👤 Chia-Jen 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-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, Yupu Liu, Yuqi Liu, Yuqiang Liu, Yuqing Liu, Yurong Liu, Yuru Liu, Yusen Liu, Yutao Liu, Yutian Liu, Yuting Liu, Yutong Liu, Yuwei Liu, Yuxi Liu, Yuxia Liu, Yuxiang Liu, Yuxin Liu, Yuxuan Liu, Yuyan Liu, Yuyi Liu, Yuyu Liu, Yuyuan Liu, Yuzhen Liu, Yv-Xuan Liu, Z H Liu, Z Q Liu, Z Z Liu, Zaiqiang Liu, Zan Liu, Zaoqu Liu, Ze Liu, Zefeng Liu, Zekun Liu, Zeming Liu, Zengfu Liu, Zeyu Liu, Zezhou Liu, Zhangyu Liu, Zhangyuan Liu, Zhansheng Liu, Zhao Liu, Zhaoguo Liu, Zhaoli Liu, Zhaorui Liu, Zhaotian Liu, Zhaoxiang Liu, Zhaoxun Liu, Zhaoyang Liu, Zhe Liu, Zhekai Liu, Zheliang Liu, Zhen Liu, Zhen-Lin Liu, Zhendong Liu, Zhenfang Liu, Zhenfeng Liu, Zheng Liu, Zheng-Hong Liu, Zheng-Yu Liu, ZhengYi Liu, Zhengbing Liu, Zhengchuang Liu, Zhengdong Liu, Zhenghao Liu, Zhengkun Liu, Zhengtang Liu, Zhengting Liu, Zhenguo Liu, Zhengxia Liu, Zhengye Liu, Zhenhai Liu, Zhenhao Liu, Zhenhua Liu, Zhenjiang Liu, Zhenjiao Liu, Zhenjie Liu, Zhenkui Liu, Zhenlei Liu, Zhenmi Liu, Zhenming Liu, Zhenna Liu, Zhenqian Liu, Zhenqiu Liu, Zhenwei Liu, Zhenxing Liu, Zhenxiu Liu, Zhenzhen Liu, Zhenzhu Liu, Zhi Liu, Zhi Y Liu, Zhi-Fen Liu, Zhi-Guo Liu, Zhi-Jie Liu, Zhi-Kai Liu, Zhi-Ping Liu, Zhi-Ren Liu, Zhi-Wen Liu, Zhi-Ying Liu, Zhicheng Liu, Zhifang Liu, Zhigang Liu, Zhiguo Liu, Zhihan Liu, Zhihao Liu, Zhihong Liu, Zhihua Liu, Zhihui Liu, Zhijia Liu, Zhijie Liu, Zhikui Liu, Zhili Liu, Zhiming Liu, Zhipeng Liu, Zhiping Liu, Zhiqian Liu, Zhiqiang Liu, Zhiru Liu, Zhirui Liu, Zhishuo Liu, Zhitao Liu, Zhiteng Liu, Zhiwei Liu, Zhixiang Liu, Zhixue Liu, Zhiyan Liu, Zhiying Liu, Zhiyong Liu, Zhiyuan Liu, Zhong Liu, Zhong Wu Liu, Zhong-Hua Liu, Zhong-Min Liu, Zhong-Qiu Liu, Zhong-Wu Liu, Zhong-Ying Liu, Zhongchun Liu, Zhongguo Liu, Zhonghua Liu, Zhongjian Liu, Zhongjuan Liu, Zhongmin Liu, Zhongqi Liu, Zhongqiu Liu, Zhongwei Liu, Zhongyu Liu, Zhongyue Liu, Zhongzhong Liu, Zhou Liu, Zhou-di Liu, Zhu Liu, Zhuangjun Liu, Zhuanhua Liu, Zhuo Liu, Zhuoyuan Liu, Zi Hao Liu, Zi-Hao Liu, Zi-Lun Liu, Zi-Ye Liu, Zi-wen Liu, Zichuan Liu, Zihang Liu, Zihao Liu, Zihe Liu, Ziheng Liu, Zijia Liu, Zijian Liu, Zijing J Liu, Zimeng Liu, Ziqian Liu, Ziqin Liu, Ziteng Liu, Zitian Liu, Ziwei Liu, Zixi Liu, Zixuan Liu, Ziyang Liu, Ziying Liu, Ziyou Liu, Ziyuan Liu, Ziyue Liu, Zong-Chao Liu, Zong-Yuan Liu, Zonghua Liu, Zongjun Liu, Zongtao Liu, Zongxiang Liu, Zu-Guo Liu, Zuguo Liu, Zuohua Liu, Zuojin Liu, Zuolu Liu, Zuyi Liu, Zuyun Liu
articles

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

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

Association between polymorphisms of the adenylate cyclase 3 gene rs2241759 and the effect of high-intensity interval training on blood lipid profiles.

Junren Lai, Li Gong, Yan Liu +3 more · 2025 · PeerJ · added 2026-04-24
One of the recognized effects of systematic physical activity is the improvement of physical fitness, with a negative correlation found between physical fitness and cardiovascular and cardiometabolic Show more
One of the recognized effects of systematic physical activity is the improvement of physical fitness, with a negative correlation found between physical fitness and cardiovascular and cardiometabolic risk. The purpose of this study is to analyze the influence of single nucleotide polymorphisms (SNPs) of the adenylate cyclase 3 ( In the 12-week HIIT program, a total of 237 Chinese Han college students with non-regular exercise habits were recruited, and these volunteers participated in the training three times a week. Baseline and after the HIIT program, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were measured, respectively. DNA was extracted from the white blood cells of volunteers and genotyping was carried out. The PLINK v1.09 software was used to conduct quality control screening on the obtained SNPs, and a linear regression model was constructed to analyze the association between (1) Through the analysis of Illumina CGA chip scanning, a total of 22 SNPs of the (1) The implementation of a 12-week HIIT regimen can significantly enhance the blood lipid status of college students. (2) The locus rs2241759 of the Show less
📄 PDF DOI: 10.7717/peerj.19271
ADCY3

Combined Cold Exposure and Exercise Improves NAFLD: Mechanistic Insights.

Xue Geng, Zhijian Rao, Jianhong Zhang +7 more · 2025 · Medicine and science in sports and exercise · added 2026-04-24
Nonalcoholic fatty liver disease (NAFLD) affects a quarter of the global population and poses a remarkably serious threat to human health. The effect and potential molecular mechanisms of combined col Show more
Nonalcoholic fatty liver disease (NAFLD) affects a quarter of the global population and poses a remarkably serious threat to human health. The effect and potential molecular mechanisms of combined cold exposure and exercise intervention on NAFLD remain unclear. A high-fat diet-induced NAFLD mouse model was used. Twenty-four NAFLD mice were divided into three groups and subjected to cold exposure (5°C), regular-temperature exercise (22°C), or combined cold exposure and exercise (5°C) for 8 wk, 5 d·wk -1 , once daily for 1 h each session. Intervention effects were evaluated through bodyweight, liver mass, liver/bodyweight ratio, blood lipid profile, circulating fibroblast growth factor 21 (FGF21) levels, and liver histopathology. Immunoblotting and quantitative PCR were used to assess the protein and gene expression of liver FGF21, β-klotho, and FGFR1 to preliminarily elucidate the molecular mechanisms underlying NAFLD improvement by combined cold exposure and exercise. Compared with cold exposure or regular-temperature exercise alone, combined cold exposure and exercise significantly reduced the bodyweight, liver weight, and liver/bodyweight ratio in the NAFLD mice. The levels of blood lipids, circulating FGF21, and liver glycogen also significantly decreased. Furthermore, the combined intervention significantly reduced liver fat deposition and fibrosis and significantly increased the expression of FGFR1 and β-klotho proteins, suggesting the activation of the FGF21-β-klotho/FGFR1 signaling pathway. This preclinical study demonstrates that combined cold exposure and exercise synergistically alleviates NAFLD progression in animal models, primarily by activating the FGF21-β-klotho/FGFR1 pathway to enhance lipid metabolism and reduce liver injury. These findings highlight the translational potential of dual environmental and behavioral interventions, providing a mechanistic foundation for developing nonpharmacological therapies targeting metabolic pathways in humans, particularly for NAFLD patients resistant to conventional lifestyle modifications or pharmacotherapy. Show less
no PDF DOI: 10.1249/MSS.0000000000003719
FGFR1

Unveiling Biomarkers and Therapeutic Targets in Systemic Sclerosis and Lupus Erythematosus Through Transcriptomic Profiling.

Ang-Jun Liu, Jian-Ruei Ciou, Po-Chang Wu +3 more · 2025 · International journal of rheumatic diseases · Blackwell Publishing · added 2026-04-24
This study aims to investigate the molecular differences and commonalities between systemic sclerosis (SSc) and systemic lupus erythematosus (SLE) by analyzing RNA-sequencing (RNA-seq) data. By focusi Show more
This study aims to investigate the molecular differences and commonalities between systemic sclerosis (SSc) and systemic lupus erythematosus (SLE) by analyzing RNA-sequencing (RNA-seq) data. By focusing on differentially expressed genes and enriched pathways, the investigation seeks to identify unique biomarkers, shared pathways, and potential therapeutic targets for these autoimmune diseases. This study involved 10 patients with SSc and 24 with SLE who did not receive immunosuppressants. RNA-seq data from patients with SSc and SLE were analyzed using DESeq2 to identify differentially expressed genes. Functional and pathway enrichment analyses were conducted and comparative analyses were performed. We identified 2055 differentially expressed genes (DEGs) between patients with SSc and controls. Notably, the expression of the shared gene RGS5 was significantly downregulated in both SLE and SSc, with a more pronounced downregulation in SSc. Additionally, the expression of the key transcription factor EGR1 was upregulated in SSc, whereas that of BLK, ITGAM, and IFNG was upregulated in SLE. Network analysis identified hub genes-AP3D1, FTX, USP47, CUX1, ZC3H4, CAND1, INTS1, TRNT1, MTERF1, and SETD1B-that may play critical roles in the progression of both SLE and SSc. These findings suggest that RGS5 could serve as a shared biomarker for vascular dysfunction, while EGR1 and BLK may represent therapeutic targets in SSc and SLE. Overall, this analysis enhances understanding of distinct and overlapping gene expression signatures in SSc and SLE, providing a foundation for future targeted treatment strategies and requiring further validation in larger cohorts. Show less
no PDF DOI: 10.1111/1756-185X.70308
ZC3H4

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

Lipids, apolipoproteins, carbohydrates, and risk of hematological malignancies.

Qianwei Liu, Dang Wei, Niklas Hammar +6 more · 2025 · European journal of epidemiology · Springer · added 2026-04-24
Previous studies have investigated the role of metabolic factors in risk of hematological malignancies with contradicting findings. Existing studies are generally limited by potential concern of rever Show more
Previous studies have investigated the role of metabolic factors in risk of hematological malignancies with contradicting findings. Existing studies are generally limited by potential concern of reverse causality and confounding by inflammation. Therefore, we aimed to investigate the associations of glucose, lipid, and apolipoprotein biomarkers with the risk of hematological malignancy. We performed a study of over 560,000 individuals of the Swedish AMORIS cohort, with measurements of biomarkers for carbohydrate, lipid, and apolipoprotein metabolism during 1985-1996 and follow-up until 2020. We conducted a prospective cohort study and used Cox models to investigate the association of nine different metabolic biomarkers (glucose, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), LDL-C/HDL-C, triglyceride (TG), apolipoprotein B (ApoB), apolipoprotein A-I (ApoA I), and ApoB/ApoA-I) with risk of hematological malignancy, after excluding the first five years of follow-up and adjustment for inflammatory biomarkers. We observed a decreased risk of hematological malignancy associated with one SD increase of TC (HR 0.93; 95% CI 0.91-0.96), LDL-C (HR 0.94; 95% CI 0.91-0.97), HDL-C (HR 0.92; 95% CI 0.86-0.99), and ApoA-I (HR 0.96; 95% CI 0.93-0.996). Our study highlights a decreased risk of hematological malignancy associated with a higher level of TC, LDL-C, HDL-C, and ApoA-I. Show less
📄 PDF DOI: 10.1007/s10654-025-01207-y
APOB

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

AMPK/SIRT1/PGC-1α Signaling Pathway: Molecular Mechanisms and Targeted Strategies From Energy Homeostasis Regulation to Disease Therapy.

Junyang Chen, Boya Liu, Xinlei Yao +8 more · 2025 · CNS neuroscience & therapeutics · Blackwell Publishing · added 2026-04-24
The AMPK/SIRT1/PGC-1α pathway serves as a central regulator of cellular energy homeostasis, coordinating metabolic stress responses, epigenetic modifications, and transcriptional programs. Its dysfunc Show more
The AMPK/SIRT1/PGC-1α pathway serves as a central regulator of cellular energy homeostasis, coordinating metabolic stress responses, epigenetic modifications, and transcriptional programs. Its dysfunction is implicated in the pathogenesis of a wide spectrum of complex modern diseases, spanning neurodegeneration, metabolic syndromes, and chronic inflammatory conditions. This review examines the pathway's role as an integrative hub and its potential as a therapeutic target. We synthesize current mechanistic evidence from molecular, cellular, and preclinical studies to elucidate the pathway's operational logic and the consequences of its dysregulation. The analysis is structured around key disease paradigms-including Alzheimer's disease, Parkinson's disease, diabetes, cardiovascular injury, stroke, and chronic kidney disease-to dissect its tissue-specific pathophysiological impacts. The AMPK/SIRT1/PGC-1α axis operates through a core positive feedback loop: AMPK activation elevates NAD+, thereby activating SIRT1, which in turn deacetylates and activates PGC-1α to drive mitochondrial biogenesis and function, further reinforcing SIRT1 activity. Disruption of this cascade manifests in disease-specific mechanisms: promoting Aβ production via BACE1/γ-secretase in Alzheimer's; impairing α-synuclein clearance in Parkinson's; disrupting GLUT4 translocation and insulin signaling in diabetes; exacerbating oxidative damage and mitochondrial dysfunction in cardiovascular and neuronal injury; and accelerating fibrosis and sustained inflammation in renal and pulmonary diseases via NLRP3 and TGF-β/Smad3 signaling. The AMPK/SIRT1/PGC-1α pathway represents a cornerstone target at the intersection of metabolism, aging, and disease. Current therapeutic strategies-including pharmacological activators (e.g., metformin, SRT1720), natural compounds (e.g., resveratrol), lifestyle interventions (e.g., exercise, caloric restriction), and emerging technologies (e.g., gene editing, exosomal miRNAs)-offer multidimensional avenues for intervention. Future research must prioritize elucidating tissue-specific regulatory mechanisms, such as AMPK isoform diversity and PGC-1α interactome dynamics, to enable precision therapeutics and successful clinical translation for a range of complex disorders. Show less
📄 PDF DOI: 10.1111/cns.70657
BACE1

NSP6 regulates calcium overload-induced autophagic cell death and is regulated by KLHL22-mediated ubiquitination.

Xingyu Tao, Yanan Wang, Jiangbo Jin +10 more · 2025 · Journal of advanced research · Elsevier · added 2026-04-24
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a substantial global threat. SARS-CoV-2 nonstructural proteins (NSPs) are essential for impeding the host replication mechanism while Show more
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a substantial global threat. SARS-CoV-2 nonstructural proteins (NSPs) are essential for impeding the host replication mechanism while also assisting in the production and organization of new viral components. However, NSPs are not incorporated into viral particles, and their subsequent fate within host cells remains poorly understood. Additionally, their role in viral pathogenesis requires further investigation. This study aimed to discover the ultimate fate of NSP6 in host cells and to elucidate its role in viral pathogenesis. We investigated the effects of NSP6 on cell death and explored the underlying mechanism; moreover, we examined the degradation mechanism of NSP6 in human cells, along with analysing its correlation with coronavirus disease 2019 (COVID-19) severity in patient peripheral blood mononuclear cells (PBMCs). NSP6 was demonstrated to induce cell death. Specifically, NSP6 interacted with EI24 autophagy-associated transmembrane protein (EI24) to increase intracellular Ca This study reveals that KLHL22-mediated ubiquitination controls NSP6 stability and that NSP6 induces autophagic cell death via calcium overload, highlighting its cytotoxic role and suggesting therapeutic strategies that target calcium signaling or promote NSP6 degradation as potential interventions against COVID-19. Show less
no PDF DOI: 10.1016/j.jare.2025.05.031
PIK3C3

Osteoclast-derived arachidonic acid triggers dormant lung adenocarcinoma cell activation.

Xingyu Liu, Rong Qiu, Pengcheng Gui +7 more · 2025 · iScience · Elsevier · added 2026-04-24
Dormant lung adenocarcinoma (LUAD) cells in the bone microenvironment can re-emerge as metastatic disease through osteoclast interactions. Using a 3D dormancy model and a mouse bone metastasis model, Show more
Dormant lung adenocarcinoma (LUAD) cells in the bone microenvironment can re-emerge as metastatic disease through osteoclast interactions. Using a 3D dormancy model and a mouse bone metastasis model, this study reveals that arachidonic acid (AA) is the initiating molecule transferred from osteoclasts to dormant LUAD cells, triggering their activation. Dormant LUAD cells uptake AA through CD36, which activates the PPARγ-ANGPTL4 pathway and activates tumor cells. There is a dose-response relationship in the activation effect of AA, and inhibiting AA metabolism prevents this reactivation. The study also finds that the serum levels of AA and ANGPTL4 are significantly elevated in patients with clinical bone metastases compared to those without. This research confirms that osteoclasts transmit AA via the CD36-PPARγ-ANGPTL4 axis to activate dormant LUAD cells, suggesting that AA and ANGPTL4 may serve as valuable biomarkers and potential clinical applications in treatment and prediction of LUAD bone metastasis. Show less
📄 PDF DOI: 10.1016/j.isci.2025.112167
ANGPTL4

PNPLA3-I148M is a neomorph that interferes with two primary hepatic triglyceride clearance pathways.

David J Sherman, Lei Liu, Edward L LaGory +8 more · 2025 · Cell reports · Elsevier · added 2026-04-24
The common variant PNPLA3-I148M, globally, is the most significant genetic risk factor for fatty liver disease. However, it is unclear precisely how I148M drives disease risk. Using human hepatoma cel Show more
The common variant PNPLA3-I148M, globally, is the most significant genetic risk factor for fatty liver disease. However, it is unclear precisely how I148M drives disease risk. Using human hepatoma cells expressing endogenous I148M, we find that the variant impairs cellular secretion of apolipoprotein B (ApoB), the scaffolding protein of very-low-density lipoprotein (VLDL). This is not due to loss-of-function of wild-type PNPLA3. Expression of human I148M in primary hepatocytes and mice also hinders VLDL secretion. Lipidomic profiling reveals a shift from polyunsaturated phosphatidylcholine to polyunsaturated triglycerides in I148M cells, reducing membrane fluidity and, concomitantly, VLDL biogenesis. ApoB secretion is substantially rescued in I148M cells overexpressing ABHD5/CGI-58, an I148M-binding partner that normally activates ATGL/PNPLA2-mediated triglyceride lipolysis. Conversely, knocking down CGI-58 or PNPLA2 mimics I148M. We propose that I148M is a neomorph that exacerbates fatty liver risk by simultaneously impeding two major CGI-58-dependent pathways for liver triglyceride clearance: lipolysis and secretion. Show less
no PDF DOI: 10.1016/j.celrep.2025.116371
APOB

Stenotrophomonas muris-first discovered as a potential human pathogen with strong virulence and antibiotic resistance, associated with bloodstream infections.

Jiaying Liu, Xu Dong, Yanghui Xiang +8 more · 2025 · Microbiology spectrum · added 2026-04-24
For the first time,
📄 PDF DOI: 10.1128/spectrum.02770-24
ANGPTL4

Correlation between lipid metabolism levels and pregnancy outcomes.

Jingjing Guo, Haifan Qiu, Jianping Wang +3 more · 2025 · Frontiers in medicine · Frontiers · added 2026-04-24
To establish the reference interval for the serum lipid index in pregnant women and to explore the relationship between lipid metabolism levels and pregnancy outcomes. Data were derived from 446 pregn Show more
To establish the reference interval for the serum lipid index in pregnant women and to explore the relationship between lipid metabolism levels and pregnancy outcomes. Data were derived from 446 pregnancy women and 317 healthy non-pregnant women. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein (a) [Lp(a)], and hypersensitive C-reactive protein (hs-CRP) were measured in both groups. The mean and standard deviation of each index were calculated to establish the reference range of normal serum lipid levels in pregnant women in mid-to-late pregnancy. The associations between serum lipid levels and perinatal outcomes were assessed statistically. There were no significant differences in age, pregnancy, or parity between the adverse outcome and normal delivery groups, but the caesarean section rate was significantly higher in the adverse outcome group. The levels of hs-CRP, TG, TC, HDL-C, LDL-C, and ApoA1 were significantly higher in the adverse outcome group. Elevated hs-CRP, TG, and HDL-C levels were risk factors for adverse pregnancy outcomes. According to the receiver operating characteristic curve, the optimal threshold of the combined diagnosis of these three indicators to predict adverse pregnancy outcomes was 0.534, and the area under the curve was 0.822. The establishment of lipid reference intervals in the second and third trimesters of pregnancy can effectively evaluate lipid metabolism in pregnant women, and the measurement of lipid metabolism in pregnant women is helpful in predicting adverse pregnancy outcomes. Show less
📄 PDF DOI: 10.3389/fmed.2025.1530525
APOB

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

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

Insights Into Causal Effects of Genetically Proxied Lipids and Lipid-Modifying Drug Targets on Cardiometabolic Diseases.

Liwan Fu, Qin Liu, Hong Cheng +3 more · 2025 · Journal of the American Heart Association · added 2026-04-24
The differential impact of serum lipids and their targets for lipid modification on cardiometabolic disease risk is debated. This study used Mendelian randomization to investigate the causal relations Show more
The differential impact of serum lipids and their targets for lipid modification on cardiometabolic disease risk is debated. This study used Mendelian randomization to investigate the causal relationships and underlying mechanisms. Genetic variants related to lipid profiles and targets for lipid modification were sourced from the Global Lipids Genetics Consortium. Summary data for 10 cardiometabolic diseases were compiled from both discovery and replication data sets. Expression quantitative trait loci data from relevant tissues were employed to evaluate significant lipid-modifying drug targets. Comprehensive analyses including colocalization, mediation, and bioinformatics were conducted to validate the results and investigate potential mediators and mechanisms. Significant causal associations were identified between lipids, lipid-modifying drug targets, and various cardiometabolic diseases. Notably, genetic enhancement of LPL (lipoprotein lipase) was linked to reduced risks of myocardial infarction (odds ratio [OR] The study substantiates the causal role of lipids in specific cardiometabolic diseases, highlighting LPL as a potent drug target. The effects of LPL are suggested to be influenced by changes in glucose and blood pressure, providing insights into its mechanism of action. Show less
📄 PDF DOI: 10.1161/JAHA.124.038857
LPL

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

The therapeutic potential of recombinant ANGPTL4 in Parkinson's disease: Evidence from in vivo and in vitro studies.

Hualing Li, Junjie Wei, Zhiyi Zheng +6 more · 2025 · Free radical biology & medicine · Elsevier · added 2026-04-24
The established body of knowledge attests to the pivotal influence of ANGPTL4 on lipid metabolism and vascular biology. Nevertheless, its potential implication in neurodegenerative disease remains to Show more
The established body of knowledge attests to the pivotal influence of ANGPTL4 on lipid metabolism and vascular biology. Nevertheless, its potential implication in neurodegenerative disease remains to be fully characterized. The present investigation delves into the involvement of ANGPTL4 in the pathological progression of PD, both in vitro and in vivo. PD models were induced by intraperitoneal administration of MPTP and LPS in WT and ANGPTL4 The observations unveiled that ANGPTL4 deficiency exacerbated behavioral aberrations, intensified dopaminergic neuron loss, and stimulated microglial activation along with p21-dependent senescence. There was an elevation in the expression of proinflammatory cytokines in the PD model. Furthermore, the administration of rANGPTL4 protein reversed the observed phenotypes in ANGPTL4 Our findings posit a salutary role for ANGPTL4 in counteracting PD, rendering it a prospective therapeutic target for the development of innovative drugs aimed at treating neuroinflammation-associated neurological diseases, including PD. Show less
no PDF DOI: 10.1016/j.freeradbiomed.2024.12.009
ANGPTL4

Single-cell transcriptomic profiling reveals liver fibrosis in colorectal cancer liver metastasis.

Yiqiao Deng, Chengyao Guo, Xiaomeng Liu +14 more · 2025 · Experimental & molecular medicine · Nature · added 2026-04-24
Tumor fibrosis is recognized as a malignant hallmark in various solid tumors; however, the clinical importance and associated molecular characteristics of tumor fibrosis in liver metastases (LM) from Show more
Tumor fibrosis is recognized as a malignant hallmark in various solid tumors; however, the clinical importance and associated molecular characteristics of tumor fibrosis in liver metastases (LM) from colorectal cancer (CRLM) remain poorly understood. Here we show that patients with CRLM whose liver metastases (LM) exhibited tumor fibrosis (Fibrosis+ LM) had significantly worse progression-free survival (P = 0.025) and overall survival (P = 0.008). Single-cell RNA sequencing revealed that the tumor microenvironment of the Fibrosis+ LM was characterized by T cells with an exhausted phenotype, macrophages displaying a profibrotic and suppressive phenotype and fibrosis-promoting fibroblasts. Further investigation highlighted the pivotal role of VCAN_eCAF in remodeling the tumor fibrosis in the tumor microenvironment of Fibrosis+ LM, emphasizing potential targetable interactions such as FGF23 or FGF3-FGFR1. Validation through multiplex immunohistochemistry/immunofluorescence and spatial transcriptomics supported these findings. Here we present a comprehensive single-cell atlas of tumor fibrosis in LM, revealing the intricate multicellular environment and molecular features associated with it. These insights deepen our understanding of tumor fibrosis mechanisms and inform improved clinical diagnosis and treatment strategies. Show less
📄 PDF DOI: 10.1038/s12276-025-01573-3
FGFR1

A novel piperine derivative HJJ₃₅ inhibits colorectal cancer progression by modulating EMT signaling pathways.

Wenhao Cheng, Shunfang Liu, Jingliang He +8 more · 2025 · Biochemical and biophysical research communications · Elsevier · added 2026-04-24
Colorectal cancer (CRC) is a fatal cancer prevalent worldwide, and epithelial-mesenchymal transition (EMT) is a key factor in tumor invasion and metastasis. Piperine, a natural alkaloid known for its Show more
Colorectal cancer (CRC) is a fatal cancer prevalent worldwide, and epithelial-mesenchymal transition (EMT) is a key factor in tumor invasion and metastasis. Piperine, a natural alkaloid known for its antitumor properties, faces limitations in clinical use due to its moderate potency. To address this, our team synthesized and validated a new derivative, HJJ₃₅, which has shown potent antitumor activity against CRC cells. We assessed HJJ₃₅'s inhibitory effects on the colon cancer cell line HCT116 through MTT, colony formation, and assays for cell migration and invasion. To uncover HJJ₃₅'s molecular mechanisms, we utilized transcriptomics, weighted gene co-expression network analysis (WGCNA), and machine learning to identify key EMT-related genes. Western blot and immunofluorescence experiments confirmed the expression changes of these key proteins. Our findings indicate that HJJ₃₅ significantly suppressed the proliferation, migration, and invasion of HCT116 cells in vitro, outperforming piperine. We discovered that HJJ₃₅ downregulated the expression of COL12A1, PJA2, VCAN, MEF2C, DPYD, and DDR2 genes in HCT116 cells, which resulted in a decrease in the EMT regulator SNAI1, thus inhibiting EMT in these cells. In summary, this study presents novel evidence that the piperine derivative HJJ₃₅ inhibits the migration and invasion of colorectal cancer cells through SNAI1-mediated EMT. Show less
no PDF DOI: 10.1016/j.bbrc.2025.151323
SNAI1

MicroRNA-6084 orchestrates angiogenesis and liver metastasis in colorectal cancer via extracellular vesicles.

Yang Zhang, Xuyang Yang, Su Zhang +10 more · 2025 · JCI insight · added 2026-04-24
The prognosis for colorectal cancer (CRC) patients with liver metastasis remains poor, and the molecular mechanisms driving CRC liver metastasis are not fully understood. Tumor-derived hypoxia-induced Show more
The prognosis for colorectal cancer (CRC) patients with liver metastasis remains poor, and the molecular mechanisms driving CRC liver metastasis are not fully understood. Tumor-derived hypoxia-induced extracellular vesicles have emerged as key players in inducing angiogenesis by transferring noncoding RNAs. However, the specific role of CRC-derived hypoxic extracellular vesicles (H-EVs) in regulating premetastatic microenvironment (PMN) formation by inducing angiogenesis remains unclear. Our study demonstrates that H-EVs induce angiogenesis and liver metastasis. Through microRNA microarray analysis, we identified a reduction in miR-6084 levels within H-EVs. We found that miR-6084 inhibited angiogenesis by being transferred to endothelial cells via EVs. In endothelial cells, miR-6084 directly targeted angiopoietin like 4 (ANGPTL4) mRNA, thereby suppressing angiogenesis through the ANGPTL4-mediated JAK2/STAT3 pathway. Furthermore, we uncovered that specificity protein 1 (SP1) acted as a transcription factor regulating miR-6084 transcription, while hypoxia-inducible factor 1A (HIF1A) decreased miR-6084 expression by promoting SP1 protein dephosphorylation and facilitating ubiquitin-proteasome degradation in SW620 cells. In clinical samples, we observed low expression of miR-6084 in plasma-derived EVs from CRC patients with liver metastasis. In summary, our findings suggest that CRC-derived H-EVs promote angiogenesis and liver metastasis through the HIF1A/SP1/miR-6084/ANGPTL4 axis. Additionally, miR-6084 holds promise as a diagnostic and prognostic biomarker for CRC liver metastasis. Show less
📄 PDF DOI: 10.1172/jci.insight.189503
ANGPTL4

Pharmacogenetic study of antipsychotic-induced lipid and BMI changes in Chinese schizophrenia patients: A Genome-Wide Association Study.

Kenneth Chi-Yin Wong, Perry Bok-Man Leung, Benedict Ka-Wa Lee +8 more · 2025 · Translational psychiatry · Nature · added 2026-04-24
Second-generation antipsychotics (SGAs) are widely used to treat schizophrenia (SCZ), but they often induce metabolic side effects like dyslipidemia and obesity. We conducted genome-wide association s Show more
Second-generation antipsychotics (SGAs) are widely used to treat schizophrenia (SCZ), but they often induce metabolic side effects like dyslipidemia and obesity. We conducted genome-wide association studies (GWASs) to identify genetic variants associated with SGA-induced lipid and BMI changes in Chinese SCZ patients. A longitudinal cohort of Chinese SCZ receiving SGAs was followed for up to 18.7 years (mean = 5.7 years, SD = 3.3 years). We analysed the patients' genotypes (N = 669), lipid profiles, and BMI using 19 316 prescription records and 3 917 to 7 596 metabolic measurements per outcome. Linear mixed models were employed to evaluate seven SGAs' random effects on metabolic changes for each patient, followed by GWAS and gene set analyses with Bonferroni and FDR correction. Five SNPs achieved p-value < 5 × 10 Show less
📄 PDF DOI: 10.1038/s41398-025-03499-w
APOA5

Dietary Tannic Acid Promotes Growth Performance and Resistance Against

Liqin Ji, Yisen Shangguan, Chen Chen +6 more · 2025 · Antioxidants (Basel, Switzerland) · MDPI · added 2026-04-24
To investigate the effect of tannic acid (TA) on the growth, disease resistance, and intestinal health of Chinese soft-shelled turtles, individual turtles were fed with 0 g/kg (CG), 0.5 g/kg, 1 g/kg, Show more
To investigate the effect of tannic acid (TA) on the growth, disease resistance, and intestinal health of Chinese soft-shelled turtles, individual turtles were fed with 0 g/kg (CG), 0.5 g/kg, 1 g/kg, 2 g/kg, and 4 g/kg TA diets for 98 days. Afterwards, the turtles' disease resistance was tested using Show less
📄 PDF DOI: 10.3390/antiox14010112
APOA5

Integrative analysis of serum lipids and chronic gastritis: causal insights from mendelian randomization and experimental models.

Xinqiao Chu, Yaning Biao, Hongzheng Li +9 more · 2025 · Lipids in health and disease · BioMed Central · added 2026-04-24
Lipid metabolism may be linked to chronic gastritis, but its causal role remains unclear. While current research emphasizes inflammation, mucosal changes, immune regulation, genetics, and the gut micr Show more
Lipid metabolism may be linked to chronic gastritis, but its causal role remains unclear. While current research emphasizes inflammation, mucosal changes, immune regulation, genetics, and the gut microbiota, the contribution of lipid metabolism is understudied. This study aims to evaluate the impact of serum lipids and the mechanistic roles of lipid-lowering drug targets in chronic gastritis. We conducted a cross-sectional study using data from real world. Multivariable logistic regression was performed to assess the association between serum lipid profiles and gastritis. Mendelian randomization (MR) analyses based on genome-wide association study (GWAS) datasets were performed to detect the causal relationship of serum lipids, plasma lipid species, and lipid-lowering drug targets. Experimental validation was conducted using high-fat diet (HFD)-fed mice and chemically induced CAG rat models. Four thousand sixty one person, including 1,023 patients with chronic atrophic gastritis (CAG), 1,742 with non-atrophic gastritis (NAG), and 1,296 as healthy population were included in the analysis. Through covariates adjustment, TC, ApoA1, and HDL-C showed to be associated with an increased risk of chronic gastritis, whereas TG exhibited a protective effect. MR analysis confirmed a significant inverse causal relationship between TG and gastritis (OR = 0.889, 95% CI: 0.825-0.958). Ten plasma lipid species and lipid-lowering gene targets, including LPL and APOC3, were identified as causally associated with disease risk. Mediation analysis revealed six plasma lipid species as potential intermediaries linking genetic variation to gastritis. In vivo experiments demonstrated progressive hepatic steatosis and mild gastric mucosal changes in HFD-fed mice. Immunohistochemical analysis further revealed a significant reduction in LPL and APOC3 expression in gastric tissue (P < 0.05). In the CAG rat model, histological analysis revealed hepatocyte disarray, edema, and gastric mucosal atrophy. Elevated levels of TNF-α, IL-6, IL-1β and decreased levels of GAS-17 and PG I/II were also observed (P < 0.05). Western blot analyses further confirmed the downregulation of LPL and APOC3 expression in gastric tissue (P < 0.05). This study provides genetic and experimental evidence, supporting a causal role of lipid metabolism in chronic gastritis. LPL and APOC3 are implicated in its pathogenesis, highlighting potential lipid-targeted strategies for prevention and treatment. Show less
📄 PDF DOI: 10.1186/s12944-025-02782-5
APOC3

Genotype and phenotype correlation analysis in 27 families with multiple osteochondroma and validation by ATDC5 chondrocyte models.

XiaoYan Guo, Mingrui Lin, Shan Xu +4 more · 2025 · Bone & joint research · added 2026-04-24
This study aimed to explore the genotype and phenotype correlation of patients with multiple osteochondroma (MO), and validate phenotypic differences in ATDC5 cell model with Mutation analysis was emp Show more
This study aimed to explore the genotype and phenotype correlation of patients with multiple osteochondroma (MO), and validate phenotypic differences in ATDC5 cell model with Mutation analysis was employed in 27 families with MO using polymerase chain reaction (PCR)-Sanger sequencing and targeted next-generation sequencing (t-NGS). ATDC5 cell model with A total of 27 pathogenic mutations were identified in Clinical research identified nine novel mutations in Show less
📄 PDF DOI: 10.1302/2046-3758.1410.BJR-2024-0477.R1
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

Establishment of an induced pluripotent stem cell line (HMUCPi001-A) from a hypertrophic cardiomyopathy patient carrying MYBPC3 c.3072C > A mutation.

Dongping Liu, Mingyu Yang, Shasha Fan +6 more · 2025 · Stem cell research · Elsevier · added 2026-04-24
Hypertrophic cardiomyopathy (HCM) is an inherited cardiovascular disorder characterized by left ventricular hypertrophy and an elevated risk of sudden cardiac death. Cardiac myosin binding protein C ( Show more
Hypertrophic cardiomyopathy (HCM) is an inherited cardiovascular disorder characterized by left ventricular hypertrophy and an elevated risk of sudden cardiac death. Cardiac myosin binding protein C (MYBPC3) is the most frequently mutated gene leading to HCM. In this study, peripheral blood mononuclear cells isolated from an HCM patient harboring a heterozygous MYBPC3 missense mutation (c.3072C > A; p.S1024R) were reprogrammed via Sendai virus vectors to generate a patient-specific induced pluripotent stem cell (iPSC) line. The iPSC line exhibits normal morphology and karyotype, alongside definitive hallmarks of pluripotency, including trilineage differentiation potential. Show less
no PDF DOI: 10.1016/j.scr.2025.103841
MYBPC3

Identification of SEC16B as a novel regulator of glucose homeostasis.

Ruo-Xin Zhang, An-Qi Li, Xin-Yuan Zhao +7 more · 2025 · Diabetologia · Springer · added 2026-04-24
Glucose homeostasis, essential for metabolic health, requires coordinated insulin and glucagon activity to maintain blood glucose balance. Dysregulation of glucose homeostasis causes hyperglycaemia an Show more
Glucose homeostasis, essential for metabolic health, requires coordinated insulin and glucagon activity to maintain blood glucose balance. Dysregulation of glucose homeostasis causes hyperglycaemia and glucose intolerance, hallmark features of type 2 diabetes. While SEC16 homologue B (SEC16B), an endoplasmic reticulum export factor, has been linked to obesity, type 2 diabetes and lipid metabolism, its role in glucose regulation remains poorly defined. This study aims to investigate SEC16B's contribution to glucose homeostasis by systematically dissecting its conserved physiological mechanisms across species. To interrogate SEC16B's role, we combined Drosophila genetics (RNA interference-mediated dSec16 knockdown) with murine models (Sec16b deletion) under standard or high-fat diet conditions. Glucose and insulin tolerance tests assessed glucose homeostasis. Mechanistic insights into beta cell dysfunction were derived from immunostaining, glucose-stimulated insulin secretion assays and RNA-seq profiling of murine pancreatic islets. Both disruption of dSec16 in Drosophila and Sec16b deletion in mice triggered glucose intolerance under standard diet conditions, recapitulating conserved metabolic dysfunction. In addition, Sec16b loss impaired glycaemic control in mice fed a high-fat diet. Mechanistically, Sec16b deficiency impairs insulin secretion by downregulating cholinergic signalling and compromising intracellular Ca Our study reveals SEC16B, a genome-wide association study-identified obesity risk gene, as an evolutionarily conserved regulator of glucose homeostasis. By linking SEC16B to cholinergic-driven insulin secretion and calcium dynamics, we resolve a mechanistic gap in beta cell dysfunction and metabolic disease. This finding provides novel insights into the mechanisms underlying glucose homeostasis and may enhance our understanding of potential treatments for metabolic diseases. Show less
no PDF DOI: 10.1007/s00125-025-06501-8
SEC16B

Cold-resistant lactic acid bacteria in Jerusalem artichoke silage: quality, microbiome and metabolome dynamics during aerobic exposure on the Qinghai-Tibet Plateau.

Xiaoqiang Wei, Lihui Wang, Haiwang Zhang +6 more · 2025 · Frontiers in microbiology · Frontiers · added 2026-04-24
Forage scarcity during the cold season poses a major challenge to livestock farming on the Qinghai-Tibet Plateau. Jerusalem artichoke (
📄 PDF DOI: 10.3389/fmicb.2025.1699658
LPL

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

SMURF2 inhibition attenuates cardiac hypertrophy through blocking ubiquitination degradation of AXIN1.

Ying Kang, Yan Zu, Qi-Yao Fan +6 more · 2025 · Acta pharmacologica Sinica · Nature · added 2026-04-24
Cardiac hypertrophy as one of the major predisposing factors for chronic heart failure lacks effective interventions. It has been shown that protein ubiquitination plays an important role in cardiac h Show more
Cardiac hypertrophy as one of the major predisposing factors for chronic heart failure lacks effective interventions. It has been shown that protein ubiquitination plays an important role in cardiac hypertrophy. SMURF2 (SMAD-specific E3 ubiquitin ligase 2) is an important member of NEDD4 (neuronal precursor cell expressed developmentally downregulated 4) family of HECT E3 ubiquitin ligases. In this study we investigated the regulatory role of SMURF2 in cardiac hypertrophy. Experiment models were established in mice by transverse aortic constriction (TAC) in vivo, as well as in neonatal rat cardiomyocytes (NRCMs) by treatment with angiotensin II (Ang II, 1 μM) in vitro. We showed that the expression levels of SMURF2 were significantly elevated in cardiac tissues from patients with cardiac hypertrophy and the two experiment models. In NRCMs, SMURF2 knockdown or treatment with a specific SMURF2 inhibitor heclin (8 μM) significantly inhibited Ang II-induced cardiomyocyte hypertrophy, evidenced by reduced mRNA levels of Anp, Bnp and β-Mhc as well as cell surface. Prophylactic or therapeutic administration of heclin (10 mg·kg Show less
no PDF DOI: 10.1038/s41401-025-01597-5
AXIN1