👤 Yanfen Liu

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3182
Articles
1983
Name variants
Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Chih Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Huazhen Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, 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

Perfluorooctanoic acid (PFOA) disrupts cardiac performance of thick-shell mussel by inserting into the binding pocket of proliferator-activated receptor gamma and thereby causing lipid metabolism disorders.

Yingying Yu, Kuankuan Yuan, Difei Tong +7 more · 2025 · Environmental pollution (Barking, Essex : 1987) · Elsevier · added 2026-04-24
Invertebrates constitute the largest group of animals on Earth, accounting for approximately 97 % of all animal species. Although the heart of invertebrates could be a sensitive target for environment Show more
Invertebrates constitute the largest group of animals on Earth, accounting for approximately 97 % of all animal species. Although the heart of invertebrates could be a sensitive target for environmental pollution, potential cardiotoxicity for most contaminants has received little attention. In this study, perfluorooctanoic acid (PFOA) and thick-shell mussels (Mytilus coruscus) were used to investigate the effect of PFOA on cardiac performance and the potential underlying mechanisms. Heart beat monitoring demonstrated that four-week exposure to 0.5 and 5.0 μg/L of PFOA resulted in bradycardia and arrhythmia in thick-shell mussels. Moreover, considerably more triglyceride (TG) accumulation, higher lipoprotein lipase (LPL) and lipase (LPS) activities, and disruption of lipid metabolism-related genes were observed in the hearts of PFOA-exposed mussels. In addition, comparable adverse impacts were detected in mussels treated with proliferator-activated receptor gamma (PPARγ) agonist whereas the PFOA-induced effects were fully or partially alleviated by PPARγ antagonist. Furthermore, molecular docking and molecular dynamics simulation revealed a high binding affinity of PFOA to the PPARγ of 12 invertebrates, including thick-shell mussels. In general, our data suggest that PFOA may pose a severe threat to cardiac performance of invertebrate species by inserting into the binding pocket of PPARγ, and thereby causing cardiac lipid metabolism disorders. Show less
no PDF DOI: 10.1016/j.envpol.2025.126369
LPL

Retraction Note: Interleukin-27 ameliorates coxsackievirus-B3-induced viral myocarditis by inhibiting Th17 cells.

Hengshan Zhu, Chuang Lou, Ping Liu · 2025 · Virology journal · BioMed Central · added 2026-04-24
📄 PDF DOI: 10.1186/s12985-025-02824-5
IL27

LXRα agonists ameliorates acute rejection after liver transplantation via ABCA1/MAPK and PI3K/AKT/mTOR signaling axis in macrophages.

Xiaoyan Qin, Dingheng Hu, Qi Li +6 more · 2025 · Molecular medicine (Cambridge, Mass.) · BioMed Central · added 2026-04-24
Liver X receptor α (LXRα) plays an important role in inflammatory immune response induced by hepatic ischemia-reperfusion injury (IRI) and acute rejection (AR). Macrophage M1-polarization play an impo Show more
Liver X receptor α (LXRα) plays an important role in inflammatory immune response induced by hepatic ischemia-reperfusion injury (IRI) and acute rejection (AR). Macrophage M1-polarization play an important role in the occurrence and development of AR. Although the activation of LXR has anti-inflammatory effects, the role of LXRα in AR after liver transplantation (LT) has not been elucidated. We aimed to investigate LXRα anti-inflammatory and macrophage polarization regulation effects and mechanisms in acute rejection rat models. LXRα anti-inflammatory and liver function protective effects was initially measured in primary Kupffer cells and LT rat models. Subsequently, a flow cytometry assay was used to detect the regulation effect of LXRα in macrophage polarization. HE staining, TUNEL and ELISA were used to evaluate the co-treatment effects of TO901317 and tacrolimus on hepatic apoptosis and liver acute rejection after LT. In this study, we found that LPS can inhibit the expression of LXRα and activate MAPK pathway and PI3K/AKT/mTOR. We also found that LXRα agonist (TO901317) could improve liver function and rat survival after LT by activating the level of ABCA1 and inhibiting MAPK. TO901317 could inhibit macrophage M1-polarization by activating PI3K/AKT/mTOR signal pathway to improve the liver lesion of AR rats after liver transplantation. Additionally, co-treatment with TO901317 and tacrolimus more effectively alleviated the damaging effects of AR following LT than either drug alone. Our results suggest that the activation of LXRα can improve liver function and rat survival after LT by regulate ABCA1/MAPK and PI3K/AKT/mTOR signaling axis in macrophages. Show less
no PDF DOI: 10.1186/s10020-025-01153-1
NR1H3

[Mechanism of elaidic acid-induced lipid accumulation in vascular smooth muscle cells].

Chenyang Xiao, Shuang Song, Jiyong Yin +4 more · 2025 · Wei sheng yan jiu = Journal of hygiene research · added 2026-04-24
To investigate the molecular mechanisms underlying EA(elaidic acid)-induced lipid accumulation in VSMCs(vascular smooth muscle cells). CCK-8 assay determined the effects of EA(0-2.8 mmol/L) on MOVAS(m Show more
To investigate the molecular mechanisms underlying EA(elaidic acid)-induced lipid accumulation in VSMCs(vascular smooth muscle cells). CCK-8 assay determined the effects of EA(0-2.8 mmol/L) on MOVAS(murine aortic vascular smooth muscle cells)to select experimental concentrations. Oil Red O staining combined with quantitative lipid droplet analysis was conducted to examine the effects of EA on intracellular lipid droplet accumulation. Intracellular total cholesterol(TC) and triglyceride(TG) levels were quantified spectrophotometrically to assess EA's effects on intracellular lipid levels. Western blot analyzed protein expression of PPARγ, LXRα, ABCA1, and ABCG1 to delineate EA's pro-foamogenic mechanism. EA dose-dependently suppressed MOVAS viability(P<0.01). EA-treated groups exhibited significant increases in lipid droplet area/number and TC/TG content versus controls(P<0.01). EA downregulated PPARγ and LXRα protein expression(P<0.05), subsequently suppressing downstream targets ABCA1 and ABCG1(P<0.05). EA disrupts lipid metabolism in VSMCs by inhibiting the PPARγ-LXRα-ABCA1/ABCG1 signaling pathway, thereby inducing lipid accumulation and promoting foam cell formation. Show less
no PDF DOI: 10.19813/j.cnki.weishengyanjiu.2025.04.021
NR1H3

Recent advances in immunotherapy targeting CETP proteins for atherosclerosis prevention.

Wenhui Hu, Han Feng, Ying Liu +8 more · 2025 · Human vaccines & immunotherapeutics · Taylor & Francis · added 2026-04-24
Cholesteryl ester transfer protein (CETP) plays a key role in lipoprotein metabolism, and its activity has been linked to the risk of atherosclerosis (AS). CETP inhibitors, such as obicetrapib, repres Show more
Cholesteryl ester transfer protein (CETP) plays a key role in lipoprotein metabolism, and its activity has been linked to the risk of atherosclerosis (AS). CETP inhibitors, such as obicetrapib, represent a novel approach in immunotherapy to reduce the risk of atherosclerotic cardiovascular disease (ASCVD) by targeting lipid metabolism. In addition, CETP vaccines are being explored as a novel strategy for the prevention and treatment of ASCVD by inducing the body to produce antibodies against CETP, which is expected to reduce CETP activity, thereby increasing high-density lipoproteins (HDL) levels. This paper provides a comprehensive overview of the structure of CETP, the mechanisms of lipid transfer and the progress of immunotherapy in the last decade, which provides possible ideas for future development of novel drugs and optimization of immunization strategies. Show less
📄 PDF DOI: 10.1080/21645515.2025.2462466
CETP

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

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

Absence of the intracellular lipolytic inhibitor G0S2 enhances intravascular triglyceride clearance and abolishes diet-induced hypertriglyceridemia.

Yongbin Chen, Scott M Johnson, Stephanie D Burr +4 more · 2025 · The Journal of clinical investigation · added 2026-04-24
The interplay between intracellular and intravascular lipolysis is crucial for maintaining circulating lipid levels and systemic energy homeostasis. Adipose triglyceride lipase (ATGL) and lipoprotein Show more
The interplay between intracellular and intravascular lipolysis is crucial for maintaining circulating lipid levels and systemic energy homeostasis. Adipose triglyceride lipase (ATGL) and lipoprotein lipase (LPL), the primary triglyceride (TG) lipases responsible for these two spatially separate processes, are highly expressed in adipose tissue. Yet the mechanisms underlying their coordinated regulation remain undetermined. Here, we demonstrate that genetic ablation of G0S2, a specific inhibitory protein of ATGL, completely abolished diet-induced hypertriglyceridemia and significantly attenuated atherogenesis in mice. These effects were attributable to enhanced whole-body TG clearance, not altered hepatic TG secretion. Specifically, G0S2 deletion increased circulating LPL concentration and activity, predominantly through LPL production from white adipose tissue (WAT). Strikingly, transplantation of G0S2-deficient WAT normalized plasma TG levels in mice with hypertriglyceridemia. In conjunction with improved insulin sensitivity and decreased ANGPTL4 expression, the absence of G0S2 enhanced the stability of LPL protein in adipocytes, a phenomenon that could be reversed upon ATGL inhibition. Collectively, these findings highlight the pivotal role of adipocyte G0S2 in regulating both intracellular and intravascular lipolysis, and the possibility of targeting G0S2 as a viable pharmacological approach to reducing levels of circulating TGs. Show less
📄 PDF DOI: 10.1172/JCI181754
ANGPTL4

Machine learning models for predicting short-term progression in patients with stage 4 chronic kidney disease: a multi-center validation study.

Jingshu Li, Xuanyi Du, Rui Zhang +7 more · 2025 · Scientific reports · Nature · added 2026-04-24
End-stage renal disease (ESRD) is associated with high morbidity and mortality. Identifying patients with stage 4 chronic kidney disease (CKD) at risk of short-term progression to ESRD remains challen Show more
End-stage renal disease (ESRD) is associated with high morbidity and mortality. Identifying patients with stage 4 chronic kidney disease (CKD) at risk of short-term progression to ESRD remains challenging. Accurate prediction can improve advanced care planning and patient outcomes. This study aimed to develop and validate a machine learning (ML) model for predicting progression within 25 weeks (approximately six months) of ESRD in patients with stage 4 CKD. Electronic health records (EHRs) of patients with stage 4 CKD were analyzed. Nine ML models including Ridge regression (Ridge), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were used to predict short-term progression to ESRD within 25 weeks. The models were trained and externally validated using the data of 346 and 105 patients. Of the 451 patients with stage 4 CKD, 219 developed ESRD. Among the evaluated models, XGBoost demonstrated the best overall performance. In the internal validation, it achieved an area under the curve (AUC) of 0.93, an accuracy of 0.90, and an F1 score of 0.89. In the external validation, XGBoost maintained the highest AUC (0.85), accuracy (0.79), and F1 score (0.79), along with the highest average precision (0.89) and a low log-loss (0.48), indicating strong discriminative ability and good generalizability. The top predictive features included high-density lipoprotein cholesterol, Alb, Cys C, ApoB, FGB, Bun, Neutrophil, and Total cholesterol. This study demonstrated the feasibility of ML for assessing ESRD prognosis based on easily accessible clinical features. XGBoost demonstrated superior performance in both internal and external validation, suggesting its potential for future patient screening. Show less
📄 PDF DOI: 10.1038/s41598-025-23037-4
APOB

Expression, Characteristics, and Clinical Target Prediction of PIK3C3/ vps34 in Gastric Cancer.

Chenglou Zhu, Wenhan Liu, Mingxu Da · 2025 · Current cancer drug targets · Bentham Science · added 2026-04-24
This study aimed to investigate the expression pattern of phosphatidylinositol 3-kinase class III (PIK3C3/vps34) in gastric cancer (GC) tissues and their juxtaposed normal counterparts and its correla Show more
This study aimed to investigate the expression pattern of phosphatidylinositol 3-kinase class III (PIK3C3/vps34) in gastric cancer (GC) tissues and their juxtaposed normal counterparts and its correlation with the clinicopathological attributes and prognostic outlook of afflicted individuals. Immunohistochemical (IHC) staining was used to ascertain the expression levels of PIK3C3/vps34 across 60 GC tissues juxtaposed with their normal counterparts. Statistical methodologies were used to scrutinize the correlation between PIK3C3/vps34 expression and clinicopathological features, along with prognostic implications for GC patients. In GC tissues, the positive expression rate of PIK3C3/vps34 was 23.3% (14/60), which contrasted sharply with the markedly elevated rate of 66.7% (40/60) observed in adjacent tissues. The positive expression proportion of PIK3C3/vps34 within GC tissues exhibited a notable decrease than in adjacent tissues (P < 0.05). The expression of PIK3C3/vps34 inversely correlated with tumor size, degree of tissue differentiation, depth of tumor infiltration, and incidence of lymph node metastasis (P < 0.05), whereas no significant associations were found with patient sex, age, tumor location, TNM staging, or distant metastasis (P > 0.05). As the tumor diameter increases, the degree of tissue differentiation diminishes, tumor infiltration depth intensifies, lymph node metastasis emerges, the TNM stage progresses, and PIK3C3/vps34 expression level within GC tissues declines correspondingly. Kaplan-Meier survival analysis unveiled a prolonged survival duration among GC patients exhibiting heightened PIK3C3/vps34 expression than in their counterparts with diminished expression (HR=0.66, 95% CI: 0.55-0.80), demonstrating statistical significance (P < 0.05). Protein interaction analysis revealed noteworthy interactions involving PIK3C3 with Beclin 1, UVRAG, and ATG14. PIK3C3/vps34 is downregulated in GC tissues, exerting a pivotal role in tumorigenesis, and is intimately linked with the prognostic trajectory of GC patients. It may serve as a significant biomarker for prognostic evaluation and a promising molecular therapeutic target for GC. Show less
no PDF DOI: 10.2174/0115680096334160240916102105
PIK3C3

The Oncopromoting Gene RBM6 Inhibits Prostate Tumour Cell Migration During Epithelial-to-Mesenchymal Transition.

Ruoyang Liu, Yu Liu, Long Zhang +7 more · 2025 · Journal of cellular and molecular medicine · Blackwell Publishing · added 2026-04-24
RBM6, implicated in the progression of multiple tumour types but unexplored in prostate tumours, was found to indicate potential therapeutic implications due to its elevated expression in prostate tum Show more
RBM6, implicated in the progression of multiple tumour types but unexplored in prostate tumours, was found to indicate potential therapeutic implications due to its elevated expression in prostate tumours. To elucidate its molecular function, scratch tests, transwell migration and invasion assays were conducted, with PCR and western blot analyses verifying molecular regulatory relationships. RNA pulldown and RNA immunoprecipitation tests were also employed to investigate underlying mechanisms. Results indicate that RBM6 enhances prostate cell migration by suppressing CDH1, yet ZEB1 overexpression alleviates this suppression. Notably, under these conditions, RBM6's inhibitory effect on MMP16 becomes more pronounced, reducing cell migration ability. Thus, under normal conditions, RBM6 promotes prostate tumour cell migration, but in the context of high ZEB1 expression, it inhibits migration. This shift in RBM6's regulatory capacity towards downstream genes underscores the importance of considering objective conditions in studying RBM6 molecules. Show less
no PDF DOI: 10.1111/jcmm.70397
RBM6

Lipids, Lipid-Lowering Drug Target Genes, and COPD Risk: A Mendelian Randomization Study.

Guobing Jia, Tao Guo, Lei Liu +1 more · 2025 · Chronic obstructive pulmonary diseases (Miami, Fla.) · added 2026-04-24
Some studies suggest that statins could reduce the risk of chronic obstructive pulmonary disease (COPD), but it is unclear if this effect is related to their lipid-lowering properties. The causal link Show more
Some studies suggest that statins could reduce the risk of chronic obstructive pulmonary disease (COPD), but it is unclear if this effect is related to their lipid-lowering properties. The causal link between serum lipid levels and COPD risk remains uncertain. This study aims to clarify this potential causal relationship and evaluate the impact of lipid-lowering drug target genes on COPD. Mendelian randomization (MR) was used to investigate causal associations between lipid levels, lipid-lowering drug target genes, and COPD risk. Data were obtained from publicly available genome-wide association study databases. The inverse variance weighted method was the primary statistical approach for evaluating causal effects, complemented by various sensitivity analyses. MR analysis demonstrated a causal relationship between low-density lipoprotein cholesterol (LDL-C) and a reduced risk of COPD (odds ratio [OR]=0.90, 95% confidence interval [CI]=0.85-0.95, P=1.50×10⁻⁴). Causal relationships were also identified for 2 lipid-lowering drug target genes, This study genetically identified causal relationships between serum LDL-C levels, the 2 coding genes Show less
no PDF DOI: 10.15326/jcopdf.2025.0632
LPL

Improving neuroblastoma risk prediction through a polygenic risk score derived from genome-wide association study-identified loci.

Wenli Zhang, Jinhong Zhu, Mengzhen Zhang +7 more · 2025 · Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · added 2026-04-24
Neuroblastoma is the most common extracranial solid tumor in children and has complex genetic underpinnings. Previous genome-wide association studies (GWASs) have identified many loci associated with Show more
Neuroblastoma is the most common extracranial solid tumor in children and has complex genetic underpinnings. Previous genome-wide association studies (GWASs) have identified many loci associated with neuroblastoma susceptibility; however, their application in risk prediction for Chinese children has not been systematically explored. This study seeks to enhance neuroblastoma risk prediction by validating these loci and evaluating their performance in polygenic risk models. We validated 35 GWAS-identified neuroblastoma susceptibility loci in a cohort of Chinese children, consisting of 402 neuroblastoma patients and 473 healthy controls. Genotyping these polymorphisms was conducted via the TaqMan method. Univariable and multivariable logistic regression analyses revealed the genetic loci significantly associated with neuroblastoma risk. We constructed polygenic risk models by combining these loci and assessed their predictive performance via area under the curve (AUC) analysis. We also established a polygenic risk scoring (PRS) model for risk prediction by adopting the PLINK method. Fourteen loci, including ten protective polymorphisms from Our findings validate multiple loci as neuroblastoma risk factors in Chinese children and demonstrate the utility of polygenic risk models, particularly the PRS, in improving risk prediction. These results suggest that integrating multiple genetic variants into a PRS can enhance neuroblastoma risk stratification and potentially improve early diagnosis by guiding targeted screening programs for high-risk children. Show less
no PDF DOI: 10.21147/j.issn.1000-9604.2025.01.01
HSD17B12

Chronic inflammation response as a key factor in polycystic ovary syndrome among patients with bipolar disorder.

Jieyu Liu, Zhuohui Chen, Ziwei Teng +8 more · 2025 · Journal of affective disorders · Elsevier · added 2026-04-24
This study aimed to investigate serum inflammatory factor levels of polycystic ovary syndrome (PCOS) in female patients with bipolar disorder (BD) to explore the related inflammatory molecular mechani Show more
This study aimed to investigate serum inflammatory factor levels of polycystic ovary syndrome (PCOS) in female patients with bipolar disorder (BD) to explore the related inflammatory molecular mechanisms preliminarily. The study recruited 72 female drug-naïve patients with BD and 98 female healthy controls (HCs). Demographic information, menstrual cycles, sex hormone levels, and ovarian ultrasound data were collected from them. Additionally, their serum inflammatory factor levels and the proteomics of peripheral blood mononuclear cells were analyzed. The levels of interleukin (IL)-8 and IL-13 were significantly higher in patients with BD than in HCs (p < 0.05), and the IL-8 level was higher in BD patients with PCOS than in those without (adjusted p = 0.07). Bioinformatics analysis revealed that downregulated genes with significant differences between the two groups were all involved in immune-inflammatory-related pathways, and the expression of downregulated genes BTN3A2, MAP2K5, JCHAIN-B, and DMAP1 showed substantial differences and consistent trends between the two groups. IL-8-related chronic inflammatory response is closely associated with PCOS in BD patients, and genes such as BTN3A2 may mediate this chronic inflammatory response by negatively regulating the abnormal differentiation of T helper 17 cells, serving as one of the mechanisms underlying its pathogenesis. Show less
no PDF DOI: 10.1016/j.jad.2025.02.072
MAP2K5

Lymphatic Endothelial Branched-Chain Amino Acid Catabolic Defects Undermine Cardiac Lymphatic Integrity and Drive HFpEF.

Xiong Guo, Chong Huang, Ling Zhang +18 more · 2025 · Circulation · added 2026-04-24
Heart failure with preserved ejection fraction (HFpEF) has become the most prevalent type of heart failure, but effective treatments are lacking. Cardiac lymphatics play a crucial role in maintaining Show more
Heart failure with preserved ejection fraction (HFpEF) has become the most prevalent type of heart failure, but effective treatments are lacking. Cardiac lymphatics play a crucial role in maintaining heart health by draining fluids and immune cells. However, their involvement in HFpEF remains largely unexplored. We examined cardiac lymphatic alterations in mice with HFpEF with comorbid obesity and hypertension, and in heart tissues from patients with HFpEF. Using genetically engineered mouse models and various cellular and molecular techniques, we investigated the role of cardiac lymphatics in HFpEF and the underlying mechanisms. In mice with HFpEF, cardiac lymphatics displayed substantial structural and functional anomalies, including decreased lymphatic endothelial cell (LEC) density, vessel fragmentation, reduced branch connections, and impaired capacity to drain fluids and immune cells. LEC numbers and marker expression levels were also decreased in heart tissues from patients with HFpEF. Stimulating lymphangiogenesis with an adeno-associated virus expressing an engineered variant of vascular endothelial growth factor C (VEGFC Our study provides evidence that cardiac lymphatic disruption, driven by impaired BCAA catabolism in LECs, is a key factor contributing to HFpEF. These findings unravel the crucial role of BCAA catabolism in modulating lymphatic biology, and suggest that preserving cardiac lymphatic integrity may present a novel therapeutic strategy for HFpEF. Show less
📄 PDF DOI: 10.1161/CIRCULATIONAHA.124.071741
BCKDK

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

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

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

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

Quinolinic acid protects mouse liver from high-fat diet induced MASLD by inhibiting lipid uptake gene expression.

Bao Wang, Delong Zhen, Jin Wei +4 more · 2025 · European journal of pharmacology · Elsevier · added 2026-04-24
Quinolinic acid (QA) is a metabolite of tryptophan catabolism involved in the biosynthesis of nicotinamide adenine dinucleotide (NAD). It has been extensively studied in the context of neuropsychiatri Show more
Quinolinic acid (QA) is a metabolite of tryptophan catabolism involved in the biosynthesis of nicotinamide adenine dinucleotide (NAD). It has been extensively studied in the context of neuropsychiatric disorders in the past decades. Recent studies have also linked high plasma QA levels to obesity, metabolic dysfunction-associated steatotic liver disease (MASLD) and diabetes. In the present study, we have explored the impact of long-term oral QA administration on glucose and lipid metabolism in mice. We observed a protective role for QA in preventing hepatic lipid accumulation in high-fat-diet fed mice, whereas oral administration of NAD showed opposite effects. We further demonstrated that QA reduces hepatic lipid uptake by inhibiting the expression of lipoprotein lipase (LPL) and fatty acid translocase (CD36) in liver, thereby mitigating liver lipid accumulation in the context of a high-fat diet. Our data suggest that QA is an important regulator of lipid homeostasis and has potential as a therapeutic target for MASLD. Show less
no PDF DOI: 10.1016/j.ejphar.2025.178065
LPL

The integrated analysis of transcriptomics and metabolomics reveals the effects of tea polyphenols on lipid metabolism in lion-head geese.

Zhiqi Fu, Chunpeng Liu, Tao Zeng +5 more · 2025 · Poultry science · Elsevier · added 2026-04-24
Tea polyphenols are a class of natural plant compounds with potent antioxidant properties, and their critical role in regulating lipid metabolism has been demonstrated in numerous studies. However, sy Show more
Tea polyphenols are a class of natural plant compounds with potent antioxidant properties, and their critical role in regulating lipid metabolism has been demonstrated in numerous studies. However, systematic research on the effects of tea polyphenols on lipid metabolism in lion-head geese remains limited. In this study, we examined the impact of tea polyphenols on lipid metabolism in geese through an integrative analysis of transcriptomics and metabolomics. A total of 240 healthy male lion-head geese with similar body weights at 1 day of age were randomly allocated into two treatment groups (6 replicates per group, with 20 geese per replicate). The control group received a basal diet, while the experimental group was supplemented with 1000 mg/kg of tea polyphenols (50.4 % catechin purity) in the basal diet for 18 weeks. The results indicated that serum total antioxidant capacity (T-AOC) and glutathione peroxidase (GSH-Px) activities were significantly increased (P < 0.05), while malondialdehyde (MDA) levels were significantly decreased (P < 0.05) in the tea polyphenol group compared to the control group. Additionally, serum triglycerides (TG), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) activities were significantly lower (P < 0.05) in the tea polyphenol group than in the control group. Hepatic transcriptomic analysis further revealed that tea polyphenols significantly modulated the expression of several genes involved in lipid metabolism, including angiopoietin-like 4 (ANGPTL4), which plays a role in regulating lipid homeostasis, as well as glycerophosphodiester phosphodiesterase domain containing 2 (GDPD2), immunoglobulin heavy chain (IGH), proto-oncogene protein c-fos (FOS), and matrix metallopeptidase 1 (MMP1), etc. Serum metabolomic analysis also demonstrated significant alterations in lipid metabolites induced by tea polyphenols, including the downregulation of fatty acyl metabolites such as L-Palmitoylcarnitine and Hexadecanal. Moreover, the combined analysis revealed a strong positive correlation between ANGPTL4 and the organic compounds of steroidal saponins, such as Glucoconvallasaponin B, and negative correlations with glycerophospholipid metabolites, such as LysoPC (P-16:0). The comprehensive analysis suggests that the inclusion of tea polyphenols in the diet enhances the antioxidant capacity of lion-head geese, improves hepatic lipid profiles, and regulates lipid metabolism via modulating lipid metabolism-related genes and metabolites. Show less
📄 PDF DOI: 10.1016/j.psj.2025.104958
ANGPTL4

Prenatal bisphenol analogs exposure and placental DNA hypomethylation of genes in the PPAR signaling pathway: Insights for bisphenol analogs' effects on infant anthropometry.

Honglei Ji, Haijun Zhu, Ziliang Wang +7 more · 2025 · Environmental research · Elsevier · added 2026-04-24
Prenatal exposure to bisphenol analogs (BPs) may pose hazards to offspring's health; however, their underlying mechanisms remain to be elucidated. DNA methylation, a major epigenetic mechanism, may be Show more
Prenatal exposure to bisphenol analogs (BPs) may pose hazards to offspring's health; however, their underlying mechanisms remain to be elucidated. DNA methylation, a major epigenetic mechanism, may be involved in early programming following environmental disturbances. In this prospective study, we investigated associations between prenatal BPs exposure and the placental DNA methylation levels of 14 candidate genes in the peroxisome proliferator-activated receptor (PPAR) signaling pathway among 205 mother-infant pairs and explored the potential mediating role of the DNA methylation in the association of prenatal BPs exposure with anthropometric measurements of infants aged 1 year. We observed a general pattern that prenatal BPs exposure was associated with the DNA hypomethylation of candidate genes, with associations consistently and notably observed for PPAR α (PPARA), retinoid X receptor α (RXRA), acetyl-CoA acyltransferase 1, and acyl-CoA dehydrogenase medium chain (ACADM) in linear regression and Bayesian kernel machine regression. Both models identified bisphenol F (BPF) as the predominant compound. We found inverse associations between the placental DNA methylation levels of most candidate genes, such as PPARA, RXRA, ACADM, and nuclear receptor subfamily 1 group H member 3 (NR1H3), and the length-for-age z-score, arm circumference-for-age z-score, subscapular skinfold-for-age z-score, and abdominal skinfold thickness of the infants. The DNA methylation levels of RXRA and NR1H3 could mediate the associations between prenatal BPF exposure and increased infant anthropometric measurements, with mediating portions ranging from 23.02% to 30.53%. Our findings shed light on the potential mechanisms underlying the effects of prenatal BPs exposure on infant growth and call for urgent actions for risk assessment and regulation of BPF. Future cohort studies with larger sample sizes are warranted to confirm our findings. Show less
no PDF DOI: 10.1016/j.envres.2024.120476
NR1H3

Lipid metabolism-associated immune gene LPL promotes M1 macrophage polarization and inhibits breast cancer progression.

Lu Yang, Xuan Fang, Xu Liu +2 more · 2025 · Tissue & cell · Elsevier · added 2026-04-24
Breast cancer (BRCA) ranks among the most frequently diagnosed malignancies worldwide. Immune infiltration plays a critical role in tumor progression and therapeutic response. However, the precise mec Show more
Breast cancer (BRCA) ranks among the most frequently diagnosed malignancies worldwide. Immune infiltration plays a critical role in tumor progression and therapeutic response. However, the precise mechanisms underlying immune infiltration in BRCA remain incompletely understood. Machine learning (support vector machine-recursive feature elimination and least absolute shrinkage and selection operator regression) and weighted gene co-expression network were utilized to screen hub genes. An immune infiltration assessment was carried out via TIMER and CIBERSORT. The prognostic and survival of risk model and immune infiltration-associated hub genes were analyzed through Kaplan-Meier survival analysis, Cox regression, and ROC curve evaluation. Cell functional assays and xenograft models in vivo were utilized to examine lipoprotein lipase (LPL) function. The impact of LPL on macrophage polarization was evaluated using THP-1-derived macrophages and immunohistochemistry analysis of immune infiltration (CD4, CD8, and F4/80) in vivo. 10 hub immune regulators were identified in BRCA, which were associated with lipid metabolism. Hub genes and a prognostic risk model exhibited high predictive accuracy for BRCA patient survival and prognosis. Overexpression of LPL inhibited BRCA cell proliferation, migration, and invasion while promoting M1-like macrophage polarization. In vivo, LPL overexpression significantly suppressed tumor growth and enhanced immune cell infiltration, as indicated by the elevation of CD4 + and F4/80 + cells along with a decline in CD8 + macrophage abundance. This study identifies a novel lipid metabolism-related gene signature and demonstrates that LPL overexpression modulates macrophage polarization and inhibits BRCA progression. Show less
no PDF DOI: 10.1016/j.tice.2025.103071
LPL

Pediatric diffuse low-grade glioma with oligodendroglioma-like features: A case report.

Pengwei Hou, Chengzhu Cai, Meiyan Liu +2 more · 2025 · Experimental and therapeutic medicine · added 2026-04-24
The present case report presents the diagnostic challenges of pediatric diffuse low-grade glioma (pDLGG) with oligodendroglioma-like features. The patient, an 11-year-old girl, presented with refracto Show more
The present case report presents the diagnostic challenges of pediatric diffuse low-grade glioma (pDLGG) with oligodendroglioma-like features. The patient, an 11-year-old girl, presented with refractory epilepsy and brain imaging did not provide a clear diagnosis. Intraoperatively, the tumor appeared gray-yellow to gray-red, with moderate texture and unclear borders, consistent with LGG. Postoperative pathology showed diffuse infiltrative growth of the tumor, with pleomorphic cell morphology and oligodendroglioma-like gliocyte proliferation. Staining was positive for markers such as glial fibrillary acidic protein and Olig-2. Genomic analysis revealed BRAF V600E, fibroblast growth factor receptor (FGFR)1 and FGFR4 mutations, but no IDH mutations or other related mutations. The final diagnosis was pDLGG with alterations in the MAPK pathway. The present case underscores the importance of molecular and histological features in the diagnosis of pDLGG, especially when clinical and imaging characteristics are atypical, as molecular diagnostics provide key insights for disease classification. Show less
📄 PDF DOI: 10.3892/etm.2025.12985
FGFR1

Emotional regulation and mental health profiles among Chinese junior middle school students: evidence for the dual-factor model.

Jichang Guo, Yanpei Pan, Yan Zhao +2 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24
This study explored latent mental health profiles among adolescents in southwestern China and the association with emotional regulation using the dual-factor model framework. 1,682 junior middle schoo Show more
This study explored latent mental health profiles among adolescents in southwestern China and the association with emotional regulation using the dual-factor model framework. 1,682 junior middle school students completed the LPA revealed three profiles: Troubled (31.51%, high negative symptoms/low well-being), complete mental health (61.30%, low negative symptoms/high well-being), and more troubled (7.19%, severe negative symptoms/extremely low well-being). Cognitive reappraisal positively predicted complete mental health (vs. Troubled; Three distinct profiles emerged, differing from the traditional dual-factor model. Cognitive reappraisal protects mental health, while expressive suppression correlates with poorer outcomes, highlighting the need for targeted interventions promoting cognitive reappraisal. Show less
📄 PDF DOI: 10.3389/fpsyg.2025.1708381
LPA

Identifying social isolation profiles among young and middle-aged patients with cancer in Western China: A latent profile analysis.

Lu Yang, Xia Liu, Huiqiong Xu +1 more · 2025 · Asia-Pacific journal of oncology nursing · Elsevier · added 2026-04-24
To identify the various profiles of social isolation among 18-59-year-old patients with cancer in Western China and examine their demographic, clinical, and cultural predictors. This cross-sectional s Show more
To identify the various profiles of social isolation among 18-59-year-old patients with cancer in Western China and examine their demographic, clinical, and cultural predictors. This cross-sectional study included 300 patients from a tertiary hospital who completed standardized assessments of social isolation (Social Avoidance Scale, UCLA Loneliness Scale) and family functioning. Latent Profile Analysis (LPA) was used to identify the subgroups, and multinomial logistic regression was used to analyze predictors of the profiles. Three distinct latent profiles were identified: "avoidance-dominant" (52.3%), which was characterized by high levels of social avoidance (12.52 ​± ​1.38) and low loneliness (30.87 ​± ​6.89), "loneliness-dominant" (27.0%), which was characterized by high levels of loneliness (53.15 ​± ​6.24) and low social avoidance (2.07 ​± ​1.38), and "balanced" (20.7%), which was characterized by balanced scores on both the measures. Individuals with fatigue, employment status, personality traits, and family dynamics significantly predicted profile membership ( Social isolation was heterogeneous among young and middle-aged patients with cancer. Fatigue significantly predicted distinct patterns of social isolation. Furthermore, exploratory findings indicated a potential role of religious beliefs in the avoidance-dominant profile; however, replication with larger samples is required. Family dynamics may buffer the risk of isolation in patients prone to avoidance, whereas those dominated by loneliness may lack such safeguards. Health care providers can implement tailored interventions to mitigate social isolation based on these varying profiles. Show less
📄 PDF DOI: 10.1016/j.apjon.2025.100794
LPA

Identification of

Xiao-Yun Lei, Meng-Wen Zhang, Hui Sun +13 more · 2025 · Journal of medical genetics · added 2026-04-24
The microtubule actin crosslinking factor 1 ( Trios-based whole-exome sequencing was performed on a cohort with generalised epilepsy from the China Epilepsy Gene 1.0 project. The spatial-temporal expr Show more
The microtubule actin crosslinking factor 1 ( Trios-based whole-exome sequencing was performed on a cohort with generalised epilepsy from the China Epilepsy Gene 1.0 project. The spatial-temporal expression, single-cell sequencing and genotype-phenotype correlation were analysed to explore the role of Two de novo heterozygous and eight biallelic Show less
📄 PDF DOI: 10.1136/jmg-2025-110699
MACF1

Pseudogene Lamr1-ps1 Aggravates Early Spatial Learning Memory Deficits in Alzheimer's Disease Model Mice.

Zhuoze Wu, Xiaojie Liu, Yuntai Wang +3 more · 2025 · Neuroscience bulletin · Springer · added 2026-04-24
Alzheimer's disease (AD), a neurodegenerative disorder with complex etiologies, manifests through a cascade of pathological changes before clinical symptoms become apparent. Among these early changes, Show more
Alzheimer's disease (AD), a neurodegenerative disorder with complex etiologies, manifests through a cascade of pathological changes before clinical symptoms become apparent. Among these early changes, alterations in the expression of non-coding RNAs (ncRNAs) have emerged as pivotal events. In this study, we focused on the aberrant expression of ncRNAs and revealed that Lamr1-ps1, a pseudogene of the laminin receptor, significantly exacerbates early spatial learning and memory deficits in APP/PS1 mice. Through a combination of bioinformatics prediction and experimental validation, we identified the miR-29c/Bace1 pathway as a potential regulatory mechanism by which Lamr1-ps1 influences AD pathology. Importantly, augmenting the miR-29c-3p levels in mice ameliorated memory deficits, underscoring the therapeutic potential of targeting miR-29c-3p in early AD intervention. This study not only provides new insights into the role of pseudogenes in AD but also consolidates a foundational basis for considering miR-29c as a viable therapeutic target, offering a novel avenue for AD research and treatment strategies. Show less
📄 PDF DOI: 10.1007/s12264-024-01336-6
BACE1

Single-cell transcriptomic atlas of the chicken cecum reveals cellular responses and state shifts during Eimeria tenella infection.

Jun-Hao Tu, Bo-Gong Liu, Bing-Jin Lin +7 more · 2025 · BMC genomics · BioMed Central · added 2026-04-24
Eimeria tenella (E. tenella) infection is a major cause of coccidiosis in chickens, leading to significant economic losses in the poultry industry due to its impact on the cecum. This study presents a Show more
Eimeria tenella (E. tenella) infection is a major cause of coccidiosis in chickens, leading to significant economic losses in the poultry industry due to its impact on the cecum. This study presents a comprehensive single-cell atlas of the chicken cecal epithelium by generating 7,394 cells using 10X Genomics single-cell RNA sequencing (scRNA-seq). We identified 13 distinct cell types, including key immune and epithelial populations, and characterized their gene expression profiles and cell-cell communication networks. Integration of this single-cell data with bulk RNA-seq data from E. tenella-infected chickens revealed significant alterations in cell type composition and state, particularly a marked decrease in APOB Show less
📄 PDF DOI: 10.1186/s12864-025-11302-9
APOB

Genetic Reasons for Phenotypic Diversity in Neuronal Ceroid Lipofuscinoses and High-Resolution Imaging as a Marker of Retinal Disease.

Jennifer Huey, Pankhuri Gupta, Benjamin Wendel +9 more · 2024 · Ophthalmology science · Elsevier · added 2026-04-24
To describe the clinical characteristics, natural history, genetic landscape, and phenotypic spectrum of neuronal ceroid lipofuscinosis (NCL)-associated retinal disease. Multicenter retrospective coho Show more
To describe the clinical characteristics, natural history, genetic landscape, and phenotypic spectrum of neuronal ceroid lipofuscinosis (NCL)-associated retinal disease. Multicenter retrospective cohort study complemented by a cross-sectional examination. Twelve pediatric subjects with biallelic variants in 5 NCL-causing genes (CLN3 lysosomal/endosomal transmembrane protein [ Review of clinical notes, retinal imaging, electroretinography (ERG), and molecular genetic testing. Two subjects underwent a cross-sectional examination comprising adaptive optics scanning laser ophthalmoscopy imaging of the retina and optoretinography (ORG). Clinical/demographic data, multimodal retinal imaging data, electrophysiology parameters, and molecular genetic testing. Our cohort included a diverse set of subjects with Our cohort data demonstrates that the underlying genetic variants drive the phenotypic diversity in different forms of NCL. Genetic testing can provide molecular diagnosis and ensure appropriate disease management and support for children and their families. With intravitreal enzyme replacement therapy on the horizon as a potential treatment option for NCL-associated retinal degeneration, precise structural and functional measures will be required to more accurately monitor disease progression. We show that adaptive optics imaging and ORG can be used as highly sensitive methods to track early retinal changes, which can be used to establish eligibility for future therapies and provide metrics for determining the efficacy of interventions on a cellular scale. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. Show less
📄 PDF DOI: 10.1016/j.xops.2024.100560
CLN3

Potent and Protease Resistant Azapeptide Agonists of the GLP-1 and GIP Receptors.

Tristan C Dinsmore, Jamie Liu, Jiayuan Miao +5 more · 2024 · Angewandte Chemie (International ed. in English) · Wiley · added 2026-04-24
The gut-derived peptide hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) play important physiological roles including glucose homeostasis and appetite su Show more
The gut-derived peptide hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) play important physiological roles including glucose homeostasis and appetite suppression. Stabilized agonists of the GLP-1 receptor (GLP-1R) and dual agonists of GLP-1R and GIP receptor (GIPR) for the management of type 2 diabetes and obesity have generated widespread enthusiasm and have become blockbuster drugs. These therapeutics are refractory to the action of dipeptidyl peptidase-4 (DPP4), that catalyzes rapid removal of the two N-terminal residues of the native peptides, in turn severely diminishing their activity profiles. Here we report that a single atom change from carbon to nitrogen in the backbone of the entire peptide makes them refractory to DPP4 action while still retaining full potency and efficacy at their respective receptors. This was accomplished by use of aza-amino acids, that are bioisosteric replacements for α-amino acids that perturb the structural backbone and local side chain conformations. Molecular dynamics simulations reveal that aza-amino acid can populate the same conformational space that GLP-1 adopts when bound to the GLP-1R. The insertion of an aza-amino acid at the second position from the N-terminus in semaglutide and in a dual agonist of GLP-1R and GIPR further demonstrates its capability as a viable alternative to current DPP4 resistance strategies while offering additional structural variation that may influence downstream signaling. Show less
no PDF DOI: 10.1002/anie.202410237
GIPR

Peripheral Blood Levels of IL-27, IFABP, and DAO at Early Onset as Predictors of 28-Day Mortality in Enterogenic Sepsis Patients: A Single-Center, Prospective Pilot Study.

Yin Ni, Renhua Sun, Bangchuan Hu +3 more · 2024 · Infection and drug resistance · added 2026-04-24
Currently, there is a lack of serum biomarkers that can accurately predict the short-term prognosis of enterogenic sepsis. 99 patients with enterogenic sepsis were categorized based on their Acute Gas Show more
Currently, there is a lack of serum biomarkers that can accurately predict the short-term prognosis of enterogenic sepsis. 99 patients with enterogenic sepsis were categorized based on their Acute Gastrointestinal Injury (AGI) grade on the third day of ICU admission into four groups: no AGI, AGI grade I, AGI grade II, and AGI (III+IV). Additionally, patients were classified into survival and death groups according to their 28-day clinical outcomes. Peripheral venous blood samples were collected to measure levels of interleukin (IL)-27, intestinal fatty acid-binding protein (IFABP), and diamine oxidase (DAO). Receiver operating characteristic (ROC) curves were generated to assess the ability of IL-27, IFABP, and DAO to predict the short-term prognosis of patients with enterogenic sepsis. On the third day, both the survival and death groups exhibited elevated serum levels of IL-27 and IFABP compared to the first day, while levels of DAO were lower than those observed on day one. Furthermore, a significant positive correlation was observed between IL-27 and both IFABP and DAO, with stronger correlations evident on day three compared to day one. As the Acute Gastrointestinal Injury (AGI) grading increased, levels of IL-27, IFABP, and DAO rose correspondingly, correlating with a gradual decrease in survival rates, all demonstrating statistical significance (all P < 0.05). The Area Under the Curve (AUC) values for IL-27, IFABP, and DAO on the third day, predicting short-term prognosis for intestinal sepsis patients, were 0.714, 0.772, and 0.724, respectively. Notably, these values surpassed those of the first day, with IFABP on the third day exhibiting the highest predictive capability. IL-27, IFABP, and DAO levels measured on the third day of hospitalization can accurately predict the short-term prognosis of enterogenic sepsis. Show less
📄 PDF DOI: 10.2147/IDR.S496918
IL27