👤 Junwen Liu

🔍 Search 📋 Browse 🏷️ Tags ❤️ Favourites ➕ Add 🧪 BiometalDB 🧬 Extraction
3182
Articles
1983
Name variants
Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Chih Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Huazhen Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, 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

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

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

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

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

Systemic angiogenic protein changes following propranolol therapy in infantile hemangioma: a multi-target perspective.

Lijun Liang, Yan Zhang, Yan Ma +3 more · 2025 · Frontiers in pharmacology · Frontiers · added 2026-04-24
Infantile hemangioma (IH) is a common benign vascular tumor in infants, often requiring intervention due to potential functional impairment and cosmetic concerns. Propranolol, a nonselective β-adrener Show more
Infantile hemangioma (IH) is a common benign vascular tumor in infants, often requiring intervention due to potential functional impairment and cosmetic concerns. Propranolol, a nonselective β-adrenergic receptor blocker, is the first-line therapy for IH, yet its mechanisms remain incompletely elucidated. This prospective study investigated the systemic angiogenic protein profile changes in response to propranolol in 14 treatment-naïve IH infants compared to 14 healthy controls using antibody array analysis. We identified twenty-six angiogenic proteins significantly downregulated in pretreatment IH patients compared to healthy controls. After 3 months of propranolol treatment, six proteins including HB-EGF, TGFα, ANGPTL4, Follistatin, Tie-1 and PLGF were significantly upregulated. Bioinformatic enrichment analysis revealed that these proteins are involved in key biological processes and signaling pathways, including epithelial cell proliferation, angiogenesis regulation, VEGF signaling, ERBB-EGFR axis, Ras-MAPK, and PI3K-Akt pathways. These results suggest that propranolol treatment is associated with a rebalancing of dysregulated angiogenic proteins in IH, through modulating both pro- and anti-angiogenic factors to rebalance vascular homeostasis. Our study provides novel insights into the systems-level pharmacological actions of propranolol and proposes potential biomarkers for treatment response evaluation. Show less
📄 PDF DOI: 10.3389/fphar.2025.1706048
ANGPTL4

Metabolic Risk Factors Evaluation of Obstructive Sleep Apnea Among Patients with Polycystic Ovary Syndrome.

Ling-Xia Ha, Jin-Juan Wang, Ying-Ying Yuan +2 more · 2025 · International journal of women's health · added 2026-04-24
Women diagnosed with PCOS exhibit a high prevalence of obstructive sleep apnea (OSA). This study aims to assess risk factors of OSA among patients with PCOS. This retrospective study included 126 pati Show more
Women diagnosed with PCOS exhibit a high prevalence of obstructive sleep apnea (OSA). This study aims to assess risk factors of OSA among patients with PCOS. This retrospective study included 126 patients with PCOS who were categorized into an OSA group (n = 30) and a non-OSA group (n = 96) according to the apnea-hypopnea index (AHI). A control group comprised 72 patients without PCOS who presented during the same period for infertility due to fallopian tube, pelvic, or male factors. Patients with PCOS A multivariate logistic regression model was used to analyze independent risk factors for OSA in the PCOS group. Patients with PCOS had significantly higher AHI values and elevated values for various physical indicators, including body mass index (BMI) and neck, waist, and hip circumferences; prolactin (PRL); fasting plasma glucose (FPG); insulin (FINS); triglycerides (TG); homeostasis model assessment of insulin resistance (HOMA-IR); 2-hour postprandial glucose (2-hPG) and insulin (2-hINS); AHI; and oxygen desaturation index (ODI). Conversely, levels of high-density lipoprotein cholesterol (HDL-C) and lowest oxygen saturation (LSaO OSA in PCOS patients is linked to metabolic indicators. High neck circumference and BMI levels were independent risk factors, highlighting the need for OSA in routine PCOS screening, particularly in the context of metabolic dysregulation. Show less
📄 PDF DOI: 10.2147/IJWH.S543184
APOB

Admixture mapping identifies complex trait associations with local ancestry in the

Wilfredo G Gonzalez Rivera, Youwen Liu, Tara Mirmira +6 more · 2025 · medRxiv : the preprint server for health sciences · added 2026-04-24
Genetic studies have largely focused on homogeneous populations, limiting our understanding of the genetic architecture of complex traits in admixed individuals. The advent of diverse biobanks like th Show more
Genetic studies have largely focused on homogeneous populations, limiting our understanding of the genetic architecture of complex traits in admixed individuals. The advent of diverse biobanks like the Show less
no PDF DOI: 10.64898/2025.12.29.25343152
ZPR1

Insulin resistance as potential mediator linking ApoB/ApoA1 to MAFLD, but not inflammation.

Mengying Yang, Xiaoman Liu, Qianqian Li +2 more · 2025 · Therapeutic advances in endocrinology and metabolism · SAGE Publications · added 2026-04-24
Metabolic-associated fatty liver disease (MAFLD) is closely associated with insulin resistance (IR) and systemic inflammation. Apolipoprotein A1 (ApoA1) and Apolipoprotein B (ApoB), as notable non-tra Show more
Metabolic-associated fatty liver disease (MAFLD) is closely associated with insulin resistance (IR) and systemic inflammation. Apolipoprotein A1 (ApoA1) and Apolipoprotein B (ApoB), as notable non-traditional lipid markers, have demonstrated distinct advantages in identifying risks related to metabolic syndrome and coronary atherosclerosis, yet its association with MAFLD and the mediating roles of IR/inflammation remain unclear. This retrospective investigation involved 1061 participants, categorized into a non-MAFLD group ( The MAFLD group exhibited markedly elevated levels of neutrophils/lymphocytes, neutrophils/platelets, systemic immune inflammation index, systemic inflammation response index, pan-immune-inflammation value and triglyceride-glucose index (TyG), TyG body mass index (TyGBMI), and metabolic score for insulin resistance (METS-IR) compared to the non-MAFLD group. Logistic regression analysis revealed that ApoB/ApoA1, TyG, TyGBMI, and METS-IR were markedly linked to MAFLD risk. Spearman's correlation analysis identified substantial positive links between ApoB/ApoA1 and TyG ( Our findings clarify the complex interrelationships between ApoB/ApoA1, MAFLD risk, inflammation, and IR, and for the first time, demonstrate that IR may act as a key potential mediator in the link between ApoB/ApoA1 and MAFLD, rather than systemic inflammation. This suggests that IR may serve a more prominent role than chronic systemic inflammation in the association between lipid metabolism and MAFLD risk, and intervening in IR may be more effective than anti-inflammatory therapy in blocking the progression from lipid metabolism disorders to MAFLD. Show less
📄 PDF DOI: 10.1177/20420188251378318
APOB

Gestational exposure to PM

Jianong Lv, Ruiyang Ding, Chen Liang +8 more · 2025 · Journal of advanced research · Elsevier · added 2026-04-24
Increasing epidemiological studies suggested that maternal exposure to fine particulate matter (PM This study aimed to investigate PM In the present study, we first identified that angiopoietin-like 4 Show more
Increasing epidemiological studies suggested that maternal exposure to fine particulate matter (PM This study aimed to investigate PM In the present study, we first identified that angiopoietin-like 4 (ANGPTL4), sirtuin 3 (SIRT3), and D2-hydroxyglutarate (D2-HG) may be potential biomarkers for PM These findings suggested that PM Show less
no PDF DOI: 10.1016/j.jare.2025.11.022
ANGPTL4

New heights in CT differentiation of adrenal lesions and a rational definition of non-enhancement.

Lichun Liu, Fangmei Zhu, Zongfeng Niu +4 more · 2025 · BMC medical imaging · BioMed Central · added 2026-04-24
To explore the stratification and identification of adrenal lipid-poor adenomas (LPAs), adrenal cysts (ACs), and adrenal ganglioneuromas (AGNs) from each other using contrast-enhanced computed tomogra Show more
To explore the stratification and identification of adrenal lipid-poor adenomas (LPAs), adrenal cysts (ACs), and adrenal ganglioneuromas (AGNs) from each other using contrast-enhanced computed tomography (CT). Pathologically confirmed, 348 patients were categorized into Model 1 (260 LPAs, 34 ACs), Model 2 (260 LPAs, 54 AGNs), and Model 3 (34 ACs, 54 AGNs). Statistical analyses were performed on the differences in the degree of enhancement in the arterial/venous phase (DEap/DEvp) (in HU) and the corresponding graded variables for the arterial/venous phase (GVap/GVvp). Models were evaluated via receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer‒Lemeshow (HL) test. The values of the area under the curve (AUC) for DEap, DEvp, GVap, and GVvp in Models 1-3 were 0.996, 1.000, 0.993, and 0.999; 0.980, 0.978, 0.961, and 0.975; and 0.734, 0.892, 0.725, and 0.883, respectively. The p values of the HL test were 0.984, 1.000, and 0.113, respectively. The DEvp interval values (in HU) for the LPAs, ACs, and AGNs were [4.9, 190.2] HU, [-3.7, 4.2] HU, and [-4.8, 41.8] HU, respectively. The GVap and GVvp ranges for the LPAs, ACs, and AGNs were [1, 6], [0, 2], and [0, 2] and [1, 6], [0, 1], and [0, 5], respectively. DEvp enhanced discrimination in Models 1 and 3, whereas DEap performed better in Model 2. Lesions with DEvp < 4.5 HU are likely represent non-enhancing pathology (e.g., cysts). When both GVap and GVvp are 0, when both GVap and GVvp are [2, 6], and when GVap is [3, 6] and GVvp is 6, LPA, AC, and AGN are excluded. Not applicable. Show less
📄 PDF DOI: 10.1186/s12880-025-01916-6
LPA

Gene-Lifestyle Interactions in Renal Dysfunction: Polygenic Risk Modulation via Plant-Based Diets, Coffee Intake, and Bioactive Compound Interactions.

Meiling Liu, Da-Sol Kim, Sunmin Park · 2025 · Nutrients · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/nu17050916
CPS1

The Predictive Value of Monocyte-to-HDL Cholesterol Ratio in Patients with Dilated Cardiomyopathy and Associated Pulmonary Hypertension.

Fengfeng Deng, Jianqi Sun, Lixia Liu +6 more · 2025 · Cardiovascular & hematological agents in medicinal chemistry · Bentham Science · added 2026-04-24
Pulmonary Hypertension (PH) is a significant contributor to cardiac mortality in Dilated Cardiomyopathy (DCM) patients. Inflammatory processes and oxidative stress play pivotal roles in the advancemen Show more
Pulmonary Hypertension (PH) is a significant contributor to cardiac mortality in Dilated Cardiomyopathy (DCM) patients. Inflammatory processes and oxidative stress play pivotal roles in the advancement of Pulmonary Hypertension (PH). The Monocyte-to-High-- Density-Lipoprotein Cholesterol Ratio (MHR), a newly identified biomarker indicative of inflammatory and oxidative stress, has not been extensively researched in the context of pulmonary hypertension, especially within the scope of dilated cardiomyopathy. Given the reason mentioned above, our research explores the correlation between the MHR and the severity of PH in patients suffering from DCM. In this study, we conducted a retrospective review of medical data from 107 individuals diagnosed with non-ischemic DCM, evaluating their clinical profiles, biochemical indicators, MHR, and echocardiographic parameters. We analyzed the relationships between Pulmonary Arterial Systolic Pressure (PASP) and the Ejection Fraction of the Left Ventricle (LVEF). Utilizing logistic regression analysis, we determined the predictors of PH. Findings indicated that the DCM-PH group exhibited a significantly larger male population and elevated New York Heart Association (NYHA) classification scores (both with p-values <0.001 and 0.01, respectively) compared to the DCM-only group. A positive association was observed between the PASP and parameters, such as the Dimensions of the Left Atrium (LAD) and Left Ventricle in Systole (LVDs), Monocyte (M) levels, Direct Bilirubin (DB), and MHR. Conversely, an inverse relationship was noted with serum lipid profiles, including Total Cholesterol (TC), HDL Cholesterol (HDL-c), and apolipoprotein A1. LVEF demonstrated positive linkage with the same lipid profiles and the Left Ventricular Posterior Wall Thickness (LVPWT) yet showed negative correlations with the NYHA classification, Red Blood Cell Distribution Width Standard Deviation (RDW-SD), Total Bilirubin (TB), Direct Bilirubin (DB), and dimensions of the left ventricle in diastole and systole, as well as MHR. Through logistic regression analysis, several factors were recognized as significant predictors for the severity of PH within the DCM cohort, with weight (OR1.20, CI 1.022-1.409, p=0.026), RDW-SD (OR1.988, CI 1.015-3.895, p=0.045), LVPW (OR3.577, CI 1.307-9.792, p=0.013), LVDd (OR1.333, CI 1.058-1.680, p=0.015), MHR (OR3.575, CI 1.502-8.506, p=0.032), and TB (OR1.416, CI 1.014-1.979, p=0.041) showing positive associations, while apoB (OR0.001 CI0.001-0.824, p=0.045) exhibiting negative associations, all with p-values <0.05. Higher MHR and LVD correlate with increased PASP and reduced LVEF in DCMPH patients. MHR and LVPW are independent predictors of PH severity, indicating their potential as novel severity markers in DCM-related PH. Show less
no PDF DOI: 10.2174/0118715257294388250326034612
APOB

The Batten disease gene Cln3 is required for the activation of intestinal stem cell during regeneration via JAK/STAT signaling in

Zihua Yu, Jinhua Yan, Zhiming Liu +3 more · 2025 · Frontiers in cell and developmental biology · Frontiers · added 2026-04-24
CLN3 mutation causes Juvenile neuronal ceroid lipofuscinosis (JNCL, also known as Batten disease), an early onset neurodegenerative disorder. Patients who suffer from Batten disease often die at an ea Show more
CLN3 mutation causes Juvenile neuronal ceroid lipofuscinosis (JNCL, also known as Batten disease), an early onset neurodegenerative disorder. Patients who suffer from Batten disease often die at an early age. However, the mechanisms underlying how CLN3 loss develops Batten disease remain largely unclear. Here, using Show less
📄 PDF DOI: 10.3389/fcell.2025.1508714
CLN3

[Allogeneic hematopoietic stem cell transplantation for pediatric acute leukemia harboring the

Yu Chen, Yong-Bing Zhu, Jia-Si Zhang +4 more · 2025 · Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · added 2026-04-24
A retrospective analysis was conducted on the clinical course of two children with
no PDF DOI: 10.7499/j.issn.1008-8830.2503092
MLLT10

Prognostic value and molecular mechanism of photodynamic therapy and apoptosis related gene FGFR1 in bladder cancer.

Lei Wang, Yanchun Wang, Yanqing Wang +4 more · 2025 · Frontiers in oncology · Frontiers · added 2026-04-24
Photodynamic therapy (PDT) is an innovative non-invasive therapy for human cancer treatment. The significance of apoptosis-related genes (ARGs) in the prognosis of bladder cancer (BLCA) has gradually Show more
Photodynamic therapy (PDT) is an innovative non-invasive therapy for human cancer treatment. The significance of apoptosis-related genes (ARGs) in the prognosis of bladder cancer (BLCA) has gradually emerged. Therefore, this study aims to investigate the prognostic significance and pathogenesis of PDT related genes (PDTRGs)-ARGs in BLCA cases. Based on the BLCA data in TCGA, PDTRGs-ARGs with prognostic value in BLCA patients were screened. Subsequently, the prognostic value and diagnostic performance of all candidate genes were evaluated by univariate Cox regression analysis and ROC curves. Then, GSEA, GSVA and immune microenvironment analysis were conducted based on candidate genes. Finally, the molecular mechanisms of key candidate genes in BLCA patients were initially explored by qRT-PCR, CCK-8 analysis, Transwell Assay and Western Blotting. A total of 5 ARGs-PDTRGs (EMP1, FGFR1, PLPPR4, JUN, TNFRSF25) were screened as prognostic biomarkers for BLCA. Survival analysis revealed significant differences in overall survival of the five prognostic biomarkers in the high/low expression groups. ROC curve analysis revealed that the five prognostic biomarkers had strong prognostic predictive ability. QRT-PCR proved that the expression of EMP1, FGFR1, PLPPR4 and JUN was obviously reduced, while TNFRSF25 was markedly increased in BLCA tissue samples and cell lines. The following research confirmed that FGFR1 inhibited the biological process of T24 cells by activating cGMP-PKG pathway. Five ARGs-PDTRGs (EMP1, FGFR1, PLPPR4, JUN, TNFRSF25) were screened as prognostic biomarkers for BLCA. Among them, FGFR1 inhibits the biological process of T24 cells via activating cGMP-PKG pathway. Show less
📄 PDF DOI: 10.3389/fonc.2025.1578695
FGFR1

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

Illuminating the FGFR fusion landscape in Chinese patients: unveiling novel molecular insights and clinical implications.

Zhuo Liu, Dandan Zhao, Baoming Wang +14 more · 2025 · The oncologist · Oxford University Press · added 2026-04-24
Despite the increasing approval and ongoing clinical trials of FGFR-targeted therapies, accurately detecting FGFR fusions remains a challenge due to limited research, low incidence rates, complex fusi Show more
Despite the increasing approval and ongoing clinical trials of FGFR-targeted therapies, accurately detecting FGFR fusions remains a challenge due to limited research, low incidence rates, complex fusion partner distribution, and unique kinase domain distribution. We conducted a multicenter study to comprehensively profile FGFR fusions in the largest Chinese pan-cancer cohort to date, comprising 118 FGFR fusions from 114 individuals. Both DNA- and RNA-based sequencing approaches were utilized to reveal novel and fundamental features of FGFR fusion. Our research reveals an incidence rate of 0.96% for FGFR rearrangements within this Chinese cohort, including a high incidence rate of FGFR fusions (40%) in parotid gland carcinoma. However, this is based on a small sample size of 5 tumors and should be interpreted cautiously pending validation in larger cohorts. We also uncovered distinct breakpoint distribution patterns across various FGFR rearrangements. For example, a primary breakpoint in intron17 of FGFR2 was predominant (21/22), while FGFR1/3 breakpoints displayed substantial diversity. For the first time, we identified "hot" breakpoints in FGFR1 intron17, exon18, and FGFR3's 3' untranslated region. These findings underline the importance of incorporating these regions in targeted sequencing to ensure comprehensive detection of FGFR1/3 fusions. Notably, we observed a predilection for intrachromosomal distribution in common FGFR1/2/3 fusions. In contrast, most novel fusions (12/15) exhibited an interchromosomal distribution pattern, indicating variations in the fusion formation mechanism. Importantly, our study demonstrates the substantial incremental value of RNA-NGS or other orthogonal methods in confirming the functionality of FGFR rearrangements initially identified by DNA sequencing. In our cohort, 46% (6/13) of rare FGFR1/2/3 fusions lacked detectable RNA transcripts; however, this does not definitively indicate non-functionality as factors such as low RNA quality, expression below detection limits, or nonsense-mediated decay may contribute. Therefore, RNA-based validation is critical for accurately identifying potentially targetable FGFR fusions and guiding therapy. Our findings offer critical novel insights into functional FGFR fusions and bear considerable clinical implications for identifying individuals whose tumors are most likely to respond favorably to FGFR-targeted therapies. Show less
📄 PDF DOI: 10.1093/oncolo/oyaf347
FGFR1

Clinical Relationship Between Serum ApoB, HER2, and Myocardial Ischemia Risk in Breast Cancer Patients.

Yeyan Lei, Dongmei Li, Shuang Bai +3 more · 2025 · Cancer reports (Hoboken, N.J.) · Wiley · added 2026-04-24
The risk factors and clinical prediction of cardiovascular comorbidities in patients with breast cancer have not been fully clarified. This retrospective case-control study was designed to investigate Show more
The risk factors and clinical prediction of cardiovascular comorbidities in patients with breast cancer have not been fully clarified. This retrospective case-control study was designed to investigate the factors affecting myocardial ischemia occurrence in breast cancer patients. A total of 194 cases (144 breast cancer and 50 benign breast tumor patients) were included. Univariate and multivariable Cox regression found that ApoB, age, and HER2 were significant factors responsible for the myocardial ischemia occurrence in breast cancer patients. By comparing the significance of ApoB in breast cancer patients versus benign breast tumor patients, it was observed that ApoB and HER2 were crucial predictors of myocardial ischemia in breast cancer patients compared to those with benign breast tumors. These factors were utilized to construct the clinical prediction model, achieving a combined area under the curve (AUC) of 0.583. The decision curve analysis (DCA) indicated that the model-predicted population, within a threshold ranging from 0.35 to 0.70, would experience a therapeutically clinical net benefit. Kaplan-Meier plot indicated that ApoB We demonstrated that ApoB and HER2 were potential factors in predicting the myocardial ischemia occurrence in breast cancer patients. This study will help provide clinical evidence for the early prediction of cardiovascular comorbidities in breast cancer patients. Show less
📄 PDF DOI: 10.1002/cnr2.70075
APOB

Integrating network pharmacology, quantitative transcriptomic analysis, and experimental validation revealed the mechanism of cordycepin in the treatment of obesity.

Yu Liao, Mingchao Wang, Fuli Qin +2 more · 2025 · Frontiers in pharmacology · Frontiers · added 2026-04-24
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically Show more
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically identify Cpn's therapeutic targets and pathways in Western diet (WD)-induced obesity using integrated network pharmacology, transcriptomics, and experimental validation. A Western diet (WD)-induced mice model was used to evaluate the effectiveness of Cpn in ameliorating obesity. A network pharmacology analysis was then employed to identify the potential anti-obesity targets of Cpn. GO functional enrichment and KEGG pathway analysis were performed to elucidate the potential functions of the identified targets, followed by constructing a protein-protein interaction network to screen the core targets. Meanwhile, quantitative transcriptomics was conducted to validate and broaden the network pharmacology findings. Finally, molecular docking and quantitative real-time PCR assay were used for the core target validation. Cpn treatment effectively alleviated obesity-related symptoms in WD-induced mice. The metabolic pathway, insulin signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, lipid and atherosclerosis pathway, and core targets including CPS1, HRAS, MAPK14, PAH, ALDOB, AKT1, GSK3B, HSP90AA1, BHMT2, EGFR, CASP3, MAT1A, APOM, APOA2, APOC3, and APOA1 are involved in regulating the therapeutic effect of Cpn. This study comprehensively uncovers the potential mechanism of Cpn against obesity based on network pharmacology and quantitative transcriptomics, which provides evidence for revealing the pathogenesis of obesity, suggesting that Cpn is a possible lead compound for anti-obesity treatment. Show less
📄 PDF DOI: 10.3389/fphar.2025.1571480
APOC3

Targeting ApoA5-Associated Hypertriglyceridemia to Ameliorate Acute Pancreatitis: Insights From a Knockout Hamster Model.

Sijing Shi, Kaikai Lu, Yijun Tao +6 more · 2025 · MedComm · Wiley · added 2026-04-24
📄 PDF DOI: 10.1002/mco2.70555
APOA5

Family caregivers' needs at 6 months after pancreatic cancer surgery: A latent profile analysis.

Linglong Liu, Xiaoping Fang, Xinbo Wang +8 more · 2025 · International journal of nursing studies advances · Elsevier · added 2026-04-24
Family caregivers ('carers') bear the highest care burden during the postoperative survivorship period of pancreatic cancer, given its poor prognosis. Most carers report unmet needs when taking on car Show more
Family caregivers ('carers') bear the highest care burden during the postoperative survivorship period of pancreatic cancer, given its poor prognosis. Most carers report unmet needs when taking on caregiving responsibilities during this period. Thoroughly investigating carers' needs is essential for helping families address practical care challenges. However, this important topic remains underexplored. To assess the need levels and identify need subgroups among carers of patients with pancreatic cancer 6 months after surgery and demographic predictors contributing to heterogeneity. Cross-sectional study. Participants were recruited from the pancreas centres of four tertiary A-level comprehensive hospitals in Jiangsu Province, China. 240 patients with pancreatic cancer and their carers ('dyads') participated in the survey. Carers completed the Comprehensive Needs Assessment Tool in Cancer for Carers, the Activities of Daily Living Scale for patients, and the General Demographic Information Questionnaire for dyads. Latent profile analysis (LPA) was used to categorise carers' needs. Non-parametric and chi-square tests were used to examine differences in need scores and sociodemographic characteristics among subgroups. Multiple logistic regression (MLR) was used to analyse sociodemographic impacts. Six months post-surgery, the total carers' need score was 41.83 ± 22.65 points, indicating a moderate level, with the highest needs reported for healthcare personnel, information and knowledge, and facilities and services. The LPA results revealed that carers were divided into five distinct subgroups based on differing levels of need across the domains assessed by the Comprehensive Needs Assessment Tool in Cancer for Carers, with proportions of 8.8 %, 22.5 %, 8.3 %, 55 %, and 5.4 %. Subgroup membership was predicted by four factors: carers' sex (odds ratio [OR]: 11.08, 95 % confidence interval [CI]: 1.64, 74.99, We have highlighted the complex individualised needs of carers of patients with pancreatic cancer. Through LPA and MLR, we identified distinct need subgroups and their predictors. Healthcare professionals may be able to improve dyads' health by tailoring support to each subgroup's specific needs and issues. Registration number: ChiCTR2400079415, registered 03/01/2024, first recruitment 04/02/2024. Show less
📄 PDF DOI: 10.1016/j.ijnsa.2025.100416
LPA

BACE1 inhibition via miR-6838-5p overexpression inhibits insulin resistance and the immune response in HFD-induced obesity in mice model.

Yubo Han, Zhenhua Quan, Miao Tian +4 more · 2025 · Immunopharmacology and immunotoxicology · Taylor & Francis · added 2026-04-24
Obesity is a chronic inflammatory disorder, which promotes the progression of metabolic disorders. MicroRNA (miR)-6838-5p is dysregulated and participates in the progression of several disorder models Show more
Obesity is a chronic inflammatory disorder, which promotes the progression of metabolic disorders. MicroRNA (miR)-6838-5p is dysregulated and participates in the progression of several disorder models. To explore the role and mechanism of miR-6838-5p in insulin resistance. Mice were fed with high-fat diet (HFD) to construct an obesity animal model. The role of miR-6838-5p was evaluated by insulin tolerance test (ITT), glucose tolerance test (GTT), homeostasis model assessment of insulin resistance (HOMA-IR) analysis, enzyme-linked immunosorbent assay (ELISA) and western blot assays. The potential target of miR-6838-5p was screened through the starBase online website and confirmed by the luciferase assay. HFD supply induced a prominent increase in the body weight, white adipose tissue (WAT) weight, the area under the curve (AUC) of GTT and ITT, HOMA-IR, the serum level of insulin and the serum concentrations and relative protein levels of interleukin (IL)-1β, IL-6 and monocyte chemoattractant protein-1 (MCP-1) accompanied with reduced levels of IL-10 in mice. The level of miR-6838-5p was reduced in HFD-fed mice. Upregulation of miR-6838-5p partly reversed the above-mentioned indicators. Moreover, miR-6838-5p directly targeted to β-site amyloid precursor protein cleaving enzyme1 (BACE1) and negatively regulated the BACE1 expression. Downregulation of BACE1 improved insulin sensitivity and inflammatory mediators release involving in AKT/GSK3β signaling pathway in HFD-fed mice. Besides, overexpression of BACE1 counteracted the depressant role of miR-6838-5p overexpression in insulin resistance and inflammatory factors release in HFD-fed mice. MiR-6838-5p/BACE1 axis regulated insulin resistance and inflammatory factors release in HFD-fed mice. Show less
no PDF DOI: 10.1080/08923973.2024.2430668
BACE1

GBA1 Gene-Associated Transcriptomic Signatures Reveal Risk Genes in Parkinson's Disease.

Yanjun Liu, Xi Luo, Ronan M T Fleming · 2025 · Biomedicines · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/biomedicines13112799
GIPR

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

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

C-type natriuretic peptide regulates lipid metabolism through a NPRB-PPAR pathway in the intramuscular and subcutaneous adipocytes in chickens.

Huayun Huang, Longzhou Liu, Zhong Liang +5 more · 2025 · Scientific reports · Nature · added 2026-04-24
Natriuretic peptides (NPs) have an important role in lipid metabolism in skeletal muscle and adipose tissue in animals. C-type natriuretic peptide (CNP) is an important NP, but the molecular mechanism Show more
Natriuretic peptides (NPs) have an important role in lipid metabolism in skeletal muscle and adipose tissue in animals. C-type natriuretic peptide (CNP) is an important NP, but the molecular mechanisms that underlie its activity are not completely understood. Treatment of intramuscular fat (IMF) and subcutaneous fat (SCF) adipocytes with CNP led to decreased differentiation, promoted proliferation and lipolysis, and increased the expression of natriuretic peptide receptor B (NPRB) mRNA. Silencing natriuretic peptide C (NPPC) had the opposite results in IMF and SCF adipocytes. Transcriptome analysis found 665 differentially expressed genes (DEGs) in IMF adipocytes and 991 in SCF adipocytes. Seven genes in IMF adipocytes (FABP4, APOA1, ACOX2, ADIPOQ, CD36, FABP5, and LPL) and eight genes in SCF adipocytes (ACOX3, ACSL1, APOA1, CPT1A, CPT2, FABP4, PDPK1 and PPARα) are related to fat metabolism. Fifteen genes were found to be enriched in the peroxisome proliferator-activated receptor (PPAR) pathway. Integrated analysis identified 113 intersection genes in IMF and SCF adipocytes, two of which (APOA1 and FABP4) were enriched in the PPAR pathway. In conclusion, CNP may regulated lipid metabolism through the NPRB-PPAR pathway in both IMF and SCF adipocytes, FABP4 and APOA1 may be the key genes that mediated CNP regulation of fat deposition. Show less
📄 PDF DOI: 10.1038/s41598-025-86433-w
LPL

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

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

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

Correlation between lipid metabolism levels and pregnancy outcomes.

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

Construction and differential analysis of testicular atlas between 10-week-old and 23-week-old ducks using single-cell RNA sequencing.

Zhiyun Tao, Wenjuan Xu, Weitao Song +7 more · 2025 · Poultry science · Elsevier · added 2026-04-24
While spermatogenesis has been extensively characterized in mammals, its molecular underpinnings in avian species remain largely unexplored. To address this knowledge gap, we performed single-cell tra Show more
While spermatogenesis has been extensively characterized in mammals, its molecular underpinnings in avian species remain largely unexplored. To address this knowledge gap, we performed single-cell transcriptomic profiling of duck testes across developmental stages (10-week immature vs. 23-week mature). Our analysis generated a comprehensive cellular atlas comprising 54,702 cells, resolving eight germ cell clusters (three spermatogonia [SPG], three spermatocytes [SPC], two spermatozoa [SPT]) and nine somatic populations, including peritubular myoid cells, immune subsets (T cells, macrophages, granulocytes), endothelial cells, Leydig cells, and three Sertoli cell subtypes, each defined by unique marker gene signatures. Furthermore, novel marker genes were identified, including EXFABP for granulocyte, ARHGAP15 for T cell regulation, FDX1 specific to Leydig cells (LC), and TSSK3/TSSK2 linked to elongated spermatid formation (SPT). Notably, we identified some novel molecular markers distinguishing these populations. Pseudotemporal trajectory reconstruction of germline development revealed stage-specific enrichment of ribosome, endoplasmic reticulum protein processing, and autophagy pathways. Core regulators MRPL13, MRPL2, MRPL22, MRPS14, MRPS7 (ribosome), HSPA5 (ER stress response), and PIK3C3 (autophagy) emerged as molecular hubs showing progressive downregulation during differentiation. Comparative transcriptomic analysis of germ cells and Sertoli cells between immature (IMT) and mature (MT) testes revealed significant enrichment of the spliceosome pathway in both germ and Sertoli cells. Critical spliceosome components SNRPG, SF3B3, and SNRPF exhibited coordinated downregulation during testicular maturation, suggesting their role as negative regulators of spermatogenic progression. This study establishes the first high-resolution cellular blueprint of avian spermatogenesis, delineating regulatory networks of duck testis cell development. Our findings provide valuable datasets and mechanistic insights into the evolutionary specialization of reproductive strategies in poultry. Show less
no PDF DOI: 10.1016/j.psj.2025.105715
PIK3C3

Multisystem comorbidities and shared genetic pathways in calcific aortic stenosis: a phenome-wide and Mendelian randomisation analysis.

Zihao Zhou, Yidan Zheng, Shiyan Hu +13 more · 2025 · Heart (British Cardiac Society) · added 2026-04-24
Calcific aortic stenosis (CAS) is frequently accompanied by systemic comorbidities, but their causal relationships and shared genetic architecture remain poorly defined. We aimed to map the multisyste Show more
Calcific aortic stenosis (CAS) is frequently accompanied by systemic comorbidities, but their causal relationships and shared genetic architecture remain poorly defined. We aimed to map the multisystem comorbidity network of CAS and clarify underlying genetic mechanisms. In 467 484 participants from the UK Biobank, observational and polygenic phenome-wide association studies evaluated associations between CAS and 1571 phenotypes, integrating disease-trajectory analyses to visualise temporal patterns. Associations replicated across observational and polygenic analyses were tested using two-sample Mendelian randomisation (MR) based on 22 CAS-related variants from FinnGen. Polygenic risk score (PRS) analyses excluding specific genes assessed their contributions, particularly LPA and plasma lipoprotein(a) (Lp(a)) levels. CAS was associated with higher risks of 42 cardiovascular and non-cardiovascular conditions, most prominently metabolic, endocrine, haematological and respiratory disorders. Temporal analyses showed that circulatory and metabolic diseases typically precede other comorbidities in CAS trajectories. MR findings were consistent with causal effects of CAS on multiple cardiovascular diseases, iron-deficiency anaemia, mental disorders and pleural effusion. When LPA variants were removed from the CAS PRS or plasma Lp(a) concentration was adjusted for, most associations lost significance, indicating a shared LPA/Lp(a)-mediated genetic pathway. CAS is embedded within a broad multisystem comorbidity network, driven largely by genetic variation at LPA and elevated Lp(a). These findings highlight pleiotropic mechanisms linking valvular calcification with systemic disease and support LPA-targeted therapies as a promising avenue for reducing the multisystem burden of CAS. Show less
no PDF DOI: 10.1136/heartjnl-2025-326058
LPA

Targeting polyunsaturated fatty acids desaturase FADS1 inhibits renal cancer growth via ATF3-mediated ER stress response.

Gioia Heravi, Zhenjie Liu, Mackenzie Herroon +11 more · 2025 · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · Elsevier · added 2026-04-24
Fatty Acid Desaturase 1 (FADS1) is a rate-limiting enzyme controlling the bioproduction of long-chain polyunsaturated fatty acids (PUFAs). Increasing studies suggest that FADS1 is a potential cancer t Show more
Fatty Acid Desaturase 1 (FADS1) is a rate-limiting enzyme controlling the bioproduction of long-chain polyunsaturated fatty acids (PUFAs). Increasing studies suggest that FADS1 is a potential cancer target. Our previous research has demonstrated the significant role of FADS1 in cancer biology and patient survival, especially in kidney cancers. We aim to explore the underlying mechanism in this study. We found that pharmacological inhibition or knockdown of the expression of FADS1 significantly reduced the intracellular conversion of long-chain PUFAs, effectively inhibits renal cancer cell proliferation, and induces cell cycle arrest. The stable knockdown of FADS1 also significantly inhibits tumor formation in vivo. Mechanistically, we showed that while FADS1 inhibition induces endoplasmic reticulum (ER) stress, FADS1 expression is augmented by ER-stress inducer, suggesting a necessary role of PUFA production in response to ER stress. FADS1-inhibition sensitized cellular response to ER stress inducers, leading to cell apoptosis. Also, FADS1 inhibition-induced ER stress leads to activation of the PERK/eIF2α/ATF4/ATF3 pathway. Inhibiting PERK or knockdown of ATF3 rescued FADS1 inhibition-induced ER stress and cell growth suppression, while ATF3-overexpression aggravates the FADS1 inhibition-induced cell growth suppression and leads to cell death. Metabolomic analysis revealed that FADS1 inhibition results in decreased level of UPD-N-Acetylglucosamine, a critical mediator of the unfolded protein response, as well as impaired biosynthesis of nucleotides, possibly accounting for the cell cycle arrest. Our findings suggest that PUFA desaturation is crucial for rescuing cancer cells from persistent ER stress, supporting FADS1 as a new therapeutic target. Show less
📄 PDF DOI: 10.1016/j.biopha.2025.118006
FADS1

Development of a novel targeted LC-MS/MS methods for the typing of cardiac amyloidosis.

Kaijuan Wang, Ruichen Liu, Li Li +7 more · 2025 · Analytica chimica acta · Elsevier · added 2026-04-24
The treatment and prognosis of cardiac amyloidosis (CA) depend heavily on the accurate identification of amyloid protein types. Histopathological methods are the most commonly used approach, but often Show more
The treatment and prognosis of cardiac amyloidosis (CA) depend heavily on the accurate identification of amyloid protein types. Histopathological methods are the most commonly used approach, but often produce inconclusive results. The application of mass spectrometry with laser microdissection mass spectrometry based on non-targeted proteomics in CA diagnosis is gradually being recognized, but it is expensive, time-consuming, and still in the early stages of scientific research applications. This study aims to establish a novel typing method based on targeted semi-quantitative proteomics to address the shortcomings of existing methods. Formalin-fixed, paraffin-embedded (FFPE) myocardial tissue samples from 52 CA and 52 hypertrophic cardiomyopathy (HCM) patients were analyzed. A semi-quantitative typing method was developed using triple quadrupole mass spectrometry, with laser microdissection mass spectrometry (LMD-MS) serving as the reference standard. A total of 52 peptides were analyzed. Key amyloid-associated proteins (AAPs) -apolipoprotein A-IV (apo A-IV), apolipoprotein E (apo E), and serum amyloid P component (SAP) - showed high diagnostic accuracy, with AUC values of 0.964, 0.999, and 0.923, respectively. Transthyretin (TTR), immunoglobulin light chains- κ (IGL - κ), and IGL-λ were semi-quantified using normalized scores (NS) adjusted for microdissection and peptide peak areas. An NS This targeted semi-quantitative mass spectrometry method has high consistency with non-targeted LMD-MS typing, with an accuracy higher than IHC (100 % vs. 30.8 %), while compensating for the shortcomings of non-targeted proteomics. It provides a practical method for CA typing in routine clinical laboratories and may help identify rare subtypes of amyloidosis in the future. Show less
no PDF DOI: 10.1016/j.aca.2025.344453
APOA4