👤 Shangyu Liu

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3182
Articles
1983
Name variants
Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Chih Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Huazhen Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, Yanyan Liu, Yanyao Liu, Yanying Liu, Yanyun Liu, Yao Liu, Yao-Hui Liu, Yaobo Liu, Yaoquan Liu, Yaou Liu, Yaowen Liu, Yaoyao Liu, Yaozhong Liu, Yaping Liu, Yaqiong Liu, Yarong Liu, Yaru Liu, Yating Liu, Yaxin Liu, Ye Liu, Ye-Dan Liu, Yehai Liu, Yen-Chen Liu, Yen-Chun Liu, Yen-Nien Liu, Yeqing Liu, Yi Liu, Yi-Chang Liu, Yi-Chien Liu, Yi-Han Liu, Yi-Hung Liu, Yi-Jia Liu, Yi-Ling Liu, Yi-Meng Liu, Yi-Ming Liu, Yi-Yun Liu, Yi-Zhang Liu, YiRan Liu, Yibin Liu, Yibing Liu, Yicun Liu, Yidan Liu, Yidong Liu, Yifan Liu, Yifu Liu, Yihao Liu, Yiheng Liu, Yihui Liu, Yijing Liu, Yilei Liu, Yili Liu, Yilin Liu, Yimei Liu, Yiming Liu, Yin Liu, Yin-Ping Liu, Yinchu Liu, Yinfang Liu, Ying Liu, Ying Poi Liu, Yingchun Liu, Yinghua Liu, Yinghuan Liu, Yinghui Liu, Yingjun Liu, Yingli Liu, Yingwei Liu, Yingxia Liu, Yingyan Liu, Yingyi Liu, Yingying Liu, Yingzi Liu, Yinhe Liu, Yinhui Liu, Yining Liu, Yinjiang Liu, Yinping Liu, Yinuo Liu, Yiping Liu, Yiqing Liu, Yitian Liu, Yiting Liu, Yitong Liu, Yiwei Liu, Yiwen Liu, Yixiang Liu, Yixiao Liu, Yixuan Liu, Yiyang Liu, Yiyi Liu, Yiyuan Liu, Yiyun Liu, Yizhi Liu, Yizhuo Liu, Yong Liu, Yong Mei Liu, Yong-Chao Liu, Yong-Hong Liu, Yong-Jian Liu, Yong-Jun Liu, Yong-Tai Liu, Yong-da Liu, Yongchao Liu, Yonggang Liu, Yonggao Liu, Yonghong Liu, Yonghua Liu, Yongjian Liu, Yongjie Liu, Yongjun Liu, Yongli Liu, Yongmei Liu, Yongming Liu, Yongqiang Liu, Yongshuo Liu, Yongtai Liu, Yongtao Liu, Yongtong Liu, Yongxiao Liu, Yongyue Liu, You Liu, You-ping Liu, Youan Liu, Youbin Liu, Youdong Liu, Youhan Liu, Youlian Liu, Youwen Liu, Yu Liu, Yu Xuan Liu, Yu-Chen Liu, Yu-Ching Liu, Yu-Hui Liu, Yu-Li Liu, Yu-Lin Liu, Yu-Peng Liu, Yu-Wei Liu, Yu-Zhang Liu, YuHeng Liu, Yuan Liu, Yuan-Bo Liu, Yuan-Jie Liu, Yuan-Tao Liu, YuanHua Liu, Yuanchu Liu, Yuanfa Liu, Yuanhang Liu, Yuanhui Liu, Yuanjia Liu, Yuanjiao Liu, Yuanjun Liu, Yuanliang Liu, Yuantao Liu, Yuantong Liu, Yuanxiang Liu, Yuanxin Liu, Yuanxing Liu, Yuanying Liu, Yuanyuan Liu, Yubin Liu, Yuchen Liu, Yue Liu, Yuecheng Liu, Yuefang Liu, Yuehong Liu, Yueli Liu, Yueping Liu, Yuetong Liu, Yuexi Liu, Yuexin Liu, Yuexing Liu, Yueyang Liu, Yueyun Liu, Yufan Liu, Yufei Liu, Yufeng Liu, Yuhao Liu, Yuhe Liu, Yujia Liu, Yujiang Liu, Yujie Liu, Yujun Liu, Yulan Liu, Yuling Liu, Yulong Liu, Yumei Liu, Yumiao Liu, Yun Liu, Yun-Cai Liu, Yun-Qiang Liu, Yun-Ru Liu, Yun-Zi Liu, Yunfen Liu, Yunfeng Liu, Yuning Liu, Yunjie Liu, Yunlong Liu, Yunqi Liu, Yunqiang Liu, Yuntao Liu, Yunuan Liu, Yunuo Liu, Yunxia Liu, Yunyun Liu, Yuping Liu, Yupu Liu, Yuqi Liu, Yuqiang Liu, Yuqing Liu, Yurong Liu, Yuru Liu, Yusen Liu, Yutao Liu, Yutian Liu, Yuting Liu, Yutong Liu, Yuwei Liu, Yuxi Liu, Yuxia Liu, Yuxiang Liu, Yuxin Liu, Yuxuan Liu, Yuyan Liu, Yuyi Liu, Yuyu Liu, Yuyuan Liu, Yuzhen Liu, Yv-Xuan Liu, Z H Liu, Z Q Liu, Z Z Liu, Zaiqiang Liu, Zan Liu, Zaoqu Liu, Ze Liu, Zefeng Liu, Zekun Liu, Zeming Liu, Zengfu Liu, Zeyu Liu, Zezhou Liu, Zhangyu Liu, Zhangyuan Liu, Zhansheng Liu, Zhao Liu, Zhaoguo Liu, Zhaoli Liu, Zhaorui Liu, Zhaotian Liu, Zhaoxiang Liu, Zhaoxun Liu, Zhaoyang Liu, Zhe Liu, Zhekai Liu, Zheliang Liu, Zhen Liu, Zhen-Lin Liu, Zhendong Liu, Zhenfang Liu, Zhenfeng Liu, Zheng Liu, Zheng-Hong Liu, Zheng-Yu Liu, ZhengYi Liu, Zhengbing Liu, Zhengchuang Liu, Zhengdong Liu, Zhenghao Liu, Zhengkun Liu, Zhengtang Liu, Zhengting Liu, Zhenguo Liu, Zhengxia Liu, Zhengye Liu, Zhenhai Liu, Zhenhao Liu, Zhenhua Liu, Zhenjiang Liu, Zhenjiao Liu, Zhenjie Liu, Zhenkui Liu, Zhenlei Liu, Zhenmi Liu, Zhenming Liu, Zhenna Liu, Zhenqian Liu, Zhenqiu Liu, Zhenwei Liu, Zhenxing Liu, Zhenxiu Liu, Zhenzhen Liu, Zhenzhu Liu, Zhi Liu, Zhi Y Liu, Zhi-Fen Liu, Zhi-Guo Liu, Zhi-Jie Liu, Zhi-Kai Liu, Zhi-Ping Liu, Zhi-Ren Liu, Zhi-Wen Liu, Zhi-Ying Liu, Zhicheng Liu, Zhifang Liu, Zhigang Liu, Zhiguo Liu, Zhihan Liu, Zhihao Liu, Zhihong Liu, Zhihua Liu, Zhihui Liu, Zhijia Liu, Zhijie Liu, Zhikui Liu, Zhili Liu, Zhiming Liu, Zhipeng Liu, Zhiping Liu, Zhiqian Liu, Zhiqiang Liu, Zhiru Liu, Zhirui Liu, Zhishuo Liu, Zhitao Liu, Zhiteng Liu, Zhiwei Liu, Zhixiang Liu, Zhixue Liu, Zhiyan Liu, Zhiying Liu, Zhiyong Liu, Zhiyuan Liu, Zhong Liu, Zhong Wu Liu, Zhong-Hua Liu, Zhong-Min Liu, Zhong-Qiu Liu, Zhong-Wu Liu, Zhong-Ying Liu, Zhongchun Liu, Zhongguo Liu, Zhonghua Liu, Zhongjian Liu, Zhongjuan Liu, Zhongmin Liu, Zhongqi Liu, Zhongqiu Liu, Zhongwei Liu, Zhongyu Liu, Zhongyue Liu, Zhongzhong Liu, Zhou Liu, Zhou-di Liu, Zhu Liu, Zhuangjun Liu, Zhuanhua Liu, Zhuo Liu, Zhuoyuan Liu, Zi Hao Liu, Zi-Hao Liu, Zi-Lun Liu, Zi-Ye Liu, Zi-wen Liu, Zichuan Liu, Zihang Liu, Zihao Liu, Zihe Liu, Ziheng Liu, Zijia Liu, Zijian Liu, Zijing J Liu, Zimeng Liu, Ziqian Liu, Ziqin Liu, Ziteng Liu, Zitian Liu, Ziwei Liu, Zixi Liu, Zixuan Liu, Ziyang Liu, Ziying Liu, Ziyou Liu, Ziyuan Liu, Ziyue Liu, Zong-Chao Liu, Zong-Yuan Liu, Zonghua Liu, Zongjun Liu, Zongtao Liu, Zongxiang Liu, Zu-Guo Liu, Zuguo Liu, Zuohua Liu, Zuojin Liu, Zuolu Liu, Zuyi Liu, Zuyun Liu
articles

Retraction of "Design, Synthesis, and Biological Evaluation of Notopterol Derivatives as Triple Inhibitors of AChE/BACE1/GSK3β for the Treatment of Alzheimer's Disease".

Nan Wang, Wenjie Liu, Lijun Zhou +11 more · 2025 · ACS omega · ACS Publications · added 2026-04-24
[This retracts the article DOI: 10.1021/acsomega.2c03368.].
📄 PDF DOI: 10.1021/acsomega.5c06137
BACE1

Shen-Hong-Tong-Luo formula ameliorates atherosclerosis by enhancing macrophage efferocytosis through activating the PPARγ/mfge8 pathway.

Yanyu Shi, Zepeng Zhang, Jiaqi Liu +7 more · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
Atherosclerosis (AS) is a chronic inflammatory disorder driven by dysregulated lipid metabolism and remains a leading cause of cardiovascular morbidity. The Shen-Hong-Tong-Luo (SHTL) preparation has d Show more
Atherosclerosis (AS) is a chronic inflammatory disorder driven by dysregulated lipid metabolism and remains a leading cause of cardiovascular morbidity. The Shen-Hong-Tong-Luo (SHTL) preparation has demonstrated clinical benefit in stabilizing atherosclerotic plaques, yet its molecular mechanisms are not fully defined. This research sought to elucidate the protective effects exerted by SHTL on AS progression. To investigate the impact of SHTL on macrophage function and plaque stability, we utilized ApoE SHTL markedly attenuated the progression of AS, demonstrated by reduced plaque formation within both the aortic root and aorta, diminished plasma lipid concentrations, and suppressed inflammatory responses. SHTL demonstrates significant anti-inflammatory and lipid-regulatory effects, attenuating AS progression through the PPARγ/Mfge8 pathway, thereby enhancing macrophage efferocytosis. These findings highlight a novel mechanism by which SHTL may contribute to preventing and treating atherosclerotic diseases. Show less
📄 PDF DOI: 10.3389/fimmu.2025.1727378
APOE

Risk Assessment of Serum Biomarkers with Perioperative Neurocognitive Dysfunction in Elderly Patients Undergoing Thoracic Surgery: A Prospective Cohort Study.

Zhijing Zhang, Di Wang, Riguang Zhong +6 more · 2025 · Cellular and molecular neurobiology · Springer · added 2026-04-24
Perioperative neurocognitive disorder (PND) is a common complication following thoracic surgery and often leading to poor outcomes. Despite ongoing research, effective treatments for late PND remain l Show more
Perioperative neurocognitive disorder (PND) is a common complication following thoracic surgery and often leading to poor outcomes. Despite ongoing research, effective treatments for late PND remain limited. Identifying reliable biomarkers for early diagnosis is, therefore, essential. A prospective cohort study was conducted with 60 elderly patients undergoing thoracic surgery. Serum samples were collected within 10 minutes prior to anesthesia and following extubation to measure adiponectin (APN), cyclic adenosine monophosphate (cAMP), protein kinase A (PKA), aquaporin-4 (AQP4) and brain-derived neurotrophic factor (BDNF). Among PND patients, serum APN, PKA, AQP4, and BDNF levels were markedly decreased compared with the normal group. While serum cAMP (HR = 1.087, p = 0.695, 95% CI [0.284-4.166]) and PKA (HR = 0.996, p = 0.09, 95% CI [0.491-0.947]) were not significantly correlated with PND, serum APN (HR = 0.307, 95% CI [0.113-0.835], p = 0.021), AQP4 (HR = 0.204, 95% CI [0.060-0.697], p = 0.011), and BDNF (HR = 0.382, 95% CI [0.177-0.823], p = 0.014) were protective factors against PND. ROC analysis demonstrated that APN (AUC = 0.68, 95% CI [0.51-0.87]), AQP4 (AUC = 0.73, 95% CI [0.59-0.87]), BDNF (AUC = 0.73, 95% CI [0.59-0.87]), and the model of combining those biomarkers (AUC = 0.91, 95% CI [0.83-0.99]) could predict PND. PND patients exhibited a lower protective stress response to surgical trauma. High serum APN, AQP4, and BDNF levels were independent protective factors for PND, and a combined model of these biomarkers showed predictive potential for PND. Show less
📄 PDF DOI: 10.1007/s10571-025-01636-z
BDNF

Retraction Notice to: circRAPGEF5 Contributes to Papillary Thyroid Proliferation and Metastatis by Regulation miR-198/FGFR1.

Weiwei Liu, Ji Zhao, Mingming Jin +1 more · 2025 · Molecular therapy. Nucleic acids · Elsevier · added 2026-04-24
[This retracts the article DOI: 10.1016/j.omtn.2019.01.003.].
📄 PDF DOI: 10.1016/j.omtn.2025.102613
FGFR1

Effect of lutein supplementation on blood lipids and advanced glycation end products in adults with central obesity: a double-blind randomized controlled trial.

Juan Zhou, Shanshan Wang, Qiang Wang +11 more · 2025 · Food & function · Royal Society of Chemistry · added 2026-04-24
Central obesity poses a significant health threat. Lutein-rich fruits and vegetables may help manage obesity. Limited evidence suggests that lutein exerts health effects by inhibiting advanced glycati Show more
Central obesity poses a significant health threat. Lutein-rich fruits and vegetables may help manage obesity. Limited evidence suggests that lutein exerts health effects by inhibiting advanced glycation end products (AGEs), but data on its effects in centrally obese individuals are sparse. Thus, we aimed to investigate the effects of lutein supplementation in subjects with central obesity. A double-blind, randomized controlled trial was conducted involving patients with central obesity. Anthropometric indices, dietary intake, metabolic parameters, carotenoid and AGEs levels were compared between those receiving a 32-week intervention of 10 mg d Show less
no PDF DOI: 10.1039/d4fo05578k
APOB

Multi-Target Inhibitor of ZJQ- 3 F Against AChE/BACE1/GSK3β Targets Improves the Cognitive Impairment of APP/PS1/Tau Triple-Transgenic Mouse Models of Alzheimer's Disease.

Nan Wang, Xin-Zhu Li, Xiao-Wen Jiang +10 more · 2025 · Molecular neurobiology · Springer · added 2026-04-24
Alzheimer's disease (AD) is a multifactorial neuropathology characterized by the accumulation of amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs) and cholinergic system dysfunction. At presen Show more
Alzheimer's disease (AD) is a multifactorial neuropathology characterized by the accumulation of amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs) and cholinergic system dysfunction. At present, there is no effective treatment strategy for AD. Our previous research showed that ZJQ-3F acts as an inhibitor of AChE/BACE1/GSK3β, and showed good blood-brain barrier permeability, appropriate bioavailability and oral safety. In order to further study, the protective effect of ZJQ-3F on APP/PS1/Tau transgenic mice was determined. APP/PS1/Tau transgenic mice model of AD was treated with ZJQ-3F from the age of 8 to 12 months, and then behavioral tests was conducted. Western blot, immunohistochemistry and immunofluorescence staining were used to evaluate the level of tau protein, Aβ plaques and synaptic function. Our results revealed that administration of ZJQ-3F could improve the cognitive function of APP/PS1/Tau transgenic mice. In addition, compared with APP/PS1/Tau mice, the protein expression levels of tau protein phosphorylation site at Ser396, Thr212 and Thr181 in the cortex and hippocampus of ZJQ-3F treated mice was significantly decreased. Moreover, the results showed that ZJQ-3F significantly reduced the deposition of Aβ in the cortex and hippocampus. Furthermore, the results indicated that the protein expression levels of PSD95, SYP and SYT in the cortex and hippocampus were increased markedly after ZJQ-3F was given. Our studies suggest that the chronic administration of ZJQ-3F can improve learning and memory ability, reduce tau protein phosphorylation, reduce Aβ deposition and improve synaptic dysfunction in APP/PS1/Tau transgenic model of AD, indicating that ZJQ-3F can be used as a multi-target inhibitor to slow down the progress of AD. Show less
📄 PDF DOI: 10.1007/s12035-025-04982-7
BACE1

Biomarker-based depression risk prediction in chronic heart failure patients: an interpretable machine learning approach.

Yuxuan Tao, Chenglong Yao, Runjia Liu +4 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Chronic heart failure (CHF) is frequently complicated by depression, which worsens prognosis but remains underdiagnosed due to symptom overlap and a lack of objective screening tools. Although biomark Show more
Chronic heart failure (CHF) is frequently complicated by depression, which worsens prognosis but remains underdiagnosed due to symptom overlap and a lack of objective screening tools. Although biomarkers reflecting lipid metabolism, insulin resistance, inflammation, and neuro-immuno-endocrine imbalance have been implicated in both CHF and depression, their predictive value for psychiatric outcomes in CHF patients is unclear. This study aimed to develop and validate interpretable machine learning (ML) models for predicting depression risk in CHF patients via the use of clinical and biomarker data. We retrospectively enrolled 3, 110 CHF patients admitted between January 2015 and December 2024 at Guang'anmen Hospital. Demographic, clinical, and laboratory indicators, including apolipoprotein B (ApoB), the triglyceride-glucose (TyG) index, and a novel glycated TyG (gTyG) index, were collected. Logistic regression and restricted cubic spline analyses were used to assess dose-response associations between biomarkers and depression. Eight ML algorithms were trained and evaluated, with model interpretability assessed via SHapley Additive exPlanation (SHAP). Among the 3, 110 patients, 37.3% had comorbid depression. Elevated ApoB and gTyG indices were strongly associated with depression risk in both the unadjusted and fully adjusted models (ApoB Q4 vs. Q1: OR 5.41, 95% CI 3.72-7.87; gTyG Q4 vs. Q1: OR 2.88, 95% CI 1.88-4.41; both P < 0.001), demonstrating clear nonlinear dose-response relationships. The TyG index was associated with depression in the crude analyses but lost significance after adjustment. Among the ML models, the RF model achieved the best performance (AUC 0.933 in training, accuracy 0.814, sensitivity 0.939). SHAP analysis revealed that the ApoB and gTyG indices were the most influential predictors. A user-friendly web application was developed for individualized risk prediction. This study demonstrated that the ApoB and gTyG index are robust biomarkers for predicting depression risk in CHF patients. The RF model provided the highest predictive accuracy and interpretability, highlighting its potential utility for early risk stratification and targeted intervention. The incorporation of these biomarkers into routine clinical practice may facilitate timely identification and management of depression in CHF patients, ultimately improving patient outcomes. Show less
📄 PDF DOI: 10.3389/fendo.2025.1737713
APOB

Heparan sulfate regulates myofibroblast heterogeneity and function to mediate niche homeostasis during alveolar morphogenesis.

Hua He, Chong Ma, Wei Wei +10 more · 2025 · Nature communications · Nature · added 2026-04-24
Postnatal respiration requires bulk formation of alveoli that produces extensive surface area for gas diffusion from epithelium to the circulatory system. Alveolar morphogenesis initiates at late gest Show more
Postnatal respiration requires bulk formation of alveoli that produces extensive surface area for gas diffusion from epithelium to the circulatory system. Alveolar morphogenesis initiates at late gestation or postnatal stage during mammalian development and is mediated by coordination among multiple cell types. Here we show that fibroblast-derived Heparan Sulfate Glycosaminoglycan (HS-GAG) is essential for maintaining a niche that supports alveolar formation by modulating both biophysical and biochemical cues. Gli1-CreER mediated deletion of HS synthase gene Ext1 in lung fibroblasts results in enlarged and simplified alveolar structures. Ablation of HS results in loss of a subset of PDGFRα Show less
📄 PDF DOI: 10.1038/s41467-025-57163-4
EXT1

10,12-Conjugated linoleic acid alleviates lipid accumulation in primary chicken hepatocytes via the ERK1/2-AMPK pathway.

Xuelan Liu, Peipei Yan, Heng Zhang +3 more · 2025 · Poultry science · Elsevier · added 2026-04-24
Conjugated linoleic acid (CLA) isomers have been reported to reduce body weight and promote glycolipid metabolism in animals. In a preliminary study, we revealed that trans-10, cis-12-CLA (10,12-CLA) Show more
Conjugated linoleic acid (CLA) isomers have been reported to reduce body weight and promote glycolipid metabolism in animals. In a preliminary study, we revealed that trans-10, cis-12-CLA (10,12-CLA) plays an important role in modulating lipid metabolism in chickens. However, the underlying mechanism remains unclear. In this study, we constructed an isolated in vitro model with primary chicken hepatocytes to investigate the effect of 10,12-CLA on lipid metabolism. 10,12-CLA inhibited lipid accumulation by decreasing the mRNA expression of sterol regulatory element-binding protein-1c (SREBP-1c), SREBP2, 3‑hydroxy-3-methylglutaryl-CoA reductase (HMGCR), fatty acid synthase (FAS), adipose triacylglyceride lipase (ACC), and lipoprotein lipase (LPL) and increasing the mRNA expression of peroxisome proliferator-activated receptor α (PPARα), carnitine palmitoyltransferase 1 (CPT1) and adipose triacylglyceride lipase (ATGL). Furthermore, 10,12-CLA treatment activated the protein expression of extracellular signal-regulated kinase 1/2 (ERK1/2) and AMP-activated protein kinase (AMPK), whereas treatment with the ERK1/2 inhibitor U0126 reversed the inhibitory effects of 10,12-CLA on lipid accumulation by blocking the ERK1/2-AMPK pathway, leading to increased lipid accumulation and triglyceride content in primary chicken hepatocytes. These findings suggest that in chicken hepatocytes, 10,12-CLA alleviates hepatocyte lipid deposition by activating the ERK1/2-AMPK pathway, promoting fatty acid oxidation and reducing lipid synthesis, revealing the potential mechanism through which 10,12-CLA regulates hepatic lipid metabolism in chickens. Show less
📄 PDF DOI: 10.1016/j.psj.2025.105904
LPL

Hypertrophic Cardiomyopathy: Genes and Mechanisms.

Jinli Chen, Yang Xing, Jie Sun +4 more · 2025 · Frontiers in bioscience (Landmark edition) · added 2026-04-24
Hypertrophic cardiomyopathy (HCM) is a hereditary disease of the myocardium characterized by asymmetric hypertrophy (mainly the left ventricle) not caused by pressure or volume load. Most cases of HCM Show more
Hypertrophic cardiomyopathy (HCM) is a hereditary disease of the myocardium characterized by asymmetric hypertrophy (mainly the left ventricle) not caused by pressure or volume load. Most cases of HCM are caused by genetic mutations, particularly in the gene encoding cardiac myosin, such as Show less
no PDF DOI: 10.31083/FBL25714
MYBPC3

Targeting the self-amplifying loop between ATF6 and SNAI1 to inhibit epithelial‒mesenchymal transition in fibrotic lesions.

Tong Wu, Yan Liu, Jiyuan Ma +10 more · 2025 · Theranostics · added 2026-04-24
no PDF DOI: 10.7150/thno.109442
SNAI1

Endothelial cells-derived SEMA3G suppresses glioblastoma stem cells by inducing c-Myc degradation.

Peng-Xiang Min, Li-Li Feng, Yi-Xuan Zhang +12 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
The poor prognosis of glioblastoma (GBM) patients is attributed mainly to abundant neovascularization and presence of glioblastoma stem cells (GSCs). GSCs are preferentially localized to the perivascu Show more
The poor prognosis of glioblastoma (GBM) patients is attributed mainly to abundant neovascularization and presence of glioblastoma stem cells (GSCs). GSCs are preferentially localized to the perivascular niche to maintain stemness. However, the effect of abnormal communication between endothelial cells (ECs) and GSCs on GBM progression remains unknown. Here, we reveal that ECs-derived SEMA3G, which is aberrantly expressed in GBM patients, impairs GSCs by inducing c-Myc degradation. SEMA3G activates NRP2/PLXNA1 in a paracrine manner, subsequently inducing the inactivation of Cdc42 and dissociation of Cdc42 and WWP2 in GSCs. Once released, WWP2 interacts with c-Myc and mediates c-Myc degradation via ubiquitination. Genetic deletion of Sema3G in ECs accelerates GBM growth, whereas SEMA3G overexpression or recombinant SEMA3G protein prolongs the survival of GBM bearing mice. These findings illustrate that ECs play an intrinsic inhibitory role in GSCs stemness via the SMEA3G-c-Myc distal regulation paradigm. Targeting SEMA3G signaling may have promising therapeutic benefits for GBM patients. Show less
no PDF DOI: 10.1038/s41418-025-01534-3
WWP2

AAV9-mediated MYBPC3 gene therapy with optimized expression cassette enhances cardiac function and survival in MYBPC3 cardiomyopathy models.

Amara Greer-Short, Anna Greenwood, Elena C Leon +27 more · 2025 · Nature communications · Nature · added 2026-04-24
Hypertrophic cardiomyopathy (HCM) affects approximately 600,000 people in the United States. Loss-of-function mutations in Myosin Binding Protein C3, MYBPC3, are the most common genetic cause of HCM, Show more
Hypertrophic cardiomyopathy (HCM) affects approximately 600,000 people in the United States. Loss-of-function mutations in Myosin Binding Protein C3, MYBPC3, are the most common genetic cause of HCM, with the majority of mutations resulting in haploinsufficiency. To restore cardiac MYBPC3, we use an adeno-associated virus (AAV9) vector and engineer an optimized expression cassette with a minimal promoter and cis-regulatory elements (TN-201) to enhance packaging efficiency and cardiomyocyte expression. Rather than simply preventing cardiac dysfunction preclinically, we demonstrate in a symptomatic MYBPC3-deficient murine model the ability of AAV gene therapy to reverse cardiac hypertrophy and systolic dysfunction, improve diastolic dysfunction, and prolong survival. Dose-ranging efficacy studies exhibit restoration of wild-type MYBPC3 protein levels and saturation of cardiac improvement at the clinically relevant dose of 3E13 vg/kg, outperforming a previously published construct. These findings suggest that TN-201 may offer therapeutic benefits in MYBPC3-associated cardiomyopathy, pending further validation in clinical settings. Show less
📄 PDF DOI: 10.1038/s41467-025-57481-7
MYBPC3

Latent profiles of volume management behaviors and their relationship with symptom distress in patients with chronic heart failure.

Weiwei Liu, Yuzhong Gu, Qingqing Yang +1 more · 2025 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
To explore the latent categories of volume management behaviors in patients with chronic heart failure (CHF) and analyze their relationship with symptom distress. This cross-sectional study utilized a Show more
To explore the latent categories of volume management behaviors in patients with chronic heart failure (CHF) and analyze their relationship with symptom distress. This cross-sectional study utilized a convenience sampling method to select 552 CHF patients from the cardiology departments of Nantong Sixth People's Hospital and Nantong Fourth People's Hospital. Volume management behaviors were assessed using the Volume Management Behavior Scale, and symptom distress was evaluated using the Symptom Distress Questionnaire (SDQ), which measures the severity of eight core symptoms. Latent Profile Analysis (LPA) was employed to identify behavioral categories. Multivariate Analysis of Variance (MANOVA) and multiple linear regression were used to analyze differences in symptom distress across behavioral categories and to examine the independent predictive effect of behavioral classification on symptom distress. The volume management behaviors of CHF patients were classified into three latent categories: active management type (43.1%), selective adherence type (27.7%), and passive dependence type (29.2%). Symptom distress scores showed a significant increasing trend across the three categories (active type: 10.5 ± 3.8; selective type: 13.2 ± 4.1; passive type: 16.3 ± 5.2, CHF patients exhibit three distinct clinical patterns of volume management behaviors, with the passive dependence type associated with the highest symptom burden. Behavioral category is a significant predictor of symptom distress. These findings provide an empirical basis for developing precise intervention strategies tailored to different behavioral phenotypes. Show less
📄 PDF DOI: 10.3389/fcvm.2025.1698319
LPA

Dysregulated serum lipid profile and atherosclerosis in untreated female Takayasu arteritis patients: a propensity score-matched analysis.

Q Zang, F Li, Y Ju +6 more · 2025 · Scandinavian journal of rheumatology · Taylor & Francis · added 2026-04-24
Recent studies suggest that dyslipidaemia may play a critical role in the progression of cardiovascular disease in Takayasu arteritis (TA), although the exact relationship between dyslipidaemia and TA Show more
Recent studies suggest that dyslipidaemia may play a critical role in the progression of cardiovascular disease in Takayasu arteritis (TA), although the exact relationship between dyslipidaemia and TA disease activity remains unclear, which is the focus of this study. We evaluated dyslipidaemia and atherosclerosis in a cohort of untreated female patients. Fifty untreated female patients with TA (median age 30 years) and 98 healthy controls matched for age and body mass index (median age 30 years) were assessed for lipid profiles [total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), ApoB, ApoE, lipoprotein(a)], inflammatory markers [C-reactive protein (CRP), erythrocyte sedimentation rate (ESR)], and atherosclerotic plaque frequency. TA patients exhibited significantly higher levels of TG and the non-HDL-C/HDL-C ratio than the control group, whereas TC, HDL-C, LDL-C, and ApoA1 levels were significantly lower. Pearson's correlation analysis indicated a positive correlation between CRP and ApoB, as well as the non-HDL-C/HDL-C ratio, and negative correlations with TG, HDL-C, and ApoA1. Atherosclerotic plaques were detected in 14.3% of the TA patients. Multivariate regression analysis revealed that the presence of atherosclerotic plaques was associated only with age, independent of inflammatory markers and lipoprotein levels. The results of this study indicate that untreated female TA patients exhibit a markedly dysregulated serum lipid profile. Atherosclerosis in early TA was not related to lipids or markers of inflammation. Show less
no PDF DOI: 10.1080/03009742.2025.2488096
APOB

GLP-1/GIP dual agonist tirzepatide alleviates mice model of Parkinson's disease by promoting mitochondrial homeostasis.

Ruixue Tian, Kexin Liu, Hurong Lai +3 more · 2025 · International immunopharmacology · Elsevier · added 2026-04-24
The prevailing treatment of Parkinson's disease (PD) is not yet satisfactory. The present investigate the neuroprotective effect of the GLP-1/GIP dual agonist tirzepatide and examine the potential mec Show more
The prevailing treatment of Parkinson's disease (PD) is not yet satisfactory. The present investigate the neuroprotective effect of the GLP-1/GIP dual agonist tirzepatide and examine the potential mechanisms involved. Analysis of GLP1 receptor (GLP1R) and GIPR expression alterations in dopaminergic neurons from PD patients in the GSE238129 dataset. The MPTP-induced subacute PD mice was treated with tirzepatide, semaglutide and levodopa. Behavioral tests and brain histopathology of mice were evaluated. The transmission electron microscopy revealed the presence of ultrastructural alterations in the mitochondrial morphology. The ATP level was assessed in substantia nigra. Western blot and immunohistochemical staining were employed to quantify Drp1 and mitophagy proteins. Furthermore, Drp1 inhibitor and mitophagy activator were used to treat MPTP-induced subacute PD mice, and lysosome inhibitor chloroquine (CQ) and the autophagy inhibitor 3-methyladenine (3-MA) were used in SY5Y cells for validation. The gene expression levels of both GLP1R and GIPR were significantly downregulated in dopaminergic neurons derived from PD patients. Tirzepatide could significantly ameliorate MPTP-induced the loss of tyrosine hydroxylase (TH) protein in the substantia nigra. There was no statistically difference observed between one-third doses of tirzepatide when compared with semaglutide and levodopa. In addition, tirzepatide not only improved mitochondrial ultrastructure, but also enhanced mitochondrial ATP content. Tirzepatide was found to reduce Drp1 expression and reverse the expressions of mitophagy-related proteins, including Pink1, Parkin, and p62. There was no statistically difference observed between one-third doses of tirzepatide compared with semaglutide in mitochondrial energy control. In addition, we observed that MPTP-induced subacute PD mice treated with a Drp1 inhibitor and mitophagy activator exhibited therapeutic effects. In SY5Y cells, lysosomal and autophagy inhibitors significantly reduced mitochondrial membrane potential, ATP levels, and the NAD+/NADH ratio. This study demonstrates that the benefits of tirzepatide extend to mitochondrial networks, achieved by means of the inhibition of mitochondrial pathological fission, the promotion of mitophagy, in MPTP-induced subacute PD mice or cells model. Show less
no PDF DOI: 10.1016/j.intimp.2025.115443
GIPR

Construction and validation of a lung adenocarcinoma prognostic model based on neutrophil extracellular traps and oxidative stress-related genes.

Long Xu, Yuanyuan Zhao, Shuxi Song +3 more · 2025 · European journal of medical research · BioMed Central · added 2026-04-24
Lung adenocarcinoma (LUAD) is a major cause of cancer-related morbidity and mortality globally, with challenges in prognosis and treatment due to its complex pathogenesis and heterogeneous tumor micro Show more
Lung adenocarcinoma (LUAD) is a major cause of cancer-related morbidity and mortality globally, with challenges in prognosis and treatment due to its complex pathogenesis and heterogeneous tumor microenvironment (TME). Neutrophil extracellular traps (NETs) and oxidative stress play critical roles in tumor progression: NETs promote tumor cell adhesion, migration, and immune suppression, while oxidative stress induces DNA damage and activates pro-tumor signaling pathways. Moreover, oxidative stress is an important inducer of NETs, and their crosstalk shapes the LUAD immune microenvironment. However, systematic exploration of LUAD immunotherapeutic response prediction based on NETs and oxidative stress-related genes remains lacking. The gene set related to oxidative stress was obtained from MSigDB. The gene set related to NETs was sourced from relevant literature. Transcriptomic and clinical data were integrated from The Cancer Genome Atlas (TCGA)-LUAD (training set) and GSE31210 (validation set). Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to screen gene modules and characteristic scores related to NETs and oxidative stress signatures. Differentially expressed genes (DEGs) were screened, and prognostic model was established using univariate and LASSO Cox regression. Immune infiltration was analyzed using ESTIMATE algorithm, MCP-counter and ssGSEA methods. And we developed a nomogram incorporating clinicopathological features and RiskScore model, and performed drug sensitivity analysis. Finally, the biological role of CPS1 in lung cancer cells was investigated through CCK-8, wound-healing, and Transwell experiments. 22 co-expression modules were screened, among which the brown module showed significant correlations with NETs and oxidative stress signature scores. This module was intersected with DEGs, yielding 624 overlapping genes implicated in immune-relevant pathways (like leukocyte differentiation, neutrophil activation involved in immune response). A prognostic model was established utilizing 8 key genes (ADGRE3, ARHGEF3, CD79A, CLEC7A, CPS1, EPHB2, LARGE2, and OAS3). In the TCGA database, the model demonstrated robust prognostic discrimination (area under the curve (AUC) > 0.6), with high-risk patients exhibiting shorter overall survival (OS) (p < 0.05). Its stability was validated in GSE31210 (AUC > 0.6). The RiskScore showed negative correlations with immune infiltration (like T cells, CD8 T cells, and natural killer cells) as well as immune/stromal scores. A nomogram model combining RiskScore with N staging was developed and validated, demonstrating strong predictive accuracy through calibration and decision curve analyses. High-risk patients were more sensitive to drugs like BI-2536, BMS-509744, and Pyrimethamine. Finally, in vitro tests showed that CPS1 knockdown markedly decreased the viability, migration, and invasion of lung cancer cells. The constructed prognostic model by NETs and oxidative stress-relevant genes effectively predicts LUAD prognosis, correlates with immune microenvironment characteristics, and guides drug sensitivity, providing novel insights for LUAD prognostic assessment and personalized therapy. Show less
📄 PDF DOI: 10.1186/s40001-025-03553-9
CPS1

Improving Light-Responsive Efficiency of Type II Porous Liquid by Tailoring the Functionality of Host.

Yang Liu, Han-Yan Jin, Meng-Meng Li +6 more · 2025 · Angewandte Chemie (International ed. in English) · Wiley · added 2026-04-24
Light-responsive porous liquids (LPLs) attract significant attention for their controllable gas uptake under light irradiation, while their preparation has remained a great challenge. Here we report t Show more
Light-responsive porous liquids (LPLs) attract significant attention for their controllable gas uptake under light irradiation, while their preparation has remained a great challenge. Here we report the fabrication of type II LPLs with enhanced light-responsive efficiency by tailoring the host's functionality for the first time. The functionality of light-responsive metal-organic cage (MOC-RL, constructed from dicopper and responsive ligands) is modified by introducing the second long-chain alkyl ligand, producing MOC-RL-AL as a new host. A spatially hindered solvent based on polyethylene glycol, IL-NTf Show less
no PDF DOI: 10.1002/anie.202501191
LPL

Identification and validation of MMP1 as a biomarker associated with mitochondrial oxidative stress in liver hepatocellular carcinoma.

Zhihui Wang, Hao Zhou, Lie Zhang +2 more · 2025 · Scientific reports · Nature · added 2026-04-24
Mitochondrial oxidative stress plays a critical role in cancer development and progression. However, there is limited research on the relationship between mitochondrial oxidative stress and liver hepa Show more
Mitochondrial oxidative stress plays a critical role in cancer development and progression. However, there is limited research on the relationship between mitochondrial oxidative stress and liver hepatocellular carcinoma (LIHC). Mitochondrial oxidative stress-related genes were collected from Genecards Portal. Prognosis-linked genes (PLGs) were identified by univariate Cox regression analysis. A risk model was constructed based on the PLGs using least absolute shrinkage and selection operator (LASSO) analysis. Receiver operating characteristic (ROC) curves were used to determine the predictive ability of the model. The expression levels of the prognostic genes were verified in the cell lines. Cell proliferation, apoptosis, and invasion assays were conducted to investigate the functional role of the target gene. We constructed a novel risk model based on 9 prognostic genes (CYP2C19, CASQ2, LPL, TXNRD1, CACNA1S, SLC6A3, OXTR, BIRC5, and MMP1). Survival analysis showed that patients with a low-risk score had a much better overall survival (OS). Prognostic risk score was found to be an independent predictor of prognosis. Patients in the high-risk group had a less favorable tumor microenvironment characterized by a lower degree of immune cell infiltration. Among the nine prognostic genes, MMP1, identified as the most promising candidate, demonstrated the capacity to enhance tumor cell proliferation and invasion. Our investigation reveals the oncogenic role of mitochondrial oxidative stress in LIHC. For the first time, we established a risk prediction model for mitochondrial oxidative stress in patients with LIHC. MMP1 has the potential to function as a promising biomarker in LIHC. Show less
📄 PDF DOI: 10.1038/s41598-025-10076-0
LPL

Recent Advancements in Understanding the Role and Mechanisms of Angiopoietin-like Proteins in Diabetic Retinopathy.

Xinling Zhang, Dongang Liu, Yuting Qiu +7 more · 2025 · Metabolites · MDPI · added 2026-04-24
Angiopoietin-like proteins (ANGPTLs) represent a family of secreted glycoproteins that are extensively expressed in vivo and are integral to various pathophysiological processes, including glucose and Show more
Angiopoietin-like proteins (ANGPTLs) represent a family of secreted glycoproteins that are extensively expressed in vivo and are integral to various pathophysiological processes, including glucose and lipid metabolism, stem cell proliferation, local inflammation, vascular permeability, and angiogenesis. Particularly interesting is ANGPTL4, which has been identified as a significant factor in the development and progression of diabetic retinopathy (DR), thus becoming a central focus of DR research. ANGPTLs modulate metabolic pathways, enhance vascular permeability, and facilitate pathological angiogenesis, in addition to causing intraocular inflammation. As promising molecular targets, ANGPTLs not only serve as biomarkers for predicting the onset and progression of DR but also present therapeutic potential through antibody-based interventions. This paper discusses the pathogenesis of DR and the potential applications of ANGPTLs in early diagnosis and targeted therapy. It provides references for advancing precision diagnosis and personalized treatment strategies through more profound ANGPTLs research in the future. Show less
📄 PDF DOI: 10.3390/metabo15060352
ANGPTL4

G3BP1, a stress granule core protein, ameliorates metabolic dysfunction-associated fatty liver disease by attenuating hepatocyte lipid deposition.

Xingjing Liu, Huimei Yu, Tongtong Hu +7 more · 2025 · Diabetes, obesity & metabolism · Blackwell Publishing · added 2026-04-24
Abnormal lipid accumulation is an important cause of metabolic dysfunction-associated fatty liver disease (MAFLD) progression and can induce several stress responses within cells. This study is the fi Show more
Abnormal lipid accumulation is an important cause of metabolic dysfunction-associated fatty liver disease (MAFLD) progression and can induce several stress responses within cells. This study is the first to explore the role and molecular mechanism of stress granules (SGs) in MAFLD. A gene knock-down model of G3BP1, a core SG molecule in mice and HepG2 cells, was constructed to explore the role of SGs in MAFLD induced in vivo by a high-fat diet or in vitro by palmitic acid (PA). Methods included metabolic phenotyping; western blotting; qPCR; and immunofluorescence, haematoxylin/eosin and masson staining. The downstream molecules of G3BP1 and its specific molecular mechanism were screened using RNA sequencing (RNA-seq). G3BP1 and TIA1 expression were upregulated in high-fat diet-fed mouse liver tissues and PA-induced HepG2 cells, and the two molecules showed significantly increased colocalisation. G3BP1 knock-down slightly increased TIA1 expression in the livers of obese mice but not in lean mice. G3BP1 deficiency aggravated liver lipid deposition and insulin resistance in obese mice, and this phenotype was confirmed in vitro in PA-induced hepatocytes. RNA-seq demonstrated that G3BP1 slowed down MAFLD progression by inhibiting APOC3, possibly through a mechanistic suppression of APOC3 entry into the nucleus. This study reveals for the first time a protective role for SGs in MAFLD. Specifically, knocking down the core G3BP1 molecule in SGs aggravated the progression of fatty acid-induced MAFLD through a mechanism that may involve the nuclear entry of APOC3. These findings provide a new therapeutic direction for MAFLD. Show less
no PDF DOI: 10.1111/dom.16302
APOC3

PYK2 promotes cell proliferation and epithelial-mesenchymal transition in endometriosis by phosphorylating Snail1.

Lu Liu, Lan Liu, Chenjing Yue +3 more · 2025 · Molecular medicine (Cambridge, Mass.) · BioMed Central · added 2026-04-24
Endometriosis can lead to decreased endometrial receptivity, reduced rates of implantation, and diminished ovarian reserve. Currently, more than 50% of infertile women are found to suffer from endomet Show more
Endometriosis can lead to decreased endometrial receptivity, reduced rates of implantation, and diminished ovarian reserve. Currently, more than 50% of infertile women are found to suffer from endometriosis. However the etiology and pathogenesis of endometriosis are still poorly understood. Epithelial-mesenchymal transition (EMT) has been confirmed to be involved in endometriosis. PYK2 is a non-receptor tyrosine kinase that affects cell proliferation, survival, and migration by regulating intracellular signaling pathways. PYK2 plays a regulatory role in the EMT process by affecting the expression of genes associated with EMT through the influence of transcription factors. Snail1 (Snail1) plays a key role in the EMT process and is highly expressed in endometriosis tissues. On the other hand, Snail1 affects the invasive and metastatic ability of endometriosis cells mainly by regulating the EMT process. However, the upstream mechanisms that regulate the process of Snail1 protein stability in endometriosis are not clear. We identified a non-receptor tyrosine kinase, proline-rich tyrosine kinase 2 (PYK2 or PTK2B), and examined the expression of PYK2 in endometriosis. The relevant plasmids were constructed. This study enrolled 20 patients with laparoscopically confirmed endometriosis meeting ASRM diagnostic criteria, collecting ectopic lesions (14 ovarian endometriotic cysts and 6 deep infiltrating nodules) along with matched eutopic endometrial tissues (15 proliferative phase, 5 secretory phase) as controls. All tissue specimens underwent immunohistochemical analysis. Human endometrial stromal cells (HESC) were isolated from normal endometrium of 3 control patients for in vitro meconium induction. Ectopic endometrial stromal cells (EESC) were obtained from 5 ectopic lesions. Protein extracts from both ectopic tissues and cells were subjected to Western blot and co-immunoprecipitation (Co-IP) interaction validation. Functional assays (proliferation/migration/invasion) were performed using EESC and 11Z cell lines with triplicate biological replicates. Co-IP experiments were performed to verify the interaction between PYK2 and Snail1, as well as to determine the specific location of this interaction. Additionally, we examined the effect of PYK2 on endometriosis cells in vitro and whether VS-6063 inhibits the biological functions of endometriosis cells. Endometriosis models were established in 20 five-week-old female C57BL/6 mice, randomly allocated into experimental (n = 10) and control (n = 10) groups. Statistical analyses were conducted using GraphPad Prism 7.0, employing parametric tests for normally distributed data and non-parametric methods otherwise, with Benjamini-Hochberg correction for multiple comparisons. PYK2 is highly expressed in endometriosis tissues. It acts as a new binding partner of Snail1 and enhances EMT in endometriosis by increasing the phosphorylation of Snail1. Additionally, PYK2 promotes the proliferation, migration, and invasion of endometriosis cells while inhibiting decidualization. We demonstrated that VS-6063 inhibited the proliferation, migration, and invasion of endometriosis cells in vitro, as well as the growth of endometriotic lesions in vivo. PYK2 is a novel binding partner of Snail1. PYK2 promotes the occurrence and development of endometriosis by up-regulating Snail1, which could be a promising therapeutic target for endometriosis. Show less
no PDF DOI: 10.1186/s10020-025-01218-1
SNAI1

Proteomics reveals genetic mechanisms of cold resistance in Hezuo pig liver tissue.

Yali Zhang, Xiaoli Gao, Chao Liu +4 more · 2025 · Journal of proteomics · Elsevier · added 2026-04-24
Cold stress poses a significant challenge to pig farming in northern China, leading to reduced productivity and, in severe cases, even mortality. However, the mechanisms underlying cold resistance in Show more
Cold stress poses a significant challenge to pig farming in northern China, leading to reduced productivity and, in severe cases, even mortality. However, the mechanisms underlying cold resistance in pigs are not well understood. To explore the genetic mechanism of cold resistance in pigs under low-temperature conditions, the cold-tolerant Hezuo pig was selected as a model. DIA proteomics analysis was performed on liver tissues from Hezuo pigs after 24 h of exposure to low-temperature treatments. The results showed that approximately 149 differential abundance proteins (DAPs) were detected (95 up-regulated and 54 down-regulated). GO analysis showed that these DAPs were mainly associated with lipid metabolism, vesicle fusion, and membrane function. KEGG analysis showed that these DAPs were primarily enriched in lipid metabolism-related pathways such as cholesterol metabolism and vitamin digestion and absorption. Comprehensive analysis identified APOA4, APOA2, SREBF2, ATP23, STX2, USO1, ETFA, RAB11FIP1, ETNPPL, and SGMS1 as potential key proteins involved in cold resistance mechanisms. The mRNA expression of the genes for two key candidate proteins (APOA4 and SREBF2), which are involved in lipid metabolism, was analyzed using qRT-PCR, revealing a significant up-regulation after low-temperature treatment. These findings provide significant insights into the mechanisms of cold resistance in animals and may serve as candidate markers for further studies on cold tolerance. SIGNIFICANCE: Cold resistance is one of the key traits in pigs and involves multiple complex coordinated regulatory mechanisms. However, its genetic mechanisms are not completely understood. In this study, a DIA proteomics approach was used to identify proteins and pathways associated with cold resistance in the liver of low-temperature-treated Hezuo pigs. These findings offer novel candidate proteins and key pathways for investigating the molecular mechanisms of cold resistance in Hezuo pigs, providing a base for further elucidating the mechanisms of cold tolerance in pigs. Show less
no PDF DOI: 10.1016/j.jprot.2025.105420
APOA4

Identification of oxidative stress-related genes in papillary thyroid carcinoma and their common targets with resveratrol based on bioinformatics, network pharmacology, and molecular docking.

Dongliang Shi, Liang Chen, Chenhao Li +5 more · 2025 · Discover oncology · Springer · added 2026-04-24
This study aims to identify oxidative stress-related genes (OSGs) in papillary thyroid carcinoma (PTC) and their common targets with resveratrol. Oxidative stress-related differentially expressed gene Show more
This study aims to identify oxidative stress-related genes (OSGs) in papillary thyroid carcinoma (PTC) and their common targets with resveratrol. Oxidative stress-related differentially expressed genes (OS-DEGs) were identified by intersecting datasets. The screened core genes were utilized to construct a prognostic model, and their prognostic value, along with their associations with clinical pathological characteristics and immune infiltration, was assessed. Subsequently, the core targets at the intersection of resveratrol and oxidative stress (OS) in PTC were screened, and their binding properties with resveratrol were analyzed. By conducting cross-database analysis, 38 OS-DEGs were identified, and 3 core genes APOE、CDKN2A、APOD were determined. The prognostic model based on core genes exhibited robust prognostic capabilities. The core genes displayed significant correlations with various clinical pathological parameters and a range of immune cells. Additionally, 13 targets of resveratrol for antioxidative stress were screened from databases. 6 high-performing targets, JUN, TGFB1, BCL2, CDKN1A, FOS, ICAM1, were revealed by topological analysis, all exhibiting binding energies lower than - 5.0 kcal/mol. Our study is the pioneering research to provide new insights into the diagnosis, prognosis, and treatment of PTC through the analysis of OSGs, presenting potential clinical implications. Furthermore, this research reveals the molecular functions associated with resveratrol and its pharmacological targets regulating OS in PTC for the first time. Show less
📄 PDF DOI: 10.1007/s12672-025-04170-y
APOE

Metabolic Coordination Structures Contribute to Diabetic Myocardial Dysfunction.

Teng Wu, Tongsheng Huang, Honglin Ren +26 more · 2025 · Circulation research · added 2026-04-24
Individuals with diabetes are susceptible to cardiac dysfunction and heart failure, potentially resulting in mortality. Metabolic disorders frequently occur in patients with diabetes, and diabetes usu Show more
Individuals with diabetes are susceptible to cardiac dysfunction and heart failure, potentially resulting in mortality. Metabolic disorders frequently occur in patients with diabetes, and diabetes usually leads to remodeling of heart structure and cardiac dysfunction. However, the contribution and underlying mechanisms of metabolic and structural coupling in diabetic cardiac dysfunction remain elusive. Two mouse models of type 2 diabetes (T2DM) were used to assess alterations in glucose/lipid metabolism and cardiac structure. The potential metabolic-structural coupling molecule ACBP (acyl-coenzyme A-binding protein) was screened from 4 published datasets of T2DM-associated heart disease. In vivo loss-of-function and gain-of-function approaches were used to investigate the role of ACBP in diabetic cardiac dysfunction. The underlying mechanisms of metabolic and structural coupling were investigated by stable-isotope tracing metabolomics, coimmunoprecipitation coupled with mass spectrometry, and chromatin immunoprecipitation sequencing. Diabetic mouse hearts exhibit enhanced lipid metabolism and impaired ultrastructure with marked cardiac systolic and diastolic dysfunction. Analysis of 4 T2DM public datasets revealed that Our findings demonstrated that ACBP mediates the bidirectional regulation of cardiomyocyte metabolic and structural associations and identified a promising therapeutic target for ameliorating cardiac dysfunction in patients with T2DM. Show less
no PDF DOI: 10.1161/CIRCRESAHA.124.326044
MYBPC3

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

Chaoyi Chen, Yanhua Hao, Weilan Xu +3 more · 2025 · BMC public health · BioMed Central · added 2026-04-24
Chronic diseases have become a major public health challenge facing the world. Identifying key factors and developing effective management strategies to promote proactive health behaviors in patients Show more
Chronic diseases have become a major public health challenge facing the world. Identifying key factors and developing effective management strategies to promote proactive health behaviors in patients is crucial for improving health outcomes. This study aims to construct a comprehensive model of proactive health behaviors in chronic disease patients, elucidate multilevel determinants, and guide targeted policy interventions in China. A cross-sectional survey was conducted among 805 patients with chronic diseases in China. Latent profile analysis (LPA) was conducted to identify distinct profiles of proactive health behaviors among patients. Binary logistic regression analysis was used to verify and analyze the determinants affecting the proactive health behaviors of patients. Among the 805 participants, 471 were classified as highly proactive, and 334 were classified as less proactive. The average score for proactive health behaviors was 70.37 ± 10.93. Several factors positively predicted proactive health behaviors: patients aged > 74 years (AOR = 8.85, 95% CI 2.06-39.45), married patients (AOR = 1.78, 95% CI 1.02-3.11), urban residents (AOR= 1.33, 95% CI 1.04-1.70), those with stronger health intentions (AOR = 1.42, 95% CI 1.28-1.60), higher self-efficacy (AOR = 1.12, 95% CI 1.04-1.20), positive health beliefs (AOR = 1.21, 95% CI 1.09-1.34)), and greater community support (AOR = 1.18, 95% CI 1.07-1.32). Regarding policy support, perceiving an adequate upper payment limit for drugs was associated with twice the odds of proactive health behaviors (AOR = 2.61, 95% CI 1.44-4.78). Additionally, age and the medication reimbursement policy for drug expenses exerted negative effects on proactive health behaviors (β = -0.507, P < 0.01). Governments should transform medical insurance from a passive payer into an active health investor. By incorporating behavioral economics principles, such a reform reallocates policy design, resources, and decision-making power toward disadvantaged populations. This shift breaks the "well-intentioned policy trap", achieving lower medical costs alongside improved population health. Show less
📄 PDF DOI: 10.1186/s12889-025-25564-1
LPA

Triaptosis and Cancer: Next Hope?

Zi-Zhan Li, Kan Zhou, Jinmei Wu +5 more · 2025 · Research (Washington, D.C.) · added 2026-04-24
Cancer persists as one of the most formidable global public health crises and socioeconomic burdens of our era, compelling the scientific community to develop innovative and diversified therapeutic mo Show more
Cancer persists as one of the most formidable global public health crises and socioeconomic burdens of our era, compelling the scientific community to develop innovative and diversified therapeutic modalities to revolutionize clinical management and enhance patient outcomes. The recent seminal discovery by Swamynathan et al. has unveiled menadione, a vitamin K precursor, as a potent inducer of triaptosis-a novel regulated cell death pathway mediated through the oxidative modulation of phosphatidylinositol 3-kinase PIK3C3/VPS34. This mechanistically distinct cell death paradigm, characterized by its intimate association with endosomal dysfunction and oxidative stress-induced cellular catastrophe, has demonstrated remarkable therapeutic efficacy in preclinical prostate cancer models, outperforming conventional therapeutic regimens and emerging as a potential paradigm-shifting strategy in oncology. This comprehensive review provides a critical synthesis of the triaptosis discovery landscape, elucidating its molecular intricacies and pathophysiological implications. We systematically examine the multifaceted roles of endosomal biology in oncogenesis and tumor progression, while offering a nuanced perspective on redox homeostasis in malignant cells and the therapeutic potential of oxidative stress modulation. Furthermore, we address the inherent dichotomy of oxidative stress induction in cancer therapy, balancing its therapeutic promise against potential adverse effects. Looking toward the horizon of cancer research, we explore transformative therapeutic strategies leveraging triaptosis induction and its potential applications beyond oncology, aiming to catalyze a new era of precision medicine that ultimately enhances patient survival and quality of life. Show less
no PDF DOI: 10.34133/research.0880
PIK3C3

Transcriptomic and Proteomic Insights Into Buffalo Milk Fat Synthesis and the Role of IGFBP4 in BMECs.

Xinyue Shen, Chaobin Qin, Zhixiang Wang +5 more · 2025 · FASEB journal : official publication of the Federation of American Societies for Experimental Biology · added 2026-04-24
The content and composition of milk fat are critical determinants influencing milk flavor, nutritional value, and economic significance. Buffalo milk is characterized by its high-fat content and compl Show more
The content and composition of milk fat are critical determinants influencing milk flavor, nutritional value, and economic significance. Buffalo milk is characterized by its high-fat content and complex lipid profile, characterized by elevated levels of health-beneficial fatty acids such as linoleic acid, α-linolenic acid, and arachidonic acid. However, the molecular regulatory mechanisms governing milk fat synthesis in buffaloes remain incompletely elucidated. This study employed transcriptomic analysis of milk fat globules (MFGs) from buffaloes exhibiting high and low milk fat content, identifying 15 949 annotated genes, including 234 differentially expressed genes (DEGs). Functional enrichment analysis revealed that these DEGs were predominantly associated with cell proliferation and differentiation, glyconeogenesis, and reproductive system development. Notably, the expression of IGFBP4, AGPAT4, GPAT3, GPR84, and PC exhibited positive correlations with buffalo milk fat content, identifying them as potential candidate genes regulating milk fat synthesis. Proteomic profiling identified 1678 proteins, including 53 differentially expressed proteins (DEPs). Enrichment analysis indicated that DEPs were primarily involved in nucleotide metabolism, the tricarboxylic acid (TCA) cycle, glycerophospholipid metabolism, and TGF-β signaling. Integrated analysis revealed potential interactions involving the IGFBP4 and PC genes, as well as the ACO1, TMED7, and APRT proteins, highlighting IGFBP4 as a pivotal regulator of milk fat synthesis. Functional validation demonstrated that overexpression or knockdown of IGFBP4 in buffalo mammary epithelial cells (BMECs) significantly modulated cell proliferation and altered the expression of key milk fat synthesis-related genes (FABP3, LPL, SCD, ACACA, and FASN), indicating that IGFBP4 can promote de novo fatty acid synthesis and intracellular lipid storage while inhibiting exogenous fatty acid uptake. Collectively, this study provides novel mechanistic insights into the regulation of milk fat synthesis in buffaloes and establishes a foundation for enhancing lactation traits through targeted genetic breeding strategies. Show less
📄 PDF DOI: 10.1096/fj.202502191R
LPL

L3MBTL3 and STAT3 collaboratively upregulate SNAIL expression to promote metastasis in female breast cancer.

Jianpeng Xiao, Jie Wang, Jialun Li +11 more · 2025 · Nature communications · Nature · added 2026-04-24
The STAT3 pathway promotes epithelial-mesenchymal transition, migration, invasion and metastasis in cancer. STAT3 upregulates the transcription of the key epithelial-mesenchymal transition transcripti Show more
The STAT3 pathway promotes epithelial-mesenchymal transition, migration, invasion and metastasis in cancer. STAT3 upregulates the transcription of the key epithelial-mesenchymal transition transcription factor SNAIL in a DNA binding-independent manner. However, the mechanism by which STAT3 is recruited to the SNAIL promoter to upregulate its expression is still elusive. In our study, the lysine methylation binding protein L3MBTL3 is positively associated with metastasis and poor prognosis in female patients with breast cancer. L3MBTL3 also promotes epithelial-mesenchymal transition and metastasis in breast cancer. Mechanistic analysis reveals that L3MBTL3 interacts with STAT3 and recruits STAT3 to the SNAIL promoter to increase SNAIL transcription levels. The interaction between L3MBTL3 and STAT3 is required for SNAIL transcription upregulation and metastasis in breast cancer, while the methylated lysine binding activity of L3MBTL3 is not required for these functions. In conclusion, L3MBTL3 and STAT3 synergistically upregulate SNAIL expression to promote breast cancer metastasis. Show less
no PDF DOI: 10.1038/s41467-024-55617-9
SNAI1

Silencing IncRNA BACE1-AS alleviates neuronal injury and neurological deficits after ischemic stroke via targeting miR-103a-3p.

Kaijie Yu, Fang Liu, Tianrui Yu +3 more · 2025 · Neurological research · Taylor & Francis · added 2026-04-24
To investigate the role of lncRNA BACE1-AS in neuronal injury and neurological deficits after ischemic stroke and explore its underlying molecular mechanism. MCAO rat model and OGD/R cell model were e Show more
To investigate the role of lncRNA BACE1-AS in neuronal injury and neurological deficits after ischemic stroke and explore its underlying molecular mechanism. MCAO rat model and OGD/R cell model were established. BACE1-AS expression was detected by RT-qPCR. Neurological function was evaluated by mNSS and MWM test. Inflammatory factors (TNF-α, IL-6, IL-10), neuronal injury markers (NSE, GFAP), and apoptosis-related markers (Bcl-2, Bax, Caspase-3) were detected by ELISA and RT-qPCR. Bioinformatics analysis, dual-luciferase reporter assay, and RIP assay were used to validate the targeting relationship between BACE1-AS and miR-103a-3p. BACE1-AS was significantly upregulated in both MCAO rats and OGD/R-treated SH-SY5Y cells. Silencing BACE1-AS alleviated neurological deficits, reduced pro-inflammatory cytokine levels, and inhibited neuronal apoptosis. Mechanistically, BACE1-AS targeted miR-103a-3p, and inhibiting miR-103a-3p reversed the neuroprotective effects of BACE1-AS silencing in vivo and in vitro. Silencing BACE1-AS mitigates neuronal injury and neurological deficits after ischemic stroke by targeting miR-103a-3p, providing a novel therapeutic target for ischemic stroke. Show less
no PDF DOI: 10.1080/01616412.2025.2568025
BACE1