👤 Jinfeng Wang

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Also published as: A Wang, Ai-Ling Wang, Ai-Ting Wang, Aihua Wang, Aijun Wang, Aili Wang, Aimin Wang, Aiting Wang, Aixian Wang, Aiyun Wang, Aizhong Wang, Alexander Wang, Alice Wang, Allen Wang, Anlai Wang, Anli Wang, Annette Wang, Anni Wang, Anqi Wang, Anthony Z Wang, Anxiang Wang, Anxin Wang, Ao Wang, Aoli Wang, B R Wang, B Wang, Baihan Wang, Baisong Wang, Baitao Wang, Bangchen Wang, Banghui Wang, Bangmao Wang, Bangshing Wang, Bao Wang, Bao-Long Wang, Baocheng Wang, Baofeng Wang, Baogui Wang, Baojun Wang, Baoli Wang, Baolong Wang, Baoming Wang, Baosen Wang, Baowei Wang, Baoying Wang, Baoyun Wang, Bei Bei Wang, Bei Wang, Beibei Wang, Beilan Wang, Beilei Wang, Ben Wang, Benjamin H Wang, Benzhong Wang, Bi Wang, Bi-Dar Wang, Biao Wang, Bicheng Wang, Bijue Wang, Bin Wang, Bin-Xue Wang, Binbin Wang, Bing Qing Wang, Bing Wang, Binghai Wang, Binghan Wang, Bingjie Wang, Binglong Wang, Bingnan Wang, Bingyan Wang, Bingyu Wang, Binquan Wang, Biqi Wang, Bo Wang, Bochu Wang, Boyu Wang, Bruce Wang, C Wang, C Z Wang, Cai Ren Wang, Cai-Hong Wang, Cai-Yun Wang, Cailian Wang, Caiqin Wang, Caixia Wang, Caiyan Wang, Can Wang, Cangyu Wang, Carol A Wang, Catherine Ruiyi Wang, Cenxuan Wang, Chan Wang, Chang Wang, Chang-Yun Wang, Changduo Wang, Changjing Wang, Changliang Wang, Changlong Wang, Changqian Wang, Changtu Wang, Changwei Wang, Changying Wang, Changyu Wang, Changyuan Wang, Changzhen Wang, Chao Wang, Chao-Jun Wang, Chao-Yung Wang, Chaodong Wang, Chaofan Wang, Chaohan Wang, Chaohui Wang, Chaojie Wang, Chaokui Wang, Chaomeng Wang, Chaoqun Wang, Chaoxian Wang, Chaoyi Wang, Chaoyu Wang, Chaozhan Wang, Charles C N Wang, Chau-Jong Wang, Chen Wang, Chen-Cen Wang, Chen-Ma Wang, Chen-Yu Wang, Chenchen Wang, Chenfei Wang, Cheng An Wang, Cheng Wang, Cheng-Cheng Wang, Cheng-Jie Wang, Cheng-zhang Wang, Chengbin Wang, Chengcheng Wang, Chenggang Wang, Chenghao Wang, Chenghua Wang, Chengjian Wang, Chengjun Wang, Chenglin Wang, Chenglong Wang, Chengniu Wang, Chengqiang Wang, Chengshuo Wang, Chenguang Wang, Chengwen Wang, Chengyan Wang, Chengyu Wang, Chengze Wang, Chenji Wang, Chenliang Wang, Chenwei Wang, Chenxi Wang, Chenxin Wang, Chenxuan Wang, Chenyang Wang, Chenyao Wang, Chenyin Wang, Chenyu Wang, Chenzi Wang, Chi Chiu Wang, Chi Wang, Chi-Ping Wang, Chia-Chuan Wang, Chia-Lin Wang, Chien-Hsun Wang, Chien-Wei Wang, Chih-Chun Wang, Chih-Hao Wang, Chih-Hsien Wang, Chih-Liang Wang, Chih-Yang Wang, Chih-Yuan Wang, Chijia Wang, Ching C Wang, Ching-Jen Wang, Chiou-Miin Wang, Chong Wang, Chongjian Wang, Chonglong Wang, Chongmin Wang, Chongze Wang, Christina Wang, Christine Wang, Chu Wang, Chuan Wang, Chuan-Chao Wang, Chuan-Hui Wang, Chuan-Jiang Wang, Chuan-Wen Wang, Chuang Wang, Chuanhai Wang, Chuansen Wang, Chuansheng Wang, Chuanxin Wang, Chuanyue Wang, Chuduan Wang, Chun Wang, Chun-Chieh Wang, Chun-Juan Wang, Chun-Li Wang, Chun-Lin Wang, Chun-Ting Wang, Chun-Xia Wang, Chung-Hsi Wang, Chung-Hsing Wang, Chung-Teng Wang, Chunguo Wang, Chunhong Wang, Chuning Wang, Chunjiong Wang, Chunjuan Wang, Chunle Wang, Chunli Wang, Chunlong Wang, Chunmei Wang, Chunsheng Wang, Chunting Wang, Chunxia Wang, Chunxue Wang, Chunyan Wang, Chunyang Wang, Chunyi Wang, Chunyu Wang, Chuyao Wang, Cindy Wang, Ciyang Wang, Cong Wang, Congcong Wang, Congrong Wang, Congrui Wang, Cui Wang, Cui-Fang Wang, Cui-Shan Wang, Cuili Wang, Cuiling Wang, Cuizhe Wang, Cun-Yu Wang, Cunchuan Wang, Cunyi Wang, D Wang, Da Wang, Da-Cheng Wang, Da-Li Wang, Da-Yan Wang, Da-Zhi Wang, Dadong Wang, Dai Wang, Daijun Wang, Daiwei Wang, Daixi Wang, Dajia Wang, Dake Wang, Dali Wang, Dalong Wang, Dalu Wang, Dan Wang, Dan-Dan Wang, Danan Wang, Dandan Wang, Danfeng Wang, Dang Wang, Dangfeng Wang, Danling Wang, Danqing Wang, Danxin Wang, Danyang Wang, Dao Wen Wang, Dao-Wen Wang, Dao-Xin Wang, Daolong Wang, Daoping Wang, Daozhong Wang, Dapeng Wang, Daping Wang, Daqi Wang, Daqing Wang, David Q H Wang, David Q-H Wang, David Wang, Dawei Wang, Dayan Wang, Dayong Wang, Dazhi Wang, De-He Wang, Dedong Wang, Dehao Wang, Deli Wang, Delin Wang, Delong Wang, Demin Wang, Deming Wang, Dengbin Wang, Dennis Qing Wang, Dennis Wang, Deqi Wang, Deshou Wang, Dezhong Wang, Di Wang, Dinghui Wang, Dingting Wang, Dingxiang Wang, Dong D Wang, Dong Hao Wang, Dong Wang, Dong-Dong Wang, Dong-Jie Wang, Dong-Mei Wang, DongWei Wang, Dongdong Wang, Donggen Wang, Donghao Wang, Donghong Wang, Donghui Wang, Dongliang Wang, Donglin Wang, Dongmei Wang, Dongqin Wang, Dongshi Wang, Dongxia Wang, Dongxu Wang, Dongyan Wang, Dongyang Wang, Dongyi Wang, Dongying Wang, Dongyu Wang, Doudou Wang, Du Wang, Duan Wang, Duanyang Wang, Duo-Ping Wang, E Wang, Edward Wang, En-bo Wang, En-hua Wang, Endi Wang, Enhua Wang, Er-Jin Wang, Erfei Wang, Erika Y Wang, Ermao Wang, Erming Wang, Ertao Wang, Eryao Wang, Eunice S Wang, Exing Wang, F Wang, Fa-Kai Wang, Fan Wang, Fanchang Wang, Fang Wang, Fang-Tao Wang, Fangfang Wang, Fangjie Wang, Fangjun Wang, Fangyan Wang, Fangyong Wang, Fangyu Wang, Fanhua Wang, Fanwen Wang, Fanxiong Wang, Fei Wang, Fei-Fei Wang, Fei-Yan Wang, Feida Wang, Feifei Wang, Feijie Wang, Feimiao Wang, Feixiang Wang, Feiyan Wang, Fen Wang, Feng Wang, Feng-Sheng Wang, Fengchong Wang, Fengge Wang, Fenghua Wang, Fengliang Wang, Fenglin Wang, Fengling Wang, Fengqiang Wang, Fengyang Wang, Fengying Wang, Fengyong Wang, Fengyun Wang, Fengzhen Wang, Fengzhong Wang, Fu Wang, Fu-Sheng Wang, Fu-Yan Wang, Fu-Zhen Wang, Fubao Wang, Fubing Wang, Fudi Wang, Fuhua Wang, Fuqiang Wang, Furong Wang, Fuwen Wang, Fuxin Wang, Fuyan Wang, G Q Wang, G Wang, G-W Wang, Gan Wang, Gang Wang, Ganggang Wang, Ganglin Wang, Gangyang Wang, Ganyu Wang, Gao T Wang, Gao Wang, Gaofu Wang, Gaopin Wang, Gavin Wang, Ge Wang, Geng Wang, Genghao Wang, Gengsheng Wang, Gongming Wang, Guan Wang, Guan-song Wang, Guandi Wang, Guanduo Wang, Guang Wang, Guang-Jie Wang, Guang-Rui Wang, Guangdi Wang, Guanghua Wang, Guanghui Wang, Guangliang Wang, Guangming Wang, Guangsuo Wang, Guangwen Wang, Guangyan Wang, Guangzhi Wang, Guanrou Wang, Guanru Wang, Guansong Wang, Guanyun Wang, Gui-Qi Wang, Guibin Wang, Guihu Wang, Guihua Wang, Guimin Wang, Guiping Wang, Guiqun Wang, Guixin Wang, Guixue Wang, Guiying Wang, Guo-Du Wang, Guo-Hua Wang, Guo-Liang Wang, Guo-Ping Wang, Guo-Quan Wang, Guo-hong Wang, GuoYou Wang, Guobin Wang, Guobing Wang, Guodong Wang, Guohang Wang, Guohao Wang, Guoliang Wang, Guoling Wang, Guoping Wang, Guoqian Wang, Guoqiang Wang, Guoqing Wang, Guorong Wang, Guowen Wang, Guoxiang Wang, Guoxiu Wang, Guoyi Wang, Guoying Wang, Guozheng Wang, H J Wang, H Wang, H X Wang, H Y Wang, H-Y Wang, Hai Bo Wang, Hai Wang, Hai Yang Wang, Hai-Feng Wang, Hai-Jun Wang, Hai-Long Wang, Haibin Wang, Haibing Wang, Haibo Wang, Haichao Wang, Haichuan Wang, Haifei Wang, Haifeng Wang, Haihe Wang, Haihong Wang, Haihua Wang, Haijiao Wang, Haijing Wang, Haijiu Wang, Haikun Wang, Hailei Wang, Hailin Wang, Hailing Wang, Hailong Wang, Haimeng Wang, Haina Wang, Haining Wang, Haiping Wang, Hairong Wang, Haitao Wang, Haiwei Wang, Haixia Wang, Haixin Wang, Haixing Wang, Haiyan Wang, Haiying Wang, Haiyong Wang, Haiyun Wang, Haizhen Wang, Han Wang, Hanbin Wang, Hanbing Wang, Hanchao Wang, Handong Wang, Hang Wang, Hangzhou Wang, Hanmin Wang, Hanping Wang, Hanqi Wang, Hanying Wang, Hanyu Wang, Hanzhi Wang, Hao Wang, Hao-Ching Wang, Hao-Hua Wang, Hao-Tian Wang, Hao-Yu Wang, Haobin Wang, Haochen Wang, Haohao Wang, Haohui Wang, Haojie Wang, Haolong Wang, Haomin Wang, Haoming Wang, Haonan Wang, Haoping Wang, Haoqi Wang, Haoran Wang, Haowei Wang, Haoxin Wang, Haoyang Wang, Haoyu Wang, Haozhou Wang, He Wang, He-Cheng Wang, He-Ling Wang, He-Ping Wang, He-Tong Wang, Hebo Wang, Hechuan Wang, Heling Wang, Hemei Wang, Heming Wang, Heng Wang, Heng-Cai Wang, Hengjiao Wang, Hengjun Wang, Hequn Wang, Hesuiyuan Wang, Heyong Wang, Hezhi Wang, Hong Wang, Hong Yi Wang, Hong-Gang Wang, Hong-Hui Wang, Hong-Kai Wang, Hong-Qin Wang, Hong-Wei Wang, Hong-Xia Wang, Hong-Yan Wang, Hong-Yang Wang, Hong-Ying Wang, Hongbin Wang, Hongbing Wang, Hongbo Wang, Hongcai Wang, Hongda Wang, Hongdan Wang, Hongfang Wang, Hongjia Wang, Hongjian Wang, Hongjie Wang, Hongjuan Wang, Hongkun Wang, Honglei Wang, Hongli Wang, Honglian Wang, Honglun Wang, Hongmei Wang, Hongpin Wang, Hongqian Wang, Hongshan Wang, Hongsheng Wang, Hongtao Wang, Hongwei Wang, Hongxia Wang, Hongxin Wang, Hongyan Wang, Hongyang Wang, Hongyi Wang, Hongyin Wang, Hongying Wang, Hongyu Wang, Hongyuan Wang, Hongyue Wang, Hongyun Wang, Hongze Wang, Hongzhan Wang, Hongzhuang Wang, Horng-Dar Wang, Houchun Wang, Hsei-Wei Wang, Hsueh-Chun Wang, Hu WANG, Hua Wang, Hua-Qin Wang, Hua-Wei Wang, Huabo Wang, Huafei Wang, Huai-Zhou Wang, Huaibing Wang, Huaili Wang, Huaizhi Wang, Huajin Wang, Huajing Wang, Hualin Wang, Hualing Wang, Huan Wang, Huan-You Wang, Huang Wang, Huanhuan Wang, Huanyu Wang, Huaquan Wang, Huating Wang, Huawei Wang, Huaxiang Wang, Huayang Wang, Huei Wang, Hui Miao Wang, Hui Wang, Hui-Hui Wang, Hui-Li Wang, Hui-Nan Wang, Hui-Yu Wang, HuiYue Wang, Huie Wang, Huiguo Wang, Huihua Wang, Huihui Wang, Huijie Wang, Huijun Wang, Huilun Wang, Huimei Wang, Huimin Wang, Huina Wang, Huiping Wang, Huiquan Wang, Huiqun Wang, Huishan Wang, Huiting Wang, Huiwen Wang, Huixia Wang, Huiyan Wang, Huiyang Wang, Huiyao Wang, Huiying Wang, Huiyu Wang, Huizhen Wang, Huizhi Wang, Huming Wang, I-Ching Wang, Iris X Wang, Isabel Z Wang, J J Wang, J P Wang, J Q Wang, J Wang, J Z Wang, J-Y Wang, Jacob E Wang, James Wang, Jeffrey Wang, Jen-Chun Wang, Jen-Chywan Wang, Jennifer E Wang, Jennifer T Wang, Jennifer X Wang, Jenny Y Wang, Jeremy R Wang, Jeremy Wang, Ji M Wang, Ji Wang, Ji-Nuo Wang, Ji-Yang Wang, Ji-Yao Wang, Ji-zheng Wang, Jia Bei Wang, Jia Bin Wang, Jia Wang, Jia-Liang Wang, Jia-Lin Wang, Jia-Mei Wang, Jia-Peng Wang, Jia-Qi Wang, Jia-Qiang Wang, Jia-Ying Wang, Jia-Yu Wang, Jiabei Wang, Jiabo Wang, Jiafeng Wang, Jiafu Wang, Jiahao Wang, Jiahui Wang, Jiajia Wang, Jiakun Wang, Jiale Wang, Jiali Wang, Jialiang Wang, Jialin Wang, Jialing Wang, Jiamin Wang, Jiaming Wang, Jian Wang, Jian'an Wang, Jian-Bin Wang, Jian-Guo Wang, Jian-Hong Wang, Jian-Long Wang, Jian-Wei Wang, Jian-Xiong Wang, Jian-Yong Wang, Jian-Zhi Wang, Jian-chun Wang, Jianan Wang, Jianbing Wang, Jianbo Wang, Jianding Wang, Jianfang Wang, Jianfei Wang, Jiang Wang, Jiangbin Wang, Jiangbo Wang, Jianghua Wang, Jianghui Wang, Jiangong Wang, Jianguo Wang, Jianhao Wang, Jianhua Wang, Jianhui Wang, Jiani Wang, Jianjiao Wang, Jianjie Wang, Jianjun Wang, Jianle Wang, Jianli Wang, Jianlin Wang, Jianliu Wang, Jianlong Wang, Jianmei Wang, Jianmin Wang, Jianning Wang, Jianping Wang, Jianqin Wang, Jianqing Wang, Jianqun Wang, Jianru Wang, Jianshe Wang, Jianshu Wang, Jiantao Wang, Jianwei Wang, Jianwu Wang, Jianxiang Wang, Jianxin Wang, Jianye Wang, Jianying Wang, Jianyong Wang, Jianyu Wang, Jianzhang Wang, Jianzhi Wang, Jiao Wang, Jiaojiao Wang, Jiapan Wang, Jiaping Wang, Jiaqi Wang, Jiaqian Wang, Jiatao Wang, Jiawei Wang, Jiawen Wang, Jiaxi Wang, Jiaxin Wang, Jiaxing Wang, Jiaxuan Wang, Jiayan Wang, Jiayang Wang, Jiayi Wang, Jiaying Wang, Jiayu Wang, Jiazheng Wang, Jiazhi Wang, Jie Jin Wang, Jie Wang, Jieda Wang, Jieh-Neng Wang, Jiemei Wang, Jieqi Wang, Jieyan Wang, Jieyu Wang, Jifei Wang, Jiheng Wang, Jihong Wang, Jiliang Wang, Jilin Wang, Jin Wang, Jin'e Wang, Jin-Bao Wang, Jin-Cheng Wang, Jin-Da Wang, Jin-E Wang, Jin-Juan Wang, Jin-Liang Wang, Jin-Xia Wang, Jin-Xing Wang, Jincheng Wang, Jindan Wang, Jinfei Wang, Jinfu Wang, Jing J Wang, Jing Wang, Jing-Hao Wang, Jing-Huan Wang, Jing-Jing Wang, Jing-Long Wang, Jing-Min Wang, Jing-Shi Wang, Jing-Wen Wang, Jing-Xian Wang, Jing-Yi Wang, Jing-Zhai Wang, Jingang Wang, Jingchun Wang, Jingfan Wang, Jingfeng Wang, Jingheng Wang, Jinghong Wang, Jinghua Wang, Jinghuan Wang, Jingjing Wang, Jingkang Wang, Jinglin Wang, Jingmin Wang, Jingnan Wang, Jingqi Wang, Jingru Wang, Jingtong Wang, Jingwei Wang, Jingwen Wang, Jingxiao Wang, Jingyang Wang, Jingyi Wang, Jingying Wang, Jingyu Wang, Jingyue Wang, Jingyun Wang, Jingzhou Wang, Jinhai Wang, Jinhao Wang, Jinhe Wang, Jinhua Wang, Jinhuan Wang, Jinhui Wang, Jinjie Wang, Jinjin Wang, Jinkang Wang, Jinling Wang, Jinlong Wang, Jinmeng Wang, Jinning Wang, Jinping Wang, Jinqiu Wang, Jinrong Wang, Jinru Wang, Jinsong Wang, Jintao Wang, Jinxia Wang, Jinxiang Wang, Jinyang Wang, Jinyu Wang, Jinyue Wang, Jinyun Wang, Jinzhu Wang, Jiou Wang, Jipeng Wang, Jiqing Wang, Jiqiu Wang, Jisheng Wang, Jiu Wang, Jiucun Wang, Jiun-Ling Wang, Jiwen Wang, Jixuan Wang, Jiyan Wang, Jiying Wang, Jiyong Wang, Jizheng Wang, John Wang, Jou-Kou Wang, Joy Wang, Ju Wang, Juan Wang, Jue Wang, Jueqiong Wang, Jufeng Wang, Julie Wang, Juling Wang, Jun Kit Wang, Jun Wang, Jun Yi Wang, Jun-Feng Wang, Jun-Jie Wang, Jun-Jun Wang, Jun-Ling Wang, Jun-Sheng Wang, Jun-Sing Wang, Jun-Zhuo Wang, Jundong Wang, Junfeng Wang, Jung-Pan Wang, Junhong Wang, Junhua Wang, Junhui Wang, Junjiang Wang, Junjie Wang, Junjun Wang, Junkai Wang, Junke Wang, Junli Wang, Junlin Wang, Junling Wang, Junmei Wang, Junmin Wang, Junpeng Wang, Junping Wang, Junqin Wang, Junqing Wang, Junrui Wang, Junsheng Wang, Junshi Wang, Junshuang Wang, Junwen Wang, Junxiao Wang, Junya Wang, Junying Wang, Junyu Wang, Justin Wang, Jutao Wang, Juxiang Wang, K Wang, Kai Wang, Kai-Kun Wang, Kai-Wen Wang, Kaicen Wang, Kaihao Wang, Kaihe Wang, Kaihong Wang, Kaijie Wang, Kaijuan Wang, Kailu Wang, Kaiming Wang, Kaining Wang, Kaiting Wang, Kaixi Wang, Kaixu Wang, Kaiyan Wang, Kaiyuan Wang, Kaiyue Wang, Kan Wang, Kangli Wang, Kangling Wang, Kangmei Wang, Kangning Wang, Ke Wang, Ke-Feng Wang, KeShan Wang, Kehan Wang, Kehao Wang, Kejia Wang, Kejian Wang, Kejun Wang, Keke Wang, Keming Wang, Kenan Wang, Keqing Wang, Kesheng Wang, Kexin Wang, Keyan Wang, Keyi Wang, Keyun Wang, Kongyan Wang, Kuan Hong Wang, Kui Wang, Kun Wang, Kunhua Wang, Kunpeng Wang, Kunzheng Wang, L F Wang, L M Wang, L Wang, L Z Wang, L-S Wang, Laidi Wang, Laijian Wang, Laiyuan Wang, Lan Wang, Lan-Wan Wang, Lan-lan Wang, Lanlan Wang, Larry Wang, Le Wang, Le-Xin Wang, Ledan Wang, Lee-Kai Wang, Lei P Wang, Lei Wang, Lei-Lei Wang, Leiming Wang, Leishen Wang, Leli Wang, Leran Wang, Lexin Wang, Leying Wang, Li Chun Wang, Li Dong Wang, Li Wang, Li-Dong Wang, Li-E Wang, Li-Juan Wang, Li-Li Wang, Li-Na Wang, Li-San Wang, Li-Ting Wang, Li-Xin Wang, Li-Yong Wang, LiLi Wang, Lian Wang, Lianchun Wang, Liang Wang, Liang-Yan Wang, Liangfu Wang, Lianghai Wang, Liangli Wang, Liangliang Wang, Liangxu Wang, Lianshui Wang, Lianyong Wang, Libo Wang, Lichan Wang, Lichao Wang, Liewei Wang, Lifang Wang, Lifei Wang, Lifen Wang, Lifeng Wang, Ligang Wang, Lihong Wang, Lihua Wang, Lihui Wang, Lijia Wang, Lijin Wang, Lijing Wang, Lijuan Wang, Lijun Wang, Liling Wang, Lily Wang, Limeng Wang, Limin Wang, Liming Wang, Lin Wang, Lin-Fa Wang, Lin-Yu Wang, Lina Wang, Linfang Wang, Ling Jie Wang, Ling Wang, Ling-Ling Wang, Lingbing Wang, Lingda Wang, Linghua Wang, Linghuan Wang, Lingli Wang, Lingling Wang, Lingyan Wang, Lingzhi Wang, Linhua Wang, Linhui Wang, Linjie Wang, Linli Wang, Linlin Wang, Linping Wang, Linshu Wang, Linshuang Wang, Lintao Wang, Linxuan Wang, Linying Wang, Linyuan Wang, Liping Wang, Liqing Wang, Liqun Wang, Lirong Wang, Litao Wang, Liting Wang, Liu Wang, Liusong Wang, Liuyang Wang, Liwei Wang, Lixia Wang, Lixian Wang, Lixiang Wang, Lixin Wang, Lixing Wang, Lixiu Wang, Liyan Wang, Liyi Wang, Liying Wang, Liyong Wang, Liyuan Wang, Liyun Wang, Long Wang, Longcai Wang, Longfei Wang, Longsheng Wang, Longxiang Wang, Lou-Pin Wang, Lu Wang, Lu-Lu Wang, Lueli Wang, Lufang Wang, Luhong Wang, Luhui Wang, Lujuan Wang, Lulu Wang, Luofu Wang, Luping Wang, Luting Wang, Luwen Wang, Luxiang Wang, Luya Wang, Luyao Wang, Luyun Wang, Lynn Yuning Wang, M H Wang, M Wang, M Y Wang, M-J Wang, Maiqiu Wang, Man Wang, Mangju Wang, Manli Wang, Mao-Xin Wang, Maochun Wang, Maojie Wang, Maoju Wang, Mark Wang, Mei Wang, Mei-Gui Wang, Mei-Xia Wang, Meiding Wang, Meihui Wang, Meijun Wang, Meiling Wang, Meixia Wang, Melissa T Wang, Meng C Wang, Meng Wang, Meng Yu Wang, Meng-Dan Wang, Meng-Lan Wang, Meng-Meng Wang, Meng-Ru Wang, Meng-Wei Wang, Meng-Ying Wang, Meng-hong Wang, Mengge Wang, Menghan Wang, Menghui Wang, Mengjiao Wang, Mengjing Wang, Mengjun Wang, Menglong Wang, Menglu Wang, Mengmeng Wang, Mengqi Wang, Mengru Wang, Mengshi Wang, Mengwen Wang, Mengxiao Wang, Mengya Wang, Mengyao Wang, Mengying Wang, Mengyuan Wang, Mengyue Wang, Mengyun Wang, Mengze Wang, Mengzhao Wang, Mengzhi Wang, Mian Wang, Miao Wang, Mimi Wang, Min Wang, Min-sheng Wang, Ming Wang, Ming-Chih Wang, Ming-Hsi Wang, Ming-Jie Wang, Ming-Wei Wang, Ming-Yang Wang, Ming-Yuan Wang, Mingchao Wang, Mingda Wang, Minghua Wang, Minghuan Wang, Minghui Wang, Mingji Wang, Mingjin Wang, Minglei Wang, Mingliang Wang, Mingmei Wang, Mingming Wang, Mingqiang Wang, Mingrui Wang, Mingsong Wang, Mingxi Wang, Mingxia Wang, Mingxun Wang, Mingya Wang, Mingyang Wang, Mingyi Wang, Mingyu Wang, Mingzhi Wang, Mingzhu Wang, Minjie Wang, Minjun Wang, Minmin Wang, Minxian Wang, Minxiu Wang, Minzhou Wang, Miranda C Wang, Mo Wang, Mofei Wang, Monica Wang, Mu Wang, Mutian Wang, Muxiao Wang, Muxuan Wang, N Wang, Na Wang, Nan Wang, Nana Wang, Nanbu Wang, Nannan Wang, Nanping Wang, Neng Wang, Ni Wang, Niansong Wang, Ning Wang, Ningjian Wang, Ningli Wang, Ningyuan Wang, Nuan Wang, Oliver Wang, Ouchen Wang, P Jeremy Wang, P L Wang, P N Wang, P Wang, Pai Wang, Pan Wang, Pan-Pan Wang, Panfeng Wang, Panliang Wang, Pei Chang Wang, Pei Wang, Pei-Hua Wang, Pei-Jian Wang, Pei-Juan Wang, Pei-Wen Wang, Pei-Yu Wang, Peichang Wang, Peigeng Wang, Peihe Wang, Peijia Wang, Peijuan Wang, Peijun Wang, Peilin Wang, Peipei Wang, Peirong Wang, Peiwen Wang, Peixi Wang, Peiyao Wang, Peiyin Wang, Peng Wang, Peng-Cheng Wang, Pengbo Wang, Pengchao Wang, Pengfei Wang, Pengjie Wang, Pengju Wang, Penglai Wang, Penglong Wang, Pengpu Wang, Pengtao Wang, Pengxiang Wang, Pengyu Wang, Pin Wang, Ping Wang, Pingchuan Wang, Pingfeng Wang, Pingping Wang, Pintian Wang, Po-Jen Wang, Pu Wang, Q Wang, Q Z Wang, Qi Wang, Qi-Bing Wang, Qi-En Wang, Qi-Jia Wang, Qi-Qi Wang, Qian Wang, Qian-Liang Wang, Qian-Wen Wang, Qian-Zhu Wang, Qian-fei Wang, Qianbao Wang, Qiang Wang, Qiang-Sheng Wang, Qiangcheng Wang, Qianghu Wang, Qiangqiang Wang, Qianjin Wang, Qianliang Wang, Qianqian Wang, Qianrong Wang, Qianru Wang, Qianwen Wang, Qianxu Wang, Qiao Wang, Qiao-Ping Wang, Qiaohong Wang, Qiaoqi Wang, Qiaoqiao Wang, Qifan Wang, Qifei Wang, Qifeng Wang, Qigui Wang, Qihao Wang, Qihua Wang, Qijia Wang, Qiming Wang, Qin Wang, Qing Jun Wang, Qing K Wang, Qing Kenneth Wang, Qing Mei Wang, Qing Wang, Qing-Bin Wang, Qing-Dong Wang, Qing-Jin Wang, Qing-Liang Wang, Qing-Mei Wang, Qing-Yan Wang, Qing-Yuan Wang, Qing-Yun Wang, QingDong Wang, Qingchun Wang, Qingfa Wang, Qingfeng Wang, Qinghang Wang, Qingliang Wang, Qinglin Wang, Qinglu Wang, Qingming Wang, Qingping Wang, Qingqing Wang, Qingshi Wang, Qingshui Wang, Qingsong Wang, Qingtong Wang, Qingyong Wang, Qingyu Wang, Qingyuan Wang, Qingyun Wang, Qingzhong Wang, Qinqin Wang, Qinrong Wang, Qintao Wang, Qinwen Wang, Qinyun Wang, Qiong Wang, Qiqi Wang, Qirui Wang, Qishan Wang, Qiu-Ling Wang, Qiu-Xia Wang, Qiuhong Wang, Qiuli Wang, Qiuling Wang, Qiuning Wang, Qiuping Wang, Qiushi Wang, Qiuting Wang, Qiuyan Wang, Qiuyu Wang, Qiwei Wang, Qixue Wang, Qiyu Wang, Qiyuan Wang, Quan Wang, Quan-Ming Wang, Quanli Wang, Quanren Wang, Quanxi Wang, Qun Wang, Qunxian Wang, Qunzhi Wang, R Wang, Ran Wang, Ranjing Wang, Ranran Wang, Re-Hua Wang, Ren Wang, Rencheng Wang, Renjun Wang, Renqian Wang, Renwei Wang, Renxi Wang, Renxiao Wang, Renyuan Wang, Rihua Wang, Rikang Wang, Rixiang Wang, Robert Yl Wang, Rong Wang, Rong-Chun Wang, Rong-Rong Wang, Rong-Tsorng Wang, RongRong Wang, Rongjia Wang, Rongping Wang, Rongyun Wang, Ru Wang, RuNan Wang, Ruey-Yun Wang, Rufang Wang, Ruhan Wang, Rui Wang, Rui-Hong Wang, Rui-Min Wang, Rui-Ping Wang, Rui-Rui Wang, Ruibin Wang, Ruibing Wang, Ruibo Wang, Ruicheng Wang, Ruifang Wang, Ruijing Wang, Ruimeng Wang, Ruimin Wang, Ruiming Wang, Ruinan Wang, Ruining Wang, Ruiquan Wang, Ruiwen Wang, Ruixian Wang, Ruixin Wang, Ruixuan Wang, Ruixue Wang, Ruiying Wang, Ruizhe Wang, Ruizhi Wang, Rujie Wang, Ruling Wang, Ruming Wang, Runci Wang, Runuo Wang, Runze Wang, Runzhi Wang, Ruo-Nan Wang, Ruo-Ran Wang, Ruonan Wang, Ruosu Wang, Ruoxi Wang, Rurong Wang, Ruting Wang, Ruxin Wang, Ruxuan Wang, Ruyue Wang, S L Wang, S S Wang, S Wang, S X Wang, Sa A Wang, Sa Wang, Saifei Wang, Saili Wang, Sainan Wang, Saisai Wang, Sangui Wang, Sanwang Wang, Sasa Wang, Sen Wang, Seok Mui Wang, Seungwon Wang, Sha Wang, Shan Wang, Shan-Shan Wang, Shang Wang, Shangyu Wang, Shanshan Wang, Shao-Kang Wang, Shaochun Wang, Shaohsu Wang, Shaokun Wang, Shaoli Wang, Shaolian Wang, Shaoshen Wang, Shaowei Wang, Shaoyi Wang, Shaoying Wang, Shaoyu Wang, Shaozheng Wang, Shasha Wang, Shau-Chun Wang, Shawn Wang, Shen Wang, Shen-Nien Wang, Shenao Wang, Sheng Wang, Sheng-Min Wang, Sheng-Nan Wang, Sheng-Ping Wang, Sheng-Quan Wang, Sheng-Yang Wang, Shengdong Wang, Shengjie Wang, Shengli Wang, Shengqi Wang, Shengya Wang, Shengyao Wang, Shengyu Wang, Shengyuan Wang, Shenqi Wang, Sheri Wang, Shi Wang, Shi-Cheng Wang, Shi-Han Wang, Shi-Qi Wang, Shi-Xin Wang, Shi-Yao Wang, Shibin Wang, Shichao Wang, Shicung Wang, Shidong Wang, Shifa Wang, Shifeng Wang, Shih-Wei Wang, Shihan Wang, Shihao Wang, Shihua Wang, Shijie Wang, Shijin Wang, Shijun Wang, Shikang Wang, Shimiao Wang, Shiqi Wang, Shiqiang Wang, Shitao Wang, Shitian Wang, Shiwen Wang, Shixin Wang, Shixuan Wang, Shiyang Wang, Shiyao Wang, Shiyin Wang, Shiyu Wang, Shiyuan Wang, Shiyue Wang, Shizhi Wang, Shouli Wang, Shouling Wang, Shouzhi Wang, Shu Wang, Shu-Huei Wang, Shu-Jin Wang, Shu-Ling Wang, Shu-Na Wang, Shu-Song Wang, Shu-Xia Wang, Shu-qiang Wang, Shuai Wang, Shuaiqin Wang, Shuang Wang, Shuang-Shuang Wang, Shuang-Xi Wang, Shuangyuan Wang, Shubao Wang, Shudan Wang, Shuge Wang, Shuguang Wang, Shuhe Wang, Shuiliang Wang, Shuiyun Wang, Shujin Wang, Shukang Wang, Shukui Wang, Shun Wang, Shuning Wang, Shunjun Wang, Shunran Wang, Shuo Wang, Shuping Wang, Shuqi Wang, Shuqing Wang, Shuren Wang, Shusen Wang, Shusheng Wang, Shushu Wang, Shuu-Jiun Wang, Shuwei Wang, Shuxia Wang, Shuxin Wang, Shuya Wang, Shuye Wang, Shuyue Wang, Shuzhe Wang, Shuzhen Wang, Shuzhong Wang, Shyi-Gang P Wang, Si Wang, Sibo Wang, Sidan Wang, Sihua Wang, Sijia Wang, Silas L Wang, Silu Wang, Simeng Wang, Siqi Wang, Siqing Wang, Siwei Wang, Siyang Wang, Siyi Wang, Siying Wang, Siyu Wang, Siyuan Wang, Siyue Wang, Song Wang, Songjiao Wang, Songlin Wang, Songping Wang, Songsong Wang, Songtao Wang, Sophie H Wang, Stephani Wang, Su'e Wang, Su-Guo Wang, Su-Hua Wang, Sufang Wang, Sugai Wang, Sui Wang, Suiyan Wang, Sujie Wang, Sujuan Wang, Suli Wang, Sun Wang, Supeng Perry Wang, Suxia Wang, Suyun Wang, Suzhen Wang, T Q Wang, T Wang, T Y Wang, Taian Wang, Taicheng Wang, Taishu Wang, Tammy C Wang, Tao Wang, Taoxia Wang, Teng Wang, Tengfei Wang, Theodore Wang, Thomas T Y Wang, Tian Wang, Tian-Li Wang, Tian-Lu Wang, Tian-Tian Wang, Tian-Yi Wang, Tiancheng Wang, Tiange Wang, Tianhao Wang, Tianhu Wang, Tianhui Wang, Tianjing Wang, Tianjun Wang, Tianlin Wang, Tiannan Wang, Tianpeng Wang, Tianqi Wang, Tianqin Wang, Tianqing Wang, Tiansheng Wang, Tiansong Wang, Tiantian Wang, Tianyi Wang, Tianying Wang, Tianyuan Wang, Tielin Wang, Tienju Wang, Tieqiao Wang, Timothy C Wang, Ting Chen Wang, Ting Wang, Ting-Chen Wang, Ting-Hua Wang, Ting-Ting Wang, Tingting Wang, Tingye Wang, Tingyu Wang, Tom J Wang, Tong Wang, Tong-Hong Wang, Tongsong Wang, Tongtong Wang, Tongxia Wang, Tongxin Wang, Tongyao Wang, Tony Wang, Tzung-Dau Wang, Victoria Wang, Vivian Wang, W Wang, Wanbing Wang, Wanchun Wang, Wang Wang, Wangxia Wang, Wanliang Wang, Wanxia Wang, Wanyao Wang, Wanyi Wang, Wanyu Wang, Wayseen Wang, Wei Wang, Wei-En Wang, Wei-Feng Wang, Wei-Lien Wang, Wei-Qi Wang, Wei-Ting Wang, Wei-Wei Wang, Weicheng Wang, Weiding Wang, Weidong Wang, Weifan Wang, Weiguang Wang, Weihao Wang, Weihong Wang, Weihua Wang, Weijian Wang, Weijie Wang, Weijun Wang, Weilin Wang, Weiling Wang, Weilong Wang, Weimin Wang, Weina Wang, Weining Wang, Weipeng Wang, Weiqin Wang, Weiqing Wang, Weirong Wang, Weiwei Wang, Weiwen Wang, Weixiao Wang, Weixue Wang, Weiyan Wang, Weiyu Wang, Weiyuan Wang, Weizhen Wang, Weizhi Wang, Weizhong Wang, Wen Wang, Wen-Chang Wang, Wen-Der Wang, Wen-Fei Wang, Wen-Jie Wang, Wen-Jun Wang, Wen-Qing Wang, Wen-Xuan Wang, Wen-Yan Wang, Wen-Ying Wang, Wen-Yong Wang, Wen-mei Wang, Wenbin Wang, Wenbo Wang, Wence Wang, Wenchao Wang, Wencheng Wang, Wendong Wang, Wenfei Wang, Wengong Wang, Wenhan Wang, Wenhao Wang, Wenhe Wang, Wenhui Wang, Wenjie Wang, Wenjing Wang, Wenju Wang, Wenjuan Wang, Wenjun Wang, Wenkai Wang, Wenkang Wang, Wenke Wang, Wenming Wang, Wenqi Wang, Wenqiang Wang, Wenqing Wang, Wenran Wang, Wenrui Wang, Wentao Wang, Wentian Wang, Wenting Wang, Wenwen Wang, Wenxia Wang, Wenxian Wang, Wenxiang Wang, Wenxiu Wang, Wenxuan Wang, Wenya Wang, Wenyan Wang, Wenyi Wang, Wenying Wang, Wenyu Wang, Wenyuan Wang, Wenzhou Wang, William Wang, Won-Jing Wang, Wu-Wei Wang, Wuji Wang, Wuqing Wang, Wusan Wang, X E Wang, X F Wang, X O Wang, X S Wang, X Wang, X-T Wang, Xi Wang, Xi-Hong Wang, Xi-Rui Wang, Xia Wang, Xian Wang, Xian-e Wang, Xianding Wang, Xianfeng Wang, Xiang Wang, Xiang-Dong Wang, Xiangcheng Wang, Xiangding Wang, Xiangdong Wang, Xiangguo Wang, Xianghua Wang, Xiangkun Wang, Xiangrong Wang, Xiangru Wang, Xiangwei Wang, Xiangyu Wang, Xianna Wang, Xianqiang Wang, Xianrong Wang, Xianshi Wang, Xianshu Wang, Xiansong Wang, Xiantao Wang, Xianwei Wang, Xianxing Wang, Xianze Wang, Xianzhe Wang, Xianzong Wang, Xiao Ling Wang, Xiao Qun Wang, Xiao Wang, Xiao-Ai Wang, Xiao-Fei Wang, Xiao-Hui Wang, Xiao-Jie Wang, Xiao-Juan Wang, Xiao-Lan Wang, Xiao-Li Wang, Xiao-Lin Wang, Xiao-Ming Wang, Xiao-Pei Wang, Xiao-Qian Wang, Xiao-Qun Wang, Xiao-Tong Wang, Xiao-Xia Wang, Xiao-Yi Wang, Xiao-Yun Wang, Xiao-jian WANG, Xiao-liang Wang, Xiaobin Wang, Xiaobo Wang, Xiaochen Wang, Xiaochuan Wang, Xiaochun Wang, Xiaodan Wang, Xiaoding Wang, Xiaodong Wang, Xiaofang Wang, Xiaofei Wang, Xiaofen Wang, Xiaofeng Wang, Xiaogang Wang, Xiaohong Wang, Xiaohu Wang, Xiaohua Wang, Xiaohui Wang, Xiaojia Wang, Xiaojian Wang, Xiaojiao Wang, Xiaojie Wang, Xiaojing Wang, Xiaojuan Wang, Xiaojun Wang, Xiaokun Wang, Xiaole Wang, Xiaoli Wang, Xiaoliang Wang, Xiaolin Wang, Xiaoling Wang, Xiaolong Wang, Xiaolu Wang, Xiaolun Wang, Xiaoman Wang, Xiaomei Wang, Xiaomeng Wang, Xiaomin Wang, Xiaoming Wang, Xiaona Wang, Xiaonan Wang, Xiaoning Wang, Xiaoqi Wang, Xiaoqian Wang, Xiaoqin Wang, Xiaoqing Wang, Xiaoqiu Wang, Xiaoqun Wang, Xiaorong Wang, Xiaorui Wang, Xiaoshan Wang, Xiaosong Wang, Xiaotang Wang, Xiaoting Wang, Xiaotong Wang, Xiaowei Wang, Xiaowen Wang, Xiaowu Wang, Xiaoxia Wang, Xiaoxiao Wang, Xiaoxin Wang, Xiaoxin X Wang, Xiaoxuan Wang, Xiaoya Wang, Xiaoyan Wang, Xiaoyang Wang, Xiaoye Wang, Xiaoying Wang, Xiaoyu Wang, Xiaozhen Wang, Xiaozhi Wang, Xiaozhong Wang, Xiaozhu Wang, Xichun Wang, Xidi Wang, Xietong Wang, Xifeng Wang, Xifu Wang, Xijun Wang, Xike Wang, Xin Wang, Xin Wei Wang, Xin-Hua Wang, Xin-Liang Wang, Xin-Ming Wang, Xin-Peng Wang, Xin-Qun Wang, Xin-Shang Wang, Xin-Xin Wang, Xin-Yang Wang, Xin-Yue Wang, Xinbo Wang, Xinchang Wang, Xinchao Wang, Xinchen Wang, Xincheng Wang, Xinchun Wang, Xindi Wang, Xindong Wang, Xing Wang, Xing-Huan Wang, Xing-Jin Wang, Xing-Jun Wang, Xing-Lei Wang, Xing-Ping Wang, Xing-Quan Wang, Xingbang Wang, Xingchen Wang, Xingde Wang, Xingguo Wang, Xinghao Wang, Xinghui Wang, Xingjie Wang, Xingjin Wang, Xinglei Wang, Xinglong Wang, Xingqin Wang, Xinguo Wang, Xingxin Wang, Xingxing Wang, Xingye Wang, Xingyu Wang, Xingyue Wang, Xingyun Wang, Xinhui Wang, Xinjing Wang, Xinjun Wang, Xinke Wang, Xinkun Wang, Xinli Wang, Xinlin Wang, Xinlong Wang, Xinmei Wang, Xinqi Wang, Xinquan Wang, Xinran Wang, Xinrong Wang, Xinru Wang, Xinrui Wang, Xinshuai Wang, Xintong Wang, Xinwen Wang, Xinxin Wang, Xinyan Wang, Xinyang Wang, Xinye Wang, Xinyi Wang, Xinying Wang, Xinyu Wang, Xinyue Wang, Xinzhou Wang, Xiong Wang, Xiongjun Wang, Xiru Wang, Xitian Wang, Xiu-Lian Wang, Xiu-Ping Wang, Xiufen Wang, Xiujuan Wang, Xiujun Wang, Xiurong Wang, Xiuwen Wang, Xiuyu Wang, Xiuyuan Hugh Wang, Xixi Wang, Xixiang Wang, Xiyan Wang, Xiyue Wang, Xizhi Wang, Xu Wang, Xu-Hong Wang, Xuan Wang, Xuan-Ren Wang, Xuan-Ying Wang, Xuanwen Wang, Xuanyi Wang, Xubo Wang, Xudong Wang, Xue Wang, Xue-Feng Wang, Xue-Hua Wang, Xue-Lei Wang, Xue-Lian Wang, Xue-Rui Wang, Xue-Yao Wang, Xue-Ying Wang, Xuebin Wang, Xueding Wang, Xuedong Wang, Xuefei Wang, Xuefeng Wang, Xueguo Wang, Xuehao Wang, Xuejie Wang, Xuejing Wang, Xueju Wang, Xuejun Wang, Xuekai Wang, Xuelai Wang, Xuelian Wang, Xuelin Wang, Xuemei Wang, Xuemin Wang, Xueping Wang, Xueqian Wang, Xueqin Wang, Xuesong Wang, Xueting Wang, Xuewei Wang, Xuewen Wang, Xuexiang Wang, Xueyan Wang, Xueyi Wang, Xueying Wang, Xueyun Wang, Xuezhen Wang, Xuezheng Wang, Xufei Wang, Xujing Wang, Xuliang Wang, Xumeng Wang, Xun Wang, Xuping Wang, Xuqiao Wang, Xuru Wang, Xusheng Wang, Xv Wang, Y Alan Wang, Y B Wang, Y H Wang, Y L Wang, Y P Wang, Y Wang, Y Y Wang, Y Z Wang, Y-H Wang, Y-S Wang, Ya Qi Wang, Ya Wang, Ya Xing Wang, Ya-Han Wang, Ya-Jie Wang, Ya-Long Wang, Ya-Nan Wang, Ya-Ping Wang, Ya-Qin Wang, Ya-Zhou Wang, Yachen Wang, Yachun Wang, Yadong Wang, Yafang Wang, Yafen Wang, Yahong Wang, Yahui Wang, Yajie Wang, Yajing Wang, Yajun Wang, Yake Wang, Yakun Wang, Yali Wang, Yalin Wang, Yaling Wang, Yalong Wang, Yan Ming Wang, Yan Wang, Yan-Chao Wang, Yan-Chun Wang, Yan-Feng Wang, Yan-Ge Wang, Yan-Jiang Wang, Yan-Jun Wang, Yan-Ming Wang, Yan-Yang Wang, Yan-Yi Wang, Yan-Zi Wang, Yana Wang, Yanan Wang, Yanbin Wang, Yanbing Wang, Yanchun Wang, Yancun Wang, Yanfang Wang, Yanfei Wang, Yanfeng Wang, Yang Wang, Yang-Yang Wang, Yange Wang, Yanggan Wang, Yangpeng Wang, Yangyang Wang, Yangyufan Wang, Yanhai Wang, Yanhong Wang, Yanhua Wang, Yanhui Wang, Yani Wang, Yanjin Wang, Yanjun Wang, Yankun Wang, Yanlei Wang, Yanli Wang, Yanliang Wang, Yanlin Wang, Yanling Wang, Yanmei Wang, Yanming Wang, Yanni Wang, Yanong Wang, Yanping Wang, Yanqing Wang, Yanru Wang, Yanting Wang, Yanwen Wang, Yanxia Wang, Yanxing Wang, Yanyang Wang, Yanyun Wang, Yanzhe Wang, Yanzhu Wang, Yao Wang, Yaobin Wang, Yaochun Wang, Yaodong Wang, Yaohe Wang, Yaokun Wang, Yaoling Wang, Yaolou Wang, Yaoxian Wang, Yaoxing Wang, Yaozhi Wang, Yapeng Wang, Yaping Wang, Yaqi Wang, Yaqian Wang, Yaqiong Wang, Yaru Wang, Yatao Wang, Yating Wang, Yawei Wang, Yaxian Wang, Yaxin Wang, Yaxiong Wang, Yaxuan Wang, Yayu Wang, Yazhou Wang, Ye Wang, Ye-Ran Wang, Yefu Wang, Yeh-Han Wang, Yehan Wang, Yeming Wang, Yen-Feng Wang, Yen-Sheng Wang, Yeou-Lih Wang, Yeqi Wang, Yezhou Wang, Yi Fan Wang, Yi Lei Wang, Yi Wang, Yi-Cheng Wang, Yi-Chuan Wang, Yi-Ming Wang, Yi-Ni Wang, Yi-Ning Wang, Yi-Shan Wang, Yi-Shiuan Wang, Yi-Shu Wang, Yi-Tao Wang, Yi-Ting Wang, Yi-Wen Wang, Yi-Xin Wang, Yi-Xuan Wang, Yi-Yi Wang, Yi-Ying Wang, Yi-Zhen Wang, Yi-sheng Wang, YiLi Wang, Yian Wang, Yibin Wang, Yibing Wang, Yichen Wang, Yicheng Wang, Yichuan Wang, Yifan Wang, Yifei Wang, Yigang Wang, Yige Wang, Yihan Wang, Yihao Wang, Yihe Wang, Yijin Wang, Yijing Wang, Yijun Wang, Yikang Wang, Yike Wang, Yilin Wang, Yilu Wang, Yimeng Wang, Yiming Wang, Yin Wang, Yin-Hu Wang, Yinan Wang, Yinbo Wang, Yindan Wang, Ying Wang, Ying-Piao Wang, Ying-Wei Wang, Ying-Zi Wang, Yingbo Wang, Yingcheng Wang, Yingchun Wang, Yingfei Wang, Yingge Wang, Yinggui Wang, Yinghui Wang, Yingjie Wang, Yingmei Wang, Yingna Wang, Yingping Wang, Yingqiao Wang, Yingtai Wang, Yingte Wang, Yingwei Wang, Yingwen Wang, Yingxiong Wang, Yingxue Wang, Yingyi Wang, Yingying Wang, Yingzi Wang, Yinhuai Wang, Yining E Wang, Yinong Wang, Yinsheng Wang, Yintao Wang, Yinuo Wang, Yinxiong Wang, Yinyin Wang, Yiou Wang, Yipeng Wang, Yiping Wang, Yiqi Wang, Yiqiao Wang, Yiqin Wang, Yiqing Wang, Yiquan Wang, Yirong Wang, Yiru Wang, Yirui Wang, Yishan Wang, Yishu Wang, Yitao Wang, Yiting Wang, Yiwei Wang, Yiwen Wang, Yixi Wang, Yixian Wang, Yixuan Wang, Yiyan Wang, Yiyi Wang, Yiying Wang, Yizhe Wang, Yong Wang, Yong-Bo Wang, Yong-Gang Wang, Yong-Jie Wang, Yong-Jun Wang, Yong-Tang Wang, Yongbin Wang, Yongdi Wang, Yongfei Wang, Yongfeng Wang, Yonggang Wang, Yonghong Wang, Yongjie Wang, Yongjun Wang, Yongkang Wang, Yongkuan Wang, Yongli Wang, Yongliang Wang, Yonglun Wang, Yongmei Wang, Yongming Wang, Yongni Wang, Yongqiang Wang, Yongqing Wang, Yongrui Wang, Yongsheng Wang, Yongxiang Wang, Yongyi Wang, Yongzhong Wang, You Wang, Youhua Wang, Youji Wang, Youjie Wang, Youli Wang, Youzhao Wang, Youzhi Wang, Yu Qin Wang, Yu Tian Wang, Yu Wang, Yu'e Wang, Yu-Chen Wang, Yu-Fan Wang, Yu-Fen Wang, Yu-Hang Wang, Yu-Hui Wang, Yu-Ping Wang, Yu-Ting Wang, Yu-Wei Wang, Yu-Wen Wang, Yu-Ying Wang, Yu-Zhe Wang, Yu-Zhuo Wang, Yuan Wang, Yuan-Hung Wang, Yuanbo Wang, Yuanfan Wang, Yuanjiang Wang, Yuanli Wang, Yuanqiang Wang, Yuanqing Wang, Yuanyong Wang, Yuanyuan Wang, Yuanzhen Wang, Yubing Wang, Yubo Wang, Yuchen Wang, Yucheng Wang, Yuchuan Wang, Yudong Wang, Yue Wang, Yue-Min Wang, Yue-Nan Wang, YueJiao Wang, Yuebing Wang, Yuecong Wang, Yuegang Wang, Yuehan Wang, Yuehong Wang, Yuehu Wang, Yuehua Wang, Yuelong Wang, Yuemiao Wang, Yueshen Wang, Yueting Wang, Yuewei Wang, Yuexiang Wang, Yuexin Wang, Yueying Wang, Yueze Wang, Yufei Wang, Yufeng Wang, Yugang Wang, Yuh-Hwa Wang, Yuhan Wang, Yuhang Wang, Yuhua Wang, Yuhuai Wang, Yuhuan Wang, Yuhui Wang, Yujia Wang, Yujiao Wang, Yujie Wang, Yujiong Wang, Yulai Wang, Yulei Wang, Yuli Wang, Yuliang Wang, Yulin Wang, Yuling Wang, Yulong Wang, Yumei Wang, Yumeng Wang, Yumin Wang, Yuming Wang, Yun Wang, Yun Yong Wang, Yun-Hui Wang, Yun-Jin Wang, Yun-Xing Wang, Yunbing Wang, Yunce Wang, Yunchao Wang, Yuncong Wang, Yunduan Wang, Yunfang Wang, Yunfei Wang, Yunhan Wang, Yunhe Wang, Yunong Wang, Yunpeng Wang, Yunqiong Wang, Yuntai Wang, Yunzhang Wang, Yunzhe Wang, Yunzhi Wang, Yupeng Wang, Yuping Wang, Yuqi Wang, Yuqian Wang, Yuqiang Wang, Yuqin Wang, Yusha Wang, Yushe Wang, Yusheng Wang, Yutao Wang, Yuting Wang, Yuwei Wang, Yuwen Wang, Yuxiang Wang, Yuxing Wang, Yuxuan Wang, Yuxue Wang, Yuyan Wang, Yuyang Wang, Yuyin Wang, Yuying Wang, Yuyong Wang, Yuzhong Wang, Yuzhou Wang, Yuzhuo Wang, Z P Wang, Z Wang, Z-Y Wang, Zai Wang, Zaihua Wang, Ze Wang, Zechen Wang, Zehao Wang, Zehua Wang, Zekun Wang, Zelin Wang, Zeneng Wang, Zengtao Wang, Zeping Wang, Zexin Wang, Zeying Wang, Zeyu Wang, Zeyuan Wang, Zezhou Wang, Zhan Wang, Zhang Wang, Zhanggui Wang, Zhangshun Wang, Zhangying Wang, Zhanju Wang, Zhao Wang, Zhao-Jun Wang, Zhaobo Wang, Zhaofeng Wang, Zhaofu Wang, Zhaohai Wang, Zhaohui Wang, Zhaojing Wang, Zhaojun Wang, Zhaoming Wang, Zhaoqing Wang, Zhaosong Wang, Zhaotong Wang, Zhaoxi Wang, Zhaoxia Wang, Zhaoyu Wang, Zhe Wang, Zhehai Wang, Zhehao Wang, Zhen Wang, ZhenXue Wang, Zhenbin Wang, Zhenchang Wang, Zhenda Wang, Zhendan Wang, Zhendong Wang, Zheng Wang, Zhengbing Wang, Zhengchun Wang, Zhengdong Wang, Zhenghui Wang, Zhengkun Wang, Zhenglong Wang, Zhenguo Wang, Zhengwei Wang, Zhengxuan Wang, Zhengyang Wang, Zhengyi Wang, Zhengyu Wang, Zhenhua Wang, Zhenning Wang, Zhenqian Wang, Zhenshan Wang, Zhentang Wang, Zhenwei Wang, Zhenxi Wang, Zhenyu Wang, Zhenze Wang, Zhenzhen Wang, Zheyi Wang, Zheyue Wang, Zhezhi Wang, Zhi Wang, Zhi Xiao Wang, Zhi-Gang Wang, Zhi-Guo Wang, Zhi-Hao Wang, Zhi-Hong Wang, Zhi-Hua Wang, Zhi-Jian Wang, Zhi-Long Wang, Zhi-Qin Wang, Zhi-Wei Wang, Zhi-Xiao Wang, Zhi-Xin Wang, Zhibo Wang, Zhichao Wang, Zhicheng Wang, Zhicun Wang, Zhidong Wang, Zhifang Wang, Zhifeng Wang, Zhifu Wang, Zhigang Wang, Zhige Wang, Zhiguo Wang, Zhihao Wang, Zhihong Wang, Zhihua Wang, Zhihui Wang, Zhiji Wang, Zhijian Wang, Zhijie Wang, Zhijun Wang, Zhilun Wang, Zhimei Wang, Zhimin Wang, Zhipeng Wang, Zhiping Wang, Zhiqi Wang, Zhiqian Wang, Zhiqiang Wang, Zhiqing Wang, Zhiren Wang, Zhiruo Wang, Zhisheng Wang, Zhitao Wang, Zhiting Wang, Zhiwu Wang, Zhixia Wang, Zhixiang Wang, Zhixiao Wang, Zhixin Wang, Zhixing Wang, Zhixiong Wang, Zhixiu Wang, Zhiying Wang, Zhiyong Wang, Zhiyou Wang, Zhiyu Wang, Zhiyuan Wang, Zhizheng Wang, Zhizhong Wang, Zhong Wang, Zhong-Hao Wang, Zhong-Hui Wang, Zhong-Ping Wang, Zhong-Yu Wang, ZhongXia Wang, Zhongfang Wang, Zhongjing Wang, Zhongli Wang, Zhonglin Wang, Zhongqun Wang, Zhongsu Wang, Zhongwei Wang, Zhongyi Wang, Zhongyu Wang, Zhongyuan Wang, Zhongzhi Wang, Zhou Wang, Zhou-Ping Wang, Zhoufeng Wang, Zhouguang Wang, Zhuangzhuang Wang, Zhugang Wang, Zhulin Wang, Zhulun Wang, Zhuo Wang, Zhuo-Hui Wang, Zhuo-Jue Wang, Zhuo-Xin Wang, Zhuowei Wang, Zhuoying Wang, Zhuozhong Wang, Zhuqing Wang, Zi Wang, Zi Xuan Wang, Zi-Hao Wang, Zi-Qi Wang, Zi-Yi Wang, Zicheng Wang, Zifeng Wang, Zihan Wang, Ziheng Wang, Zihua Wang, Zihuan Wang, Zijian Wang, Zijie Wang, Zijue Wang, Zijun Wang, Zikang Wang, Zikun Wang, Ziliang Wang, Zilin Wang, Ziling Wang, Zilong Wang, Zining Wang, Ziping Wang, Ziqi Wang, Ziqian Wang, Ziqiang Wang, Ziqing Wang, Ziqiu Wang, Zitao Wang, Ziwei Wang, Zixi Wang, Zixia Wang, Zixian Wang, Zixiang Wang, Zixu Wang, Zixuan Wang, Ziyi Wang, Ziying Wang, Ziyu Wang, Ziyun Wang, Zongbao Wang, Zonggui Wang, Zongji Wang, Zongkui Wang, Zongqi Wang, Zongwei Wang, Zou Wang, Zulong Wang, Zumin Wang, Zun Wang, Zunxian Wang, Zuo Wang, Zuoheng Wang, Zuoyan Wang, Zusen Wang
articles
Xiang Hong, Mengjie Zhao, Furong Tan +5 more · 2026 · BMC microbiology · BioMed Central · added 2026-04-24
To investigate the association between vaginal microbiota structure in early pregnancy and gestational diabetes mellitus (GDM) and to characterize microbial signatures for early screening for GDM. The Show more
To investigate the association between vaginal microbiota structure in early pregnancy and gestational diabetes mellitus (GDM) and to characterize microbial signatures for early screening for GDM. The present study was a nested case-control study recruiting pregnant women from the Nanjing Gulou Maternal-Child Health Center, China. Vaginal swabs were collected before 20 weeks of gestation for 16S rRNA sequencing. Following 1:3 propensity score matching, 45 GDM cases and 135 controls were enrolled. The final analysis included 42 GDM cases and 121 controls. A random forest model was used to explore the genera of vaginal differential microbiota associated with GDM. Based on these findings, latent profile analysis (LPA) was conducted to explore potential types of vaginal microbiota, and logistic regression was used to analyze the association between vaginal microbiota types and GDM. The GDM group exhibited elevated alpha diversity (Chao1 index, The composition and structure of vaginal microbiota in early pregnancy are different in the two groups. The vaginal microbiota in early pregnancy, which is characterized by co-dominated by The online version contains supplementary material available at 10.1186/s12866-026-04910-2. Show less
📄 PDF DOI: 10.1186/s12866-026-04910-2
LPA
XiaoSong Pei, Fei Wang, Xiaomin Liu +7 more · 2026 · Oncogene · Nature · added 2026-04-24
High-grade serous ovarian cancer (HGSC) is the most aggressive subtype of ovarian epithelial cancer (OEC), with characters of late-stage diagnosis, high recurrence rate, and poor survival outcomes. Fu Show more
High-grade serous ovarian cancer (HGSC) is the most aggressive subtype of ovarian epithelial cancer (OEC), with characters of late-stage diagnosis, high recurrence rate, and poor survival outcomes. Fucosyltransferase 8 (FUT8) is responsible for α1,6-core fucosylation biosynthesis, and aberrant FUT8/α1,6-core fucosylation level is involved in tumor progression. However, the roles and mechanisms of protein FUT8 and α1,6-core fucosylation in HGSC tumorigenesis and progression remain elusive. Here, our study confirms that elevated levels of FUT8/α1,6-core fucose in the tissues and serum of HGSC patients, and the elevation is associated with poor patient prognosis. By applying glycoproteomic assay, we globally screen and identify NCEH1 as the specific scaffold protein of α1,6-core fucosylation. Alpha 1,6-core fucose modification stabilizes NCEH1 by preventing its degradation through proteasomal pathway. Importantly, combined with non-targeted metabolomics analysis, α1,6-core fucosylated NCEH1 facilitates LPA secretion, driving M2-like polarization of tumor-associated macrophages in the tumor microenvironment, thus leading to oncogenesis and peritoneal metastasis of HGSC in vitro and in vivo. These findings broaden the understanding of FUT8/α1,6-core fucosylation/NCEH1 in HGSC progression and metastasis, and offer glycosylated diagnostic indicators and targets for therapeutic strategies in HGSC. Show less
📄 PDF DOI: 10.1038/s41388-026-03703-1
LPA
Wen Hao, Yuyao Qiu, Zekun Zhang +7 more · 2026 · Sleep · Oxford University Press · added 2026-04-24
The impact of obstructive sleep apnea (OSA) on subsequent cardiovascular events in patients with acute coronary syndrome (ACS) remains debated. This study aims to investigate whether the association o Show more
The impact of obstructive sleep apnea (OSA) on subsequent cardiovascular events in patients with acute coronary syndrome (ACS) remains debated. This study aims to investigate whether the association of OSA with cardiovascular events is affected by lipoprotein (a) [Lp(a)] levels. This is a sub-analysis of prospective cohort study (OSA-ACS, NCT03362385) enrolled ACS patients. OSA defined as an apnea-hypopnea index ≥15 events/h. The effects of OSA on subsequent cardiovascular outcomes were evaluated across varying Lp(a) thresholds. Coronary plaque features by coronary computed tomography angiography were also analyzed. A total of 1137 patients were enrolled, 608 patients (53.5%) were diagnosed with OSA. At a median follow-up of 3.6 years, OSA was associated with a higher risk of major adverse cardiovascular and cerebrovascular events (MACCE) in patients with Lp(a) level > median (HR 1.59, 95% CI 1.12-2.26, p=.009), but not in patients with Lp(a) level ≤ median (HR 1.09, 95% CI 0.80-1.49, p=.60). There were consistent increases in HRs for MACCE in the OSA group with Lp(a) levels rising, as stratified by tertiles or quartiles of Lp(a). In patients with Lp(a) level > median, OSA demonstrated a higher prevalence of ≥1 high-risk plaque (HRP) feature (51.4% vs. 33.3%, p=.03) and low-attenuation plaque (50.0% vs. 32.8, p=.04) per vessel than non-OSA. OSA was associated with a continuously increased cardiovascular risk and a higher prevalence of HRP features as Lp(a) levels rose. Lp(a) may help identify ACS patients at higher cardiovascular risk, in whom the efficacy of OSA treatment should be further investigated. Show less
no PDF DOI: 10.1093/sleep/zsag062
LPA
Wei Wang, Xiaoxu Han, Xin Xu +2 more · 2026 · Journal of neurointerventional surgery · added 2026-04-24
Fear of disease progression (FoP) is a common psychological concern among patients with unruptured intracranial aneurysms (UIAs). However, heterogeneity in FoP and the role of psychological resilience Show more
Fear of disease progression (FoP) is a common psychological concern among patients with unruptured intracranial aneurysms (UIAs). However, heterogeneity in FoP and the role of psychological resilience before treatment remain insufficiently understood. In this cross-sectional study, patients with UIAs scheduled for endovascular treatment were recruited. FoP was assessed using the Fear of Progression Questionnaire-Short Form (FoP-Q-12), and psychological resilience was measured with the Connor-Davidson Resilience Scale (CD-RISC-25). Latent profile analysis (LPA) was conducted to identify distinct FoP profiles. Least Absolute Shrinkage and Selection Operator (LASSO) regression followed by multinomial logistic regression were used to examine factors associated with profile membership. Five distinct FoP profiles were identified: Minimal Fear-Good Adjustment, Mild Emotionally Elevated Fear, Moderate Emotionally Reactive Fear, Moderate Functionally Concerned Fear, and High Pervasive Fear. Multinomial logistic regression showed that higher psychological resilience-particularly the tenacity dimension-was associated with lower odds of belonging to the Mild Emotionally Elevated Fear, Moderate Emotionally Reactive Fear, and Moderate Functionally Concerned Fear profiles, compared with the Minimal Fear-Good Adjustment profile. No significant association was observed between tenacity and the High Pervasive Fear profile. Sensitivity analyses using alternative resilience indicators yielded consistent results. Among patients with UIAs prior to endovascular treatment, FoP exhibits marked heterogeneity. Psychological resilience, especially tenacity, is differentially associated with specific FoP profiles. These findings support the value of profile-based psychological assessment to inform targeted psychosocial support during treatment planning. Show less
no PDF DOI: 10.1136/jnis-2026-025036
LPA
Junying Wang, Ning Jia · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
Depression and anxiety are highly prevalent and often co-occurring mental health issues among adolescents, with comorbidity leading to poorer outcomes and additional challenges. Left-behind adolescent Show more
Depression and anxiety are highly prevalent and often co-occurring mental health issues among adolescents, with comorbidity leading to poorer outcomes and additional challenges. Left-behind adolescents-a unique group experiencing disrupted parent-child relationships and limited social support-may face a higher risk of such comorbidity. Yet, few studies have examined the depression-anxiety network in this population. Latent profile analysis (LPA) identified subgroups with similar symptom patterns, and network analysis visualized the structure of comorbidities. Network comparison tests evaluated differences across subgroups. Based on the "Science Database of People Mental Health" managed by the National Population Health Data Center (China), a total of 3,205 left-behind adolescents (1,538 males; 1,667 females) were included. The Patient Health Questionnaire-9 and the Generalized Anxiety Disorder Scale-7 were used to assess depression and anxiety among left-behind adolescents. Three distinct profiles were identified: high-comorbidity (8.2%), moderate-comorbidity (28.7%), and low-comorbidity (63.1%). Network structures and global strength differed significantly between subgroups. "Restlessness" was the central bridge symptom in the high-comorbidity group, while "Nervousness" was central in the moderate- and low-comorbidity groups. These findings suggest tailored interventions targeting subgroup-specific bridge symptoms-such as restlessness or nervousness-may improve outcomes for left-behind adolescents with comorbid depression and anxiety. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1751196
LPA
Bin Ma, Jingjing Wang, Mengyuan Zhang +2 more · 2026 · BMC nursing · BioMed Central · added 2026-04-24
To evaluate the current status and latent profiles of caregiver self-care contributions for patients with chronic obstructive pulmonary disease (COPD) and examine the associations between demographic Show more
To evaluate the current status and latent profiles of caregiver self-care contributions for patients with chronic obstructive pulmonary disease (COPD) and examine the associations between demographic characteristics, health literacy, confidence in self-care contributions, family intimacy, and profile membership. We recruited 275 dyads of patients with COPD and their family caregivers from five tertiary hospitals between May and November 2022 using convenience sampling. Latent profile analysis (LPA) was used to identify distinct profiles of caregiver self-care contributions. Univariate analysis and multinomial logistic regression were subsequently conducted to examine associations between participant characteristics and profile membership. LPA identified four distinct profiles of caregiver self-care contributions: low-contributing, under-monitored, maintenance-prioritized, and high-contributing. Significant differences were observed across these profiles in terms of patients' symptom severity, exacerbation frequency, number of hospitalizations, caregivers' education levels, caregiving duration, health literacy, confidence in self-management contributions, and family intimacy using univariate analysis. Multinomial logistic regression analysis revealed that caregivers' education levels, caregiving duration, confidence in self-management contributions, and health literacy were significant predictors of profile membership. Caregiver self-care contributions for patients with COPD can be characterized by four distinct profiles, with caregivers' educational level, health literacy, and confidence in self-management identified as key factors associated with profile membership. Show less
📄 PDF DOI: 10.1186/s12912-026-04503-4
LPA
Miaomiao Chen, Shailing Ma, Xiaohui Liu +5 more · 2026 · Frontiers in reproductive health · Frontiers · added 2026-04-24
China's total fertility rate has reached a critically low level, dropping to approximately 1.0 by the end of 2023which is significantly below the population replacement level of 1.5. This decline refl Show more
China's total fertility rate has reached a critically low level, dropping to approximately 1.0 by the end of 2023which is significantly below the population replacement level of 1.5. This decline reflects a marked reduction in fertility intention among reproductive-aged women, exacerbating population aging and threatening long-term labor supply and social sustainability. Despite policy adjustments and governmental support initiatives, intended outcomes have not been realized. Current literature largely focuses on isolated determinants of fertility intention, overlooking heterogeneity within the population. Moreover, the pathways through which psychosocial factors operate across different subgroups remain poorly understood. Data for this study were derived from the 2021 Psychological and Behavioral Investigation of Chinese Residents (PBICR 2021), a nationally representative cross-sectional survey. Latent profile analysis (LPA) was employed to identify subtypes of fertility intention among reproductive-aged women, followed by multinomial logistic regression, which examined factors associated with different profiles. Among 2,973 reproductive-aged female participants, three distinct fertility intention profiles were identified via latent profile analysis: the Fertility Intention Decline Group (25.1%), the Low Fertility Intention Group (51.3%), and the High Fertility Intention Group (23.6%). Multinomial logistic regression analysis revealed that, compared with the Fertility Intention Decline Group, the Low Fertility Intention Group was significantly associated with family type, aged 20-40 years, residential location, having 2 children, and retirement status (all Fertility intention among reproductive-aged women demonstrates significant heterogeneity. This study identified three distinct latent profiles, each characterized by unique patterns of influencing factors. The findings highlight the necessity of moving beyond one-size-fits-all policy approaches and emphasize the importance of developing tailored interventions that account for the specific characteristics and determinants of each subgroup. Show less
📄 PDF DOI: 10.3389/frph.2026.1758039
LPA
Yali Jiang, Chunyi Wang, Yangfan Hu +4 more · 2026 · Nursing in critical care · Blackwell Publishing · added 2026-04-24
Studies of surrogate decision-makers (SDMs) in the intensive care unit (ICU) often report high average levels of family decision-making self-efficacy (FDMSE). However, these findings contrast with the Show more
Studies of surrogate decision-makers (SDMs) in the intensive care unit (ICU) often report high average levels of family decision-making self-efficacy (FDMSE). However, these findings contrast with the significant decision conflict commonly observed in clinical practice. This discrepancy suggests that high aggregate FDMSE scores may mask underlying subgroups with distinct experiences. Identifying these latent profiles is essential for understanding the true experiences of ICU SDMs. This study aimed to identify distinct latent profiles of FDMSE among ICU SDMs and explore key influencing factors. A cross-sectional study was conducted among SDMs of ICU patients. Exploratory and confirmatory factor analysis (EFA/CFA) was performed to examine the factor structure of the Chinese FDMSE scale. The verified factor structure was then used for latent profile analysis (LPA). Lastly, univariate and multivariate analyses were performed to identify the main influencing factors. A total of 350 ICU SDMs were included in the analysis. The three-factor model, including treatment decision-making, comfort promotion decision-making, and facing death decision-making, provided a good fit for the Chinese FDMSE scale. Two profiles emerged: 'weak family decision-making self-efficacy', accounting for 55.9% of cases, and 'strong family decision-making self-efficacy', represented by the remaining 44.1%. The 'strong family decision-making self-efficacy' group was more likely to be observed in families where the patients held religious beliefs and were diagnosed with cancer, and where the family decision-makers held religious beliefs, had higher incomes, and had engaged in prior discussions about treatment preferences. This study verified the multi-dimensionality and heterogeneity of the FDMSE of ICU SDMs through EFA, CFA and LPA. The identification of a subgroup with low FDMSE differs from previous studies. Key modifiable factors include socio-economic resources, prior communication of the patients' preferences, and spiritual and cultural background, which serve as crucial levers for strengthening the decision-support framework in critical care settings. By identifying two distinct FDMSE profiles and key influencing factors, it offers critical care nurses a new perspective to design targeted interventions, thereby enhancing their ability to provide personalised decision support. Critical care nurses should receive structured end-of-life communication training to address the shared vulnerability of ICU SDMs in facing death decision-making self-efficacy across both profiles. Show less
no PDF DOI: 10.1111/nicc.70398
LPA
Mengru Wang, Fudong Hu, Rongyan Jiang +1 more · 2026 · PloS one · PLOS · added 2026-04-24
Lipoprotein(a) [Lp(a)] promotes atherosclerotic plaque vulnerability through pro-inflammatory and thrombogenic pathways, while the CatLet© angiographic score quantifies coronary lesion complexity. We Show more
Lipoprotein(a) [Lp(a)] promotes atherosclerotic plaque vulnerability through pro-inflammatory and thrombogenic pathways, while the CatLet© angiographic score quantifies coronary lesion complexity. We hypothesized that their integration would improve prognostication in acute myocardial infarction (AMI) after emergency percutaneous coronary intervention (ePCI). In this retrospective cohort, 307 AMI patients undergoing successful ePCI (2020-2022) were stratified by 1-year major adverse cardiovascular/cerebrovascular events (MACCE). Serum Lp(a) and troponin I were measured post-admission. CatLet© and Gensini scores were assessed by blinded analysts. Multivariable logistic regression and ROC analyses evaluated predictive performance. MACCE patients (n = 78) exhibited higher Lp(a) (135.99 ± 33.07vs. 123.35 ± 42.70nmol/L, P = 0.0178) and CatLet© scores (33.58 ± 9.04vs. 30.80 ± 8.24, P = 0.0012) versus controls. Lp(a) (OR=2.339,95%CI:1.519-3.603, P <  0.001) and CatLet© score (OR=1.092, 95%CI:1.027-1.161, P = 0.005) independently predicted MACCE. The combined model Lp(a)≥70.70 nmol/L + CatLet© ≥ 18.6) significantly outperformed individual markers (AUC 0.862 [95%CI:0.83-0.96] vs. 0.780/0.833; DeLong's test confirmed the superiority of the combined model over individual predictors (P = 0.0089, Z = 2.64 vs. Lp(a); P = 0.034, Z = 2.12 vs. CatLet© score), with 88% sensitivity and 83% specificity. The Lp(a)-CatLet© synergy enhances MACCE risk stratification in ePCI-treated AMI, reflecting complementary pathobiological (Lp(a)-driven plaque vulnerability) and anatomical (CatLet©-quantified complexity) pathways. This dual-parameter approach could support post-PCI risk stratification and follow-up planning. Show less
📄 PDF DOI: 10.1371/journal.pone.0342704
LPA
Laixi Kong, Xiucheng Ma, Cui Yang +3 more · 2026 · European journal of psychotraumatology · Taylor & Francis · added 2026-04-24
📄 PDF DOI: 10.1080/20008066.2026.2629072
LPA
Xueyan Wang, Yingying Wang, Xinyue Chen +4 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Digital literacy has become a core competency for nursing professionals, enabling them to adapt to modern healthcare environments and engage effectively with emerging technologies. It is closely linke Show more
Digital literacy has become a core competency for nursing professionals, enabling them to adapt to modern healthcare environments and engage effectively with emerging technologies. It is closely linked to innovative behavior, which is essential for problem solving and advancing nursing practice. Despite its importance, limited research has examined differences in digital literacy among undergraduate nursing students and how these differences influence innovation. A cross-sectional study was conducted using a convenience sample of 450 undergraduate nursing students from four universities in Anhui Province, China. Participants completed a general information questionnaire, the Undergraduate Digital Literacy Scale, and the Innovative Behavior Scale. Latent profile analysis (LPA) was employed to classify students into distinct digital literacy profiles, while logistic regression and one-way ANOVA were used to explore factors influencing profile membership and the relationship between digital literacy and innovative behavior. Three latent profiles were identified: a "Low Digital Literacy" group (34.1%), a "Moderate Digital Literacy" group (15.9%), and a "High Digital Literacy" group (50.0%). Significant differences were observed across profiles in relation to gender, age, academic year, and frequency of artificial intelligence (AI) use in the past 6 months. Importantly, students with higher digital literacy consistently exhibited stronger innovative behavior ( Digital literacy among undergraduate nursing students is heterogeneous and shaped by demographic and experiential factors. Targeted educational interventions tailored to distinct literacy profiles are needed to bridge gaps, promote equity, and strengthen innovation. By integrating AI and advanced digital tools into nursing curricula, educators can enhance students' competencies and better prepare them to thrive in an increasingly digital and intelligent healthcare landscape. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1717234
LPA
Dan Lei, Wei Liang, Fengying Yang +3 more · 2026 · BMC pediatrics · BioMed Central · added 2026-04-24
This study examined the relationship between motor competence (MC) and Physical Activity (PA) in school-aged children, and assessed the mediating role of physical fitness, based on the Model of the Re Show more
This study examined the relationship between motor competence (MC) and Physical Activity (PA) in school-aged children, and assessed the mediating role of physical fitness, based on the Model of the Relationship between Children’s Motor Development and Obesity Risk. From March to April 2022, 1,026 children (53.6% boys, mean age 8.93 years) from four public primary schools in Shijiazhuang City, China, were recruited via stratified cluster sampling. MC was assessed using the Test of Gross Motor Development, 3rd edition (TGMD-3), PA was measured via a three-axis accelerometer, and physical fitness was evaluated according to the Chinese National Student Physical Health Standards (2014 revision). Data were analyzed using SPSS 26.0, with mediation tested via the bias-corrected bootstrap method (10,000 resamples). Ball skills ( Ball skills are critical for promoting MVPA in school-aged children, with physical fitness acting as a significant mediator. Systematic ball skill training is recommended as a core strategy to enhance physical activity via improved fitness. Show less
📄 PDF DOI: 10.1186/s12887-026-06590-3
LPA
Klaus G Parhofer, Ulrich Julius, Anna Laura Herzog +12 more · 2026 · European heart journal · Oxford University Press · added 2026-04-24
Lipoprotein apheresis (LA) is the only approved treatment for patients with elevated lipoprotein(a) [Lp(a)]. The Lp(a)FRONTIERS APHERESIS trial investigated whether pelacarsen reduces the need for LA Show more
Lipoprotein apheresis (LA) is the only approved treatment for patients with elevated lipoprotein(a) [Lp(a)]. The Lp(a)FRONTIERS APHERESIS trial investigated whether pelacarsen reduces the need for LA in patients from Germany with elevated Lp(a) and established cardiovascular disease (CVD). Adult patients with Lp(a) levels >60 mg/dl who had undergone ≥35 LA sessions in the prior year were randomized to receive pelacarsen 80 mg or placebo every 4 weeks for 52 weeks. Weekly LA sessions were performed if the Lp(a) measurement from the prior visit was >60 mg/dL. The primary endpoint was the rate of performed LA sessions normalized to the weekly LA schedule (the number of actual LA sessions divided by the number of planned LA sessions during the 52-week period). Secondary endpoints were time to LA avoidance (for ≥24 consecutive weeks) and total LA avoidance from week 12 to week 52. Fifty-one patients were randomized (mean age 61.7 years, mean Lp(a) at baseline 85.4 mg/dL, and mean 44.0 LA sessions in the past 12 months), with 25 of 26 (96.2%) in the pelacarsen arm and 23 of 25 (92.0%) in the placebo arm completing the study. Baseline characteristics were generally balanced between treatment arms. Pelacarsen reduced the mean rates of LA (0.16 vs 0.93 in placebo, odds ratio 0.006, 95% confidence interval [CI] 0.003, 0.013; P < .0001) and substantially increased the hazard of achieving LA avoidance (hazard ratio: 88.3; P = .0014; median time to achieve LA avoidance: 6.1 weeks) and total LA avoidance (odds ratio: 163.2; P = .0005). The placebo-adjusted Lp(a) change from baseline at week 52 was -72% (95% CI: -79%, -61%; P < .0001). Treatment emergent adverse events were similar between arms, except for mostly mild injection site erythema (pelacarsen 38.5%; placebo 0%). Pelacarsen is a highly effective and well-tolerated Lp(a)-targeted therapy that substantially reduces the need for LA in patients with elevated Lp(a) and established CVD. NCT05305664. Show less
no PDF DOI: 10.1093/eurheartj/ehag073
LPA
Fang Wu, Juan Zhang, Adan Fu +6 more · 2026 · Diabetes, metabolic syndrome and obesity : targets and therapy · added 2026-04-24
Using latent profile analysis (LPA) based on Self-Determination Theory (SDT), this study aimed to explore the profiles of health behavior motivation among Chinese patients with prediabetes and examine Show more
Using latent profile analysis (LPA) based on Self-Determination Theory (SDT), this study aimed to explore the profiles of health behavior motivation among Chinese patients with prediabetes and examine the relationship between these profiles and self-management ability. A cross-sectional study was conducted involving 335 patients with prediabetes. The questionnaires were used to assess health behavior motivation, self-management ability, satisfaction of basic psychological needs and disease knowledge level. Latent profile analysis was performed based on five subscale scores of the health behavior motivation measure. Three distinct latent profiles were identified: a "Self-Determined" profile (C1,29.55%, n=99), a "Non Self-Determined" profile (C2, 55.82%, n=187), and a "Conflicted" profile (C3, 14.63%, n=49). Patients in the C1 profile demonstrated higher levels of autonomy and competence. Patients in the C2 profile were characterized by better disease knowledge and lower relatedness. Compared to patients in the C3 profile, patients in both the C1 and C2 profiles exhibited significantly lower self-management ability. The heterogeneity in health behavior motivation profiles must be considered in the design and clinical practice of personalized interventions for prediabetes. Profile-specific strategies serve as the foundation for enhancing patients' self-management ability and sustaining healthy behaviors. Show less
📄 PDF DOI: 10.2147/DMSO.S567404
LPA
BiXia Yuan, QingHua Lai, Jing Wu +5 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
From a positive psychology perspective, this study aimed to identify the latent profiles of spiritual well-being and analyze the serial mediation mechanism of family care and spiritual coping in the r Show more
From a positive psychology perspective, this study aimed to identify the latent profiles of spiritual well-being and analyze the serial mediation mechanism of family care and spiritual coping in the relationship between spiritual well-being and health-related quality of life (HRQoL). The findings are intended to inform strategies for improving the spiritual well-being of maintenance hemodialysis (MHD) patients. A cross-sectional design was employed with 220 MHD patients recruited from two tertiary hospitals in Guangdong, China (August 2023-January 2024). Assessments were conducted using the Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-SP-12), Family Care Index, Spiritual Coping Questionnaire (SCQ), and Short Form-12 Health Survey (SF-12). Latent profile analysis (LPA) was employed to identify heterogeneous subgroups based on spiritual well-being. A chain mediation model was then used to examine the mediating effects of family care and spiritual coping. HRQoL scores averaged 56.50 ± 22.29. Significant correlations emerged: spiritual well-being ( Spiritual well-being indirectly influences the quality of life in MHD patients through the serial mediation of family care and spiritual coping. Clinicians should recognize the heterogeneity in spiritual well-being and integrate routine spiritual screening into nursing assessments to identify patients with low spiritual well-being. It is recommended to develop family-based education and support programs, along with interventions that combine family care and spiritual coping strategies, so as to improve patients' long-term health outcomes. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1668699
LPA
Xiaolan Pu, Mei Ju, Xingping Han +3 more · 2026 · BMJ open · added 2026-04-24
To identify distinct latent frailty profiles using latent profile analysis (LPA) in patients undergoing radiotherapy for head and neck cancer (HNC) and to examine the factors associated with profile m Show more
To identify distinct latent frailty profiles using latent profile analysis (LPA) in patients undergoing radiotherapy for head and neck cancer (HNC) and to examine the factors associated with profile membership. A cross-sectional study. This research used data acquired from a major tertiary referral hospital in China. This study recruited 391 HNC patients receiving radiotherapy. Validated instruments included a demographic questionnaire, Charlson Comorbidity Index (CCI), Pittsburgh Sleep Quality Index, FRAIL Scale, Hospital Anxiety and Depression Scale and Perceived Social Support Scale. Profile membership associations were assessed using χ The frailty status of patients can be divided into three different categories: (1) robust group (23.0%), (2) prefrail group (49.6%) and (3) frail group (27.4%). Frailty demonstrated independent associations with nine clinical parameters in adjusted regression models: radiotherapy session frequency, social support, age, CCI score, educational attainment, metastasis, nutritional risk, radiation-induced injuries and serum albumin levels (p<0.05). The distinct frailty profiles identified by LPA can inform the future development of targeted care protocols for specific subgroups (eg, the frail group), with a focus on key predictors such as age and nutritional risk. Show less
📄 PDF DOI: 10.1136/bmjopen-2025-108357
LPA
Xinyan Li, Zhongsu Wang, Juan Liang +3 more · 2026 · Journal of cardiovascular pharmacology · added 2026-04-24
Lipoprotein(a) [Lp(a)] is a genetically determined independent risk factor for atherosclerotic cardiovascular disease (ASCVD) that drives a significant residual risk through proatherogenic, proinflamm Show more
Lipoprotein(a) [Lp(a)] is a genetically determined independent risk factor for atherosclerotic cardiovascular disease (ASCVD) that drives a significant residual risk through proatherogenic, proinflammatory, and prothrombotic pathways. However, current mainstay lipid-lowering therapies such as statins have limited efficacy in reducing Lp(a) levels, highlighting a critical therapeutic gap. This review aims to synthesize evidence on the role of Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) inhibitors in targeting Lp(a). We systematically searched PubMed and Embase for clinical trials and mechanistic studies (2010-2025), using the PRISMA and AMSTAR-2 frameworks to ensure methodological rigor and demonstrated that PCSK9 inhibitors (eg, alirocumab, evolocumab, and tafolecimab) not only reduced low-density lipoprotein (LDL-C) by 55%-60% but also lowered Lp(a) by 20%-30%. The efficacy of these agents varies ethnically, with tafolecimab showing superior performance in East Asian populations, which is partly attributable to the higher prevalence of the PCSK9 R46L loss-of-function allele. Mechanistically, PCSK9 inhibitors lowered Lp(a) levels through 2 pathways: suppression of hepatic synthesis and enhanced plasma clearance. This evidence supports the 2023 ESC guidelines, which issued a Class IIa recommendation for PCSK9 inhibitor use in patients with ASCVD and elevated Lp(a) levels. Given the evolving landscape, further research is warranted to confirm the role of these therapies in precision medicine paradigms for managing Lp(a)-associated risks. Show less
no PDF DOI: 10.1097/FJC.0000000000001794
LPA
Yuecong Wang, Xin Wang, Chengcai Wen +6 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Occupational stress in nursing is a critical issue that can have significant implications for both workforce stability and personal health. This study aimed to identify subgroups of occupational stres Show more
Occupational stress in nursing is a critical issue that can have significant implications for both workforce stability and personal health. This study aimed to identify subgroups of occupational stress among Chinese female clinical nurses using latent profile analysis, compare sociodemographic differences across these subgroups, and examine their associations with premenstrual syndrome (PMS). A cross-sectional study was conducted among female nurses in tertiary hospitals in Huai'an City, Jiangsu Province, China, from November to December 2023. We recruited participants via convenience sampling, and 400 valid questionnaires were collected. Data were collected using a researcher-developed general information questionnaire, the standardized Chinese Nurses Stressor Scale (35 items), and the Premenstrual Syndrome Scale. Latent profile analysis (LPA) was performed with Mplus 8.0 to identify occupational stress subtypes. Sociodemographic predictors of these subtypes were explored using chi-square tests and multivariate logistic regression in SPSS 25.0. The association between stress subtypes and PMS symptoms was assessed using ANOVA. A Three clinical female nurse occupational stress subtypes were identified: overall low-stress (38.3%, This study identified significant heterogeneity in occupational stress among clinical female nurses, categorized into three distinct subtypes differing in stress levels and demographic characteristics. These findings highlight the importance of considering individual differences when developing interventions to address occupational stress. The study advocates for the implementation of intervention strategies targeting different types of stress in nursing education and organizational reform to better support nurses in fulfilling their responsibilities. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1683290
LPA
Xiangying Xie, Juan Su, Qian Zhou +4 more · 2026 · Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver · Elsevier · added 2026-04-24
Depression and anxiety were not only common but also with serious consequence in inflammatory bowel diseases (IBD) patients. The current study endeavors to define distinct depression and anxiety profi Show more
Depression and anxiety were not only common but also with serious consequence in inflammatory bowel diseases (IBD) patients. The current study endeavors to define distinct depression and anxiety profiles of IBD patients and identify central symptoms within different profiles to facilitate targeted interventions. The research employed K-means Clustering to delineate the depression and anxiety profiles, followed by a repetition of the analysis using Latent Profile Analysis (LPA). Furthermore, network analysis was utilized to identify central symptoms within the various profiles. K‑means Clustering identified Cluster 1 (38.89%), Cluster 2 (45.33%) and Cluster 3 (15.78%), while LPA yielded the low-risk group (39.56%), the mild-risk group (44.22%) and the high-risk group (16.22%). A majority of patients in the three clusters were predominantly in a single LPA-derived patient class (96.1-99.0%). Network analysis revealed that connections within each symptom in PHQ-9 and GAD-7 were stronger than those between symptoms. Furthermore, PHQ 6 ("guilt"), PHQ2 ("sad mood")and GAD 7 ("feeling afraid") were identified as the central symptoms in Cluster 1. PHQ2 ("sad mood"), GAD 3("excessive worry") and GAD 1 ("nervousness") emerged as the central symptoms in Cluster 2. Additionally, GAD3 ("excessive worry"), GAD 4 ("trouble relaxing") and GAD 6("irritability") were identified as the central symptoms in Cluster 3. We defined three distinct depression and anxiety profiles among IBD patients and pinpointed central symptoms within each profile. These findings underscore the importance of directing research towards those central symptoms within each profile in order to develop targeted intervention strategies. Show less
no PDF DOI: 10.1016/j.dld.2026.01.222
LPA
Hua Hua Ma, Caiping Zhao, Yongni Wang +2 more · 2026 · Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy · Wiley · added 2026-04-24
This study aimed to identify latent classes of adherence for serum phosphorus control and their influencing factors among patients receiving Peritoneal Dialysis with hyperphosphatemia. This cross-sect Show more
This study aimed to identify latent classes of adherence for serum phosphorus control and their influencing factors among patients receiving Peritoneal Dialysis with hyperphosphatemia. This cross-sectional study using convenience sampling was conducted among patients receiving peritoneal dialysis with hyperphosphatemia between December 2024 and May 2025. Participants were assessed using the Phosphate Control Adherence Scale, Self-Efficacy Scale, and Family Care and Social Support Scale. Demographic and clinical data were also collected. Latent profile analysis (LPA) was used to identify adherence subgroups. Univariate analysis and multicollinearity diagnostics were performed, followed by binary logistic regression to determine predictors of adherence. Blood phosphorus control adherence can be classified into two categories: the low-level medical adherence group, characterized by poor dietary self-control (19.93%), and the high-level medical adherence group, marked by effective medication adherence (80.07%). The results indicated that residence conditions, types of medication, and self-efficacy significantly influenced blood phosphorus control adherence among patients with various forms of PD hyperphosphatemia (all p < 0.05). Patients with hyperphosphatemia undergoing peritoneal dialysis exhibit heterogeneity in adherence to serum phosphorus control. This indicates that healthcare providers should identify the adherence characteristics of different patient groups at an early stage and implement targeted intervention strategies to enhance patients' adherence to serum phosphorus management. Show less
no PDF DOI: 10.1002/1744-9987.70125
LPA
Chenlin Li, Yanping Qiu, Nan Zheng +3 more · 2026 · BMC public health · BioMed Central · added 2026-04-24
This cross-sectional study aimed to examine the associations between the 24-h movement behaviors and mental health among university students in China, and to determine the optimal behavioral balance b Show more
This cross-sectional study aimed to examine the associations between the 24-h movement behaviors and mental health among university students in China, and to determine the optimal behavioral balance based on the top 5% of model-predicted mental health outcomes using compositional data analysis. A total of 6,084 university students aged 17–24 years in Southwest China self-reported their daily durations of moderate-to-vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behavior (SED), and sleep (SLP). They were stratified by gender and then randomly and equally assigned to the “recommendation” group and the “validation” group. Using compositional data analysis, time-use compositions (MVPA, LPA, SED, SLP) were transformed into isometric log-ratios (with quadratic terms as needed) and subsequently used in regression models to predict the three mental health outcomes. All possible combinations of motion components were examined to determine the combination with the highest correlation (top 5%) for each outcome. Through research and analysis of the recommendation groups, the optimal combination of average (range) time usage is determined as follows: for males, MVPA 92 (60–110) min/day, LPA 361 (310–400) min/day, SED 372 (350–480) min/day, SLP 614 (530–680) min/day; for females, MVPA 58 (40–90) min/day, LPA 290 (180–390) min/day, SED 445 min (400–560), SLP 665 (580–740) min/day. The recommended durations served as benchmarks for the validation group. Participants who met the optimal 24-h movement behavior time showed significantly lower depression (males: β = –1.290, The optimal 24-h movement behavior time differs between men and women. Males tend to require a longer optimal MVPA duration than females, while females require a longer optimal SLP duration than males. The findings provide valuable reference for developing 24-h movement guidelines and promoting healthy and balanced lifestyles among university students. [Image: see text] The online version contains supplementary material available at 10.1186/s12889-026-26534-x. Show less
📄 PDF DOI: 10.1186/s12889-026-26534-x
LPA
Leying Zhao, Cong Zhao, Yaoxian Wang · 2026 · Circulation · added 2026-04-24
no PDF DOI: 10.1161/CIRCULATIONAHA.125.076090
LPA
Luyi Xu, Tingting Lin, Zheng Wang +3 more · 2026 · BMC geriatrics · BioMed Central · added 2026-04-24
This study aimed to identify the heterogeneity of attitudes toward ageing among older adults in the “early transition period” (the initial 2–4 weeks after nursing homes transition from home to nursing Show more
This study aimed to identify the heterogeneity of attitudes toward ageing among older adults in the “early transition period” (the initial 2–4 weeks after nursing homes transition from home to nursing homes). and the mediation effect of self-efficacy between attitudes toward ageing and quality of life (QoL). A total of 300 older adults were enrolled from October 2023 to May 2024. Participants completed the General Information Questionnaire, the Attitudes to Ageing Questionnaire (AAQ), the World Health Organization Quality of Life-Brief (WHOQOL-BREF), and the General Self-Efficacy Scale (GSES). Latent profile analysis (LPA), R3STEP methods, BCH methods, and mediation analysis were conducted to analyze the data. LPA categorized the attitudes toward ageing into three profiles: most negative (18.333%), moderately negative (64.000%), and positive (17.667%). Attitudes toward ageing profiles were associated with the following factors: age, pension, number of children, number of chronic diseases, ADL, willingness to reside in nursing homes, and social isolation. Self-efficacy partially mediates between attitudes toward ageing and the three dimensions of QoL (physical health, psychological health, and environmental health). Older adults during the “early transition period” had negative attitudes toward ageing. It may be related to the Chinese traditional interpersonal communication mode, family culture, and various maladaptive problems. Older adults who have two or more children, chronic diseases, no pension, moderate to severe dependency, involuntary admission to nursing homes, and social isolation are associated with more negative attitudes toward ageing. Mediation analysis reminds that self-efficacy can be used as intervention targets to improve the QoL. The online version contains supplementary material available at 10.1186/s12877-026-07007-7. Show less
📄 PDF DOI: 10.1186/s12877-026-07007-7
LPA
Qingyu Wang, Meijing Zhou, Sha Li +4 more · 2026 · Journal of nursing management · added 2026-04-24
To investigate potential types of food avoidance among patients with inflammatory bowel disease (IBD) and identify the contributing factors. Food avoidance may be an important risk factor for poor phy Show more
To investigate potential types of food avoidance among patients with inflammatory bowel disease (IBD) and identify the contributing factors. Food avoidance may be an important risk factor for poor physical and mental health in patients with IBD. However, there is limited research on food avoidance within the Chinese context. Between July 2022 and December 2023, patients with IBD during appointment at the First Affiliated Hospital with Nanjing Medical University was investigated with paper questionnaires to assess food avoidance, food category avoidance, fear of disease progression, negative illness perception, IBD-related self-efficacy, and social support. Demographic and disease-related characteristics were also collected. Latent profile analysis (LPA) was used to examine food avoidance in patients with IBD, and the correlates were investigated using regression analysis. LPA showed that respondents could be classified into three groups in terms of food avoidance, namely, the mild-food avoidance adaptation group ( Patients with IBD may exhibit long-term, spontaneous food avoidance, which often presents at high levels. Furthermore, patients with IBD exhibit considerable heterogeneity in their food avoidance patterns, categorizing them into three distinct categories. Future dietary management strategies should be tailored based on the specific characteristics and predictive factors of these food avoidance patterns. Given the prevalence and heterogeneity of food avoidance in patients with IBD, nurse managers should implement stratified interventions tailored to patient characteristics. Training nurses in culturally sensitive dietary education and emotional regulation strategies may improve the management of food-related behaviors and support patients' adaptive coping with the disease. Show less
📄 PDF DOI: 10.1155/jonm/3669996
LPA
Dan Jiang, Yi-Ling Liu, Jian Liu +7 more · 2026 · Lipids in health and disease · BioMed Central · added 2026-04-24
Calcific aortic valve disease (CAVD) is a cardiovascular disease closely associated with aging. The role of lipoprotein(a) [Lp(a)] has attracted considerable attention in recent years. However, limite Show more
Calcific aortic valve disease (CAVD) is a cardiovascular disease closely associated with aging. The role of lipoprotein(a) [Lp(a)] has attracted considerable attention in recent years. However, limited research has simultaneously explored the relationships between Lp(a), age, and CAVD. This study sought to assess the relationship linking Lp(a), time-weighted Lp(a), and CAVD. A total of 5,156 inpatients with comprehensive clinical data were recruited for this study. The associations of Lp(a) and time-weighted Lp(a) with CAVD were examined via multivariate logistic regression analysis, alongside the application of restricted cubic spline analysis. The diagnostic utility of Lp(a) and time-weighted Lp(a) for CAVD was assessed by constructing receiver operating characteristic (ROC) curves. CAVD prevalence rose with age, whereas the rate of increase diminished with advancing age. The average Lp(a) level in the young populations with CAVD was more than twice that in the No-CAVD group, particularly among those aged 55 years or younger. The prevalence of CAVD in non-elderly populations was markedly 2–4 fold greater in the higher Lp(a) group (> 30 mg/dL) than in the lower Lp(a) group (≤ 30 mg/dL). Multivariate adjusted odds ratios ‌(ORs) for CAVD increased with advancing Lp(a) or age. Time-weighted Lp(a), which takes into account both age and Lp(a), was more strongly linked to elevated CAVD risk than Lp(a) alone. Time-weighted Lp(a) enhanced the diagnostic value of CAVD, improving both sensitivity and specificity. The risk of CAVD is strongly associated with both age and elevated Lp(a) levels. Time-weighted Lp(a), which integrates these factors, serves as a superior indicator that better captures cumulative long-term Lp(a) variation and yields stronger CAVD risk stratification. The online version contains supplementary material available at 10.1186/s12944-026-02884-8. Show less
📄 PDF DOI: 10.1186/s12944-026-02884-8
LPA
Tingting Li, Lin Wang, Wenyu Li +3 more · 2026 · Angiology · SAGE Publications · added 2026-04-24
The present study aimed to investigate the combined impact of lipoprotein (a) [Lp(a)] and low-density lipoprotein (LDL) subfractions on cardiovascular outcomes in patients with acute coronary syndrome Show more
The present study aimed to investigate the combined impact of lipoprotein (a) [Lp(a)] and low-density lipoprotein (LDL) subfractions on cardiovascular outcomes in patients with acute coronary syndrome (ACS). The study enrolled 2061 ACS patients from Tianjin Chest Hospital. Participants were categorized into 4 groups based on their Lp(a) and the concentration of the sixth component particles of LDL(LDL-P6). The primary endpoint was the occurrence of major adverse cardiovascular events (MACE). The relationship between LDL-P6, Lp(a), and MACE was evaluated. Over a mean follow-up period of 5.4 years, 456 (22.1%) patients experienced MACE. Multivariate analysis identified both LDL-P6 and Lp(a) as significant independent predictors of MACE in ACS patients. Those in the highest-risk group had a substantially higher incidence of MACE compared with the lowest-risk group (HR 5.718; 95% CI 3.703-8.829; Show less
no PDF DOI: 10.1177/00033197251415207
LPA
Yangjuan Bao, Lili Yang, Jing-Yi Zhao +4 more · 2026 · PeerJ · added 2026-04-24
This study aimed to identify distinct patterns of chronic disease resource utilization among patients with chronic obstructive pulmonary disease (COPD) and to examine their association with illness un Show more
This study aimed to identify distinct patterns of chronic disease resource utilization among patients with chronic obstructive pulmonary disease (COPD) and to examine their association with illness uncertainty. A cross-sectional study. This study enrolled COPD patients hospitalized in the Department of Respiratory Medicine at a tertiary hospital in Zhejiang Province, China, between April and December 2023. All participants completed a general information form, the Chronic Illness Resource Survey (CIRS), and the Mishel Uncertainty in Illness Scale (MUIS). Latent profile analysis (LPA) was conducted to identify subgroups of resource utilization patterns. Subsequently, hierarchical linear regression was employed to assess the associations between these patterns and illness uncertainty. Ethical approval was obtained from the Institutional Review Board of the Fourth Affiliated Hospital of Zhejiang University (Approval No. K2022057). A total of 308 participants were included. Two latent classes of resource utilization were identified: the Suboptimal Utilization Group ( Distinct patterns of chronic disease resource utilization exist among COPD patients and are significantly associated with illness uncertainty. Healthcare providers should recognize these subgroups and implement targeted interventions to enhance access to disease-related support resources, thereby mitigating illness uncertainty. Understanding COPD patients' varying patterns of resource utilization enables healthcare professionals and related industries to deliver personalized, resource-based interventions tailored to individual needs, ultimately reducing illness-related uncertainty and improving disease management outcomes. Show less
📄 PDF DOI: 10.7717/peerj.20674
LPA
Qianqian Xiao, Man Wang, Shitao Wang +2 more · 2026 · Journal of translational medicine · BioMed Central · added 2026-04-24
Atherosclerotic cardiovascular disease (ASCVD) continues to be a major global health burden, with substantial residual cardiovascular risk remaining. Growing evidence highlights the liver’s pivotal ro Show more
Atherosclerotic cardiovascular disease (ASCVD) continues to be a major global health burden, with substantial residual cardiovascular risk remaining. Growing evidence highlights the liver’s pivotal role in the onset and development of ASCVD through multiple interconnected pathways. As the metabolic center of the body, the liver regulates the synthesis, secretion, and clearance of several atherogenic lipoproteins while simultaneously serving as a systemic inflammation amplifier, producing cytokines, acute-phase proteins, and coagulation factors. Traditional liver-targeted therapies, such as statins, have demonstrated that regulating liver metabolism can confer significant cardiovascular benefits. Subsequently, advances in nucleic acid-based drugs and in vivo gene-editing tools have broadened this strategy, enabling accurate and durable modulation of hepatic gene expression. However, recent clinical trials suggest that improvements in laboratory biomarkers do not always translate into proportional reductions in major adverse cardiovascular events. Moreover, the long-term safety and durability of lipid nanoparticles and gene-editing platforms remain ongoing concerns. Future research should focus on the classification of patients based on multiple omics data, and distinguish those whose main problem is metabolic disorder from those who are mainly at high risk of inflammation, thereby facilitating personalized therapeutic targeting. Overall, current evidence indicates that the liver represents a convergent therapeutic target for modulating both lipid metabolism and inflammation, offering a promising opportunity for deeper and more durable cardiovascular risk reduction. Show less
📄 PDF DOI: 10.1186/s12967-025-07647-0
LPA
Yali Jiang, Juanjuan Zhao, Kun Li +10 more · 2026 · BMC medical education · BioMed Central · added 2026-04-24
Massive open online courses (MOOCs) have transformed global education, yet their long-term effectiveness and evolving learner engagement remain underexplored. This study aims to comprehensively evalua Show more
Massive open online courses (MOOCs) have transformed global education, yet their long-term effectiveness and evolving learner engagement remain underexplored. This study aims to comprehensively evaluate a nursing MOOC over six years, examining learner engagement, identifying distinct learner profiles, and assessing changes across different developmental stages to inform future MOOC design. A retrospective study was conducted on 4171 completers of the Medical Nursing MOOC on a Chinese MOOC platform, covering eleven semesters from 2018 to 2023. Latent profile analysis (LPA) categorized learners based on unit test scores, and profile distributions were compared across the MOOC's developmental stages. The Medical Nursing MOOC attracted 69,642 registrants with a 5.99% completion rate. Among the 4171 individuals who completed the course, latent profile analysis identified six distinct learner types, demonstrating significant differences in overall learning effect (H = 2823.604, P < 0.001). The chi-squared analysis revealed significant differences between the proportions of the six profiles regarding MOOC developmental stages (χ Findings highlight the evolving role of MOOCs in nursing education. Despite challenges in long-term engagement, the increasing proportion of highly engaged learners and declining dropout rates indicate growing effectiveness and sustainability. These insights provide evidence-based guidance for optimizing MOOC design and implementation. Show less
📄 PDF DOI: 10.1186/s12909-026-08679-w
LPA
Mingliang Sun, Wenxin Lin, Rui Gong +7 more · 2026 · Lipids in health and disease · BioMed Central · added 2026-04-24
TyHGB is a novel insulin resistance (IR)-related indicator, and its association with coronary heart disease (CHD) remains unclear. Additionally, studies have shown a close correlation between the diag Show more
TyHGB is a novel insulin resistance (IR)-related indicator, and its association with coronary heart disease (CHD) remains unclear. Additionally, studies have shown a close correlation between the diagonal earlobe crease (DELC) and CHD, yet it has not been fully applied in clinical practice to date. Therefore, this study constructed and validated a diagnostic model for CHD by combining TyHGB and DELC. A total of 1664 patients suspected of CHD who underwent coronary angiography (CAG) in the Department of Cardiology, Chengde Central Hospital from September 2021 to April 2025 were recruited for this study. Participants were categorized into a CHD group ( Age, sex, hypertension, diabetes, CR, Lp(a), TyHGB, and DELC were identified as independent risk factors for CHD through multivariate logistic regression analysis ( Both TyHGB and DELC have been identified as independent risk factors for CHD, with a linear relationship observed between TyHGB levels and CHD risk. A diagnostic model for CHD, developed by integrating TyHGB, DELC, and traditional risk factors, demonstrates strong diagnostic efficacy. The online version contains supplementary material available at 10.1186/s12944-026-02880-y. Show less
📄 PDF DOI: 10.1186/s12944-026-02880-y
LPA