👤 L Liu

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

Integrating metabolomics and transcriptomics to analyze the differences of breast muscle quality and flavor formation between Daweishan mini chicken and broiler.

Jingying Zhao, Xuehai Ge, Tao Li +10 more · 2024 · Poultry science · Elsevier · added 2026-04-24
The quality and flavor of chicken are affected by muscle metabolites and related regulatory genes, and the molecular regulation mechanism of meat quality is different among different breeds of chicken Show more
The quality and flavor of chicken are affected by muscle metabolites and related regulatory genes, and the molecular regulation mechanism of meat quality is different among different breeds of chicken. In this study, 40 one-day-old Daweishan mini chicken (DM) and Cobb broiler (CB) were selected from each group, with 4 replicates and 10 chickens in each replicate. The chickens were reared until 90 d of age under the same management conditions. Then, metabolomics and transcriptomics data of 90-day-old DM (n = 4) and CB (n = 4) were integrated to analyze metabolites affecting breast muscle quality and flavor, and to explore the important genes regulating meat quality and flavor related metabolites. The results showed that a total of 38 significantly different metabolites (SDMs) and 420 differentially expressed genes (DEGs) were detected in the breast muscle of the 2 breeds. Amino acid and lipid metabolism may be the cause of meat quality and flavor difference between DM and CB chickens, involving metabolites such as L-methionine, betaine, N6, N6, N6-Trimethyl-L-lysine, L-anserine, glutathione, glutathione disulfide, L-threonine, N-Acetyl-L-aspartic acid, succinate, choline, DOPC, SOPC, alpha-linolenic acid, L-palmitoylcarnitine, etc. Important regulatory genes with high correlation with flavor amino acids (GATM, GSTO1) and lipids (PPARG, LPL, PLIN1, SCD, ANGPTL4, FABP7, GK, B4GALT6, UGT8, PLPP4) were identified by correlation analysis, and the gene-metabolite interaction network of breast muscle mass and flavor formation in DM chicken was constructed. This study showed that there were significant differences in breast metabolites between DM and CB chickens, mainly in amino acid and lipid metabolites. These 2 kinds of substances may be the main reasons for the difference in breast muscle quality and flavor between the 2 breeds. In general, this study could provide a theoretical basis for further research on the molecular regulatory mechanism of the formation of breast muscle quality and flavor differences between DM and CB chickens, and provide a reference for the development, utilization and genetic breeding of high-quality meat chicken breeds. Show less
📄 PDF DOI: 10.1016/j.psj.2024.103920
ANGPTL4

Genetic correlation between genes targeted by lipid-lowering drugs and venous thromboembolism: A drug-target Mendelian randomization study.

Min Li, Hangyu Duan, Jinwen Luo +5 more · 2024 · Medicine · added 2026-04-24
Dyslipidemia has been established as a potential risk factor for venous thromboembolism (VTE) in several observational studies. Statins and novel lipid-modifying agents are being explored for their po Show more
Dyslipidemia has been established as a potential risk factor for venous thromboembolism (VTE) in several observational studies. Statins and novel lipid-modifying agents are being explored for their potential in VTE prevention, encompassing deep vein thrombosis (DVT), and pulmonary embolism (PE). Nonetheless, conclusive evidence supporting the effectiveness remains uncertain. Without definitive proof, the current recommendation of lipid-lowering drugs (LLDs) for preventing VTE, either primarily or secondarily, is not support. An investigation into the impact of 8 classes of LLDs on VTE was conducted using a drug-target Mendelian randomization approach. The drug categories examined included 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), apolipoprotein B, proprotein convertase subtilisin/kexin type 9, Niemann-Pick C1-like 1, lipoprotein lipase (LPL), angiopoietin-like 3, apolipoprotein C3 (APOC3), and peroxisome proliferator-activated receptor alpha. Leveraging genetic variants situated proximate to or within drug-target genes linked with low-density lipoprotein and triglycerides, we acted as proxies for LLDs. The UK Biobank study was the source of data on VTE, PE, and DVT of lower extremities (LEDVT). We employed the inverse-variance weighted method for the core analysis in Mendelian randomization, complemented by sensitivity analysis to investigate horizontal pleiotropy and heterogeneity. Employing genetic proxies to inhibit HMGCR revealed a notable correlation with reduced LEDVT risk (odds ratio [OR]: 0.995, 95% CI: 0.992-0.998, P = .002), VTE (OR: 0.994, 95% CI: 0.988-1.000, P = .033), but a no significant association with PE (OR: 1.000, 95% CI: 0.994-1.002, P = .246). The suppression of APOB was linked with an elevated risk of experiencing LEDVT (OR: 1.002, 95% CI: 1.001-1.004, P = .006), VTE (OR: 1.005, 95% CI: 1.002-1.007, P < .001), and PE (OR: 1.002, 95% CI: 1.000-1.004, P = .031). Similarly, the activation of LPL was associated with increased risks for VTE (OR: 1.003, 95% CI: 1.001-1.005, P = .003) and PE (OR: 1.003, 95% CI: 1.002-1.005, P < .001). Additionally, the inhibition of APOC3 was linked to a higher DVT risk (OR: 1.002, 95% CI: 1.000-1.004, P = .038). Research has shown that HMGCR, out of 8 lipid-lowering drug-targets evaluated, exhibited a significant correlation with VTE and LEDVT, highlighting its potential as an effective target for the treatment or prevention of these conditions. In contrast, APOB, LPL, and APOC3 each contribute to an increased risk of VTE, PE, and LEDVT in various degrees, pharmacovigilance for VTE, PE, and LEDVT risk among users of APOB inhibitors, LPL activation, and APOC3 inhibitors may be warranted. Show less
📄 PDF DOI: 10.1097/MD.0000000000040770
APOB

A novel gene-based model for prognosis prediction of head and neck squamous cell carcinoma.

Yanxi Li, Peiran Li, Yuqi Liu +1 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Head and neck squamous cell carcinoma (HNSCC) is a significant global health challenge. The identification of reliable prognostic biomarkers and construction of an accurate prognostic model are crucia Show more
Head and neck squamous cell carcinoma (HNSCC) is a significant global health challenge. The identification of reliable prognostic biomarkers and construction of an accurate prognostic model are crucial. In this study, mRNA expression data and clinical data of HNSCC patients from The Cancer Genome Atlas were used. Overlapping candidate genes (OCGs) were identified by intersecting differentially expressed genes and prognosis-related genes. Best prognostic genes were selected using the least absolute shrinkage and selection operator Cox regression based on OCGs, and a risk score was developed using the Cox coefficient of each gene. The prognostic power of the risk score was assessed using Kaplan-Meier survival analysis and time-dependent receiver operating characteristic analysis. Univariate and multivariate Cox regression were performed to identify independent prognostic parameters, which were used to construct a nomogram. The predictive accuracy of the nomogram was evaluated using calibration plots. Functional enrichment analysis of risk score related genes was performed to explore the potential biological functions and pathways. External validation was conducted using data from the Gene Expression Omnibus and ArrayExpress databases. FADS3, TNFRSF12A, TJP3, and FUT6 were screened to be significantly related to prognosis in HNSCC patients. The risk score effectively stratified patients into high-risk group with poor overall survival (OS) and low-risk group with better OS. Risk score, age, clinical M stage and clinical N stage were regarded as independent prognostic parameters by Cox regression analysis and used to construct a nomogram. The nomogram performed well in 1-, 2-, 3-, 5- and 10-year survival predictions. Functional enrichment analysis suggested that tight junction was closely related to the cancer. In addition, the prognostic power of the risk score was validated by external datasets. This study constructed a gene-based model integrating clinical prognostic parameters to accurately predict prognosis in HNSCC patients. Show less
📄 PDF DOI: 10.1016/j.heliyon.2024.e29449
FADS3

High-Speed Centrifugation Efficiently Removes Immunogenic Elements in Osteochondral Allografts.

Yongsheng Ma, Qitai Lin, Wenming Yang +10 more · 2024 · Orthopaedic surgery · Blackwell Publishing · added 2026-04-24
The current clinical pulse lavage technique for flushing fresh osteochondral allografts (OCAs) to remove immunogenic elements from the subchondral bone is ineffective. This study aimed to identify the Show more
The current clinical pulse lavage technique for flushing fresh osteochondral allografts (OCAs) to remove immunogenic elements from the subchondral bone is ineffective. This study aimed to identify the optimal method for removing immunogenic elements from OCAs. We examined five methods for the physical removal of immunogenic elements from OCAs from the femoral condyle of porcine knees. We distributed the OCAs randomly into the following seven groups: (1) control, (2) saline, (3) ultrasound, (4) vortex vibration (VV), (5) low-pulse lavage (LPL), (6) high-pulse lavage (HPL), and (7) high-speed centrifugation (HSC). OCAs were evaluated using weight measurement, micro-computed tomography (micro-CT), macroscopic and histological evaluation, DNA quantification, and chondrocyte activity testing. Additionally, the subchondral bone was zoned to assess the bone marrow and nucleated cell contents. One-way ANOVA and paired two-tailed Student's t-test are used for statistical analysis. Histological evaluation and DNA quantification showed no significant reduction in marrow elements compared to the control group after the OCAs were treated with saline, ultrasound, or VV treatments; however, there was a significant reduction in marrow elements after LPL, HPL, and HSC treatments. Furthermore, HSC more effectively reduced the marrow elements of OCAs in the middle and deep zones compared with LPL (p < 0.0001) and HPL (p < 0.0001). Macroscopic evaluation revealed a significant reduction in blood, lipid, and marrow elements in the subchondral bone after HSC. Micro-CT, histological analyses, and chondrocyte viability results showed that HSC did not damage the subchondral bone and cartilage; however, LPL and HPL may damage the subchondral bone. HSC may play an important role in decreasing immunogenicity and therefore potentially increasing the success of OCA transplantation. Show less
📄 PDF DOI: 10.1111/os.13991
LPL

Evolutionary Identification of the Requirement of the Second Intracellular Loop for the Constitutive Activity of Melanocortin-4 Receptors.

Bingxin Xu, Jindong Yao, Wenqi Song +11 more · 2024 · ACS pharmacology & translational science · ACS Publications · added 2026-04-24
Melanocortin-4 receptor (MC4R) functions as a crucial neuroendocrine G protein-coupled receptor (GPCR) in the central nervous system of mammals, displaying agonist-independent constitutive activity th Show more
Melanocortin-4 receptor (MC4R) functions as a crucial neuroendocrine G protein-coupled receptor (GPCR) in the central nervous system of mammals, displaying agonist-independent constitutive activity that is mainly determined by its N-terminal domain. We previously reported that zebrafish MC4R exhibited a much higher basal cAMP level in comparison to mammalian MC4Rs. However, the functional evolution of constitutive activities in chordate MC4Rs remains to be elucidated. Here we cloned and compared the constitutive activities of MC4Rs from nine vertebrate species and showed that the additive action of the N-terminus with the extracellular region or transmembrane domain exhibited a combined pharmacological effect on the MC4R constitutive activity. In addition, we demonstrated that four residues of F149, Q156, V163, and K164 of the second intracellular loop played a vital role in determining MC4R constitutive activity. This study provided novel insights into functional evolution and identified a key motif essential for constitutive modulation of MC4R signaling. Show less
no PDF DOI: 10.1021/acsptsci.3c00169
MC4R

Transcriptional changes impact hepatic proteome in autophagy-impaired liver.

Kamal Baral, Spandan Joshi, Adriana Lopez +11 more · 2024 · FEBS open bio · Wiley · added 2026-04-24
Hepatic proteomes are intricately controlled through biosynthesis, extracellular secretion, and intrahepatic degradation. Autophagy governs lysosome-mediated intrahepatic degradation and the hepatic p Show more
Hepatic proteomes are intricately controlled through biosynthesis, extracellular secretion, and intrahepatic degradation. Autophagy governs lysosome-mediated intrahepatic degradation and the hepatic proteome. When autophagy is impaired, it leads to the accumulation of intrahepatic proteins, causing proteinopathy. This study investigates whether autophagy can modulate the hepatic proteome non-degradatively. Utilizing conditional, inducible, and hepatotoxin models of hepatic autophagy impairment, we assessed the overall hepatic proteome expression using Coomassie brilliant blue (CBB) staining and liquid chromatography-tandem mass spectrometry (LC/MS). We pinpointed and confirmed four specific hepatic proteins-Cps1, Ahcy, Ca3, and Gstm1-that were selectively modified in autophagy-deficient livers. Expression of Cps1, Ahcy, and Ca3 were significantly reduced, while Gstm1 expression increased in livers with autophagy impairment. Interestingly, these changes in hepatic protein levels were not due to defective autophagic degradation but were associated with alterations in mRNA transcript levels. Moreover, as a result of autophagic dysfunction, sustained activation of the nuclear erythroid-derived 2-like 2 (Nrf2) transcription factor, transcriptionally regulated the mRNA levels of these proteins. Our findings indicate that autophagy can influence hepatic proteins not solely via traditional degradative routes but also through non-degradative transcriptional processes by modulating Nrf2. Show less
📄 PDF DOI: 10.1002/2211-5463.13898
CPS1

Neurexin-3 in the paraventricular nucleus of the hypothalamus regulates body weight and glucose homeostasis independently of food intake.

Mingdao Mu, Haoyu Sun, Shuyan Geng +8 more · 2024 · Molecular brain · BioMed Central · added 2026-04-24
Neurexin-3 (Nrxn3) has been genetically associated with obesity, but the underlying neural mechanisms remain poorly understood. This study aimed to investigate the role of Nrxn3 in the paraventricular Show more
Neurexin-3 (Nrxn3) has been genetically associated with obesity, but the underlying neural mechanisms remain poorly understood. This study aimed to investigate the role of Nrxn3 in the paraventricular nucleus of the hypothalamus (PVN) in regulating energy balance and glucose homeostasis. We found that Nrxn3 expression in the PVN was upregulated in response to metabolic stressors, including cold exposure and fasting. Using Cre-loxP technology, we selectively ablated Nrxn3 in CaMKIIα-expressing neurons of the PVN in male mice. This genetic manipulation resulted in marked weight gain attributable to increased adiposity and impaired glucose tolerance, without affecting food intake. Our findings identify PVN CaMKIIα-expressing neurons as a critical locus where Nrxn3 modulates energy balance by regulating adipogenesis and glucose metabolism, independently of appetite. These results reveal a novel neural mechanism potentially linking Nrxn3 dysfunction to obesity pathogenesis, suggesting that targeting PVN Nrxn3-dependent neural pathways may inform new therapeutic approaches for obesity prevention and treatment. Show less
no PDF DOI: 10.1186/s13041-024-01124-3
NRXN3

Dihydroartemisinin inhibited vasculogenic mimicry in gastric cancer through the FGF2/FGFR1 signaling pathway.

Huina Wang, Qingzhu Ding, Haihua Zhou +5 more · 2024 · Phytomedicine : international journal of phytotherapy and phytopharmacology · Elsevier · added 2026-04-24
Vasculogenic mimicry (VM) is a novel model for supplying blood to multiple tumors, including gastric cancer (GC), and is a potential target for its treatment. Dihydroartemisinin (DHA) is a potential n Show more
Vasculogenic mimicry (VM) is a novel model for supplying blood to multiple tumors, including gastric cancer (GC), and is a potential target for its treatment. Dihydroartemisinin (DHA) is a potential natural antitumor substance that inhibits the progression of tumors in many ways. The research aimed to evaluate the impact of DHA on VM formation and its mechanisms. The IC50 of DHA, DHA's effect on proliferation, invasion, and migration in GC cells and VM formation in both cell and animal models were determined through wound healing, MTT, EdU, colony formation, and Transwell assays. Genomics was employed to identify genes related to DHA inhibition of VM formation, and to analyze their relationship to VM formation. qRT‒PCR and western blot (WB) analysis were carried out to analyze the changes in protein and mRNA levels after DHA treatment and the changes in VM-associated protein biomarkers after blocking target gene-related pathways. The mechanism by which DHA inhibits VM in GC was elucidated in vivo. DHA reduced the invasion, proliferation, and migration of GC cells and inhibited VM in cells and in vivo. A total of 220 DEGs were identified in the DHA-treated HGC-27 cells. Among the 146 downregulated genes, fibroblast growth Factor 2 (FGF2) was most closely associated with angiogenesis and VM. The level of FGF2 in GC tissues with VM was markedly greater than in VM lacking tissues. Treatment with DHA or FGFR1 blockade suppressed VM formation and reduced VM-related biomarker proteins. DHA suppressed tumor progression and VM formation by reducing FGF2 in xenograft mouse models. Per our knowledge, this is the first study to demonstrate the inhibitory effect of DHA on VM, providing a novel strategy for the treatment of GC. Show less
no PDF DOI: 10.1016/j.phymed.2024.155962
FGFR1

[Association between

Jun-Ren Lai, Li Gong, Yan Liu +3 more · 2024 · Sheng li xue bao : [Acta physiologica Sinica] · added 2026-04-24
This study aimed to analyze the impact of single nucleotide polymorphism (SNP) of
no PDF
ADCY3

Transcriptional activation of the Myc gene by glucose in β-cells requires a ChREBP-dependent 3-D chromatin interaction between the Myc and Pvt1 genes.

Liora S Katz, Gabriel Brill, Peng Wang +8 more · 2024 · Molecular metabolism · Elsevier · added 2026-04-24
All forms of diabetes result from insufficient functional β-cell mass. Thus, achieving the therapeutic goal of expanding β-cell mass requires a better mechanistic understanding of how β-cells prolifer Show more
All forms of diabetes result from insufficient functional β-cell mass. Thus, achieving the therapeutic goal of expanding β-cell mass requires a better mechanistic understanding of how β-cells proliferate. Glucose is a natural β-cell mitogen that mediates its effects in part through the glucose-responsive transcription factor, carbohydrate response element binding protein (ChREBP) and the anabolic transcription factor, MYC. However, mechanistic details by which glucose activates Myc at the transcriptional level are poorly understood. Here, siRNA was used to test the role of ChREBP in the glucose response of MYC, ChIP and ChIPseq to identify potential regulatory binding sites, chromatin conformation capture to identify DNA/DNA interactions, and an adenovirus was constructed to expresses x-dCas9 and an sgRNA that specifically disrupts the recruitment of ChREBP to a specific targeted ChoRE. We found that ChREBP is essential for glucose-mediated transcriptional induction of Myc, and for increases in Myc mRNA and protein abundance. Further, ChIPseq revealed that the carbohydrate response element (ChoRE) nearest to the Myc transcriptional start site (TSS) is immediately upstream of the gene encoding the lncRNA, Pvt1, 60,000 bp downstream of the Myc gene. Chromatin Conformation Capture (3C) confirmed a glucose-dependent interaction between these two sites. Transduction with an adenovirus expressing x-dCas9 and an sgRNA specifically targeting the highly conserved Pvt1 ChoRE, attenuates ChREBP recruitment, decreases Myc-Pvt1 DNA/DNA interaction, and decreases expression of the Pvt1 and Myc genes in response to glucose. Importantly, isolated and dispersed rat islet cells transduced with the ChoRE-disrupting adenovirus also display specific decreases in ChREBP-dependent, glucose-mediated expression of Pvt1 and Myc, as well as decreased glucose-stimulated β-cell proliferation. The mitogenic glucose response of Myc is mediated via glucose-dependent recruitment of ChREBP to the promoter of the Pvt1 gene and subsequent DNA looping with the Myc promoter. Show less
📄 PDF DOI: 10.1016/j.molmet.2023.101848
MLXIPL

Identification of PPAR-related differentially expressed genes liver hepatocellular carcinoma and construction of a prognostic model based on data analysis and molecular docking.

Yumeng Wang, Shuqiang Li, Zihang Liu +3 more · 2024 · Journal of cellular and molecular medicine · Blackwell Publishing · added 2026-04-24
Liver hepatocellular carcinoma (LIHC) is a significant global health issue with limited treatment options. In this study, single-cell RNA sequencing (scRNA-seq) data were used to explore the molecular Show more
Liver hepatocellular carcinoma (LIHC) is a significant global health issue with limited treatment options. In this study, single-cell RNA sequencing (scRNA-seq) data were used to explore the molecular mechanisms of LIHC development and identify potential targets for therapy. The expression of peroxisome proliferator-activated receptors (PPAR)-related genes was analysed in LIHC samples, and primary cell populations, including natural killer cells, T cells, B cells, myeloid cells, endothelial cells, fibroblasts and hepatocytes, were identified. Analysis of the differentially expressed genes (DEGs) between normal and tumour tissues revealed significant changes in gene expression in various cell populations. PPAR activity was evaluated using the 'AUCell' R software, which indicated higher scores in the normal versus the malignant hepatocytes. Furthermore, the DEGs showed significant enrichment of pathways related to lipid and glucose metabolism, cell development, differentiation and inflammation. A prognostic model was then constructed using 8 PPARs-related genes, including FABP5, LPL, ACAA1, PPARD, FABP4, PLIN1, HMGCS2 and CYP7A1, identified using least absolute shrinkage and selection operator-Cox regression analysis, and validated in the TCGA-LIHC, ICGI-LIRI and GSE14520 datasets. Patients with low-risk scores had better prognosis in all cohorts. Based on the expression of the eight model genes, two clusters of patients were identified by ConsensusCluster analysis. We also predicted small-molecule drugs targeting the model genes, and identified perfluorohexanesulfonic acid, triflumizole and perfluorononanoic acid as potential candidates. Finally, wound healing assay confirmed that PPARD can promote the migration of liver cancer cells. Overall, our study offers novel perspectives on the molecular mechanisms of LIHC and potential areas for therapeutic intervention, which may facilitate the development of more effective treatment regimens. Show less
📄 PDF DOI: 10.1111/jcmm.18304
LPL

Proteomics Analysis of Renal Cell Line Caki-2 with AFMID Overexpression and Potential Biomarker Discovery in Urine.

Jiameng Sun, Jinchun Chang, Zhengguang Guo +4 more · 2024 · Journal of proteome research · ACS Publications · added 2026-04-24
Aromatic caninurine formamase (AFMID) is an enzyme involved in the tryptophan pathway, metabolizing N-formylkynurenine to kynurenine. AFMID had been found significantly downregulated in clear cell ren Show more
Aromatic caninurine formamase (AFMID) is an enzyme involved in the tryptophan pathway, metabolizing N-formylkynurenine to kynurenine. AFMID had been found significantly downregulated in clear cell renal cell carcinoma (ccRCC) in both tissue and urine samples. Although ccRCC is characterized by a typical Warburg-like phenotype, mitochondrial dysfunction, and elevated fat deposition, it is unknown whether AFMID plays a role in tumorigenesis and the development of ccRCC. In the present study, AFMID overexpression had inhibitory effects for ccRCC cells, decreasing the rate of cell proliferation. Quantitative proteomics showed that AFMID overexpression altered cellular signaling pathways involved in cell growth and cellular metabolism pathways, including lipid metabolism and inositol phosphate metabolism. Further urine proteomic analysis indicated that cellular function dysfunction with AFMID overexpression could be reflected in the urine. The activity of predicted upregulators DDX58, TREX1, TGFB1, SMARCA4, and TNF in ccRCC cells and urine showed opposing change trends. Potential urinary biomarkers were tentatively discovered and further validated using an independent cohort. The protein panel of APOC3, UMOD, and CILP achieved an AUC value of 0.862 for the training cohort and 0.883 for the validation cohort. The present study is of significance in terms of highlighting various aspects of pathway changes associated with AFMID enzymes, discovering potential specific biomarkers for potential patient diagnosis, and therapeutic targeting. Show less
no PDF DOI: 10.1021/acs.jproteome.4c00431
APOC3

Multi-omics analysis reveals associations between host gene expression, gut microbiota, and metabolites in chickens.

Kai Shi, Xiangping Liu, Ying Duan +4 more · 2024 · Journal of animal science · Oxford University Press · added 2026-04-24
Egg-laying is an important trait in chickens, and it is affected by many factors, such as hormones regulated by the hypothalamic-pituitary axis and precursors synthesized by the liver. Recent studies Show more
Egg-laying is an important trait in chickens, and it is affected by many factors, such as hormones regulated by the hypothalamic-pituitary axis and precursors synthesized by the liver. Recent studies showed that gut microbiota was associated with egg-laying, however, its underlying mechanism remains unclear. We comprehensively analyzed the host transcriptome, gut microbiota, and metabolome in broiler breeder hens during the pre-laying, peak-laying, and late-laying periods. The transcriptome analysis of the tissues related to the hypothalamic-pituitary-liver (HPL) axis revealed dynamic gene expression during egg-laying periods. Differentially expressed genes (DEGs) (i.e., PENK, NPY, AVP, PRL, RLN3, and FST) from the hypothalamus and pituitary gland were involved in female gonadal development, hormone secretion, response to endogenous stimulus, liver development, and amide metabolism. In liver, DEGs (i.e., FABP3, VTG1, LPL, APOA5, APOV1, and RBP5) were enriched in efferocytosis, sphingolipid metabolism, amide, and peptide biosynthesis. Alpha and beta diversity changed significantly in cecum microbiota during different laying periods. The abundance of Firmicutes was decreased and the abundance of Bacteroidota was increased during the peak-laying period. Functional analysis showed that the biosynthesis of secondary metabolites, amino acids, purine, and steroid hormones was altered during laying. The metabolome analysis from cecal contents showed that amino acid metabolism and steroid hormone biosynthesis changed during laying. Integrated analysis of the cecal microbiota and metabolites showed the genus Megasphaera was involved in amino acid metabolism, which included 3-phenyllatic acid, quinic acid, caffeic acid, and folic acid, and the genus Hungatella participated in steroid hormone biosynthesis through its strong correlation with estradiol. These results explored the dynamic changes in tissues related to the HPL axis and cecal microbiota and provided new insights into the interaction between the host and microbiota during egg-laying in chickens. Show less
no PDF DOI: 10.1093/jas/skae263
APOA5

Correction: The ANGPTL4-HIF-1α loop: a critical regulator of renal interstitial fibrosis.

Yan Li, Shuang Chen, Qian Yang +5 more · 2024 · Journal of translational medicine · BioMed Central · added 2026-04-24
no PDF DOI: 10.1186/s12967-024-05586-w
ANGPTL4

IL-27 Gene Therapy Ameliorates IPEX Syndrome Caused by Germline Mutation of Foxp3 Gene: A Major Role for Induction of IL-10.

Jin-Qing Liu, Ali Jabbari, Cho-Hao Lin +7 more · 2024 · Journal of immunology (Baltimore, Md. : 1950) · added 2026-04-24
Inactivating mutations of Foxp3, the master regulator of regulatory T cell development and function, lead to immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome in mice and Show more
Inactivating mutations of Foxp3, the master regulator of regulatory T cell development and function, lead to immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome in mice and humans. IPEX is a fatal autoimmune disease, with allogeneic stem cell transplant being the only available therapy. In this study, we report that a single dose of adeno-associated virus (AAV)-IL-27 to young mice with naturally occurring Foxp3 mutation (Scurfy mice) substantially ameliorates clinical symptoms, including growth retardation and early fatality. Correspondingly, AAV-IL-27 gene therapy significantly prevented naive T cell activation, as manifested by downregulation of CD62L and upregulation of CD44, and immunopathology typical of IPEX. Because IL-27 is known to induce IL-10, a key effector molecule of regulatory T cells, we evaluated the contribution of IL-10 induction by crossing IL-10-null allele to Scurfy mice. Although IL-10 deficiency does not affect the survival of Scurfy mice, it largely abrogated the therapeutic effect of AAV-IL-27. Our study revealed a major role for IL-10 in AAV-IL-27 gene therapy and demonstrated that IPEX is amenable to gene therapy. Show less
📄 PDF DOI: 10.4049/jimmunol.2400056
IL27

Clinical and genetic characteristics of a case of Koolen-De Vries syndrome caused by KANSL1 gene mutation and literature review: A case report.

Haozheng Zhang, Limei Yuan, Meili Fan +6 more · 2024 · Medicine · added 2026-04-24
Koolen-De Vries syndrome (KdVS, OMIM: 612452), also known as 17q21.31 microdeletion syndrome, is an autosomal dominant genetic disease. In the study, we analyze of clinical phenotype and gene variatio Show more
Koolen-De Vries syndrome (KdVS, OMIM: 612452), also known as 17q21.31 microdeletion syndrome, is an autosomal dominant genetic disease. In the study, we analyze of clinical phenotype and gene variation of a child with Koolen-De Vries syndrome, review the literature to improve the understanding of the disease. The patient is a male, aged 1 month and 3 days. The patient has poor airway development, difficulty weaning from respiratory support, seizures, and recurrent low granulocyte counts. High-throughput sequencing showed a heterozygous mutation NM₀₀₁₁₉₃₄₆₆.1: c.1574₁₅₇₈del (P.525HFS *24) in the KANSL1 gene of the proband, which was considered a new mutation since neither of his parents carried this mutation based on Sanger sequencing results. Combining clinical features and genetic results, the proband was diagnosed as KdVS. The patient was in good condition after receiving bronchoscopy and laser interventional therapy, meeting the criteria for discharge. Follow-up for 1 year and 6 months indicated that the patient's physical signs were normal and there was no recurrence. According to literature review, KdVS is a multi-organ disease characterized by feeding difficulties, seizures, characteristic facial features, dysplasia of the respiratory system and cardiac abnormalities. In this study, laryngeal malacia accounted for 23.2% of the clinical manifestations of KdVS patients, limb convulsions/seizures accounted for 62.5%, and cardiac development defects accounted for 23.5%. The disease was rare in China and had a variety of clinical manifestations. The summary of reported cases can enable doctors to have more understanding of the disease. The new mutations enrich the KANSL1 gene mutation spectrum. Show less
📄 PDF DOI: 10.1097/MD.0000000000040923
KANSL1

Longitudinal plasma proteomic profiling of EML4-ALK positive lung cancer receiving ALK-TKIs therapy.

Shasha Wang, Xuezhi Hao, Liyuan Dai +12 more · 2024 · Lung cancer (Amsterdam, Netherlands) · Elsevier · added 2026-04-24
Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) has demonstrated remarkable therapeutic effects in ALK-positive non-small cell lung cancer (NSCLC) patients. Identifying prognostic bio Show more
Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) has demonstrated remarkable therapeutic effects in ALK-positive non-small cell lung cancer (NSCLC) patients. Identifying prognostic biomarkers can enhance the clinical efficacy of relapsed or refractory patients. We profiled 737 plasma proteins from 159 pre-treatment and on-treatment plasma samples of 63 ALK-positive NSCLC patients using data-independent acquisition-mass spectrometry (DIA-MS). The consensus clustering algorithm was used to identify subtypes with distinct biological features. A plasma-based prognostic model was constructed using the LASSO-Cox method. We performed the Mfuzz analysis to classify the patterns of longitudinal changes in plasma proteins during treatment. 52 baseline plasma samples from another independent ALK-TKI treatment cohort were collected to validate the potential prognostic markers using ELISA. We identified three subtypes of ALK-positive NSCLC with distinct biological features and clinical efficacy. Patients in subgroup 1 exhibited activated humoral immunity and inflammatory responses, increased expression of positive acute-phase response proteins, and the worst prognosis. Then we constructed and verified a prognostic model that predicts the efficacy of ALK-TKI therapy using the expression levels of five plasma proteins (SERPINA4, ATRN, APOA4, TF, and MYOC) at baseline. Next, we explored the longitudinal changes in plasma protein expression during treatment and identified four distinct change patterns (Clusters 1-4). The longitudinal changes of acute-phase proteins during treatment can reflect the treatment status and tumor progression of patients. Finally, we validated the prognostic efficacy of baseline plasma CRP, SAA1, AHSG, SERPINA4, and TF in another independent NSCLC cohort undergoing ALK-TKI treatment. This study contributes to the search for prognostic and drug-resistance biomarkers in plasma samples for ALK-TKI therapy and provides new insights into the mechanism of drug resistance and the selection of follow-up treatment. Show less
no PDF DOI: 10.1016/j.lungcan.2024.107503
APOA4

Redox-modulated SNX25 as a novel regulator of GPCR-G protein signaling from endosomes.

Yulong Zhang, Zhijun Yu, Mingwei Sun +10 more · 2024 · Redox biology · Elsevier · added 2026-04-24
GPCR-G protein signaling from endosomes plays a crucial role in various physiological and pathological processes. However, the mechanism by which endosomal G protein signaling is terminated remains la Show more
GPCR-G protein signaling from endosomes plays a crucial role in various physiological and pathological processes. However, the mechanism by which endosomal G protein signaling is terminated remains largely unknown. In this study, we aimed to investigate the regulatory mechanisms involved in terminating the signaling of Gα subunits from endosomes. Through structural analysis and cell-based assays, we have discovered that SNX25, a protein that targets endosomes via its PXA or PXC domain, interacts with regulator of G protein signaling (RGS) proteins (including RGS2, RGS4, RGS8, and RGS17) in a redox-regulated manner. The interaction between SNX25 and these RGS proteins enhances their GTPase-accelerating activity towards Gα Show less
no PDF DOI: 10.1016/j.redox.2024.103253
RGS17

Dissecting the mediating role of inflammatory factors in the interaction between metabolites and sepsis: insights from bidirectional Mendelian randomization.

Fangchen Gong, Wenbin Liu, Lei Pei +10 more · 2024 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Sepsis, a life-threatening condition, involves complex interactions among metabolic alterations, inflammatory mediators, and host responses. This study utilized a bidirectional Mendelian randomization Show more
Sepsis, a life-threatening condition, involves complex interactions among metabolic alterations, inflammatory mediators, and host responses. This study utilized a bidirectional Mendelian randomization approach to investigate the causal relationships between 1400 metabolites and sepsis, and the mediating role of inflammatory factors. We identified 36 metabolites significantly associated with sepsis (p < 0.05), with AXIN1, FGF-19, FGF-23, IL-4, and OSM showing an inverse association, suggesting a protective role, while IL-2 exhibited a positive correlation, indicating a potential risk factor. Among these metabolites, Piperine and 9-Hydroxystearate demonstrated particularly interesting protective effects against sepsis. Piperine's protective effect was mediated through its interaction with AXIN1, contributing to a 16.296% reduction in sepsis risk. This suggests a potential pathway where Piperine influences sepsis outcomes by modulating AXIN1 levels. 9-Hydroxystearate also exhibited a protective role against sepsis, mediated through its positive association with FGF-19 and negative association with IL-2, contributing 9.436% and 12.565%, respectively, to its protective effect. Experimental validation confirmed significantly elevated IL-2 levels and reduced FGF-19, AXIN1, piperine, and 9-hydroxyoctadecanoic acid levels in sepsis patients compared to healthy controls. Piperine levels positively correlated with AXIN1, while 9-hydroxyoctadecanoic acid levels negatively correlated with IL-2 and positively correlated with FGF-19, supporting the Mendelian randomization findings. Our findings provide insights into the molecular mechanisms of sepsis, highlighting the unique roles and contributions of specific metabolites and their interactions with inflammatory mediators. This study enhances our understanding of sepsis pathophysiology and opens avenues for targeted therapeutic interventions and biomarker development for sepsis management. However, further research is essential to validate these pathways across diverse populations and fully explore the roles of these metabolites in sepsis. Show less
📄 PDF DOI: 10.3389/fendo.2024.1377755
AXIN1

Precision targeting of β-catenin induces tumor reprogramming and immunity in hepatocellular cancers.

Brandon M Lehrich, Evan R Delgado, Tyler M Yasaka +31 more · 2024 · Research square · added 2026-04-24
First-line immune checkpoint inhibitor (ICI) combinations show responses in subsets of hepatocellular carcinoma (HCC) patients. Nearly half of HCCs are Wnt-active with mutations in
📄 PDF DOI: 10.21203/rs.3.rs-5494074/v1
AXIN1

Inhibition of autophagosome-lysosome fusion contributes to TDCIPP-induced Aβ1-42 production in N2a-APPswe cells.

Chunli Zou, Tingting Yang, Xinfeng Huang +4 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Alzheimer's disease is the most common form of dementia and is characterized by cognitive impairment. The disruption of autophagosome-lysosome function has been linked to the pathogenesis of Alzheimer Show more
Alzheimer's disease is the most common form of dementia and is characterized by cognitive impairment. The disruption of autophagosome-lysosome function has been linked to the pathogenesis of Alzheimer's disease. Tris (1,3-dichloro-2-propyl) phosphate (TDCIPP) is a widely used organophosphorus flame retardant that has the potential to cause neuronal damage. We found that TDCIPP significantly increased the expression of β-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1), presenilin-1 (PS1) and Aβ42. Proteomic studies with TMT labeling revealed changes in the profiles of N2a-APPswe cells after exposure to TDCIPP. Proteomic and bioinformatics analyses revealed that lysosomal proteins were dysregulated in N2a-APPswe cells after treatment with TDCIPP. The LC3, P62, CTSD, and LAMP1 levels were increased after TDCIPP exposure, and dysregulated protein expression was validated by Western blotting. The exposure to TDCIPP led to the accumulation of autophagosomes, and this phenomenon was enhanced in the presence of chloroquine (CQ). Our results revealed for the first time that TDCIPP could be a potential environmental risk factor for AD development. The inhibition of autophagosome-lysosome fusion may have a significant impact on the generation of Aβ1-42 in response to TDCIPP. Show less
📄 PDF DOI: 10.1016/j.heliyon.2024.e26832
BACE1

Protective effects of betaine on the early fatty liver in laying hens through ameliorating lipid metabolism and oxidative stress.

Chaohui Wang, Xi Sun, Xiaoying Liu +4 more · 2024 · Frontiers in nutrition · Frontiers · added 2026-04-24
Fatty liver syndrome (FLS) is a prevalent nutritional and metabolic disease that mainly occurs in caged laying hens, causing substantial losses in the poultry industry. The study was carried out to ex Show more
Fatty liver syndrome (FLS) is a prevalent nutritional and metabolic disease that mainly occurs in caged laying hens, causing substantial losses in the poultry industry. The study was carried out to explore the protective effect and potential mechanism of betaine on early FLS. There were three groups: Con group (basal diet), FLS group (Dexamethasone injection + basal diet) and betaine group (Dexamethasone injection + basal diet with 8 g/kg betaine). Birds in FLS and betaine groups were treated with subcutaneous dexamethasone injection once a day at a dosage of 4.50 mg/kg body weight for 7 days. The results revealed that DXM treatment significantly increased the liver index, serum aspartate aminotransferase (AST), total protein (TP), total bilirubin (TBIL), total biliary acid (TBA), total cholesterol (TC), high density lipoprotein cholesterol (HDL-c), low density lipoprotein cholesterol (LDL-c), and glucose (GLU) ( Dexamethasone treatment could establish the early FLS model in laying hens with hepatic lipid accumulation and no inflammation, which could be attenuated by dietary betaine addition. Show less
📄 PDF DOI: 10.3389/fnut.2024.1505357
APOA4

Effects of Dietary Supplementation with Chitosan on the Muscle Composition, Digestion, Lipid Metabolism, and Stress Resistance of Juvenile Tilapia (

Qin Zhang, Yi Xie, Yuanhui Zhang +4 more · 2024 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
The aim of this study was to investigate the effects of dietary chitosan supplementation on the muscle composition, digestion, lipid metabolism, and stress resistance, and their related gene expressio Show more
The aim of this study was to investigate the effects of dietary chitosan supplementation on the muscle composition, digestion, lipid metabolism, and stress resistance, and their related gene expression, of juvenile tilapia ( Show less
📄 PDF DOI: 10.3390/ani14040541
LPL

Integrative proteome and metabolome analyses reveal molecular basis underlying growth and nutrient composition in the Pacific oyster, Crassostrea gigas.

Ying Tan, Yongjing Li, Liting Ren +3 more · 2024 · Journal of proteomics · Elsevier · added 2026-04-24
In order to comprehend the molecular basis of growth, nutrient composition, and color pigmentation in oysters, comparative proteome and metabolome analyses of two selectively bred oyster strains with Show more
In order to comprehend the molecular basis of growth, nutrient composition, and color pigmentation in oysters, comparative proteome and metabolome analyses of two selectively bred oyster strains with contrasting growth rate and shell color were used in this study. A total of 289 proteins and 224 metabolites were identified differentially expressed between the two strains. We identified a series of specifically enriched functional clusters implicated in protein biosynthesis (RPL4, MRPS7, and CARS), fatty acid metabolism (ACSL5, PEX3, ACOXI, CPTIA, FABP6, and HSD17B12), energy metabolism (FH, PPP1R7, CLAM2, and RGN), cell proliferation (MYB, NFYC, DOHH, TOP2a, SMARCA5, and SMARCC2), material transport (ABCB1, ABCB8, VPS16, and VPS33a), and pigmentation (RDH7, RDH13, Retsat, COX15, and Cyp3a9). Integrated proteome and metabolome analyses indicate that fast-growing strain utilize energy-efficient mechanisms of ATP generation while promoting protein and polyunsaturated fatty acid synthesis, activating the cell cycle to increase cell proliferation and thus promoting their biomass increase. These results uncovered molecular mechanisms underlying growth regulation, nutrition quality, and pigmentation and provided candidate biomarkers for molecular breeding in oysters. SIGNIFICANCE: Rapid growth has always been the primary breeding objective to increase the production profits of Pacific oyster (Crassostrea gigas), while favorable nutritional quality and beautiful color add commercial value. In recent years, proteomic and metabolomic techniques have been widely used in marine organisms, although these techniques are seldom utilized to study oyster growth and development. In this study, two C. gigas strains with contrasted phenotypes in growth and shell color provided an ideal model for unraveling the molecular basis of growth and nutrient composition through a comparison of the proteome and metabolome. Since proteins and metabolites are the critical undertakers and the end products of cellular regulatory processes, identifying the differentially expressed proteins and metabolites would allow for discovering biomarkers and pathways that were implicated in cell growth, proliferation, and other critical functions. This work provides valuable resources in assistance with molecular breeding of oyster strains with superior production traits of fast-growth and high-quality nutrient value. Show less
no PDF DOI: 10.1016/j.jprot.2023.105021
HSD17B12

Extending the G1 phase improves the production of lipophilic compounds in yeast by boosting enzyme expression and increasing cell size.

He Hao, Mingdong Yao, Ying Wang +6 more · 2024 · Proceedings of the National Academy of Sciences of the United States of America · National Academy of Sciences · added 2026-04-24
Cell phase engineering can significantly impact protein synthesis and cell size, potentially enhancing the production of lipophilic products. This study investigated the impact of G1 phase extension o Show more
Cell phase engineering can significantly impact protein synthesis and cell size, potentially enhancing the production of lipophilic products. This study investigated the impact of G1 phase extension on resource allocation, metabolic functions, and the unfolded protein response (UPR) in yeast, along with the potential for enhancing the production of lipophilic compounds. In brief, the regulation of the G1 phase was achieved by deleting Show less
📄 PDF DOI: 10.1073/pnas.2413486121
CLN3

The molecular regulated mechanism of METTL3 and FTO in lipid metabolism of Hu sheep.

Bowen Chen, Chao Yuan, Tingting Guo +3 more · 2024 · Genomics · Elsevier · added 2026-04-24
Balanced lipid metabolism can improve the growth performance and meat quality of livestock. The m6A methylation-related genes METTL3 and FTO play important roles in animal lipid metabolism; however, t Show more
Balanced lipid metabolism can improve the growth performance and meat quality of livestock. The m6A methylation-related genes METTL3 and FTO play important roles in animal lipid metabolism; however, the mechanism through which they regulate lipid metabolism in sheep is unclear. We established lipid deposition models of hepatocytes and preadipocytes in Hu sheep. In the hepatocyte lipid deposition model, the genes expression levels of FABP4, Accα, ATGL and METTL3, METTL14, and FTO-were significantly up-regulated after lipid deposition (P < 0.05). Transcriptomic and metabolomic analyses showed that lipid deposition had a significant effect on MAPK, steroid biosynthesis, and glycerophospholipid metabolism pathway in hepatocytes. The m6A methylation level decreased but the difference was not significant after METTL3 interference, and the expression levels of FABP4 and ATGL increased significantly (P < 0.05); the m6A methylation level significantly increased following METTL3 overexpression, and LPL and ATGL expression levels significantly decreased (P < 0.05), indicating that overexpression of METTL3 inhibited the expression of lipid deposition-related genes in a m6A-dependent manner. The m6A methylation level was significantly increased, ATGL expression was significantly decreased (P < 0.05), and LPL, FABP4, and Accα expression was not significantly changed following FTO interference (P > 0.05); the m6A methylation level was significantly decreased after FTO overexpression, and LPL, FABP4, and ATGL expression was significantly increased (P < 0.05), indicating that FTO overexpression increased the expression of lipid deposition-related genes in a m6A-dependent manner. Transcriptomic and metabolomic analyses showed that m6A methylation modification mainly regulated lipid metabolism through triglyceride metabolism, adipocytokine signaling, MAPK signaling, and fat digestion and absorption in hepatocytes. In the lipid deposition model of preadipocytes, the regulation of gene expression is the same as that in hepatocytes. METTL3 significantly inhibited the expression of lipid deposition-related genes, whereas FTO overexpression promoted lipid deposition. Our study provides a theoretical basis and reference for accurately regulating animal lipid deposition by mastering METTL3 and FTO genes to promote high-quality animal husbandry. Show less
no PDF DOI: 10.1016/j.ygeno.2024.110945
LPL

Novel Melanocortin-3 and -4 Receptor Functional Variants in Asian Children With Severe Obesity.

Siong Gim Ong, Roghayeh Dehghan, Rajkumar Dorajoo +4 more · 2024 · The Journal of clinical endocrinology and metabolism · added 2026-04-24
Genetic variants in melanocortin 3 receptor (MC3R) and melanocortin 4 receptor (MC4R) genes are strongly associated with childhood obesity. This study aims to identify and functionally characterize MC Show more
Genetic variants in melanocortin 3 receptor (MC3R) and melanocortin 4 receptor (MC4R) genes are strongly associated with childhood obesity. This study aims to identify and functionally characterize MC3R and MC4R variants in an Asian cohort of children with severe early-onset obesity. Whole-exome sequencing was performed to screen for MC3R and MC4R coding variants in 488 Asian children with severe early-onset obesity (body mass index for age ≥97th percentile). Functionality of the identified variants were determined via measurement of intracellular cyclic adenosine monophosphate (cAMP) concentrations and luciferase activity. Four MC3R and 2 MC4R heterozygous nonsynonymous rare variants were detected. There were 3 novel variants: MC3R c.151G > C (p.Val51Leu), MC4R c.127C > A (p.Gln43Lys), and MC4R c.272T > G (p.Met91Arg), and 3 previously reported variants: MC3R c.127G > A (p.Glu43Lys), MC3R c.97G > A (p.Ala33Thr), and MC3R c.437T > A (p.Ile146Asn). Both MC3R c.127G > A (p.Glu43Lys) and MC4R c.272T > G (p.Met91Arg) variants demonstrated defective downstream cAMP signaling activity. The MC4R c.127C > A (p.Gln43Lys) variant showed reduced cAMP signaling activity at low substrate concentration but the signaling activity was restored at high substrate concentration. The MC3R c.151G > C (p.Val51Leu) variant did not show a significant reduction in cAMP signaling activity compared to wild-type (WT) MC3R. Coexpression studies of the WT and variant MC3R/MC4R showed that the heterozygous variants did not exhibit dominant negative effect. Our functional assays demonstrated that MC3R c.127G > A (p.Glu43Lys) and MC4R c.272T > G (p.Met91Arg) variants might predispose individuals to early-onset obesity, and further studies are needed to establish the causative effect of these variants in the pathogenesis of obesity. Show less
no PDF DOI: 10.1210/clinem/dgad602
MC4R

Nanofiber-induced hierarchically-porous magnesium phosphate bone cements accelerate bone regeneration by inhibiting Notch signaling.

Jingteng Chen, Ling Yu, Tian Gao +11 more · 2024 · Bioactive materials · Elsevier · added 2026-04-24
Magnesium phosphate bone cements (MPC) have been recognized as a viable alternative for bone defect repair due to their high mechanical strength and biodegradability. However, their poor porosity and Show more
Magnesium phosphate bone cements (MPC) have been recognized as a viable alternative for bone defect repair due to their high mechanical strength and biodegradability. However, their poor porosity and permeability limit osteogenic cell ingrowth and vascularization, which is critical for bone regeneration. In the current study, we constructed a novel hierarchically-porous magnesium phosphate bone cement by incorporating extracellular matrix (ECM)-mimicking electrospun silk fibroin (SF) nanofibers. The SF-embedded MPC (SM) exhibited a heterogeneous and hierarchical structure, which effectively facilitated the rapid infiltration of oxygen and nutrients as well as cell ingrowth. Besides, the SF fibers improved the mechanical properties of MPC and neutralized the highly alkaline environment caused by excess magnesium oxide. Bone marrow stem cells (BMSCs) adhered excellently on SM, as illustrated by formation of more pseudopodia. CCK8 assay showed that SM promoted early proliferation of BMSCs. Our study also verified that SM increased the expression of OPN, RUNX2 and BMP2, suggesting enhanced osteogenic differentiation of BMSCs. We screened for osteogenesis-related pathways, including FAK signaing, Wnt signaling and Notch signaling, and found that SM aided in the process of bone regeneration by suppressing the Notch signaling pathway, proved by the downregulation of NICD1, Hes1 and Hey2. In addition, using a bone defect model of rat calvaria, the study revealed that SM exhibited enhanced osteogenesis, bone ingrowth and vascularization compared with MPC alone. No adverse effect was found after implantation of SM Show less
📄 PDF DOI: 10.1016/j.bioactmat.2024.03.021
HEY2

The relevance between abnormally elevated serum ceramide and cognitive impairment in Alzheimer's disease model mice and its mechanism.

Xin Liu, Yongzeng Jin, Xinyi Cheng +4 more · 2024 · Psychopharmacology · Springer · added 2026-04-24
The plasma ceramide levels in Alzheimer's disease (AD) patients are found abnormally elevated, which is related to cognitive decline. This research was aimed to investigate the mechanisms of aberrant Show more
The plasma ceramide levels in Alzheimer's disease (AD) patients are found abnormally elevated, which is related to cognitive decline. This research was aimed to investigate the mechanisms of aberrant elevated ceramides in the pathogenesis of AD. The ICR mice intracerebroventricularly injected with Aβ Ceramide was positively related to the increased p-tau and impaired cognitive function. The increased generation of ceramide and endoplasmic reticulum stress in the hypothalamus was positively related to fatty acid synthesis and NF-κB signaling via brain-liver axis. Show less
📄 PDF DOI: 10.1007/s00213-024-06530-y
MC4R

Identifying Hub Genes Related to Vascular Endothelial Cell Permeability Through Bioinformatics and Verification.

Yunjiang Zheng, Qianyi Chen, Lei Cao +3 more · 2024 · Clinical laboratory · added 2026-04-24
In this study, we aimed to identify the hub genes responsible for increased vascular endothelial cell permeability. We applied the weighted Gene Expression Omnibus (GEO) database to mine dataset GSE17 Show more
In this study, we aimed to identify the hub genes responsible for increased vascular endothelial cell permeability. We applied the weighted Gene Expression Omnibus (GEO) database to mine dataset GSE178331 and ob-tained the most relevant high-throughput sequenced genes for an increased permeability of vascular endothelial cells due to inflammation. We constructed two weighted gene co-expression network analysis (WGCNA) networks, and the differential expression of high-throughput sequenced genes related to endothelial cell permeability were screened from the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differential genes. Their degree values were obtained from the topological properties of protein-protein interaction (PPI) networks of differential genes, and the hub genes associated with an increased endothelial cell permeability were analyzed. Reverse transcription-polymerase chain reaction (RT-PCR) and western blotting techniques were used to detect the presence of these hub genes in TNF-α induced mRNA and the protein expression in endothelial cells. In total, 1,475 differential genes were mainly enriched in the cell adhesion and TNF-α signaling pathway. With TNF-α inducing an increase in the endothelial cell permeability and significantly increasing mRNA and protein expression levels, we identified three hub genes, namely PTGS2, ICAM1, and SNAI1. There was a significant difference in the high-dose TNF-α group and in the low-dose TNF-α group compared to the control group, in the endothelial cell permeability experiment (p = 0.008 vs. p = 0.02). Measurement of mRNA and protein levels of PTGS2, ICAM1, and SNAI1 by western blotting analysis showed that there was a significant impact on TNF-α and that there was a significant dose-dependent relationship (p < 0.05 vs. p < 0.01). The three hub genes identified through bioinformatics analyses in the present study may serve as biomarkers of increased vascular endothelial cell permeability. The findings offer valuable insights into the progress and mechanism of vascular endothelial cell permeability. Show less
no PDF DOI: 10.7754/Clin.Lab.2024.231125
SNAI1