👤 Han 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-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

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

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

Heparan sulfate regulates myofibroblast heterogeneity and function to mediate niche homeostasis during alveolar morphogenesis.

Hua He, Chong Ma, Wei Wei +10 more · 2025 · Nature communications · Nature · added 2026-04-24
Postnatal respiration requires bulk formation of alveoli that produces extensive surface area for gas diffusion from epithelium to the circulatory system. Alveolar morphogenesis initiates at late gest Show more
Postnatal respiration requires bulk formation of alveoli that produces extensive surface area for gas diffusion from epithelium to the circulatory system. Alveolar morphogenesis initiates at late gestation or postnatal stage during mammalian development and is mediated by coordination among multiple cell types. Here we show that fibroblast-derived Heparan Sulfate Glycosaminoglycan (HS-GAG) is essential for maintaining a niche that supports alveolar formation by modulating both biophysical and biochemical cues. Gli1-CreER mediated deletion of HS synthase gene Ext1 in lung fibroblasts results in enlarged and simplified alveolar structures. Ablation of HS results in loss of a subset of PDGFRα Show less
📄 PDF DOI: 10.1038/s41467-025-57163-4
EXT1

NFIA-dependent upregulation of SMC4 promotes metastasis and metabolic reprogramming in glioma.

Guoyin Li, Yukui Zhao, Yubo He +4 more · 2025 · Frontiers in oncology · Frontiers · added 2026-04-24
Gliomas, particularly glioblastoma, are aggressive brain tumors with poor prognosis and unmet therapeutic needs. Structural maintenance of chromosomes 4 (SMC4), a core component of the condensin compl Show more
Gliomas, particularly glioblastoma, are aggressive brain tumors with poor prognosis and unmet therapeutic needs. Structural maintenance of chromosomes 4 (SMC4), a core component of the condensin complex, is dysregulated in multiple cancers, but its role in glioma metabolism and metastasis remains unclear. Using integrated multi-omics analyses of glioma datasets, we assessed SMC4 expression and its correlation with clinical outcomes. Functional studies in U-251MG and LN229 glioma cells including CCK-8, EdU, cell cycle, Transwell, and wound-healing assays were combined with subcutaneous xenograft and tail-vein metastasis mouse models to evaluate SMC4's effects on proliferation, migration, invasion, and metastasis. ECAR/OCR and rescue experiments validated SMC4's role in glycolysis. Luciferase reporter and ChIP assays identified nuclear factor I A (NFIA) as an upstream transcriptional regulator of SMC4. A prognostic model (SRRS) was developed via LASSO regression and validated across cohorts. SMC4 was significantly overexpressed in glioma tissues, with higher expression correlating with advanced tumor grades and poorer patient survival (AUC > 0.82). Mechanistically, SMC4 promoted G1/S cell cycle transition and proliferation SMC4 drives glioma progression through dual mechanisms TGF-β/SMAD-mediated metastasis and LDHA-dependent glycolysis regulated by NFIA. This extends beyond its known role in TGF-β activation by identifying NFIA as an upstream regulator and metabolic reprogramming as a novel function. The SRRS and nomogram provide robust tools for prognosis and personalized therapy, supporting the NFIA/SMC4 axis and downstream effectors as potential therapeutic targets for glioma. Show less
no PDF DOI: 10.3389/fonc.2025.1624370
SNAI1

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

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

[Association study of FADS2 gene rs174575 and rs2845574 single nucleotide polymorphisms with blood pressure and lipid levels in pregnant women].

Yuwen Guo, Huai Bai, Linbo Guan +4 more · 2025 · Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics · added 2026-04-24
To assess the association between the single nucleotide polymorphisms (SNP) rs174575 and rs2845574 of the fatty acid desaturase 2 (FADS2) gene and gestational diabetes mellitus (GDM). A total of 1 514 Show more
To assess the association between the single nucleotide polymorphisms (SNP) rs174575 and rs2845574 of the fatty acid desaturase 2 (FADS2) gene and gestational diabetes mellitus (GDM). A total of 1 514 pregnant women who visited West China Second University Hospital of Sichuan University between January 1, 2013 and December 31, 2021 were enrolled in this study. Among them, 583 were diagnosed with gestational diabetes mellitus (GDM group), and 931 had normal pregnancies (control group). The SNPs rs174575 and rs2845574 of the FADS2 gene were analyzed using Sanger DNA sequencing. Plasma levels of insulin (INS), apolipoprotein A1 (apoA1) and apolipoprotein B (apoB) were measured using enzymatic methods, chemiluminescence and immunoturbidimetry. This study was approved by the Medical Ethics Committee of the West China Second University Hospital of Sichuan University (Ethics No.: 2020-036). The main genotype at the rs174575 C/G and rs2845574 C/T loci were CC in both GDM and control groups. No significant difference was found between the GDM and control groups regarding the genotypic or allelic frequencies of rs174575 and rs2845574 sites (P > 0.05). Among the GDM group, individuals with the GG genotype at the rs174575 site had lower plasma HDL-C levels compared to those with the CC genotype (P < 0.05), and had higher atherogenic indices (AI) compared with the CC and CG genotype (P < 0.05; P < 0.05). Individuals with the TT genotype at the rs2845574 site had higher AI compared with the CT genotype (P < 0.05). Among the control group, individuals with the GG genotype had lower diastolic blood pressure (DBP) compared to those with the CC genotype (P < 0.05). Additional subgroup analysis demonstrated that the rs174575 polymorphism was associated with AI levels in obesity subgroup of GDM, TG levels in non-obese subgroup of control and DBP levels in the obese subgroup of control (P < 0.05; P < 0.05; P < 0.05). The FADS2 rs174575 and rs2845574 polymorphisms in GDM patients are associated wit HDL-C and AI levels, and the FADS2 rs174575 polymorphisms was also associated with DBP levels in normal pregnant women. The AI and DBP levels have a BMI-dependent effect. Show less
no PDF DOI: 10.3760/cma.j.cn511374-20221221-00866
APOB

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

Isotemporal substitution from sedentary behavior or sleep to physical activity: associations with depression risk in older adults-a systematic review and meta-analysis.

Jiali Wang, Yilin Wang, Yue Wang +3 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24
As global population aging intensifies, mental health issues in older adults are increasingly prominent, with depression being particularly prevalent and detrimental. The study investigated how substi Show more
As global population aging intensifies, mental health issues in older adults are increasingly prominent, with depression being particularly prevalent and detrimental. The study investigated how substituting sedentary behavior (SB) and sleep (SLP) with physical activity (PA) affects depression risk in this population. Meta-analysis was conducted by searching four databases: PubMed, Scopus, SPORTdiscus, and PsycINFO (via EBSCOhost platform) for relevant studies published until January 2025. Regression coefficients (β) with 95% confidence intervals (CIs) for depressive symptoms were estimated. Publication bias was assessed using funnel plots and Egger's tests, and heterogeneity was evaluated using Q tests and the I Among 18,912 participants (53.45% female, ≥60 years) across nine studies, replacing SB with MVPA significantly reduced depression (β = -0.12, 95% CI: -0.20, -0.04), subgroup analyses indicated that reallocating 10, 30 and 60 min/day of SB to MVPA ( Substituting SB and SLP with MVPA is significantly associated with a reduction in depression, whereas no significant association is observed when replaced by LPA. https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=546666, identifier CRD42024546666. Show less
📄 PDF DOI: 10.3389/fpsyg.2025.1682987
LPA

Preoperative lipid levels and prognosis in non-small cell lung cancer: A retrospective age-stratified study.

Yv-Xuan Liu, Jing Chen, Chen Liu +4 more · 2025 · Science progress · SAGE Publications · added 2026-04-24
BackgroundAlthough abnormalities in circulating lipids and lipoproteins are associated with increased cancer risk, their specific impact on lung cancer progression and prognosis is still unclear. This Show more
BackgroundAlthough abnormalities in circulating lipids and lipoproteins are associated with increased cancer risk, their specific impact on lung cancer progression and prognosis is still unclear. This study retrospectively assessed the influence of preoperative lipid and lipoprotein levels on non-small cell lung cancer progression and prognosis, stratified by age.MethodsIn this retrospective study, we analyzed 849 patients to investigate the association between lipid markers and lung cancer progression, and examined postoperative prognosis in a subset of 222 patients. Data was analyzed using restricted cubic spline curves, Kaplan-Meier survival analysis, and Cox proportional hazards models.ResultsA significant nonlinear relationship was observed between total cholesterol (TC), high-density lipoprotein (HDL), ApoB, ApoAI, ApoE, and baseline tumor diameter (BSLD) (PTC = 0.025; PHDL < 0.001; PApoB = 0.037; PApoAI =0.001; PApoE < 0.001). In contrast, Lp(a) showed a significant linear relationship with BSLD (P = 0.002). The Cox regression analysis revealed that triglyceride (TG) (hazard ratio (HR) = 0.50, 95% confidence interval (CI): 0.28-0.92, P = 0.025) was significantly negatively associated with lung cancer mortality in patients under 58 years. For patients over 58 years, higher ApoB levels were linked to a reduced risk of lung cancer death (HR = 0.59, 95% CI: 0.36-0.97, P = 0.038).ConclusionThis study reveals a significant negative correlation between ApoAI and HDL levels with BSLD, while Lp(a) shows a positive correlation. In terms of long-term prognosis, high-serum ApoB are associated with a lower mortality risk in all lung cancer patients, and high-serum TG levels associated with reduced mortality risk in patients aged under 58 while high-serum TC levels associated with reduced mortality risk in patients over 58, with high Lp(a) levels indicating a greater risk of mortality in older patients. Show less
📄 PDF DOI: 10.1177/00368504251352375
APOB

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

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

Molecular Characterization and Functional Insights into Goose IGF2BP2 During Skeletal Muscle Development.

Cui Wang, Yi Liu, Jiuli Dai +2 more · 2025 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) is an RNA-binding protein known to play critical roles in metabolism, cell proliferation, and tumorigenesis. Although its involvement in m Show more
Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) is an RNA-binding protein known to play critical roles in metabolism, cell proliferation, and tumorigenesis. Although its involvement in muscle development has been documented in several species, the function of goose IGF2BP2 remains largely unexplored. In this study, we cloned and characterized the full-length cDNA and genomic DNA sequences of goose IGF2BP2. The cDNA is 2957 bp in length and contains a 1662 bp open reading frame encoding a 553-amino acid protein with five conserved RNA-binding domains. The genomic sequence spans 12,183 bp and consists of 12 exons and 11 introns. A total of 60 genetic variants were identified, including a deletion of a G base at position 2299 (g.2299delG) that results in a frameshift mutation. Expression analysis revealed high levels of IGF2BP2 mRNA in the liver, heart, and muscle tissues of female geese across embryonic (E25d), growing (A70d), and laying (L270d) stages, consistent with a potential role in muscle development ( Show less
📄 PDF DOI: 10.3390/ani16010058
MYBPC3

DCTP-Net: Dual-Branch CLIP-Enhance Textual Prompt-Aware Network for Acute Ischemic Stroke Lesion Segmentation From CT Image.

Jiahao Liu, Hongqing Zhu, Ziying Wang +6 more · 2025 · IEEE journal of biomedical and health informatics · IEEE · added 2026-04-24
Detecting early ischemic lesions (EIL) in computed tomography (CT) images is crucial for reducing diagnostic time and minimizing neuron loss due to oxygen deprivation. This paper introduces DCTP-Net, Show more
Detecting early ischemic lesions (EIL) in computed tomography (CT) images is crucial for reducing diagnostic time and minimizing neuron loss due to oxygen deprivation. This paper introduces DCTP-Net, a dual-branch network for segmenting acute ischemic stroke lesions in CT images, consisting of a segmentation branch and a prompt-aware branch. The segmentation branch uses an encoder-decoder network as the backbone to identify lesions, where the encoder fuses CT image features with prompt features from the prompt-aware branch. To enhance semantic feature extraction and reduce the impact of cerebral structural details, we introduce a cross-collaboration dynamic connection (CCDC) module to link the encoder and decoder. The prompt-aware branch includes a learnable prompt (LP) block to incorporate cerebral prior knowledge, and the prompt-aware encoder (PAE) combines the LP block with multi-level features from the segmentation branch for more precise representation. Additionally, we propose a CLIP-enhance textual prompt (CETP) module that utilizes the CLIP text encoder to generate specialized convolutional parameters for the segmentation head. These parameters are tailored to the unique characteristics of each input image, improving segmentation performance. Qualitative and quantitative studies reveal that DCTP-Net outperforms the current state-of-the-art, IS-Net, with Dice score increases of 3.9% on AISD and 3.8% on ISLES2018, demonstrating its superiority in EIL segmentation. Show less
no PDF DOI: 10.1109/JBHI.2024.3471627
CETP

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

Bioinformatic analysis identifies LPL as a critical gene in diabetic kidney disease via lipoprotein metabolism.

Qian Dong, Huan Xu, Pengjie Xu +2 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Diabetic kidney disease (DKD) is a common and serious complication of diabetes, affecting approximately 40% of patients with the condition. The pathogenesis of DKD is complex, involving multiple proce Show more
Diabetic kidney disease (DKD) is a common and serious complication of diabetes, affecting approximately 40% of patients with the condition. The pathogenesis of DKD is complex, involving multiple processes such as metabolism, inflammation, and fibrosis. Given its increasing incidence and associated mortality, there is an urgent need to identify novel pathogenic genes and therapeutic targets. This study systematically identified hub DKD-associated genes and their potential molecular mechanisms through bioinformatic analysis. Gene expression datasets from DKD patients and healthy controls were obtained from the GEO database. Hub genes were screened using differential expression analysis, weighted gene co-expression network analysis (WGCNA), LASSO regression, random forest (RF) algorithms, and consensus clustering for DKD patient classification. Additionally, immune cell infiltration analysis was performed on differentially expressed genes to explore the relationship between hub genes and the immune microenvironment. Potential drugs targeting LPL were predicted based on gene-drug interaction analysis. Immunohistochemistry was used to verify the expression of LPL and TNF-α in kidney tissues from patients with varying degrees of DKD severity, as well as their relationship with kidney function impairment. This study revealed that LPL, a lipoprotein metabolism gene, plays a crucial role in DKD, participating in cholesterol and glycerolipid metabolism as well as PPAR signaling. LPL expression was negatively correlated with pro-inflammatory M1 macrophages and various subsets of T cells, including naïve CD4 T cells and gamma delta T cells, while positively correlated with follicular helper T cells, suggesting its immune-regulation effects in DKD progression. Potential LPL-targeting drugs, such as Ibrolipim, anabolic steroid, and acarbose, might mitigate DKD. LPL expression was decreased with DKD severity and was correlated with TNF-α and kidney dysfunction markers, indicating its key role in DKD progression. LPL is a pivotal regulator of lipid metabolism and immune inflammation in DKD. Potential drugs targeting LPL offer new candidates for precision treatment of DKD. These findings lay a theoretical foundation for understanding the molecular mechanisms of DKD and developing LPL-based therapeutic strategies. Show less
📄 PDF DOI: 10.3389/fendo.2025.1620032
LPL

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

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

Research status and future perspectives of IL‑27 in the treatment of stroke (Review).

Weiqin Liu, Zhenyou Zou, Wenyang Li +4 more · 2025 · International journal of molecular medicine · added 2026-04-24
Stroke is a life‑threatening cerebrovascular disorder categorized into two major subtypes: Ischemic and hemorrhagic. Characterized by high morbidity and mortality rates, its clinical management remain Show more
Stroke is a life‑threatening cerebrovascular disorder categorized into two major subtypes: Ischemic and hemorrhagic. Characterized by high morbidity and mortality rates, its clinical management remains challenging due to limited therapeutic options. Interleukin (IL)‑27, a pleiotropic cytokine with demonstrated neuroprotective potential, has emerged as a promising candidate for stroke intervention. IL‑27 exerts immunomodulatory effects within the central nervous system, including suppression of proinflammatory T‑cell proliferation and induction of regulatory T‑cell differentiation. These mechanisms collectively attenuate neuroinflammation, mitigate neuronal apoptosis and prevent neurodegenerative processes. The efficacy of IL‑27 in reducing cerebral damage in both ischemic and hemorrhagic stroke models has been validated, although clinical translation remains to be achieved. The present review summarizes: i) The epidemiology of stroke; ii) the immunoregulatory functions of IL‑27 and its neuroprotective mechanisms across stroke subtypes; iii) innovative brain‑targeted delivery approaches; iv) IL‑27 clinical applicability with supporting evidence; and v) possible risks and solutions in clinical applications. By collating the current knowledge, the present study provides a translational framework for advancing IL‑27‑based therapies in stroke management. Show less
📄 PDF DOI: 10.3892/ijmm.2025.5557
IL27

The Impact of Cannabidiol (CBD) on Lipid Absorption and Lymphatic Transport in Rats.

Qi Zhu, Qing Yang, Ling Shen +2 more · 2025 · Nutrients · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/nu17061034
APOA4

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

Latent profile analysis of depression and anxiety comorbidity in patients with acute ischemic stroke: a prospective study.

Dongli Chen, Hong Zhang, Yuqi Xiu +5 more · 2025 · Frontiers in psychiatry · Frontiers · added 2026-04-24
Stroke is a leading cause of mortality and disability globally, with post-stroke depression and post-stroke anxiety being common and significant complications that hinder recovery and adversely affect Show more
Stroke is a leading cause of mortality and disability globally, with post-stroke depression and post-stroke anxiety being common and significant complications that hinder recovery and adversely affect quality of life. Although these conditions frequently co-occur, their heterogeneity remains poorly understood. This study integrates the Health Ecology Model (HEM) and employs Latent Profile Analysis (LPA) to identify distinct psychological profiles of depression and anxiety among patients with acute ischemic stroke (AIS), as well as to investigate their multilevel determinants. Patients with AIS from a tertiary hospital in Guangdong Province, China, from January to November 2024 were included. Within one week of stroke onset, the data of sociodemographic, clinical characteristics, swallowing function, stroke severity, activities of daily living, resilience and social support were collected according to the HEM guidelines. The Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7 were used to assess the depression and anxiety symptoms of the patients three months after stroke onset. LPA was employed to identify distinct psychological profiles, and variables with a A total of 551 patients with AIS were included in the study, 49 were lost to follow-up or withdrew, resulting in a final analytic sample of 502 participants (91.11%). Three distinct psychological profiles were identified: no depression-anxiety (67.93%), high-risk depression-anxiety (21.12%) and major depression-anxiety (10.95%). In the multivariate analysis, the results indicated that occupation (OR = 0.61, 95% CI [0.40-0.93]), National Institutes of Health Stroke Scale (NIHSS, OR = 1.60, 95% CI [1.06-2.42]), Barthel Index (BI, OR = 1.67, 95% CI [1.27-2.19]) and hypertension (OR = 2.37, 95% CI [1.29-4.35]) were independent predictors of the high-risk depression-anxiety profile, while NIHSS (OR = 2.33, 95% CI [1.42-3.85]), BI (OR = 2.65, 95% CI [1.62-4.35]) and resilience (OR = 0.92, 95% CI [0.87-0.98]) were significantly associated with the major depression-anxiety profile. This study reveals significant heterogeneity in psychological distress among AIS survivors. Key predictors of post-stroke emotional comorbidity include occupation, hypertension, stroke severity, activities of daily living and low resilience. Early identification of high-risk individuals can significantly enhance screening and intervention strategies, particularly by focusing on symptoms such as anhedonia and nervousness. Future research should focus on longitudinal designs and objective biomarkers to better understand the mechanisms behind post-stroke emotional comorbidity. Show less
📄 PDF DOI: 10.3389/fpsyt.2025.1651116
LPA

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

Apolipoprotein E ε4 homozygosity exacerbates retinal and cerebral microvascular dysfunction in Alzheimer's disease: A mediation analysis of vascular contributions to cognitive decline.

Yuntao Liu, HanYu Zhu, Youjie Wang +11 more · 2025 · Alzheimer's & dementia : the journal of the Alzheimer's Association · Wiley · added 2026-04-24
Apolipoprotein E (APOE) ε4 is the strongest genetic risk factor for Alzheimer's disease (AD), with homozygous carriers (ε4/ε4) experiencing accelerated cognitive decline. While its role in amyloid and Show more
Apolipoprotein E (APOE) ε4 is the strongest genetic risk factor for Alzheimer's disease (AD), with homozygous carriers (ε4/ε4) experiencing accelerated cognitive decline. While its role in amyloid and tau pathology is established, its impact on retinal and cerebral microvasculature remains underexplored. A total of 107 AD (46 non-carriers, 42 heterozygotes, 19 homozygotes) underwent optical coherence tomography angiography (OCTA) to assess retinal microvasculature and magnetic resonance imaging (MRI) -derived peak width of skeletonized mean diffusivity (PSMD) to evaluate cerebral small vessel disease. Plasma biomarkers (Aβ Homozygous APOE ε4 carriers exhibited the most severe reduction in retinal microvascular density and higher PSMD (p < 0.001). Superficial retinal vessels and PSMD partially mediated APOE ε4's association with cognitive impairment. APOE ε4 homozygosity exacerbates retinal and cerebral microvascular dysfunction, which partially mediates cognitive impairment in AD. Apolipoprotein E (APOE) ε4 homozygosity is associated with the most severe reductions in retinal microvascular densities and elevated cerebral small vessel disease (peak width of skeletonized mean diffusivity [PSMD]) in Alzheimer's disease (AD). Vascular dysfunction (retinal and cerebral) correlates with lower Aβ42, higher p-tau217/Aβ Show less
📄 PDF DOI: 10.1002/alz.71000
APOE

GIPR agonism and antagonism decrease body weight and food intake via different mechanisms in male mice.

Robert M Gutgesell, Ahmed Khalil, Arkadiusz Liskiewicz +21 more · 2025 · Nature metabolism · Nature · added 2026-04-24
Agonists and antagonists of the glucose-dependent insulinotropic polypeptide receptor (GIPR) enhance body weight loss induced by glucagon-like peptide-1 receptor (GLP-1R) agonism. However, while GIPR Show more
Agonists and antagonists of the glucose-dependent insulinotropic polypeptide receptor (GIPR) enhance body weight loss induced by glucagon-like peptide-1 receptor (GLP-1R) agonism. However, while GIPR agonism decreases body weight and food intake in a GLP-1R-independent manner via GABAergic GIPR Show less
📄 PDF DOI: 10.1038/s42255-025-01294-x
GIPR

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

Novel Double MYH7/MYBPC3 Variants in a Chinese Family of Hypertrophic Cardiomyopathy with Early-Onset and Sudden Death.

Bo Wang, Jia Zhao, Yanmin Zhang +14 more · 2025 · Cardiology · added 2026-04-24
Hypertrophic cardiomyopathy (HCM) is a common inherited heart condition. Traditional genetic testing is typically conducted on the proband only, with family members undergoing Sanger sequencing, which Show more
Hypertrophic cardiomyopathy (HCM) is a common inherited heart condition. Traditional genetic testing is typically conducted on the proband only, with family members undergoing Sanger sequencing, which may overlook other pathogenic variants. This study explores the gene sequencing strategy in a three-generation family based on genetic carrier status and examines the relationship between phenotypic characteristics and genotype. High-throughput second-generation sequencing was performed on the proband to analyze HCM-related pathogenic genes. Subsequently, the identified pathogenic variants were validated by Sanger sequencing in the proband and family members. Clinical, electrocardiographic, and echocardiographic assessments were conducted for family members. Second-generation sequencing of the proband (III7) revealed a pathogenic variant MYBPC3-P453Lfs. Initially, no HCM-related pathogenic variants were detected in another patient (III11), prompting additional sequencing of III11, which identified the MYH7-G823E pathogenic variant. Both patients had severe left ventricular outflow tract obstruction. Sanger sequencing showed that five family members carried both mutations. Among them, three died suddenly before age 40, one required an implantable cardioverter defibrillator for arrhythmias, and one developed HCM before adulthood. Cardiac magnetic resonance imaging (MRI) of patients carrying both mutations showed myocardial fibrosis of 32.75%, significantly higher than the 6.98% observed in patients carrying only one mutation. In families with varying HCM phenotypes, second-generation sequencing should be considered for all members. In this family, carrying one variant led to outflow tract obstruction, while carrying both variants resulted in severe disease, including sudden death and early onset. Cardiac MRI is crucial for assessing the severity of the disease within the family. Show less
no PDF DOI: 10.1159/000548235
MYBPC3

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

Development of Neuroprotective Agents for the Treatment of Alzheimer's Disease using Conjugates of Serotonin with Sesquiterpene Lactones.

Margarita Neganova, Junqi Liu, Yulia Aleksandrova +7 more · 2024 · Current medicinal chemistry · Bentham Science · added 2026-04-24
Sesquiterpene lactones are secondary plant metabolites with a wide variety of biological activities. The process of lactone conjugation to other pharmacophores can increase the efficacy and specificit Show more
Sesquiterpene lactones are secondary plant metabolites with a wide variety of biological activities. The process of lactone conjugation to other pharmacophores can increase the efficacy and specificity of the conjugated agent effect on molecular targets in various diseases, including brain pathologies. Derivatives of biogenic indoles, including neurotransmitter serotonin, are of considerable interest as potential pharmacophores. Most of these compounds have neurotropic activity and, therefore, can be used in the synthesis of new drugs with neuroprotective properties. The aim of this experimental synthesis was to generate potential treatment agents for Alzheimer's disease using serotonin conjugated with natural sesquiterpene lactones. Three novel compounds were obtained via the Michael reaction and used for biological testing. The obtained conjugates demonstrated complex neuroprotective activities. Serotonin conjugated to isoalantolactone exhibited strong antioxidant and mitoprotective activities. The agent was also found to inhibit β-site amyloid precursor protein cleaving enzyme 1 (BACE-1), prevent the aggregation of β-amyloid peptide 1-42, and protect SH-SY5Y neuroblastoma cells from neurotoxins such as glutamate and H In conclusion, the obtained results indicate that serotonin conjugates to sesquiterpene lactones are promising agents for the treatment of symptoms associated with Alzheimer's disease. Show less
no PDF DOI: 10.2174/0929867330666221125105253
BACE1