👤 Weida 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, 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

Association between 24-hour movement behaviors and depressive symptoms among urban older adults in china: a compositional isotemporal substitution analysis.

Hu Ji, Glenn Roswal, Jing Min Liu +2 more · 2025 · Frontiers in psychiatry · Frontiers · added 2026-04-24
To examine the association between 24-hour movement behaviors and depressive symptoms in older adults using compositional data analysis, and to investigate the dose-response characteristics of time re Show more
To examine the association between 24-hour movement behaviors and depressive symptoms in older adults using compositional data analysis, and to investigate the dose-response characteristics of time reallocations between movement behaviors in relation to depressive symptoms. A cross-sectional study was conducted among 1093 urban-dwelling older adults aged 60 years and above in selected communities of Jinan City, Shandong Province, China, between April 2024 and September 2024. The Chinese version of the International Physical Activity Questionnaire-Long Form (IPAQ-LF) was used to estimate time spent in moderate to vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behavior (SB), and sleep (SLP) across a typical 24-hour day. The Chinese version of the Patient Health Questionnaire Depression Scale-9 item (PHQ-9) was applied to assess depressive symptoms. Compositional isotemporal substitution models were employed to explore the associations between time reallocations among 24-hour movement behaviors and depressive symptoms, accounting for the co-dependent nature of time-use data. (1) The geometric means of time spent in MVPA, LPA, SB, and SLP were 25.33 minutes, 141.26 minutes, 738.10 minutes, and 455.15 minutes, respectively. Variation matrix analysis revealed the highest log-ratio variance between MVPA and SB (0.168), and the lowest between SLP and SB (0.031). (2) The prevalence of screening-positive depressive symptoms was 16.29% among Chinese urban older adults. (3) Results from compositional linear regression models showed that time allocated to MVPA, LPA, and SLP (relative to the remaining movement behaviors) was negatively associated with depressive symptoms, while time spent in SB was positively associated. (4) Dose-response analysis further indicated that: (a) MVPA substitutions with other movement behaviors exhibited nonlinear and markedly asymmetric effects on depressive symptoms; (b) replacing MVPA with LPA, SB, or SLP resulted in increasingly larger changes in predicted scores as substitution duration increased, whereas the reverse substitution (MVPA for other movement behaviors) produced progressively smaller changes; and (c) substitutions between SB and LPA displayed linear and symmetrical effects. The findings provide evidence of an association between 24-hour movement behaviors and depressive symptoms in Chinese urban-dwelling older adults and reinforce the importance of achieving a balance between different types of movement behaviors over a 24-hour period for mental health. Show less
📄 PDF DOI: 10.3389/fpsyt.2025.1706591
LPA

Case Report: A rare case of hepatoid carcinoma of the ovary with genomic profiling and long-term follow-up: diagnostic and therapeutic perspectives.

Yufang Zuo, Xuan Liu, Yajun Pang +7 more · 2025 · Frontiers in oncology · Frontiers · added 2026-04-24
Hepatoid carcinoma of the ovary (HCO) is a highly uncommon and aggressive neoplasm originating from the surface epithelial cells of the ovary, characterized by hepatocyte-like differentiation. To date Show more
Hepatoid carcinoma of the ovary (HCO) is a highly uncommon and aggressive neoplasm originating from the surface epithelial cells of the ovary, characterized by hepatocyte-like differentiation. To date, most information on HCO is derived from case reports, with fewer than 50 documented cases globally. In this case report, we present a detailed account of the diagnosis, treatment, and prognosis of a patient diagnosed as having bilateral HCO, which is even rarer. Targeted next-generation sequencing revealed somatic mutations in PIK3C3 and TP53, with no BRCA1/2 alterations, and a molecular profile consistent with microsatellite stability and low tumor mutational burden. We also review the current literature to situate our findings within the broader context of existing knowledge. Given the rarity of bilateral HCO, our objective is to contribute to the existing body of knowledge by providing a comprehensive description of its clinical features, molecular characteristics, and treatment strategies. This effort may enhance understanding of this rare malignancy and offer insights to improve patient outcomes in clinical practice. Show less
no PDF DOI: 10.3389/fonc.2025.1631424
PIK3C3

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

Genome-wide association studies identify genetic determinants of synucleinopathy biomarkers.

Emma N Somerville, Lang Liu, Michael Ta +4 more · 2025 · medRxiv : the preprint server for health sciences · added 2026-04-24
α-synucleinopathies are clinically and biologically heterogeneous disorders lacking reliable biomarkers to assist with early diagnosis, disease progression, patient stratification, and therapeutic tar Show more
α-synucleinopathies are clinically and biologically heterogeneous disorders lacking reliable biomarkers to assist with early diagnosis, disease progression, patient stratification, and therapeutic targeting. Genetic variation is known to impact biomarker levels, influencing their utility and interpretation in research and clinical settings. We aimed to identify common genetic modulators of biomarker levels implicated in α-synucleinopathy pathogenesis. Genome-wide association studies (GWASs) were conducted on 63 CSF, plasma, and urine biomarkers in 581 individuals from the Parkinson's Progression Markers Initiative (PPMI). Analyses were adjusted for age, sex, disease status, and principal components. PD- and DLB-risk loci associations were separately assessed for each GWAS. We confirm strong associations between urine bis(monoacylglycerol)phosphate (BMP) isoforms and the variants The present study reveals established and novel genetic modulators of potential α-synucleinopathy biomarkers, demonstrating that genetic background significantly shapes biomarker levels. These genetic influences should be accounted for when conducting biomarker-based research, clinical trials, or therapeutic development to ensure accurate interpretation and improve their translational relevance. Show less
📄 PDF DOI: 10.64898/2025.12.29.25343142
APOE

Neural mechanisms and social support for mental health in old age by martial arts exercise.

Yong Jiang, Pengsong Li, Yulong Yang +9 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24
The increasing global aging population presents significant challenges related to cognitive decline, mental health disorders, and social isolation. Martial arts exercise emerges as a multifaceted inte Show more
The increasing global aging population presents significant challenges related to cognitive decline, mental health disorders, and social isolation. Martial arts exercise emerges as a multifaceted intervention promoting mental health and cognitive vitality among older adults by integrating physical activity, cognitive engagement, and social interaction. This review synthesizes evidence on the neural mechanisms underlying the benefits of martial arts, highlighting their role in enhancing brain-derived neurotrophic factor (BDNF) expression, neuroplasticity, and neural connectivity, which support improved executive functions, memory, and emotional regulation. Both hard martial arts and soft practices, such as Tai Chi, offer distinct advantages in addressing age-related cognitive and psychosocial challenges. Additionally, martial arts foster strong social support systems, reducing loneliness and enhancing emotional resilience through community engagement and shared achievement. Physical and functional benefits, including improved strength, balance, and cardiovascular health, further contribute to overall well-being. Despite promising results, current studies are limited by heterogeneity in martial arts styles, short intervention durations, and variable methodologies. Future research should focus on long-term, standardized interventions employing advanced neuroimaging and biomarker assessments to better elucidate mechanisms and optimize training protocols. Integrating martial arts into health promotion strategies holds substantial potential for enhancing mental health, cognitive resilience, and quality of life in aging populations. Show less
📄 PDF DOI: 10.3389/fpsyg.2025.1733310
BDNF

Relationship Between Chronic Pain and Breast Cancer: Insight From Genetic Correlation Analyses and 2-Sample Mendelian Randomization.

Haojie Yang, Xiaoyan Xie, Liling Lin +5 more · 2025 · Clinical breast cancer · Elsevier · added 2026-04-24
To evaluate potential genetic causal relationships between chronic pain subtypes like migraine and multi-site chronic pain (MCP) and their impact on breast cancer occurrence and survival rates. The as Show more
To evaluate potential genetic causal relationships between chronic pain subtypes like migraine and multi-site chronic pain (MCP) and their impact on breast cancer occurrence and survival rates. The association between chronic pain and breast cancer was reported before, yet the causal nature between them remained uncertain. Data on chronic pain and breast cancer were sourced from publicly available European genome-wide association study (GWAS) datasets. Genetic association between chronic pain and breast cancer phenotypes was assessed using linkage disequilibrium genetic correlation (LDSC). Colocalization analysis further identified potential shared causal variation. Based on Inverse variance weighted method, 2-sample Mendelian Randomization (MR) was conducted to investigate causal associations between migraine, MCP, and breast cancer or breast cancer survival. Sensitive analysis was conducted to ensure the absence of heterogeneity and horizontal pleiotropy. LDSC demonstrated significant genetic correlations between migraine and both estrogen receptor-negative (ER-) and overall breast cancer, while also revealing a notable genetic association between MCP and ER- and ER+ breast cancer, as well as overall breast cancer. Through colocalization analysis, potential involvement of rs2183271, located in MLLT10 gene, in regulating MCP and ER+ breast cancer was identified. MR analysis revealed the association between migraine and elevated risk of ER- breast cancer (IVW, P = 4.95 × 10 Our results provided new insights into the role of migraine and MCP in breast cancer, paving the way for targeted preventive strategies and future investigations. Show less
no PDF DOI: 10.1016/j.clbc.2025.02.004
MLLT10

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

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

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

Neural Mechanisms of the Impact of Rotated Terrain Symbols on Spatial Representation in Orienteers: Evidence from Eye-Tracking and Whole-Brain fNIRS Synchronization.

Shijia Ou, Tianyu Liu, Yang Liu · 2025 · Behavioral sciences (Basel, Switzerland) · MDPI · added 2026-04-24
Spatial representation is a core element of spatial cognition in orienteering, but the visual-spatial neural modulation mechanisms underlying spatial representations with differently oriented maps hav Show more
Spatial representation is a core element of spatial cognition in orienteering, but the visual-spatial neural modulation mechanisms underlying spatial representations with differently oriented maps have not yet been systematically elucidated. This study recruited 67 orienteering athletes as participants and employed a single-factor (map orientation: normal vs. rotated) between-subjects experimental design. Eye-tracking and functional near-infrared spectroscopy (fNIRS) techniques were used simultaneously to collect behavioral, eye movement, and brain activity data, investigating the effects of map orientation on visual attention and brain activity characteristics during terrain symbol representation processing in orienteering athletes. The results revealed that compared to the normal orientation, the rotated orientation led to significantly decreased task accuracy, significantly prolonged reaction times, and significantly increased saccade amplitude and pupil diameter. Brain activation analysis showed that the rotated orientation elicited significantly higher activation levels in the right dorsolateral prefrontal cortex (R-DLPFC), bilateral parietal lobe cortex (L-PL, R-PL), right temporal lobe (R-TL), and visual cortex (VC) compared to the normal orientation, along with enhanced functional connectivity. Correlation analysis revealed that under normal map orientation, accuracy was positively correlated with both saccade amplitude and pupil diameter; accuracy was positively correlated with activation in the R-DLPFC; saccade amplitude was positively correlated with activation in the R-DLPFC and R-PL; and pupil diameter was positively correlated with activation in the R-DLPFC. Under rotated map orientation, accuracy was positively correlated with saccade amplitude and pupil diameter, and pupil diameter was positively correlated with activation in both the L-PL and R-PL. The results indicate that map orientation significantly influences the visual search patterns and neural activity characteristics of orienteering athletes, impacting task performance through the coupling mode of visual-neural activity. Show less
📄 PDF DOI: 10.3390/bs15101314
LPL

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

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

Dual-Mode Method for the Sensitive Detection of β-Secretase (BACE1) Based on Surface-Enhanced Raman Scattering and Dark-Field Microscopy.

Xiaowan Zhang, Xiaolei Song, Chenchen Wang +4 more · 2025 · Analytical chemistry · ACS Publications · added 2026-04-24
β-Secretase (BACE1), a key enzyme to producing neurotoxic β-amyloid, is a potential biomarker of Alzheimer's disease (AD). Developing a sensitive and efficient detection method for BACE1 activity is s Show more
β-Secretase (BACE1), a key enzyme to producing neurotoxic β-amyloid, is a potential biomarker of Alzheimer's disease (AD). Developing a sensitive and efficient detection method for BACE1 activity is significant for AD progression evaluation. Due to the poor cleavage efficiency and acidic working conditions of BACE1, developing probes with high stability and strong signals is challenging for its detection. This work proposed a dual-mode BACE1 detection method based on surface-enhanced Raman scattering and dark-field microscopy. 4-Mercaptobenzoic acid (4-MBA), as the internal Raman reporter of Au@Ag nanoparticles (NPs), shows stable and enhanced Raman signals in an acidic environment. The plasmonic Au Show less
no PDF DOI: 10.1021/acs.analchem.5c02512
BACE1

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

Genetic associations of metabolic factors and therapeutic drug targets with polycystic ovary syndrome.

Changlong Zhang, Yuxuan Li, Yang Wang +6 more · 2025 · Journal of advanced research · Elsevier · added 2026-04-24
Polycystic ovary syndrome (PCOS) is frequently accompanied with metabolic dysfunctions, yet the causal relationships between metabolic factors and PCOS remain to be conclusively established and etiolo Show more
Polycystic ovary syndrome (PCOS) is frequently accompanied with metabolic dysfunctions, yet the causal relationships between metabolic factors and PCOS remain to be conclusively established and etiology-based therapies are lacking. To comprehensively identify the metabolic causal factors and potential drug targets for PCOS. This genetic association study was conducted using bidirectional two-sample Mendelian Randomization (MR), multivariable MR (MVMR) and drug-target MR. Considering metabolic sexual dimorphism, female-specific genome-wide association studies (GWASs) for metabolic factors were obtained. To ensure the robustness of the findings, an additional independent PCOS GWAS dataset was utilized for replication. The PCOS cohort included 10,074 PCOS cases (mean age 28 to 45 years) and 103,164 controls (mean age 27 to 60 years) of European ancestry. All participants were female. Employing two-sample MR analysis, we found that genetically proxied body mass index (BMI) (OR = 3.40 [95 % CI, 2.65-4.36]), triglyceride (TG) (OR = 1.54 [95 % CI, 1.17-2.04]), low-density lipoprotein cholesterol (LDL-c) (OR = 1.37 [95 % CI, 1.07-1.76]), and type 2 diabetes (T2D) (OR = 1.24 [95 % CI, 1.09-1.41]) were significantly associated with an increased risk of PCOS, whereas genetically predicted high-density lipoprotein cholesterol (HDL-c) (OR = 0.61 [95 % CI, 0.47-0.80]) decreased the odds of PCOS. Stepwise MVMR established a hierarchy of interactions among these metabolic factors, identifying BMI and HDL-c as the most prominent causal factors. Notably, drug-target MR analysis identified incretin-based therapeutics, PCSK9 inhibitors, LPL gene therapy, sulfonylureas, and thiazolidinediones as potential therapeutics for PCOS. All these findings were validated in an independent dataset. This study offered insights into the roles of obesity, diabetes, and dyslipidemia in PCOS etiology and therapeutics, underscoring the necessity for managing metabolic health in women and paving the way for tailored therapeutic strategies for PCOS based on its metabolic underpinnings. Show less
📄 PDF DOI: 10.1016/j.jare.2024.10.038
LPL

Multivariate genome-wide analyses of insulin resistance unravel novel loci and therapeutic targets for cardiometabolic health.

Chaojie Ye, Chun Dou, Dong Liu +13 more · 2025 · Nature communications · Nature · added 2026-04-24
Limited identification of insulin resistance-associated loci hinders understanding of its role in cardiometabolic health, impeding therapeutic strategies. We apply three multivariate genome-wide assoc Show more
Limited identification of insulin resistance-associated loci hinders understanding of its role in cardiometabolic health, impeding therapeutic strategies. We apply three multivariate genome-wide association study approaches on homeostatic model assessment for insulin resistance, insulin resistance index, fasting insulin, and ratio of triglycerides to high-density lipoprotein cholesterol from MAGIC and UK Biobank to develop a comprehensive phenotype ('mvIR'), and identify 217 independent loci, including 24 novel loci. The mvIR is causally associated with higher risks of 17 cardiometabolic diseases and five aging phenotypes, independent of adiposity and sarcopenia. We outline 21 of 2644 druggable genes for insulin resistance by Mendelian randomization and colocalization, where six genes (AKT1, ERBB3, FCGR1A, FGFR1, LPL, NR1H3) encode targets for approved drugs with consistent directions in alleviating insulin resistance, with no significant side effects revealed by phenome-wide association study. This study uncovers novel loci and therapeutic targets to inform strategies promoting insulin resistance-centered cardiometabolic health and longevity. Show less
📄 PDF DOI: 10.1038/s41467-025-64985-9
FGFR1

Adipocyte heparan sulfate determines type 2 diabetes susceptibility in mice via FGF1-Mediated glucose regulation.

Chung-Jui Yu, Ariane R Pessentheiner, Sihao Liu +21 more · 2025 · Molecular metabolism · Elsevier · added 2026-04-24
Obesity is the principal driver of insulin resistance, and lipodystrophy is also linked with insulin resistance, emphasizing the vital role of adipose tissue in glucose homeostasis. The quality of adi Show more
Obesity is the principal driver of insulin resistance, and lipodystrophy is also linked with insulin resistance, emphasizing the vital role of adipose tissue in glucose homeostasis. The quality of adipose tissue expansion is a critical determinant of insulin resistance predisposition, with individuals suffering from metabolic unhealthy adipose expansion exhibiting greater risk. Adipocytes are pivotal in orchestrating metabolic adjustments in response to nutrient intake and cell intrinsic factors that positively regulate these adjustments are key to prevent Type-2 diabetes. Employing unique genetic mouse models, we established the critical involvement of heparan sulfate (HS), a fundamental element of the adipocyte glycocalyx, in upholding glucose homeostasis during dietary stress. Genetic models that compromise adipocyte HS accelerate the development of high-fat diet-induced hyperglycemia and insulin resistance, independent of weight gain. Mechanistically, we show that perturbations in adipocyte HS disrupts endogenous FGF1 signaling, a key nutrient-sensitive effector. Furthermore, compromising adipocyte HS composition detrimentally impacts FGF1-FGFR1-mediated endocrinization, with no significant improvement observed in glucose homeostasis. Our data establish adipocyte HS composition as a determinant of Type 2 diabetes susceptibility and the critical dependency of the endogenous adipocyte FGF1 metabolic pathway on HS. Show less
📄 PDF DOI: 10.1016/j.molmet.2025.102267
FGFR1

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes.

Fanqi Liang, Man Zheng, Jingjiu Lu +2 more · 2025 · Scientific reports · Nature · added 2026-04-24
Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminatin Show more
Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminating in septic shock and multiple organ dysfunction syndrome. A pivotal element in the pathogenesis and progression of sepsis involves the significant disruption of oncological metabolic networks, where cells within the pathological milieu exhibit metabolic functions that diverge from their healthy counterparts. Among these, purine metabolism plays a crucial role in nucleic acid synthesis. However, the contribution of Purine Metabolism Genes (PMGs) to the defense mechanisms against sepsis remains inadequately explored. Leveraging bioinformatics, this study aimed to identify and substantiate potential PMGs implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 75 candidate PMGs. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were employed to assess the biological significance and pathways associated with these genes. Additionally, Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methodologies were implemented to identify key hub genes and evaluate the diagnostic potential of nine selected PMGs in sepsis identification. The study also examined the correlation between these hub PMGs and related genes, with validation conducted through expression level analysis using the GSE13904 and GSE65682 datasets. The study identified twelve PMGs correlated with sepsis, namely AK9, ENTPD3, NUDT16, GMPR2, PKM, RRM2B, POLR2J, POLE3, ADCY3, ADCY4, ADSSL1, and AMPD1. Functional analysis revealed their involvement in critical processes such as purine nucleotide and ribose phosphate metabolism. The diagnostic capability of these PMGs to effectively differentiate sepsis cases underscored their potential as biomarkers. This research elucidates twelve PMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings highlight the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets. Show less
📄 PDF DOI: 10.1038/s41598-024-82998-0
ADCY3

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

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

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

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

Targeting WNT5A noncanonical signaling attenuates renal fibrosis progression in acute kidney injury.

Sijie Gu, Haoran Feng, Xiaomei Li +10 more · 2025 · Molecular therapy : the journal of the American Society of Gene Therapy · Elsevier · added 2026-04-24
Preventing the progression from acute kidney injury (AKI) to chronic kidney disease (CKD) remains a considerable clinical challenge. In this study, we elucidate the role of WNT5A in accelerating the A Show more
Preventing the progression from acute kidney injury (AKI) to chronic kidney disease (CKD) remains a considerable clinical challenge. In this study, we elucidate the role of WNT5A in accelerating the AKI-to-CKD transition and its underlying mechanisms. Renal biopsies from patients with AKI showed marked upregulation of WNT5A and its receptor, CD146, in proximal tubules, with higher expression in patients with CKD progression. In murine AKI models, Wnt5a knockdown attenuated CKD progression. Conversely, proximal tubular overexpression of Wnt5a exacerbated renal fibrosis in ischemia-reperfusion injury (IRI) mice, which was alleviated by Box5, a specific WNT5A antagonist. In vitro, WNT5A overexpression in transforming growth factor β (TGF-β)-stimulated HK-2 cells promoted CD146 upregulation, activated JNK phosphorylation, and enhanced SNAI1 expression. The genetic silencing of WNT5A/CD146 and JNK inhibition suppresses SNAI1 expression and attenuates fibrotic responses. Mechanistically, JNK-mediated c-JUN phosphorylation promoted its interaction with KLF5 at the SNAI1 promoter, driving renal fibrosis. Elevated serum levels of soluble CD146 correlated with renal function in patients with AKI and were higher in patients exhibiting CKD progression. Inhibition of WNT5A could serve as a therapeutic target for delaying renal fibrosis in AKI progression. Show less
no PDF DOI: 10.1016/j.ymthe.2025.06.039
SNAI1

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

Potassium molybdate blocks APN-dependent coronavirus entry by degrading receptor via PIK3C3-mediated autophagy.

Yunhang Zhang, Na Zhang, Yue Zhang +11 more · 2025 · Journal of virology · added 2026-04-24
Swine enteric coronaviruses pose a significant challenge to the global pig industry, inflicting severe diarrhea and high mortality rates among piglets, and resulting in substantial economic losses. In Show more
Swine enteric coronaviruses pose a significant challenge to the global pig industry, inflicting severe diarrhea and high mortality rates among piglets, and resulting in substantial economic losses. In our clinical practice, we observed that the addition of potassium molybdate (PM) to the feed could dramatically reduce diarrhea and diarrhea-related mortality in piglets. However, the underlying mechanisms remain elusive and merit further investigation. In this study, we revealed that PM effectively inhibited the infection of both aminopeptidase N (APN)-dependent coronaviruses, transmissible gastroenteritis virus (TGEV), and porcine respiratory coronavirus (PRCV), both Show less
no PDF DOI: 10.1128/jvi.01449-24
PIK3C3

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

A Multi-Task Self-Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors.

Xin Yang, Yang Wang, Ye Lin +4 more · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Studying the molecular properties of drugs and their interactions with human targets aids in better understanding the clinical performance of drugs and guides drug development. In computer-aided drug Show more
Studying the molecular properties of drugs and their interactions with human targets aids in better understanding the clinical performance of drugs and guides drug development. In computer-aided drug discovery, it is crucial to utilize effective molecular feature representations for predicting molecular properties and designing ligands with high binding affinity to targets. However, designing an effective multi-task and self-supervised strategy remains a significant challenge for the pretraining framework. In this study, a multi-task self-supervised deep learning framework is proposed, MTSSMol, which utilizes ≈10 million unlabeled drug-like molecules for pretraining to identify potential inhibitors of fibroblast growth factor receptor 1 (FGFR1). During the pretraining of MTSSMol, molecular representations are learned through a graph neural networks (GNNs) encoder. A multi-task self-supervised pretraining strategy is proposed to fully capture the structural and chemical knowledge of molecules. Extensive computational tests on 27 datasets demonstrate that MTSSMol exhibits exceptional performance in predicting molecular properties across different domains. Moreover, MTSSMol's capability is validated to identify potential inhibitors of FGFR1 through molecular docking using RoseTTAFold All-Atom (RFAA) and molecular dynamics simulations. Overall, MTSSMol provides an effective algorithmic framework for enhancing molecular representation learning and identifying potential drug candidates, offering a valuable tool to accelerate drug discovery processes. All of the codes are freely available online at https:// github.com/zhaoqi106/MTSSMol. Show less
📄 PDF DOI: 10.1002/advs.202412987
FGFR1

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

Aqueous Extract of

Andong Wu, Jiayi Dong, Jiankun Liu +10 more · 2025 · Nutrients · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/nu18010021
APOE