👤 Jiantao Liu

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3182
Articles
1983
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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, 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

Bidirectional causal associations between plasma metabolites and bipolar disorder.

Qian Zhao, Ancha Baranova, Dongming Liu +2 more · 2025 · Molecular psychiatry · Nature · added 2026-04-24
Altered levels of human plasma metabolites have been implicated in the etiology of bipolar disorder (BD). However, the causality between metabolites and the disease was not well described. We performe Show more
Altered levels of human plasma metabolites have been implicated in the etiology of bipolar disorder (BD). However, the causality between metabolites and the disease was not well described. We performed a bidirectional metabolome-wide Mendelian randomization (MR) analysis to evaluate the potential causal relationships between 871 plasma metabolites and BD. We used DrugBank and ChEMBL to evaluate whether related metabolites are potential therapeutic targets. Finally, Bayesian colocalization analysis was performed to identify shared genomic loci BD and identified metabolites. Our MR results showed that six metabolites were significantly associated with a reduced risk of BD, including arachidonate (20:4n6) (OR: 0.90, 95% CI: 0.84-0.95) and sphingomyelin (d18:2/24:1, d18:1/24:2) (OR: 0.92, 95% CI: 0.87-0.96), while five metabolites were significantly associated with an increased risk of BD, including 1-palmitoyl-2-linoleoyl-GPE (16:0/18:2) (OR: 1.09, 95% CI: 1.05-1.13). However, our reverse MR analysis showed that BD was not associated with the levels of any metabolite. Additionally, the leave-one-out analysis revealed SNPs within chromosome 11 loci harboring MYRF, FADS1, and FADS2 as ones with the potential to influence partial causal effects. Druggability evaluation showed that 10 of the BD-related metabolites, such as sphingomyelin and cytidine, have been targeted by pharmacologic intervention. Colocalization analysis highlighted one colocalized region (chromosome 11q12) shared by 11 metabolites and BD and pointed to some genes as possible players, including FADS1, FADS2, FADS3, and SYT7. Our study supported a causal role of plasma metabolites in the susceptibility to BD, and the identified metabolites may provide a new avenue for the prevention and treatment of BD. Show less
📄 PDF DOI: 10.1038/s41380-025-02977-3
FADS1

3D printing combined with thermally induced phase separation for engineering hierarchical osteogenic PLA scaffolds.

Xinyi Yun, Ziyue Li, Zi Yan +13 more · 2025 · Materials today. Bio · Elsevier · added 2026-04-24
Accelerated population aging and rising incidence of bone defects have intensified the need for advanced bone regeneration strategies. While tissue-engineered scaffolds fabricated via 3D printing offe Show more
Accelerated population aging and rising incidence of bone defects have intensified the need for advanced bone regeneration strategies. While tissue-engineered scaffolds fabricated via 3D printing offer promising alternatives to conventional grafts, most techniques fail to replicate the multi-scale fibrous architecture of native bone extracellular matrix, limiting their biofunctionality. To address this, we developed a hybrid manufacturing strategy integrating low-temperature thermally induced phase separation with extrusion-based 3D printing of polylactic acid (PLA) scaffolds. By optimizing solvent ratios (THF: DMF = 3:1) and freezing temperatures (-196 °C-4 °C), we produced scaffolds with tunable micro-nano fibrous surfaces and macroporous structures. Key findings revealed that scaffolds processed at -196 °C (PLA-196) exhibited the highest porosity (pore size: 6.01 ± 2.06 μm), superior hydrophilicity, and enhanced compressive modulus. These scaffolds significantly promoted BMSC adhesion, proliferation, and osteogenic differentiation via activation of Show less
📄 PDF DOI: 10.1016/j.mtbio.2025.102621
MACF1

Transcriptome analysis of postnatal mouse cardiac tissue growth and development.

Xiao-Cong Zhu, Sheng-Nan Wang, Lin Jiang +1 more · 2025 · Yi chuan = Hereditas · added 2026-04-24
Postnatal cardiac function in mammals is closely associated with cardiomyocyte proliferation and hypertrophy. However, the molecular mechanisms regulating cardiomyocyte proliferation and hypertrophy h Show more
Postnatal cardiac function in mammals is closely associated with cardiomyocyte proliferation and hypertrophy. However, the molecular mechanisms regulating cardiomyocyte proliferation and hypertrophy have not yet been fully elucidated. Therefore, phenotypic measurements and transcriptomic sequencing were performed on myocardial tissues from 7-day-old (P7) and 3-month-old (3m) female C57BL/6 mice to investigate changes in cardiomyocytes during growth and development and to identify key genes regulating myocardial growth and development. In comparison to 7-day-old mice, 3-month-old mice exhibited a significant increase in heart weight ( Show less
no PDF DOI: 10.16288/j.yczz.24-328
HEY2

FGF22 Secreted by Hair Papilla Cells Regulates Hair Follicle Stem Cell Proliferation and Differentiation.

Yu Luo, Tong Xiao, Binpeng Xi +5 more · 2025 · Biomolecules · MDPI · added 2026-04-24
Hair follicle stem cells (HFSCs) are resident stem cells within hair follicles (HFs) that possess self-renewal and differentiation capacities, serving as a critical model for regenerative medicine res Show more
Hair follicle stem cells (HFSCs) are resident stem cells within hair follicles (HFs) that possess self-renewal and differentiation capacities, serving as a critical model for regenerative medicine research. Their dynamic interaction with dermal papilla cells (DPCs) plays a decisive role in HF development and cycling. Show less
📄 PDF DOI: 10.3390/biom15111560
FGFR1

Single-cell transcriptomic profiling of heart reveals ANGPTL4 linking fibroblasts and angiogenesis in heart failure with preserved ejection fraction.

Guoxing Li, Huilin Zhao, Zhe Cheng +3 more · 2025 · Journal of advanced research · Elsevier · added 2026-04-24
Despite the high morbidity and mortality, the effective therapies for heart failure with preserved fraction (HFpEF) are limited as the poor understand of its pathophysiological basis. This study was a Show more
Despite the high morbidity and mortality, the effective therapies for heart failure with preserved fraction (HFpEF) are limited as the poor understand of its pathophysiological basis. This study was aimed to characterize the cellular heterogeneity and potential mechanisms of HFpEF at single-cell resolution. An HFpEF mouse model was induced by a high-fat diet with N-nitro-L-arginine methyl ester. Cells from the hearts were subjected to single-cell sequencing. The key protein expression was measured with Immunohistochemistry and immunofluorescence staining. In HFpEF hearts, myocardial fibroblasts exhibited higher levels of fibrosis. Furthermore, an increased number of fibroblasts differentiated into high-metabolism and high-fibrosis phenotypes. The expression levels of genes encoding certain pro-angiogenic secreted proteins were decreased in the HFpEF group, as confirmed by bulk RNA sequencing. Additionally, the proportion of the endothelial cell (EC) lineages in the HFpEF group was significantly downregulated, with low angiogenesis and high apoptosis phenotypes observed in these EC lineages. Interestingly, the fibroblasts in the HFpEF heart might cross-link with the EC lineages via over-secretion of ANGPTL4, thus displaying an anti-angiogenic function. Immunohistochemistry and immunofluorescence staining then revealed the downregulation of vascular density and upregulation of ANGPTL4 expression in HFpEF hearts. Finally, we predicted ANGPTL4as a potential druggable target using DrugnomeAI. In conclusion, this study comprehensively characterized the angiogenesis impairment in HFpEF hearts at single-cell resolution and proposed that ANGPTL4 secretion by fibroblasts may be a potential mechanism underlying this angiogenic abnormality. Show less
📄 PDF DOI: 10.1016/j.jare.2024.02.006
ANGPTL4

Aqueous Extract of

Andong Wu, Jiayi Dong, Jiankun Liu +10 more · 2025 · Nutrients · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/nu18010021
APOE

Relationship Between Chronic Pain and Breast Cancer: Insight From Genetic Correlation Analyses and 2-Sample Mendelian Randomization.

Haojie Yang, Xiaoyan Xie, Liling Lin +5 more · 2025 · Clinical breast cancer · Elsevier · added 2026-04-24
To evaluate potential genetic causal relationships between chronic pain subtypes like migraine and multi-site chronic pain (MCP) and their impact on breast cancer occurrence and survival rates. The as Show more
To evaluate potential genetic causal relationships between chronic pain subtypes like migraine and multi-site chronic pain (MCP) and their impact on breast cancer occurrence and survival rates. The association between chronic pain and breast cancer was reported before, yet the causal nature between them remained uncertain. Data on chronic pain and breast cancer were sourced from publicly available European genome-wide association study (GWAS) datasets. Genetic association between chronic pain and breast cancer phenotypes was assessed using linkage disequilibrium genetic correlation (LDSC). Colocalization analysis further identified potential shared causal variation. Based on Inverse variance weighted method, 2-sample Mendelian Randomization (MR) was conducted to investigate causal associations between migraine, MCP, and breast cancer or breast cancer survival. Sensitive analysis was conducted to ensure the absence of heterogeneity and horizontal pleiotropy. LDSC demonstrated significant genetic correlations between migraine and both estrogen receptor-negative (ER-) and overall breast cancer, while also revealing a notable genetic association between MCP and ER- and ER+ breast cancer, as well as overall breast cancer. Through colocalization analysis, potential involvement of rs2183271, located in MLLT10 gene, in regulating MCP and ER+ breast cancer was identified. MR analysis revealed the association between migraine and elevated risk of ER- breast cancer (IVW, P = 4.95 × 10 Our results provided new insights into the role of migraine and MCP in breast cancer, paving the way for targeted preventive strategies and future investigations. Show less
no PDF DOI: 10.1016/j.clbc.2025.02.004
MLLT10

Exercise-Induced Changes in Brain-Derived Neurotrophic Factor in Neurodegenerative Diseases: A Bayesian Network Meta-Analysis.

Zhuolin Tang, Mingyue Yin, Kai Xu +4 more · 2025 · Journal of geriatric psychiatry and neurology · SAGE Publications · added 2026-04-24
ObjectivesThis study aimed to compare the effects of different exercise interventions on brain-derived neurotrophic factor (BDNF) levels in patients with neurodegenerative diseases and to explore regu Show more
ObjectivesThis study aimed to compare the effects of different exercise interventions on brain-derived neurotrophic factor (BDNF) levels in patients with neurodegenerative diseases and to explore regulatory factors.MethodsSearched PubMed, Scopus, Web of Science Core Collection, CNKI and Cochrane Library databases up to March 15, 2025. Bayesian network meta-analysis was conducted using R software, and meta-regression analyzed the moderating effects of training period and frequency.Results42 randomized controlled trials covering 1482 patients were included. The Surface Under the Cumulative Ranking (SUCRA) indicated that stretching training (SUCRA = 78.92) and high-intensity interval training (SUCRA = 69.73) were ranked higher than other exercise modalities and exhibited more favorable effect on BDNF enhancement, although neither demonstrated statistically significant superiority over the blank control. In contrast, combined training (SUCRA = 35.58), aerobic training (SUCRA = 35.17), and resistance training (SUCRA = 12.98) showed relatively lower potential for BDNF enhancement (blank control SUCRA = 67.62). Meta-regression analysis showed that the effect of combined training was significantly and positively correlated with intervention period ( Show less
no PDF DOI: 10.1177/08919887251409415
BDNF bayesian network meta-analysis brain-derived neurotrophic factor exercise interventions meta-regression neurodegenerative diseases neuroscience neurotrophic factors

Molecular mechanisms of synovial pathology in osteoarthritis: insights from CXCL10 and MC4R expression.

Tao Yang, Hong Liu, Jian Chen · 2025 · Genes & genomics · Springer · added 2026-04-24
Osteoarthritis (OA) is a common progressive joint disorder marked by synovial inflammation, cartilage degeneration, the formation of osteophytes, though its underlying molecular mechanisms remain uncl Show more
Osteoarthritis (OA) is a common progressive joint disorder marked by synovial inflammation, cartilage degeneration, the formation of osteophytes, though its underlying molecular mechanisms remain unclear. This study integrated bioinformatics and experimental validation to identify key genes in OA synovium and their association with immune infiltration. Analysis of the GSE82107 dataset (10 OA, 7 controls) revealed 909 differentially expressed genes (525 upregulated, 384 downregulated). WGCNA identified the "midnightblue" module, and its intersection with DEGs yielded 122 genes enriched in cytokine-cytokine receptor interaction, JAK-STAT signaling, and autophagy pathways. Protein-protein interaction analysis highlighted FLT3LG, MC4R, CXCL10, CARTPT, and LHX2 as core genes (AUC 0.743-0.871). Immune infiltration analysis showed elevated M0 macrophages in OA, with CXCL10 showing a strong positive correlation with M1 macrophage infiltration (r = 0.74), and MC4R correlating with the presence of follicular helper T cells (r = 0.85). In vitro, OA-derived fibroblast-like synoviocytes exhibited CXCL10 upregulation, MC4R downregulation, and increased IL-6, IL-8, and TNF-α secretion, which were markedly reduced by CXCL10 knockdown or MC4R overexpression. Synovial tissue assays confirmed these expression patterns. CXCL10 and MC4R may represent promising diagnostic markers and therapeutic targets, offering new insights into OA immunopathogenesis and precision intervention. Show less
📄 PDF DOI: 10.1007/s13258-025-01679-y
MC4R

Exploring the Causal Relationships between Lipid Biomarkers and Anti-VEGF Treatment Response in Patients with Neovascular Age-related Macular Degeneration.

Feixiang He, Qifang Chen, Peilin Gu +4 more · 2025 · Ophthalmology science · Elsevier · added 2026-04-24
To identify the connections between lipid biomarkers and the anti-VEGF therapy response in patients with neovascular age-related macular degeneration (nAMD). A bidirectional and multivariable Mendelia Show more
To identify the connections between lipid biomarkers and the anti-VEGF therapy response in patients with neovascular age-related macular degeneration (nAMD). A bidirectional and multivariable Mendelian randomization study. The summary statistics for anti-VEGF nAMD treatment response included a total of 128 responders, 51 nonresponders, and 6 908 005 genetic variants available for analysis. The sample size of lipid biomarkers is 441 016 and 12 321 875 genetic variants available for analysis. Two-sample Mendelian randomization (MR) method was conducted to exhaustively appraise the causalities among 13 lipid biomarkers and the risk of different anti-VEGF treatment responses (including visual acuity [VA] and central retinal thickness [CRT]) for nAMD subtypes. Thirteen lipid biomarkers, VA, and CRT. A positive causal relationship was identified between triglycerides (TGs), apolipoproteins (Apos) E2, ApoE3, total cholesterol (TC), and VA response to anti-VEGF therapy in patients with nAMD, as confirmed by MR-Egger, weighted median, and weighted mode models. The MR-Egger model yielded statistically significant results for TC, ApoA-I, ApoB, and ApoA-V in relation to the CRT response to anti-VEGF treatment in patients with nAMD. In the reverse MR, the MR-Egger model identified significant causal relationships between ApoA-I, low-density lipoprotein cholesterol (LDL-c), ApoE3, and ApoF and the VA response. However, this was not the case in the weighted median and weighted mode models. In the MR-Egger model, ApoB, LDL-c, ApoE3, and ApoM were identified as significantly influencing the CRT response. In the multisample MR analysis, TC, high-density lipoprotein cholesterol, LDL-c, and TG were found to be causally related to VA response, and TC was also identified as being causally related to the CRT response to anti-VEGF therapy in patients with nAMD. This MR study suggests unidirectional causality between TG and ApoE3 and the response to anti-VEGF treatment in patients with nAMD. The author(s) have no proprietary or commercial interest in any materials discussed in this article. Show less
📄 PDF DOI: 10.1016/j.xops.2025.100711
APOB

Genome-wide analysis identifies susceptibility loci for heart failure and nonischemic cardiomyopathy subtype in the East Asian populations.

Yi Han, Yun Hong, Yan Gao +11 more · 2025 · PLoS genetics · PLOS · added 2026-04-24
Heart failure (HF) is a serious cardiovascular condition resulting from abnormalities in multiple biological processes, affecting over 64 million people worldwide. We sought to expand our understandin Show more
Heart failure (HF) is a serious cardiovascular condition resulting from abnormalities in multiple biological processes, affecting over 64 million people worldwide. We sought to expand our understanding of the genetic basis of HF and more specific NICM subtype in the East Asian populations and evaluate the biological pathways underlying subclinical left ventricular dysfunction. We conducted a meta-analysis of genome-wide association studies (GWAS) for all-cause HF in the East Asian populations (N cases ~ 13,385) and a more precise definition of nonischemic cardiomyopathy (NICM) subtype in multi-ancestry populations (N cases~3,603). We identified a low-frequency East-Asian enriched coding variant near MYBPC3 and a NICM specific locus. Follow up analyses demonstrated male-specific HF association at the MYBPC3 locus, and highlighted SVIL as a candidate causal gene for NICM. Moreover, we demonstrated that SVIL deficiency aggravated cardiomyocyte hypertrophy, apoptosis and impaired cell viability in phenylephrine (PE)-treated H9C2 cells. In addition, the gene expression level of B-type natriuretic peptide (BNP) which was deemed as a hallmark for HF was further elevated by SVIL silencing in PE-stimulated H9C2 cells. RNA-sequencing analysis of H9C2 cells revealed that the function of SVIL might be mediated through pathways relevant to regulation and differentiation of heart muscle. These results enhance our understanding of the genetic architecture of HF in the East Asian populations, and provide important insight into the biological pathways underlying NICM and sex-specific relevance of the MYBPC3 locus that warrants further replication in another datasets. Show less
📄 PDF DOI: 10.1371/journal.pgen.1011897
MYBPC3

Tracing the Genetic Heritage of the Kirgiz People: Dual-Wave Admixture and Ancestry-Biased Adaptation.

Shuanghui Chen, Yan Lu, Hao Chen +6 more · 2025 · Molecular biology and evolution · Oxford University Press · added 2026-04-24
The Kirgiz, a Turkic-speaking ethnic group with a rich nomadic heritage, represent a pivotal population for understanding human migration and adaptation in Central Asia. However, their genetic origins Show more
The Kirgiz, a Turkic-speaking ethnic group with a rich nomadic heritage, represent a pivotal population for understanding human migration and adaptation in Central Asia. However, their genetic origins and admixture history remain largely unexplored. Here, we present the first comprehensive genomic study of Kirgiz populations from Xinjiang, China (XJ.KGZ, n = 36) and their counterparts in Kyrgyzstan (KRG), integrating genome-wide data of 2,406 global individuals. Our analyses reveal four primary ancestry components in XJ.KGZ: East Asian (41.7%), Siberian (25.6%), West Eurasian (25.2%), and South Asian (7.6%). Despite close genetic affinity (FST = 0.13%), XJ.KGZ and KRG diverged ∼447 years ago, with limited gene flow post-split. A two-wave admixture model elucidates their demographic history: an initial East-West Eurasian mixture ∼2,225 years ago, likely reflecting west-east contacts during the period of the Warring States and the Qin Dynasty, followed by secondary admixture events (∼875 to 425 years ago) linked to historical migrations under Mongol and post-Mongol rule. Local adaptation signatures implicate genes critical for cellular tight junction (e.g. PATJ), pathogen invasion (e.g. OR14I1), and cardiac functions (e.g. RYR2) with allele frequency deviations suggesting ancestry-specific selection. While no classical high-altitude adaptation genes (e.g. EPAS1) showed selection signals, RYR2 and C10orf67-implicated in hypoxia response in Tibetan fauna-displayed Western ancestry bias, hinting at convergent adaptation mechanisms. This study advances our understanding of the genetic makeup and admixture history of the Kirgiz people and provides novel insights into human dispersal in Central Asia. Show less
no PDF DOI: 10.1093/molbev/msaf196
PATJ

Identification of SEC16B as a novel regulator of glucose homeostasis.

Ruo-Xin Zhang, An-Qi Li, Xin-Yuan Zhao +7 more · 2025 · Diabetologia · Springer · added 2026-04-24
Glucose homeostasis, essential for metabolic health, requires coordinated insulin and glucagon activity to maintain blood glucose balance. Dysregulation of glucose homeostasis causes hyperglycaemia an Show more
Glucose homeostasis, essential for metabolic health, requires coordinated insulin and glucagon activity to maintain blood glucose balance. Dysregulation of glucose homeostasis causes hyperglycaemia and glucose intolerance, hallmark features of type 2 diabetes. While SEC16 homologue B (SEC16B), an endoplasmic reticulum export factor, has been linked to obesity, type 2 diabetes and lipid metabolism, its role in glucose regulation remains poorly defined. This study aims to investigate SEC16B's contribution to glucose homeostasis by systematically dissecting its conserved physiological mechanisms across species. To interrogate SEC16B's role, we combined Drosophila genetics (RNA interference-mediated dSec16 knockdown) with murine models (Sec16b deletion) under standard or high-fat diet conditions. Glucose and insulin tolerance tests assessed glucose homeostasis. Mechanistic insights into beta cell dysfunction were derived from immunostaining, glucose-stimulated insulin secretion assays and RNA-seq profiling of murine pancreatic islets. Both disruption of dSec16 in Drosophila and Sec16b deletion in mice triggered glucose intolerance under standard diet conditions, recapitulating conserved metabolic dysfunction. In addition, Sec16b loss impaired glycaemic control in mice fed a high-fat diet. Mechanistically, Sec16b deficiency impairs insulin secretion by downregulating cholinergic signalling and compromising intracellular Ca Our study reveals SEC16B, a genome-wide association study-identified obesity risk gene, as an evolutionarily conserved regulator of glucose homeostasis. By linking SEC16B to cholinergic-driven insulin secretion and calcium dynamics, we resolve a mechanistic gap in beta cell dysfunction and metabolic disease. This finding provides novel insights into the mechanisms underlying glucose homeostasis and may enhance our understanding of potential treatments for metabolic diseases. Show less
no PDF DOI: 10.1007/s00125-025-06501-8
SEC16B

Branched-Chain α Keto-Acid Dehydrogenase Kinase-Mediated AKT Phosphorylation Promotes RCC Tumorigenesis and Drug Resistance.

Qin Tian, Jinxiang Wang, Qiji Li +16 more · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Advanced renal cell carcinoma (RCC) primarily relies on targeted and immune-based therapies, yet these treatments often face limitations due to inefficacy and drug resistance. Branched-chain α-keto-ac Show more
Advanced renal cell carcinoma (RCC) primarily relies on targeted and immune-based therapies, yet these treatments often face limitations due to inefficacy and drug resistance. Branched-chain α-keto-acid dehydrogenase kinase (BCKDK) has been implicated in promoting RCC metastasis, but its specific substrates and the mechanisms underlying its regulation of RCC progression remain poorly understood. This study uncovers a novel mechanism whereby BCKDK-mediated AKT phosphorylation drives RCC tumorigenesis and drug resistance. Elevated BCKDK expression correlates with poor prognosis in RCC clinical samples. BCKDK deficiency inhibits RCC cell proliferation and tumorigenesis both in vitro and in vivo. Mechanistic investigations reveal that BCKDK directly binds to and regulates the phosphorylation of AKT. BCKDK-mediated phosphorylation of AKT decreases ubiquitin-mediated AKT protein degradation, and promotes tumorigenesis via activation of the AKT/mTOR signaling pathway. RNA sequencing identifies BCKDK's involvement in the drug metabolism network and apoptotic signaling pathways. The BCKDK/AKT/ABCB1 axis mediates doxorubicin resistance. Targeting BCKDK/AKT inhibits the growth of RCC patient-derived organoids (PDOs), enhances doxorubicin-induced apoptosis in RCC cells, and suppresses tumor growth in vivo. These findings identify a previously unrecognized phosphorylation substrate of BCKDK and highlight the critical role of the BCKDK/AKT signaling axis in RCC progression, offering a promising target for therapeutic intervention. Show less
📄 PDF DOI: 10.1002/advs.202411081
BCKDK

Dietary Se-enrich Cardamine violifolia supplementation decreases lipid deposition and improves antioxidant status in the liver of aging laying hens.

Mengke Yan, Xin Cong, Hui Wang +7 more · 2025 · Poultry science · Elsevier · added 2026-04-24
Aging-related lipid metabolic disorder is related to oxidative stress. Selenium (Se)-enriched Cardamine violifolia (SEC) is known for its excellent antioxidant function. The objective of this study wa Show more
Aging-related lipid metabolic disorder is related to oxidative stress. Selenium (Se)-enriched Cardamine violifolia (SEC) is known for its excellent antioxidant function. The objective of this study was to evaluate the effects of SEC on antioxidant capacity and lipid metabolism in the liver of aged laying hens. A total of 450 sixty-five-wk-old Roman laying hens were randomly divided into 5 treatments: a basal diet (without Se supplementation, CON) and basal diets supplemented with 0.3 mg/kg Se from sodium selenite (SS), 0.3 mg/kg Se from Se-enriched yeast (SEY), 0.3 mg/kg Se from SEC (SEC), or 0.3 mg/kg Se from SEC and 0.3 mg/kg Se from SEY (SEC + SEY). The experiment lasted for 8 wk. The results showed that dietary SEC + SEY supplementation decreased (P < 0.05) triglyceride (in the plasma and liver) and total cholesterol levels (in the plasma), and increased (P < 0.05) HDL-C concentration in plasma compared to CON diet. Compared with CON diet, SEC and/or SEY supplementation decreased (P < 0.05) the mRNA expression of hepatic ACC, FAS and HMGCR, and increased (P < 0.05) PPARα, VTG-II, Apo-VLDL II and ApoB expression. Dietary SEC + SEY and SEY supplementation increased (P < 0.05) Se content in egg yolk and breast muscle compared to CON diet. Dietary SEC, SEY or SEC + SEY supplementation increased (P < 0.05) the activity of antioxidant enzymes (GSH-PX, T-AOC and T-SOD) in the plasma and liver and decreased (P < 0.05) MDA content in the plasma compared to CON diet. Dietary Se supplementation promoted (P < 0.05) mRNA expression of Nrf2 in the liver. In contrast, dietary SEY and SEC supplementation resulted in a decrease (P < 0.05) of hepatic Keap1 mRNA expression compared to CON diet. Dietary SEC + SEY and/or SEC supplementation increased (P < 0.05) mRNA expression of Selenof, GPX1 and GPX4 in the liver compared with CON diet. In conclusion, dietary SEC (0.3 mg/kg Se) or SEC (0.3 mg/kg Se) + SEY (0.3 mg/kg Se) improved the antioxidant capacity and the lipid metabolism in the liver of aged laying hens, which might be associated with regulating Nrf2/Keap1 signaling pathway. Show less
📄 PDF DOI: 10.1016/j.psj.2024.104620
APOB

Multi-Angle Bioactivity Cartography for Computational Screening and Mechanistic Analysis of AChE Inhibitors From Yellow Gastrodia elata.

Ruijun Sun, Yuchi Zhang, Jingying Xu +7 more · 2025 · Archiv der Pharmazie · Wiley · added 2026-04-24
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-se Show more
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-serving as promising reservoirs of such inhibitors. Nevertheless, comprehensive screening and mechanistic characterization of their inhibitory potential remain limited. This study sought to identify potent AChE inhibitors from YGE, investigate their mechanisms of action, and assess their therapeutic prospects for AD. Methodologically, an integrated approach was employed, combining ultrafiltration-liquid chromatography (UF-LC) for rapid inhibitor screening, molecular docking and dynamics simulations for mechanistic insight, two-stage high-speed countercurrent chromatography for compound isolation, enzyme kinetics to delineate inhibition modalities, and network pharmacology to uncover relevant AD-related targets. The findings identified seven active constituents with notable AChE inhibition, among which parishins A and G were obtained at high purity (98.26% and 97.26%, respectively) and exhibited mixed-type inhibition with low IC Show less
no PDF DOI: 10.1002/ardp.70174
BACE1

Targeting VSMCs to suppress macrophage-like phenotype switching that restrains atherogenesis by immunosuppressive oligodeoxynucleotide (A151) functionalized selenium nanoparticles.

Jingru Wang, Bo Yao, Yutian Zhang +13 more · 2025 · Journal of nanobiotechnology · BioMed Central · added 2026-04-24
Macrophage-like phenotype switching of vascular smooth muscle cells (VSMCs) is a crucial mechanism driving atherogenesis. Inhibition of a phenotype switch to macrophage-like cells is a promising strat Show more
Macrophage-like phenotype switching of vascular smooth muscle cells (VSMCs) is a crucial mechanism driving atherogenesis. Inhibition of a phenotype switch to macrophage-like cells is a promising strategy to prevent atherosclerosis (AS), and targeted nanotherapeutics represent one approach for implementing this strategy. To this end, we designed immunosuppressive oligodeoxynucleotide A151 functionalized selenium nanoparticles with a spearhead LacNAc (LN-A151-SeNPs) that target macrophage-like VSMCs. Nano characterization showed that the uniformity and stability of nanoparticles were optimized by modification with LacNAc and A151, resulting in an average diameter of 88.90 ± 1.45 nm, Zeta potentials of -21.1 ± 1.5 mV, a A151:Se molar ratio of 1:60 and mass ratio of 1.68:1. The effects of LN-A151-SeNPs on inhibiting VSMCs phenotype switching and attenuation of AS were investigated using [Image: see text] The online version contains supplementary material available at 10.1186/s12951-025-03925-7. Show less
📄 PDF DOI: 10.1186/s12951-025-03925-7
APOE

Comorbidity patterns and immune-metabolic differences in patients with acute-episode of schizophrenia spectrum disorders.

Guoping Wu, Zhe Dong, Zhongcai Li +12 more · 2025 · Schizophrenia (Heidelberg, Germany) · Nature · added 2026-04-24
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause Show more
Patients with schizophrenia (SCZ) face multiple health challenges due to the complication of chronic diseases and psychiatric disorders. Among these, cardiovascular comorbidities are the leading cause of their life expectancy being 15-20 years shorter than that of the general population. Identifying comorbidity patterns and uncovering differences in immune and metabolic function are crucial steps toward improving prevention and management strategies. A retrospective cross-sectional study was conducted using electronic medical records of inpatients discharged between 2015 and 2024 from a municipal psychiatric hospital in China. The study included patients diagnosed with Schizophrenia, Schizotypal, and Delusional Disorders (SSDs) (ICD-10: F20-F29). Comorbidity patterns were identified through latent class analysis (LCA) based on the 20 most common comorbid conditions among SSD patients. To investigate differences in peripheral blood metabolic and immune function, linear regression or generalized linear models were applied to 44 laboratory test indicators collected during the acute episode. The Benjamini-Hochberg method was used for p-value correction, and the false discovery rate (FDR) was calculated, with statistical significance set at FDR < 0.05. Among 3,697 inpatients with SSDs, four distinct comorbidity clusters were identified: SSDs only (Class 1), High-Risk Metabolic Multisystem Disorders (Class 2, n = 39), Low-Risk Metabolic Multisystem Disorders (Class 3, n = 573), and Sleep Disorders (Class 4, n = 205). Compared to Class 1, Class 2 exhibited significantly elevated levels of apolipoprotein A (ApoA; β = 90.62), apolipoprotein B (ApoB; β = 0.181), mean platelet volume (MPV; β = 0.994), red cell distribution width-coefficient of variation (RDW-CV; β = 1.182), antistreptolysin O (ASO; β = 276.80), and absolute lymphocyte count (ALC; β = 0.306), along with reduced apolipoprotein AI (ApoAI; β = -0.173) and hematocrit (HCT; β = -35.13). Class 3 showed moderate increases in low-density lipoprotein cholesterol (LDL-C; β = 0.113), MPV (β = 0.267), white blood cell count (WBC; β = 0.476), and absolute neutrophil count (ANC; β = 0.272), with decreased HCT (β = -9.81). Class 4 was characterized by elevated aggregate index of systemic inflammation (AISI; β = 81.07), neutrophil-to-lymphocyte ratio (NLR; β = 0.465), and systemic inflammation response index (SIRI; β = 0.346), indicating a heightened inflammatory state. The comorbidity patterns of patients with SCZ can be distinctly classified. During the acute episode, those with comorbid metabolic disorders exhibit a higher risk of cardiovascular diseases and immune system abnormalities, while patients with comorbid sleep disorders present a pronounced systemic inflammatory state and immune dysfunction. This study provides a basis for the chronic disease management and anti-inflammatory treatment, while also offering objective biomarker insights for transdiagnostic research. Show less
📄 PDF DOI: 10.1038/s41537-025-00646-6
APOB

POU6F2 promotes liver metastasis of gastric adenocarcinoma by dual mechanism of transcriptional upregulation of SNAI1 and IGF2/PI3K/AKT signaling-induced conversion of hepatic stellate cells into cancer-associated fibroblasts.

Chunxiao Yang, Zhiqing Gao, Ruiming Tang +16 more · 2025 · British journal of cancer · Nature · added 2026-04-24
Activation of cancer-associated fibroblasts (CAFs) plays an important role in tumor metastasis. The purpose of this study is to investigate the role of POU6F2 in conversion of hepatic stellate cells ( Show more
Activation of cancer-associated fibroblasts (CAFs) plays an important role in tumor metastasis. The purpose of this study is to investigate the role of POU6F2 in conversion of hepatic stellate cells (HSCs) into CAFs in liver metastasis of gastric adenocarcinoma (GAC). POU6F2 expression was examined by real-time PCR, Western blot and immunohistochemical staining. The functional roles of POU6F2 in GAC liver metastasis were investigated both cellular experiments in vitro and in vivo using a mouse model of subcutaneous splenic injection. ChIP and ELISA assays were used to explore the underlying molecular mechanism of POU6F2 in liver metastasis of GAC. Here we reported that POU6F2 was upregulated in GAC tissue with liver metastasis, which predicted poor early liver metastasis. Upregulating POU6F2 promoted EMT, invasion and migration of GAC cells in vitro, and the liver metastasis of GAC cells in vivo. Mechanic investigation further revealed that upregulating POU6F2 promoted the invasion and metastasis of GAC by transcriptional upregulation of EMT-inducer SNAI1, and promoting the conversion of HSCs into CAFs dependent on transcriptional upregulation of IGF2-induced activation of PI3K/AKT signaling. Our findings uncover a novel dual mechanism by which POU6F2 promotes liver metastasis of GAC. Show less
no PDF DOI: 10.1038/s41416-025-03017-1
SNAI1

Social Bonds Retain Oxytocin-Mediated Brain-Liver Axis to Retard Atherosclerosis.

Seien Ko, Atsushi Anzai, Xueyuan Liu +15 more · 2025 · Circulation research · added 2026-04-24
Social interaction with others is essential to life. Although social isolation and loneliness have been implicated as increased risks of cardiometabolic and cardiovascular diseases and all-cause morta Show more
Social interaction with others is essential to life. Although social isolation and loneliness have been implicated as increased risks of cardiometabolic and cardiovascular diseases and all-cause mortality, the cellular and molecular mechanisms by which social connection maintains cardiometabolic and cardiovascular health remain largely unresolved. To investigate how social connection protects against cardiometabolic and cardiovascular diseases, atherosclerosis-prone, high-fat diet-fed These results identify a novel brain-liver axis that links sociality to hepatic lipid metabolism, thus proposing a potential therapeutic strategy for loneliness-associated atherosclerosis progression. Show less
no PDF DOI: 10.1161/CIRCRESAHA.124.324638
ANGPTL4

Hepatic ceramide synthesis links systemic inflammation to organelle dysfunction in cancer.

Ying Liu, Ting Miao, Alice Wang +10 more · 2025 · bioRxiv : the preprint server for biology · Cold Spring Harbor Laboratory · added 2026-04-24
Paraneoplastic syndromes arise when tumor-derived cytokines reprogram distant organs. Although mediators such as Interleukin-6 have been implicated, how these signals impair host organ function remain Show more
Paraneoplastic syndromes arise when tumor-derived cytokines reprogram distant organs. Although mediators such as Interleukin-6 have been implicated, how these signals impair host organ function remains incompletely defined. Here, we identify a cytokine-lipid axis that drives hepatic autophagy dysfunction. Specifically, in Show less
📄 PDF DOI: 10.1101/2025.10.01.679814
LPL

A new perspective on cognitive impairment in major depressive disorder: Changes in gut microbiota and their inflammatory regulatory mechanisms.

Xiaodong Song, Qilin Zhong, Rongxu Zhang +10 more · 2025 · Journal of affective disorders · Elsevier · added 2026-04-24
Cognitive impairments in major depressive disorder (MDD) affect patients' social functioning, with underlying mechanisms involving gut microbiota and inflammatory factors remaining unclear. The study Show more
Cognitive impairments in major depressive disorder (MDD) affect patients' social functioning, with underlying mechanisms involving gut microbiota and inflammatory factors remaining unclear. The study analyzed cognitive function, gut microbiota changes, and inflammatory factor levels in 39 unmedicated MDD patients and 41 healthy controls, employing correlation and moderation effect analysis. MDD patients scored lower than controls in cognitive functions like information processing speed, attention/vigilance, verbal learning, visual learning and social cognition. They showed reduced gut microbiota diversity and increased levels of inflammatory markers (TNF-α, IL-1, IL-6, IL-17, IL-27, IL-33). Sellimonas abundance correlated negatively with attention/vigilance, moderated by TNF-α, IL-27, and IL-33. This relationship was stronger at lower inflammation levels. MDD patients exhibit multi-domain cognitive dysfunction alongside pro-inflammatory states and disrupted gut microbiota. The abundance of Sellimonas significantly predicts attention/vigilance deficits. Inflammatory factors modulate the impact of gut microbiota on cognitive function, suggesting chronic low-grade inflammation as a key risk factor for cognitive impairment in MDD. Show less
no PDF DOI: 10.1016/j.jad.2025.119648
IL27

The joint predictive roles of achievement task values and academic achievement on trajectories of school satisfaction among elementary school students: A latent transition analysis.

Meiru Deng, E Scott Huebner, Wang Liu +1 more · 2025 · The British journal of educational psychology · Blackwell Publishing · added 2026-04-24
Exploring how different profiles of academic task values (ATVs) and academic achievement (AA) influence students' school satisfaction should enrich understanding of how school satisfaction develops in Show more
Exploring how different profiles of academic task values (ATVs) and academic achievement (AA) influence students' school satisfaction should enrich understanding of how school satisfaction develops in specific academic situations. This study aimed to identify profiles of ATV (attainment value [AV], utility value [UV]) and AA, as well as heterogeneous trajectories of school satisfaction, and then examine the joint predictive effects of ATV and AA on the development of school satisfaction. A total of 3548 Chinese elementary school students (M Data were collected through self-reports of ATVs and school satisfaction, while AA data were collected from final examination scores at the end of each semester. Latent profile analysis (LPA) and latent class growth analysis revealed six profiles of ATV and AA (i.e., congruent subgroups: congruent-low, congruent-high, congruent-moderate and incongruent subgroups: high ATV-low AA, lower UV-moderate AA, low ATV-moderate AA); and four heterogeneous trajectories of school satisfaction (i.e., high-decreasing, low-stable, high-stable, low-increasing). Latent transition analysis (LTA) indicated that students with congruent levels of AA and ATV were more likely to transition into the optimal school satisfaction groups (high-stable and low-increasing groups), whereas students with greater gaps between AA and ATV were more likely to transition into the adverse groups (i.e., low-stable and high-decreasing groups). These findings highlight the need for educators to tailor interventions to distinct groups to optimize students' school satisfaction. Show less
no PDF DOI: 10.1111/bjep.70054
LPA

Selenium Nanoparticles vs Selenite Fertilizers: Implications for Toxicological Profiles, Antioxidant Defense, and Ferroptosis Pathways.

Qiting Fang, Zhonghua Liu, Kaixi Wang · 2025 · Journal of agricultural and food chemistry · ACS Publications · added 2026-04-24
Selenium (Se) foliar fertilizers enhance crop nutrition and address human selenium deficiency, while improper application may lead to excessive intake and residue accumulation. Our study comprehensive Show more
Selenium (Se) foliar fertilizers enhance crop nutrition and address human selenium deficiency, while improper application may lead to excessive intake and residue accumulation. Our study comprehensively assessed the toxicity and function of novel selenium nanoparticles and traditional sodium selenite fertilizers across cell, zebrafish, and murine models. Both fertilizers enhanced antioxidant pathways at low doses, but selenium nanoparticles exhibited stronger antioxidant and ferroptosis-modulating effects with lower toxicity at a high dose. Sodium selenite increased total and lipid ROS production, leading to decreased viability of cells and increased distortion and mortality of zebrafish. In mice, sodium selenite induced hepatic toxicity and decreased GPX4. Transcriptome analysis revealed that sodium selenite downregulated c-JUN and APOA4, weakening the antioxidant defense, whereas selenium nanoparticles promoted ferroptosis resistance through FGF21. These findings suggest selenium nanoparticles as a safer alternative for Se biofortification, mitigating health risks while supporting food security and environmental sustainability. Show less
no PDF DOI: 10.1021/acs.jafc.5c02034
APOA4

RNA G-quadruplex removal promotes a translational switch after meiosis resumption.

Qiong-Wen Lu, Shao-Yuan Liu, Xiu-Quan Liao +6 more · 2025 · Nucleic acids research · Oxford University Press · added 2026-04-24
Oocyte maturation-coupled mRNA post-transcriptional regulation is essential for the establishment of developmental potential. Previously, oocyte mRNA translation efficiencies focused on the trans-regu Show more
Oocyte maturation-coupled mRNA post-transcriptional regulation is essential for the establishment of developmental potential. Previously, oocyte mRNA translation efficiencies focused on the trans-regulation of key RNA-binding protein (RBPs), rarely related to RNA structure. RNA G-quadruplexes (rG4s) are four-stranded RNA secondary structures involved in many different aspects of RNA metabolism. In this study, we have developed a low-input technique for rG4 detection (G4-LACE-seq) in mouse oocytes and found that rG4s were widely distributed in maternal transcripts, with enrichment in untranslated regions, and they underwent transcriptome-wide removal during meiotic maturation. The rG4-selective small-molecule ligand BYBX stabilized rG4s in the oocyte transcriptome and impaired spindle assembly and meiotic cell cycle progression. The proteomic spectrum results revealed that rG4 accumulation weakened the binding of a large number of RBPs to mRNAs, especially those associated with translational initiation. Ribosomal immunoprecipitation and translational reporter assays further proved that rG4s in the untranslated regions negatively affected the translational efficiency of key maternal mRNAs. Overexpression DEAH/RHA family helicase-36 partially reverses BYBX-induced oocyte developmental defects, suggesting its importance in rG4 regulation. Collectively, this study describes the distribution, dynamic changes, and regulation of rG4s in the mouse maternal transcriptome. Before meiosis resumption, a large number of rG4s in oocytes are necessary to maintain the translatome at a low level, and DHX36-mediated rG4 removal promotes a translational switch and is required for successful maternal-to-zygotic transition. Show less
📄 PDF DOI: 10.1093/nar/gkaf067
DHX36

Rational Design and Identification of ISM7594 as a Tissue-Agnostic FGFR2/3 Inhibitor.

Yazhou Wang, Jinxin Liu, Yihong Zhang +14 more · 2025 · Journal of medicinal chemistry · ACS Publications · added 2026-04-24
The selective inhibition of fibroblast growth factor receptors (FGFR) presents a significant challenge due to the high degree of sequence and the close structural similarity of the subtypes. Herein, w Show more
The selective inhibition of fibroblast growth factor receptors (FGFR) presents a significant challenge due to the high degree of sequence and the close structural similarity of the subtypes. Herein, we designed selective dual FGFR2/3 inhibitors based on the in-depth understanding of protein-ligand interaction contributions. We efficiently identified ISM7594 ( Show less
no PDF DOI: 10.1021/acs.jmedchem.5c00928
FGFR1

Oncometabolite D-2HG drives tumor metastasis and protumoral macrophage polarization by targeting FTO/m

Siyue Zhang, Ning Zhang, Tong Wan +10 more · 2025 · Journal of experimental & clinical cancer research : CR · BioMed Central · added 2026-04-24
D-2-hydroxyglutarate (D-2HG), an oncometabolite derived from the tricarboxylic acid cycle. Previous studies have reported the diverse effects of D-2HG in pathophysiological processes, yet its role in Show more
D-2-hydroxyglutarate (D-2HG), an oncometabolite derived from the tricarboxylic acid cycle. Previous studies have reported the diverse effects of D-2HG in pathophysiological processes, yet its role in breast cancer remains largely unexplored. We applied an advanced biosensor approach to detect the D-2HG levels in breast cancer samples. We then investigated the biological functions of D-2HG through multiple in vitro and in vivo assays. A joint MeRIP-seq and RNA-seq strategy was used to identify the target genes regulated by D-2HG-mediated N6-methyladenosine (m We found that D-2HG accumulated in triple-negative breast cancer (TNBC), exerting oncogenic effects both in vitro and in vivo by promoting TNBC cell growth and metastasis. Mechanistically, D-2HG enhanced global m Our study unveils a previously unrecognized role for D-2HG-mediated RNA modification in TNBC progression and targeting the D-2HG/FTO/m Show less
📄 PDF DOI: 10.1186/s13046-025-03282-1
ANGPTL4

Elevated serum lipoprotein(a) as a prognostic marker for reduced survival in pancreatic cancer.

Jinyue Liu, Yueping Jiang, Yueyi Xing +5 more · 2025 · BMC gastroenterology · BioMed Central · added 2026-04-24
This study aimed to assess the prognostic significance of serum lipoprotein(a) [Lp(a)] levels regarding overall survival (OS) and progression-free survival (PFS) among patients diagnosed with pancreat Show more
This study aimed to assess the prognostic significance of serum lipoprotein(a) [Lp(a)] levels regarding overall survival (OS) and progression-free survival (PFS) among patients diagnosed with pancreatic cancer (PC). A retrospective cohort of 364 pathologically confirmed PC patients treated at the Affiliated Hospital of Qingdao University between January 2019 and December 2022 was analyzed. The optimal cutoff for Lp(a) was identified using X-tile software, allowing categorization into high and low Lp(a) groups. To minimize selection bias, propensity score matching (PSM) was utilized. Survival outcomes were compared using Kaplan-Meier curves and log-rank tests. Cox proportional hazards models were applied to identify independent prognostic variables affecting OS and PFS. Patients with high Lp(a) had significantly shorter OS and PFS both before and after PSM (post-PSM OS: 12.28 vs. 27.67 months, P = 0.003; PFS: 7.00 vs. 11.30 months, P = 0.002). Multivariate Cox analysis confirmed high Lp(a) as an independent predictor of poor OS [HR = 2.11 (1.17-3.81), P = 0.013] and PFS [HR = 2.14 (1.20-3.83), P = 0.010]. In the surgical subgroup (n = 215), high Lp(a) was also associated with worse OS (16.43 vs. 35.47 months, P = 0.02) and PFS (8.40 vs. 11.77 months, P = 0.036). Multivariate analysis in this subgroup showed that high Lp(a) remained an independent risk factor for OS [HR = 2.82 (1.36-5.87), P = 0.006] and PFS [HR = 2.01 (1.06-3.86), P = 0.034]. Elevated serum Lp(a) is an independent predictor of reduced OS and PFS in patients with pancreatic cancer. In contrast to conventional lipid profiles, the genetic stability of Lp(a) makes it a reliable baseline prognostic marker. Show less
📄 PDF DOI: 10.1186/s12876-025-04573-9
LPA

Impact of learning APOE genotype on cognitively unimpaired adults: a pre-screening cohort study of the Alzheimer's Prevention Initiative Generation Study 1.

Jessica B Langbaum, Angela R Bradbury, Brian L Egleston +16 more · 2025 · The lancet. Healthy longevity · Elsevier · added 2026-04-24
The apolipoprotein E (APOE) gene is the best established genetic risk factor for Alzheimer's disease in later life, with the ε4 allele conferring higher risk. APOE disclosure is becoming increasingly Show more
The apolipoprotein E (APOE) gene is the best established genetic risk factor for Alzheimer's disease in later life, with the ε4 allele conferring higher risk. APOE disclosure is becoming increasingly common in the clinical care of people with Alzheimer's disease and in cognitively unimpaired adults. In this study, we aimed to describe changes in measures of genetic disease knowledge and psychiatric symptoms following APOE disclosure to cognitively unimpaired adults. Data were collected as part of the screening phase of the global, multicentre, Alzheimer's Prevention Initiative Generation Study 1 (NCT02565511). Eligible individuals were cognitively unimpaired (Mini-Mental State Exam total score ≥24), aged 60-75 years, and psychologically pre-screened for readiness (by measures of depressive symptoms and anxiety) to receive their APOE genotype from a health-care provider. Participants were assessed before disclosure, and 2-7 days, 6 weeks, 6 months, and 12 months after disclosure. Multivariable linear and ordinal logistic regressions were used to compare changes in genetic disease knowledge, anxiety, depression, and distress by APOE4 genotype status, adjusting for key covariates, with a focus on 2-7 days after disclosure. Multiple imputation by chained equations methods was used to account for missing outcome data. The trial took place between Nov 30, 2015, and Sept 23, 2019. In total, 9496 participants (including 790 APOE4 homozygotes, 4869 heterozygotes, and 3837 non-carriers) learned their APOE genotype from a health-care provider as part of Generation Study 1 screening. 4038 (42·5%) participants were in the 65-69-year age group, 5790 (61·0%) were female, 3706 (39·0%) were male, and 8862 (93·3%) self-identified as White. Increase in genetic disease knowledge 2-7 days after disclosure was greater in APOE4 homozygotes (mean 1·19 [SD 3·95]) than in heterozygotes (0·78 [3·95], p=0·042) and non-carriers (0·29 [3·96], p=0·0002). Disease-specific distress 2-7 days after disclosure increased more in homozygotes (2·25 [6·42]) than in heterozygotes (0·53 [5·08], p<0·0001) and non-carriers (0·79 [4·95], p<0·0001). Levels of anxiety 2-7 days after disclosure increased in homozygotes (0·17 [2·95]) but decreased in heterozygotes (-0·67 [2·68], p<0·0001) and non-carriers (-0·66 [2·67], p<0·0001). There were no significant changes in depressive symptoms following disclosure for any APOE4 group. Notably, for all APOE4 groups, increases in distress and anxiety were small and did not reach predefined levels of clinical concern. In cognitively unimpaired, psychologically pre-screened adults, APOE disclosure by a trained health-care provider was generally safe and well tolerated, consistent with results from previous studies. To our knowledge, this is the largest study experience of APOE disclosure to date, especially for homozygotes, and is notable for the older age of participants compared with previous research. These results are timely and important given anticipated increases in APOE disclosure to guide clinical decision making once an Alzheimer's disease prevention treatment is approved for cognitively unimpaired adults or if patients' family members are interested in genetic testing. Scalable approaches for returning Alzheimer's disease risk information are critical to meeting anticipated demand. Results from this study may be useful to bolster clinical translatability of disclosure programmes. The National Institute on Aging, Alzheimer's Association, Banner Alzheimer's Foundation, GHR Foundation, F-Prime Biomedical Research Initiative (FBRI), and Novartis Pharma. Show less
📄 PDF DOI: 10.1016/j.lanhl.2025.100778
APOE

Identifying internet addiction profiles and bridge connectors among Chinese college students and evaluating CBT vs. CBT+MBI interventions via a randomized controlled trial.

Shuhong Liang, Yaxu Yu, Shuang Liu +2 more · 2025 · Journal of behavioral addictions · added 2026-04-24
The Interaction of Person-Affect-Cognition-Execution (I-PACE) model offers a framework for understanding the interplay between cognitive, affective, and behavioral factors in internet addiction (IA). Show more
The Interaction of Person-Affect-Cognition-Execution (I-PACE) model offers a framework for understanding the interplay between cognitive, affective, and behavioral factors in internet addiction (IA). Our study aims to explore the heterogeneity of IA, identify bridge connectors, and compare the efficacy of cognitive behavioral therapy combined with mindfulness-based intervention (CBT+MBI) versus CBT alone in reducing IA levels among Chinese college students. In study 1, 1,030 Chinese college students completed assessments of IA, automatic thoughts, self-control, and anxiety. Latent profile analysis (LPA) was employed to identify distinct symptom profiles of IA across individuals. Network analysis (NA) identified bridge connectors for targeted intervention. In study 2, 36 participants randomly selected from the high IA and low IA groups of study 1 were randomly assigned to CBT+MBI, CBT alone, or a control group. The CBT+MBI group received an 8-week dual-modality intervention and the CBT alone received an 8-week CBT intervention, both designed to target the bridge connectors identified via NA in Study 1, while the control group only completed basic questionnaires. In study 1, LPA identified four subgroups: regular, at-risk, low IA, and high IA groups. NA pinpointed automatic thoughts and anxiety as bridge connectors. In study 2, targeted interventions significantly reduced college students' levels of IA. CBT+MBI resulted in greater and more sustained improvements compared to CBT alone, with effects maintained for six-month post-intervention. Our study not only reinforces the I-PACE model but also provides actionable strategies for designing evidence-based, multidimensional interventions to reduce addictive behaviors among college students. Show less
📄 PDF DOI: 10.1556/2006.2025.00086
LPA