👤 Hongmei 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, 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

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset.

Zijian Wang, Radek Zenkl, Latifa Greche +33 more · 2025 · Plant phenomics (Washington, D.C.) · Elsevier · added 2026-04-24
Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection an Show more
Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 ​%. However, the precision for stems with 54 ​% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies. Show less
📄 PDF DOI: 10.1016/j.plaphe.2025.100084
LPA

Sex Differences in the Relationship Between Lipid Ratios and the Risk of Carotid Plaque.

Ling-Ling Wang, Zi-Xiang Xu, Bo-Qian Sun +3 more · 2025 · Angiology · SAGE Publications · added 2026-04-24
Lipid ratio is a balance between atherogenesis and antiatherogenesis. it is an important predictive marker of carotid plaque. The lipid ratios, which include non-high-density lipoprotein cholesterol ( Show more
Lipid ratio is a balance between atherogenesis and antiatherogenesis. it is an important predictive marker of carotid plaque. The lipid ratios, which include non-high-density lipoprotein cholesterol (non-HDL-C)/high-density lipoprotein cholesterol (HDL-C), remnant cholesterol (RC)/HDL-C, apolipoprotein B (ApoB)/apolipoprotein A1 (ApoA1), low-density lipoprotein cholesterol (LDL-C)/HDL-C, ApoB/HDL-C, total cholesterol (TC)/HDL-C, triglycerides (TG)/HDL-C, were included and analyzed. Sex differences in the relationship between lipid ratios and carotid plaque were discussed. The risk of carotid plaque was found to be significantly associated with the Non-HDL-C /HDL-C, RC/HDL-C, ApoB/ApoA1, LDL-C /HDL-C, ApoB/HDL-C, TC/HDL-C in females but not in males. The ApoB/HDL risk presented the highest relationship with carotid plaque in females only. The predictive value of the aforementioned lipid ratios for carotid plaque was observed in females only. Show less
no PDF DOI: 10.1177/00033197251316624
APOB

Bayesian fine-mapping and Mendelian randomization leveraging expression quantitative trait loci reveal novel candidate causal genes for body conformation traits in cattle.

Jun Teng, Chongwei Duan, Xinyi Zhang +9 more · 2025 · Journal of dairy science · added 2026-04-24
Cattle body size measurements constitute the conformation traits that facilitate their production, fertility, and longevity status. Prioritizing functional variants and causal genes of conformation tr Show more
Cattle body size measurements constitute the conformation traits that facilitate their production, fertility, and longevity status. Prioritizing functional variants and causal genes of conformation traits is essential for understanding their genetic basis. In this study, we conducted single-trait and multitrait GWAS for 20 body conformation traits using imputed sequence data in 7,674 Chinese Holstein individuals and identified 27 QTL regions. Leveraging these QTL regions, we performed multitrait Bayesian fine-mapping to identify 30 independent credible sets of putative causal variants. Incorporating GWAS and cis-acting expression QTL data, Mendelian randomization was used to infer 153 putative causal gene-trait relationships. The previously reported genes, such as CCND2, TMTC2, and NRG3, were confirmed in our study. Of note, several novel candidate causal genes were also identified, such as C1R, RIMS1, SERPINB8, NETO2, TTYH3, TTC3, ANAPC4, and PSMD13. Our results provide new insights into the regulatory mechanisms of body conformation traits in cattle. Show less
no PDF DOI: 10.3168/jds.2025-26361
ANAPC4

Selenium alleviates high-fat diet induced hepatocyte lipid accumulation via exosome miR-22/FGFR1 pathway in grass carp.

Chi Wang, Xiaotian Zhang, Guohao Liu +6 more · 2025 · The Journal of nutritional biochemistry · Elsevier · added 2026-04-24
The current study aims to investigate whether exosomal miRNAs are involved in lipid reduction by selenium (Se) in the liver of grass carp, through miRNA sequencing, transfection of miRNA mimic (miR-22 Show more
The current study aims to investigate whether exosomal miRNAs are involved in lipid reduction by selenium (Se) in the liver of grass carp, through miRNA sequencing, transfection of miRNA mimic (miR-22m) or inhibitor (miR-22i), isolation of hepatocyte-derived exosomes and treatment, and detection of lipid metabolism-related genes and proteins. The miRNAs sequencing and bioinformatics revealed that miR-22 was most abundantly expressed in the differentially expressed miRNAs after selenium treatment, and was enriched in lipid metabolism-related pathways. Moreover, Se significantly up-regulated the miR-22 levels and reduced the lipid content in liver or hepatocytes of grass carp. Furthermore, the miR-22m significantly increased levels of miR-22 and reduced lipid content in grass carp hepatocytes, which were consistent with the Se-treatment. However, the miR-22i reversed these trends. Besides, the miR-22 suppressed the FGFR1-PI3K-AKT-mTOR signaling pathway and its downstream genes related to lipid synthesis. More importantly, the Se-treated hepatocyte-exosomes which were enriched in the miR-22 significantly reduced the triglycerides content in the oleic acid-treated hepatocytes. In summary, Se alleviated high fat-induced lipid accumulation in grass carp liver by up-regulating the expression of miR-22 which negatively regulates FGFR1 and its downstream regulatory genes. Moreover, exosomes participate in the lipid reduction by Se, which may be through carrying miR-22. Show less
no PDF DOI: 10.1016/j.jnutbio.2025.109907
FGFR1

[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

IL-27Ra promotes the progression of neonatal necrotizing enterocolitis.

Yuling Yang, Yingyan Liu, Limei Zhong +5 more · 2025 · Scientific reports · Nature · added 2026-04-24
Neonatal necrotizing enterocolitis (NEC) is a life-threatening gastrointestinal disease of premature infants, characterized by immune dysregulation and compromised intestinal barrier integrity. Interl Show more
Neonatal necrotizing enterocolitis (NEC) is a life-threatening gastrointestinal disease of premature infants, characterized by immune dysregulation and compromised intestinal barrier integrity. Interleukin-27 receptor α (IL-27Ra), a critical component of the JAK-STAT signaling pathway, exhibits dual pro- and anti-inflammatory roles in various inflammatory conditions. However, its role in NEC pathogenesis remains unclear. To elucidate the functional role of IL-27Ra in NEC development and assess its potential as a therapeutic target. A multi-tiered approach was employed, including integrative analysis of clinical NEC specimens by single-cell and bulk RNA sequencing, and a neonatal mouse NEC model. NEC was induced in mice via hyperosmolar formula feeding combined with LPS gavage, intermittent hypoxia, and cold stress. Additional experiments included immunofluorescence staining for IL-27Ra, cytokine profiling (ELISA, quantitative real-time PCR (qPCR)), use of IL-27Ra knockout (IL-27Ra Show less
📄 PDF DOI: 10.1038/s41598-025-26899-w
IL27

High-density lipoprotein cholesterol and nuclear factor I A in type 2 diabetes and mild cognitive impairment: biomarkers and mechanistic insights.

Jin Zhao, Tingying Zhang, Yunling Zhu +5 more · 2025 · Archives of medical science : AMS · added 2026-04-24
Type 2 diabetes (T2D) and mild cognitive impairment (MCI) are interrelated conditions that significantly impair quality of life. This study aimed to identify a feasible biomarker for assessing T2D-MCI Show more
Type 2 diabetes (T2D) and mild cognitive impairment (MCI) are interrelated conditions that significantly impair quality of life. This study aimed to identify a feasible biomarker for assessing T2D-MCI risk and to evaluate a potential therapeutic strategy. We integrated data from the National Health and Nutrition Examination Survey (NHANES) with Mendelian randomization (MR) to investigate genetic causal relationships between T2D, MCI, and their shared biomarkers. Transcriptomic analysis identified T2D-associated genes. Clinical trials evaluated the short-term effects of modified fasting therapy (MFT) on glucose regulation and cognitive function. Cellular assays and patient samples were used to validate the regulatory roles of key genes in biochemical markers and downstream signaling pathways. Among 6,356 T2D and 1,138 MCI subjects, vitamin D, high-density lipoprotein cholesterol (HDL-C), globulin, and creatinine were associated with both conditions. MR analysis showed that higher HDL-C levels reduced T2D risk (0.9059, 95% CI: 0.8666-0.9470) but increased MCI risk (OR = 1.0482, 95% CI: 1.0216-1.0755). Nuclear factor I A ( HDL-C has divergent genetic effects on T2D and MCI. Show less
📄 PDF DOI: 10.5114/aoms/208529
APOE

Single nucleotide polymorphisms in ovarian cancer impacting lipid metabolism and prognosis: an integrated TCGA database analysis.

Haoyu Wang, Tian Tu, Lijun Yin +2 more · 2025 · BMC cancer · BioMed Central · added 2026-04-24
Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers in Show more
Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers intricately linked to OC's molecular pathogenesis represents a critical avenue for enhancing patient outcomes and survival prospects. In this comprehensive study, we embarked on a bioinformatics journey, leveraging the vast repository of single nucleotide polymorphism (SNP) data from OC patients available within the TCGA database. Our overarching goal was to unearth the genetic underpinnings of OC, shedding light on potential prognostic markers that could significantly impact clinical decision-making and patient care. Our meticulous analysis led to the discovery of five mutated genes-APOB, BRCA1, COL6A3, LRP1, and LRP1B-engaged in the intricate world of lipid metabolism. These genes, previously unexplored in the context of OC, emerged as prominent figures in our investigation, showcasing their potential roles in OC progression. The intricate interplay between lipid metabolism and cancer development has garnered considerable attention in recent years, and our findings underscore the relevance of these genes in the context of OC. To fortify our discoveries, we delved into the realm of survival analysis, a pivotal component of our investigation. The results yielded compelling evidence of significant correlations between patient survival and the expression levels of the aforementioned genes. This critical insight underscores the potential utility of these genes as prognostic markers, illuminating a path toward more personalized and effective approaches to patient care. Our study represents a multifaceted approach to unraveling the complex molecular pathogenesis of OC. By harnessing the power of high-throughput data mining, we uncovered genetic insights that may reshape our understanding of this formidable disease. We complemented these findings with advanced techniques such as RT-qPCR and Western blot, further dissecting the intricacies of OC's molecular landscape. This holistic approach not only deepens our understanding but also provides essential bioinformatics information that holds promise in assessing patient prognosis. In summary, our study represents a significant stride in the quest to decode the molecular intricacies of ovarian cancer. Our findings spotlight the potential prognostic significance of APOB, BRCA1, COL6A3, LRP1, and LRP1B, inviting further exploration into their roles in OC progression. Ultimately, our research carries the potential to shape the future of OC management, offering a glimpse into a more personalized and effective approach to patient care. Show less
📄 PDF DOI: 10.1186/s12885-025-13841-6
APOB

IL-27 regulates macrophage ferroptosis by inhibiting the Nrf2/HO1 signaling pathway in sepsis-induced ARDS.

Meng Xiong, Renjie Luo, Zhijiao Zhang +4 more · 2025 · Inflammation research : official journal of the European Histamine Research Society ... [et al.] · Springer · added 2026-04-24
Acute respiratory distress syndrome (ARDS) is a clinical syndrome characterized by high morbidity and mortality rates. Sepsis-induced ARDS involves excessive inflammatory responses, which are modulate Show more
Acute respiratory distress syndrome (ARDS) is a clinical syndrome characterized by high morbidity and mortality rates. Sepsis-induced ARDS involves excessive inflammatory responses, which are modulated by macrophages. This study aimed to elucidate the effect of Recombinant Mouse IL-27 Protein on macrophage ferroptosis and polarization, as well as its impact on sepsis-induced ARDS. A cecal ligation and puncture (CLP)-induced sepsis model was established using wild-type (WT) or IL27R In vitro, IL-27 alone did not alter the expression of proteins linked to the ferroptosis pathway or macrophage polarization. Contrastingly, the combination of IL-27 with LPS further amplified LPS-induced alterations in the ferroptosis pathway, thereby promoting macrophage M1 polarization and inhibiting M2 polarization. Additionally, IL-27 + LPS increased ROS levels in macrophages. A sepsis-induced ARDS mouse model was then established via CLP. In vivo, IL-27 exacerbated CLP-induced lung injury in WT mice. Additionally, it decreased the expression levels of ferroptosis-related proteins (Nrf2, HO-1, GPX4) and increased those of Ptgs2 in the lung tissue of septic mice. Besides, GSH and SOD levels in lung tissue were also reduced. Moreover, IL-27 also promoted M1 polarization and inhibited M2 polarization in macrophages. In IL27R Oltipraz may alleviate ARDS-related lung injury by up-regulating Nrf2 expression and concurrently inhibiting macrophage ferroptosis. Show less
📄 PDF DOI: 10.1007/s00011-024-01986-2
IL27

Quantitative proteomic analysis reveals potential serum diagnostic markers for colorectal adenoma.

Chengli Yu, Xin Huang, Yating Cao +7 more · 2025 · Frontiers in molecular biosciences · Frontiers · added 2026-04-24
Colorectal cancer (CRC) is one of the leading causes of cancer-related death, and most CRCs arise from colorectal adenomas. Early detection and removal of precancerous lesions during the adenoma-carci Show more
Colorectal cancer (CRC) is one of the leading causes of cancer-related death, and most CRCs arise from colorectal adenomas. Early detection and removal of precancerous lesions during the adenoma-carcinoma sequence can significantly reduce CRC risk. However, current clinical practice lacks rapid, noninvasive screening tools for reliable adenoma detection. Proteomic analysis was performed on serum samples from patients with inflammatory polyps (non-neoplastic), patients with adenomas, and healthy controls to identify key differentially expressed proteins capable of distinguishing adenoma patients. The alterations in these candidate proteins were further validated by ELISA to evaluate their potential as diagnostic biomarkers for colorectal adenoma. In two independent cohorts, we identified two candidate biomarkers, apolipoprotein A4 (APOA4) and filamin A (FLNA), through a multi-step selection process involving ANOVA p-value screening, sparse partial least squares discriminant analysis (sPLS-DA), and LASSO regression analysis. These candidates were subsequently validated in a third cohort using ELISA. The ELISA results for APOA4 were discordant with the liquid chromatography-tandem mass spectrometry (LC-MS/MS) findings. In contrast, FLNA levels measured by ELISA showed a progressive decrease from healthy controls to patients with inflammatory polyps and further to those with adenomas. We propose FLNA as a potential biomarker for the diagnosis of colorectal adenomas. The areas under the ROC curves exceeded 0.7 for both key clinical comparisons: 0.810 for adenomas versus healthy controls, and 0.734 for adenomas versus inflammatory polyps. Overall, this study not only enhances our understanding of the serum proteome in colorectal adenoma but also identifies FLNA as a promising biomarker for its clinical diagnosis. Show less
📄 PDF DOI: 10.3389/fmolb.2025.1628587
APOA4

Impact of learning APOE genotype on cognitively unimpaired adults: a pre-screening cohort study of the Alzheimer's Prevention Initiative Generation Study 1.

Jessica B Langbaum, Angela R Bradbury, Brian L Egleston +16 more · 2025 · The lancet. Healthy longevity · Elsevier · added 2026-04-24
The apolipoprotein E (APOE) gene is the best established genetic risk factor for Alzheimer's disease in later life, with the ε4 allele conferring higher risk. APOE disclosure is becoming increasingly Show more
The apolipoprotein E (APOE) gene is the best established genetic risk factor for Alzheimer's disease in later life, with the ε4 allele conferring higher risk. APOE disclosure is becoming increasingly common in the clinical care of people with Alzheimer's disease and in cognitively unimpaired adults. In this study, we aimed to describe changes in measures of genetic disease knowledge and psychiatric symptoms following APOE disclosure to cognitively unimpaired adults. Data were collected as part of the screening phase of the global, multicentre, Alzheimer's Prevention Initiative Generation Study 1 (NCT02565511). Eligible individuals were cognitively unimpaired (Mini-Mental State Exam total score ≥24), aged 60-75 years, and psychologically pre-screened for readiness (by measures of depressive symptoms and anxiety) to receive their APOE genotype from a health-care provider. Participants were assessed before disclosure, and 2-7 days, 6 weeks, 6 months, and 12 months after disclosure. Multivariable linear and ordinal logistic regressions were used to compare changes in genetic disease knowledge, anxiety, depression, and distress by APOE4 genotype status, adjusting for key covariates, with a focus on 2-7 days after disclosure. Multiple imputation by chained equations methods was used to account for missing outcome data. The trial took place between Nov 30, 2015, and Sept 23, 2019. In total, 9496 participants (including 790 APOE4 homozygotes, 4869 heterozygotes, and 3837 non-carriers) learned their APOE genotype from a health-care provider as part of Generation Study 1 screening. 4038 (42·5%) participants were in the 65-69-year age group, 5790 (61·0%) were female, 3706 (39·0%) were male, and 8862 (93·3%) self-identified as White. Increase in genetic disease knowledge 2-7 days after disclosure was greater in APOE4 homozygotes (mean 1·19 [SD 3·95]) than in heterozygotes (0·78 [3·95], p=0·042) and non-carriers (0·29 [3·96], p=0·0002). Disease-specific distress 2-7 days after disclosure increased more in homozygotes (2·25 [6·42]) than in heterozygotes (0·53 [5·08], p<0·0001) and non-carriers (0·79 [4·95], p<0·0001). Levels of anxiety 2-7 days after disclosure increased in homozygotes (0·17 [2·95]) but decreased in heterozygotes (-0·67 [2·68], p<0·0001) and non-carriers (-0·66 [2·67], p<0·0001). There were no significant changes in depressive symptoms following disclosure for any APOE4 group. Notably, for all APOE4 groups, increases in distress and anxiety were small and did not reach predefined levels of clinical concern. In cognitively unimpaired, psychologically pre-screened adults, APOE disclosure by a trained health-care provider was generally safe and well tolerated, consistent with results from previous studies. To our knowledge, this is the largest study experience of APOE disclosure to date, especially for homozygotes, and is notable for the older age of participants compared with previous research. These results are timely and important given anticipated increases in APOE disclosure to guide clinical decision making once an Alzheimer's disease prevention treatment is approved for cognitively unimpaired adults or if patients' family members are interested in genetic testing. Scalable approaches for returning Alzheimer's disease risk information are critical to meeting anticipated demand. Results from this study may be useful to bolster clinical translatability of disclosure programmes. The National Institute on Aging, Alzheimer's Association, Banner Alzheimer's Foundation, GHR Foundation, F-Prime Biomedical Research Initiative (FBRI), and Novartis Pharma. Show less
📄 PDF DOI: 10.1016/j.lanhl.2025.100778
APOE

CCL26-CX3CR1 Axis Mediates a Feedback Loop between Cancer Cell and PMN-MDSCs to Promote CD8

Xiaotao Jiang, Hui Wu, Ning Yan +14 more · 2025 · Research (Washington, D.C.) · added 2026-04-24
The development of an immunosuppressive microenvironment is a critical factor in stomach carcinogenesis. Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) serve a pivotal function in medi Show more
The development of an immunosuppressive microenvironment is a critical factor in stomach carcinogenesis. Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) serve a pivotal function in mediating immune suppression. However, the precise mechanisms underlying PMN-MDSCs infiltration into the tumor immune microenvironment (TIME) and their immunosuppressive functions remain poorly understood. In this investigation, we observed that PMN-MDSCs were up-regulated during stomach carcinogenesis, with gastric cancer (GC) cells secreting CCL26 to promote the infiltration of PMN-MDSCs into the TIME via the CX3CR1 receptor. The infiltrating CX3CR1 Show less
no PDF DOI: 10.34133/research.1002
SNAI1

HDAC3 activates endothelial NLRP3 inflammasome and promotes atherosclerosis via inhibiting the acetylation of specificity protein 1.

Lifang Chen, Wei Zhang, Huan Chen +11 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
Histone deacetylase 3 (HDAC3) is an epigenetic modifying enzyme closely linked to the development of atherosclerosis. Endothelial inflammation is a critical factor in atherosclerosis. However, the rol Show more
Histone deacetylase 3 (HDAC3) is an epigenetic modifying enzyme closely linked to the development of atherosclerosis. Endothelial inflammation is a critical factor in atherosclerosis. However, the role of HDAC3 in mediating epigenetic modifications and regulating endothelial inflammation in atherosclerosis remains unclear. This study aims to investigate the impact of HDAC3 on endothelial inflammation and its contribution to atherosclerosis. Firstly, single-cell transcriptomic analysis identified elevated expression of HDAC3 and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) in inflammatory endothelial cells of atherosclerotic plaques in symptomatic patients. Endothelial-specific knockout HDAC3 in an apolipoprotein E knockout (ApoE Show less
📄 PDF DOI: 10.1038/s41418-025-01620-6
APOE

Transcriptome analysis reveals reduced lipid accumulation and mitochondrial metabolic remodeling in ADCY3-overexpressing adipocytes.

Lu Liu, Houxue Cui, Zhongfang Xiang +2 more · 2025 · Functional & integrative genomics · Springer · added 2026-04-24
Excessive adipose tissue accumulation adversely impacts the health of both humans and livestock. Adenylyl cyclase 3 (ADCY3) is a promising anti-obesity target, yet its regulatory role in adipogenesis Show more
Excessive adipose tissue accumulation adversely impacts the health of both humans and livestock. Adenylyl cyclase 3 (ADCY3) is a promising anti-obesity target, yet its regulatory role in adipogenesis remains incompletely understood. Our findings revealed a dynamic pattern of ADCY3 expression during adipogenesis and lipid droplet (LDs) accumulation. Functional analyses demonstrated that ADCY3 overexpression impaired adipogenesis by downregulating adipogenic transcription factors CEBPα and PPARγ. Furthermore, it reduced both the number and size of LDs through suppressing triglyceride synthesis and fatty acid metabolism, concomitantly downregulating key genes involved in LDs formation (PLIN1, CIDEC, FIT2, and Seipin), as well as factors mediating glycerol ester synthesis and fatty acid metabolism (DGAT1, DGAT2, ACC, SCD, FASN, and ACSL1). Transcriptomic profiling revealed that ADCY3 overexpression suppressed PPARγ signaling, leading to the downregulation of oxidative phosphorylation genes encoded by both the nuclear and mitochondrial genomes. Our results implicate ADCY3 in the regulation of lipid metabolism, with the speculative involvement of mitochondrial metabolic remodeling. This perspective offers a framework for developing future interventions against excessive lipid deposition. Show less
no PDF DOI: 10.1007/s10142-025-01789-6
ADCY3

Biomimetic non-collagenous proteins-calcium phosphate complex with superior osteogenesis via regulating macrophage IL-27 secretion.

Shenglong Tan, Xinghong Luo, Yifan Wang +9 more · 2025 · Biomaterials · Elsevier · added 2026-04-24
Traumatic defects or non-union fractures presents a substantial challenge in the fields of tissue engineering and regenerative medicine. Although synthetic calcium phosphate-based biomaterials (CaPs) Show more
Traumatic defects or non-union fractures presents a substantial challenge in the fields of tissue engineering and regenerative medicine. Although synthetic calcium phosphate-based biomaterials (CaPs) such as dibasic calcium phosphate anhydrate (DCPA) are commonly employed for bone repair, their inadequate cellular immune responses significantly impede sustained degradation and optimal osteogenesis. In this study, drawing inspiration from the key structure of an acidic non-collagenous protein-CaP complex (ANCPs-CaP) essential for natural bone formation, we prepared biomimetic mineralized dibasic calcium phosphate (MDCPA). This preparation utilized plant-derived non-collagenous protein Zein as the organic template and acidic artificial saliva as the mineralization medium. Physicochemical property analysis revealed that MDCPA is a complex of Zein and DCPA, which mimics the composite of the natural ANCP-CaP. Moreover, MDCPA exhibited enhanced biodegradability and osteogenic potential. Mechanistic insight revealed that MDCPA can be phagocytized and degraded by macrophages via the FCγRIII receptor, leading to the release of interleukin 27 (IL-27), which promotes osteogenic differentiation by osteoimmunomodulation. The critical role of IL-27 in osteogenesis is further confirmed using IL-27 gene knockout mice. Additionally, MDCPA demonstrates effective healing of critical-sized defects in rat cranial bones within only 4 w, providing a promising basis and valuable insights for critical-sized bone defects regeneration. Show less
no PDF DOI: 10.1016/j.biomaterials.2024.122917
IL27

A novel splicing variant of CNTRL::FGFR1 in myeloid/lymphoid neoplasm with eosinophilia and rearrangement of FGFR1.

Xianqi Feng, Xueting Bai, Hong Zhang +7 more · 2025 · Journal of hematopathology · Springer · added 2026-04-24
Background Myeloid/lymphoid neoplasm with eosinophilia and rearrangement of FGFR1(MLN-FGFR1), also referred to as 8p11 myeloproliferative syndrome (EMS), arises from aberrant FGFR1 gene rearrangement Show more
Background Myeloid/lymphoid neoplasm with eosinophilia and rearrangement of FGFR1(MLN-FGFR1), also referred to as 8p11 myeloproliferative syndrome (EMS), arises from aberrant FGFR1 gene rearrangement in bone marrow hematopoietic stem cells, resulting in the transformation of myeloid/lymphoid cells into neoplastic growths. The clinical and laboratory features of affected individuals are influenced by the specific partner genes. Purpose This article aims to report a case of MLN-FGFR1 involving a novel CNTRL::FGFR1 splicing variant and to discuss its clinicopathological characteristics and treatment challenges. Methods/Results We report a case of MLN-FGFR1 in a 35-year-old male patient presenting with leukocytosis, lymphadenopathy, hepatosplenomegaly, and a mixed population of B lymphoblasts, T lymphoblasts, and monoblasts in the bone marrow and lymph nodes. Comprehensive molecular profiling, including chromosomal karyotyping, fluorescence in situ hybridization (FISH), targeted transcriptome sequencing, reverse transcription polymerase chain reaction (RT-PCR), and Sanger sequencing, identified a novel splicing variant of the CNTRL::FGFR1 fusion, resulting from a t(8;9)(p11;q33) translocation. This novel splicing variant involves an in-frame fusion between exon 38 of CNTRL and exon 11 of FGFR1, retaining the kinase domain of FGFR1 and leading to its constitutive activation. Despite multiple treatment regimens, the patient failed to achieve complete remission (CR). Conclusion The findings highlight the urgent need for targeted therapies, such as FGFR inhibitors, to improve outcomes in patients with FGFR1-rearranged malignancies. Show less
📄 PDF DOI: 10.1007/s12308-025-00670-6
FGFR1

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

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

Improving Light-Responsive Efficiency of Type II Porous Liquid by Tailoring the Functionality of Host.

Yang Liu, Han-Yan Jin, Meng-Meng Li +6 more · 2025 · Angewandte Chemie (International ed. in English) · Wiley · added 2026-04-24
Light-responsive porous liquids (LPLs) attract significant attention for their controllable gas uptake under light irradiation, while their preparation has remained a great challenge. Here we report t Show more
Light-responsive porous liquids (LPLs) attract significant attention for their controllable gas uptake under light irradiation, while their preparation has remained a great challenge. Here we report the fabrication of type II LPLs with enhanced light-responsive efficiency by tailoring the host's functionality for the first time. The functionality of light-responsive metal-organic cage (MOC-RL, constructed from dicopper and responsive ligands) is modified by introducing the second long-chain alkyl ligand, producing MOC-RL-AL as a new host. A spatially hindered solvent based on polyethylene glycol, IL-NTf Show less
no PDF DOI: 10.1002/anie.202501191
LPL

CAUSAL ASSOCIATION BETWEEN SEPSIS AND FIBROBLAST GROWTH FACTORS AS WELL AS THEIR RECEPTORS LEVELS: A TWO-SAMPLE MENDELIAN RANDOMIZATION STUDY.

Junru Dai, Bangbo Xia, Ning Liu +1 more · 2025 · Shock (Augusta, Ga.) · added 2026-04-24
Objective: The potential association between sepsis risk and circulating levels of fibroblast growth factors (FGFs) and their receptors (FGFRs) has been a focus of research; however, the causal relati Show more
Objective: The potential association between sepsis risk and circulating levels of fibroblast growth factors (FGFs) and their receptors (FGFRs) has been a focus of research; however, the causal relationship between them remains to be elucidated. We hypothesize a causal association between genetically predicted FGFs, FGFRs, and sepsis risk, and we conduct a Mendelian randomization (MR) study to validate this hypothesis. Methods: We utilized a two-sample MR design to assess the effect of genetic variants associated with various FGFs (FGF1, FGF2, FGF7, FGF16, FGF19, FGF21, FGF23, FGF5) and FGFRs (FGFR1, FGFR2, FGFR3, α-Klotho) on sepsis risk, using genome-wide association study summary statistics. Our MR analyses employed the inverse-variance weighted (IVW) method, along with weighted median, weighted mode, and MR-Egger regression, supplemented by sensitivity analyses to ensure robustness. Results: The MR analysis identified an unequal number of instrumental variables ranging from 2 to 17 for FGFs and FGFRs when sepsis was the outcome. No significant correlation was found between genetically determined FGF levels and sepsis risk by IVW analysis (all P > 0.05). Correspondingly, similar nonsignificant associations were observed for FGFRs (all P > 0.05). Other MR methods corroborated the IVW findings. Sensitivity analyses, including Cochran's Q test, MR-Egger, and MR pleiotropy residual sum and outlier, indicated no significant heterogeneity or pleiotropy in the relationships, with the exception of a nonsignificant correlation between FGFR1 and sepsis that persisted after the exclusion of an outlier (odds ratio, 0.84; P = 0.34). Conclusion: The analysis found no significant causal associations between FGFs, their receptors, and sepsis risk, indicating a need for further research on their complex interactions. Show less
no PDF DOI: 10.1097/SHK.0000000000002565
FGFR1

Blood and urine biomarkers and myocardial infarction: A 2-sample and multivariate combination of Mendelian randomization.

Yu Ding, Haoyang Ling, Xiuyan Chen +6 more · 2025 · Medicine · added 2026-04-24
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any m Show more
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any means to prevent several risks associated with MI. Blood and urine tests are frequently employed in clinical examinations to detect cardiovascular diseases at an early stage. Mendelian randomization (MR) is commonly employed to explore disease-trait relationships and uncover therapeutic targets. Our goal was to explore the genetic links between 35 blood and urine biomarkers and MI. Blood and urine biomarker MR correlations with MI risk were studied. In version R10, the UK Biobank and Finnish databases included blood and urine marker data and MI data (26,060 cases and 343,079 controls). We performed bidirectional 2-sample MR with 4 methods: inverse variance weighted, MR-Egger, weighted median, and weighted mode. Final causal associations were determined by inverse variance weighted. Sensitivity analyses (heterogeneity, pleiotropy) were conducted. MR-PRESSO and PhenoScanner were used to exclude invalid instruments. We used multivariate MR to filter the most important genes without including other positive genes. To identify positive gene pathways and gene networks that cause MI, we employed GeneMANIA for gene prediction. The findings revealed a positive genetic association between the 8 blood and urine biomarker levels and an elevated risk of MI. There are apolipoprotein B (APOB), glycated hemoglobin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, sex hormone-binding globulin, triglycerides, and urate. Moreover, APOB, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol selectively affect MI through the rejection of other positive gene stems. Finally, APOB and numerous genes strongly impact MI development. APOB collaborates with related genes to regulate plasma lipoprotein particle levels, sterol homeostasis, organization, lipid homeostasis, and remodeling in MI. Our research further reveals the causal relationship between MI and blood/urine biomarkers, providing a new perspective for the prevention, diagnosis, and treatment of MI. Blood and urine marker tests can subsequently be conducted based on these results to detect MI and study the underlying mechanisms linking these metabolites to MI. Show less
no PDF DOI: 10.1097/MD.0000000000046146
APOB

Transcriptomic and Proteomic Insights Into Buffalo Milk Fat Synthesis and the Role of IGFBP4 in BMECs.

Xinyue Shen, Chaobin Qin, Zhixiang Wang +5 more · 2025 · FASEB journal : official publication of the Federation of American Societies for Experimental Biology · added 2026-04-24
The content and composition of milk fat are critical determinants influencing milk flavor, nutritional value, and economic significance. Buffalo milk is characterized by its high-fat content and compl Show more
The content and composition of milk fat are critical determinants influencing milk flavor, nutritional value, and economic significance. Buffalo milk is characterized by its high-fat content and complex lipid profile, characterized by elevated levels of health-beneficial fatty acids such as linoleic acid, α-linolenic acid, and arachidonic acid. However, the molecular regulatory mechanisms governing milk fat synthesis in buffaloes remain incompletely elucidated. This study employed transcriptomic analysis of milk fat globules (MFGs) from buffaloes exhibiting high and low milk fat content, identifying 15 949 annotated genes, including 234 differentially expressed genes (DEGs). Functional enrichment analysis revealed that these DEGs were predominantly associated with cell proliferation and differentiation, glyconeogenesis, and reproductive system development. Notably, the expression of IGFBP4, AGPAT4, GPAT3, GPR84, and PC exhibited positive correlations with buffalo milk fat content, identifying them as potential candidate genes regulating milk fat synthesis. Proteomic profiling identified 1678 proteins, including 53 differentially expressed proteins (DEPs). Enrichment analysis indicated that DEPs were primarily involved in nucleotide metabolism, the tricarboxylic acid (TCA) cycle, glycerophospholipid metabolism, and TGF-β signaling. Integrated analysis revealed potential interactions involving the IGFBP4 and PC genes, as well as the ACO1, TMED7, and APRT proteins, highlighting IGFBP4 as a pivotal regulator of milk fat synthesis. Functional validation demonstrated that overexpression or knockdown of IGFBP4 in buffalo mammary epithelial cells (BMECs) significantly modulated cell proliferation and altered the expression of key milk fat synthesis-related genes (FABP3, LPL, SCD, ACACA, and FASN), indicating that IGFBP4 can promote de novo fatty acid synthesis and intracellular lipid storage while inhibiting exogenous fatty acid uptake. Collectively, this study provides novel mechanistic insights into the regulation of milk fat synthesis in buffaloes and establishes a foundation for enhancing lactation traits through targeted genetic breeding strategies. Show less
📄 PDF DOI: 10.1096/fj.202502191R
LPL

Branched-chain amino acid and cancer: metabolism, immune microenvironment and therapeutic targets.

Hao Xiong, Ruiqi Liu, Keke Xu +7 more · 2025 · Journal of translational medicine · BioMed Central · added 2026-04-24
Cancer is one of the major diseases threatening human health in the world. According to the latest global cancer statistics from the International Agency for Research on Cancer (IARC), there were appr Show more
Cancer is one of the major diseases threatening human health in the world. According to the latest global cancer statistics from the International Agency for Research on Cancer (IARC), there were approximately 20 million new cancer cases and 10 million cancer deaths worldwide. Amidst this global health concern, branched chain amino acids have emerged as key players, playing an important role in the occurrence and development of cancer. In certain malignancies like colorectal cancer, the average level of BCAA in tumor tissues is twice that in normal tissues. BCAA metabolism is intricately associated with the progression of multiple tumors and is modulated by diverse enzymes, including BCAT, BCKDH, and BCKDK. The metabolism of BCAA involves multiple enzymes and biochemical processes via signaling pathways such as PI3K/AKT/mTOR and AMPK/mTOR, etc. In addition, mTOR inhibitors show potential value in cancer treatment by regulating the metabolism and signaling pathways of tumor cells, which provides a new direction for anticancer efforts. Simultaneously, BCAAs are closely associated with tumor immunity, including NK cells, CD4 Show less
📄 PDF DOI: 10.1186/s12967-025-06664-3
BCKDK

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

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

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

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

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

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

The multi-protective role of dietary betaine in largemouth bass (

Yi-Jia Liu, Jian Guo, Chang-Da Li +2 more · 2025 · Frontiers in physiology · Frontiers · added 2026-04-24
Intensive aquaculture frequently utilizes high-fat diets (HF) as a cost-effective strategy, yet this practice often induces hepatic steatosis, oxidative stress, and chronic inflammation in carnivorous Show more
Intensive aquaculture frequently utilizes high-fat diets (HF) as a cost-effective strategy, yet this practice often induces hepatic steatosis, oxidative stress, and chronic inflammation in carnivorous fish. Betaine, a natural methyl donor, has shown potential as a functional feed additive, but its comprehensive protective mechanisms under HF stress remain to be fully elucidated. Juvenile largemouth bass (Micropterus salmoides) were fed one of four isonitrogenous diets for 8 weeks: a normal-fat control (Control), a high-fat diet (HF), and two high-fat diets supplemented with 0.5% (HFB0.5) or 1.0% (HFB1) betaine. Growth performance, digestive enzyme activities, serum biochemical parameters, hepatic antioxidant capacity, and the expression of genes related to antioxidant defense, lipid metabolism, and inflammation were analyzed. The HF group exhibited significantly impaired growth, digestive function, and antioxidant capacity, along with elevated lipid peroxidation, dyslipidemia, and pro-inflammatory cytokine expression. Betaine supplementation restored growth performance and feed efficiency to control levels, ameliorated digestive enzyme activities (particularly enhancing lipase), and activated the hepatic Nrf2-Keap1 pathway, upregulating antioxidant genes (nrf2, sod1, cat, gpx, ho-1, gr) and enhancing enzyme activities. Betaine also improved serum lipid profiles, upregulated genes related to fatty acid oxidation (pparα, cpt-1) and lipolysis (lpl, hsl), suppressed lipogenic genes (srebp-1, fas), and rebalanced inflammatory cytokines by reducing tnf-α and il-1β while increasing tgf-β1 and il-10. Dietary betaine effectively counteracts HF-induced metabolic stress in M. salmoides through coordinated multi-pathway regulation. It enhances antioxidant defense, reprograms hepatic lipid metabolism toward catabolism, and restores inflammatory homeostasis. These findings underscore betaine's role as a multi-functional feed additive capable of mitigating HF-related metabolic disorders and promoting overall health in carnivorous fish aquaculture. Show less
📄 PDF DOI: 10.3389/fphys.2025.1742669
LPL

Disruption of cerebral cholesterol homeostasis by PS-NPs: astrocytic endoplasmic reticulum stress.

Lei Tian, Yizhe Wei, Jianping Ma +10 more · 2025 · Journal of nanobiotechnology · BioMed Central · added 2026-04-24
Cholesterol plays a crucial role in regulating synaptic membrane fluidity and ion channels. Due to the blood-brain barrier, cholesterol in the brain is primarily self-synthesized by astrocytes. Howeve Show more
Cholesterol plays a crucial role in regulating synaptic membrane fluidity and ion channels. Due to the blood-brain barrier, cholesterol in the brain is primarily self-synthesized by astrocytes. However, limited research has been conducted on the effects of polystyrene nanoplastic (PS-NPs) on intracranial cholesterol metabolic pathways. In this study, we exposed whole-brain organoids (WBOs) to PS-NPs and identified significant changes in endoplasmic reticulum stress and cholesterol biosynthesis pathways through whole-transcriptome sequencing. To investigate potential mechanisms of altered cholesterol pathways, we constructed a Transwell neuronal-astrocyte co-culture model. Results demonstrated that PS-NPs induced significant endoplasmic reticulum stress in astrocytes, specifically manifested by elevated levels of ATF4 and CHOP, along with increased autophagy indicated by the elevated LC3-II/I ratio. PS-NPs significantly inhibited the AKT/ACLY pathway of cholesterol biosynthesis, leading to marked reductions in acetyl-CoA and cholesterol within astrocytes (P < 0.05). In addition, PS-NPs led to a significant reduction of apolipoprotein APOE, which hindered cholesterol transport and ultimately inhibited synaptin (SYN) formation. In summary, PS-NPs induce endoplasmic reticulum stress and autophagy in astrocytes, impair cholesterol de novo synthesis and apolipoprotein-mediated transport, ultimately inhibiting neuronal synaptogenesis. Furthermore, specific inhibition of ERs restored cholesterol synthesis in astrocytes and neuronal synapses. This study demonstrates that PS-NPs produce neurotoxic effects by affecting cholesterol homeostasis in the brain. Show less
📄 PDF DOI: 10.1186/s12951-025-03949-z
APOE

Ursolic Acid Inhibits Triple-Negative Breast Cancer Progression by Modulating the FGFR1/AKT/ERK Pathway: Evidence from Network Pharmacology and Experimental Validation.

Ziming Chen, Weiqiang Guo, Yahan Gao +6 more · 2025 · Anti-cancer agents in medicinal chemistry · Bentham Science · added 2026-04-24
Ursolic acid (UA) exhibits antitumor activity; however, its effects and mechanisms on triple-negative breast cancer (TNBC) cells are not well understood. The present study aimed to explore the anti- T Show more
Ursolic acid (UA) exhibits antitumor activity; however, its effects and mechanisms on triple-negative breast cancer (TNBC) cells are not well understood. The present study aimed to explore the anti- TNBC mechanisms of UA by network pharmacology and experimental validation. TNBC cell lines MDA-MB-231 and BT-549 cells were treated with UA. A CCK-8 assay was performed to detect cell growth, while flow cytometry assessed cell cycle arrest and apoptosis. The underlying mechanism and potential targets of UA for TNBC treatment were investigated by network pharmacology, including PharmMapper database, GO, KEGG enrichment, and PPI analysis. The protein expressions and phosphorylation levels of FGFR1, AKT, and ERK were measured by western blot. Pull-down assay, cellular thermal shift assay (CETSA), and molecular docking were used to analyze the interaction between UA and FGFR1. Xenograft models were established to examine the effect of UA on TNBC tumor growth. UA effectively reduced cell viability, induced apoptosis, and arrested cell cycle in TNBC cells. Moreover, UA significantly regulated the expression of Bcl-2 and Bax to induce apoptosis. The results of network pharmacology and western blot suggested that UA reduced FGFR1/AKT/ERK pathway. Furthermore, pull-down, CETSA, and molecular docking results revealed that UA directly bound to FGFR1. In the xenograft model, UA inhibited the growth by suppressing FGFR1. In this study, we employed network pharmacology and experimental approaches to elucidate the mechanism of UA on TNBC. The results demonstrated that UA targeted FGFR1 to inhibit TNBC via mediating FGFR1/AKT/ERK pathway. Our findings demonstrate that UA inhibits the FGFR1/AKT/ERK pathway by directly targeting FGFR1, thereby suppressing TNBC progression and supporting its potential as a therapeutic agent for TNBC treatment. Show less
no PDF DOI: 10.2174/0118715206379579250722053647
FGFR1

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

Liraglutide improves cognition function in streptozotocin-induced diabetic rats by downregulating β-secretase and γ-secretase and alleviating oxidative stress in HT-22 cells.

Lou-Yan Ma, Song-Fang Liu, Zheng-Quan Ma +11 more · 2025 · Endocrine journal · added 2026-04-24
Diabetes has been regarded as an independent risk factor for Alzheimer's disease (AD). Liraglutide could improve cognition in AD mouse models, but its precise mechanism remains unclear. In this study, Show more
Diabetes has been regarded as an independent risk factor for Alzheimer's disease (AD). Liraglutide could improve cognition in AD mouse models, but its precise mechanism remains unclear. In this study, we used STZ-induced diabetic rats and HT-22 cells to investigate the effects of liraglutide. The MWM test, MTT assay, ELISA, western blot, and immunofluorescence were used in this research. Diabetic rats induced by STZ displayed a longer escape latency and entered the target zone less frequently (p < 0.05) in the MWM test. Intraperitoneal injection of liraglutide improved the cognition of diabetic rats (p < 0.05) and reduced Aβ42 expression in the hippocampus (p < 0.05). In vivo experiments showed that HT-22 cell viability decreased in the HG group, but liraglutide (100 nmol/L and 1 μmol/L) enhanced HT-22 cell viability (p < 0.05). Oxidative stress markers were upregulated in HT-22 cells in the HG group, while liraglutide treatment significantly reduced these markers (p < 0.05). Western blot and immunofluorescence analyses demonstrated increased levels of Aβ, BACE1, and γ-secretase in HT-22 cells in the HG group (p < 0.05), whereas these levels were reduced in the liraglutide treatment group (p < 0.05). These effects were reversed by the nuclear factor kappa B (NF-κB) and extracellular signal-regulated kinase 1/2 (ERK1/2) inhibitors (p < 0.05). These findings suggest that liraglutide improved the cognition of diabetic rats and might exert its protective effects by reducing oxidative stress, downregulating BACE1 and γ-secretase expression, and decreasing Aβ deposition via the NF-κB and ERK1/2 pathways. Show less
📄 PDF DOI: 10.1507/endocrj.EJ23-0723
BACE1