👤 Guilin 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, 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, Zian Zhang, Zicheng Zhang, Ziding Zhang, Ziguo Zhang, Zihan Zhang, Ziheng Zhang, Zijian Zhang, Zijiao Zhang, Zijing Zhang, Zikai Zhang, Zilong Zhang, Zilu Zhang, Ziping Zhang, Ziqi Zhang, Zishuo Zhang, Zixiong Zhang, Zixu Zhang, Zixuan Zhang, Ziyang Zhang, Ziyi Zhang, Ziyin Zhang, Ziyu Zhang, Ziyue Zhang, Zizhen Zhang, Zongping Zhang, Zongquan Zhang, Zongwang Zhang, Zongxiang Zhang, Zu-Xuan Zhang, Zufa Zhang, Zuoyi Zhang
articles

The significance of urine extracellular vesicle DNA methylation detection in the diagnosis and classification of prostate cancer.

Ting Ding, Yanjun Diao, Ruiqing Fu +11 more · 2025 · Journal of advanced research · Elsevier · added 2026-04-24
As one of the most common malignant tumors in men, prostate cancer (PCa) still lacks convenient, non-invasive and highly specific diagnostic markers. The advantages of Extracellular vesicle (EV) DNA i Show more
As one of the most common malignant tumors in men, prostate cancer (PCa) still lacks convenient, non-invasive and highly specific diagnostic markers. The advantages of Extracellular vesicle (EV) DNA in tumor diagnosis have gradually attracted the attention of researchers. However, methylation detection, which is more advantageous than mutation detection in tumor diagnosis, has not been widely practiced in EV DNA, and its value in PCa diagnosis also remains underexplored. This study aims to establish and optimize an EV DNA methylation detection system and evaluate its diagnostic and classification potential for PCa. We characterized EV DNA biological properties, optimized pretreatment strategies, validated its correlation with genomic DNA methylation, and explored urine EV DNA methylation targets in 86 benign prostatic hyperplasia (BPH) and 109 PCa patients across three cohorts (screening: 30 BPH/33 PCa; training: 27 BPH/30 PCa; validation: 29 BPH/46 PCa). Heterogeneous biological characteristics were observed among DNA from different subtypes of EV, but methylation profiles remained consistent across subtypes and post-DNase I treatment. EV DNA accurately reflected the methylation state of source cell genomic DNA. By combining our screening results with data from the TCGA database and previously reported, we developed a panel consisting of 667 PCa-specific methylation targets for detection. Among these, six methylation sites (MACF1、LINC01359-1、LINC01359-2、ADCY4、GAPLINC、C19orf25) demonstrated high diagnostic value for PCa, enabling construction of PCa and aggressive PCa differential diagnosis model with AUCs up to 0.74 and 0.91 respectively. The diagnostic value of these six markers was further confirmed using methylight PCR in the validation cohort which also displayed promising performance as a tool for diagnosing PCa. This study highlights the potential of urine EV DNA methylation as a novel diagnostic marker for PCa and lays a foundation for future EV DNA research. Show less
no PDF DOI: 10.1016/j.jare.2025.09.056
MACF1

Polystyrene/polylactic acid microplastics impair transzonal projections and oocyte maturation via gut microbiota-mediated lipoprotein lipase inhibition.

Jiacheng Zhang, Hangqi Hu, Yutian Zhu +11 more · 2025 · Journal of hazardous materials · Elsevier · added 2026-04-24
This study focuses on the impacts of polystyrene/polylactic acid microplastics (PS/PLA-MPs) on ovarian reserve and oocyte maturation in female mice, along with the underlying mechanisms. 1 μm PS-MPs a Show more
This study focuses on the impacts of polystyrene/polylactic acid microplastics (PS/PLA-MPs) on ovarian reserve and oocyte maturation in female mice, along with the underlying mechanisms. 1 μm PS-MPs and PLA-MPs were prepared, with PLA-MPs having a rougher surface and broader size distribution. In vitro, PLA-MPs showed higher cytotoxicity to granulosa cells compared to PS-MPs. In vivo, MPs exposure disrupted the estrous cycle, and damaged ovarian reserve. Granulosa cell apoptosis and cytokine activation led to transzonal projection retraction, oocyte oxidative stress, meiotic abnormalities, and reduced oocyte retrieval and polar body extrusion rate, thus reducing litter size. PS-MPs induced more severe intestinal and ovarian impairment. Analysis of feces 16S rRNA, serum metabolomics, and ovarian RNA sequencing revealed that lipoprotein lipase (LPL) was suppressed by both MPs, linking gut microbiota, lipid metabolism, and ovarian injury. Fecal microbiota transplantation as a rescue strategy in MPs exposed mice upregulated LPL, alleviating ovarian reserve decline. In PLA-MPs exposed mice, ovarian reserve related indicators partially recovered after a two-week exposure cessation. These results clarify the similarities and differences in how PS-MPs and PLA-MPs impair ovarian function via gut-ovary axis and lipid metabolism dysregulation. Show less
no PDF DOI: 10.1016/j.jhazmat.2025.139475
LPL

Copper metabolism-related biomarkers and therapeutic targets for diabetic nephropathy.

Qiaofang Yan, Yuanyuan Du, Fei Huang +9 more · 2025 · PeerJ · added 2026-04-24
Diabetic nephropathy (DN) is the most intractable complication of diabetes. Despite decades of research, accurate diagnostic markers and effective therapeutic drugs are still elusive. Abnormal copper Show more
Diabetic nephropathy (DN) is the most intractable complication of diabetes. Despite decades of research, accurate diagnostic markers and effective therapeutic drugs are still elusive. Abnormal copper metabolism is also implicated in diabetes and its complications. This study aims to identify copper metabolism-related biomarkers and potential drugs for DN. DN datasets and copper metabolism-related genes (CMGs) were obtained from Gene Expression Omnibus (GEO) and GeneCards. Differentially expressed CMGs (DE-CMGs) were identified using the limma package and the Venn algorithm. Functional enrichment analysis and protein-protein interaction (PPI) network were performed to identify candidate hub genes. The single gene with an area under the receiver operating characteristic (ROC) curve > 0.7 was identified as a potential diagnostic biomarker of DN. Finally, these biomarkers were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in high-glucose-treated human proximal tubular (HK-2) cells. These validated hub genes were used to construct a combined prediction model, confirmed by additional GSE30528 and GSE30529 datasets. The correlation analysis between the expression level of the hub genes and the estimated glomerular filtration rate (eGFR) was carried out. Additionally, immune cell infiltration and potential target drugs were investigated for these biomarkers. Five hub genes associated with copper metabolism, namely CD36, CCL2, CASP3, LPL, and APOC3, were identified as biomarkers for the early diagnosis of DN. Utilizing multiple biomarkers enhanced diagnostic accuracy and specificity. CD36, CCL2, and CASP3 correlated negatively with eGFR levels, while LPL and APOC3 correlated positively. Additionally, these hub genes were significantly linked to various immune cell types, including macrophages M1 and M2, T cells, gamma delta resting dendritic cells, neutrophils, and NK cells. Furthermore, 15 agents targeting these biomarkers were retrieved from the DrugBank database. Our study identified key genes possibly related to copper metabolism in the pathological mechanism of DN that could serve as novel targets for the diagnosis and therapy of DN. Show less
📄 PDF DOI: 10.7717/peerj.20468
APOC3

Multi-Angle Bioactivity Cartography for Computational Screening and Mechanistic Analysis of AChE Inhibitors From Yellow Gastrodia elata.

Ruijun Sun, Yuchi Zhang, Jingying Xu +7 more · 2025 · Archiv der Pharmazie · Wiley · added 2026-04-24
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-se Show more
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-serving as promising reservoirs of such inhibitors. Nevertheless, comprehensive screening and mechanistic characterization of their inhibitory potential remain limited. This study sought to identify potent AChE inhibitors from YGE, investigate their mechanisms of action, and assess their therapeutic prospects for AD. Methodologically, an integrated approach was employed, combining ultrafiltration-liquid chromatography (UF-LC) for rapid inhibitor screening, molecular docking and dynamics simulations for mechanistic insight, two-stage high-speed countercurrent chromatography for compound isolation, enzyme kinetics to delineate inhibition modalities, and network pharmacology to uncover relevant AD-related targets. The findings identified seven active constituents with notable AChE inhibition, among which parishins A and G were obtained at high purity (98.26% and 97.26%, respectively) and exhibited mixed-type inhibition with low IC Show less
no PDF DOI: 10.1002/ardp.70174
BACE1

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

Hui-Hui Liu, Chen-Xi Song, Sha Li +12 more · 2025 · MedComm · Wiley · added 2026-04-24
This study aimed to investigate the effect of lipoprotein(a) (Lp(a)) on major adverse cardiovascular events (MACEs) among individuals with chronic coronary syndrome (CCS) according to ABO blood groups Show more
This study aimed to investigate the effect of lipoprotein(a) (Lp(a)) on major adverse cardiovascular events (MACEs) among individuals with chronic coronary syndrome (CCS) according to ABO blood groups. Two independent cohorts of patients with CCS were included consecutively. Blood groups and Lp(a) levels were measured. Patients with the AB group were excluded due to the small sample size. In the exploratory cohort ( Show less
📄 PDF DOI: 10.1002/mco2.70505
LPA

Dyslipidemia and aging: the non-linear association between atherogenic index of plasma (AIP) and aging acceleration.

QianKun Yang, XianJie Zhu, Li Zhang +1 more · 2025 · Cardiovascular diabetology · BioMed Central · added 2026-04-24
Dyslipidemia has been proved to play a pivotal role in biological aging. Atherogenic Index of Plasma (AIP), derived from serum triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C), is an Show more
Dyslipidemia has been proved to play a pivotal role in biological aging. Atherogenic Index of Plasma (AIP), derived from serum triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C), is an effective biomarker of dyslipidemia. However, whether AIP can be used as an indicator of biological aging remains unclear. This study aims to investigate the relationship between AIP and biological aging in the US adult population. 4,471 American adults with age over 20 years from the National Health and Nutrition Examination Survey (NHANES) database were included in this study. Biological aging was assessed by phenotypic age acceleration (PhenoAgeAccel). Multivariable linear regression models, subgroup analyses and interaction tests were employed to explore the association between AIP and PhenoAgeAccel. Furthermore, adjusted restricted cubic spline (RCS) analyses were employed to assess potential nonlinear relationships, while mediation analysis was utilized to identify the mediating effects of homeostatic model assessment of insulin resistance (HOMA-IR). Besides, network pharmacology was performed to determine the potential mechanisms underlying dyslipidemia-related aging acceleration. A total of 4,471 participants were included in this study, the median chronological age, PhenoAge and PhenoAgeAccel for the overall population were 49 (35-64) years, 42.85 (27.30-59.68) years, and - 6.92 (- 10.52 to -2.46) years, respectively. In the fully adjusted model, one unit increase of AIP was correlated with 1.820-year increase in PhenoAgeAccel (β = 1.820, 95% CI: 1.085-2.556), which was more pronounced among individuals being female, diabetic and hypertensive. Furthermore, RCS analysis revealed a nonlinear relationship between AIP and PhenoAgeAccel, with an inflection point identified at -0.043 for AIP via threshold and saturation effect analysis. AIP demonstrated a positive correlation with PhenoAgeAccel both before (β = 6.550, 95% CI: 5.070-8.030) and after (β = 3.898, 95% CI: 2.474-5.322) this inflection point. Additionally, HOMA-IR was found to mediate 39.21% of the association between AIP and PhenoAgeAccel. Finally, network pharmacology analysis identified INS, APOE, APOB, IL6, IL10, PPARG, MTOR, ACE, PPARGC1A, and SERPINE1 as core targets in biological aging, which were functionally linked to key signaling pathways like AMPK, apelin, JAK-STAT, FoxO, etc. CONCLUSIONS: An elevated AIP was notably and positively correlated with accelerated aging, suggesting that AIP may serve as an effective predictor to evaluate accelerated aging. Show less
📄 PDF DOI: 10.1186/s12933-025-02695-8
APOB

Ferroptosis-related hub genes and immune cell dynamics as diagnostic biomarkers in age-related macular degeneration.

Jinquan Chen, Zhao Long, Dandan Shi +2 more · 2025 · European journal of medical research · BioMed Central · added 2026-04-24
Age-related Macular Degeneration (AMD) is widely acknowledged as a principal cause of vision loss in the elderly. Currently, the therapeutic interventions available in clinical practice fail to achiev Show more
Age-related Macular Degeneration (AMD) is widely acknowledged as a principal cause of vision loss in the elderly. Currently, the therapeutic interventions available in clinical practice fail to achieve satisfactory outcomes. Therefore, it is imperative that we approach the progress of AMD from novel perspectives in order to explore new therapeutic strategies. We obtained transcriptomic data from the macular and the peripheral retina from patients with AMD and a control group from the Gene Expression Omnibus (GEO) database. Through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we identified differentially expressed genes (DEGs) that were significantly enriched in functions associated with ferroptosis. Subsequent application of machine learning techniques enabled the identification of key hub genes, whose diagnostic potential was further validated. Additionally, the expression of these hub genes was corroborated in both animal and cellular models. Finally, we performed a functional enrichment analysis of these hub genes. In the macula of patients with AMD, 452 DEGs were identified, while in the peripheral retina, 222 DEGs were discovered. Within the macula, 19 genes were associated with ferroptosis, compared to 3 in the peripheral retina. Consequently, the macular was selected as the primary focus of the study. Subsequent screening of these 19 genes using LASSO regression, Support Vector Machine (SVM), and Random Forest algorithms identified four hub genes: FADS1, TFAP2A, AKR1C3, and TTPA. Consequently, we utilized cigarette smoke extract (CSE) to either stimulate retinal pigment epithelial (RPE) cells in vitro or administer it via intravitreal injection, thereby establishing in vitro and in vivo models of AMD. Results from RT-PCR and Western blot analyses revealed an upregulation of FADS1, AKR1C3, and TTPA, while TFAP2A exhibited decreased expression. Finally, we investigated the infiltration of immune cells within the macular and performed a functional enrichment analysis of the hub genes. We identified four key ferroptosis-related genes (FRGs)-FADS1, AKR1C3, TFAP2A, and TTPA-that possess diagnostic relevance for AMD and correlate with immune cell infiltration. Moreover, significant changes in both mRNA and protein expression levels of these genes have been observed in in vitro experiments and mice models. Show less
📄 PDF DOI: 10.1186/s40001-025-03044-x
FADS1

Meta-Analysis and Machine Learning Prediction of Protein Corona Composition across Nanoparticle Systems in Biological Media.

Alexa Canchola, Keyuan Li, Kunpeng Chen +12 more · 2025 · ACS nano · ACS Publications · added 2026-04-24
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokine Show more
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. Yet systematic analyses are hampered by the absence of standardized, richly annotated data sets. Here, we introduce the Protein Corona Database (PC-DB), which compiles data from 83 studies (2000-2024) and integrates 817 NP formulations with quantitative profiles of 2497 adsorbed proteins. The PC-DB exposes pronounced heterogeneity in NP materials (metal 28.8%, silica 22.8%, lipid-based 14.8%), surface modifications, sizes (1-1400 nm), and ζ-potentials (-70 to +70 mV). Subsequent meta-analysis shows that silica, polystyrene, and lipid-based NPs smaller than 100 nm with moderately negative to neutral ζ-potentials preferentially bind the lipoproteins APOE and APOB-100, which are linked to receptor-mediated uptake and enhanced delivery efficiency. In contrast, metal and metal-oxide NPs carrying highly negative surface charge enrich complement component C3, indicating a greater likelihood of immune recognition and clearance. Interpretable machine learning models (LightGBM and XGBoost; ROC-AUC > 0.85) confirm NP size, ζ-potential, and incubation time as the most influential predictors of protein adsorption. These results delineate how physicochemical parameters dictate PC composition and illustrate the power of predictive modeling to guide rational NP design. Show less
📄 PDF DOI: 10.1021/acsnano.5c08608
APOB

Myocardial dysfunction caused by MyBPC3 P459fs mutation in hypertrophic cardiomyopathy: evidence from multi-omics approaches and super-resolution imaging.

Yupeng Wu, Yuzhu Zhang, Qirui Zheng +5 more · 2025 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Mutations in the sarcomere protein, particularly in cardiac myosin binding protein C gene ( We used multi-omics approaches and super-resolution imaging to explore the effects of MyBPC3 P459fs mutation Show more
Mutations in the sarcomere protein, particularly in cardiac myosin binding protein C gene ( We used multi-omics approaches and super-resolution imaging to explore the effects of MyBPC3 P459fs mutation on humans and cells. HCM patients carrying MyBPC3 P459fs mutation (MyBPC3-P459fs HCMs) and healthy controls (HCs) were evaluated for myocardial function using both conventional and advanced echocardiography. In parallel, H9C2 myocardial cells infected with either MyBPC3 P459fs mutation (P459fs cells) or its wild type (WT cells) were investigated for myocardial fiber formation and the potential pathways behind this using super-resolution imaging and metabolomics and proteomics. First, conventional and advanced echocardiography showed that MyBPC3-P459fs HCMs exhibited left ventricular diastolic and systolic dysfunction. Subsequently, super-resolution imaging indicated that P459fs cells formed fewer and shorter myocardial fibers in the cytoplasm compared to WT cells. Moreover, our metabolomic and proteomic data suggested several key components of mitochondrial membrane integrity, myocardial remodeling, myocardial energy metabolism, oxidative stress, inflammation, and actin binding capacity were significantly altered in response to P459fs mutation. This investigation indicated myocardial dysfunction and myocardial fiber disarray in clinical HCMs with MyBPC3 P459fs mutation and added potential pathways underlying this. These findings provided a link between the observed structural and functional disorders in MyBPC3 P459fs mutation and its onset of HCM pathogenesis and might have a significant translational contribution to effective treatment in HCM patients with MyBPC3 P459fs mutation. Show less
📄 PDF DOI: 10.3389/fcvm.2025.1529921
MYBPC3

Transcriptional activation of MACF1 by NR2F1 drives WNT-mediated focal adhesion and metastasis in lung adenocarcinoma.

Yi Zhou, Jing Wang, Yangcheng Sun +2 more · 2025 · European journal of medical research · BioMed Central · added 2026-04-24
To investigate the regulatory role of MACF1 and its upstream transcriptional control in focal adhesion remodeling and tumor progression in lung adenocarcinoma (LUAD). We employed in vitro loss- and ga Show more
To investigate the regulatory role of MACF1 and its upstream transcriptional control in focal adhesion remodeling and tumor progression in lung adenocarcinoma (LUAD). We employed in vitro loss- and gain-of-function assays using shRNA-mediated knockdown and ectopic overexpression of MACF1 and NR2F1 in LUAD cell lines (H1299 and Calu-3). Cell proliferation, adhesion, and migration were assessed by CCK-8, EdU, crystal violet, and Transwell assays. In vivo tumor growth and metastasis were evaluated using subcutaneous and tail vein xenograft models in nude mice. RNA-seq and GSEA were performed to identify MACF1-regulated pathways, followed by nuclear-cytoplasmic fractionation, dual-luciferase reporter assays, and immunofluorescence to assess WNT/β-catenin activity. ChIP-qPCR and ChIP-seq data from ENCODE were used to validate NR2F1 binding to the MACF1 promoter. MACF1 knockdown significantly suppressed LUAD cell proliferation, DNA replication, adhesion, and migration, and reduced tumor burden and lung metastases in vivo. Mechanistically, MACF1 activated WNT/β-catenin signaling by promoting CTNNB1 nuclear translocation, which upregulated focal adhesion genes (Paxillin, FAK, ITGB1). CTNNB1 agonist TWS119 restored focal adhesion in MACF1-deficient cells. Bioinformatic prediction and ChIP validation identified NR2F1 as a transcription factor directly targeting the MACF1 promoter. NR2F1 deficiency reduced MACF1 expression and phenocopied its functional loss, while MACF1 overexpression rescued the impaired phenotype. Our study uncovers a previously unrecognized NR2F1-MACF1-WNT axis that drives focal adhesion formation and LUAD progression. Targeting this regulatory circuit may offer new avenues for anti-metastatic therapy in lung adenocarcinoma. 1. NR2F1 is identified as a direct upstream transcription factor that activates MACF1 expression in LUAD. 2. MACF1 promotes LUAD cell proliferation, adhesion, and migration by enhancing focal adhesion assembly. 3. MACF1 activates the WNT/CTNNB1 signaling cascade, facilitating CTNNB1 nuclear translocation and downstream target expression. 4. Loss of MACF1 impairs focal adhesion formation and metastatic potential both in vitro and in xenograft and tail vein models. 5. The NR2F1-MACF1-WNT axis represents a novel regulatory circuit driving LUAD metastasis and offers potential therapeutic targets. Show less
📄 PDF DOI: 10.1186/s40001-025-03332-6
MACF1

A Novel IgG-IgM Autoantibody Panel Enhances Detection of Early-stage Lung Adenocarcinoma from Benign Nodules.

Rongrong Luo, Xiying Li, Ruyun Gao +13 more · 2025 · Genomics, proteomics & bioinformatics · Oxford University Press · added 2026-04-24
Autoantibodies hold promise for diagnosing lung cancer. However, their effectiveness in early-stage detection needs improvement. In this study, we investigated novel IgG and IgM autoantibodies for det Show more
Autoantibodies hold promise for diagnosing lung cancer. However, their effectiveness in early-stage detection needs improvement. In this study, we investigated novel IgG and IgM autoantibodies for detecting early-stage lung adenocarcinoma (Early-LUAD) by employing a multi-step approach, including Human Proteome Microarray (HuProtTM) discovery, focused microarray verification, and ELISA validation, on 1246 individuals consisting of 634 patients with Early-LUAD (stage 0-I), 280 patients with benign lung disease (BLD), and 332 normal healthy controls (NHCs). HuProtTM selected 417 IgG/IgM candidates, and focused microarray further verified 55 significantly elevated IgG/IgM autoantibodies targeting 32 tumor-associated antigens in Early-LUAD compared to BLD/NHC/BLD+NHC. A novel panel of 10 autoantibodies (ELAVL4-IgM, GDA-IgM, GIMAP4-IgM, GIMAP4-IgG, MGMT-IgM, UCHL1-IgM, DCTPP1-IgM, KCMF1-IgM, UCHL1-IgG, and WWP2-IgM) demonstrated a sensitivity of 70.5% and a specificity of 77.0% or 80.0% for distinguishing Early-LUAD from BLD or NHC in ELISA validation. Positive predictive values for distinguishing Early-LUAD from BLD with nodules ≤ 8 mm, 9-20 mm, and > 20 mm significantly increased from 47.27%, 52.00%, and 62.90% [low-dose computed tomography (LDCT) alone] to 79.17%, 71.13%, and 87.88% (10-autoantibody panel combined with LDCT), respectively. The combined risk score (CRS), based on the 10-autoantibody panel, sex, and imaging maximum diameter, effectively stratified the risk for Early-LUAD. Individuals with 10 ≤ CRS ≤ 25 and CRS > 25 indicated a higher risk of Early-LUAD compared to the reference (CRS < 10), with adjusted odds ratios of 5.28 [95% confidence interval (CI): 3.18-8.76] and 9.05 (95% CI: 5.40-15.15), respectively. This novel panel of IgG and IgM autoantibodies offers a complementary approach to LDCT in distinguishing Early-LUAD from benign nodules. Show less
no PDF DOI: 10.1093/gpbjnl/qzae085
WWP2

[Analysis of gene expression in synovial fluid and blood of patients with knee osteoarthritis of Yang deficiency and blood stasis type].

Hao-Tian Hua, Zhong-Yi Zhang, Zhao-Kai Jin +5 more · 2025 · Zhongguo gu shang = China journal of orthopaedics and traumatology · added 2026-04-24
To reveal the molecular basis of knee osteoarthritis (KOA) with Yang deficiency and blood stasis syndrome by analyzing the gene expression profiles in synovial fluid and blood of KOA patients with thi Show more
To reveal the molecular basis of knee osteoarthritis (KOA) with Yang deficiency and blood stasis syndrome by analyzing the gene expression profiles in synovial fluid and blood of KOA patients with this syndrome. A total of 80 KOA patients were recruited from October 2022 to June 2024, including 40 cases in the non- Logistic regression analysis showed that compared with KOA patients with non-Yang deficiency and blood stasis syndrome, those with Yang deficiency and blood stasis syndrome had increased BMI, LDL, fibrinogen, total cholesterol, and D-dimer, and decreased HDL, with a clear correlation between the two groups. There were 562 differential genes in the blood, among which 322 were up-regulated and 240 were down-regulated;755 differential genes were found in the synovial fluid, with 350 up-regulated and 405 down-regulated. KEGG signaling pathway analysis of synovial fluid revealed changes in lipid metabolism-related pathways, including cholesterol metabolism, fatty acid metabolism, and PPARG signaling pathway. Analysis of the involved differential genes identified 6 genes in synovial fluid that were closely related to lipid metabolism, namely LRP1, LPL, ACOT6, TM6SF2, DGKK, and PPARG. Subsequently, PCR and immunohistochemical verification were performed using synovial fluid and cartilage samples, and the results were consistent with those of microarray sequencing. This study explores the clinical and genomic correlation between traditional Chinese medicine syndromes and knee osteoarthritis from the perspective of lipid metabolism, and proves that abnormal lipid metabolism is closely related to KOA with Show less
no PDF DOI: 10.12200/j.issn.1003-0034.20241087
LPL

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
LPL

METTL3-dependent DLG2 inhibits the malignant progression of cervical cancer by inactivating the Hippo/YAP signaling.

Mei Pu, Xia Xiao, Shasha Lv +6 more · 2025 · Hereditas · BioMed Central · added 2026-04-24
Discs large homolog 2 (DLG2) has been implicated in cancer development, yet its role in cervical cancer remains unclear. This study aims to explore the regulatory mechanism of DLG2 in cervical cancer Show more
Discs large homolog 2 (DLG2) has been implicated in cancer development, yet its role in cervical cancer remains unclear. This study aims to explore the regulatory mechanism of DLG2 in cervical cancer and its clinical implications. Quantitative reverse transcription polymerase chain reaction and western blotting assays were employed to detect RNA and protein expression, respectively. Colony formation assay, 5-Ethynyl-2'-deoxyuridine assay, flow cytometry, and transwell assays were conducted for cell functional analysis. A xenograft mouse model assay was performed to analyze tumor tumorigenesis in vivo. m6A RNA immunoprecipitation assay was used to analyze the association of METTL3 and DLG2. DLG2 was underexpressed in cervical cancer tissues and cells. Elevating DLG2 levels significantly suppressed cervical cancer cell proliferation, migration, and invasion, while promoting apoptosis. Additionally, DLG2 overexpression led to the deactivation of the Hippo/YAP signaling pathway. In vivo, DLG2 overexpression was shown to reduce tumor formation. We also discovered that METTL3 destabilized DLG2 mRNA through an m6A-dependent mechanism. Moreover, lowering DLG2 expression mitigated the effects of METTL3 silencing on cervical cancer cell malignancy. DLG2 acted as a tumor suppressor in cervical cancer by inhibiting the Hippo/YAP signaling pathway. The METTL3-dependent regulation of DLG2 mRNA stability could be a critical factor in cervical cancer progression. Show less
📄 PDF DOI: 10.1186/s41065-025-00365-z
DLG2

Genetic association of lipids characteristics and lipid lowering drug target genes with sepsis.

Yu Wang, Haiyue Zhang, Yuanyuan Zhan +4 more · 2025 · PloS one · PLOS · added 2026-04-24
Sepsis is a severe systemic infection that can result in organ dysfunction and mortality. Dyslipidemia emerges as a key player in the intricate web of sepsis pathogenesis. Yet, the causal relationship Show more
Sepsis is a severe systemic infection that can result in organ dysfunction and mortality. Dyslipidemia emerges as a key player in the intricate web of sepsis pathogenesis. Yet, the causal relationship between blood lipid profiles and sepsis risk remains uncertain. This study aims to investigate the association between genetically predicted lipid traits, drug targets, and sepsis. The UK Biobank's Genome-wide association studies (GWAS) produced data on lipid and apolipoprotein characteristics. Four independent GWAS datasets were used to generate the sepsis statistics. The study utilized the two-sample Mendelian randomization (MR) approach, which incorporates multivariable (MVMR) models, to assess the correlations between sepsis risk and lipid-related parameters. To gain further insight, expression quantitative trait loci (eQTL) data were used to investigate the significant drug targets for lipid-lowering. Increasing ApoA-1 levels was associated with a diminished risk of sepsis (under 75) (OR 0.927, 95% CI 0.861-0.999; p = 0.047). This inverse correlation persevered even after performing multivariable MR. Elevated levels of HDL-C were associated with a decreased risk of sepsis (under 75) (OR 0.897, 95% CI 0.838-0.960; P = 0.002) and incidence of sepsis (OR 0.883, 95% CI 0.820-0.951; P = 0.001), which was consistent across sensitivity analyses. Furthermore, a decrease in total cholesterol exhibited a causal effect on sepsis in multivariable MR (OR 0.779, 95% CI 0.642-0.944; P = 0.01). The genetic variants related to lowering LDL-C, located near the HMGCR and LDLR genes, were predicted to elevate the risk of sepsis. Moreover, genetic mimicry near the ANGPTL3 and LPL gene suggested that reducing the activity of ANGPTL3 and LPL (mimicking antisense anti-ANGPTL3 and LPL agents) was forecasted to decrease sepsis risk. Genetically inferred elevated ApoA-1, total cholesterol, and HDL-C manifest a protective effect against sepsis. Within the 9 lipid-lowering drug targets investigated ANGPTL3 and LPL exhibit potential as candidate drug targets for sepsis. Show less
📄 PDF DOI: 10.1371/journal.pone.0331023
LPL

Heterogeneity in medical coping modes and the moderating role of social support on social disability in patients after percutaneous coronary intervention.

Jia Zhang, Song Bin Huang, Dan Ni Peng +3 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24
This study aimed to identify heterogeneous patterns of medical coping modes (MCM) and to examine the moderating role of social support in the relationship between these patterns and social disability Show more
This study aimed to identify heterogeneous patterns of medical coping modes (MCM) and to examine the moderating role of social support in the relationship between these patterns and social disability in young and middle-aged patients after percutaneous coronary intervention (PCI). A cross-sectional study was conducted among 129 post-PCI patients from a single center in China. Participants completed the Medical Coping Modes Questionnaire (MCMQ), the Social Support Rating Scale (SSRS), and the Social Disability Screening Schedule (SDSS). Latent profile analysis (LPA) was used to identify distinct coping patterns. The moderation effect of social support was tested using the Johnson-Neyman technique. Two distinct coping profiles were identified via LPA: "Adaptive Copers" (55.1%), characterized by higher confrontation and lower avoidance/resignation, and "Maladaptive Copers" (44.9%), showing the opposite pattern. A counterintuitive finding emerged, with the Maladaptive Copers reporting significantly lower social disability scores. Furthermore, beyond this profile differentiation, social support demonstrated a significant U-shaped moderating effect in the coping-disability relationship. Its moderating role was statistically significant only at very low (<39.884) and very high (>52.924) levels of support. This study reveals two key findings: first, post-PCI patients are heterogeneous in coping, comprising adaptive and maladaptive subgroups; second, the impact of these coping styles on social disability is non-linearly moderated by social support. Clinicians should assess both coping profiles and social support levels to tailor interventions effectively. Show less
📄 PDF DOI: 10.3389/fpsyg.2025.1731898
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

Discovery of First Branched-Chain Ketoacid Dehydrogenase Kinase (BDK) Inhibitor Clinical Candidate PF-07328948.

Kevin J Filipski, Luis A Martinez-Alsina, Matthew R Reese +31 more · 2025 · Journal of medicinal chemistry · ACS Publications · added 2026-04-24
Inhibition of branched-chain ketoacid dehydrogenase kinase (BDK or BCKDK), a negative regulator of branched-chain amino acid (BCAA) metabolism, is hypothesized to treat cardio-metabolic diseases. From Show more
Inhibition of branched-chain ketoacid dehydrogenase kinase (BDK or BCKDK), a negative regulator of branched-chain amino acid (BCAA) metabolism, is hypothesized to treat cardio-metabolic diseases. From a starting point with potential idiosyncratic toxicity risk, modification to a benzothiophene core and discovery of a cryptic pocket allowed for improved potency with 3-aryl substitution to arrive at PF-07328948, which was largely devoid of protein covalent binding liability. This BDK inhibitor was shown also to be a BDK degrader in cells and in vivo rodent studies. Plasma biomarkers, including BCAAs and branched-chain ketoacids (BCKAs), were lowered in vivo with enhanced pharmacodynamic effect upon chronic dosing due to BDK degradation. This molecule improves metabolic and heart failure end points in rodent models. PF-07328948 is the first known selective BDK inhibitor candidate to be examined in clinical studies, with Phase 1 single ascending dose data showing good tolerability and a pharmacokinetic profile commensurate with once-daily dosing. Show less
no PDF DOI: 10.1021/acs.jmedchem.4c02230
BCKDK

Negative modulation of maternal iodine deficiency and excess on milk lipid synthesis and secretion in lactating rats.

Ying Zhang, Xin Zhao, Ning Yu +10 more · 2025 · The Journal of nutritional biochemistry · Elsevier · added 2026-04-24
Iodine is an essential micronutrient for developmental processes in the early stages; however, data on the effect of maternal iodine nutrition on milk lipids are limited. We aimed to explore the effec Show more
Iodine is an essential micronutrient for developmental processes in the early stages; however, data on the effect of maternal iodine nutrition on milk lipids are limited. We aimed to explore the effect of inadequate and excessive iodine intake on milk lipid metabolism and its mechanisms preliminarily. Rats were treated with different concentrations of potassium iodide water to construct animal models of iodine deficiency and excess. Iodine excess reduced serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels during lactation. Iodine deficiency had no significant effect on blood lipid indicators. In early and late lactation, iodine deficiency and excess inhibited triglyceride (TG) levels in milk; in mid-lactation, the inhibitory effect of iodine deficiency was attenuated. Under iodine deficiency and excess, the level of TG and the expression of THRα1, THRβ1, ACC1, FAS, THRSP, BTN1A1, and ADFP proteins in the mammary gland were decreased during lactation; a decrease in LPL protein expression was observed in early and late lactation; and a decline of XOR protein expression was reported in mid and late lactation. Blood lipid metabolism was less sensitive to iodine deficiency during lactation. Iodine excess has a more profound effect on blood lipid metabolism, causing dyslipidemia in lactating rats. Long-term iodine deficiency and excess may have a negative role in the mechanisms regulating milk lipid synthesis and secretion by affecting thyroid hormones to inhibit the milk TG level. Show less
no PDF DOI: 10.1016/j.jnutbio.2025.110085
LPL

LncRNA A2ml2 inhibits fatty liver hemorrhage syndrome progression and function as ceRNA to target LPL by sponging miR-143-5p.

Qingxing Xiao, Sibao Yang, Yuwei Yang +7 more · 2025 · Poultry science · Elsevier · added 2026-04-24
Fatty liver hemorrhage syndrome (FLHS) is the most common metabolic diseases in laying hens during the late-laying period, and it causes a significant economic burden on the poultry industry. The comp Show more
Fatty liver hemorrhage syndrome (FLHS) is the most common metabolic diseases in laying hens during the late-laying period, and it causes a significant economic burden on the poultry industry. The competing endogenous RNA plays crucial roles in the occurrence and development of fatty liver. Based on the previously constructed lncRNA-miRNA-mRNA networks, we selected the axis of ENSGALT00000079786-LPL-miR-143-5p for further study to elucidate its mechanistic role in development of fatty liver. In this study, we identified a novel highly conserved lncRNA (ENSGALT00000079786) in poultry, which we designated as lncRNA A2ml2 based on its chromosomal location. Fluorescent in situ hybridization (FISH) revealed that lncRNA A2ml2 was localized in both the nucleus and cytoplasm. Dual-luciferase reporter assay validated the targeted relationship between lncRNA A2ml2, miR-143-5p, and the LPL gene. To further analyze the lncRNA A2ml2 and miR-143-5p function, lncRNA A2ml2 overexpression vector was successfully constructed and transfected into Leghorn male hepatocellular (LMH) cells, which could remarkably inhibit cellular lipid deposition was detected by oil red staining (P < 0.01), the opposite occurred for miR-143-5p (P < 0.01). qPCR demonstrated an inverse correlation between miR-143-5p expression and lncRNA A2ml2 expression, and confirmed that miR-143-5p directly target lncRNA A2ml2. Similarly, we found an inverse correlation between expression of LPL and the expression of miR-143-5p. To further investigate the interactions among these three factors and their effects on cellular lipid metabolism, we assessed the expression levels of LPL by co-transfecting lncRNA A2ml2 with miR-143-5p mimic and miR-143-5p mimic binding site mutants. Co-transfection experiments showed that miR-143-5p diminished the promoting effect of lncRNA A2ml2 on LPL. Meanwhile, miR-143-5p has the capacity to mitigate the suppressive impact of lncRNA A2ml2 overexpression on lipid accumulation in LMH cells. The results revealed that lncRNA A2ml2 attenuated hepatic lipid accumulation through negatively regulating miR-143-5p and enhancing LPL expression in LMH cells. Our findings offer novel insights into ceRNA-mediated in FLHS and identify a novel lncRNA as a potential molecular biomarker. Show less
📄 PDF DOI: 10.1016/j.psj.2025.105003
LPL

Unveiling circulating targets in pancreatic cancer: Insights from proteogenomic evidence and clinical cohorts.

Haokang Feng, Zhixue Chen, Jianang Li +13 more · 2025 · iScience · Elsevier · added 2026-04-24
Pancreatic cancer (PC), characterized by the absence of effective biomarkers and therapies, remains highly fatal. Data regarding the correlations between PC risk and individual plasma proteome known f Show more
Pancreatic cancer (PC), characterized by the absence of effective biomarkers and therapies, remains highly fatal. Data regarding the correlations between PC risk and individual plasma proteome known for minimally invasive biomarkers are scarce. Here, we analyzed 1,345 human plasma proteins using proteome-wide association studies, identifying 78 proteins significantly associated with PC risk. Of these, four proteins (ROR1, FN1, APOA5, and ABO) showed the most substantial causal link to PC, confirmed through Mendelian randomization and colocalization analyses. Data from two clinical cohorts further demonstrated that FN1 and ABO were notably overexpressed in both blood and tumor samples from PC patients, compared to healthy controls or para-tumor tissues. Additionally, elevated FN1 and ABO levels correlated with shorter median survival in patients. Multiple drugs targeting FN1 or ROR1 are available or in clinical trials. These findings suggest that plasma protein FN1 associated with PC holds potential as both prognostic biomarkers and therapeutic targets. Show less
📄 PDF DOI: 10.1016/j.isci.2024.111693
APOA5

Integration of gut microbiome and lipid metabolism reveals the anti-cancer effects of pentadecanoic acid on bladder cancer.

Ya-Ting Chen, Jing Sui, Yu Yang +16 more · 2025 · BMC medicine · BioMed Central · added 2026-04-24
Pentadecanoic acid (PEA), an odd-chain fatty acid derived from diet by the gut microbiome, has garnered increasing attention for its systemic health-promoting properties. Its potential role in bladder Show more
Pentadecanoic acid (PEA), an odd-chain fatty acid derived from diet by the gut microbiome, has garnered increasing attention for its systemic health-promoting properties. Its potential role in bladder cancer (BC) occurrence and invasion, however, remains unclear. Large-scale cohorts' analyses were performed to assess the association between dietary PEA and BC occurrence and invasion. In vitro and in vivo experiments, including EJ and T24 BC cell assays and a BBN-induced mouse model, were conducted to experimentally assess the impact of PEA on BC. Serum proteomics, gut microbiome, and targeted fecal lipidomics analyses were employed to explore the underlying mechanisms. Dietary PEA was negatively associated with BC occurrence and invasion in cohort analyses. PEA suppressed EJ and T24 BC cell migration, invasion, and proliferation, while inhibiting BC development in a BBN-induced mouse model. In vivo serum proteomics identified differentially expressed lipid-related proteins (e.g., Apoe and Apob) following PEA treatment, implicating its modulation of lipid metabolism pathways. Considering the essential role of the gut-bladder axis, the gut microbiome analysis exhibited that PEA markedly altered bacteria (e.g., g_Alistipes) and fungi (e.g., o_Erysiphales, g_Teberdinia, and g_Gibberella), with concomitant lipid metabolism changes. Furthermore, targeted fecal lipidomics demonstrated the shifts in key lipids, such as phosphatidylethanolamines (PE) involved in essential lipid clusters, suggesting regulation by gut microbiome linked to BC development. Collectively, our findings demonstrate that PEA mitigates BC by reshaping the gut microbiome and modulating lipid metabolism, providing new insights into its molecular and therapeutic potential. Show less
📄 PDF DOI: 10.1186/s12916-025-04554-5
APOB

SIRT7 regulates T-cell antitumor immunity through modulation BCAA and fatty acid metabolism.

Zuojian Hu, Yingji Chen, Jielin Lei +11 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
SIRT7, one of the least studied members of the Sirtuins family, is an NAD
no PDF DOI: 10.1038/s41418-025-01490-y
BCKDK

Sparstolonin B and Curcumin alleviate atherogenesis by modulating T helper 17-stromal cell interactions associated with interleukin-17 receptor A (IL-17RA) - transforming growth factor-β-activated kinase 1 (TAK1) - nuclear factor-kappa B (NF-κB) signaling.

Guofu Zhong, Qingqing Liu, Qing Zhang +11 more · 2025 · Phytomedicine : international journal of phytotherapy and phytopharmacology · Elsevier · added 2026-04-24
Sparstolonin B (SSNB) and Curcumin (Cur), from a pair of compatible herbs, were previously identified as anti-inflammation and T helper 17 (Th17) modulation reagents. However, their compatible roles i Show more
Sparstolonin B (SSNB) and Curcumin (Cur), from a pair of compatible herbs, were previously identified as anti-inflammation and T helper 17 (Th17) modulation reagents. However, their compatible roles in atherosclerosis (AS) and underlying mechanisms remain uninvestigated. In vivo, the apoE The gene-disease interaction and hub gene network reveals Th17-associated genes in the pathogenesis of atherosclerosis. In vitro, SSNB and Cur reduced oxLDL-induced BMDC activation by downregulating CD36. SSNB showed stronger inhibition to inflammatory activation of DC, while Cur more intensively suppressed co-stimulatory molecules. For the Th17/Treg bias in co-culture of BMDC and CD4 Our findings reveal, for the first time, that SSNB and Cur alleviate AS by modulating Th17-stromal cell interactions, with the IL-17RA-TAK1-NF-κB pathway as a related mediator. Notably, SSNB and Cur exhibit distinct anti-atherogenic roles. SSNB primarily targets TLR4/CD36 to inhibit DC activation, Th17 differentiation, VSMC inflammation and mainly inhibited TAK1 phosphorylation, while Cur more significant inhibited macrophage inflammation, and more directly inhibited NF-κB P65 phosphorylation. This study will be valuable for developing novel and precise adjuvant therapies for AS. Show less
no PDF DOI: 10.1016/j.phymed.2025.157578
APOE

A novel splicing variant of CNTRL::FGFR1 in myeloid/lymphoid neoplasm with eosinophilia and rearrangement of FGFR1.

Xianqi Feng, Xueting Bai, Hong Zhang +7 more · 2025 · Journal of hematopathology · Springer · added 2026-04-24
Background Myeloid/lymphoid neoplasm with eosinophilia and rearrangement of FGFR1(MLN-FGFR1), also referred to as 8p11 myeloproliferative syndrome (EMS), arises from aberrant FGFR1 gene rearrangement Show more
Background Myeloid/lymphoid neoplasm with eosinophilia and rearrangement of FGFR1(MLN-FGFR1), also referred to as 8p11 myeloproliferative syndrome (EMS), arises from aberrant FGFR1 gene rearrangement in bone marrow hematopoietic stem cells, resulting in the transformation of myeloid/lymphoid cells into neoplastic growths. The clinical and laboratory features of affected individuals are influenced by the specific partner genes. Purpose This article aims to report a case of MLN-FGFR1 involving a novel CNTRL::FGFR1 splicing variant and to discuss its clinicopathological characteristics and treatment challenges. Methods/Results We report a case of MLN-FGFR1 in a 35-year-old male patient presenting with leukocytosis, lymphadenopathy, hepatosplenomegaly, and a mixed population of B lymphoblasts, T lymphoblasts, and monoblasts in the bone marrow and lymph nodes. Comprehensive molecular profiling, including chromosomal karyotyping, fluorescence in situ hybridization (FISH), targeted transcriptome sequencing, reverse transcription polymerase chain reaction (RT-PCR), and Sanger sequencing, identified a novel splicing variant of the CNTRL::FGFR1 fusion, resulting from a t(8;9)(p11;q33) translocation. This novel splicing variant involves an in-frame fusion between exon 38 of CNTRL and exon 11 of FGFR1, retaining the kinase domain of FGFR1 and leading to its constitutive activation. Despite multiple treatment regimens, the patient failed to achieve complete remission (CR). Conclusion The findings highlight the urgent need for targeted therapies, such as FGFR inhibitors, to improve outcomes in patients with FGFR1-rearranged malignancies. Show less
📄 PDF DOI: 10.1007/s12308-025-00670-6
FGFR1

Altered brain network dynamics and functional connectivity in subjective cognitive decline: an edge-centric network study.

Xiaofan Wei, Baiwan Zhou, Juanling Li +2 more · 2025 · Frontiers in aging neuroscience · Frontiers · added 2026-04-24
To explore neurodynamic bases underlying subjective cognitive decline (SCD) based on edge-centric functional network. 211 SCD patients and 210 healthy controls (HC) were recruited from the Alzheimer's Show more
To explore neurodynamic bases underlying subjective cognitive decline (SCD) based on edge-centric functional network. 211 SCD patients and 210 healthy controls (HC) were recruited from the Alzheimer's Disease Neuroimaging Initiative. Edge time series (ETS) were obtained based on resting-state functional magnetic resonance data. The top 10% co-fluctuation signals of all time points in ETS were extracted to construct the high-amplitude frame networks, and the co-fluctuation signals from the remaining time points were used to construct the low-amplitude frame networks. In both network states, the graph theory and network-based statistics (NBS) analyses were used to compare SCD and HC. The correlation of the imaging indicators with cognitive scores and apolipoprotein E (APOE) ε4 genes was performed by Spearman correlation analysis. SCD exhibited lower peak amplitude and longer trough-to-trough duration (TTD) compared to HC. In both network states, the normalized clustering coefficient, normalized characteristic path length, small-worldness, and global efficiency of SCD were significantly reduced, and the altered nodal centralities of SCD predominantly exhibited a decreasing trend. However, the high-amplitude frame network identified more altered brain regions compared to the low-amplitude frame network. Furthermore, a SCD-related subnetwork was found in the high-amplitude frame network, which was composed of 11 brain regions and 13 edges. TTD was positively related to the number of APOE ε4 genes; the normalized characteristic path length, the betweenness centrality of right postcentral gyrus, and the connection between bilateral angular gyrus were correlated with cognitive scores. Our findings demonstrate that the edge-centric network framework reveals details of brain network alterations in SCD through different perspectives, and these alterations hold potential as novel biomarkers for SCD. Show less
📄 PDF DOI: 10.3389/fnagi.2025.1596537
APOE

Illuminating the FGFR fusion landscape in Chinese patients: unveiling novel molecular insights and clinical implications.

Zhuo Liu, Dandan Zhao, Baoming Wang +14 more · 2025 · The oncologist · Oxford University Press · added 2026-04-24
Despite the increasing approval and ongoing clinical trials of FGFR-targeted therapies, accurately detecting FGFR fusions remains a challenge due to limited research, low incidence rates, complex fusi Show more
Despite the increasing approval and ongoing clinical trials of FGFR-targeted therapies, accurately detecting FGFR fusions remains a challenge due to limited research, low incidence rates, complex fusion partner distribution, and unique kinase domain distribution. We conducted a multicenter study to comprehensively profile FGFR fusions in the largest Chinese pan-cancer cohort to date, comprising 118 FGFR fusions from 114 individuals. Both DNA- and RNA-based sequencing approaches were utilized to reveal novel and fundamental features of FGFR fusion. Our research reveals an incidence rate of 0.96% for FGFR rearrangements within this Chinese cohort, including a high incidence rate of FGFR fusions (40%) in parotid gland carcinoma. However, this is based on a small sample size of 5 tumors and should be interpreted cautiously pending validation in larger cohorts. We also uncovered distinct breakpoint distribution patterns across various FGFR rearrangements. For example, a primary breakpoint in intron17 of FGFR2 was predominant (21/22), while FGFR1/3 breakpoints displayed substantial diversity. For the first time, we identified "hot" breakpoints in FGFR1 intron17, exon18, and FGFR3's 3' untranslated region. These findings underline the importance of incorporating these regions in targeted sequencing to ensure comprehensive detection of FGFR1/3 fusions. Notably, we observed a predilection for intrachromosomal distribution in common FGFR1/2/3 fusions. In contrast, most novel fusions (12/15) exhibited an interchromosomal distribution pattern, indicating variations in the fusion formation mechanism. Importantly, our study demonstrates the substantial incremental value of RNA-NGS or other orthogonal methods in confirming the functionality of FGFR rearrangements initially identified by DNA sequencing. In our cohort, 46% (6/13) of rare FGFR1/2/3 fusions lacked detectable RNA transcripts; however, this does not definitively indicate non-functionality as factors such as low RNA quality, expression below detection limits, or nonsense-mediated decay may contribute. Therefore, RNA-based validation is critical for accurately identifying potentially targetable FGFR fusions and guiding therapy. Our findings offer critical novel insights into functional FGFR fusions and bear considerable clinical implications for identifying individuals whose tumors are most likely to respond favorably to FGFR-targeted therapies. Show less
📄 PDF DOI: 10.1093/oncolo/oyaf347
FGFR1

The Clinical Value of Apolipoprotein C3 Combined with FAR and RWT in Heart Failure with Preserved Ejection Fraction.

Qingyu Zhang, Zongliang Yu · 2025 · International journal of general medicine · added 2026-04-24
Heart failure with preserved ejection fraction (HFpEF) is becoming increasingly prevalent, yet clinical practice lacks specific biomarkers, early diagnostic tools, and reliable risk assessment methods Show more
Heart failure with preserved ejection fraction (HFpEF) is becoming increasingly prevalent, yet clinical practice lacks specific biomarkers, early diagnostic tools, and reliable risk assessment methods. Given the growing burden of HFpEF, identifying novel diagnostic markers is crucial. This study investigates the diagnostic potential of apolipoprotein C3 (ApoC3) in HFpEF and its correlation with ventricular structure. We analyzed data from HFpEF patients admitted to the Kunshan Branch of Gusu College of Nanjing Medical University and the First People's Hospital of Kunshan (March-December 2023). Controls included HFrEF+HFmrEF patients and healthy individuals. Enzyme-linked immunosorbent assay (ELISA) was used to detect the concentration of ApoC3 in all collected cases. The receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic performance of ApoC3 alone and combined with the fibrinogen-to-albumin ratio (FAR) in plasma, and the relative wall thickness (RWT) in echocardiography for HFpEF. After exclusions, 80 HFpEF patients (39 male, 41 female), 41 HFrEF+HFmrEF patients (27 male, 14 female), and 79 healthy controls (53 male, 26 female) were included. ApoC3 levels were significantly higher in HFpEF (63136.03±12,113.07 ng/mL) than in HFrEF+HFmrEF (55580.84±13,685.35 ng/mL) and controls (53090.31±5893.25 ng/mL, P<0.001). ROC analysis demonstrated that ApoC3 alone (AUC=0.836) and the combined index (ApoC3+FAR+RWT, AUC=0.891) effectively distinguished HFpEF. Both also aided in differentiating HFpEF from HFrEF+HFmrEF (AUC=0.702 vs 0.823). ApoC3 is a promising biomarker for HFpEF diagnosis, and the combined index (ApoC3+FAR+RWT) enhances diagnostic accuracy. These findings may improve early detection and clinical management of HFpEF. Show less
📄 PDF DOI: 10.2147/IJGM.S532542
APOC3

Safety and efficacy of inclisiran in treating hypercholesterolemia: A systemic review and meta-analysis.

Zhiling Cheng, Meiling Gao, Yang Liu +4 more · 2025 · Nutrition, metabolism, and cardiovascular diseases : NMCD · Elsevier · added 2026-04-24
To evaluate the efficacy and safety of inclisiran in the treatment of hypercholesterolemia. Randomized controlled trials comparing inclisiran with a placebo were searched until April 2024. Overall, 8 Show more
To evaluate the efficacy and safety of inclisiran in the treatment of hypercholesterolemia. Randomized controlled trials comparing inclisiran with a placebo were searched until April 2024. Overall, 8 studies involving 4947 patients were included. Inclisiran reduced low-density lipoprotein cholesterol (mean difference [MD]: -46.95 %; 95 % confidence interval [CI]: -53.26 to -40.46; P < 0.05), proprotein convertase subtilisin/kexin type 9 (MD: -70.80 %; 95 % CI: -76.52 to -65.08; P < 0.05), serum total cholesterol (MD: -29.47 %; 95 % CI: -32.56 to -26.39; P < 0.05), non-high-density lipoprotein cholesterol (MD: -40.46 %; 95 % CI: -45.24 to -35.68; P < 0.05), apolipoprotein B (MD: -36.77 %; 95 % CI: -40.94 to -32.61; P < 0.05), and lipoprotein(a) (MD: -20.04 %; 95 % CI: -24.2 to -15.87; P < 0.05) levels but increased high-density lipoprotein cholesterol level (MD: 6.09 %; 95 % CI: 3.63 to 8.55; P < 0.05). The incidences of adverse events, serious adverse events, headache, nasopharyngitis, and muscular adverse reactions were not significantly different between the inclisiran and placebo groups. Inclisiran reduced the incidence of cardiovascular adverse reactions (odds ratio [OR] = 0.79; 95 % CI: 0.65 to 0.96; P = 0.02) and increased the incidence of injection-site reactions (OR = 4.79; 95 % CI: 2.18 to 10.52; P < 0.05). Inclisiran is effective in treating hypercholesterolemia and has a good safety profile. Show less
no PDF DOI: 10.1016/j.numecd.2024.10.017
APOB

Stochastic neuropeptide signals compete to calibrate the rate of satiation.

Stephen X Zhang, Angela Kim, Joseph C Madara +10 more · 2025 · Nature · Nature · added 2026-04-24
Neuropeptides have important roles in neural plasticity, spiking and behaviour
📄 PDF DOI: 10.1038/s41586-024-08164-8
MC4R