👤 Yuanying Liu

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

FGF1

Qunwu Tang, Zhewei Cheng, Sixiu Liu +6 more · 2024 · Biochemical pharmacology · Elsevier · added 2026-04-24
Translocator protein (18 kDa) (TSPO) plays an important role in retinal neuroinflammation in the early stage of diabetic retinopathy (DR). Studies have found that a FGF1 variant (FGF1
no PDF DOI: 10.1016/j.bcp.2024.116039
FGFR1

Proximity hybridization induced bipedal DNA walker and rolling circle amplification for label-free electrochemical detection of apolipoprotein A4.

Chenyi Zhuo, Dehong Yu, Jiuying Cui +7 more · 2024 · Bioelectrochemistry (Amsterdam, Netherlands) · Elsevier · added 2026-04-24
Apolipoprotein A4 (Apo-A4) is considered as a prospective molecular biomarker for diagnosis of depression due to its neurosynaptic toxicity. We develop a proximity hybridization-induced DNAzyme-driven Show more
Apolipoprotein A4 (Apo-A4) is considered as a prospective molecular biomarker for diagnosis of depression due to its neurosynaptic toxicity. We develop a proximity hybridization-induced DNAzyme-driven bipedal DNA walker strategy for Apo-A4 quantification based on rolling circle amplification (RCA) triggered by poly adenine binding to Ag nanoparticles (AgNPs). With the help of DNAzyme, the free-running bipedal DNA walker can quickly and sequentially shear a molecular beacon that acts as a primer to initiate the RCA process, producing a large number of long DNA strands containing numerous adenines. The long repetitive adenine strands then absorb large amounts of AgNPs on the electrode interface, which is then electrochemically stripped of the AgNPs. The method has a linear detection range of 0.001 ∼ 100 ng mL Show less
no PDF DOI: 10.1016/j.bioelechem.2023.108596
APOA4

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

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

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

Identification of coagulation diagnostic biomarkers related to the severity of spinal cord injury.

Jianfeng Li, Junhong Li, Xianlong Li +9 more · 2024 · International immunopharmacology · Elsevier · added 2026-04-24
Blood always shows coagulation changes after spinal cord injury (SCI), and identifying these blood changes may be helpful for diagnosis and treatment of SCI. Nevertheless, studies to date on blood coa Show more
Blood always shows coagulation changes after spinal cord injury (SCI), and identifying these blood changes may be helpful for diagnosis and treatment of SCI. Nevertheless, studies to date on blood coagulation changes after SCI in humans are not comprehensive. Therefore, this study aims to identify blood coagulation diagnostic biomarkers and immune changes related to SCI and its severity levels. Human blood sequencing datasets were obtained from public databases. Differentially expressed coagulation-related genes were analyzed (DECRGs). Enrichment analysis and assessment of immune changes were conducted. Weighted gene co-expression network analysis, least absolute shrinkage and selection operator logistic regression were used to identify biomarkers. Validation for these biomarkers was performed. The correlation between biomarkers and immune cells was evaluated. Transcription factors, miRNA, lncRNA, and drugs that can regulate biomarkers were analyzed. DECRGs associated with SCI and its different grades were identified, showing enrichment in altered coagulation and immune-related signaling pathways. ADAM9, CD55, and STAT4 were identified as coagulation diagnostic biomarkers for SCI. IRF4 and PABPC4 were identified as coagulation diagnostic biomarkers for American Spinal Injury Association Impairment Scale (AIS) A grade of SCI. GP9 was designated as a diagnostic biomarker for AIS D grade of SCI. Immune changes in blood of SCI and its different grades were observed. Correlation between diagnostic biomarkers and immune cells were identified. Transcription factors, miRNA, lncRNA, and drugs that can regulate diagnostic biomarker expression were discovered. Therefore, detecting the expression of these putative diagnostic biomarkers and related immune changes may be helpful for predicting the severity of SCI. Uncovering potential regulatory mechanisms for biomarkers may be beneficial for further research. Show less
no PDF DOI: 10.1016/j.intimp.2024.112505
PABPC4

Genetic Associations of Lipids and Lipid-Modifying Drug Targets With Type 2 Diabetes in the Chinese Population.

Jiawen Lu, Yang Liu, Zhenqian Wang +4 more · 2024 · JACC. Asia · Elsevier · added 2026-04-24
Dyslipidemia is a recognized risk factor for type 2 diabetes (T2D), yet the genetic basis and causal nature remain unclear, particularly in Chinese populations. The authors investigated the causal eff Show more
Dyslipidemia is a recognized risk factor for type 2 diabetes (T2D), yet the genetic basis and causal nature remain unclear, particularly in Chinese populations. The authors investigated the causal effects of genetically predicted lipid levels on T2D risk and explored the potential effects of lipid-modifying drugs. Leveraging data from the Kunshan Community cohort in China, we analyzed the associations between low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides (TGs) with T2D risk using genetic risk scores, 1-sample univariable, multivariable, and nonlinear Mendelian randomization (MR) analyses. Two-sample MR using summary-level data from Global Lipid Genetics Consortium and Biobank Japan was used for validation. Drug-target MR was used to examine the impact of lipid-modifying drug targets on T2D. Lower genetic risk scores of LDL-C (OR per SD: 0.97 [95% CI: 0.95-0.99]; Our findings suggested potential adverse effects of lower LDL-C, TG levels, as well as long-term use of APOC3 inhibitors on T2D risk in Chinese populations. These findings highlight the need for cautious lipid management strategies in T2D prevention. Show less
📄 PDF DOI: 10.1016/j.jacasi.2024.07.011
APOC3

Chronic Lead Exposure in Adult Mice: Associations with miR-671/CDR1as Regulation, NF-κB Signaling, and Alzheimer's Disease-like Pathology.

Mengyun Qiao, Haitao Yang, Li Liu +9 more · 2024 · Toxics · MDPI · added 2026-04-24
Long-term exposure to lead (Pb) can result in chronic damage to the body through accumulation in the central nervous system (CNS) leading to neurodegenerative diseases, such as Alzheimer's disease (AD Show more
Long-term exposure to lead (Pb) can result in chronic damage to the body through accumulation in the central nervous system (CNS) leading to neurodegenerative diseases, such as Alzheimer's disease (AD). This study delves into the intricate role of miR-671/CDR1as regulation in the etiology of AD-like lesions triggered by chronic Pb exposure in adult mice. To emulate the chronic effects of Pb, we established a rodent model spanning 10 months of controlled Pb administration, dividing 52 C57BL/6J mice into groups receiving varying concentrations of Pb (1, 2, or 4 g/L) alongside an unexposed control. Blood Pb levels were monitored using serum samples to ensure accurate dosing and to correlate with observed toxicological outcomes. Utilizing the Morris water maze, a robust behavioral assay for assessing cognitive functions, we documented a dose-dependent decline in learning and memory capabilities among the Pb-exposed mice. Histopathological examination of the hippocampal tissue revealed tell-tale signs of AD-like neurodegeneration, characterized by the accumulation of amyloid plaques and neurofibrillary tangles. At the molecular level, a significant upregulation of AD-associated genes, namely amyloid precursor protein (APP), β-secretase 1 (BACE1), and tau, was observed in the hippocampal tissue of Pb-exposed mice. This was accompanied by a corresponding surge in the protein levels of APP, BACE1, amyloid-β (Aβ), and phosphorylated tau (p-tau), further implicating Pb in the dysregulation of these key AD markers. The expression of CDR1as, a long non-coding RNA implicated in AD pathogenesis, was found to be suppressed in Pb-exposed mice. This observation suggests a potential mechanistic link between Pb-induced neurotoxicity and the dysregulation of the CDR1as/miR-671 axis, which warrants further investigation. Moreover, our study identified a dose-dependent alteration in the intracellular and extracellular levels of the transcription factor nuclear factor-kappa B (NF-κB). This finding implicates Pb in the modulation of NF-κB signaling, a pathway that plays a pivotal role in neuroinflammation and neurodegeneration. In conclusion, our findings underscored the deleterious effects of Pb exposure on the CNS, leading to the development of AD-like pathology. The observed modulation of NF-κB signaling and miR-671/CDR1as regulation provides a plausible mechanistic framework for understanding the neurotoxic effects of Pb and its potential contribution to AD pathogenesis. Show less
📄 PDF DOI: 10.3390/toxics12060410
BACE1

Lipid Regulatory Element Interact with

Guanghui Li, Xibi Fang, Yinuo Liu +6 more · 2024 · Journal of agricultural and food chemistry · ACS Publications · added 2026-04-24
The
no PDF DOI: 10.1021/acs.jafc.4c02434
LPL

The potential molecular mechanism underlying gypenoside amelioration of atherosclerosis in ApoE

Xing Ju, Yufeng Liu, Ying Wang +9 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Gypenosides (Gyp) are bioactive components of
📄 PDF DOI: 10.1016/j.heliyon.2024.e29164
FADS1

Identification of the novel markers of PPAR signalling affecting immune microenvironment and immunotherapy response of lung adenocarcinoma patients.

Wei Zhang, Junhui Liu, Xin Ren +7 more · 2024 · Journal of cellular and molecular medicine · Blackwell Publishing · added 2026-04-24
Peroxisome proliferator-activated receptors (PPARs) are essential for cellular physiological processes. However, there is less research on the PPAR-related genes in lung adenocarcinoma (LUAD). Open-ac Show more
Peroxisome proliferator-activated receptors (PPARs) are essential for cellular physiological processes. However, there is less research on the PPAR-related genes in lung adenocarcinoma (LUAD). Open-access data were get from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. All the analysis were conducted in the R software based on different R packages. In this study, we gauged the PPAR score employing a set of 72 PPAR-associated genes and probed the biological impact of this score on lung adenocarcinoma (LUAD). Subsequently, we established a unique signature composed of eight PPAR-related genes (ANGPTL4, ACSL3, ADIPOQ, FABP1, SLC27A1, ACOX2, PPARD and OLR1) to forecast the prognosis of LUAD. The signature's effectiveness in predicting survival was validated through the receiver operating characteristic curve in the TCGA-LUAD cohort. As per the pathway enrichment analysis, several crucial oncogenic pathways and metabolic processes were enriched in high-risk individuals. Further, we observed that these high-risk patients exhibited heightened genomic instability. Additionally, compared to the low-risk cohort, high-risk patients demonstrated diminished immune components and function. Intriguingly, high-risk patients exhibited a potential heightened sensitivity to immunotherapy and certain drugs, including Gefitinib, Afatinib, Erlotinib, IAP₅₆₂₀, Sapitinib, LCL161, Lapatinib and AZD3759. The prognosis model based on eight PPAR-related genes has satisfactory prognosis prediction efficiency. Meanwhile, our results can provide direction for future studies in the relevant aspects. Show less
📄 PDF DOI: 10.1111/jcmm.17877
ANGPTL4

Plantaginis Semen Ameliorates Hyperuricemia Induced by Potassium Oxonate.

Tian Liu, Liting Wang, Li Ji +5 more · 2024 · International journal of molecular sciences · MDPI · added 2026-04-24
Plantaginis semen is the dried ripe seed of
📄 PDF DOI: 10.3390/ijms25158548
LPL

Genetic architectures of the human hippocampus and those involved in neuropsychiatric traits.

Caibo Ning, Meng Jin, Yimin Cai +28 more · 2024 · BMC medicine · BioMed Central · added 2026-04-24
The hippocampus, with its complex subfields, is linked to numerous neuropsychiatric traits. While most research has focused on its global structure or a few specific subfields, a comprehensive analysi Show more
The hippocampus, with its complex subfields, is linked to numerous neuropsychiatric traits. While most research has focused on its global structure or a few specific subfields, a comprehensive analysis of hippocampal substructures and their genetic correlations across a wide range of neuropsychiatric traits remains underexplored. Given the hippocampus's high heritability, considering hippocampal and subfield volumes (HASV) as endophenotypes for neuropsychiatric conditions is essential. We analyzed MRI-derived volumetric data of hippocampal and subfield structures from 41,525 UK Biobank participants. Genome-wide association studies (GWAS) on 24 HASV traits were conducted, followed by genetic correlation, overlap, and Mendelian randomization (MR) analyses with 10 common neuropsychiatric traits. Polygenic risk scores (PRS) based on HASV traits were also evaluated for predicting these traits. Our analysis identified 352 independent genetic variants surpassing a significance threshold of 2.1 × 10 These findings highlight the extensive distribution of pleiotropic genetic determinants between HASVs and neuropsychiatric traits. Moreover, they suggest a significant potential for effectively managing and intervening in these diseases during their early stages. Show less
📄 PDF DOI: 10.1186/s12916-024-03682-8
KANSL1

A positive feedback between PDIA3P1 and OCT4 promotes the cancer stem cell properties of esophageal squamous cell carcinoma.

Tao Huang, Qi You, Dengjun Huang +9 more · 2024 · Cell communication and signaling : CCS · BioMed Central · added 2026-04-24
Increasing evidence has indicated that long non-coding RNAs (lncRNAs) have been proven to regulate esophageal cancer progression. The lncRNA protein disulfide isomerase family A member 3 pseudogene 1 Show more
Increasing evidence has indicated that long non-coding RNAs (lncRNAs) have been proven to regulate esophageal cancer progression. The lncRNA protein disulfide isomerase family A member 3 pseudogene 1 (PDIA3P1) has been shown to promote cancer stem cell properties; however, its mechanism of action remains unclear. In this study, we investigated the regulation of esophageal cancer stem cell properties by the interaction of PDIA3P1 with proteins. The GEPIA2 and Gene Expression Omnibus databases were used to analyze gene expression. PDIA3P1 expression in human esophageal squamous cell carcinoma (ESCC) tissues and cell lines was detected by quantitative real-time polymerase chain reaction (qRT-PCR). Loss-of-function experiments were performed to determine the effects of PDIA3P1 on ESCC cell proliferation, migration, and invasion. The sphere formation assay, number of side population cells, and CD271 + /CD44 + cells were detected by flow cytometry to identify the cancer stem cell properties. RNA immunoprecipitation (RIP), RNA pull-down, co-immunoprecipitation (co-IP), dual luciferase reporter, and cleavage under targets and tagmentation (CUT&Tag) assays were performed to elucidate the underlying molecular mechanisms. PDIA3P1 expression was upregulated in ESCC cell lines and tissues. Functionally, higher PDIA3P1 expression promoted cell proliferation, invasion, and metastasis and inhibited apoptosis in esophageal cancer. Importantly, PDIA3P1 promoted cancer stem cell properties in ESCC. Mechanistically, PDIA3P1 interacted with and stabilized octamer-binding transcription factor 4 (OCT4) by eliminating its ubiquitination by the ubiquitinating enzyme WW domain-containing protein 2 (WWP2). Moreover, as a transcription factor, OCT4 bound to the PDIA3P1 promoter and promoted its transcription. Our research revealed a novel mechanism by which a positive feedback loop exists between PDIA3P1 and OCT4. It also demonstrated that the PDIA3P1-WWP2-OCT4 loop is beneficial for promoting the cancer stem cell properties of ESCC. Owing to this regulatory relationship, the PDIA3P1-WWP2-OCT4-positive feedback loop might be used in the diagnosis and prognosis, as well as in the development of novel therapeutics for esophageal cancer. Show less
no PDF DOI: 10.1186/s12964-024-01475-3
WWP2

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

The inflammatory microenvironment of nasal polyps in patients with chronic rhinosinusitis and the relationship of this microenvironment with the nasal microbiome.

Weigang Gan, Yu Xiang, Bo Wei +2 more · 2024 · Asian journal of surgery · Elsevier · added 2026-04-24
We investigated the characteristics of the microbial community of the nasal sinuses in patients with chronic rhinosinusitis with nasal polyps (CRSwNP) and identified the correlations of the nasal micr Show more
We investigated the characteristics of the microbial community of the nasal sinuses in patients with chronic rhinosinusitis with nasal polyps (CRSwNP) and identified the correlations of the nasal microbiome with the inflammatory microenvironment of the nasal cavity. We collected matched nasal secretion and polyp tissue samples from 77 CRSwNP patients. Then, we extracted microbial DNA from cotton swabs, used high-throughput sequencing technology based on 16S ribosomal RNA (rRNA) to detect the bacterial community composition, and detected cytokines such as interleukin (IL)-5, IL-8, IL-17a, IL-17e, IL-18, IL-27 and interferon (INF)-gamma in the polyp tissue samples using Luminex. Eosinophils and neutrophils in the peripheral blood and polyp tissue were counted, and the relationships between inflammatory factors or inflammatory cell counts and nasal microbial diversity were analyzed. Among the inflammatory factors evaluated, IL-5 had a positive rate of 32.47%, IFN-γ had a positive rate of 84.42%, IL-17A and IL-17E had positive rates of 75.32%, IL-18 had a positive rate of 94.81%, IL-27 had a positive rate of 68.83%, and IL-8 had a positive rate of 100%. IL-17a and IL-27 were negatively correlated with both Enterobacter and Anaerococcus, IL-8 was negatively correlated with both Enterobacter and Staphylococcus, IL-18 was positively correlated with Candidatus Arthromitus and negatively correlated with Haemophilus, and IL-27 was positively correlated with Faecalibaculum. Lactobacillus and Enterococcus were positively correlated with the degree of neutrophil infiltration in nasal polyp tissue. In Southwest China, inflammation of the nasal polyps exhibits a variety of patterns. Enterobacteria and anaerobic bacteria may be correlated with the inflammatory pattern of nasal polyps. The neutrophil-mediated inflammatory response plays an important role in patients with CRSwNP in Southwest China. Show less
no PDF DOI: 10.1016/j.asjsur.2023.08.096
IL27

Co-Colorectal cancer stem cells employ the FADS1/DDA axis to evade NK cell-mediated immunosuppression after co-cultured with NK cells under hypoxia.

Shan Geng, Lei Zhu, Yanping Wang +4 more · 2024 · International immunopharmacology · Elsevier · added 2026-04-24
Colorectal cancer (CRC) ranks as China's second most common cancer and fifth top cancer death cause. The study highlights the role of Natural Killer (NK) cells in targeting cancer stem cells (CSCs) th Show more
Colorectal cancer (CRC) ranks as China's second most common cancer and fifth top cancer death cause. The study highlights the role of Natural Killer (NK) cells in targeting cancer stem cells (CSCs) that evade immune responses in CRC. Colorectal cancer stem cells (CCSCs) were stem from HT-29 cells and co-cultured with NK cells under normoxic or hypoxic conditions. The impact of this co-culture was evaluated using CCK8 assays for NK cell viability, ELISA for cytokine level changes, and flow cytometry for assessing NK cell apoptosis and activation. Comprehensive metabolomic and transcriptomic analyses were also performed to identify key genes and metabolites involved in the interaction between CCSCs and NK cells Co-culture of CCSCs with NK cells under hypoxia reduced NK cytotoxicity, increased NK apoptosis, and altered cytokine secretion by decreasing IFN-γ and TNF-α levels while increasing IL-6. Transcriptomic and metabolomic analysis identified 4 genes (FADS1, ALDH3A2, GCSH, MTCL1) and 3 metabolites (glyoxylic acid, spermine, DDA) as significant. Interfering with FADS1 counteracted the suppression of IFN-γ and TNF-α induced by CSC cells. Curiously, this inhibition caused by si-FADS1 could be neutralized by the addition of exogenous DDA. Co-culturing with NK cells notably increased spermine levels. Exogenous spermine resulted in a significant reduction in HT-29 cell death rates at 32 µM, 64 µM, and 128 µM, compared to NK cells without spermine. Our research explored CCSCs employed the FADS1/DDA axis to evade NK cell-mediated immunosuppression after co-cultured with NK cells under hypoxia. Show less
no PDF DOI: 10.1016/j.intimp.2024.113535
FADS1

Dysregulated palmitic acid metabolism promotes the formation of renal calcium-oxalate stones through ferroptosis induced by polyunsaturated fatty acids/phosphatidic acid.

Rui Wang, Jingdong Zhang, Haotian Ren +5 more · 2024 · Cellular and molecular life sciences : CMLS · Springer · added 2026-04-24
The pathogenesis of renal calcium-oxalate (CaOx) stones is complex and influenced by various metabolic factors. In parallel, palmitic acid (PA) has been identified as an upregulated lipid metabolite i Show more
The pathogenesis of renal calcium-oxalate (CaOx) stones is complex and influenced by various metabolic factors. In parallel, palmitic acid (PA) has been identified as an upregulated lipid metabolite in the urine and serum of patients with renal CaOx stones via untargeted metabolomics. Thus, this study aimed to mechanistically assess whether PA is involved in stone formation. Lipidomics analysis of PA-treated renal tubular epithelial cells compared with the control samples revealed that α-linoleic acid and α-linolenic acid were desaturated and elongated, resulting in the formation of downstream polyunsaturated fatty acids (PUFAs). In correlation, the levels of fatty acid desaturase 1 and 2 (FADS1 and FADS2) and peroxisome proliferator-activated receptor α (PPARα) in these cells treated with PA were increased relative to the control levels, suggesting that PA-induced upregulation of PPARα, which in turn upregulated these two enzymes, forming the observed PUFAs. Lipid peroxidation occurred in these downstream PUFAs under oxidative stress and Fenton Reaction. Furthermore, transcriptomics analysis revealed significant changes in the expression levels of ferroptosis-related genes in PA-treated renal tubular epithelial cells, induced by PUFA peroxides. In addition, phosphatidyl ethanolamine binding protein 1 (PEBP1) formed a complex with 15-lipoxygenase (15-LO) to exacerbate PUFA peroxidation under protein kinase C ζ (PKC ζ) phosphorylation, and PKC ζ was activated by phosphatidic acid derived from PA. In conclusion, this study found that the formation of renal CaOx stones is promoted by ferroptosis of renal tubular epithelial cells resulting from PA-induced dysregulation of PUFA and phosphatidic acid metabolism, and PA can promote the renal adhesion and deposition of CaOx crystals by injuring renal tubular epithelial cells, consequently upregulating adhesion molecules. Accordingly, this study provides a new theoretical basis for understanding the correlation between fatty acid metabolism and the formation of renal CaOx stones, offering potential targets for clinical applications. Show less
📄 PDF DOI: 10.1007/s00018-024-05145-y
FADS1

Blood lipid profiles associated with metastatic sites in advanced gastric cancer.

Hui Zhang, Yiming Liu, Li Feng +9 more · 2024 · BMC gastroenterology · BioMed Central · added 2026-04-24
This study explored the correlation between peripheral blood lipid levels and clinicopathological parameters in patients with advanced gastric cancer (GC), focusing on changes in lipid levels during d Show more
This study explored the correlation between peripheral blood lipid levels and clinicopathological parameters in patients with advanced gastric cancer (GC), focusing on changes in lipid levels during disease progression. Pathological features and serum lipid profiles of 179 patients with stage III-IV gastric adenocarcinoma were analyzed. Lipid parameters examined included total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), apolipoprotein AI (Apo AI), apolipoprotein B (Apo B), lipoprotein(a) (Lp(a)), among others. The total cholesterol-lymphocyte score (TL score) and BMI were also calculated. The association between lipid parameters and clinicopathological characteristics such as age, gender, family history, and metastasis sites was assessed. In GC patients, females had higher TG levels than males. Patients with peritoneal metastasis had significantly lower levels of TC, LDL-C, Apo B, and B/A ratio. Those with lung metastasis exhibited higher LDL-C levels and lower levels of VLDL-C. No significant associations were found between lipid levels and metastasis to distant lymph nodes, liver, or bone. Female patients with ovarian metastasis had significantly lower VLDL-C levels. Multivariate analysis revealed low TC as an independent risk factor for peritoneal metastasis, high LDL-C and low VLDL-C levels for lung metastasis, and younger age and low VLDL-C for ovarian metastasis. Specific blood lipid levels are significantly associated with metastatic sites in advanced gastric cancer. Lipid profiles could serve as potential biomarkers for predicting metastatic sites in GC patients. Show less
📄 PDF DOI: 10.1186/s12876-024-03479-2
APOB

Corrigendum to "MicroRNA-30c functions as a tumor suppressor via targeting SNAI1 in esophageal squamous cell carcinoma" [Biomed. Pharmacother. 98 (2018) 680-686].

Teng Ma, Ye Zhao, Qitong Lu +4 more · 2024 · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · Elsevier · added 2026-04-24
no PDF DOI: 10.1016/j.biopha.2024.117376
SNAI1

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

Why are we still in need for novel anti-obesity medications?

Aaron Novikoff, Gerald Grandl, Xue Liu +1 more · 2024 · The Lancet regional health. Europe · Elsevier · added 2026-04-24
From the pioneering moment in 1987 when the insulinotropic effect of glucagon-like peptide 1 (GLP-1) was first demonstrated in humans, to today's pharmaceutical gold rush for GLP-1-based treatments of Show more
From the pioneering moment in 1987 when the insulinotropic effect of glucagon-like peptide 1 (GLP-1) was first demonstrated in humans, to today's pharmaceutical gold rush for GLP-1-based treatments of obesity, the journey of GLP-1 pharmacology has been nothing short of extraordinary. The sequential conceptual developments of long-acting GLP-1 receptor (GLP-1R) mono-agonists, GLP-1R/glucose-dependent insulinotropic polypeptide receptor (GIPR) dual-agonists, and GLP-1R/GIPR/glucagon receptor (GcgR) triple agonists, have led to profound body weight-lowering capacities, with benefits that extend past obesity and towards obesity-associated diseases. The GLP-1R/GIPR dual-agonist tirzepatide has demonstrated a remarkable 23% body weight reduction in individuals with obesity over 72 weeks, eclipsing the average result achieved by certain types of bariatric surgery. Meanwhile, the GLP-1R/GIPR/GcgR triple-agonist retatrutide achieves similar body weight loss (∼25%) in just two-thirds of the time, potentially surpassing the efficacy of Roux-en-Y gastric bypass. These remarkable achievements rightfully raise the question whether and why there is still need for novel anti-obesity medications (AOMs) in the future. Show less
📄 PDF DOI: 10.1016/j.lanepe.2024.101098
GIPR

ZPR1 is an immunodiagnostic biomarker and promotes tumor progression in esophageal squamous cell carcinoma.

Guiying Sun, Hua Ye, Huijuan Liu +8 more · 2024 · Cancer science · Blackwell Publishing · added 2026-04-24
To evaluate the potential of zinc finger protein 1 (ZPR1) as a diagnostic biomarker and explore the underlying role for esophageal squamous cell carcinoma (ESCC). A human proteome microarray was custo Show more
To evaluate the potential of zinc finger protein 1 (ZPR1) as a diagnostic biomarker and explore the underlying role for esophageal squamous cell carcinoma (ESCC). A human proteome microarray was customized to identify anti-ZPR1 autoantibody, and enzyme-linked immunosorbent assay (ELISA) was adopted to assess the diagnostic performance of anti-ZPR1 autoantibody in 294 patients with ESCC and 294 normal controls. The expression of ZPR1 protein was measured by immunohistochemistry. The effect of ZPR1 on the proliferation, migration, and invasion of ESCC cells was investigated through CCK-8, wound healing, and Transwell assays. The expression level of anti-ZPR1 autoantibody (fold change = 2.77) in ESCC patients was higher than that in normal controls. The receiver operating characteristic (ROC) analysis manifested anti-ZPR1 autoantibody achieved area under the ROC curve (AUC) of 0.726 and 0.734 to distinguish ESCC from normal controls with sensitivity of 50.0% and 42.3%, and specificity of 91.0% and 92.0% in the test group and validation group, respectively. The positive rate of ZPR1 protein was significantly higher in ESCC tissues (75.5%, 80/106) than paracancerous tissues (9.4%, 5/53). Compared with the human normal esophageal cell line, the expression level of ZPR1 mRNA and protein in ESCC lines (KYSE150, Eca109, and TE1) had an increased trend. The knockdown or overexpression of ZPR1 reduced and enhanced the proliferation, migration, and invasion of ESCC cell, respectively. ZPR1 was a potential immunodiagnostic biomarker for noninvasive detection and could be a promotional factor in tumor progression of ESCC. Show less
no PDF DOI: 10.1111/cas.16004
ZPR1

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

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

The effects of

Lei Gao, Yan-Jun Xiong, Ya-Xue Liang +6 more · 2024 · Frontiers in immunology · Frontiers · added 2026-04-24
Inflammatory cytokines have crucial roles in the pathogenesis of tuberculosis (TB), and interleukin (IL)-27 and IL-35 have a pro-inflammatory and anti-inflammatory effect on many diseases, including i Show more
Inflammatory cytokines have crucial roles in the pathogenesis of tuberculosis (TB), and interleukin (IL)-27 and IL-35 have a pro-inflammatory and anti-inflammatory effect on many diseases, including infectious diseases. Therefore, we evaluated the relationship between Show less
📄 PDF DOI: 10.3389/fimmu.2024.1267624
IL27

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

Metformin promotes anti-tumor immunity in

Zhiguo Wang, Kunlin Li, Conghua Lu +12 more · 2024 · Oncology research · added 2026-04-24
Metformin has pleiotropic effects beyond glucose reduction, including tumor inhibition and immune regulation. It enhanced the anti-tumor effects of programmed cell death protein 1 (PD-1) inhibitors in Show more
Metformin has pleiotropic effects beyond glucose reduction, including tumor inhibition and immune regulation. It enhanced the anti-tumor effects of programmed cell death protein 1 (PD-1) inhibitors in serine/threonine kinase 11 ( We performed untargeted metabolomics using liquid chromatography (LC)-mass spectrometry (MS)/MS system and conducted cell experiments to verify the results of bioinformatics analysis. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, most metabolites were annotated into metabolism, including nucleotide metabolism. Next, the differentially expressed metabolites in H460 (refers to H460 cells), H460_met (refers to metformin-treated H460 cells), and H460_KO_met (refers to metformin-treated Relying on AXIN1, metformin upregulated multiple nucleotide metabolites which promoted STING signaling and the killing of activated T cells in Show less
📄 PDF DOI: 10.32604/or.2024.052664
AXIN1

LRP1 Repression by SNAIL Results in ECM Remodeling in Genetic Risk for Vascular Diseases.

Lu Liu, Joséphine Henry, Yingwei Liu +4 more · 2024 · Circulation research · added 2026-04-24
Genome-wide association studies implicate common genetic variations in the Fine mapping analyses included Bayesian colocalization to identify the most likely causal variant. Human induced pluripotent Show more
Genome-wide association studies implicate common genetic variations in the Fine mapping analyses included Bayesian colocalization to identify the most likely causal variant. Human induced pluripotent stem cells were genome-edited using CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR associated protein 9) to delete or modify candidate enhancer regions and generate Multitrait colocalization analyses pointed at rs11172113 as the most likely causal variant in Our findings support allele-specific Show less
no PDF DOI: 10.1161/CIRCRESAHA.124.325269
SNAI1

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