👤 Yupu 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, 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, 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

Exercise-Induced Changes in Brain-Derived Neurotrophic Factor in Neurodegenerative Diseases: A Bayesian Network Meta-Analysis.

Zhuolin Tang, Mingyue Yin, Kai Xu +4 more · 2025 · Journal of geriatric psychiatry and neurology · SAGE Publications · added 2026-04-24
ObjectivesThis study aimed to compare the effects of different exercise interventions on brain-derived neurotrophic factor (BDNF) levels in patients with neurodegenerative diseases and to explore regu Show more
ObjectivesThis study aimed to compare the effects of different exercise interventions on brain-derived neurotrophic factor (BDNF) levels in patients with neurodegenerative diseases and to explore regulatory factors.MethodsSearched PubMed, Scopus, Web of Science Core Collection, CNKI and Cochrane Library databases up to March 15, 2025. Bayesian network meta-analysis was conducted using R software, and meta-regression analyzed the moderating effects of training period and frequency.Results42 randomized controlled trials covering 1482 patients were included. The Surface Under the Cumulative Ranking (SUCRA) indicated that stretching training (SUCRA = 78.92) and high-intensity interval training (SUCRA = 69.73) were ranked higher than other exercise modalities and exhibited more favorable effect on BDNF enhancement, although neither demonstrated statistically significant superiority over the blank control. In contrast, combined training (SUCRA = 35.58), aerobic training (SUCRA = 35.17), and resistance training (SUCRA = 12.98) showed relatively lower potential for BDNF enhancement (blank control SUCRA = 67.62). Meta-regression analysis showed that the effect of combined training was significantly and positively correlated with intervention period ( Show less
no PDF DOI: 10.1177/08919887251409415
BDNF bayesian network meta-analysis brain-derived neurotrophic factor exercise interventions meta-regression neurodegenerative diseases neuroscience neurotrophic factors

Neuroinflammation in an Optimized Model of Lysophosphatidic Acid (LPA)-Induced Post-hemorrhagic Hydrocephalus.

Paloma Sánchez-Pavón, Carter R Palmer, Christine S Liu +3 more · 2025 · Neurochemical research · Springer · added 2026-04-24
Post-hemorrhagic hydrocephalus (PHH) is a neurological disease that primarily affects premature infants and involves infiltration of blood into the brain's ventricles followed by excessive accumulatio Show more
Post-hemorrhagic hydrocephalus (PHH) is a neurological disease that primarily affects premature infants and involves infiltration of blood into the brain's ventricles followed by excessive accumulation of cerebrospinal fluid (CSF), leading to ventricular enlargement and increased intracranial pressure. The precise mechanisms driving PHH development and persistence are incompletely understood and lack disease-modifying treatments. Using a mouse model of PHH, we have identified transcriptomic, proteomic, and cellular features of PHH involving neuroimmune and neurovascular alterations recapitulating those reported in human disease. Improvement upon a lysophosphatidic acid (LPA)-induced PHH mouse model was combined with unbiased proteomic and single-nucleus transcriptomics that identified microglial molecular pathways propagating PHH. Pharmacological depletion of microglia in vivo significantly reduced PHH-associated ventriculomegaly. These data identify microglial and neurovascular elements in the development of PHH, implicating them as other potentially tractable therapeutic targets beyond LPA receptors, towards developing medical treatments for PHH. Show less
📄 PDF DOI: 10.1007/s11064-025-04542-4
LPA

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

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

RNA G-quadruplex removal promotes a translational switch after meiosis resumption.

Qiong-Wen Lu, Shao-Yuan Liu, Xiu-Quan Liao +6 more · 2025 · Nucleic acids research · Oxford University Press · added 2026-04-24
Oocyte maturation-coupled mRNA post-transcriptional regulation is essential for the establishment of developmental potential. Previously, oocyte mRNA translation efficiencies focused on the trans-regu Show more
Oocyte maturation-coupled mRNA post-transcriptional regulation is essential for the establishment of developmental potential. Previously, oocyte mRNA translation efficiencies focused on the trans-regulation of key RNA-binding protein (RBPs), rarely related to RNA structure. RNA G-quadruplexes (rG4s) are four-stranded RNA secondary structures involved in many different aspects of RNA metabolism. In this study, we have developed a low-input technique for rG4 detection (G4-LACE-seq) in mouse oocytes and found that rG4s were widely distributed in maternal transcripts, with enrichment in untranslated regions, and they underwent transcriptome-wide removal during meiotic maturation. The rG4-selective small-molecule ligand BYBX stabilized rG4s in the oocyte transcriptome and impaired spindle assembly and meiotic cell cycle progression. The proteomic spectrum results revealed that rG4 accumulation weakened the binding of a large number of RBPs to mRNAs, especially those associated with translational initiation. Ribosomal immunoprecipitation and translational reporter assays further proved that rG4s in the untranslated regions negatively affected the translational efficiency of key maternal mRNAs. Overexpression DEAH/RHA family helicase-36 partially reverses BYBX-induced oocyte developmental defects, suggesting its importance in rG4 regulation. Collectively, this study describes the distribution, dynamic changes, and regulation of rG4s in the mouse maternal transcriptome. Before meiosis resumption, a large number of rG4s in oocytes are necessary to maintain the translatome at a low level, and DHX36-mediated rG4 removal promotes a translational switch and is required for successful maternal-to-zygotic transition. Show less
📄 PDF DOI: 10.1093/nar/gkaf067
DHX36

Physical activity is associated with a lower risk of pre-sarcopenic obesity in adolescents: evidence from Northwest China.

Yingli Xu, Longkai Shi, Linlin Chen +2 more · 2025 · Scientific reports · Nature · added 2026-04-24
Little is known about the association between physical activity and the risk of pre-sarcopenic obesity (pre-SO) among adolescents. Hence, this study aimed to examine the association between physical a Show more
Little is known about the association between physical activity and the risk of pre-sarcopenic obesity (pre-SO) among adolescents. Hence, this study aimed to examine the association between physical activity and pre-SO in a sample of 2143 adolescents aged 12 to 18 years from Yinchuan, China. The pre-SO was defined by three criteria: low skeletal muscle mass adjusted by weight (SMM/W) combined with body mass index (BMI), fat mass percentage (FMP), and waist circumference (WC). After adjusting for age, smoking, drinking, sleep time, and high-fat food consumption, participants with high physical activity (HPA) had a lower risk of pre-SO compared to those with low physical activity (LPA) according to the obesity criteria of FMP (OR   0.63, 95% CI, 0.48-0.83, P < 0.05), and WC (OR 0.71, 95% CI, 0.52-0.96, P < 0.05). Additionally, restricted cubic spline models showed a linear dose-response association between total physical activity (TPA) and pre-SO no matter what obesity criteria were adopted (all P overall trend < 0.05, all P non-linear > 0.50). Subgroup analyses revealed that individuals with higher TPA levels exhibited a decreased risk of pre-SO in boys according to the obesity criteria of FMP, and WC. In conclusion, HPA is associated with a reduced risk of pre-SO in adolescents, especially among boys. Show less
📄 PDF DOI: 10.1038/s41598-025-28449-w
LPA

Elevation of p53 sensitizes obese kidney to adriamycin-induced aberrant lipid homeostasis via repressing HNF4α-mediated FGF21 sensitivity.

Jiahao Li, Yufeng Tang, Guangping Lu +7 more · 2025 · Journal of advanced research · Elsevier · added 2026-04-24
Lipid metabolism disorders have been confirmed to be closely related to kidney injury caused by adriamycin (ADR) and obesity, respectively. However, it has not been explored whether lipid metabolism d Show more
Lipid metabolism disorders have been confirmed to be closely related to kidney injury caused by adriamycin (ADR) and obesity, respectively. However, it has not been explored whether lipid metabolism disorders appear progressively more severe after ADR-based chemotherapy in the obese state, and the specific molecular mechanism needs to be further clarified. This study was designed to examine the role of p53-fibroblast growth factor 21 (FGF21) axis in ADR-induced renal injury aggravated by high-fat diet (HFD). We engineered Fgf21 KO mice and used long-term (4 months) and short-term (0.5 months) HFD feeding, and ADR-injected mice, as well as STZ-induced type 1 diabetic mice and type 2 (db/db) diabetic mice to produce an in vivo model of nephrotoxicity. The specific effects of p53/FGF21 on the regulation of lipid metabolism disorders and its downstream mediators in kidney were subsequently elucidated using a combination of functional and pathological analysis, RNA-sequencing, molecular biology, and in vitro approaches. Long-term HFD feeding mice exhibited compromised effects of FGF21 on alleviation of renal dysfunction and lipid accumulation following ADR administration. However, these impairments were reversed by p53 inhibitor (pifithrin-α, PFT-α). PFT-α sensitized FGF21 actions in kidney tissues, while knockout of Fgf21 impaired the protective effects of PFT-α on lipid metabolism. Mechanistically, p53 impaired the renal expression of FGF receptor-1 (FGFR1) and thereby developed gradually into FGF21 resistance via inhibiting hepatocyte nuclear factor 4 alpha (HNF4α)-mediated transcriptional activation of Fgfr1. More importantly, exogenous supplementation of FGF21 or PFT-α could not only alleviate ADR-induced lipid metabolism disorder aggravated by HFD, but also reduce lipid accumulation caused by diabetic nephropathy. Given the difficulties in developing the long-acting recombinant FGF21 analogs for therapeutic applications, sensitizing obesity-impaired FGF21 actions by suppression of p53 might be a therapeutic strategy for maintaining renal metabolic homeostasis during chemotherapy. Show less
📄 PDF DOI: 10.1016/j.jare.2024.07.014
FGFR1

Exploring Interactions of Volatile Constituents From Polygonum multiflorum With Inflammation-Related Targets via Dual-Ligand Docking and MD Simulations.

Wei Wang, Zhaosu Song, Ye Chen +6 more · 2025 · Journal of food science · Blackwell Publishing · added 2026-04-24
Polygonum multiflorum Thunb., a plant rich in diverse bioactive constituents, has been widely used in East Asia in functional foods and medicine to ameliorate inflammatory disorders through its multi- Show more
Polygonum multiflorum Thunb., a plant rich in diverse bioactive constituents, has been widely used in East Asia in functional foods and medicine to ameliorate inflammatory disorders through its multi-component activity. The effectiveness of these botanical extracts is thought to involve complex interactions among diverse constituents; however, the molecular basis of such interactions remains insufficiently understood. In this study, we explored the anti-inflammatory properties of the ethanol extract of Polygonum multiflorum (PME) through a combination of chemical profiling and computational analysis. PME was found to reduce the production of nitric oxide, inducible nitric oxide synthase, and interleukin-6 in LPS-stimulated RAW 264.7 macrophages. Using HS-SPME-GC-MS in conjunction with network pharmacology, we identified 32 volatile constituents, among which five core compounds were predicted to be associated with three inflammation-related targets: ESR1, FASN, and NR1H3. Dual-ligand molecular docking and molecular dynamics simulations suggested that the sequence of ligand binding may influence the stability and interaction patterns of protein-ligand complexes, offering insights into possible mechanisms of synergy and antagonism mediated by key residues such as ARG394 in ESR1. Overall, these findings contribute to a better understanding of how binding order and structural context may shape constituent-target interactions, providing a basis for the further development of multi-component natural product strategies against inflammation. This study underscores the relevance of incorporating multi-ligand dynamics into natural product research and presents an integrated experimental-computational framework to investigate the cooperative or competitive behaviors of functional food constituents, thereby supporting the rational design of optimized multi-target formulations. Show less
no PDF DOI: 10.1111/1750-3841.70708
NR1H3

Macrophage marker gene-driven prognostic models for esophageal cancer: integrating multi-omics analysis and therapeutic strategies.

Zehan Li, Huazhen Wu, Chuzhong Wei +15 more · 2025 · 3 Biotech · Springer · added 2026-04-24
By integrating single-cell and bulk RNA-sequencing data for esophageal cancer (ESCA), we developed and validated a seven-macrophage-gene prognostic signature (FCN1, SCARB2, ATF5, PHLDA2, GLIPR1, CHORD Show more
By integrating single-cell and bulk RNA-sequencing data for esophageal cancer (ESCA), we developed and validated a seven-macrophage-gene prognostic signature (FCN1, SCARB2, ATF5, PHLDA2, GLIPR1, CHORDC1, and BCKDK). This signature effectively stratified patients into high- and low-risk groups with significantly different overall survival, achieving area under the curve (AUC) values greater than 0.7 for 1-, 2-, and 3-year survival prediction. A high-risk status correlated with an immunosuppressive tumor microenvironment, characterized by lower infiltration of B cells and CD8 + T cells, and was associated with reduced sensitivity to multiple chemotherapeutic agents, including Cisplatin and 5-Fluorouracil. Conversely, a low-risk status was linked to greater immune cell infiltration and higher predicted chemosensitivity. At the single-cell level, pseudotime analysis revealed that macrophage maturation significantly correlated with a decreasing risk score, suggesting that mature macrophages may contribute to a favorable prognosis. Furthermore, cell communication analysis identified high-risk macrophages as dominant drivers of a pro-tumorigenic microenvironment via signaling pathways, such as SPP1 and complement. In conclusion, this seven-gene signature is a robust prognostic biomarker that offers a new strategy for personalized risk assessment and treatment selection in ESCA. The online version contains supplementary material available at 10.1007/s13205-025-04452-w. Show less
no PDF DOI: 10.1007/s13205-025-04452-w
BCKDK

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

C6orf223 promotes colorectal cancer growth and metastasis by facilitating PRMT5-MEP50 multiprotein complex assembling.

Yufeng Qiao, Zhenzhen Wu, Peng Wang +18 more · 2025 · The Journal of clinical investigation · added 2026-04-24
Protein arginine methyltransferase 5 (PRMT5) complexes with methylosome protein 50 (MEP50) play crucial roles in tumor progress. However, the regulatory mechanism of governing the PRMT5-MEP50 hetero-o Show more
Protein arginine methyltransferase 5 (PRMT5) complexes with methylosome protein 50 (MEP50) play crucial roles in tumor progress. However, the regulatory mechanism of governing the PRMT5-MEP50 hetero-octameric complex remains unclear. Here, we demonstrate that C6orf223, to our knowledge an uncharacterized protein, facilitates PRMT5-MEP50 multiprotein complex assembling, thereby promoting colorectal cancer (CRC) growth and metastasis. C6orf223 forms dimers through disulfide bonds, with its N-terminal arginine-enriched region binding to the C-terminal negatively charged groove of PRMT5, thus stabilizing PRMT5-MEP50 multiprotein and enhancing PRMT5 methyltransferase activity. Consequently, PRMT5-mediated H4R3me2s substantially decreases the expression of the tumor suppressor GATA5, leading to the upregulation of multiple oncogenic target genes including WWTR1, FGFR1, and CLU. Targeting C6orf223 using siRNAs encapsulated in ferritin protein shells effectively suppresses CRC tumor growth and metastasis. Collectively, our findings characterize the role of C6orf223 in facilitating PRMT5-MEP50 hetero-octameric complex assembling and suggest that C6orf223 could serve as a potential therapeutic target for CRC. Show less
📄 PDF DOI: 10.1172/JCI186052
FGFR1

Latent profiles of digital health literacy and perceived stigma in burn patients: a cross-sectional study.

Fengwen Yue, Liping Liu, Qingjiang Huang +3 more · 2025 · Frontiers in public health · Frontiers · added 2026-04-24
Prior research has consistently demonstrated that higher levels of digital health literacy contribute positively to improved mental health outcomes and overall quality of life among patients. Neverthe Show more
Prior research has consistently demonstrated that higher levels of digital health literacy contribute positively to improved mental health outcomes and overall quality of life among patients. Nevertheless, the interplay between digital health literacy and the experience of perceived stigma-particularly among burn patients-remains underexplored, and the potential heterogeneity within this relationship has not been adequately addressed. This cross-sectional study, conducted from June to July 2025, recruited 534 burn patients (mean age 31.05 ± 9.52 years; 61.0% male) from three tertiary hospitals in Sichuan Province, China. Participants completed validated scales assessing digital health literacy, social support, appearance anxiety, perceived stigma, and demographics. Data were analyzed using Pearson correlations, latent profile analysis (LPA) with fit indices, univariate analyses (chi-square tests and Digital health literacy was negatively correlated with perceived stigma ( This study confirms heterogeneity in digital health literacy and perceived stigma among burn patients, with social support and appearance anxiety as key influencers. Findings support targeted interventions to enhance digital health literacy and reduce perceived stigma, advancing precision psychological care for burn survivors. Show less
📄 PDF DOI: 10.3389/fpubh.2025.1702458
LPA

A new species of the genus

Tao Zhang, Siyu Yang, Haijun Jiang +7 more · 2025 · ZooKeys · added 2026-04-24
The genus
📄 PDF DOI: 10.3897/zookeys.1262.164459
APOB

Deficiency of EXT1 and FGFR3 genes promotes chondrocyte differentiation, leading to the induction of osteochondroma formation.

Hongrong Zhang, Zhencun Tang, Shiying Shen +6 more · 2025 · Bone · Elsevier · added 2026-04-24
This study aims to investigate the roles of the EXT1 and FGFR3 genes in the development of osteochondromas, focusing specifically on their potential interactions in chondrocyte proliferation, differen Show more
This study aims to investigate the roles of the EXT1 and FGFR3 genes in the development of osteochondromas, focusing specifically on their potential interactions in chondrocyte proliferation, differentiation, and tumor formation. In vitro, the ATDC5 chondroprogenitor cell line was used to examine the effects of inactivation of both EXT1 and FGFR3. In vivo, a mouse model with dual gene knockout of Ext1 and Fgfr3 was constructed to further explore these genes' roles in tumor formation by observing the incidence and distribution patterns of osteochondromas. The in vitro experiments demonstrated that ATDC5 cells with reduced expression of EXT1 and FGFR3 genes exhibited enhanced chondrogenic differentiation. In vivo, Fgfr3 The EXT1 and FGFR3 genes play crucial regulatory roles in the development of osteochondromas. Deficiencies in Ext1 and Fgfr3 can induce the formation of osteochondromas. Show less
no PDF DOI: 10.1016/j.bone.2024.117370
EXT1

ApoB/ApoA-Ι is associated with major cardiovascular events and readmission risk of patients after percutaneous coronary intervention in one year.

Jie Zhang, Mengyu Liu, Ju Gao +4 more · 2025 · Scientific reports · Nature · added 2026-04-24
Percutaneous coronary intervention (PCI) is a practical and effective method for treating coronary heart disease (CHD). This study aims to explore the influencing factors of major cardiovascular event Show more
Percutaneous coronary intervention (PCI) is a practical and effective method for treating coronary heart disease (CHD). This study aims to explore the influencing factors of major cardiovascular events (MACEs) and hospital readmission risk within one year following PCI treatment. Additionally, it seeks to assess the clinical value of Apolipoprotein B/Apolipoprotein A-I (ApoB/ApoA-I) in predicting the risk of one-year MACEs and readmission post-PCI. A retrospective study included 1938 patients who underwent PCI treatment from January 2010 to December 2018 at Shandong Provincial Hospital affiliated with Shandong First Medical University. Patient demographics, medications, and biochemical indicators were recorded upon admission, with one-year follow-up post-operation. Univariate and multivariate Cox proportional hazards regression models were utilized to establish the relationship between ApoB/ApoA-I levels and MACEs/readmission. Predictive nomograms were constructed to forecast MACEs and readmission, with the accuracy of the nomograms assessed using the concordance index. Subgroup analyses were conducted to explore the occurrence of MACEs and readmission. We observed a correlation between ApoB/ApoA-I and other lipid indices, including total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) (P < 0.001). Univariate and multivariate Cox regression analyses demonstrated that ApoB/ApoA-I is an independent risk factor for MACEs in post-PCI patients (P = 0.038). Within one year, the incidence of MACEs significantly increased in the high-level ApoB/ApoA-I group (ApoB/ApoA-I ratio ≥ 0.824) (P = 0.038), while the increase in readmission incidence within one year was not statistically significant. Furthermore, a nomogram predicting one-year MACEs was established (Concordance Index: 0.668). Subgroup analysis revealed that ApoB/ApoA-I was associated with the occurrence of both MACEs and readmission in male patients, those using CCB/ARB/ACEI, those without multivessel diseases, or those with LDL-C < 2.6 mmol/L. The ApoB/ApoA-I ratio serves as an independent risk factor for one-year MACEs in post-PCI patients and correlates closely with other blood lipid indicators. ApoB/ApoA-I demonstrates significant predictive value for the occurrence of MACEs within one year.Trial registration Chinese clinical trial registry: No.ChiCTR22000597-23. Show less
📄 PDF DOI: 10.1038/s41598-024-84092-x
APOB

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

Decoding the Therapeutic Effects of Acupuncture in Hemorrhagic Stroke Using Single-Cell RNA Sequencing.

Chen Ruan, Jia Du, Wentao Zhang +8 more · 2025 · CNS neuroscience & therapeutics · Wiley · added 2026-04-24
Acupuncture has been proposed as a therapeutic intervention for stroke recovery, yet the underlying molecular mechanisms remain poorly understood. In this study, we used a mouse model of hemorrhagic s Show more
Acupuncture has been proposed as a therapeutic intervention for stroke recovery, yet the underlying molecular mechanisms remain poorly understood. In this study, we used a mouse model of hemorrhagic stroke induced by autologous blood injection to investigate the effects of acupuncture on post-stroke recovery at the cellular and molecular levels, utilizing single-cell RNA sequencing. Our findings revealed that acupuncture modulates the gene expression of microglia, astrocytes, and oligodendrocytes, three major glial cell types, which may contribute to the improvement of stroke-induced phenotypes. Notably, we identified a potential role of the APOE-TREM2 signaling axis, with ligand-binding interactions enhancing microglia activation and promoting their neuroprotective functions. These findings also suggested that acupuncture may promote microglia-astrocyte interactions, leading to enhanced neuroinflammation resolution and tissue repair. Our study provided new insights into the cellular mechanisms underlying acupuncture's therapeutic effects in stroke recovery and highlighted the potential of targeting glial cell-mediated pathways, including APOE-TREM2, as a strategy for improving post-stroke rehabilitation. Show less
📄 PDF DOI: 10.1002/cns.70689
APOE

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

High-abundance serum glycoproteins as valuable resources for glycopeptide standards.

Jun Li, Didi Liu, Yingjie Zhang +3 more · 2025 · Carbohydrate polymers · Elsevier · added 2026-04-24
High-abundance serum proteins, mostly modified by N-glycans, are usually depleted from human sera to achieve in-depth analyses of serum proteome and sub-proteomes. In this study, we show that these hi Show more
High-abundance serum proteins, mostly modified by N-glycans, are usually depleted from human sera to achieve in-depth analyses of serum proteome and sub-proteomes. In this study, we show that these high-abundance glycoproteins (HAGPs) can be used as valuable standard glycopeptide resources, as long as the structural features of their glycans have been well defined at the glycosite-specific level. By directly analyzing intact glycopeptides enriched from serum, we identified 1322 unique glycopeptides at 48 N-glycosites from the top 12 HAGPs (19 subclasses). These HAGPs could be further classified into four major groups based on the structural features of their attached N-glycans. Immunoglobins including IGHG1/2/3/4, IGHA1/2 and IGHM were mostly modified by core fucosylated and bisected N-glycans with rarely sialic acids. Alpha-1-acid glycoproteins (ORM1/2) and haptoglobins (HP) were mainly modified by tri-and tetra-antennary (40 %) N-glycans with antenna-fucoses and sialic acids. Complement components C3 and C4A/B were highly modified by oligo-mannose glycans. The other HAGPs including SERPINA1, A2M, TF, FGB/G and APOB mainly contain bi-antennary complex glycans with the common core structure and (sialyl-) LacNAc branch structures. These HAGPs are easily detected by LC-MS analysis and therefore could be used as standard glycopeptides for glycoproteomic methodology studies as well as possible clinical utilities. Show less
no PDF DOI: 10.1016/j.carbpol.2024.122746
APOB

Modulation of β secretase and neuroinflammation by biomimetic nanodelivery system for Alzheimer's disease therapy.

Xiaolei Song, Chenchen Wang, Qin Ding +8 more · 2025 · Journal of controlled release : official journal of the Controlled Release Society · Elsevier · added 2026-04-24
Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disorder. The vicious circle between amyloid-β peptide (Aβ) overgeneration and microglial dysfunction is an important path Show more
Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disorder. The vicious circle between amyloid-β peptide (Aβ) overgeneration and microglial dysfunction is an important pathological event that promotes AD progression. However, therapeutic strategies toward only Aβ or microglial modulation still have many problems. Herein, inspired by the Aβ transportation, an Aβ-derived peptide (CKLVFFAED) engineered biomimetic nanodelivery system (MK@PC-R NPs) is reported for realizing BBB penetration and reprogram neuron and microglia in AD lesion sites. This hollow mesoporous Prussian blue-based MK@PC-R NPs carrying curcumin and miRNA-124 can down-regulate β secretase expression, thereby inhibiting Aβ production and reducing Aβ-induced neurotoxicity. Meanwhile, MK@PC-R NPs with excellent antioxidant and anti-inflammatory properties could normalize the microglial phenotype and promote Aβ degradation, providing neuroprotection. As expected, after treatment with MK@PC-R NPs, the Aβ burdens, neuron damages, neuroinflammation, and memory deficits of transgenic AD mice (APP/PS1 mice) are significantly attenuated. Overall, this biomimetic nanodelivery system with anti-Aβ and anti-inflammatory properties provides a promising strategy for the multi-target therapy of early AD. Show less
no PDF DOI: 10.1016/j.jconrel.2024.12.060
BACE1

The joint predictive roles of achievement task values and academic achievement on trajectories of school satisfaction among elementary school students: A latent transition analysis.

Meiru Deng, E Scott Huebner, Wang Liu +1 more · 2025 · The British journal of educational psychology · Blackwell Publishing · added 2026-04-24
Exploring how different profiles of academic task values (ATVs) and academic achievement (AA) influence students' school satisfaction should enrich understanding of how school satisfaction develops in Show more
Exploring how different profiles of academic task values (ATVs) and academic achievement (AA) influence students' school satisfaction should enrich understanding of how school satisfaction develops in specific academic situations. This study aimed to identify profiles of ATV (attainment value [AV], utility value [UV]) and AA, as well as heterogeneous trajectories of school satisfaction, and then examine the joint predictive effects of ATV and AA on the development of school satisfaction. A total of 3548 Chinese elementary school students (M Data were collected through self-reports of ATVs and school satisfaction, while AA data were collected from final examination scores at the end of each semester. Latent profile analysis (LPA) and latent class growth analysis revealed six profiles of ATV and AA (i.e., congruent subgroups: congruent-low, congruent-high, congruent-moderate and incongruent subgroups: high ATV-low AA, lower UV-moderate AA, low ATV-moderate AA); and four heterogeneous trajectories of school satisfaction (i.e., high-decreasing, low-stable, high-stable, low-increasing). Latent transition analysis (LTA) indicated that students with congruent levels of AA and ATV were more likely to transition into the optimal school satisfaction groups (high-stable and low-increasing groups), whereas students with greater gaps between AA and ATV were more likely to transition into the adverse groups (i.e., low-stable and high-decreasing groups). These findings highlight the need for educators to tailor interventions to distinct groups to optimize students' school satisfaction. Show less
no PDF DOI: 10.1111/bjep.70054
LPA

ENO1-related gene signature predicts prognosis and therapeutic response in diffuse large B-cell lymphoma.

Wenli Yan, Xiaoxi Liu, Beibei Gao +6 more · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
Alpha-enolase (ENO1), the enzyme catalyzing 2-phosphoglycerate conversion to phosphoenolpyruvate, is highly expressed in diffuse large B-cell lymphoma (DLBCL) and correlates with adverse clinical outc Show more
Alpha-enolase (ENO1), the enzyme catalyzing 2-phosphoglycerate conversion to phosphoenolpyruvate, is highly expressed in diffuse large B-cell lymphoma (DLBCL) and correlates with adverse clinical outcomes. Thus, understanding the relationship between ENO1-related gene (ERG) network and DLBCL is imperative. Here, we integrated multi-omics profiling (RIP-seq, RNA-seq, and protein interactome analysis) to identify ERGs and established a prognostic model by machine learning algorithms. We identified eleven hub genes (CHERP, SYNE2, INTS1, FAP, MMP9, LRP5, RBM8A, PRMT5, SLC25A6, PABPC4, PSTPIP2) using RNA sequencing, RNA immunoprecipitation sequencing, and protein interaction profiling. A prognostic model was constructed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression in the GSE10846 dataset and validated in two independent cohorts. DLBCL patients were stratified into high- and low-risk groups based on the model, and clinical characteristics were compared. The tumor immune microenvironment (TIME) was analyzed using CIBERSORT and xCell algorithms to explore correlations with the ERG score. Drug sensitivity assays in DLBCL cell lines were performed to validate the model's predictive capacity for chemotherapy response. Furthermore, the functional role of PABPC4, a key gene in the scoring system, was investigated through A prognostic model including 11 hub genes was established. Patients in the high-risk group exhibited worse clinical outcomes and an immunosuppressive TIME, characterized by altered expression of immune checkpoint-related proteins. This group demonstrated increased sensitivity to vincristine, etoposide, and oxaliplatin. Knockdown of PABPC4 significantly inhibited cell proliferation, reduced colony formation, and delayed tumor growth The ERG scoring system offers a robust and precise tool for predicting survival and guiding personalized treatment in DLBCL patients. Show less
no PDF DOI: 10.3389/fimmu.2025.1644020
PABPC4

IL-27 attenuated macrophage injury and inflammation induced by Mycobacterium tuberculosis by activating autophagy.

Yushan Zhou, Yuxuan Zhang, Yanli Li +3 more · 2025 · In vitro cellular & developmental biology. Animal · Springer · added 2026-04-24
Interleukin-27 (IL-27) is a cytokine that is reported to be highly expressed in the peripheral blood of patients with pulmonary tuberculosis (PTB). IL-27-mediated signaling pathways, which exhibit ant Show more
Interleukin-27 (IL-27) is a cytokine that is reported to be highly expressed in the peripheral blood of patients with pulmonary tuberculosis (PTB). IL-27-mediated signaling pathways, which exhibit anti- Mycobacterium tuberculosis (Mtb) properties, have also been demonstrated in macrophages infected with Mtb. However, the exact mechanism remains unclear. This study aimed to clarify the potential molecular mechanisms through which IL-27 enhances macrophage resistance to Mtb infection. Both normal and PTB patients provided bronchoalveolar lavage fluid (BALF). Peripheral blood mononuclear cells (PBMCs) were isolated from healthy individuals and stimulated with 50 ng/mL macrophage-colony stimulating factor (M-CSF) to obtain monocyte-derived macrophages (MDMs). Using 100 ng/mL phorbol 12-myristate 13-acetate (PMA), THP-1 cells were induced to differentiate into THP-1-derived macrophage-like cells (TDMs). Both MDMs and TDMs were subsequently infected with the Mtb strain H37Rv and treated with 50 ng/mL IL-27 prior to infection. The damage and inflammation of macrophages were examined using flow cytometry, enzyme-linked immunosorbent assay (ELISA), and Western blotting. Patients with PTB had elevated levels of IL-27 in their BALF. Preconditioning with IL-27 was shown to reduce H37Rv-induced MDMs and TDMs apoptosis while also decreasing the levels of Cleaved Caspase-3, Bax and the proinflammatory cytokines TNF-α, IL-1β, and IL-6, promoting the expression of Bcl-2 and the anti-inflammatory factors IL-10 and IL-4. Silencing of the IL-27 receptor IL-27Ra increased macrophage damage and inflammation triggered by H37Rv. Mechanistically, IL-27 activates autophagy by inhibiting TLR4/NF-κB signaling and activating the PI3K/AKT signaling pathway, thereby inhibiting H37Rv-induced macrophage apoptosis and the inflammatory response. Our study suggests that IL-27 alleviates H37Rv-induced macrophage injury and the inflammatory response by activating autophagy and that IL-27 may be a new target for the treatment of PTB. Show less
📄 PDF DOI: 10.1007/s11626-024-00989-x
IL27

Daily activity accumulation patterns and depressive symptoms among adolescents: a latent profile approach.

Yuwei Liu, Nan Zheng, Huan Chen +3 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24
This study aims to identify and characterize daily activity accumulation patterns (bouts of physical activity and sedentary behavior) among adolescents and then to explore the associations between the Show more
This study aims to identify and characterize daily activity accumulation patterns (bouts of physical activity and sedentary behavior) among adolescents and then to explore the associations between these groups and depressive symptoms. A total of 521 adolescents aged 13-18 years from Wuhan and Changsha, China, were included. Bouts of physical activity (PA) and sedentary behavior (SED) were measured using accelerometers. The Center for Epidemiologic Studies Depression Scale was used to assess participants' depressive symptoms. Latent profile analysis was employed to identify distinct groups based on their activity patterns. Three distinct groups were identified: "Prolonged sitters" ( The synergistic effect of strategies to reduce total SED duration by limiting SED bouts to 30 min or less and increasing light physical activity (LPA) may also be effective in alleviating depressive symptoms in adolescents. Show less
📄 PDF DOI: 10.3389/fpsyg.2025.1683685
LPA

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

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

Tracing the Genetic Heritage of the Kirgiz People: Dual-Wave Admixture and Ancestry-Biased Adaptation.

Shuanghui Chen, Yan Lu, Hao Chen +6 more · 2025 · Molecular biology and evolution · Oxford University Press · added 2026-04-24
The Kirgiz, a Turkic-speaking ethnic group with a rich nomadic heritage, represent a pivotal population for understanding human migration and adaptation in Central Asia. However, their genetic origins Show more
The Kirgiz, a Turkic-speaking ethnic group with a rich nomadic heritage, represent a pivotal population for understanding human migration and adaptation in Central Asia. However, their genetic origins and admixture history remain largely unexplored. Here, we present the first comprehensive genomic study of Kirgiz populations from Xinjiang, China (XJ.KGZ, n = 36) and their counterparts in Kyrgyzstan (KRG), integrating genome-wide data of 2,406 global individuals. Our analyses reveal four primary ancestry components in XJ.KGZ: East Asian (41.7%), Siberian (25.6%), West Eurasian (25.2%), and South Asian (7.6%). Despite close genetic affinity (FST = 0.13%), XJ.KGZ and KRG diverged ∼447 years ago, with limited gene flow post-split. A two-wave admixture model elucidates their demographic history: an initial East-West Eurasian mixture ∼2,225 years ago, likely reflecting west-east contacts during the period of the Warring States and the Qin Dynasty, followed by secondary admixture events (∼875 to 425 years ago) linked to historical migrations under Mongol and post-Mongol rule. Local adaptation signatures implicate genes critical for cellular tight junction (e.g. PATJ), pathogen invasion (e.g. OR14I1), and cardiac functions (e.g. RYR2) with allele frequency deviations suggesting ancestry-specific selection. While no classical high-altitude adaptation genes (e.g. EPAS1) showed selection signals, RYR2 and C10orf67-implicated in hypoxia response in Tibetan fauna-displayed Western ancestry bias, hinting at convergent adaptation mechanisms. This study advances our understanding of the genetic makeup and admixture history of the Kirgiz people and provides novel insights into human dispersal in Central Asia. Show less
no PDF DOI: 10.1093/molbev/msaf196
PATJ

Oral Sheep Milk-Derived Exosome Therapeutics for

Zhimin Wu, Shuo Yan, Huimin Zhang +6 more · 2025 · International journal of molecular sciences · MDPI · added 2026-04-24
Cadmium (Cd) contamination in plants and soil poses significant risks to livestock, particularly sheep. Cd exposure often leads to severe gastrointestinal diseases in sheep that are difficult to treat Show more
Cadmium (Cd) contamination in plants and soil poses significant risks to livestock, particularly sheep. Cd exposure often leads to severe gastrointestinal diseases in sheep that are difficult to treat. Milk-derived exosomes, particularly those from sheep milk (SM-Exo), have shown potential in treating gastrointestinal disorders, though their efficacy in Cd-induced colitis remains unclear. In this study, we investigated the therapeutic potential of SM-Exo in a Cd-induced colitis model. Hu sheep were exposed to Cd, and their fecal microbiota were collected to prepare bacterial solutions for fecal microbiota transplantation (FMT) in mice. The changes in gut microbiota and gene expression were analyzed through microbiome and transcriptomics. Our results showed that prior to treatment, harmful bacteria (e.g., Show less
📄 PDF DOI: 10.3390/ijms26073299
ADCY3

Demoralization profiles and their association with active aging in Chinese older adults with disabilities: a latent profile analysis.

Lulu Wu, Ziqing Qi, Yue Zhang +5 more · 2025 · Frontiers in public health · Frontiers · added 2026-04-24
To identify latent profiles of demoralization among older adults with disabilities, analyze their influencing factors, and examine their associations with active aging. From February to July 2025, a c Show more
To identify latent profiles of demoralization among older adults with disabilities, analyze their influencing factors, and examine their associations with active aging. From February to July 2025, a convenience sample of 411 older adults with disabilities was recruited from a tertiary hospital in Anhui Province, China. Data were collected using a general information questionnaire, the Chinese version of the Demoralization Scale, and the Active Aging Scale. Latent profile analysis (LPA) was performed based on demoralization subscale scores. Univariate and multinominal analyses were employed to investigate the influencing factors, and the Kruskal-Wallis The prevalence of demoralization syndrome was 49.1%. LPA identified three distinct profiles: the Well-Adapted Group (53.3%), the Disheartened-Helpless Group (23.8%), and the Fully Demoralized Group (22.9%). The Kruskal-Wallis Nearly half of the older adults with disabilities experienced demoralization, with heterogeneous subgroups identified. The active aging status of demoralized subgroups requires urgent attention. These findings suggest the need for targeted interventions tailored to the characteristics of each profile to improve mental health and promote active aging in this population. Show less
📄 PDF DOI: 10.3389/fpubh.2025.1715566
LPA

Regulation of partial endothelial-to-mesenchymal transition by circATXN1 in ischemic diseases.

Yue Li, Zhe Zheng, Yanze Li +14 more · 2025 · Nature communications · Nature · added 2026-04-24
Ischemic injury induces a partial mesenchymal shift in endothelial cells (ECs), contributing to impaired vascular regeneration. However, the molecular regulators of this transitional state remain poor Show more
Ischemic injury induces a partial mesenchymal shift in endothelial cells (ECs), contributing to impaired vascular regeneration. However, the molecular regulators of this transitional state remain poorly defined. To address this, we performed circular RNA profiling of endothelial cells under ischemic-like conditions and identified a marked upregulation of a circular RNA, named circATXN1. Functional studies revealed that circATXN1 knockdown modulates endothelial phenotype and vascular response after ischemia. Functional studies have shown that knockdown of circATXN1 can regulate the endothelial cell phenotype and vascular response after ischemia. Mechanistically, circATXN1 knockdown enhances the demethylase protein ALKBH5 to reduce the RNA methylation level of the key transcription factor SLUG, thereby stabilizing SLUG. In animal models, suppression of circATXN1 enhances angiogenesis and improves recovery following ischemic injury. Here, we show that circATXN1 regulates partial endothelial-to-mesenchymal transition (EndMT) and angiogenesis by controlling SLUG mRNA methylation dynamics, highlighting its potential as a therapeutic target in ischemic disease. Show less
no PDF DOI: 10.1038/s41467-025-61596-2
SNAI1

Transcriptome analysis of postnatal mouse cardiac tissue growth and development.

Xiao-Cong Zhu, Sheng-Nan Wang, Lin Jiang +1 more · 2025 · Yi chuan = Hereditas · added 2026-04-24
Postnatal cardiac function in mammals is closely associated with cardiomyocyte proliferation and hypertrophy. However, the molecular mechanisms regulating cardiomyocyte proliferation and hypertrophy h Show more
Postnatal cardiac function in mammals is closely associated with cardiomyocyte proliferation and hypertrophy. However, the molecular mechanisms regulating cardiomyocyte proliferation and hypertrophy have not yet been fully elucidated. Therefore, phenotypic measurements and transcriptomic sequencing were performed on myocardial tissues from 7-day-old (P7) and 3-month-old (3m) female C57BL/6 mice to investigate changes in cardiomyocytes during growth and development and to identify key genes regulating myocardial growth and development. In comparison to 7-day-old mice, 3-month-old mice exhibited a significant increase in heart weight ( Show less
no PDF DOI: 10.16288/j.yczz.24-328
HEY2

FGF22 Secreted by Hair Papilla Cells Regulates Hair Follicle Stem Cell Proliferation and Differentiation.

Yu Luo, Tong Xiao, Binpeng Xi +5 more · 2025 · Biomolecules · MDPI · added 2026-04-24
Hair follicle stem cells (HFSCs) are resident stem cells within hair follicles (HFs) that possess self-renewal and differentiation capacities, serving as a critical model for regenerative medicine res Show more
Hair follicle stem cells (HFSCs) are resident stem cells within hair follicles (HFs) that possess self-renewal and differentiation capacities, serving as a critical model for regenerative medicine research. Their dynamic interaction with dermal papilla cells (DPCs) plays a decisive role in HF development and cycling. Show less
📄 PDF DOI: 10.3390/biom15111560
FGFR1