👤 Zhongxu Zhang

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Also published as: A-Mei Zhang, Ai Zhang, Ai-Min Zhang, Aiguo Zhang, Aihua Zhang, Aijun Zhang, Aileen Zhang, Ailin Zhang, Aimei Zhang, Aimin Zhang, Aixiang Zhang, Alaina Zhang, Alex R Zhang, Amy L Zhang, An Zhang, An-Qi Zhang, Anan Zhang, Andrew Zhang, Ang Zhang, Anli Zhang, Anqi Zhang, Anwei Zhang, Anying Zhang, Ao Zhang, Bangke Zhang, Bangzhou Zhang, Bao Long Zhang, Bao-Fu Zhang, Bao-Rong Zhang, Baohu Zhang, Baojing Zhang, Baojun Zhang, Baoren Zhang, Baorong Zhang, Baotong Zhang, Bei B Zhang, Bei Zhang, Bei-Bei Zhang, Beiyu Zhang, Ben Zhang, Benjian Zhang, Benyou Zhang, Bi-Tian Zhang, Biao Zhang, Bicheng Zhang, Bikui Zhang, Bin Zhang, Binbin Zhang, Bing Zhang, Bing-Qi Zhang, Bingbing Zhang, Bingkun Zhang, Bingqiang Zhang, Bingxue Zhang, Bingye Zhang, Bixia Zhang, Bo Zhang, Bo-Fei Zhang, Bo-Heng Zhang, Bo-Ya Zhang, Bochuan Zhang, Bofang Zhang, Bohao Zhang, Bohong Zhang, Bohua Zhang, Bojian Zhang, Bolin Zhang, Boping Zhang, Boqing Zhang, Bosheng Zhang, Bowei Zhang, Bowen Zhang, Boxi Zhang, Boxiang Zhang, Boya Zhang, Boyan Zhang, C D Zhang, C H Zhang, C Zhang, Cai Zhang, Cai-Ling Zhang, Caihong Zhang, Caiping Zhang, Caiqing Zhang, Caishi Zhang, Caiyi Zhang, Caiying Zhang, Caiyu Zhang, Can Zhang, Cathy C Zhang, Chan-na Zhang, Chang Zhang, Chang-Hua Zhang, Changhua Zhang, Changhui Zhang, Changjiang Zhang, Changjing Zhang, Changlin Zhang, Changlong Zhang, Changquan Zhang, Changteng Zhang, Changwang Zhang, Channa Zhang, Chao Zhang, Chao-Hua Zhang, Chao-Sheng Zhang, Chao-Yang Zhang, ChaoDong Zhang, Chaobao Zhang, Chaoke Zhang, Chaoqiang Zhang, Chaoyang Zhang, Chaoyue Zhang, Chen Zhang, Chen-Qi Zhang, Chen-Ran Zhang, Chen-Song Zhang, Chen-Xi Zhang, Chen-Yan Zhang, Chen-Yang Zhang, Chenan Zhang, Chenfei Zhang, Cheng Cheng Zhang, Cheng Zhang, Cheng-Lin Zhang, Cheng-Wei Zhang, Chengbo Zhang, Chengcheng Zhang, Chengfei Zhang, Chenggang Zhang, Chengkai Zhang, Chenglong Zhang, Chengnan Zhang, Chengrui Zhang, Chengsheng Zhang, Chengshi Zhang, Chenguang Zhang, Chengwu Zhang, Chengxiang Zhang, Chengxiong Zhang, Chengyu Zhang, Chenhong Zhang, Chenhui Zhang, Chenjie Zhang, Chenlin Zhang, Chenlu Zhang, Chenmin Zhang, Chenming Zhang, Chenrui Zhang, Chenshuang Zhang, Chenxi Zhang, Chenyan Zhang, Chenyang Zhang, Chenyi Zhang, Chenzi Zhang, Chi Zhang, Chong Zhang, Chong-Hui Zhang, Chongguo Zhang, Chonghe Zhang, Chris Zhiyi Zhang, Chu-Yue Zhang, Chuan Zhang, Chuanfu Zhang, Chuankuan Zhang, Chuankuo Zhang, Chuanmao Zhang, Chuantao Zhang, Chuanxin Zhang, Chuanyong Zhang, Chuchu Zhang, Chumeng Zhang, Chun Zhang, Chun-Lan Zhang, Chun-Mei Zhang, Chun-Qing Zhang, Chungu Zhang, Chunguang Zhang, Chunhai Zhang, Chunhong Zhang, Chunhua Zhang, Chunjun Zhang, Chunli Zhang, Chunling Zhang, Chunqing Zhang, Chunxia Zhang, Chunxiang Zhang, Chunxiao Zhang, Chunyan Zhang, Chunying Zhang, Churen Zhang, Chuting Zhang, Chuyue Zhang, Ci Zhang, Claire Y Zhang, Claire Zhang, Clarence K Zhang, Cong Zhang, Congen Zhang, Cuihua Zhang, Cuijuan Zhang, Cuilin Zhang, Cuiping Zhang, Cuiyu Zhang, Cun Zhang, Da Zhang, Da-Qi Zhang, Da-Wei Zhang, Dachuan Zhang, Dadong Zhang, Daguo Zhang, Dai Zhang, Dalong Zhang, Daming Zhang, Dan Zhang, Dan-Dan Zhang, DanDan Zhang, Danfeng Zhang, Danhua Zhang, Danning Zhang, Danyan Zhang, Danyang Zhang, Daolai Zhang, Daoyong Zhang, Dapeng Zhang, David Y Zhang, David Zhang, Dawei Zhang, Daxin Zhang, Dayi Zhang, De-Jun Zhang, Dekai Zhang, Delai Zhang, Deng-Feng Zhang, Dengke Zhang, Deqiang Zhang, Detao Zhang, Deyi Zhang, Deyin Zhang, Di Zhang, Dian Ming Zhang, Dianbo Zhang, Dianzheng Zhang, Ding Zhang, Dingdong Zhang, Dinghu Zhang, Dingkai Zhang, Dingyi Zhang, Dingyu Zhang, Dong Zhang, Dong-Hui Zhang, Dong-Mei Zhang, Dong-Wei Zhang, Dong-Ying Zhang, Dong-cui Zhang, Dong-juan Zhang, Dong-qiang Zhang, Dongdong Zhang, Dongfeng Zhang, Donghua Zhang, Donghui Zhang, Dongjian Zhang, Dongjie Zhang, Donglei Zhang, Dongmei Zhang, Dongsheng Zhang, Dongxin Zhang, Dongyan Zhang, Dongyang Zhang, Dongying Zhang, Donna D Zhang, Donna Zhang, Duo Zhang, Duoduo Zhang, Duowen Zhang, En Zhang, Enhui Zhang, Enming Zhang, Erchen Zhang, F P Zhang, F Zhang, Fa Zhang, Famin Zhang, Fan Zhang, Fang Zhang, Fanghong Zhang, Fangmei Zhang, Fangting Zhang, Fangyuan Zhang, Fei Zhang, Fei-Ran Zhang, Feifei Zhang, Feixue Zhang, Fen Zhang, Feng Zhang, Fengqing Zhang, Fengshi Zhang, Fengshuo Zhang, Fengwei Zhang, Fengxi Zhang, Fengxia Zhang, Fengxu Zhang, Fomin Zhang, Fred Zhang, Fu-Ping Zhang, Fubo Zhang, Fugui Zhang, Fuhan Zhang, Fujun Zhang, Fukang Zhang, Fuming Zhang, Fuqiang Zhang, Fuquan Zhang, Furen Zhang, Fushun Zhang, Fuxing Zhang, Fuyang Zhang, Fuyuan Zhang, G Zhang, G-Y Zhang, Gan Zhang, Gang Zhang, Ganlin Zhang, Gaoxin Zhang, Gary Zhang, Ge Zhang, Geng Zhang, Genglin Zhang, Genxi Zhang, Geyang Zhang, Gong Zhang, Gu Zhang, Guan-Yan Zhang, Guang Zhang, Guang-Qiong Zhang, Guang-Xian Zhang, Guang-Ya Zhang, Guanghui Zhang, Guangji Zhang, Guanglei Zhang, Guangliang Zhang, Guangping Zhang, Guangqiong Zhang, Guangxian Zhang, Guangxin Zhang, Guangye Zhang, Guangyong Zhang, Guangyuan Zhang, Guanqun Zhang, Gui-Ping Zhang, Guicheng Zhang, Guihua Zhang, Guijie Zhang, Guili Zhang, Guiliang Zhang, Guilin Zhang, Guimin Zhang, Guiping Zhang, Guisen Zhang, Guixia Zhang, Guixiang Zhang, Gumuyang Zhang, Guo-Fang Zhang, Guo-Fu Zhang, Guo-Guo Zhang, Guo-Liang Zhang, Guo-Wei Zhang, Guo-Xiong Zhang, Guoan Zhang, Guochao Zhang, Guodong Zhang, Guofang Zhang, Guofeng Zhang, Guofu Zhang, Guoguo Zhang, Guohua Zhang, Guohui Zhang, Guojun Zhang, Guoli Zhang, Guoliang Zhang, Guolong Zhang, Guomin Zhang, Guoming Zhang, Guoping Zhang, Guoqiang Zhang, Guoqing Zhang, Guorui Zhang, Guosen Zhang, Guowei Zhang, Guoxin Zhang, Guoying Zhang, Guozhi Zhang, H D Zhang, H F Zhang, H L Zhang, H P Zhang, H W Zhang, H X Zhang, H Y Zhang, H Zhang, H-F Zhang, Hai Zhang, Hai-Bo Zhang, Hai-Feng Zhang, Hai-Gang Zhang, Hai-Han Zhang, Hai-Liang Zhang, Hai-Man Zhang, Hai-Ying Zhang, Haibei Zhang, Haibing Zhang, Haibo Zhang, Haicheng Zhang, Haifeng Zhang, Haihong Zhang, Haihua Zhang, Haijiao Zhang, Haijun Zhang, Haikuo Zhang, Hailei Zhang, Hailian Zhang, Hailiang Zhang, Hailin Zhang, Hailing Zhang, Hailong Zhang, Hailou Zhang, Haiming Zhang, Hainan Zhang, Haipeng Zhang, Haisan Zhang, Haisen Zhang, Haitao Zhang, Haiwang Zhang, Haiwei Zhang, Haixia Zhang, Haiyan Zhang, Haiyang Zhang, Haiying Zhang, Haiyue Zhang, Han Zhang, Hanchao Zhang, Hang Zhang, Hanqi Zhang, Hanrui Zhang, Hansi Zhang, Hanting Zhang, Hanwang Zhang, Hanwen Zhang, Hanxu Zhang, Hanyin Zhang, Hanyu Zhang, Hao Zhang, Hao-Chen Zhang, Hao-Yu Zhang, Haohao Zhang, Haojian Zhang, Haojie Zhang, Haojun Zhang, Haokun Zhang, Haolin Zhang, Haomin Zhang, Haonan Zhang, Haopeng Zhang, Haoran Zhang, Haotian Zhang, Haowen Zhang, Haoxing Zhang, Haoyu Zhang, Haoyuan Zhang, Haoyue Zhang, Haozheng Zhang, He Zhang, Hefang Zhang, Hejun Zhang, Heng Zhang, Hengming Zhang, Hengrui Zhang, Hengyuan Zhang, Heping Zhang, Hong Zhang, Hong-Jie Zhang, Hong-Sheng Zhang, Hong-Xing Zhang, Hong-Yu Zhang, Hong-Zhen Zhang, Hongbin Zhang, Hongbing Zhang, Hongcai Zhang, Hongfeng Zhang, Hongfu Zhang, Honghe Zhang, Honghong Zhang, Honghua Zhang, Hongjia Zhang, Hongjie Zhang, Hongjin Zhang, Hongju Zhang, Hongjuan Zhang, Honglei Zhang, Hongliang Zhang, Hongmei Zhang, Hongmin Zhang, Hongquan Zhang, Hongrong Zhang, Hongrui Zhang, Hongsen Zhang, Hongtao Zhang, Hongting Zhang, Hongwu Zhang, Hongxia Zhang, Hongxin Zhang, Hongxing Zhang, Hongya Zhang, Hongyan Zhang, Hongyang Zhang, Hongyi Zhang, Hongying Zhang, Hongyou Zhang, Hongyuan Zhang, Hongyun Zhang, Hongzhong Zhang, Hongzhou Zhang, Houbin Zhang, Hu Zhang, Hua Zhang, Hua-Min Zhang, Hua-Xiong Zhang, Huabing Zhang, Huafeng Zhang, Huaiyong Zhang, Huajia Zhang, Huan Zhang, Huan-Tian Zhang, Huanmin Zhang, Huanqing Zhang, Huanxia Zhang, Huanyu Zhang, Huaqi Zhang, Huaqiu Zhang, Huawei Zhang, Huawen Zhang, Huayang Zhang, Huayong Zhang, Huayu Zhang, Hugang Zhang, Huhan Zhang, Hui Hua Zhang, Hui Z Zhang, Hui Zhang, Hui-Jun Zhang, Hui-Wen Zhang, Huibing Zhang, Huifang Zhang, Huihui Zhang, Huijie Zhang, Huijun Zhang, Huili Zhang, Huilin Zhang, Huimao Zhang, Huimin Zhang, Huiming Zhang, Huiping Zhang, Huiqing Zhang, Huiru Zhang, Huiting Zhang, Huixin Zhang, Huiying Zhang, Huiyu Zhang, Huiyuan Zhang, Huize Zhang, Huizhen Zhang, Igor Ying Zhang, J B Zhang, J R Zhang, J Y Zhang, J Zhang, J-Y Zhang, Jamie Zhang, Jason Z Zhang, Jennifer Y Zhang, Jerry Z Zhang, Ji Yao Zhang, Ji Zhang, Ji-Yuan Zhang, Jia Zhang, Jia-Bao Zhang, Jia-Si Zhang, Jia-Su Zhang, Jia-Xuan Zhang, Jiabi Zhang, Jiachao Zhang, Jiachen Zhang, Jiacheng Zhang, Jiahai Zhang, Jiahao Zhang, Jiahe Zhang, Jiajia Zhang, Jiajing Zhang, Jiaming Zhang, Jian Zhang, Jian-Guo Zhang, Jian-Ping Zhang, Jian-Xu Zhang, Jianan Zhang, Jianbin Zhang, Jianbo Zhang, Jianchao Zhang, Jianduan Zhang, Jianeng Zhang, Jianfa Zhang, Jiang Zhang, Jiangang Zhang, Jianghong Zhang, Jianglin Zhang, Jiangmei Zhang, Jiangtao Zhang, Jianguang Zhang, Jianguo Zhang, Jiangyan Zhang, Jianhai Zhang, Jianhong Zhang, Jianhua Zhang, Jianhui Zhang, Jianing Zhang, Jianjun Zhang, Jiankang Zhang, Jiankun Zhang, Jianliang Zhang, Jianling Zhang, Jianmei Zhang, Jianmin Zhang, Jianming Zhang, Jiannan Zhang, Jianping Zhang, Jianqiong Zhang, Jianshe Zhang, Jianting Zhang, Jianwei Zhang, Jianwen Zhang, Jianwu Zhang, Jianxia Zhang, Jianxiang Zhang, Jianxin Zhang, Jianying Zhang, Jianyong Zhang, Jianzhao Zhang, Jiao Zhang, Jiaqi Zhang, Jiasheng Zhang, Jiawei Zhang, Jiawen Zhang, Jiaxin Zhang, Jiaxing Zhang, Jiayan Zhang, Jiayi Zhang, Jiayin Zhang, Jiaying Zhang, Jiayu Zhang, Jiayuan Zhang, Jibin Zhang, Jicai Zhang, Jie Zhang, Jiecheng Zhang, Jiehao Zhang, Jiejie Zhang, Jieming Zhang, Jieping Zhang, Jieqiong Zhang, Jieying Zhang, Jifa Zhang, Jifeng Zhang, Jihang Zhang, Jimei Zhang, Jiming Zhang, Jimmy Zhang, Jin Zhang, Jin-Ge Zhang, Jin-Jing Zhang, Jin-Man Zhang, Jin-Ru Zhang, Jin-Rui Zhang, Jin-Yu Zhang, Jinbiao Zhang, Jinfan Zhang, Jinfang Zhang, Jinfeng Zhang, Jing Jing Zhang, Jing Zhang, Jing-Bo Zhang, Jing-Chang Zhang, Jing-Fa Zhang, Jing-Lve Zhang, Jing-Nan Zhang, Jing-Qiu Zhang, Jing-Zhan Zhang, JingZi Zhang, Jingchuan Zhang, Jingchun Zhang, Jingdan Zhang, Jingdong Zhang, Jingfa Zhang, Jinghui Zhang, Jingjing Zhang, Jinglan Zhang, Jingli Zhang, Jingliang Zhang, Jinglu Zhang, Jingmei Zhang, Jingmian Zhang, Jingning Zhang, Jingping Zhang, Jingqi Zhang, Jingrong Zhang, Jingru Zhang, Jingshuang Zhang, Jingsong Zhang, Jingtian Zhang, Jingting Zhang, Jingwei Zhang, Jingwen Zhang, Jingxi Zhang, Jingxiao Zhang, Jingxuan Zhang, Jingxue Zhang, Jingyao Zhang, Jingyi Zhang, Jingying Zhang, Jingyu Zhang, Jingyuan Zhang, Jingyue Zhang, Jingzhe Zhang, Jinhua Zhang, Jinhui Zhang, Jinjin Zhang, Jinjing Zhang, Jinliang Zhang, Jinlong Zhang, Jinming Zhang, Jinquan Zhang, Jinrui Zhang, Jinsong Zhang, Jinsu Zhang, Jintao Zhang, Jinwei Zhang, Jinxiu Zhang, Jinyi Zhang, Jinying Zhang, Jinyu Zhang, Jinze Zhang, Jinzhou Zhang, Jiqiang Zhang, Jiquan Zhang, Jishou Zhang, Jishui Zhang, Jitai Zhang, Jiuchun Zhang, Jiupan Zhang, Jiuwei Zhang, Jiuxuan Zhang, Jixia Zhang, Jixing Zhang, Jiyang Zhang, Joe Z Zhang, John H Zhang, John Z H Zhang, Joshua Zhang, Joyce Zhang, Juan Zhang, Juan-Juan Zhang, Jue Zhang, Juliang Zhang, Jun Zhang, Jun-Feng Zhang, Jun-Jie Zhang, Jun-Xiao Zhang, Jun-Xiu Zhang, Jun-ying Zhang, June Zhang, Junfeng Zhang, Junhan Zhang, Junhang Zhang, Junhua Zhang, Junhui Zhang, Junjie Zhang, Junjing Zhang, Junkai Zhang, Junli Zhang, Junling Zhang, Junlong Zhang, Junmei Zhang, Junmin Zhang, Junpei Zhang, Junpeng Zhang, Junping Zhang, Junqing Zhang, Junran Zhang, Junru Zhang, Junsheng Zhang, Juntai Zhang, Junwei Zhang, Junxia Zhang, Junxiao Zhang, Junxing Zhang, Junxiu Zhang, Junyan Zhang, Junyi Zhang, Junying Zhang, Junyu Zhang, Junzhi Zhang, Juqing Zhang, K Y Zhang, K Zhang, Kai Zhang, Kai-Jie Zhang, Kai-Qiang Zhang, Kaichuang Zhang, Kaige Zhang, Kaihua Zhang, Kaihui Zhang, Kailin Zhang, Kailing Zhang, Kaiming Zhang, Kainan Zhang, Kaitai Zhang, Kaituo Zhang, Kaiwen Zhang, Kaiyi Zhang, Kan Zhang, Kang Zhang, Kang-Ling Zhang, Kangjun Zhang, Kangning Zhang, Karen Zhang, Ke Zhang, Ke-Wen Zhang, Ke-lan Zhang, Kefen Zhang, Kejia Zhang, Kejian Zhang, Kejin Zhang, Kejun Zhang, Keke Zhang, Keshan Zhang, Kewen Zhang, Keyi Zhang, Keyong Zhang, Keyu Zhang, Kezhong Zhang, Kongyong Zhang, Kui Zhang, Kui-ming Zhang, Kun Zhang, Kunning Zhang, Kunshan Zhang, Kunyi Zhang, Kuo Zhang, L F Zhang, L Zhang, L-S Zhang, Laihong Zhang, Lan Zhang, Lanfang Zhang, Lanju Zhang, Lanjun Zhang, Lanlan Zhang, Lantian Zhang, Lanyue Zhang, Le Zhang, Le-Le Zhang, Lechi Zhang, Lei Zhang, Lei-Lei Zhang, Lei-Sheng Zhang, Leilei Zhang, Leili Zhang, Leitao Zhang, Leiying Zhang, Lele Zhang, Leli Zhang, Leo H Zhang, Li Zhang, Li-Fen Zhang, Li-Jie Zhang, Li-Ke Zhang, Li-ping Zhang, Lian Zhang, Lian-Lian Zhang, Lianbo Zhang, Lianfeng Zhang, Liang Zhang, Liang-Rong Zhang, Liangdong Zhang, Liangliang Zhang, Liangming Zhang, Lianjun Zhang, Lianmei Zhang, Lianqin Zhang, Lianxin Zhang, Libo Zhang, Lichao Zhang, Lichen Zhang, Licheng Zhang, Lichuan Zhang, Licui Zhang, Lida Zhang, Lie Zhang, Lifan Zhang, Lifang Zhang, Liguo Zhang, Lihong Zhang, Lihua Zhang, Lijian Zhang, Lijiao Zhang, Lijie Zhang, Lijuan Zhang, Lijun Zhang, Lilei Zhang, Lili Zhang, Limei Zhang, Limin Zhang, Liming Zhang, Lin Zhang, Lin-Jie Zhang, Lina Zhang, Linan Zhang, Linbo Zhang, Linda S Zhang, Ling Xia Zhang, Ling Zhang, Ling-Yu Zhang, Lingjie Zhang, Lingli Zhang, Lingling Zhang, Lingna Zhang, Lingqiang Zhang, Lingxiao Zhang, Lingyan Zhang, Lingyu Zhang, Lining Zhang, Linjing Zhang, Linli Zhang, Linlin Zhang, Lintao Zhang, Linyou Zhang, Linyuan Zhang, Liping Zhang, Liqian Zhang, Lirong Zhang, Lishuang Zhang, Litao Zhang, Liu Zhang, Liuming Zhang, Liuwei Zhang, Liwei Zhang, Liwen Zhang, Lixia Zhang, Lixing Zhang, Liyan Zhang, Liyi Zhang, Liyin Zhang, Liying Zhang, Liyu Zhang, Liyuan Zhang, Liyun Zhang, Lizhi Zhang, Long Zhang, Longlong Zhang, Longxin Zhang, Longzhen Zhang, Lu Zhang, Lu-Pei Zhang, Lu-Yang Zhang, Luanluan Zhang, Lucia Zhang, Lufei Zhang, Lukuan Zhang, Lulu Zhang, Lun Zhang, Lunan Zhang, Luning Zhang, Luo Zhang, Luo-Meng Zhang, Luoping Zhang, Lupei Zhang, Lusha Zhang, Luwen Zhang, Luyao Zhang, Luyun Zhang, Luzheng Zhang, Lv-Lang Zhang, M H Zhang, M J Zhang, M M Zhang, M Q Zhang, M X Zhang, M Zhang, Man Zhang, Manjin Zhang, Mao Zhang, Maomao Zhang, Mei Zhang, Mei-Fang Zhang, Mei-Ling Zhang, Mei-Qing Zhang, Mei-Ya Zhang, Mei-Zhen Zhang, MeiLu Zhang, Meidi Zhang, Meijia Zhang, Meiling Zhang, Meimei Zhang, Meishan Zhang, Meiwei Zhang, Meixia Zhang, Meixian Zhang, Meiyu Zhang, Melissa C Zhang, Melody Zhang, Meng Zhang, Meng-Jie Zhang, Meng-Wen Zhang, Meng-Ying Zhang, Mengdi Zhang, Mengguo Zhang, Menghao Zhang, Menghuan Zhang, Menghui Zhang, Mengjia Zhang, Mengjie Zhang, Mengliang Zhang, Menglu Zhang, Mengmeng Zhang, Mengmin Zhang, Mengna Zhang, Mengnan Zhang, Mengni Zhang, Mengqi Zhang, Mengqiu Zhang, Mengren Zhang, Mengshi Zhang, Mengxi Zhang, Mengxian Zhang, Mengxue Zhang, Mengying Zhang, Mengyuan Zhang, Mengyue Zhang, Mengzhao Zhang, Mengzhen Zhang, Mi Zhang, Mianzhi Zhang, Miao Zhang, Miao-Miao Zhang, Miaomiao Zhang, Miaoran Zhang, Michael Zhang, Min Zhang, Minfang Zhang, Ming Zhang, Ming-Jun Zhang, Ming-Liang Zhang, Ming-Ming Zhang, Ming-Rong Zhang, Ming-Yu Zhang, Ming-Zhu Zhang, Mingai Zhang, Mingchang Zhang, Mingdi Zhang, Mingfa Zhang, Mingfeng Zhang, Minghang Zhang, Minghao Zhang, Minghui Zhang, Mingjie Zhang, Mingjiong Zhang, Mingjun Zhang, Mingming Zhang, Mingqi Zhang, Mingtong Zhang, Mingxiang Zhang, Mingxiu Zhang, Mingxuan Zhang, Mingxue Zhang, Mingyang A Zhang, Mingyang Zhang, Mingyao Zhang, Mingyi Zhang, Mingying Zhang, Mingyu Zhang, Mingyuan Zhang, Mingyue Zhang, Mingzhao Zhang, Mingzhen Zhang, Minhong Zhang, Minying Zhang, Minyue Zhang, Minzhi Zhang, Minzhu Zhang, Mo Zhang, Mo-Ruo Zhang, Mu Zhang, Muqing Zhang, Muxin Zhang, Muzi Zhang, N Zhang, Na Zhang, Naijin Zhang, Naiqi Zhang, Naisheng Zhang, Naixia Zhang, Nan Yang Zhang, Nan Zhang, Nan-Nan Zhang, Nana Zhang, Nannan Zhang, Nasha Zhang, Ni Zhang, Niankai Zhang, Nianxiang Zhang, Nieke Zhang, Ning Zhang, Ning-Ping Zhang, Ninghan Zhang, Ningkun Zhang, Ningning Zhang, Ningzhen Zhang, Ningzhi Zhang, Nisi Zhang, Nong Zhang, Nu Zhang, P Zhang, Pan Zhang, Pan-Pan Zhang, Panpan Zhang, Pei Zhang, Pei-Weng Zhang, Pei-Zhuo Zhang, PeiFeng Zhang, Peichun Zhang, Peijing Zhang, Peijun Zhang, Peilin Zhang, Peiqin Zhang, Peiwen Zhang, Peiyi Zhang, Peizhen Zhang, Peng Zhang, Peng-Cheng Zhang, Peng-Fei Zhang, Pengbo Zhang, Pengcheng Zhang, Pengfei Zhang, Pengpeng Zhang, Pengwei Zhang, Pengyuan Zhang, Pili Zhang, Ping Zhang, Ping-Fan Zhang, Pingchuan Zhang, Pinggen Zhang, Pingmei Zhang, Pu-Hong Zhang, Pumin Zhang, Q L Zhang, Q Y Zhang, Q Zhang, Q-D Zhang, Qi Zhang, Qi-Ai Zhang, Qi-Lei Zhang, Qi-Min Zhang, QiYue Zhang, Qian Jun Zhang, Qian ZHANG, Qian-Qian Zhang, Qian-Wen Zhang, Qiang Zhang, Qiang-Sheng Zhang, Qiangsheng Zhang, Qiangyan Zhang, Qianhui Zhang, Qianjun Zhang, Qiannan Zhang, Qianqian Zhang, Qianru Zhang, Qiao-Xia Zhang, Qiaofang Zhang, Qiaojun Zhang, Qiaoxuan Zhang, Qifan Zhang, Qiguo Zhang, Qihao Zhang, Qihong Zhang, Qilong Zhang, Qilu Zhang, Qimin Zhang, Qin Zhang, Qing Zhang, Qing-Hui Zhang, Qing-Zhu Zhang, Qingchao Zhang, Qingcheng Zhang, Qingchuan Zhang, Qingfeng Zhang, Qinghong Zhang, Qinghua Zhang, Qingjiong Zhang, Qingjun Zhang, Qingling Zhang, Qingna Zhang, Qingqing Zhang, Qingquan Zhang, Qingrun Zhang, Qingshuang Zhang, Qingtian Zhang, Qingxiu Zhang, Qingxue Zhang, Qingyu Zhang, Qingyue Zhang, Qingyun Zhang, Qinjun Zhang, Qiong Zhang, Qishu Zhang, Qiu Zhang, Qiuting Zhang, Qiuxia Zhang, Qiuyang Zhang, Qiuyue Zhang, Qiwei Zhang, Qiyong Zhang, Quan Zhang, Quan-bin Zhang, Quanfu Zhang, Quanqi Zhang, Quanquan Zhang, Qun Zhang, Qun-Feng Zhang, Qunchen Zhang, Qunfeng Zhang, Qunyuan Zhang, R Zhang, Ran Zhang, Ranran Zhang, Ren Zhang, Renbo Zhang, Renhe Zhang, Renliang Zhang, Renshuai Zhang, Rey M Zhang, Richard Zhang, Rong Zhang, Rong-Kai Zhang, Rongcai Zhang, Rongchao Zhang, Rongguang Zhang, Rongrong Zhang, Rongxin Zhang, Rongxu Zhang, Rongying Zhang, Rongyu Zhang, Ru Zhang, Rugang Zhang, Rui Long Zhang, Rui Xue Zhang, Rui Yan Zhang, Rui Zhang, Rui-Nan Zhang, Rui-Ning Zhang, Rui-fang Zhang, Ruihao Zhang, Ruihong Zhang, Ruikun Zhang, Ruilin Zhang, Ruiling Zhang, Ruimin Zhang, Ruiqi Zhang, Ruiqian Zhang, Ruisan Zhang, Ruixia Zhang, Ruixin Zhang, Ruixue Zhang, Ruiyan Zhang, Ruiyang Zhang, Ruiying Zhang, Ruizhe Zhang, Ruizhi Zhang, Ruizhong Zhang, Rulin Zhang, Run Zhang, Runcheng Zhang, Runxiang Zhang, Runyun Zhang, Runze Zhang, Ruo-Xin Zhang, Ruohan Zhang, Ruoshi Zhang, Ruotian Zhang, Ruoxuan Zhang, Ruoying Zhang, Rusi Zhang, Ruth Zhang, Ruxiang Zhang, Ruxuan Zhang, Ruyi Zhang, S Y Zhang, S Z Zhang, S Zhang, Sai Zhang, Saidan Zhang, Saifei Zhang, Sainan Zhang, Sanbao Zhang, Sen Zhang, Sha Zhang, Shan Zhang, Shan-Shan Zhang, Shanchun Zhang, Shang Zhang, Shangxiong Zhang, Shanhong Zhang, Shanshan Zhang, Shanxiang Zhang, Shao Kang Zhang, Shao Zhang, Shao-Qi Zhang, Shaochuan Zhang, Shaochun Zhang, Shaofei Zhang, Shaofeng Zhang, Shaohua Zhang, Shaojun Zhang, Shaoyang Zhang, Shaozhao Zhang, Shaozhen Zhang, Shasha Zhang, Shen Zhang, Sheng Zhang, Sheng-Dao Zhang, Sheng-Hong Zhang, Sheng-Qiang Zhang, Sheng-Xiao Zhang, Shengchi Zhang, Shengding Zhang, Shengkun Zhang, Shenglai Zhang, Shenglan Zhang, Shenglei Zhang, Shengli Zhang, Shengming Zhang, Shengnan Zhang, Shengye Zhang, Shenqi Zhang, Shenqian Zhang, Shi Zhang, Shi-Han Zhang, Shi-Jie Zhang, Shi-Meng Zhang, Shi-Qian Zhang, Shi-Yao Zhang, ShiSong Zhang, Shichao Zhang, Shihan Zhang, Shijun Zhang, Shikai Zhang, Shilei Zhang, Shimao Zhang, Shining Zhang, Shiping Zhang, Shiqi Zhang, Shiquan Zhang, Shiti Zhang, Shitian Zhang, Shiwen Zhang, Shiwu Zhang, Shiyao Zhang, Shiyi Zhang, Shiyu Zhang, Shiyun Zhang, Shou-Mei Zhang, Shou-Peng Zhang, Shouyue Zhang, Shu Zhang, Shu-Dong Zhang, Shu-Fan Zhang, Shu-Fang Zhang, Shu-Min Zhang, Shu-Ming Zhang, Shu-Yang Zhang, Shu-Zhen Zhang, Shuai Zhang, Shuai-Nan Zhang, Shuaishuai Zhang, Shuang Zhang, Shuangjie Zhang, Shuanglu Zhang, Shuangxin Zhang, Shubing Zhang, Shuchen Zhang, Shucong Zhang, Shuer Zhang, Shuge Zhang, Shuhong Zhang, Shuijun Zhang, Shujun Zhang, Shuli Zhang, Shulong Zhang, Shun Zhang, Shun-Bo Zhang, Shunfen Zhang, Shunming Zhang, Shuo Zhang, Shupeng Zhang, Shuran Zhang, Shurui Zhang, Shushan Zhang, Shuwan Zhang, Shuwei Zhang, Shuxia Zhang, Shuya Zhang, Shuyan Zhang, Shuyang Zhang, Shuye Zhang, Shuyi Zhang, Shuyuan Zhang, Si Zhang, Si-Zhong Zhang, Sibin Zhang, Sifan Zhang, Sihe Zhang, Simeng Zhang, Simin Zhang, Siqi Zhang, Sisi Zhang, Sixue Zhang, Siyuan Zhang, Siyue Zhang, Sizhong Zhang, Song Zhang, Song-Yang Zhang, Songlin Zhang, Songying Zhang, Sophia L Zhang, Stanley Weihua Zhang, Stephen X Zhang, Su Zhang, Sujiang Zhang, Sulin Zhang, Sumei Zhang, Suming Zhang, Suping Zhang, Susie Zhang, Suya Zhang, Suyang Zhang, Suzhen Zhang, T Zhang, Tangjuan Zhang, Tao Zhang, Tao-Lan Zhang, Taojun Zhang, Taoyuan Zhang, Teng Zhang, Tengfang Zhang, Terry Jianguo Zhang, Ti Zhang, Tian Zhang, Tian-Guang Zhang, Tian-Yu Zhang, Tiane Zhang, Tianfeng Zhang, Tianliang Zhang, Tianlong Zhang, Tianpeng Zhang, Tianshu Zhang, Tiantian Zhang, Tianxi Zhang, Tianxiao Zhang, Tianxin Zhang, Tianyang Zhang, Tianye Zhang, Tianyi Zhang, Tianyu Zhang, Tie-mei Zhang, Tiefeng Zhang, Tiehua Zhang, Tiejun Zhang, Ting Ting Zhang, Ting Zhang, Ting-Ting Zhang, Tinghu Zhang, Tingting Zhang, Tingxue Zhang, Tingying Zhang, Tong Xuan Zhang, Tong Zhang, Tong-Cun Zhang, Tongcun Zhang, Tongfu Zhang, Tonghan Zhang, Tonghua Zhang, Tonghui Zhang, Tongran Zhang, Tongshuo Zhang, Tongtong Zhang, Tongwu Zhang, Tongxin Zhang, Tongxue Zhang, Tuo Zhang, Vita Zhang, W G Zhang, W X Zhang, W Zhang, Wancong Zhang, Wang-Dong Zhang, Wangang Zhang, Wangping Zhang, Wanjiang Zhang, Wanjun Zhang, Wannian Zhang, Wanqi Zhang, Wanting Zhang, Wanying Zhang, Wanyu Zhang, Wei Zhang, Wei-Jia Zhang, Wei-Na Zhang, Wei-Yi Zhang, Weibo Zhang, Weichen Zhang, Weifeng Zhang, Weiguo Zhang, Weihua Zhang, Weijian Zhang, Weikang Zhang, Weili Zhang, Weilin Zhang, Weiling Zhang, Weilong Zhang, Weimin Zhang, Weina Zhang, Weipeng Zhang, Weiping J Zhang, Weiqin Zhang, Weisen Zhang, Weiwei Zhang, Weixia Zhang, Weiyi Zhang, Weiyu Zhang, Weizheng Zhang, Weizhou Zhang, Wen Jun Zhang, Wen Zhang, Wen-Hong Zhang, Wen-Jie Zhang, Wen-Jing Zhang, Wen-Xin Zhang, Wen-Xuan Zhang, Wenbin Zhang, Wenbo Zhang, Wenchao Zhang, Wencheng Zhang, Wencong Zhang, Wendi Zhang, Wenguang Zhang, Wenhao Zhang, Wenhong Zhang, Wenhua Zhang, Wenhui Zhang, Wenji Zhang, Wenjia Zhang, Wenjing Zhang, Wenjuan Zhang, Wenjun Zhang, Wenkai Zhang, Wenkui Zhang, Wenli Zhang, Wenlong Zhang, Wenlu Zhang, Wenming Zhang, Wenqian Zhang, Wenru Zhang, Wentao Zhang, Wenting Zhang, Wenwen Zhang, Wenxi Zhang, Wenxiang Zhang, Wenxin Zhang, Wenxue Zhang, Wenya Zhang, Wenyang Zhang, Wenyi Zhang, Wenyuan Zhang, Wenzhong Zhang, Wuhu Zhang, X N Zhang, X X Zhang, X Y Zhang, X Zhang, X-T Zhang, X-Y Zhang, Xi Zhang, Xi'an Zhang, Xi-Feng Zhang, XiHe Zhang, Xia Zhang, Xian Zhang, Xian-Bo Zhang, Xian-Li Zhang, Xian-Man Zhang, Xiang Yang Zhang, Xiang Zhang, Xiangbin Zhang, Xiangfei Zhang, Xianglian Zhang, Xiangsong Zhang, Xiangwu Zhang, Xiangyang Zhang, Xiangyu Zhang, Xiangzheng Zhang, Xianhong Zhang, Xianhua Zhang, Xianjing Zhang, Xianpeng Zhang, Xianxian Zhang, Xiao Bin Zhang, Xiao Min Zhang, Xiao Yu Cindy Zhang, Xiao Zhang, Xiao-Chang Zhang, Xiao-Cheng Zhang, Xiao-Chong Zhang, Xiao-Feng Zhang, Xiao-Hong Zhang, Xiao-Hua Zhang, Xiao-Jun Zhang, Xiao-Lei Zhang, Xiao-Lin Zhang, Xiao-Ling Zhang, Xiao-Meng Zhang, Xiao-Ming Zhang, Xiao-Qi Zhang, Xiao-Qian Zhang, Xiao-Shuo Zhang, Xiao-Wei Zhang, Xiao-Xuan Zhang, Xiao-Yong Zhang, Xiao-Yu Zhang, Xiao-bo Zhang, Xiao-yan Zhang, XiaoLin Zhang, XiaoPing Zhang, XiaoYi Zhang, Xiaobao Zhang, Xiaobiao Zhang, Xiaobo Zhang, Xiaochang Zhang, Xiaochen Zhang, Xiaochun Zhang, Xiaocong Zhang, Xiaocui Zhang, Xiaodan Zhang, Xiaodong Zhang, Xiaofan Zhang, Xiaofang Zhang, Xiaofei Zhang, Xiaofeng Zhang, Xiaogang Zhang, Xiaohan Zhang, Xiaohong Zhang, Xiaohui Zhang, Xiaojia Zhang, Xiaojian Zhang, Xiaojie Zhang, Xiaojin Zhang, Xiaojing Zhang, Xiaojun Zhang, Xiaokui Zhang, Xiaolan Zhang, Xiaolei Zhang, Xiaoli Zhang, Xiaoling Zhang, Xiaolong Zhang, Xiaomei Zhang, Xiaomeng Zhang, Xiaomin Zhang, Xiaoming Zhang, Xiaoning Zhang, Xiaonyun Zhang, Xiaopei Zhang, Xiaopo Zhang, Xiaoqi Zhang, Xiaoqing Zhang, Xiaorong Zhang, Xiaosheng Zhang, Xiaotian Michelle Zhang, Xiaotian Zhang, Xiaotong Zhang, Xiaotun Zhang, Xiaowan Zhang, Xiaowei Zhang, Xiaoxi Zhang, Xiaoxia Zhang, Xiaoxian Zhang, Xiaoxiao Zhang, Xiaoxin Zhang, Xiaoxue Zhang, Xiaoyan Zhang, Xiaoying Zhang, Xiaoyu Zhang, Xiaoyuan Zhang, Xiaoyue Zhang, Xiaoyun Zhang, Xiaozhe Zhang, Xiayin Zhang, Xibo Zhang, Xieyi Zhang, Xijiang Zhang, Xilin Zhang, Xiling Zhang, Ximei Zhang, Xin Zhang, Xin-Hui Zhang, Xin-Xin Zhang, Xin-Yan Zhang, Xin-Ye Zhang, Xin-Yuan Zhang, Xinan Zhang, Xinbao Zhang, Xinbo Zhang, Xincheng Zhang, Xindang Zhang, Xindong Zhang, Xinfeng Zhang, Xinfu Zhang, Xing Yu Zhang, Xing Zhang, Xingan Zhang, Xingang Zhang, Xingcai Zhang, Xingen Zhang, Xinglai Zhang, Xingong Zhang, Xingwei Zhang, Xingxing Zhang, Xingxu Zhang, Xingyi Zhang, Xingyu Zhang, Xingyuan Zhang, Xinhai Zhang, Xinhan Zhang, Xinhe Zhang, Xinheng Zhang, Xinhong Zhang, Xinhua Zhang, Xinjiang Zhang, Xinjing Zhang, Xinjun Zhang, Xinke Zhang, Xinlei Zhang, Xinlian Zhang, Xinlin Zhang, Xinling Zhang, Xinlong Zhang, Xinlu Zhang, Xinmin Zhang, Xinping Zhang, Xinqiao Zhang, Xinquan Zhang, Xinran Zhang, Xinrui Zhang, Xinruo Zhang, Xintao Zhang, Xinwei Zhang, Xinwu Zhang, Xinxin Zhang, Xinyao Zhang, Xinye Zhang, Xinyi Zhang, Xinyu Zhang, Xinyue Zhang, Xiong Zhang, Xiongjun Zhang, Xiongze Zhang, Xipeng Zhang, Xiping Zhang, Xiu Qi Zhang, Xiu-Juan Zhang, Xiu-Li Zhang, Xiu-Peng Zhang, Xiujie Zhang, Xiujun Zhang, Xiulan Zhang, Xiuming Zhang, Xiupeng Zhang, Xiuping Zhang, Xiuqin Zhang, Xiuqing Zhang, Xiuse Zhang, Xiushan Zhang, Xiuwen Zhang, Xiuxing Zhang, Xiuxiu Zhang, Xiuyin Zhang, Xiuyue Zhang, Xiuyun Zhang, Xiuzhen Zhang, Xixi Zhang, Xixun Zhang, Xiyu Zhang, Xu Dong Zhang, Xu Zhang, Xu-Chao Zhang, Xu-Jun Zhang, Xu-Mei Zhang, Xuan Zhang, Xudan Zhang, Xudong Zhang, Xue Zhang, Xue-Ping Zhang, Xue-Qin Zhang, Xue-Qing Zhang, XueWu Zhang, Xuebao Zhang, Xuebin Zhang, Xuefei Zhang, Xueguang Zhang, Xuehai Zhang, Xuehong Zhang, Xuehui Zhang, Xuejiao Zhang, Xuejun C Zhang, Xueli Zhang, Xuelian Zhang, Xuelong Zhang, Xueluo Zhang, Xuemei Zhang, Xuemin Zhang, Xueming Zhang, Xuening Zhang, Xueping Zhang, Xueqia Zhang, Xueqian Zhang, Xueqin Zhang, Xueting Zhang, Xuewei Zhang, Xuewen Zhang, Xuexi Zhang, Xueya Zhang, Xueyan Zhang, Xueyi Zhang, Xueying Zhang, Xuezhi Zhang, Xufang Zhang, Xuhao Zhang, Xujun Zhang, Xunming Zhang, Xuting Zhang, Xutong Zhang, Xuxiang Zhang, Y H Zhang, Y L Zhang, Y Y Zhang, Y Zhang, Y-H Zhang, Ya Zhang, Ya-Juan Zhang, Ya-Li Zhang, Ya-Long Zhang, Ya-Meng Zhang, Yachen Zhang, Yadi Zhang, Yadong Zhang, Yafang Zhang, Yafei Zhang, Yafeng Zhang, Yaguang Zhang, Yahua Zhang, Yajie Zhang, Yajing Zhang, Yajun Zhang, Yakun Zhang, Yalan Zhang, Yali Zhang, Yaling Zhang, Yameng Zhang, Yamin Zhang, Yaming Zhang, Yan Zhang, Yan-Chun Zhang, Yan-Ling Zhang, Yan-Min Zhang, Yan-Qing Zhang, Yanan Zhang, Yanbin Zhang, Yanbing Zhang, Yanchao Zhang, Yandong Zhang, Yanfei Zhang, Yanfen Zhang, Yanfeng Zhang, Yang Zhang, Yang-Yang Zhang, Yangfan Zhang, Yanghui Zhang, Yangqianwen Zhang, Yangyang Zhang, Yangyu Zhang, Yanhong Zhang, Yanhua Zhang, Yani Zhang, Yanjiao Zhang, Yanju Zhang, Yanjun Zhang, Yanli Zhang, Yanlin Zhang, Yanling Zhang, Yanman Zhang, Yanmin Zhang, Yanming Zhang, Yanna Zhang, Yannan Zhang, Yanping Zhang, Yanqiao Zhang, Yanquan Zhang, Yanru Zhang, Yanting Zhang, Yanxia Zhang, Yanxiang Zhang, Yanyan Zhang, Yanyi Zhang, Yanyu Zhang, Yao Zhang, Yao-Hua Zhang, Yaodong Zhang, Yaoxin Zhang, Yaoyang Zhang, Yaoyao Zhang, Yaozhengtai Zhang, Yaping Zhang, Yaqi Zhang, Yaru Zhang, Yashuo Zhang, Yating Zhang, Yawei Zhang, Yaxin Zhang, Yaxuan Zhang, Yayong Zhang, Yazhuo Zhang, Ye Zhang, Yefan Zhang, Yeqian Zhang, Yerui Zhang, Yeting Zhang, Yexiang Zhang, Yi J Zhang, Yi Ping Zhang, Yi Zhang, Yi-Chi Zhang, Yi-Feng Zhang, Yi-Ge Zhang, Yi-Hang Zhang, Yi-Hua Zhang, Yi-Min Zhang, Yi-Ming Zhang, Yi-Qi Zhang, Yi-Wei Zhang, Yi-Wen Zhang, Yi-Xuan Zhang, Yi-Yue Zhang, Yi-yi Zhang, YiJie Zhang, YiPei Zhang, Yibin Zhang, Yibo Zhang, Yichen Zhang, Yichi Zhang, Yidan Zhang, Yidong Zhang, Yifan Zhang, Yifang Zhang, Yige Zhang, Yiguo Zhang, Yihan Zhang, Yihang Zhang, Yihao Zhang, Yiheng Zhang, Yihong Zhang, Yihui Zhang, Yijing Zhang, Yikai Zhang, Yikun Zhang, Yili Zhang, Yiliang Zhang, Yilin Zhang, Yimei Zhang, Yimeng Zhang, Yimin Zhang, Yiming Zhang, Yin Jiang Zhang, Yin Zhang, Yin-Hong Zhang, Yina Zhang, Yinci Zhang, Ying E Zhang, Ying Zhang, Ying-Jun Zhang, Ying-Lin Zhang, Ying-Qian Zhang, Yingang Zhang, Yingchao Zhang, Yinghui Zhang, Yingjie Zhang, Yingli Zhang, Yingmei Zhang, Yingna Zhang, Yingnan Zhang, Yingqi Zhang, Yingqian Zhang, Yingyi Zhang, Yingying Zhang, Yingze Zhang, Yingzi Zhang, Yinhao Zhang, Yinjiang Zhang, Yintang Zhang, Yinzhi Zhang, Yinzhuang Zhang, Yipeng Zhang, Yiping Zhang, Yiqian Zhang, Yiqing Zhang, Yiren Zhang, Yirong Zhang, Yitian Zhang, Yiting Zhang, Yiwan Zhang, Yiwei Zhang, Yiwen Zhang, Yixia Zhang, Yixin Zhang, Yiyao Zhang, Yiyi Zhang, Yiyuan Zhang, Yizhe Zhang, Yizhi Zhang, Yong Zhang, Yong-Guo Zhang, Yong-Liang Zhang, Yong-hong Zhang, Yongbao Zhang, Yongchang Zhang, Yongchao Zhang, Yongci Zhang, Yongfa Zhang, Yongfang Zhang, Yongfeng Zhang, Yonggang Zhang, Yonggen Zhang, Yongguang Zhang, Yongguo Zhang, Yongheng Zhang, Yonghong Zhang, Yonghui Zhang, Yongjie Zhang, Yongjiu Zhang, Yongjuan Zhang, Yonglian Zhang, Yongliang Zhang, Yonglong Zhang, Yongpeng Zhang, Yongping Zhang, Yongqiang Zhang, Yongsheng Zhang, Yongwei Zhang, Yongxiang Zhang, Yongxing Zhang, Yongyan Zhang, Yongyun Zhang, You-Zhi Zhang, Youjin Zhang, Youmin Zhang, Youti Zhang, Youwen Zhang, Youyi Zhang, Youying Zhang, Youzhong Zhang, Yu Chen Zhang, Yu Zhang, Yu-Bo Zhang, Yu-Chi Zhang, Yu-Fei Zhang, Yu-Hui Zhang, Yu-Jie Zhang, Yu-Jing Zhang, Yu-Qi Zhang, Yu-Qiu Zhang, Yu-Yu Zhang, Yu-Zhe Zhang, YuHang Zhang, YuHong Zhang, Yuan Zhang, Yuan-Wei Zhang, Yuan-Yuan Zhang, Yuanchao Zhang, Yuanhao Zhang, Yuanhui Zhang, Yuanping Zhang, Yuanqiang Zhang, Yuanqing Zhang, Yuansheng Zhang, Yuanxi Zhang, Yuanxiang Zhang, Yuanyi Zhang, Yuanyuan Zhang, Yuanzhen Zhang, Yuanzhuang Zhang, Yubin Zhang, Yucai Zhang, Yuchao Zhang, Yuchen Zhang, Yuchi Zhang, Yue Zhang, Yue-Bo Zhang, Yue-Ming Zhang, Yuebin Zhang, Yuebo Zhang, Yuehong Zhang, Yuehua Zhang, Yuejuan Zhang, Yuemei Zhang, Yueqi Zhang, Yueru Zhang, Yuetong Zhang, Yufang Zhang, Yufeng Zhang, Yuhan Zhang, Yuhao Zhang, Yuheng Zhang, Yuhua Zhang, Yuhui Zhang, Yujia Zhang, Yujiao Zhang, Yujie Zhang, Yujin Zhang, Yujing Zhang, Yujuan Zhang, Yuke Zhang, Yukun Zhang, Yulin Zhang, Yuling Zhang, Yulong Zhang, Yumei Zhang, Yumeng Zhang, Yumin Zhang, Yun Zhang, Yun-Feng Zhang, Yun-Lin Zhang, Yun-Mei Zhang, Yun-Sheng Zhang, Yun-Xiang Zhang, Yunfan Zhang, Yunfei Zhang, Yunfeng Zhang, Yunhai Zhang, Yunhang Zhang, Yunhe Zhang, Yunhui Zhang, Yuning Zhang, Yunjia Zhang, Yunli Zhang, Yunmei Zhang, Yunpeng Zhang, Yunqi Zhang, Yunqiang Zhang, Yunqing Zhang, Yunsheng Zhang, Yunxia Zhang, Yupei Zhang, Yupeng Zhang, Yuping Zhang, Yuqi Zhang, Yuqing Zhang, Yurou Zhang, Yuru Zhang, Yusen Zhang, Yushan Zhang, Yutian Zhang, Yuting Zhang, Yutong Zhang, Yuwei Zhang, Yuxi Zhang, Yuxia Zhang, Yuxin Zhang, Yuxuan Zhang, Yuyan Zhang, Yuyanan Zhang, Yuyang Zhang, Yuying Zhang, Yuyu Zhang, Yuyuan Zhang, Yuzhe Zhang, Yuzhi Zhang, Yuzhou Zhang, Yuzhu Zhang, Yvonne Zhang, Z Zhang, Z-K Zhang, Zai-Rong Zhang, Zaifeng Zhang, Zaijun Zhang, Zaiqi Zhang, Zebang Zhang, Zekun Zhang, Zemin Zhang, Zeming Zhang, Zeng Zhang, Zengdi Zhang, Zengfu Zhang, Zenglei Zhang, Zengli Zhang, Zengqiang Zhang, Zengrong Zhang, Zengtie Zhang, Zepeng Zhang, Zewei Zhang, Zewen Zhang, Zeyan Zhang, Zeyuan Zhang, Zhan-Xiong Zhang, Zhangjin Zhang, Zhanhao Zhang, Zhanjie Zhang, Zhanjun Zhang, Zhanming Zhang, Zhanyi Zhang, Zhao Zhang, Zhao-Huan Zhang, Zhao-Ming Zhang, Zhaobo Zhang, Zhaocong Zhang, Zhaofeng Zhang, Zhaohua Zhang, Zhaohuai Zhang, Zhaohuan Zhang, Zhaohui Zhang, Zhaomin Zhang, Zhaoping Zhang, Zhaoqi Zhang, Zhaotian Zhang, Zhaoxue Zhang, Zhe Zhang, Zhehua Zhang, Zhemei Zhang, Zhen Zhang, Zhen-Dong Zhang, Zhen-Jie Zhang, Zhen-Shan Zhang, Zhen-Tao Zhang, Zhen-lin Zhang, Zhenfeng Zhang, Zheng Zhang, Zhengbin Zhang, Zhengfen Zhang, Zhenglang Zhang, Zhengliang Zhang, Zhengxiang Zhang, Zhengxing Zhang, Zhengyu Zhang, Zhengyun Zhang, Zhenhao Zhang, Zhenhua Zhang, Zhenlin Zhang, Zhenqiang Zhang, Zhentao Zhang, Zhenyang Zhang, Zhenyu Zhang, Zhenzhen Zhang, Zhenzhu Zhang, Zhewei Zhang, Zhewen Zhang, Zheyuan Zhang, Zhezhe Zhang, Zhi Zhang, Zhi-Chang Zhang, Zhi-Jie Zhang, Zhi-Jun Zhang, Zhi-Peng Zhang, Zhi-Qing Zhang, Zhi-Shuai Zhang, Zhi-Shuo Zhang, Zhi-Xin Zhang, Zhibo Zhang, Zhicheng Zhang, Zhicong Zhang, Zhifei Zhang, Zhigang Zhang, Zhiguo Zhang, Zhihan Zhang, Zhihao Zhang, Zhihong Zhang, Zhihua Zhang, Zhihui Zhang, Zhijian Zhang, Zhijiao Zhang, Zhijing Zhang, Zhijun Zhang, Zhikun Zhang, Zhimin Zhang, Zhiming Zhang, Zhiping Zhang, Zhiqian Zhang, Zhiqiang Zhang, Zhiqiao Zhang, Zhiru Zhang, Zhishang Zhang, Zhishuai Zhang, Zhiwang Zhang, Zhiwen Zhang, Zhixia Zhang, Zhixin Zhang, Zhiyan Zhang, Zhiyao Zhang, Zhiye Zhang, Zhiyi Zhang, Zhiyong Zhang, Zhiyu Zhang, Zhiyuan Zhang, Zhiyun Zhang, Zhizhong Zhang, Zhong Zhang, Zhong-Bai Zhang, Zhong-Yi Zhang, Zhong-Yin Zhang, Zhong-Yuan Zhang, Zhongheng Zhang, Zhongjie Zhang, Zhonglin Zhang, Zhongqi Zhang, Zhongwei Zhang, Zhongxin Zhang, Zhongyang Zhang, Zhongyi Zhang, Zhou Zhang, Zhu Zhang, Zhu-Qin Zhang, Zhuang Zhang, Zhuo Zhang, Zhuo-Ya Zhang, Zhuohua Zhang, Zhuojun Zhang, Zhuorong Zhang, Zhuoya Zhang, Zhuqin Zhang, Zhuqing Zhang, Zhuzhen Zhang, Zi-Feng Zhang, Zi-Jian Zhang, Zian Zhang, Zicheng Zhang, Ziding Zhang, Ziguo Zhang, Zihan Zhang, Ziheng Zhang, Zijian Zhang, Zijiao Zhang, Zijing Zhang, Zikai Zhang, Zilong Zhang, Zilu Zhang, Ziping Zhang, Ziqi Zhang, Zishuo Zhang, Zixiong Zhang, Zixu Zhang, Zixuan Zhang, Ziyang Zhang, Ziyi Zhang, Ziyin Zhang, Ziyu Zhang, Ziyue Zhang, Zizhen Zhang, Zongping Zhang, Zongquan Zhang, Zongwang Zhang, Zongxiang Zhang, Zu-Xuan Zhang, Zufa Zhang, Zuoyi Zhang
articles
Xiang Hong, Mengjie Zhao, Furong Tan +5 more · 2026 · BMC microbiology · BioMed Central · added 2026-04-24
To investigate the association between vaginal microbiota structure in early pregnancy and gestational diabetes mellitus (GDM) and to characterize microbial signatures for early screening for GDM. The Show more
To investigate the association between vaginal microbiota structure in early pregnancy and gestational diabetes mellitus (GDM) and to characterize microbial signatures for early screening for GDM. The present study was a nested case-control study recruiting pregnant women from the Nanjing Gulou Maternal-Child Health Center, China. Vaginal swabs were collected before 20 weeks of gestation for 16S rRNA sequencing. Following 1:3 propensity score matching, 45 GDM cases and 135 controls were enrolled. The final analysis included 42 GDM cases and 121 controls. A random forest model was used to explore the genera of vaginal differential microbiota associated with GDM. Based on these findings, latent profile analysis (LPA) was conducted to explore potential types of vaginal microbiota, and logistic regression was used to analyze the association between vaginal microbiota types and GDM. The GDM group exhibited elevated alpha diversity (Chao1 index, The composition and structure of vaginal microbiota in early pregnancy are different in the two groups. The vaginal microbiota in early pregnancy, which is characterized by co-dominated by The online version contains supplementary material available at 10.1186/s12866-026-04910-2. Show less
📄 PDF DOI: 10.1186/s12866-026-04910-2
LPA
Wen Hao, Yuyao Qiu, Zekun Zhang +7 more · 2026 · Sleep · Oxford University Press · added 2026-04-24
The impact of obstructive sleep apnea (OSA) on subsequent cardiovascular events in patients with acute coronary syndrome (ACS) remains debated. This study aims to investigate whether the association o Show more
The impact of obstructive sleep apnea (OSA) on subsequent cardiovascular events in patients with acute coronary syndrome (ACS) remains debated. This study aims to investigate whether the association of OSA with cardiovascular events is affected by lipoprotein (a) [Lp(a)] levels. This is a sub-analysis of prospective cohort study (OSA-ACS, NCT03362385) enrolled ACS patients. OSA defined as an apnea-hypopnea index ≥15 events/h. The effects of OSA on subsequent cardiovascular outcomes were evaluated across varying Lp(a) thresholds. Coronary plaque features by coronary computed tomography angiography were also analyzed. A total of 1137 patients were enrolled, 608 patients (53.5%) were diagnosed with OSA. At a median follow-up of 3.6 years, OSA was associated with a higher risk of major adverse cardiovascular and cerebrovascular events (MACCE) in patients with Lp(a) level > median (HR 1.59, 95% CI 1.12-2.26, p=.009), but not in patients with Lp(a) level ≤ median (HR 1.09, 95% CI 0.80-1.49, p=.60). There were consistent increases in HRs for MACCE in the OSA group with Lp(a) levels rising, as stratified by tertiles or quartiles of Lp(a). In patients with Lp(a) level > median, OSA demonstrated a higher prevalence of ≥1 high-risk plaque (HRP) feature (51.4% vs. 33.3%, p=.03) and low-attenuation plaque (50.0% vs. 32.8, p=.04) per vessel than non-OSA. OSA was associated with a continuously increased cardiovascular risk and a higher prevalence of HRP features as Lp(a) levels rose. Lp(a) may help identify ACS patients at higher cardiovascular risk, in whom the efficacy of OSA treatment should be further investigated. Show less
no PDF DOI: 10.1093/sleep/zsag062
LPA
Cailing Liu, Yueyuan He, Xue Yang +5 more · 2026 · International journal of women's health · added 2026-04-24
This study aimed to assess the childbirth readiness of women in their third trimester of pregnancy and to identify distinct readiness profiles using latent profile analysis (LPA). Additionally, it exp Show more
This study aimed to assess the childbirth readiness of women in their third trimester of pregnancy and to identify distinct readiness profiles using latent profile analysis (LPA). Additionally, it explored the factors influencing childbirth readiness in order to guide targeted interventions for improved maternal and neonatal outcomes. A cross-sectional study was conducted among women in their third trimester of pregnancy between May and November 2024. Eligible participants completed a general information questionnaire, the Childbirth Readiness Scale (CRS), the Childbirth Attitude Questionnaire (CAQ), and the Perceived Social Support Scale (PSSS). LPA identified three groups with distinct childbirth readiness levels: "Low Readiness - Childbirth Knowledge Deficit" (37.9%), "Moderate Readiness - Good Lifestyle Habits" (47.9%), and "High Readiness - Rich Health Knowledge" (14.2%). In addition, gestational age, previous childbirth history, adverse pregnancy outcomes, childbirth attitudes, and social support had different influences on women in different latent profiles of childbirth readiness. There was significant heterogeneity in childbirth readiness among women in their third trimester. Women with lower readiness-especially in childbirth knowledge-would greatly benefit from targeted educational programs, whereas those with moderate readiness levels would find enhanced emotional and psychological support most advantageous. These findings support the implementation of profile-based, personalized prenatal care strategies to improve childbirth preparedness and optimize maternal and neonatal outcomes. Show less
📄 PDF DOI: 10.2147/IJWH.S574855
LPA
Li He, Wen-Wen Yu, Hao-Tian Zheng +4 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Hemodialysis, as one of the main alternative treatment methods for end-stage renal disease, has received much attention in recent years. Due to the particularity of hemodialysis treatment, patients ha Show more
Hemodialysis, as one of the main alternative treatment methods for end-stage renal disease, has received much attention in recent years. Due to the particularity of hemodialysis treatment, patients have a relatively high risk of infection during the treatment process. Hemodialysis nurses, who are the main executors of the treatment operations and have the most contact with patients, have a close relationship with the infection risk of patients. The level of their hospital infection prevention and control literacy is closely related to the infection risk of patients. To explore the current level of knowledge, attitudes, and practices (KAP) of hospital infection prevention and control among haemodialysis nurses in the Sichuan Province, China, and identified their potential categories. This provided evidence-based recommendations for improving infection control management in hemodialysis departments. A cross-sectional study was conducted From July 15 to August 15, 2025 using a convenience sampling method to survey 470 hemodialysis nurses from 78 hospitals in Sichuan Province. Participants were licensed nurses with over 3 months of hemodialysis experience. Data were collected using the A total of 460 valid questionnaires were collected, with an effective response rate of 97.87%. The average scores for knowledge, attitudes, and practices related to hospital infection prevention and control among haemodialysis nurses were 4.67 ± 0.43, 4.59 ± 0.43, and 4.74 ± 0.34, respectively. Three latent profile models were constructed, with the two-class model identified as the optimal solution, which were defined as the "Low KAP Group" (25.9%) and "High KAP Group" (74.1%). Logistic regression analysis revealed that sex, responsibility for infection control, hospital level, annual number of infection control training sessions, organizational support, and work engagement were significant influencing factors ( The KAP level of haemodialysis nurses in hospital infection prevention and control was relatively high. Hospital managers should tailor supportive work environments on the basis of the individual characteristics and work engagement of haemodialysis nurses to improve the KAP level of nosocomial infection prevention and control among haemodialysis nurses. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1734891
LPA
Xiao Huang, Darui Gao, Wenya Zhang +7 more · 2026 · Biology of sex differences · BioMed Central · added 2026-04-24
Cancer patients face a markedly elevated risk of thromboembolism (TE), including both venous thromboembolism (VTE) and arterial thromboembolism (ATE), which contribute substantially to morbidity and m Show more
Cancer patients face a markedly elevated risk of thromboembolism (TE), including both venous thromboembolism (VTE) and arterial thromboembolism (ATE), which contribute substantially to morbidity and mortality in this population. This study examined sex disparities in associations between sleep, sedentary behavior (SB), light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and TE risk, in cancer patients using data from the UK Biobank. A longitudinal cohort analysis of 6,765 cancer patients (2,774 men and 3,991 women) from the accelerometry subsample was conducted using Cox proportional hazards and isotemporal substitution models stratified by sex. The incidence of VTE was 3.0% in men versus 2.2% in women, while ATE incidence was 5.0% versus 2.2%, respectively. Compared with high LPA, medium and low durations were associated with 2.75- and 2.88-fold higher VTE risk only in men. Reallocating 1 h per day from sleep or SB to LPA reduced VTE risk by 24% and 19% in men. Low MVPA was associated with 3.35- and 1.59-fold higher ATE risk in women and men, respectively. Reallocating 1 h per day from sleep, SB, or LPA to MVPA reduced ATE risk by 71%, 70%, and 66%, respectively, only in women. LPA was associated with a lower risk of VTE only in male cancer patients, whereas MVPA was linked to a lower risk of ATE in female patients, indicating sex-specific associations between movement behaviors and TE risk. Show less
📄 PDF DOI: 10.1186/s13293-026-00867-z
LPA
Bin Ma, Jingjing Wang, Mengyuan Zhang +2 more · 2026 · BMC nursing · BioMed Central · added 2026-04-24
To evaluate the current status and latent profiles of caregiver self-care contributions for patients with chronic obstructive pulmonary disease (COPD) and examine the associations between demographic Show more
To evaluate the current status and latent profiles of caregiver self-care contributions for patients with chronic obstructive pulmonary disease (COPD) and examine the associations between demographic characteristics, health literacy, confidence in self-care contributions, family intimacy, and profile membership. We recruited 275 dyads of patients with COPD and their family caregivers from five tertiary hospitals between May and November 2022 using convenience sampling. Latent profile analysis (LPA) was used to identify distinct profiles of caregiver self-care contributions. Univariate analysis and multinomial logistic regression were subsequently conducted to examine associations between participant characteristics and profile membership. LPA identified four distinct profiles of caregiver self-care contributions: low-contributing, under-monitored, maintenance-prioritized, and high-contributing. Significant differences were observed across these profiles in terms of patients' symptom severity, exacerbation frequency, number of hospitalizations, caregivers' education levels, caregiving duration, health literacy, confidence in self-management contributions, and family intimacy using univariate analysis. Multinomial logistic regression analysis revealed that caregivers' education levels, caregiving duration, confidence in self-management contributions, and health literacy were significant predictors of profile membership. Caregiver self-care contributions for patients with COPD can be characterized by four distinct profiles, with caregivers' educational level, health literacy, and confidence in self-management identified as key factors associated with profile membership. Show less
📄 PDF DOI: 10.1186/s12912-026-04503-4
LPA
Shaowei Liu, Bin Ma, Yanju Liu +3 more · 2026 · BMC psychiatry · BioMed Central · added 2026-04-24
Non-suicidal self-injury (NSSI) is highly prevalent among adolescents with depression, yet the heterogeneity of underlying temperamental risk factors remains poorly understood. Traditional variable-ce Show more
Non-suicidal self-injury (NSSI) is highly prevalent among adolescents with depression, yet the heterogeneity of underlying temperamental risk factors remains poorly understood. Traditional variable-centered approaches fail to capture how distinct affective temperaments co-occur within individuals. This study aimed to identify latent profiles of affective temperaments and examine their association with NSSI, exploring the statistical mediating role of cognitive emotion regulation (CER). A cross-sectional study was conducted from February 2025 to September 2025 at the First Hospital of Hebei Medical University. A total of 290 adolescents (aged 10–19) diagnosed with Major Depressive Disorder were recruited, with 282 valid responses included in the final analysis. Participants completed the TEMPS-A, CERQ, and ASHS. Latent Profile Analysis (LPA) was utilized to identify temperament subgroups. Mediation analysis with bootstrapping was performed to test the indirect effects of CER strategies. LPA identified three distinct profiles: Resilient/Low-risk (Class 1, 32.6%), Anxious-Depressive (Class 2, 46.1%), and Mixed-Dysregulated (Class 3, 21.3%). The Mixed-Dysregulated group, characterized by simultaneous elevations in depressive, anxious, irritable, and cyclothymic temperaments, exhibited the highest frequency (45.2 ± 21.3 times/year) and prevalence (98.8%) of NSSI compared to other groups ( The findings delineate a specific “Mixed-Dysregulated” risk phenotype within adolescent depression that is associated with severe NSSI. Interventions should move beyond standard depression care to target cognitive flexibility and emotional regulation skills. Statistical mediation analysis suggests that this risk is mediated by maladaptive cognitive emotion regulation strategies. Not applicable. Show less
📄 PDF DOI: 10.1186/s12888-026-07910-8
LPA
Wenjuan Zhao, Jie Zhong, Xiaobin Lai +3 more · 2026 · Journal of nursing management · added 2026-04-24
Identifying high-performing advanced practice nursing roles and understanding the factors that contribute to their effectiveness are critical for advancing professional development, optimizing workfor Show more
Identifying high-performing advanced practice nursing roles and understanding the factors that contribute to their effectiveness are critical for advancing professional development, optimizing workforce deployment, and ensuring long-term sustainability in nursing. This study aimed to (1) identify distinct latent profiles of advanced practice nursing among specialist nurses in mainland China, (2) quantitatively examine the individual and contextual factors associated with high performance, as characterized by these profiles, and (3) qualitatively confirm the significant factors using explanatory semistructured interviews in the high-performance groups. A mixed-methods sequential explanatory design was used, in which quantitative data were collected first and subsequently explained through qualitative interviews. Certified specialist nurses from 16 hospitals across urban and rural areas of Shanghai were included. Latent profile analysis (LPA) was conducted using the five domains from the Advanced Practice Role Delineation tool as manifest indicators to classify nurses into distinct performance profiles. Multinomial logistic regression was used to examine potential determinants (e.g., job position) of group membership. Additionally, a backpropagation neural network (BPNN) was developed to rank the importance of contributing factors. Specialist nurses identified as high performers in the quantitative phase were purposively sampled for explanatory semistructured qualitative interviews. Three latent profiles emerged: high performance (26.1%), moderate performance (46.3%), and low performance (27.6%). Compared to APNs, staff nurses had significantly lower odds of belonging to the high-performance group ( Identifying the profiles of advanced practice nursing roles provides valuable insights for optimizing APN performance and informing targeted management and policy strategies. High-performing specialist nurses are positioned at the nexus of individual capability, interdisciplinary collaboration, and institutional support. Show less
📄 PDF DOI: 10.1155/jonm/3528145
LPA
Xiaozhao Lu, Ziyao Yuan, Xiaoyu Lin +13 more · 2026 · Diabetes, obesity & metabolism · Blackwell Publishing · added 2026-04-24
Lipoprotein(a) [Lp(a)] and diabetes mellitus (DM) are independent risk factors for worse outcomes in coronary artery disease (CAD) patients. Evidence of their joint association is limited. We aimed to Show more
Lipoprotein(a) [Lp(a)] and diabetes mellitus (DM) are independent risk factors for worse outcomes in coronary artery disease (CAD) patients. Evidence of their joint association is limited. We aimed to investigate the combined effect of elevated Lp(a) and DM on survival outcomes in CAD patients. This study included 65 547 CAD patients (62.6 ± 10.7 years, 27.7% female) from CIN-II and RED-CARPET cohorts. Patients were stratified into four groups by Lp(a) levels (< or ≥ 30 mg/dL) and DM status. Multivariable Cox regression models estimated associations with cardiovascular and all-cause mortality, examining additive and multiplicative interactions. During a median follow-up of 5.5 years, 10 686 (16.3%) patients died from all causes and 5106 (7.8%) died from cardiovascular causes. Patients with Lp(a) ≥ 30 mg/dL and DM were independently associated with cardiovascular mortality (adjusted hazard ratio [aHR]: 1.28, 95% CI: 1.20-1.35; aHR: 1.53, 95% CI: 1.44-1.62, all p < 0.001, respectively). Compared to patients with Lp(a) < 30 mg/dL without DM, the aHRs were 1.26 (95% CI: 1.16-1.36, p < 0.001), 1.51 (95% CI: 1.40-1.62, p < 0.001) and 2.00 (95% CI: 1.83-2.18, p < 0.001) for those with Lp(a) ≥ 30 mg/dL without DM, Lp(a) < 30 mg/dL with DM and Lp(a) ≥ 30 mg/dL with DM, respectively. Significant additive interaction between elevated Lp(a) and DM on cardiovascular mortality was observed, with 12% of the excess risk attributed. Similar associations were observed in all-cause mortality. In patients with CAD, elevated Lp(a) and DM act synergistically to increase the risk of cardiovascular and all-cause mortality, suggesting that both risks should be considered to integrate management. Show less
no PDF DOI: 10.1111/dom.70603
LPA
Fangping Song, Yao Sang, Xiuyan Fang +2 more · 2026 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Osteoporosis has emerged as a growing public health concern due to its high prevalence and substantial economic burden on both individuals and society. Recent studies have identified the serum uric ac Show more
Osteoporosis has emerged as a growing public health concern due to its high prevalence and substantial economic burden on both individuals and society. Recent studies have identified the serum uric acid to high-density lipoprotein cholesterol ratio (UHR) as a novel predictive biomarker for various diseases. However, its association with bone mineral density (BMD) remains unclear. This study evaluated the association of the UHR and forearm BMD (FR-BMD) in a middle-aged and elderly cohort. We also assessed the interaction effects of age, sex, and body mass index (BMI). A total of 4,958 adults aged ≥50 years were enrolled from health examinees at Heze Municipal Hospital (2022-2025). We collected demographic data, serum lipids, and uric acid levels. Measurements of FR-BMD were performed on the left distal radius (1/3 site) utilizing dual-energy X-ray absorptiometry. Multivariate linear regression analyses evaluated the UHR-BMD relationship, supplemented by subgroup analyses and interaction tests. Nonlinear associations were assessed using generalized additive models with smoothing curves. After adjusting for age, sex, BMI, Alb, ALP, ALT, BUN, TP, Scr, Lp(a), TC, GGT and hypertension, a higher UHR was significantly associated with lower FR-BMD [β=-0.076, 95%CI(-0.138~-0.015), P = 0.015]. Significant interaction effects were observed for age and sex ( The association of UHR with FR-BMD is significantly modified by age and sex in middle-aged and elderly populations. Nonlinear relationships exist in males <60 years, females ≥60 years and non-overweight individuals. The potential of UHR as a novel indicator for bone health assessment in select populations is highlighted by our results. Show less
📄 PDF DOI: 10.3389/fendo.2026.1710027
LPA
Yibo Zhang, Longying Tian, Ying Zhang +2 more · 2026 · BMC nursing · BioMed Central · added 2026-04-24
Patient safety competency (PSC) is a core element of nursing practice, essential for ensuring high-quality and safe patient care. Newly recruited nurses often face challenges such as transition shock, Show more
Patient safety competency (PSC) is a core element of nursing practice, essential for ensuring high-quality and safe patient care. Newly recruited nurses often face challenges such as transition shock, limited clinical experience, and fragmented safety education, which may hinder their ability to maintain patient safety. Most studies have assessed PSC using total scale scores, overlooking internal heterogeneity within this group. This study aimed to identify latent profiles of PSC among newly recruited nurses and explore the influencing factors to provide evidence for targeted competency development and management strategies. From July to August 2023, a convenience sample of newly recruited nurses was obtained from seven tertiary grade-A hospitals in Shandong Province, China. Data were collected using the General Information Questionnaire, the Transition Shock Scale of Newly Graduated Nurses, the Nurses' Perception of Organizational Support Scale, and the Patient Safety Nurse Competency Evaluation Scale. Latent Profile Analysis (LPA) was conducted to identify the potential subgroups of patient safety competency among newly recruited nurses. Univariate analysis and multivariate logistic regression were performed to examine the influencing factors associated with different latent profile categories. The patient safety competency of newly recruited nurses was categorized into 3 potential profiles: "high safety competency group" (36.9%), "medium safety competency group" (49.4%), and "low safety competency group" (13.7%). The results of the logistic regression analysis revealed that education level, average number of night shifts per week, participation in safety training, involvement in patient safety-related projects, transition shock, and perceived organizational support were significant predictors of patient safety competency among newly recruited nurses (P < 0.05). This study identified three distinct latent profiles of patient safety competency among newly recruited nurses, revealing a moderate overall competency level with notable heterogeneity. Nursing managers should pay particular attention to nurses with moderate and low competency levels and implement targeted, evidence-based interventions to strengthen their patient safety competency and promote safer clinical practice. Not applicable. Show less
no PDF DOI: 10.1186/s12912-026-04494-2
LPA
Boyang Xiang, Ruiqi Zhang, Yujia Zhou +4 more · 2026 · European journal of preventive cardiology · Oxford University Press · added 2026-04-24
Observational studies have yielded conflicting evidence regarding the interdependence between lipoprotein(a) [Lp(a)]-related cardiovascular risk and systemic inflammation. It remains unclear whether c Show more
Observational studies have yielded conflicting evidence regarding the interdependence between lipoprotein(a) [Lp(a)]-related cardiovascular risk and systemic inflammation. It remains unclear whether combined targeting of Lp(a) and inflammation provides additive cardiovascular benefits. This study aimed to investigate the associations between genetically predicted lower Lp(a) and cardiovascular disease (CVD) across interleukin-6 (IL-6) signalling levels and the combined effects of lower Lp(a) and IL-6 signalling activity on CVD risk. This study included UK Biobank participants of European ancestry. Genetic scores for LPA and IL-6 receptor (IL6R)-mediated signalling were calculated to mimic the effects of therapies targeting Lp(a) and IL-6 signalling, respectively. We investigated the associations of separate and combined exposure to lower Lp(a) and IL-6 signalling with coronary heart disease (CHD), ischaemic stroke (IS), heart failure (HF), atrial fibrillation (AF), peripheral artery disease (PAD), and aortic aneurysm (AA), using Mendelian randomization analyses and validating the findings in observational analyses. This study included 408 687 UK Biobank individuals (mean age, 57 years; 54% women). Genetically predicted lower Lp(a) was associated with reduced risks of CHD [odds ratio (OR) per 50 mg/dL reduction in Lp(a) levels, 0.68; 95% confidence interval (CI), 0.65-0.71], IS (0.89, 0.80-0.98), PAD (0.68, 0.62-0.76), HF (0.82, 0.77-0.88), and AA (0.71, 0.61-0.82). Genetically lower IL-6 signalling was associated with lower risks of CHD (OR per 0.5 log[mg/L] reduction in log-transformed C-reactive protein levels, 0.67; 95% CI, 0.55-0.82), AF (0.72, 0.55-0.94), and AA (0.43, 0.23-0.83). The genetic association between Lp(a) and CVD was consistent among individuals with different IL-6 signalling activity (P for difference > 0.05). Combined exposure to genetically predicted lower Lp(a) and IL-6 signalling was associated with an additive decrease in CHD risk (lower Lp(a): 0.67, 0.63-0.71; lower IL-6 signalling: 0.61, 0.46-0.80; combined: 0.25, 0.21-0.30; P for interaction = 0.144). In observational analyses, IL-6 levels below the median and Lp(a) concentrations below 50 mg/dL were also independently and additively associated with lower CHD risk (Lp(a) < 50 mg/dL: hazard ratio, 0.82; 95% CI, 0.72-0.93; IL-6 < median: 0.79, 0.65-0.96; combined: 0.65, 0.56-0.74; P for interaction = 0.102). Lower Lp(a) levels were associated with a reduced risk of CVD, independent of IL-6 signalling activity. Combined exposure to genetic variants lowering Lp(a) and downregulating IL-6 signalling was associated with an additive reduction in cardiovascular risk. These findings indicate that concurrent Lp(a)-lowering and anti-inflammatory therapies may reduce residual cardiovascular risk through additive effects. Show less
no PDF DOI: 10.1093/eurjpc/zwag090
LPA
Fang Wu, Juan Zhang, Adan Fu +6 more · 2026 · Diabetes, metabolic syndrome and obesity : targets and therapy · added 2026-04-24
Using latent profile analysis (LPA) based on Self-Determination Theory (SDT), this study aimed to explore the profiles of health behavior motivation among Chinese patients with prediabetes and examine Show more
Using latent profile analysis (LPA) based on Self-Determination Theory (SDT), this study aimed to explore the profiles of health behavior motivation among Chinese patients with prediabetes and examine the relationship between these profiles and self-management ability. A cross-sectional study was conducted involving 335 patients with prediabetes. The questionnaires were used to assess health behavior motivation, self-management ability, satisfaction of basic psychological needs and disease knowledge level. Latent profile analysis was performed based on five subscale scores of the health behavior motivation measure. Three distinct latent profiles were identified: a "Self-Determined" profile (C1,29.55%, n=99), a "Non Self-Determined" profile (C2, 55.82%, n=187), and a "Conflicted" profile (C3, 14.63%, n=49). Patients in the C1 profile demonstrated higher levels of autonomy and competence. Patients in the C2 profile were characterized by better disease knowledge and lower relatedness. Compared to patients in the C3 profile, patients in both the C1 and C2 profiles exhibited significantly lower self-management ability. The heterogeneity in health behavior motivation profiles must be considered in the design and clinical practice of personalized interventions for prediabetes. Profile-specific strategies serve as the foundation for enhancing patients' self-management ability and sustaining healthy behaviors. Show less
📄 PDF DOI: 10.2147/DMSO.S567404
LPA
Yao Gao, Tao Dong, Ancha Baranova +9 more · 2026 · Molecular psychiatry · Nature · added 2026-04-24
Major depressive disorder (MDD) in adolescents is a critical public health concern, yet objective diagnostic biomarkers remain lacking. We conducted an integrative lipidomics study across human cohort Show more
Major depressive disorder (MDD) in adolescents is a critical public health concern, yet objective diagnostic biomarkers remain lacking. We conducted an integrative lipidomics study across human cohorts and a chronic unpredictable mild stress (CUMS) rat model. Targeted UPLC-MS/MS profiling was applied to a training cohort (95 MDD, 40 controls), and untargeted UPLC-HRMS profiling to an independent cohort (56 MDD, 37 controls). Candidate biomarkers were identified using univariate tests, partial least squares discriminant analysis, and three feature-selection methods (Boruta, LASSO, RFE), with predictive performance evaluated by cross-validation and external replication. Translational relevance was examined in CUMS rats through behavioral assays and lipidomic profiling of serum and brain tissues. Pathway enrichment and regression models explored metabolic context and clinical associations. In the training cohort, we found that 244 lipids were significantly altered, highlighting altered glycerophospholipid, glycerolipid, and sphingolipid metabolism. A 29-lipid panel achieved 90.4% cross-validation accuracy, while a reduced 7-lipid subset reached 94.8%. In the validation cohort, an 8-lipid panel achieved 71.2% accuracy, and a minimal 2-lipid set-LPA(18:2) and SPH(d16:1)-reached 72.1%. Cross-species analysis confirmed consistent downregulation of SPH(d16:1) in serum of both humans and rats, and of LPC(0:0/16:0) specifically in the rat prefrontal cortex. Regression analyses linked sex, age, and anxiety severity to lipid alterations. This cross-platform, cross-species study identifies reproducible lipid signatures of adolescent MDD, highlights SPH(d16:1) and LPC(0:0/16:0) as translational biomarkers, and implicates glycerophospholipid metabolism in MDD pathophysiology, providing a foundation for biomarker-guided diagnostics and therapeutics. Show less
📄 PDF DOI: 10.1038/s41380-026-03486-7
LPA
Haiying Yang, Lihong Sun, Ying Zhang · 2026 · Frontiers in psychiatry · Frontiers · added 2026-04-24
This study examined heterogeneous patterns of trauma-related adaptation among Chinese adolescents during the post-COVID-19 recovery phase, focusing on the co-occurrence of posttraumatic distress (PTD) Show more
This study examined heterogeneous patterns of trauma-related adaptation among Chinese adolescents during the post-COVID-19 recovery phase, focusing on the co-occurrence of posttraumatic distress (PTD) and posttraumatic growth (PTG). We also investigated how modifiable psychosocial protective and vulnerability factors were associated with membership in different adaptation profiles. A large-scale cross-sectional survey was administered to 5, 044 students (aged 9-17 years; 46.6% male) from 15 primary and secondary schools in Wuhan, China. Validated instruments assessed posttraumatic stress symptoms (PCL-C), posttraumatic growth (PTGI), depressive symptoms (CES-D), and anxiety (SAS). Protective and vulnerability factors included resilience (CD-RISC), perceived social support (SSRS), physical activity (PARS-3), school belonging (PSSM), adaptive coping (SCSQ), and trait anxiety (TAI). Latent profile analysis (LPA) was used to identify adaptation profiles, and multinomial logistic regression examined how modifiable psychosocial factors were associated with profile membership. LPA revealed four empirically derived profiles: a High Distress/High Growth-Moderate PTSD profile (76.9%), a Low Distress-High Growth profile (4.8%), a Low Growth-Moderate Distress profile (3.9%), and a High Distress/High Growth-High PTSD profile (14.4%). The vast majority of adolescents showed some degree of both PTD and PTG, consistent with dual-process perspectives. In multinomial models, higher resilience, social support, school belonging, adaptive coping, and physical activity were associated with greater likelihood of belonging to the Low Distress-High Growth profile rather than more distressed profiles, whereas higher trait anxiety was associated with increased odds of membership in profiles characterized by greater distress. In this large school-based sample of Chinese adolescents, distress and growth frequently co-occurred and clustered into distinct adaptation profiles that differed systematically in psychosocial resources. Resilience, social connectedness, school belonging, and physical activity emerged as promising targets for trauma-informed, school-based support, whereas trait anxiety appeared to mark heightened vulnerability. Given the cross-sectional and single-region design, these findings should be interpreted as exploratory, and longitudinal and cross-cultural studies are needed to clarify temporal and contextual influences on adolescent trauma adaptation. Show less
📄 PDF DOI: 10.3389/fpsyt.2026.1720487
LPA
Jingwen Zhang, Ann Marie Navar, Lale Tokgozoglu · 2026 · European heart journal · Oxford University Press · added 2026-04-24
Lipoprotein(a) [Lp(a)] is a significant, genetically determined contributor to the risk of atherosclerotic cardiovascular disease (ASCVD), which remains the leading cause of mortality worldwide despit Show more
Lipoprotein(a) [Lp(a)] is a significant, genetically determined contributor to the risk of atherosclerotic cardiovascular disease (ASCVD), which remains the leading cause of mortality worldwide despite successes in the management of LDL cholesterol. Lipoprotein(a) possesses increased atherogenicity, contributing to residual cardiovascular risk. Elevated Lp(a) levels affect a substantial proportion of the population, rendering this a potentially high-impact therapeutic target, but currently available lipid-lowering agents and lifestyle interventions have minimal impact on lowering Lp(a), and lipoprotein apheresis is the sole effective-but impractical-method to significantly reduce Lp(a). Recent advances in Lp(a)-targeted therapies, notably nucleic acid-based approaches (e.g. antisense oligonucleotides and small interfering RNAs) and a small molecule inhibitor of Lp(a) synthesis, demonstrated substantial and often durable Lp(a)-lowering effects in Phase II trials. Phase III trials of these agents are now underway to examine the impact of lowering Lp(a) levels on atherosclerotic cardiovascular disease outcomes, and their results may transform the landscape of cardiovascular risk reduction and management for patients with elevated Lp(a). This review summarizes existing lipid-lowering therapies' limited effects on Lp(a), provides an update on the array of emerging therapeutics and their safety and efficacy, and discusses ongoing Phase III trials as well as other potential benefits of Lp(a)-lowering, such as slowing progression of calcific aortic valve stenosis. Show less
no PDF DOI: 10.1093/eurheartj/ehag092
LPA
Ziliang Wu, Chen Qiu, Meimei Pan +6 more · 2026 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
Lipoprotein(a) [Lp(a)] has been recognized as a genetically determined and independent contributor to atherosclerotic cardiovascular disease. However, its role in lower extremity arterial disease (LEA Show more
Lipoprotein(a) [Lp(a)] has been recognized as a genetically determined and independent contributor to atherosclerotic cardiovascular disease. However, its role in lower extremity arterial disease (LEAD) among individuals with metabolic dysfunction-associated steatotic liver disease (MASLD) remains insufficiently studied. Given the overlapping metabolic disturbances in both conditions, such as insulin resistance and lipid abnormalities, a potential relationship between Lp(a) and peripheral vascular injury in MASLD is biologically plausible. This study aimed to investigate the cross-sectional association between circulating Lp(a) concentrations and the presence of LEAD in a well-characterized MASLD population. A total of 468 MASLD patients undergoing routine health check-ups were included. Lp(a) levels were stratified into three categories: <10 mg/dL, 10–30 mg/dL, and ≥ 30 mg/dL. LEAD was diagnosed using duplex ultrasonography. Multivariable logistic regression models were used to assess the relationship between Lp(a) levels and the presence of LEAD, with adjustments for demographic variables, metabolic conditions, and lipid-related parameters. Subgroup analyses were conducted to assess potential effect modification. LEAD was diagnosed in 61.5% ( Elevated Lp(a) levels were associated with a higher prevalence of LEAD in patients with MASLD. Although the magnitude of association per unit increase was modest, higher Lp(a) concentrations were associated with greater LEAD prevalence. These findings should be interpreted cautiously and viewed as hypothesis-generating, particularly with respect to subgroup analyses. Prospective studies are needed to clarify causality and clinical relevance. The online version contains supplementary material available at 10.1186/s12872-026-05600-7. Show less
📄 PDF DOI: 10.1186/s12872-026-05600-7
LPA
Xiangying Xie, Juan Su, Qian Zhou +4 more · 2026 · Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver · Elsevier · added 2026-04-24
Depression and anxiety were not only common but also with serious consequence in inflammatory bowel diseases (IBD) patients. The current study endeavors to define distinct depression and anxiety profi Show more
Depression and anxiety were not only common but also with serious consequence in inflammatory bowel diseases (IBD) patients. The current study endeavors to define distinct depression and anxiety profiles of IBD patients and identify central symptoms within different profiles to facilitate targeted interventions. The research employed K-means Clustering to delineate the depression and anxiety profiles, followed by a repetition of the analysis using Latent Profile Analysis (LPA). Furthermore, network analysis was utilized to identify central symptoms within the various profiles. K‑means Clustering identified Cluster 1 (38.89%), Cluster 2 (45.33%) and Cluster 3 (15.78%), while LPA yielded the low-risk group (39.56%), the mild-risk group (44.22%) and the high-risk group (16.22%). A majority of patients in the three clusters were predominantly in a single LPA-derived patient class (96.1-99.0%). Network analysis revealed that connections within each symptom in PHQ-9 and GAD-7 were stronger than those between symptoms. Furthermore, PHQ 6 ("guilt"), PHQ2 ("sad mood")and GAD 7 ("feeling afraid") were identified as the central symptoms in Cluster 1. PHQ2 ("sad mood"), GAD 3("excessive worry") and GAD 1 ("nervousness") emerged as the central symptoms in Cluster 2. Additionally, GAD3 ("excessive worry"), GAD 4 ("trouble relaxing") and GAD 6("irritability") were identified as the central symptoms in Cluster 3. We defined three distinct depression and anxiety profiles among IBD patients and pinpointed central symptoms within each profile. These findings underscore the importance of directing research towards those central symptoms within each profile in order to develop targeted intervention strategies. Show less
no PDF DOI: 10.1016/j.dld.2026.01.222
LPA
Ningying Zhou, Feng Zhang, Min Liu +4 more · 2026 · Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology · Taylor & Francis · added 2026-04-24
Inadequate childbirth readiness can adversely affect the birthing experience of pregnant women and may even influence their willingness to have further children. This study aimed to explore the determ Show more
Inadequate childbirth readiness can adversely affect the birthing experience of pregnant women and may even influence their willingness to have further children. This study aimed to explore the determinants of childbirth readiness and the network relationships among these factors, thereby providing evidence to improve childbirth readiness. This cross-sectional study surveyed 350 pregnant women attending Wuxi Maternity and Child Health Care Hospital. Latent profile analysis (LPA) was first performed using the four domains of the Childbirth Readiness Scale to identify subgroups of childbirth readiness, and potential associated factors were then screened using univariate analysis and multinomial logistic regression. A Bayesian network model was employed to construct the structural relationships of factors influencing childbirth readiness. Childbirth readiness was categorised into three levels: poor (26%), good (30.9%), and complete (43.1%). Univariate analysis revealed significant differences across the three categories in relation to age, parity, pregnancy complications, antenatal exercise, planned pregnancy, self-efficacy, eHealth literacy, fear of childbirth, and family support ( Previous studies on childbirth readiness have mainly relied on regression models, which are unable to elucidate the intrinsic interconnections among influencing factors. By constructing a Bayesian model, this study demonstrated that women with high self-efficacy, no fear of childbirth, high eHealth literacy, and multiparity had the highest probability of achieving complete childbirth readiness (83.3%). Show less
no PDF DOI: 10.1080/01443615.2026.2626380
LPA
Minjie Zheng, Xinxin Shi, Zhijuan Xie +2 more · 2026 · Journal of psychosomatic research · Elsevier · added 2026-04-24
Illness perceptions have been associated with outcomes in patients with atrial fibrillation (AF). This study aimed to identify distinct illness perception profiles in patients with AF and examine thei Show more
Illness perceptions have been associated with outcomes in patients with atrial fibrillation (AF). This study aimed to identify distinct illness perception profiles in patients with AF and examine their associations with psychological and physical responses. A total of 150 patients with AF were enrolled in this study. Illness perception profiles were identified using latent profile analysis (LPA). Model fit indices were evaluated to determine the optimal class solution. Logistic regression analyses were conducted to examine the associations between illness perception profiles and psychological and physical outcomes, including Generalized anxiety disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), 12-Item Short Form Health Survey (SF-12) and University of Toronto atrial fibrillation severity scale (AFSS). A two-class model was identified as optimal, comprising a "Reactive-Minimizing" profile (Class 1, 49%) and a "Symptom-Helplessness" profile (Class 2, 51%). Univariate logistic analysis revealed significant differences between classes in age, AF type, work status, PHQ-9, AFSS-symptoms, and AFSS-burden. In the multivariable logistic regression adjusted for age and sex (logistic outcome: Class 2 vs. Class 1), higher AFSS-burden scores were independently associated with the "Symptom-Helplessness" profile (OR = 1.26, 95% CI: 1.09-1.45, p = 0.001). Conversely, higher PHQ-9 scores were associated with the "Reactive-Minimizing" profile (OR for Symptom-Helplessness = 0.92, 95% CI: 0.86-0.99, p = 0.018). Person-centered illness perception profiling revealed two distinct cognitive-emotional patterns in patients with AF that were associated with depressive symptoms and symptom burden, highlighting their potential value for individualized psychological and clinical management. Show less
no PDF DOI: 10.1016/j.jpsychores.2026.112540
LPA
Dan Jiang, Yi-Ling Liu, Jian Liu +7 more · 2026 · Lipids in health and disease · BioMed Central · added 2026-04-24
Calcific aortic valve disease (CAVD) is a cardiovascular disease closely associated with aging. The role of lipoprotein(a) [Lp(a)] has attracted considerable attention in recent years. However, limite Show more
Calcific aortic valve disease (CAVD) is a cardiovascular disease closely associated with aging. The role of lipoprotein(a) [Lp(a)] has attracted considerable attention in recent years. However, limited research has simultaneously explored the relationships between Lp(a), age, and CAVD. This study sought to assess the relationship linking Lp(a), time-weighted Lp(a), and CAVD. A total of 5,156 inpatients with comprehensive clinical data were recruited for this study. The associations of Lp(a) and time-weighted Lp(a) with CAVD were examined via multivariate logistic regression analysis, alongside the application of restricted cubic spline analysis. The diagnostic utility of Lp(a) and time-weighted Lp(a) for CAVD was assessed by constructing receiver operating characteristic (ROC) curves. CAVD prevalence rose with age, whereas the rate of increase diminished with advancing age. The average Lp(a) level in the young populations with CAVD was more than twice that in the No-CAVD group, particularly among those aged 55 years or younger. The prevalence of CAVD in non-elderly populations was markedly 2–4 fold greater in the higher Lp(a) group (> 30 mg/dL) than in the lower Lp(a) group (≤ 30 mg/dL). Multivariate adjusted odds ratios ‌(ORs) for CAVD increased with advancing Lp(a) or age. Time-weighted Lp(a), which takes into account both age and Lp(a), was more strongly linked to elevated CAVD risk than Lp(a) alone. Time-weighted Lp(a) enhanced the diagnostic value of CAVD, improving both sensitivity and specificity. The risk of CAVD is strongly associated with both age and elevated Lp(a) levels. Time-weighted Lp(a), which integrates these factors, serves as a superior indicator that better captures cumulative long-term Lp(a) variation and yields stronger CAVD risk stratification. The online version contains supplementary material available at 10.1186/s12944-026-02884-8. Show less
📄 PDF DOI: 10.1186/s12944-026-02884-8
LPA
Yanghong Zou, Chunhai Zhang, Hui Bian +5 more · 2026 · International immunopharmacology · Elsevier · added 2026-04-24
The abuse of methamphetamine (METH) is associated with an increased risk of Parkinson's disease (PD), whereas microglial polarization and glucose metabolism disorders are closely related to the progre Show more
The abuse of methamphetamine (METH) is associated with an increased risk of Parkinson's disease (PD), whereas microglial polarization and glucose metabolism disorders are closely related to the progression of PD. This study aimed to investigate the specific molecular mechanism underlying the promotion of PD progression by METH through the regulation of microglial polarization and glycolysis. METH-induced C57BL/6 mice and BV2 cells were used to construct PD-like neurotoxicity animal and cell models for experimental investigation. Behavioral tests, immunohistochemistry and Nissl staining were used to assess the behavioral ability and neuronal damage of the animals. The levels of related proteins, inflammatory cytokines and glycolysis were detected using immunofluorescence, ELISA, Western blotting, and CCK-8 assays. METH treatment significantly promoted behavioral disorders in PD mice, reduced the number of TH-positive neurons, and aggravated neuronal damage in the substantia nigra (SN). In addition, METH decreased the M2 marker proteins Arg-1 and CD206 and increased the M1 marker proteins iNOS and CD86; the proinflammatory cytokines TNF-α, IL-β, and IL-6; and glucose uptake, glucose consumption and lactic acid production, thus promoting M1 polarization and glycolytic activity in BV2 cells. In terms of the underlying molecular mechanism, METH treatment significantly increased the level of LPA. METH promotes LPA expression via upregulation of LIPH expression, and activates the PI3K/AKT pathway. Knockdown of LIPH or treatment with BrP-LPA reduces the ability of METH to promote M1 microglial polarization and glycolytic activity. Furthermore, the addition of the PI3K/AKT signaling pathway activator 740 YP weakened the inhibitory effect of BrP-LPA on the above process. METH may promote M1 polarization and glycolytic activity in microglia by activating LIPH/LPA/PI3K/AKT signaling, thus promoting the progression of PD. Show less
no PDF DOI: 10.1016/j.intimp.2026.116306
LPA
Shifan Deng, Xinli Zheng, Han Chu +5 more · 2026 · Poultry science · Elsevier · added 2026-04-24
Through the selective breeding of superior strains, livestock and poultry can achieve enhanced disease resistance and production performance, thereby improving farming efficiency and increasing chicke Show more
Through the selective breeding of superior strains, livestock and poultry can achieve enhanced disease resistance and production performance, thereby improving farming efficiency and increasing chicken meat yield. This study analyzed the expression of gut health-related genes, cecal microbiota, and untargeted serum metabolomics in Wenchang chickens from the NS strain (Normal strain) and the AFS strain (Antibiotic-free strain), and explored the relationships between their cecal microbiota and serum metabolites. Our results show that in the ileum, antioxidant-related indicators T-AOC (P < 0.05), T-SOD (P < 0.05), and GSH-PX (P < 0.05) were significantly higher in the AFS strain than in the NS strain, while MDA (P < 0.05) was significantly lower in the AFS strain than in the NS strain. The mRNA expression level of RORγt/FoxP3, which is related to immune regulation, was significantly lower in the AFS strain than in the NS strain (P < 0.05). The differential microorganisms in the cecum primarily included Muribaculum, Cryptobacteroides, Blautia, Enterocloster, Lachnoclostridium, Hydrogenoanaerobacterium, Ruminococcus, Subdoligranulum, Clostridioides, and Evtepia. The main differential metabolites in serum included folinic acid, biotin, lysophosphatidic acid (LPA), 3-hydroxy-3-methylbutanoic acid, 3-hydroxybutyric acid, and others. The differential metabolites are primarily enriched in the following metabolic pathways: gap junction, glycolipid metabolism, and fatty acid biosynthesis. In addition, the Pearson correlation analysis between the gut microbiota and serum metabolites showed that Blautia was positively correlated with folinic acid (P < 0.05) and biotin (P < 0.05); Lachnoclostridium was positively correlated with biotin (P < 0.01); and Ruminococcus was positively correlated with 3-hydroxybutyric acid (P < 0.05). This study mainly elucidates the metabolic characteristics of the antibiotic-free Wenchang chicken strain by analyzing gut microbiota and serum metabolites. Show less
📄 PDF DOI: 10.1016/j.psj.2026.106506
LPA
Mingliang Sun, Wenxin Lin, Rui Gong +7 more · 2026 · Lipids in health and disease · BioMed Central · added 2026-04-24
TyHGB is a novel insulin resistance (IR)-related indicator, and its association with coronary heart disease (CHD) remains unclear. Additionally, studies have shown a close correlation between the diag Show more
TyHGB is a novel insulin resistance (IR)-related indicator, and its association with coronary heart disease (CHD) remains unclear. Additionally, studies have shown a close correlation between the diagonal earlobe crease (DELC) and CHD, yet it has not been fully applied in clinical practice to date. Therefore, this study constructed and validated a diagnostic model for CHD by combining TyHGB and DELC. A total of 1664 patients suspected of CHD who underwent coronary angiography (CAG) in the Department of Cardiology, Chengde Central Hospital from September 2021 to April 2025 were recruited for this study. Participants were categorized into a CHD group ( Age, sex, hypertension, diabetes, CR, Lp(a), TyHGB, and DELC were identified as independent risk factors for CHD through multivariate logistic regression analysis ( Both TyHGB and DELC have been identified as independent risk factors for CHD, with a linear relationship observed between TyHGB levels and CHD risk. A diagnostic model for CHD, developed by integrating TyHGB, DELC, and traditional risk factors, demonstrates strong diagnostic efficacy. The online version contains supplementary material available at 10.1186/s12944-026-02880-y. Show less
📄 PDF DOI: 10.1186/s12944-026-02880-y
LPA
Miao Yu, Libin Yao, Sanjeev Shahi +12 more · 2026 · Radiology · added 2026-04-24
Background Although substantial evidence has demonstrated the impact of obesity on brain structure and cognition, the heterogeneity of adiposity-particularly in terms of fat distribution patterns-and Show more
Background Although substantial evidence has demonstrated the impact of obesity on brain structure and cognition, the heterogeneity of adiposity-particularly in terms of fat distribution patterns-and its differential neurologic effects remain poorly understood. Purpose To identify body fat distribution patterns with MRI and latent profile analysis (LPA) and their associations with brain structure measurements, cognition, and neurologic diseases. Materials and Methods This secondary analysis used prospective data from the UK Biobank, including health records and MRI scans of the brain, heart, and abdomen. Fat distribution profiles were classified using LPA based on eight body mass index (BMI)-adjusted MRI-derived fat quantification metrics. Differences in brain volume, white matter properties, cognition, and the risk of neurologic disorders were analyzed across profiles and relative to a benchmark lean profile; analyses were stratified by sex. Group differences were examined using analysis of covariance (ANCOVA) or rank-based ANCOVA. Results Among 25 997 participants (mean age, 55 years ± 7.4 [SD]; 13 536 female participants), LPA identified six profiles of body fat distribution in both sexes. Four high-adiposity patterns were identified, including the pancreatic-predominant profile (profile 1), with elevated proton density fat fraction (mean BMI-adjusted Show less
no PDF DOI: 10.1148/radiol.252610
LPA
Zeyu Xu, Yi Fang, Yong Peng +2 more · 2026 · Journal of clinical biochemistry and nutrition · added 2026-04-24
Despite substantial progress in the management of cardiovascular disease (CVD), lipoprotein(a) [Lp(a)] persists as a genetically determined risk factor that remains insufficiently explored. Both extre Show more
Despite substantial progress in the management of cardiovascular disease (CVD), lipoprotein(a) [Lp(a)] persists as a genetically determined risk factor that remains insufficiently explored. Both extremely high and low levels of Lp(a) are linked to adverse outcomes. Current diagnostic assays for Lp(a) lack standardization, and conventional lipid-lowering therapies exert minimal effects on its levels, resulting in limited treatment options specifically targeting Lp(a). To address these gaps, we conducted a comprehensive molecular and clinical review of Lp(a), examining its unique structure, genetic determinants, metabolic pathways, and the factors influencing its plasma concentration. Furthermore, we discuss emerging therapeutic strategies aimed at targeting Lp(a). Show less
📄 PDF DOI: 10.3164/jcbn.25-94
LPA
Yuanyuan Zhang, Bochun Kang · 2026 · BMC psychology · BioMed Central · added 2026-04-24
AI literacy is increasingly important in college students' academic achievement, daily life, and future employability. However, current research predominantly overlooks the heterogeneity in students' Show more
AI literacy is increasingly important in college students' academic achievement, daily life, and future employability. However, current research predominantly overlooks the heterogeneity in students' AI literacy, especially how individual psychological characteristics and features of AI technology contribute to this variation. This oversight limits the formulation of tailored strategies to meet the students' various demands in an era shaped by rapid AI advancement. This study aims to adopt an individual-centered approach to identify distinct AI literacy profiles among college students. In addition, it investigates, based on affordance theory, how positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism predict assignment to different profiles. A total of 808 Chinese college students participated in this survey. Latent profile analysis (LPA) was employed to classify students into distinct AI literacy profiles. Multinomial logistic regression was conducted to examine how psychological and technological factors predict profile classification. This study identified four distinct AI literacy profiles among college students: preliminary contact type, ethical orientation type, balanced development type, and behavioral conservatism type. These profiles showed significant differences in positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism, highlighting diverse psychological and technological characteristics inherent to each group. This study underscores the heterogeneity of AI literacy within the college student population and detects four distinct AI literacy profiles with unique psychological and technological traits. The findings indicate that students' AI literacy is profoundly affected by emotional tendencies, motivational drives, and technological variables, highlighting the need for tailored educational strategies that address the distinct psychological and technological drivers of each literacy profile. Show less
📄 PDF DOI: 10.1186/s40359-026-04047-x
LPA
Xiao Liang, Raffy C F Chan, Justin A Haegele +8 more · 2026 · Research in developmental disabilities · Elsevier · added 2026-04-24
Physical inactivity is a health concern for children and adolescents with neurodevelopmental disorders (NDDs) as it directly increases their risk of developing various health problems. Evidence on dif Show more
Physical inactivity is a health concern for children and adolescents with neurodevelopmental disorders (NDDs) as it directly increases their risk of developing various health problems. Evidence on differences in accelerometer-assessed physical activity between children and adolescents with and without NDDs is inconclusive. And age- and body mass index (BMI)-related effects on physical activity remain unclear. The systematic literature searches were performed in 6 databases up to March 2025. Methodological quality was evaluated by the Newcastle-Ottawa Scales. Data were pooled using a random-effects model. Hedges' g was used to express the effect size index with 95 % confidence interval (CI). Meta-regression on age and BMI was also performed to investigate the potential moderating effects. Out of the 2167 studies initially identified, 28 were included in the analysis, which comprised total physical activity (TPA), moderate-to-vigorous physical activity (MVPA), and light physical activity (LPA) included in the meta-analysis, respectively. These studies involved 1060 children and adolescents with NDDs and 1820 without, aged 6.6-16.9 years. A small-to-moderate effect size exists for the difference in TPA (g=-0.299) and MVPA (g=-0.479) between children and adolescents with and without NDD, particularly indicating a difference in 12.7 min of MVPA daily. The difference in LPA was not significant (g=0.450, p = 0.125). The decline in MVPA with age was more pronounced in those with NDDs, and the difference in MVPA was smaller for those with lower BMI. The variation in MVPA differences by age and BMI highlights the need to develop better physical activity habits and reduce these disparities for children and adolescents with NDDs. Show less
no PDF DOI: 10.1016/j.ridd.2026.105233
LPA
Yuejun Huang, Han Zhang, Yu Chen +1 more · 2026 · Scientific reports · Nature · added 2026-04-24
This study aimed to investigate the latent profiles of clinical nurse preceptors (CNPs)' compassion fatigue (CF), identify the influencing factors, and examine their association with work alienation. Show more
This study aimed to investigate the latent profiles of clinical nurse preceptors (CNPs)' compassion fatigue (CF), identify the influencing factors, and examine their association with work alienation. Between July and August 2025, 340 nurse preceptors from a tertiary grade A general hospital in Zhejiang Province were recruited as participants using convenience sampling. The Chinese version of the Professional Quality of Life Scale Version 5 (ProQOL-5) and the Work Alienation Scale (WAS) were used to assess compassion fatigue and work alienation, respectively. Demographic information was also collected from the participants. Latent Profile Analysis (LPA) was employed to identify potential profiles of compassion fatigue. After screening variables through univariate analysis and multicollinearity tests, multinomial logistic regression was used to assess the influencing factors. Furthermore, a one-way ANOVA was conducted to examine differences in work alienation among different potential profiles, and the results were interpreted based on the job demands-resources (JD-R) model theoretical framework. A total of 320 CNPs were included in the final analysis. The findings of the latent profile analysis indicated that three latent profiles of CNPs' compassion fatigue were identified: high-satisfaction-low-exhaustion group (n = 56, 17.5%), moderate compassion fatigue group (n = 160, 50%), and severe exhaustion group (n = 104, 32.5%). Multinomial logistic regression analysis showed that age, marital status, education, years of preceptorship, experience, employment type, and professional title were significant predictors of compassion fatigue among CNPs. There were statistically significant differences in the work alienation scores among the three latent profiles (P < 0.001). CNPs' compassion fatigue can be categorised into three types, with significant heterogeneity observed among them. Notable differences exist in work alienation among CNPs with different compassion fatigue types. These findings suggest that clinical managers and educators should develop targeted interventions and support systems based on these circumstances. Therefore, formulating such management strategies is crucial for alleviating work alienation among CNPs and will help improve nurse retention rates and the quality of clinical education. Show less
📄 PDF DOI: 10.1038/s41598-025-33648-6
LPA
Tong Cheng, Ying Zhang, Mengnan Zhang +13 more · 2026 · Child: care, health and development · Blackwell Publishing · added 2026-04-24
The associations between 24-h movement behaviours (24 h MBs) and emotional and behavioural problems (EBPs) in early years are not well understood. This study examined these associations in a nationall Show more
The associations between 24-h movement behaviours (24 h MBs) and emotional and behavioural problems (EBPs) in early years are not well understood. This study examined these associations in a nationally representative sample of Chinese preschoolers. As part of the Chinese cohort of the SUNRISE International Study of Movement Behaviors in the Early Years main study, this research recruited 1316 children aged 3-4 years through multistage stratified cluster sampling in urban and rural areas across seven major administrative regions in China. Moderate- to vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA) and sedentary behaviour (SED) were measured using 24-h accelerometry over five consecutive days. Sleep duration was parent-reported. EBPs were evaluated using the parent-rated Strengths and Difficulties Questionnaire (SDQ), which assesses total difficulties, internalising problems, externalising problems and prosocial behaviour. Compositional multiple linear regression was employed to analyse the relationships between 24 h MBs and EBPs. Compositional isotemporal substitution was also utilised to predict changes in EBPs due to reallocating time among 24 h MBs. Isotemporal substitution analyses revealed that replacing as little as 1 min of MVPA, LPA or SED with sleep was associated with significant reductions in total difficulties (β Increasing LPA by reducing MVPA or SED was significantly associated with improvements in internalising and conduct problems, whereas increasing sleep to decrease MVPA or SED-even by small amounts-was consistently associated with improvements in EBPs across all SDQ subscales. However, increasing LPA at the expense of sleep exacerbates total difficulties and externalising problems. Promoting diverse LPA opportunities alongside sufficient sleep, while maintaining a balance between them, is essential for supporting preschoolers' emotional and behavioural development. Show less
📄 PDF DOI: 10.1111/cch.70239
LPA