang, Daqi Wang, Daqing Wang, David Q H Wang, David Q-H Wang, David Wang, Dawei Wang, Dayan Wang, Dayong Wang, Dazhi Wang, De-He Wang, Dedong Wang, Dehao Wang, Deli Wang, Delin Wang, Delong Wang, Demin Wang, Deming Wang, Dengbin Wang, Dennis Qing Wang, Dennis Wang, Deqi Wang, Deshou Wang, Dezhong Wang, Di Wang, Dinghui Wang, Dingting Wang, Dingxiang Wang, Dong D Wang, Dong Hao Wang, Dong Wang, Dong-Dong Wang, Dong-Jie Wang, Dong-Mei Wang, DongWei Wang, Dongdong Wang, Donggen Wang, Donghao Wang, Donghong Wang, Donghui Wang, Dongliang Wang, Donglin Wang, Dongmei Wang, Dongqin Wang, Dongshi Wang, Dongxia Wang, Dongxu Wang, Dongyan Wang, Dongyang Wang, Dongyi Wang, Dongying Wang, Dongyu Wang, Doudou Wang, Du Wang, Duan Wang, Duanyang Wang, Duo-Ping Wang, E Wang, Edward Wang, En-bo Wang, En-hua Wang, Endi Wang, Enhua Wang, Er-Jin Wang, Erfei Wang, Erika Y Wang, Ermao Wang, Erming Wang, Ertao Wang, Eryao Wang, Eunice S Wang, Exing Wang, F Wang, Fa-Kai Wang, Fan Wang, Fanchang Wang, Fang Wang, Fang-Tao Wang, Fangfang Wang, Fangjie Wang, Fangjun Wang, Fangyan Wang, Fangyong Wang, Fangyu Wang, Fanhua Wang, Fanwen Wang, Fanxiong Wang, Fei Wang, Fei-Fei Wang, Fei-Yan Wang, Feida Wang, Feifei Wang, Feijie Wang, Feimiao Wang, Feixiang Wang, Feiyan Wang, Fen Wang, Feng Wang, Feng-Sheng Wang, Fengchong Wang, Fengge Wang, Fenghua Wang, Fengliang Wang, Fenglin Wang, Fengling Wang, Fengqiang Wang, Fengyang Wang, Fengying Wang, Fengyong Wang, Fengyun Wang, Fengzhen Wang, Fengzhong Wang, Fu Wang, Fu-Sheng Wang, Fu-Yan Wang, Fu-Zhen Wang, Fubao Wang, Fubing Wang, Fudi Wang, Fuhua Wang, Fuqiang Wang, Furong Wang, Fuwen Wang, Fuxin Wang, Fuyan Wang, G Q Wang, G Wang, G-W Wang, Gan Wang, Gang Wang, Ganggang Wang, Ganglin Wang, Gangyang Wang, Ganyu Wang, Gao T Wang, Gao Wang, Gaofu Wang, Gaopin Wang, Gavin Wang, Ge Wang, Geng Wang, Genghao Wang, Gengsheng Wang, Gongming Wang, Guan Wang, Guan-song Wang, Guandi Wang, Guanduo Wang, Guang Wang, Guang-Jie Wang, Guang-Rui Wang, Guangdi Wang, Guanghua Wang, Guanghui Wang, Guangliang Wang, Guangming Wang, Guangsuo Wang, Guangwen Wang, Guangyan Wang, Guangzhi Wang, Guanrou Wang, Guanru Wang, Guansong Wang, Guanyun Wang, Gui-Qi Wang, Guibin Wang, Guihu Wang, Guihua Wang, Guimin Wang, Guiping Wang, Guiqun Wang, Guixin Wang, Guixue Wang, Guiying Wang, Guo-Du Wang, Guo-Hua Wang, Guo-Liang Wang, Guo-Ping Wang, Guo-Quan Wang, Guo-hong Wang, GuoYou Wang, Guobin Wang, Guobing Wang, Guodong Wang, Guohang Wang, Guohao Wang, Guoliang Wang, Guoling Wang, Guoping Wang, Guoqian Wang, Guoqiang Wang, Guoqing Wang, Guorong Wang, Guowen Wang, Guoxiang Wang, Guoxiu Wang, Guoyi Wang, Guoying Wang, Guozheng Wang, H J Wang, H Wang, H X Wang, H Y Wang, H-Y Wang, Hai Bo Wang, Hai Wang, Hai Yang Wang, Hai-Feng Wang, Hai-Jun Wang, Hai-Long Wang, Haibin Wang, Haibing Wang, Haibo Wang, Haichao Wang, Haichuan Wang, Haifei Wang, Haifeng Wang, Haihe Wang, Haihong Wang, Haihua Wang, Haijiao Wang, Haijing Wang, Haijiu Wang, Haikun Wang, Hailei Wang, Hailin Wang, Hailing Wang, Hailong Wang, Haimeng Wang, Haina Wang, Haining Wang, Haiping Wang, Hairong Wang, Haitao Wang, Haiwei Wang, Haixia Wang, Haixin Wang, Haixing Wang, Haiyan Wang, Haiying Wang, Haiyong Wang, Haiyun Wang, Haizhen Wang, Han Wang, Hanbin Wang, Hanbing Wang, Hanchao Wang, Handong Wang, Hang Wang, Hangzhou Wang, Hanmin Wang, Hanping Wang, Hanqi Wang, Hanying Wang, Hanyu Wang, Hanzhi Wang, Hao Wang, Hao-Ching Wang, Hao-Hua Wang, Hao-Tian Wang, Hao-Yu Wang, Haobin Wang, Haochen Wang, Haohao Wang, Haohui Wang, Haojie Wang, Haolong Wang, Haomin Wang, Haoming Wang, Haonan Wang, Haoping Wang, Haoqi Wang, Haoran Wang, Haowei Wang, Haoxin Wang, Haoyang Wang, Haoyu Wang, Haozhou Wang, He Wang, He-Cheng Wang, He-Ling Wang, He-Ping Wang, He-Tong Wang, Hebo Wang, Hechuan Wang, Heling Wang, Hemei Wang, Heming Wang, Heng Wang, Heng-Cai Wang, Hengjiao Wang, Hengjun Wang, Hequn Wang, Hesuiyuan Wang, Heyong Wang, Hezhi Wang, Hong Wang, Hong Yi Wang, Hong-Gang Wang, Hong-Hui Wang, Hong-Kai Wang, Hong-Qin Wang, Hong-Wei Wang, Hong-Xia Wang, Hong-Yan Wang, Hong-Yang Wang, Hong-Ying Wang, Hongbin Wang, Hongbing Wang, Hongbo Wang, Hongcai Wang, Hongda Wang, Hongdan Wang, Hongfang Wang, Hongjia Wang, Hongjian Wang, Hongjie Wang, Hongjuan Wang, Hongkun Wang, Honglei Wang, Hongli Wang, Honglian Wang, Honglun Wang, Hongmei Wang, Hongpin Wang, Hongqian Wang, Hongshan Wang, Hongsheng Wang, Hongtao Wang, Hongwei Wang, Hongxia Wang, Hongxin Wang, Hongyan Wang, Hongyang Wang, Hongyi Wang, Hongyin Wang, Hongying Wang, Hongyu Wang, Hongyuan Wang, Hongyue Wang, Hongyun Wang, Hongze Wang, Hongzhan Wang, Hongzhuang Wang, Horng-Dar Wang, Houchun Wang, Hsei-Wei Wang, Hsueh-Chun Wang, Hu WANG, Hua Wang, Hua-Qin Wang, Hua-Wei Wang, Huabo Wang, Huafei Wang, Huai-Zhou Wang, Huaibing Wang, Huaili Wang, Huaizhi Wang, Huajin Wang, Huajing Wang, Hualin Wang, Hualing Wang, Huan Wang, Huan-You Wang, Huang Wang, Huanhuan Wang, Huanyu Wang, Huaquan Wang, Huating Wang, Huawei Wang, Huaxiang Wang, Huayang Wang, Huei Wang, Hui Miao Wang, Hui Wang, Hui-Hui Wang, Hui-Li Wang, Hui-Nan Wang, Hui-Yu Wang, HuiYue Wang, Huie Wang, Huiguo Wang, Huihua Wang, Huihui Wang, Huijie Wang, Huijun Wang, Huilun Wang, Huimei Wang, Huimin Wang, Huina Wang, Huiping Wang, Huiquan Wang, Huiqun Wang, Huishan Wang, Huiting Wang, Huiwen Wang, Huixia Wang, Huiyan Wang, Huiyang Wang, Huiyao Wang, Huiying Wang, Huiyu Wang, Huizhen Wang, Huizhi Wang, Huming Wang, I-Ching Wang, Iris X Wang, Isabel Z Wang, J J Wang, J P Wang, J Q Wang, J Wang, J Z Wang, J-Y Wang, Jacob E Wang, James Wang, Jeffrey Wang, Jen-Chun Wang, Jen-Chywan Wang, Jennifer E Wang, Jennifer T Wang, Jennifer X Wang, Jenny Y Wang, Jeremy R Wang, Jeremy Wang, Ji M Wang, Ji Wang, Ji-Nuo Wang, Ji-Yang Wang, Ji-Yao Wang, Ji-zheng Wang, Jia Bei Wang, Jia Bin Wang, Jia Wang, Jia-Liang Wang, Jia-Lin Wang, Jia-Mei Wang, Jia-Peng Wang, Jia-Qi Wang, Jia-Qiang Wang, Jia-Ying Wang, Jia-Yu Wang, Jiabei Wang, Jiabo Wang, Jiafeng Wang, Jiafu Wang, Jiahao Wang, Jiahui Wang, Jiajia Wang, Jiakun Wang, Jiale Wang, Jiali Wang, Jialiang Wang, Jialin Wang, Jialing Wang, Jiamin Wang, Jiaming Wang, Jian Wang, Jian'an Wang, Jian-Bin Wang, Jian-Guo Wang, Jian-Hong Wang, Jian-Long Wang, Jian-Wei Wang, Jian-Xiong Wang, Jian-Yong Wang, Jian-Zhi Wang, Jian-chun Wang, Jianan Wang, Jianbing Wang, Jianbo Wang, Jianding Wang, Jianfang Wang, Jianfei Wang, Jiang Wang, Jiangbin Wang, Jiangbo Wang, Jianghua Wang, Jianghui Wang, Jiangong Wang, Jianguo Wang, Jianhao Wang, Jianhua Wang, Jianhui Wang, Jiani Wang, Jianjiao Wang, Jianjie Wang, Jianjun Wang, Jianle Wang, Jianli Wang, Jianlin Wang, Jianliu Wang, Jianlong Wang, Jianmei Wang, Jianmin Wang, Jianning Wang, Jianping Wang, Jianqin Wang, Jianqing Wang, Jianqun Wang, Jianru Wang, Jianshe Wang, Jianshu Wang, Jiantao Wang, Jianwei Wang, Jianwu Wang, Jianxiang Wang, Jianxin Wang, Jianye Wang, Jianying Wang, Jianyong Wang, Jianyu Wang, Jianzhang Wang, Jianzhi Wang, Jiao Wang, Jiaojiao Wang, Jiapan Wang, Jiaping Wang, Jiaqi Wang, Jiaqian Wang, Jiatao Wang, Jiawei Wang, Jiawen Wang, Jiaxi Wang, Jiaxin Wang, Jiaxing Wang, Jiaxuan Wang, Jiayan Wang, Jiayang Wang, Jiayi Wang, Jiaying Wang, Jiayu Wang, Jiazheng Wang, Jiazhi Wang, Jie Jin Wang, Jie Wang, Jieda Wang, Jieh-Neng Wang, Jiemei Wang, Jieqi Wang, Jieyan Wang, Jieyu Wang, Jifei Wang, Jiheng Wang, Jihong Wang, Jiliang Wang, Jilin Wang, Jin Wang, Jin'e Wang, Jin-Bao Wang, Jin-Cheng Wang, Jin-Da Wang, Jin-E Wang, Jin-Juan Wang, Jin-Liang Wang, Jin-Xia Wang, Jin-Xing Wang, Jincheng Wang, Jindan Wang, Jinfei Wang, Jinfeng Wang, Jinfu Wang, Jing J Wang, Jing Wang, Jing-Hao Wang, Jing-Huan Wang, Jing-Jing Wang, Jing-Long Wang, Jing-Min Wang, Jing-Shi Wang, Jing-Wen Wang, Jing-Xian Wang, Jing-Yi Wang, Jing-Zhai Wang, Jingang Wang, Jingchun Wang, Jingfan Wang, Jingfeng Wang, Jingheng Wang, Jinghong Wang, Jinghua Wang, Jinghuan Wang, Jingjing Wang, Jingkang Wang, Jinglin Wang, Jingmin Wang, Jingnan Wang, Jingqi Wang, Jingru Wang, Jingtong Wang, Jingwei Wang, Jingwen Wang, Jingxiao Wang, Jingyang Wang, Jingyi Wang, Jingying Wang, Jingyu Wang, Jingyue Wang, Jingyun Wang, Jingzhou Wang, Jinhai Wang, Jinhao Wang, Jinhe Wang, Jinhua Wang, Jinhuan Wang, Jinhui Wang, Jinjie Wang, Jinjin Wang, Jinkang Wang, Jinling Wang, Jinlong Wang, Jinmeng Wang, Jinning Wang, Jinping Wang, Jinqiu Wang, Jinrong Wang, Jinru Wang, Jinsong Wang, Jintao Wang, Jinxia Wang, Jinxiang Wang, Jinyang Wang, Jinyu Wang, Jinyue Wang, Jinyun Wang, Jinzhu Wang, Jiou Wang, Jipeng Wang, Jiqing Wang, Jiqiu Wang, Jisheng Wang, Jiu Wang, Jiucun Wang, Jiun-Ling Wang, Jiwen Wang, Jixuan Wang, Jiyan Wang, Jiying Wang, Jiyong Wang, Jizheng Wang, John Wang, Jou-Kou Wang, Joy Wang, Ju Wang, Juan Wang, Jue Wang, Jueqiong Wang, Jufeng Wang, Julie Wang, Juling Wang, Jun Kit Wang, Jun Wang, Jun Yi Wang, Jun-Feng Wang, Jun-Jie Wang, Jun-Jun Wang, Jun-Ling Wang, Jun-Sheng Wang, Jun-Sing Wang, Jun-Zhuo Wang, Jundong Wang, Junfeng Wang, Jung-Pan Wang, Junhong Wang, Junhua Wang, Junhui Wang, Junjiang Wang, Junjie Wang, Junjun Wang, Junkai Wang, Junke Wang, Junli Wang, Junlin Wang, Junling Wang, Junmei Wang, Junmin Wang, Junpeng Wang, Junping Wang, Junqin Wang, Junqing Wang, Junrui Wang, Junsheng Wang, Junshi Wang, Junshuang Wang, Junwen Wang, Junxiao Wang, Junya Wang, Junying Wang, Junyu Wang, Justin Wang, Jutao Wang, Juxiang Wang, K Wang, Kai Wang, Kai-Kun Wang, Kai-Wen Wang, Kaicen Wang, Kaihao Wang, Kaihe Wang, Kaihong Wang, Kaijie Wang, Kaijuan Wang, Kailu Wang, Kaiming Wang, Kaining Wang, Kaiting Wang, Kaixi Wang, Kaixu Wang, Kaiyan Wang, Kaiyuan Wang, Kaiyue Wang, Kan Wang, Kangli Wang, Kangling Wang, Kangmei Wang, Kangning Wang, Ke Wang, Ke-Feng Wang, KeShan Wang, Kehan Wang, Kehao Wang, Kejia Wang, Kejian Wang, Kejun Wang, Keke Wang, Keming Wang, Kenan Wang, Keqing Wang, Kesheng Wang, Kexin Wang, Keyan Wang, Keyi Wang, Keyun Wang, Kongyan Wang, Kuan Hong Wang, Kui Wang, Kun Wang, Kunhua Wang, Kunpeng Wang, Kunzheng Wang, L F Wang, L M Wang, L Wang, L Z Wang, L-S Wang, Laidi Wang, Laijian Wang, Laiyuan Wang, Lan Wang, Lan-Wan Wang, Lan-lan Wang, Lanlan Wang, Larry Wang, Le Wang, Le-Xin Wang, Ledan Wang, Lee-Kai Wang, Lei P Wang, Lei Wang, Lei-Lei Wang, Leiming Wang, Leishen Wang, Leli Wang, Leran Wang, Lexin Wang, Leying Wang, Li Chun Wang, Li Dong Wang, Li Wang, Li-Dong Wang, Li-E Wang, Li-Juan Wang, Li-Li Wang, Li-Na Wang, Li-San Wang, Li-Ting Wang, Li-Xin Wang, Li-Yong Wang, LiLi Wang, Lian Wang, Lianchun Wang, Liang Wang, Liang-Yan Wang, Liangfu Wang, Lianghai Wang, Liangli Wang, Liangliang Wang, Liangxu Wang, Lianshui Wang, Lianyong Wang, Libo Wang, Lichan Wang, Lichao Wang, Liewei Wang, Lifang Wang, Lifei Wang, Lifen Wang, Lifeng Wang, Ligang Wang, Lihong Wang, Lihua Wang, Lihui Wang, Lijia Wang, Lijin Wang, Lijing Wang, Lijuan Wang, Lijun Wang, Liling Wang, Lily Wang, Limeng Wang, Limin Wang, Liming Wang, Lin Wang, Lin-Fa Wang, Lin-Yu Wang, Lina Wang, Linfang Wang, Ling Jie Wang, Ling Wang, Ling-Ling Wang, Lingbing Wang, Lingda Wang, Linghua Wang, Linghuan Wang, Lingli Wang, Lingling Wang, Lingyan Wang, Lingzhi Wang, Linhua Wang, Linhui Wang, Linjie Wang, Linli Wang, Linlin Wang, Linping Wang, Linshu Wang, Linshuang Wang, Lintao Wang, Linxuan Wang, Linying Wang, Linyuan Wang, Liping Wang, Liqing Wang, Liqun Wang, Lirong Wang, Litao Wang, Liting Wang, Liu Wang, Liusong Wang, Liuyang Wang, Liwei Wang, Lixia Wang, Lixian Wang, Lixiang Wang, Lixin Wang, Lixing Wang, Lixiu Wang, Liyan Wang, Liyi Wang, Liying Wang, Liyong Wang, Liyuan Wang, Liyun Wang, Long Wang, Longcai Wang, Longfei Wang, Longsheng Wang, Longxiang Wang, Lou-Pin Wang, Lu Wang, Lu-Lu Wang, Lueli Wang, Lufang Wang, Luhong Wang, Luhui Wang, Lujuan Wang, Lulu Wang, Luofu Wang, Luping Wang, Luting Wang, Luwen Wang, Luxiang Wang, Luya Wang, Luyao Wang, Luyun Wang, Lynn Yuning Wang, M H Wang, M Wang, M Y Wang, M-J Wang, Maiqiu Wang, Man Wang, Mangju Wang, Manli Wang, Mao-Xin Wang, Maochun Wang, Maojie Wang, Maoju Wang, Mark Wang, Mei Wang, Mei-Gui Wang, Mei-Xia Wang, Meiding Wang, Meihui Wang, Meijun Wang, Meiling Wang, Meixia Wang, Melissa T Wang, Meng C Wang, Meng Wang, Meng Yu Wang, Meng-Dan Wang, Meng-Lan Wang, Meng-Meng Wang, Meng-Ru Wang, Meng-Wei Wang, Meng-Ying Wang, Meng-hong Wang, Mengge Wang, Menghan Wang, Menghui Wang, Mengjiao Wang, Mengjing Wang, Mengjun Wang, Menglong Wang, Menglu Wang, Mengmeng Wang, Mengqi Wang, Mengru Wang, Mengshi Wang, Mengwen Wang, Mengxiao Wang, Mengya Wang, Mengyao Wang, Mengying Wang, Mengyuan Wang, Mengyue Wang, Mengyun Wang, Mengze Wang, Mengzhao Wang, Mengzhi Wang, Mian Wang, Miao Wang, Mimi Wang, Min Wang, Min-sheng Wang, Ming Wang, Ming-Chih Wang, Ming-Hsi Wang, Ming-Jie Wang, Ming-Wei Wang, Ming-Yang Wang, Ming-Yuan Wang, Mingchao Wang, Mingda Wang, Minghua Wang, Minghuan Wang, Minghui Wang, Mingji Wang, Mingjin Wang, Minglei Wang, Mingliang Wang, Mingmei Wang, Mingming Wang, Mingqiang Wang, Mingrui Wang, Mingsong Wang, Mingxi Wang, Mingxia Wang, Mingxun Wang, Mingya Wang, Mingyang Wang, Mingyi Wang, Mingyu Wang, Mingzhi Wang, Mingzhu Wang, Minjie Wang, Minjun Wang, Minmin Wang, Minxian Wang, Minxiu Wang, Minzhou Wang, Miranda C Wang, Mo Wang, Mofei Wang, Monica Wang, Mu Wang, Mutian Wang, Muxiao Wang, Muxuan Wang, N Wang, Na Wang, Nan Wang, Nana Wang, Nanbu Wang, Nannan Wang, Nanping Wang, Neng Wang, Ni Wang, Niansong Wang, Ning Wang, Ningjian Wang, Ningli Wang, Ningyuan Wang, Nuan Wang, Oliver Wang, Ouchen Wang, P Jeremy Wang, P L Wang, P N Wang, P Wang, Pai Wang, Pan Wang, Pan-Pan Wang, Panfeng Wang, Panliang Wang, Pei Chang Wang, Pei Wang, Pei-Hua Wang, Pei-Jian Wang, Pei-Juan Wang, Pei-Wen Wang, Pei-Yu Wang, Peichang Wang, Peigeng Wang, Peihe Wang, Peijia Wang, Peijuan Wang, Peijun Wang, Peilin Wang, Peipei Wang, Peirong Wang, Peiwen Wang, Peixi Wang, Peiyao Wang, Peiyin Wang, Peng Wang, Peng-Cheng Wang, Pengbo Wang, Pengchao Wang, Pengfei Wang, Pengjie Wang, Pengju Wang, Penglai Wang, Penglong Wang, Pengpu Wang, Pengtao Wang, Pengxiang Wang, Pengyu Wang, Pin Wang, Ping Wang, Pingchuan Wang, Pingfeng Wang, Pingping Wang, Pintian Wang, Po-Jen Wang, Pu Wang, Q Wang, Q Z Wang, Qi Wang, Qi-Bing Wang, Qi-En Wang, Qi-Jia Wang, Qi-Qi Wang, Qian Wang, Qian-Liang Wang, Qian-Wen Wang, Qian-Zhu Wang, Qian-fei Wang, Qianbao Wang, Qiang Wang, Qiang-Sheng Wang, Qiangcheng Wang, Qianghu Wang, Qiangqiang Wang, Qianjin Wang, Qianliang Wang, Qianqian Wang, Qianrong Wang, Qianru Wang, Qianwen Wang, Qianxu Wang, Qiao Wang, Qiao-Ping Wang, Qiaohong Wang, Qiaoqi Wang, Qiaoqiao Wang, Qifan Wang, Qifei Wang, Qifeng Wang, Qigui Wang, Qihao Wang, Qihua Wang, Qijia Wang, Qiming Wang, Qin Wang, Qing Jun Wang, Qing K Wang, Qing Kenneth Wang, Qing Mei Wang, Qing Wang, Qing-Bin Wang, Qing-Dong Wang, Qing-Jin Wang, Qing-Liang Wang, Qing-Mei Wang, Qing-Yan Wang, Qing-Yuan Wang, Qing-Yun Wang, QingDong Wang, Qingchun Wang, Qingfa Wang, Qingfeng Wang, Qinghang Wang, Qingliang Wang, Qinglin Wang, Qinglu Wang, Qingming Wang, Qingping Wang, Qingqing Wang, Qingshi Wang, Qingshui Wang, Qingsong Wang, Qingtong Wang, Qingyong Wang, Qingyu Wang, Qingyuan Wang, Qingyun Wang, Qingzhong Wang, Qinqin Wang, Qinrong Wang, Qintao Wang, Qinwen Wang, Qinyun Wang, Qiong Wang, Qiqi Wang, Qirui Wang, Qishan Wang, Qiu-Ling Wang, Qiu-Xia Wang, Qiuhong Wang, Qiuli Wang, Qiuling Wang, Qiuning Wang, Qiuping Wang, Qiushi Wang, Qiuting Wang, Qiuyan Wang, Qiuyu Wang, Qiwei Wang, Qixue Wang, Qiyu Wang, Qiyuan Wang, Quan Wang, Quan-Ming Wang, Quanli Wang, Quanren Wang, Quanxi Wang, Qun Wang, Qunxian Wang, Qunzhi Wang, R Wang, Ran Wang, Ranjing Wang, Ranran Wang, Re-Hua Wang, Ren Wang, Rencheng Wang, Renjun Wang, Renqian Wang, Renwei Wang, Renxi Wang, Renxiao Wang, Renyuan Wang, Rihua Wang, Rikang Wang, Rixiang Wang, Robert Yl Wang, Rong Wang, Rong-Chun Wang, Rong-Rong Wang, Rong-Tsorng Wang, RongRong Wang, Rongjia Wang, Rongping Wang, Rongyun Wang, Ru Wang, RuNan Wang, Ruey-Yun Wang, Rufang Wang, Ruhan Wang, Rui Wang, Rui-Hong Wang, Rui-Min Wang, Rui-Ping Wang, Rui-Rui Wang, Ruibin Wang, Ruibing Wang, Ruibo Wang, Ruicheng Wang, Ruifang Wang, Ruijing Wang, Ruimeng Wang, Ruimin Wang, Ruiming Wang, Ruinan Wang, Ruining Wang, Ruiquan Wang, Ruiwen Wang, Ruixian Wang, Ruixin Wang, Ruixuan Wang, Ruixue Wang, Ruiying Wang, Ruizhe Wang, Ruizhi Wang, Rujie Wang, Ruling Wang, Ruming Wang, Runci Wang, Runuo Wang, Runze Wang, Runzhi Wang, Ruo-Nan Wang, Ruo-Ran Wang, Ruonan Wang, Ruosu Wang, Ruoxi Wang, Rurong Wang, Ruting Wang, Ruxin Wang, Ruxuan Wang, Ruyue Wang, S L Wang, S S Wang, S Wang, S X Wang, Sa A Wang, Sa Wang, Saifei Wang, Saili Wang, Sainan Wang, Saisai Wang, Sangui Wang, Sanwang Wang, Sasa Wang, Sen Wang, Seok Mui Wang, Seungwon Wang, Sha Wang, Shan Wang, Shan-Shan Wang, Shang Wang, Shangyu Wang, Shanshan Wang, Shao-Kang Wang, Shaochun Wang, Shaohsu Wang, Shaokun Wang, Shaoli Wang, Shaolian Wang, Shaoshen Wang, Shaowei Wang, Shaoyi Wang, Shaoying Wang, Shaoyu Wang, Shaozheng Wang, Shasha Wang, Shau-Chun Wang, Shawn Wang, Shen Wang, Shen-Nien Wang, Shenao Wang, Sheng Wang, Sheng-Min Wang, Sheng-Nan Wang, Sheng-Ping Wang, Sheng-Quan Wang, Sheng-Yang Wang, Shengdong Wang, Shengjie Wang, Shengli Wang, Shengqi Wang, Shengya Wang, Shengyao Wang, Shengyu Wang, Shengyuan Wang, Shenqi Wang, Sheri Wang, Shi Wang, Shi-Cheng Wang, Shi-Han Wang, Shi-Qi Wang, Shi-Xin Wang, Shi-Yao Wang, Shibin Wang, Shichao Wang, Shicung Wang, Shidong Wang, Shifa Wang, Shifeng Wang, Shih-Wei Wang, Shihan Wang, Shihao Wang, Shihua Wang, Shijie Wang, Shijin Wang, Shijun Wang, Shikang Wang, Shimiao Wang, Shiqi Wang, Shiqiang Wang, Shitao Wang, Shitian Wang, Shiwen Wang, Shixin Wang, Shixuan Wang, Shiyang Wang, Shiyao Wang, Shiyin Wang, Shiyu Wang, Shiyuan Wang, Shiyue Wang, Shizhi Wang, Shouli Wang, Shouling Wang, Shouzhi Wang, Shu Wang, Shu-Huei Wang, Shu-Jin Wang, Shu-Ling Wang, Shu-Na Wang, Shu-Song Wang, Shu-Xia Wang, Shu-qiang Wang, Shuai Wang, Shuaiqin Wang, Shuang Wang, Shuang-Shuang Wang, Shuang-Xi Wang, Shuangyuan Wang, Shubao Wang, Shudan Wang, Shuge Wang, Shuguang Wang, Shuhe Wang, Shuiliang Wang, Shuiyun Wang, Shujin Wang, Shukang Wang, Shukui Wang, Shun Wang, Shuning Wang, Shunjun Wang, Shunran Wang, Shuo Wang, Shuping Wang, Shuqi Wang, Shuqing Wang, Shuren Wang, Shusen Wang, Shusheng Wang, Shushu Wang, Shuu-Jiun Wang, Shuwei Wang, Shuxia Wang, Shuxin Wang, Shuya Wang, Shuye Wang, Shuyue Wang, Shuzhe Wang, Shuzhen Wang, Shuzhong Wang, Shyi-Gang P Wang, Si Wang, Sibo Wang, Sidan Wang, Sihua Wang, Sijia Wang, Silas L Wang, Silu Wang, Simeng Wang, Siqi Wang, Siqing Wang, Siwei Wang, Siyang Wang, Siyi Wang, Siying Wang, Siyu Wang, Siyuan Wang, Siyue Wang, Song Wang, Songjiao Wang, Songlin Wang, Songping Wang, Songsong Wang, Songtao Wang, Sophie H Wang, Stephani Wang, Su'e Wang, Su-Guo Wang, Su-Hua Wang, Sufang Wang, Sugai Wang, Sui Wang, Suiyan Wang, Sujie Wang, Sujuan Wang, Suli Wang, Sun Wang, Supeng Perry Wang, Suxia Wang, Suyun Wang, Suzhen Wang, T Q Wang, T Wang, T Y Wang, Taian Wang, Taicheng Wang, Taishu Wang, Tammy C Wang, Tao Wang, Taoxia Wang, Teng Wang, Tengfei Wang, Theodore Wang, Thomas T Y Wang, Tian Wang, Tian-Li Wang, Tian-Lu Wang, Tian-Tian Wang, Tian-Yi Wang, Tiancheng Wang, Tiange Wang, Tianhao Wang, Tianhu Wang, Tianhui Wang, Tianjing Wang, Tianjun Wang, Tianlin Wang, Tiannan Wang, Tianpeng Wang, Tianqi Wang, Tianqin Wang, Tianqing Wang, Tiansheng Wang, Tiansong Wang, Tiantian Wang, Tianyi Wang, Tianying Wang, Tianyuan Wang, Tielin Wang, Tienju Wang, Tieqiao Wang, Timothy C Wang, Ting Chen Wang, Ting Wang, Ting-Chen Wang, Ting-Hua Wang, Ting-Ting Wang, Tingting Wang, Tingye Wang, Tingyu Wang, Tom J Wang, Tong Wang, Tong-Hong Wang, Tongsong Wang, Tongtong Wang, Tongxia Wang, Tongxin Wang, Tongyao Wang, Tony Wang, Tzung-Dau Wang, Victoria Wang, Vivian Wang, W Wang, Wanbing Wang, Wanchun Wang, Wang Wang, Wangxia Wang, Wanliang Wang, Wanxia Wang, Wanyao Wang, Wanyi Wang, Wanyu Wang, Wayseen Wang, Wei Wang, Wei-En Wang, Wei-Feng Wang, Wei-Lien Wang, Wei-Qi Wang, Wei-Ting Wang, Wei-Wei Wang, Weicheng Wang, Weiding Wang, Weidong Wang, Weifan Wang, Weiguang Wang, Weihao Wang, Weihong Wang, Weihua Wang, Weijian Wang, Weijie Wang, Weijun Wang, Weilin Wang, Weiling Wang, Weilong Wang, Weimin Wang, Weina Wang, Weining Wang, Weipeng Wang, Weiqin Wang, Weiqing Wang, Weirong Wang, Weiwei Wang, Weiwen Wang, Weixiao Wang, Weixue Wang, Weiyan Wang, Weiyu Wang, Weiyuan Wang, Weizhen Wang, Weizhi Wang, Weizhong Wang, Wen Wang, Wen-Chang Wang, Wen-Der Wang, Wen-Fei Wang, Wen-Jie Wang, Wen-Jun Wang, Wen-Qing Wang, Wen-Xuan Wang, Wen-Yan Wang, Wen-Ying Wang, Wen-Yong Wang, Wen-mei Wang, Wenbin Wang, Wenbo Wang, Wence Wang, Wenchao Wang, Wencheng Wang, Wendong Wang, Wenfei Wang, Wengong Wang, Wenhan Wang, Wenhao Wang, Wenhe Wang, Wenhui Wang, Wenjie Wang, Wenjing Wang, Wenju Wang, Wenjuan Wang, Wenjun Wang, Wenkai Wang, Wenkang Wang, Wenke Wang, Wenming Wang, Wenqi Wang, Wenqiang Wang, Wenqing Wang, Wenran Wang, Wenrui Wang, Wentao Wang, Wentian Wang, Wenting Wang, Wenwen Wang, Wenxia Wang, Wenxian Wang, Wenxiang Wang, Wenxiu Wang, Wenxuan Wang, Wenya Wang, Wenyan Wang, Wenyi Wang, Wenying Wang, Wenyu Wang, Wenyuan Wang, Wenzhou Wang, William Wang, Won-Jing Wang, Wu-Wei Wang, Wuji Wang, Wuqing Wang, Wusan Wang, X E Wang, X F Wang, X O Wang, X S Wang, X Wang, X-T Wang, Xi Wang, Xi-Hong Wang, Xi-Rui Wang, Xia Wang, Xian Wang, Xian-e Wang, Xianding Wang, Xianfeng Wang, Xiang Wang, Xiang-Dong Wang, Xiangcheng Wang, Xiangding Wang, Xiangdong Wang, Xiangguo Wang, Xianghua Wang, Xiangkun Wang, Xiangrong Wang, Xiangru Wang, Xiangwei Wang, Xiangyu Wang, Xianna Wang, Xianqiang Wang, Xianrong Wang, Xianshi Wang, Xianshu Wang, Xiansong Wang, Xiantao Wang, Xianwei Wang, Xianxing Wang, Xianze Wang, Xianzhe Wang, Xianzong Wang, Xiao Ling Wang, Xiao Qun Wang, Xiao Wang, Xiao-Ai Wang, Xiao-Fei Wang, Xiao-Hui Wang, Xiao-Jie Wang, Xiao-Juan Wang, Xiao-Lan Wang, Xiao-Li Wang, Xiao-Lin Wang, Xiao-Ming Wang, Xiao-Pei Wang, Xiao-Qian Wang, Xiao-Qun Wang, Xiao-Tong Wang, Xiao-Xia Wang, Xiao-Yi Wang, Xiao-Yun Wang, Xiao-jian WANG, Xiao-liang Wang, Xiaobin Wang, Xiaobo Wang, Xiaochen Wang, Xiaochuan Wang, Xiaochun Wang, Xiaodan Wang, Xiaoding Wang, Xiaodong Wang, Xiaofang Wang, Xiaofei Wang, Xiaofen Wang, Xiaofeng Wang, Xiaogang Wang, Xiaohong Wang, Xiaohu Wang, Xiaohua Wang, Xiaohui Wang, Xiaojia Wang, Xiaojian Wang, Xiaojiao Wang, Xiaojie Wang, Xiaojing Wang, Xiaojuan Wang, Xiaojun Wang, Xiaokun Wang, Xiaole Wang, Xiaoli Wang, Xiaoliang Wang, Xiaolin Wang, Xiaoling Wang, Xiaolong Wang, Xiaolu Wang, Xiaolun Wang, Xiaoman Wang, Xiaomei Wang, Xiaomeng Wang, Xiaomin Wang, Xiaoming Wang, Xiaona Wang, Xiaonan Wang, Xiaoning Wang, Xiaoqi Wang, Xiaoqian Wang, Xiaoqin Wang, Xiaoqing Wang, Xiaoqiu Wang, Xiaoqun Wang, Xiaorong Wang, Xiaorui Wang, Xiaoshan Wang, Xiaosong Wang, Xiaotang Wang, Xiaoting Wang, Xiaotong Wang, Xiaowei Wang, Xiaowen Wang, Xiaowu Wang, Xiaoxia Wang, Xiaoxiao Wang, Xiaoxin Wang, Xiaoxin X Wang, Xiaoxuan Wang, Xiaoya Wang, Xiaoyan Wang, Xiaoyang Wang, Xiaoye Wang, Xiaoying Wang, Xiaoyu Wang, Xiaozhen Wang, Xiaozhi Wang, Xiaozhong Wang, Xiaozhu Wang, Xichun Wang, Xidi Wang, Xietong Wang, Xifeng Wang, Xifu Wang, Xijun Wang, Xike Wang, Xin Wang, Xin Wei Wang, Xin-Hua Wang, Xin-Liang Wang, Xin-Ming Wang, Xin-Peng Wang, Xin-Qun Wang, Xin-Shang Wang, Xin-Xin Wang, Xin-Yang Wang, Xin-Yue Wang, Xinbo Wang, Xinchang Wang, Xinchao Wang, Xinchen Wang, Xincheng Wang, Xinchun Wang, Xindi Wang, Xindong Wang, Xing Wang, Xing-Huan Wang, Xing-Jin Wang, Xing-Jun Wang, Xing-Lei Wang, Xing-Ping Wang, Xing-Quan Wang, Xingbang Wang, Xingchen Wang, Xingde Wang, Xingguo Wang, Xinghao Wang, Xinghui Wang, Xingjie Wang, Xingjin Wang, Xinglei Wang, Xinglong Wang, Xingqin Wang, Xinguo Wang, Xingxin Wang, Xingxing Wang, Xingye Wang, Xingyu Wang, Xingyue Wang, Xingyun Wang, Xinhui Wang, Xinjing Wang, Xinjun Wang, Xinke Wang, Xinkun Wang, Xinli Wang, Xinlin Wang, Xinlong Wang, Xinmei Wang, Xinqi Wang, Xinquan Wang, Xinran Wang, Xinrong Wang, Xinru Wang, Xinrui Wang, Xinshuai Wang, Xintong Wang, Xinwen Wang, Xinxin Wang, Xinyan Wang, Xinyang Wang, Xinye Wang, Xinyi Wang, Xinying Wang, Xinyu Wang, Xinyue Wang, Xinzhou Wang, Xiong Wang, Xiongjun Wang, Xiru Wang, Xitian Wang, Xiu-Lian Wang, Xiu-Ping Wang, Xiufen Wang, Xiujuan Wang, Xiujun Wang, Xiurong Wang, Xiuwen Wang, Xiuyu Wang, Xiuyuan Hugh Wang, Xixi Wang, Xixiang Wang, Xiyan Wang, Xiyue Wang, Xizhi Wang, Xu Wang, Xu-Hong Wang, Xuan Wang, Xuan-Ren Wang, Xuan-Ying Wang, Xuanwen Wang, Xuanyi Wang, Xubo Wang, Xudong Wang, Xue Wang, Xue-Feng Wang, Xue-Hua Wang, Xue-Lei Wang, Xue-Lian Wang, Xue-Rui Wang, Xue-Yao Wang, Xue-Ying Wang, Xuebin Wang, Xueding Wang, Xuedong Wang, Xuefei Wang, Xuefeng Wang, Xueguo Wang, Xuehao Wang, Xuejie Wang, Xuejing Wang, Xueju Wang, Xuejun Wang, Xuekai Wang, Xuelai Wang, Xuelian Wang, Xuelin Wang, Xuemei Wang, Xuemin Wang, Xueping Wang, Xueqian Wang, Xueqin Wang, Xuesong Wang, Xueting Wang, Xuewei Wang, Xuewen Wang, Xuexiang Wang, Xueyan Wang, Xueying Wang, Xueyun Wang, Xuezhen Wang, Xuezheng Wang, Xufei Wang, Xujing Wang, Xuliang Wang, Xumeng Wang, Xun Wang, Xuping Wang, Xuqiao Wang, Xuru Wang, Xusheng Wang, Xv Wang, Y Alan Wang, Y B Wang, Y H Wang, Y L Wang, Y P Wang, Y Wang, Y Y Wang, Y Z Wang, Y-H Wang, Y-S Wang, Ya Qi Wang, Ya Wang, Ya Xing Wang, Ya-Han Wang, Ya-Jie Wang, Ya-Long Wang, Ya-Nan Wang, Ya-Ping Wang, Ya-Qin Wang, Ya-Zhou Wang, Yachen Wang, Yachun Wang, Yadong Wang, Yafang Wang, Yafen Wang, Yahong Wang, Yahui Wang, Yajie Wang, Yajing Wang, Yajun Wang, Yake Wang, Yakun Wang, Yali Wang, Yalin Wang, Yaling Wang, Yalong Wang, Yan Ming Wang, Yan Wang, Yan-Chao Wang, Yan-Chun Wang, Yan-Feng Wang, Yan-Ge Wang, Yan-Jiang Wang, Yan-Jun Wang, Yan-Ming Wang, Yan-Yang Wang, Yan-Yi Wang, Yan-Zi Wang, Yana Wang, Yanan Wang, Yanbin Wang, Yanbing Wang, Yanchun Wang, Yancun Wang, Yanfang Wang, Yanfei Wang, Yanfeng Wang, Yang Wang, Yang-Yang Wang, Yange Wang, Yanggan Wang, Yangpeng Wang, Yangyang Wang, Yangyufan Wang, Yanhai Wang, Yanhong Wang, Yanhua Wang, Yanhui Wang, Yani Wang, Yanjin Wang, Yanjun Wang, Yankun Wang, Yanlei Wang, Yanli Wang, Yanliang Wang, Yanlin Wang, Yanling Wang, Yanmei Wang, Yanming Wang, Yanni Wang, Yanong Wang, Yanping Wang, Yanqing Wang, Yanru Wang, Yanting Wang, Yanwen Wang, Yanxia Wang, Yanxing Wang, Yanyang Wang, Yanyun Wang, Yanzhe Wang, Yanzhu Wang, Yao Wang, Yaobin Wang, Yaochun Wang, Yaodong Wang, Yaohe Wang, Yaokun Wang, Yaoling Wang, Yaolou Wang, Yaoxian Wang, Yaoxing Wang, Yaozhi Wang, Yapeng Wang, Yaping Wang, Yaqi Wang, Yaqian Wang, Yaqiong Wang, Yaru Wang, Yatao Wang, Yating Wang, Yawei Wang, Yaxian Wang, Yaxin Wang, Yaxiong Wang, Yaxuan Wang, Yayu Wang, Yazhou Wang, Ye Wang, Ye-Ran Wang, Yefu Wang, Yeh-Han Wang, Yehan Wang, Yeming Wang, Yen-Feng Wang, Yen-Sheng Wang, Yeou-Lih Wang, Yeqi Wang, Yezhou Wang, Yi Fan Wang, Yi Lei Wang, Yi Wang, Yi-Cheng Wang, Yi-Chuan Wang, Yi-Ming Wang, Yi-Ni Wang, Yi-Ning Wang, Yi-Shan Wang, Yi-Shiuan Wang, Yi-Shu Wang, Yi-Tao Wang, Yi-Ting Wang, Yi-Wen Wang, Yi-Xin Wang, Yi-Xuan Wang, Yi-Yi Wang, Yi-Ying Wang, Yi-Zhen Wang, Yi-sheng Wang, YiLi Wang, Yian Wang, Yibin Wang, Yibing Wang, Yichen Wang, Yicheng Wang, Yichuan Wang, Yifan Wang, Yifei Wang, Yigang Wang, Yige Wang, Yihan Wang, Yihao Wang, Yihe Wang, Yijin Wang, Yijing Wang, Yijun Wang, Yikang Wang, Yike Wang, Yilin Wang, Yilu Wang, Yimeng Wang, Yiming Wang, Yin Wang, Yin-Hu Wang, Yinan Wang, Yinbo Wang, Yindan Wang, Ying Wang, Ying-Piao Wang, Ying-Wei Wang, Ying-Zi Wang, Yingbo Wang, Yingcheng Wang, Yingchun Wang, Yingfei Wang, Yingge Wang, Yinggui Wang, Yinghui Wang, Yingjie Wang, Yingmei Wang, Yingna Wang, Yingping Wang, Yingqiao Wang, Yingtai Wang, Yingte Wang, Yingwei Wang, Yingwen Wang, Yingxiong Wang, Yingxue Wang, Yingyi Wang, Yingying Wang, Yingzi Wang, Yinhuai Wang, Yining E Wang, Yinong Wang, Yinsheng Wang, Yintao Wang, Yinuo Wang, Yinxiong Wang, Yinyin Wang, Yiou Wang, Yipeng Wang, Yiping Wang, Yiqi Wang, Yiqiao Wang, Yiqin Wang, Yiqing Wang, Yiquan Wang, Yirong Wang, Yiru Wang, Yirui Wang, Yishan Wang, Yishu Wang, Yitao Wang, Yiting Wang, Yiwei Wang, Yiwen Wang, Yixi Wang, Yixian Wang, Yixuan Wang, Yiyan Wang, Yiyi Wang, Yiying Wang, Yizhe Wang, Yong Wang, Yong-Bo Wang, Yong-Gang Wang, Yong-Jie Wang, Yong-Jun Wang, Yong-Tang Wang, Yongbin Wang, Yongdi Wang, Yongfei Wang, Yongfeng Wang, Yonggang Wang, Yonghong Wang, Yongjie Wang, Yongjun Wang, Yongkang Wang, Yongkuan Wang, Yongli Wang, Yongliang Wang, Yonglun Wang, Yongmei Wang, Yongming Wang, Yongni Wang, Yongqiang Wang, Yongqing Wang, Yongrui Wang, Yongsheng Wang, Yongxiang Wang, Yongyi Wang, Yongzhong Wang, You Wang, Youhua Wang, Youji Wang, Youjie Wang, Youli Wang, Youzhao Wang, Youzhi Wang, Yu Qin Wang, Yu Tian Wang, Yu Wang, Yu'e Wang, Yu-Chen Wang, Yu-Fan Wang, Yu-Fen Wang, Yu-Hang Wang, Yu-Hui Wang, Yu-Ping Wang, Yu-Ting Wang, Yu-Wei Wang, Yu-Wen Wang, Yu-Ying Wang, Yu-Zhe Wang, Yu-Zhuo Wang, Yuan Wang, Yuan-Hung Wang, Yuanbo Wang, Yuanfan Wang, Yuanjiang Wang, Yuanli Wang, Yuanqiang Wang, Yuanqing Wang, Yuanyong Wang, Yuanyuan Wang, Yuanzhen Wang, Yubing Wang, Yubo Wang, Yuchen Wang, Yucheng Wang, Yuchuan Wang, Yudong Wang, Yue Wang, Yue-Min Wang, Yue-Nan Wang, YueJiao Wang, Yuebing Wang, Yuecong Wang, Yuegang Wang, Yuehan Wang, Yuehong Wang, Yuehu Wang, Yuehua Wang, Yuelong Wang, Yuemiao Wang, Yueshen Wang, Yueting Wang, Yuewei Wang, Yuexiang Wang, Yuexin Wang, Yueying Wang, Yueze Wang, Yufei Wang, Yufeng Wang, Yugang Wang, Yuh-Hwa Wang, Yuhan Wang, Yuhang Wang, Yuhua Wang, Yuhuai Wang, Yuhuan Wang, Yuhui Wang, Yujia Wang, Yujiao Wang, Yujie Wang, Yujiong Wang, Yulai Wang, Yulei Wang, Yuli Wang, Yuliang Wang, Yulin Wang, Yuling Wang, Yulong Wang, Yumei Wang, Yumeng Wang, Yumin Wang, Yuming Wang, Yun Wang, Yun Yong Wang, Yun-Hui Wang, Yun-Jin Wang, Yun-Xing Wang, Yunbing Wang, Yunce Wang, Yunchao Wang, Yuncong Wang, Yunduan Wang, Yunfang Wang, Yunfei Wang, Yunhan Wang, Yunhe Wang, Yunong Wang, Yunpeng Wang, Yunqiong Wang, Yuntai Wang, Yunzhang Wang, Yunzhe Wang, Yunzhi Wang, Yupeng Wang, Yuping Wang, Yuqi Wang, Yuqian Wang, Yuqiang Wang, Yuqin Wang, Yusha Wang, Yushe Wang, Yusheng Wang, Yutao Wang, Yuting Wang, Yuwei Wang, Yuwen Wang, 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, Zhongjing 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
Yue Peng, Yan Pu, Yuyang Wang +3 more · 2026 · Frontiers in psychiatry · Frontiers · added 2026-04-24
To ascertain the level of psychological resilience, examine the latent profiles of individuals within infertile couples who experience recurrent implantation failure (RIF), identify the relevant influ Show more
To ascertain the level of psychological resilience, examine the latent profiles of individuals within infertile couples who experience recurrent implantation failure (RIF), identify the relevant influencing factors, and lay a foundation for developing customized intervention strategies. Convenience sampling was adopted in this study. Participants were selected from individuals in infertile couples with RIF who attended the Second West China Hospital of Sichuan University between November 2024 and July 2025. Data were collected via a general information questionnaire and validated scales assessing psychological resilience, social support, sleep quality, family adaptability and cohesion, anxiety, and depression. Latent profile analysis (LPA) was performed to explore the psychological resilience profiles of individuals with RIF, while univariate analysis and multivariate Logistic regression analyses were employed to identify the influencing factors associated with different profile categories. A total of 303 valid questionnaires were collected, including 194 from females and 109 from males. The overall psychological resilience score was (26.66 ± 6.319). Latent profile analysis categorized psychological resilience into three subgroups: the low tenacity-low strength subgroup (31.4%), the moderate tenacity-moderate strength subgroup (53.1%), and the high tenacity-high strength subgroup (15.5%); Multivariate Logistic regression analysis indicated that gender, family adaptability and depression severity (all Marked interindividual heterogeneity exists in the psychological resilience of individuals with RIF. Gender, family adaptability and depression severity serve as the core influencing factors. In clinical practice, stratified and targeted interventions should be delivered according to distinct psychological resilience subgroups. It yields clinical implications for an association between improved psychological resilience among individuals from couples with RIF and enhanced treatment adherence. Show less
📄 PDF DOI: 10.3389/fpsyt.2026.1798373
LPA
Lingxue Chen, Jing Yang, Li Wang +5 more · 2026 · Diabetes, obesity & metabolism · Blackwell Publishing · added 2026-04-24
Based on self-determination theory (SDT), this study aimed to identify latent profiles of health motivation characteristics among young and middle-aged individuals with prediabetes and to examine thei Show more
Based on self-determination theory (SDT), this study aimed to identify latent profiles of health motivation characteristics among young and middle-aged individuals with prediabetes and to examine their associations with self-management behaviours and metabolic risk indicators. This cross-sectional study recruited individuals with prediabetes from January 2024 to January 2025 using a convenience sampling method, enrolling 309 participants. Health behaviour motivation, basic psychological needs, prediabetes-related disease knowledge and self-management were assessed using validated questionnaires. Latent profile analysis (LPA) was conducted to identify distinct subgroups. Multinomial logistic regression was used to examine demographic, lifestyle and clinical factors associated with profile membership. Three types of health motivation characteristics were identified: high psychological need satisfaction-autonomous motivation profile (24.0%), moderate psychological need satisfaction-externally controlled motivation dominant profile (15.0%) and low psychological need satisfaction-low motivation profile (61.0%). After adjustment, BMI, comorbidity history and occupation were significantly associated with profile membership, whereas distance to primary healthcare facilities showed a non-robust pattern. Significant heterogeneity exists in health motivation characteristics among young and middle-aged individuals with prediabetes, with the low psychological need satisfaction-low motivation profile representing the largest proportion. Incorporating motivation-oriented stratification into diabetes prevention strategies may provide a useful framework for delivering tailored interventions and supporting more sustainable self-management. Show less
no PDF DOI: 10.1111/dom.70726
LPA
Ni Wang, Liang Shang, Ting Zhou · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
To explore the latent profiles, core associated factors, and complex mechanisms of work ability among healthcare workers in large tertiary hospitals in China. A cross-sectional study was conducted fro Show more
To explore the latent profiles, core associated factors, and complex mechanisms of work ability among healthcare workers in large tertiary hospitals in China. A cross-sectional study was conducted from July to October 2025. A convenience sample of 1,590 healthcare workers from a large tertiary hospital in Shaanxi Province was assessed using the Work Ability Index (WAI), the Maslach Burnout Inventory-General Survey (MBI-GS), and the Pittsburgh Sleep Quality Index (PSQI). Latent profile analysis (LPA) was employed to identify potential categories of work ability. Multivariable logistic regression analysis was performed to determine independently associated factors and to construct a nomogram prediction model. An additive interaction model and structural equation modeling (SEM) were used to analyze the joint effect and the influential pathways of job burnout and sleep disorder. LPA identified two distinct categories: "Good Work Ability" (73%) and "Poor Work Ability" (27%). Multivariable regression analysis indicated that job burnout (OR = 3.770, 95% CI: 2.510-5.661) and sleep disorder (OR = 2.890, 95% CI: 2.121-3.939) were the factors most strongly associated with poor work ability. Longer working years (≥21 years) and higher professional titles (intermediate/senior) were also associated with an increased likelihood of poor work ability. In contrast, higher education (master's degree or above) and regular physical exercise were associated with a decreased likelihood. The predictive nomogram model demonstrated good discriminative ability (AUCs of 0.781 and 0.740 for the training and validation sets, respectively) and clinical utility. Interaction analysis revealed a significant positive additive interaction between job burnout and sleep disorder (RERI = 5.164, AP = 47.453%). SEM supported a model in which job burnout was not only directly and negatively associated with work ability ( Among healthcare workers in large tertiary hospitals in China, job burnout and sleep disorder are two core and synergistic factors associated with work ability. The prediction model based on multiple factors can provide a practical tool for the early identification of high-risk individuals. Future occupational health intervention programs need to adopt integrated strategies, targeting both the alleviation of job burnout and the improvement of sleep quality as dual core objectives, and implement precise prevention and control for key populations such as those with long service years and high professional titles to maintain and enhance the work ability of healthcare workers. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1787439
LPA
Yihong Gao, Jingjin Shao, Zhi Wang · 2026 · Journal of adolescence · Wiley · added 2026-04-24
The role of parenting styles during early adolescence has always been a subject of significant concern. However, previous studies have predominantly treated parenting styles as a static construct, lea Show more
The role of parenting styles during early adolescence has always been a subject of significant concern. However, previous studies have predominantly treated parenting styles as a static construct, leading to a limited understanding of their dynamic patterns. This study employed a longitudinal person-centered perspective to examine the stability of and transitions in parenting style profiles during this critical period, as well as their associations with adolescents' internalizing and externalizing problem behaviors. Data were obtained in November 2023 (T1) and November 2024 (T2) from 893 Chinese students (53.5% female; M The analysis identified three distinct parenting profiles: harsh, supportive, and low-involved. Each profile demonstrated a high degree of stability over time, although some meaningful transitions were observed. Adolescents who consistently experienced supportive parenting or transitions toward the supportive profile generally reported lower levels of internalizing and externalizing problems. Conversely, those exposed to stable harsh parenting or a shift toward the harsh profile showed higher levels of these problems. Furthermore, internalizing problems appeared to be more susceptible to changes in parenting profiles than externalizing problems. The findings underscore the potential for positive shifts in parenting styles to serve as protective factors against problem behaviors in early adolescence, offering valuable implications for prevention and intervention strategies. Show less
no PDF DOI: 10.1002/jad.70146
LPA
Shuhe Wang, Zhongguo Liu · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
This study aimed to use latent profile analysis (LPA) to identify heterogeneous configurational patterns of short video addiction and emotion dysregulation among college students, and to systematicall Show more
This study aimed to use latent profile analysis (LPA) to identify heterogeneous configurational patterns of short video addiction and emotion dysregulation among college students, and to systematically examine the predictive effects of cognitive reappraisal, emotional loneliness, and sociodemographic factors on latent profile membership. A cross-sectional survey design was employed. From April to July 2025, full-time undergraduate students were recruited from multiple universities in Shandong Province using a combination of convenience sampling and snowball sampling. Participants completed online questionnaires including the Short Video Addiction Scale, the Emotion Dysregulation Inventory (EDI), the Cognitive Reappraisal Scale, and the Emotional Loneliness Scale. A total of 1,168 valid questionnaires were obtained. LPA identified four optimal profiles: Profile 1 ("low short video addiction-low emotion dysregulation"), Profile 2 ("medium to lower short video addiction-medium to lower emotion dysregulation"), Profile 3 ("medium to upper short video addiction-medium to upper emotion dysregulation"), and Profile 4 ("high short video addiction-high emotion dysregulation"). Multivariable logistic regression analyses indicated that, with Profile 4 as the reference category, cognitive reappraisal significantly increased the likelihood of membership in lower-risk profiles, whereas emotional loneliness significantly decreased the likelihood of membership in lower-risk profiles. Among sociodemographic factors, being female and having an urban background significantly increased the likelihood of membership in Profile 1 (vs. Profile 4); being a non-only child and having no part-time work experience significantly predicted membership in Profile 3. Marked heterogeneity exists among college students in the measured dimensions of short-form video addiction and emotion dysregulation, and the two constructs exhibit highly concordant co-variation. The findings provide empirical support for developing risk-stratified and precision-oriented mental health intervention strategies. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1789207
LPA
Jiarou Chen, Kaiyue Han, Xingxing Liao +6 more · 2026 · Frontiers in psychiatry · Frontiers · added 2026-04-24
Executive function (EF) deficits are a core cognitive feature of autism spectrum disorder (ASD) and are closely associated with social responsiveness. Previous research has primarily focused on childr Show more
Executive function (EF) deficits are a core cognitive feature of autism spectrum disorder (ASD) and are closely associated with social responsiveness. Previous research has primarily focused on children with ASD, whereas how specific executive components relate to social functioning in adults remains less clear. This study examined whether patterns of association between EF and social responsiveness differ between children and adults with and without ASD. Data were obtained from the Autism Brain Imaging Data Exchange II (ABIDE II), including 423 participants aged 8-23 years (ASD = 184; controls = 239). EF was evaluated using the Behavior Rating Inventory of Executive Function (BRIEF/BRIEF-A), and social responsiveness was assessed with the Social Responsiveness Scale (SRS). Covariates of age, sex, and full-scale IQ (FIQ) were controlled using entropy balancing in children and multiple regression in adults. Hierarchical regression, moderated mediation analysis, and latent profile analysis (LPA) were conducted to examine the moderation, mediation, and heterogeneity effects, respectively. Across both child and adult samples, individuals with ASD exhibited significantly higher T-scores than controls on nearly all BRIEF and SRS subdomains after covariate adjustment (all adjusted p < 0.01), indicating widespread EF and social responsiveness impairments. Moderation analyses revealed no significant age group × EF interaction, indicating that the association between EF and social responsiveness was consistent across development. Mediation analysis revealed age-specific pathways, with EF broadly mediating social responsiveness in adults but showing more selective mediation in children. LPA identified four distinct subtypes, which were independent of age, sex, and FIQ. EF-social responsiveness associations were evident across development, but the functional contribution of specific executive components became more differentiated with age. Working memory showed greater relative prominence in adulthood. Latent profile analysis revealed heterogeneity in how executive difficulties align with social challenges, supporting developmentally informed assessment and clinical interpretation rather than direct treatment recommendations. Show less
📄 PDF DOI: 10.3389/fpsyt.2026.1729973
LPA
Shuting Yin, Yuxiang Yuan, Huiqun Wang +2 more · 2026 · Patient preference and adherence · added 2026-04-24
To identify latent self-management profiles in people living with HIV (PLWH) with dyslipidemia and factors associated with profile membership, thereby facilitating targeted clinical intervention. A cr Show more
To identify latent self-management profiles in people living with HIV (PLWH) with dyslipidemia and factors associated with profile membership, thereby facilitating targeted clinical intervention. A cross-sectional survey was conducted from December 2024 to June 2025 among 333 PLWH with dyslipidemia at Nanjing Second Hospital. Data were collected via sociodemographic/disease-related questionnaire, the HIV Self-Management Scale (HIVSMS), and the Health Literacy Management Scale (HLMS). Latent profile analysis (LPA) was performed in Mplus 8.3, and multinomial logistic regression was used to examine factors associated with profile membership. Fit indices (entropy = 0.993) supported a three-profile solution: low self-management-low social support-seeking (C1, 42.3%), moderate self-management-stable (C2, 37.8%), and high self-management-emotion regulation dominant (C3, 19.8%). Seeking social support was relatively low across profiles. Compared with C1, C2 membership was significantly associated with higher education and income, lipid-lowering medication use (OR 3.735, 95% CI 1.597-8.736), and CD4 350-500 cells/μL, and was less likely among participants with VL >1000 copies/mL or chronic comorbidities (all P < 0.05). Compared with C1, C3 membership was significantly associated with HIV infection duration ≥5 years, higher education and income, CD4 >500 cells/μL, and higher HDL-C, and was less likely among those with VL >1000 copies/mL (OR 0.037, 95% CI 0.004-0.380) or chronic comorbidities (all P < 0.05). Compared with C2, C3 membership was independently associated with higher health literacy (HL) (OR 1.038 per point, 95% CI 1.012-1.064) and was less likely among those with LDL-C ≥3 mmol/L (P < 0.05). We identified three distinct self-management profiles among PLWH with dyslipidemia. Profile membership was significantly associated with HL and socioeconomic, HIV-related, lipid-related, and comorbidity factors, supporting the need for profile-tailored strategies to improve self-management. Show less
📄 PDF DOI: 10.2147/PPA.S584419
LPA
Zumin Wang, Jun Gao, Wenhao Ping +2 more · 2026 · Sensors (Basel, Switzerland) · MDPI · added 2026-04-24
Accurate classification of intestinal polyps is crucial for preventing colorectal cancer but is hindered by visual similarity among subtypes and endoscopic variability. While deep learning aids in dia Show more
Accurate classification of intestinal polyps is crucial for preventing colorectal cancer but is hindered by visual similarity among subtypes and endoscopic variability. While deep learning aids in diagnosis, single-modal models face efficiency-accuracy trade-offs and ignore pathological semantics. We propose a multimodal framework that integrates endoscopic images with structured pathological descriptions to bridge this gap. We propose LPA-Tuning CLIP, which incorporates three key innovations: replacing CLIP's instance-level contrastive loss with cross-modal projection matching (CMPM) with ID loss to explicitly optimize intraclass compactness and interclass separation through label-aware image-text similarity matrices; introducing structured clinical semantic templates that encode WHO diagnostic criteria into hierarchical text prompts for consistent pathology annotations; and developing medical-aware augmentation that preserves lesion features while reducing domain shifts. The experimental results demonstrate that our proposed method achieves an accuracy of 85.8% and an F1 score of 0.862 on the internal test set, establishing a new state-of-the-art performance for intestinal polyp classification. This study proposes a multimodal polyp classification paradigm that achieves 85.8% accuracy on three-subtype classification via endoscopic image-pathology text joint representation learning, outperforming unimodal baselines by 8.7% and a multimodal baseline by 4.3%. Show less
📄 PDF DOI: 10.3390/s26061764
LPA
Xiaoqing Wang, Ruisen Chen, Panqin Ye +1 more · 2026 · Behavioral sciences (Basel, Switzerland) · MDPI · added 2026-04-24
This study explores the influence of congruence and incongruence in father-mother co-parenting on adolescent depression, as well as the mediating effect of self-esteem. A total of 1389 adolescents com Show more
This study explores the influence of congruence and incongruence in father-mother co-parenting on adolescent depression, as well as the mediating effect of self-esteem. A total of 1389 adolescents completed questionnaires assessing their levels of depression and self-esteem, while their fathers and mothers correspondingly reported on their own co-parenting behaviors using the Parental Co-parenting Scale in this cross-sectional study. Dates were analyzed using LPA, RSA, and mediation consecutively. The results show that: (1) We identified three distinct co-parenting profiles: positive parental co-parenting, negative parental co-parenting, and mixed parental co-parenting. (2) In cases of congruent parental co-parenting, high positive parental co-parenting was associated with lower adolescent depression, whereas high negative parental co-parenting was linked to higher depression, and the difference manifests in different forms among boys and girls. Girls showed nonlinear changes in depression while boys exhibited linear trends. (3) In cases of incongruence in parental co-parenting, mothers' co-parenting exerted a stronger influence on boys' depression, while girls were not affected by mothers' and fathers' discrepancies. (4) Self-esteem mediated the relationship between parental co-parenting (in)congruence and depression across both genders. This study provides evidence for the mechanism through which parental coparenting influences adolescent depression and offers a basis for future interventions targeting adolescent depression. Show less
📄 PDF DOI: 10.3390/bs16030448
LPA
Meizhu Ding, Yinggao Li, Shasha Yao +1 more · 2026 · Annals of vascular surgery · Elsevier · added 2026-04-24
The pathogenesis of aortic aneurysm (AA) remains unclear, and there are no effective therapeutic drugs or targets. Circulating plasma proteins are considered biomarkers of AA and potential therapeutic Show more
The pathogenesis of aortic aneurysm (AA) remains unclear, and there are no effective therapeutic drugs or targets. Circulating plasma proteins are considered biomarkers of AA and potential therapeutic targets for AA. This study aimed to systematically evaluate the causal effects of plasma proteins on AA using a multi-cohort Mendelian randomization (MR) approach. Protein quantitative trait loci (pQTLs) was obtained from 9 published proteome genome-wide association studies (GWAS) and AA GWAS data from the FinnGen cohort. Independent pQTLs were selected as instrumental variables (IVs). Two-sample MR analysis was performed using inverse-variance weighted, MR-Egger regression, weighted median, weighted mode, and simple mode methods. Heterogeneity and pleiotropy were assessed using Cochran's Q test, I A total of 8,285 pQTLs for 4,421 proteins were retained as IVs. Using cis-pQTLs for IVs,MR analysis identified 154 proteins associated with thoracic aortic aneurysm (TAA; 76 protective and 78 risk factors) and 211 proteins with abdominal aortic aneurysm (AAA; 112 protective and 99 risk factors) Using cis-pQTLs combined with trans-pQTLs as IVs, MR analysis identified 236 proteins associated with TAA and 309 proteins with AAA. A subset of these associations survived FDR correction (FDR < 0.05), representing the most robust findings. Comparison of the TAA and AAA proteomic profiles revealed both shared proteins (e.g., AHSG, MMP7, RARRES2, THBS2, CCL25) and condition-specific proteins (e.g., OVCA2, STAT3, and HPSE for TAA; PLAU, LPA, SERPING1, and SMPDL3A for AAA), reflecting the distinct embryonic origins and pathological drivers of these two conditions. Steiger filtering confirmed the expected direction of effect from circulating proteins to AA. Colocalization analysis found evidence of shared causal variants between multiple proteins and AA. Pathway enrichment analysis revealed involvement in stress response, immune regulation, cytokine-cytokine receptor interaction, and metabolic processes. Nearly two-thirds of the associated proteins were classified as druggable or potentially druggable targets. This study identified a large number of potentially novel pathogenic proteins and therapeutic targets for AA, providing important references for elucidating the molecular pathogenesis of AA and advancing drug development. These findings warrant further validation through experimental studies and prospective clinical investigations. Show less
no PDF DOI: 10.1016/j.avsg.2026.03.008
LPA
Chaoyi Wang, Dong Yang, Jiangbo Hu +1 more · 2026 · Journal of Intelligence · MDPI · added 2026-04-24
The engagement and burnout profiles of preschool teachers are closely linked to young children's developmental outcomes. This study investigated engagement and burnout profiles among 529 Chinese presc Show more
The engagement and burnout profiles of preschool teachers are closely linked to young children's developmental outcomes. This study investigated engagement and burnout profiles among 529 Chinese preschool teachers in relation to their emotional states, varying experiences, and professional backgrounds. The sample predominantly consisted of early-career educators, with 47.8% aged between 21 and 30 years and 33.1% having 0-5 years of work experience. Using a quantitative cross-sectional design and latent profile analysis (LPA), this study identified four distinct profiles: slightly exhausted (48.58%), moderately burned out (18.53%), engaged (25.90%), and highly burned out (6.99%). Positive emotional states, such as enjoyment, were associated with higher work engagement, while anxiety was associated with a higher probability of belonging to burnout profiles. In contrast, perceived career success and negative emotions like anger did not significantly predict work engagement and burnout profiles. Teachers with extensive teaching experience and pre-service early childhood education (ECE) training were more likely to maintain high work engagement. This study highlights the critical role of emotional states and professional ECE training in promoting preschool teachers' work engagement and sustainable practice, particularly among early-career teachers. Show less
📄 PDF DOI: 10.3390/jintelligence14030046
LPA
Erin E Kishman, Shawn D Youngstedt, Xuewen Wang · 2026 · Clocks & sleep · MDPI · added 2026-04-24
There are limited data on the dynamic changes in daily composition of movement behaviors (sleep; moderate-to-vigorous physical activity, MVPA; light physical activity, LPA; and sedentary time, SED) an Show more
There are limited data on the dynamic changes in daily composition of movement behaviors (sleep; moderate-to-vigorous physical activity, MVPA; light physical activity, LPA; and sedentary time, SED) and their associations with body weight in postpartum women. The purpose of this study was to examine associations of reallocating time in one behavior to another with body weight, at different times in the first year postpartum. The study included 86 women who delivered a singleton infant at ≥37 weeks gestation. Physical activity and sleep were measured via actigraphy in early, mid-, and late postpartum. Body weight was measured at each timepoint. Isotemporal substitution models were used to examine the association of reallocating ten minutes of one behavior (MVPA, LPA, SED, or sleep) to another, with body weight. Participants spent most of their day in SED (~52-53%), followed by sleep (~30%), LPA (~12-13%), and then MVPA (~2%) throughout the first year postpartum. In early and mid-postpartum, but not late postpartum, reallocating 10 min of MVPA to LPA, SED, or sleep was associated with lower body weight (range: 3.07-4.03 kg lower). In early and late postpartum, reallocating 10 min of SED to LPA was associated with a lower body weight (4.03 kg and 1.04 kg, respectively). In participants who slept ≥7 h per day, reallocating sleep to LPA in early postpartum, and MVPA time to LPA in mid-postpartum was associated with lower body weight. In those who slept <7 h, no significant associations with body weight were found when reallocating time from one behavior to another. Encouraging LPA throughout the postpartum period may be beneficial for weight loss, and having enough sleep may be especially important for early to mid-postpartum. Future research examining the impact of changes in LPA on body weight in the postpartum period are needed, along with postpartum specific 24 h movement guidelines. Show less
📄 PDF DOI: 10.3390/clockssleep8010012
LPA
Wenzhuo Xu, Hao Guo, Kele Jiang +9 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
In recent years, the global incidence of Non-Suicidal Self-Injury (NSSI) has risen, posing a significant challenge in public health. Adolescents are the main group affected. A cross-sectional study wa Show more
In recent years, the global incidence of Non-Suicidal Self-Injury (NSSI) has risen, posing a significant challenge in public health. Adolescents are the main group affected. A cross-sectional study was conducted using a self-administered questionnaire to collect data from 6,311 adolescents in Hefei, China. This study employed the Compositional Isotemporal Substitution Model (CISM, a statistical method that estimates health effects of replacing time in one behavior with another while accounting for the interdependent, compositional nature of 24-h time-use data) to examine the impact of Screen Time (ST), Non-Screen-based Sedentary Time (NSST), Physical Activity, and Sleep Time on NSSI among adolescents. Compositional logistic regression analysis revealed that, relative to the remaining behavioral components, higher Light Physical Activity (LPA) ( The findings highlight those reasonably allocating adolescents' daily activities, reducing ST, can help lower the risk of NSSI among adolescents. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1737730
LPA
Ning Su, Jiayu Hu, Borui Shang +4 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24
Older adults increasingly rely on digital health resources, yet evidence regarding the relationship between eHealth literacy (eHL) and 24-hour movement behaviors (24-HMB), including physical activity Show more
Older adults increasingly rely on digital health resources, yet evidence regarding the relationship between eHealth literacy (eHL) and 24-hour movement behaviors (24-HMB), including physical activity (PA), sedentary behavior (SB), and sleep, remains underexplored. This study examined the associations between eHL and 24-HMB in Chinese older adults and examined self-efficacy as a potential mediator and moderator. Using a convenience sampling approach, 564 community-dwelling older adults (aged 60-74 years) were recruited from four urban Chinese cities via an online survey. A total of 553 valid cases were retained for analyses. eHL was assessed using the eHealth Literacy Scale-Web 3.0, and self-efficacy was assessed using a validated Self-Efficacy Scale. PA and SB were assessed objectively using ActiGraph GT3X+ accelerometers over three consecutive days (two weekdays and one weekend day). Sleep duration was derived from accelerometer-based estimates anchored by daily sleep logs. Multiple linear regression analyses were conducted to examine associations, and mediation and moderation were tested using PROCESS macro (Model 4 and Model 1, respectively), adjusting for age, sex, and education. After adjustment for covariates ( In this cross-sectional, urban, device-using sample of older adults, higher eHL was associated with a more favorable 24-HMB profile, particularly higher LPA and lower SB, while associations with sleep duration were weaker. Self-efficacy showed modest indirect associations consistent with partial mediation for PA and SB and also acted as a moderator of several associations. Given the observational design and modest effect sizes, findings should be interpreted cautiously and require confirmation in longitudinal or experimental studies with more representative sampling and improved sleep assessment. Show less
📄 PDF DOI: 10.3389/fmed.2026.1746861
LPA
Chao-Yun Cheng, Yih-Jer Wu, Chih-Fan Yeh +25 more · 2026 · Journal of the Formosan Medical Association = Taiwan yi zhi · Elsevier · added 2026-04-24
Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic Show more
Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve disease (CAVD). Structurally composed of a low-density lipoprotein (LDL)-like particle covalently linked to apolipoprotein(a) [apo(a)], Lp(a) exhibits unique atherogenic, thrombogenic, and inflammatory properties, largely due to its role as a carrier of oxidized phospholipids (OxPL). Plasma Lp(a) concentrations are predominantly determined by the number of kringle IV type 2 (KIV-2) repeats in the LPA gene, with minimal influence from lifestyle or environmental factors. Despite substantial evidence linking elevated Lp(a) to cardiovascular risk, clinical testing remains underutilized, especially in East Asian countries. In Taiwan, although population-level Lp(a) concentrations are comparatively low, a significant subset exceeds risk thresholds, with local studies confirming its prognostic value in coronary artery disease and ischemic stroke. Barriers, including limited physician awareness, implementation barriers, and therapeutic nihilism, contribute to its under-recognition. This review highlights the molecular features of Lp(a), its pathogenesis of cardiovascular disorders, epidemiology, and current barriers and future advances in diagnostic testing, with a particular focus on implications for cardiovascular risk management in Taiwan. Show less
no PDF DOI: 10.1016/j.jfma.2026.03.073
LPA
Jiaqi Zuo, Jie Zhang, Ying Tang +10 more · 2026 · The Plant cell · Oxford University Press · added 2026-04-24
Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties Show more
Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties make cytosolic accumulation cytotoxic, necessitating its sequestration into vacuoles for safe storage. Here, we present the cryo-EM structures of the rice vacuolar phytate transporter, OsMRP5, captured in distinct functional states. These structures reveal the molecular basis of OsMRP5 function as an ATP-binding cassette (ABC) transporter. OsMRP5 employs a specialized substrate-recognition mechanism, uniquely adapted to bind the fully hydrophilic InsP6 through extensive electrostatic and hydrogen-bonding interactions within two distinct, highly polar binding sites in its central cavity. A distinctive electropositive tunnel, positioned above the central cavity, forms a continuous pathway connecting the InsP6-binding pocket to the vacuolar export site. This tunnel likely generates an electrostatic attraction that facilitates the movement of the highly anionic InsP6 through the transporter. By mapping mutations from low-phytic acid (lpa) crop variants onto the OsMRP5 structures, we pinpoint their conserved locations critical for transporter function and validate their impact experimentally. These results reveal how OsMRP5 recognizes and transports the highly charged InsP6 molecules into vacuoles, providing a molecular framework for targeted manipulation of this agriculturally important transporter. Show less
no PDF DOI: 10.1093/plcell/koag088
LPA
Shang Gao, Qiyuan Wang, Keyao Kang +3 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Job satisfaction is a critical factor influencing workplace efficiency and employee well-being. In the context of Industry 5.0 transformation, understanding the latent profiles of job satisfaction and Show more
Job satisfaction is a critical factor influencing workplace efficiency and employee well-being. In the context of Industry 5.0 transformation, understanding the latent profiles of job satisfaction and their relationship with mental health outcomes, such as depression, anxiety, and digital-intelligence job insecurity, is critical for promoting employee well-being and organizational sustainability. This study aims to explore the latent profiles of job satisfaction among industrial workers and explore their associations with mental health outcomes. This study used cross-sectional data from 3,420 male frontline workers from a large automobile manufacturing enterprise in Jilin Province, China in April 2024. Latent profile analysis (LPA) was employed to identify distinct latent profiles of job satisfaction among industrial workers, while hierarchical linear regression analysis was used to analyze the relationship between job satisfaction and psychological health outcomes (depression, anxiety and digital-intelligence job insecurity). The score of job satisfaction among industrial workers in Jilin Province was 3.62 ± 0.90. Four profiles were identified: very low (5.97%), low-to-moderate (31.14%), moderately high (42.63%), and high job satisfaction (20.26%). Depression and anxiety showed a clear level-gradient pattern across profiles, whereas digital-intelligence job insecurity displayed a non-monotonic pattern with higher levels in the low-to-moderate and moderately high profiles. Work stress showed consistent associations with all outcomes, and job satisfaction profiles remained associated with depression and anxiety after covariate and stress adjustment; associations with digital-intelligence job insecurity were smaller but detectable. This study examined heterogeneity in job satisfaction among frontline industrial workers and its associations with mental health outcomes. Latent profile analysis identified four job satisfaction profiles. Job satisfaction profile membership remained strongly associated with depression and anxiety. Digital-intelligence job insecurity showed a non-monotonic pattern across profiles. These findings suggest that an individual-centered profile approach provides actionable differentiation of mental health symptom burden across distinct job satisfaction patterns, supporting more targeted workplace strategies. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1772767
LPA
Chuqin Xiong, Shuge Wang, Peiran Guo +6 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24
Nursing interns often face maladjustment during the early stages of clinical practice, which not only directly affects their physical and mental health as well as work efficiency but also significantl Show more
Nursing interns often face maladjustment during the early stages of clinical practice, which not only directly affects their physical and mental health as well as work efficiency but also significantly inhibits their proactive feedback-seeking behavior (FSB). As an active self-regulation strategy, FSB can enhance interns' work initiative and promote role transition. However, existing research has yet to thoroughly investigate the potential heterogeneity and categorical characteristics of FSB within this population, and the role of psychological resources such as career adaptability in shaping these patterns requires further investigation. To investigate the status of FSB in early-stage nursing interns, identify latent subgroups via latent profile analysis (LPA), and analyze associated factors, thereby providing evidence for targeted clinical educational interventions. Multicenter cross-sectional research. This study employed a multistage stratified cluster sampling to survey 1,308 early-stage nursing interns from nine universities in Hubei, China, between June and September 2024. Data were collected using a demographic questionnaire, Feedback-Seeking Behavior Scale, and Career Adapt-Abilities Scale. LPA was employed to delineate FSB profiles and multivariate logistic regression analysis to examine the associated predictors. A total of 1,370 questionnaires were distributed, with 1,308 valid responses, yielding an effective response rate of 95.47%. The mean score on the feedback-seeking behavior scale was 5.06 ± 1.08. LPA identified three distinct feedback-seeking profiles: low (20.87%), moderate (38.3%), and high (40.83%). Education level, student cadre experience, internship hospital type, and career adaptability were significant predictors of profile membership ( FSB among early-stage nursing interns exhibited heterogeneity. Nursing educators and managers should implement tiered interventions: for the low and moderate feedback-seeking groups, career guidance and feedback awareness cultivation should be strengthened; for the high feedback-seeking group, peer modeling should be encouraged. This strategy can enhance proactive FSB, supports role transition and professional identity, and promotes long-term nursing workforce stability. Show less
📄 PDF DOI: 10.3389/fmed.2026.1664329
LPA
Di Dai, Qingping Zhou, Yusupujiang Tuersun +6 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
Negative Emotional symptoms such as depression and anxiety do not exist independently, often co-occurring in the same individual, and heterogeneity exists between individuals suffering from depression Show more
Negative Emotional symptoms such as depression and anxiety do not exist independently, often co-occurring in the same individual, and heterogeneity exists between individuals suffering from depression and anxiety; however, prior research has rarely investigated heterogeneity in a person-centered manner and from the perspective of college students. The main purpose of this study was to explore this heterogeneity and its association with e-Health literacy (e-HL) using Latent profile analysis (LPA), a person-centered statistical method. A total of 7,503 Chinese college students from 10 regions (including Guangdong Province, Shanghai Municipality, and Jiangsu Province) were surveyed using the Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9) to assess anxiety and depressive symptoms. LPA was employed to identify potential profiles of negative emotional symptoms and validate their robustness; binary logistic regression was used to explore differences in demographic characteristics (sex, grade ranking), sociological factors (family residential background, per capita monthly family income), and lifestyle factors (adherence to physical activity, smoking status, alcohol consumption) across profiles; analysis of variance (ANOVA) was applied to compare e-HL levels among different profiles. The two-class model was identified as the optimal classification of negative emotional symptoms: low/no negative emotional symptoms (61.49%) and high negative emotional symptoms (38.51%). Female college students, those with low per capita monthly family income, lack of regular physical exercise, and alcohol consumption habits were more likely to be categorized into the high negative emotional symptoms group (all Reliance on self-report measures may lead to recall bias and social desirability bias; the cross-sectional design cannot establish causal relationships between variables; digital addiction, a potential confounding factor that may co-occur with negative emotional symptoms and influence e-HL, was not included in the analysis. This study identified two distinct latent profiles of negative emotional symptoms among Chinese college students and their key predictive factors using LPA. The findings highlight the need for stratified early screening for high-risk groups (females, low-income families, inactive individuals, and drinkers) and the development of targeted interventions. Enhancing e-HL could be a potential pathway to improve mental health outcomes, providing actionable insights for scientific and effective mental health management in colleges and universities. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1760468
LPA
Dinuo Xin, Dina Xin, Ying Wang +3 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
This study aimed to investigate the current status of career calling among novice nurses, to identify potential subtypes and their population characteristics, and to further explore the factors associ Show more
This study aimed to investigate the current status of career calling among novice nurses, to identify potential subtypes and their population characteristics, and to further explore the factors associated with the different subtypes. A cross-sectional descriptive study was used. From January to February 2024, 845 novice nurses from 11 hospitals in Shanxi Province were selected for an online questionnaire survey using convenience sampling. The demographic questionnaire, transition shock of newly graduated nurses scale, medical staff resilience scale, and career calling scale were used as study instruments. Latent profile analysis (LPA) was used to explore the subtypes of novice nurses' career calling, and multifactorial logistic regression was used to analyze the related factors of novice nurses' career calling. Three subtypes of career calling of novice nurses in this study were identified, namely, lacking-calling group (10.3%), stable-calling group (50.0%), and sufficient-calling group (39.7%). Education, weekly working hours, weekly frequency of night shifts, reasons for choosing nursing, level of transition shock, and level of resilience were significantly associated with the three latent profiles of career calling of novice nurses in this study. Novice nurses' career calling presents 3 latent profiles and is heterogeneous in this study. Nursing administrators could pay attention to the differences in the level of career calling of novice nurses and adopt targeted management strategies based on the type of characteristics of the population in order to improve the level of career calling of novice nurses, help them develop their careers, and stabilize the nursing workforce. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1651190
LPA
Yunyun Liu, Xiangrui Li, Ting Zhao +9 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
Fear of progression (FoP) is a prevalent psychological issue among stroke patients. Previous studies failing to distinguish characteristics of patient groups with varying FoP levels. Latent profile an Show more
Fear of progression (FoP) is a prevalent psychological issue among stroke patients. Previous studies failing to distinguish characteristics of patient groups with varying FoP levels. Latent profile analysis (LPA) classifies individuals into distinct subgroups via continuous FoP indicators, boosting classification accuracy by accounting for variable uncertainty. Given FoP's heterogeneity, investigating FoP profiles and their influencing factors in stroke patients is clinically significant for personalized psychological care and improved patient quality of life. A total of 366 stroke patients were selected as study subjects through convenience sampling, and a cross-sectional survey was conducted. FoP was assessed using the Fear of Progression Questionnaire-Short Form (FoP-Q-SF, 2 dimensions, 12 items). Independent variables included demographic characteristics, clinical indicators, the Recurrence Risk Perception Scale for Stroke patients (RRPSS), and the Medical Coping Modes Questionnaire (MCMQ). LPA was performed on the FoP-Q-SF items to identify subgroups. The R3STEP method was used to analyze influencing factors of subgroup membership, and the BCH method was applied to compare differences in distal outcomes across subgroups. Statistical significance was set at The study sample had a mean age of 63.93 ± 10.58 years, with 70.5% males and 65.0% first-ever stroke patients. Two latent profiles were identified: Low-FoP Adaptive Type (C1, 48.6%) and High-FoP Sustained Type (C2, 51.4%). The R3STEP showed that age 18-59 years (OR = 0.476, 95%CI = 0.245-0.924, This study revealed significant heterogeneity in FoP among stroke patients. Age, hypertension comorbidity, excessive recurrence risk perception, MCMQ-confrontation, and MCMQ-avoidance were associated with high FoP. Healthcare providers should prioritize identifying high-risk individuals and develop tailored interventions to reduce FoP and improve rehabilitation outcomes. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1741344
LPA
Xingrong He, Jing Wang, Lingyan Ye +3 more · 2026 · Patient preference and adherence · added 2026-04-24
This study aimed to identify distinct in-hospital cardiac rehabilitation (CR) adherence profiles and explore their associated clinical and sociodemographic factors among patients following percutaneou Show more
This study aimed to identify distinct in-hospital cardiac rehabilitation (CR) adherence profiles and explore their associated clinical and sociodemographic factors among patients following percutaneous coronary intervention (PCI). A cross-sectional survey was conducted among patients undergoing Phase I cardiac rehabilitation following percutaneous coronary intervention (PCI) who were hospitalized in the cardiology department between June and July 2025 (n=384). Data were collected using a general information questionnaire and a treatment adherence questionnaire (Since the study population consisted of inpatients undergoing PCI followed by phase I cardiac rehabilitation, the dimension of follow-up compliance was excluded). LPA, a person-centered method that identifies unobserved subgroups (profiles) based on response patterns, was prespecified to classify CR adherence profiles. Multinomial logistic regression was performed to examine factors associated with profile membership. Clinical indicators (number of diseased vessels, LVEF, LDL-C, and serum creatinine) were included as candidate predictors; after LASSO selection, LDL-C and number of diseased vessels were retained and entered the final multinomial logistic regression model as continuous variables (original values). Three distinct CR adherence profiles were identified: Low CR Adherence (125/384, 32.55%), Medium CR Adherence (169/384, 44.01%), and High CR Adherence (90/384, 23.44%). Profile membership was significantly associated with gender, living situation, family monthly income, residential distance, smartphone use/proficiency and LDL-C ( CR adherence among post-PCI patients was overall moderate-to-low, with substantial heterogeneity across adherence patterns. The associated sociodemographic and contextual factors may help inform profile-based, tailored support to improve CR adherence after PCI. Given the cross-sectional design, these associations are non-causal and should be validated in future multicenter longitudinal and intervention studies. Show less
📄 PDF DOI: 10.2147/PPA.S589177
LPA
Ling Sun, Zhen Zeng, Jie Wang +5 more · 2026 · Foods (Basel, Switzerland) · MDPI · added 2026-04-24
Hot air drying is widely used in edible mushroom processing, but often leads to quality changes, including browning and flavor changes. This study focused on
📄 PDF DOI: 10.3390/foods15050812
LPA
Zhiji Wang, Lin Wang, Shijie Liu +4 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
24-h activity encompasses four categories: light-intensity physical activity (LPA), moderate-to-vigorous-intensity physical activity (MVPA), sedentary behavior (SB), and sleep (SP). This study aims to Show more
24-h activity encompasses four categories: light-intensity physical activity (LPA), moderate-to-vigorous-intensity physical activity (MVPA), sedentary behavior (SB), and sleep (SP). This study aims to investigate the effects of different physical activity components on executive function in older adults with chronic diseases and to examine substitution effects among activity components. The findings provide scientific evidence to inform physical activity interventions for improving executive function in older adults with chronic diseases. A total of 105 older adults (72.64 ± 6.82 years) were recruited. Following questionnaire screening, 75 older adults with chronic diseases were ultimately included. Accelerometers objectively measured participants' daily SP, SB, LPA, and MVPA. Executive function was objectively assessed using the Stroop task, N-back task, and More-odd-shifting task. Component linear regression equation assessed the relationship between different activities and executive function in older adults with chronic diseases. The dose-response effects of "one-for-one" substitutions between different activity behaviors were explored. Component linear regression results showed that SB positively correlated with inhibitory control ( SP and MVPA significantly improve inhibitory control in older adults with chronic diseases, while LPA significantly enhances their working memory. It is recommended that older adults with chronic diseases adjust their daily time structure by increasing diverse physical activities, ensuring adequate sleep duration, and reducing sedentary behavior to improve executive function. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1733294
LPA
Zheyuan Xia, Yukuan Miao, Leran Tang +4 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
To identify latent profiles of proactive health behaviors in patients with hypertension, examine the category-specific influencing factors. Proactive health behavior, as an emerging concept, refers to Show more
To identify latent profiles of proactive health behaviors in patients with hypertension, examine the category-specific influencing factors. Proactive health behavior, as an emerging concept, refers to a self-motivated approach to systematically managing health-related factors in order to actively maintain and promote one's health status. However, existing studies have largely focused on describing the overall level of such behaviors among patients with hypertension, with insufficient exploration of behavioral heterogeneity within this population. Moreover, there has been a lack of systematic integration of established behavioral theories to explain the multifactorial mechanisms underlying different behavioral patterns, which limits the development of precise nursing interventions. A cross-sectional study was performed, involving 352 patients with hypertension from 8 communities in Anhui Province from September to December 2025. The survey tools included self-designed demographic and clinical instrument, the Proactive Health Behavior Scale for Hypertensive Patients, the Self-Efficacy Scale for Hypertensive Patients, the Health Literacy Management Scale (HeLMS). Latent profile analysis (LPA) was used to identify subtypes of proactive health behavior among hypertension patients. Multinomial logistic regression analysis was applied to determine the factors associated with the identified subtypes. A total of 352 questionnaires were distributed, yielding 321 valid responses (a response rate of 91.2%). The total score of proactive health behavior was 89.57 ± 22.99 points. The LPA revealed four profiles of proactive health behavior: the positive proactive health behavior profile (Class 1, The proactive health behavior among hypertension patients was at a moderate level, revealing four distinct behavioral categories with significant differences. Guided by the Health Belief Model, profile-specific influencing factors were analyzed, which informed the development of tailored intervention strategies. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1789975
LPA
Ruoxuan Zhang, Xin Wang, Angela Y M Leung +8 more · 2026 · Journal of nursing management · added 2026-04-24
Given the globalization of the nursing workforce, psychological empowerment represents a critical intrinsic determinant of nurses' mobility intentions, specifically regarding cross-border work. To ide Show more
Given the globalization of the nursing workforce, psychological empowerment represents a critical intrinsic determinant of nurses' mobility intentions, specifically regarding cross-border work. To identify latent profiles of nurses' psychological empowerment, examine associated factors, and explore the relationship between these profiles and cross-border working intention. A cross-sectional multicenter study was conducted from March to September 2023. Using convenience sampling, clinical nurses were recruited through liaisons from nursing societies in nine cities of Guangdong Province. Data were collected through questionnaires covering sociodemographic questionnaire, psychological empowerment, and cross-border working intention, with analyses including chi-square tests, logistic regression, and latent profile analysis (LPA) performed using SPSS 23.0 and Mplus 8.3. A total of 3671 valid questionnaires were collected, and 39.5% of the respondents reported cross-border intentions. LPA identified three psychological empowerment profiles among nurses, ranked from high to low: the core-driven empowerment profile (16.94%), the adaptive empowerment profile (70.42%), and the constrained empowerment profile (12.64%). The nurses with lower salary, intermediate title, and without specialist nurse qualification were more likely to fall into the constrained empowerment profile. Psychological empowerment was positively correlated with nurses' cross-border work intention. The core-driven profile showed the highest cross-border work intention (50.6%), followed by the adaptive (38.2%) and constrained profiles (31.7%). For cross-border work, the constrained profile prioritized salary (87.1%) as the key concern, while the core-driven profile focused more on good promotion opportunities (70.3%). Psychological empowerment exerts a positive impact on clinical nurses' cross-border work intention, with the three identified empowerment profiles exhibiting divergent motivational priorities and decision logics. These findings highlight the need for subgroup-specific strategies to balance nursing workforce mobility and stability. The findings support a differentiated human resource strategy based on nurses' psychological empowerment profiles. For core-driven nurses, institutions should provide international career development channels to strengthen their domestic job embeddedness. For adaptive nurses, tailored skill training and decision-making autonomy should be offered to guide their mobility aspirations. For constrained nurses, competitive compensation and family support services should be prioritized to address their stability needs and rebuild professional confidence. These targeted measures balance talent mobility and domestic workforce stability. Show less
📄 PDF DOI: 10.1155/jonm/8714790
LPA
Dongxue Liu, Yihan Pan, Hairong Wang +1 more · 2026 · Journal of exercise science and fitness · Elsevier · added 2026-04-24
This study used a group-based multi-trajectory model (GBMTM) to identify distinct muscle health trajectories and examine their associations with physical activity (PA) in middle-aged and older adults. Show more
This study used a group-based multi-trajectory model (GBMTM) to identify distinct muscle health trajectories and examine their associations with physical activity (PA) in middle-aged and older adults. Data were obtained from 2818 middle-aged and older adults (aged ≥40 years) in the China Health and Retirement Longitudinal Study (2011-2015). Muscle health was assessed using muscle mass (appendicular skeletal muscle mass index), muscle strength (handgrip strength), and physical performance (5-time chair stand test). PA was assessed using the International Physical Activity Questionnaire Short Form. A GBMTM was applied to jointly identify longitudinal trajectories of muscle mass, muscle strength, and physical performance, and to evaluate their associations with PA. In this study, four muscle health trajectories were identified: low-function declining, moderate-function declining, moderate-function stable, and high-function stable group. Engaging in ≥150 min/wk of light PA (LPA), moderate PA (MPA), or vigorous PA (VPA) was associated with the moderate-function stable group (LPA: aOR = 3.44, 95% CI: 1.94 - 6.11; MPA: aOR = 2.83, 95% CI: 1.67 - 4.96; VPA: aOR = 2.88, 95% CI: 1.61 - 5.13) and the high-function stable group (LPA: aOR = 5.20, 95% CI: 2.44 - 11.19; MPA: aOR = 4.10, 95% CI: 1.92 - 8.73; VPA: aOR = 3.42, 95% CI: 1.55 - 8.55). In older adults aged ≥70 years, associations persisted for MPA and VPA. Distinct muscle health trajectories highlight individualized muscle aging and inform personalized PA guidance. Regular PA ≥150 min/wk across intensities was associated with more favorable longitudinal muscle health. Show less
📄 PDF DOI: 10.1016/j.jesf.2026.200462
LPA
Xintong Ma, Wei Li, Yuanyuan Liu +8 more · 2026 · BMC psychiatry · BioMed Central · added 2026-04-24
Adolescence is a critical period for rapid emotional and cognitive development. Depression and cognitive impairment frequently co-occur in this population, yet their comorbidity patterns and symptom-l Show more
Adolescence is a critical period for rapid emotional and cognitive development. Depression and cognitive impairment frequently co-occur in this population, yet their comorbidity patterns and symptom-level interactions remain insufficiently explored. A total of 2,244 students (mean age = 16.8 ± 0.84 years; 1,218 males, 1,026 females) from a high school in Heilongjiang Province, China, were recruited. Depressive symptoms and cognitive impairment were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Perceived Deficits Questionnaire–Depression (PDQ-D). Latent profile analysis (LPA) was applied to identify subgroups, followed by network analysis to examine central symptoms (expected influence, EI), bridge symptoms (bridge expected influence, BEI), and network differences (NCT). The optimal LPA model identified three comorbidity subgroups: low, moderate, and high. NCT revealed significant differences in network structure and global strength between the low–moderate (S = 1.514, Adolescent Depression and Cognitive Impairment can be classified into low, moderate, and high comorbidity subgroups. Somatic symptoms emerged as the central symptom, while prospective memory impairment and interpersonal problems were identified as key bridge symptoms, suggesting potential intervention targets for early screening and stratified treatment. Not applicable. The online version contains supplementary material available at 10.1186/s12888-026-07946-w. Show less
📄 PDF DOI: 10.1186/s12888-026-07946-w
LPA
Wenyan Xu, Jiayin Qin, Yang Yang +3 more · 2026 · Journal of tissue viability · Elsevier · added 2026-04-24
Diabetic foot (DF) is a serious diabetes complication that increases ulceration, amputation and mortality risks. Effective foot self-care can prevent up to 85% of ulcer events. This study aimed to ass Show more
Diabetic foot (DF) is a serious diabetes complication that increases ulceration, amputation and mortality risks. Effective foot self-care can prevent up to 85% of ulcer events. This study aimed to assess foot self-care behaviors among middle-aged and older DF patients, evaluate the impact of social support, and explore the mediating effects of frailty and fear of progression (FoP). We also identified patient subtypes using latent profile analysis. A cross-sectional study was conducted in a tertiary hospital from November 2024 to March 2025. A total of 361 patients with DF aged ≥45 years completed validated questionnaires, including the Social Support Rating Scale (SSRS), FRAIL Scale, FoP-Q-SF, and DFSQ-UMA. Structural equation modeling (SEM) assessed mediation effects, and latent profile analysis (LPA) identified subgroups based on frailty and FoP. A total of 383 questionnaires were distributed, with 361 valid responses collected, resulting in an effective response rate of 94.3%. The average score for foot self-care behavior was 58.52 ± 13.46, while levels of social support, frailty, and FoP were all at moderate levels. SEM indicated that Social support significantly predicted better foot self-care behavior (β = 0.225, P < 0.01). Frailty and FoP partially mediated this relationship (mediation effect: 6.68%). LPA identified three types of physical and mental profiles: Low FoP - Low Frailty Group (75.1%), Moderate FoP - Moderate Frailty Group (15.2%), and High FoP - High Frailty Group (9.7%). Importantly, patients in the High FoP-High Frailty Group demonstrated the lowest foot self-care behavior (mean = 43.70, P < 0.001), indicating the highest potential risk for ulcer occurrence and poor tissue outcomes. Social support enhances foot self-care in DF patients through reduced Frailty and FoP. Tailored interventions targeting high-risk subgroups may improve tissue outcomes and prevent ulcers. Show less
no PDF DOI: 10.1016/j.jtv.2026.100991
LPA
Ziye Luo, Yuan Yuan, Rahila Hafeez +3 more · 2026 · Food chemistry · Elsevier · added 2026-04-24
Yucha, a traditional fermented rice-fish product, faces challenges in inconsistent quality and safety. In this study, 69 lactic acid bacteria (LAB) were isolated from Yucha and shrimp paste in Hainan, Show more
Yucha, a traditional fermented rice-fish product, faces challenges in inconsistent quality and safety. In this study, 69 lactic acid bacteria (LAB) were isolated from Yucha and shrimp paste in Hainan, China. Four strains, Lactiplantibacillus plantarum Lpl-YC37, Lacticaseibacillus paracasei Lpa-XJ120, and Pediococcus pentosaceus Ppe-YC39 and Ppe-XJ37 were selected as starters based on probiotic property and safety evaluation. Inoculation with these LAB starters significantly enriched beneficial metabolites, with Ppe-XJ37 showing a four-fold increase in acetic acid, the dominant short-chain fatty acids. Additionally, all LAB inoculation enhanced free amino acids, particularly L-glycine, improving flavor and nutritional value. Crucially, LAB inoculation drastically suppressed biogenic amines, reducing putrescine from 55.23 μg g Show less
no PDF DOI: 10.1016/j.foodchem.2026.148614
LPA