👤 Changya 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, 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, 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

Tracking the Risk of Cardiovascular Disease after Almond and Oat Milk Intervene or Statin Medication with a Powerful Reflex SH-SAW POCT Platform.

Chia-Hsuan Cheng, Hiromi Yatsuda, Han-Hsiang Chen +3 more · 2024 · Sensors (Basel, Switzerland) · MDPI · added 2026-04-24
Cardiovascular disease (CVD) represents the leading cause of death worldwide. For individuals at elevated risk for cardiovascular disease, early detection and monitoring of lipid status is imperative. Show more
Cardiovascular disease (CVD) represents the leading cause of death worldwide. For individuals at elevated risk for cardiovascular disease, early detection and monitoring of lipid status is imperative. The majority of lipid measurements conducted in hospital settings employ optical detection, which necessitates the use of relatively large-sized detection machines. It is, therefore, necessary to develop point-of-care testing (POCT) for lipoprotein in order to monitor CVD. To enhance the management and surveillance of CVD, this study sought to develop a POCT approach for apolipoprotein B (ApoB) utilizing a shear horizontal surface acoustic wave (SH-SAW) platform to assess the risk of heart disease. The platform employs a reflective SH-SAW sensor to reduce the sensor size and enhance the phase-shifted signals. In this study, the platform was utilized to monitor the impact of a weekly almond and oat milk or statins intervention on alterations in CVD risk. The SH-SAW ApoB test exhibited a linear range of 0 to 212 mg/dL, and a coefficient correlation (R) of 0.9912. Following a four-week intervention period, both the almond and oat milk intervention (-23.3%, Show less
📄 PDF DOI: 10.3390/s24206517
APOB

Associations Between Gene Variants of Lipid-Lowering Drug Targets and Adverse Outcomes After Ischemic Stroke.

Lulu Sun, Qilu Zhang, Mengyao Shi +9 more · 2024 · Journal of the American Heart Association · added 2026-04-24
The association of lipid-lowering drug targets and their gene variants with cardiovascular diseases has been previously clarified. However, the relationship between gene variants of lipid-lowering dru Show more
The association of lipid-lowering drug targets and their gene variants with cardiovascular diseases has been previously clarified. However, the relationship between gene variants of lipid-lowering drug targets and the adverse prognosis of ischemic stroke patients remains unclear. Multiple single-nucleotide polymorphisms associated with 6 lipid-lowering drug targets were genotyped for patients with ischemic stroke. The primary outcome was death or major disability within 2 years after ischemic stroke. Genetic risk score was constructed from significant single-nucleotide polymorphisms identified via additive models, which was calculated by multiplying the number of risk alleles at each locus by the corresponding beta coefficient and then summing the products. The rs2006760-C of the rs2006760-C of Show less
📄 PDF DOI: 10.1161/JAHA.124.036544
CETP

Effect of gut hormones on bone metabolism and their possible mechanisms in the treatment of osteoporosis.

Hongyu Liu, Huimin Xiao, Sufen Lin +4 more · 2024 · Frontiers in pharmacology · Frontiers · added 2026-04-24
Bone is a highly dynamic organ that changes with the daily circadian rhythm. During the day, bone resorption is suppressed due to eating, while it increases at night. This circadian rhythm of the skel Show more
Bone is a highly dynamic organ that changes with the daily circadian rhythm. During the day, bone resorption is suppressed due to eating, while it increases at night. This circadian rhythm of the skeleton is regulated by gut hormones. Until now, gut hormones that have been found to affect skeletal homeostasis include glucagon-like peptide-1 (GLP-1), glucagon-like peptide-2 (GLP-2), glucose-dependent insulinotropic polypeptide (GIP), and peptide YY (PYY), which exerts its effects by binding to its cognate receptors (GLP-1R, GLP-2R, GIPR, and Y1R). Several studies have shown that GLP-1, GLP-2, and GIP all inhibit bone resorption, while GIP also promotes bone formation. Notably, PYY has a strong bone resorption-promoting effect. In addition, gut microbiota (GM) plays an important role in maintaining bone homeostasis. This review outlines the roles of GLP-1, GLP-2, GIP, and PYY in bone metabolism and discusses the roles of gut hormones and the GM in regulating bone homeostasis and their potential mechanisms. Show less
📄 PDF DOI: 10.3389/fphar.2024.1372399
GIPR

Identification and experimental verification of immune-related hub genes in intervertebral disc degeneration.

Zeling Huang, Xuefeng Cai, Xiaofeng Shen +6 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Inflammation and immune factors are the core of intervertebral disc degeneration (IDD), but the immune environment and epigenetic regulation process of IDD remain unclear. This study aims to identify Show more
Inflammation and immune factors are the core of intervertebral disc degeneration (IDD), but the immune environment and epigenetic regulation process of IDD remain unclear. This study aims to identify immune-related diagnostic candidate genes for IDD, and search for potential pathogenesis and therapeutic targets for IDD. Gene expression datasets were obtained from the Gene Expression Omnibus (GEO). Differential expression immune genes (Imm-DEGs) were identified through weighted gene correlation network analysis (WGCNA) and linear models for microarray data analysis (Limma). LASSO algorithm was used to identify feature genes related to IDD, which were compared with core node genes in PPI network to obtain hub genes. Based on the coefficients of hub genes, a risk model was constructed, and the diagnostic value of hub genes was further evaluated through receiver operating characteristic (ROC) analysis. Xcell, an immunocyte analysis tool, was used to estimate the infiltration of immune cells. Finally, nucleus pulposus cells were co-cultured with macrophages to create an M1 macrophage immune inflammatory environment, and the changes of hub genes were verified. Combined with the results of WGCNA and Limma gene differential analysis, a total of 30 Imm-DEGs were identified. Imm-DEGs enriched in multiple pathways related to immunity and inflammation. LASSO algorithm identified 10 feature genes from Imm-DEGs that significantly affected IDD, and after comparison with core node genes in the PPI network of Imm-DEGs, 6 hub genes (NR1H3, SORT1, PTGDS, AGT, IRF1, TGFB2) were determined. Results of ROC curves and external dataset validation showed that the risk model constructed with the 6 hub genes had high diagnostic value for IDD. Immunocyte infiltration analysis showed the presence of various dysregulated immune cells in the degenerative nucleus pulposus tissue. In vitro experimental results showed that the gene expression of NR1H3, SORT1, PTGDS, IRF1, and TGFB2 in nucleus pulposus cells in the immune inflammatory environment was up-regulated, but the change of AGT was not significant. The hub genes NR1H3, SORT1, PTGDS, IRF1, and TGFB2 can be used as immunorelated biomarkers for IDD, and may be potential targets for immune regulation therapy for IDD. Show less
no PDF DOI: 10.1016/j.heliyon.2024.e34530
NR1H3

Associations of CSF BACE1 with amyloid pathology, neurodegeneration, and cognition in Alzheimer's disease.

Feng Gao, Mengguo Zhang, Qiong Wang +8 more · 2024 · Acta neuropathologica · Springer · added 2026-04-24
Β-site amyloid precursor protein (APP) cleaving enzyme (BACE1) is a crucial protease in the production of amyloid-β (Aβ) in Alzheimer's disease (AD) patients. However, the side effects observed in cli Show more
Β-site amyloid precursor protein (APP) cleaving enzyme (BACE1) is a crucial protease in the production of amyloid-β (Aβ) in Alzheimer's disease (AD) patients. However, the side effects observed in clinical trials of BACE1 inhibitors, including reduction in brain volume and cognitive worsening, suggest that the exact role of BACE1 in AD pathology is not fully understood. To further investigate this, we examined cerebrospinal fluid (CSF) levels of BACE1 and its cleaved product sAPPβ that reflects BACE1 activity in the China Aging and Neurodegenerative Disorder Initiative cohort. We found significant correlations between CSF BACE1 or sAPPβ levels and CSF Aβ40, Aβ42, and Aβ42/Aβ40 ratio, but not with amyloid deposition detected by 18F-Florbetapir PET. Additionally, CSF BACE1 and sAPPβ levels were positively associated with cortical thickness in multiple brain regions, and higher levels of sAPPβ were linked to increased cortical glucose metabolism in frontal and supramarginal areas. Interestingly, individuals with higher baseline levels of CSF BACE1 exhibited slower rates of brain volume reduction and cognitive worsening over time. This suggests that increased levels and activity of BACE1 may not be the determining factor for amyloid deposition, but instead, may be associated with increased neuronal activity and potentially providing protection against neurodegeneration in AD. Show less
📄 PDF DOI: 10.1007/s00401-024-02750-w
BACE1

Characterizing and identifying of miRNAs involved in berberine modulating glucose metabolism of Megalobrama amblycephala.

Mingyang Liu, Chang He, Tingting Zhu +8 more · 2024 · Fish physiology and biochemistry · Springer · added 2026-04-24
The present study, as one part of a larger project that aimed to investigate the effects of dietary berberine (BBR) on fish growth and glucose regulation, mainly focused on whether miRNAs involve in B Show more
The present study, as one part of a larger project that aimed to investigate the effects of dietary berberine (BBR) on fish growth and glucose regulation, mainly focused on whether miRNAs involve in BBR's modulation of glucose metabolism in fish. Blunt snout bream Megalobrama amblycephala (average weight of 20.36 ± 1.44 g) were exposed to the control diet (NCD, 30% carbohydrate), the high-carbohydrate diet (HCD, 43% carbohydrate) and the berberine diet (HCB, HCD supplemented with 50 mg/kg BBR). After 10 weeks' feeding trial, intraperitoneal injection of glucose was conducted, and then, the plasma and liver were sampled at 0 h, 1 h, 2 h, 6 h, and 12 h. The results showed the plasma glucose levels in all groups rose sharply and peaked at 1 h after glucose injection. Unlike the NCD and HCB groups, the plasma glucose in the HCD group did not decrease after 1 h, while remained high level until at 2 h. The NCD group significantly increased liver glycogen content at times 0-2 h compared to the other two groups and then liver glycogen decreased sharply until at times 6-12 h. To investigate the role of BBR that may cause the changes in plasma glucose and liver glycogen, miRNA high-throughput sequencing was performed on three groups of liver tissues at 2 h time point. Eventually, 20 and 12 differentially expressed miRNAs (DEMs) were obtained in HCD vs NCD and HCB vs HCD, respectively. Through function analyzing, we found that HCD may affect liver metabolism under glucose loading through the NF-κB pathway; and miRNAs regulated by BBR mainly play roles in adipocyte lipolysis, niacin and nicotinamide metabolism, and amino acid transmembrane transport. In the functional exploration of newly discovered novel:Chr12₁₈₈₉₂, we found its target gene, adenylate cyclase 3 (adcy3), was widely involved in lipid decomposition, amino acid metabolism, and other pathways. Furthermore, a targeting relationship of novel:Chr12₁₈₈₉₂ and adcy3 was confirmed by double luciferase assay. Thus, BBR may promote novel:Chr12₁₈₈₉₂ to regulate the expression of adcy3 and participate in glucose metabolism. Show less
📄 PDF DOI: 10.1007/s10695-024-01362-1
ADCY3

Macrophage-specific FGFR1 deletion alleviates high-fat-diet-induced liver inflammation by inhibiting the MAPKs/TNF pathways.

Yan-Ni Zhao, Zhou-di Liu, Tao Yan +7 more · 2024 · Acta pharmacologica Sinica · Nature · added 2026-04-24
Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disease that is substantially associated with obesity-induced chronic inflammation. Macrophage activation and macrophage-medicated infla Show more
Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disease that is substantially associated with obesity-induced chronic inflammation. Macrophage activation and macrophage-medicated inflammation play crucial roles in the development and progression of NAFLD. Furthermore, fibroblast growth factor receptor 1 (FGFR1) has been shown to be essentially involved in macrophage activation. This study investigated the role of FGFR1 in the NAFLD pathogenesis and indicated that a high-fat diet (HFD) increased p-FGFR1 levels in the mouse liver, which is associated with increased macrophage infiltration. In addition, macrophage-specific FGFR1 knockout or administration of FGFR1 inhibitor markedly protected the liver from HFD-induced lipid accumulation, fibrosis, and inflammatory responses. The mechanistic study showed that macrophage-specific FGFR1 knockout alleviated HFD-induced liver inflammation by suppressing the activation of MAPKs and TNF signaling pathways and reduced fat deposition in hepatocytes, thereby inhibiting the activation of hepatic stellate cells. In conclusion, the results of this research revealed that FGFR1 could protect the liver of HFD-fed mice by inhibiting MAPKs/TNF-mediated inflammatory responses in macrophages. Therefore, FGFR1 can be employed as a target to prevent the development and progression of NAFLD. Show less
no PDF DOI: 10.1038/s41401-024-01226-7
FGFR1

Low PDE4A expression promoted the progression of ovarian cancer by inducing Snail nuclear translocation.

Jinlong Wang, Qiuying Gu, Yuexi Liu +5 more · 2024 · Experimental cell research · Elsevier · added 2026-04-24
Widespread metastasis is the primary reason for the high mortality associated with ovarian cancer (OC), and effective targeted therapy for tumor aggressiveness is still insufficient in clinical practi Show more
Widespread metastasis is the primary reason for the high mortality associated with ovarian cancer (OC), and effective targeted therapy for tumor aggressiveness is still insufficient in clinical practice. Therefore, it is urgent to find new targets to improve prognosis of patients. PDE4A is a cyclic nucleotide phosphodiesterase that plays a crucial role in the occurrence and development in various malignancies. Our study firstly reported the function of PDE4A in OC. Expression of PDE4A was validated through bioinformatics analysis, RT-qPCR, Western blot, and immunohistochemistry. Additionally, its impact on cell growth and motility was assessed via in vitro and in vivo experiments. PDE4A was downregulated in OC tissues compared with normal tissues and low PDE4A expression was correlated with poor clinical outcomes in OC patients. The knockdown of PDE4A significantly promoted the proliferation, migration and invasion of OC cells while overexpression of PDE4A resulted in the opposite effect. Furthermore, smaller and fewer tumor metastatic foci were observed in mice bearing PDE4A-overexpressing OVCAR3 cells. Mechanistically, downregulation of PDE4A expression can induce epithelial-mesenchymal transition (EMT) and nuclear translocation of Snail, which suggests that PDE4A plays a pivotal role in suppressing OC progression. Notably, Rolipram, the PDE4 inhibitor, mirrored the effects observed with PDE4A deletion. In summary, the downregulation of PDE4A appears to facilitate OC progression by modulating the Snail/EMT pathway, underscoring the potential of PDE4A as a therapeutic target against ovarian cancer metastasis. Show less
no PDF DOI: 10.1016/j.yexcr.2024.114100
SNAI1

Genetic overlap and causality between COVID-19 and multi-site chronic pain: the importance of immunity.

Yanjing Chen, Ping Liu, Zhiyi Zhang +5 more · 2024 · Frontiers in immunology · Frontiers · added 2026-04-24
The existence of chronic pain increases susceptibility to virus and is now widely acknowledged as a prominent feature recognized as a major manifestation of long-term coronavirus disease 2019 (COVID-1 Show more
The existence of chronic pain increases susceptibility to virus and is now widely acknowledged as a prominent feature recognized as a major manifestation of long-term coronavirus disease 2019 (COVID-19) infection. Given the ongoing COVID-19 pandemic, it is imperative to explore the genetic associations between chronic pain and predisposition to COVID-19. We conducted genetic analysis at the single nucleotide polymorphism (SNP), gene, and molecular levels using summary statistics of genome-wide association study (GWAS) and analyzed the drug targets by summary data-based Mendelian randomization analysis (SMR) to alleviate the multi-site chronic pain in COVID-19. Additionally, we performed a latent causal variable (LCV) method to investigate the causal relationship between chronic pain and susceptibility to COVID-19. The cross-trait meta-analysis identified 19 significant SNPs shared between COVID-19 and chronic pain. Coloc analysis indicated that the posterior probability of association (PPH4) for three loci was above 70% in both critical COVID-19 and COVID-19, with the corresponding top three SNPs being rs13135092, rs7588831, and rs13135092. A total of 482 significant overlapped genes were detected from MAGMA and CPASSOC results. Additionally, the gene ANAPC4 was identified as a potential drug target for treating chronic pain (P=7.66E-05) in COVID-19 (P=8.23E-03). Tissue enrichment analysis highlighted that the amygdala (P=7.81E-04) and prefrontal cortex (P=8.19E-05) as pivotal in regulating chronic pain of critical COVID-19. KEGG pathway enrichment further revealed the enrichment of pleiotropic genes in both COVID-19 (P=3.20E-03,Padjust=4.77E-02,hsa05171) and neurotrophic pathways (P=9.03E-04,Padjust =2.55E-02,hsa04621). Finally, the latent causal variable (LCV) model was applied to find the genetic component of critical COVID-19 was causal for multi-site chronic pain (P=0.015), with a genetic causality proportion (GCP) of was 0.60. In this study, we identified several functional genes and underscored the pivotal role of the inflammatory system in the correlation between the paired traits. Notably, heat shock proteins emerged as potential objective biomarkers for chronic pain symptoms in individuals with COVID-19. Additionally, the ubiquitin system might play a role in mediating the impact of COVID-19 on chronic pain. These findings contribute to a more comprehensive understanding of the pleiotropy between COVID-19 and chronic pain, offering insights for therapeutic trials. Show less
📄 PDF DOI: 10.3389/fimmu.2024.1277720
ANAPC4

Correlation between serum ANGPTL4 levels and white matter hyperintensity and cognitive impairment in patients with cerebral small vessel disease.

Jianhua Zhao, Shiyun Zhang, Xiaoting Wang +6 more · 2024 · Brain and behavior · Wiley · added 2026-04-24
To investigate the correlation between serum angiopoietin-like protein 4 (ANGPTL4) levels, white matter hyperintensity (WMH), and cognitive impairment (CI) in patients with cerebral small vessel disea Show more
To investigate the correlation between serum angiopoietin-like protein 4 (ANGPTL4) levels, white matter hyperintensity (WMH), and cognitive impairment (CI) in patients with cerebral small vessel disease (CSVD). This cross-sectional study enrolled 171 patients with CSVD who attended the First Affiliated Hospital of Xinxiang Medical University from December 2021 to July 2022. All subjects underwent a 3.0T head magnetic resonance imaging, neuropsychology assessment, and blood sampling. Serum ANGPTL4 levels were detected by enzyme-linked immunosorbent assay and the severity of WMH was assessed by the Fazekas scale. According to the Montreal Cognitive Assessment (MoCA) scale, subjects were divided into normal cognition group (NC, n = 80) and CI group (n = 91). According to the total Fazekas scores, subjects were divided into a mild WMH group (n = 84), a moderate WMH group (n = 70), and a severe WMH group (n = 17). Serum ANGPTL4 levels were significantly higher in the CI group than in the NC group (p < .05) and were negatively correlated with mini-mental state examination scores and MoCA scores (r = -0.26, -0.341, p < .05). Serum ANGPTL4 levels increased significantly in the mild to moderate WMH group but tended to decrease in the severe WMH group. Binary logistic regression analysis showed that ANGPTL4 was an independent influencing factor for CSVD-CI (OR = 2.062, 95% CI (1.591-2.674), p < .001). The area under curve of ANGPTL4 for CSVD-CI was 0.847 (0.791-0.903). ANGPTL4 may be involved in the process of white matter damage and CI in CSVD patients and shows a diagnostic value for CSVD-CI. Show less
📄 PDF DOI: 10.1002/brb3.3401
ANGPTL4

Identification of gene signatures and molecular mechanisms underlying the mutual exclusion between psoriasis and leprosy.

You-Wang Lu, Rong-Jing Dong, Lu-Hui Yang +6 more · 2024 · Scientific reports · Nature · added 2026-04-24
Leprosy and psoriasis rarely coexist, the specific molecular mechanisms underlying their mutual exclusion have not been extensively investigated. This study aimed to reveal the underlying mechanism re Show more
Leprosy and psoriasis rarely coexist, the specific molecular mechanisms underlying their mutual exclusion have not been extensively investigated. This study aimed to reveal the underlying mechanism responsible for the mutual exclusion between psoriasis and leprosy. We obtained leprosy and psoriasis data from ArrayExpress and GEO database. Differential expression analysis was conducted separately on the leprosy and psoriasis using DEseq2. Differentially expressed genes (DEGs) with opposite expression patterns in psoriasis and leprosy were identified, which could potentially involve in their mutual exclusion. Enrichment analysis was performed on these candidate mutually exclusive genes, and a protein-protein interaction (PPI) network was constructed to identify hub genes. The expression of these hub genes was further validated in an external dataset to obtain the critical mutually exclusive genes. Additionally, immune cell infiltration in psoriasis and leprosy was analyzed using single-sample gene set enrichment analysis (ssGSEA), and the correlation between critical mutually exclusive genes and immune cells was also examined. Finally, the expression pattern of critical mutually exclusive genes was evaluated in a single-cell transcriptome dataset. We identified 1098 DEGs in the leprosy dataset and 3839 DEGs in the psoriasis dataset. 48 candidate mutually exclusive genes were identified by taking the intersection. Enrichment analysis revealed that these genes were involved in cholesterol metabolism pathways. Through PPI network analysis, we identified APOE, CYP27A1, FADS1, and SOAT1 as hub genes. APOE, CYP27A1, and SOAT1 were subsequently validated as critical mutually exclusive genes on both internal and external datasets. Analysis of immune cell infiltration indicated higher abundance of 16 immune cell types in psoriasis and leprosy compared to normal controls. The abundance of 6 immune cell types in psoriasis and leprosy positively correlated with the expression levels of APOE and CYP27A1. Single-cell data analysis demonstrated that critical mutually exclusive genes were predominantly expressed in Schwann cells and fibroblasts. This study identified APOE, CYP27A1, and SOAT1 as critical mutually exclusive genes. Cholesterol metabolism pathway illustrated the possible mechanism of the inverse association of psoriasis and leprosy. The findings of this study provide a basis for identifying mechanisms and therapeutic targets for psoriasis. Show less
📄 PDF DOI: 10.1038/s41598-024-52783-0
FADS1

Autoantibodies to BACE1 promote Aβ accumulation and neurodegeneration in Alzheimer's disease.

Ye-Ran Wang, Xiao-Qin Zeng, Jun Wang +10 more · 2024 · Acta neuropathologica · Springer · added 2026-04-24
The profile of autoantibodies is dysregulated in patients with Alzheimer's disease (AD). Autoantibodies to beta-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1) are present in human bloo Show more
The profile of autoantibodies is dysregulated in patients with Alzheimer's disease (AD). Autoantibodies to beta-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1) are present in human blood. This study aims to investigate the clinical relevance and pathophysiological roles of autoantibodies to BACE1 in AD. Clinical investigations were conducted in two independent cohorts, the Chongqing cohort, and the Australian Imaging, Biomarkers, and Lifestyle (AIBL) cohort. The Chongqing cohort included 55 AD patients, 28 patients with non-AD dementia, and 70 cognitively normal subjects (CN). The AIBL cohort included 162 Aβ-PET Show less
📄 PDF DOI: 10.1007/s00401-024-02814-x
BACE1

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

[The efficacy and safety of ibrutinib in the treatment of lymphoplasmacytic lymphoma/Waldenström macroglobulinemia].

Y S Huang, W J Xiong, J J Yuan +11 more · 2024 · Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · added 2026-04-24
📄 PDF DOI: 10.3760/cma.j.cn121090-20240301-00077
LPL

rTM reprograms macrophages via the HIF-1α/METTL3/PFKM axis to protect mice against sepsis.

Chen Yao, Hanyong Zhu, Binbin Ji +18 more · 2024 · Cellular and molecular life sciences : CMLS · Springer · added 2026-04-24
The metabolic reprogramming of macrophages is a potential therapeutic strategy for sepsis treatment, but the mechanism underlying this reprogramming remains unclear. Since glycolysis can drive macroph Show more
The metabolic reprogramming of macrophages is a potential therapeutic strategy for sepsis treatment, but the mechanism underlying this reprogramming remains unclear. Since glycolysis can drive macrophage phenotype switching, the rate-limiting enzymes in glycolysis may be key to treating sepsis. Here, we found that, compared with other isoenzymes, the expression of 6-phosphofructokinase, muscle type (PFKM) was the most upregulated in monocytes from septic patients. Recombinant thrombomodulin (rTM) treatment downregulated the protein expression of PFKM in macrophages. Both rTM treatment and Pfkm knockout protected mice from sepsis and reduced the production of the proinflammatory cytokines IL-1β, IL-6, TNF-α, and IL-27, whereas PFKM overexpression increased the production of these cytokines. Mechanistically, rTM treatment inhibited glycolysis in macrophages by decreasing PFKM expression in a hypoxia-inducible factor-1α (HIF-1α)-dependent manner. HIF-1α overexpression increased methyltransferase-like 3 (METTL3) expression, elevated the m Show less
📄 PDF DOI: 10.1007/s00018-024-05489-5
IL27

Naringenin inhibits the microsomal triglyceridetransfer protein/apolipoprotein B axis to inhibit intestinal metaplasia progression.

Xiangming Huang, Mengqiu Zhang, Lina Gu +9 more · 2024 · Phytotherapy research : PTR · Wiley · added 2026-04-24
Intestinal metaplasia (IM) is a premalignant condition that increases the risk for subsequent gastric cancer (GC). Traditional Chinese medicine generally plays a role in the treatment of IM, and the p Show more
Intestinal metaplasia (IM) is a premalignant condition that increases the risk for subsequent gastric cancer (GC). Traditional Chinese medicine generally plays a role in the treatment of IM, and the phytochemical naringenin used in Chinese herbal medicine has shown therapeutic potential for the treatment of gastric diseases. However, naringenin's specific effect on IM is not yet clearly understood. Therefore, this study identified potential gene targets for the treatment of IM through bioinformatics analysis and experiment validation. Two genes (MTTP and APOB) were selected as potential targets after a comparison of RNA-seq results of clinical samples, the GEO dataset (GSE78523), and naringenin-related genes from the GeneCards database. The results of both cell and animal experiments suggested that naringenin can improve the changes in the intestinal epithelial metaplasia model via MTTP/APOB expression. In summary, naringenin likely inhibits the MTTP/APOB axis and therefore inhibits IM progression. These results support the development of naringenin as an anti-IM agent and may contribute to the discovery of novel IM therapeutic targets. Show less
no PDF DOI: 10.1002/ptr.8279
APOB

The Long Non-Coding RNA SNHG1 Predicts Severity of Acute Pancreatitis and Stimulates Pancreatic Cell Apoptosis and Inflammatory Response.

Zhuo Chen, Shengnan Liu, Junsheng Wang +1 more · 2024 · Journal of environmental pathology, toxicology and oncology : official organ of the International Society for Environmental Toxicology and Cancer · added 2026-04-24
Acute pancreatitis (AP) is a common digestive emergency, needs early prediction and recognition. The study examined the clinical value of long non-coding RNA SNHG1 in AP, and explored its related mech Show more
Acute pancreatitis (AP) is a common digestive emergency, needs early prediction and recognition. The study examined the clinical value of long non-coding RNA SNHG1 in AP, and explored its related mechanism for AP. A total of 288 AP cases and 150 healthy persons were recruited, the AP patients were grouped based on AP severity. AR42J cells were treated with 100nM caerulein to stimulate AP in vitro. qRT-PCR was performed for mRNA detection. Receiver operating characteristic (ROC) curve was drawn for diagnostic significance evaluation. The relationship of SNHG1 and miR-140-3p was verified via luciferase reporter and RNA immunoprecipitation (RIP) assay. AP cases had high expression of SNHG1, and it can differentiate AP cases from healthy people with the area under the curve (AUC) of 0.899. Severe AP cases had high values of SNHG1, which was independently related to AP severity. SNHG1 knockdown relieved caerulein-induced AR42J cell apoptosis and inflammatory response. miR-140-3p interacted with SNHG1, and reversed the role of SNHG1 in caerulein-induced AR42J cell injury. RAB21 was a candidate target of miR-140-3p, and was at high expression in AP cell models. SNHG1 may be a promising biomarker for the detection of AP, and serves as a potential biological marker for further risk stratification in the management of AP. SNHG1 knockdown can relieve inflammatory responses and pancreatic cell apoptosis by absorbing miR-140-3p. Show less
no PDF DOI: 10.1615/JEnvironPatholToxicolOncol.2024053229
RAB21

Global RNA Interaction and Transcriptome Profiles Demonstrate the Potential Anti-Oncogenic Targets and Pathways of RBM6 in HeLa Cells.

Ping Peng, Qingqing Yin, Wei Sun +4 more · 2024 · Frontiers in bioscience (Landmark edition) · added 2026-04-24
The fate and functions of RNAs are coordinately regulated by RNA-binding proteins (RBPs), which are often dysregulated in various cancers. Known as a splicing regulator, RNA-binding motif protein 6 (R Show more
The fate and functions of RNAs are coordinately regulated by RNA-binding proteins (RBPs), which are often dysregulated in various cancers. Known as a splicing regulator, RNA-binding motif protein 6 (RBM6) harbors tumor-suppressor activity in many cancers; however, there is a lack of research on the molecular targets and regulatory mechanisms of RBM6. In this study, we constructed an Using The Cancer Genome Atlas dataset, we found that higher expression of In summary, our study highlights the important role of RBM6, as well as the downstream targets and regulated pathways, suggesting the potential regulatory mechanisms of RBM6 in the development of cancer. Show less
no PDF DOI: 10.31083/j.fbl2909330
RBM6

The integration of multi-omics analysis and machine learning for the identification of prognostic assessment and immunotherapy efficacy through aging-associated genes in lung cancer.

Wei Lu, Yun Zhou, Ruixuan Zhao +3 more · 2024 · Aging · Impact Journals · added 2026-04-24
Recent years revealed key molecules in lung cancer research, yet their exact roles in disease onset and progression remain uncertain. Lung cancer's heterogeneity complicates prognosis prediction. This Show more
Recent years revealed key molecules in lung cancer research, yet their exact roles in disease onset and progression remain uncertain. Lung cancer's heterogeneity complicates prognosis prediction. This study integrates pivotal molecules to evaluate patient prognosis and immunotherapy efficacy. The WGCNA algorithm identified module genes linked to immunity. The Lasso-Cox method built a prognostic model for outcome prediction. GO and KEGG analyses explored gene pathways. ssGSEA quantified immune cell types and functions. The riskScore predicts the effectiveness of immunotherapy based on its correlation with DNA repair and immune checkpoint genes. Single-cell sequencing examined key gene expression across cell types. Using WGCNA, we identified the MEbrown module related to immunity. Lasso-Cox selected "BLK," "ITGB4," "PRKCH," and "SNAI1" for the prognostic model. MF analysis revealed enriched functions including antigen binding, GTPase regulator activity. In terms of BP, processes like immune signaling and mitotic division were enriched. CC enrichment included immunoglobulin complexes and chromosomal regions. Enriched pathways encompassed Cell cycle, Focal adhesion, Cellular senescence, and p53 signaling. ssGSEA evaluated immune cell abundance. RiskScore correlated with CTLA4 and PD1 through MMR and immune checkpoint analysis. Single-cell analysis indicated gene expression across cell types for BLK, ITGB4, PRKCH, and SNAI1. In summary, our developed prognostic model utilizing age-related genes effectively predicts lung cancer prognosis and the efficacy of immune therapy. Show less
no PDF DOI: 10.18632/aging.205464
SNAI1

Association of

Ziyang Liu, Yang Zhou, Menglong Jin +8 more · 2024 · PeerJ · added 2026-04-24
Dyslipidemia plays a very important role in the occurrence and development of cardiovascular disease (CVD). Genetic factors, including single nucleotide polymorphisms (SNPs), are one of the main risks Show more
Dyslipidemia plays a very important role in the occurrence and development of cardiovascular disease (CVD). Genetic factors, including single nucleotide polymorphisms (SNPs), are one of the main risks of dyslipidemia. 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) is not only the rate-limiting enzyme step of endogenous cholesterol production, but also the therapeutic target of statins. We investigated 405 Han Chinese and 373 Uyghur people who took statins for a period of time, recorded their blood lipid levels and baseline data before and after oral statin administration, and extracted DNA from each subject for SNP typing of In this study, for rs17671591, the CC We found that Show less
📄 PDF DOI: 10.7717/peerj.18144
APOB

Multiomics analysis reveals the potential mechanism of high-fat diet in dextran sulfate sodium-induced colitis mice model.

Yuyang Zhao, Zhimin Chen, Ruiyi Dong +6 more · 2024 · Food science & nutrition · Wiley · added 2026-04-24
A high-fat diet (HFD) is recognized as an important contributor to inflammatory bowel disease (IBD). However, the precise underlying mechanism of HFD on IBD remains elusive. This study aimed to invest Show more
A high-fat diet (HFD) is recognized as an important contributor to inflammatory bowel disease (IBD). However, the precise underlying mechanism of HFD on IBD remains elusive. This study aimed to investigate the potential mechanism by which HFD affects IBD using 16S rRNA-sequencing and RNA-seq technology. Results indicated that HFD-treated mice exhibited notable alternations in the structure and composition of the gut microbiota, with some of these alternations being associated with the pathogenesis of IBD. Analysis of the colon transcriptome revealed 11 hub genes and 7 hub pathways among control, DSS-induced colitis, and HFD + DSS-treated groups. Further analysis explores the relationship between the hub pathways and genes, as well as the hub genes and gut microbiota. Overall, the findings indicate that the impact of HFD on DSS-induced colitis may be linked to intestinal dysbiosis and specific genes such as Show less
📄 PDF DOI: 10.1002/fsn3.4426
APOA4

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

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

Integrative analysis reveals chemokines CCL2 and CXCL5 mediated shear stress-induced aortic dissection formation.

Chao Xue, Liqing Jiang, Bin Zhang +12 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Aortic dissection (AD) is a critical emergency in cardiovascular disease. AD occurs only in specific sites of the aorta, and the variation of shear stress in different aortic segments is a possible ca Show more
Aortic dissection (AD) is a critical emergency in cardiovascular disease. AD occurs only in specific sites of the aorta, and the variation of shear stress in different aortic segments is a possible cause not reported. This study investigated the key molecules involved in shear stress-induced AD through quantitative bioinformatic analysis of a public RNA sequencing database and clinical tissue sample validation. Gene expression data from the GSE153434, GSE147026, and GSE52093 datasets were downloaded from the Gene Expression Omnibus. Next, differently expressed genes (DEGs) in each dataset were identified and integrated to identify common AD DEGs. STRING, Cytoscape, and MCODE were used to identify hub genes and crucial clustering modules, and Connectivity Map (CMap) was used to identify positive and negative agents. The same procedure was performed for the GSE160611 dataset to obtain shear stress-induced human aortic endothelial cell (HAEC) DEGs. After the integration of these two DEGs sets to identify shear stress-associated hub DEGs in AD, Gene Ontology Enrichment Analysis was performed. The common chemokine receptors and ligands in AD were identified by analyzing AD's three RNA sequencing datasets. Their origin was verified by analyzing AD single-cell sequencing data and validated by immunoblotting and immunofluorescence. We identified 100 down-regulated and 50 up-regulated AD common DEGs. Enrichment results showed that common DEGs were closely related to blood vessel morphogenesis, muscle structure development, muscle tissue development, and chemotaxis. Among those DEGs, MYC, CCL2, and SPP1 are the three molecules with the highest degree. A crucial cluster of 15 genes was identified using MCODE, which contained inflammation-related genes with elevated expression and muscle cell-related genes with decreased expression, and CCL2 is central to immune-related genes. CMap confirmed MEK inhibitors and ALK inhibitors as possible therapeutic agents for AD. Moreover, 366 shear stress-associated DEGs in HAEC were identified in the GSE160611 dataset. After taking the intersection, we identified five shear stress-associated hub DEGs in AD (ANGPTL4, SNAI2, CCL2, GADD45B, and PROM1), and the enrichment analysis indicated they were related to the endothelial cell apoptotic process. Chemokine CCL2 was the molecule with a high degree in both DEG sets. Besides CCL2, CXCL5 was the only chemokine ligand differentially expressed in the three datasets. Additionally, immunoblotting confirmed the increased expression of CCL2 and CXCL5 in clinical tissue samples. Further research at the single-cell level revealed that CCL2 has multiple origins, and CXCL5 is macrophage-derived. Through integrative analysis, we identified core common AD DEGs and possible therapeutic agents based on these DEGs. We elucidated that the chemokine CCL2 and CXCL5-mediated "Endothelial-Monocyte-Neutrophil" axis may contribute to the development of shear stress-induced AD. These findings provide possible therapeutic targets for the prevention and treatment of AD. Show less
📄 PDF DOI: 10.1016/j.heliyon.2023.e23312
ANGPTL4

RNA Sequencing Reveals That the Genes Related to Cell Cycle and Glycolysis Play an Essential Role in IL-27-Induced Keratinocyte Hyperproliferation.

Zijun Wu, Ruijing Wang, Yuanjun Liu +2 more · 2024 · Journal of inflammation research · added 2026-04-24
Psoriasis is characterized by accelerated proliferation of epidermal keratinocytes. IL-27 is relevant to psoriasis pathogenesis. We previously found that IL-27 stimulates the proliferation of keratino Show more
Psoriasis is characterized by accelerated proliferation of epidermal keratinocytes. IL-27 is relevant to psoriasis pathogenesis. We previously found that IL-27 stimulates the proliferation of keratinocytes. However, the mRNAs involved in the process have not been fully studied. This study aims to identify potential pathways and hub genes associated with proliferation in keratinocytes with IL-27 intervention by bioinformatics analysis. The mRNA expression profiles from HaCaT cells with or without IL-27 treated were analyzed by bioinformatics tools. The protein-protein interaction (PPI) network was constructed to screen gene clusters and hub genes associated with proliferation. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to identify the function of the mRNAs. The GEO database and quantitative real-time PCR (qPCR) were used to verify the expression levels of hub genes in psoriatic skin lesions and IL-27-treated psoriasiform keratinocytes, respectively. We found 1257 differentially expressed genes and screened 2 crucial gene clusters. GO analysis revealed that Cluster 1 was mainly enriched in "Mitotic sister chromatid segregation" and "Spindle". Cluster 2 was mainly enriched in the "Pyruvate metabolic process" and "Oxidoreductase complex". KEGG analysis showed that Cluster 1 and Cluster 2 were mainly enriched in "Cell cycle" and "Glycolysis/Gluconeogenesis", respectively. We then identified 6 hub genes enriched in the two pathways, including IL-27 possibly promotes glycolysis, mitochondrial oxidative phosphorylation, and cell cycle progression in keratinocytes. Additionally, we identified Show less
📄 PDF DOI: 10.2147/JIR.S481835
IL27

Genetically predicted gene expression effects on changes in red blood cell and plasma polyunsaturated fatty acids.

Nikhil K Khankari, Timothy Su, Qiuyin Cai +8 more · 2024 · medRxiv : the preprint server for health sciences · Cold Spring Harbor Laboratory · added 2026-04-24
Polyunsaturated fatty acids (PUFAs) including omega-3 and omega-6 are obtained from diet and can be measured objectively in plasma or red blood cells (RBCs) membrane biomarkers, representing different Show more
Polyunsaturated fatty acids (PUFAs) including omega-3 and omega-6 are obtained from diet and can be measured objectively in plasma or red blood cells (RBCs) membrane biomarkers, representing different dietary exposure windows. Show less
no PDF DOI: 10.1101/2024.12.17.24319171
FADS1

Human Conjunctival Transcriptome in Acanthamoeba Keratitis: An Exploratory Study.

Gerami D Seitzman, Jeremy D Keenan, Thomas M Lietman +11 more · 2024 · Cornea · added 2026-04-24
The purpose of this study was to identify conjunctival transcriptome differences in patients with Acanthamoeba keratitis compared with keratitis with no known associated pathogen. The host conjunctiva Show more
The purpose of this study was to identify conjunctival transcriptome differences in patients with Acanthamoeba keratitis compared with keratitis with no known associated pathogen. The host conjunctival transcriptome of 9 patients with Acanthamoeba keratitis (AK) is compared with the host conjunctival transcriptome of 13 patients with pathogen-free keratitis. Culture and/or confocal confirmed Acanthamoeba in 8 of 9 participants with AK who underwent metagenomic RNA sequencing as the likely pathogen. Cultures were negative in all 13 cases where metagenomic RNA sequencing did not identify a pathogen. Transcriptome analysis identified 36 genes differently expressed between patients with AK and patients with presumed sterile, or pathogen-free, keratitis. Gene enrichment analysis revealed that some of these genes participate in several biologic pathways important for cellular signaling, ion transport and homeostasis, glucose transport, and mitochondrial metabolism. Notable relatively differentially expressed genes with potential relevance to Acanthamoeba infection included CPS1 , SLC35B4 , STEAP2 , ATP2B2 , NMNAT3 , and AKAP12 . This research suggests that the local transcriptome in Acanthamoeba keratitis may be sufficiently robust to be detected in the conjunctiva and that corneas infected with Acanthamoeba may be distinguished from the inflamed cornea where no pathogen was identified. Given the low sensitivity for corneal cultures, identification of differentially expressed genes may serve as a suggestive transcriptional signature allowing for a complementary diagnostic technique to identify this blinding parasite. Knowledge of differentially expressed genes may also direct investigation of disease pathophysiology and suggest novel pathways for therapeutic targets. Show less
📄 PDF DOI: 10.1097/ICO.0000000000003545
CPS1

Development and validation of a nutrition-related genetic-clinical-radiological nomogram associated with behavioral and psychological symptoms in Alzheimer's disease.

Jiwei Jiang, Yaou Liu, Anxin Wang +11 more · 2024 · Chinese medical journal · added 2026-04-24
Few evidence is available in the early prediction models of behavioral and psychological symptoms of dementia (BPSD) in Alzheimer's disease (AD). This study aimed to develop and validate a novel genet Show more
Few evidence is available in the early prediction models of behavioral and psychological symptoms of dementia (BPSD) in Alzheimer's disease (AD). This study aimed to develop and validate a novel genetic-clinical-radiological nomogram for evaluating BPSD in patients with AD and explore its underlying nutritional mechanism. This retrospective study included 165 patients with AD from the Chinese Imaging, Biomarkers, and Lifestyle (CIBL) cohort between June 1, 2021, and March 31, 2022. Data on demographics, neuropsychological assessments, single-nucleotide polymorphisms of AD risk genes, and regional brain volumes were collected. A multivariate logistic regression model identified BPSD-associated factors, for subsequently constructing a diagnostic nomogram. This nomogram was internally validated through 1000-bootstrap resampling and externally validated using a time-series split based on the CIBL cohort data between June 1, 2022, and February 1, 2023. Area under receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical applicability of the nomogram. Factors independently associated with BPSD were: CETP rs1800775 (odds ratio [OR] = 4.137, 95% confidence interval [CI]: 1.276-13.415, P  = 0.018), decreased Mini Nutritional Assessment score (OR = 0.187, 95% CI: 0.086-0.405, P  <0.001), increased caregiver burden inventory score (OR = 8.993, 95% CI: 3.830-21.119, P  <0.001), and decreased brain stem volume (OR = 0.006, 95% CI: 0.001-0.191, P  = 0.004). These variables were incorporated into the nomogram. The area under the ROC curve was 0.925 (95% CI: 0.884-0.967, P  <0.001) in the internal validation and 0.791 (95% CI: 0.686-0.895, P  <0.001) in the external validation. The calibration plots showed favorable consistency between the prediction of nomogram and actual observations, and the DCA showed that the model was clinically useful in both validations. A novel nomogram was established and validated based on lipid metabolism-related genes, nutritional status, and brain stem volumes, which may allow patients with AD to benefit from early triage and more intensive monitoring of BPSD. Chictr.org.cn , ChiCTR2100049131. Show less
📄 PDF DOI: 10.1097/CM9.0000000000002914
CETP

ANGPTL4 regulates ovarian cancer progression by activating the ERK1/2 pathway.

Jiaqi Xu, Fei Wu, Yue Zhu +8 more · 2024 · Cancer cell international · BioMed Central · added 2026-04-24
Ovarian cancer (OC) has the highest mortality rate among all gynecological malignancies. A hypoxic microenvironment is a common feature of solid tumors, including ovarian cancer, and an important driv Show more
Ovarian cancer (OC) has the highest mortality rate among all gynecological malignancies. A hypoxic microenvironment is a common feature of solid tumors, including ovarian cancer, and an important driving factor of tumor cell survival and chemo- and radiotherapy resistance. Previous research identified the hypoxia-associated gene angiopoietin-like 4 (ANGPTL4) as both a pro-angiogenic and pro-metastatic factor in tumors. Hence, this work aimed to further elucidate the contribution of ANGPTL4 to OC progression. The expression of hypoxia-associated ANGPTL4 in human ovarian cancer was examined by bioinformatics analysis of TCGA and GEO datasets. The CIBERSORT tool was used to analyze the distribution of tumor-infiltrating immune cells in ovarian cancer cases in TCGA. The effect of ANGPTL4 silencing and overexpression on the proliferation and migration of OVCAR3 and A2780 OC cells was studied in vitro, using CCK-8, colony formation, and Transwell assays, and in vivo, through subcutaneous tumorigenesis assays in nude mice. GO enrichment analysis and WGCNA were performed to explore biological processes and genetic networks associated with ANGPTL4. The results obtained were corroborated in OC cells in vitro by western blotting. Screening of hypoxia-associated genes in OC-related TCGA and GEO datasets revealed a significant negative association between ANGPTL4 expression and patient survival. Based on CIBERSORT analysis, differential representation of 14 distinct tumor-infiltrating immune cell types was detected between low- and high-risk patient groups. Silencing of ANGPTL4 inhibited OVCAR3 and A2780 cell proliferation and migration in vitro and reduced the growth rate of xenografted OVCAR3 cells in vivo. Based on results from WGCNA and previous studies, western blot assays in cultured OC cells demonstrated that ANGPTL4 activates the Extracellular signal-related kinases 1 and 2 (ERK1/2) pathway and this results in upregulation of c-Myc, Cyclin D1, and MMP2 expression. Suggesting that the above mechanism mediates the pro-oncogenic actions of ANGPTL4T in OC, the pro-survival effects of ANGPTL4 were largely abolished upon inhibition of ERK1/2 signaling with PD98059. Our work suggests that the hypoxia-associated gene ANGPTL4 stimulates OC progression through activation of the ERK1/2 pathway. These findings may offer a new prospect for targeted therapies for the treatment of OC. Show less
📄 PDF DOI: 10.1186/s12935-024-03246-z
ANGPTL4

Unveiling the potential of estrogen: Exploring its role in neuropsychiatric disorders and exercise intervention.

Wenke He, Sen Zhang, Zhengtang Qi +1 more · 2024 · Pharmacological research · Elsevier · added 2026-04-24
Neuropsychiatric disorders shorten human life spans through multiple ways and become major threats to human health. Exercise can regulate the estrogen signaling, which may be involved in depression, A Show more
Neuropsychiatric disorders shorten human life spans through multiple ways and become major threats to human health. Exercise can regulate the estrogen signaling, which may be involved in depression, Alzheimer's disease (AD) and Parkinson's disease (PD), and other neuropsychiatric disorders as well in their sex differences. In nervous system, estrogen is an important regulator of cell development, synaptic development, and brain connectivity. Therefore, this review aimed to investigate the potential of estrogen system in the exercise intervention of neuropsychiatric disorders to better understand the exercise in neuropsychiatric disorders and its sex specific. Exercise can exert a protective effect in neuropsychiatric disorders through regulating the expression of estrogen and estrogen receptors, which are involved in neuroprotection, neurodevelopment, and neuronal glucose homeostasis. These processes are mediated by the downstream factors of estrogen signaling, including N-myc downstream regulatory gene 2 (Ndrg2), serotonin (5-HT), delta like canonical Notch ligand 1 (DLL1), NOD-like receptor thermal protein domain associated protein 3 (NLRP3), etc. In addition, exercise can act on the estrogen response element (ERE) fragment in the genes of estrogenic downstream factors like β-amyloid precursor protein cleavase 1 (BACE1). However, there are few studies on the relationship between exercise, the estrogen signaling pathway, and neuropsychiatric disorders. Hence, we review how the estrogen signaling mediates the mechanism of exercise intervention in neuropsychiatric disorders. We aim to provide a theoretical perspective for neuropsychiatric disorders affecting female health and provide theoretical support for the design of exercise prescriptions. Show less
no PDF DOI: 10.1016/j.phrs.2024.107201
BACE1

Integrating tertiary lymphoid structure-associated genes into computational models to evaluate prognostication and immune infiltration in pancreatic cancer.

Ying Ma, Xuesong Li, Jin Zhang +6 more · 2024 · Journal of leukocyte biology · Oxford University Press · added 2026-04-24
Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor response to all therapeutic modalities and dismal prognosis. The presence of tertiary lymphoid structures (TLSs) in various solid cance Show more
Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor response to all therapeutic modalities and dismal prognosis. The presence of tertiary lymphoid structures (TLSs) in various solid cancers is of crucial prognostic significance, highlighting the intricate interplay between the tumor microenvironment and immune cells aggregation. However, the extent to which TLSs and immune status affect PDAC prognosis remains incompletely understood. Here, we sought to unveil the unique properties of TLSs in PDAC by leveraging both single-cell and bulk transcriptomics, culminating in a risk model that predicts clinical outcomes. We used TLS scores based on a 12-gene (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13) and 9-gene (PTGDS, RBP5, EIF1AY, CETP, SKAP1, LAT, CCR6, CD1D, and CD79B) signature, respectively, and examined their distribution in cell clusters of single-cell data from PDAC samples. The markers involved in these clusters were selected to develop a prognostic model using The Cancer Genome Atlas Program database as the training cohort and Gene Expression Omnibus database as the validation cohort. Further, we compared the immune infiltration, drug sensitivity, and enriched and differentially expressed genes between the high- and low-risk groups in our model. Therefore, we established a risk model that has significant implications for the prognostic assessment of PADC patients with remarkable differences in immune infiltration and chemosensitivity between the low- and high-risk groups. This paradigm established by TLS-related cell marker genes provides a prognostic prediction and a panel of novel therapeutic targets for exploring potential immunotherapy. Show less
no PDF DOI: 10.1093/jleuko/qiae067
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