👤 Wen Liu

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

E3 ligase HERC5-catalyzed UGDH isgylation promotes SNAI1-mediated tumor metastasis and cisplatin resistance in oral squamous cell carcinoma.

Xu Zhang, Fayu Liu, Qigen Fang +2 more · 2025 · Biology direct · BioMed Central · added 2026-04-24
Oral squamous cell carcinoma (OSCC) is one of the leading causes of cancer-related mortality worldwide due to its high aggressive potential and drug resistance. Previous studies have revealed an impor Show more
Oral squamous cell carcinoma (OSCC) is one of the leading causes of cancer-related mortality worldwide due to its high aggressive potential and drug resistance. Previous studies have revealed an important function of HECT And RLD Domain Containing E3 Ubiquitin Protein Ligase 5 (HERC5) in cancer. Six GEO gene microarrays identified HERC5 as a significant upregulated gene in OSCC tissues or cells (log2 Fold change > 1 and adj.p < 0.05). This study aimed to explore the role and underlying mechanisms of HERC5 in OSCC development. High HERC5 expression in OSCC tissues was confirmed by our hospital validation cohort and positively correlated with primary tumor stages. Subsequent functional studies demonstrated that knockdown of HERC5 inhibited the migratory and invasive capabilities with decrease of Vimentin and increase of E-cadherin in OSCC cells. In cisplatin treatment, cell survival rates were significantly reduced in HERC5-silencing OSCC cells, accompanied by the increase in cytotoxicity, DNA damage and apoptosis. OSCC cell-derived tumor xenograft displayed that HERC5 depletion inhibited pulmonary metastasis as well as restored the cisplatin-induced tumor burden. In line with this, overexpression of HERC5 yielded the opposite alterations both in vivo and in vitro. Mechanistically, UDP-glucose 6-dehydrogenase (UGDH) was identified as a HERC5-binding protein. Cysteine residue at position 994 in the HECT domain of HERC5 catalyzed the conjugation of ubiquitin-like protein Interferon-induced 15 kDa protein (ISG15) to UGDH (ISGylation of UGDH) and facilitated its phosphorylation, therefore enhancing SNAI1 mRNA stability. SNAI1 depletion inhibited HERC5 overexpression-triggered invasion and cisplatin resistance of OSCC cells. Our study indicates that HERC5 may be a promising therapeutic target for OSCC. Show less
no PDF DOI: 10.1186/s13062-025-00622-1
SNAI1

Serum BDNF helps identify favorable subgroups in HCC patients treated with PD-L1 inhibitors and anti-angiogenic TKIs.

Caifeng Gong, Jinglong Huang, Dandan Cao +10 more · 2025 · Therapeutic advances in medical oncology · SAGE Publications · added 2026-04-24
Immune checkpoint inhibitors (ICIs) combined with antiangiogenic agents have become a standard strategy for advanced hepatocellular carcinoma (HCC). There remains an urgent need for effective biomarke Show more
Immune checkpoint inhibitors (ICIs) combined with antiangiogenic agents have become a standard strategy for advanced hepatocellular carcinoma (HCC). There remains an urgent need for effective biomarkers to guide treatment, with C-reactive protein and alpha-fetoprotein in immunotherapy (CRAFITY) scores and cytokine levels representing promising candidates. We aimed to assess the efficacy, safety, and potential biomarkers of anlotinib plus TQB2450 in patients with advanced HCC. This study was a single-arm, phase Ib trial. Twenty-five patients with advanced HCC were enrolled. Patients received an intravenous infusion of TQB2450 (1200 mg, on Day 1) and oral administration of anlotinib (initiated at 10 mg, once a day, from Day 1 to Day 14), which was repeated every 3 weeks. Blood was collected at baseline for serum cytokine analysis. After a median follow-up of 41.80 months, the median progression-free survival (mPFS) was 5.49 months, and the median overall survival (mOS) was 8.94 months. Treatment-related adverse events (TRAEs) occurred in 22 patients, with grade ⩾3 TRAEs observed in 12 patients. Patients who achieved clinical benefit (CB) had higher baseline serum brain-derived neurotrophic factor (BDNF) levels than non-CB patients (median, 227.97 vs 129.26 pg/ml, Anlotinib plus TQB2450 demonstrated promising efficacy with manageable safety in advanced HCC. Elevated serum BDNF levels might serve as a potential positive prognostic marker and, together with ECOG score, may help complement the CRAFITY score in identifying subgroups that could benefit from ICIs and antiangiogenic therapy. Show less
📄 PDF DOI: 10.1177/17588359251407052
BDNF

Shunt configuration's role in shaping hemodynamics of reverse Potts shunt in pediatric pulmonary arterial hypertension.

Jinlong Liu, Xiafeng Yu, Jiwen Xiong +4 more · 2025 · Frontiers in bioengineering and biotechnology · Frontiers · added 2026-04-24
Reverse Potts shunt is a promising yet high-risk therapy for pediatric pulmonary arterial hypertension. Postoperative hemodynamics is critically influenced by shunt configuration but is difficult to p Show more
Reverse Potts shunt is a promising yet high-risk therapy for pediatric pulmonary arterial hypertension. Postoperative hemodynamics is critically influenced by shunt configuration but is difficult to predict. This study aimed to quantify the effects of shunt size and location on hemodynamics to guide surgical planning. Based on a patient-specific model, four postoperative models with two different shunt locations [left pulmonary artery (LPA)-descending aorta (DAO) and pulmonary artery bifurcation-aortic arch] and three conduit sizes (4, 5, and 6 mm) were created. The direct Potts shunt model was created by a direct side-to-side anastomosis between the LPA and DAO with a 6-mm circular opening. Quantitative parameters including the shunt ratio (SR), which was defined as the percentage of the shunt flow rates to the total pulmonary inflow rate, lower limb oxygen saturation, and pressure were analyzed. Increasing the shunt size from 4 mm to 6 mm elevated the SR from 6.01% to 9.80%, concurrently reducing lower limb oxygen saturation from 89.57% to 86.52%. When taking 11,000 Pa as the threshold, this increased SR resulted in a reduction of the high-pressure area from 17.32% of the total pulmonary artery area to almost zero. Meanwhile, the high-pressure area on the aorta expanded from 8.72% of the total aortic area to 14.94%. These results indicated a reduction in the right ventricular afterload and an increase in the left ventricular afterload. Notably, a 6-mm shunt at the pulmonary artery bifurcation yielded a significantly larger SR than at the LPA (9.80% vs. 2.68%), which is attributed to a higher pressure gradient at the pulmonary artery bifurcation (1,201 Pa vs. 162 Pa). The shunt location had a greater impact on the SR than shunt size within the 4 mm-6 mm range in this specific case. A 6-mm shunt at the pulmonary artery bifurcation yielded a significantly larger SR than at the LPA, which is attributed to the higher preoperative pressure gradient at the bifurcation site. Left heart function is as critical as right heart function in maintaining pressure balance and determining outcomes, as the shunt flow increases the left ventricular afterload. Show less
📄 PDF DOI: 10.3389/fbioe.2025.1697468
LPA

TMEM106A mediates atherosclerosis progression through macrophage-centered immune responses and chemokine signaling.

Menglong Gao, Xingbang Liu, Zhen Fang +5 more · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
Atherosclerosis (AS) remains a leading cause of cardiovascular morbidity and mortality, characterized by intricate interactions between immune dysregulation and lipid metabolism abnormalities-identify Show more
Atherosclerosis (AS) remains a leading cause of cardiovascular morbidity and mortality, characterized by intricate interactions between immune dysregulation and lipid metabolism abnormalities-identifying key mediators in its pathogenesis is critical for improving diagnostics and therapies. This study focuses on Transmembrane Protein 106A (TMEM106A) to clarify its role and clinical relevance in AS progression. Public transcriptomic datasets (GSE43292, GSE100927, GSE28829) were analyzed to assess TMEM106A expression and diagnostic value; single-cell RNA-seq data (GSE159677) defined its cellular localization. Immune infiltration (ssGSEA, Cibersort, xCell) and CellChat (intercellular communication) analyses explored its immune associations. TMEM106A was significantly upregulated in AS samples across datasets, with strong diagnostic efficacy (AUC 0.80-0.95). Single-cell analysis confirmed its specific enrichment in macrophages, with functional links to immune-related pathways. TMEM106A promoted macrophage infiltration, foam cell formation, oxidative stress, and inflammatory responses, while regulating PLCB2 in chemokine signaling; silencing TMEM106A alleviated these pro-atherosclerotic effects. TMEM106A contributes to AS progression by modulating macrophage-mediated immune responses and chemokine signaling, as validated in experimental models. These findings support its potential as a clinically relevant biomarker and promising therapeutic target for AS intervention. Show less
📄 PDF DOI: 10.3389/fimmu.2025.1681645
APOE

Tumor Small Extracellular Vesicle-Transmitted LncRNA CATED Promotes Platinum-Resistance in High-Grade Serous Ovarian Cancer.

Yi Liu, Hanyuan Liu, Chenchen Zhu +5 more · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
High-grade serous ovarian cancer (HGSOC) is the most lethal type of gynecological cancer, and platinum-resistance is a serious challenge in its treatment. Long non-coding RNAs (lncRNAs) play critical Show more
High-grade serous ovarian cancer (HGSOC) is the most lethal type of gynecological cancer, and platinum-resistance is a serious challenge in its treatment. Long non-coding RNAs (lncRNAs) play critical regulatory roles in the occurrence and development of cancers. Here, using RNA sequencing of tumor small extracellular vesicles (sEVs) from HGSOC patients, the lncRNA CATED is identified as significantly upregulated in both tumors and tumor-derived sEVs in platinum-resistant HGSOC, and low CATED levels correlate with good prognosis. Functionally, CATED enhances cisplatin resistance by promoting cell proliferation and inhibiting apoptosis in vitro and in vivo. These effects could be transferred via CATED-overexpressing sEVs from donor cells and HGSOC tumor sEVs. Mechanistically, CATED binds to and upregulates DHX36 via PIAS1-mediated SUMOylation at the K105 site, and elevated DHX36 levels increase downstream RAP1A protein levels by enhancing RAP1A mRNA translation, consequently activating the MAPK pathway to promote platinum-resistance in HGSOC. Antisense oligonucleotide mediated knockdown of CATED reverse platinum-resistance in sEV-transmitted mouse models via the DHX36-RAP1A-MAPK pathway. This study newly identifies a sEV-transmitted lncRNA CATED in driving HGSOC platinum-resistance and elucidates the mechanism it regulates the interacting protein through SUMOylation. These findings also provide a novel strategy for improving chemotherapy in HGSOC by targeting CATED. Show less
📄 PDF DOI: 10.1002/advs.202505963
DHX36

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

Single-cell transcriptomics reveals apolipoprotein A4-mediated metabolic-immune reprogramming in lymphocytes during early obesity-related chronic kidney disease.

Yang Wei, Ting Zhang, Yingying Jin +4 more · 2025 · Acta biochimica et biophysica Sinica · added 2026-04-24
Obesity-induced metabolic inflammation is a key driver of chronic kidney disease (CKD), with immune dysregulation, particularly among lymphocytes, contributing to early disease pathology. To explore t Show more
Obesity-induced metabolic inflammation is a key driver of chronic kidney disease (CKD), with immune dysregulation, particularly among lymphocytes, contributing to early disease pathology. To explore the role of apolipoprotein A4 (Apoa4) in regulating immune cell metabolism and function, we establish high-fat diet-induced obese (DIO) models using wild-type and Show less
📄 PDF DOI: 10.3724/abbs.2025171
APOA4

Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele.

Xinruo Zhang, Jennifer A Brody, Mariaelisa Graff +122 more · 2025 · Nature communications · Nature · added 2026-04-24
Xinruo Zhang, Jennifer A Brody, Mariaelisa Graff, Heather M Highland, Nathalie Chami, Hanfei Xu, Zhe Wang, Kendra R Ferrier, Geetha Chittoor, Navya Shilpa Josyula, Mariah Meyer, Shreyash Gupta, Xihao Li, Zilin Li, Matthew A Allison, Diane M Becker, Lawrence F Bielak, Joshua C Bis, Meher Preethi Boorgula, Donald W Bowden, Jai G Broome, Erin J Buth, Christopher S Carlson, Kyong-Mi Chang, Sameer Chavan, Yen-Feng Chiu, Lee-Ming Chuang, Matthew P Conomos, Dawn L DeMeo, Mengmeng Du, Ravindranath Duggirala, Celeste Eng, Alison E Fohner, Barry I Freedman, Melanie E Garrett, Xiuqing Guo, Chris Haiman, Benjamin D Heavner, Bertha Hidalgo, James E Hixson, Yuk-Lam Ho, Brian D Hobbs, Donglei Hu, Qin Hui, Chii-Min Hwu, Rebecca D Jackson, Deepti Jain, Rita R Kalyani, Sharon L R Kardia, Tanika N Kelly, Ethan M Lange, Michael LeNoir, Changwei Li, Loic Le Marchand, Merry-Lynn N McDonald, Caitlin P McHugh, Alanna C Morrison, Take Naseri, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Jeffrey O'Connell, Christopher J O'Donnell, Nicholette D Palmer, James S Pankow, James A Perry, Ulrike Peters, Michael H Preuss, D C Rao, Elizabeth A Regan, Sefuiva M Reupena, Dan M Roden, Jose Rodriguez-Santana, Colleen M Sitlani, Jennifer A Smith, Hemant K Tiwari, Ramachandran S Vasan, Zeyuan Wang, Daniel E Weeks, Jennifer Wessel, Kerri L Wiggins, Lynne R Wilkens, Peter W F Wilson, Lisa R Yanek, Zachary T Yoneda, Wei Zhao, Sebastian Zöllner, Donna K Arnett, Allison E Ashley-Koch, Kathleen C Barnes, John Blangero, Eric Boerwinkle, Esteban G Burchard, April P Carson, Daniel I Chasman, Yii-der Ida Chen, Joanne E Curran, Myriam Fornage, Victor R Gordeuk, Jiang He, Susan R Heckbert, Lifang Hou, Marguerite R Irvin, Charles Kooperberg, Ryan L Minster, Braxton D Mitchell, Mehdi Nouraie, Bruce M Psaty, Laura M Raffield, Alexander P Reiner, Stephen S Rich, Jerome I Rotter, M Benjamin Shoemaker, Nicholas L Smith, Kent D Taylor, Marilyn J Telen, Scott T Weiss, Yingze Zhang, Nancy Heard-Costa, Yan V Sun, Xihong Lin, L Adrienne Cupples, Leslie A Lange, Ching-Ti Liu, Ruth J F Loos, Kari E North, Anne E Justice Show less
Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data fr Show more
Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10 Show less
no PDF DOI: 10.1038/s41467-025-58420-2
POC5

The truncated AXIN1 isoform promotes hepatocellular carcinoma metastasis through SRSF9-mediated exon 9 skipping.

Qian-Qian Zhang, Ying-Shuang Miao, Jun-Yi Hu +3 more · 2025 · Molecular and cellular biochemistry · Springer · added 2026-04-24
Axis inhibitor protein 1 (AXIN1) is a protein recognized for inhibiting tumor growth and is commonly involved in cancer development. In this study, we explored the potential molecular mechanisms that Show more
Axis inhibitor protein 1 (AXIN1) is a protein recognized for inhibiting tumor growth and is commonly involved in cancer development. In this study, we explored the potential molecular mechanisms that connect alternative splicing of AXIN1 to the metastasis of hepatocellular carcinoma (HCC). Transcriptome sequencing, RT‒PCR, qPCR and Western blotting were utilized to determine the expression levels of AXIN1 in human HCC tissues and HCC cells. The effects of the AXIN1 exon 9 alternative splice isoform and SRSF9 on the migration and invasion of HCC cells were assessed through wound healing and Transwell assays, respectively. The interaction between SRSF9 and AXIN1 was investigated using UV crosslink RNA immunoprecipitation, RNA pulldown, and RNA immunoprecipitation assays. Furthermore, the involvement of the AXIN1 isoform and SRSF9 in HCC metastasis was validated in a nude mouse model. AXIN1-L (exon 9 including) expression was downregulated, while AXIN1-S (exon 9 skipping) was upregulated in HCC. SRSF9 promotes the production of AXIN1-S by interacting with the sequence of exons 8 and 10 of AXIN1. AXIN1-S significantly promoted HCC cells migration and invasion by activating the Wnt pathway, while the opposite effects were observed for AXIN1-L. In vivo experiments demonstrated that AXIN1-L inhibited HCC metastasis, whereas SRSF9 promoted HCC metastasis in part by regulating the level of AXIN1-S. AXIN1, a tumor suppressor protein that targets the AXIN1/Wnt/β-catenin signaling axis, may be a promising prognostic factor and a valuable therapeutic target for HCC. Show less
📄 PDF DOI: 10.1007/s11010-024-05012-1
AXIN1

Rodent Models for Atherosclerosis.

Linghong Zeng, Jingshu Chi, Meiqi Zhu +4 more · 2025 · International journal of molecular sciences · MDPI · added 2026-04-24
Atherosclerosis, a leading cause of cardiovascular disease, is driven by a complex interplay of dyslipidemia, inflammation, and arterial plaque formation and progression. Animal models are indispensab Show more
Atherosclerosis, a leading cause of cardiovascular disease, is driven by a complex interplay of dyslipidemia, inflammation, and arterial plaque formation and progression. Animal models are indispensable to elucidate the pathogenesis and develop novel therapies. Rodent models are widely utilized due to their cost-effectiveness, reproducibility, and rapid disease progression. However, notable species differences exist in lipoprotein composition and lipid metabolism pathways. Mice and rats exhibit an HDL-dominant profile, whereas Syrian golden hamsters express cholesteryl ester transfer protein (CETP) and display a higher LDL fraction, but lower than that of humans, offering a model closer to human metabolically. Divergent CETP activity across species further complicates the translational relevance of the findings from these models for atherosclerosis and related metabolic disorders. This review systematically examines the key factors in rodent model selection and optimization, with consideration on the roles of sex and age. We focus on three commonly used and well-characterized rodent strains prone to atherosclerosis: C57BL/6J mice, Sprague-Dawley (SD) rats, Wistar rats, and golden hamsters. On Show less
📄 PDF DOI: 10.3390/ijms27010378
APOE

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

Schisandrin B alleviates metabolic associated fatty liver disease by regulating the PPARγ signaling pathway and gut microbiota in mice.

Jiuchen Wan, Chenjian Lang, Meng Gao +5 more · 2025 · Frontiers in pharmacology · Frontiers · added 2026-04-24
The aim of this study was to investigate the improving effect of Schisandrin B (Sch B) on metabolic associated fatty liver disease (MAFLD) by regulating the PPARγ signaling pathway and gut microbiota, Show more
The aim of this study was to investigate the improving effect of Schisandrin B (Sch B) on metabolic associated fatty liver disease (MAFLD) by regulating the PPARγ signaling pathway and gut microbiota, and its mechanism in mice. Male C57BL/6 mice were fed with a high-fat diet (HFD) continuously for 16 weeks to establish a MAFLD model. The levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-10 (IL-10), and lipopolysaccharide (LPS) in serum, as well as the level of malondialdehyde (MDA), and the activities of glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) in the liver tissue were measured. Changes in the gut microbiota of mice was analyzed by 16S rRNA sequencing technology. The expression levels of PPARγ, Plin2, Pck1, Acsl4, and Fads1 proteins, as well as those of zonula occludins 1 (ZO-1) and Occludin proteins in the colon tissue were detected by Western Blot. The results showed that Sch B could alleviate the structure disorder, ballooning degeneration, inflammatory cell infiltration, liver lipid droplets, and fibrosis in liver tissue, lower the levels of AST, ALT, TG, TC, LDL-C, and LPS, increase the level of HDL-C and lower the levels of TNF-α and IL-6 in serum, increase the level of IL-10, and lower the level of MDA and increase the activities of SOD and GSH-Px in liver tissue in MAFLD mice. Sch B could increase the expression levels of PPARγ, Pck1, and Fads1 proteins, but decrease Plin2 and Acsl4 proteins in liver tissue. Sch B could improve the diversity and abundance of the gut microbiota, restore the normal composition of the gut microbiota at the phylum and genus levels, alleviate the disruption of the gut barrier caused by HFD, and enhance the expression of ZO-1 and Occludin proteins in colon tissue in MAFLD mice. This study showed Sch B can improve HFD-induced MAFLD, and the mechanism may be through regulating the PPARγ, Plin2, PCk1, Acsl4 and Fads1 signaling pathway, restoring the diversity of gut microbiota, and improving the gut barrier to delay the progression of MAFLD. Show less
📄 PDF DOI: 10.3389/fphar.2025.1583307
FADS1

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

Prenatal androgen exposure delays puberty onset in female offspring rats: possible roles of leptin and α-MSH.

Runfei Ge, Yongting Yuan, Jingqi Liu +7 more · 2025 · Endocrine · Springer · added 2026-04-24
To clarify the possible mechanism of leptin and α-MSH on the onset of puberty in female offspring rats after prenatal androgen exposure. Sixteen 8-week-old specific pathogen free (SPF) healthy Sprague Show more
To clarify the possible mechanism of leptin and α-MSH on the onset of puberty in female offspring rats after prenatal androgen exposure. Sixteen 8-week-old specific pathogen free (SPF) healthy Sprague Dawley (SD) pregnant rats were randomly divided into the testosterone-treated group (TG, female offspring termed PNA group) or the olive oil control group (OOG, female offspring termed VEH group). The female offspring rats of two groups were raised to 21 days (PND21) and weaned. Six female offspring rats at PND21 (VEH:PNA = 3:3) were randomly selected for transcriptome sequencing. Twenty-seven offspring female rats were randomly divided into three groups (VEHI:VEHII:PNA = 9:9:9). VEHI group was observed until the onset of puberty, VEHII and PNA groups were observed until the 8th week. Compared with VEH group, onset of puberty was not observed in PNA group, and hypothalamic Pomc gene expression at PND21 was lower. Compared with the VEHI group, the body weight, abdominal fat, serum testosterone (T), dehydroepiandrosterone (DHEA) and leptin (LEP) levels were upregulated in the PNA group, while serum gonadotropin-releasing hormone (GnRH), mRNA of hypothalamic estrogen receptor α (ERα), α-melanocyte stimulating hormone (α-MSH), melanocortin receptor-4 (MC4R), GnRH and adipose AR, and the protein of androgen receptor (AR) and leptin receptor (LEPR) in the hypothalamic arcuate nucleus (ARC) were decreased. In the PNA group, there were positive correlations between serum DHEA and mRNA of hypothalamic ERα, MC4R and AR, negative correlations between mRNA of adipose AR and serum T and free testosterone (FT). Prenatal androgen exposure delayed the onset of puberty in female offspring, the possible mechanism of which is that prenatal androgen exposure may increase the levels of androgen and LEP, decreases their sensitivity and the expression of AR, LEPR, and MC4R, reducing GnRH secretion. Show less
📄 PDF DOI: 10.1007/s12020-025-04388-4
MC4R

BCAA metabolism: the Achilles' heel of ovarian cancer, polycystic ovary syndrome, and premature ovarian failure.

Tian Zeng, Yitong Liu, Xing Tang +7 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Branched-chain amino acids (BCAAs), including valine, leucine and isoleucine, are essential nutrient signals that influence mammalian animal metabolism. Many enzymes are involved in the metabolism of Show more
Branched-chain amino acids (BCAAs), including valine, leucine and isoleucine, are essential nutrient signals that influence mammalian animal metabolism. Many enzymes are involved in the metabolism of BCAAs, such as branched-chain amino acid transaminases (BCATs), branched-chain α-keto acid dehydrogenase (BCKDH), and BCKDH kinase (BCKDK). The aberrant expression of enzymes involved in BCAA metabolism and an imbalance in BCAA amino acid intake can lead to disordered metabolism. Aberrant BCAA metabolism can lead to several diseases, such as human ovarian disease, including ovarian cancer (OC), polycystic ovary syndrome (PCOS), and premature ovarian failure (POF), which are common gynaecological diseases. The overexpression of BCATs is found in OC, which promotes BCAA catalysis to provide a large amount of energy for tumorigenesis. However, BCKDK is overexpressed in epithelial ovarian cancer (EOC), which promotes proliferation and migration via MEK-ERK. In addition, several studies have reported that high levels of BCAAs are increased in the plasma of PCOS and POF patients. This review focuses on the role of BCAA metabolism and potential management methods for OC, PCOS and POF. Show less
📄 PDF DOI: 10.3389/fendo.2025.1579477
BCKDK

A Novel Approach for the Early Identification of Genetic Risk Factors for Alzheimer's Disease Using EEG and Psychometric Data.

Shyamal Y Dharia, Qian Liu, Stephen D Smith +1 more · 2025 · IEEE journal of biomedical and health informatics · IEEE · added 2026-04-24
Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with impairments in memory and executive functions. Despite significant advancements in identifying genetic risk factors Show more
Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with impairments in memory and executive functions. Despite significant advancements in identifying genetic risk factors, the high cost and limited accessibility of genetic testing remain major barriers. In this work, we propose a cost-effective screening approach that leverages EEG recordings and psychometric test scores to predict an individual's genetic risk for AD. Our Convolutional Neural Network (CNN) model shows promising performance: it achieved an F1 score of 72.21% in distinguishing APOE-ϵ4/PICALM GG non-carriers (N) from APOE-ϵ4 carriers with the risky PICALM GG alleles (A+P+). It reached an F1 score of 60.78% for differentiating non-carriers (N) from APOE-ϵ4 carriers without the risky alleles (A+P-), and 65.12% when separating A+P- from A+P+. To enhance interpretability, we employ Grad-CAM, which reveals that EEG features contribute more significantly to gene prediction than psychometric measures. Notably, our model also identifies three key psychometric tests, MINI COPE (which assesses emotional coping skills), the California Verbal Learning Test (CVLT), and NEO Neuroticism, as associated with higher AD risk, consistent with prior research. Moreover, our results align with earlier findings reporting increased theta-band power among high-risk individuals. Finally, Higuchi Fractal Dimension (HFD) features drove most of the EEG-based prediction capability, as shown through our ablation study. This study highlights the potential of integrating neurophysiological and cognitive assessments to develop accessible and reliable screening tools for AD genetic risk, enabling earlier diagnoses. The code has been released at https://github.com/ Shyamal-Dharia/EEG-Psycho-Genes-AD. Show less
no PDF DOI: 10.1109/JBHI.2025.3639217
APOE

New heights in CT differentiation of adrenal lesions and a rational definition of non-enhancement.

Lichun Liu, Fangmei Zhu, Zongfeng Niu +4 more · 2025 · BMC medical imaging · BioMed Central · added 2026-04-24
To explore the stratification and identification of adrenal lipid-poor adenomas (LPAs), adrenal cysts (ACs), and adrenal ganglioneuromas (AGNs) from each other using contrast-enhanced computed tomogra Show more
To explore the stratification and identification of adrenal lipid-poor adenomas (LPAs), adrenal cysts (ACs), and adrenal ganglioneuromas (AGNs) from each other using contrast-enhanced computed tomography (CT). Pathologically confirmed, 348 patients were categorized into Model 1 (260 LPAs, 34 ACs), Model 2 (260 LPAs, 54 AGNs), and Model 3 (34 ACs, 54 AGNs). Statistical analyses were performed on the differences in the degree of enhancement in the arterial/venous phase (DEap/DEvp) (in HU) and the corresponding graded variables for the arterial/venous phase (GVap/GVvp). Models were evaluated via receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer‒Lemeshow (HL) test. The values of the area under the curve (AUC) for DEap, DEvp, GVap, and GVvp in Models 1-3 were 0.996, 1.000, 0.993, and 0.999; 0.980, 0.978, 0.961, and 0.975; and 0.734, 0.892, 0.725, and 0.883, respectively. The p values of the HL test were 0.984, 1.000, and 0.113, respectively. The DEvp interval values (in HU) for the LPAs, ACs, and AGNs were [4.9, 190.2] HU, [-3.7, 4.2] HU, and [-4.8, 41.8] HU, respectively. The GVap and GVvp ranges for the LPAs, ACs, and AGNs were [1, 6], [0, 2], and [0, 2] and [1, 6], [0, 1], and [0, 5], respectively. DEvp enhanced discrimination in Models 1 and 3, whereas DEap performed better in Model 2. Lesions with DEvp < 4.5 HU are likely represent non-enhancing pathology (e.g., cysts). When both GVap and GVvp are 0, when both GVap and GVvp are [2, 6], and when GVap is [3, 6] and GVvp is 6, LPA, AC, and AGN are excluded. Not applicable. Show less
📄 PDF DOI: 10.1186/s12880-025-01916-6
LPA

Comprehensive dietary patterns explain acquired cystic kidney disease risk through genetic and metabolomic mechanisms.

Chenhao Xu, Junjie Zhao, Kan Wu +9 more · 2025 · Frontiers in nutrition · Frontiers · added 2026-04-24
Acquired renal cysts (ARC) are associated with kidney function decline, necessitating novel dietary pattern (DP) analyses in large cohorts. This UK Biobank prospective cohort study (2006-2010) include Show more
Acquired renal cysts (ARC) are associated with kidney function decline, necessitating novel dietary pattern (DP) analyses in large cohorts. This UK Biobank prospective cohort study (2006-2010) included participants with ≥2 dietary records, excluding those with severe kidney damage. The constructed comprehensive dietary pattern integration (CDPI) utilized reduced rank regression (RRR) and latent profile analysis (LPA). ARC cases (ICD-10: N28.1) were assessed via Cox regression for risk and dose-response, with NMR metabolites examined as mediators. Among 119,709 participants (median follow-up: 10.57 years), 850 ARC cases were identified. Lipid-rich and hyperglycemic diets increased ARC risk [e.g., HRs for G1.DP1: 1.080 (1.024, 1.139); G1.DP2: 1.144 (1.048, 1.249)], while micronutrient-rich diets showed weak protective effects [G4.DP1: 0.943 (0.892, 0.998)]. LPA confirmed RRR findings, and 7/251 NMR metabolites had significant mediating effects. Diets high in fat (cheese, butter, pizza) and sugar (chocolate, sugary drinks) elevated ARC risk, whereas micronutrient- and fiber-rich diets (vegetables, fruit, lean poultry, nuts, eggs) were protective. Key mediators included branched-chain amino acids, IGF-1, and RBC distribution width. Show less
📄 PDF DOI: 10.3389/fnut.2025.1611656
LPA

FGFR1-amplified colorectal cancer: a distinct prognostic subtype identified through integrated genomic and immunohistochemical profiling.

X Lyu, R Cai, B Han +10 more · 2025 · ESMO open · Elsevier · added 2026-04-24
Fibroblast growth factor receptor (FGFR) alterations are established therapeutic targets in cholangiocarcinoma and urothelial carcinoma but remain understudied in colorectal cancer (CRC). This study i Show more
Fibroblast growth factor receptor (FGFR) alterations are established therapeutic targets in cholangiocarcinoma and urothelial carcinoma but remain understudied in colorectal cancer (CRC). This study investigates the prevalence, clinicopathological correlates, and prognostic impact of FGFR alterations in CRC. We analyzed 608 stage I-IV CRC samples (2014-2024) through next-generation sequencing (NGS) and immunohistochemistry (IHC). FGFR genomic status was correlated with survival outcomes using Kaplan-Meier and Cox regression analyses. External validation of FGFR genomic alterations was carried out using the 19 datasets (n = 6998) with prognostic impact validated through The Cancer Genome Atlas Colon and Rectum Adenocarcinoma (COREAD) dataset (Firehose Legacy, n = 640), both accessed via cBioPortal database. Large-scale genomic profiling of CRC [n = 7606 (608 in-house + 6998 public cohorts)] identified FGFR1 amplification (3.8% prevalence) as the predominant FGFR alteration subtype. Multivariable analysis confirmed FGFR alterations as independent predictors of poor disease-free survival [DFS; hazard ratio (HR) 2.58, P = 0.0002] and progression-free survival (PFS; HR 2.17, P = 0.0011), with FGFR1 amplification showing strongest prognostic impact (DFS HR 2.91, PFS HR 2.52, P < 0.01). Notably, the prognostic magnitude of FGFR alterations was comparable to KRAS/BRAF mutations in both localized and metastatic CRC. In addition, we established a semiquantitative immunoreactive score (IRS) system achieving 95.2% concordance with NGS (κ = 0.901), enabling reliable FGFR1 screening in routine pathology workflows. This study provides the first comprehensive characterization of FGFR genomic alterations in CRC through large-scale profiling (n = 7606), establishing FGFR1 amplification as the predominant alteration. Unlike FGFR2/3-driven malignancies, FGFR1-amplified CRC exhibited aggressive clinical behavior and inferior survival outcomes across disease stages. To address the diagnostic challenges in routine practice, we further developed a validated immunohistochemical scoring system (IRS), establishing a cost-effective and clinically feasible alternative to molecular assays for identifying FGFR1-driven CRC subsets. Show less
📄 PDF DOI: 10.1016/j.esmoop.2025.105561
FGFR1

Multi-omics analysis of copper metabolism-related molecular subtypes and risk stratification for osteosarcoma.

Yang Zhang, Wen Liu, Dayong Liu +5 more · 2025 · Discover oncology · Springer · added 2026-04-24
As the most common primary malignant bone tumor, further investigation into risk stratification for osteosarcoma (OS) prognosis is of significant clinical importance. Copper is essential for bone meta Show more
As the most common primary malignant bone tumor, further investigation into risk stratification for osteosarcoma (OS) prognosis is of significant clinical importance. Copper is essential for bone metabolism; however, its specific role in OS remains unclear. The expression characteristics of copper metabolism related genes (CORGs) in OS were revealed by single cell sequencing. Prognosis-associated CORGs were identified, and a CORG-related scoring system and risk model were established using bioinformatics approaches, including univariate and multivariate Cox regression analyses and LASSO analysis. We further analyzed immune microenvironment infiltration, molecular subtypes and clinicopathological characteristics. The impact of selected CORG with high-risk coefficient on OS cells was tested by qRT-PCR, western blot, siRNA, colony formation analysis and Transwell in vitro. We successfully developed an OS scoring system related to copper metabolism and validated its independent prognostic value in patients with OS. The potential clinical value of CORG scoring system was analyzed. APOA4 was selected for in vitro experiments and its effect on the proliferation and invasion ability of OS cells was verified. We established a copper metabolism-related scoring system to effectively stratify the risk of OS patients. Our results provide a new basis for the role of copper metabolism in OS and provide new potential targets for the treatment of OS. Show less
📄 PDF DOI: 10.1007/s12672-025-02273-0
APOA4

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

Genetic association of circulating lipids and lipid-lowering drug targets with vascular calcification.

Pengfei Zhang, Wenting Wang, Qian Xu +5 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. Show more
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. This study explores the genetic causal associations of different circulating lipids and lipid-lowering drug targets with coronary artery calcification (CAC) and abdominal aortic artery calcification (AAC). We obtained single-nucleotide polymorphisms (SNPs) and expression quantitative trait loci (eQTLs) associated with seven circulating lipids and 13 lipid-lowering drug targets from publicly available genome-wide association studies and eQTL databases. Causal associations were investigated by univariable, multivariable, drug-target, and summary data-based Mendelian randomization (MR) analyses. Potential mediation effects of metabolic risk factors were evaluated. MR analysis revealed that genetic proxies for low-density lipoprotein cholesterol (LDL-C), triglycerides (TC) and Lipoprotein (a) (Lp(a)) were causally associated with CAC severity, and apolipoprotein B (apoB) level was causally associated with AAC severity. A significant association was detected between hepatic Lipoprotein(A) (LPA) gene expression and CAC severity. Colocalisation analysis supported the hypothesis that the association between LPA expression and CAC quantity is driven by different causal variant sites within the ±1 Mb flanking region of LPA. Serum calcium and phosphorus had causal associations with CAC severity. Inhibitors targeting LPA might represent CAC drug candidates. Moreover, T2DM, hypercalcemia, and hyperphosphatemia are positively causally associated with CAC severity, while chronic kidney disease and estimated glomerular filtration rate are not. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.119136
APOB

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

Association between 24-hour movement behaviors and depressive symptoms among urban older adults in china: a compositional isotemporal substitution analysis.

Hu Ji, Glenn Roswal, Jing Min Liu +2 more · 2025 · Frontiers in psychiatry · Frontiers · added 2026-04-24
To examine the association between 24-hour movement behaviors and depressive symptoms in older adults using compositional data analysis, and to investigate the dose-response characteristics of time re Show more
To examine the association between 24-hour movement behaviors and depressive symptoms in older adults using compositional data analysis, and to investigate the dose-response characteristics of time reallocations between movement behaviors in relation to depressive symptoms. A cross-sectional study was conducted among 1093 urban-dwelling older adults aged 60 years and above in selected communities of Jinan City, Shandong Province, China, between April 2024 and September 2024. The Chinese version of the International Physical Activity Questionnaire-Long Form (IPAQ-LF) was used to estimate time spent in moderate to vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behavior (SB), and sleep (SLP) across a typical 24-hour day. The Chinese version of the Patient Health Questionnaire Depression Scale-9 item (PHQ-9) was applied to assess depressive symptoms. Compositional isotemporal substitution models were employed to explore the associations between time reallocations among 24-hour movement behaviors and depressive symptoms, accounting for the co-dependent nature of time-use data. (1) The geometric means of time spent in MVPA, LPA, SB, and SLP were 25.33 minutes, 141.26 minutes, 738.10 minutes, and 455.15 minutes, respectively. Variation matrix analysis revealed the highest log-ratio variance between MVPA and SB (0.168), and the lowest between SLP and SB (0.031). (2) The prevalence of screening-positive depressive symptoms was 16.29% among Chinese urban older adults. (3) Results from compositional linear regression models showed that time allocated to MVPA, LPA, and SLP (relative to the remaining movement behaviors) was negatively associated with depressive symptoms, while time spent in SB was positively associated. (4) Dose-response analysis further indicated that: (a) MVPA substitutions with other movement behaviors exhibited nonlinear and markedly asymmetric effects on depressive symptoms; (b) replacing MVPA with LPA, SB, or SLP resulted in increasingly larger changes in predicted scores as substitution duration increased, whereas the reverse substitution (MVPA for other movement behaviors) produced progressively smaller changes; and (c) substitutions between SB and LPA displayed linear and symmetrical effects. The findings provide evidence of an association between 24-hour movement behaviors and depressive symptoms in Chinese urban-dwelling older adults and reinforce the importance of achieving a balance between different types of movement behaviors over a 24-hour period for mental health. Show less
📄 PDF DOI: 10.3389/fpsyt.2025.1706591
LPA

Machine learning models for predicting short-term progression in patients with stage 4 chronic kidney disease: a multi-center validation study.

Jingshu Li, Xuanyi Du, Rui Zhang +7 more · 2025 · Scientific reports · Nature · added 2026-04-24
End-stage renal disease (ESRD) is associated with high morbidity and mortality. Identifying patients with stage 4 chronic kidney disease (CKD) at risk of short-term progression to ESRD remains challen Show more
End-stage renal disease (ESRD) is associated with high morbidity and mortality. Identifying patients with stage 4 chronic kidney disease (CKD) at risk of short-term progression to ESRD remains challenging. Accurate prediction can improve advanced care planning and patient outcomes. This study aimed to develop and validate a machine learning (ML) model for predicting progression within 25 weeks (approximately six months) of ESRD in patients with stage 4 CKD. Electronic health records (EHRs) of patients with stage 4 CKD were analyzed. Nine ML models including Ridge regression (Ridge), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were used to predict short-term progression to ESRD within 25 weeks. The models were trained and externally validated using the data of 346 and 105 patients. Of the 451 patients with stage 4 CKD, 219 developed ESRD. Among the evaluated models, XGBoost demonstrated the best overall performance. In the internal validation, it achieved an area under the curve (AUC) of 0.93, an accuracy of 0.90, and an F1 score of 0.89. In the external validation, XGBoost maintained the highest AUC (0.85), accuracy (0.79), and F1 score (0.79), along with the highest average precision (0.89) and a low log-loss (0.48), indicating strong discriminative ability and good generalizability. The top predictive features included high-density lipoprotein cholesterol, Alb, Cys C, ApoB, FGB, Bun, Neutrophil, and Total cholesterol. This study demonstrated the feasibility of ML for assessing ESRD prognosis based on easily accessible clinical features. XGBoost demonstrated superior performance in both internal and external validation, suggesting its potential for future patient screening. Show less
📄 PDF DOI: 10.1038/s41598-025-23037-4
APOB

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

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

Social Isolation and Its Influencing Factors in Maintenance Haemodialysis Patients: A Latent Profile Analysis.

Luwen Zhang, Fangli Liu, Jinghui Liu +1 more · 2025 · Journal of advanced nursing · Blackwell Publishing · added 2026-04-24
To explore latent profiles of social isolation in maintenance haemodialysis (MHD) patients and to analyse the factors influencing different latent profiles. Multicentre cross-sectional study. Between Show more
To explore latent profiles of social isolation in maintenance haemodialysis (MHD) patients and to analyse the factors influencing different latent profiles. Multicentre cross-sectional study. Between November 2024 to March 2025, 305 MHD patients from the haemodialysis centres of three hospitals in Henan Province, China, were recruited using a convenience sampling method. All participants completed the general information questionnaire, Lubben Social Network Scale 6 (LSNS-6), UCLA Loneliness Scale-6 (ULS-6) and Personal Mastery Scale. Latent Profile Analysis (LPA) was used to classify the participants into potential subgroups with different types of social isolation. The influencing factors of profiles were explored by univariate analysis and multiple logistic regression analysis. Social isolation of 305 patients can be divided into three profiles: the family-friend dual isolation group (14.10%), friend isolation-only group (47.54%), and social network well-being group (38.36%). Multivariable logistic regression analysis revealed that monthly personal income, living arrangement, social participation, dialysis time, post-dialysis fatigue, number of comorbidities, loneliness and personal mastery were identified as factors influencing the profiles. There is heterogeneity in social isolation among MHD patients. It is therefore necessary to implement targeted intervention measures based on the distinct characteristics of each subgroup to facilitate their social reintegration. Nurses should identify differences in social isolation among MHD patients. It is necessary to establish tripartite connections between families, hospitals and communities, and develop personalised psychosocial interventions to alleviate social isolation. The study identified distinct subgroups of social isolation among MHD patients, while emphasising the impact of psychological resources such as loneliness and personal mastery on social isolation. This may offer critical insights for nurses to develop targeted interventions for patients' social health. The study followed the STROBE guidelines for cross-sectional studies. No patient or public involvement. Show less
no PDF DOI: 10.1111/jan.70452
LPA

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

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