👤 Zhongxu Zhang

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Also published as: A-Mei Zhang, Ai Zhang, Ai-Min Zhang, Aiguo Zhang, Aihua Zhang, Aijun Zhang, Aileen Zhang, Ailin Zhang, Aimei Zhang, Aimin Zhang, Aixiang Zhang, Alaina Zhang, Alex R Zhang, Amy L Zhang, An Zhang, An-Qi Zhang, Anan Zhang, Andrew Zhang, Ang Zhang, Anli Zhang, Anqi Zhang, Anwei Zhang, Anying Zhang, Ao Zhang, Bangke Zhang, Bangzhou Zhang, Bao Long Zhang, Bao-Fu Zhang, Bao-Rong Zhang, Baohu Zhang, Baojing Zhang, Baojun Zhang, Baoren Zhang, Baorong Zhang, Baotong Zhang, Bei B Zhang, Bei Zhang, Bei-Bei Zhang, Beiyu Zhang, Ben Zhang, Benjian Zhang, Benyou Zhang, Bi-Tian Zhang, Biao Zhang, Bicheng Zhang, Bikui Zhang, Bin Zhang, Binbin Zhang, Bing Zhang, Bing-Qi Zhang, Bingbing Zhang, Bingkun Zhang, Bingqiang Zhang, Bingxue Zhang, Bingye Zhang, Bixia Zhang, Bo Zhang, Bo-Fei Zhang, Bo-Heng Zhang, Bo-Ya Zhang, Bochuan Zhang, Bofang Zhang, Bohao Zhang, Bohong Zhang, Bohua Zhang, Bojian Zhang, Bolin Zhang, Boping Zhang, Boqing Zhang, Bosheng Zhang, Bowei Zhang, Bowen Zhang, Boxi Zhang, Boxiang Zhang, Boya Zhang, Boyan Zhang, C D Zhang, C H Zhang, C Zhang, Cai Zhang, Cai-Ling Zhang, Caihong Zhang, Caiping Zhang, Caiqing Zhang, Caishi Zhang, Caiyi Zhang, Caiying Zhang, Caiyu Zhang, Can Zhang, Cathy C Zhang, Chan-na Zhang, Chang Zhang, Chang-Hua Zhang, Changhua Zhang, Changhui Zhang, Changjiang Zhang, Changjing Zhang, Changlin Zhang, Changlong Zhang, Changquan Zhang, Changteng Zhang, Changwang Zhang, Channa Zhang, Chao Zhang, Chao-Hua Zhang, Chao-Sheng Zhang, Chao-Yang Zhang, ChaoDong Zhang, Chaobao Zhang, Chaoke Zhang, Chaoqiang Zhang, Chaoyang Zhang, Chaoyue Zhang, Chen Zhang, Chen-Qi Zhang, Chen-Ran Zhang, Chen-Song Zhang, Chen-Xi Zhang, Chen-Yan Zhang, Chen-Yang Zhang, Chenan Zhang, Chenfei Zhang, Cheng Cheng Zhang, Cheng Zhang, Cheng-Lin Zhang, Cheng-Wei Zhang, Chengbo Zhang, Chengcheng Zhang, Chengfei Zhang, Chenggang Zhang, Chengkai Zhang, Chenglong Zhang, Chengnan Zhang, Chengrui Zhang, Chengsheng Zhang, Chengshi Zhang, Chenguang Zhang, Chengwu Zhang, Chengxiang Zhang, Chengxiong Zhang, Chengyu Zhang, Chenhong Zhang, Chenhui Zhang, Chenjie Zhang, Chenlin Zhang, Chenlu Zhang, Chenmin Zhang, Chenming Zhang, Chenrui Zhang, Chenshuang Zhang, Chenxi Zhang, Chenyan Zhang, Chenyang Zhang, Chenyi Zhang, Chenzi Zhang, Chi Zhang, Chong Zhang, Chong-Hui Zhang, Chongguo Zhang, Chonghe Zhang, Chris Zhiyi Zhang, Chu-Yue Zhang, Chuan Zhang, Chuanfu Zhang, Chuankuan Zhang, Chuankuo Zhang, Chuanmao Zhang, Chuantao Zhang, Chuanxin Zhang, Chuanyong Zhang, Chuchu Zhang, Chumeng Zhang, Chun Zhang, Chun-Lan Zhang, Chun-Mei Zhang, Chun-Qing Zhang, Chungu Zhang, Chunguang Zhang, Chunhai Zhang, Chunhong Zhang, Chunhua Zhang, Chunjun Zhang, Chunli Zhang, Chunling Zhang, Chunqing Zhang, Chunxia Zhang, Chunxiang Zhang, Chunxiao Zhang, Chunyan Zhang, Chunying Zhang, Churen Zhang, Chuting Zhang, Chuyue Zhang, Ci Zhang, Claire Y Zhang, Claire Zhang, Clarence K Zhang, Cong Zhang, Congen Zhang, Cuihua Zhang, Cuijuan Zhang, Cuilin Zhang, Cuiping Zhang, Cuiyu Zhang, Cun Zhang, Da Zhang, Da-Qi Zhang, Da-Wei Zhang, Dachuan Zhang, Dadong Zhang, Daguo Zhang, Dai Zhang, Dalong Zhang, Daming Zhang, Dan Zhang, Dan-Dan Zhang, DanDan Zhang, Danfeng Zhang, Danhua Zhang, Danning Zhang, Danyan Zhang, Danyang Zhang, Daolai Zhang, Daoyong Zhang, Dapeng Zhang, David Y Zhang, David Zhang, Dawei Zhang, Daxin Zhang, Dayi Zhang, De-Jun Zhang, Dekai Zhang, Delai Zhang, Deng-Feng Zhang, Dengke Zhang, Deqiang Zhang, Detao Zhang, Deyi Zhang, Deyin Zhang, Di Zhang, Dian Ming Zhang, Dianbo Zhang, Dianzheng Zhang, Ding Zhang, Dingdong Zhang, Dinghu Zhang, Dingkai Zhang, Dingyi Zhang, Dingyu Zhang, Dong Zhang, Dong-Hui Zhang, Dong-Mei Zhang, Dong-Wei Zhang, Dong-Ying Zhang, Dong-cui Zhang, Dong-juan Zhang, Dong-qiang Zhang, Dongdong Zhang, Dongfeng Zhang, Donghua Zhang, Donghui Zhang, Dongjian Zhang, Dongjie Zhang, Donglei Zhang, Dongmei Zhang, Dongsheng Zhang, Dongxin Zhang, Dongyan Zhang, Dongyang Zhang, Dongying Zhang, Donna D Zhang, Donna Zhang, Duo Zhang, Duoduo Zhang, Duowen Zhang, En Zhang, Enhui Zhang, Enming Zhang, Erchen Zhang, F P Zhang, F Zhang, Fa Zhang, Famin Zhang, Fan Zhang, Fang Zhang, Fanghong Zhang, Fangmei Zhang, Fangting Zhang, Fangyuan Zhang, Fei Zhang, Fei-Ran Zhang, Feifei Zhang, Feixue Zhang, Fen Zhang, Feng Zhang, Fengqing Zhang, Fengshi Zhang, Fengshuo Zhang, Fengwei Zhang, Fengxi Zhang, Fengxia Zhang, Fengxu Zhang, Fomin Zhang, Fred Zhang, Fu-Ping Zhang, Fubo Zhang, Fugui Zhang, Fuhan Zhang, Fujun Zhang, Fukang Zhang, Fuming Zhang, Fuqiang Zhang, Fuquan Zhang, Furen Zhang, Fushun Zhang, Fuxing Zhang, Fuyang Zhang, Fuyuan Zhang, G Zhang, G-Y Zhang, Gan Zhang, Gang Zhang, Ganlin Zhang, Gaoxin Zhang, Gary Zhang, Ge Zhang, Geng Zhang, Genglin Zhang, Genxi Zhang, Geyang Zhang, Gong Zhang, Gu Zhang, Guan-Yan Zhang, Guang Zhang, Guang-Qiong Zhang, Guang-Xian Zhang, Guang-Ya Zhang, Guanghui Zhang, Guangji Zhang, Guanglei Zhang, Guangliang Zhang, Guangping Zhang, Guangqiong Zhang, Guangxian Zhang, Guangxin Zhang, Guangye Zhang, Guangyong Zhang, Guangyuan Zhang, Guanqun Zhang, Gui-Ping Zhang, Guicheng Zhang, Guihua Zhang, Guijie Zhang, Guili Zhang, Guiliang Zhang, Guilin Zhang, Guimin Zhang, Guiping Zhang, Guisen Zhang, Guixia Zhang, Guixiang Zhang, Gumuyang Zhang, Guo-Fang Zhang, Guo-Fu Zhang, Guo-Guo Zhang, Guo-Liang Zhang, Guo-Wei Zhang, Guo-Xiong Zhang, Guoan Zhang, Guochao Zhang, Guodong Zhang, Guofang Zhang, Guofeng Zhang, Guofu Zhang, Guoguo Zhang, Guohua Zhang, Guohui Zhang, Guojun Zhang, Guoli Zhang, Guoliang Zhang, Guolong Zhang, Guomin Zhang, Guoming Zhang, Guoping Zhang, Guoqiang Zhang, Guoqing Zhang, Guorui Zhang, Guosen Zhang, Guowei Zhang, Guoxin Zhang, Guoying Zhang, Guozhi Zhang, H D Zhang, H F Zhang, H L Zhang, H P Zhang, H W Zhang, H X Zhang, H Y Zhang, H Zhang, H-F Zhang, Hai Zhang, Hai-Bo Zhang, Hai-Feng Zhang, Hai-Gang Zhang, Hai-Han Zhang, Hai-Liang Zhang, Hai-Man Zhang, Hai-Ying Zhang, Haibei Zhang, Haibing Zhang, Haibo Zhang, Haicheng Zhang, Haifeng Zhang, Haihong Zhang, Haihua Zhang, Haijiao Zhang, Haijun Zhang, Haikuo Zhang, Hailei Zhang, Hailian Zhang, Hailiang Zhang, Hailin Zhang, Hailing Zhang, Hailong Zhang, Hailou Zhang, Haiming Zhang, Hainan Zhang, Haipeng Zhang, Haisan Zhang, Haisen Zhang, Haitao Zhang, Haiwang Zhang, Haiwei Zhang, Haixia Zhang, Haiyan Zhang, Haiyang Zhang, Haiying Zhang, Haiyue Zhang, Han Zhang, Hanchao Zhang, Hang Zhang, Hanqi Zhang, Hanrui Zhang, Hansi Zhang, Hanting Zhang, Hanwang Zhang, Hanwen Zhang, Hanxu Zhang, Hanyin Zhang, Hanyu Zhang, Hao Zhang, Hao-Chen Zhang, Hao-Yu Zhang, Haohao Zhang, Haojian Zhang, Haojie Zhang, Haojun Zhang, Haokun Zhang, Haolin Zhang, Haomin Zhang, Haonan Zhang, Haopeng Zhang, Haoran Zhang, Haotian Zhang, Haowen Zhang, Haoxing Zhang, Haoyu Zhang, Haoyuan Zhang, Haoyue Zhang, Haozheng Zhang, He Zhang, Hefang Zhang, Hejun Zhang, Heng Zhang, Hengming Zhang, Hengrui Zhang, Hengyuan Zhang, Heping Zhang, Hong Zhang, Hong-Jie Zhang, Hong-Sheng Zhang, Hong-Xing Zhang, Hong-Yu Zhang, Hong-Zhen Zhang, Hongbin Zhang, Hongbing Zhang, Hongcai Zhang, Hongfeng Zhang, Hongfu Zhang, Honghe Zhang, Honghong Zhang, Honghua Zhang, Hongjia Zhang, Hongjie Zhang, Hongjin Zhang, Hongju Zhang, Hongjuan Zhang, Honglei Zhang, Hongliang Zhang, Hongmei Zhang, Hongmin Zhang, Hongquan Zhang, Hongrong Zhang, Hongrui Zhang, Hongsen Zhang, Hongtao Zhang, Hongting Zhang, Hongwu Zhang, Hongxia Zhang, Hongxin Zhang, Hongxing Zhang, Hongya Zhang, Hongyan Zhang, Hongyang Zhang, Hongyi Zhang, Hongying Zhang, Hongyou Zhang, Hongyuan Zhang, Hongyun Zhang, Hongzhong Zhang, Hongzhou Zhang, Houbin Zhang, Hu Zhang, Hua Zhang, Hua-Min Zhang, Hua-Xiong Zhang, Huabing Zhang, Huafeng Zhang, Huaiyong Zhang, Huajia Zhang, Huan Zhang, Huan-Tian Zhang, Huanmin Zhang, Huanqing Zhang, Huanxia Zhang, Huanyu Zhang, Huaqi Zhang, Huaqiu Zhang, Huawei Zhang, Huawen Zhang, Huayang Zhang, Huayong Zhang, Huayu Zhang, Hugang Zhang, Huhan Zhang, Hui Hua Zhang, Hui Z Zhang, Hui Zhang, Hui-Jun Zhang, Hui-Wen Zhang, Huibing Zhang, Huifang Zhang, Huihui Zhang, Huijie Zhang, Huijun Zhang, Huili Zhang, Huilin Zhang, Huimao Zhang, Huimin Zhang, Huiming Zhang, Huiping Zhang, Huiqing Zhang, Huiru Zhang, Huiting Zhang, Huixin Zhang, Huiying Zhang, Huiyu Zhang, Huiyuan Zhang, Huize Zhang, Huizhen Zhang, Igor Ying Zhang, J B Zhang, J R Zhang, J Y Zhang, J Zhang, J-Y Zhang, Jamie Zhang, Jason Z Zhang, Jennifer Y Zhang, Jerry Z Zhang, Ji Yao Zhang, Ji Zhang, Ji-Yuan Zhang, Jia Zhang, Jia-Bao Zhang, Jia-Si Zhang, Jia-Su Zhang, Jia-Xuan Zhang, Jiabi Zhang, Jiachao Zhang, Jiachen Zhang, Jiacheng Zhang, Jiahai Zhang, Jiahao Zhang, Jiahe Zhang, Jiajia Zhang, Jiajing Zhang, Jiaming Zhang, Jian Zhang, Jian-Guo Zhang, Jian-Ping Zhang, Jian-Xu Zhang, Jianan Zhang, Jianbin Zhang, Jianbo Zhang, Jianchao Zhang, Jianduan Zhang, Jianeng Zhang, Jianfa Zhang, Jiang Zhang, Jiangang Zhang, Jianghong Zhang, Jianglin Zhang, Jiangmei Zhang, Jiangtao Zhang, Jianguang Zhang, Jianguo Zhang, Jiangyan Zhang, Jianhai Zhang, Jianhong Zhang, Jianhua Zhang, Jianhui Zhang, Jianing Zhang, Jianjun Zhang, Jiankang Zhang, Jiankun Zhang, Jianliang Zhang, Jianling Zhang, Jianmei Zhang, Jianmin Zhang, Jianming Zhang, Jiannan Zhang, Jianping Zhang, Jianqiong Zhang, Jianshe Zhang, Jianting Zhang, Jianwei Zhang, Jianwen Zhang, Jianwu Zhang, Jianxia Zhang, Jianxiang Zhang, Jianxin Zhang, Jianying Zhang, Jianyong Zhang, Jianzhao Zhang, Jiao Zhang, Jiaqi Zhang, Jiasheng Zhang, Jiawei Zhang, Jiawen Zhang, Jiaxin Zhang, Jiaxing Zhang, Jiayan Zhang, Jiayi Zhang, Jiayin Zhang, Jiaying Zhang, Jiayu Zhang, Jiayuan Zhang, Jibin Zhang, Jicai Zhang, Jie Zhang, Jiecheng Zhang, Jiehao Zhang, Jiejie Zhang, Jieming Zhang, Jieping Zhang, Jieqiong Zhang, Jieying Zhang, Jifa Zhang, Jifeng Zhang, Jihang Zhang, Jimei Zhang, Jiming Zhang, Jimmy Zhang, Jin Zhang, Jin-Ge Zhang, Jin-Jing Zhang, Jin-Man Zhang, Jin-Ru Zhang, Jin-Rui Zhang, Jin-Yu Zhang, Jinbiao Zhang, Jinfan Zhang, Jinfang Zhang, Jinfeng Zhang, Jing Jing Zhang, Jing Zhang, Jing-Bo Zhang, Jing-Chang Zhang, Jing-Fa Zhang, Jing-Lve Zhang, Jing-Nan Zhang, Jing-Qiu Zhang, Jing-Zhan Zhang, JingZi Zhang, Jingchuan Zhang, Jingchun Zhang, Jingdan Zhang, Jingdong Zhang, Jingfa Zhang, Jinghui Zhang, Jingjing Zhang, Jinglan Zhang, Jingli Zhang, Jingliang Zhang, Jinglu Zhang, Jingmei Zhang, Jingmian Zhang, Jingning Zhang, Jingping Zhang, Jingqi Zhang, Jingrong Zhang, Jingru Zhang, Jingshuang Zhang, Jingsong Zhang, Jingtian Zhang, Jingting Zhang, Jingwei Zhang, Jingwen Zhang, Jingxi Zhang, Jingxiao Zhang, Jingxuan Zhang, Jingxue Zhang, Jingyao Zhang, Jingyi Zhang, Jingying Zhang, Jingyu Zhang, Jingyuan Zhang, Jingyue Zhang, Jingzhe Zhang, Jinhua Zhang, Jinhui Zhang, Jinjin Zhang, Jinjing Zhang, Jinliang Zhang, Jinlong Zhang, Jinming Zhang, Jinquan Zhang, Jinrui Zhang, Jinsong Zhang, Jinsu Zhang, Jintao Zhang, Jinwei Zhang, Jinxiu Zhang, Jinyi Zhang, Jinying Zhang, Jinyu Zhang, Jinze Zhang, Jinzhou Zhang, Jiqiang Zhang, Jiquan Zhang, Jishou Zhang, Jishui Zhang, Jitai Zhang, Jiuchun Zhang, Jiupan Zhang, Jiuwei Zhang, Jiuxuan Zhang, Jixia Zhang, Jixing Zhang, Jiyang Zhang, Joe Z Zhang, John H Zhang, John Z H Zhang, Joshua Zhang, Joyce Zhang, Juan Zhang, Juan-Juan Zhang, Jue Zhang, Juliang Zhang, Jun Zhang, Jun-Feng Zhang, Jun-Jie Zhang, Jun-Xiao Zhang, Jun-Xiu Zhang, Jun-ying Zhang, June Zhang, Junfeng Zhang, Junhan Zhang, Junhang Zhang, Junhua Zhang, Junhui Zhang, Junjie Zhang, Junjing Zhang, Junkai Zhang, Junli Zhang, Junling Zhang, Junlong Zhang, Junmei Zhang, Junmin Zhang, Junpei Zhang, Junpeng Zhang, Junping Zhang, Junqing Zhang, Junran Zhang, Junru Zhang, Junsheng Zhang, Juntai Zhang, Junwei Zhang, Junxia Zhang, Junxiao Zhang, Junxing Zhang, Junxiu Zhang, Junyan Zhang, Junyi Zhang, Junying Zhang, Junyu Zhang, Junzhi Zhang, Juqing Zhang, K Y Zhang, K Zhang, Kai Zhang, Kai-Jie Zhang, Kai-Qiang Zhang, Kaichuang Zhang, Kaige Zhang, Kaihua Zhang, Kaihui Zhang, Kailin Zhang, Kailing Zhang, Kaiming Zhang, Kainan Zhang, Kaitai Zhang, Kaituo Zhang, Kaiwen Zhang, Kaiyi Zhang, Kan Zhang, Kang Zhang, Kang-Ling Zhang, Kangjun Zhang, Kangning Zhang, Karen Zhang, Ke Zhang, Ke-Wen Zhang, Ke-lan Zhang, Kefen Zhang, Kejia Zhang, Kejian Zhang, Kejin Zhang, Kejun Zhang, Keke Zhang, Keshan Zhang, Kewen Zhang, Keyi Zhang, Keyong Zhang, Keyu Zhang, Kezhong Zhang, Kongyong Zhang, Kui Zhang, Kui-ming Zhang, Kun Zhang, Kunning Zhang, Kunshan Zhang, Kunyi Zhang, Kuo Zhang, L F Zhang, L Zhang, L-S Zhang, Laihong Zhang, Lan Zhang, Lanfang Zhang, Lanju Zhang, Lanjun Zhang, Lanlan Zhang, Lantian Zhang, Lanyue Zhang, Le Zhang, Le-Le Zhang, Lechi Zhang, Lei Zhang, Lei-Lei Zhang, Lei-Sheng Zhang, Leilei Zhang, Leili Zhang, Leitao Zhang, Leiying Zhang, Lele Zhang, Leli Zhang, Leo H Zhang, Li Zhang, Li-Fen Zhang, Li-Jie Zhang, Li-Ke Zhang, Li-ping Zhang, Lian Zhang, Lian-Lian Zhang, Lianbo Zhang, Lianfeng Zhang, Liang Zhang, Liang-Rong Zhang, Liangdong Zhang, Liangliang Zhang, Liangming Zhang, Lianjun Zhang, Lianmei Zhang, Lianqin Zhang, Lianxin Zhang, Libo Zhang, Lichao Zhang, Lichen Zhang, Licheng Zhang, Lichuan Zhang, Licui Zhang, Lida Zhang, Lie Zhang, Lifan Zhang, Lifang Zhang, Liguo Zhang, Lihong Zhang, Lihua Zhang, Lijian Zhang, Lijiao Zhang, Lijie Zhang, Lijuan Zhang, Lijun Zhang, Lilei Zhang, Lili Zhang, Limei Zhang, Limin Zhang, Liming Zhang, Lin Zhang, Lin-Jie Zhang, Lina Zhang, Linan Zhang, Linbo Zhang, Linda S Zhang, Ling Xia Zhang, Ling Zhang, Ling-Yu Zhang, Lingjie Zhang, Lingli Zhang, Lingling Zhang, Lingna Zhang, Lingqiang Zhang, Lingxiao Zhang, Lingyan Zhang, Lingyu Zhang, Lining Zhang, Linjing Zhang, Linli Zhang, Linlin Zhang, Lintao Zhang, Linyou Zhang, Linyuan Zhang, Liping Zhang, Liqian Zhang, Lirong Zhang, Lishuang Zhang, Litao Zhang, Liu Zhang, Liuming Zhang, Liuwei Zhang, Liwei Zhang, Liwen Zhang, Lixia Zhang, Lixing Zhang, Liyan Zhang, Liyi Zhang, Liyin Zhang, Liying Zhang, Liyu Zhang, Liyuan Zhang, Liyun Zhang, Lizhi Zhang, Long Zhang, Longlong Zhang, Longxin Zhang, Longzhen Zhang, Lu Zhang, Lu-Pei Zhang, Lu-Yang Zhang, Luanluan Zhang, Lucia Zhang, Lufei Zhang, Lukuan Zhang, Lulu Zhang, Lun Zhang, Lunan Zhang, Luning Zhang, Luo Zhang, Luo-Meng Zhang, Luoping Zhang, Lupei Zhang, Lusha Zhang, Luwen Zhang, Luyao Zhang, Luyun Zhang, Luzheng Zhang, Lv-Lang Zhang, M H Zhang, M J Zhang, M M Zhang, M Q Zhang, M X Zhang, M Zhang, Man Zhang, Manjin Zhang, Mao Zhang, Maomao Zhang, Mei Zhang, Mei-Fang Zhang, Mei-Ling Zhang, Mei-Qing Zhang, Mei-Ya Zhang, Mei-Zhen Zhang, MeiLu Zhang, Meidi Zhang, Meijia Zhang, Meiling Zhang, Meimei Zhang, Meishan Zhang, Meiwei Zhang, Meixia Zhang, Meixian Zhang, Meiyu Zhang, Melissa C Zhang, Melody Zhang, Meng Zhang, Meng-Jie Zhang, Meng-Wen Zhang, Meng-Ying Zhang, Mengdi Zhang, Mengguo Zhang, Menghao Zhang, Menghuan Zhang, Menghui Zhang, Mengjia Zhang, Mengjie Zhang, Mengliang Zhang, Menglu Zhang, Mengmeng Zhang, Mengmin Zhang, Mengna Zhang, Mengnan Zhang, Mengni Zhang, Mengqi Zhang, Mengqiu Zhang, Mengren Zhang, Mengshi Zhang, Mengxi Zhang, Mengxian Zhang, Mengxue Zhang, Mengying Zhang, Mengyuan Zhang, Mengyue Zhang, Mengzhao Zhang, Mengzhen Zhang, Mi Zhang, Mianzhi Zhang, Miao Zhang, Miao-Miao Zhang, Miaomiao Zhang, Miaoran Zhang, Michael Zhang, Min Zhang, Minfang Zhang, Ming Zhang, Ming-Jun Zhang, Ming-Liang Zhang, Ming-Ming Zhang, Ming-Rong Zhang, Ming-Yu Zhang, Ming-Zhu Zhang, Mingai Zhang, Mingchang Zhang, Mingdi Zhang, Mingfa Zhang, Mingfeng Zhang, Minghang Zhang, Minghao Zhang, Minghui Zhang, Mingjie Zhang, Mingjiong Zhang, Mingjun Zhang, Mingming Zhang, Mingqi Zhang, Mingtong Zhang, Mingxiang Zhang, Mingxiu Zhang, Mingxuan Zhang, Mingxue Zhang, Mingyang A Zhang, Mingyang Zhang, Mingyao Zhang, Mingyi Zhang, Mingying Zhang, Mingyu Zhang, Mingyuan Zhang, Mingyue Zhang, Mingzhao Zhang, Mingzhen Zhang, Minhong Zhang, Minying Zhang, Minyue Zhang, Minzhi Zhang, Minzhu Zhang, Mo Zhang, Mo-Ruo Zhang, Mu Zhang, Muqing Zhang, Muxin Zhang, Muzi Zhang, N Zhang, Na Zhang, Naijin Zhang, Naiqi Zhang, Naisheng Zhang, Naixia Zhang, Nan Yang Zhang, Nan Zhang, Nan-Nan Zhang, Nana Zhang, Nannan Zhang, Nasha Zhang, Ni Zhang, Niankai Zhang, Nianxiang Zhang, Nieke Zhang, Ning Zhang, Ning-Ping Zhang, Ninghan Zhang, Ningkun Zhang, Ningning Zhang, Ningzhen Zhang, Ningzhi Zhang, Nisi Zhang, Nong Zhang, Nu Zhang, P Zhang, Pan Zhang, Pan-Pan Zhang, Panpan Zhang, Pei Zhang, Pei-Weng Zhang, Pei-Zhuo Zhang, PeiFeng Zhang, Peichun Zhang, Peijing Zhang, Peijun Zhang, Peilin Zhang, Peiqin Zhang, Peiwen Zhang, Peiyi Zhang, Peizhen Zhang, Peng Zhang, Peng-Cheng Zhang, Peng-Fei Zhang, Pengbo Zhang, Pengcheng Zhang, Pengfei Zhang, Pengpeng Zhang, Pengwei Zhang, Pengyuan Zhang, Pili Zhang, Ping Zhang, Ping-Fan Zhang, Pingchuan Zhang, Pinggen Zhang, Pingmei Zhang, Pu-Hong Zhang, Pumin Zhang, Q L Zhang, Q Y Zhang, Q Zhang, Q-D Zhang, Qi Zhang, Qi-Ai Zhang, Qi-Lei Zhang, Qi-Min Zhang, QiYue Zhang, Qian Jun Zhang, Qian ZHANG, Qian-Qian Zhang, Qian-Wen Zhang, Qiang Zhang, Qiang-Sheng Zhang, Qiangsheng Zhang, Qiangyan Zhang, Qianhui Zhang, Qianjun Zhang, Qiannan Zhang, Qianqian Zhang, Qianru Zhang, Qiao-Xia Zhang, Qiaofang Zhang, Qiaojun Zhang, Qiaoxuan Zhang, Qifan Zhang, Qiguo Zhang, Qihao Zhang, Qihong Zhang, Qilong Zhang, Qilu Zhang, Qimin Zhang, Qin Zhang, Qing Zhang, Qing-Hui Zhang, Qing-Zhu Zhang, Qingchao Zhang, Qingcheng Zhang, Qingchuan Zhang, Qingfeng Zhang, Qinghong Zhang, Qinghua Zhang, Qingjiong Zhang, Qingjun Zhang, Qingling Zhang, Qingna Zhang, Qingqing Zhang, Qingquan Zhang, Qingrun Zhang, Qingshuang Zhang, Qingtian Zhang, Qingxiu Zhang, Qingxue Zhang, Qingyu Zhang, Qingyue Zhang, Qingyun Zhang, Qinjun Zhang, Qiong Zhang, Qishu Zhang, Qiu Zhang, Qiuting Zhang, Qiuxia Zhang, Qiuyang Zhang, Qiuyue Zhang, Qiwei Zhang, Qiyong Zhang, Quan Zhang, Quan-bin Zhang, Quanfu Zhang, Quanqi Zhang, Quanquan Zhang, Qun Zhang, Qun-Feng Zhang, Qunchen Zhang, Qunfeng Zhang, Qunyuan Zhang, R Zhang, Ran Zhang, Ranran Zhang, Ren Zhang, Renbo Zhang, Renhe Zhang, Renliang Zhang, Renshuai Zhang, Rey M Zhang, Richard Zhang, Rong Zhang, Rong-Kai Zhang, Rongcai Zhang, Rongchao Zhang, Rongguang Zhang, Rongrong Zhang, Rongxin Zhang, Rongxu Zhang, Rongying Zhang, Rongyu Zhang, Ru Zhang, Rugang Zhang, Rui Long Zhang, Rui Xue Zhang, Rui Yan Zhang, Rui Zhang, Rui-Nan Zhang, Rui-Ning Zhang, Rui-fang Zhang, Ruihao Zhang, Ruihong Zhang, Ruikun Zhang, Ruilin Zhang, Ruiling Zhang, Ruimin Zhang, Ruiqi Zhang, Ruiqian Zhang, Ruisan Zhang, Ruixia Zhang, Ruixin Zhang, Ruixue Zhang, Ruiyan Zhang, Ruiyang Zhang, Ruiying Zhang, Ruizhe Zhang, Ruizhi Zhang, Ruizhong Zhang, Rulin Zhang, Run Zhang, Runcheng Zhang, Runxiang Zhang, Runyun Zhang, Runze Zhang, Ruo-Xin Zhang, Ruohan Zhang, Ruoshi Zhang, Ruotian Zhang, Ruoxuan Zhang, Ruoying Zhang, Rusi Zhang, Ruth Zhang, Ruxiang Zhang, Ruxuan Zhang, Ruyi Zhang, S Y Zhang, S Z Zhang, S Zhang, Sai Zhang, Saidan Zhang, Saifei Zhang, Sainan Zhang, Sanbao Zhang, Sen Zhang, Sha Zhang, Shan Zhang, Shan-Shan Zhang, Shanchun Zhang, Shang Zhang, Shangxiong Zhang, Shanhong Zhang, Shanshan Zhang, Shanxiang Zhang, Shao Kang Zhang, Shao Zhang, Shao-Qi Zhang, Shaochuan Zhang, Shaochun Zhang, Shaofei Zhang, Shaofeng Zhang, Shaohua Zhang, Shaojun Zhang, Shaoyang Zhang, Shaozhao Zhang, Shaozhen Zhang, Shasha Zhang, Shen Zhang, Sheng Zhang, Sheng-Dao Zhang, Sheng-Hong Zhang, Sheng-Qiang Zhang, Sheng-Xiao Zhang, Shengchi Zhang, Shengding Zhang, Shengkun Zhang, Shenglai Zhang, Shenglan Zhang, Shenglei Zhang, Shengli Zhang, Shengming Zhang, Shengnan Zhang, Shengye Zhang, Shenqi Zhang, Shenqian Zhang, Shi Zhang, Shi-Han Zhang, Shi-Jie Zhang, Shi-Meng Zhang, Shi-Qian Zhang, Shi-Yao Zhang, ShiSong Zhang, Shichao Zhang, Shihan Zhang, Shijun Zhang, Shikai Zhang, Shilei Zhang, Shimao Zhang, Shining Zhang, Shiping Zhang, Shiqi Zhang, Shiquan Zhang, Shiti Zhang, Shitian Zhang, Shiwen Zhang, Shiwu Zhang, Shiyao Zhang, Shiyi Zhang, Shiyu Zhang, Shiyun Zhang, Shou-Mei Zhang, Shou-Peng Zhang, Shouyue Zhang, Shu Zhang, Shu-Dong Zhang, Shu-Fan Zhang, Shu-Fang Zhang, Shu-Min Zhang, Shu-Ming Zhang, Shu-Yang Zhang, Shu-Zhen Zhang, Shuai Zhang, Shuai-Nan Zhang, Shuaishuai Zhang, Shuang Zhang, Shuangjie Zhang, Shuanglu Zhang, Shuangxin Zhang, Shubing Zhang, Shuchen Zhang, Shucong Zhang, Shuer Zhang, Shuge Zhang, Shuhong Zhang, Shuijun Zhang, Shujun Zhang, Shuli Zhang, Shulong Zhang, Shun Zhang, Shun-Bo Zhang, Shunfen Zhang, Shunming Zhang, Shuo Zhang, Shupeng Zhang, Shuran Zhang, Shurui Zhang, Shushan Zhang, Shuwan Zhang, Shuwei Zhang, Shuxia Zhang, Shuya Zhang, Shuyan Zhang, Shuyang Zhang, Shuye Zhang, Shuyi Zhang, Shuyuan Zhang, Si Zhang, Si-Zhong Zhang, Sibin Zhang, Sifan Zhang, Sihe Zhang, Simeng Zhang, Simin Zhang, Siqi Zhang, Sisi Zhang, Sixue Zhang, Siyuan Zhang, Siyue Zhang, Sizhong Zhang, Song Zhang, Song-Yang Zhang, Songlin Zhang, Songying Zhang, Sophia L Zhang, Stanley Weihua Zhang, Stephen X Zhang, Su Zhang, Sujiang Zhang, Sulin Zhang, Sumei Zhang, Suming Zhang, Suping Zhang, Susie Zhang, Suya Zhang, Suyang Zhang, Suzhen Zhang, T Zhang, Tangjuan Zhang, Tao Zhang, Tao-Lan Zhang, Taojun Zhang, Taoyuan Zhang, Teng Zhang, Tengfang Zhang, Terry Jianguo Zhang, Ti Zhang, Tian Zhang, Tian-Guang Zhang, Tian-Yu Zhang, Tiane Zhang, Tianfeng Zhang, Tianliang Zhang, Tianlong Zhang, Tianpeng Zhang, Tianshu Zhang, Tiantian Zhang, Tianxi Zhang, Tianxiao Zhang, Tianxin Zhang, Tianyang Zhang, Tianye Zhang, Tianyi Zhang, Tianyu Zhang, Tie-mei Zhang, Tiefeng Zhang, Tiehua Zhang, Tiejun Zhang, Ting Ting Zhang, Ting Zhang, Ting-Ting Zhang, Tinghu Zhang, Tingting Zhang, Tingxue Zhang, Tingying Zhang, Tong Xuan Zhang, Tong Zhang, Tong-Cun Zhang, Tongcun Zhang, Tongfu Zhang, Tonghan Zhang, Tonghua Zhang, Tonghui Zhang, Tongran Zhang, Tongshuo Zhang, Tongtong Zhang, Tongwu Zhang, Tongxin Zhang, Tongxue Zhang, Tuo Zhang, Vita Zhang, W G Zhang, W X Zhang, W Zhang, Wancong Zhang, Wang-Dong Zhang, Wangang Zhang, Wangping Zhang, Wanjiang Zhang, Wanjun Zhang, Wannian Zhang, Wanqi Zhang, Wanting Zhang, Wanying Zhang, Wanyu Zhang, Wei Zhang, Wei-Jia Zhang, Wei-Na Zhang, Wei-Yi Zhang, Weibo Zhang, Weichen Zhang, Weifeng Zhang, Weiguo Zhang, Weihua Zhang, Weijian Zhang, Weikang Zhang, Weili Zhang, Weilin Zhang, Weiling Zhang, Weilong Zhang, Weimin Zhang, Weina Zhang, Weipeng Zhang, Weiping J Zhang, Weiqin Zhang, Weisen Zhang, Weiwei Zhang, Weixia Zhang, Weiyi Zhang, Weiyu Zhang, Weizheng Zhang, Weizhou Zhang, Wen Jun Zhang, Wen Zhang, Wen-Hong Zhang, Wen-Jie Zhang, Wen-Jing Zhang, Wen-Xin Zhang, Wen-Xuan Zhang, Wenbin Zhang, Wenbo Zhang, Wenchao Zhang, Wencheng Zhang, Wencong Zhang, Wendi Zhang, Wenguang Zhang, Wenhao Zhang, Wenhong Zhang, Wenhua Zhang, Wenhui Zhang, Wenji Zhang, Wenjia Zhang, Wenjing Zhang, Wenjuan Zhang, Wenjun Zhang, Wenkai Zhang, Wenkui Zhang, Wenli Zhang, Wenlong Zhang, Wenlu Zhang, Wenming Zhang, Wenqian Zhang, Wenru Zhang, Wentao Zhang, Wenting Zhang, Wenwen Zhang, Wenxi Zhang, Wenxiang Zhang, Wenxin Zhang, Wenxue Zhang, Wenya Zhang, Wenyang Zhang, Wenyi Zhang, Wenyuan Zhang, Wenzhong Zhang, Wuhu Zhang, X N Zhang, X X Zhang, X Y Zhang, X Zhang, X-T Zhang, X-Y Zhang, Xi Zhang, Xi'an Zhang, Xi-Feng Zhang, XiHe Zhang, Xia Zhang, Xian Zhang, Xian-Bo Zhang, Xian-Li Zhang, Xian-Man Zhang, Xiang Yang Zhang, Xiang Zhang, Xiangbin Zhang, Xiangfei Zhang, Xianglian Zhang, Xiangsong Zhang, Xiangwu Zhang, Xiangyang Zhang, Xiangyu Zhang, Xiangzheng Zhang, Xianhong Zhang, Xianhua Zhang, Xianjing Zhang, Xianpeng Zhang, Xianxian Zhang, Xiao Bin Zhang, Xiao Min Zhang, Xiao Yu Cindy Zhang, Xiao Zhang, Xiao-Chang Zhang, Xiao-Cheng Zhang, Xiao-Chong Zhang, Xiao-Feng Zhang, Xiao-Hong Zhang, Xiao-Hua Zhang, Xiao-Jun Zhang, Xiao-Lei Zhang, Xiao-Lin Zhang, Xiao-Ling Zhang, Xiao-Meng Zhang, Xiao-Ming Zhang, Xiao-Qi Zhang, Xiao-Qian Zhang, Xiao-Shuo Zhang, Xiao-Wei Zhang, Xiao-Xuan Zhang, Xiao-Yong Zhang, Xiao-Yu Zhang, Xiao-bo Zhang, Xiao-yan Zhang, XiaoLin Zhang, XiaoPing Zhang, XiaoYi Zhang, Xiaobao Zhang, Xiaobiao Zhang, Xiaobo Zhang, Xiaochang Zhang, Xiaochen Zhang, Xiaochun Zhang, Xiaocong Zhang, Xiaocui Zhang, Xiaodan Zhang, Xiaodong Zhang, Xiaofan Zhang, Xiaofang Zhang, Xiaofei Zhang, Xiaofeng Zhang, Xiaogang Zhang, Xiaohan Zhang, Xiaohong Zhang, Xiaohui Zhang, Xiaojia Zhang, Xiaojian Zhang, Xiaojie Zhang, Xiaojin Zhang, Xiaojing Zhang, Xiaojun Zhang, Xiaokui Zhang, Xiaolan Zhang, Xiaolei Zhang, Xiaoli Zhang, Xiaoling Zhang, Xiaolong Zhang, Xiaomei Zhang, Xiaomeng Zhang, Xiaomin Zhang, Xiaoming Zhang, Xiaoning Zhang, Xiaonyun Zhang, Xiaopei Zhang, Xiaopo Zhang, Xiaoqi Zhang, Xiaoqing Zhang, Xiaorong Zhang, Xiaosheng Zhang, Xiaotian Michelle Zhang, Xiaotian Zhang, Xiaotong Zhang, Xiaotun Zhang, Xiaowan Zhang, Xiaowei Zhang, Xiaoxi Zhang, Xiaoxia Zhang, Xiaoxian Zhang, Xiaoxiao Zhang, Xiaoxin Zhang, Xiaoxue Zhang, Xiaoyan Zhang, Xiaoying Zhang, Xiaoyu Zhang, Xiaoyuan Zhang, Xiaoyue Zhang, Xiaoyun Zhang, Xiaozhe Zhang, Xiayin Zhang, Xibo Zhang, Xieyi Zhang, Xijiang Zhang, Xilin Zhang, Xiling Zhang, Ximei Zhang, Xin Zhang, Xin-Hui Zhang, Xin-Xin Zhang, Xin-Yan Zhang, Xin-Ye Zhang, Xin-Yuan Zhang, Xinan Zhang, Xinbao Zhang, Xinbo Zhang, Xincheng Zhang, Xindang Zhang, Xindong Zhang, Xinfeng Zhang, Xinfu Zhang, Xing Yu Zhang, Xing Zhang, Xingan Zhang, Xingang Zhang, Xingcai Zhang, Xingen Zhang, Xinglai Zhang, Xingong Zhang, Xingwei Zhang, Xingxing Zhang, Xingxu Zhang, Xingyi Zhang, Xingyu Zhang, Xingyuan Zhang, Xinhai Zhang, Xinhan Zhang, Xinhe Zhang, Xinheng Zhang, Xinhong Zhang, Xinhua Zhang, Xinjiang Zhang, Xinjing Zhang, Xinjun Zhang, Xinke Zhang, Xinlei Zhang, Xinlian Zhang, Xinlin Zhang, Xinling Zhang, Xinlong Zhang, Xinlu Zhang, Xinmin Zhang, Xinping Zhang, Xinqiao Zhang, Xinquan Zhang, Xinran Zhang, Xinrui Zhang, Xinruo Zhang, Xintao Zhang, Xinwei Zhang, Xinwu Zhang, Xinxin Zhang, Xinyao Zhang, Xinye Zhang, Xinyi Zhang, Xinyu Zhang, Xinyue Zhang, Xiong Zhang, Xiongjun Zhang, Xiongze Zhang, Xipeng Zhang, Xiping Zhang, Xiu Qi Zhang, Xiu-Juan Zhang, Xiu-Li Zhang, Xiu-Peng Zhang, Xiujie Zhang, Xiujun Zhang, Xiulan Zhang, Xiuming Zhang, Xiupeng Zhang, Xiuping Zhang, Xiuqin Zhang, Xiuqing Zhang, Xiuse Zhang, Xiushan Zhang, Xiuwen Zhang, Xiuxing Zhang, Xiuxiu Zhang, Xiuyin Zhang, Xiuyue Zhang, Xiuyun Zhang, Xiuzhen Zhang, Xixi Zhang, Xixun Zhang, Xiyu Zhang, Xu Dong Zhang, Xu Zhang, Xu-Chao Zhang, Xu-Jun Zhang, Xu-Mei Zhang, Xuan Zhang, Xudan Zhang, Xudong Zhang, Xue Zhang, Xue-Ping Zhang, Xue-Qin Zhang, Xue-Qing Zhang, XueWu Zhang, Xuebao Zhang, Xuebin Zhang, Xuefei Zhang, Xueguang Zhang, Xuehai Zhang, Xuehong Zhang, Xuehui Zhang, Xuejiao Zhang, Xuejun C Zhang, Xueli Zhang, Xuelian Zhang, Xuelong Zhang, Xueluo Zhang, Xuemei Zhang, Xuemin Zhang, Xueming Zhang, Xuening Zhang, Xueping Zhang, Xueqia Zhang, Xueqian Zhang, Xueqin Zhang, Xueting Zhang, Xuewei Zhang, Xuewen Zhang, Xuexi Zhang, Xueya Zhang, Xueyan Zhang, Xueyi Zhang, Xueying Zhang, Xuezhi Zhang, Xufang Zhang, Xuhao Zhang, Xujun Zhang, Xunming Zhang, Xuting Zhang, Xutong Zhang, Xuxiang Zhang, Y H Zhang, Y L Zhang, Y Y Zhang, Y Zhang, Y-H Zhang, Ya Zhang, Ya-Juan Zhang, Ya-Li Zhang, Ya-Long Zhang, Ya-Meng Zhang, Yachen Zhang, Yadi Zhang, Yadong Zhang, Yafang Zhang, Yafei Zhang, Yafeng Zhang, Yaguang Zhang, Yahua Zhang, Yajie Zhang, Yajing Zhang, Yajun Zhang, Yakun Zhang, Yalan Zhang, Yali Zhang, Yaling Zhang, Yameng Zhang, Yamin Zhang, Yaming Zhang, Yan Zhang, Yan-Chun Zhang, Yan-Ling Zhang, Yan-Min Zhang, Yan-Qing Zhang, Yanan Zhang, Yanbin Zhang, Yanbing Zhang, Yanchao Zhang, Yandong Zhang, Yanfei Zhang, Yanfen Zhang, Yanfeng Zhang, Yang Zhang, Yang-Yang Zhang, Yangfan Zhang, Yanghui Zhang, Yangqianwen Zhang, Yangyang Zhang, Yangyu Zhang, Yanhong Zhang, Yanhua Zhang, Yani Zhang, Yanjiao Zhang, Yanju Zhang, Yanjun Zhang, Yanli Zhang, Yanlin Zhang, Yanling Zhang, Yanman Zhang, Yanmin Zhang, Yanming Zhang, Yanna Zhang, Yannan Zhang, Yanping Zhang, Yanqiao Zhang, Yanquan Zhang, Yanru Zhang, Yanting Zhang, Yanxia Zhang, Yanxiang Zhang, Yanyan Zhang, Yanyi Zhang, Yanyu Zhang, Yao Zhang, Yao-Hua Zhang, Yaodong Zhang, Yaoxin Zhang, Yaoyang Zhang, Yaoyao Zhang, Yaozhengtai Zhang, Yaping Zhang, Yaqi Zhang, Yaru Zhang, Yashuo Zhang, Yating Zhang, Yawei Zhang, Yaxin Zhang, Yaxuan Zhang, Yayong Zhang, Yazhuo Zhang, Ye Zhang, Yefan Zhang, Yeqian Zhang, Yerui Zhang, Yeting Zhang, Yexiang Zhang, Yi J Zhang, Yi Ping Zhang, Yi Zhang, Yi-Chi Zhang, Yi-Feng Zhang, Yi-Ge Zhang, Yi-Hang Zhang, Yi-Hua Zhang, Yi-Min Zhang, Yi-Ming Zhang, Yi-Qi Zhang, Yi-Wei Zhang, Yi-Wen Zhang, Yi-Xuan Zhang, Yi-Yue Zhang, Yi-yi Zhang, YiJie Zhang, YiPei Zhang, Yibin Zhang, Yibo Zhang, Yichen Zhang, Yichi Zhang, Yidan Zhang, Yidong Zhang, Yifan Zhang, Yifang Zhang, Yige Zhang, Yiguo Zhang, Yihan Zhang, Yihang Zhang, Yihao Zhang, Yiheng Zhang, Yihong Zhang, Yihui Zhang, Yijing Zhang, Yikai Zhang, Yikun Zhang, Yili Zhang, Yiliang Zhang, Yilin Zhang, Yimei Zhang, Yimeng Zhang, Yimin Zhang, Yiming Zhang, Yin Jiang Zhang, Yin Zhang, Yin-Hong Zhang, Yina Zhang, Yinci Zhang, Ying E Zhang, Ying Zhang, Ying-Jun Zhang, Ying-Lin Zhang, Ying-Qian Zhang, Yingang Zhang, Yingchao Zhang, Yinghui Zhang, Yingjie Zhang, Yingli Zhang, Yingmei Zhang, Yingna Zhang, Yingnan Zhang, Yingqi Zhang, Yingqian Zhang, Yingyi Zhang, Yingying Zhang, Yingze Zhang, Yingzi Zhang, Yinhao Zhang, Yinjiang Zhang, Yintang Zhang, Yinzhi Zhang, Yinzhuang Zhang, Yipeng Zhang, Yiping Zhang, Yiqian Zhang, Yiqing Zhang, Yiren Zhang, Yirong Zhang, Yitian Zhang, Yiting Zhang, Yiwan Zhang, Yiwei Zhang, Yiwen Zhang, Yixia Zhang, Yixin Zhang, Yiyao Zhang, Yiyi Zhang, Yiyuan Zhang, Yizhe Zhang, Yizhi Zhang, Yong Zhang, Yong-Guo Zhang, Yong-Liang Zhang, Yong-hong Zhang, Yongbao Zhang, Yongchang Zhang, Yongchao Zhang, Yongci Zhang, Yongfa Zhang, Yongfang Zhang, Yongfeng Zhang, Yonggang Zhang, Yonggen Zhang, Yongguang Zhang, Yongguo Zhang, Yongheng Zhang, Yonghong Zhang, Yonghui Zhang, Yongjie Zhang, Yongjiu Zhang, Yongjuan Zhang, Yonglian Zhang, Yongliang Zhang, Yonglong Zhang, Yongpeng Zhang, Yongping Zhang, Yongqiang Zhang, Yongsheng Zhang, Yongwei Zhang, Yongxiang Zhang, Yongxing Zhang, Yongyan Zhang, Yongyun Zhang, You-Zhi Zhang, Youjin Zhang, Youmin Zhang, Youti Zhang, Youwen Zhang, Youyi Zhang, Youying Zhang, Youzhong Zhang, Yu Chen Zhang, Yu Zhang, Yu-Bo Zhang, Yu-Chi Zhang, Yu-Fei Zhang, Yu-Hui Zhang, Yu-Jie Zhang, Yu-Jing Zhang, Yu-Qi Zhang, Yu-Qiu Zhang, Yu-Yu Zhang, Yu-Zhe Zhang, YuHang Zhang, YuHong Zhang, Yuan Zhang, Yuan-Wei Zhang, Yuan-Yuan Zhang, Yuanchao Zhang, Yuanhao Zhang, Yuanhui Zhang, Yuanping Zhang, Yuanqiang Zhang, Yuanqing Zhang, Yuansheng Zhang, Yuanxi Zhang, Yuanxiang Zhang, Yuanyi Zhang, Yuanyuan Zhang, Yuanzhen Zhang, Yuanzhuang Zhang, Yubin Zhang, Yucai Zhang, Yuchao Zhang, Yuchen Zhang, Yuchi Zhang, Yue Zhang, Yue-Bo Zhang, Yue-Ming Zhang, Yuebin Zhang, Yuebo Zhang, Yuehong Zhang, Yuehua Zhang, Yuejuan Zhang, Yuemei Zhang, Yueqi Zhang, Yueru Zhang, Yuetong Zhang, Yufang Zhang, Yufeng Zhang, Yuhan Zhang, Yuhao Zhang, Yuheng Zhang, Yuhua Zhang, Yuhui Zhang, Yujia Zhang, Yujiao Zhang, Yujie Zhang, Yujin Zhang, Yujing Zhang, Yujuan Zhang, Yuke Zhang, Yukun Zhang, Yulin Zhang, Yuling Zhang, Yulong Zhang, Yumei Zhang, Yumeng Zhang, Yumin Zhang, Yun Zhang, Yun-Feng Zhang, Yun-Lin Zhang, Yun-Mei Zhang, Yun-Sheng Zhang, Yun-Xiang Zhang, Yunfan Zhang, Yunfei Zhang, Yunfeng Zhang, Yunhai Zhang, Yunhang Zhang, Yunhe Zhang, Yunhui Zhang, Yuning Zhang, Yunjia Zhang, Yunli Zhang, Yunmei Zhang, Yunpeng Zhang, Yunqi Zhang, Yunqiang Zhang, Yunqing Zhang, Yunsheng Zhang, Yunxia Zhang, Yupei Zhang, Yupeng Zhang, Yuping Zhang, Yuqi Zhang, Yuqing Zhang, Yurou Zhang, Yuru Zhang, Yusen Zhang, Yushan Zhang, Yutian Zhang, Yuting Zhang, Yutong Zhang, Yuwei Zhang, Yuxi Zhang, Yuxia Zhang, Yuxin Zhang, Yuxuan Zhang, Yuyan Zhang, Yuyanan Zhang, Yuyang Zhang, Yuying Zhang, Yuyu Zhang, Yuyuan Zhang, Yuzhe Zhang, Yuzhi Zhang, Yuzhou Zhang, Yuzhu Zhang, Yvonne Zhang, Z Zhang, Z-K Zhang, Zai-Rong Zhang, Zaifeng Zhang, Zaijun Zhang, Zaiqi Zhang, Zebang Zhang, Zekun Zhang, Zemin Zhang, Zeming Zhang, Zeng Zhang, Zengdi Zhang, Zengfu Zhang, Zenglei Zhang, Zengli Zhang, Zengqiang Zhang, Zengrong Zhang, Zengtie Zhang, Zepeng Zhang, Zewei Zhang, Zewen Zhang, Zeyan Zhang, Zeyuan Zhang, Zhan-Xiong Zhang, Zhangjin Zhang, Zhanhao Zhang, Zhanjie Zhang, Zhanjun Zhang, Zhanming Zhang, Zhanyi Zhang, Zhao Zhang, Zhao-Huan Zhang, Zhao-Ming Zhang, Zhaobo Zhang, Zhaocong Zhang, Zhaofeng Zhang, Zhaohua Zhang, Zhaohuai Zhang, Zhaohuan Zhang, Zhaohui Zhang, Zhaomin Zhang, Zhaoping Zhang, Zhaoqi Zhang, Zhaotian Zhang, Zhaoxue Zhang, Zhe Zhang, Zhehua Zhang, Zhemei Zhang, Zhen Zhang, Zhen-Dong Zhang, Zhen-Jie Zhang, Zhen-Shan Zhang, Zhen-Tao Zhang, Zhen-lin Zhang, Zhenfeng Zhang, Zheng Zhang, Zhengbin Zhang, Zhengfen Zhang, Zhenglang Zhang, Zhengliang Zhang, Zhengxiang Zhang, Zhengxing Zhang, Zhengyu Zhang, Zhengyun Zhang, Zhenhao Zhang, Zhenhua Zhang, Zhenlin Zhang, Zhenqiang Zhang, Zhentao Zhang, Zhenyang Zhang, Zhenyu Zhang, Zhenzhen Zhang, Zhenzhu Zhang, Zhewei Zhang, Zhewen Zhang, Zheyuan Zhang, Zhezhe Zhang, Zhi Zhang, Zhi-Chang Zhang, Zhi-Jie Zhang, Zhi-Jun Zhang, Zhi-Peng Zhang, Zhi-Qing Zhang, Zhi-Shuai Zhang, Zhi-Shuo Zhang, Zhi-Xin Zhang, Zhibo Zhang, Zhicheng Zhang, Zhicong Zhang, Zhifei Zhang, Zhigang Zhang, Zhiguo Zhang, Zhihan Zhang, Zhihao Zhang, Zhihong Zhang, Zhihua Zhang, Zhihui Zhang, Zhijian Zhang, Zhijiao Zhang, Zhijing Zhang, Zhijun Zhang, Zhikun Zhang, Zhimin Zhang, Zhiming Zhang, Zhiping Zhang, Zhiqian Zhang, Zhiqiang Zhang, Zhiqiao Zhang, Zhiru Zhang, Zhishang Zhang, Zhishuai Zhang, Zhiwang Zhang, Zhiwen Zhang, Zhixia Zhang, Zhixin Zhang, Zhiyan Zhang, Zhiyao Zhang, Zhiye Zhang, Zhiyi Zhang, Zhiyong Zhang, Zhiyu Zhang, Zhiyuan Zhang, Zhiyun Zhang, Zhizhong Zhang, Zhong Zhang, Zhong-Bai Zhang, Zhong-Yi Zhang, Zhong-Yin Zhang, Zhong-Yuan Zhang, Zhongheng Zhang, Zhongjie Zhang, Zhonglin Zhang, Zhongqi Zhang, Zhongwei Zhang, Zhongxin Zhang, Zhongyang Zhang, Zhongyi Zhang, Zhou Zhang, Zhu Zhang, Zhu-Qin Zhang, Zhuang Zhang, Zhuo Zhang, Zhuo-Ya Zhang, Zhuohua Zhang, Zhuojun Zhang, Zhuorong Zhang, Zhuoya Zhang, Zhuqin Zhang, Zhuqing Zhang, Zhuzhen Zhang, Zi-Feng Zhang, Zi-Jian Zhang, Zian Zhang, Zicheng Zhang, Ziding Zhang, Ziguo Zhang, Zihan Zhang, Ziheng Zhang, Zijian Zhang, Zijiao Zhang, Zijing Zhang, Zikai Zhang, Zilong Zhang, Zilu Zhang, Ziping Zhang, Ziqi Zhang, Zishuo Zhang, Zixiong Zhang, Zixu Zhang, Zixuan Zhang, Ziyang Zhang, Ziyi Zhang, Ziyin Zhang, Ziyu Zhang, Ziyue Zhang, Zizhen Zhang, Zongping Zhang, Zongquan Zhang, Zongwang Zhang, Zongxiang Zhang, Zu-Xuan Zhang, Zufa Zhang, Zuoyi Zhang
articles
Sascha N Goonewardena, Shanshan Yao, Tomasz Jurga +20 more · 2026 · The Journal of clinical investigation · added 2026-04-24
Elevated lipoprotein(a) [Lp(a)] is associated with a higher risk of atherosclerotic cardiovascular disease (ASCVD). Although Lp(a) is a genetically determined risk factor, the plasma proteomic feature Show more
Elevated lipoprotein(a) [Lp(a)] is associated with a higher risk of atherosclerotic cardiovascular disease (ASCVD). Although Lp(a) is a genetically determined risk factor, the plasma proteomic features associated with Lp(a) and whether they provide information about ASCVD risk beyond Lp(a) concentration are not well characterized. We sought to identify plasma proteomic features associated with Lp(a) concentration and to evaluate whether an Lp(a)-associated proteomic signature is associated with ASCVD phenotypes in young, healthy adults. In the Coronary Artery Risk Development in Young Adults (CARDIA) study, we measured Year 7 Lp(a) and 184 cardiovascular proteins using the Olink proximity extension assay in 3,920 participants without prior coronary heart disease. Lp(a)-associated proteomic signatures were derived using LASSO regression in a split-sample design and tested for association with coronary artery calcification (CAC), incident CHD, and hs-CRP over 27 years of follow-up. External replication was performed in the UK Biobank (n=37,996). Lp(a) was associated with CAC (OR 1.23 [1.13-1.34]; p<0.0001) and incident CHD (HR 1.23 [1.07-1.41]; p=0.004). Lp(a) correlated with proteomic features reflecting immune activation, coagulation, and vascular dysfunction. A quantitative Lp(a) proteomic score was independently associated with incident CAC (standardized beta = 0.40, p<0.0001) and hs-CRP (standardized beta = 0.11, p = 0.00015) after adjustment for Lp(a) concentration. In the UK Biobank, a recalibrated Lp(a)-associated proteomic score was associated with CRP, incident CHD, and all-cause mortality. In young adults, Lp(a) is associated with distinct proteomic features that independently predict ASCVD phenotypes beyond Lp(a) concentration, generating hypotheses regarding biological pathways linked to Lp(a)-related cardiovascular risk. Show less
no PDF DOI: 10.1172/JCI204287
LPA
Yang Xu, Mingyu Liao, MeiLu Zhang · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Insufficient physical activity is prevalent among perinatal women, and digital health interventions offer a promising avenue to promote engagement in physical activity within this population. However, Show more
Insufficient physical activity is prevalent among perinatal women, and digital health interventions offer a promising avenue to promote engagement in physical activity within this population. However, previous studies have relied heavily on self-reported data, lacking a systematic synthesis based on objective measurements. This study aims to systematically evaluate the effects of digital health interventions on objectively measured physical activity and sedentary behavior in perinatal women. A systematic search was conducted in PubMed, Embase, Web of Science, and the Cochrane Library databases from inception to December 20, 2025. Fourteen randomized controlled trials (RCTs) involving 2,101 participants were included. The Risk of Bias 2.0 (RoB 2.0) tool was used to assess bias risk, random-effects models were employed to pool effect sizes, and the quality of evidence was evaluated using the GRADE system. The meta-analysis showed that, following the exclusion of outliers via sensitivity analysis, digital health interventions significantly increased daily step counts (MD = 0.68, Digital health interventions can effectively and robustly enhance daily baseline activity levels in perinatal women, with the observed increments potentially reaching the minimal effective dose for improving metabolic health. However, current intervention designs face challenges in driving high-intensity behavior change and disrupting sedentary habits. Future research should explore more targeted and personalized intervention strategies. This systematic review and meta-analysis has been registered in PROSPERO (www.crd.york.ac.uk/prospero), identifier CRD420261280936. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1786474
LPA
Xiaoxiao Song, Xiaoyan Wang, Xindi Wang +4 more · 2026 · Cyberpsychology, behavior and social networking · SAGE Publications · added 2026-04-24
With the widespread use of smartphones among adolescents, smartphone addiction has become a growing mental health concern. Adolescents' limited self-regulation makes them particularly vulnerable to us Show more
With the widespread use of smartphones among adolescents, smartphone addiction has become a growing mental health concern. Adolescents' limited self-regulation makes them particularly vulnerable to using smartphones to escape real-life stress, heightening addiction risk. However, the heterogeneity of addictive behaviors and the dynamic role of experiential avoidance have been underexplored. This 6-month longitudinal study surveyed 547 Chinese primary and secondary students using the Smartphone Addiction Scale (SAS) and the Acceptance and Action Questionnaire-II (AAQ-II). Latent profile analysis (LPA) and latent transition analysis (LTA) were applied to identify subgroups and examine transitions between these subgroups. Cross-lagged panel network analysis (CLPN) revealed key symptom interactions between experiential avoidance and addiction. The study identified two addiction subgroups: a stable "low-risk group" (84.9 percent) and a "high-risk group," 51.4 percent of whom transitioned to low risk over time. Logistic regression showed that experiential avoidance significantly predicted high-risk membership (odds ratios [OR] = 1.083-1.102) and deterioration within the low-risk group (OR = 1.036). The CLPN identified "online intimacy" (SPA-3) and "hesitation and overcautious" (EA-7) as driver nodes, with "withdrawal symptoms" (SPA-2) serving as a central node. These findings emphasize the crucial role of experiential avoidance in adolescent smartphone addiction and suggest symptom-level targets for early intervention. The results support acceptance and commitment therapy (ACT) as a promising approach for reducing smartphone addiction among youth. Show less
no PDF DOI: 10.1177/21522715261441401
LPA
Shunming Zhang, Yan Borné, Le Ma +2 more · 2026 · Nutrition, metabolism, and cardiovascular diseases : NMCD · Elsevier · added 2026-04-24
We examined whether the excess cardiovascular disease (CVD) risk among adults with steatotic liver disease (SLD) subtypes could be reduced or eliminated through joint control of low-density lipoprotei Show more
We examined whether the excess cardiovascular disease (CVD) risk among adults with steatotic liver disease (SLD) subtypes could be reduced or eliminated through joint control of low-density lipoprotein cholesterol (LDL-C), lipoprotein(a) [Lp(a)], and high-sensitivity C-reactive protein (hs-CRP). This prospective cohort study included 291,995 participants from the UK Biobank, comprising 77,187 with metabolic dysfunction-associated steatotic liver disease (MASLD), 22,190 with metabolic dysfunction and alcohol-associated liver disease (MetALD), 5474 with alcohol-associated liver disease (ALD), and 187,144 without SLD. Cox proportional hazards models were used to assess CVD risk associated with numbers of LDL-C, Lp(a), and hs-CRP controlled within the target range. During 12 years of median follow-up, 24,251 CVD events were documented, with 19,661 coronary heart disease and 5600 stroke. Among individuals with various SLD subtypes, those with all three factors controlled had the lowest risks of CVD, with HRs (95% CIs) of 0.65 (0.58, 0.72) in MASLD, 0.61 (0.49, 0.76) in MetALD, and 0.57 (0.35, 0.93) in ALD when comparing to zero-factor control. In addition, among individuals with SLD subtypes achieving all three factors within target ranges, the HRs (95% CIs) of CVD were 0.97 (0.88, 1.07) in MASLD, 0.90 (0.75, 1.08) in MetALD, and 0.63 (0.42, 0.95) in ALD, as compared with non-SLD controls. Similar association patterns were observed for coronary heart disease and stroke. Participants with various SLD subtypes who had optimally controlled LDL-C, Lp(a), and hs-CRP showed no excess or even lower risk of CVD as compared with the general population. Not available. Show less
no PDF DOI: 10.1016/j.numecd.2026.104722
LPA
Tao Sun, Wenhao Zhang, Pingyan Fei +2 more · 2026 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
In recent years, the impact of lipoprotein(a) (Lp(a)) on the prognosis of coronary heart disease has been increasingly recognized. Lp(a) is an independent risk factor for cardiovascular disease, and s Show more
In recent years, the impact of lipoprotein(a) (Lp(a)) on the prognosis of coronary heart disease has been increasingly recognized. Lp(a) is an independent risk factor for cardiovascular disease, and studies have shown that homocysteine (HCY) may influence the association between Lp(a) and the risk of recurrent cardiovascular events. This study investigates the association between Lp(a) levels and recurrent cardiovascular events in patients with varying HCY concentrations. We conducted a 36-month follow-up on 530 patients with coronary heart disease and divided them into low-Lp(a) and high-Lp(a) groups based on Lp(a) levels. The incidence rates of major adverse cardiovascular events (MACE) and acute coronary events (ACE) were compared between the two groups. The association between elevated Lp(a) and cardiovascular risk in different subgroups(based on HCY concentration) was analyzed using Kaplan-Meier curves and Cox proportional hazards models. Elevated Lp(a) remained a significant risk factor for both MACE (HR = 2.07, 95% CI = 1.37-3.12, P = 0.001) and ACE (HR = 2.83, 95% CI = 1.67-4.81, P = 0.001) overall. In subgroup analyses, elevated Lp(a) in patients with moderate-to-high HCY levels constituted a high-risk cohort for MACE and ACE occurrence (HR = 1.87, 95% CI = 1.01-3.46, P = 0.046;HR = 2.85, 95% CI = 1.32-6.18, P = 0.008). Among those with low HCY levels, elevated Lp(a) showed no association with either MACE or ACE (P > 0.05). When HCY is elevated, patients with increased Lp(a) experience amplified risk of recurrent cardiovascular events. This association shifts when HCY is at low levels. Future efforts should emphasize combined assessment of Lp(a) and HCY and explore targeted intervention strategies to reduce residual cardiovascular risk. Show less
no PDF DOI: 10.1186/s12872-026-05847-0
LPA
Tianxiang Fan, Qiyu Xie, Jiawei Chen +13 more · 2026 · Rheumatology (Oxford, England) · Oxford University Press · added 2026-04-24
To explore the associations between accelerometer-measured physical activity patterns and cardiovascular diseases (CVD), CVD-cause mortality, and all-cause mortality in people with osteoarthritis (OA) Show more
To explore the associations between accelerometer-measured physical activity patterns and cardiovascular diseases (CVD), CVD-cause mortality, and all-cause mortality in people with osteoarthritis (OA). OA participants from the UK biobank with ≥36 h of accelerometer data, collected over one-week, were analyzed. Moderate to vigorous physical activity (MVPA) patterns were classified as: 'weekend warriors' (≥150 min/week, >50% on 1-2 days), active regular (>150 min/week), or inactive (<150 min/week). Mean min per week of light physical activity (LPA) were categorized into quartiles based on the distribution in the analytical sample. Among 10 210 study participants (mean age 58.1 ± 7.1 years; 64.5% female) followed for a median of 6.9 years, there were 1,538 incident cases of CVD, and 358 deaths, including 90 from CVD. Compared with inactive MVPA, both weekend warrior (adjusted hazard ratio, aHR (95% CIs); 0.73 (0.64-0.82)) and active regular MVPA (0.75 (0.65-0.87)) significantly lowered the risks of incident CVD. Notably, only the weekend warrior group showed significant reductions in CVD-cause mortality (0.55, 0.33-0.92), and all-cause mortality (0.75 (0.59-0.96)). Higher levels of LPA may link to lower CVD, CVD-cause mortality, and all-cause mortality risks in a dose-response manner. Subgroup analysis indicated that more prominent associations were found in individuals with a body mass index >30 or those aged over 60. Engaging in a weekend warrior pattern may confer unique survival benefits for OA patients, especially among older adults and those with obesity. LPA may have dose-dependent protective effects for CVD and mortality risk in OA patients. Show less
no PDF DOI: 10.1093/rheumatology/keag179
LPA
Tingting Xiao, Yaming Yang, Yue Xiao +6 more · 2026 · Healthcare (Basel, Switzerland) · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/healthcare14070862
LPA
Xin Yang, Haiyan Xiang, Weiming Qian +5 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Falls have long been a significant safety concern worldwide, not only compromising the physical and psychological health of older adults and limiting their social engagement but also imposing substant Show more
Falls have long been a significant safety concern worldwide, not only compromising the physical and psychological health of older adults and limiting their social engagement but also imposing substantial economic and caregiving burdens. Evidence on fall risk perception among Chinese community-dwelling older adults remains limited, especially for those transitioning to community living after hospital discharge. This research examined the subtypes of fall risk perception of Chinese community-dwelling older adults in the post-discharge transition and to explore subgroup characteristics and associated factors. A cross-sectional survey was conducted between January 2024 to March 2025 in Hangzhou, Zhejiang Province. A self-designed questionnaire was used to collect demographic and health-related information, The Fall Risk Perception Scale for Community-dwelling Older Adults was used to assess the fall risk perception, the objective fall risk was assessed by Morse Fall Scale. Latent profile analysis (LPA) was performed to extract latent classes of fall risk perception, and multinomial regression analyses were used to identify differences between these categories. A total of 468 older adults were included, with 56.0% were male. Three fall risk perception subtypes were identified by LPA: Low Perception-Social Context Desensitized Type (29.2%), Moderate Perception - Balanced Type (43.4%), and High Perception - Bio-behaviorally Salient Type (27.4%). Individuals who were aged with 70-79 (OR = 0.46, 95% CI: 0.27-0.77), with college education or above (OR = 0.31, 95% CI: 0.13-0.76), those who underwent surgery during hospitalization (OR = 0.26, 95% CI: 0.15-0.43), reported difficulty falling asleep (OR = 0.40, 95% CI: 0.20-0.82), and those with a history of falls (OR = 0.44, 95% CI: 0.24-0.81) were significantly more likely to be in the High Perception - Bio-behaviorally Salient Type. Compared to objective fall risk level, a third of participants (31.4%) correctly estimated their fall risk, 23.1% overestimated it and 45.5% underestimated it. Most older adults possess a Moderate Perception - Balanced Type toward fall risk. Key determinants of heightened risk perception included advanced age, higher education, fall history, and recent surgical experience. Tailored, profile-specific risk communication strategies are essential to improve perceptual accuracy during the hospital-to-home transition may support post-discharge fall prevention. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1759157
LPA
Xue Rao, Haixin Wang, Bingzi Shi +1 more · 2026 · Seminars in oncology nursing · Elsevier · added 2026-04-24
The purpose of this study was to explore the latent profiles of ambivalence over emotional expression (AEE) in breast cancer patients and its influencing factors. From July 2024 to June 2025, breast c Show more
The purpose of this study was to explore the latent profiles of ambivalence over emotional expression (AEE) in breast cancer patients and its influencing factors. From July 2024 to June 2025, breast cancer patients were recruited using a convenience sampling method from a tertiary hospital in China. A total of 388 participants completed demographic and clinical characteristic questionnaires, the Ambivalence Over Emotional Expression Questionnaire (AEQ), the Perceived Stress Scale-14 (PSS-14), the Social Support Rating Scale (SSRS), and the Irrational Beliefs Scale (IBS). Latent profile analysis (LPA) was used to identify AEE subgroups, followed by univariate analysis, ANOVA, and multinomial logistic regression to examine associated influencing factors. Based on the level of AEE, breast cancer patients were divided into 3 sub groups: "low conflict-active disclosure group " (34.5%), "moderate conflict-inhibition and regret group " (46.5%), and "high conflict-inhibition and regret group " (19.3%). The multivariate logistic regression analysis showed that retirement status, perceived stress, social support and irrational beliefs were factors influencing participants' AEE (P < .05). There was significant variability in AEE among 3 subgroups of breast cancer patients. Retirement status, perceived stress, social support, and irrational beliefs have an impact on AEE in breast cancer patients. It is crucial for healthcare professionals to promptly identify high-risk groups and implement targeted interventions to improve AEE. This study can help healthcare providers identify patients at high risk of AEE, enabling early intervention and targeted psychological nursing interventions. Healthcare providers can assist patients in establishing correct beliefs about their illness and alleviating perceived stress, thereby reducing the negative impact of AEE. Show less
no PDF DOI: 10.1016/j.soncn.2026.152239
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Zhenzhen Zhang, Yuhan Xu, Jinzhen Jin +2 more · 2026 · Scientific reports · Nature · added 2026-04-24
no PDF DOI: 10.1038/s41598-026-48179-x
LPA
Jianlei Liu, Yaling Cui, Hongyu Wang +2 more · 2026 · Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society · Blackwell Publishing · added 2026-04-24
With global population aging, the number of older adults in Chinese nursing homes is rising rapidly, and depression is the most prevalent mental health problem in this population. Most previous studie Show more
With global population aging, the number of older adults in Chinese nursing homes is rising rapidly, and depression is the most prevalent mental health problem in this population. Most previous studies assessed depression via total scale scores, ignoring individual heterogeneity of depressive symptoms. This study aimed to identify distinct depressive symptom profiles and their associated factors in this population. Data were derived from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), with 353 valid nursing home older adults included. Depressive symptoms, anxiety and functional status were assessed using the CESD-10, GAD-7 and IADL scales. Latent profile analysis (LPA), univariate tests and multinomial logistic regression were performed, with supplementary effect size and sensitivity analyses to verify result robustness. Three distinct depressive symptom profiles were identified: low level (39%, n = 135), medium level (52%, n = 187) and high level (9%, n = 31). Town residence and anxiety were risk factors for moderate depression, while good self-rated health, regular exercise and social activity participation were protective factors. Good self-rated health protected against severe depression, while occasional television/radio viewing and anxiety were risk factors. Anxiety was the only independent correlate of high-level versus medium-level depression (OR = 1.322, p < 0.001). Supplementary analyses confirmed the robustness of core findings. The CESD-10, as a screening tool, has limited diagnostic efficacy for clinical depression, and the cross-sectional design cannot confirm causal relationships. Depressive symptoms in Chinese nursing home older adults show significant heterogeneity with three distinct latent profiles. Early screening and targeted stratified interventions should be implemented for this population to improve quality of life and promote healthy aging. Show less
no PDF DOI: 10.1111/psyg.70166
LPA
Yinhu Tan, Hang Li, Shuangxin Zhang +5 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Frailty is associated with increased risks of falls, disability, hospitalization, and mortality. The 24-h movement behaviors (24HMB) framework conceptualizes sleep, sedentary behavior (SB), light-inte Show more
Frailty is associated with increased risks of falls, disability, hospitalization, and mortality. The 24-h movement behaviors (24HMB) framework conceptualizes sleep, sedentary behavior (SB), light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) as mutually constrained components of daily time use and may inform frailty prevention and management. This scoping review maps evidence on associations between 24HMB and frailty and identifies methodological gaps to inform future research and nursing practice. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and follows Joanna Briggs Institute (JBI) guidance. We searched PubMed, Embase, CINAHL, and Web of Science. We included observational studies of adults aged ≥18 years. Exposures were objectively measured or validated self-reported sleep, SB, LPA, and MVPA, including step counts, breaks in SB, isotemporal substitution models (ISM), and compositional data analysis (CoDA). Outcomes were frailty or prefrailty assessed using validated instruments. Quality was appraised with JBI tools. Thirty-three studies showed good methodological quality. Longer SB, particularly prolonged, uninterrupted bouts, was associated with higher frailty. Greater MVPA was consistently associated with lower frailty. Light-intensity physical activity was generally beneficial but often attenuated when MVPA or total activity volume was modeled. Sleep fragmentation and poor sleep quality were associated with frailty. Isotemporal substitution models and compositional data analysis indicated that reallocating sedentary time to MVPA would yield the largest theoretical benefit, followed by reallocating to LPA. Higher daily step counts and more frequent or higher-intensity breaks in SB were associated with lower frailty. Evidence supports a 24-h integrated movement-behavior approach centered on MVPA, combined with reducing prolonged SB and improving sleep quality, for the prevention and nursing management of frailty. The study design and analytical protocol were prospectively registered on the Open Science Framework (OSF). The unique identifier is S39Y4, and the publicly accessible URL is https://doi.org/10.17605/OSF.IO/S39Y4. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1780746
LPA
Munkhtuya Myagmarsuren, Hayley G Law, Wei Zhang +7 more · 2026 · Journal of lipid research · Elsevier · added 2026-04-24
Lipoprotein(a) [Lp(a)] is a genetically determined cardiovascular risk factor. Additionally, Lp(a) levels are affected by dietary saturated fat (SFA) reduction. We previously reported an Lp(a) increas Show more
Lipoprotein(a) [Lp(a)] is a genetically determined cardiovascular risk factor. Additionally, Lp(a) levels are affected by dietary saturated fat (SFA) reduction. We previously reported an Lp(a) increase in response to SFA reduction in both white and black cohorts. However, less is known whether diets impact Lp(a)'s oxidized phospholipids (OxPL) and lipid components. We assessed responses of Lp(a)-OxPL concentration, Lp(a)-OxPL subspecies abundance, and the Lp(a)-lipidome to SFA reduction [from 16% energy with the average American diet (AAD) to 6% energy with a DASH-type diet] in 166 African-Americans. Responses by variability in Lp(a) levels and apolipoprotein(a) [apo(a)] sizes were tested. Mean age was 35 years; 70% were women; mean BMI was 28 kg/m Show less
no PDF DOI: 10.1016/j.jlr.2026.101032
LPA
Kuiliang Li, Lei Ren, Rui Lang +7 more · 2026 · Stress and health : journal of the International Society for the Investigation of Stress · Wiley · added 2026-04-24
Compared with non-left-behind children (NLBC), left-behind children (LBC) face a higher risk of academic stress, depression, and anxiety symptoms due to separation from their parents; however, the het Show more
Compared with non-left-behind children (NLBC), left-behind children (LBC) face a higher risk of academic stress, depression, and anxiety symptoms due to separation from their parents; however, the heterogeneity of academic stress profiles and their relationships with the symptom network remain insufficiently explored. To address this gap, a cross-sectional survey of 10,524 Chinese children compared LBC (n = 2487) and NLBC. Latent profile analysis (LPA) was first conducted to identify academic stress subgroups among LBC. Subsequently, depression-anxiety symptom networks were estimated using Ising and Gaussian graphical models (GGM), with edge weights derived from regularised logistic regression (Ising) and partial correlation (GGM). Simulated interventions were further evaluated via the NodeIdentifyR algorithm (NIRA). Overall, compared to NLBC, LBC exhibited higher levels of academic stress, depression, and anxiety (ps < 0.001, Cliff's δ = 0.076; Cohen's d = 0.067). LPA revealed three academic stress subgroups: moderate (31.44%), high (9.17%), and low (59.39%). The severity of depression and anxiety symptoms increased with the level of academic stress. The high stress subgroup displayed a sparse network with stronger edges (e.g., A1 'Sudden Fear'-A4 'Physical Symptoms', edge weight = 2.10) compared to moderate- and low-academic stress subgroups. Core nodes with the strongest expected influence were A8 ('Decision Hesitation', moderate subgroup), A2 ('Worry', high subgroup), and D1/D6 ('Sadness' and 'Failure', low subgroup). Simulated interventions indicated that alleviating A8 'Decision Hesitation' or A2 'Worry' most effectively reduced symptom risk (16.66%-30.76%), whereas D8 'Motor' and A7 'Early Departure' were associated with maximal symptom aggravation. Taken together, by integrating LPA-derived academic stress profiles with symptom network analysis, this study reveals distinct symptom associations across subgroups. In the high stress subgroup, symptom A2 ('Worry') is a core intervention target; in the low stress subgroup, A7 ('Early Departure') holds preventive potential. These findings underscore subgroup-specific interventions tailored to individual stress profiles. Show less
no PDF DOI: 10.1002/smi.70172
LPA
Wen Guo, Fei Lin, Chengxiao Yu +5 more · 2026 · Frontiers in nutrition · Frontiers · added 2026-04-24
Given that abnormal lipid metabolism is a hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), this study seeks to investigate the relationship between serum lipoprotein(a) [L Show more
Given that abnormal lipid metabolism is a hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), this study seeks to investigate the relationship between serum lipoprotein(a) [Lp(a)] levels and the progression or regression of MASLD. A total of 12,962 participants undergoing transient elastography at the Health Promotion Center of the First Affiliated Hospital of Nanjing Medical University were included in the first cross-sectional study (Study 1). The longitudinal study (Study 2) included 17,661 individuals from the same center, each with at least two health check-ups involving abdominal ultrasonography. Another cross-sectional study (Study 3) included 5,927 individuals from the UK Biobank cohort who had undergone both magnetic resonance imaging proton density fat fraction (MRI-PDFF) and Lp(a) testing. Cross-sectional analysis (Study 1) revealed that elevated Lp(a) levels were inversely correlated with the severity of both hepatic steatosis and fibrosis. Longitudinal data (Study 2) further demonstrated that baseline serum Lp(a) levels were decreased in participants with the incident of MASLD, while increased in participants with the regression of MASLD during the follow-up period. A lower baseline Lp(a) level was an independent factor for new-onset MASLD and non-regression of MASLD: the fully adjusted hazard ratios (HR) were 0.895 (95%CI 0.834-0.962, Serum Lp(a) levels are inversely associated with both the progression and regression of MASLD, indicating its potential role in reflecting disease dynamics. Show less
📄 PDF DOI: 10.3389/fnut.2026.1722393
LPA
Jiarou Chen, Kaiyue Han, Xingxing Liao +6 more · 2026 · Frontiers in psychiatry · Frontiers · added 2026-04-24
Executive function (EF) deficits are a core cognitive feature of autism spectrum disorder (ASD) and are closely associated with social responsiveness. Previous research has primarily focused on childr Show more
Executive function (EF) deficits are a core cognitive feature of autism spectrum disorder (ASD) and are closely associated with social responsiveness. Previous research has primarily focused on children with ASD, whereas how specific executive components relate to social functioning in adults remains less clear. This study examined whether patterns of association between EF and social responsiveness differ between children and adults with and without ASD. Data were obtained from the Autism Brain Imaging Data Exchange II (ABIDE II), including 423 participants aged 8-23 years (ASD = 184; controls = 239). EF was evaluated using the Behavior Rating Inventory of Executive Function (BRIEF/BRIEF-A), and social responsiveness was assessed with the Social Responsiveness Scale (SRS). Covariates of age, sex, and full-scale IQ (FIQ) were controlled using entropy balancing in children and multiple regression in adults. Hierarchical regression, moderated mediation analysis, and latent profile analysis (LPA) were conducted to examine the moderation, mediation, and heterogeneity effects, respectively. Across both child and adult samples, individuals with ASD exhibited significantly higher T-scores than controls on nearly all BRIEF and SRS subdomains after covariate adjustment (all adjusted p < 0.01), indicating widespread EF and social responsiveness impairments. Moderation analyses revealed no significant age group × EF interaction, indicating that the association between EF and social responsiveness was consistent across development. Mediation analysis revealed age-specific pathways, with EF broadly mediating social responsiveness in adults but showing more selective mediation in children. LPA identified four distinct subtypes, which were independent of age, sex, and FIQ. EF-social responsiveness associations were evident across development, but the functional contribution of specific executive components became more differentiated with age. Working memory showed greater relative prominence in adulthood. Latent profile analysis revealed heterogeneity in how executive difficulties align with social challenges, supporting developmentally informed assessment and clinical interpretation rather than direct treatment recommendations. Show less
📄 PDF DOI: 10.3389/fpsyt.2026.1729973
LPA
Shuting Yin, Yuxiang Yuan, Huiqun Wang +2 more · 2026 · Patient preference and adherence · added 2026-04-24
To identify latent self-management profiles in people living with HIV (PLWH) with dyslipidemia and factors associated with profile membership, thereby facilitating targeted clinical intervention. A cr Show more
To identify latent self-management profiles in people living with HIV (PLWH) with dyslipidemia and factors associated with profile membership, thereby facilitating targeted clinical intervention. A cross-sectional survey was conducted from December 2024 to June 2025 among 333 PLWH with dyslipidemia at Nanjing Second Hospital. Data were collected via sociodemographic/disease-related questionnaire, the HIV Self-Management Scale (HIVSMS), and the Health Literacy Management Scale (HLMS). Latent profile analysis (LPA) was performed in Mplus 8.3, and multinomial logistic regression was used to examine factors associated with profile membership. Fit indices (entropy = 0.993) supported a three-profile solution: low self-management-low social support-seeking (C1, 42.3%), moderate self-management-stable (C2, 37.8%), and high self-management-emotion regulation dominant (C3, 19.8%). Seeking social support was relatively low across profiles. Compared with C1, C2 membership was significantly associated with higher education and income, lipid-lowering medication use (OR 3.735, 95% CI 1.597-8.736), and CD4 350-500 cells/μL, and was less likely among participants with VL >1000 copies/mL or chronic comorbidities (all P < 0.05). Compared with C1, C3 membership was significantly associated with HIV infection duration ≥5 years, higher education and income, CD4 >500 cells/μL, and higher HDL-C, and was less likely among those with VL >1000 copies/mL (OR 0.037, 95% CI 0.004-0.380) or chronic comorbidities (all P < 0.05). Compared with C2, C3 membership was independently associated with higher health literacy (HL) (OR 1.038 per point, 95% CI 1.012-1.064) and was less likely among those with LDL-C ≥3 mmol/L (P < 0.05). We identified three distinct self-management profiles among PLWH with dyslipidemia. Profile membership was significantly associated with HL and socioeconomic, HIV-related, lipid-related, and comorbidity factors, supporting the need for profile-tailored strategies to improve self-management. Show less
📄 PDF DOI: 10.2147/PPA.S584419
LPA
Wenzhuo Xu, Hao Guo, Kele Jiang +9 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
In recent years, the global incidence of Non-Suicidal Self-Injury (NSSI) has risen, posing a significant challenge in public health. Adolescents are the main group affected. A cross-sectional study wa Show more
In recent years, the global incidence of Non-Suicidal Self-Injury (NSSI) has risen, posing a significant challenge in public health. Adolescents are the main group affected. A cross-sectional study was conducted using a self-administered questionnaire to collect data from 6,311 adolescents in Hefei, China. This study employed the Compositional Isotemporal Substitution Model (CISM, a statistical method that estimates health effects of replacing time in one behavior with another while accounting for the interdependent, compositional nature of 24-h time-use data) to examine the impact of Screen Time (ST), Non-Screen-based Sedentary Time (NSST), Physical Activity, and Sleep Time on NSSI among adolescents. Compositional logistic regression analysis revealed that, relative to the remaining behavioral components, higher Light Physical Activity (LPA) ( The findings highlight those reasonably allocating adolescents' daily activities, reducing ST, can help lower the risk of NSSI among adolescents. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1737730
LPA
Hui Song, Qiang Geng, Yaowen Xu +6 more · 2026 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
To evaluate the predictive value of novel lipid parameters for coronary lesion severity in pCAD and to develop a nomogram-based prediction model. Patients newly diagnosed with pCAD at Qingdao Municipa Show more
To evaluate the predictive value of novel lipid parameters for coronary lesion severity in pCAD and to develop a nomogram-based prediction model. Patients newly diagnosed with pCAD at Qingdao Municipal Hospital (2021-2024) were enrolled and randomly assigned to training and validation cohorts in a 7:3 ratio. Coronary lesion severity was assessed using the Gensini score (GS), with patients stratified into mild or significant stenosis groups. Spearman correlation analysis was performed between GS and lipid parameters. Key predictors were selected using LASSO regression, and independent risk factors were identified by multivariable logistic regression to construct the nomogram model. The model's discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Lp(a), non-HDL-C, RC, FFA, and BAR were positively correlated with GS (r = 0.34, 0.34, 0.18, 0.19, 0.18; all The proposed nomogram provides an effective tool for identifying pCAD patients with severe coronary artery stenosis, demonstrating robust predictive accuracy and potential clinical utility. Show less
📄 PDF DOI: 10.3389/fcvm.2026.1745711
LPA
Li Zhang, Fengyi Li, Yaru Wu +3 more · 2026 · Cancer management and research · added 2026-04-24
This study aims to identify distinct mindfulness profiles among young and middle-aged lymphoma patients and to examine the mediating role of psychological resilience in the relationship between these Show more
This study aims to identify distinct mindfulness profiles among young and middle-aged lymphoma patients and to examine the mediating role of psychological resilience in the relationship between these mindfulness profiles and social function deficits. From November 2024 to June 2025, a total of 324 young and middle-aged lymphoma patients were recruited using convenience sampling from a tertiary cancer hospital in Urumqi, Xinjiang, China. Participants completed the Mindful Attention Awareness Scale, the 10-item Connor-Davidson Resilience Scale, and the Social Dysfunction Screening Scale. We used latent profile analysis (LPA) to identify distinct mindfulness profiles and tested the mediating role of psychological resilience with the Bootstrap method. Latent profile analysis identified three distinct mindfulness profiles among the patients: a low mindfulness type (29.3%), a moderate mindfulness type (40.1%), and a high mindfulness type (30.6%). Furthermore, psychological resilience partially mediated the relationship between these mindfulness profiles and social function deficits. Young and middle-aged lymphoma patients exhibit heterogeneous mindfulness profiles. Higher mindfulness can enhance psychological resilience, which in turn alleviates social function deficits. Therefore, healthcare providers should develop personalized interventions targeting psychological resilience based on patients' specific mindfulness profiles to improve their social function. Show less
📄 PDF DOI: 10.2147/CMAR.S570129
LPA
Jiaqi Zuo, Jie Zhang, Ying Tang +10 more · 2026 · The Plant cell · Oxford University Press · added 2026-04-24
Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties Show more
Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties make cytosolic accumulation cytotoxic, necessitating its sequestration into vacuoles for safe storage. Here, we present the cryo-EM structures of the rice vacuolar phytate transporter, OsMRP5, captured in distinct functional states. These structures reveal the molecular basis of OsMRP5 function as an ATP-binding cassette (ABC) transporter. OsMRP5 employs a specialized substrate-recognition mechanism, uniquely adapted to bind the fully hydrophilic InsP6 through extensive electrostatic and hydrogen-bonding interactions within two distinct, highly polar binding sites in its central cavity. A distinctive electropositive tunnel, positioned above the central cavity, forms a continuous pathway connecting the InsP6-binding pocket to the vacuolar export site. This tunnel likely generates an electrostatic attraction that facilitates the movement of the highly anionic InsP6 through the transporter. By mapping mutations from low-phytic acid (lpa) crop variants onto the OsMRP5 structures, we pinpoint their conserved locations critical for transporter function and validate their impact experimentally. These results reveal how OsMRP5 recognizes and transports the highly charged InsP6 molecules into vacuoles, providing a molecular framework for targeted manipulation of this agriculturally important transporter. Show less
no PDF DOI: 10.1093/plcell/koag088
LPA
Chuqin Xiong, Shuge Wang, Peiran Guo +6 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24
Nursing interns often face maladjustment during the early stages of clinical practice, which not only directly affects their physical and mental health as well as work efficiency but also significantl Show more
Nursing interns often face maladjustment during the early stages of clinical practice, which not only directly affects their physical and mental health as well as work efficiency but also significantly inhibits their proactive feedback-seeking behavior (FSB). As an active self-regulation strategy, FSB can enhance interns' work initiative and promote role transition. However, existing research has yet to thoroughly investigate the potential heterogeneity and categorical characteristics of FSB within this population, and the role of psychological resources such as career adaptability in shaping these patterns requires further investigation. To investigate the status of FSB in early-stage nursing interns, identify latent subgroups via latent profile analysis (LPA), and analyze associated factors, thereby providing evidence for targeted clinical educational interventions. Multicenter cross-sectional research. This study employed a multistage stratified cluster sampling to survey 1,308 early-stage nursing interns from nine universities in Hubei, China, between June and September 2024. Data were collected using a demographic questionnaire, Feedback-Seeking Behavior Scale, and Career Adapt-Abilities Scale. LPA was employed to delineate FSB profiles and multivariate logistic regression analysis to examine the associated predictors. A total of 1,370 questionnaires were distributed, with 1,308 valid responses, yielding an effective response rate of 95.47%. The mean score on the feedback-seeking behavior scale was 5.06 ± 1.08. LPA identified three distinct feedback-seeking profiles: low (20.87%), moderate (38.3%), and high (40.83%). Education level, student cadre experience, internship hospital type, and career adaptability were significant predictors of profile membership ( FSB among early-stage nursing interns exhibited heterogeneity. Nursing educators and managers should implement tiered interventions: for the low and moderate feedback-seeking groups, career guidance and feedback awareness cultivation should be strengthened; for the high feedback-seeking group, peer modeling should be encouraged. This strategy can enhance proactive FSB, supports role transition and professional identity, and promotes long-term nursing workforce stability. Show less
📄 PDF DOI: 10.3389/fmed.2026.1664329
LPA
Yunyun Liu, Xiangrui Li, Ting Zhao +9 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
Fear of progression (FoP) is a prevalent psychological issue among stroke patients. Previous studies failing to distinguish characteristics of patient groups with varying FoP levels. Latent profile an Show more
Fear of progression (FoP) is a prevalent psychological issue among stroke patients. Previous studies failing to distinguish characteristics of patient groups with varying FoP levels. Latent profile analysis (LPA) classifies individuals into distinct subgroups via continuous FoP indicators, boosting classification accuracy by accounting for variable uncertainty. Given FoP's heterogeneity, investigating FoP profiles and their influencing factors in stroke patients is clinically significant for personalized psychological care and improved patient quality of life. A total of 366 stroke patients were selected as study subjects through convenience sampling, and a cross-sectional survey was conducted. FoP was assessed using the Fear of Progression Questionnaire-Short Form (FoP-Q-SF, 2 dimensions, 12 items). Independent variables included demographic characteristics, clinical indicators, the Recurrence Risk Perception Scale for Stroke patients (RRPSS), and the Medical Coping Modes Questionnaire (MCMQ). LPA was performed on the FoP-Q-SF items to identify subgroups. The R3STEP method was used to analyze influencing factors of subgroup membership, and the BCH method was applied to compare differences in distal outcomes across subgroups. Statistical significance was set at The study sample had a mean age of 63.93 ± 10.58 years, with 70.5% males and 65.0% first-ever stroke patients. Two latent profiles were identified: Low-FoP Adaptive Type (C1, 48.6%) and High-FoP Sustained Type (C2, 51.4%). The R3STEP showed that age 18-59 years (OR = 0.476, 95%CI = 0.245-0.924, This study revealed significant heterogeneity in FoP among stroke patients. Age, hypertension comorbidity, excessive recurrence risk perception, MCMQ-confrontation, and MCMQ-avoidance were associated with high FoP. Healthcare providers should prioritize identifying high-risk individuals and develop tailored interventions to reduce FoP and improve rehabilitation outcomes. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1741344
LPA
Niuniu Zhou, Yuzhong Gu, Jianyun Liu +4 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24
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
LPA
Yesheng Ling, Yang Chen, Xianguan Yu +1 more · 2026 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
To assess the predictive value of serum lipoprotein(a) [Lp(a)] for contrast-induced nephropathy in patients with type 2 diabetes mellitus (T2DM). Consecutive T2DM patients who underwent coronary angio Show more
To assess the predictive value of serum lipoprotein(a) [Lp(a)] for contrast-induced nephropathy in patients with type 2 diabetes mellitus (T2DM). Consecutive T2DM patients who underwent coronary angiography (CAG) or percutaneous coronary intervention (PCI) between January 2019 and December 2021 were enrolled. Baseline Lp(a) was measured before the operation. CIN was defined as an increase in serum creatinine of more than 25% or 44 μmol within 72 h of contrast administration. The relationship between Lp(a) and CIN risk was analyzed. A total of 928 T2DM patients were included. CIN developed in 11.1% (103/928) of patients. The Lp(a) level was significantly higher in patients with CIN than in non-CIN patients (311.12 ± 278.66 vs. 254.19 ± 274.56 mg/L, A higher serum Lp(a) level indicates an increased risk of CIN in T2DM patients undergoing CAG or PCI and can serve as an independent predictor of CIN in this population. This study's findings will aid in the clinical prevention and treatment of contrast agent-induced kidney disease. Show less
📄 PDF DOI: 10.3389/fcvm.2026.1733119
LPA
Jian Zhang, Xilin Yu, Lili Wei +3 more · 2026 · Nursing ethics · SAGE Publications · added 2026-04-24
PurposeThis study aims to explore the latent classes of compassion fatigue among intensive care unit (ICU) nurses and identify the factors that influence their compassion fatigue.MethodsBetween Novemb Show more
PurposeThis study aims to explore the latent classes of compassion fatigue among intensive care unit (ICU) nurses and identify the factors that influence their compassion fatigue.MethodsBetween November 2024 and February 2025, 1029 ICU nurses were selected as study participants using convenience sampling. Data were gathered through general demographic questionnaires, the Chinese version of the Short Scale of Compassion Fatigue (CFSS), the Occupational Stress Scale, the Perceived Social Support Scale, as well as the Professional Identity Scale. A latent profile analysis (LPA) was conducted based on the three dimensions of the CFSS as observed indicators. Additionally, factors influencing outcomes were analyzed using both univariate and multivariate logistic regression methods.Ethical considerationsThis study was approved by the Institutional Review Board of the Affiliated Hospital of Qingdao University.ResultsA total of 1029 valid questionnaires were obtained, resulting in an effective response rate of 93.46%. The average score on the ICU Nurse Compassion Fatigue Scale was 60.00 ± 27.36 points. Three distinct profiles were identified: low compassion fatigue-low secondary trauma type (33.04%), moderate compassion fatigue-overall fluctuation type (48.30%), and high compassion fatigue-high burnout type (18.66%). Multivariate logistic regression analysis revealed that health status, sleep quality, highest education level, occupational stress, professional identity, and social support significantly influence the potential compassion fatigue profiles among critical care nurses ( Show less
no PDF DOI: 10.1177/09697330261426430
LPA
Ruoxuan Zhang, Xin Wang, Angela Y M Leung +8 more · 2026 · Journal of nursing management · added 2026-04-24
Given the globalization of the nursing workforce, psychological empowerment represents a critical intrinsic determinant of nurses' mobility intentions, specifically regarding cross-border work. To ide Show more
Given the globalization of the nursing workforce, psychological empowerment represents a critical intrinsic determinant of nurses' mobility intentions, specifically regarding cross-border work. To identify latent profiles of nurses' psychological empowerment, examine associated factors, and explore the relationship between these profiles and cross-border working intention. A cross-sectional multicenter study was conducted from March to September 2023. Using convenience sampling, clinical nurses were recruited through liaisons from nursing societies in nine cities of Guangdong Province. Data were collected through questionnaires covering sociodemographic questionnaire, psychological empowerment, and cross-border working intention, with analyses including chi-square tests, logistic regression, and latent profile analysis (LPA) performed using SPSS 23.0 and Mplus 8.3. A total of 3671 valid questionnaires were collected, and 39.5% of the respondents reported cross-border intentions. LPA identified three psychological empowerment profiles among nurses, ranked from high to low: the core-driven empowerment profile (16.94%), the adaptive empowerment profile (70.42%), and the constrained empowerment profile (12.64%). The nurses with lower salary, intermediate title, and without specialist nurse qualification were more likely to fall into the constrained empowerment profile. Psychological empowerment was positively correlated with nurses' cross-border work intention. The core-driven profile showed the highest cross-border work intention (50.6%), followed by the adaptive (38.2%) and constrained profiles (31.7%). For cross-border work, the constrained profile prioritized salary (87.1%) as the key concern, while the core-driven profile focused more on good promotion opportunities (70.3%). Psychological empowerment exerts a positive impact on clinical nurses' cross-border work intention, with the three identified empowerment profiles exhibiting divergent motivational priorities and decision logics. These findings highlight the need for subgroup-specific strategies to balance nursing workforce mobility and stability. The findings support a differentiated human resource strategy based on nurses' psychological empowerment profiles. For core-driven nurses, institutions should provide international career development channels to strengthen their domestic job embeddedness. For adaptive nurses, tailored skill training and decision-making autonomy should be offered to guide their mobility aspirations. For constrained nurses, competitive compensation and family support services should be prioritized to address their stability needs and rebuild professional confidence. These targeted measures balance talent mobility and domestic workforce stability. Show less
📄 PDF DOI: 10.1155/jonm/8714790
LPA
Jingjing Ma, Weifei Yu, Qihang Xu +2 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
While family resilience is a recognized determinant of adaptation following stroke, the distinct, empirically derived profiles of family resilience among Chinese stroke survivor-caregiver dyads have n Show more
While family resilience is a recognized determinant of adaptation following stroke, the distinct, empirically derived profiles of family resilience among Chinese stroke survivor-caregiver dyads have not been clearly delineated. Identifying these profiles and their determinants is crucial for developing targeted interventions. To identify latent profiles of family resilience and examine the socio-demographic and clinical factors associated with profile membership among stroke patient-caregiver dyads in China. In this cross-sectional study, a convenience sample of 773 stroke survivor-caregiver dyads was recruited from three hospitals in Zhejiang Province, China. Latent profile analysis (LPA) was conducted on the 20-item Family Resilience Questionnaire (FRQ). Multinomial logistic regression was used to determine factors associated with profile membership. LPA supported a four-profile solution: Profile 1 "Low-Functioning Families" (22%), Profile 2 "Moderately Resilient - Low Cohesive Families" (24%), Profile 3 "Highly Resilient - Well-Functioning Families" (31%), and Profile 4 "High-Functioning - Optimistically Resilient Families" (24%). Multinomial logistic regression revealed that lower caregiver competence (higher FCTI scores) was strongly associated with profile membership (standardized aORs ranged from 2.58 to 43.19), whereas higher perceived social support (PSSS) was a significant protective factor (standardized aORs ranged from 0.03 to 0.19). Caregiver relationship and payment source were also significantly associated with profile membership. Family resilience among Chinese stroke families manifests in four distinct profiles, which are differentiated predominantly by caregiver competence and perceived social support. Our findings advocate for a precision family support paradigm, shifting from one-size-fits-all approaches to interventions tailored to distinct resilience profiles. Given the strong association, intervention programs should prioritize enhancing core caregiver competencies as a primary leverage point for building family resilience. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1749638
LPA
Xintong Ma, Wei Li, Yuanyuan Liu +8 more · 2026 · BMC psychiatry · BioMed Central · added 2026-04-24
Adolescence is a critical period for rapid emotional and cognitive development. Depression and cognitive impairment frequently co-occur in this population, yet their comorbidity patterns and symptom-l Show more
Adolescence is a critical period for rapid emotional and cognitive development. Depression and cognitive impairment frequently co-occur in this population, yet their comorbidity patterns and symptom-level interactions remain insufficiently explored. A total of 2,244 students (mean age = 16.8 ± 0.84 years; 1,218 males, 1,026 females) from a high school in Heilongjiang Province, China, were recruited. Depressive symptoms and cognitive impairment were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Perceived Deficits Questionnaire–Depression (PDQ-D). Latent profile analysis (LPA) was applied to identify subgroups, followed by network analysis to examine central symptoms (expected influence, EI), bridge symptoms (bridge expected influence, BEI), and network differences (NCT). The optimal LPA model identified three comorbidity subgroups: low, moderate, and high. NCT revealed significant differences in network structure and global strength between the low–moderate (S = 1.514, Adolescent Depression and Cognitive Impairment can be classified into low, moderate, and high comorbidity subgroups. Somatic symptoms emerged as the central symptom, while prospective memory impairment and interpersonal problems were identified as key bridge symptoms, suggesting potential intervention targets for early screening and stratified treatment. Not applicable. The online version contains supplementary material available at 10.1186/s12888-026-07946-w. Show less
📄 PDF DOI: 10.1186/s12888-026-07946-w
LPA
Bingyuan Lu, Linlin Ma, Fei Xia +5 more · 2026 · Frontiers in psychiatry · Frontiers · added 2026-04-24
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
LPA