👤 Zian Zhang

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Also published as: A-Mei Zhang, Ai Zhang, Ai-Min Zhang, Aiguo Zhang, Aihua Zhang, Aijun Zhang, Aileen Zhang, Ailin Zhang, Aimei Zhang, Aimin Zhang, Aixiang Zhang, Alaina Zhang, Alex R Zhang, Amy L Zhang, An Zhang, An-Qi Zhang, Anan Zhang, Andrew Zhang, Ang Zhang, Anli Zhang, Anqi Zhang, Anwei Zhang, Anying Zhang, Ao Zhang, Bangke Zhang, Bangzhou Zhang, Bao Long Zhang, Bao-Fu Zhang, Bao-Rong Zhang, Baohu Zhang, Baojing Zhang, Baojun Zhang, Baoren Zhang, Baorong Zhang, Baotong Zhang, Bei B Zhang, Bei Zhang, Bei-Bei Zhang, Beiyu Zhang, Ben Zhang, Benjian Zhang, Benyou Zhang, Bi-Tian Zhang, Biao Zhang, Bicheng Zhang, Bikui Zhang, Bin Zhang, Binbin Zhang, Bing Zhang, Bing-Qi Zhang, Bingbing Zhang, Bingkun Zhang, Bingqiang Zhang, Bingxue Zhang, Bingye Zhang, Bixia Zhang, Bo Zhang, Bo-Fei Zhang, Bo-Heng Zhang, Bo-Ya Zhang, Bochuan Zhang, Bofang Zhang, Bohao Zhang, Bohong Zhang, Bohua Zhang, Bojian Zhang, Bolin Zhang, Boping Zhang, Boqing Zhang, Bosheng Zhang, Bowei Zhang, Bowen Zhang, Boxi Zhang, Boxiang Zhang, Boya Zhang, Boyan Zhang, C D Zhang, C H Zhang, C Zhang, Cai Zhang, Cai-Ling Zhang, Caihong Zhang, Caiping Zhang, Caiqing Zhang, Caishi Zhang, Caiyi Zhang, Caiying Zhang, Caiyu Zhang, Can Zhang, Cathy C Zhang, Chan-na Zhang, Chang Zhang, Chang-Hua Zhang, Changhua Zhang, Changhui Zhang, Changjiang Zhang, Changjing Zhang, Changlin Zhang, Changlong Zhang, Changquan Zhang, Changteng Zhang, Changwang Zhang, Channa Zhang, Chao Zhang, Chao-Hua Zhang, Chao-Sheng Zhang, Chao-Yang Zhang, ChaoDong Zhang, Chaobao Zhang, Chaoke Zhang, Chaoqiang Zhang, Chaoyang Zhang, Chaoyue Zhang, Chen Zhang, Chen-Qi Zhang, Chen-Ran Zhang, Chen-Song Zhang, Chen-Xi Zhang, Chen-Yan Zhang, Chen-Yang Zhang, Chenan Zhang, Chenfei Zhang, Cheng Cheng Zhang, Cheng Zhang, Cheng-Lin Zhang, Cheng-Wei Zhang, Chengbo Zhang, Chengcheng Zhang, Chengfei Zhang, Chenggang Zhang, Chengkai Zhang, Chenglong Zhang, Chengnan Zhang, Chengrui Zhang, Chengsheng Zhang, Chengshi Zhang, Chenguang Zhang, Chengwu Zhang, Chengxiang Zhang, Chengxiong Zhang, Chengyu Zhang, Chenhong Zhang, Chenhui Zhang, Chenjie Zhang, Chenlin Zhang, Chenlu Zhang, Chenmin Zhang, Chenming Zhang, Chenrui Zhang, Chenshuang Zhang, Chenxi Zhang, Chenyan Zhang, Chenyang Zhang, Chenyi Zhang, Chenzi Zhang, Chi Zhang, Chong Zhang, Chong-Hui Zhang, Chongguo Zhang, Chonghe Zhang, Chris Zhiyi Zhang, Chu-Yue Zhang, Chuan Zhang, Chuanfu Zhang, Chuankuan Zhang, Chuankuo Zhang, Chuanmao Zhang, Chuantao Zhang, Chuanxin Zhang, Chuanyong Zhang, Chuchu Zhang, Chumeng Zhang, Chun Zhang, Chun-Lan Zhang, Chun-Mei Zhang, Chun-Qing Zhang, Chungu Zhang, Chunguang Zhang, Chunhai Zhang, Chunhong Zhang, Chunhua Zhang, Chunjun Zhang, Chunli Zhang, Chunling Zhang, Chunqing Zhang, Chunxia Zhang, Chunxiang Zhang, Chunxiao Zhang, Chunyan Zhang, Chunying Zhang, Churen Zhang, Chuting Zhang, Chuyue Zhang, Ci Zhang, Claire Y Zhang, Claire Zhang, Clarence K Zhang, Cong Zhang, Congen Zhang, Cuihua Zhang, Cuijuan Zhang, Cuilin Zhang, Cuiping Zhang, Cuiyu Zhang, Cun Zhang, Da Zhang, Da-Qi Zhang, Da-Wei Zhang, Dachuan Zhang, Dadong Zhang, Daguo Zhang, Dai Zhang, Dalong Zhang, Daming Zhang, Dan Zhang, Dan-Dan Zhang, DanDan Zhang, Danfeng Zhang, Danhua Zhang, Danning Zhang, Danyan Zhang, Danyang Zhang, Daolai Zhang, Daoyong Zhang, Dapeng Zhang, David Y Zhang, David Zhang, Dawei Zhang, Daxin Zhang, Dayi Zhang, De-Jun Zhang, Dekai Zhang, Delai Zhang, Deng-Feng Zhang, Dengke Zhang, Deqiang Zhang, Detao Zhang, Deyi Zhang, Deyin Zhang, Di Zhang, Dian Ming Zhang, Dianbo Zhang, Dianzheng Zhang, Ding Zhang, Dingdong Zhang, Dinghu Zhang, Dingkai Zhang, Dingyi Zhang, Dingyu Zhang, Dong Zhang, Dong-Hui Zhang, Dong-Mei Zhang, Dong-Wei Zhang, Dong-Ying Zhang, Dong-cui Zhang, Dong-juan Zhang, Dong-qiang Zhang, Dongdong Zhang, Dongfeng Zhang, Donghua Zhang, Donghui Zhang, Dongjian Zhang, Dongjie Zhang, Donglei Zhang, Dongmei Zhang, Dongsheng Zhang, Dongxin Zhang, Dongyan Zhang, Dongyang Zhang, Dongying Zhang, Donna D Zhang, Donna Zhang, Duo Zhang, Duoduo Zhang, Duowen Zhang, En Zhang, Enhui Zhang, Enming Zhang, Erchen Zhang, F P Zhang, F Zhang, Fa Zhang, Famin Zhang, Fan Zhang, Fang Zhang, Fanghong Zhang, Fangmei Zhang, Fangting Zhang, Fangyuan Zhang, Fei Zhang, Fei-Ran Zhang, Feifei Zhang, Feixue Zhang, Fen Zhang, Feng Zhang, Fengqing Zhang, Fengshi Zhang, Fengshuo Zhang, Fengwei Zhang, Fengxi Zhang, Fengxia Zhang, Fengxu Zhang, Fomin Zhang, Fred Zhang, Fu-Ping Zhang, Fubo Zhang, Fugui Zhang, Fuhan Zhang, Fujun Zhang, Fukang Zhang, Fuming Zhang, Fuqiang Zhang, Fuquan Zhang, Furen Zhang, Fushun Zhang, Fuxing Zhang, Fuyang Zhang, Fuyuan Zhang, G Zhang, G-Y Zhang, Gan Zhang, Gang Zhang, Ganlin Zhang, Gaoxin Zhang, Gary Zhang, Ge Zhang, Geng Zhang, Genglin Zhang, Genxi Zhang, Geyang Zhang, Gong Zhang, Gu Zhang, Guan-Yan Zhang, Guang Zhang, Guang-Qiong Zhang, Guang-Xian Zhang, Guang-Ya Zhang, Guanghui Zhang, Guangji Zhang, Guanglei Zhang, Guangliang Zhang, Guangping Zhang, Guangqiong Zhang, Guangxian Zhang, Guangxin Zhang, Guangye Zhang, Guangyong Zhang, Guangyuan Zhang, Guanqun Zhang, Gui-Ping Zhang, Guicheng Zhang, Guihua Zhang, Guijie Zhang, Guili Zhang, Guiliang Zhang, Guilin Zhang, Guimin Zhang, Guiping Zhang, Guisen Zhang, Guixia Zhang, Guixiang Zhang, Gumuyang Zhang, Guo-Fang Zhang, Guo-Fu Zhang, Guo-Guo Zhang, Guo-Liang Zhang, Guo-Wei Zhang, Guo-Xiong Zhang, Guoan Zhang, Guochao Zhang, Guodong Zhang, Guofang Zhang, Guofeng Zhang, Guofu Zhang, Guoguo Zhang, Guohua Zhang, Guohui Zhang, Guojun Zhang, Guoli Zhang, Guoliang Zhang, Guolong Zhang, Guomin Zhang, Guoming Zhang, Guoping Zhang, Guoqiang Zhang, Guoqing Zhang, Guorui Zhang, Guosen Zhang, Guowei Zhang, Guoxin Zhang, Guoying Zhang, Guozhi Zhang, H D Zhang, H F Zhang, H L Zhang, H P Zhang, H W Zhang, H X Zhang, H Y Zhang, H Zhang, H-F Zhang, Hai Zhang, Hai-Bo Zhang, Hai-Feng Zhang, Hai-Gang Zhang, Hai-Han Zhang, Hai-Liang Zhang, Hai-Man Zhang, Hai-Ying Zhang, Haibei Zhang, Haibing Zhang, Haibo Zhang, Haicheng Zhang, Haifeng Zhang, Haihong Zhang, Haihua Zhang, Haijiao Zhang, Haijun Zhang, Haikuo Zhang, Hailei Zhang, Hailian Zhang, Hailiang Zhang, Hailin Zhang, Hailing Zhang, Hailong Zhang, Hailou Zhang, Haiming Zhang, Hainan Zhang, Haipeng Zhang, Haisan Zhang, Haisen Zhang, Haitao Zhang, Haiwang Zhang, Haiwei Zhang, Haixia Zhang, Haiyan Zhang, Haiyang Zhang, Haiying Zhang, Haiyue Zhang, Han Zhang, Hanchao Zhang, Hang Zhang, Hanqi Zhang, Hanrui Zhang, Hansi Zhang, Hanting Zhang, Hanwang Zhang, Hanwen Zhang, Hanxu Zhang, Hanyin Zhang, Hanyu Zhang, Hao Zhang, Hao-Chen Zhang, Hao-Yu Zhang, Haohao Zhang, Haojian Zhang, Haojie Zhang, Haojun Zhang, Haokun Zhang, Haolin Zhang, Haomin Zhang, Haonan Zhang, Haopeng Zhang, Haoran Zhang, Haotian Zhang, Haowen Zhang, Haoxing Zhang, Haoyu Zhang, Haoyuan Zhang, Haoyue Zhang, Haozheng Zhang, He Zhang, Hefang Zhang, Hejun Zhang, Heng Zhang, Hengming Zhang, Hengrui Zhang, Hengyuan Zhang, Heping Zhang, Hong Zhang, Hong-Jie Zhang, Hong-Sheng Zhang, Hong-Xing Zhang, Hong-Yu Zhang, Hong-Zhen Zhang, Hongbin Zhang, Hongbing Zhang, Hongcai Zhang, Hongfeng Zhang, Hongfu Zhang, Honghe Zhang, Honghong Zhang, Honghua Zhang, Hongjia Zhang, Hongjie Zhang, Hongjin Zhang, Hongju Zhang, Hongjuan Zhang, Honglei Zhang, Hongliang Zhang, Hongmei Zhang, Hongmin Zhang, Hongquan Zhang, Hongrong Zhang, Hongrui Zhang, Hongsen Zhang, Hongtao Zhang, Hongting Zhang, Hongwu Zhang, Hongxia Zhang, Hongxin Zhang, Hongxing Zhang, Hongya Zhang, Hongyan Zhang, Hongyang Zhang, Hongyi Zhang, Hongying Zhang, Hongyou Zhang, Hongyuan Zhang, Hongyun Zhang, Hongzhong Zhang, Hongzhou Zhang, Houbin Zhang, Hu Zhang, Hua Zhang, Hua-Min Zhang, Hua-Xiong Zhang, Huabing Zhang, Huafeng Zhang, Huaiyong Zhang, Huajia Zhang, Huan Zhang, Huan-Tian Zhang, Huanmin Zhang, Huanqing Zhang, Huanxia Zhang, Huanyu Zhang, Huaqi Zhang, Huaqiu Zhang, Huawei Zhang, Huawen Zhang, Huayang Zhang, Huayong Zhang, Huayu Zhang, Hugang Zhang, Huhan Zhang, Hui Hua Zhang, Hui Z Zhang, Hui Zhang, Hui-Jun Zhang, Hui-Wen Zhang, Huibing Zhang, Huifang Zhang, Huihui Zhang, Huijie Zhang, Huijun Zhang, Huili Zhang, Huilin Zhang, Huimao Zhang, Huimin Zhang, Huiming Zhang, Huiping Zhang, Huiqing Zhang, Huiru Zhang, Huiting Zhang, Huixin Zhang, Huiying Zhang, Huiyu Zhang, Huiyuan Zhang, Huize Zhang, Huizhen Zhang, Igor Ying Zhang, J B Zhang, J R Zhang, J Y Zhang, J Zhang, J-Y Zhang, Jamie Zhang, Jason Z Zhang, Jennifer Y Zhang, Jerry Z Zhang, Ji Yao Zhang, Ji Zhang, Ji-Yuan Zhang, Jia Zhang, Jia-Bao Zhang, Jia-Si Zhang, Jia-Su Zhang, Jia-Xuan Zhang, Jiabi Zhang, Jiachao Zhang, Jiachen Zhang, Jiacheng Zhang, Jiahai Zhang, Jiahao Zhang, Jiahe Zhang, Jiajia Zhang, Jiajing Zhang, Jiaming Zhang, Jian Zhang, Jian-Guo Zhang, Jian-Ping Zhang, Jian-Xu Zhang, Jianan Zhang, Jianbin Zhang, Jianbo Zhang, Jianchao Zhang, Jianduan Zhang, Jianeng Zhang, Jianfa Zhang, Jiang Zhang, Jiangang Zhang, Jianghong Zhang, Jianglin Zhang, Jiangmei Zhang, Jiangtao Zhang, Jianguang Zhang, Jianguo Zhang, Jiangyan Zhang, Jianhai Zhang, Jianhong Zhang, Jianhua Zhang, Jianhui Zhang, Jianing Zhang, Jianjun Zhang, Jiankang Zhang, Jiankun Zhang, Jianliang Zhang, Jianling Zhang, Jianmei Zhang, Jianmin Zhang, Jianming Zhang, Jiannan Zhang, Jianping Zhang, Jianqiong Zhang, Jianshe Zhang, Jianting Zhang, Jianwei Zhang, Jianwen Zhang, Jianwu Zhang, Jianxia Zhang, Jianxiang Zhang, Jianxin Zhang, Jianying Zhang, Jianyong Zhang, Jianzhao Zhang, Jiao Zhang, Jiaqi Zhang, Jiasheng Zhang, Jiawei Zhang, Jiawen Zhang, Jiaxin Zhang, Jiaxing Zhang, Jiayan Zhang, Jiayi Zhang, Jiayin Zhang, Jiaying Zhang, Jiayu Zhang, Jiayuan Zhang, Jibin Zhang, Jicai Zhang, Jie Zhang, Jiecheng Zhang, Jiehao Zhang, Jiejie Zhang, Jieming Zhang, Jieping Zhang, Jieqiong Zhang, Jieying Zhang, Jifa Zhang, Jifeng Zhang, Jihang Zhang, Jimei Zhang, Jiming Zhang, Jimmy Zhang, Jin Zhang, Jin-Ge Zhang, Jin-Jing Zhang, Jin-Man Zhang, Jin-Ru Zhang, Jin-Rui Zhang, Jin-Yu Zhang, Jinbiao Zhang, Jinfan Zhang, Jinfang Zhang, Jinfeng Zhang, Jing Jing Zhang, Jing Zhang, Jing-Bo Zhang, Jing-Chang Zhang, Jing-Fa Zhang, Jing-Lve Zhang, Jing-Nan Zhang, Jing-Qiu Zhang, Jing-Zhan Zhang, JingZi Zhang, Jingchuan Zhang, Jingchun Zhang, Jingdan Zhang, Jingdong Zhang, Jingfa Zhang, Jinghui Zhang, Jingjing Zhang, Jinglan Zhang, Jingli Zhang, Jingliang Zhang, Jinglu Zhang, Jingmei Zhang, Jingmian Zhang, Jingning Zhang, Jingping Zhang, Jingqi Zhang, Jingrong Zhang, Jingru Zhang, Jingshuang Zhang, Jingsong Zhang, Jingtian Zhang, Jingting Zhang, Jingwei Zhang, Jingwen Zhang, Jingxi Zhang, Jingxiao Zhang, Jingxuan Zhang, Jingxue Zhang, Jingyao Zhang, Jingyi Zhang, Jingying Zhang, Jingyu Zhang, Jingyuan Zhang, Jingyue Zhang, Jingzhe Zhang, Jinhua Zhang, Jinhui Zhang, Jinjin Zhang, Jinjing Zhang, Jinliang Zhang, Jinlong Zhang, Jinming Zhang, Jinquan Zhang, Jinrui Zhang, Jinsong Zhang, Jinsu Zhang, Jintao Zhang, Jinwei Zhang, Jinxiu Zhang, Jinyi Zhang, Jinying Zhang, Jinyu Zhang, Jinze Zhang, Jinzhou Zhang, Jiqiang Zhang, Jiquan Zhang, Jishou Zhang, Jishui Zhang, Jitai Zhang, Jiuchun Zhang, Jiupan Zhang, Jiuwei Zhang, Jiuxuan Zhang, Jixia Zhang, Jixing Zhang, Jiyang Zhang, Joe Z Zhang, John H Zhang, John Z H Zhang, Joshua Zhang, Joyce Zhang, Juan Zhang, Juan-Juan Zhang, Jue Zhang, Juliang Zhang, Jun Zhang, Jun-Feng Zhang, Jun-Jie Zhang, Jun-Xiao Zhang, Jun-Xiu Zhang, Jun-ying Zhang, June Zhang, Junfeng Zhang, Junhan Zhang, Junhang Zhang, Junhua Zhang, Junhui Zhang, Junjie Zhang, Junjing Zhang, Junkai Zhang, Junli Zhang, Junling Zhang, Junlong Zhang, Junmei Zhang, Junmin Zhang, Junpei Zhang, Junpeng Zhang, Junping Zhang, Junqing Zhang, Junran Zhang, Junru Zhang, Junsheng Zhang, Juntai Zhang, Junwei Zhang, Junxia Zhang, Junxiao Zhang, Junxing Zhang, Junxiu Zhang, Junyan Zhang, Junyi Zhang, Junying Zhang, Junyu Zhang, Junzhi Zhang, Juqing Zhang, K Y Zhang, K Zhang, Kai Zhang, Kai-Jie Zhang, Kai-Qiang Zhang, Kaichuang Zhang, Kaige Zhang, Kaihua Zhang, Kaihui Zhang, Kailin Zhang, Kailing Zhang, Kaiming Zhang, Kainan Zhang, Kaitai Zhang, Kaituo Zhang, Kaiwen Zhang, Kaiyi Zhang, Kan Zhang, Kang Zhang, Kang-Ling Zhang, Kangjun Zhang, Kangning Zhang, Karen Zhang, Ke Zhang, Ke-Wen Zhang, Ke-lan Zhang, Kefen Zhang, Kejia Zhang, Kejian Zhang, Kejin Zhang, Kejun Zhang, Keke Zhang, Keshan Zhang, Kewen Zhang, Keyi Zhang, Keyong Zhang, Keyu Zhang, Kezhong Zhang, Kongyong Zhang, Kui Zhang, Kui-ming Zhang, Kun Zhang, Kunning Zhang, Kunshan Zhang, Kunyi Zhang, Kuo Zhang, L F Zhang, L Zhang, L-S Zhang, Laihong Zhang, Lan Zhang, Lanfang Zhang, Lanju Zhang, Lanjun Zhang, Lanlan Zhang, Lantian Zhang, Lanyue Zhang, Le Zhang, Le-Le Zhang, Lechi Zhang, Lei Zhang, Lei-Lei Zhang, Lei-Sheng Zhang, Leilei Zhang, Leili Zhang, Leitao Zhang, Leiying Zhang, Lele Zhang, Leli Zhang, Leo H Zhang, Li Zhang, Li-Fen Zhang, Li-Jie Zhang, Li-Ke Zhang, Li-ping Zhang, Lian Zhang, Lian-Lian Zhang, Lianbo Zhang, Lianfeng Zhang, Liang Zhang, Liang-Rong Zhang, Liangdong Zhang, Liangliang Zhang, Liangming Zhang, Lianjun Zhang, Lianmei Zhang, Lianqin Zhang, Lianxin Zhang, Libo Zhang, Lichao Zhang, Lichen Zhang, Licheng Zhang, Lichuan Zhang, Licui Zhang, Lida Zhang, Lie Zhang, Lifan Zhang, Lifang Zhang, Liguo Zhang, Lihong Zhang, Lihua Zhang, Lijian Zhang, Lijiao Zhang, Lijie Zhang, Lijuan Zhang, Lijun Zhang, Lilei Zhang, Lili Zhang, Limei Zhang, Limin Zhang, Liming Zhang, Lin Zhang, Lin-Jie Zhang, Lina Zhang, Linan Zhang, Linbo Zhang, Linda S Zhang, Ling Xia Zhang, Ling Zhang, Ling-Yu Zhang, Lingjie Zhang, Lingli Zhang, Lingling Zhang, Lingna Zhang, Lingqiang Zhang, Lingxiao Zhang, Lingyan Zhang, Lingyu Zhang, Lining Zhang, Linjing Zhang, Linli Zhang, Linlin Zhang, Lintao Zhang, Linyou Zhang, Linyuan Zhang, Liping Zhang, Liqian Zhang, Lirong Zhang, Lishuang Zhang, Litao Zhang, Liu Zhang, Liuming Zhang, Liuwei Zhang, Liwei Zhang, Liwen Zhang, Lixia Zhang, Lixing Zhang, Liyan Zhang, Liyi Zhang, Liyin Zhang, Liying Zhang, Liyu Zhang, Liyuan Zhang, Liyun Zhang, Lizhi Zhang, Long Zhang, Longlong Zhang, Longxin Zhang, Longzhen Zhang, Lu Zhang, Lu-Pei Zhang, Lu-Yang Zhang, Luanluan Zhang, Lucia Zhang, Lufei Zhang, Lukuan Zhang, Lulu Zhang, Lun Zhang, Lunan Zhang, Luning Zhang, Luo Zhang, Luo-Meng Zhang, Luoping Zhang, Lupei Zhang, Lusha Zhang, Luwen Zhang, Luyao Zhang, Luyun Zhang, Luzheng Zhang, Lv-Lang Zhang, M H Zhang, M J Zhang, M M Zhang, M Q Zhang, M X Zhang, M Zhang, Man Zhang, Manjin Zhang, Mao Zhang, Maomao Zhang, Mei Zhang, Mei-Fang Zhang, Mei-Ling Zhang, Mei-Qing Zhang, Mei-Ya Zhang, Mei-Zhen Zhang, MeiLu Zhang, Meidi Zhang, Meijia Zhang, Meiling Zhang, Meimei Zhang, Meishan Zhang, Meiwei Zhang, Meixia Zhang, Meixian Zhang, Meiyu Zhang, Melissa C Zhang, Melody Zhang, Meng Zhang, Meng-Jie Zhang, Meng-Wen Zhang, Meng-Ying Zhang, Mengdi Zhang, Mengguo Zhang, Menghao Zhang, Menghuan Zhang, Menghui Zhang, Mengjia Zhang, Mengjie Zhang, Mengliang Zhang, Menglu Zhang, Mengmeng Zhang, Mengmin Zhang, Mengna Zhang, Mengnan Zhang, Mengni Zhang, Mengqi Zhang, Mengqiu Zhang, Mengren Zhang, Mengshi Zhang, Mengxi Zhang, Mengxian Zhang, Mengxue Zhang, Mengying Zhang, Mengyuan Zhang, Mengyue Zhang, Mengzhao Zhang, Mengzhen Zhang, Mi Zhang, Mianzhi Zhang, Miao Zhang, Miao-Miao Zhang, Miaomiao Zhang, Miaoran Zhang, Michael Zhang, Min Zhang, Minfang Zhang, Ming Zhang, Ming-Jun Zhang, Ming-Liang Zhang, Ming-Ming Zhang, Ming-Rong Zhang, Ming-Yu Zhang, Ming-Zhu Zhang, Mingai Zhang, Mingchang Zhang, Mingdi Zhang, Mingfa Zhang, Mingfeng Zhang, Minghang Zhang, Minghao Zhang, Minghui Zhang, Mingjie Zhang, Mingjiong Zhang, Mingjun Zhang, Mingming Zhang, Mingqi Zhang, Mingtong Zhang, Mingxiang Zhang, Mingxiu Zhang, Mingxuan Zhang, Mingxue Zhang, Mingyang A Zhang, Mingyang Zhang, Mingyao Zhang, Mingyi Zhang, Mingying Zhang, Mingyu Zhang, Mingyuan Zhang, Mingyue Zhang, Mingzhao Zhang, Mingzhen Zhang, Minhong Zhang, Minying Zhang, Minyue Zhang, Minzhi Zhang, Minzhu Zhang, Mo Zhang, Mo-Ruo Zhang, Mu Zhang, Muqing Zhang, Muxin Zhang, Muzi Zhang, N Zhang, Na Zhang, Naijin Zhang, Naiqi Zhang, Naisheng Zhang, Naixia Zhang, Nan Yang Zhang, Nan Zhang, Nan-Nan Zhang, Nana Zhang, Nannan Zhang, Nasha Zhang, Ni Zhang, Niankai Zhang, Nianxiang Zhang, Nieke Zhang, Ning Zhang, Ning-Ping Zhang, Ninghan Zhang, Ningkun Zhang, Ningning Zhang, Ningzhen Zhang, Ningzhi Zhang, Nisi Zhang, Nong Zhang, Nu Zhang, P Zhang, Pan Zhang, Pan-Pan Zhang, Panpan Zhang, Pei Zhang, Pei-Weng Zhang, Pei-Zhuo Zhang, PeiFeng Zhang, Peichun Zhang, Peijing Zhang, Peijun Zhang, Peilin Zhang, Peiqin Zhang, Peiwen Zhang, Peiyi Zhang, Peizhen Zhang, Peng Zhang, Peng-Cheng Zhang, Peng-Fei Zhang, Pengbo Zhang, Pengcheng Zhang, Pengfei Zhang, Pengpeng Zhang, Pengwei Zhang, Pengyuan Zhang, Pili Zhang, Ping Zhang, Ping-Fan Zhang, Pingchuan Zhang, Pinggen Zhang, Pingmei Zhang, Pu-Hong Zhang, Pumin Zhang, Q L Zhang, Q Y Zhang, Q Zhang, Q-D Zhang, Qi Zhang, Qi-Ai Zhang, Qi-Lei Zhang, Qi-Min Zhang, QiYue Zhang, Qian Jun Zhang, Qian ZHANG, Qian-Qian Zhang, Qian-Wen Zhang, Qiang Zhang, Qiang-Sheng Zhang, Qiangsheng Zhang, Qiangyan Zhang, Qianhui Zhang, Qianjun Zhang, Qiannan Zhang, Qianqian Zhang, Qianru Zhang, Qiao-Xia Zhang, Qiaofang Zhang, Qiaojun Zhang, Qiaoxuan Zhang, Qifan Zhang, Qiguo Zhang, Qihao Zhang, Qihong Zhang, Qilong Zhang, Qilu Zhang, Qimin Zhang, Qin Zhang, Qing Zhang, Qing-Hui Zhang, Qing-Zhu Zhang, Qingchao Zhang, Qingcheng Zhang, Qingchuan Zhang, Qingfeng Zhang, Qinghong Zhang, Qinghua Zhang, Qingjiong Zhang, Qingjun Zhang, Qingling Zhang, Qingna Zhang, Qingqing Zhang, Qingquan Zhang, Qingrun Zhang, Qingshuang Zhang, Qingtian Zhang, Qingxiu Zhang, Qingxue Zhang, Qingyu Zhang, Qingyue Zhang, Qingyun Zhang, Qinjun Zhang, Qiong Zhang, Qishu Zhang, Qiu Zhang, Qiuting Zhang, Qiuxia Zhang, Qiuyang Zhang, Qiuyue Zhang, Qiwei Zhang, Qiyong Zhang, Quan Zhang, Quan-bin Zhang, Quanfu Zhang, Quanqi Zhang, Quanquan Zhang, Qun Zhang, Qun-Feng Zhang, Qunchen Zhang, Qunfeng Zhang, Qunyuan Zhang, R Zhang, Ran Zhang, Ranran Zhang, Ren Zhang, Renbo Zhang, Renhe Zhang, Renliang Zhang, Renshuai Zhang, Rey M Zhang, Richard Zhang, Rong Zhang, Rong-Kai Zhang, Rongcai Zhang, Rongchao Zhang, Rongguang Zhang, Rongrong Zhang, Rongxin Zhang, Rongxu Zhang, Rongying Zhang, Rongyu Zhang, Ru Zhang, Rugang Zhang, Rui Long Zhang, Rui Xue Zhang, Rui Yan Zhang, Rui Zhang, Rui-Nan Zhang, Rui-Ning Zhang, Rui-fang Zhang, Ruihao Zhang, Ruihong Zhang, Ruikun Zhang, Ruilin Zhang, Ruiling Zhang, Ruimin Zhang, Ruiqi Zhang, Ruiqian Zhang, Ruisan Zhang, Ruixia Zhang, Ruixin Zhang, Ruixue Zhang, Ruiyan Zhang, Ruiyang Zhang, Ruiying Zhang, Ruizhe Zhang, Ruizhi Zhang, Ruizhong Zhang, Rulin Zhang, Run Zhang, Runcheng Zhang, Runxiang Zhang, Runyun Zhang, Runze Zhang, Ruo-Xin Zhang, Ruohan Zhang, Ruoshi Zhang, Ruotian Zhang, Ruoxuan Zhang, Ruoying Zhang, Rusi Zhang, Ruth Zhang, Ruxiang Zhang, Ruxuan Zhang, Ruyi Zhang, S Y Zhang, S Z Zhang, S Zhang, Sai Zhang, Saidan Zhang, Saifei Zhang, Sainan Zhang, Sanbao Zhang, Sen Zhang, Sha Zhang, Shan Zhang, Shan-Shan Zhang, Shanchun Zhang, Shang Zhang, Shangxiong Zhang, Shanhong Zhang, Shanshan Zhang, Shanxiang Zhang, Shao Kang Zhang, Shao Zhang, Shao-Qi Zhang, Shaochuan Zhang, Shaochun Zhang, Shaofei Zhang, Shaofeng Zhang, Shaohua Zhang, Shaojun Zhang, Shaoyang Zhang, Shaozhao Zhang, Shaozhen Zhang, Shasha Zhang, Shen Zhang, Sheng Zhang, Sheng-Dao Zhang, Sheng-Hong Zhang, Sheng-Qiang Zhang, Sheng-Xiao Zhang, Shengchi Zhang, Shengding Zhang, Shengkun Zhang, Shenglai Zhang, Shenglan Zhang, Shenglei Zhang, Shengli Zhang, Shengming Zhang, Shengnan Zhang, Shengye Zhang, Shenqi Zhang, Shenqian Zhang, Shi Zhang, Shi-Han Zhang, Shi-Jie Zhang, Shi-Meng Zhang, Shi-Qian Zhang, Shi-Yao Zhang, ShiSong Zhang, Shichao Zhang, Shihan Zhang, Shijun Zhang, Shikai Zhang, Shilei Zhang, Shimao Zhang, Shining Zhang, Shiping Zhang, Shiqi Zhang, Shiquan Zhang, Shiti Zhang, Shitian Zhang, Shiwen Zhang, Shiwu Zhang, Shiyao Zhang, Shiyi Zhang, Shiyu Zhang, Shiyun Zhang, Shou-Mei Zhang, Shou-Peng Zhang, Shouyue Zhang, Shu Zhang, Shu-Dong Zhang, Shu-Fan Zhang, Shu-Fang Zhang, Shu-Min Zhang, Shu-Ming Zhang, Shu-Yang Zhang, Shu-Zhen Zhang, Shuai Zhang, Shuai-Nan Zhang, Shuaishuai Zhang, Shuang Zhang, Shuangjie Zhang, Shuanglu Zhang, Shuangxin Zhang, Shubing Zhang, Shuchen Zhang, Shucong Zhang, Shuer Zhang, Shuge Zhang, Shuhong Zhang, Shuijun Zhang, Shujun Zhang, Shuli Zhang, Shulong Zhang, Shun Zhang, Shun-Bo Zhang, Shunfen Zhang, Shunming Zhang, Shuo Zhang, Shupeng Zhang, Shuran Zhang, Shurui Zhang, Shushan Zhang, Shuwan Zhang, Shuwei Zhang, Shuxia Zhang, Shuya Zhang, Shuyan Zhang, Shuyang Zhang, Shuye Zhang, Shuyi Zhang, Shuyuan Zhang, Si Zhang, Si-Zhong Zhang, Sibin Zhang, Sifan Zhang, Sihe Zhang, Simeng Zhang, Simin Zhang, Siqi Zhang, Sisi Zhang, Sixue Zhang, Siyuan Zhang, Siyue Zhang, Sizhong Zhang, Song Zhang, Song-Yang Zhang, Songlin Zhang, Songying Zhang, Sophia L Zhang, Stanley Weihua Zhang, Stephen X Zhang, Su Zhang, Sujiang Zhang, Sulin Zhang, Sumei Zhang, Suming Zhang, Suping Zhang, Susie Zhang, Suya Zhang, Suyang Zhang, Suzhen Zhang, T Zhang, Tangjuan Zhang, Tao Zhang, Tao-Lan Zhang, Taojun Zhang, Taoyuan Zhang, Teng Zhang, Tengfang Zhang, Terry Jianguo Zhang, Ti Zhang, Tian Zhang, Tian-Guang Zhang, Tian-Yu Zhang, Tiane Zhang, Tianfeng Zhang, Tianliang Zhang, Tianlong Zhang, Tianpeng Zhang, Tianshu Zhang, Tiantian Zhang, Tianxi Zhang, Tianxiao Zhang, Tianxin Zhang, Tianyang Zhang, Tianye Zhang, Tianyi Zhang, Tianyu Zhang, Tie-mei Zhang, Tiefeng Zhang, Tiehua Zhang, Tiejun Zhang, Ting Ting Zhang, Ting Zhang, Ting-Ting Zhang, Tinghu Zhang, Tingting Zhang, Tingxue Zhang, Tingying Zhang, Tong Xuan Zhang, Tong Zhang, Tong-Cun Zhang, Tongcun Zhang, Tongfu Zhang, Tonghan Zhang, Tonghua Zhang, Tonghui Zhang, Tongran Zhang, Tongshuo Zhang, Tongtong Zhang, Tongwu Zhang, Tongxin Zhang, Tongxue Zhang, Tuo Zhang, Vita Zhang, W G Zhang, W X Zhang, W Zhang, Wancong Zhang, Wang-Dong Zhang, Wangang Zhang, Wangping Zhang, Wanjiang Zhang, Wanjun Zhang, Wannian Zhang, Wanqi Zhang, Wanting Zhang, Wanying Zhang, Wanyu Zhang, Wei Zhang, Wei-Jia Zhang, Wei-Na Zhang, Wei-Yi Zhang, Weibo Zhang, Weichen Zhang, Weifeng Zhang, Weiguo Zhang, Weihua Zhang, Weijian Zhang, Weikang Zhang, Weili Zhang, Weilin Zhang, Weiling Zhang, Weilong Zhang, Weimin Zhang, Weina Zhang, Weipeng Zhang, Weiping J Zhang, Weiqin Zhang, Weisen Zhang, Weiwei Zhang, Weixia Zhang, Weiyi Zhang, Weiyu Zhang, Weizheng Zhang, Weizhou Zhang, Wen Jun Zhang, Wen Zhang, Wen-Hong Zhang, Wen-Jie Zhang, Wen-Jing Zhang, Wen-Xin Zhang, Wen-Xuan Zhang, Wenbin Zhang, Wenbo Zhang, Wenchao Zhang, Wencheng Zhang, Wencong Zhang, Wendi Zhang, Wenguang Zhang, Wenhao Zhang, Wenhong Zhang, Wenhua Zhang, Wenhui Zhang, Wenji Zhang, Wenjia Zhang, Wenjing Zhang, Wenjuan Zhang, Wenjun Zhang, Wenkai Zhang, Wenkui Zhang, Wenli Zhang, Wenlong Zhang, Wenlu Zhang, Wenming Zhang, Wenqian Zhang, Wenru Zhang, Wentao Zhang, Wenting Zhang, Wenwen Zhang, Wenxi Zhang, Wenxiang Zhang, Wenxin Zhang, Wenxue Zhang, Wenya Zhang, Wenyang Zhang, Wenyi Zhang, Wenyuan Zhang, Wenzhong Zhang, Wuhu Zhang, X N Zhang, X X Zhang, X Y Zhang, X Zhang, X-T Zhang, X-Y Zhang, Xi Zhang, Xi'an Zhang, Xi-Feng Zhang, XiHe Zhang, Xia Zhang, Xian Zhang, Xian-Bo Zhang, Xian-Li Zhang, Xian-Man Zhang, Xiang Yang Zhang, Xiang Zhang, Xiangbin Zhang, Xiangfei Zhang, Xianglian Zhang, Xiangsong Zhang, Xiangwu Zhang, Xiangyang Zhang, Xiangyu Zhang, Xiangzheng Zhang, Xianhong Zhang, Xianhua Zhang, Xianjing Zhang, Xianpeng Zhang, Xianxian Zhang, Xiao Bin Zhang, Xiao Min Zhang, Xiao Yu Cindy Zhang, Xiao Zhang, Xiao-Chang Zhang, Xiao-Cheng Zhang, Xiao-Chong Zhang, Xiao-Feng Zhang, Xiao-Hong Zhang, Xiao-Hua Zhang, Xiao-Jun Zhang, Xiao-Lei Zhang, Xiao-Lin Zhang, Xiao-Ling Zhang, Xiao-Meng Zhang, Xiao-Ming Zhang, Xiao-Qi Zhang, Xiao-Qian Zhang, Xiao-Shuo Zhang, Xiao-Wei Zhang, Xiao-Xuan Zhang, Xiao-Yong Zhang, Xiao-Yu Zhang, Xiao-bo Zhang, Xiao-yan Zhang, XiaoLin Zhang, XiaoPing Zhang, XiaoYi Zhang, Xiaobao Zhang, Xiaobiao Zhang, Xiaobo Zhang, Xiaochang Zhang, Xiaochen Zhang, Xiaochun Zhang, Xiaocong Zhang, Xiaocui Zhang, Xiaodan Zhang, Xiaodong Zhang, Xiaofan Zhang, Xiaofang Zhang, Xiaofei Zhang, Xiaofeng Zhang, Xiaogang Zhang, Xiaohan Zhang, Xiaohong Zhang, Xiaohui Zhang, Xiaojia Zhang, Xiaojian Zhang, Xiaojie Zhang, Xiaojin Zhang, Xiaojing Zhang, Xiaojun Zhang, Xiaokui Zhang, Xiaolan Zhang, Xiaolei Zhang, Xiaoli Zhang, Xiaoling Zhang, Xiaolong Zhang, Xiaomei Zhang, Xiaomeng Zhang, Xiaomin Zhang, Xiaoming Zhang, Xiaoning Zhang, Xiaonyun Zhang, Xiaopei Zhang, Xiaopo Zhang, Xiaoqi Zhang, Xiaoqing Zhang, Xiaorong Zhang, Xiaosheng Zhang, Xiaotian Michelle Zhang, Xiaotian Zhang, Xiaotong Zhang, Xiaotun Zhang, Xiaowan Zhang, Xiaowei Zhang, Xiaoxi Zhang, Xiaoxia Zhang, Xiaoxian Zhang, Xiaoxiao Zhang, Xiaoxin Zhang, Xiaoxue Zhang, Xiaoyan Zhang, Xiaoying Zhang, Xiaoyu Zhang, Xiaoyuan Zhang, Xiaoyue Zhang, Xiaoyun Zhang, Xiaozhe Zhang, Xiayin Zhang, Xibo Zhang, Xieyi Zhang, Xijiang Zhang, Xilin Zhang, Xiling Zhang, Ximei Zhang, Xin Zhang, Xin-Hui Zhang, Xin-Xin Zhang, Xin-Yan Zhang, Xin-Ye Zhang, Xin-Yuan Zhang, Xinan Zhang, Xinbao Zhang, Xinbo Zhang, Xincheng Zhang, Xindang Zhang, Xindong Zhang, Xinfeng Zhang, Xinfu Zhang, Xing Yu Zhang, Xing Zhang, Xingan Zhang, Xingang Zhang, Xingcai Zhang, Xingen Zhang, Xinglai Zhang, Xingong Zhang, Xingwei Zhang, Xingxing Zhang, Xingxu Zhang, Xingyi Zhang, Xingyu Zhang, Xingyuan Zhang, Xinhai Zhang, Xinhan Zhang, Xinhe Zhang, Xinheng Zhang, Xinhong Zhang, Xinhua Zhang, Xinjiang Zhang, Xinjing Zhang, Xinjun Zhang, Xinke Zhang, Xinlei Zhang, Xinlian Zhang, Xinlin Zhang, Xinling Zhang, Xinlong Zhang, Xinlu Zhang, Xinmin Zhang, Xinping Zhang, Xinqiao Zhang, Xinquan Zhang, Xinran Zhang, Xinrui Zhang, Xinruo Zhang, Xintao Zhang, Xinwei Zhang, Xinwu Zhang, Xinxin Zhang, Xinyao Zhang, Xinye Zhang, Xinyi Zhang, Xinyu Zhang, Xinyue Zhang, Xiong Zhang, Xiongjun Zhang, Xiongze Zhang, Xipeng Zhang, Xiping Zhang, Xiu Qi Zhang, Xiu-Juan Zhang, Xiu-Li Zhang, Xiu-Peng Zhang, Xiujie Zhang, Xiujun Zhang, Xiulan Zhang, Xiuming Zhang, Xiupeng Zhang, Xiuping Zhang, Xiuqin Zhang, Xiuqing Zhang, Xiuse Zhang, Xiushan Zhang, Xiuwen Zhang, Xiuxing Zhang, Xiuxiu Zhang, Xiuyin Zhang, Xiuyue Zhang, Xiuyun Zhang, Xiuzhen Zhang, Xixi Zhang, Xixun Zhang, Xiyu Zhang, Xu Dong Zhang, Xu Zhang, Xu-Chao Zhang, Xu-Jun Zhang, Xu-Mei Zhang, Xuan Zhang, Xudan Zhang, Xudong Zhang, Xue Zhang, Xue-Ping Zhang, Xue-Qin Zhang, Xue-Qing Zhang, XueWu Zhang, Xuebao Zhang, Xuebin Zhang, Xuefei Zhang, Xueguang Zhang, Xuehai Zhang, Xuehong Zhang, Xuehui Zhang, Xuejiao Zhang, Xuejun C Zhang, Xueli Zhang, Xuelian Zhang, Xuelong Zhang, Xueluo Zhang, Xuemei Zhang, Xuemin Zhang, Xueming Zhang, Xuening Zhang, Xueping Zhang, Xueqia Zhang, Xueqian Zhang, Xueqin Zhang, Xueting Zhang, Xuewei Zhang, Xuewen Zhang, Xuexi Zhang, Xueya Zhang, Xueyan Zhang, Xueyi Zhang, Xueying Zhang, Xuezhi Zhang, Xufang Zhang, Xuhao Zhang, Xujun Zhang, Xunming Zhang, Xuting Zhang, Xutong Zhang, Xuxiang Zhang, Y H Zhang, Y L Zhang, Y Y Zhang, Y Zhang, Y-H Zhang, Ya Zhang, Ya-Juan Zhang, Ya-Li Zhang, Ya-Long Zhang, Ya-Meng Zhang, Yachen Zhang, Yadi Zhang, Yadong Zhang, Yafang Zhang, Yafei Zhang, Yafeng Zhang, Yaguang Zhang, Yahua Zhang, Yajie Zhang, Yajing Zhang, Yajun Zhang, Yakun Zhang, Yalan Zhang, Yali Zhang, Yaling Zhang, Yameng Zhang, Yamin Zhang, Yaming Zhang, Yan Zhang, Yan-Chun Zhang, Yan-Ling Zhang, Yan-Min Zhang, Yan-Qing Zhang, Yanan Zhang, Yanbin Zhang, Yanbing Zhang, Yanchao Zhang, Yandong Zhang, Yanfei Zhang, Yanfen Zhang, Yanfeng Zhang, Yang Zhang, Yang-Yang Zhang, Yangfan Zhang, Yanghui Zhang, Yangqianwen Zhang, Yangyang Zhang, Yangyu Zhang, Yanhong Zhang, Yanhua Zhang, Yani Zhang, Yanjiao Zhang, Yanju Zhang, Yanjun Zhang, Yanli Zhang, Yanlin Zhang, Yanling Zhang, Yanman Zhang, Yanmin Zhang, Yanming Zhang, Yanna Zhang, Yannan Zhang, Yanping Zhang, Yanqiao Zhang, Yanquan Zhang, Yanru Zhang, Yanting Zhang, Yanxia Zhang, Yanxiang Zhang, Yanyan Zhang, Yanyi Zhang, Yanyu Zhang, Yao Zhang, Yao-Hua Zhang, Yaodong Zhang, Yaoxin Zhang, Yaoyang Zhang, Yaoyao Zhang, Yaozhengtai Zhang, Yaping Zhang, Yaqi Zhang, Yaru Zhang, Yashuo Zhang, Yating Zhang, Yawei Zhang, Yaxin Zhang, Yaxuan Zhang, Yayong Zhang, Yazhuo Zhang, Ye Zhang, Yefan Zhang, Yeqian Zhang, Yerui Zhang, Yeting Zhang, Yexiang Zhang, Yi J Zhang, Yi Ping Zhang, Yi Zhang, Yi-Chi Zhang, Yi-Feng Zhang, Yi-Ge Zhang, Yi-Hang Zhang, Yi-Hua Zhang, Yi-Min Zhang, Yi-Ming Zhang, Yi-Qi Zhang, Yi-Wei Zhang, Yi-Wen Zhang, Yi-Xuan Zhang, Yi-Yue Zhang, Yi-yi Zhang, YiJie Zhang, YiPei Zhang, Yibin Zhang, Yibo Zhang, Yichen Zhang, Yichi Zhang, Yidan Zhang, Yidong Zhang, Yifan Zhang, Yifang Zhang, Yige Zhang, Yiguo Zhang, Yihan Zhang, Yihang Zhang, Yihao Zhang, Yiheng Zhang, Yihong Zhang, Yihui Zhang, Yijing Zhang, Yikai Zhang, Yikun Zhang, Yili Zhang, Yiliang Zhang, Yilin Zhang, Yimei Zhang, Yimeng Zhang, Yimin Zhang, Yiming Zhang, Yin Jiang Zhang, Yin Zhang, Yin-Hong Zhang, Yina Zhang, Yinci Zhang, Ying E Zhang, Ying Zhang, Ying-Jun Zhang, Ying-Lin Zhang, Ying-Qian Zhang, Yingang Zhang, Yingchao Zhang, Yinghui Zhang, Yingjie Zhang, Yingli Zhang, Yingmei Zhang, Yingna Zhang, Yingnan Zhang, Yingqi Zhang, Yingqian Zhang, Yingyi Zhang, Yingying Zhang, Yingze Zhang, Yingzi Zhang, Yinhao Zhang, Yinjiang Zhang, Yintang Zhang, Yinzhi Zhang, Yinzhuang Zhang, Yipeng Zhang, Yiping Zhang, Yiqian Zhang, Yiqing Zhang, Yiren Zhang, Yirong Zhang, Yitian Zhang, Yiting Zhang, Yiwan Zhang, Yiwei Zhang, Yiwen Zhang, Yixia Zhang, Yixin Zhang, Yiyao Zhang, Yiyi Zhang, Yiyuan Zhang, Yizhe Zhang, Yizhi Zhang, Yong Zhang, Yong-Guo Zhang, Yong-Liang Zhang, Yong-hong Zhang, Yongbao Zhang, Yongchang Zhang, Yongchao Zhang, Yongci Zhang, Yongfa Zhang, Yongfang Zhang, Yongfeng Zhang, Yonggang Zhang, Yonggen Zhang, Yongguang Zhang, Yongguo Zhang, Yongheng Zhang, Yonghong Zhang, Yonghui Zhang, Yongjie Zhang, Yongjiu Zhang, Yongjuan Zhang, Yonglian Zhang, Yongliang Zhang, Yonglong Zhang, Yongpeng Zhang, Yongping Zhang, Yongqiang Zhang, Yongsheng Zhang, Yongwei Zhang, Yongxiang Zhang, Yongxing Zhang, Yongyan Zhang, Yongyun Zhang, You-Zhi Zhang, Youjin Zhang, Youmin Zhang, Youti Zhang, Youwen Zhang, Youyi Zhang, Youying Zhang, Youzhong Zhang, Yu Chen Zhang, Yu Zhang, Yu-Bo Zhang, Yu-Chi Zhang, Yu-Fei Zhang, Yu-Hui Zhang, Yu-Jie Zhang, Yu-Jing Zhang, Yu-Qi Zhang, Yu-Qiu Zhang, Yu-Yu Zhang, Yu-Zhe Zhang, YuHang Zhang, YuHong Zhang, Yuan Zhang, Yuan-Wei Zhang, Yuan-Yuan Zhang, Yuanchao Zhang, Yuanhao Zhang, Yuanhui Zhang, Yuanping Zhang, Yuanqiang Zhang, Yuanqing Zhang, Yuansheng Zhang, Yuanxi Zhang, Yuanxiang Zhang, Yuanyi Zhang, Yuanyuan Zhang, Yuanzhen Zhang, Yuanzhuang Zhang, Yubin Zhang, Yucai Zhang, Yuchao Zhang, Yuchen Zhang, Yuchi Zhang, Yue Zhang, Yue-Bo Zhang, Yue-Ming Zhang, Yuebin Zhang, Yuebo Zhang, Yuehong Zhang, Yuehua Zhang, Yuejuan Zhang, Yuemei Zhang, Yueqi Zhang, Yueru Zhang, Yuetong Zhang, Yufang Zhang, Yufeng Zhang, Yuhan Zhang, Yuhao Zhang, Yuheng Zhang, Yuhua Zhang, Yuhui Zhang, Yujia Zhang, Yujiao Zhang, Yujie Zhang, Yujin Zhang, Yujing Zhang, Yujuan Zhang, Yuke Zhang, Yukun Zhang, Yulin Zhang, Yuling Zhang, Yulong Zhang, Yumei Zhang, Yumeng Zhang, Yumin Zhang, Yun Zhang, Yun-Feng Zhang, Yun-Lin Zhang, Yun-Mei Zhang, Yun-Sheng Zhang, Yun-Xiang Zhang, Yunfan Zhang, Yunfei Zhang, Yunfeng Zhang, Yunhai Zhang, Yunhang Zhang, Yunhe Zhang, Yunhui Zhang, Yuning Zhang, Yunjia Zhang, Yunli Zhang, Yunmei Zhang, Yunpeng Zhang, Yunqi Zhang, Yunqiang Zhang, Yunqing Zhang, Yunsheng Zhang, Yunxia Zhang, Yupei Zhang, Yupeng Zhang, Yuping Zhang, Yuqi Zhang, Yuqing Zhang, Yurou Zhang, Yuru Zhang, Yusen Zhang, Yushan Zhang, Yutian Zhang, Yuting Zhang, Yutong Zhang, Yuwei Zhang, Yuxi Zhang, Yuxia Zhang, Yuxin Zhang, Yuxuan Zhang, Yuyan Zhang, Yuyanan Zhang, Yuyang Zhang, Yuying Zhang, Yuyu Zhang, Yuyuan Zhang, Yuzhe Zhang, Yuzhi Zhang, Yuzhou Zhang, Yuzhu Zhang, Yvonne Zhang, Z Zhang, Z-K Zhang, Zai-Rong Zhang, Zaifeng Zhang, Zaijun Zhang, Zaiqi Zhang, Zebang Zhang, Zekun Zhang, Zemin Zhang, Zeming Zhang, Zeng Zhang, Zengdi Zhang, Zengfu Zhang, Zenglei Zhang, Zengli Zhang, Zengqiang Zhang, Zengrong Zhang, Zengtie Zhang, Zepeng Zhang, Zewei Zhang, Zewen Zhang, Zeyan Zhang, Zeyuan Zhang, Zhan-Xiong Zhang, Zhangjin Zhang, Zhanhao Zhang, Zhanjie Zhang, Zhanjun Zhang, Zhanming Zhang, Zhanyi Zhang, Zhao Zhang, Zhao-Huan Zhang, Zhao-Ming Zhang, Zhaobo Zhang, Zhaocong Zhang, Zhaofeng Zhang, Zhaohua Zhang, Zhaohuai Zhang, Zhaohuan Zhang, Zhaohui Zhang, Zhaomin Zhang, Zhaoping Zhang, Zhaoqi Zhang, Zhaotian Zhang, Zhaoxue Zhang, Zhe Zhang, Zhehua Zhang, Zhemei Zhang, Zhen Zhang, Zhen-Dong Zhang, Zhen-Jie Zhang, Zhen-Shan Zhang, Zhen-Tao Zhang, Zhen-lin Zhang, Zhenfeng Zhang, Zheng Zhang, Zhengbin Zhang, Zhengfen Zhang, Zhenglang Zhang, Zhengliang Zhang, Zhengxiang Zhang, Zhengxing Zhang, Zhengyu Zhang, Zhengyun Zhang, Zhenhao Zhang, Zhenhua Zhang, Zhenlin Zhang, Zhenqiang Zhang, Zhentao Zhang, Zhenyang Zhang, Zhenyu Zhang, Zhenzhen Zhang, Zhenzhu Zhang, Zhewei Zhang, Zhewen Zhang, Zheyuan Zhang, Zhezhe Zhang, Zhi Zhang, Zhi-Chang Zhang, Zhi-Jie Zhang, Zhi-Jun Zhang, Zhi-Peng Zhang, Zhi-Qing Zhang, Zhi-Shuai Zhang, Zhi-Shuo Zhang, Zhi-Xin Zhang, Zhibo Zhang, Zhicheng Zhang, Zhicong Zhang, Zhifei Zhang, Zhigang Zhang, Zhiguo Zhang, Zhihan Zhang, Zhihao Zhang, Zhihong Zhang, Zhihua Zhang, Zhihui Zhang, Zhijian Zhang, Zhijiao Zhang, Zhijing Zhang, Zhijun Zhang, Zhikun Zhang, Zhimin Zhang, Zhiming Zhang, Zhiping Zhang, Zhiqian Zhang, Zhiqiang Zhang, Zhiqiao Zhang, Zhiru Zhang, Zhishang Zhang, Zhishuai Zhang, Zhiwang Zhang, Zhiwen Zhang, Zhixia Zhang, Zhixin Zhang, Zhiyan Zhang, Zhiyao Zhang, Zhiye Zhang, Zhiyi Zhang, Zhiyong Zhang, Zhiyu Zhang, Zhiyuan Zhang, Zhiyun Zhang, Zhizhong Zhang, Zhong Zhang, Zhong-Bai Zhang, Zhong-Yi Zhang, Zhong-Yin Zhang, Zhong-Yuan Zhang, Zhongheng Zhang, Zhongjie Zhang, Zhonglin Zhang, Zhongqi Zhang, Zhongwei Zhang, Zhongxin Zhang, Zhongxu Zhang, Zhongyang Zhang, Zhongyi Zhang, Zhou Zhang, Zhu Zhang, Zhu-Qin Zhang, Zhuang Zhang, Zhuo Zhang, Zhuo-Ya Zhang, Zhuohua Zhang, Zhuojun Zhang, Zhuorong Zhang, Zhuoya Zhang, Zhuqin Zhang, Zhuqing Zhang, Zhuzhen Zhang, Zi-Feng Zhang, Zi-Jian Zhang, Zicheng Zhang, Ziding Zhang, Ziguo Zhang, Zihan Zhang, Ziheng Zhang, Zijian Zhang, Zijiao Zhang, Zijing Zhang, Zikai Zhang, Zilong Zhang, Zilu Zhang, Ziping Zhang, Ziqi Zhang, Zishuo Zhang, Zixiong Zhang, Zixu Zhang, Zixuan Zhang, Ziyang Zhang, Ziyi Zhang, Ziyin Zhang, Ziyu Zhang, Ziyue Zhang, Zizhen Zhang, Zongping Zhang, Zongquan Zhang, Zongwang Zhang, Zongxiang Zhang, Zu-Xuan Zhang, Zufa Zhang, Zuoyi Zhang
articles

Systemic inflammation modulates lipoprotein(a)-associated coronary stenosis in the chronic coronary syndromes.

Lu Shen, Wenqing Zhai, Ping Jiang +6 more · 2025 · American journal of preventive cardiology · Elsevier · added 2026-04-24
Recent researches highlight the interdependence of lipoprotein(a) [Lp(a)] and Lp(a)-associated cardiovascular risk with the background inflammatory burden. This study aimed to investigate whether syst Show more
Recent researches highlight the interdependence of lipoprotein(a) [Lp(a)] and Lp(a)-associated cardiovascular risk with the background inflammatory burden. This study aimed to investigate whether systemic inflammation modulates Lp(a)-associated coronary stenosis in chronic coronary syndromes (CCS). A total of 1513 participants undergoing angiography at a tertiary cardiology center in China were included in our retrospective, cross-sectional study. Participants were categorized into normal, mild, and severe groups based on the Gensini Scores, which quantitatively assess stenosis severity. Multinomial logistic models were calculated according to accompanying systemic inflammation concentration. Participants with elevated Lp(a) levels had a high coronary stenosis risk: fully adjusted model odds ratios (ORs) [95% confidence intervals (CIs)] for the mild vs. normal and severe vs. normal groups were 1.47 (1.11-1.96) and 1.68 (1.21-2.33). Notably, the strongest Lp(a)-coronary stenosis associations after multi-variable adjustment persisted only in low inflammation concentration [systemic inflammation response index (SIRI) < 0.64)] [mild vs. normal, OR 2.03, 95% CI 1.17-3.54, Elevated Lp(a) correlates with coronary stenosis only in low inflammation concentration. Considering systemic inflammation in personalized Lp(a)-lowering therapies is more conducive for CCS managements. Show less
📄 PDF DOI: 10.1016/j.ajpc.2025.101324
LPA

Precision Management of Dyslipidemia in Atherosclerosis: Mechanisms, Therapeutic Strategies, and Future Directions.

Ganyuan Deng, Qun Kong, Maomao Zhang +1 more · 2025 · Journal of visualized experiments : JoVE · added 2026-04-24
Dyslipidemia is a central driver in the initiation and progression of atherosclerosis (AS). The chronic inflammation and endothelial injury triggered by dyslipidemia are key pathological events in AS Show more
Dyslipidemia is a central driver in the initiation and progression of atherosclerosis (AS). The chronic inflammation and endothelial injury triggered by dyslipidemia are key pathological events in AS development. Elucidating the molecular network underlying dyslipidemia and developing precise interventions are critical for achieving precision prevention and treatment of AS. Recent studies have demonstrated that sterol regulatory element-binding protein 1 (SREBP1) and lipoprotein(a) [Lp(a)] play pivotal roles in the regulation of lipid synthesis and transport. Additionally, gut microbiota-derived metabolites, such as trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs), can activate inflammatory pathways and promote lipid deposition via inter-organ signaling axes, thereby accelerating the progression of AS. However, clinical studies have revealed that even when low-density lipoprotein cholesterol (LDL-C) levels are within the recommended range, a significant number of patients continue to experience cardiovascular events. This indicates the widespread presence of "residual risk". Such residual risk is primarily driven by elevated non-high-density lipoprotein cholesterol (non-HDL-C), abnormal levels of Lp(a), and imbalances in the triglyceride to HDL-C (TG/HDL-C) ratio, highlighting the limitations of traditional therapies in comprehensive lipid profile management. Emerging targeted therapies, including proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, small interfering RNA (siRNA)-based treatments, and Lp(a)-lowering agents like pelacarsen, represent promising strategies for more precise lipid modulation. With the continuous advancement of related research, the precise management of AS will increasingly rely on deeper mechanistic insights and individualized therapeutic strategies. Current strategies for AS prevention and treatment focus on understanding key pathways, including lipid metabolism, inflammation, and vascular dysfunction, to develop targeted therapies. The integration of the 2023 Chinese Guidelines for Lipid Management, imaging, and AI-assisted decision-making will promote data-driven, precision medicine. Personalized drug selection, efficacy monitoring, and long-term follow-up will optimize clinical outcomes and enhance prevention strategies for high-risk patients. Show less
no PDF DOI: 10.3791/69357
LPA

The relationship between preschool-based movement behaviors and physical fitness: a compositional data analysis.

Jin Guo, Yingying Ding, Yihua Bao +6 more · 2025 · Pediatric research · Nature · added 2026-04-24
Physical fitness in preschoolers, encompassing muscular strength, speed-agility, balance, and cardiorespiratory fitness, serves as a key health indicator. While preschools are ideal settings for promo Show more
Physical fitness in preschoolers, encompassing muscular strength, speed-agility, balance, and cardiorespiratory fitness, serves as a key health indicator. While preschools are ideal settings for promoting physical fitness, the association between preschool-based movement behaviors and physical fitness remains unclear. This cross-sectional study included 1144 Chinese preschoolers aged 3-6 years. Preschool-based movement behaviors including moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), and sedentary behavior (SB), were measured using ActiGraph GT9X accelerometers. Physical fitness was assessed via the PREFIT battery, which includes handgrip strength, standing long jump, 4 × 10 m shuttle run, one-leg stance, and 20 m shuttle run. Compositional linear regression and isotemporal substitution modeling were employed to examine associations and time-reallocation effects, respectively. Greater amounts of MVPA during preschool hours were positively associated with better performance in muscular strength, speed-agility, and cardiorespiratory fitness. Reallocating time from SB or LPA to MVPA enhanced physical fitness, whereas substituting MVPA with SB or LPA reduced fitness levels, demonstrating an asymmetric effect. Moderate-to-vigorous physical activity during preschool hours significantly enhances physical fitness. Prioritizing the implementation of physical activity programs to increase MVPA in preschool settings is crucial for improving physical fitness and addressing insufficient MVPA in this age group. First large-scale study (N = 1144) demonstrating preschool-based moderate-to-vigorous physical activity (MVPA) is essential for developing preschoolers' muscular strength, speed-agility, and cardiorespiratory fitness, complementing existing 24-h movement behavior research. Reveals critical asymmetry: Reducing MVPA time significantly harms fitness, with losses exceeding the benefits from equivalent MVPA increases. Provides objective evidence to guide policymakers in optimizing preschool schedules to prioritize MVPA for enhancing children's physical fitness. Show less
📄 PDF DOI: 10.1038/s41390-025-04501-3
LPA

Accelerometer-measured intensity-specific physical activity, genetic susceptibility, and back pain risk: a UK biobank cohort study.

Yuanpeng Zhu, Di Liu, Xiangjie Yin +3 more · 2025 · The spine journal : official journal of the North American Spine Society · Elsevier · added 2026-04-24
Current clinical guidelines lack clear, quantitative recommendations on intensity-specific physical activity (PA) levels for preventing back pain. Moreover, accelerometer-based evidence regarding dose Show more
Current clinical guidelines lack clear, quantitative recommendations on intensity-specific physical activity (PA) levels for preventing back pain. Moreover, accelerometer-based evidence regarding dose-response relationships and interactions between PA and genetic susceptibility remains limited. To determine the relationships between accelerometer-measured total and intensity-specific PA and incident back pain, and to assess potential effect modification by polygenic risk scores (PRS). Prospective, large-scale, population-based study using UK Biobank data. UK Biobank participants who wore wrist accelerometers for 7 days (N=71,601). Incident back pain, defined as the first recorded ICD-10 dorsalgia code (M54). Total PA, light PA (LPA), and moderate-to-vigorous PA (MVPA) were derived using validated machine-learning algorithms from raw accelerometer data. Dose-response relationships were modeled using restricted cubic splines within Cox proportional hazards models, with adjustment for and stratification by a polygenic risk score (PRS). Point estimates for the population attributable fraction (PAF) were then calculated. Body mass index (BMI) mediation was assessed. Over a median follow-up of 7.0 years, total PA and MVPA exhibited nonlinear inverse associations with incident back pain, independent of genetic risk, with thresholds at approximately 35 milli-g (total PA) and 60 min/day (MVPA). The adjusted PAF was 15.9% for low MVPA and 9.9% for low total PA. Associations were strongest for MVPA, followed by total PA; no significant association was observed for LPA. Within both PRS strata, risk declined monotonically across PA quartiles, with similar effect sizes and no PA × PRS interaction. Notably, participants with high PRS and high PA had lower risk than those with low PRS and low PA. BMI mediated 26.2% of the total PA association and 15.5% of the MVPA association. Accelerometer-measured MVPA robustly reduces back-pain risk, independent of genetic predisposition. Future guidelines should provide clear, intensity-specific recommendations and account for the observed nonlinear dose-response to optimize prevention. Show less
no PDF DOI: 10.1016/j.spinee.2025.10.021
LPA

Cyberbullying and cybervictimization patterns among left-behind and non-left-behind adolescents in China: a latent profile analysis.

Lei Liu, Bixia Zhang, Meilin Song +3 more · 2025 · BMC psychology · BioMed Central · added 2026-04-24
Left-behind adolescents in China may face heightened risks of involvement in cyberbullying due to their psychological vulnerability and complex social circumstances. Considering the potential heteroge Show more
Left-behind adolescents in China may face heightened risks of involvement in cyberbullying due to their psychological vulnerability and complex social circumstances. Considering the potential heterogeneity within this population, this study aimed to identify distinct patterns of cyberbullying and cybervictimization among left-behind adolescents and to explore how reactive anger, left-behind patterns, gender, and grade level predict membership in these subgroups. A total of 1,351 junior high school students (752 left-behind, 599 non-left-behind) were recruited from five schools. Latent profile analysis (LPA) was used to identify distinct patterns, and multinomial logistic regression was used to examine the relationships between predictors and various profiles. (1) Three distinct profiles of cyberbullying and cybervictimization were identified among left-behind adolescents. (2) Left-behind adolescents were more likely to experience cybervictimization compared to their non-left-behind peers. (3) Reactive anger, left-behind patterns, gender, and grade level significantly predicted subgroup membership. These findings underscore the importance of developing targeted interventions and considering the specific psychosocial vulnerabilities of left-behind youth. Show less
📄 PDF DOI: 10.1186/s40359-025-03493-3
LPA

[Latent classes and their associated factors of sleep quality among police officers in Inner Mongolia Autonomous Region in 2021].

Yifei Dou, Ying Li, Meng Zhang · 2025 · Wei sheng yan jiu = Journal of hygiene research · added 2026-04-24
To explore the latent classes and their associated factors of sleep quality among police officers, and to analyze the potential heterogeneity in sleep quality within this population. A total of 1162 p Show more
To explore the latent classes and their associated factors of sleep quality among police officers, and to analyze the potential heterogeneity in sleep quality within this population. A total of 1162 police officers were selected using cluster random sampling in the Inner Mongolia Autonomous Region between September and December 2021. Participants completed a basic information questionnaire and the Pittsburgh sleep quality index(PSQI). Latent profile analysis(LPA) was employed to examine heterogeneity in sleep quality, and multinomial Logistic regression was used to identify associated factors of the latent profiles. The mean age of participants was(43.08±8.98) years. The sample comprised 920 males(79.2%) and 242 females(20.8%), 987(84.9%) were married and 175(15.1%) were single, 644(55.4%) had a high school education or below, and 518(44.6%) had college education or above. By department, 607(52.2%) worked in grassroots police stations, 200(17.2%) were criminal police, and 355(30.6%) served in other units. Significant heterogeneity in sleep quality was identified, revealing four distinct latent classes: good sleep group(n=821, 70.6%), moderate sleep group(n=46, 4.0%), sleep-disordered group(n=249, 21.4%), and medication-assisted sleep group(n=46, 4.0%). Using the good sleepers as the reference group, multinomial Logistic regression indicated that older age was a significant risk factor for belonging to the medication-assisted sleep group(OR=1.348, 95%CI 1.078-1.822). Higher education level was a protective factor against membership in the moderate sleep group(OR=4.101, 95%CI 1.304-12.893). Serving as a grassroots police station officer or criminal police officer was a significant risk factor for membership in both the moderate sleep group(OR = 3.329, 95%CI 1.338-8.284; OR=4.188, 95%CI 1.415-12.396) and sleep-disordered group(OR=1.701, 95%CI 1.196-2.420; OR=1.587, 95%CI 1.073-2.533). Sleep quality among police officers demonstrates significant heterogeneity. Age, police department assignment, and educational level are key associated factors of distinct latent classes of sleep quality. Show less
no PDF DOI: 10.19813/j.cnki.weishengyanjiu.2025.05.015
LPA

Lipoprotein(a) Lipidome: Responses to Reduced Dietary Saturated Fat Intake in Two Randomized Controlled Feeding Trials.

Munkhtuya Myagmarsuren, Hayley G Law, Wei Zhang +8 more · 2025 · Nutrients · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/nu17193113
LPA

Latent profiles and correlates factors of cognitive function in older adults: a cross-sectional study.

Jin Xiang, Yan Xiong, Heting Liang +5 more · 2025 · Frontiers in aging neuroscience · Frontiers · added 2026-04-24
This study aimed to identify the latent profiles of cognitive function among community-dwelling and institutionalized older adults, and to examine their associated influencing factors, in order to inf Show more
This study aimed to identify the latent profiles of cognitive function among community-dwelling and institutionalized older adults, and to examine their associated influencing factors, in order to inform the development of targeted interventions. A convenience sampling method was used to select 6,708 elderly people aged 60 years and older from six communities and nine long-term care institutions across China, who were assessed using a general information questionnaire, Mini-Mental State Examination (MMSE), the Frailty Scale, the Anxiety Scale, the Depression Scale, and the Pittsburgh Sleep Quality Index. Latent profile analysis (LPA) was performed based on the MMSE scores, and multiple logistic regression was used to analyse the influencing factors of cognitive function categories. A total of three cognitive function profiles were identified: High cognitive Function group (41.2%), Moderate Cognitive Function Group (48.2%) and Low cognitive Function group (10.7%). Higher Frailty [odds ratio (ORs) = 1.070-1.246], higher depressive symptom scores (OR = 1.059-1.191) and poorer sleep quality (higher PSQI; OR = 1.088) were associated with higher odds of belonging to the Moderate/Low cognitive profiles, whereas adequate social support (Yes vs. No; OR = 0.530-0.696), selected middle-income categories versus ≥¥6,000 in per-capita monthly household income (OR = 0.462-0.735) and male sex (OR = 0.556-0.876) were associated with lower odds. Cognitive function among older adults can be classified into three distinct latent profiles, each associated with different influencing factors. These findings underscore the need for stratified and personalized interventions at the community level to support stratified screening and tailored community programs; given the cross-sectional design, these associations do not establish causality or intervention effects. Show less
📄 PDF DOI: 10.3389/fnagi.2025.1622804
LPA

Accumulation patterns of sedentary and breaks and adiposity risk in Chinese children and adolescents: a latent profile analysis.

Ting Li, Li-Juan Wang, Ai-Jun Cui +4 more · 2025 · BMC public health · BioMed Central · added 2026-04-24
This study aimed to identify and characterize the sedentary behavior (SED) and breaks accumulation patterns of children and adolescents and investigate the associations of these derived patterns with Show more
This study aimed to identify and characterize the sedentary behavior (SED) and breaks accumulation patterns of children and adolescents and investigate the associations of these derived patterns with adiposity indicators. A total of 348 children and 562 adolescents from China participated in this study. Accelerometers were used to measure the bouts of SED and breaks. Adiposity indicators included body mass index (BMI) z-score, fat mass percentage (FM%), and fat mass index (FMI). Latent profile analysis was used to identify the SED and breaks accumulation patterns on the basis of 11 compositions of SED bouts and breaks. Mixed-effects multivariable linear regression models were used to analyze the associations of accumulation patterns with adiposity indicators. Four accumulation patterns were identified in children: "prolonged sitters" (N = 77, 22.1%), "shortened sitters" (N = 90, 25.9%), "LPA breakers" (N = 69, 19.8%), and "MVPA breakers"(N = 112, 32.2%). "MVPA breakers" had significantly lower BMI z-score, FM%, and FMI than "prolonged sitters." No significant differences in adiposity indicators were observed among the other three patterns. In adolescents, "prolonged sitters" (N = 250, 44.5%), "moderate sitters" (N = 211, 37.5%), and "breakers" (N = 101, 18.0%) were identified. "Breakers" had the lowest BMI z-score, FM%, and FMI among the three groups, followed by "moderate sitters" and "prolonged sitters." Different accumulation patterns of SED and breaks were identified for children and adolescents in China. Among them, "MVPA breakers" and "Breakers" are most beneficial to maintain a normal weight status. Health promotion efforts could consider increasing MVPA and decreasing SED time for children and restricting SED to at least 30 min for adolescents to improve their adiposity indicators. Show less
📄 PDF DOI: 10.1186/s12889-025-24540-z
LPA

Association between device-measured physical activities, type 2 diabetes, and life expectancy over the next 10 years: a prospective longitudinal study.

Ziqiang Lin, Jiade Chen, Yutai Cai +16 more · 2025 · BMC public health · BioMed Central · added 2026-04-24
The mediation effect of 24-hour physical activities on the association between type 2 diabetes and mortality is unclear. Additionally, Little evidence was found on the isotemporal substitution effect Show more
The mediation effect of 24-hour physical activities on the association between type 2 diabetes and mortality is unclear. Additionally, Little evidence was found on the isotemporal substitution effect of 24-hour physical activities components on changing Life expectancy among patients with type 2 diabetes diagnosed. To address the abovementioned research gap, the study has a two-fold aims: first, to examine the mediation effect of 24-hour physical activities in type 2 diabetes and mortality; and second, to address how reallocating time on different daily activities would affect life expectancy. Analysis was conducted on the accelerometer data of 103,359 participants in the UK Biobank, with a median age of 57 years (range 39 to 70). Compositional mediation cox model was conducted to analyze the mediating effects of 24-hour physical activities. Additionally, the cohort Life table method was utilized to estimate the changes of Life-years over the next 10 years resulting from the substitution effect of different physical activities. During a mean follow-up of 13.95 (range 2.95-16.28) years, 2,649 deaths were recorded. Diabetes was significantly associated with increased time spent engaging in sedentary behavior (SB), and reduced time spent on moderate-to-vigorous physical activity (MVPA) and light-intensive physical activity (LPA), thereby demonstrating an association with higher mortality risk. The indirect effect of physical activity (HR = 1.27, 95% CI 1.23-1.30) accounted for 41.9% of the total effect of diabetes on mortality. Furthermore, the Life expectancy gains with a maximum of 1.32 years over the next 10 years was found when reallocating SB time to MVPA. The results revealed that 24-hour physical activities might mediate the association between diabetes and mortality. Therefore, promoting participation in MVPA and reducing sedentary activities among diabetes patients was expected to have a positive effect on Life expectancy over the next 10 years. Show less
📄 PDF DOI: 10.1186/s12889-025-24662-4
LPA

Development of a nomogram model for predicting coronary heart disease in patients with metabolic-associated fatty liver disease.

Zhengliang Li, Xiaokai Chen, Juan Wang +6 more · 2025 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
To investigate the risk factors associated with coronary heart disease (CHD) in patients with metabolic-associated fatty liver disease (MAFLD) and develop a nomogram prediction model. This study inclu Show more
To investigate the risk factors associated with coronary heart disease (CHD) in patients with metabolic-associated fatty liver disease (MAFLD) and develop a nomogram prediction model. This study included 394 patients with MAFLD who underwent coronary angiography at The Affiliated Hospital of Qingdao University between December 2019 and December 2024. The study cohort was divided in a 7:3 ratio into training and validation sets comprising 277 and 117 cases, respectively. The training group was further divided into the MAFLD-only ( Of the 394 MAFLD cases, 313 had CHD-related complications. Of the 277 patients in the training set, 220 had CHD, and of the 117 patients in the validation set, 93 had CHD. LASSO regression analysis revealed that the following variables were associated with the risk of CHD: sex, lipoprotein(a) (Lp[a]), low-density lipoprotein cholesterol, white blood cell count (WBC), glycated triglyceride-glucose index (TyG), and atherosclerosis index (AIP). Multivariate logistic regression analysis revealed that sex, Lp(a), WBC, TyG, and AIP were independent risk factors for CHD in MAFLD cases. A nomogram was constructed and an ROC curve was plotted, based on which the optimal cutoff value was determined as 0.698. The area under the curve of the nomogram in the training and validation cohorts was 0.860 (95% CI = 0.807-0.913) and 0.843 (95% CI = 0.757-0.929), respectively. Calibration curves for CHD risk probability showed good agreement between the nomogram's predicted probabilities and the observed event rates. DCA demonstrated the net clinical benefit of the constructed nomogram. Sex, Lp(a), WBC, TyG, and AIP emerged as independent risk factors for CHD in patients with MAFLD and the nomogram prediction model constructed using these factors could effectively predict CHD occurrence. Show less
📄 PDF DOI: 10.3389/fcvm.2025.1652321
LPA

Validation and clinical application of the Chinese version of the rapid response scale.

Ziying Chen, Junhao Shen, Yifang Chen +7 more · 2025 · BMC psychiatry · BioMed Central · added 2026-04-24
Existing depression assessment tools inadequately detect rapid symptom changes after antidepressant treatments. This study aimed to translate, validate, and explore the clinical application of the Chi Show more
Existing depression assessment tools inadequately detect rapid symptom changes after antidepressant treatments. This study aimed to translate, validate, and explore the clinical application of the Chinese version of the McIntyre and Rosenblat Rapid Response Scale (CMRRRS) for use during the treatment of rapid-onset depression. The McIntyre and Rosenblat Rapid Response Scale was translated and culturally adapted for use in Chinese settings. Briefly, 71 patients with major depressive disorder who were receiving intravenous esketamine were assessed using the CMRRRS and other validated scales. Properties were examined, including internal consistency, construct validity, and responsiveness to change. For patient classification, Latent Profile Analysis (LPA) and Kernel Density Estimation (KDE) curves were used. The Minimum Clinically Important Difference was computed to explore the smallest change related to clinical improvement. The CMRRRS showed high reliability and robust validity. Factor analysis explained over 60% of the total variance. LPA distinguished three patient classes, while KDE curves determined that a cut-off of 5 points was optimal for detecting clinically meaningful changes. The CMRRRS is a reliable, valid, and sensitive tool for monitoring rapid symptom changes in patients with depression treated with esketamine. It allows real-time symptom monitoring and personalized treatment adjustments. Further studies are warranted to explore its broader applicability. Show less
📄 PDF DOI: 10.1186/s12888-025-07413-y
LPA

Life's Essential 8, phenotypic age, and cardiovascular age in prediabetes: predictive value in the UK Biobank and protein mediation in cardiovascular-diabetes comorbidity.

Chaoping Chen, Chenhao Li, Qingru Zhu +4 more · 2025 · European journal of medical research · BioMed Central · added 2026-04-24
Lifestyle improvement may help reverse prediabetes. Indicators such as Life's Essential 8 (LE8) and biological aging measures (phenotypic age, cardiovascular biological age) partially reflect metaboli Show more
Lifestyle improvement may help reverse prediabetes. Indicators such as Life's Essential 8 (LE8) and biological aging measures (phenotypic age, cardiovascular biological age) partially reflect metabolic status in prediabetes, but their predictive value for cardiovascular mortality and stroke in this population remains unclear. We analyzed data from 74,678 White participants with prediabetes in the UK Biobank, defined by either HbA1c (5.7-6.4%) or fasting glucose (6.1-6.9 mmol/L). Follow-up continued until October 10, 2023. Cox regression was used to examine associations between LE8, phenotypic age (PhenoAge), cardiovascular biological age (CBA), and outcomes of cardiovascular (CVD) mortality and stroke. Restricted cubic spline (RCS) models identified biological age risk thresholds. Mediation analysis assessed whether proteins such as CST3, EFEMP1, FES, IGFBP2, IGFBP6, LPA, PCSK9, and TIMP1 mediated these effects. Over a median follow-up of 13.4 years, 2263 participants died from CVD causes. Each 1-year increase in CBA or PhenoAge was associated with a ~ 10% higher risk of CVD mortality (CBA aHR = 1.10; PhenoAge aHR = 1.09; both P < 0.001), while each 1-point increase in LE8 score was linked to a 3% lower risk (HR = 0.97, P < 0.001). The risk biological ages for these two indicators were also identified: PhenoAge ≥ 58.52 years and CBA ≥ 62.42 years. Similar trends were observed for stroke. Mediation analysis revealed that CST3, TIMP1, IGFBP2, and IGFBP6 contributed to the biological pathways between aging/lifestyle and CVD outcomes. The combined LE8 and PhenoAge model showed the strongest predictive performance for CVD mortality (AUC = 0.716) and stroke (AUC = 0.638) over 15 years. LE8 combined with phenotypic age provides prognostic value for CVD outcomes in prediabetes. These findings highlight the potential of lifestyle modification and delayed biological aging in reversing prediabetes and underscore comorbidity-related proteins as promising therapeutic targets. Show less
📄 PDF DOI: 10.1186/s40001-025-03218-7
LPA

Current status of career adaptability among Chinese cardiovascular specialist nurses: a latent profile analysis.

Yanling Du, Chao Wu, Ziyue Gai +6 more · 2025 · BMC nursing · BioMed Central · added 2026-04-24
This study aimed to explore the career adaptability status of cardiovascular specialist nurses (CSNs) through latent profile analysis (LPA), identify distinct subgroups and their demographic features, Show more
This study aimed to explore the career adaptability status of cardiovascular specialist nurses (CSNs) through latent profile analysis (LPA), identify distinct subgroups and their demographic features, and determine factors influencing different adaptability categories. CSNs play a vital role in treating and rehabilitating patients with cardiovascular conditions. However, the existing literature offers limited insights into the career adaptability of CSNs in China. A multicenter, cross-sectional survey involving 659 Chinese CSNs was conducted. LPA was utilized to classify career adaptability profiles based on responses to the Career Adaptation Abilities Scale Short Form (CAAS-SF). Influencing factors were assessed using the Conditions of Work Effectiveness Questionnaire-II (CWEQ-II) and the General Self-Efficacy Scale (GSES). Differences among identified profiles were analyzed through ANOVA, chi-square tests, and multinomial logistic regression to explore relevant socio-demographic characteristics and influencing variables. A four-profile model provided the best fit, identifying groups labeled as “high adaptability” (Class 4, These findings provide evidence to assist nursing administrators in developing training programs to enhance CSNs’ career adaptability. The variables identified as associated with profile membership may enable more tailored training strategies. Show less
📄 PDF DOI: 10.1186/s12912-025-03887-z
LPA

Patterns and predictors of post-traumatic growth and fear of disease progression in breast cancer patients: a latent profile analysis.

Keying Guo, Haipeng Li, Weina Du +4 more · 2025 · Frontiers in psychiatry · Frontiers · added 2026-04-24
The primary aim of this study is to explore distinct patterns of post-traumatic growth (PTG) and fear of cancer progression (FOP) among breast cancer patients through latent profile analysis (LPA). Ad Show more
The primary aim of this study is to explore distinct patterns of post-traumatic growth (PTG) and fear of cancer progression (FOP) among breast cancer patients through latent profile analysis (LPA). Additionally, we assessed the differences in demographic and disease-related factors among breast cancer patients with varying patterns. Finally, we examined the influence of socio-demographic, disease-related, social support, anxiety, depression, and post-traumatic stress disorder (PTSD) factors on the varying patterns, aiming to assist healthcare providers in developing more effective psychological care strategies for breast cancer patients. A questionnaire survey was conducted on 752 breast cancer patients. Latent profile analysis was employed to explore the patterns of post-traumatic growth and fear of cancer progression in these patients, and multiple logistic regression analysis was used to identify the predictive factors for the different patterns. Based on the fit indices of latent class analysis, a three-class model was identified as the optimal solution, which included the Resisting group, Struggling group, and Growth group. In the Resisting group (24.33%), patients reported low levels of post-traumatic growth and high levels of fear of cancer progression; in the Struggling group (46.14%), patients exhibited moderate levels of post-traumatic growth and low levels of fear of cancer progression; in the Growth group (29.52%), patients demonstrated high levels of post-traumatic growth and moderate levels of fear of cancer progression. Additionally, the multiple logistic regression analysis reveals that marital status, place of residence, education level, disease stage, social support, anxiety, and post-traumatic stress disorder levels in breast cancer patients serve as significant factors influencing the distinct patterns of post-traumatic growth and fear of progression. This study suggests that there is heterogeneity in the PTG and FOP patterns in breast cancer patients. It provides a research basis for promoting the psychological recovery of breast cancer patients and highlights the importance of focusing on the positive effects of PTG while mitigating the negative impact of FOP. Healthcare providers can implement targeted nursing interventions based on the different patterns observed in breast cancer patients. Show less
📄 PDF DOI: 10.3389/fpsyt.2025.1604787
LPA

Associations between 24-hour movement behaviors and physical fitness in adolescents: A cross-sectional study using objective and subjective measures.

Zhiqiang Ren, Yutai Cai, Qiaoman Mo +5 more · 2025 · Journal of sports sciences · Taylor & Francis · added 2026-04-24
This study aimed to investigate the association between objectively and subjectively measured 24-hour movement behaviors and physical fitness, and explore how the reallocation of time between 24-hour Show more
This study aimed to investigate the association between objectively and subjectively measured 24-hour movement behaviors and physical fitness, and explore how the reallocation of time between 24-hour movement behaviors is associated with changes in physical fitness in adolescents. A total of 690 adolescents aged 14-17 years (55% girls) were included in this cross-sectional study conducted in Foshan, China. Moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behavior, and sleep were assessed using accelerometers in combination with a questionnaire. Physical fitness was tested through body mass index, forced vital capacity, 50-m sprint, standing long jump, sit-and-reach, gender-specific 800/1000-m run, and pull-ups/sit-ups. MVPA was significantly associated with better performance in the 50-m sprint ( Show less
no PDF DOI: 10.1080/02640414.2025.2567791
LPA

Pelvic floor support changes in postpartum pelvic organ prolapse with involvement of single and multiple compartments.

Yujiao Zhao, Luyang Ma, Weijun Li +9 more · 2025 · BMC pregnancy and childbirth · BioMed Central · added 2026-04-24
To investigate longitudinal changes in pelvic floor support in primiparous women with pelvic organ prolapse (POP) after vaginal delivery, focusing on single- and multiple-compartment involvement. Two Show more
To investigate longitudinal changes in pelvic floor support in primiparous women with pelvic organ prolapse (POP) after vaginal delivery, focusing on single- and multiple-compartment involvement. Two hundred primiparas after vaginal delivery were prospectively enrolled and underwent pelvic floor MRI at six weeks postpartum. POP was diagnosed and classified into subgroups (single or multiple compartments involved) based on MRI findings. Primiparas with POP underwent repeat MRI at four months postpartum. Pelvic floor measurements, including injury score and functional parameters of the levator ani muscle (puborectal hiatus line, H line; muscular pelvic floor relaxation line, M line; levator hiatus area, LHA; iliococcygeus angle, ICA; levator plate angle, LPA), were assessed on MRI. Measurements were compared among POP subgroups and a normal control group (without POP) at six weeks postpartum. Additionally, changes between six weeks and four months postpartum were analyzed within POP subgroups. Based on MRI criteria, approximately 41.5% of primiparas were diagnosed with POP, predominantly cystoceles commonly associated with uterine prolapse. Functional parameters of the levator ani, except for LPA at rest, were significantly increased in POP subgroups compared to controls. At four months postpartum, M line, H line, and LPA significantly decreased, and prolapsed organs were elevated in cases with multiple compartments involved, compared to six weeks postpartum. No significant changes were observed in cases with single-compartment involvement during follow-up. A substantial proportion of primiparas experienced postpartum POP. Impaired levator ani function contributed to POP. Pelvic floor support improved during early postpartum in cases with multiple-compartment involvement. Show less
📄 PDF DOI: 10.1186/s12884-025-08044-7
LPA

Characterizing Gray matter atrophy patterns associated with accelerometer-measured sedentary behavior: a population-based study.

Minle Tian, Xiaolei Han, Ming Mao +12 more · 2025 · Brain imaging and behavior · Springer · added 2026-04-24
Evidence has linked self-reported sedentary behaviors with dementia and cognitive impairment; however, the underlying mechanisms remain poorly understood. We investigated the associations of accelerom Show more
Evidence has linked self-reported sedentary behaviors with dementia and cognitive impairment; however, the underlying mechanisms remain poorly understood. We investigated the associations of accelerometer-measured sedentary behavior patterns with gray matter atrophy patterns in rural-dwelling older adults, while taking into account the manner in which sedentary time is accrued (in short or long bouts). This community-based study involved 911 dementia-free older adults (age ≥ 60 years, 59% women) who participated in both ActiGraph and brain MRI substudies within MIND-China (2018-2020). Sedentary behavior parameters (total sedentary time, mean sedentary bout duration, and sedentary breaks) were recorded with accelerometers. Regional gray matter volumes (GMV) were measured using voxel-based morphometry (VBM) methods. Data were analyzed using the general linear regression models, restricted cubic spline curves, and VBM analysis. There was an inverted U-shaped association between daily sedentary time and GMV in temporal, cingulate, and medial temporal cortex, while longer mean sedentary bout duration was linearly related to decreased GMV in total, frontal, temporal, insula, cingulate, and medial temporal cortex. Greater daily time spent in light or moderate-to-vigorous physical activity (LPA and MVPA) was correlated with larger insula GMV. The VBM analysis suggested that prolonged daily total sedentary time and mean sedentary bout duration were significantly associated with smaller GMV in extensive brain regions, especially in thalamus and insula. In conclusion, gray matter atrophy associated with sedentary behavior in older adults is characterized by reduced GMV in global, frontal, temporal, medial temporal, and cingulate cortex, especially in the insula and thalamus regions. Show less
📄 PDF DOI: 10.1007/s11682-025-01054-1
LPA

Correlation of

Jia-Xin Xu, Ye Wu, Lin Zhang +3 more · 2025 · World journal of cardiology · added 2026-04-24
Coronary heart disease (CHD) is a prominent cause of mortality and disability worldwide. Like most complex diseases, the risk of CHD in individuals is regulated by the interaction between genetic fact Show more
Coronary heart disease (CHD) is a prominent cause of mortality and disability worldwide. Like most complex diseases, the risk of CHD in individuals is regulated by the interaction between genetic factors and lifestyle. To investigate the influence of A total of 324 patients with CHD and 143 control participants were involved in this study. Single nucleotide polymorphisms rs429358 and rs7412 in the In the CHD group, the frequencies of In the Teochew population, the Show less
📄 PDF DOI: 10.4330/wjc.v17.i9.110278
LPA

Latent profile analysis of nurses' knowledge, attitudes, and practices regarding pressure injury prevention: a multicenter large-sample study.

Caili Li, Xiaoqian Lu, Liyan Zhang +10 more · 2025 · BMC nursing · BioMed Central · added 2026-04-24
This study aimed to analyze latent profiles and characteristics of nurses' knowledge, attitudes, and practices (KAP) regarding pressure injury (PI) prevention, as well as influencing factors across di Show more
This study aimed to analyze latent profiles and characteristics of nurses' knowledge, attitudes, and practices (KAP) regarding pressure injury (PI) prevention, as well as influencing factors across distinct profiles. A convenience sampling method was employed to recruit nurses from hospitals at various tiers in Guangxi Zhuang Autonomous Region between July and August 2024. Data were collected using a General Information Questionnaire and a Nurse PI-KAP Questionnaire. Latent profile analysis (LPA) identified distinct PI-KAP profiles, while univariate analysis and multinomial logistic regression determined profile-specific influencing factors. Among 17,253 enrolled nurses, the total PI-KAP score was 63.44 ± 7.69. Three latent profiles emerged: low-level PI-KAP (12.82%), moderate-level PI-KAP (52.23%), and high-level PI-KAP (34.95%). Multinomial logistic regression revealed that hospital tier, years of experience, education level, professional title, gender, and attitudes toward PI training significantly influenced PI-KAP profiles (p < .05). Heterogeneity exists in nurses' PI-KAP profiles, with a substantial proportion demonstrating suboptimal competency. Nursing administrators should establish hierarchical training systems tailored to PI-KAP characteristics. Capacity-building strategies include prioritizing training for core nurses, optimizing resource allocation, and establishing tiered hospital assistance mechanisms to enhance team-based PI prevention capabilities. Not applicable. Show less
📄 PDF DOI: 10.1186/s12912-025-03875-3
LPA

Latent Profile Analysis of Depression and Its Influencing Factors Among Frail Older Adults in China.

Lingling Ye, Penghao Fan, Siyuan Zhang +1 more · 2025 · Behavioral sciences (Basel, Switzerland) · MDPI · added 2026-04-24
The present investigation set out to examine potential categories regarding depressive symptoms in frail senior individuals in China and to identify the contributing variables associated with each cat Show more
The present investigation set out to examine potential categories regarding depressive symptoms in frail senior individuals in China and to identify the contributing variables associated with each category, with the goal of informing more targeted mental health interventions. Data were drawn from the 2018 China Health and Retirement Longitudinal Survey, commonly called CHARLS, which comprised an overall cohort of 1083 qualifying respondents. A latent profile analysis (LPA) revealed the following four distinct depression profiles: a Low Depression-High Loneliness Group (38.4%), a Moderately Low Depression-High Suicidal Ideation Group (7.5%), a Moderately High Depression-High Negative Emotion Group (33.4%), and a High Depression-High Suicidal Ideation Group (20.7%). Ordered multi-categorical logistic regression and restricted cubic spline analyses revealed that age, gender, body pain, pension insurance, sleep duration, and frailty index were significant predictors of depression classification. These findings suggest that depressive symptoms among frail older individuals in China are markedly heterogeneous, highlighting the need to develop differentiated intervention strategies for distinct depression risk groups to promote their mental health. Show less
📄 PDF DOI: 10.3390/bs15091217
LPA

Joint association of body mass index and lipoprotein(a) with atrial fibrillation prevalence: an observational and Mendelian randomization study.

Wenxing Guo, Huan Shi, Xinpeng You +5 more · 2025 · Polish archives of internal medicine · added 2026-04-24
Although previous studies have demonstrated that lipoprotein(a) (Lp[a]) and body mass index (BMI) are associated with atrial fibrillation (AF), their joint effect on AF remains poorly understood. Our Show more
Although previous studies have demonstrated that lipoprotein(a) (Lp[a]) and body mass index (BMI) are associated with atrial fibrillation (AF), their joint effect on AF remains poorly understood. Our primary objective was to examine the combined influence of BMI and Lp(a) on AF occurrence. The study included 8886 patients, among whom 205 were diagnosed with persistent AF. The joint association of BMI and Lp(a) with AF was evaluated. A mediation Mendelian randomization (MR) analysis was also performed. In comparison with the individuals with a higher Lp(a) level (≥30 mg/dl) and BMI equal to or above 24 kg/m2, those with a lower Lp(a) level and BMI had the lowest prevalence of AF (odds ratio, 0.96; 95% CI, 0.95-0.97; P <0.001), especially at the age of 50-69 years, and the lowest risk of stroke (hazard ratio [HR], 0.28; 95% CI, 0.12-0.68; P = 0.004), heart failure (HF; HR, 0.24; 95% CI, 0.08-0.66; P = 0.006), and major adverse cardiovascular events (MACE; HR, 0.35; 95% CI, 0.18-0.66; P = 0.001). Mediation MR analysis highlighted the coexposure effects of Lp(a) levels and BMI on AF and their independent influence on AF development. Lower BMI and Lp(a) levels were associated with a reduced prevalence of AF as well as a lower risk of stroke, HF, and MACE. Mediation analysis showed that neither BMI nor Lp(a) mediated the effect of the other, suggesting that their contributions to AF risk operate through independent pathways. Show less
no PDF DOI: 10.20452/pamw.17123
LPA

Latent Motivation Profiles and Doping in Sport and Exercise: An Integrative Approach Based on Achievement Goal and Self-Determination Theories.

Ian David Boardley, Shuge Zhang, Scott Alec Gunning +1 more · 2025 · Scandinavian journal of medicine & science in sports · Blackwell Publishing · added 2026-04-24
Utilizing adult sport (Study 1: N = 290; M
📄 PDF DOI: 10.1111/sms.70138
LPA

Discovery of Novel Autotaxin Inhibitors Bearing the 4,5,6,7-Tetrahydro-7H-Pyrazolo[3,4-c]Pyridine-7-One Core for Pulmonary Fibrosis Therapy.

Jiahe Zhang, Jiachen Zhang, Jiandong Li +3 more · 2025 · Archiv der Pharmazie · Wiley · added 2026-04-24
Pulmonary fibrosis (PF) is a progressive and fatal disease, and recent studies have revealed its key role in the autotaxin (ATX)-lysophosphatidic acid (LPA) signaling pathway, revealing the therapeuti Show more
Pulmonary fibrosis (PF) is a progressive and fatal disease, and recent studies have revealed its key role in the autotaxin (ATX)-lysophosphatidic acid (LPA) signaling pathway, revealing the therapeutic potential of targeting ATX. Herein, starting from PAT-409, a series of novel ATX inhibitors containing the 4,5,6,7-tetrahydro-7H-pyrazolo[3,4-c]pyridin-7-one core were designed to improve the pharmacological activity and physicochemical properties. The most promising compound 19 exhibited potent ATX inhibition (IC Show less
no PDF DOI: 10.1002/ardp.70095
LPA

[Mechanism of naringin in ameliorating lipid-bone metabolism disorders in postmenopausal osteoporotic rats via activation of FXR/FGF19 pathway].

Xin Zhang, Yun-Teng Xu, Xi Chen +4 more · 2025 · Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica · added 2026-04-24
This study aims to investigate the effect and mechanism of naringin on anti-osteoporosis by regulating lipid-bone balance. Thirty healthy female SD rats(8-week-old, SPF grade) were selected and random Show more
This study aims to investigate the effect and mechanism of naringin on anti-osteoporosis by regulating lipid-bone balance. Thirty healthy female SD rats(8-week-old, SPF grade) were selected and randomly divided into a sham group, an ovariectomy group, and a naringin group. Except for the sham group, postmenopausal osteoporosis models were established for both the ovariectomy group and the naringin group by removing bilateral ovaries. Rats in the naringin group were given a naringin suspension at a dose of 100 mg·kg~(-1), while those in sham and ovariectomy groups were administered an equivalent volume of saline. Following the treatment once daily for 12 weeks, an enzyme-linked immunosorbent assay(ELISA) was used to detect the changes in the content of serum estradiol(E₂₎ and bone metabolism biomarkers, including procollagen type Ⅰ N-terminal propeptide(PINP), osteocalcin(OC), and tartrate-resistant acid phosphatase 5(TRACP5). Micro-CT analysis was performed to assess structural alterations in the femoral trabeculae of rats and analyze morphometric parameters of the bone. Hematoxylin-eosin(HE) and Masson staining were used to observe the histopathological changes in the bone tissue. Western blot was employed to analyze the protein expression level of osteogenesis-and adipogenesis-related factors, including peroxisome proliferator-activated receptor gamma(PPARγ), lipoprotein lipase(LPL), RUNT-related transcription factor 2(RUNX2), osterix(OSX), farnesoid X receptor(FXR), and fibroblast growth factor 19(FGF19). Additionally, immunohistochemistry was employed to evaluate the expression of key metabolic pathway proteins FXR and FGF19. After 12-week treatment, compared with the sham group, the ovariectomy group exhibited a significantly reduced level of serum E₂, PINP, and OC, alongside significantly elevated TRACP5. Compared with the ovariectomy group, the levels of serum E₂, PINP, and OC in the naringin group were significantly increased, while the level of TRACP5 was significantly decreased. Compared with the sham group, the ovariectomy group exhibited a decrease in trabecular number and continuity, sparse and disorganized arrangements, and partial formation of voids. The group also showed decreased bone mineral density(BMD), bone volume fraction(BV/TV), trabecular number(Tb.N), and trabecular thickness(Tb.Th), coupled with increased trabecular separation(Tb.Sp). Compared with the ovariectomy group, naringin intervention resulted in improved bone microarchitecture, characterized by increased trabecular number and continuity, more compact arrangements, and a significant reduction in voids. Quantitatively, this was reflected in elevated levels of BMD, BV/TV, Tb.N, and Tb.Th, alongside a significant decrease in Tb.Sp. Under light microscopy, fragmented trabeculae, uneven collagen staining, disorganized arrangements, and an expanded number and size of marrow adipocyte vacuoles were observed in the ovariectomy group, whereas naringin administration attenuated these pathological alterations. Compared with the sham group, the ovariectomy group showed a significant increase in the expression of adipogenic proteins PPARγ and LPL, alongside significant decreases in the expression of osteogenic proteins(RUNX2 and OSX) and of FXR and FGF19 proteins. In contrast, the naringin group exhibited a reversal of these trends compared to the ovariectomy group, with decreased PPARγ and LPL expression and increased RUNX2, OSX, FXR, and FGF19 expression. These findings demonstrate that naringin modulates lipid-bone metabolism homeostasis in postmenopausal osteoporotic rats, ameliorating trabecular microstructure and attenuating bone marrow adipogenesis, with its therapeutic effects mechanistically linked to the FXR/FGF19 signaling pathway. Show less
no PDF DOI: 10.19540/j.cnki.cjcmm.20250908.401
LPL

Cold-resistant lactic acid bacteria in Jerusalem artichoke silage: quality, microbiome and metabolome dynamics during aerobic exposure on the Qinghai-Tibet Plateau.

Xiaoqiang Wei, Lihui Wang, Haiwang Zhang +6 more · 2025 · Frontiers in microbiology · Frontiers · added 2026-04-24
Forage scarcity during the cold season poses a major challenge to livestock farming on the Qinghai-Tibet Plateau. Jerusalem artichoke (
📄 PDF DOI: 10.3389/fmicb.2025.1699658
LPL

Optimization of row-ratios in mechanized hybrid rice seed production: a study on pollen dispersal and seed-setting characteristics at two ecological sites.

Ziyu Li, Guangyi Chen, Wei Li +10 more · 2025 · Frontiers in plant science · Frontiers · added 2026-04-24
To explore the optimal row-ratio in mechanized hybrid rice seed production, a field experiment was conducted in 2024 at Qionglai and Mianzhu using 'Tiantai A' × 'Taihui 808'. Three row-ratio treatment Show more
To explore the optimal row-ratio in mechanized hybrid rice seed production, a field experiment was conducted in 2024 at Qionglai and Mianzhu using 'Tiantai A' × 'Taihui 808'. Three row-ratio treatments (H1: 18:6, H2: 24:6, and H3: 30:6) were tested using agricultural unmanned aerial vehicles (AUAVs) for pollination assistance. The results showed that row-ratio had little effect on sterile line flowering dynamics. The index of flowers meeting (IFM) was 0.71-0.72 at Qionglai and 0.81-0.86 at Mianzhu, with 11 to 12 days of flowering duration. As the row-ratio increased, total pollen quantity in the panicle layer and grain filling rate (GFR) decreased, while grain infection rate (GIR) increased. The responses of grain blighted rate (GBR), grain empty rate (GER), and fertilization success rate (FSR) to row-ratio varied between sites. Pollen density and GFR followed the pattern of near region (NR) > central region (CR) > far region (FR). Within the panicle, pollen density was generally highest in the upper panicle layer (UPL), followed by the middle (MPL) and lower (LPL) layers, with partial exceptions observed in the H2 and H3 treatments at Mianzhu. The vertical distribution of GFR varied by site: at Qionglai, it was apical parts of panicle (APP) > median parts (MPP) > basal parts (BPP), whereas at Mianzhu the order was MPP > APP > BPP. With wider row-ratios, yield per unit area (YUA) and GFR declined (H1 > H2 > H3), while 1,000-grain weight increased or decreased and then increased. Under H1, yields reached 2,107.50 kg ha Show less
📄 PDF DOI: 10.3389/fpls.2025.1704773
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Integrated multi-omics unravels solanesol's multi-organ protection mechanisms against insulin resistance in type 2 diabetic mice.

Wenji Zhang, Wenli Cheng, Jiaqi Fu +5 more · 2025 · Journal of advanced research · Elsevier · added 2026-04-24
Integrated multi-omics analysis has revolutionized the investigation of plant-derived compounds for type 2 diabetes mellitus (T2DM). Solanesol, a bioactive constituent from Solanaceae plants, exhibits Show more
Integrated multi-omics analysis has revolutionized the investigation of plant-derived compounds for type 2 diabetes mellitus (T2DM). Solanesol, a bioactive constituent from Solanaceae plants, exhibits high oral bioavailability and translational potential for multi-target therapeutics. This study aimed to elucidate the multi-target mechanisms and multi-organ protective effects of solanesol in T2DM management through integrated multi-omics approaches, to bridge the gap between phytochemical discovery and clinical translation. In Lepr Solanesol improved glucose tolerance, insulin sensitivity, and reduced serum lipids, hepatic gluconeogenesis, uric acid, white adipose mass, pancreatic/hepatic inflammation, and renal fibrosis. Mechanistically, solanesol: 1) enriched beneficial gut microbiota (Alistipes, Anaerotruncus, and Parasutterella) and increased levels of long-chain unsaturated fatty acids; 2) rebalanced the dysfunctional mitochondrial oxidative phosphorylation​​ microenvironment by modulating the expression and the activities of respiratory chain Complexes I-V; 3) modulated hepatic lipid metabolism by ​inhibiting​​ de novo ​​lipogenesis​​ via the Acly-Acaca-Fasn pathway, promoting cholesterol efflux and fatty acid oxidation​​ through Abca1/Fabp5, and attenuating inflammation​​ via Lpl-PPARδ downregulation. Solanesol demonstrates multi-organ protective effects through gut microbiota-metabolite crosstalk and hepatic lipid/redox homeostasis regulation. Its multi-target efficacy and oral bioavailability position it as a novel, clinically translatable candidate for T2DM management. Show less
no PDF DOI: 10.1016/j.jare.2025.12.025
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Integrated single-cell RNA-seq and bulk RNA-seq analysis to investigate key adipogenesis genes in adipose-derived stem cells.

Tongtong Zhang, Zhongming Cai, Haoran Li +3 more · 2025 · PloS one · PLOS · added 2026-04-24
Adipogenic differentiation of adipose-derived stem cells (ADSCs) is fundamental to both adipose tissue homeostasis and clinical applications, particularly fat grafting. However, the global and stage-s Show more
Adipogenic differentiation of adipose-derived stem cells (ADSCs) is fundamental to both adipose tissue homeostasis and clinical applications, particularly fat grafting. However, the global and stage-specific transcriptional regulatory networks underlying ADSC adipogenesis remain incompletely elucidated. In this study, we integrated bulk and single-cell RNA-seq datasets across multiple time points of ADSC adipogenesis to identify core regulators of differentiation and maturation. A total of 41 genes were consistently upregulated during early differentiation, among which eight hub genes (FABP4, FASN, FABP5, ADIPOQ, PLIN1, LPL, CIDEC, and ACSL1) formed a tightly connected protein-protein interaction (PPI) module associated with lipid metabolism, lipid droplet formation, and adipocyte maturation. Further integration of differentially expressed lncRNAs and miRNAs led to the construction of a ceRNA network involving 7 mRNAs, 9 miRNAs, and 4 lncRNAs, comprising 34 predicted lncRNA-miRNA-mRNA regulatory axes. To identify temporal transcriptional regulators, we defined five genes (TTC14, MBNL2, UBR3, ABCD2, and SORT1) as early-stage inducers of adipogenesis, and four genes (UQCR11, NDUFB4, S100A10, and PRDX3) as late-stage regulators involved in maintaining the mature phenotype. These stage-specific regulators showed distinct temporal expression patterns and were validated by qPCR. GeneMANIA network analysis further revealed that early-stage regulators were enriched in lipid transport and lipase activity regulation, while late-stage regulators were associated with mitochondrial electron transport and energy metabolism. These findings highlight the stage-dependent transcriptional landscape of ADSC adipogenesis and provide candidate regulatory targets for modulating adipocyte differentiation and stability. Show less
📄 PDF DOI: 10.1371/journal.pone.0335152
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Lipidomic and Transcriptomic Reveals Variations in Lipid Deposition During Goose Fatty Liver Formation.

Qi Zhang, Chuning Bai, Mingai Zhang +6 more · 2025 · Biology · MDPI · added 2026-04-24
Goose foie gras production requires force-feeding with high-energy feed, disrupting hepatic lipid homeostasis and causing excessive lipid accumulation. To investigate the formation mechanism, we colle Show more
Goose foie gras production requires force-feeding with high-energy feed, disrupting hepatic lipid homeostasis and causing excessive lipid accumulation. To investigate the formation mechanism, we collected liver samples from Landes geese at pre-force-feeding (D0), mid-force-feeding (D16), and terminal-force-feeding (D25) stages. Overfeeding shifted liver color from reddish-brown to yellow, significantly increasing size and weight. Histological analysis revealed pronounced lipid droplet accumulation in hepatocytes. Biochemical analysis indicated force-feeding groups (D16, D25) exhibited continuous and significant decreases in liver moisture, crude ash, and crude protein content compared to D0, while crude fat increased substantially. Integrated transcriptomic and lipidomic analyses identified 497 differentially expressed genes (DEGs) and 368 differential lipid molecules (DLMs) between D16 and D0, and 303 DEGs and 172 DLMs between D25 and D16. KEGG enrichment highlighted four pathways associated with fatty liver formation: glycerolipid metabolism, adipocytokine signaling pathway, ErbB signaling pathway, and MAPK signaling pathway. Within these, key genes ( Show less
📄 PDF DOI: 10.3390/biology14111617
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