👤 Kang-le 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, 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, Shangyu 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

Advancing the clinical assessment of glomerular podocyte pathology in kidney biopsies via super-resolution microscopy and angiopoietin-like 4 staining.

Xiaojing Liu, Suxia Wang, Gang Liu +7 more · 2025 · Theranostics · added 2026-04-24
📄 PDF DOI: 10.7150/thno.101498
ANGPTL4

Dipeptidyl Peptidase-4 Inhibitors Improved Lipid Levels in Patients With Type 2 Diabetes: A Meta-analysis of Randomized Clinical Trials.

Lijia Zhao, Jie Meng, Jingjing Li +5 more · 2025 · Nutrition reviews · Oxford University Press · added 2026-04-24
Dipeptidyl peptidase-4 inhibitors (DPP-4i) serve as an incretin-based hypoglycemic class for the treatment of type 2 diabetes (T2D). DPP-4i have been reported to produce a pleiotropic effect on lipid Show more
Dipeptidyl peptidase-4 inhibitors (DPP-4i) serve as an incretin-based hypoglycemic class for the treatment of type 2 diabetes (T2D). DPP-4i have been reported to produce a pleiotropic effect on lipid profiles in addition to regulation of glucose homeostasis. The aim of this systematic review and meta-analysis was to quantitatively evaluate the impact of DPP-4i on lipid parameters in patients with T2D. PubMed, Embase, and The Cochrane Library were systematically searched for randomized controlled trials. Trials were identified if changes in lipid parameters, including low-density-lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), high-density-lipoprotein cholesterol (HDL-C), non-HDL-C, and apolipoprotein B (ApoB) were reported. A total of 95 publications were identified. DPP-4i significantly reduced levels of LDL-C (-3.48 mg/dL; 95% CI, -4.77 to -2.20; I2 = 70%, P < .00001), TC (-2.59 mg/dL; 95% CI, -3.88 to -1.29; I2 = 73%, P < .0001), TG (-5.39 mg/dL; 95% CI, -8.04 to -2.75; I2 = 77%, P < .0001), and non-HDL-C (-6.27 mg/dL; 95% CI, -10.94 to -1.60; I2 = 53%, P = .008). No significant effect was found on HDL-C (-0.32 mg/dL; 95% CI, -1.19 to 0.55; I2 = 97%, P = .47) and ApoB (-0.88 mg/dL; 95% CI, -3.36 to 1.60; I2 = 36%, P = .49) during DPP-4i treatment. DDP-4i significantly improved lipid parameters including LDL-C, TC, TG, and non-HDL-C in patients with T2D. This underscores the potential cardiovascular benefits of DPP-4i and their role in improving diabetes-related outcomes. PROSPERO registration no. CRD42020175999. Show less
no PDF DOI: 10.1093/nutrit/nuaf209
APOB

Distinct FGF-FGFR sets regulate inhibitory presynaptic differentiation from parvalbumin- and somatostatin-positive interneurons.

Zhengdong Wei, Shasha Zhang, Keke Bai +11 more · 2025 · Development (Cambridge, England) · added 2026-04-24
Twenty types of GABAergic interneurons form intricate networks to fine-tune neural circuits in the brain. Parvalbumin-positive (PV+) and somatostatin-positive (SST+) interneurons, which are the two la Show more
Twenty types of GABAergic interneurons form intricate networks to fine-tune neural circuits in the brain. Parvalbumin-positive (PV+) and somatostatin-positive (SST+) interneurons, which are the two largest populations of neocortical interneurons, innervate the soma and/or proximal dendrites, and distal dendrites of pyramidal neurons, respectively. Using PV- and SST-specific knockout mouse models, we show that PV+ interneurons require FGFR2, which responds to FGF7, to drive PV+ inhibitory presynaptic maturation on perisomatic regions of Layer V pyramidal neurons. In contrast, SST+ interneurons rely on both FGFR1 and FGFR2, which respond to FGF10 or FGF22, to promote SST+ inhibitory presynaptic maturation on distal dendrites of pyramidal neurons in cortical Layer I. Mechanistically, FGF-FGFR signaling sustains VGAT protein levels in interneurons through PP2A and Akt pathways. Together, these findings demonstrate that distinct FGF ligand-receptor combinations regulate inhibitory presynaptic differentiation by PV+ and SST+ interneurons, contributing to the formation of compartment-specific synaptic patterns. Show less
no PDF DOI: 10.1242/dev.204532
FGFR1

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

Super-enhancers and Mef2c: Novel regulators of cardiac hypertrophy via the Hey2/Notch/p38 signaling pathway.

Ruixuan Wang, Lina Ba, Rui Wang +6 more · 2025 · European journal of pharmacology · Elsevier · added 2026-04-24
Cardiac hypertrophy (CH), a pathological response to stress, is intricately regulated by the dynamic control of gene expression. This study explored the role of super-enhancers (SEs) and the transcrip Show more
Cardiac hypertrophy (CH), a pathological response to stress, is intricately regulated by the dynamic control of gene expression. This study explored the role of super-enhancers (SEs) and the transcription factor Mef2c in CH regulation. Using a transverse aortic constriction (TAC) mouse model, we demonstrated that inhibition of SEs with JQ-1, a BET inhibitor, significantly attenuated hypertrophic responses, as evidenced by reduced heart weight indices, enhanced cardiac function, and decreased expression of hypertrophic marker proteins BNP and β-MHC. Further analysis revealed that Mef2c, a key transcription factor, is driven by SEs in CH. In vivo and in vitro overexpression of Mef2c promotes CH, while deletion of the Mef2c SE region alleviates this condition. Mechanistically, we identified Hey2 as a downstream target of Mef2c and demonstrated that Mef2c regulates CH through the Hey2/Notch/p38 signaling pathway. Our findings provide novel insights into the molecular mechanisms underlying CH and suggest potential therapeutic targets for its treatment. Show less
no PDF DOI: 10.1016/j.ejphar.2025.177771
HEY2

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

FH-2001 is a novel FGFR/VEGFR dual inhibitor with immune-modulating activity.

Aiguo Liu, Longfei Huang, Xin Gao +4 more · 2025 · Anti-cancer drugs · added 2026-04-24
Multiple cancers are driven by aberrant fibroblast growth factor receptor (FGFR) signaling and vascular endothelial growth factor receptor (VEGFR)-linked angiogenesis. Several therapeutic agents targe Show more
Multiple cancers are driven by aberrant fibroblast growth factor receptor (FGFR) signaling and vascular endothelial growth factor receptor (VEGFR)-linked angiogenesis. Several therapeutic agents targeting FGFR and VEGFR have been developed and approved for use in solid cancers; however, there is still a high unmet medical need for new agents that have a more powerful antitumor activity and a broader antitumor spectrum. Here, we report the discovery of FH-2001, a novel and potent FGFR/VEGFR dual inhibitor, with additional activity of modulating programmed cell death ligand 1 (PD-L1) gene expression. In biochemical assays, FH-2001 showed potent inhibition of FGFR1, 2, 3, and 4, with half-maximal inhibitory concentration (IC 50 ) of 0.2, 0.2, 0.4, and 2.0 nM, respectively, and VEGFR1, 2, and 3, with IC 50 values of 2.0, 0.3, and 0.5 nM, respectively. FH-2001 significantly suppressed the cell growth of FGFR- or VEGFR-driven cancer cell lines. In representative cell line- and patient-derived tumor xenografts with aberrant FGFR or VEGFR signaling, FH-2001 substantially inhibited tumor growth. Furthermore, FH-2001 demonstrated marked antitumor activities when treated alone or combined with PD-L1 or PD-1 antibody in syngeneic mouse models. Flow cytometric analysis revealed that FH-2001 alone or in combination with anti-PD-L1 increased T and natural killer cells and decreased myeloid cells in the tumor microenvironment. Mechanistically, FH-2001 treatment dramatically reduced c-Myc and PD-L1 mRNA and protein levels in a dose-dependent manner in vitro . Taken together, FH-2001 is a promising dual-target inhibitor of FGFR and VEGFR and also modulates cancer immunity, while its robust antitumor activity positions it as a potentially class-leading anticancer agent. Show less
no PDF DOI: 10.1097/CAD.0000000000001743
FGFR1

CXCR4-LASP1-G9a-SNAIL axis drives NEPC transdifferentiation via induction of EMT and downregulation of REST.

Liangliang Liu, Itzel Astiazarán Rascón, Dong Lin +15 more · 2025 · Cell genomics · Elsevier · added 2026-04-24
Phenotypic switching is an emerging driver of cancer treatment resistance, yet early signals regulating this process remain unclear. Here, using longitudinal single-cell RNA sequencing, we mapped diff Show more
Phenotypic switching is an emerging driver of cancer treatment resistance, yet early signals regulating this process remain unclear. Here, using longitudinal single-cell RNA sequencing, we mapped differentiation trajectories in the LTL331 prostate adenocarcinoma patient-derived xenograft (PDX) model undergoing neuroendocrine prostate cancer (NEPC) transformation post castration. Our analyses identified a key differentiation node marked by epithelial-mesenchymal transition (EMT) and repressor element-1 silencing transcription factor (REST) downregulation driven by the CXCR4-LASP1-G9a-SNAIL axis. Mechanistically, CXCR4 activation promotes nuclear translocation of LASP1 that links G9a and SNAIL via SH3/proline-rich motif and LIM/SNAG domain interactions, enabling SNAIL-mediated REST repression via promoter E-box motifs. Inhibition of CXCR4 or G9a reversed LTL331R NEPC cells toward a luminal androgen receptor-active phenotype. CXCR4-targeted radioligands enabled both imaging and inhibition of NEPC tumors in vivo. These findings highlight the CXCR4-LASP1-G9a-SNAIL axis as a key regulator of epigenetic and transcriptional reprogramming in NEPC transdifferentiation and support its therapeutic targeting in aggressive NEPC. Show less
no PDF DOI: 10.1016/j.xgen.2025.100916
SNAI1

Precision engineering of anti-atherosclerotic herbal nanomedicine: from machine learning-aided active components screening to optimized metal-phenolic network codelivery.

Yao Chen, Meiting Lu, Lu Zhang +9 more · 2025 · Drug delivery and translational research · Springer · added 2026-04-24
Atherosclerosis (AS), a chronic inflammatory disease linked to oxidative stress and lipid imbalance, remains a major cardiovascular threat. Traditional herbs Salvia miltiorrhiza and Carthamus tinctori Show more
Atherosclerosis (AS), a chronic inflammatory disease linked to oxidative stress and lipid imbalance, remains a major cardiovascular threat. Traditional herbs Salvia miltiorrhiza and Carthamus tinctorius exhibit multi-target anti-AS potential, yet their compositional complexity limits clinical translation. This study aimed to systematically identify core anti-AS components from these herbs and enhance their anti-AS efficacy via machine learning-aided screening and nanotechnology-driven codelivery. We initially pioneered a machine learning-aided hybrid strategy integrating network pharmacology and quantitative activity relationship (QSAR) modeling to identify four core anti-AS polyphenols (i.e., salvianic acid A, salvianolic acid B, protocatechuic acid, and hydroxysafflor yellow A). Subsequently, a quaternary metal-phenolic network (SSPH-MPN) was engineered for plaque-targeted codelivery, optimized via the median-effect principle for achieving a synergistic effect based on ROS scavenging efficacy. The optimized SSPH-MPN was characterized by a series of studies, including molecular dynamics simulations, UV, DLS, TEM, FTIR, XPS, and ICP-MS. The anti-AS effect of the optimized SSPH-MPN was evaluated by monitoring oxidative status (ROS levels, antioxidant enzymes SOD, GSH-Px, MDA, T-AOC), inflammatory markers (IL-1β, IL-6, TNF-α), lipid metabolism (DiI-oxLDL uptake, cholesterol efflux, blood lipid levels, lipid accumulation), and plaque areas. The results demonstrated that the optimized SSPH-MPN showed great efficiency in inhibiting lipid uptake and accumulation, and mediating cholesterol efflux in RAW 264.7 cells, and exhibited improved lipid metabolism, attenuated oxidative stress and inflammation, thus acquired diminished plaque area in apoE Show less
📄 PDF DOI: 10.1007/s13346-025-02023-3
APOE

Blood and urine biomarkers and myocardial infarction: A 2-sample and multivariate combination of Mendelian randomization.

Yu Ding, Haoyang Ling, Xiuyan Chen +6 more · 2025 · Medicine · added 2026-04-24
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any m Show more
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any means to prevent several risks associated with MI. Blood and urine tests are frequently employed in clinical examinations to detect cardiovascular diseases at an early stage. Mendelian randomization (MR) is commonly employed to explore disease-trait relationships and uncover therapeutic targets. Our goal was to explore the genetic links between 35 blood and urine biomarkers and MI. Blood and urine biomarker MR correlations with MI risk were studied. In version R10, the UK Biobank and Finnish databases included blood and urine marker data and MI data (26,060 cases and 343,079 controls). We performed bidirectional 2-sample MR with 4 methods: inverse variance weighted, MR-Egger, weighted median, and weighted mode. Final causal associations were determined by inverse variance weighted. Sensitivity analyses (heterogeneity, pleiotropy) were conducted. MR-PRESSO and PhenoScanner were used to exclude invalid instruments. We used multivariate MR to filter the most important genes without including other positive genes. To identify positive gene pathways and gene networks that cause MI, we employed GeneMANIA for gene prediction. The findings revealed a positive genetic association between the 8 blood and urine biomarker levels and an elevated risk of MI. There are apolipoprotein B (APOB), glycated hemoglobin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, sex hormone-binding globulin, triglycerides, and urate. Moreover, APOB, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol selectively affect MI through the rejection of other positive gene stems. Finally, APOB and numerous genes strongly impact MI development. APOB collaborates with related genes to regulate plasma lipoprotein particle levels, sterol homeostasis, organization, lipid homeostasis, and remodeling in MI. Our research further reveals the causal relationship between MI and blood/urine biomarkers, providing a new perspective for the prevention, diagnosis, and treatment of MI. Blood and urine marker tests can subsequently be conducted based on these results to detect MI and study the underlying mechanisms linking these metabolites to MI. Show less
no PDF DOI: 10.1097/MD.0000000000046146
APOB

FGFR1-related congenital hypogonadotropic hypogonadism: a case report and literature review.

Miao Sun, Yan Liu, Maolin Liu +5 more · 2025 · Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology · Taylor & Francis · added 2026-04-24
Congenital hypogonadotropic hypogonadism (CHH) is a rare condition characterized by incomplete pubertal development, infertility, and gonadotropin-releasing hormone deficiency, associated with mutatio Show more
Congenital hypogonadotropic hypogonadism (CHH) is a rare condition characterized by incomplete pubertal development, infertility, and gonadotropin-releasing hormone deficiency, associated with mutations in more than 50 genes. We aimed to conduct an etiological analysis of a CHH Chinese family and summarize the clinical presentations and genetic changes of reported similar cases. Whole-exome sequencing (WES) was performed to identify the molecular cause in the proband. In silico tools were employed to analyze the pathogenicity of the variants. Reported cases with similar clinical features and associated genes were summarized by searching through PubMed/MEDLINE using keywords 'FGFR1,' 'CHH,' and 'Kallmann syndrome (KS).' Genetic analysis revealed a novel likely pathogenic deletion mutation in the FGFR1 gene (NM₀₂₃₁₁₀.3: c.263₂₆₄del (Val88Alafs*22)) in a Chinese family exhibiting micropenis and underdeveloped testes. A total of 38 cases with CHH or KS have been previously reported. This study identified a novel FGFR1 deletion variant responsible for CHH, expanding the known mutational spectrum of FGFR1. Typical manifestations include delayed puberty and diverse presentations. The genotype-phenotype correlation in CHH remains unclear and may involve oligogenic effects and epigenetic regulation. Show less
no PDF DOI: 10.1080/09513590.2025.2571656
FGFR1

VRK1 promotes epithelial-mesenchymal transition in hepatocellular carcinoma mediated by SNAI1 via phosphorylating CHD1L.

Jing Li, Zan Song, Xue Dong +12 more · 2025 · Cell death & disease · Nature · added 2026-04-24
Vaccinia-related kinase 1 (VRK1) is involved in numerous cellular processes, including DNA repair, cell cycle and cell proliferation. However, its roles and molecular mechanism underlying the progress Show more
Vaccinia-related kinase 1 (VRK1) is involved in numerous cellular processes, including DNA repair, cell cycle and cell proliferation. However, its roles and molecular mechanism underlying the progression of hepatocellular carcinoma (HCC) are yet largely unexplored. Here, we demonstrated that VRK1 expression is elevated in HCC tumor tissues, which is associated with high tumor stage and poor prognosis in HCC patients. In vitro and in vivo experiments manifested that VRK1 overexpression significantly promotes cell proliferation, colony formation, migration and tumor growth of HCC by inducing epithelial-mesenchymal transition (EMT) program. Mechanistically, immunoprecipitation combined with mass spectrometry analysis determined that VRK1 interacts with CHD1L, which mediates the phosphorylation of CHD1L at serine 122 site. RNA-seq revealed that one of the key downstream target genes of VRK1 is SNAI1, by which VRK1 promotes EMT process and HCC progression. Furthermore, VRK1 upregulates SNAI1 expression through phosphorylating CHD1L. In conclusion, these findings suggested that VRK1/CHD1L/SNAI1 axis acts as a cancer-driving pathway to promote the proliferation and EMT of HCC, indicating that targeting VRK1 may be an attractive therapeutic strategy of HCC. Show less
no PDF DOI: 10.1038/s41419-025-07641-w
SNAI1

From repetition to habit: how variability in daily physical activity relates to physical activity habits.

Jie Wen, Yujie Liu, Rui Cao +2 more · 2025 · Psychology & health · Taylor & Francis · added 2026-04-24
Repetition of physical activity (PA) contributes to the formation of PA habit. However, daily repetitions of PA of varied intensities might differ in their impact on PA habits. This study investigated Show more
Repetition of physical activity (PA) contributes to the formation of PA habit. However, daily repetitions of PA of varied intensities might differ in their impact on PA habits. This study investigated the effect of daily variability in PA on various facets of PA habits: lack of intention (LOI), lack of control (LOC) and efficiency of PA. Daily time spent on light-, moderate- and vigorous-intensity of PA (LPA, MPA and VPA) were assessed for 14 consecutive days among 182 college students. PA habits were measured afterwards. The results of mixed-effects random location-scale model showed that LOI was negatively predicted by variability in daily LPA; and that LOC was negatively predicted by daily variability in LPA and MPA. These findings suggest interventions of PA habit formation should focus on different facets of PA habits and consider the impact of daily repetition of PA of varied intensities. Show less
no PDF DOI: 10.1080/08870446.2025.2567333
LPA

Transcriptome analysis reveals reduced lipid accumulation and mitochondrial metabolic remodeling in ADCY3-overexpressing adipocytes.

Lu Liu, Houxue Cui, Zhongfang Xiang +2 more · 2025 · Functional & integrative genomics · Springer · added 2026-04-24
Excessive adipose tissue accumulation adversely impacts the health of both humans and livestock. Adenylyl cyclase 3 (ADCY3) is a promising anti-obesity target, yet its regulatory role in adipogenesis Show more
Excessive adipose tissue accumulation adversely impacts the health of both humans and livestock. Adenylyl cyclase 3 (ADCY3) is a promising anti-obesity target, yet its regulatory role in adipogenesis remains incompletely understood. Our findings revealed a dynamic pattern of ADCY3 expression during adipogenesis and lipid droplet (LDs) accumulation. Functional analyses demonstrated that ADCY3 overexpression impaired adipogenesis by downregulating adipogenic transcription factors CEBPα and PPARγ. Furthermore, it reduced both the number and size of LDs through suppressing triglyceride synthesis and fatty acid metabolism, concomitantly downregulating key genes involved in LDs formation (PLIN1, CIDEC, FIT2, and Seipin), as well as factors mediating glycerol ester synthesis and fatty acid metabolism (DGAT1, DGAT2, ACC, SCD, FASN, and ACSL1). Transcriptomic profiling revealed that ADCY3 overexpression suppressed PPARγ signaling, leading to the downregulation of oxidative phosphorylation genes encoded by both the nuclear and mitochondrial genomes. Our results implicate ADCY3 in the regulation of lipid metabolism, with the speculative involvement of mitochondrial metabolic remodeling. This perspective offers a framework for developing future interventions against excessive lipid deposition. Show less
no PDF DOI: 10.1007/s10142-025-01789-6
ADCY3

TAC-ECG: A task-adaptive classification method for electrocardiogram based on cross-modal contrastive learning and low-rank convolutional adapter.

Rongjia Wang, Xunde Dong, Xiuling Liu +5 more · 2025 · Computer methods and programs in biomedicine · Elsevier · added 2026-04-24
Cardiovascular diseases are one of the major health threats to humans. Researchers have proposed numerous deep learning-based methods for the automatic analysis of electrocardiogram (ECG), achieving e Show more
Cardiovascular diseases are one of the major health threats to humans. Researchers have proposed numerous deep learning-based methods for the automatic analysis of electrocardiogram (ECG), achieving encouraging results. However, many existing methods are limited to task-specific model training and require retraining or full fine-tuning when confronted with a new ECG classification task, thus lacking flexibility in clinical applications. In this study, we propose a Task-Adaptive Classification method for ECG (TAC-ECG) based on cross-modal contrastive learning and low-rank convolutional adapters. TAC-ECG comprises two main phases. In the first phase, inspired by the Contrastive Language-Image Pre-training, we design the Contrastive ECG-Text Pre-training (CETP) to pre-train a robust ECG encoder. In the second phase, the pre-trained ECG encoder is frozen and integrated with a lightweight plug-in, the Low-Rank Convolutional Adapter (LRC-Adapter), forming an extensible ECG classification model. The frozen encoder extracts more discriminative features from the ECG signal, while the LRC-Adapter enables task-specific adaptation. For diverse ECG classification tasks, TAC-ECG only requires training the LRC-Adapter. This mechanism enables TAC-ECG to efficiently perform different ECG classification tasks, significantly reducing resource consumption and deployment costs in multi-tasking scenarios compared to traditional fully fine-tuned methods. We conducted extensive experiments using six different network architectures as ECG encoders. Specifically, we performed ECG classification experiments on four datasets: CPSC2018, Cinc2017, PTB-XL, and Chapman, targeting 9-category, 3-category, 5-category, and 4-category classifications respectively. The TAC-ECG achieved highly competitive results using only approximately 3% of the trainable parameters and approximately 25% of the total parameters compared to the fully fine-tuned method. These results demonstrates the effectiveness and practicality of the TAC-ECG method. The TAC-ECG offers a flexible and efficient method for ECG classification, enabling rapid adaptation to diverse tasks and enhancing clinical diagnostic practicality. Show less
no PDF DOI: 10.1016/j.cmpb.2025.108918
CETP

Exogenous lactate infusion enhanced VMH neuroplasticity and visceral fat reduction via PGC-1α-ERα pathway in female rats.

Shuai Tian, Jing Han, Zhaomin Zhang +3 more · 2025 · European journal of applied physiology · Springer · added 2026-04-24
High-intensity exercise promotes visceral adipose tissue (VAT) breakdown in females via the hypothalamic ERα pathway, and exogenous lactate infusion combined with aerobic training (AT) mimics this eff Show more
High-intensity exercise promotes visceral adipose tissue (VAT) breakdown in females via the hypothalamic ERα pathway, and exogenous lactate infusion combined with aerobic training (AT) mimics this effect. However, whether lactate administration can independently mediate hypothalamic plasticity and VAT catabolism as a standalone nutritional strategy remains unexplored. Firstly, using a two-factor design (Lactate × AT) in female SD rats, we showed that long-term exogenous lactate infusion independently induced co-expression of Estrogen receptor α (ERα) and Brain-derived neurotrophic factor (BDNF) in the ventromedial hypothalamus (VMH) and elevated local field potential spectral power in specific bands. These neural adaptations were accompanied by increased resting metabolic rate, enhanced fat oxidation, and enhanced lipolysis, thereby preventing excessive VAT accumulation induced by a high-fat diet. Furthermore, pharmacological inhibition confirmed that Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-α (PGC-1α) acts as a co-upstream signal of ERα and BDNF mediating this process. Our findings reveal that standalone lactate administration induces functional plasticity and metabolic reprogramming through the VMH PGC-1α-ERα pathway, independent of exercise, and effectively suppresses pathological VAT accumulation in female rats. This study identifies potential nutritional interventions and mechanistic targets for preventing female-centered obesity. Show less
📄 PDF DOI: 10.1007/s00421-025-06097-2
BDNF

Revealing the binding mechanism of BACE1 inhibitors through molecular dynamics simulations.

Yanjun Zhang, Dongqiang Miao, Senchen Liu +1 more · 2025 · Journal of biomolecular structure & dynamics · Taylor & Francis · added 2026-04-24
Alzheimer's disease is a debilitating neurodegenerative disorder, and the Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE1) is a key therapeutic target in its treatment. This study employs Show more
Alzheimer's disease is a debilitating neurodegenerative disorder, and the Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE1) is a key therapeutic target in its treatment. This study employs molecular dynamics simulations and binding energy analysis to investigate the binding interactions between BACE1 and four selected small molecules: CNP520, D9W, NB641, and NB360. The binding model analysis indicates that the binding of BACE1 with four molecules are stable, except the loop regions show significant fluctuation. The binding free energy analyses reveal that NB360 exhibits the highest binding affinity with BACE1, surpassing other molecules (CNP520, D9W, and NB641). Detailed energy component assessments highlight the critical roles of electrostatic interactions and van der Waals forces in the binding process. Furthermore, residue contribution analysis identifies key amino acids influencing the binding process across all systems. Hydrogen bond analysis reveals a limited number of bonds between BACE1 and each small molecule, highlighting the importance of structural modifications to enable more stable hydrogen bonds. This research provides valuable insights into the molecular mechanisms of potential Alzheimer's disease therapeutics, guiding the way for improved drug design and the development of effective treatments targeting BACE1. Show less
no PDF DOI: 10.1080/07391102.2024.2319676
BACE1

Development of a genetically encoded melanocortin sensor for high sensitivity in vivo imaging.

Yoon Namkung, Tal Slutzki, Joao Pedroso +4 more · 2025 · Molecular metabolism · Elsevier · added 2026-04-24
The central melanocortin system, composed of peptides derived from pro-opiomelanocortin (POMC) such as the melanocyte-stimulating hormones (α-, β-, γ-MSH) and melanocortin 4 receptors (MC4R), along wi Show more
The central melanocortin system, composed of peptides derived from pro-opiomelanocortin (POMC) such as the melanocyte-stimulating hormones (α-, β-, γ-MSH) and melanocortin 4 receptors (MC4R), along with the agouti-related protein (AgRP), plays a pivotal role in controlling energy balance. To elucidate the dynamic role of α-MSH release in regulating appetite, specific, sensitive, and spatiotemporally resolved genetic sensors are required. The melanocortin 1 receptor (MC1R) scaffold was leveraged for its robust plasma membrane expression, high affinity for melanocortins and low affinity for AgRP to design a α-MSH selective sensor for in vivo use. This was achieved by integrating circularly permuted green fluorescent protein (cpGFP) into the receptor, which we named Fluorescence Amplified Receptor sensor for Melanocortin (FLARE The FLARE FLARE Show less
📄 PDF DOI: 10.1016/j.molmet.2025.102254
MC4R

Integrating network pharmacology, quantitative transcriptomic analysis, and experimental validation revealed the mechanism of cordycepin in the treatment of obesity.

Yu Liao, Mingchao Wang, Fuli Qin +2 more · 2025 · Frontiers in pharmacology · Frontiers · added 2026-04-24
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically Show more
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically identify Cpn's therapeutic targets and pathways in Western diet (WD)-induced obesity using integrated network pharmacology, transcriptomics, and experimental validation. A Western diet (WD)-induced mice model was used to evaluate the effectiveness of Cpn in ameliorating obesity. A network pharmacology analysis was then employed to identify the potential anti-obesity targets of Cpn. GO functional enrichment and KEGG pathway analysis were performed to elucidate the potential functions of the identified targets, followed by constructing a protein-protein interaction network to screen the core targets. Meanwhile, quantitative transcriptomics was conducted to validate and broaden the network pharmacology findings. Finally, molecular docking and quantitative real-time PCR assay were used for the core target validation. Cpn treatment effectively alleviated obesity-related symptoms in WD-induced mice. The metabolic pathway, insulin signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, lipid and atherosclerosis pathway, and core targets including CPS1, HRAS, MAPK14, PAH, ALDOB, AKT1, GSK3B, HSP90AA1, BHMT2, EGFR, CASP3, MAT1A, APOM, APOA2, APOC3, and APOA1 are involved in regulating the therapeutic effect of Cpn. This study comprehensively uncovers the potential mechanism of Cpn against obesity based on network pharmacology and quantitative transcriptomics, which provides evidence for revealing the pathogenesis of obesity, suggesting that Cpn is a possible lead compound for anti-obesity treatment. Show less
📄 PDF DOI: 10.3389/fphar.2025.1571480
APOC3

Sleep hygiene profiling, dysfunctional sleep beliefs, and their impacts on cancer-related fatigue in lung cancer patients undergoing chemotherapy.

Zhige Yan, Xiajun Guo, Ying Hu +2 more · 2025 · Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · Springer · added 2026-04-24
To elucidate the accurate roles of dysfunctional sleep beliefs in modulating cancer-related fatigue (CRF), identify distinct sleep hygiene profiles, and assess whether and how these profiles serve as Show more
To elucidate the accurate roles of dysfunctional sleep beliefs in modulating cancer-related fatigue (CRF), identify distinct sleep hygiene profiles, and assess whether and how these profiles serve as mediators in lung cancer patients undergoing chemotherapy. This study recruited 396 lung cancer patients receiving chemotherapy between May and December 2023. Participants completed the Sleep Hygiene Index, Brief Fatigue Inventory, and Dysfunctional Beliefs and Attitudes about Sleep Scale. Latent profile analysis (LPA) was conducted to identify profiles of sleep hygiene, and mediation analysis was performed to explore the impacts of sleep hygiene profiles and dysfunctional sleep beliefs on CRF. LPA revealed three distinct sleep hygiene profiles: normal (33.3%), excellent (50.3%), and poor (16.4%). Family monthly disposable income, radiotherapy, and performance status were identified as influential factors distinguishing these profiles. Additionally, the dimensions of dysfunctional sleep beliefs and sleep hygiene profiles showed different correlations with CRF. With the normal sleep hygiene group as reference, mediation analysis revealed that poor sleep hygiene serves as a mediator between sleep worry of dysfunctional sleep beliefs and CRF (SE = 0.010, 95% CI [0.006, 0.047]). This study contributes to understanding the heterogeneity in sleep hygiene in lung cancer patients undergoing chemotherapy and elucidates the underlying mechanisms of the relationship between sleep worry of dysfunctional cognitions and CRF. Clinical healthcare providers developing targeted interventions in terms of sleep beliefs and sleep hygiene might be helpful to alleviate CRF in this population. Show less
no PDF DOI: 10.1007/s00520-025-10109-4
LPA

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

Latent profiles of quality of life and their correlations with social support and self-efficacy of patients with hematologic malignancies.

Xuran Cao, Yaxin Du, Chuanxin Liu · 2025 · Scientific reports · Nature · added 2026-04-24
Quality of life (QoL) subtypes were identified via latent profile analysis (LPA), and their correlations with social support and self-efficacy were assessed in 284 patients with hematologic malignanci Show more
Quality of life (QoL) subtypes were identified via latent profile analysis (LPA), and their correlations with social support and self-efficacy were assessed in 284 patients with hematologic malignancies (HMs). The results were as follows: (1) LPA revealed three QoL subtypes of patients with HMs, namely, the high-QoL group, the medium-QoL group, and the low-QoL group. (2) The high-QoL group had higher levels of social support than the medium-QoL group did, and they also had higher levels of self-efficacy than both the medium- and low-QoL groups did. These results contribute to the identification of heterogeneous QoL features among patients with HMs and their correlations with social support and self-efficacy. Moreover, this study has clinical implications for improving the QoL of patients with HMs and promoting their physical and mental health. Show less
📄 PDF DOI: 10.1038/s41598-025-99662-w
LPA

FGF19-Activated Hepatic Stellate Cells Release ANGPTL4 that Promotes Colorectal Cancer Liver Metastasis.

Xueying Fan, Baoting Li, Fan Zhang +4 more · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Liver and lung are the most common metastatic sites in colorectal cancer (CRC), where the tumor microenvironment (TME) plays a crucial role in the progression and metastasis of CRC. Understanding the Show more
Liver and lung are the most common metastatic sites in colorectal cancer (CRC), where the tumor microenvironment (TME) plays a crucial role in the progression and metastasis of CRC. Understanding the interactions between various types of cells in the TME can suggest innovative therapeutic strategies. Using single-cell RNA sequencing (scRNA-Seq) and clinical samples, fibroblast growth factor-19 (FGF19, rodent FGF15) is found to mediate a significant interaction between CRC cells and cancer-associated fibroblasts (CAFs), activating the hepatic stellate cells (HSCs)-to-CAFs differentiation. In various CRC metastatic mouse models, it is shown that FGF15 has a more pronounced effect on liver metastasis compared to pulmonary metastasis. More importantly, the differentially expressed genes (DEGs) are also identified from the RNA-Seq dataset upon the activation of HSCs by FGF19 and compared the DEGs in matched primary and metastatic mRNA samples from patients with CRC liver metastasis (CRCLM), it is found that the ANGPTL4 gene is significantly associated with HSCs activation. Different mouse models also demonstrated the impact of the FGF19/ANGPTL4 axis on the severity of CRCLM. Importantly, disruption of this axis significantly inhibits CRCLM in vivo. This study is among the first to demonstrate the impact of the FGF19/ANGPTL4 axis on CRCLM, offering a novel therapeutic strategy. Show less
📄 PDF DOI: 10.1002/advs.202413525
ANGPTL4

Stationary screen time, blood lipids, and atherosclerotic cardiovascular disease incidence.

Yunjie Liu, Lanjin Xu, Panting Liu +2 more · 2025 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
The associations between screen time exposure, blood lipids, and Atherosclerotic Cardiovascular Disease (ASCVD) incidence have been less studied. We aimed to examine the associations of exposure to sc Show more
The associations between screen time exposure, blood lipids, and Atherosclerotic Cardiovascular Disease (ASCVD) incidence have been less studied. We aimed to examine the associations of exposure to screen time with blood total cholesterol (TC), HDL-C, LDL-C, triglycerides (TG), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and ASCVD risk score, and risk of subsequent ASCVD incidence. A nationwide sample of 7124 China Health and Nutrition Survey 2009 participants were followed up to 2015 for ASCVD incidence. The stationary screen time exposure was assessed through self-reported daily hours of using television, and computers. A total of 292 ASCVD events occurred during 35,310 follow-up person-years. Per 1-h increases in daily screen time exposure were associated with a higher 0.34% (0.12% to 0.56%), 0.47% (0.09% to 0.86%), and 0.51% (0.19% to 0.83%) increases in blood TC, LDL-C, and ApoB levels. A higher risk of incident ASCVD was associated with per log-transformed unit increase in blood LDL-C (adjusted HR = 1.51, 95% CI 1.04 to 2.18), and ApoB (adjusted HR = 1.80, 95% CI 1.12 to 2.92). The elevated blood TC, blood LDL-C, blood ApoA1 and ApoB levels significantly mediated the association between screen time exposure and ASCVD incidence. Urban dwellers, middle-aged adults, and females were particularly associated with a higher ASCVD risk with screen time exposure. The results of this nationwide cohort supported the associations of screen time exposure with elevated blood LDL-C, and ApoB levels, which consistently contributed to an increased risk of subsequent ASCVD incidence. Show less
📄 PDF DOI: 10.1186/s12872-025-04568-0
APOB

Dietary Exposure to 2,2',4,4'-Tetrabromodiphenyl Ether (BDE-47) Causes Inflammation in the Liver of Common Carp (

Shuhuang Chen, Nian Han, Yujie Huang +5 more · 2025 · International journal of molecular sciences · MDPI · added 2026-04-24
2,2',4,4'-tetrabromodiphenyl ether (BDE-47) is a common environmental contaminant and widely detected in aquatic surroundings, while only a few reports exist on the hazard mechanism in economic aquati Show more
2,2',4,4'-tetrabromodiphenyl ether (BDE-47) is a common environmental contaminant and widely detected in aquatic surroundings, while only a few reports exist on the hazard mechanism in economic aquatic animals. It has been shown that 40 and 4000 ng/g of BDE-47 dietary exposure over 42 days significantly increased the levels of blood triglycerides, glucose, and liver glycogen in carp ( Show less
📄 PDF DOI: 10.3390/ijms262010152
LPL

Effect of cholesterol on distribution, cell uptake, and protein corona of lipid microspheres at sites of cardiovascular inflammatory injury.

Lingyan Li, Xingjie Wu, Qianqian Guo +9 more · 2025 · Journal of pharmaceutical analysis · Elsevier · added 2026-04-24
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, over Show more
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, overshadowing its influence on the biological effects of formulations. In this study, we reevaluated the delivery effect of CH using widely applied lipid microspheres (LM) as a model DDS. We conducted comprehensive investigations into the impact of CH on the distribution, cell uptake, and protein corona (PC) of LM at sites of cardiovascular inflammatory injury. The results demonstrated that moderate CH promoted the accumulation of LM at inflamed cardiac and vascular sites without exacerbating damage while partially mitigating pathological damage. Then, the slow cellular uptake rate observed for CH@LM contributed to a prolonged duration of drug efficacy. Network pharmacology and molecular docking analyses revealed that CH depended on LM and exerted its biological effects by modulating peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in vascular endothelial cells and estrogen receptor alpha (ERα) protein levels in myocardial cells, thereby enhancing LM uptake at cardiovascular inflammation sites. Proteomics analysis unveiled a serum adsorption pattern for CH@LM under inflammatory conditions showing significant adsorption with CH metabolism-related apolipoprotein family members such as apolipoprotein A-V (Apoa5); this may be a major contributing factor to their prolonged circulation Show less
📄 PDF DOI: 10.1016/j.jpha.2024.101182
APOA5

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

Pterostilbene mitigates experimental pulmonary arterial hypertension by inhibiting endothelial-to-mesenchymal transition.

Jie Wang, Yu Zhang, Junjun Liu +3 more · 2025 · Frontiers in pharmacology · Frontiers · added 2026-04-24
The natural compound pterostilbene (PTE) has multiple cardiovascular protective effects. However, its effects on pulmonary arterial hypertension (PAH)-associated vascular remodeling remain to be eluci Show more
The natural compound pterostilbene (PTE) has multiple cardiovascular protective effects. However, its effects on pulmonary arterial hypertension (PAH)-associated vascular remodeling remain to be elucidated. This study investigated the effects of PTE on monocrotaline (MCT)-induced PAH in rats Experimental PAH was established by subcutaneous injection of MCT (50 mg/kg) in Sprague-Dawley rats, which were then randomly divided into vehicle or PTE (15 mg/kg via gavage) treatment groups. Endothelial-to-mesenchymal transition (EndMT) was modeled in hPAECs by treating with transforming growth factor-β, tumor necrosis factor-α, and interleukin-1β in combination. In rats with MCT-induced PAH, administration of PTE resulted in a reduction in right ventricular systolic pressure, thereby alleviating right ventricular hypertrophy. This was accompanied by mitigation of the remodeling of pulmonary arteries. PTE mitigates MCT-induced PAH and vascular remodeling in rats, at least in part, by inhibiting HMGA-mediated EndMT, suggesting that PTE may be a useful complementary medicine in the treatment of PAH. Show less
no PDF DOI: 10.3389/fphar.2025.1621700
SNAI1

Enhanced articular exposure using a lateral patellar ligament approach in Schatzker IV-C tibial plateau fractures: A cadaveric and clinical case series.

Run Fang, Kehao Wang, Yulong Liu +3 more · 2025 · Science progress · SAGE Publications · added 2026-04-24
BackgroundSchatzker IV-C tibial plateau fractures pose a significant challenge for adequate visualization and reduction of the lateral articular surface through a solitary posteromedial (PM) approach. Show more
BackgroundSchatzker IV-C tibial plateau fractures pose a significant challenge for adequate visualization and reduction of the lateral articular surface through a solitary posteromedial (PM) approach. This study aimed to evaluate the effectiveness of an adjunctive lateral patellar ligament (LPL) approach in enhancing articular exposure, assessed through cadaveric modeling and a clinical case series.MethodsIn a cadaveric study, eight preserved knee specimens were dissected using a combined PM and LPL approach. The exposed articular area was quantitatively measured using calibrated digital imaging and ImageJ software before and after the LPL approach was established. Clinically, a case series of 10 patients with Schatzker IV-C fractures underwent open reduction and internal fixation via the combined approach between October 2021 and December 2023. Outcome measures included intraoperative exposure, 12-month postoperative Knee Society Score (KSS), and complications.ResultsThe addition of the LPL approach resulted in a 96% increase in the mean exposed articular area (from 8.4 cm² to 16.5 cm²; Show less
📄 PDF DOI: 10.1177/00368504251392607
LPL

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

Chenhao Xu, Junjie Zhao, Kan Wu +9 more · 2025 · Frontiers in nutrition · Frontiers · added 2026-04-24
Acquired renal cysts (ARC) are associated with kidney function decline, necessitating novel dietary pattern (DP) analyses in large cohorts. This UK Biobank prospective cohort study (2006-2010) include Show more
Acquired renal cysts (ARC) are associated with kidney function decline, necessitating novel dietary pattern (DP) analyses in large cohorts. This UK Biobank prospective cohort study (2006-2010) included participants with ≥2 dietary records, excluding those with severe kidney damage. The constructed comprehensive dietary pattern integration (CDPI) utilized reduced rank regression (RRR) and latent profile analysis (LPA). ARC cases (ICD-10: N28.1) were assessed via Cox regression for risk and dose-response, with NMR metabolites examined as mediators. Among 119,709 participants (median follow-up: 10.57 years), 850 ARC cases were identified. Lipid-rich and hyperglycemic diets increased ARC risk [e.g., HRs for G1.DP1: 1.080 (1.024, 1.139); G1.DP2: 1.144 (1.048, 1.249)], while micronutrient-rich diets showed weak protective effects [G4.DP1: 0.943 (0.892, 0.998)]. LPA confirmed RRR findings, and 7/251 NMR metabolites had significant mediating effects. Diets high in fat (cheese, butter, pizza) and sugar (chocolate, sugary drinks) elevated ARC risk, whereas micronutrient- and fiber-rich diets (vegetables, fruit, lean poultry, nuts, eggs) were protective. Key mediators included branched-chain amino acids, IGF-1, and RBC distribution width. Show less
📄 PDF DOI: 10.3389/fnut.2025.1611656
LPA