👤 Chin-Chih Liu

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3182

Articles

1983

Name variants

Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Huazhen Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, Yanyan Liu, Yanyao Liu, Yanying Liu, Yanyun Liu, Yao Liu, Yao-Hui Liu, Yaobo Liu, Yaoquan Liu, Yaou Liu, Yaowen Liu, Yaoyao Liu, Yaozhong Liu, Yaping Liu, Yaqiong 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articles

The association between short video addiction and emotion dysregulation among college students: a latent profile analysis and its influencing factors.

Shuhe Wang, Zhongguo Liu · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24

This study aimed to use latent profile analysis (LPA) to identify heterogeneous configurational patterns of short video addiction and emotion dysregulation among college students, and to systematicall Show more

This study aimed to use latent profile analysis (LPA) to identify heterogeneous configurational patterns of short video addiction and emotion dysregulation among college students, and to systematically examine the predictive effects of cognitive reappraisal, emotional loneliness, and sociodemographic factors on latent profile membership. A cross-sectional survey design was employed. From April to July 2025, full-time undergraduate students were recruited from multiple universities in Shandong Province using a combination of convenience sampling and snowball sampling. Participants completed online questionnaires including the Short Video Addiction Scale, the Emotion Dysregulation Inventory (EDI), the Cognitive Reappraisal Scale, and the Emotional Loneliness Scale. A total of 1,168 valid questionnaires were obtained. LPA identified four optimal profiles: Profile 1 ("low short video addiction-low emotion dysregulation"), Profile 2 ("medium to lower short video addiction-medium to lower emotion dysregulation"), Profile 3 ("medium to upper short video addiction-medium to upper emotion dysregulation"), and Profile 4 ("high short video addiction-high emotion dysregulation"). Multivariable logistic regression analyses indicated that, with Profile 4 as the reference category, cognitive reappraisal significantly increased the likelihood of membership in lower-risk profiles, whereas emotional loneliness significantly decreased the likelihood of membership in lower-risk profiles. Among sociodemographic factors, being female and having an urban background significantly increased the likelihood of membership in Profile 1 (vs. Profile 4); being a non-only child and having no part-time work experience significantly predicted membership in Profile 3. Marked heterogeneity exists among college students in the measured dimensions of short-form video addiction and emotion dysregulation, and the two constructs exhibit highly concordant co-variation. The findings provide empirical support for developing risk-stratified and precision-oriented mental health intervention strategies. Show less

📄 PDF DOI: 10.3389/fpsyg.2026.1789207

Symptom Burden in Patients with Acute Coronary Syndrome After Percutaneous Coronary Intervention: A Network and Latent Profile Analysis.

Shuqin Hong, Xiuni Gan, Wen Zhou +8 more · 2026 · Patient preference and adherence · added 2026-04-24

Shuqin Hong, Xiuni Gan, Wen Zhou, Yan Gao, Ran Li, Li Liu, Xinyuan Hu, Qiulan Zheng, Miao Huang, Lanlan Lou, Lian Tan Show less

To describe the network structure and heterogeneity of symptom burden in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI), and to examine factors associated w Show more

To describe the network structure and heterogeneity of symptom burden in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI), and to examine factors associated with different symptom burden profiles to inform risk-stratified management after PCI. A convenience sample of 261 patients with ACS who underwent PCI at a tertiary hospital in Chongqing between November 2024 and August 2025 was recruited. Data were collected using a demographic questionnaire, the Cardiac Symptom Survey, and the Seattle Angina Questionnaire. Network analysis was conducted to identify inter-symptom associations and the structural characteristics of the symptom network. Latent profile analysis (LPA) was performed to classify symptom burden patterns, and multinomial logistic regression analysis was used to explore factors associated with profile membership. Network analysis indicated that depression was the most central symptom (strength Symptom burden in patients with ACS after PCI demonstrates substantial individual heterogeneity. Depression occupies a central position within the symptom network, and BMI is associated with moderate and high symptom burden profiles. These findings suggest that integrating symptom network characteristics and BMI status into post-PCI assessment may facilitate risk-stratified management and targeted psychological and weight-related interventions to improve recovery outcomes. Show less

📄 PDF DOI: 10.2147/PPA.S580130

Associations between eHealth literacy and 24-hour movement behaviors in older adults: the mediating and moderating roles of self-efficacy.

Ning Su, Jiayu Hu, Borui Shang +4 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24

Ning Su, Jiayu Hu, Borui Shang, Xiang Wang, Wei Liang, Lin Zhou, Hao Liu Show less

Older adults increasingly rely on digital health resources, yet evidence regarding the relationship between eHealth literacy (eHL) and 24-hour movement behaviors (24-HMB), including physical activity Show more

Older adults increasingly rely on digital health resources, yet evidence regarding the relationship between eHealth literacy (eHL) and 24-hour movement behaviors (24-HMB), including physical activity (PA), sedentary behavior (SB), and sleep, remains underexplored. This study examined the associations between eHL and 24-HMB in Chinese older adults and examined self-efficacy as a potential mediator and moderator. Using a convenience sampling approach, 564 community-dwelling older adults (aged 60-74 years) were recruited from four urban Chinese cities via an online survey. A total of 553 valid cases were retained for analyses. eHL was assessed using the eHealth Literacy Scale-Web 3.0, and self-efficacy was assessed using a validated Self-Efficacy Scale. PA and SB were assessed objectively using ActiGraph GT3X+ accelerometers over three consecutive days (two weekdays and one weekend day). Sleep duration was derived from accelerometer-based estimates anchored by daily sleep logs. Multiple linear regression analyses were conducted to examine associations, and mediation and moderation were tested using PROCESS macro (Model 4 and Model 1, respectively), adjusting for age, sex, and education. After adjustment for covariates ( In this cross-sectional, urban, device-using sample of older adults, higher eHL was associated with a more favorable 24-HMB profile, particularly higher LPA and lower SB, while associations with sleep duration were weaker. Self-efficacy showed modest indirect associations consistent with partial mediation for PA and SB and also acted as a moderator of several associations. Given the observational design and modest effect sizes, findings should be interpreted cautiously and require confirmation in longitudinal or experimental studies with more representative sampling and improved sleep assessment. Show less

📄 PDF DOI: 10.3389/fmed.2026.1746861

2026 Consensus and review of Lipoprotein(a) from Taiwan Society of Lipid and Atherosclerosis: Molecular pathogenesis, epidemiology, clinical implications, and advances in diagnostic strategies.

Chao-Yun Cheng, Yih-Jer Wu, Chih-Fan Yeh +25 more · 2026 · Journal of the Formosan Medical Association = Taiwan yi zhi · Elsevier · added 2026-04-24

Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic Show more

Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve disease (CAVD). Structurally composed of a low-density lipoprotein (LDL)-like particle covalently linked to apolipoprotein(a) [apo(a)], Lp(a) exhibits unique atherogenic, thrombogenic, and inflammatory properties, largely due to its role as a carrier of oxidized phospholipids (OxPL). Plasma Lp(a) concentrations are predominantly determined by the number of kringle IV type 2 (KIV-2) repeats in the LPA gene, with minimal influence from lifestyle or environmental factors. Despite substantial evidence linking elevated Lp(a) to cardiovascular risk, clinical testing remains underutilized, especially in East Asian countries. In Taiwan, although population-level Lp(a) concentrations are comparatively low, a significant subset exceeds risk thresholds, with local studies confirming its prognostic value in coronary artery disease and ischemic stroke. Barriers, including limited physician awareness, implementation barriers, and therapeutic nihilism, contribute to its under-recognition. This review highlights the molecular features of Lp(a), its pathogenesis of cardiovascular disorders, epidemiology, and current barriers and future advances in diagnostic testing, with a particular focus on implications for cardiovascular risk management in Taiwan. Show less

no PDF DOI: 10.1016/j.jfma.2026.03.073

Replacing sedentary behaviour with physical activity and all-cause mortality in older adults: two-cohort study in China and the UK.

Huan Huang, Zhaojun Chen, Jiong Liu +4 more · 2026 · Journal of epidemiology and community health · added 2026-04-24

Huan Huang, Zhaojun Chen, Jiong Liu, Zhilin Huang, Dianjianyi Sun, Lu Qi, Tao Zhou Show less

Older adults typically have higher sedentary behaviour (SB) and lower physical activity (PA) than younger adults. Studies on replacing SB with PA in relation to all-cause mortality in racially diverse Show more

Older adults typically have higher sedentary behaviour (SB) and lower physical activity (PA) than younger adults. Studies on replacing SB with PA in relation to all-cause mortality in racially diverse older adults remain limited. This study included 122 966 older adults from the China Kadoorie Biobank (CKB) and 207 212 older adults from the UK Biobank (UKB). SB and PA were assessed using baseline questionnaires, with PA classified as light (LPA), moderate (MPA) or vigorous (VPA) based on metabolic equivalents. Cox proportional hazards models and isotemporal substitution models were used to examine the associations between replacing SB with different PA intensities and all-cause mortality. Longer SB (per 30 min/day increase) was associated with a higher risk of all-cause mortality in both cohorts (CKB: HR 1.013, 95% CI 1.010 to 1.017; UKB: HR 1.012, 95% CI 1.009 to 1.015). PA of any intensity was associated with a reduced risk of all-cause mortality. In the CKB, replacing 30 min/day of SB with an equivalent duration of PA showed comparable protective associations (LPA: HR 0.963, 95% CI 0.958 to 0.968; MPA: HR 0.967, 95% CI 0.961 to 0.972; VPA: HR 0.965, 95% CI 0.960 to 0.971). In the UKB, replacing 30 min/day of SB with VPA was associated with the largest reduction in mortality risk (HR: 0.950, 95% CI 0.931 to 0.970). Replacing SB with PA of any intensity was associated with reduced all-cause mortality risk in older adults, with variations across populations. These findings highlight the need for population-specific PA recommendations to promote healthy ageing. Show less

no PDF DOI: 10.1136/jech-2025-225695

Latent Physical Activity Patterns and Their Related Factors in Breast Cancer Patients During Chemotherapy Intervals.

Siqi Shi, Guangting Chang, Chunying Xie +3 more · 2026 · Patient preference and adherence · added 2026-04-24

Siqi Shi, Guangting Chang, Chunying Xie, Jingjing Xiao, Yanling Liu, Shu Cai Show less

Previous research on breast cancer patients has primarily examined singular behavioral indicators, often overlooking the coexistence and interaction between physical activity and sedentary behavior-pa Show more

Previous research on breast cancer patients has primarily examined singular behavioral indicators, often overlooking the coexistence and interaction between physical activity and sedentary behavior-particularly screen-based sedentary time. This study aims to identify the latent activity pattern categories among breast cancer patients during chemotherapy intervals and explore their associated factors to inform targeted behavioral interventions. A cross-sectional survey was conducted with 292 breast cancer patients undergoing chemotherapy intervals at four general hospitals in Foshan, Guangdong Province. Latent Profile Analysis (LPA) was applied as a person-centered analytic approach to identify distinct activity pattern profiles. Data were collected using a general information questionnaire, the Adult Sedentary Behavior Questionnaire (ASBQ), the Chinese version of the International Physical Activity Questionnaire (IPAQ-SC), the Exercise Self-Efficacy Scale (ESES), the Perceived Social Support Scale (PSSS), and the Hospital Anxiety and Depression Scale (HADS). The activity patterns of breast cancer patients were categorized into three groups: Moderate Activity-Dominant Group (37.33%), Screen-Sedentary High-Risk Group (8.22%), and Activity-Sedentary Coexistence Group (54.45%). Logistic regression analysis showed that, compared to the Moderate Activity-Dominant Group, patients with low exercise self-efficacy and higher anxiety and depression levels were more likely to be classified into the Screen-Sedentary High-Risk Group and Activity-Sedentary Coexistence Group. Higher education levels and being on medical leave were associated with a higher probability of belonging to the Activity-Sedentary Coexistence Group (all Activity patterns in breast cancer patients show significant heterogeneity. Healthcare providers should pay attention to the individual physical activity characteristics of patients and offer personalized physical activity guidance. Tailored interventions that meet the needs of breast cancer patients should be developed to improve health outcomes. Show less

📄 PDF DOI: 10.2147/PPA.S561144

Cryo-EM structures of Oryza sativa MRP5 reveal a phytate accumulation mechanism in plant vacuoles.

Jiaqi Zuo, Jie Zhang, Ying Tang +10 more · 2026 · The Plant cell · Oxford University Press · added 2026-04-24

Jiaqi Zuo, Jie Zhang, Ying Tang, Lihuan Jiang, Shuo Cao, Yan Liu, Cuicui Shen, Chuang Wang, Hao Chen, Lizhong Xiong, Ping Yin, Zhou Gong, Zhu Liu Show less

Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties Show more

Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties make cytosolic accumulation cytotoxic, necessitating its sequestration into vacuoles for safe storage. Here, we present the cryo-EM structures of the rice vacuolar phytate transporter, OsMRP5, captured in distinct functional states. These structures reveal the molecular basis of OsMRP5 function as an ATP-binding cassette (ABC) transporter. OsMRP5 employs a specialized substrate-recognition mechanism, uniquely adapted to bind the fully hydrophilic InsP6 through extensive electrostatic and hydrogen-bonding interactions within two distinct, highly polar binding sites in its central cavity. A distinctive electropositive tunnel, positioned above the central cavity, forms a continuous pathway connecting the InsP6-binding pocket to the vacuolar export site. This tunnel likely generates an electrostatic attraction that facilitates the movement of the highly anionic InsP6 through the transporter. By mapping mutations from low-phytic acid (lpa) crop variants onto the OsMRP5 structures, we pinpoint their conserved locations critical for transporter function and validate their impact experimentally. These results reveal how OsMRP5 recognizes and transports the highly charged InsP6 molecules into vacuoles, providing a molecular framework for targeted manipulation of this agriculturally important transporter. Show less

no PDF DOI: 10.1093/plcell/koag088

Potential profile and influencing factors of feedback-seeking behavior among nursing interns during the early stages of internships: a multicenter cross-sectional study.

Chuqin Xiong, Shuge Wang, Peiran Guo +6 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24

Chuqin Xiong, Shuge Wang, Peiran Guo, Yao Lei, Yanling Xiao, Qiuxia Chen, Liuliu Guo, Zhixia Zhang, Limin Liu Show less

Nursing interns often face maladjustment during the early stages of clinical practice, which not only directly affects their physical and mental health as well as work efficiency but also significantl Show more

Nursing interns often face maladjustment during the early stages of clinical practice, which not only directly affects their physical and mental health as well as work efficiency but also significantly inhibits their proactive feedback-seeking behavior (FSB). As an active self-regulation strategy, FSB can enhance interns' work initiative and promote role transition. However, existing research has yet to thoroughly investigate the potential heterogeneity and categorical characteristics of FSB within this population, and the role of psychological resources such as career adaptability in shaping these patterns requires further investigation. To investigate the status of FSB in early-stage nursing interns, identify latent subgroups via latent profile analysis (LPA), and analyze associated factors, thereby providing evidence for targeted clinical educational interventions. Multicenter cross-sectional research. This study employed a multistage stratified cluster sampling to survey 1,308 early-stage nursing interns from nine universities in Hubei, China, between June and September 2024. Data were collected using a demographic questionnaire, Feedback-Seeking Behavior Scale, and Career Adapt-Abilities Scale. LPA was employed to delineate FSB profiles and multivariate logistic regression analysis to examine the associated predictors. A total of 1,370 questionnaires were distributed, with 1,308 valid responses, yielding an effective response rate of 95.47%. The mean score on the feedback-seeking behavior scale was 5.06 ± 1.08. LPA identified three distinct feedback-seeking profiles: low (20.87%), moderate (38.3%), and high (40.83%). Education level, student cadre experience, internship hospital type, and career adaptability were significant predictors of profile membership ( FSB among early-stage nursing interns exhibited heterogeneity. Nursing educators and managers should implement tiered interventions: for the low and moderate feedback-seeking groups, career guidance and feedback awareness cultivation should be strengthened; for the high feedback-seeking group, peer modeling should be encouraged. This strategy can enhance proactive FSB, supports role transition and professional identity, and promotes long-term nursing workforce stability. Show less

📄 PDF DOI: 10.3389/fmed.2026.1664329

Heterogeneity in dual-career stress: an integrative person-centered and distribution-sensitive analysis of its asymmetric effects on adolescent football players.

Zhengri Quan, Guannan Liu, Hang Yin +1 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24

Zhengri Quan, Guannan Liu, Hang Yin, Dan Pang Show less

This study examined the heterogeneous nature of dual-career stress and its asymmetric associations with on adolescent athletes, aiming to: (1) identify distinct stress profiles based on academic, trai Show more

This study examined the heterogeneous nature of dual-career stress and its asymmetric associations with on adolescent athletes, aiming to: (1) identify distinct stress profiles based on academic, training, and role-conflict stressors; (2) assess whether stress associations vary across levels of athletic burnout and academic performance; and (3) test whether stress profiles moderate these relationships. A two-wave longitudinal study included 843 adolescent male football players in China. Latent Profile Analysis (LPA) categorized participants using three stressor subscales at Time 1. Quantile Regression (QR) at Time 2 (6 months later) analyzed the association between total stress and athletic burnout and academic performance across five quantiles (τ = 0.10-0.90), with stress profile as moderator, controlling for social support, time management, and demographics. LPA revealed four profiles: Balanced Moderates (37.2%), Academically Overwhelmed (28.1%), Sport-Centric Strained (22.0%), and Dual-Track Distressed (12.7%). QR showed the positive association between stress and burnout increased across quantiles (β = 0.41 at τ = 0.10 to 0.78 at τ = 0.90), with the strongest association observed among already burnt-out athletes most. For academic performance, the negative association between stress and performance was strongest at lower quantiles (β = -0.71 at τ = 0.10) and weaker at higher quantiles (β = -0.29 at τ = 0.90). Stress profiles significantly moderate these relationships: the Dual-Track Distressed profile showed the strongest association with on burnout (β = 0.89), while Academically Overwhelmed and Dual-Track Distressed profiles showed the strongest negative association with on academic performance (β = -0.79 and -0.92, respectively). Dual-career stress experiences and impacts are highly heterogeneous. Adolescents cluster into meaningful stress profiles, and stress is most strongly associated with negative outcomes among those already at extremes of burnout or poor academic performance. Findings underscore the need for personalized interventions tailored to athletes' specific stress profiles and outcome levels, supporting holistic development in dual-career contexts. Show less

📄 PDF DOI: 10.3389/fpsyg.2026.1789877

Negative emotional symptoms and e-Health literacy among Chinese college students: a latent profile analysis.

Di Dai, Qingping Zhou, Yusupujiang Tuersun +6 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24

Di Dai, Qingping Zhou, Yusupujiang Tuersun, Yuying Xie, Yao Yu, Siyuan Liu, Chenxi Wang, Zhenning Liang, Yi Qian Show less

Negative Emotional symptoms such as depression and anxiety do not exist independently, often co-occurring in the same individual, and heterogeneity exists between individuals suffering from depression Show more

Negative Emotional symptoms such as depression and anxiety do not exist independently, often co-occurring in the same individual, and heterogeneity exists between individuals suffering from depression and anxiety; however, prior research has rarely investigated heterogeneity in a person-centered manner and from the perspective of college students. The main purpose of this study was to explore this heterogeneity and its association with e-Health literacy (e-HL) using Latent profile analysis (LPA), a person-centered statistical method. A total of 7,503 Chinese college students from 10 regions (including Guangdong Province, Shanghai Municipality, and Jiangsu Province) were surveyed using the Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9) to assess anxiety and depressive symptoms. LPA was employed to identify potential profiles of negative emotional symptoms and validate their robustness; binary logistic regression was used to explore differences in demographic characteristics (sex, grade ranking), sociological factors (family residential background, per capita monthly family income), and lifestyle factors (adherence to physical activity, smoking status, alcohol consumption) across profiles; analysis of variance (ANOVA) was applied to compare e-HL levels among different profiles. The two-class model was identified as the optimal classification of negative emotional symptoms: low/no negative emotional symptoms (61.49%) and high negative emotional symptoms (38.51%). Female college students, those with low per capita monthly family income, lack of regular physical exercise, and alcohol consumption habits were more likely to be categorized into the high negative emotional symptoms group (all Reliance on self-report measures may lead to recall bias and social desirability bias; the cross-sectional design cannot establish causal relationships between variables; digital addiction, a potential confounding factor that may co-occur with negative emotional symptoms and influence e-HL, was not included in the analysis. This study identified two distinct latent profiles of negative emotional symptoms among Chinese college students and their key predictive factors using LPA. The findings highlight the need for stratified early screening for high-risk groups (females, low-income families, inactive individuals, and drinkers) and the development of targeted interventions. Enhancing e-HL could be a potential pathway to improve mental health outcomes, providing actionable insights for scientific and effective mental health management in colleges and universities. Show less

📄 PDF DOI: 10.3389/fpsyg.2026.1760468

Fear of progression and association factors in stroke patients: a latent profile analysis.

Yunyun Liu, Xiangrui Li, Ting Zhao +9 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24

Yunyun Liu, Xiangrui Li, Ting Zhao, Bo Wan, Leyao Han, Yaya Xi, Jingying Xiong, Meishan Zhang, Yingqiao Wang, Xinman Dou, Weiping Li, Xinglei Wang Show less

Fear of progression (FoP) is a prevalent psychological issue among stroke patients. Previous studies failing to distinguish characteristics of patient groups with varying FoP levels. Latent profile an Show more

Fear of progression (FoP) is a prevalent psychological issue among stroke patients. Previous studies failing to distinguish characteristics of patient groups with varying FoP levels. Latent profile analysis (LPA) classifies individuals into distinct subgroups via continuous FoP indicators, boosting classification accuracy by accounting for variable uncertainty. Given FoP's heterogeneity, investigating FoP profiles and their influencing factors in stroke patients is clinically significant for personalized psychological care and improved patient quality of life. A total of 366 stroke patients were selected as study subjects through convenience sampling, and a cross-sectional survey was conducted. FoP was assessed using the Fear of Progression Questionnaire-Short Form (FoP-Q-SF, 2 dimensions, 12 items). Independent variables included demographic characteristics, clinical indicators, the Recurrence Risk Perception Scale for Stroke patients (RRPSS), and the Medical Coping Modes Questionnaire (MCMQ). LPA was performed on the FoP-Q-SF items to identify subgroups. The R3STEP method was used to analyze influencing factors of subgroup membership, and the BCH method was applied to compare differences in distal outcomes across subgroups. Statistical significance was set at The study sample had a mean age of 63.93 ± 10.58 years, with 70.5% males and 65.0% first-ever stroke patients. Two latent profiles were identified: Low-FoP Adaptive Type (C1, 48.6%) and High-FoP Sustained Type (C2, 51.4%). The R3STEP showed that age 18-59 years (OR = 0.476, 95%CI = 0.245-0.924, This study revealed significant heterogeneity in FoP among stroke patients. Age, hypertension comorbidity, excessive recurrence risk perception, MCMQ-confrontation, and MCMQ-avoidance were associated with high FoP. Healthcare providers should prioritize identifying high-risk individuals and develop tailored interventions to reduce FoP and improve rehabilitation outcomes. Show less

📄 PDF DOI: 10.3389/fpsyg.2026.1741344

Symptom experience subtypes and their association with social isolation in older adults with diabetes and coronary heart disease: a latent profile analysis study.

Niuniu Zhou, Yuzhong Gu, Jianyun Liu +4 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24

Niuniu Zhou, Yuzhong Gu, Jianyun Liu, Lingyan Zhang, Ling Chen, Yi Lu, Tingyu Peng Show less

To identify latent classes based on symptom clusters and to explore the association between these distinct symptom experience subtypes and social isolation in older adults with comorbid diabetes melli Show more

To identify latent classes based on symptom clusters and to explore the association between these distinct symptom experience subtypes and social isolation in older adults with comorbid diabetes mellitus (DM) and coronary heart disease (CHD). A cross-sectional study was conducted among 337 older adults with DM and CHD recruited from the Department of Endocrinology and Cardiology of Nantong Sixth People's Hospital between February 2023 and October 2025. Data were collected using a general information questionnaire, the Chinese version of the Memorial Symptom Assessment Scale (MSAS), and the Lubben Social Network Scale-6 (LSNS-6). Exploratory factor analysis (EFA) was used to identify symptom clusters. Latent profile analysis (LPA) was then employed to classify patients into different symptom experience subtypes based on the symptom cluster scores. One-way ANOVA, Chi-square tests, and multiple linear regression were used to analyze the association between latent classes and social isolation. EFA extracted three symptom clusters (cardiopulmonary-fatigue, emotional-perceptual, and metabolic), accounting for 62.3% of the total variance. LPA identified three distinct latent classes: Class 1 "Low Burden-Balanced Pattern" (45.4%), Class 2 "Psycho-Somatic Co-dominant Pattern" (31.8%), and Class 3 "Metabolic-Physical Dominant Pattern" (22.8%). Univariate analysis revealed significant differences in social isolation scores (LSNS-6) across the three classes ( The findings reveal significant heterogeneity in symptom experiences among older adults with comorbid DM and CHD, which can be categorized into distinct latent classes. The subtype characterized by a Psycho-Somatic Co-dominant Pattern shows the strongest association with social isolation. In clinical practice, early identification of this high-burden subgroup may facilitate the provision of integrated interventions that address physical, psychological, and social dimensions. Show less

📄 PDF DOI: 10.3389/fmed.2026.1756120

Identifying subgroups of nurses' voice behavior in clinical settings: a latent profile analysis of work motivation and demographic predictors.

Jingting He, Yanping Ying, Qiufang Lu +6 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24

Jingting He, Yanping Ying, Qiufang Lu, Huihan Zhao, Yuan Wen, Hongyu Lu, Jiajie Ning, Pengxin Dong, Liying Liu Show less

Nurses' voice behavior is critical for patient safety and organizational improvement. However, its manifestation is not uniform among nurses. This study aimed to identify latent profiles of nurses' vo Show more

Nurses' voice behavior is critical for patient safety and organizational improvement. However, its manifestation is not uniform among nurses. This study aimed to identify latent profiles of nurses' voice behavior using Latent Profile Analysis (LPA) to understand this heterogeneity and explore its influencing factors, with a specific focus on differences across work motivation dimensions (rooted in Self-Determination Theory, SDT). A multicenter cross-sectional design was adopted. Data from 701 clinical nurses across six hospitals in Guangxi Province were analyzed: LPA identified four distinct profiles, and Multinomial Logistic Regression was used to examine predictors. Work motivation was measured by the Multidimensional Work Motivation Scale (MWMS), and voice behavior by the Voice Behavior Scale (VBS). LPA identified four distinct profiles (Conservative, 5.42%; Balanced Risk-Taker, 26.39%; Transitional, 34.38%; Challenging, 33.8%), and Multinomial Logistic Regression was used to examine predictors. Work motivation was measured by the Multidimensional Work Motivation Scale (MWMS), and voice behavior by the Voice Behavior Scale (VBS). Results showed autonomous motivation (e.g., intrinsic drive) strongly predicted active voice behavior, while amotivation predicted conservative profiles. Nurses exhibited high work motivation (MWMS: 93.02 ± 21.09) and moderately high voice behavior (VBS: 39.27 ± 8.736). The research found that nurses exhibited high work motivation and moderately high voice behavior, with autonomous motivation being a pivotal predictor. Differentiated strategies targeting intrinsic motivation enhancement are critical for fostering nursing innovation and improving care quality. Show less

📄 PDF DOI: 10.3389/fpsyg.2026.1732216

The impact of 24-h activity patterns on executive function in older adults with chronic diseases: analysis of the isotonic substitution effect.

Zhiji Wang, Lin Wang, Shijie Liu +4 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24

Zhiji Wang, Lin Wang, Shijie Liu, Li Zhan, Sijun Wu, Linxia Tang, Hong Wang Show less

24-h activity encompasses four categories: light-intensity physical activity (LPA), moderate-to-vigorous-intensity physical activity (MVPA), sedentary behavior (SB), and sleep (SP). This study aims to Show more

24-h activity encompasses four categories: light-intensity physical activity (LPA), moderate-to-vigorous-intensity physical activity (MVPA), sedentary behavior (SB), and sleep (SP). This study aims to investigate the effects of different physical activity components on executive function in older adults with chronic diseases and to examine substitution effects among activity components. The findings provide scientific evidence to inform physical activity interventions for improving executive function in older adults with chronic diseases. A total of 105 older adults (72.64 ± 6.82 years) were recruited. Following questionnaire screening, 75 older adults with chronic diseases were ultimately included. Accelerometers objectively measured participants' daily SP, SB, LPA, and MVPA. Executive function was objectively assessed using the Stroop task, N-back task, and More-odd-shifting task. Component linear regression equation assessed the relationship between different activities and executive function in older adults with chronic diseases. The dose-response effects of "one-for-one" substitutions between different activity behaviors were explored. Component linear regression results showed that SB positively correlated with inhibitory control ( SP and MVPA significantly improve inhibitory control in older adults with chronic diseases, while LPA significantly enhances their working memory. It is recommended that older adults with chronic diseases adjust their daily time structure by increasing diverse physical activities, ensuring adequate sleep duration, and reducing sedentary behavior to improve executive function. Show less

📄 PDF DOI: 10.3389/fpubh.2026.1733294

Nurses' Cross-Border Work Intentions Driven by Psychological Empowerment: A Cross-Sectional Study.

Ruoxuan Zhang, Xin Wang, Angela Y M Leung +8 more · 2026 · Journal of nursing management · added 2026-04-24

Ruoxuan Zhang, Xin Wang, Angela Y M Leung, Xiaoyan Jin, Hongxia Dai, Yan Wang, June Zhang, Renli Deng, Angela C Y Poon, Ka Wa Mio, Ming Liu Show less

Given the globalization of the nursing workforce, psychological empowerment represents a critical intrinsic determinant of nurses' mobility intentions, specifically regarding cross-border work. To ide Show more

Given the globalization of the nursing workforce, psychological empowerment represents a critical intrinsic determinant of nurses' mobility intentions, specifically regarding cross-border work. To identify latent profiles of nurses' psychological empowerment, examine associated factors, and explore the relationship between these profiles and cross-border working intention. A cross-sectional multicenter study was conducted from March to September 2023. Using convenience sampling, clinical nurses were recruited through liaisons from nursing societies in nine cities of Guangdong Province. Data were collected through questionnaires covering sociodemographic questionnaire, psychological empowerment, and cross-border working intention, with analyses including chi-square tests, logistic regression, and latent profile analysis (LPA) performed using SPSS 23.0 and Mplus 8.3. A total of 3671 valid questionnaires were collected, and 39.5% of the respondents reported cross-border intentions. LPA identified three psychological empowerment profiles among nurses, ranked from high to low: the core-driven empowerment profile (16.94%), the adaptive empowerment profile (70.42%), and the constrained empowerment profile (12.64%). The nurses with lower salary, intermediate title, and without specialist nurse qualification were more likely to fall into the constrained empowerment profile. Psychological empowerment was positively correlated with nurses' cross-border work intention. The core-driven profile showed the highest cross-border work intention (50.6%), followed by the adaptive (38.2%) and constrained profiles (31.7%). For cross-border work, the constrained profile prioritized salary (87.1%) as the key concern, while the core-driven profile focused more on good promotion opportunities (70.3%). Psychological empowerment exerts a positive impact on clinical nurses' cross-border work intention, with the three identified empowerment profiles exhibiting divergent motivational priorities and decision logics. These findings highlight the need for subgroup-specific strategies to balance nursing workforce mobility and stability. The findings support a differentiated human resource strategy based on nurses' psychological empowerment profiles. For core-driven nurses, institutions should provide international career development channels to strengthen their domestic job embeddedness. For adaptive nurses, tailored skill training and decision-making autonomy should be offered to guide their mobility aspirations. For constrained nurses, competitive compensation and family support services should be prioritized to address their stability needs and rebuild professional confidence. These targeted measures balance talent mobility and domestic workforce stability. Show less

📄 PDF DOI: 10.1155/jonm/8714790

Associations between physical activity parameters and multidimensional trajectories of muscle health in middle-aged and older adults: a group-based multi-trajectory modeling study.

Dongxue Liu, Yihan Pan, Hairong Wang +1 more · 2026 · Journal of exercise science and fitness · Elsevier · added 2026-04-24

Dongxue Liu, Yihan Pan, Hairong Wang, Xiaoguang Zhao Show less

This study used a group-based multi-trajectory model (GBMTM) to identify distinct muscle health trajectories and examine their associations with physical activity (PA) in middle-aged and older adults. Show more

This study used a group-based multi-trajectory model (GBMTM) to identify distinct muscle health trajectories and examine their associations with physical activity (PA) in middle-aged and older adults. Data were obtained from 2818 middle-aged and older adults (aged ≥40 years) in the China Health and Retirement Longitudinal Study (2011-2015). Muscle health was assessed using muscle mass (appendicular skeletal muscle mass index), muscle strength (handgrip strength), and physical performance (5-time chair stand test). PA was assessed using the International Physical Activity Questionnaire Short Form. A GBMTM was applied to jointly identify longitudinal trajectories of muscle mass, muscle strength, and physical performance, and to evaluate their associations with PA. In this study, four muscle health trajectories were identified: low-function declining, moderate-function declining, moderate-function stable, and high-function stable group. Engaging in ≥150 min/wk of light PA (LPA), moderate PA (MPA), or vigorous PA (VPA) was associated with the moderate-function stable group (LPA: aOR = 3.44, 95% CI: 1.94 - 6.11; MPA: aOR = 2.83, 95% CI: 1.67 - 4.96; VPA: aOR = 2.88, 95% CI: 1.61 - 5.13) and the high-function stable group (LPA: aOR = 5.20, 95% CI: 2.44 - 11.19; MPA: aOR = 4.10, 95% CI: 1.92 - 8.73; VPA: aOR = 3.42, 95% CI: 1.55 - 8.55). In older adults aged ≥70 years, associations persisted for MPA and VPA. Distinct muscle health trajectories highlight individualized muscle aging and inform personalized PA guidance. Regular PA ≥150 min/wk across intensities was associated with more favorable longitudinal muscle health. Show less

📄 PDF DOI: 10.1016/j.jesf.2026.200462

Latent profiles and network analysis of adolescent depression and cognitive impairment.

Xintong Ma, Wei Li, Yuanyuan Liu +8 more · 2026 · BMC psychiatry · BioMed Central · added 2026-04-24

Xintong Ma, Wei Li, Yuanyuan Liu, Xue Kang, Qi Xin, Junru Li, Kaiyue Wang, Yuke Zhang, Yanlong Li, Qingyuan Yu, Fei Yin Show less

Adolescence is a critical period for rapid emotional and cognitive development. Depression and cognitive impairment frequently co-occur in this population, yet their comorbidity patterns and symptom-l Show more

Adolescence is a critical period for rapid emotional and cognitive development. Depression and cognitive impairment frequently co-occur in this population, yet their comorbidity patterns and symptom-level interactions remain insufficiently explored. A total of 2,244 students (mean age = 16.8 ± 0.84 years; 1,218 males, 1,026 females) from a high school in Heilongjiang Province, China, were recruited. Depressive symptoms and cognitive impairment were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Perceived Deficits Questionnaire–Depression (PDQ-D). Latent profile analysis (LPA) was applied to identify subgroups, followed by network analysis to examine central symptoms (expected influence, EI), bridge symptoms (bridge expected influence, BEI), and network differences (NCT). The optimal LPA model identified three comorbidity subgroups: low, moderate, and high. NCT revealed significant differences in network structure and global strength between the low–moderate (S = 1.514, Adolescent Depression and Cognitive Impairment can be classified into low, moderate, and high comorbidity subgroups. Somatic symptoms emerged as the central symptom, while prospective memory impairment and interpersonal problems were identified as key bridge symptoms, suggesting potential intervention targets for early screening and stratified treatment. Not applicable. The online version contains supplementary material available at 10.1186/s12888-026-07946-w. Show less

📄 PDF DOI: 10.1186/s12888-026-07946-w

Flourishing and its influencing factor in inflammatory bowel disease patients: a latent profile analysis.

Bingyuan Lu, Linlin Ma, Fei Xia +5 more · 2026 · Frontiers in psychiatry · Frontiers · added 2026-04-24

Bingyuan Lu, Linlin Ma, Fei Xia, Lu Zhang, Ying Liu, Xiaoyan Yu, Renjuan Sun, Yanfang Luo Show less

Flourishing is a key positive psychological construct that has been linked to favorable health-related outcomes in patients with inflammatory bowel disease in prior research. However, current research Show more

Flourishing is a key positive psychological construct that has been linked to favorable health-related outcomes in patients with inflammatory bowel disease in prior research. However, current research often overlooks the variations in flourishing levels within this population, as well as the mechanisms through which flourishing interacts with disease progression. This study aimed to identify latent categories of flourishing among patients with inflammatory bowel disease and to analyze the potential influencing factors. This study employed a cross-sectional, descriptive exploratory design involving 316 patients diagnosed with inflammatory bowel disease. Data collection was carried out using a general information questionnaire, the Flourishing Scale (FS), the IBD Self-Efficacy Scale (IBD-SES), the Resilience Scale for Inflammatory Bowel Disease (RS-IBD), and the Social Support Rating Scale (SSRS). Latent profile analysis (LPA) was utilized to identify potential subgroups exhibiting flourishing, while multiple logistic regression analysis was conducted to evaluate the influencing factors. The flourishing of individuals with inflammatory bowel disease was classified into three latent groups: the low flourishing-low support beneficiary group ( Patients with inflammatory bowel disease demonstrate three distinct latent categories of flourishing. Healthcare professionals should implement more accurate and targeted intervention measures based on the characteristics and influencing factors of different potential categories, in order to improve the flourishing levels of patients with inflammatory bowel disease. Show less

📄 PDF DOI: 10.3389/fpsyt.2026.1751497

Core fucosylation of NCEH1 by FUT8 promotes progression of high-grade serous ovarian cancer by driving tumor-associated macrophage M2 polarization.

XiaoSong Pei, Fei Wang, Xiaomin Liu +7 more · 2026 · Oncogene · Nature · added 2026-04-24

XiaoSong Pei, Fei Wang, Xiaomin Liu, Yuyu Lei, Yu Chen, Bo Liu, Ruixuan Sun, Peiyu Li, Jianlei Bi, Shuai Liu Show less

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

Latent Profile and Influencing Factor Analysis of Childbirth Readiness Among Women in Their Third Trimester of Pregnancy: A Cross-Sectional Study.

Cailing Liu, Yueyuan He, Xue Yang +5 more · 2026 · International journal of women's health · added 2026-04-24

Cailing Liu, Yueyuan He, Xue Yang, Zheyao Ren, Yu Liu, Jingjing Wei, Yu Pan, Shanhong Zhang Show less

This study aimed to assess the childbirth readiness of women in their third trimester of pregnancy and to identify distinct readiness profiles using latent profile analysis (LPA). Additionally, it exp Show more

This study aimed to assess the childbirth readiness of women in their third trimester of pregnancy and to identify distinct readiness profiles using latent profile analysis (LPA). Additionally, it explored the factors influencing childbirth readiness in order to guide targeted interventions for improved maternal and neonatal outcomes. A cross-sectional study was conducted among women in their third trimester of pregnancy between May and November 2024. Eligible participants completed a general information questionnaire, the Childbirth Readiness Scale (CRS), the Childbirth Attitude Questionnaire (CAQ), and the Perceived Social Support Scale (PSSS). LPA identified three groups with distinct childbirth readiness levels: "Low Readiness - Childbirth Knowledge Deficit" (37.9%), "Moderate Readiness - Good Lifestyle Habits" (47.9%), and "High Readiness - Rich Health Knowledge" (14.2%). In addition, gestational age, previous childbirth history, adverse pregnancy outcomes, childbirth attitudes, and social support had different influences on women in different latent profiles of childbirth readiness. There was significant heterogeneity in childbirth readiness among women in their third trimester. Women with lower readiness-especially in childbirth knowledge-would greatly benefit from targeted educational programs, whereas those with moderate readiness levels would find enhanced emotional and psychological support most advantageous. These findings support the implementation of profile-based, personalized prenatal care strategies to improve childbirth preparedness and optimize maternal and neonatal outcomes. Show less

📄 PDF DOI: 10.2147/IJWH.S574855

Unmasking the "Mixed-Dysregulated" phenotype: latent profiles of affective temperaments and non-suicidal self-injury in adolescents with depression.

Shaowei Liu, Bin Ma, Yanju Liu +3 more · 2026 · BMC psychiatry · BioMed Central · added 2026-04-24

Shaowei Liu, Bin Ma, Yanju Liu, Xiaofang Liu, Xiuli Li, Xingxing Zhang Show less

Non-suicidal self-injury (NSSI) is highly prevalent among adolescents with depression, yet the heterogeneity of underlying temperamental risk factors remains poorly understood. Traditional variable-ce Show more

Non-suicidal self-injury (NSSI) is highly prevalent among adolescents with depression, yet the heterogeneity of underlying temperamental risk factors remains poorly understood. Traditional variable-centered approaches fail to capture how distinct affective temperaments co-occur within individuals. This study aimed to identify latent profiles of affective temperaments and examine their association with NSSI, exploring the statistical mediating role of cognitive emotion regulation (CER). A cross-sectional study was conducted from February 2025 to September 2025 at the First Hospital of Hebei Medical University. A total of 290 adolescents (aged 10–19) diagnosed with Major Depressive Disorder were recruited, with 282 valid responses included in the final analysis. Participants completed the TEMPS-A, CERQ, and ASHS. Latent Profile Analysis (LPA) was utilized to identify temperament subgroups. Mediation analysis with bootstrapping was performed to test the indirect effects of CER strategies. LPA identified three distinct profiles: Resilient/Low-risk (Class 1, 32.6%), Anxious-Depressive (Class 2, 46.1%), and Mixed-Dysregulated (Class 3, 21.3%). The Mixed-Dysregulated group, characterized by simultaneous elevations in depressive, anxious, irritable, and cyclothymic temperaments, exhibited the highest frequency (45.2 ± 21.3 times/year) and prevalence (98.8%) of NSSI compared to other groups ( The findings delineate a specific “Mixed-Dysregulated” risk phenotype within adolescent depression that is associated with severe NSSI. Interventions should move beyond standard depression care to target cognitive flexibility and emotional regulation skills. Statistical mediation analysis suggests that this risk is mediated by maladaptive cognitive emotion regulation strategies. Not applicable. Show less

📄 PDF DOI: 10.1186/s12888-026-07910-8

The Association Between 24-h Movement Behaviours and Fundamental Motor Skills of Children With Intellectual Disabilities Based on Compositional Data Analyses.

Yang Liu, Glenn Roswal, Jianing Ding +1 more · 2026 · Journal of intellectual disability research : JIDR · Blackwell Publishing · added 2026-04-24

Yang Liu, Glenn Roswal, Jianing Ding, Yaqing Yuan Show less

To explore the association between 24-h movement behaviours and fundamental motor skills in children with intellectual disabilities using compositional data analyses and to investigate the 'dose-effec Show more

To explore the association between 24-h movement behaviours and fundamental motor skills in children with intellectual disabilities using compositional data analyses and to investigate the 'dose-effect' characteristics of the reallocation between 24-h movement behaviours and fundamental motor skills. A cross-sectional study was conducted among 306 children with intellectual disabilities aged 6-10 years from 12 special education schools in Beijing and Jinan between 10 September 2023 and 27 March 2024. The ActiGraph GT3X+ accelerometer was used to estimate the amount of time spent in 24-h movement behaviours. The Test of Gross Motor Development-2 was applied to assess fundamental motor skills. The compositional isotemporal substitution was utilized to analyse the relationship between 24-h movement behaviours and fundamental motor skills. (1) After controlling the gender, age and intellectual disability level, MVPA of children with intellectual disabilities was positively associated with their FMS total score, locomotor skills and object control skills (β Special education school administrators, teachers, parents and guardians should consider 24-h movement behaviours as a whole and pay attention to their impact on children with intellectual disabilities. In the process of promoting FMS in children with intellectual disabilities, ensuring adequate sleep and trying to reallocate time from SB to MVPA and LPA may be effective methods. Show less

no PDF DOI: 10.1111/jir.70096

Therapeutic Potential of PCSK9 Inhibitors in Regulating Neuroinflammation in Acute Ischemic Stroke.

Lanlan Pu, Jiahui Liu, Shuying Kong +4 more · 2026 · CNS drugs · Springer · added 2026-04-24

Lanlan Pu, Jiahui Liu, Shuying Kong, Yuki Joyama, Xing Gong, Xiaofei Ji, Huijie Dong Show less

Acute ischemic stroke (AIS) poses a substantial risk of permanent disability and death globally, with neuroinflammation being a key driver of secondary brain damage post-stroke. Proprotein convertase Show more

Acute ischemic stroke (AIS) poses a substantial risk of permanent disability and death globally, with neuroinflammation being a key driver of secondary brain damage post-stroke. Proprotein convertase subtilisin/kexin type 9 (PCSK9), beyond its well-accepted role in cholesterol metabolism through low-density lipoprotein receptor (LDLR) degradation, has emerged as an important mediator of neuroinflammation, making it an attractive new therapeutic target. This has sparked broader discussions about the potential pleiotropic effects of PCSK9 inhibitors on brain function. Proprotein convertase subtilisin/kexin type 9 mediates inflammation post-ischemia directly and indirectly by disrupting mTOR pathways. This stimulates signaling cascades associated with inflammation. For example, the nuclear factor-κB (NF-κB), toll-like receptor 4 (TLR4), and mitogen-activated protein kinase (MAPK) pathways in microglia activation. It also brings about reaction in astrocytes and increases the release of cytokines like interleukin-1β, interleukin-6, and tumor necrosis factor-α. Proprotein convertase subtilisin/kexin type 9 interacts with apolipoprotein E receptor 2 (ApoER2) present on neurons cells, leading to further inflammatory effects. Proprotein convertase subtilisin/kexin type 9 indirectly increases lipoprotein(a) [Lp(a)], which promotes inflammation through the Lp(a)-TLR4 axis and induces endothelial dysfunction. Monoclonal antibodies (evolocumab, alirocumab) and small interfering RNA (siRNA) agents (inclisiran) are examples of PCSK9 inhibitors. According to preclinical studies, these inhibitors can mitigate neuroinflammation by blocking the M1 polarization of microglia and downregulating key pro-inflammatory factors while preserving the blood-brain barrier (BBB). They also inhibit neuronal apoptosis via the Bcl-2/Bax-caspase cascade and reduce the aggregation of β-amyloid (Aβ). Evidently, the findings from cardiac ischemia-reperfusion models show that pretreatment with PCSK9 inhibitors is effective with optimal neuroprotection. Recent clinical data support these mechanisms: PCSK9 inhibitors not only lower LDL-C and Lp(a) but also reduce systemic inflammatory markers (e.g., high-sensitivity C-reactive protein [hs-CRP], interleukin-6). Early adjunctive use of evolocumab in AIS is associated with reduced early neurological deterioration, highlighting that its effects extend beyond lipid lowering to modulating immune pathways in both the central and peripheral systems. As a promising multitarget therapeutic strategy for AIS, PCSK9 inhibitors target the interconnected pathways of lipid metabolism and neuroinflammation. Future studies should address critical challenges such as defining the optimal therapeutic time window, improving BBB penetrability, and refining patient stratification to translate their neuroprotective effects into clinical benefits for stroke patients. Show less

📄 PDF DOI: 10.1007/s40263-026-01278-9

Association of Elevated Lipoprotein(a) and Diabetes Mellitus With Survival Outcomes in Patients With Coronary Artery Disease: Findings From the CIN-II and RED-CARPET Cohorts.

Xiaozhao Lu, Ziyao Yuan, Xiaoyu Lin +13 more · 2026 · Diabetes, obesity & metabolism · Blackwell Publishing · added 2026-04-24

Xiaozhao Lu, Ziyao Yuan, Xiaoyu Lin, Zuxian Huang, Ziwei Zhou, Haozhang Huang, Yingying Li, Shaozhao Zhang, Ziwen Hui, Yihang Ling, Wei Guo, Shiqun Chen, Jiyan Chen, Jin Liu, Xiaodong Zhuang, Yong Liu Show less

Lipoprotein(a) [Lp(a)] and diabetes mellitus (DM) are independent risk factors for worse outcomes in coronary artery disease (CAD) patients. Evidence of their joint association is limited. We aimed to Show more

Lipoprotein(a) [Lp(a)] and diabetes mellitus (DM) are independent risk factors for worse outcomes in coronary artery disease (CAD) patients. Evidence of their joint association is limited. We aimed to investigate the combined effect of elevated Lp(a) and DM on survival outcomes in CAD patients. This study included 65 547 CAD patients (62.6 ± 10.7 years, 27.7% female) from CIN-II and RED-CARPET cohorts. Patients were stratified into four groups by Lp(a) levels (< or ≥ 30 mg/dL) and DM status. Multivariable Cox regression models estimated associations with cardiovascular and all-cause mortality, examining additive and multiplicative interactions. During a median follow-up of 5.5 years, 10 686 (16.3%) patients died from all causes and 5106 (7.8%) died from cardiovascular causes. Patients with Lp(a) ≥ 30 mg/dL and DM were independently associated with cardiovascular mortality (adjusted hazard ratio [aHR]: 1.28, 95% CI: 1.20-1.35; aHR: 1.53, 95% CI: 1.44-1.62, all p < 0.001, respectively). Compared to patients with Lp(a) < 30 mg/dL without DM, the aHRs were 1.26 (95% CI: 1.16-1.36, p < 0.001), 1.51 (95% CI: 1.40-1.62, p < 0.001) and 2.00 (95% CI: 1.83-2.18, p < 0.001) for those with Lp(a) ≥ 30 mg/dL without DM, Lp(a) < 30 mg/dL with DM and Lp(a) ≥ 30 mg/dL with DM, respectively. Significant additive interaction between elevated Lp(a) and DM on cardiovascular mortality was observed, with 12% of the excess risk attributed. Similar associations were observed in all-cause mortality. In patients with CAD, elevated Lp(a) and DM act synergistically to increase the risk of cardiovascular and all-cause mortality, suggesting that both risks should be considered to integrate management. Show less

no PDF DOI: 10.1111/dom.70603

Latent profile analysis of fertility intention among women of reproductive age.

Miaomiao Chen, Shailing Ma, Xiaohui Liu +5 more · 2026 · Frontiers in reproductive health · Frontiers · added 2026-04-24

Miaomiao Chen, Shailing Ma, Xiaohui Liu, Lijun Wang, Yingjie Zheng, Jiajia Lai, Jing Li, Yijia Qi Show less

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

Characterizing Sleep Disturbance Subgroups and Identifying Associated Factors in Traditional Chinese Medicine Nurses: A Latent Profile Analysis and Explainable Machine Learning Approach.

Chong Liu, Nieran Lian, Kristin K Sznajder +3 more · 2026 · Journal of nursing management · added 2026-04-24

Chong Liu, Nieran Lian, Kristin K Sznajder, Cong Li, Changqing Zou, Xiaoshi Yang Show less

Nurses in traditional Chinese medicine (TCM) departments face significant sleep challenges associated with occupational stressors. However, person-centered analyses classifying these sleep patterns re Show more

Nurses in traditional Chinese medicine (TCM) departments face significant sleep challenges associated with occupational stressors. However, person-centered analyses classifying these sleep patterns remain scarce. This study aimed to identify heterogeneous sleep disturbance subgroups via latent profile analysis (LPA) and evaluate the performance of explainable machine learning models in discriminating these subgroups based on demographic and occupational features. A cross-sectional survey enrolled 7721 nurses from 130 TCM healthcare institutions in Liaoning Province (December 2024). Data encompassed demographic, occupational, and psychological variables obtained via self-administered questionnaires, including the Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance short form 8a. LPA was employed to categorize sleep disturbance patterns. Recursive feature elimination with random forest (RFE-RF) was used to select features associated with subgroup membership for five machine learning models. Models were trained on 70% of the data and evaluated on a 30% independent test set. The optimal classification model (XGBoost) underwent interpretability analysis using Shapley additive explanations (SHAP). LPA identified three subgroups: mild-stable (29.8%), moderate-fluctuating (60%), and severe-persistent (10.2%). Machine learning models achieved test AUCs of 0.71-0.84, with XGBoost demonstrating the highest discriminatory performance (AUC = 0.84, 95%CI: 0.83-0.85) in classifying subgroups. SHAP analysis indicated that monthly income, organizational support, hospital level, self-compassion, and resilience were the top five features contributing to the model's classification output. This study characterized three distinct sleep disturbance subgroups among TCM nurses, with the majority exhibiting moderate symptoms. The sequential application of LPA and explainable machine learning demonstrated robust performance in distinguishing sleep disturbance patterns. Identifying correlates-such as lower income and resilience-may assist nurse managers in stratifying risk and tailoring interventions for those most likely to fall into the severe subgroup. Future longitudinal studies are required to validate the stability of these subgroups and establish causal relationships. Show less

📄 PDF DOI: 10.1155/jonm/1269507

A study on the motivation stratification and behavioral differentiation patterns of sports socializing among Chinese residents: an empirical exploration based on latent profile analysis and random forest models.

Yu Tian, Shuaishuai Liu, Fangjue Zhao · 2026 · BMC public health · BioMed Central · added 2026-04-24

As sports socializing is becoming a dominant lifestyle that integrates physical health with social interaction in China, understanding the underlying drivers of participation is crucial. However, trad Show more

As sports socializing is becoming a dominant lifestyle that integrates physical health with social interaction in China, understanding the underlying drivers of participation is crucial. However, traditional research predominantly relies on a “variable-centered” paradigm, which assumes population homogeneity and focuses on linear relationships between single motives and behaviors. This approach often fails to capture the complexity of how multiple motivations are configured within individuals (heterogeneity), and how these internal configurations are associated with external behavioral choices. To address this gap, this study employed a novel hybrid methodological framework combining Latent Profile Analysis (LPA) and Random Forest (RF) modeling. Based on data from 1,104 adults, LPA was first used to identify distinct motivational subgroups. Subsequently, RF algorithms, utilizing feature importance ranking and “One-vs-Rest” strategies, were applied to identify the associative patterns between these motivational profiles and key behavioral indicators, including sports types, media usage, and economic investment. The analysis identified four distinct motivational profiles: (1) Psychologically Introverted (3.6%), prioritizing internal psychological rewards over social status; (2) Physiologically Oriented (44.1%), the largest group, driven primarily by physical health needs; (3) Balanced (39.0%), exhibiting moderate levels across all motivational dimensions; and (4) High-Motivation/Comprehensively Oriented (13.3%), showing high intensity in both internal and external rewards. The RF model achieved a training accuracy of 99.9% and identified that Sports Type (specifically large-ball games), Media Channels (particularly Douyin/Rednote), and Annual Spending were the top three salient behavioral markers distinguishing these profiles. Notably, the High-Motivation group was characterized by heavy reliance on visual social media for social display. Participation in sports socializing among Chinese residents is not characterized by a singular, homogeneous motivation but features a clear internal stratification structure. The specific pattern of motivational combinations (i.e., the type) systematically maps onto external behavioral choices, where the sociocultural attributes of the sport and the media characteristics of digital social platforms constitute the key predictive markers of behavioral differentiation. The establishment of this “Motivation Type—Behavioral Signal” integrated framework promotes a theoretical shift in the sports socializing research paradigm from “homogeneity” to “heterogeneity” and deepens the understanding of the complex manifestations of Self-Determination Theory and Social Capital Theory in a sports context. It also provides precise user profiles and behavioral insights for sports social platforms, commercial clubs, and public sports service departments. Exploring service customization and policy adjustments based on different motivation-behavior patterns could potentially enhance user engagement and satisfaction, suggesting a possible direction for the development of the sports socializing industry. The online version contains supplementary material available at 10.1186/s12889-026-26780-z. Show less

📄 PDF DOI: 10.1186/s12889-026-26780-z

Impact of hypertension on associations of all-cause mortality with admission lipoprotein (a) in acute decompensated heart failure.

Ashen L Vidanage, Tianyu Xu, Zihao Chen +9 more · 2026 · International journal of cardiology. Cardiovascular risk and prevention · Elsevier · added 2026-04-24

Ashen L Vidanage, Tianyu Xu, Zihao Chen, Zhongping Yu, Chang Chen, Shilan Chen, Wengen Zhu, Jiangui He, Yugang Dong, Chen Liu, Jingjing Zhao, Fang-Fei Wei Show less

Serum lipoprotein(a) [Lp(a)] is recognized as an independent risk factor for cardiovascular disease. However, whether hypertension modifies the association between Lp(a) and adverse outcomes in acute Show more

Serum lipoprotein(a) [Lp(a)] is recognized as an independent risk factor for cardiovascular disease. However, whether hypertension modifies the association between Lp(a) and adverse outcomes in acute decompensated heart failure (ADHF) remains unclear. We investigated how hypertension status influences the relationship between Lp(a) and all-cause mortality in ADHF. We conducted a single-center retrospective observational study including 2610 patients hospitalized with ADHF. We normalized the distribution of Lp(a) by a logarithmic transformation and assessed the risk of all-cause mortality with Lp(a), using Cox regression with adjustment for potential confounders. Among 2610 patients (39.0% women; mean age, 68.8 years), 1606 (61.5%) had hypertension. Over 4.1 years (median), 1287 deaths occurred. In all patients, log-transformed Lp(a) was significantly associated with mortality (adjusted HR 1.21; 95% CI, 1.05-1.39; Increased admission Lp(a) levels were associated with a higher risk of all-cause mortality in ADHF patients with hypertension. Further studies are needed to explore the mechanistic links among Lp(a), hypertension and ADHF. Show less

📄 PDF DOI: 10.1016/j.ijcrp.2026.200594

Profiles of 24-h movement behaviors and physical fitness among preschool children: a latent profile analysis.

Bin Yang, Long Yin, Zongyu Yang +4 more · 2026 · Journal of exercise science and fitness · Elsevier · added 2026-04-24

Bin Yang, Long Yin, Zongyu Yang, Pan Liu, Fang Li, Yi Feng Chen, Xiaoming Liu Show less

This study aims to identify the 24-h movement behavior patterns of preschool children using Latent Profile Analysis based on Compositional Data Analysis (CoDA), and to examine their associations with Show more

This study aims to identify the 24-h movement behavior patterns of preschool children using Latent Profile Analysis based on Compositional Data Analysis (CoDA), and to examine their associations with physical fitness. The study employs a cross-sectional design. A total of 329 healthy children aged 4-6 years were selected. Accelerometers (ActiGraph wGT3-BT, Pensacola, FL, USA) were used to measure light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and sedentary behavior (SB), while sleep was assessed through parent and teacher questionnaires. The assessment of physical fitness was conducted in accordance with the "Chinese National Physical Fitness Test Standards" (Preschooler Section). To address the multicollinearity problems among components of physical activity (PA), CoDA was first applied, subsequently, Latent Profile Analysis was utilized to categorize 24-h movement behavior patterns, while a Generalized Ordered Logit Model (GOLM) was applied to investigate their associations with physical fitness. Three distinct behavioral patterns emerged from the analysis: the "brown bear group" (moderate PA and SB, high SP, N = 176, 53.5%), the "cheetah group" (high PA/MVPA, low SB, moderate SP, N = 102, 31%), and the "koala group" (low PA, high SB, lower SP, N = 51, 15.5%). After adjusting for potential confounding factors, it was found that compared with the "koala group", the "brown bear group" and the "cheetah group" exhibited higher levels of physical fitness, with the probability of improving their physical fitness rating being 3.69 times and 6.36 times that of the "koala group," respectively. This study highlights the significant impact of active and healthy activity patterns on the physical fitness of preschool children, providing a foundation for formulating personalized preventive and interventional approaches in early childhood. Show less

📄 PDF DOI: 10.1016/j.jesf.2026.200459

The relationship between motor competence and physical activity of school-aged children: the mediating effect of physical fitness in China.

Dan Lei, Wei Liang, Fengying Yang +3 more · 2026 · BMC pediatrics · BioMed Central · added 2026-04-24

Dan Lei, Wei Liang, Fengying Yang, Chang Liu, Lin Zhou, Zhengwei Wang Show less

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

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