👤 Zian 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, Zhongxu 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, 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

Social Isolation and Its Influencing Factors in Maintenance Haemodialysis Patients: A Latent Profile Analysis.

Luwen Zhang, Fangli Liu, Jinghui Liu +1 more · 2025 · Journal of advanced nursing · Blackwell Publishing · added 2026-04-24

Luwen Zhang, Fangli Liu, Jinghui Liu, Xia Zheng Show less

To explore latent profiles of social isolation in maintenance haemodialysis (MHD) patients and to analyse the factors influencing different latent profiles. Multicentre cross-sectional study. Between Show more

To explore latent profiles of social isolation in maintenance haemodialysis (MHD) patients and to analyse the factors influencing different latent profiles. Multicentre cross-sectional study. Between November 2024 to March 2025, 305 MHD patients from the haemodialysis centres of three hospitals in Henan Province, China, were recruited using a convenience sampling method. All participants completed the general information questionnaire, Lubben Social Network Scale 6 (LSNS-6), UCLA Loneliness Scale-6 (ULS-6) and Personal Mastery Scale. Latent Profile Analysis (LPA) was used to classify the participants into potential subgroups with different types of social isolation. The influencing factors of profiles were explored by univariate analysis and multiple logistic regression analysis. Social isolation of 305 patients can be divided into three profiles: the family-friend dual isolation group (14.10%), friend isolation-only group (47.54%), and social network well-being group (38.36%). Multivariable logistic regression analysis revealed that monthly personal income, living arrangement, social participation, dialysis time, post-dialysis fatigue, number of comorbidities, loneliness and personal mastery were identified as factors influencing the profiles. There is heterogeneity in social isolation among MHD patients. It is therefore necessary to implement targeted intervention measures based on the distinct characteristics of each subgroup to facilitate their social reintegration. Nurses should identify differences in social isolation among MHD patients. It is necessary to establish tripartite connections between families, hospitals and communities, and develop personalised psychosocial interventions to alleviate social isolation. The study identified distinct subgroups of social isolation among MHD patients, while emphasising the impact of psychological resources such as loneliness and personal mastery on social isolation. This may offer critical insights for nurses to develop targeted interventions for patients' social health. The study followed the STROBE guidelines for cross-sectional studies. No patient or public involvement. Show less

no PDF DOI: 10.1111/jan.70452

Latent profile analysis of depression and anxiety comorbidity in patients with acute ischemic stroke: a prospective study.

Dongli Chen, Hong Zhang, Yuqi Xiu +5 more · 2025 · Frontiers in psychiatry · Frontiers · added 2026-04-24

Dongli Chen, Hong Zhang, Yuqi Xiu, Chunxun Xiao, Zhili Liu, Hongchun Lin, Shaoyan Zheng, Yanchun Wu Show less

Stroke is a leading cause of mortality and disability globally, with post-stroke depression and post-stroke anxiety being common and significant complications that hinder recovery and adversely affect Show more

Stroke is a leading cause of mortality and disability globally, with post-stroke depression and post-stroke anxiety being common and significant complications that hinder recovery and adversely affect quality of life. Although these conditions frequently co-occur, their heterogeneity remains poorly understood. This study integrates the Health Ecology Model (HEM) and employs Latent Profile Analysis (LPA) to identify distinct psychological profiles of depression and anxiety among patients with acute ischemic stroke (AIS), as well as to investigate their multilevel determinants. Patients with AIS from a tertiary hospital in Guangdong Province, China, from January to November 2024 were included. Within one week of stroke onset, the data of sociodemographic, clinical characteristics, swallowing function, stroke severity, activities of daily living, resilience and social support were collected according to the HEM guidelines. The Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7 were used to assess the depression and anxiety symptoms of the patients three months after stroke onset. LPA was employed to identify distinct psychological profiles, and variables with a A total of 551 patients with AIS were included in the study, 49 were lost to follow-up or withdrew, resulting in a final analytic sample of 502 participants (91.11%). Three distinct psychological profiles were identified: no depression-anxiety (67.93%), high-risk depression-anxiety (21.12%) and major depression-anxiety (10.95%). In the multivariate analysis, the results indicated that occupation (OR = 0.61, 95% CI [0.40-0.93]), National Institutes of Health Stroke Scale (NIHSS, OR = 1.60, 95% CI [1.06-2.42]), Barthel Index (BI, OR = 1.67, 95% CI [1.27-2.19]) and hypertension (OR = 2.37, 95% CI [1.29-4.35]) were independent predictors of the high-risk depression-anxiety profile, while NIHSS (OR = 2.33, 95% CI [1.42-3.85]), BI (OR = 2.65, 95% CI [1.62-4.35]) and resilience (OR = 0.92, 95% CI [0.87-0.98]) were significantly associated with the major depression-anxiety profile. This study reveals significant heterogeneity in psychological distress among AIS survivors. Key predictors of post-stroke emotional comorbidity include occupation, hypertension, stroke severity, activities of daily living and low resilience. Early identification of high-risk individuals can significantly enhance screening and intervention strategies, particularly by focusing on symptoms such as anhedonia and nervousness. Future research should focus on longitudinal designs and objective biomarkers to better understand the mechanisms behind post-stroke emotional comorbidity. Show less

📄 PDF DOI: 10.3389/fpsyt.2025.1651116

Cognitive Function Profiles and Associated Factors in Older Adults Living with Type 2 Diabetes: A Latent Profile Analysis.

Beilei Ye, Mengxia Pan, Xiaoju Lei +2 more · 2025 · Clinical interventions in aging · added 2026-04-24

Beilei Ye, Mengxia Pan, Xiaoju Lei, Ying Wen, Qiong Zhang Show less

This study aims to explore the latent profile characteristics of cognitive function in older adults living with diabetes and analyze the influencing factors, providing theoretical evidence for early i Show more

This study aims to explore the latent profile characteristics of cognitive function in older adults living with diabetes and analyze the influencing factors, providing theoretical evidence for early intervention. A cross-sectional study design was used to select older adults living with diabetes hospitalized at a tertiary hospital as the study population. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Demographic characteristics, disease-related data (such as duration of diabetes, BMI, and HbA1c levels), and lifestyle factors (such as sleep quality, physical activity, and social support) were collected. Latent profile analysis (LPA) was employed to classify cognitive function, and ordered multinomial logistic regression was performed to analyze the influencing factors of each cognitive profile. A total of 564 patients were included. Latent profile analysis of cognitive impairment identified three categories: complete cognitive impairment (12.82%), partial cognitive impairment (54.74%), and at-risk cognitive impairment (32.44%). Logistic regression analysis revealed that gender, education level, duration of diabetes, HbA1c, diverse intellectual activities, and nutrition were independent factors influencing cognitive impairment (P<0.05). Cognitive impairment in older adults living with diabetes exhibits distinct profile characteristics and is influenced by multiple factors. Interventions should focus on improving blood glucose control, promoting diverse intellectual activities, and enhancing social support to delay the decline in cognitive function. Show less

📄 PDF DOI: 10.2147/CIA.S553115

What Makes Us Respect Older Adults: Linking Multiple Factor Profiles With Respect for Older Adults Among Chinese Adults.

Yue Cao, Nana Wu, Yanfen Liu +3 more · 2025 · Journal of applied gerontology : the official journal of the Southern Gerontological Society · SAGE Publications · added 2026-04-24

Yue Cao, Nana Wu, Yanfen Liu, Xinhe Zhang, Lilai He, Jingjin Shao Show less

ObjectiveRespect for older adults (ROA) is shaped by multiple ecological systems and personal factors. However, little is known about the potential subgroups that may differ in their constellation of Show more

ObjectiveRespect for older adults (ROA) is shaped by multiple ecological systems and personal factors. However, little is known about the potential subgroups that may differ in their constellation of influencing factors and their association with ROA.MethodsThis cross-sectional study included 1,476 community-dwelling Chinese adults aged 18-83 years ( Show less

no PDF DOI: 10.1177/07334648251406350

Association between lipoprotein(a) levels, cumulative lipoprotein(a) burden, and cardiovascular outcomes in patients with acute myocardial infarction: a large-scale cohort study.

Zhenwei Wang, Jinying Zhang, Junnan Tang · 2025 · Lipids in health and disease · BioMed Central · added 2026-04-24

To determine whether lipoprotein(a) [Lp(a)] and cumulative Lp(a) (CumLp(a)) are associated with adverse outcomes in patients with acute myocardial infarction (AMI). This cohort study included 2,634 ho Show more

To determine whether lipoprotein(a) [Lp(a)] and cumulative Lp(a) (CumLp(a)) are associated with adverse outcomes in patients with acute myocardial infarction (AMI). This cohort study included 2,634 hospitalized patients diagnosed with AMI who underwent coronary angiography at Zhongda Hospital, Southeast University, from July 2013, to December 2021. The main outcome was major adverse cardiac and cerebrovascular events (MACCE), defined as cardiovascular (CV) death, non-fatal myocardial infarction, non-fatal stroke, or unplanned revascularization—occurring singly or in combination. We used Cox proportional hazards models, with subgroup and sensitivity analyses, restricted cubic spline (RCS) modeling, and threshold-effect assessment to evaluate the relationships between Lp(a), CumLp(a), and prognosis. Across a median 55.2-month follow-up, 907 participants (34.40%) experienced a MACCE, 342 (13.00%) patients had CV death, 177 (6.70%) patients had non-fatal MI, 202 (7.70%) patients had non-fatal stroke, 399 (15.10%) patients underwent unplanned revascularization, and all-cause death occurred in 547 (20.80%) patients. Multivariable Cox regression models demonstrated a significantly increased risk of MACCE, CV death, non-fatal MI, and non-fatal stroke in both the higher Lp(a) and higher CumLp(a) groups compared with the lower groups (HRs for Lp(a): 1.652, 2.157, 3.455, and 1.930; HRs for CumLp(a): 1.697, 1.675, 3.759, and 2.032), and every one-unit rise in CumLp(a), the risk of MACCE, CV death, non-fatal MI and non-fatal stroke increased by 1.3%, 1.4%, 1.9% and 1.2%, respectively. The majority of subgroup and sensitivity checks consistently supported a stable link between Lp(a)/CumLp(a) and the risks of MACCE, CV death, non-fatal MI, and stroke. Analyses using RCS and threshold models revealed that Log Higher levels of Lp(a) and CumLp(a) are linked to a greater risk of poor outcomes among patients with AMI as the index event, highlighting their potential value for risk stratification and guiding clinical decision-making. The online version contains supplementary material available at 10.1186/s12944-025-02800-6. Show less

📄 PDF DOI: 10.1186/s12944-025-02800-6

The latent profile structure of alexithymia in the elderly and its relationship to eating behaviors: the mediating role of physical activity.

Bin Chen, Jing Yang, Wenying Huang +3 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24

Bin Chen, Jing Yang, Wenying Huang, Wen Zhang, Chao Yang, Chang Hu Show less

This study aimed to elucidate the psychological mechanisms underlying the relationship between alexithymia and problematic eating behaviors (EB) among older adults. Specifically, we examined whether p Show more

This study aimed to elucidate the psychological mechanisms underlying the relationship between alexithymia and problematic eating behaviors (EB) among older adults. Specifically, we examined whether physical activity (PA) mediated this association, and we further explored the heterogeneity of alexithymia using Latent Profile Analysis (LPA). A cross-sectional survey was conducted among 1,773 community-dwelling older adults in China. Participants completed validated questionnaires assessing alexithymia, PA, and EB. Mediation analysis tested the indirect effect of PA on the alexithymia-EB relationship, while LPA identified subgroups of individuals with distinct alexithymia profiles. Mediation analysis revealed that PA significantly mediated the relationship between alexithymia and maladaptive EB, accounting for 18% of the total effect. LPA supported a three-profile solution: pervasive alexithymia (21.15%), adaptive (72.81%), and affective-cognitive dissociation (6.04%). Profile membership was differentially associated with health behaviors, with the pervasive group showing the most unfavorable outcomes (high EB, low PA), and the adaptive group demonstrating the most favorable pattern. These findings highlight PA as a key behavioral pathway through which alexithymia contributes to maladaptive eating in older adults. Moreover, alexithymia is not uniform but heterogeneous, with distinct profiles that confer varied health behavior risks. Interventions to improve eating habits in elderly populations may benefit from tailoring strategies to alexithymia subtypes and systematically promoting PA as an adaptive regulatory mechanism. Show less

📄 PDF DOI: 10.3389/fpsyg.2025.1701168

Latent Profile Analysis of Occupational Stress and Psychological Capital and the Dual Mechanisms of Psychological Capital in Healthcare Workers' Sleep Quality.

Sijia Yang, Boya Zhang, Jian Chen +3 more · 2025 · Healthcare (Basel, Switzerland) · MDPI · added 2026-04-24

Sijia Yang, Boya Zhang, Jian Chen, Jiahui Li, Bo Zhang, Zhijun Zhou Show less

📄 PDF DOI: 10.3390/healthcare13233109

A socioecological model of multilevel determinants for proactive health behaviors among Chinese adults with chronic diseases.

Chaoyi Chen, Yanhua Hao, Weilan Xu +3 more · 2025 · BMC public health · BioMed Central · added 2026-04-24

Chaoyi Chen, Yanhua Hao, Weilan Xu, Ze Liu, Yuwei Zhang, Zhanchun Feng Show less

Chronic diseases have become a major public health challenge facing the world. Identifying key factors and developing effective management strategies to promote proactive health behaviors in patients Show more

Chronic diseases have become a major public health challenge facing the world. Identifying key factors and developing effective management strategies to promote proactive health behaviors in patients is crucial for improving health outcomes. This study aims to construct a comprehensive model of proactive health behaviors in chronic disease patients, elucidate multilevel determinants, and guide targeted policy interventions in China. A cross-sectional survey was conducted among 805 patients with chronic diseases in China. Latent profile analysis (LPA) was conducted to identify distinct profiles of proactive health behaviors among patients. Binary logistic regression analysis was used to verify and analyze the determinants affecting the proactive health behaviors of patients. Among the 805 participants, 471 were classified as highly proactive, and 334 were classified as less proactive. The average score for proactive health behaviors was 70.37 ± 10.93. Several factors positively predicted proactive health behaviors: patients aged > 74 years (AOR = 8.85, 95% CI 2.06-39.45), married patients (AOR = 1.78, 95% CI 1.02-3.11), urban residents (AOR= 1.33, 95% CI 1.04-1.70), those with stronger health intentions (AOR = 1.42, 95% CI 1.28-1.60), higher self-efficacy (AOR = 1.12, 95% CI 1.04-1.20), positive health beliefs (AOR = 1.21, 95% CI 1.09-1.34)), and greater community support (AOR = 1.18, 95% CI 1.07-1.32). Regarding policy support, perceiving an adequate upper payment limit for drugs was associated with twice the odds of proactive health behaviors (AOR = 2.61, 95% CI 1.44-4.78). Additionally, age and the medication reimbursement policy for drug expenses exerted negative effects on proactive health behaviors (β = -0.507, P < 0.01). Governments should transform medical insurance from a passive payer into an active health investor. By incorporating behavioral economics principles, such a reform reallocates policy design, resources, and decision-making power toward disadvantaged populations. This shift breaks the "well-intentioned policy trap", achieving lower medical costs alongside improved population health. Show less

📄 PDF DOI: 10.1186/s12889-025-25564-1

Comparison of image quality in 40 keV virtual monoenergetic images of dual-energy CT pulmonary angiography using deep learning and iterative reconstruction algorithms under optimized low dose scanning protocols.

Dapeng Zhang, Lulu Zhang, Juan Long +10 more · 2025 · Quantitative imaging in medicine and surgery · added 2026-04-24

Dapeng Zhang, Lulu Zhang, Juan Long, Yang Wu, He Zhang, Chong Wang, Bo Sun, Chenzi Wang, Xiaonan Sun, Aiyun Sun, Yankai Meng, Chunfeng Hu, Kai Xu Show less

Pulmonary embolism is a potentially fatal cardiovascular condition that demands prompt and accurate diagnostic imaging. Traditional single-energy computed tomography pulmonary angiography (CTPA), whil Show more

Pulmonary embolism is a potentially fatal cardiovascular condition that demands prompt and accurate diagnostic imaging. Traditional single-energy computed tomography pulmonary angiography (CTPA), while widely used, is associated with high radiation doses and substantial volumes of contrast agents, which may increase the risks of radiation-induced tissue damage and contrast-induced nephropathy (CIN), respectively. Dual-energy CTPA (DE-CTPA) presents a promising alternative, though challenges, including elevated image noise at low kilo-electron volt (keV) levels (e.g., 40 keV), persist. The primary aim of this study is to evaluate and compare the image quality of 40 keV virtual monoenergetic images (VMI) reconstructed using deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-V (ASIR-V) algorithms within the context of low-dose DE-CTPA protocols. This prospective study enrolled patients who underwent DE-CTPA between January and April 2025. Using a Revolution CT scanner, 40 keV VMI were reconstructed with four distinct algorithms: ASIR-V 50%, ASIR-V 70%, Deep learning image reconstruction with medium setting (DLIR-M), and deep learning image reconstruction with high setting (DLIR-H). Iodixanol (350 mgI/mL) was administered at a dose of 0.4 mL/kg. The image quality was assessed through both objective measures [image noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective evaluation via a Likert scale. Statistical analysis was conducted using SPSS 27.0, employing analysis of variance (ANOVA) for normally distributed data and the Kruskal-Wallis test for non-normally distributed data. A total of 75 patients with clinical suspicion of pulmonary embolism were included in the study. The mean effective dose (ED) was 3.76±1.02 mSv, with a mean CT volume dose index (CTDIvol) of 6.13±1.69 mGy and a mean dose-length product (DLP) of 221.12±59.85 mGy·cm. The mean contrast agent volume was 26.0±5.0 mL. Statistical analysis of image quality revealed significant differences between the four groups in terms of image noise, CNR, and SNR, measured at the levels of the main pulmonary artery, left pulmonary artery, and right pulmonary artery (P<0.001). Post-hoc analysis demonstrated that the DLIR-H algorithm provided the highest image quality, significantly reducing noise while enhancing CNR and SNR relative to both ASIR-V and DLIR-M (P<0.001). Compared with ASIR-V 50%, DLIR-H reduced image noise by 45% at the PA [24.25±16.18 The DLIR-H algorithm significantly enhances the image quality of 40 keV VMI images under low-dose DE-CTPA scanning protocols. It outperforms DLIR-M, ASIR-V 50%, and ASIR-V 70%, making it a promising tool for improving image quality in CTPA, particularly in clinical settings where minimizing radiation dose and contrast agent volume is essential. Show less

📄 PDF DOI: 10.21037/qims-2025-1420

Defining the surgical curative time window: identifying patients with an absence of recurrence for 5 years following surgical resection of stage I invasive non-small cell lung cancer.

Tong Li, Yang Zhang, Hong Hu +5 more · 2025 · Translational lung cancer research · added 2026-04-24

Tong Li, Yang Zhang, Hong Hu, Ting Ye, Yihua Sun, Yawei Zhang, Jiaqing Xiang, Haiquan Chen Show less

While most patients with stage I non-small cell lung cancer (NSCLC) remain recurrence-free after resection, some still develop recurrent disease. The surgical curative time window concept, defined as Show more

While most patients with stage I non-small cell lung cancer (NSCLC) remain recurrence-free after resection, some still develop recurrent disease. The surgical curative time window concept, defined as no recurrence through 5-year follow-up, helps identify potentially cured patients, yet predictive clinicopathologic features in stage I invasive NSCLC need clarification. This study sought to identify such features to enable risk-adapted surveillance. We analyzed a prospectively collected dataset of patients with stage I invasive NSCLC who underwent R0 resection between 2008 and 2015. Cox regression analysis was used to evaluate the association between clinicopathologic features and disease recurrence, aiming to identify independent prognostic factors. A total of 1,817 patients met the inclusion criteria. The 5-year cumulative incidence of recurrence was 14.6%. Female sex, tumor size ≤2 cm, lepidic-predominant adenocarcinoma (LPA) histologic type, presence of a ground-glass opacity (GGO) component, and solid component size ≤10 mm were identified as independent prognostic factors. A risk stratification system was subsequently developed, classifying patients into two groups: a low-risk group (with ≥4 factors; n=341) and an elevated-risk group (with <4 factors; n=1,476). Kaplan-Meier analysis revealed statistically significant differences in recurrence-free survival (RFS), overall survival (OS), and lung cancer-specific survival (LCSS) between the two groups (P<0.001). The low-risk group is considered to represent the population within the surgical curative time window. Patients with stage I invasive NSCLC who meet at least four of the following five criteria-female sex, tumor size ≤2 cm, solid component ≤10 mm, presence of a GGO component, and LPA histologic type-may be considered within the "surgical curative time window" and may therefore qualify for reduced surveillance intensity. Show less

📄 PDF DOI: 10.21037/tlcr-2025-894

Relationships among anxiety, psychological resilience, and physical activity in university students: variable-centred and person-centred perspectives.

Yuqing Yuan, Jing Yang, Wenying Huang +3 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24

Yuqing Yuan, Jing Yang, Wenying Huang, Chang Hu, Wen Zhang, Bin Chen Show less

Anxiety is significantly correlated with levels of physical activity in university students. This research assessed the effects of anxiety on engagement in physical activity and explored the potential Show more

Anxiety is significantly correlated with levels of physical activity in university students. This research assessed the effects of anxiety on engagement in physical activity and explored the potential mediating function of psychological resilience. Additionally, latent profile analysis (LPA) was employed to identify distinct subtypes based on anxiety and resilience levels, and to explore their associations with physical activity. Utilizing a non-probability convenience sampling approach, this cross-sectional study recruited a total of 1,436 collegiate participants from multiple universities. Data collection was carried out with the Generalized Anxiety Disorder Scale (GAD-7), the abbreviated Connor-Davidson Resilience Scale (CD-RISC-10), and the Physical Activity Rating Scale (PARS-3). Data analysis included mediation effect analysis via Bootstrap methods (Model 4) and latent profile analysis (LPA). Anxiety demonstrated a significant negative association with physical activity ( Results demonstrated that anxiety affects physical activity both directly and indirectly, with the latter effect occurring through the channel of psychological resilience. Latent profile analysis identified three distinct profiles among college students based on anxiety and psychological resilience: High Anxiety-Low Psychological Resilience, Moderate Anxiety-Moderate Psychological Resilience, and Low Anxiety-High Psychological Resilience. Marked variations in physical activity levels were observed among these subgroups. The results underscore the complex relationships among mental health indicators and health behaviors within the collegiate population. The delineation of distinct profiles offers practical implications for designing tailored intervention strategies. Show less

📄 PDF DOI: 10.3389/fpsyg.2025.1694344

Prediction model and significance of myocardial injury induced by fluorouracil combined with platinum-based chemotherapy in advanced gastric cancer based on baseline data and inflammation-nutrition-atherosclerosis factors.

Tian Zhang, Feifei Kong, Lei Cao +1 more · 2025 · Frontiers in medicine · Frontiers · added 2026-04-24

Tian Zhang, Feifei Kong, Lei Cao, Lanhua Zhao Show less

To develop and evaluate a predictive model for myocardial injury in patients with advanced gastric cancer treated with fluorouracil plus platinum-based chemotherapy, incorporating baseline characteris Show more

To develop and evaluate a predictive model for myocardial injury in patients with advanced gastric cancer treated with fluorouracil plus platinum-based chemotherapy, incorporating baseline characteristics and inflammatory, nutritional, and atherosclerotic factors. A total of 268 patients with advanced gastric cancer who received this treatment between April 2020 and September 2024 were selected and divided into a training set ( In the training set, 56 patients (29.79%) developed myocardial injury, while 23 patients (28.75%) in the validation set developed myocardial injury, with no statistically significant difference in the incidence or clinical characteristics between the two sets ( This predictive model aids in the early identification of myocardial injury, guiding clinical decision-making and improving prognosis. Show less

📄 PDF DOI: 10.3389/fmed.2025.1700554

Lipoprotein(a), ABO Blood Types and Clinical Outcomes: Novel Findings and Clinical Implications in Patients With Chronic Coronary Syndrome.

Hui-Hui Liu, Chen-Xi Song, Sha Li +12 more · 2025 · MedComm · Wiley · added 2026-04-24

Hui-Hui Liu, Chen-Xi Song, Sha Li, Yan Zhang, Dong Yin, Wei-Hua Song, Yuan-Lin Guo, Cheng-Gang Zhu, Na-Qiong Wu, Rui-Xia Xu, Qian Dong, Jie Qian, Yu-Hui Zhang, Ke-Fei Dou, Jian-Jun Li Show less

This study aimed to investigate the effect of lipoprotein(a) (Lp(a)) on major adverse cardiovascular events (MACEs) among individuals with chronic coronary syndrome (CCS) according to ABO blood groups Show more

This study aimed to investigate the effect of lipoprotein(a) (Lp(a)) on major adverse cardiovascular events (MACEs) among individuals with chronic coronary syndrome (CCS) according to ABO blood groups. Two independent cohorts of patients with CCS were included consecutively. Blood groups and Lp(a) levels were measured. Patients with the AB group were excluded due to the small sample size. In the exploratory cohort ( Show less

📄 PDF DOI: 10.1002/mco2.70505

Effect of physical activity volume on all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: evidence from the China health and retirement longitudinal study.

Xiaohong Fu, Weiwei Sun, Zengfu Zhang +3 more · 2025 · Postgraduate medical journal · Oxford University Press · added 2026-04-24

Xiaohong Fu, Weiwei Sun, Zengfu Zhang, Jia Gao, Min Guo, Rui Wang Show less

Cardiovascular-Kidney-Metabolic (CKM) syndrome is characterized by the interrelatedness of chronic kidney disease, cardiovascular disease, and metabolic disorders. Although physical activity is widely Show more

Cardiovascular-Kidney-Metabolic (CKM) syndrome is characterized by the interrelatedness of chronic kidney disease, cardiovascular disease, and metabolic disorders. Although physical activity is widely acknowledged as an effective intervention for improving the prognosis of chronic diseases, its impact on all-cause mortality among patients with CKM syndrome remains unclear. To investigate the impact of physical activity on all-cause mortality among patients with CKM syndrome. Data from the 2011 wave of the China Health and Retirement Longitudinal Study were used as the baseline, with follow-up conducted until 2013. According to the International Physical Activity Questionnaire criteria, weekly physical activity levels were divided into three categories: light-volume physical activity (LPA), moderate-volume physical activity (MPA), and vigorous-volume physical activity (VPA). Cox proportional hazards regression models were employed to assess the impact of varying levels of physical activity on all-cause mortality. Restricted cubic spline analysis was used to explore possible nonlinear relationships. A total of 3343 patients with CKM syndrome were enrolled in this study. During the 2-year follow-up period, 44 deaths were recorded. After adjusting for potential confounders, VPA was associated with a 54% lower risk of all-cause mortality (adjusted hazard ratios, 0.46; 95% confidence interval: 0.24-0.89). Dose-response relationships demonstrated that all-cause mortality decreased as physical activity increased, with a 5.8% reduction in all-cause mortality risk for every 1000 MET-min/week increment in physical activity levels. VPA was significantly associated with reduced all-cause mortality in patients with CKM syndrome. Encouraging patients with CKM syndrome to engage in increased physical activity may improve clinical outcomes. Key messages What is already known on this topic: Cardiovascular-Kidney-Metabolic (CKM) syndrome involves a complex interplay between cardiovascular disease, metabolic disorders, and chronic kidney disease. While prior studies have established that physical activity can decrease mortality risk in the general population as well as in patients with cardiovascular and metabolic syndromes, the evidence regarding its impact on individuals with CKM syndrome remains limited. Additionally, there is a lack of detailed dose-response analyses of physical activity specifically targeting this high-risk population. What this study adds: This study provides novel evidence indicating that vigorous-volume physical activity (>3000 MET-minutes/week) significantly decreases all-cause mortality by 54% among patients with CKM syndrome, whereas moderate-volume, and light-volume physical activities show no significant effects. Notably, a linear dose-response relationship was established, demonstrating that each 1000-MET increment corresponds to a 5.8% reduction in mortality risk. These findings address a critical knowledge gap by quantifying both the threshold and incremental benefits of physical activity specifically for individuals with CKM syndrome, a population characterized by unique multisystem pathophysiology. How this study might affect research, practice, or policy: The findings of this study have the potential to substantially impact clinical practice by offering evidence-based thresholds for physical activity recommendations in the management of CKM syndrome. The benefits associated with vigorous-volume physical activity (>3000 MET-minutes/week) may encourage guideline committees to formulate more precise exercise prescriptions tailored to this high-risk population. Additionally, these results can be incorporated into a multidisciplinary care framework designed for managing complex chronic conditions. Show less

no PDF DOI: 10.1093/postmj/qgaf205

Lp(a) levels and ischemic stroke recurrence in type 2 diabetes mellitus.

Aichun Cheng, Fangyuan Zhang, Aoming Jin +5 more · 2025 · Diabetology & metabolic syndrome · BioMed Central · added 2026-04-24

Aichun Cheng, Fangyuan Zhang, Aoming Jin, Jing Xue, Zhiyuan Feng, Xia Meng, Yongjun Wang, Jie Xu Show less

We investigated the association between lipoprotein(a) [Lp(a)] levels and stroke recurrence in type 2 diabetes mellitus (T2DM) patients with recent acute ischemic stroke or transient ischemic attack ( Show more

We investigated the association between lipoprotein(a) [Lp(a)] levels and stroke recurrence in type 2 diabetes mellitus (T2DM) patients with recent acute ischemic stroke or transient ischemic attack (TIA). This study included 3,311 T2DM patients with recent acute ischemic stroke or TIA and complete Lp(a) data from the Third China National Stroke Registry. The patients were categorized into three groups based on the 40th and 70th percentiles of the Lp(a): ≤13.1, 13.1 to 29.2 and ≥ 29.2 mg/dL. The primary outcome was stroke recurrence within one year, with incident cases further classified as either ischemic or hemorrhagic. Cox proportional hazards regression and restricted cubic splines were used to evaluate these associations. A total of 3311 patients (2142 men, 64.69%, median age 63) were analyzed. Restricted cubic spline analysis revealed a U-shaped relationship between Lp(a) levels and the risk of stroke recurrence. After adjusting for cardiovascular risk factors, patients with Lp(a) levels ≤ 13.1 mg/dL or ≥ 29.2 mg/dL had hazard ratios of 1.34 (95% confidence interval (CI), 1.02-1.76) and 1.35 (95% CI, 1.01-1.79), respectively, for total stroke compared to those with Lp(a) levels between 13.1 and 29.2 mg/dL. The corresponding hazard ratios were 1.36 (95% CI, 1.02-1.81) and 1.36 (95% CI, 1.01-1.83) for ischemic stroke and 0.88 (95% CI, 0.37-2.09) and 0.77 (95% CI, 0.31-1.94) for hemorrhagic stroke, respectively. Both low and high levels of Lp(a) are associated with an increased risk of stroke recurrence in T2DM patients with a recent history of acute ischemic stroke or TIA, demonstrating a U-shaped relationship. Show less

📄 PDF DOI: 10.1186/s13098-025-02005-y

Classification and influencing factors of fear of falling in elderly stroke patients: A latent profile analysis.

Yue Yang, Meiying Li, Xiaoge Ding +3 more · 2025 · Aging clinical and experimental research · Springer · added 2026-04-24

Yue Yang, Meiying Li, Xiaoge Ding, Lei Zhang, Meixiang Jin, Yinji Jin Show less

To explore the potential categories of fear of falling in elderly stroke patients and analyze the differences in characteristics and influencing factors among patients in different categories. AA tota Show more

To explore the potential categories of fear of falling in elderly stroke patients and analyze the differences in characteristics and influencing factors among patients in different categories. AA total of 386 elderly stroke patients hospitalized in the Department of Neurology of a tertiary grade A general hospital in Jilin Province from March 2024 to June 2024 were selected as research subjects using the convenience sampling method. A general information questionnaire, Modified Falls Efficacy Scale (MFES), Simplified Coping Style Questionnaire (SCSQ), and Social Support Rating Scale (SSRS) were used for the survey. Mplus 8.3 software was applied to conduct latent profile analysis (LPA) on fear of falling in elderly stroke patients to identify potential categories, and multivariate logistic regression was used to further explore the influencing factors of each category. There were 3 potential categories of fear of falling in elderly stroke patients: the high fear of falling group (21.8%), moderate fear of falling group (38.3%), and low fear of falling group (39.9%). Multivariate logistic regression analysis showed that gender, age, type of stroke diagnosis, visual status, hearing status, limb strength, coping style, and social support were the influencing factors for the potential categories of fear of falling in elderly stroke patients. Fear of falling in elderly stroke patients has obvious categorical characteristics. Medical staff should implement targeted interventions based on the characteristics and influencing factors of different potential categories to reduce patients' fear of falling. Show less

📄 PDF DOI: 10.1007/s40520-025-03236-9

Association between 24-hour movement behaviors and perceived stress in Chinese university students: a compositional data analysis.

Defeng Dong, Yanhe Qu, Dianbo Zhang +1 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24

Defeng Dong, Yanhe Qu, Dianbo Zhang, Chen Dong Show less

This study used objectively measured data and compositional data analysis to examine the relationship between 24-hour movement behaviors and perceived stress in Chinese university students. Cross-sect Show more

This study used objectively measured data and compositional data analysis to examine the relationship between 24-hour movement behaviors and perceived stress in Chinese university students. Cross-sectional data were collected from 208 Chinese university students (mean age = 20.23 years, 52.9% female). Accelerometers were used to measure 24-hour movement behaviors, including moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behavior (SB), and sleep. The Perceived Stress Scale (PSS-14) assessed perceived stress. Compositional data methods were applied to analyze the relationship between the proportion of time spent in 24-hour activities and perceived stress. Compositional regression analysis indicated that time spent in MVPA ( The proportion of time spent in MVPA and LPA was negatively associated with perceived stress among university students. Replacing sedentary behavior with MVPA or LPA was associated with lower perceived stress. However, these findings should be interpreted with caution due to the study's cross-sectional design and reliance on self-reported sleep data. Show less

📄 PDF DOI: 10.3389/fpsyg.2025.1681198

Explainable machine learning model for classifying atherosclerotic cardiovascular disease in patients with metabolic dysfunction-associated steatotic liver disease.

Zhengliang Li, Xiaokai Chen, Linlin Ren +4 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24

Zhengliang Li, Xiaokai Chen, Linlin Ren, Banghui Wang, Shimiao Ruan, Juan Wang, Wenzhong Zhang Show less

Cardiovascular disease (CVD) is the leading cause of mortality in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), yet traditional risk predictors remain limited in clin Show more

Cardiovascular disease (CVD) is the leading cause of mortality in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), yet traditional risk predictors remain limited in clinical practice. To develop machine learning (ML) models for classifying prevalent atherosclerotic cardiovascular disease (ASCVD) risk in MASLD patients, and to enhance model interpretability using SHapley Additive exPlanations (SHAP). Methods: This retrospective study included 590 MASLD patients diagnosed at the Affiliated Hospital of Qingdao University between December 2019 and December 2024. Patients were randomly divided into a training set (n=413) and a validation set (n=177), and further stratified based on ASCVD status. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. Six ML models were developed and evaluated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and F1 score. SHAP analysis was performed to interpret feature contributions. ASCVD was present in 434 of 590 patients (73.6%). The Gradient Boosting (GB) model achieved the best performance, with AUCs of 0.918 (95% CI: 0.890-0.944) in the training set and 0.817 (95% CI: 0.739-0.883) in the validation set. SHAP analysis identified the top predictors as the Cholesterol-HDL-Glucose (CHG) index, Castelli Risk Index II (CRI-II), lipoprotein(a) [Lp(a)], serum creatinine (Scr), and uric acid (UA). The GB model demonstrated strong high accuracy in identifying existing ASCVD in MASLD patients and may serve as a useful tool for early risk stratification in clinical settings. Show less

📄 PDF DOI: 10.3389/fendo.2025.1684558

Reallocation of 24-h Movement Behaviours and Their Effects on Preschoolers' Fundamental Movement Skills: A Compositional Data and Dose-Response Analysis.

Xinyu Wang, Xu Zhang, Miaomiao Wan +2 more · 2025 · Child: care, health and development · Blackwell Publishing · added 2026-04-24

Xinyu Wang, Xu Zhang, Miaomiao Wan, Jane Jie Yu, Xu Wen Show less

Physical activity (PA), sedentary behaviour (SB) and sleep (SLP)-key components of 24-h movement behaviours-have each been independently linked to motor development in preschool children. However, the Show more

Physical activity (PA), sedentary behaviour (SB) and sleep (SLP)-key components of 24-h movement behaviours-have each been independently linked to motor development in preschool children. However, the lack of understanding regarding their integrated and mutually exclusive nature has limited research on their combined impact on early health outcomes. This study employed compositional data analysis (CoDA) to examine the relationships between these behaviours and fundamental movement skills (FMS), as well as potential changes in FMS resulting from isotemporal reallocation. A cross-sectional study was conducted with 292 preschool children (3-6 years old; 149 boys and 143 girls). SB, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) were measured using accelerometers, whereas sleep duration was parent-reported. FMS, including locomotor skills, object-control skills and total motor skills (total MS), were assessed using the third edition of the Test of Gross Motor Development (TGMD-3). CoDA was used to analyse the relationship between 24-h movement behaviours and FMS. After adjusting for gender, age, family socioeconomic status (SES) and the number of children in the household, a higher proportion of MVPA was significantly positively associated with both total MS (β = 9.39, p = 0.008) and locomotor skills (β = 6.69, p = 0.003). In a 15-min isotemporal reallocation model, substituting MVPA for other behaviours resulted in significant improvements in both total MS and locomotor skills. Dose-response analysis revealed that reallocating even a small amount of time (e.g., 15 min) to MVPA resulted in meaningful benefits for FMS. Notably, this relationship was asymmetric: The negative impact of reducing MVPA outweighed the gains from increasing MVPA. These findings highlight the importance of prioritizing MVPA within the 24-h movement behaviours framework to optimize motor development in preschool-aged children. Show less

no PDF DOI: 10.1111/cch.70182

Roles of Diverse Lipokines in Metabolic Homeostasis and Their Associations With Various Diseases.

Chenxuan Liu, Xinyi Zhang, Wenxue Wu +1 more · 2025 · Comprehensive Physiology · Wiley · added 2026-04-24

Chenxuan Liu, Xinyi Zhang, Wenxue Wu, Tongxing Song Show less

Lipokines are a class of lipid-derived signaling molecules, playing essential roles in regulating metabolic homeostasis and systemic metabolism. In this review, we first comprehensively describe six m Show more

Lipokines are a class of lipid-derived signaling molecules, playing essential roles in regulating metabolic homeostasis and systemic metabolism. In this review, we first comprehensively describe six major lipokines, including palmitoleic acid (C16:1n7), 12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME), fatty acid esters of hydroxy fatty acids (FAHFAs), 12-hydroxyeicosapentaenoic acid (12-HEPE), lysophosphatidic acid (LPA), and 15-hydroxyeicosatetraenoic acid (15-HETE), focusing on their mechanistic roles in energy metabolism and inflammatory modulation as well as their cross-talk within different signaling pathways. These lipokines collectively contribute to metabolic homeostasis by regulating multiple pathways, including insulin signaling, AMPK activation, inflammatory modulation, and G-protein-coupled receptor-mediated pathways. Furthermore, we clarify the associations between lipokines and various diseases such as obesity, type 2 diabetes, cardiovascular diseases, non-alcoholic fatty liver disease, inflammatory disorders, and cancer, and discuss their potential as biomarkers and therapeutic targets. Despite current challenges, including functional complexity, limitations of model systems, and difficulties in clinical translation, lipokines demonstrate promising prospects in the prevention and treatment of metabolic diseases and application in precision medicine. Future research should prioritize the elucidation of the specific action mechanisms of different lipokines, development of highly sensitive detection methodologies, and large-scale clinical trials to facilitate the translation of the research results into practical medical applications. Show less

no PDF DOI: 10.1002/cph4.70072

Comprehensive dietary patterns explain acquired cystic kidney disease risk through genetic and metabolomic mechanisms.

Chenhao Xu, Junjie Zhao, Kan Wu +9 more · 2025 · Frontiers in nutrition · Frontiers · added 2026-04-24

Chenhao Xu, Junjie Zhao, Kan Wu, Shengzhuo Liu, Fan Zhang, Qin Cao, Zhouwei Wan, Yongquan Tang, Zhihong Liu, Hao Zeng, Xianding Wang, Jiayu Liang Show less

Acquired renal cysts (ARC) are associated with kidney function decline, necessitating novel dietary pattern (DP) analyses in large cohorts. This UK Biobank prospective cohort study (2006-2010) include Show more

Acquired renal cysts (ARC) are associated with kidney function decline, necessitating novel dietary pattern (DP) analyses in large cohorts. This UK Biobank prospective cohort study (2006-2010) included participants with ≥2 dietary records, excluding those with severe kidney damage. The constructed comprehensive dietary pattern integration (CDPI) utilized reduced rank regression (RRR) and latent profile analysis (LPA). ARC cases (ICD-10: N28.1) were assessed via Cox regression for risk and dose-response, with NMR metabolites examined as mediators. Among 119,709 participants (median follow-up: 10.57 years), 850 ARC cases were identified. Lipid-rich and hyperglycemic diets increased ARC risk [e.g., HRs for G1.DP1: 1.080 (1.024, 1.139); G1.DP2: 1.144 (1.048, 1.249)], while micronutrient-rich diets showed weak protective effects [G4.DP1: 0.943 (0.892, 0.998)]. LPA confirmed RRR findings, and 7/251 NMR metabolites had significant mediating effects. Diets high in fat (cheese, butter, pizza) and sugar (chocolate, sugary drinks) elevated ARC risk, whereas micronutrient- and fiber-rich diets (vegetables, fruit, lean poultry, nuts, eggs) were protective. Key mediators included branched-chain amino acids, IGF-1, and RBC distribution width. Show less

📄 PDF DOI: 10.3389/fnut.2025.1611656

Distinct Sleep Quality Profiles and Their Varying Levels of Stigma Among Patients Undergoing Maintenance Hemodialysis: A Cross-Sectional Study.

Hong Tan, Li Li, YiPei Zhang +1 more · 2025 · Journal of multidisciplinary healthcare · added 2026-04-24

Hong Tan, Li Li, YiPei Zhang, Huize Zhang Show less

To identify distinct sleep quality profiles among patients undergoing maintenance hemodialysis (MHD) using latent profile analysis (LPA), and examine differences in perceived stigma across these sleep Show more

To identify distinct sleep quality profiles among patients undergoing maintenance hemodialysis (MHD) using latent profile analysis (LPA), and examine differences in perceived stigma across these sleep quality subtypes. From December 2024 to March 2025, a total of 334 MHD patients were recruited via convenience sampling from the nephrology departments of two tertiary hospitals in Xinjiang, China. Data were collected using structured questionnaires, including the Pittsburgh Sleep Quality Index (PSQI), the Self-Rating Depression Scale (SDS), and the Social Impact Scale (SIS), along with sociodemographic and clinical information. LPA was employed to identify latent subgroups of sleep quality based on PSQI components. Multinomial logistic regression was used to determine predictors of sleep profile membership. Differences in stigma scores across sleep profiles were analyzed using non-parametric equivalents. Three distinct sleep profiles were identified: Class 1 - "overall better sleep", Class 2 - "short sleep duration and low efficiency", and Class 3 - "poor sleep quality with high medication use". Multinomial logistic regression identified comorbid heart failure (OR=2.867, Patients with MHD exhibit heterogeneous patterns of sleep disturbance, which are associated with varying levels of perceived stigma. Those with the poorest sleep quality and highest reliance on medication experience the most pronounced stigma. Tailored interventions addressing sleep-related issues and psychosocial factors may help reduce stigma and improve patient well-being. Show less

📄 PDF DOI: 10.2147/JMDH.S557424

White matter hyperintensity segmentation of multiple sclerosis and neuromyelitis optical spectrum disorders using 2.5D FrC-ResUnet.

Li Zhang, Kai Niu, Yinglu Sun +9 more · 2025 · Quantitative imaging in medicine and surgery · added 2026-04-24

Li Zhang, Kai Niu, Yinglu Sun, Liying An, Ziqi Zhao, Yingchun Gong, Zhuo Wang, Huimao Zhang, Yan Wang, Lan Huang, Zhiwei Shen, Chunjie Guo Show less

Assessing white matter hyperintensity (WMH) is essential for the diagnosis, treatment, and prognosis of multiple sclerosis (MS) and neuromyelitis optical spectrum disorder (NMOSD). MS and NMOSD presen Show more

Assessing white matter hyperintensity (WMH) is essential for the diagnosis, treatment, and prognosis of multiple sclerosis (MS) and neuromyelitis optical spectrum disorder (NMOSD). MS and NMOSD present dispersed small lesions alongside larger aggregated lesions that are irregularly shaped, posing challenges for the automatic segmentation of WMH on magnetic resonance images. Furthermore, research on NMOSD brain WMH segmentation is limited due to the rare nature of the disease. This study aims to propose a deep learning method for MS and NMOSD brain WMH segmentation. In this study, we propose a 2.5D Fourier Convolutional ResUnet (FrC-ResUnet). It utilizes a spectral encoder to extract global information, enabling accurate segmentation of scattered lesions. Additionally, the model incorporates the selective features module (SFM) and the convolutional block attention module (CBAM) to enhance lesion-background differentiation and outline the lesions distinctly. We evaluated our approach on the MS public and local datasets of MS and NMOSD. Compared to U-Net, ResUNet, FC-DenseNet, AttentionUNet, lesion prediction algorithm (LPA) and Sequence Adaptive Multimodal SEGmentation (SAMSEG), the 2.5D FrC-ResUnet achieved the highest Dice similarity coefficient (DSC) on three different datasets, with values of 0.710, 0.667, and 0.822, respectively. The 2.5D FrC-ResUnet demonstrates accurate and robust segmentation of NMOSD brain WMH. Meanwhile, the model excels in segmenting MS brain WMH, particularly when confronted with irregularly shaped and dispersed lesions. Show less

📄 PDF DOI: 10.21037/qims-24-2384

Understanding the Relationship Between Chinese Nurses' Attitudes Toward Incontinence-Related Dermatitis and Achievement Motivation Through a Person-Centered Method: A Theory-Driven Cross-Sectional Study.

Siyue Fan, Mufen Ye, Xiaoying Tong +9 more · 2025 · Journal of nursing management · added 2026-04-24

Siyue Fan, Mufen Ye, Xiaoying Tong, Shuangling Kang, Yanni Lin, Nengtong Zheng, Wenting Chai, Mingqi Zhang, Doudou Yu, Liping Yang, Yutao Wang, Lijuan Chen Show less

Incontinence-associated dermatitis (IAD) is a common nursing challenge in clinical practice, imposing a significant burden on both patients and healthcare providers. Studies have reported that nurses' Show more

Incontinence-associated dermatitis (IAD) is a common nursing challenge in clinical practice, imposing a significant burden on both patients and healthcare providers. Studies have reported that nurses' preventive attitudes toward IAD significantly influence its prevalence, and there may be a potential association between achievement motivation and these attitudes. Previous research on nurses' preventive attitudes toward IAD has primarily focused on overall levels, overlooking potential heterogeneity within the population. This study aimed to investigate the heterogeneity in clinical nurses' preventive attitudes toward IAD using a person-centered approach and to identify the influencing factors for different subgroups. A secondary aim was to utilize Self-Determination Theory (SDT) to elucidate the relationship between the identified attitude profiles and nurses' achievement motivation, thereby providing targeted strategies to enhance their preventive attitudes. This study selected 1058 clinical nurses from a tertiary hospital in Fujian, China, as research participants from September to October 2024. The study utilized the following instruments: a general information questionnaire, the Attitude Toward the Prevention of Incontinence-Associated Dermatitis Instrument, and the Achievement Motivation Scale. Latent profile analysis (LPA) was employed to identify the latent profiles of nurses' attitudes toward IAD prevention. At the same time, Two subgroups of nurses' attitudes toward IAD prevention were identified: the low-level group (63.42%) and the high-level, low-personal-responsibility group (36.57%). A significant correlation was found between nurses' attitudes toward IAD prevention and achievement motivation. Nurses with a more positive preventive attitude scored higher on the motivation for success dimension, while those with a less positive attitude scored higher on the motivation to avoid failure dimension. Factors influencing nurses' attitudes toward IAD prevention included position, department, number of participants in wound/ostomy/incontinence care training, satisfaction with the work atmosphere, and achievement motivation scores. This study revealed heterogeneity in nurses' attitudes toward IAD prevention. Nurses with positive attitudes tended to adopt a success-driven approach, while those with relatively negative attitudes leaned toward a failure-avoidance strategy, reflecting two fundamentally distinct coping mechanisms. Nursing managers should address these individual differences by targeting achievement motivation as an intervention point. Management strategies should be tailored to the distinct profiles; for instance, interventions for the "low-level group" should prioritize building competence through structured training, while strategies for the "high-level, low-personal-responsibility group" should focus on enhancing autonomy and personal accountability. By adopting such targeted approaches, managers can more effectively enhance nurses' preventive attitudes, thereby improving care quality and reducing IAD incidence. Show less

📄 PDF DOI: 10.1155/jonm/3381812

Lysophosphatidic acid promotes endometrial decidualization in recurrent implantation failure patients by regulating LPAR6.

Shanshan Wang, Yang Zhang, Xindong Zhang +2 more · 2025 · Frontiers in cell and developmental biology · Frontiers · added 2026-04-24

Shanshan Wang, Yang Zhang, Xindong Zhang, Xinyu Cai, Min Hong Show less

Impaired decidualization is associated with recurrent implantation failure (RIF) and lysophosphatidic acid (LPA) is known to play an important role in decidua formation. However, the specific impact o Show more

Impaired decidualization is associated with recurrent implantation failure (RIF) and lysophosphatidic acid (LPA) is known to play an important role in decidua formation. However, the specific impact of LPA in endometrial decidualization during RIF remains unclear. Metabolomics analysis was performed to identify differentially expressed metabolites (DEMs) in RIF patients Expression of the LPA receptor subtypes, LPAR1-6, was detected in both GEO datasets and clinical endometrial samples. An LPA was identified as a pivotal metabolite in RIF. Among the LPA receptors, LPAR1 and LPAR6 were highly expressed during LPA plays a significant role in the decidualization process of hESCs by regulating LPAR6, rather than LPAR1, providing insights into potential therapeutic target for RIF. Show less

📄 PDF DOI: 10.3389/fcell.2025.1652740

Study on post-traumatic stress disorder and influencing factors in adult ICU patients on mechanical ventilation using latent profile analysis: a cross-sectional survey.

Yunting Li, Xiaoli Yuan, Mi Liu +2 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24

Yunting Li, Xiaoli Yuan, Mi Liu, Yan Xiong, Jiabi Zhang Show less

This study aimed to explore the potential classification and influencing factors of post-traumatic stress disorder (PTSD) in intensive care unit (ICU) patients receiving mechanical ventilation to prov Show more

This study aimed to explore the potential classification and influencing factors of post-traumatic stress disorder (PTSD) in intensive care unit (ICU) patients receiving mechanical ventilation to provide a theoretical basis for formulating targeted intervention measures. A total of 229 patients on mechanical ventilation who were hospitalized in the intensive care unit of a Class III Grade A hospital in Zunyi from August 2023 to July 2024 were selected as research participants using a purposive sampling method. The General information questionnaire, Eysenck Personality Questionnaire Revised, Short Scale for Chinese (EPQ-RSC), Simplified Coping Style Questionnaire (SCSQ), Perceived Social Support Scale (PSSS), and Hospital Anxiety and Depression Scale (HADS) were used to assess the patients within 7 days after discharge from the ICU. One month after extubation, a cross-sectional survey was conducted using the Impact of Event Scale-Revised (IES-R). Latent profile analysis (LPA) was used to analyze the latent subtypes of PTSD, and univariate analysis and a disordered multivariate logistic regression model were used to evaluate the influencing factors associated with different types of PTSD. A total of 215 valid questionnaires were collected, and the effective recovery rate was 93.89%. The incidence of PTSD was 14.9% (95% CI: 10.12%-19.64%). There were three latent categories of PTSD among the ICU patients on mechanical ventilation: the "low-stress group" (56.8%, PTSD symptoms among mechanically ventilated ICU survivors manifest in three distinct profiles. Our findings strongly recommend early psychological screening, particularly focusing on anxiety and depression levels and patients' educational background. Medical staff should formulate targeted intervention plans based on the characteristics of different patient categories to lower the level of PTSD in patients. Show less

📄 PDF DOI: 10.3389/fpsyg.2025.1578276

Latent profile analysis of electronic health literacy and its impact on health promoting lifestyles among maintenance hemodialysis patients.

Lan Yang, Jinghua Yang, Hong Zhang +3 more · 2025 · Frontiers in public health · Frontiers · added 2026-04-24

Lan Yang, Jinghua Yang, Hong Zhang, Ning Wu, Xuejiao Han, Na Li Show less

Despite the critical role of e-Health literacy (eHL) in modern healthcare, current research predominantly concentrates on conditions such as cancer and diabetes, as well as outpatient care settings. H Show more

Despite the critical role of e-Health literacy (eHL) in modern healthcare, current research predominantly concentrates on conditions such as cancer and diabetes, as well as outpatient care settings. However, there remains a significant gap in studies specifically addressing the eHL needs of patients with maintenance hemodialysis (MHD). This study aims to explore the latent categories of eHL among MHD patients and its impact on health-promoting lifestyle (HPL). A survey was conducted using a convenience sampling method involving 500 MHD patients from three tertiary hospitals in Baoding. Data were analyzed using latent profile analysis (LPA) and a mixed regression model. This study showed that MHD patients could be classified into low (23.17%), middle (49.78%), and high (27.05%) eHL groups, with the three-class model showing optimal fit (AIC = 2321.213, BIC = 2271.168, entropy = 0.967). MHD Patients in the high literacy group scored significantly higher in all dimensions of e-HL and overall HPL (119.58 ± 13.86) compared to those in the low literacy group (91.82 ± 11.73) (all The findings suggest a heterogeneous stratification of eHL among MHD patients, closely linked to HPL. Stratified intervention strategies should be developed for different patient groups to potentially improve their health behaviors. The study provides evidence-based support for personalized health management. Show less

📄 PDF DOI: 10.3389/fpubh.2025.1630350

Core competencies and training needs of specialist-nurse clinical educators: a latent profile analysis.

Junye Tian, Meng Zhang, Lichuan Zhang +3 more · 2025 · BMC nursing · BioMed Central · added 2026-04-24

Junye Tian, Meng Zhang, Lichuan Zhang, Yujie Wang, Qian Lu, Yanming Ding Show less

The competency of specialist nurse clinical educators is crucial for the effectiveness of specialist nurse training programmes. However, variability in teaching competency and training needs among edu Show more

The competency of specialist nurse clinical educators is crucial for the effectiveness of specialist nurse training programmes. However, variability in teaching competency and training needs among educators remains insufficiently studied, especially in the context of rapidly evolving healthcare education in China. This study aimed to identify distinct core competency profiles among clinical educators for specialist nurses, examine associated socio-demographic factors, and explore differences in training needs across profiles. A cross-sectional online survey was conducted with 3,945 specialist nurse clinical educators from 30 Chinese regions. The Chinese version of the Nurse Educator Core Competency Scale (NECCS) and a self-developed training needs questionnaire were used. Latent Profile Analysis (LPA) identified competency subgroups, while multinomial logistic regression and Kruskal-Wallis tests examined associated variables and training needs. Latent Profile Analysis identified three competency profiles: foundational (8.6%), intermediate (43.0%), and advanced (48.4%), with mean scores of 43.89, 68.24, and 91.68, respectively. Educators without prior training were significantly more likely to belong to the foundational (OR = 3.195, p < 0.001) and intermediate (OR = 1.676, p < 0.001) groups compared to those with training experience. Advanced-competency educators showed the highest demand for curriculum design training, with 75% rating it as highly necessary. In contrast, educators in the intermediate group identified clinical teaching methods and techniques as their top training need (58.7%). Those in the foundational group prioritised common pedagogical methods and instructional technologies (54.7%). Clinical educator competencies vary by background characteristics and training exposure. Tailored, competency-based training is needed to address these gaps and enhance the quality of specialist nursing education. Show less

📄 PDF DOI: 10.1186/s12912-025-04006-8

Additive effects of Lp(a) and inflammation on cardiovascular risk beyond LDL-C lowering: circulating and genetic evidence from 401,548 adults.

Rui Li, Wenyue Dong, Wenxiu Wang +5 more · 2025 · Science bulletin · Elsevier · added 2026-04-24

Rui Li, Wenyue Dong, Wenxiu Wang, Youling Duan, Miao Zhang, Tao Huang, Xinwei Hua, Yi-da Tang Show less

no PDF DOI: 10.1016/j.scib.2025.10.005

Individuals with High Mindfulness Are Better at Metacognitive Ability: A Latent Profile Analysis Approach.

Lan Jiang, Shang Zhang, Jinglin Li +5 more · 2025 · Behavioral sciences (Basel, Switzerland) · MDPI · added 2026-04-24

Lan Jiang, Shang Zhang, Jinglin Li, Yuhong Gong, Na Sun, Haihong Wang, Tao Xiao, Xinfa Yi Show less

This study systematically examines the relationship between mindfulness and metacognition among Chinese college students through a person-centered analytical approach. Using latent profile analysis (L Show more

This study systematically examines the relationship between mindfulness and metacognition among Chinese college students through a person-centered analytical approach. Using latent profile analysis (LPA) of Five Facet Mindfulness Questionnaire (FFMQ) responses, we identified four distinct mindfulness profiles: (1) High Observation/Low Non-reactivity, (2) High Awareness/Judging, (3) Moderately Mindful, and (4) Highly Mindful. Gender differences were observed across profiles, with female students more represented in the Highly Mindful group. Hierarchical regression analyses revealed that mindfulness profiles significantly predicted metacognitive ability, with the Highly Mindful group demonstrating superior metacognitive self-regulation and learning strategy application. These findings contribute to the literature by identifying distinct mindfulness subtypes and their differential relationships with metacognition. The results suggest that educational interventions emphasizing non-judgmental present-moment awareness may be particularly effective for fostering students' metacognitive development, while highlighting the importance of considering individual differences in mindfulness training approaches. Show less

📄 PDF DOI: 10.3390/bs15101341

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