👤 Jincheng 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, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, Yanyan Liu, Yanyao Liu, Yanying Liu, Yanyun Liu, Yao Liu, Yao-Hui Liu, Yaobo Liu, Yaoquan Liu, Yaou Liu, Yaowen Liu, Yaoyao Liu, Yaozhong Liu, Yaping Liu, Yaqiong Liu, Yarong Liu, Yaru Liu, Yating Liu, Yaxin Liu, Ye Liu, Ye-Dan Liu, Yehai Liu, Yen-Chen Liu, Yen-Chun Liu, Yen-Nien Liu, Yeqing Liu, Yi Liu, Yi-Chang Liu, Yi-Chien Liu, Yi-Han Liu, Yi-Hung Liu, Yi-Jia Liu, Yi-Ling Liu, Yi-Meng Liu, Yi-Ming Liu, Yi-Yun Liu, Yi-Zhang Liu, YiRan Liu, Yibin Liu, Yibing Liu, Yicun Liu, Yidan Liu, Yidong Liu, Yifan Liu, Yifu Liu, Yihao Liu, Yiheng Liu, Yihui Liu, Yijing Liu, Yilei Liu, Yili Liu, Yilin Liu, Yimei Liu, Yiming Liu, Yin Liu, Yin-Ping Liu, Yinchu Liu, Yinfang Liu, Ying Liu, Ying Poi Liu, Yingchun Liu, Yinghua Liu, Yinghuan Liu, Yinghui Liu, Yingjun Liu, Yingli Liu, Yingwei Liu, Yingxia Liu, Yingyan Liu, Yingyi Liu, Yingying Liu, Yingzi Liu, Yinhe Liu, Yinhui Liu, Yining Liu, Yinjiang Liu, Yinping Liu, Yinuo Liu, Yiping Liu, Yiqing Liu, Yitian Liu, Yiting Liu, Yitong Liu, Yiwei Liu, Yiwen Liu, Yixiang Liu, Yixiao Liu, Yixuan Liu, Yiyang Liu, Yiyi Liu, Yiyuan Liu, Yiyun Liu, Yizhi Liu, Yizhuo Liu, Yong Liu, Yong Mei Liu, Yong-Chao Liu, Yong-Hong Liu, Yong-Jian Liu, Yong-Jun Liu, Yong-Tai Liu, Yong-da Liu, Yongchao Liu, Yonggang Liu, Yonggao Liu, Yonghong Liu, Yonghua Liu, Yongjian Liu, Yongjie Liu, Yongjun Liu, Yongli Liu, Yongmei Liu, Yongming Liu, Yongqiang Liu, Yongshuo Liu, Yongtai Liu, Yongtao Liu, Yongtong Liu, Yongxiao Liu, Yongyue Liu, You Liu, You-ping Liu, Youan Liu, Youbin Liu, Youdong Liu, Youhan Liu, Youlian Liu, Youwen Liu, Yu Liu, Yu Xuan Liu, Yu-Chen Liu, Yu-Ching Liu, Yu-Hui Liu, Yu-Li Liu, Yu-Lin Liu, Yu-Peng Liu, Yu-Wei Liu, Yu-Zhang Liu, YuHeng Liu, Yuan Liu, Yuan-Bo Liu, Yuan-Jie Liu, Yuan-Tao Liu, YuanHua Liu, Yuanchu Liu, Yuanfa Liu, Yuanhang Liu, Yuanhui Liu, Yuanjia Liu, Yuanjiao Liu, Yuanjun Liu, Yuanliang Liu, Yuantao Liu, Yuantong Liu, Yuanxiang Liu, Yuanxin Liu, Yuanxing Liu, Yuanying Liu, Yuanyuan Liu, Yubin Liu, Yuchen Liu, Yue Liu, Yuecheng Liu, Yuefang Liu, Yuehong Liu, Yueli Liu, Yueping Liu, Yuetong Liu, Yuexi Liu, Yuexin Liu, Yuexing Liu, Yueyang Liu, Yueyun Liu, Yufan Liu, Yufei Liu, Yufeng Liu, Yuhao Liu, Yuhe Liu, Yujia Liu, Yujiang Liu, Yujie Liu, Yujun Liu, Yulan Liu, Yuling Liu, Yulong Liu, Yumei Liu, Yumiao Liu, Yun Liu, Yun-Cai Liu, Yun-Qiang Liu, Yun-Ru Liu, Yun-Zi Liu, Yunfen Liu, Yunfeng Liu, Yuning Liu, Yunjie Liu, Yunlong Liu, Yunqi Liu, Yunqiang Liu, Yuntao Liu, Yunuan Liu, Yunuo Liu, Yunxia Liu, Yunyun Liu, Yuping Liu, Yupu Liu, Yuqi Liu, Yuqiang Liu, Yuqing Liu, Yurong Liu, Yuru Liu, Yusen Liu, Yutao Liu, Yutian Liu, Yuting Liu, Yutong Liu, Yuwei Liu, Yuxi Liu, Yuxia Liu, Yuxiang Liu, Yuxin Liu, Yuxuan Liu, Yuyan Liu, Yuyi Liu, Yuyu Liu, Yuyuan Liu, Yuzhen Liu, Yv-Xuan Liu, Z H Liu, Z Q Liu, Z Z Liu, Zaiqiang Liu, Zan Liu, Zaoqu Liu, Ze Liu, Zefeng Liu, Zekun Liu, Zeming Liu, Zengfu Liu, Zeyu Liu, Zezhou Liu, Zhangyu Liu, Zhangyuan Liu, Zhansheng Liu, Zhao Liu, Zhaoguo Liu, Zhaoli Liu, Zhaorui Liu, Zhaotian Liu, Zhaoxiang Liu, Zhaoxun Liu, Zhaoyang Liu, Zhe Liu, Zhekai Liu, Zheliang Liu, Zhen Liu, Zhen-Lin Liu, Zhendong Liu, Zhenfang Liu, Zhenfeng Liu, Zheng Liu, Zheng-Hong Liu, Zheng-Yu Liu, ZhengYi Liu, Zhengbing Liu, Zhengchuang Liu, Zhengdong Liu, Zhenghao Liu, Zhengkun Liu, Zhengtang Liu, Zhengting Liu, Zhenguo Liu, Zhengxia Liu, Zhengye Liu, Zhenhai Liu, Zhenhao Liu, Zhenhua Liu, Zhenjiang Liu, Zhenjiao Liu, Zhenjie Liu, Zhenkui Liu, Zhenlei Liu, Zhenmi Liu, Zhenming Liu, Zhenna Liu, Zhenqian Liu, Zhenqiu Liu, Zhenwei Liu, Zhenxing Liu, Zhenxiu Liu, Zhenzhen Liu, Zhenzhu Liu, Zhi Liu, Zhi Y Liu, Zhi-Fen Liu, Zhi-Guo Liu, Zhi-Jie Liu, Zhi-Kai Liu, Zhi-Ping Liu, Zhi-Ren Liu, Zhi-Wen Liu, Zhi-Ying Liu, Zhicheng Liu, Zhifang Liu, Zhigang Liu, Zhiguo Liu, Zhihan Liu, Zhihao Liu, Zhihong Liu, Zhihua Liu, Zhihui Liu, Zhijia Liu, Zhijie Liu, Zhikui Liu, Zhili Liu, Zhiming Liu, Zhipeng Liu, Zhiping Liu, Zhiqian Liu, Zhiqiang Liu, Zhiru Liu, Zhirui Liu, Zhishuo Liu, Zhitao Liu, Zhiteng Liu, Zhiwei Liu, Zhixiang Liu, Zhixue Liu, Zhiyan Liu, Zhiying Liu, Zhiyong Liu, Zhiyuan Liu, Zhong Liu, Zhong Wu Liu, Zhong-Hua Liu, Zhong-Min Liu, Zhong-Qiu Liu, Zhong-Wu Liu, Zhong-Ying Liu, Zhongchun Liu, Zhongguo Liu, Zhonghua Liu, Zhongjian Liu, Zhongjuan Liu, Zhongmin Liu, Zhongqi Liu, Zhongqiu Liu, Zhongwei Liu, Zhongyu Liu, Zhongyue Liu, Zhongzhong Liu, Zhou Liu, Zhou-di Liu, Zhu Liu, Zhuangjun Liu, Zhuanhua Liu, Zhuo Liu, Zhuoyuan Liu, Zi Hao Liu, Zi-Hao Liu, Zi-Lun Liu, Zi-Ye Liu, Zi-wen Liu, Zichuan Liu, Zihang Liu, Zihao Liu, Zihe Liu, Ziheng Liu, Zijia Liu, Zijian Liu, Zijing J Liu, Zimeng Liu, Ziqian Liu, Ziqin Liu, Ziteng Liu, Zitian Liu, Ziwei Liu, Zixi Liu, Zixuan Liu, Ziyang Liu, Ziying Liu, Ziyou Liu, Ziyuan Liu, Ziyue Liu, Zong-Chao Liu, Zong-Yuan Liu, Zonghua Liu, Zongjun Liu, Zongtao Liu, Zongxiang Liu, Zu-Guo Liu, Zuguo Liu, Zuohua Liu, Zuojin Liu, Zuolu Liu, Zuyi Liu, Zuyun Liu
articles

Bioinformatics analysis and identification of underlying biomarkers potentially linking allergic rhinitis and autophagy.

Tao Zhou, Hua Cai, Lisha Wu +3 more · 2024 · Scientific reports · Nature · added 2026-04-24
Allergic rhinitis (AR) resulted in impairing human health and quality of life seriously. There is currently no definitive remedy for AR. Recent studies have shown that autophagy may regulate airway in Show more
Allergic rhinitis (AR) resulted in impairing human health and quality of life seriously. There is currently no definitive remedy for AR. Recent studies have shown that autophagy may regulate airway inflammation. Our comprehension of autophagy and its molecular mechanism in the field of AR condition remains incomplete. Our research endeavors to bridge this knowledge deficit by investigating the correlation between AR and autophagy. The AR-related gene expression profile GSE50223 was screened and downloaded. The "limma" package of R software was utilized to identify differentially expressed genes associated with autophagy. GO, KEGG, and Gene set enrichment analyses were conducted. A PPI network of differentially expressed autophagy-related genes were established and further identified through the CytoHubba algorithm. A receiver operating characteristic curve analysis was employed to evaluate the diagnostic effectiveness of the hub genes and to examine the relationship between autophagy-related genes and AR. Finally, qRT-PCR was carried out to confirm the chosen autophagy-related genes using clinical samples. 21 autophagy-related genes in allergic rhinitis were identified. BECN1, PIK3C3, GABARAPL2, ULK2, and UVRAG were considered as significant differentially expressed autophagy-related genes. However, additional molecular biological experiments will be necessary to elucidate the underlying mechanism connecting autophagy and AR. Show less
no PDF DOI: 10.1038/s41598-024-78375-6
PIK3C3

MiR-2110 induced by chemically synthesized cinobufagin functions as a tumor-metastatic suppressor via targeting FGFR1 to reduce PTEN ubiquitination degradation in nasopharyngeal carcinoma.

Shiyi Fang, Lanzhu Peng, Mengmin Zhang +12 more · 2024 · Environmental toxicology · Wiley · added 2026-04-24
Tumor cell metastasis is the key cause of death in patients with nasopharyngeal carcinoma (NPC). MiR-2110 was cloned and identified in Epstein-Barr virus (EBV)-positive NPC, but its role is unclear in Show more
Tumor cell metastasis is the key cause of death in patients with nasopharyngeal carcinoma (NPC). MiR-2110 was cloned and identified in Epstein-Barr virus (EBV)-positive NPC, but its role is unclear in NPC. In this study, we investigated the effect of miR-2110 on NPC metastasis and its related molecular basis. In addition, we also explored whether miR-2110 can be regulated by cinobufotalin (CB) and participate in the inhibition of CB on NPC metastasis. Bioinformatics, RT-PCR, and in situ hybridization were used to observe the expression of miR-2110 in NPC tissues and cells. Scratch, Boyden, and tail vein metastasis model of nude mouse were used to detect the effect of miR-2110 on NPC metastasis. Western blot, Co-IP, luciferase activity, colocalization of micro confocal and ubiquitination assays were used to identify the molecular mechanism of miR-2110 affecting NPC metastasis. Finally, miR-2110 induced by CB participates in CB-stimulated inhibition of NPC metastasis was explored. The data showed that increased miR-2110 significantly suppresses NPC cell migration, invasion, and metastasis. Suppressing miR-2110 markedly restored NPC cell migration and invasion. Mechanistically, miR-2110 directly targeted FGFR1 and reduced its protein expression. Decreased FGFR1 attenuated its recruitment of NEDD4, which downregulated NEDD4-induced phosphatase and tensin homolog (PTEN) ubiquitination and degradation and further increased PTEN protein stability, thereby inactivating PI3K/AKT-stimulated epithelial-mesenchymal transition signaling and ultimately suppressing NPC metastasis. Interestingly, CB, a potential new inhibitory drug for NPC metastasis, significantly induced miR-2110 expression by suppressing PI3K/AKT/c-Jun-mediated transcription inhibition. Suppression of miR-2110 significantly restored cell migration and invasion in CB-treated NPC cells. Finally, a clinical sample assay indicated that reduced miR-2110 was negatively correlated with NPC lymph node metastasis and positively related to NPC patient survival prognosis. In summary, miR-2110 is a metastatic suppressor involving in CB-induced suppression of NPC metastasis. Show less
no PDF DOI: 10.1002/tox.24197
FGFR1

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

Stable Long-Persistent Luminescence from Self-Activated CaSb

Yanbing Han, Qingqing Mo, Zhuangzhuang Ma +8 more · 2024 · Nano letters · ACS Publications · added 2026-04-24
Long-persistent luminescence (LPL) materials have attracted intensive attention due to their fascinating emission after excitation. However, current LPL materials typically depend on external doping t Show more
Long-persistent luminescence (LPL) materials have attracted intensive attention due to their fascinating emission after excitation. However, current LPL materials typically depend on external doping to introduce traps or emitting centers, resulting in a complex synthesis and controllability. For the first time, we develop another category of undoped LPL materials based on antimonate CaSb Show less
no PDF DOI: 10.1021/acs.nanolett.4c04471
LPL

Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data.

Jingxian Tang, Hanfei Xu, Zihao Xin +6 more · 2024 · Human molecular genetics · Oxford University Press · added 2026-04-24
This study aims to identify BMI-associated genes by integrating aggregated summary information from different omics data. We conducted a meta-analysis to leverage information from a genome-wide associ Show more
This study aims to identify BMI-associated genes by integrating aggregated summary information from different omics data. We conducted a meta-analysis to leverage information from a genome-wide association study (n = 339 224), a transcriptome-wide association study (n = 5619), and an epigenome-wide association study (n = 3743). We prioritized the significant genes with a machine learning-based method, netWAS, which borrows information from adipose tissue-specific interaction networks. We also used the brain-specific network in netWAS to investigate genes potentially involved in brain-adipose interaction. We identified 195 genes that were significantly associated with BMI through meta-analysis. The netWAS analysis narrowed down the list to 21 genes in adipose tissue. Among these 21 genes, six genes, including FUS, STX4, CCNT2, FUBP1, NDUFS3, and RAPSN, were not reported to be BMI-associated in PubMed or GWAS Catalog. We also identified 11 genes that were significantly associated with BMI in both adipose and whole brain tissues. This study integrated three types of omics data and identified a group of genes that have not previously been reported to be associated with BMI. This strategy could provide new insights for future studies to identify molecular mechanisms contributing to BMI regulation. Show less
no PDF DOI: 10.1093/hmg/ddad212
RAPSN

Spatiotemporal heterogeneity of LMOD1 expression summarizes two modes of cell communication in colorectal cancer.

Jie-Pin Li, Yuan-Jie Liu, Yang Li +7 more · 2024 · Journal of translational medicine · BioMed Central · added 2026-04-24
Cellular communication (CC) influences tumor development by mediating intercellular junctions between cells. However, the role and underlying mechanisms of CC in malignant transformation remain unknow Show more
Cellular communication (CC) influences tumor development by mediating intercellular junctions between cells. However, the role and underlying mechanisms of CC in malignant transformation remain unknown. Here, we investigated the spatiotemporal heterogeneity of CC molecular expression during malignant transformation. It was found that although both tight junctions (TJs) and gap junctions (GJs) were involved in maintaining the tumor microenvironment (TME), they exhibited opposite characteristics. Mechanistically, for epithelial cells (parenchymal component), the expression of TJ molecules consistently decreased during normal-cancer transformation and is a potential oncogenic factor. For fibroblasts (mesenchymal component), the expression of GJs consistently increased during normal-cancer transformation and is a potential oncogenic factor. In addition, the molecular profiles of TJs and GJs were used to stratify colorectal cancer (CRC) patients, where subtypes characterized by high GJ levels and low TJ levels exhibited enhanced mesenchymal signals. Importantly, we propose that leiomodin 1 (LMOD1) is biphasic, with features of both TJs and GJs. LMOD1 not only promotes the activation of cancer-associated fibroblasts (CAFs) but also inhibits the Epithelial-mesenchymal transition (EMT) program in cancer cells. In conclusion, these findings demonstrate the molecular heterogeneity of CC and provide new insights into further understanding of TME heterogeneity. Show less
📄 PDF DOI: 10.1186/s12967-024-05369-3
LMOD1

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

Novel Insights into Causal Effects of Serum Lipids and Apolipoproteins on Cardiovascular Morpho-Functional Phenotypes.

Ankang Liu, Xiaohong Liu, Yuanhao Wei +6 more · 2024 · Cardiovascular toxicology · Springer · added 2026-04-24
Previous observational studies have explored the association between serum lipids, apolipoproteins, and adverse ventricular/aortic structure and function. However, whether a causal link exists is unce Show more
Previous observational studies have explored the association between serum lipids, apolipoproteins, and adverse ventricular/aortic structure and function. However, whether a causal link exists is uncertain. This study employed a two-sample Mendelian randomization (MR), colocalization, reverse, and multivariable MR (MVMR) approach to examine the causal associations among five serum lipids, two apolipoproteins, and 32 cardiac magnetic resonance (CMR) traits. Utilizing single-nucleotide polymorphisms (SNPs) linked to serum lipids and apolipoproteins as instrumental variables. CMR traits from seven independent genome-wide association studies served as preclinical endophenotypes, offering insights into aortic and cardiac structure/function. The primary analysis utilized a random-effects inverse variance method (IVW), followed by sensitivity and validation analyses. In the primary IVW MR analyses, genetically predicted low-density lipoprotein cholesterol (LDL-C) levels were positively correlated with increased descending aorta strain (DAo strain) (β = 0.098; P = 2.69E-07) and ascending aorta strain (AAo strain) (β = 0.079; P = 5.19E-05). Genetically predicted high-density lipoprotein cholesterol (HDL-C) levels were positively correlated with left ventricular radial peak diastolic strain rate (LV-PDSRll) (β = 0.176; P = 2.89E-05) and the left ventricular longitudinal peak diastolic strain rate (LV-PDSRrr) (β = 0.059; P = 2.44E-06), and negatively correlated with left ventricular regional wall thickness (LVRWT). While apolipoprotein B (ApoB) levels were positively correlated with AAo strain (β = 0.076; P = 1.16E-05), DAo strain (β = 0.065; P = 2.77E-05). A shared causal variant was identified to demonstrate the associations of ApoB with AAo strain and DAo strain using colocalization analysis. Sensitivity analyses confirmed the robustness of these associations. Targeting lipid and apolipoprotein levels through interventions may provide novel strategies for the primary prevention of CVDs. Show less
📄 PDF DOI: 10.1007/s12012-024-09930-w
APOB

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

ABCG2-Expressing Clonal Repopulating Endothelial Cells Serve to Form and Maintain Blood Vessels.

Yang Lin, Chang-Hyun Gil, Kimihiko Banno +25 more · 2024 · Circulation · added 2026-04-24
Most organs are maintained lifelong by resident stem/progenitor cells. During development and regeneration, lineage-specific stem/progenitor cells can contribute to the growth or maintenance of differ Show more
Most organs are maintained lifelong by resident stem/progenitor cells. During development and regeneration, lineage-specific stem/progenitor cells can contribute to the growth or maintenance of different organs, whereas fully differentiated mature cells have less regenerative potential. However, it is unclear whether vascular endothelial cells (ECs) are also replenished by stem/progenitor cells with EC-repopulating potential residing in blood vessels. It has been reported recently that some EC populations possess higher clonal proliferative potential and vessel-forming capacity compared with mature ECs. Nevertheless, a marker to identify vascular clonal repopulating ECs (CRECs) in murine and human individuals is lacking, and, hence, the mechanism for the proliferative, self-renewal, and vessel-forming potential of CRECs is elusive. We analyzed colony-forming, self-renewal, and vessel-forming potential of ABCG2 (ATP binding cassette subfamily G member 2)-expressing ECs in human umbilical vessels. To study the contribution of In human and mouse vessels, ECs with higher These results are the first to establish that a single prospective marker identifies CRECs in mice and human individuals, which holds promise to provide new cell therapies for repair of damaged vessels in patients with endothelial dysfunction. Show less
📄 PDF DOI: 10.1161/CIRCULATIONAHA.122.061833
HEY2

Bicyclic Inhibitors of Branched-Chain α-Keto Acid Dehydrogenase Kinase (BDK) with In Vivo Activity.

Jue Liang, Xiaoyu Wang, Francisco Ortiz +7 more · 2024 · ACS medicinal chemistry letters · ACS Publications · added 2026-04-24
Elevated levels of the branched chain α-amino acids valine, leucine, and isoleucine are associated with heart disease and metabolic disorders. The kinase BDK, also known as branched-chain ketoacid deh Show more
Elevated levels of the branched chain α-amino acids valine, leucine, and isoleucine are associated with heart disease and metabolic disorders. The kinase BDK, also known as branched-chain ketoacid dehydrogenase kinase (BCKDK), is a negative regulator of branched-chain α-amino acid metabolism through deactivation of BCKDC, the branched-chain α-ketoacid dehydrogenase complex. Inhibitors of BDK increase the activity of BCKDC and could be useful therapeutic leads for cardiometabolic diseases. We describe a novel bicyclic carboxy amide as an inhibitor of BDK with in vivo activity. Show less
no PDF DOI: 10.1021/acsmedchemlett.4c00362
BCKDK

Calcium saccharate/DUSP6 suppresses renal cell carcinoma glycolytic metabolism and boosts sunitinib efficacy via the ERK-AKT pathway.

Huan Liu, Longsheng Wang, Xiaokai Shi +5 more · 2024 · Biochemical pharmacology · Elsevier · added 2026-04-24
Current therapeutic options for renal cell carcinoma (RCC) are very limited, which is largely due to inadequate comprehension of molecular pathological mechanisms as well as RCC's resistance to chemot Show more
Current therapeutic options for renal cell carcinoma (RCC) are very limited, which is largely due to inadequate comprehension of molecular pathological mechanisms as well as RCC's resistance to chemotherapy. Dual-specificity phosphatase 6 (DUSP6) has been associated with numerous human diseases. However, its role in RCC is not well understood. Here, we show that diminished DUSP6 expression is linked to RCC progression and unfavorable prognosis. Mechanistically, DUSP6 serves as a tumor suppressor in RCC by intervening the TAF10 and BSCL2 via the ERK-AKT pathway. Further, DUSP6 is also transcriptionally regulated by HNF-4a. Moreover, docking experiments have indicated that DUSP6 expression is enhanced when bound by Calcium saccharate, which also inhibits RCC cell proliferation, metabolic rewiring, and sunitinib resistance. In conclusion, our study identifies Calcium saccharate as a prospective pharmacological therapeutic approach for RCC. Show less
no PDF DOI: 10.1016/j.bcp.2024.116247
DUSP6

A human obesity-associated MC4R mutation with defective Gq/11α signaling leads to hyperphagia in mice.

Peter J Metzger, Aileen Zhang, Bradley A Carlson +11 more · 2024 · The Journal of clinical investigation · added 2026-04-24
Melanocortin 4 receptor (MC4R) mutations are the most common cause of human monogenic obesity and are associated with hyperphagia and increased linear growth. While MC4R is known to activate Gsα/cAMP Show more
Melanocortin 4 receptor (MC4R) mutations are the most common cause of human monogenic obesity and are associated with hyperphagia and increased linear growth. While MC4R is known to activate Gsα/cAMP signaling, a substantial proportion of obesity-associated MC4R mutations do not affect MC4R/Gsα signaling. To further explore the role of specific MC4R signaling pathways in the regulation of energy balance, we examined the signaling properties of one such mutant, MC4R (F51L), as well as the metabolic consequences of MC4RF51L mutation in mice. The MC4RF51L mutation produced a specific defect in MC4R/Gq/11α signaling and led to obesity, hyperphagia, and increased linear growth in mice. The ability of a melanocortin agonist to acutely inhibit food intake when delivered to the paraventricular nucleus (PVN) was lost in MC4RF51L mice, as well as in WT mice in which a specific Gq/11α inhibitor was delivered to the PVN; this provided evidence that a Gsα-independent signaling pathway, namely Gq/11α, significantly contributes to the actions of MC4R on food intake and linear growth. These results suggest that a biased MC4R agonist that primarily activates Gq/11α may be a potential agent to treat obesity with limited untoward cardiovascular and other side effects. Show less
📄 PDF DOI: 10.1172/JCI165418
MC4R

FGF7 enhances the expression of ACE2 in human islet organoids aggravating SARS-CoV-2 infection.

Hao Meng, Zhiying Liao, Yanting Ji +15 more · 2024 · Signal transduction and targeted therapy · Nature · added 2026-04-24
The angiotensin-converting enzyme 2 (ACE2) is a primary cell surface viral binding receptor for SARS-CoV-2, so finding new regulatory molecules to modulate ACE2 expression levels is a promising strate Show more
The angiotensin-converting enzyme 2 (ACE2) is a primary cell surface viral binding receptor for SARS-CoV-2, so finding new regulatory molecules to modulate ACE2 expression levels is a promising strategy against COVID-19. In the current study, we utilized islet organoids derived from human embryonic stem cells (hESCs), animal models and COVID-19 patients to discover that fibroblast growth factor 7 (FGF7) enhances ACE2 expression within the islets, facilitating SARS-CoV-2 infection and resulting in impaired insulin secretion. Using hESC-derived islet organoids, we demonstrated that FGF7 interacts with FGF receptor 2 (FGFR2) and FGFR1 to upregulate ACE2 expression predominantly in β cells. This upregulation increases both insulin secretion and susceptibility of β cells to SARS-CoV-2 infection. Inhibiting FGFR counteracts the FGF7-induced ACE2 upregulation, subsequently reducing viral infection and replication in the islets. Furthermore, retrospective clinical data revealed that diabetic patients with severe COVID-19 symptoms exhibited elevated serum FGF7 levels compared to those with mild symptoms. Finally, animal experiments indicated that SARS-CoV-2 infection increased pancreatic FGF7 levels, resulting in a reduction of insulin concentrations in situ. Taken together, our research offers a potential regulatory strategy for ACE2 by controlling FGF7, thereby protecting islets from SARS-CoV-2 infection and preventing the progression of diabetes in the context of COVID-19. Show less
📄 PDF DOI: 10.1038/s41392-024-01790-8
FGFR1

Knockdown of ChREBP ameliorates retinal microvascular endothelial cell injury and angiogenic responses in diabetic retinopathy.

Yao Sui, Chunyang Du, Ming Wang +5 more · 2024 · Biochemical and biophysical research communications · Elsevier · added 2026-04-24
To examine whether and how carbohydrate response element-binding protein (ChREBP) plays a role in diabetic retinopathy. Western blotting was used to detect ChREBP expression and location following hig Show more
To examine whether and how carbohydrate response element-binding protein (ChREBP) plays a role in diabetic retinopathy. Western blotting was used to detect ChREBP expression and location following high glucose stimulation of Human Retinal Microvascular Endothelial Cells (HRMECs). Flow cytometry, TUNEL staining, and western blotting were used to evaluate apoptosis following ChREBP siRNA silencing. Cell scratch, transwell migration, and tube formation assays were used to determine cell migration and angiogenesis. Diabetic models for wild-type (WT) and ChREBP knockout (ChKO) mice were developed. Retinas of WT and ChKO animals were cultivated in vitro with vascular endothelial growth factor + high glucose to assess neovascular development. ChREBP gene knockdown inhibited thioredoxin-interacting protein and NOD-like receptor family pyrin domain containing protein 3 expression in HRMECs, which was caused by high glucose stimulation, reduced apoptosis, hindered migration, and tube formation, and repressed AKT/mTOR signaling pathway activation. Compared with WT mice, ChKO mice showed suppressed high glucose-induced alterations in retinal structure, alleviated retinal vascular leakage, and reduced retinal neovascularization. ChREBP deficiency decreased high glucose-induced apoptosis, migration, and tube formation in HRMECs as well as structural and angiogenic responses in the mouse retina; thus, it is a potential therapeutic target for diabetic retinopathy. Show less
no PDF DOI: 10.1016/j.bbrc.2023.149389
MLXIPL

Insight into the design of FGFR4 selective inhibitors in cancer therapy: Prospects and challenges.

Xiaolu Chen, Yajiao Huang, Ban Chen +3 more · 2024 · European journal of medicinal chemistry · Elsevier · added 2026-04-24
Recently, FGFR4 has become a hot target for the treatment of cancer owing to its important role in cellular physiological processes. FGFR4 has been validated to be closely related to the occurrence of Show more
Recently, FGFR4 has become a hot target for the treatment of cancer owing to its important role in cellular physiological processes. FGFR4 has been validated to be closely related to the occurrence of cancers, such as hepatocellular carcinoma, rhabdomyosarcoma, breast cancer and colorectal cancer. Hence, the development of FGFR4 small-molecule inhibitors is essential to further understanding the functions of FGFR4 in cancer and the treatment of FGFR4-dependent diseases. Given the particular structures of FGFR1-4, the development of FGFR4 selective inhibitors presents significant challenges. The non-conserved Cys552 in the hinge region of the FGFR4 complex becomes the key to the selectivity of FGFR4 and FGFR1/2/3 inhibitors. In this review, we systematically introduce the close relationship between FGFR4 and cancer, and conduct an in-depth analysis of the developing methodology, binding mechanism, kinase selectivity, pharmacokinetic characteristics of FGFR4 selectivity inhibitors, and their application in clinical research. Show less
no PDF DOI: 10.1016/j.ejmech.2023.115947
FGFR1

Reducing Nogo-B Improves Hepatic Fibrosis by Inhibiting BACe1-Mediated Autophagy.

LiLi Gao, YingJie Zhuang, ZhengYi Liu · 2024 · Tissue engineering and regenerative medicine · Springer · added 2026-04-24
Hepatic fibrosis (HF) is a histopathological change in the process of long-term liver injury caused by cytokine secretion and internal environment disturbance, resulting in excessive liver repair and Show more
Hepatic fibrosis (HF) is a histopathological change in the process of long-term liver injury caused by cytokine secretion and internal environment disturbance, resulting in excessive liver repair and fiber scar. Nogo-B protein is widely distributed in peripheral tissues and organs and can regulate the migration of endothelial cells by activating TGF-β1 in vascular remodeling after injury. Nogo-B has been shown to promote organ fibrosis. This study was to determine the role of Nogo-B in HF. An HF model was built by intraperitoneal injections with 20% carbon tetrachloride. Localization of Nogo-B was detected by FISH. The interaction between Nogo-B and BACE1 was confirmed by Co-IP. Autophagy flux was analyzed using tandem mRFP-GFP-LC3 fluorescence microscopy, electron microscopy, and western blotting. Detection of serum AST and ALT and H&E staining were utilized to detect the degree of liver injury. The HF was evaluated by Masson trichromatic staining. RT-qPCR, western blotting, and immunofluorescence were employed to detect relevant indicators. Reducing Nogo-B suppressed AST and ALT levels, the accumulation of collagen I and α-SMA, and expressions of pro-fibrotic genes in mouse liver. BACE1 was a potential downstream target of Nogo-B. Nogo-B was upregulated in TGF-β1-activated hepatic stellate cells (HSCs). Knocking down Nogo-B caused the downregulation of pro-fibrotic genes and inhibited viability of HSCs. Nogo-B knockdown prevented CCL4-induced fibrosis, accompanied by downregulation of extracellular matrix. Nogo-B inhibited HSC autophagy and increased lipid accumulation. BACE1 knockdown inhibited HSC autophagy and activation in LX-2 cells. Nogo-B knockdown prevents HF by directly inhibiting BACe1-mediated autophagy. Show less
no PDF DOI: 10.1007/s13770-024-00641-5
BACE1

[Liver X receptor attenuates renal ischemia-reperfusion injury in mice].

Ying-Zhi Huang, Zhi-Lin Luan, Shu-Jing Liu +5 more · 2024 · Sheng li xue bao : [Acta physiologica Sinica] · added 2026-04-24
Acute kidney injury (AKI) is a clinical syndrome characterized by a rapid decline in renal function. Renal ischemia-reperfusion injury (RIRI) is one of the main causes of AKI with the underlying mecha Show more
Acute kidney injury (AKI) is a clinical syndrome characterized by a rapid decline in renal function. Renal ischemia-reperfusion injury (RIRI) is one of the main causes of AKI with the underlying mechanism incompletely clarified. The liver X receptors (LXRs), including LXRα and LXRβ, are members of the nuclear receptor superfamily. It has been shown that LXRs play an important role in regulating glucose and lipid metabolism, cholesterol efflux, and inflammation. The purpose of this study was to explore the role and mechanism of LXRs in RIRI. We determined the effects of LXR activation on renal function and histological changes in a mouse RIRI model and a cellular model of hypoxia/reoxygenation (H/R). Show less
no PDF
NR1H3

Chemoenzymatic Synthesis of DNP-Functionalized FGFR1-Binding Peptides as Novel Peptidomimetic Immunotherapeutics for Treating Lung Cancer.

Xiaohui Li, Haiyan Liu, Shengjie Ding +11 more · 2024 · Journal of medicinal chemistry · ACS Publications · added 2026-04-24
Receptor-binding peptides are promising candidates for tumor target therapy. However, the inability to occupy "hot spots" on the PPI interface and rapid metabolic instability are significant limitatio Show more
Receptor-binding peptides are promising candidates for tumor target therapy. However, the inability to occupy "hot spots" on the PPI interface and rapid metabolic instability are significant limitations to their clinical application. We investigated a new strategy in which an FGFR1-binding peptide (Pep1) was site-specifically functionalized with the dinitrophenyl (DNP) hapten at the C-terminus. The resulting Pep1-DNP conjugates retained FGFR1 binding affinity and exhibited a similar potency in inhibiting FGF2-dependent cell proliferation, comparable to that of native Pep1 in vitro. In addition, three conjugates could recruit anti-DNP antibodies onto the surface of cancer cells, thereby mediating the CDC efficacy. In vivo pharmacokinetic studies and antitumor studies demonstrated that optimal conjugate Show less
no PDF DOI: 10.1021/acs.jmedchem.4c00967
FGFR1

Osteocyte-derived sclerostin impairs cognitive function during ageing and Alzheimer's disease progression.

Tianshu Shi, Siyu Shen, Yong Shi +21 more · 2024 · Nature metabolism · Nature · added 2026-04-24
Ageing increases susceptibility to neurodegenerative disorders, such as Alzheimer's disease (AD). Serum levels of sclerostin, an osteocyte-derived Wnt-β-catenin signalling antagonist, increase with ag Show more
Ageing increases susceptibility to neurodegenerative disorders, such as Alzheimer's disease (AD). Serum levels of sclerostin, an osteocyte-derived Wnt-β-catenin signalling antagonist, increase with age and inhibit osteoblastogenesis. As Wnt-β-catenin signalling acts as a protective mechanism for memory, we hypothesize that osteocyte-derived sclerostin can impact cognitive function under pathological conditions. Here we show that osteocyte-derived sclerostin can cross the blood-brain barrier of old mice, where it can dysregulate Wnt-β-catenin signalling. Gain-of-function and loss-of-function experiments show that abnormally elevated osteocyte-derived sclerostin impairs synaptic plasticity and memory in old mice of both sexes. Mechanistically, sclerostin increases amyloid β (Aβ) production through β-catenin-β-secretase 1 (BACE1) signalling, indicating a functional role for sclerostin in AD. Accordingly, high sclerostin levels in patients with AD of both sexes are associated with severe cognitive impairment, which is in line with the acceleration of Αβ production in an AD mouse model with bone-specific overexpression of sclerostin. Thus, we demonstrate osteocyte-derived sclerostin-mediated bone-brain crosstalk, which could serve as a target for developing therapeutic interventions against AD. Show less
📄 PDF DOI: 10.1038/s42255-024-00989-x
BACE1

Identification and Validation of a Novel Tertiary Lymphoid Structures-Related Prognostic Gene Signature in Hepatocellular Carcinoma.

Yin Liu, Chao Bo Li, Yun Peng Zhai +4 more · 2024 · World journal of oncology · added 2026-04-24
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors originating from the digestive system. Tertiary lymphoid structures (TLS), non-lymphoid tissues outside of the lymphoid organs Show more
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors originating from the digestive system. Tertiary lymphoid structures (TLS), non-lymphoid tissues outside of the lymphoid organs, are closely connected to chronic inflammation and tumorigenesis. However, the detailed relationship between TLS and HCC prognosis remained unclear. In this study, we aimed to construct a TLS-related gene signature for predicting the prognosis of HCC patients. The Cancer Genome Atlas (TCGA) clinical data from 369 HCC tissues and 50 normal liver tissues were utilized to examine the differential expression of TLS-related genes. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, the prognostic model was constructed using the TCGA cohort and validated in the GSE14520 cohort and International Cancer Genome Consortium (ICGC) cohort. The Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were employed to validate the predictive ability of the prognostic model. Furthermore, Cox regression analysis was applied to identify whether the TLS score could be employed as an independent prognosis factor. A nomogram was developed to predict the survival probability of HCC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for TLS-related genes. Genetic mutation analysis, the CIBERSORT algorithm, and single-sample gene set enrichment analysis (ssGSEA) were used to assess the tumor mutation landscape and immune infiltration. Finally, the role of the TLS score in HCC therapy was investigated. Six genes were included in the construction of our prognostic model (CETP, DNASE1L3, PLAC8, SKAP1, C7, and VNN2), and we validated its accuracy. Survival analysis showed that patients in the high-TLS score group had a significantly better overall survival than those in the low-TLS score group. Univariate, multivariate Cox regression analysis and the establishment of a nomogram indicated that the TLS score could independently function as a potential prognostic marker. A significant association between TLS score and immunity was revealed by an analysis of gene alterations and immune cell infiltration. In addition, two subtypes of the TLS score could accurately predict the effectiveness of sorafenib, transcatheter arterial chemoembolization (TACE), and immunotherapy in HCC patients. In this research, we conducted and validated a prognostic model associated with TLS that may be helpful for predicting clinical outcomes and treatment responsiveness for HCC patients. Show less
📄 PDF DOI: 10.14740/wjon1893
CETP

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.

Hao Li, Zebei Han, Yu Sun +11 more · 2024 · Nature communications · Nature · added 2026-04-24
Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent ad Show more
Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity. Show less
📄 PDF DOI: 10.1038/s41467-024-50426-6
DLG2

Multi-Omics Approaches Uncovered Critical mRNA-miRNA-lncRNA Networks Regulating Multiple Birth Traits in Goat Ovaries.

Weibing Lv, Ren An, Xinmiao Li +9 more · 2024 · International journal of molecular sciences · MDPI · added 2026-04-24
The goat breeding industry on the Tibetan Plateau faces strong selection pressure to enhance fertility. Consequently, there is an urgent need to develop goat lines with higher fertility and adaptabili Show more
The goat breeding industry on the Tibetan Plateau faces strong selection pressure to enhance fertility. Consequently, there is an urgent need to develop goat lines with higher fertility and adaptability. The ovary, as a key organ determining reproductive performance, is regulated by a complex transcriptional network involving numerous protein-coding and non-coding genes. However, the molecular mechanisms of the key mRNA-miRNA-lncRNA regulatory network in goat ovaries remain largely unknown. This study focused on the histology and differential mRNA/miRNA/lncRNA between Chuanzhong black goat (CBG, high productivity, multiple births) and Tibetan goat (TG, strong adaptability, single birth) ovaries. Histomorphological analysis showed that the medulla proportion in CBG ovaries was significantly reduced compared to TG. RNA-Seq and small RNA-Seq analysis identified 1218 differentially expressed (DE) mRNAs, 100 DE miRNAs, and 326 DE lncRNAs, which were mainly enriched in ovarian steroidogenesis, oocyte meiosis, biosynthesis of amino acids and protein digestion, and absorption signaling pathways. Additionally, five key mRNA-miRNA-lncRNA interaction networks regulating goat reproductive performance were identified, including Show less
📄 PDF DOI: 10.3390/ijms252212466
AKAP6

Low-Density Lipoprotein Cholesterol Testing Following Myocardial Infarction Hospitalization Among Medicare Beneficiaries.

Lisandro D Colantonio, Zhixin Wang, Jenna Jones +7 more · 2024 · JACC. Advances · Elsevier · added 2026-04-24
Low-density lipoprotein cholesterol (LDL-C) is used to guide lipid-lowering therapy after a myocardial infarction (MI). Lack of LDL-C testing represents a missed opportunity for optimizing therapy and Show more
Low-density lipoprotein cholesterol (LDL-C) is used to guide lipid-lowering therapy after a myocardial infarction (MI). Lack of LDL-C testing represents a missed opportunity for optimizing therapy and reducing cardiovascular risk. The purpose of this study was to estimate the proportion of Medicare beneficiaries who had their LDL-C measured within 90 days following MI hospital discharge. We conducted a retrospective cohort study of Medicare beneficiaries ≥66 years of age with an MI hospitalization between 2016 and 2020. The primary analysis used data from all beneficiaries with fee-for-service coverage and pharmacy benefits (532,767 MI hospitalizations). In secondary analyses, we used data from a 5% random sample of beneficiaries with fee-for-service coverage without pharmacy benefits (10,394 MI hospitalizations), and from beneficiaries with Medicare Advantage (176,268 MI hospitalizations). The proportion of beneficiaries who had their LDL-C measured following MI hospital discharge was estimated accounting for the competing risk of death. In the primary analysis (mean age 76.9 years, 84.4% non-Hispanic White), 29.9% of beneficiaries had their LDL-C measured within 90 days following MI hospital discharge. Among Hispanic, Asian, non-Hispanic White, and non-Hispanic Black beneficiaries, the 90-day postdischarge LDL-C testing was 33.8%, 32.5%, 30.0%, and 26.0%, respectively. Postdischarge LDL-C testing within 90 days was highest in the Middle Atlantic (36.4%) and lowest in the West North Central (23.4%) U.S. regions. In secondary analyses, the 90-day postdischarge LDL-C testing was 26.9% among beneficiaries with fee-for-service coverage without pharmacy benefits, and 28.6% among beneficiaries with Medicare Advantage coverage. LDL-C testing following MI hospital discharge among Medicare beneficiaries was low. Show less
📄 PDF DOI: 10.1016/j.jacadv.2023.100753
APOB

Human Biodistribution and Radiation Dosimetry of the Targeting Fibroblast Growth Factor Receptor 1-Positive Tumors Tracer [

Huiqing Yuan, Xiaoshan Chen, Mengmeng Zhao +8 more · 2024 · Cancer biotherapy & radiopharmaceuticals · added 2026-04-24
no PDF DOI: 10.1089/cbr.2024.0073
FGFR1

Luminescence properties of a green-emitting mechanoluminescent phosphor CaSrGa

Rui Shi, Wei Liu, Lin Li +4 more · 2024 · RSC advances · Royal Society of Chemistry · added 2026-04-24
In this study, both mechanoluminescence (ML) and long persistent luminescence (LPL) characteristics were first observed in CaSrGa
📄 PDF DOI: 10.1039/d4ra01745e
LPL

Genetic Association between Lipid-Regulating Drug Targets and Diabetic Retinopathy: A Drug Target Mendelian Randomization Study.

Shengnan Chen, Ming Zhang, Peng Yang +4 more · 2024 · Journal of lipids · added 2026-04-24
Diabetic retinopathy (DR) is a diabetic microvascular complication and a leading cause of vision loss. However, there is a lack of effective strategies to reduce the risk of DR currently. The present Show more
Diabetic retinopathy (DR) is a diabetic microvascular complication and a leading cause of vision loss. However, there is a lack of effective strategies to reduce the risk of DR currently. The present study is aimed at assessing the causal effect of lipid-regulating targets on DR risk using a two-sample Mendelian randomization (MR) study. Genetic variants within or near drug target genes, including eight lipid-regulating targets for LDL-C (HMGCR, PCSK9, and NPC1L1), HDL-C (CETP, SCARB1, and PPARG), and TG (PPARA and LPL), were selected as exposures. The exposure data were obtained from the IEU OpenGWAS project. The outcome dataset related to DR was obtained from the FinnGen research project. Inverse-variance-weighted MR (IVW-MR) was used to calculate the effect estimates by each target. Sensitivity analyses were performed to verify the robustness of the results. There was suggestive evidence that PCSK9-mediated LDL-C levels were positively associated with DR, with OR (95% CI) of 1.34 (1.02-1.77). No significant association was found between the expression of HMGCR- and NPC1L1-mediated LDL-C levels; CETP-, SCARB1-, and PPARG-mediated HDL-C levels; PPARA- and LPL-mediated TG levels; and DR risk. This is the first study to reveal a genetically causal relationship between lipid-regulating drug targets and DR risk. PCSK9-mediated LDL-C levels maybe positively associated with DR risk at the genetic level. This study provides suggestive evidence that PCSK9 inhibition may reduce the risk of DR. Show less
📄 PDF DOI: 10.1155/2024/5324127
CETP

LPL-RH suppresses bone loss in ovariectomised rat models.

Wen-Jie Chen, Xin-Liang Wang, Yu-Fan Wang +6 more · 2024 · BMC microbiology · BioMed Central · added 2026-04-24
Evidence has revealed that oestrogen deprivation-induced osteolysis is microbiota-dependent and can be treated by probiotics. However, the underlying mechanism require further investigation. This stud Show more
Evidence has revealed that oestrogen deprivation-induced osteolysis is microbiota-dependent and can be treated by probiotics. However, the underlying mechanism require further investigation. This study aims to provide additional evidence supporting the use of probiotics as an adjuvant treatment and to explore the pathophysiology of oestrogen-deprived osteolysis. Forty-five SD rats were randomly divided into five groups (n = 9). Rats from four groups were ovariectomised and treated with NS, calcium, probiotics, or calcium + probiotics, while one group underwent a sham operation and was treated with NS. The osteometabolic effects were evaluated, and the mechanistic role of the probiotic supplement was explored. Intragastric administration of Bifidobacterium animalis subsp. lactis LPL-RH (LPL-RH) markedly suppressed osteoclastic activation and bone calcium loss by downregulating TRAP enzymatic activity, the OPG/RANKL ratio, and the downstream signalling pathway RANKL/TRAF6/NF-κB/NFATc1/TRAP in ovariectomised SD rats. LPL-RH also reduced CD4 Collectively, LPL-RH suppressed osteoclastogenesis and osteolysis by modulating type 17 immunity and gut microbiome. Show less
📄 PDF DOI: 10.1186/s12866-024-03683-w
LPL

Single-nucleus multiome analysis of human cerebellum in Alzheimer's disease-related dementia.

Feixiong Cheng, Yayan Feng, Margaret Flanagan +14 more · 2024 · Research square · added 2026-04-24
Although human cerebellum is known to be neuropathologically impaired in Alzheimer's disease (AD) and AD-related dementias (ADRD), the cell type-specific transcriptional and epigenomic changes that co Show more
Although human cerebellum is known to be neuropathologically impaired in Alzheimer's disease (AD) and AD-related dementias (ADRD), the cell type-specific transcriptional and epigenomic changes that contribute to this pathology are not well understood. Here, we report single-nucleus multiome (snRNA-seq and snATAC-seq) analysis of 103,861 nuclei isolated from cerebellum from 9 human cases of AD/ADRD and 8 controls, and with frontal cortex of 6 AD donors for additional comparison. Using peak-to-gene linkage analysis, we identified 431,834 significant linkages between gene expression and cell subtype-specific chromatin accessibility regions enriched for candidate cis-regulatory elements (cCREs). These cCREs were associated with AD/ADRD-specific transcriptomic changes and disease-related gene regulatory networks, especially for RAR Related Orphan Receptor A (RORA) and E74 Like ETS Transcription Factor 1 (ELF1) in cerebellar Purkinje cells and granule cells, respectively. Trajectory analysis of granule cell populations further identified disease-relevant transcription factors, such as RORA, and their regulatory targets. Finally, we prioritized two likely causal genes, including Seizure Related 6 Homolog Like 2 (SEZ6L2) in Purkinje cells and KAT8 Regulatory NSL Complex Subunit 1 (KANSL1) in granule cells, through integrative analysis of cCREs derived from snATAC-seq, genome-wide AD/ADRD loci, and Hi-C looping data. This first cell subtype-specific regulatory landscape in the human cerebellum identified here offer novel genomic and epigenomic insights into the neuropathology and pathobiology of AD/ADRD and other neurological disorders if broadly applied. Show less
no PDF DOI: 10.21203/rs.3.rs-4871032/v1
KANSL1

Molecular mechanisms of hypothalamic-pituitary-ovarian/thyroid axis regulating age at first egg in geese.

Zhiyu He, Qingyuan Ouyang, Qingliang Chen +7 more · 2024 · Poultry science · Elsevier · added 2026-04-24
Age at first egg (AFE) has consistently garnered interest as a crucial reproductive indicator within poultry production. Previous studies have elucidated the involvement of the hypothalamic-pituitary- Show more
Age at first egg (AFE) has consistently garnered interest as a crucial reproductive indicator within poultry production. Previous studies have elucidated the involvement of the hypothalamic-pituitary-ovarian (HPO) and hypothalamic-pituitary-thyroid (HPT) axes in regulating poultry sexual maturity. Concurrently, there was evidence suggesting a potential co-regulatory relationship between these 2 axes. However, as of now, no comprehensive exploration of the key pathways and genes responsible for the crosstalk between the HPO and HPT axes in the regulation of AFE has been reported. In this study, we conducted a comparative analysis of morphological differences and performed transcriptomic analysis on the hypothalamus, pituitary, thyroid, and ovarian stroma between normal laying group (NG) and abnormal laying group (AG). Morphological results showed that the thyroid index difference (D-) value (thyroid index D-value=right thyroid index-left thyroid index) was significantly (P < 0.05) lower in the NG than in the AG, while the ovarian index was significantly (P < 0.01) higher in the NG than in the AG. Furthermore, between NG and AG, we identified 99, 415, 167, and 1182 differentially expressed genes (DEGs) in the hypothalamus, pituitary, thyroid, and ovarian stroma, respectively. Gene ontology (GO) analysis highlighted that DEGs from 4 tissues were predominantly enriched in the "biological processes" category. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that 16, 14, 3, and 26 KEGG pathways were significantly enriched (P < 0.05) in the hypothalamus, pituitary, thyroid, and ovarian stroma. The MAPK signaling pathway emerged as the sole enriched pathway across all 4 tissues. Employing an integrated analysis of the protein-protein interaction (PPI) network and correlation analysis, we found GREB1 emerged as a pivotal component within the HPO axis to regulate estrogen-related signaling in the HPT axis, meanwhile, the HPT axis influenced ovarian development by regulating thyroid hormone-related signaling mainly through OPN5. Then, 10 potential candidate genes were identified, namely IGF1, JUN, ERBB4, KDR, PGF, FGFR1, GREB1, OPN5, DIO3, and THRB. These findings establish a foundation for elucidating the physiological and genetic mechanisms by which the HPO and HPT axes co-regulate goose AFE. Show less
📄 PDF DOI: 10.1016/j.psj.2024.103478
FGFR1