👤 Hanbin Wang

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Also published as: Junli Wang, Xindi Wang, Junpeng Wang, Tingyu Wang, Guoqiang Wang, Yuxuan Wang, Hanzhi Wang, Zhi-Long Wang, Shanshan Wang, Wenfei Wang, Dengbin Wang, Yen-Sheng Wang, Chuanxin Wang, Zeyu Wang, Beibei Wang, Taicheng Wang, Xingguo Wang, Z P Wang, Yue-Min Wang, Chenghua Wang, Xianqiang Wang, Congrong Wang, Yanhai Wang, Du Wang, Xianzhe Wang, Zuoheng Wang, Yongyi Wang, Zhihui Wang, Yanhua Wang, Limeng Wang, H J Wang, Pei-Jian Wang, Yana Wang, Congrui Wang, Larry Wang, Yu-Zhuo Wang, Sihua Wang, Wanchun Wang, Jialin Wang, Xinying Wang, Shuguang Wang, Yinhuai Wang, Xiaobin Wang, Yuying Wang, Hebo Wang, Leli Wang, Jiayu Wang, Zhaojun Wang, Hai Wang, Si Wang, Re-Hua Wang, Xuping Wang, Bo Wang, Shubao Wang, Songjiao Wang, Hongjia Wang, Victoria Wang, Ling Wang, Jianjie Wang, Haining Wang, Dali Wang, Ji-Yang Wang, Cheng Wang, Weifan Wang, Yuanqiang Wang, Zhixiao Wang, Yaxian Wang, Zhigang Wang, Haochen Wang, Jia-Ying Wang, Shichao Wang, Ruosu Wang, N Wang, Haixing Wang, Guiqun Wang, Zhiting Wang, Dan Wang, Wangxia Wang, Jing-Long Wang, Yaqian Wang, Yafang Wang, Xing-Jun Wang, Dapeng Wang, Zhongyuan Wang, Junsheng Wang, Zhaohai Wang, He-Ping Wang, Minmin Wang, Wenzhou Wang, Zhaohui Wang, Yanfang Wang, Pengtao Wang, Leran Wang, Qianwen Wang, Hongkun Wang, Sa Wang, Y Alan Wang, Liyan Wang, Jou-Kou Wang, Mingda Wang, Chenfei Wang, Yuehan Wang, Simeng Wang, Yuhua Wang, Ruibin Wang, Haibo Wang, Ni Wang, Guoxiu Wang, Zhuangzhuang Wang, Yajie Wang, Zhixiang Wang, Sangui Wang, Xiantao Wang, Yan-Yang Wang, Mengjun Wang, Ruling Wang, Peihe Wang, Miao Wang, Zaihua Wang, Jun-Jie Wang, Mengyao Wang, Zhiyu Wang, Changzhen Wang, Xijun Wang, Chengjian Wang, Yiyi Wang, Mo Wang, Xiaolun Wang, Danan Wang, Fanchang Wang, Zilin Wang, Fanhua Wang, Supeng Perry Wang, Gavin Wang, Yi-Ying Wang, Yani Wang, Zhuowei Wang, Weiwei Wang, Haifeng Wang, Yi-Shiuan Wang, Yan-Chao Wang, Xiaotong Wang, Jia-Qi Wang, Yongliang Wang, Yongming Wang, Fengchong Wang, Jianyong Wang, Zeping Wang, Huaquan Wang, Xiaojia Wang, Tao Wang, Tianjun Wang, Siying Wang, Zhenze Wang, Zhijian Wang, Li Wang, Heming Wang, Jingtong Wang, Xuefei Wang, Yingqiao Wang, Xiao Qun Wang, Chun-Chieh Wang, Shuang-Xi Wang, Laiyuan Wang, Zhaoming Wang, Yinggui Wang, Qi-Jia Wang, Wen-Yan Wang, Mingming Wang, Peipei Wang, Chien-Hsun Wang, Qiuhong Wang, Monica Wang, Lexin Wang, Xiufen Wang, Yuehua Wang, Pingfeng Wang, Caiyan Wang, Weijie Wang, Yigang Wang, Jieyan Wang, Huiquan Wang, Chunsheng Wang, Yunhe Wang, Changtu Wang, Qingliang Wang, Guanghua Wang, Yongbin Wang, Zhaobo Wang, Minghui Wang, Junshi Wang, Jingyu Wang, Longsheng Wang, Fen Wang, Xianshu Wang, Jianwu Wang, Jun-Zhuo Wang, Zhixing Wang, Lei Wang, Yiyan Wang, Jinglin Wang, Jinhe Wang, Enhua Wang, Yuecong Wang, Xueying Wang, Jennifer T Wang, Xin-Hua Wang, Shijie Wang, Chun-Xia Wang, Yuanjiang Wang, Xiaojun Wang, Shunjun Wang, Chun-Juan Wang, M Wang, Jinfei Wang, Jinghuan Wang, Xuru Wang, Xiao-Lan Wang, Yu-Chen Wang, Zhi-Guo Wang, Luya Wang, Shuwei Wang, Pingchuan Wang, Qifan Wang, Xing-Quan Wang, Weiding Wang, Xuebin Wang, Yaling Wang, Chenyin Wang, Allen Wang, Liyuan Wang, Rong-Rong Wang, Wusan Wang, Wayseen Wang, Qianru Wang, Yi-Xin Wang, Hailin Wang, Yu-Hang Wang, Xuesong Wang, Haojie Wang, Wanxia Wang, Mengwen Wang, Hanping Wang, Yuhang Wang, Lueli Wang, Xinchang Wang, Oliver Wang, Shuge Wang, Jianhao Wang, Chong Wang, Kui Wang, Litao Wang, Zining Wang, Ming-Yang Wang, Hongxia Wang, Mingyi Wang, Hai Bo Wang, Bingnan Wang, Hongqian Wang, Jisheng Wang, Jiakun Wang, Maoju Wang, Xiaoqiu Wang, Dongyi Wang, Hai Yang Wang, Pengju Wang, Xiaofeng Wang, Huming Wang, Jian'an Wang, Qianrong Wang, Xiaowei Wang, Xiangkun Wang, Da Wang, Hongying Wang, Changying Wang, Changyu Wang, Xiaoqin Wang, Zhenxi Wang, Qiaoqiao Wang, Yu Tian Wang, Yupeng Wang, Xinli Wang, YueJiao Wang, Jian-chun Wang, Pengchao Wang, Xiao-Juan Wang, Siqing Wang, C Z Wang, Pengbo Wang, Baoli Wang, Yu-Zhe Wang, Gui-Qi Wang, Dazhi Wang, Yanwen Wang, Xingqin Wang, Shijin Wang, Wenming Wang, Fanxiong Wang, Tiansong Wang, Shuzhe Wang, Jie Wang, Jinling Wang, Yunfang Wang, Luyao Wang, Cun-Yu Wang, Zikang Wang, Quan-Ming Wang, Yingying Wang, Chia-Chuan Wang, Xintong Wang, Jufeng Wang, Xuejun Wang, Xiao-Qian Wang, Yijin Wang, Meng Yu Wang, Tianyi Wang, Chia-Lin Wang, Zhuo-Jue Wang, Yaohe Wang, Rong Wang, Hao-Hua Wang, Yong-Jun Wang, Xubo Wang, Dalong Wang, Yan-Ge Wang, Erika Y Wang, Ruixian Wang, Jin-Liang Wang, Shicung Wang, Saifei Wang, Jintao Wang, Zhenzhen Wang, Jiawei Wang, Beilei Wang, Huabo Wang, Huiyu Wang, Hongtao Wang, Chengjun Wang, Guo-Du Wang, Taoxia Wang, Zitao Wang, Jingwen Wang, Yibin Wang, Long Wang, Xinjing Wang, Qunzhi Wang, Liangliang Wang, Bangchen Wang, Yu-Fen Wang, Shibin Wang, Congcong Wang, Xiong Wang, Zhiren Wang, Xiaozhu Wang, Hong-Xia Wang, Qingyong Wang, Tianying Wang, Tammy C Wang, Huijie Wang, Tiansheng Wang, Mengzhao Wang, Jianshu Wang, Xinlong Wang, Benzhong Wang, Zhipeng Wang, Kaijie Wang, Xiaomin Wang, Peijun Wang, Zhiqiang Wang, Jundong Wang, Zheng Wang, Yueze Wang, Sujuan Wang, Qing-Yun Wang, Xiaoqing Wang, Zongqi Wang, Zhicun Wang, Fudi Wang, Seok Mui Wang, Wanbing Wang, Kejun Wang, Nanping Wang, Mingyang Wang, Wenxia Wang, Yaru Wang, Zikun Wang, Shidong Wang, Bei Bei Wang, Yu-Hui Wang, Rui Wang, Yige Wang, Tongxin Wang, Xiaohua Wang, Changjing Wang, Xingjin Wang, Bingjie Wang, Shaoyu Wang, Hui-Hui Wang, Zhenyu Wang, Baoying Wang, Yang-Yang Wang, Shi-Yao Wang, Lifei Wang, Fangfang Wang, Zhimei Wang, Kunpeng Wang, Binglong Wang, Daijun Wang, Qinghang Wang, Zi Wang, Shushu Wang, QingDong Wang, Qing K Wang, Fuhua Wang, Yanni Wang, Jianle Wang, Wenyan Wang, Jinning Wang, Ziqi Wang, Wei-Qi Wang, Yaolou Wang, Haoming Wang, Jian-Wei Wang, Tian Wang, Peixi Wang, Iris X Wang, Tongxia Wang, Mei-Xia Wang, Haiying Wang, Tielin Wang, Hongze Wang, Chung-Hsi Wang, Peiyao Wang, Linli Wang, Guanru Wang, Yuzhong Wang, Yunhan Wang, Jianan Wang, Menglong Wang, Yingxue Wang, Jiayi Wang, Dingxiang Wang, Ting Wang, Fenglin Wang, Jianqun Wang, Ran Wang, Kuan Hong Wang, Liusong Wang, Wen-Der Wang, Yixuan Wang, Feng Wang, Kaicen Wang, Eryao Wang, Yulei Wang, Huaibing Wang, Zhongzhi Wang, Jinrong Wang, Sujie Wang, Xiaozhong Wang, Xiao-Pei Wang, Li-Na Wang, H X Wang, Linjie Wang, Zhaosong Wang, Yafen Wang, Chuan-Wen Wang, Xiaoning Wang, Li-Xin Wang, Silas L Wang, Baocheng Wang, Hongyi Wang, Zhi-Xiao Wang, Shengjie Wang, Zhi-Hao Wang, Yaokun Wang, Shao-Kang Wang, Qunxian Wang, Jianghui Wang, Zhao Wang, Di Wang, Jianzhi Wang, Ruijing Wang, Ling Jie Wang, Qingshi Wang, Jianye Wang, Yuqiang Wang, Kangling Wang, Anxin Wang, Shengli Wang, Zhulin Wang, Hua-Wei Wang, Yiwen Wang, Yang Wang, Hanqi Wang, Changwei Wang, Honglei Wang, Yi Lei Wang, Wenkang Wang, Junjie Wang, Yazhou Wang, Peng-Cheng Wang, Chenzi Wang, Anqi Wang, Yuemiao Wang, Xuelin Wang, Rujie Wang, Dongyan Wang, Yuxue Wang, Wengong Wang, Qigui Wang, Junqing Wang, Ruhan Wang, Xinye Wang, Huihui Wang, Gengsheng Wang, Mark Wang, Zhidong Wang, Mengmeng Wang, Yuwen Wang, Liang Wang, Huaxiang Wang, Fangjun Wang, Huixia Wang, Haijiao Wang, Hong-Hui Wang, Yi-Shan Wang, Yunchao Wang, Junjun Wang, Binghai Wang, Xinguo Wang, Jun-Sing Wang, Lingzhi Wang, Yuexiang Wang, Hong-Gang Wang, Yen-Feng Wang, Xidi Wang, Jiawen Wang, Liangfu Wang, Lifeng Wang, Shihan Wang, Wentian Wang, Sa A Wang, Lee-Kai Wang, Yu-Wei Wang, Zumin Wang, Shau-Chun Wang, Jianjiao Wang, Tian-Tian Wang, Jiantao Wang, Edward Wang, Jianbo Wang, Qingfeng Wang, Wenran Wang, Xiaolin Wang, Fenghua Wang, Rongjia Wang, Shiqiang Wang, Caixia Wang, Guihu Wang, Xindong Wang, Wenxiu Wang, Xueguo Wang, YiLi Wang, Aizhong Wang, Qiqi Wang, Chengcheng Wang, D Wang, L Wang, Jianhua Wang, Qiuling Wang, Shaolian Wang, Wen-Qing Wang, Wenqing Wang, Yuchuan Wang, Guangdi Wang, Yiquan Wang, Huimei Wang, Genghao Wang, Zun Wang, Miranda C Wang, Annette Wang, Chi-Ping Wang, Hanmin Wang, Zhaoxi Wang, Shifeng Wang, Runze Wang, Mangju Wang, Junjiang Wang, Dong D Wang, Xiu-Ping Wang, Haijiu Wang, Linghuan Wang, Yiying Wang, Renqian Wang, Nana Wang, Xiangdong Wang, Shiyin Wang, Chaoyi Wang, Menghan Wang, Shuyue Wang, Yongmei Wang, Nanbu Wang, Lihua Wang, Hongyue Wang, Jianli Wang, Chunli Wang, Minghua Wang, Junkai Wang, Chenguang Wang, Siyue Wang, Jun Wang, Shu-Song Wang, Bingyan Wang, Qingping Wang, Zhong-Yu Wang, Fei-Fei Wang, Jennifer E Wang, Z-Y Wang, Dongxia Wang, Dang Wang, Zi-Hao Wang, Rihua Wang, Jutao Wang, Yanzhe Wang, Guohao Wang, Liming Wang, Yishu Wang, Xuemin Wang, Xianfeng Wang, Zixu Wang, Jingfan Wang, Guang-Jie Wang, Guixue Wang, Jiaojiao Wang, Yaxin Wang, Haibing Wang, Weizhong Wang, Hairong Wang, Hai-Jun Wang, Mingji Wang, Yongrui Wang, Huizhi Wang, Longfei Wang, Chongmin Wang, Jingyang Wang, Zhong-Ping Wang, Huanhuan Wang, Baisong Wang, Xiaohui Wang, Fengyang Wang, Wanliang Wang, Ziqiang Wang, Chuan Wang, Jeffrey Wang, Ying-Zi Wang, Ziwei Wang, Xian Wang, Hanyu Wang, Qiming Wang, Dedong Wang, Fengying Wang, Xiaoya Wang, Zhenhua Wang, Yanchun Wang, Keming Wang, Zi-Yi Wang, Dezhong Wang, Jingying Wang, Shouli Wang, Lan-lan Wang, Weiyu Wang, Yuhuai Wang, Jun Yi Wang, Wenying Wang, Xue-Feng Wang, Xing-Lei Wang, Yuehong Wang, Pengyu Wang, Yihe Wang, Guodong Wang, Weijian Wang, Wu-Wei Wang, Y Wang, Ruonan Wang, Jianbing Wang, Mian Wang, Dennis Qing Wang, Nannan Wang, Zuo Wang, Christine Wang, Ruixin Wang, Yaxiong Wang, Siwei Wang, Yuanzhen Wang, Wen-Chang Wang, Haijing Wang, X Wang, Melissa T Wang, Haixia Wang, Qianghu Wang, Hongsheng Wang, Xiurong Wang, Shaowei Wang, Shuo Wang, Zengtao Wang, Yun-Xing Wang, Songtao Wang, Mei Wang, Mengyun Wang, Qingming Wang, Ke-Feng Wang, Zhihao Wang, Haoqi Wang, X E Wang, Xin-Shang Wang, Dongmei Wang, Lingli Wang, Huai-Zhou Wang, Hua Wang, Kunzheng Wang, Mao-Xin Wang, Jingzhou Wang, Jiaqi Wang, Xingbang Wang, Wence Wang, Yongdi Wang, Xin-Qun Wang, Guoyi Wang, Jian-Guo Wang, Jiafu Wang, Pin Wang, Libo Wang, Junling Wang, J Z Wang, Haozhou Wang, Jing Wang, Hezhi Wang, T Q Wang, Xi-Hong Wang, Yuanfan Wang, Endi Wang, Hua-Qin Wang, Jeremy Wang, Songping Wang, Suyun Wang, Jiqing Wang, Shu-Ling Wang, Jennifer X Wang, Lily Wang, Yin-Hu Wang, Jen-Chywan Wang, Qingqing Wang, Shuangyuan Wang, Haihong Wang, Luyun Wang, Yake Wang, Ya-Nan Wang, Weicheng Wang, Jianxiang Wang, Zihua Wang, Lin Wang, Fu-Sheng Wang, Zongbao Wang, Tong-Hong Wang, Xianze Wang, Ting-Ting Wang, Haibin Wang, Xin-Yue Wang, Zhi-Gang Wang, Ziying Wang, Shukang Wang, Wen-Jun Wang, Delin Wang, Yating Wang, Xuehao Wang, Yefu Wang, Yi-Ning Wang, Cheng-zhang Wang, Jing J Wang, Xinglong Wang, Yanqing Wang, Tongyao Wang, Dongyang Wang, Deqi Wang, Qiao Wang, Alice Wang, Yunzhi Wang, Dayong Wang, Renxi Wang, Yeh-Han Wang, Mingya Wang, Longxiang Wang, Hualin Wang, Hailei Wang, Ao Wang, Wanyu Wang, Jiale Wang, Qiangcheng Wang, Huishan Wang, Yunqiong Wang, Xudong Wang, Xifu Wang, Wen-Xuan Wang, Dao Wen Wang, Zhi-Wei Wang, Xingchen Wang, Yanyang Wang, Yutao Wang, Huizhen Wang, Hu WANG, Y P Wang, Wen Wang, Qingsong Wang, Baofeng Wang, Ruo-Ran Wang, Yaobin Wang, Changliang Wang, Pintian Wang, Dai Wang, Su-Guo Wang, Ruting Wang, Fengzhen Wang, Qinrong Wang, HuiYue Wang, Baosen Wang, Shuhe Wang, Yifei Wang, Jiun-Ling Wang, Junhui Wang, Guangzhi Wang, Qijia Wang, Yushe Wang, Jinlong Wang, Zhouguang Wang, Huiyao Wang, Shuxin Wang, Yingyi Wang, Jing-Yi Wang, Yongxiang Wang, Zhi Wang, Dehao Wang, Yi-sheng Wang, Jiazhi Wang, Yunfei Wang, Mingjin Wang, Yaozhi Wang, Jinyu Wang, Jinmeng Wang, LiLi Wang, Shuai Wang, Yan Wang, Jun Kit Wang, Cui Wang, Zhan Wang, Dong-Jie Wang, Yangyang Wang, Xiangguo Wang, Runuo Wang, Ruimin Wang, Pengpu Wang, Nuan Wang, Guangyan Wang, Xin-Liang Wang, Minxiu Wang, Ruifang Wang, Hui Wang, Hongda Wang, Xiyan Wang, Jinxia Wang, Xinchen Wang, Haihua Wang, Delong Wang, Yayu Wang, Xue-Hua Wang, Xin-Peng Wang, Changqian Wang, Bei Wang, Ya-Han Wang, Chih-Liang Wang, P N Wang, Xiaoqian Wang, Xianshi Wang, Zhiruo Wang, Xueding Wang, Renxiao Wang, Yi-Ming Wang, Tianqi Wang, Ledan Wang, Rongyun Wang, Gan Wang, Qinqin Wang, Yuxiang Wang, Feimiao Wang, Mengyuan Wang, Chaofan Wang, Linshuang Wang, Yanhui Wang, Zhenglong Wang, Zongkui Wang, Zhenwei Wang, Xiyue Wang, Yi Fan Wang, Xiao-Ai Wang, Po-Jen Wang, Xinyang Wang, Linying Wang, Fa-Kai Wang, Yimeng Wang, Dong-Mei Wang, Anli Wang, Hui-Li Wang, Jianqing Wang, Honglun Wang, Wei-Feng Wang, Kaihao Wang, Jialing Wang, Shuren Wang, Cui-Fang Wang, Wenqi Wang, Peilin Wang, Wen-Fei Wang, Guang-Rui Wang, T Wang, Weiqing Wang, Ciyang Wang, Biao Wang, Kaihe Wang, Jieh-Neng Wang, Tony Wang, Yuehu Wang, Zhicheng Wang, Tongtong Wang, Zi Xuan Wang, Yingtai Wang, Xin-Xin Wang, Chu Wang, Tianhao Wang, Shukui Wang, Ching C Wang, Yulin Wang, Chunyang Wang, Yeqi Wang, Yinbo Wang, Kongyan Wang, Weiling Wang, Linxuan Wang, Shengya Wang, Yaqi Wang, Huating Wang, Aiting Wang, Ya Xing Wang, Daoping Wang, Shasha Wang, Wei-Lien Wang, Quanli Wang, Yanru Wang, L M Wang, Bijue Wang, H Wang, Jipeng Wang, Xiaoxia Wang, Shuu-Jiun Wang, Baitao Wang, Haimeng Wang, Chung-Hsing Wang, Weining Wang, M Y Wang, Wenwen Wang, Zhongsu Wang, Xiaochen Wang, Ligang Wang, Shaohsu Wang, Bing Qing Wang, Jiangbin Wang, Yajun Wang, Chunting Wang, Hemei Wang, En-hua Wang, H-Y Wang, Zixi Wang, Wenjing Wang, Haikun Wang, Ruxin Wang, Jianru Wang, Yongqiang Wang, Ouchen Wang, Jianyu Wang, Shen Wang, Yixi Wang, Zhi-Hong Wang, Li Dong Wang, Zhou-Ping Wang, Wen-Yong Wang, Meng-Lan Wang, Xiaojie Wang, Leying Wang, Yi-Zhen Wang, Y Y Wang, Jianlin Wang, Guoqing Wang, Jiani Wang, Guan-song Wang, You Wang, Xiangding Wang, Ke Wang, Wendong Wang, Yue Wang, Zhe Wang, K Wang, Zhuo Wang, Su'e Wang, Cangyu Wang, Erfei Wang, Xiaoming Wang, Aijun Wang, Xiaoye Wang, Jun-Sheng Wang, Wenxiang Wang, Yanjun Wang, Qiangqiang Wang, Yachun Wang, Haitao Wang, Tiancheng Wang, Gangyang Wang, Jianmin Wang, Jiabo Wang, Yijing Wang, Mengzhi Wang, Yinuo Wang, Zhou Wang, Guiying Wang, Xuezheng Wang, Shan Wang, Aoli Wang, Fuqiang Wang, Yawei Wang, Xianxing Wang, Ya-Long Wang, Yuyang Wang, Dong Hao Wang, Y-S Wang, Zelin Wang, Liqun Wang, Cunyi Wang, Qian-Zhu Wang, Yinan Wang, Panfeng Wang, Guangwen Wang, J Q Wang, Guang Wang, Yu-Ping Wang, John Wang, Jiaping Wang, Zhisheng Wang, Xuan-Ren Wang, Xiaowu Wang, Zhengyu Wang, Baowei Wang, Zhijun Wang, Zhong-Hao Wang, Fengzhong Wang, Jin-Da Wang, Zhaoqing Wang, Yuanbo Wang, Haixin Wang, Yaping Wang, Lixiu Wang, Mingxia Wang, Neng Wang, Guozheng Wang, Yan-Feng Wang, Huafei Wang, Yuhan Wang, Xingxing Wang, Wenhe Wang, Xing-Huan Wang, Xiansong Wang, Yishan Wang, Ruming Wang, Ya Qi Wang, Yueying Wang, Chunle Wang, Shihua Wang, W Wang, Hengjun Wang, Meihui Wang, Huanyu Wang, Ruinan Wang, Qiwei Wang, Zhong Wang, Shiyao Wang, Jian-Zhi Wang, Ruimeng Wang, Jinxiang Wang, Jinsong Wang, Bin-Xue Wang, Fuwen Wang, Yiou Wang, Shifa Wang, Yin Wang, Yanzhu Wang, Jia Bin Wang, Siyang Wang, Zhanggui Wang, Yueting Wang, Qingyu Wang, Qianqian Wang, Xiu-Lian Wang, Fengling Wang, Chenxi Wang, Cheng An Wang, Yipeng Wang, Weipeng Wang, Zechen Wang, Shuaiqin Wang, Xueqian Wang, Chan Wang, Guohang Wang, Cai-Yun Wang, Jiang Wang, Huei Wang, Yufeng Wang, Heng Wang, Qing-Liang Wang, Chuang Wang, Xiaofang Wang, Hao-Ching Wang, Junying Wang, Jianwei Wang, Jinhai Wang, Hanchao Wang, Penglai Wang, I-Ching Wang, S L Wang, Tianhu Wang, Sheng-Min Wang, Pan-Pan Wang, Duan Wang, Xuqiao Wang, Minghuan Wang, Wei-Wei Wang, Xiaojian Wang, Shuping Wang, Jinfu Wang, Biqi Wang, Zhenguo Wang, Fangyan Wang, Sainan Wang, Peijuan Wang, Pei-Yu Wang, Yuyan Wang, Fuxin Wang, Ji M Wang, Yange Wang, Yali Wang, Wenhui Wang, Leishen Wang, Lichan Wang, Xianna Wang, Wenbin Wang, Kenan Wang, Chih-Yuan Wang, Yanlei Wang, Ju Wang, Yanliang Wang, Keqing Wang, Bangshing Wang, Dayan Wang, Yongsheng Wang, Dinghui Wang, Zheyue Wang, Xinke Wang, Daqing Wang, Yan Ming Wang, He-Ling Wang, Shengyao Wang, Jiwen Wang, Xizhi Wang, Luxiang Wang, Dandan Wang, RongRong Wang, Heng-Cai Wang, Jindan Wang, Xiaoding Wang, Yumeng Wang, Heling Wang, Xiao-Yun Wang, Meiding Wang, Zhilun Wang, Guo-hong Wang, Na Wang, Yanli Wang, Fubing Wang, Feixiang Wang, Zhiyuan Wang, Yi-Cheng Wang, Zhengwei Wang, Wenyuan Wang, Yu-Ying Wang, Jianqin Wang, Sijia Wang, Chuansen Wang, Huawei Wang, Kaiyan Wang, Qingyuan Wang, Yujia Wang, Lian Wang, Junrui Wang, Chao-Yung Wang, Zehao Wang, Ruixue Wang, Minjun Wang, Jin Wang, Xiaoxiao Wang, Jun-Feng Wang, Binquan Wang, Shuxia Wang, Donggen Wang, Deming Wang, Chenggang Wang, Chuduan Wang, Haichuan Wang, Catherine Ruiyi Wang, Hai-Feng Wang, Anthony Z Wang, Guanghui Wang, Jiahao Wang, Xiaosong Wang, Zijue Wang, Wenbo Wang, M-J Wang, Yu Wang, Yingping Wang, Zhengbing Wang, G Q Wang, Mengjing Wang, Zhendong Wang, Kailu Wang, Jinfeng Wang, Zhiguo Wang, Yusha Wang, Jianmei Wang, Kun Wang, Lihong Wang, Haoxin Wang, Haowei Wang, Ziqing Wang, Aihua Wang, Yuanyong Wang, Sanwang Wang, Doudou Wang, Hao-Yu Wang, Peirong Wang, Wenting Wang, Yibing Wang, He Wang, Jia-Peng Wang, Shixin Wang, En-bo Wang, Dong-Dong Wang, Hualing Wang, Hongyan Wang, Shaoying Wang, Yingjie Wang, Tianqing Wang, Guo-Hua Wang, Yongfei Wang, Lijing Wang, Hongli Wang, Zixian Wang, Niansong Wang, Liangxu Wang, Xinrong Wang, X-T Wang, Zhenning Wang, Dake Wang, Yu-Ting Wang, Zonggui Wang, Daping Wang, Joy Wang, Chenji Wang, Jingmin Wang, Yuyin Wang, Jin-Cheng Wang, Jiangbo Wang, Huiyang Wang, Chi Chiu Wang, He-Cheng Wang, Zhongjing Wang, Weina Wang, Qiaohong Wang, Qintao Wang, Jenny Y Wang, Zheyi Wang, Robert Yl Wang, Zhaotong Wang, Ya Wang, Fangyu Wang, Haobin Wang, Tianyuan Wang, Xinrui Wang, Zhehao Wang, Yihan Wang, Chuan-Jiang Wang, Jianjun Wang, Yongfeng Wang, Gaofu Wang, Ying-Piao Wang, Jingwei Wang, Mengjiao Wang, Chuyao Wang, Yanping Wang, Xinchun Wang, Shu Wang, Guibin Wang, Hong-Ying Wang, Linping Wang, Yugang Wang, Xinru Wang, Fengyun Wang, Heyong Wang, Ziping Wang, Yuegang Wang, Xiangyu Wang, Haoran Wang, Xiaomei Wang, Fang Wang, Lina Wang, Guowen Wang, Liyun Wang, Qingshui Wang, Baoyun Wang, Li-Juan Wang, Tongsong Wang, Jingyun Wang, Huiguo Wang, Zhibo Wang, Lou-Pin Wang, Renjun Wang, Huiting Wang, Junfeng Wang, Zihan Wang, Linhua Wang, Zhiji Wang, Fubao Wang, Eunice S Wang, Xiaojuan Wang, Yuewei Wang, Shuang Wang, Ruey-Yun Wang, Xiaoling Wang, Weihua Wang, Yanggan Wang, Jia Wang, Chaoqun Wang, Xiao-liang Wang, Manli Wang, Yongkang Wang, Huiwen Wang, Ting Chen Wang, Yixian Wang, Xinlin Wang, Shuya Wang, Bochu Wang, Kehao Wang, Sasa Wang, Mengshi Wang, Qiu-Ling Wang, Chengshuo Wang, Mengru Wang, Yiwei Wang, Xueyun Wang, Yijun Wang, Haomin Wang, Meng C Wang, Mengxiao Wang, Huan-You Wang, Jingheng Wang, Carol A Wang, Benjamin H Wang, Penglong Wang, Pei-Wen Wang, Jian-Long Wang, Wang Wang, Jinhui Wang, Yuanqing Wang, Jacob E Wang, Jian-Xiong Wang, Wenyu Wang, Chengze Wang, Hongmei Wang, Fengqiang Wang, Zijun Wang, Shaochun Wang, Qinwen Wang, Ruicheng Wang, Aixian Wang, Yanling Wang, Lu-Lu Wang, Linyuan Wang, Yeming Wang, Ye Wang, Tian-Yi Wang, Zhichao Wang, Dangfeng Wang, Jiucun Wang, Guo-Liang Wang, Guandi Wang, Zhuo-Xin Wang, Aili Wang, Fengliang Wang, Yingzi Wang, Lirong Wang, Xuekai Wang, Wei-En Wang, Jing-Xian Wang, Hesuiyuan Wang, Yuexin Wang, Suzhen Wang, Luping Wang, Xiuyu Wang, Zicheng Wang, Jiliang Wang, Rikang Wang, Xue Wang, Shudan Wang, Chun Wang, Hongxin Wang, Chenglong Wang, Junxiao Wang, Zhiqing Wang, Shawn Wang, Shunran Wang, Tiantian Wang, Youhua Wang, Xiao-Hui Wang, Qing-Yan Wang, Hanying Wang, Qiuping Wang, Yongzhong Wang, Jin-Xia Wang, Xiao-Tong Wang, Shun Wang, Xiaoqun Wang, Ching-Jen Wang, Xin Wang, Yingwen Wang, Jia Bei Wang, Xiaodan Wang, Wenhan Wang, Jia-Yu Wang, Xiaozhi Wang, Xinkun Wang, Jinhao Wang, KeShan Wang, Shengdong Wang, Jinzhu Wang, Lihui Wang, Bicheng Wang, Chao-Jun Wang, Shaoyi Wang, Yajing Wang, Qing-Bin Wang, Feiyan Wang, Geng Wang, Chen Wang, Zhimin Wang, Cenxuan Wang, Wenjun Wang, Chuan-Chao Wang, Zexin Wang, Shu-Huei Wang, Yonggang Wang, Zhaoyu Wang, Xiaochuan Wang, Chuan-Hui Wang, Junshuang Wang, X F Wang, Li-Ting Wang, Chenxin Wang, Qiao-Ping Wang, Jingqi Wang, Xiongjun Wang, Shuang-Shuang Wang, Xu Wang, Houchun Wang, Yaodong Wang, Lujuan Wang, Jilin Wang, Peichang Wang, Keyun Wang, Ruixuan Wang, Zhangying Wang, Lianyong Wang, Dongyu Wang, Xinghui Wang, Binghan Wang, Guanduo Wang, Xian-e Wang, Guimin Wang, Xiaomeng Wang, Yuh-Hwa Wang, Jinru Wang, Mingyu Wang, Binbin Wang, Chaokui Wang, Linhui Wang, Youzhi Wang, Zhenqian Wang, Jialiang Wang, Sufang Wang, Haiyan Wang, Yankun Wang, Yingbo Wang, Zilong Wang, Xiao-Qun Wang, Lin-Fa Wang, Wenhao Wang, P Wang, Rui-Hong Wang, Xiao-jian WANG, Pei Chang Wang, Zhengkun Wang, Vivian Wang, Ying Wang, Zihuan Wang, Peiwen Wang, Chao Wang, Da-Zhi Wang, He-Tong Wang, Mofei Wang, Zezhou Wang, Liyong Wang, Bruce Wang, Hao-Tian Wang, Jin-Juan Wang, Yucheng Wang, Yong-Gang Wang, Saili Wang, Xiuwen Wang, Ruiquan Wang, Xinmei Wang, Zhezhi Wang, Xiao-Jie Wang, H Y Wang, Li-Dong Wang, Duanyang Wang, Kaiting Wang, Yikang Wang, Yichen Wang, Ting-Chen Wang, Meixia Wang, ZhenXue Wang, Juan Wang, Shouling Wang, Lan Wang, Li Chun Wang, Xingxin Wang, Ruibing Wang, Xue-Ying Wang, Bi-Dar Wang, Jiayang Wang, Suxia Wang, Yumin Wang, Qing Jun Wang, Xinbo Wang, Youli Wang, Yi-Ni Wang, Xinran Wang, Lixian Wang, Kan Wang, Ruiming Wang, Qing-Yuan Wang, Kai-Kun Wang, Yaoxian Wang, Qing-Jin Wang, Junmei Wang, Xin Wei Wang, J P Wang, Xufei Wang, Yuqin Wang, Handong Wang, Li-San Wang, Guoling Wang, Wenrui Wang, Zhongwei Wang, Shi-Han Wang, Ruoxi Wang, Huiping Wang, Mu Wang, Weihong Wang, Minzhou Wang, Yakun Wang, Da-Cheng Wang, Pengjie Wang, Qihua Wang, Ji-Nuo Wang, Deshou Wang, Xiaowen Wang, Yaochun Wang, Qihao Wang, Ruiying Wang, Tiange Wang, Xi Wang, Yindan Wang, Lixin Wang, Zhaofeng Wang, Guixin Wang, Erming Wang, Haoyu Wang, Kexin Wang, Yiqiao Wang, Qi-Qi Wang, Shuiyun Wang, Xi-Rui Wang, Cai-Hong Wang, Zhizheng Wang, Mingxun Wang, Liangli Wang, Theodore Wang, Alexander Wang, Huayang Wang, Yinyin Wang, Shuzhong Wang, Tingting Wang, Jiao Wang, Wenxian Wang, Jianghua Wang, Furong Wang, Shijun Wang, Le Wang, Guihua Wang, Xiaokun Wang, Xia Wang, Jiabei Wang, Guoying Wang, Zeyuan Wang, Jue Wang, Jin-E Wang, Jingru Wang, Chun-Li Wang, Xiaole Wang, Ermao Wang, Lanlan Wang, Ye-Ran Wang, Hao Wang, Xv Wang, Shikang Wang, Yufei Wang, Siyi Wang, Xiujuan Wang, Qinyun Wang, Xiangwei Wang, Jian-Hong Wang, David Q-H Wang, Chunjuan Wang, Weiyan Wang, Jia-Liang Wang, Yanxing Wang, Sheri Wang, Chenwei Wang, Haoping Wang, Sheng-Quan Wang, Xiangrong Wang, Xiao-Yi Wang, Huan Wang, Zhitao Wang, Xinyan Wang, J Wang, Kaixi Wang, Huihua Wang, Renwei Wang, Xiaoliang Wang, Xiao-Lin Wang, Tian-Lu Wang, Jiou Wang, Weiqin Wang, Jiamin Wang, Dennis Wang, Ji-Yao Wang, Pingping Wang, Jinyang Wang, Chen-Cen Wang, Chien-Wei Wang, Daolong Wang, Rong-Tsorng Wang, Yuwei Wang, Guo-Ping Wang, Zhentang Wang, F Wang, Xueju Wang, Saisai Wang, Zhehai Wang, Y B Wang, Xiao Wang, Guobing Wang, Kangmei Wang, Chunguo Wang, Longcai Wang, Haina Wang, Chih-Hsien Wang, Yuli Wang, Ling-Ling Wang, Zhangshun Wang, Xue-Lian Wang, Jianxin Wang, Da-Yan Wang, Xianghua Wang, Peng Wang, Yu Qin Wang, Zhao-Jun Wang, Rui-Rui Wang, Xingyue Wang, Man Wang, Daozhong Wang, Tian-Li Wang, Luhui Wang, Gaopin Wang, Mengze Wang, Jizheng Wang, Hong-Yan Wang, Dongying Wang, Wenkai Wang, Stephani Wang, Dan-Dan Wang, Yicheng Wang, Yusheng Wang, Junwen Wang, Gao Wang, Ruo-Nan Wang, Yifan Wang, Jueqiong Wang, Xuewei Wang, Jianning Wang, Yonglun Wang, Shiwen Wang, Lifang Wang, Fuyan Wang, Jian-Bin Wang, Chonglong Wang, Haiwei Wang, Yike Wang, Chunxia Wang, Kaijuan Wang, Minglei Wang, Jingxiao Wang, Luting Wang, David Wang, Ben Wang, Ji-zheng Wang, Yuncong Wang, Lei P Wang, Tingye Wang, Wenke Wang, Ping Wang, Min Wang, Qiang-Sheng Wang, Xuejing Wang, Zhanju Wang, Xixi Wang, Xiaodong Wang, Chaomeng Wang, Yanong Wang, Xinghao Wang, Jiaming Wang, Siyuan Wang, Jiu Wang, Ruizhi Wang, Qing Mei Wang, Wenyi Wang, Yiqing Wang, Cai Ren Wang, Lianchun Wang, Xing-Ping Wang, Xiaoman Wang, Yanjin Wang, Xueqin Wang, Chenliang Wang, Zhenshan Wang, Junhong Wang, Guiping Wang, Xianrong Wang, Xumeng Wang, Dajia Wang, Huang Wang, Huie Wang, Weiwen Wang, Ruiwen Wang, Qing Wang, Haohao Wang, Bao-Long Wang, P Jeremy Wang, Chengqiang Wang, Suli Wang, Lingyan Wang, Chi Wang, Meng Wang, Luwen Wang, Quan Wang, Yan-Jun Wang, Sen Wang, Ruining Wang, Xiaozhen Wang, Zhiping Wang, Xue-Yao Wang, Yuming Wang, Jingjing Wang, Jiazheng Wang, Yunong Wang, Chongze Wang, Rufang Wang, Qiuning Wang, Tiannan Wang, Liqing Wang, Wencheng Wang, Xuefeng Wang, Yongli Wang, Xinwen Wang, Runzhi Wang, Chaojie Wang, Wentao Wang, Zhifeng Wang, Yanan Wang, Mengqi Wang, Limin Wang, Donglin Wang, Shujin Wang, Chengbin Wang, Qiu-Xia Wang, Zhengxuan Wang, Yancun Wang, Yuhuan Wang, Wei Wang, G-W Wang, Bangmao Wang, Kejia Wang, Jinjin Wang, Qifei Wang, Guobin Wang, Chun-Lin Wang, Jing-Shi Wang, Jiheng Wang, Huajing Wang, Yanlin Wang, Chuansheng Wang, Cailian Wang, Beilan Wang, Luofu Wang, Yangpeng Wang, Jieqi Wang, Weilin Wang, Xiaoxuan Wang, Yangyufan Wang, Xiao-Fei Wang, Chen-Ma Wang, Yun Yong Wang, Shizhi Wang, B Wang, Yuling Wang, Yi-Yi Wang, Fanwen Wang, Aiyun Wang, Jian Wang, Chengyu Wang, Jing-Huan Wang, Ning Wang, Yichuan Wang, L F Wang, Chau-Jong Wang, Xin-Yang Wang, Yunzhe Wang, Xuewen Wang, Sheng-Ping Wang, Bi Wang, Qiuting Wang, Yan-Jiang Wang, Dongshi Wang, Yingna Wang, Jingyue Wang, Hongshan Wang, Chunjiong Wang, Hong-Yang Wang, Yingmei Wang, Danfeng Wang, Zhongyi Wang, Teng Wang, Chih-Hao Wang, Mingchao Wang, Yi-Chuan Wang, Chuning Wang, Shihao Wang, Ming-Wei Wang, Menglu Wang, Zhulun Wang, Wuji Wang, Dao-Xin Wang, Han Wang, Jincheng Wang, Thomas T Y Wang, Qingyun Wang, Guoliang Wang, Jihong Wang, Hong-Qin Wang, G Wang, Hsei-Wei Wang, Linfang Wang, Xiao Ling Wang, Ganyu Wang, Zhengdong Wang, Cuizhe Wang, Hongyu Wang, Tieqiao Wang, Lijuan Wang, Jingchun Wang, Youzhao Wang, Zijian Wang, Ziheng Wang, Xingyu Wang, Shuning Wang, Shaokun Wang, Zhifu Wang, Xinqi Wang, Jinqiu Wang, ZhongXia Wang, Yanyun Wang, Dadong Wang, Xingjie Wang, Yiting Wang, Zhongli Wang, Junyu Wang, Jianding Wang, Meng-Wei Wang, Yingge Wang, Zhenchang Wang, Qun Wang, Jin-Xing Wang, Lijun Wang, Shuqing Wang, Fu-Yan Wang, Sheng-Nan Wang, Feijie Wang, Qiuyan Wang, Ying-Wei Wang, Shitao Wang, Meng-hong Wang, Zhengyang Wang, Jinghong Wang, Zhiying Wang, Pei Wang, Weixue Wang, Shiyue Wang, Xiaohong Wang, Daiwei Wang, Jinghua Wang, S X Wang, Jian-Yong Wang, Zeying Wang, Can Wang, Kehan Wang, Yunzhang Wang, Jinping Wang, Chenchen Wang, Chun-Ting Wang, Yujiao Wang, Xinxin Wang, Ji Wang, Sui Wang, Wenqiang Wang, Yingwei Wang, Shuzhen Wang, Daixi Wang, Yanming Wang, Lin-Yu Wang, Hongyin Wang, Zhongqun Wang, Er-Jin Wang, Yi Wang, Ziyi Wang, Lianghai Wang, Zhendan Wang, Xiao-Ming Wang, Chengyan Wang, Hui Miao Wang, Jingyi Wang, Ranran Wang, Banghui Wang, Huilun Wang, Ai-Ting Wang, Wenxuan Wang, Yuan-Hung Wang, Zixuan Wang, Hailing Wang, Xuan-Ying Wang, Jiqiu Wang, Yalong Wang, Xiaogang Wang, Shu-qiang Wang, Yun-Jin Wang, Zijie Wang, Tianlin Wang, Mingqiang Wang, Lufang Wang, Jin'e Wang, Xiru Wang, Cuili Wang, GuoYou Wang, Zhizhong Wang, Haifei Wang, Guorong Wang, Xinyue Wang, Pei-Juan Wang, Jiangong Wang, Yingte Wang, Huajin Wang, Ruibo Wang, Kejian Wang, Cheng-Cheng Wang, Xusheng Wang, Shu-Na Wang, Panliang Wang, Mingxi Wang, Shenqi Wang, Zifeng Wang, Chaozhan Wang, Xiuyuan Hugh Wang, Yuping Wang, Xujing Wang, Kai Wang, Hongbing Wang, Sheng-Yang Wang, Jianfei Wang, Hang Wang, Jing-Jing Wang, Weizhi Wang, Jixuan Wang, De-He Wang, P L Wang, Ningjian Wang, Chunyi Wang, Isabel Z Wang, Yong Wang, Yiming Wang, Mingzhi Wang, Jiying Wang, Qian-Wen Wang, Shusen Wang, Xiaoting Wang, Baogui Wang, Mingsong Wang, Zixia Wang, Demin Wang, Shiyuan Wang, Qiuli Wang, C Wang, Dongliang Wang, Weixiao Wang, Yinsheng Wang, Chunmei Wang, Huaili Wang, Xuelian Wang, Yongjun Wang, Zhi-Qin Wang, Jiaying Wang, Yulong Wang, Ren Wang, Jingnan Wang, Qishan Wang, Zeneng Wang, Guangsuo Wang, Chijia Wang, Huiqun Wang, Hongcai Wang, Donghao Wang, Xing-Jin Wang, Zongji Wang, Shenao Wang, Jiaqian Wang, Xiaoying Wang, Yilin Wang, Hangzhou Wang, Wenchao Wang, Jieyu Wang, Li-E Wang, Xuezhen Wang, Liuyang Wang, Zhiqian Wang, Fang-Tao Wang, Qiong Wang, Meng-Meng Wang, Youji Wang, Jiafeng Wang, Xiaojing Wang, William Wang, Junmin Wang, Laijian Wang, Xuexiang Wang, Huiyan Wang, T Y Wang, Zhaofu Wang, Wen-mei Wang, Yalin Wang, Xinshuai Wang, Daqi Wang, Zhen Wang, Shi-Cheng Wang, Anni Wang, Chunhong Wang, Hai-Long Wang, Pan Wang, Charles C N Wang, Pengxiang Wang, Xianzong Wang, Xike Wang, Qianliang Wang, Chunyan Wang, Xuan Wang, Xiaofen Wang, Zhi-Jian Wang, Feng-Sheng Wang, Xiangru Wang, R Wang, Yi-Shu Wang, Jia-Lin Wang, Yonghong Wang, Lintao Wang, Pai Wang, Yanfei Wang, Xuanwen Wang, Lei-Lei Wang, Chenxuan Wang, James Wang, Xinhui Wang, Shengqi Wang, Yueshen Wang, Shan-Shan Wang, Dingting Wang, Zhige Wang, Jingfeng Wang, Yongqing Wang, Chenyang Wang, Ziliang Wang, Bao Wang, Xueyan Wang, Liping Wang, Xingde Wang, Weijun Wang, Sibo Wang, Yaoling Wang, Donghong Wang, Chenyu Wang, Justin Wang, Baolong Wang, Yiqi Wang, Fengyong Wang, Lichao Wang, Yachen Wang, Quanren Wang, Shiyu Wang, Boyu Wang, Aimin Wang, Zhenghui Wang, Hengjiao Wang, Xiaoxin X Wang, Weimin Wang, Mutian Wang, Zhuo-Hui Wang, Xingye Wang, Zou Wang, Yu-Wen Wang, Shaoli Wang, Xin-Ming Wang, Weirong Wang, Kangli Wang, Yaoxing Wang, Xuejie Wang, Qifeng Wang, Xiaoxin Wang, Yinghui Wang, Jianzhang Wang, Tom J Wang, Yaqiong Wang, Zongwei Wang, Yun-Hui Wang, Haiyun Wang, Zhiyou Wang, Lijin Wang, Jifei Wang, Haiyong Wang, Xiao-Xia Wang, Shyi-Gang P Wang, Chih-Yang Wang, Zhixin Wang, Jun-Jun Wang, Tianjing Wang, Zhixia Wang, Chuanhai Wang, Zhijie Wang, Silu Wang, Jianguo Wang, Ming-Hsi Wang, Liling Wang, Yanting Wang, Haolong Wang, Xue-Lei Wang, Ru Wang, Qinglin Wang, Christina Wang, Mimi Wang, Menghui Wang, Wenju Wang, Junhua Wang, S S Wang, Fangyong Wang, Lifen Wang, Zhenbin Wang, Yapeng Wang, Shaoshen Wang, B R Wang, Sugai Wang, Hequn Wang, Songlin Wang, Wenjie Wang, Xiang-Dong Wang, Ting-Hua Wang, Mingliang Wang, Chengniu Wang, Guoxiang Wang, E Wang, Xiaochun Wang, Xueting Wang, Ming-Jie Wang, Zhaojing Wang, Dongxu Wang, Yirui Wang, Jiatao Wang, Jing-Min Wang, Shih-Wei Wang, Zhengchun Wang, Chaoxian Wang, Zehua Wang, Qiyu Wang, Shuye Wang, Baojun Wang, Qing Kenneth Wang, Xichun Wang, Jianliu Wang, Junping Wang, Yudong Wang, Mingzhu Wang, Kangning Wang, Wei-Ting Wang, Hongfang Wang, Chengwen Wang, Changduo Wang, Jinkang Wang, Junya Wang, Fengge Wang, Jianping Wang, Chang Wang, Zhifang Wang, Deli Wang, Linghua Wang, Shitian Wang, Lingling Wang, Zhihua Wang, Jun-Ling Wang, Keyi Wang, Lingbing Wang, Peijia Wang, Ruizhe Wang, X O Wang, Wanyi Wang, Ganggang Wang, Pei-Hua Wang, Kaiyue Wang, Xiaojiao Wang, Xun Wang, Shiyang Wang, Ya-Ping Wang, Yirong Wang, Lixing Wang, Danyang Wang, Xiaotang Wang, Taian Wang, Ming Wang, Xiangcheng Wang, Xuemei Wang, Zhixiong Wang, Mengying Wang, Li-Yong Wang, Xinchao Wang, Jianlong Wang, Jinjie Wang, Nan Wang, Weidong Wang, Mei-Gui Wang, L-S Wang, Wuqing Wang, Z Wang, Ya-Zhou Wang, Xincheng Wang, Jing-Wen Wang, Jinyue Wang, Hongyun Wang, Huaizhi Wang, Yan-Zi Wang, Danling Wang, Dongqin Wang, Hongzhuang Wang, Chung-Teng Wang, Yan-Chun Wang, Shi-Xin Wang, Muxuan Wang, Yujie Wang, Yunbing Wang, Yahui Wang, Zhihong Wang, Xiaoshan Wang, Tienju Wang, Chiou-Miin Wang, Yuqian Wang, Shengyuan Wang, Yumei Wang, Ningyuan Wang, Minjie Wang, Zhenda Wang, Qing-Dong Wang, Horng-Dar Wang, Siqi Wang, Kaihong Wang, Hong-Kai Wang, Meiling Wang, Jiaxing Wang, Xueyi Wang, Zhuozhong Wang, Anlai Wang, Julie Wang, Jin-Bao Wang, Keke Wang, Zhang Wang, Yintao Wang, Yong-Bo Wang, Bing Wang, Dalu Wang, Minxian Wang, Zulong Wang, Gao T Wang, Gang Wang, Sophie H Wang, Xinquan Wang, Yi-Ting Wang, Honglian Wang, Ruyue Wang, Jia-Qiang Wang, Seungwon Wang, Shusheng Wang, Yanbin Wang, Chang-Yun Wang, Le-Xin Wang, Juling Wang, Haohui Wang, Chuanyue Wang, Tianqin Wang, Danqing Wang, Keyan Wang, Yeou-Lih Wang, Qinglu Wang, Sun Wang, Rui-Min Wang, Yong-Tang Wang, Xianwei Wang, Lixia Wang, Tong Wang, Xiaonan Wang, Feida Wang, Jiaxuan Wang, Mingrui Wang, Zixiang Wang, Y Z Wang, Yuliang Wang, Ming-Chih Wang, J J Wang, Huina Wang, Jingang Wang, Jinyun Wang, Min-sheng Wang, Wanyao Wang, Ziqiu Wang, Guo-Quan Wang, Xueping Wang, Qixue Wang, Hechuan Wang, Shang Wang, Chaohan Wang, M H Wang, L Z Wang, Jianhui Wang, Xifeng Wang, Xiaorong Wang, Yinong Wang, Zhixiu Wang, Jiaxi Wang, Jiahui Wang, Xiaofei Wang, Feifei Wang, Kesheng Wang, Rong-Chun Wang, Zhi-Xin Wang, Chaoyu Wang, Yongkuan Wang, Zuoyan Wang, Hsueh-Chun Wang, Xixiang Wang, Guanrou Wang, Songsong Wang, Hongyuan Wang, Yubing Wang, Xuliang Wang, Wen-Ying Wang, Xinglei Wang, Dao-Wen Wang, Yun Wang, Ze Wang, Jiyan Wang, Zai Wang, Guan Wang, Chih-Chun Wang, Yiqin Wang, X S Wang, Hongzhan Wang, Exing Wang, Shu-Jin Wang, Shangyu Wang, Shouzhi Wang, Yunduan Wang, Jiyong Wang, Dongdong Wang, Qingzhong Wang, Zi-Qi Wang, Renyuan Wang, Siyu Wang, Donghui Wang, Ming-Yuan Wang, Juxiang Wang, Muxiao Wang, Fu Wang, Fei Wang, Qiuyu Wang, Ertao Wang, Zhi Xiao Wang, Zunxian Wang, Hui-Nan Wang, Rongping Wang, Won-Jing Wang, Leiming Wang, Pu Wang, Shen-Nien Wang, Xiaona Wang, Meng-Ying Wang, Wen-Jie Wang, Jiaxin Wang, RuNan Wang, Jiemei Wang, Ningli Wang, Zhong-Hui Wang, Hong Wang, Hui-Yu Wang, Ziqian Wang, Xinzhou Wang, Zhoufeng Wang, Weiguang Wang, Zusen Wang, Jiajia Wang, Bin Wang, Shu-Xia Wang, Yu'e Wang, Laidi Wang, Xiao-Li Wang, Lu Wang, Zhugang Wang, Maojie Wang, Ganglin Wang, Xinyu Wang, Junlin Wang, Dong Wang, Yao Wang, Ya-Jie Wang, Zhiwu Wang, DongWei Wang, Hongdan Wang, Yanxia Wang, Maiqiu Wang, Guansong Wang, Qingtong Wang, Yingcheng Wang, Wenjuan Wang, Liying Wang, Xiaolong Wang, Weihao Wang, Qiushi Wang, Yingfei Wang, Haoyang Wang, Li-Li Wang, Yanbing Wang, Yingchun Wang, Guangming Wang, Kaiyuan Wang, Shiqi Wang, Qi-En Wang, Song Wang, Jing-Hao Wang, Lynn Yuning Wang, Zekun Wang, Rui-Ping Wang, Yining E Wang, Yuzhou Wang, Liu Wang, Maochun Wang, Cindy Wang, Qian-Liang Wang, Duo-Ping Wang, Linlin Wang, Taishu Wang, Xiang Wang, Qirui Wang, Baoming Wang, Liting Wang, Jiapan Wang, Lingda Wang, Xietong Wang, Jia-Mei Wang, Liwei Wang, Shaozheng Wang, Q Wang, Timothy C Wang, Mengyue Wang, Xing Wang, Yahong Wang, Yuyong Wang, Yujiong Wang, Guangliang Wang, Ya-Qin Wang, Yezhou Wang, Hongjian Wang, Su-Hua Wang, Qian-fei Wang, Meng-Dan Wang, Yuchen Wang, Hongpin Wang, Pengfei Wang, Ge Wang, Meijun Wang, Yan-Ming Wang, Haichao Wang, Tzung-Dau Wang, Runci Wang, Yan-Yi Wang, Cheng-Jie Wang, Chen-Yu Wang, Cong Wang, Yaxuan Wang, Y H Wang, Yongjie Wang, Yuntai Wang, Ranjing Wang, Yiru Wang, Anxiang Wang, Q Z Wang, Shimiao Wang, Guoping Wang, Junke Wang, Xingyun Wang, Zhengyi Wang, Shi-Qi Wang, Yanfeng Wang, Danxin Wang, Chaodong Wang, Zhiqi Wang, Chunyu Wang, Lijia Wang, Chunlong Wang, Haiping Wang, Qingfa Wang, Yu-Fan Wang, Baihan Wang, Chunxue Wang, Liewei Wang, Xinyi Wang, Fu-Zhen Wang, Qing-Mei Wang, Sheng Wang, Yi-Tao Wang, Dawei Wang, Xiaoyu Wang, Ziling Wang, Zhonglin Wang, Rurong Wang, Qingchun Wang, Qiang Wang, Suiyan Wang, Xu-Hong Wang, Jie Jin Wang, Chenyao Wang, Fei-Yan Wang, Shi Wang, Zhiyong Wang, Jieda Wang, Xiaoqi Wang, Linshu Wang, Ruxuan Wang, Qian Wang, Qianxu Wang, Fangjie Wang, Zhaoxia Wang, Jeremy R Wang, Mingmei Wang, Jingkang Wang, Jen-Chun Wang, Changyuan Wang, Chenglin Wang, Meng-Ru Wang, Tianpeng Wang, Zhongfang Wang, Xuedong Wang, Zhuoying Wang, Bingyu Wang, Xuelai Wang, Weilong Wang, Mengge Wang, Qin Wang, Da-Li Wang, Xuanyi Wang, Hongjuan Wang, Zhi-Hua Wang, Hong-Wei Wang, Yulai Wang, Gongming Wang, Yongni Wang, Mengya Wang, Yadong Wang, Chenghao Wang, Hongbo Wang, Kaiming Wang, Haonan Wang, Guanyun Wang, Yilu Wang, Quanxi Wang, Weiyuan Wang, Xiujun Wang, Liang-Yan Wang, Jianshe Wang, Yingxiong Wang, Cunchuan Wang, Jing-Zhai Wang, Yuelong Wang, Yuqi Wang, Xiaorui Wang, Qianjin Wang, Huijun Wang, Xiaobo Wang, Guoqian Wang, Luhong Wang, Kaining Wang, Chaohui Wang, Yanhong Wang, J-Y Wang, Qi-Bing Wang, Xiaohu Wang, Jiayan Wang, Cui-Shan Wang, Lulu Wang, Yong-Jie Wang, Shixuan Wang, Yuanyuan Wang, Jianying Wang, Haizhen Wang, Shuiliang Wang, Qianbao Wang, Jung-Pan Wang, Rixiang Wang, A Wang, Hanbing Wang, Caiqin Wang, Peigeng Wang, Yuan Wang, Yuzhuo Wang, Yubo Wang, Xianding Wang, Qiaoqi Wang, Cuiling Wang, Ai-Ling Wang, Hailong Wang, Yihao Wang, Lan-Wan Wang, Haihe Wang, S Wang, Sha Wang, Xiaoli Wang, David Q H Wang, Jianfang Wang, Yuting Wang, Jinhuan Wang, Kaixu Wang, Hongwei Wang, Yi-Wen Wang, Yizhe Wang, Shengyu Wang, Yanmei Wang, Huimin Wang, Youjie Wang, Kunhua Wang, Chongjian Wang, Ziyun Wang, Tianhui Wang, Huiying Wang, Yue-Nan Wang, Peiyin Wang, Hongbin Wang, Hong Yi Wang, Xinjun Wang, Yian Wang, Liyi Wang, Yunce Wang, Yi-Xuan Wang, Yitao Wang, Jiali Wang, Junqin Wang, Yuebing Wang, Yiping Wang, Yunpeng Wang, Yuxing Wang, Shuqi Wang, Ziyu Wang, Hongjie Wang, Xiaoyan Wang, Lianshui Wang, Xiaolu Wang, Wenya Wang, Fan Wang, Jinhua Wang, Sidan Wang, Lixiang Wang, Y L Wang, Xue-Rui Wang, Kai-Wen Wang, Zhongyu Wang, Xiaoyang Wang, Hongyang Wang, Rencheng Wang, Yinxiong Wang, Yuanli Wang, Zhuqing Wang, Y-H Wang, Yuhui Wang, Xitian Wang, Weizhen Wang, Qi Wang, Qiyuan Wang, Changlong Wang, Yatao Wang, Tengfei Wang, Yehan Wang
articles
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