👤 Yanyan 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, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, 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

Unraveling the shared genetic architecture of osteoarthritis and metabolic traits through multi-omics insights.

Binzhi Liao, Yumeng Mu, Mengliang Luo +8 more · 2026 · Osteoarthritis and cartilage · Elsevier · added 2026-04-24
Osteoarthritis (OA) often coexists with metabolic traits (MTs), causing significant disability. Our study aims to uncover the shared genetic mechanisms between OA and MTs, revealing novel OA-MT relate Show more
Osteoarthritis (OA) often coexists with metabolic traits (MTs), causing significant disability. Our study aims to uncover the shared genetic mechanisms between OA and MTs, revealing novel OA-MT related genes, proteins and pathways. We first explored the clinical associations between OA and MTs based on UK Biobank data. Using GWAS statistics for 9 OA subtypes and 51 MTs, we identified both global and regional genetic correlations. Multi-trait GWAS helped revealed credible genes and relevant pathways through various methods. Protein-level analyses were also conducted to identify key proteins. We developed polygenic scores (PGS), machine learning models and drug repurposing strategies were explored to translate these findings into clinical applications. We identified 152 trait pairs with significant associations and 709 local regions linked to OA-MT. Key SNVs like rs13135092 (SLC39A8) and rs34811474 (ANAPC4) were associated with multiple OA-MT pairs. Lipid and glucose metabolism emerged as central pathways, with tissue-specific enrichment analyses revealing key gene clusters in hepatocytes, arteries, and brain regions. Protein-level analyses identified 205 protein subgroups. PGS integrating MTs outperformed model based solely on OA, improving AUC by 17.5%. Causal gene-based models showed strong diagnostic accuracy (average AUC = 0.875 in external cohorts). Drug prediction highlighted fenofibrate as a promising treatment among 71 candidates. This study provides new insights into the genetic links between OA and MTs. We identified genes, proteins, and pathways related to comorbidities, revealing shared mechanisms, highlighting the potential of integrating metabolic factors to improve OA prediction, diagnosis, and treatment. Show less
no PDF DOI: 10.1016/j.joca.2025.10.010
ANAPC4

[Polystyrene nanoplastics disrupt macrophage cholesterol metabolism by adsorbing apolipoprotein E].

Ao Li, Zijia Liu, Zijie Zhao +4 more · 2026 · Sheng wu gong cheng xue bao = Chinese journal of biotechnology · added 2026-04-24
This study aimed to investigate the potential health hazards and molecular mechanisms of nanoplastic (NP) pollutants. Polystyrene nanoplastics (PS-NPs), which are prevalent in the environment and can Show more
This study aimed to investigate the potential health hazards and molecular mechanisms of nanoplastic (NP) pollutants. Polystyrene nanoplastics (PS-NPs), which are prevalent in the environment and can enter the human body, have been closely associated with the risk of cardiovascular diseases, yet their impact on cholesterol metabolism remains unclear. In this study, proteomic analysis revealed that PS-NPs specifically adsorbed 1 676 proteins following their interaction with macrophages. Bioinformatic analysis indicated that these adsorbed proteins were significantly enriched in the cholesterol metabolism pathway, with apolipoprotein E (APOE) being the most prominently adsorbed. Further molecular docking and molecular dynamics simulations demonstrated that polystyrene molecules could inhibit the interaction between APOE and cholesterol by competitively binding to key amino acid residues (e.g., LEU-202 and TRP-228) of APOE. Cell experiments confirmed that exposure to 100 μg/mL PS-NPs for 24 h significantly induced lipid accumulation in macrophages. This study reveals, from a molecular interaction perspective, a novel mechanism by which PS-NPs disrupt lipid metabolism by interfering with APOE function. It provides key evidence for elucidating the toxicological mechanism through which PS-NPs promote atherosclerosis and holds significant scientific importance for assessing their health risks. Show less
no PDF DOI: 10.13345/j.cjb.250704
APOE

Superior performance of chitosan microspheres loaded with hyperbranched polylysine for endotoxin adsorption: From molecular interactions to hemoperfusion applications.

Yuhui Feng, Ziyue Ling, Xianda Liu +4 more · 2026 · Carbohydrate polymers · Elsevier · added 2026-04-24
Sepsis triggered by lipopolysaccharide (LPS) is a life-threatening condition. Inspired by the specific capture mechanism of innate proteins like LBP and CD14, we develop oxidized chitosan microspheres Show more
Sepsis triggered by lipopolysaccharide (LPS) is a life-threatening condition. Inspired by the specific capture mechanism of innate proteins like LBP and CD14, we develop oxidized chitosan microspheres functionalized with hyperbranched polylysine (OCS-HBPL) as a sepsis detoxification agent. Isothermal titration calorimetry (ITC) reveals that HBPL-LPS binding is an enthalpy-driven process, distinct from the entropy-driven interaction of linear polylysine (LPL)-LPS. Validated by surface plasmon resonance (SPR), HBPL demonstrates superior affinity with a dissociation constant (K Show less
no PDF DOI: 10.1016/j.carbpol.2026.125269
LPL

CX3CL1 in Early Detection of Alzheimer's Disease: Plasma Dynamics Across Age and Disease Stages.

Ling Wang, Yujie Liu, Fei Li +4 more · 2026 · Annals of clinical and translational neurology · Wiley · added 2026-04-24
Alzheimer's disease (AD) is characterized by amyloid-beta plaques, tau tangles, and neuroinflammation. C-X3-C motif chemokine ligand 1 (CX3CL1, also known as fractalkine), a neuroimmune chemokine impl Show more
Alzheimer's disease (AD) is characterized by amyloid-beta plaques, tau tangles, and neuroinflammation. C-X3-C motif chemokine ligand 1 (CX3CL1, also known as fractalkine), a neuroimmune chemokine implicated in AD pathogenesis, shows inconsistent alterations in plasma/serum across studies. Specifically examining age-dependency and diagnostic utility, we investigated plasma CX3CL1 levels across the cognitive continuum (cognitively normal [CN], amnestic mild cognitive impairment [aMCI], AD) in a Chinese cohort. A total of 443 participants, including 130 patients with AD, 72 patients with aMCI, and 99 age-and sex-matched CN controls, as well as a cohort of 142 CN subjects of different ages, were enrolled from Chongqing General Hospital. Plasma CX3CL1 levels were determined using Enzyme-Linked Immunosorbent Assay (ELISA). Apolipoprotein E genotypes (APOE) were performed. The correlations between Plasma CX3CL1 levels and cognition test scores or age were analyzed. The optimal diagnostic sensitivity and specificity were determined using receiver operating characteristic curve analysis. Plasma CX3CL1 levels significantly increased with age in CN individuals. No significant sex difference was found. Plasma CX3CL1 levels did not differ significantly between APOE ε4 carriers and non-carriers. Stepwise elevation across continuum: CX3CL1 levels showed a significant stepwise increase: CN controls (1.73 ± 0.51 ng/mL) < aMCI (2.40 ± 1.06 ng/mL) < AD (4.15 ± 1.24 ng/mL) (p < 0.001 between all groups). This pattern persisted in both male and female subgroups, between the AD group and the aMCI group, between the AD group and the CN control group (p < 0.001), between the aMCI group and the CN control group, and between the male and female subgroups (p < 0.05). CX3CL1 levels negatively correlated with Mini-Mental State Examination (MMSE) scores and positively correlated with age. Plasma CX3CL1 levels exhibit a significant age-dependent increase in cognitively normal individuals, peak in midlife (40-49 years), and demonstrate a stepwise elevation across the AD continuum (CN → aMCI → AD). Strong inverse correlations with cognitive scores in disease groups and high diagnostic accuracy for AD, particularly against CN, support its role as a biomarker reflecting both physiological aging and AD-related pathological decline. Its regulation appears independent of APOE ε4 status. The midlife peak suggests potential relevance for preclinical processes, warranting further investigation of CX3CL1 as a biomarker and therapeutic target. Show less
no PDF DOI: 10.1002/acn3.70320
APOE

CD80

Yin Wang, Pan Li, Wenming Li +10 more · 2026 · Cell communication and signaling : CCS · BioMed Central · added 2026-04-24
Tc17 cells (IL-17 The percentage of Tc17 cells, monocytes and IL-1β Higher populations of Tc17 cells, IL-1β The present results show that suppressing IL-1β expression by preventing CD80 [Figure: see t Show more
Tc17 cells (IL-17 The percentage of Tc17 cells, monocytes and IL-1β Higher populations of Tc17 cells, IL-1β The present results show that suppressing IL-1β expression by preventing CD80 [Figure: see text] The online version contains supplementary material available at 10.1186/s12964-026-02785-4. Show less
📄 PDF DOI: 10.1186/s12964-026-02785-4
APOE

Identifying subgroups of nurses' voice behavior in clinical settings: a latent profile analysis of work motivation and demographic predictors.

Jingting He, Yanping Ying, Qiufang Lu +6 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
Nurses' voice behavior is critical for patient safety and organizational improvement. However, its manifestation is not uniform among nurses. This study aimed to identify latent profiles of nurses' vo Show more
Nurses' voice behavior is critical for patient safety and organizational improvement. However, its manifestation is not uniform among nurses. This study aimed to identify latent profiles of nurses' voice behavior using Latent Profile Analysis (LPA) to understand this heterogeneity and explore its influencing factors, with a specific focus on differences across work motivation dimensions (rooted in Self-Determination Theory, SDT). A multicenter cross-sectional design was adopted. Data from 701 clinical nurses across six hospitals in Guangxi Province were analyzed: LPA identified four distinct profiles, and Multinomial Logistic Regression was used to examine predictors. Work motivation was measured by the Multidimensional Work Motivation Scale (MWMS), and voice behavior by the Voice Behavior Scale (VBS). LPA identified four distinct profiles (Conservative, 5.42%; Balanced Risk-Taker, 26.39%; Transitional, 34.38%; Challenging, 33.8%), and Multinomial Logistic Regression was used to examine predictors. Work motivation was measured by the Multidimensional Work Motivation Scale (MWMS), and voice behavior by the Voice Behavior Scale (VBS). Results showed autonomous motivation (e.g., intrinsic drive) strongly predicted active voice behavior, while amotivation predicted conservative profiles. Nurses exhibited high work motivation (MWMS: 93.02 ± 21.09) and moderately high voice behavior (VBS: 39.27 ± 8.736). The research found that nurses exhibited high work motivation and moderately high voice behavior, with autonomous motivation being a pivotal predictor. Differentiated strategies targeting intrinsic motivation enhancement are critical for fostering nursing innovation and improving care quality. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1732216
LPA

The association between short video addiction and emotion dysregulation among college students: a latent profile analysis and its influencing factors.

Shuhe Wang, Zhongguo Liu · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
This study aimed to use latent profile analysis (LPA) to identify heterogeneous configurational patterns of short video addiction and emotion dysregulation among college students, and to systematicall Show more
This study aimed to use latent profile analysis (LPA) to identify heterogeneous configurational patterns of short video addiction and emotion dysregulation among college students, and to systematically examine the predictive effects of cognitive reappraisal, emotional loneliness, and sociodemographic factors on latent profile membership. A cross-sectional survey design was employed. From April to July 2025, full-time undergraduate students were recruited from multiple universities in Shandong Province using a combination of convenience sampling and snowball sampling. Participants completed online questionnaires including the Short Video Addiction Scale, the Emotion Dysregulation Inventory (EDI), the Cognitive Reappraisal Scale, and the Emotional Loneliness Scale. A total of 1,168 valid questionnaires were obtained. LPA identified four optimal profiles: Profile 1 ("low short video addiction-low emotion dysregulation"), Profile 2 ("medium to lower short video addiction-medium to lower emotion dysregulation"), Profile 3 ("medium to upper short video addiction-medium to upper emotion dysregulation"), and Profile 4 ("high short video addiction-high emotion dysregulation"). Multivariable logistic regression analyses indicated that, with Profile 4 as the reference category, cognitive reappraisal significantly increased the likelihood of membership in lower-risk profiles, whereas emotional loneliness significantly decreased the likelihood of membership in lower-risk profiles. Among sociodemographic factors, being female and having an urban background significantly increased the likelihood of membership in Profile 1 (vs. Profile 4); being a non-only child and having no part-time work experience significantly predicted membership in Profile 3. Marked heterogeneity exists among college students in the measured dimensions of short-form video addiction and emotion dysregulation, and the two constructs exhibit highly concordant co-variation. The findings provide empirical support for developing risk-stratified and precision-oriented mental health intervention strategies. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1789207
LPA

Ptch2 Deficiency Triggers Lipoma Formation and Adipogenic Transcriptome Reprogramming in Nile tilapia (

Changle Zhao, Xiang Liu, Xi Peng +5 more · 2026 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
The Hedgehog (Hh) signaling pathway is a key regulator of adipogenesis and lipid metabolism. However, the specific role of its receptor, Patched2 (Ptch2), in these processes remains unclear. Here, usi Show more
The Hedgehog (Hh) signaling pathway is a key regulator of adipogenesis and lipid metabolism. However, the specific role of its receptor, Patched2 (Ptch2), in these processes remains unclear. Here, using a CRISPR/Cas9-mediated Show less
📄 PDF DOI: 10.3390/ani16030405
LPL

UFM1 suppresses VSMCs phenotypic switching and attenuates atherosclerosis by inhibiting AKT phosphorylation.

Qianru Zhang, Mirenuer Aikebaier, Yefan Hu +5 more · 2026 · Biochemical pharmacology · Elsevier · added 2026-04-24
Atherosclerosis is a chronic and progressive inflammatory disease that can lead to adverse cardiovascular and cerebrovascular events. Phenotypic switching of vascular smooth muscle cells (VSMCs) plays Show more
Atherosclerosis is a chronic and progressive inflammatory disease that can lead to adverse cardiovascular and cerebrovascular events. Phenotypic switching of vascular smooth muscle cells (VSMCs) plays a pivotal role in its development and progression, but the upstream regulatory mechanisms remain incompletely defined. Here, we identify ubiquitin-fold modifier 1 (UFM1), a ubiquitin-like protein, as a critical regulator of VSMCs plasticity and atherogenesis. In VSMCs stimulated with oxidized low-density lipoprotein (ox-LDL), UFM1 overexpression markedly attenuated phenotypic switching, restoring contractile features and suppressing synthetic activation, accompanied by reduced proliferation and migration. In contrast, UFM1 knockdown further exacerbated these phenotypic alterations. In ApoE Show less
no PDF DOI: 10.1016/j.bcp.2026.117957
APOE

Interplay between obesity-associated insulin resistance and immune system through the lens of evolutionary medicine.

Maryam Moazzam-Jazi, Saeideh Jafarinejad-Farsangi, Leila Najd-Hassan-Bonab +4 more · 2026 · Molecular metabolism · Elsevier · added 2026-04-24
Insulin resistance (IR), commonly associated with obesity, is linked to a range of metabolic and immune-related disorders in the contemporary human population. Nevertheless, it is evolutionary well-co Show more
Insulin resistance (IR), commonly associated with obesity, is linked to a range of metabolic and immune-related disorders in the contemporary human population. Nevertheless, it is evolutionary well-conserved, suggesting its potential survival advantages to our ancestors. This review aims to explore the intricate interplay between IR and the immune system as well as its implications for the development of immune-metabolic and allergic diseases in the modern era. From an evolutionary medicine perspective, the longevity of ancient humans relied on energy storage to endure food shortages and effectively activate the immune system against various diseases. Under normal conditions, insulin induces glycogen and triglyceride synthesis in the liver and adipose tissues. However, IR directs more glucose to insulin-independent tissues, such as the immune system, which are critical for survival in adverse conditions. The persistent IR in our current lifestyle promotes low-grade inflammation, accompanied by various metabolic and allergic disorders. Critically, this evolutionary mismatch not only explains disease susceptibility but also informs therapeutic design to target immune-metabolic crosstalk. Moreover, our evolutionary analysis demonstrates that the genomic regions near the PTEN, IL27, and NUPR1 genes could play an important role in this interaction across diverse populations. Show less
📄 PDF DOI: 10.1016/j.molmet.2026.102335
IL27

Posterior communicating artery variation is linked to compromised cerebral hemodynamics in middle-aged and older adults.

Francis E Cambronero, Panpan Zhang, W Hudson Robb +8 more · 2026 · Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · SAGE Publications · added 2026-04-24
We investigate whether common circle of Willis (CoW) variants relate to cerebral blood flow (CBF) characteristics among aging adults. Vanderbilt Memory and Aging Project participants free of clinical Show more
We investigate whether common circle of Willis (CoW) variants relate to cerebral blood flow (CBF) characteristics among aging adults. Vanderbilt Memory and Aging Project participants free of clinical stroke ( Show less
📄 PDF DOI: 10.1177/0271678X261424053
APOE

2026 Consensus and review of Lipoprotein(a) from Taiwan Society of Lipid and Atherosclerosis: Molecular pathogenesis, epidemiology, clinical implications, and advances in diagnostic strategies.

Chao-Yun Cheng, Yih-Jer Wu, Chih-Fan Yeh +25 more · 2026 · Journal of the Formosan Medical Association = Taiwan yi zhi · Elsevier · added 2026-04-24
Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic Show more
Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve disease (CAVD). Structurally composed of a low-density lipoprotein (LDL)-like particle covalently linked to apolipoprotein(a) [apo(a)], Lp(a) exhibits unique atherogenic, thrombogenic, and inflammatory properties, largely due to its role as a carrier of oxidized phospholipids (OxPL). Plasma Lp(a) concentrations are predominantly determined by the number of kringle IV type 2 (KIV-2) repeats in the LPA gene, with minimal influence from lifestyle or environmental factors. Despite substantial evidence linking elevated Lp(a) to cardiovascular risk, clinical testing remains underutilized, especially in East Asian countries. In Taiwan, although population-level Lp(a) concentrations are comparatively low, a significant subset exceeds risk thresholds, with local studies confirming its prognostic value in coronary artery disease and ischemic stroke. Barriers, including limited physician awareness, implementation barriers, and therapeutic nihilism, contribute to its under-recognition. This review highlights the molecular features of Lp(a), its pathogenesis of cardiovascular disorders, epidemiology, and current barriers and future advances in diagnostic testing, with a particular focus on implications for cardiovascular risk management in Taiwan. Show less
no PDF DOI: 10.1016/j.jfma.2026.03.073
LPA

The moderation and mediation roles of white matter insults in relationship of APOE ε4 genotype with Alzheimer's disease and related phenotypes: two longitudinal studies.

Kai-Zheng Liu, Xin Zhou, Yuan-Fei Dong +5 more · 2026 · Journal of advanced research · Elsevier · added 2026-04-24
The Apolipoprotein E ε4 (APOE ε4) allele and white matter hyperintensities (WMH) have been implicated in the pathogenesis of Alzheimer's disease (AD). To investigate the dual roles of WMH in statistic Show more
The Apolipoprotein E ε4 (APOE ε4) allele and white matter hyperintensities (WMH) have been implicated in the pathogenesis of Alzheimer's disease (AD). To investigate the dual roles of WMH in statistically moderating and mediating the relationship of APOE ε4 with AD and related phenotypes, as well as the potential biological correlates. Data were derived from 34,783 non-demented participants in the UK Biobank (UKB; mean age = 55 years; follow-up = 4.3 years) and 863 in the Alzheimer's disease Neuroimaging Initiative (ADNI; mean age = 71.9 years; follow-up = 3.8 years). Multivariable models evaluated associations of APOE ε4 status, WMH, and their interaction with cognition, neurodegeneration, core pathologies, and AD risk. Mediation analyses were performed to quantify the extent to which WMH statistically explained ε4-outcome associations. Cerebrospinal fluid proteomic and bioinformatic analyses were used to explore biological clues in a subsample of ADNI (n = 708). APOE ε4 carriers exhibited larger WMH volumes (p < 0.001, UKB) and faster WMH change rates (p = 0.019, ADNI). In UKB, WMH statistically mediated a small proportion of associations between APOE ε4 and poorer numeric memory performance, smaller hippocampal volume, increased incident AD and all-cause dementia (ACD). In ADNI, WMH showed statistical mediation signals in the associations of APOE ε4 with faster rates of cognitive decline, amyloid-β (Aβ) deposition, and neurodegeneration. Notably, WMH interacted with APOE ε4 to exacerbate cognitive decline, hippocampal atrophy, and Aβ deposition. Proteomic analyses suggested that neuroinflammatory and axonal injury pathways may be associated with the observed mediating and moderating patterns. WMH mediated and enhanced the associations of APOE ε4 with AD-related phenotypes. These findings warrant further studies to clarify the underlying mechanisms and clinical implications. Show less
no PDF DOI: 10.1016/j.jare.2026.04.030
APOE

Construction of a survival model for predicting biochemical recurrence of prostate cancer based on propionate metabolism-related genes.

Baosai Lu, Yalin Niu, Xi Liu +2 more · 2026 · Translational andrology and urology · added 2026-04-24
About 20-40% of prostate cancer (PCa) develop biochemical recurrence (BCR) after surgery, and propionate metabolism may contribute to tumor progression. BCR remains a major clinical challenge in PCa, Show more
About 20-40% of prostate cancer (PCa) develop biochemical recurrence (BCR) after surgery, and propionate metabolism may contribute to tumor progression. BCR remains a major clinical challenge in PCa, as current tools based on histopathology and prostate-specific antigen (PSA) fail to capture the molecular heterogeneity driving the disease. While metabolic reprogramming is known to facilitate post-treatment adaptation, the specific role of propionate metabolism in this context remains largely unexplored. Therefore, this study aimed to systematically investigate propionate metabolism-related genes (PMRGs) to develop a novel prognostic model for the improved early prediction of recurrence. In this study, The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD), GSE70770 and 412 PMRGs were employed. Differentially expressed genes (DEGs) in PCa and control and DEGs2 in BCR and no BCR samples obtained by differential analysis were intersected with PMRGs to get candidate genes. After Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, biomarkers were identified to construct risk models. Biomarkers including In this study, PMRGs were regarded as biomarkers in PCa for risk model construction, which suggest that propionate metabolism represents a biologically relevant axis in PCa recurrence and may offer a novel framework for biomarker-driven risk assessment. Show less
📄 PDF DOI: 10.21037/tau-2025-aw-811
LPL

Normalization strategies for protein quantification in plasma astrocyte-derived extracellular vesicles: a clinical applicability study.

Kun Li, Mian-Mian Chen, Shu-Xian Xu +1 more · 2026 · BMC psychiatry · BioMed Central · added 2026-04-24
Astrocyte-derived extracellular vesicles (ADEVs) have emerged as a novel research tool in the field of central nervous system disorders. However, significant differences in yield and purity exist amon Show more
Astrocyte-derived extracellular vesicles (ADEVs) have emerged as a novel research tool in the field of central nervous system disorders. However, significant differences in yield and purity exist among extracellular vesicles (EVs) isolated by different methods, leading to considerable heterogeneity in clinical study outcomes. Therefore, establishing appropriate normalization strategies to enhance comparability across results is a key prerequisite for their clinical translation. This study included 15 patients with major depressive disorder (MDD) and 15 healthy controls (HCs). ADEVs were isolated from plasma using ultracentrifugation combined with immunoaffinity capture. Subsequently, the levels of brain-derived neurotrophic factor (BDNF), five EV biomarkers (CD9, CD63, CD81, Alix, and TSG101), and particle counts in ADEVs were quantified. In addition, plasma lipoprotein levels were measured. Our results demonstrated a lack of significant correlation between particle counts and the levels of five EV biomarkers in plasma ADEVs, whereas strong correlations were observed among the five biomarkers themselves. Normalization of BDNF levels to CD81 or CD9 revealed a significant decrease in the MDD group, whereas normalization to EV particle counts or other EV biomarkers did not show such differences. Notably, plasma levels of apolipoprotein B (APOB), low-density lipoprotein (LDL), and total cholesterol (TC) significantly interfered with the measurement of particle counts. In summary, under conventional EV isolation and detection conditions, our findings support the use of EV biomarker levels rather than particle counts as a normalization method for quantifying target proteins of ADEVs in plasma. [Image: see text] The online version contains supplementary material available at 10.1186/s12888-026-07796-6. Show less
📄 PDF DOI: 10.1186/s12888-026-07796-6
BDNF

Associations between 24-h movement behaviors and optimal mental health of Chinese university students: a compositional data analysis.

Chenlin Li, Yanping Qiu, Nan Zheng +3 more · 2026 · BMC public health · BioMed Central · added 2026-04-24
This cross-sectional study aimed to examine the associations between the 24-h movement behaviors and mental health among university students in China, and to determine the optimal behavioral balance b Show more
This cross-sectional study aimed to examine the associations between the 24-h movement behaviors and mental health among university students in China, and to determine the optimal behavioral balance based on the top 5% of model-predicted mental health outcomes using compositional data analysis. A total of 6,084 university students aged 17–24 years in Southwest China self-reported their daily durations of moderate-to-vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behavior (SED), and sleep (SLP). They were stratified by gender and then randomly and equally assigned to the “recommendation” group and the “validation” group. Using compositional data analysis, time-use compositions (MVPA, LPA, SED, SLP) were transformed into isometric log-ratios (with quadratic terms as needed) and subsequently used in regression models to predict the three mental health outcomes. All possible combinations of motion components were examined to determine the combination with the highest correlation (top 5%) for each outcome. Through research and analysis of the recommendation groups, the optimal combination of average (range) time usage is determined as follows: for males, MVPA 92 (60–110) min/day, LPA 361 (310–400) min/day, SED 372 (350–480) min/day, SLP 614 (530–680) min/day; for females, MVPA 58 (40–90) min/day, LPA 290 (180–390) min/day, SED 445 min (400–560), SLP 665 (580–740) min/day. The recommended durations served as benchmarks for the validation group. Participants who met the optimal 24-h movement behavior time showed significantly lower depression (males: β = –1.290, The optimal 24-h movement behavior time differs between men and women. Males tend to require a longer optimal MVPA duration than females, while females require a longer optimal SLP duration than males. The findings provide valuable reference for developing 24-h movement guidelines and promoting healthy and balanced lifestyles among university students. [Image: see text] The online version contains supplementary material available at 10.1186/s12889-026-26534-x. Show less
📄 PDF DOI: 10.1186/s12889-026-26534-x
LPA

From Biomarkers to New Treatments for Post-Traumatic Stress Disorder.

Albert H C Wong, Le Wang, Yuan Shen +1 more · 2026 · Neuroscience bulletin · Springer · added 2026-04-24
Post-traumatic stress disorder (PTSD) causes debilitating nightmares, flashbacks and anxiety stemming from a catastrophic, often life-threatening traumatic event. Originally described in soldiers expo Show more
Post-traumatic stress disorder (PTSD) causes debilitating nightmares, flashbacks and anxiety stemming from a catastrophic, often life-threatening traumatic event. Originally described in soldiers exposed to the horrors of battle, PTSD is now recognized in civilian victims of assault, natural disasters and mass casualty events. Most people experiencing trauma do not develop PTSD, so understanding neurobiological mechanisms is crucial to predicting risk and developing targeted treatments. There have been many studies seeking to find biomarkers for PTSD, and their results have converged on several brain regions, molecular pathways and neuropsychological functions. In this review, we focus on selected findings about the glucocorticoid receptor (GR), the chaperone protein FKBP51 (FK506 binding protein 51), BDNF (brain-derived neurotrophic factor), fear memory reconsolidation and epigenetic regulation of gene expression in the hypothalamic-pituitary-adrenal (HPA) axis, amygdala and hippocampus. Together, these disparate aspects of brain function provide an emerging model for understanding the etiology and pathophysiology of PTSD. Avoidance of lethal threats is fundamental for survival, and this stringent evolutionary requirement has conserved many components of fear memory storage and behavioural response to danger. PTSD research can therefore rely on non-human animal model systems with better face and construct validity than most other psychiatric disorders. With this advantage, advances in PTSD biomarker identification are likely closer to clinical translation than in other mental illnesses. We attempt to highlight the most promising biomarkers that could be targeted by novel treatments and propose a map for future research work. Show less
no PDF DOI: 10.1007/s12264-026-01617-2
BDNF anxiety biomarkers neurobiological mechanisms ptsd stress disorder traumatic event

Narciclasine Alleviates Endothelial Inflammation and Atherosclerosis Initiation by Inhibiting Histone Lactylation-Mediated NF-κB Activation.

Ziqian Wang, Zhengbin Zhang, Ran Xin +8 more · 2026 · Inflammation · Springer · added 2026-04-24
Glycolysis-derived lactate serves as a substrate for lysine lactylation, an epigenetic modification playing critical transcriptional regulatory roles in inflammatory diseases. Endothelial inflammation Show more
Glycolysis-derived lactate serves as a substrate for lysine lactylation, an epigenetic modification playing critical transcriptional regulatory roles in inflammatory diseases. Endothelial inflammation, characterized by upregulated glycolysis, initiates atherosclerosis, yet the contribution of histone lactylation remains undefined. Although narciclasine exhibits anti-inflammatory and antioxidant properties, its impact on endothelial inflammation in atherosclerosis is unknown. Connectivity Map (CMap) analysis predicted narciclasine as an inhibitor of oscillatory shear stress and TNF-α-induced endothelial inflammation. In vitro, treatment of human umbilical vein endothelial cells (HUVECs) with 20 nM narciclasine significantly suppressed ox-LDL-induced expression of VCAM1, ICAM1, SELE, and CCL2, reduced reactive oxygen species (ROS) production, and inhibited monocyte adhesion and migration. In vivo, administration of narciclasine (0.02 mg/kg) attenuated carotid artery endothelial inflammation and macrophage infiltration, consequently reducing early atherogenesis in partial carotid ligation model in ApoE Show less
📄 PDF DOI: 10.1007/s10753-025-02446-7
APOE

A microglial LCN2-MC4R signaling axis drives silica-induced neuronal damage via C1q release.

Xia Li, Zihao Xie, Hangbing Cao +10 more · 2026 · Journal of neuroinflammation · BioMed Central · added 2026-04-24
Silica exposure precipitates irreversible lung injury; however, its long-term neurological sequelae—and the microglial mechanisms underlying these effects—remain poorly understood. Here, we demonstrat Show more
Silica exposure precipitates irreversible lung injury; however, its long-term neurological sequelae—and the microglial mechanisms underlying these effects—remain poorly understood. Here, we demonstrate that inhaled crystalline silica induces persistent hippocampal inflammation, anxiety- and depression-like behaviors, and neuronal loss in mice. Bulk RNA sequencing, immunophenotyping, and pharmacological depletion studies revealed that microglia are the primary source of complement C1q in silica-exposed brains. Mechanistically, silica-induced lipocalin-2 (LCN2) engages the melanocortin-4 receptor (MC4R) on microglia, activating a cAMP/PKA/NF-κB cascade that transcriptionally upregulates C1q. Pharmacological blockade of MC4R (using PF) abolished C1q overproduction, normalized brain-derived neurotrophic factor levels, and restored both synaptic integrity and behavioral performance. Our findings establish the LCN2–MC4R–C1q axis as a critical microglial pathway in silica-related neurotoxicity and identify MC4R antagonism as a promising, readily translatable intervention for occupational neuroinflammation. The online version contains supplementary material available at 10.1186/s12974-026-03695-5. Show less
📄 PDF DOI: 10.1186/s12974-026-03695-5
MC4R

Knowledge, Attitudes, and Practices of Lower Limb Arteriosclerosis Obliterans among Patients: A Latent Profile Analysis.

Xinjun Liu, Qiqi Wang, Tingting Qiu +4 more · 2026 · Annals of vascular surgery · Elsevier · added 2026-04-24
This study aimed to assess the knowledge, attitudes, and practices (KAP) of patients with lower limb arteriosclerosis obliterans (ASO) toward their disease. This cross-sectional study was conducted at Show more
This study aimed to assess the knowledge, attitudes, and practices (KAP) of patients with lower limb arteriosclerosis obliterans (ASO) toward their disease. This cross-sectional study was conducted at 3 tertiary hospitals in Chengdu between August 2023 and January 2024 and included patients with lower limb ASO. Data were collected using an interviewer-administered questionnaire that captured demographic information and KAP scores. A latent profile analysis (LPA) was used to identify the KAP patterns among participants. A total of 515 nonproblematic questionnaires were collected, yielding an effective response rate of 95.72%. Among the respondents, 395 (76.85%) were male, with a disease course of 15.96 ± 17.55 months. The knowledge, attitude, and practice scores were 5.27 ± 4.69 (possible range: 0-22), 17.65 ± 2.86 (possible range: 5-25), and 107.63 ± 17.15 (possible range: 33-165), respectively. LPA identified 4 participant profiles: Profile 1 (high attitude, low practice), Profile 2 (low attitude, high practice), Profile 3 (low attitude, low practice), and Profile 4 (high attitude, high practice). Significant differences were found among profiles in residence (P = 0.028), medical insurance (P = 0.043), self-efficacy (P < 0.001), and patient activation (P < 0.001). Patients with lower limb ASO demonstrated inadequate knowledge but moderate levels of attitude and practice. Residence, medical insurance, self-efficacy, and patient activation may affect the KAP patterns of the patients. These findings suggest that tailored interventions targeting distinct patient profiles, while considering broader social determinants of health, may be critical to improving self-management and outcomes. Show less
no PDF DOI: 10.1016/j.avsg.2025.10.022
LPA

Cerebrospinal fluid proteomic signatures reveal

Zhiyuan Ning, Jeff Y L Lam, Zonghua Li +10 more · 2026 · Research square · added 2026-04-24
Cerebrospinal fluid (CSF) proteomics offers insights into molecular changes in aging and Alzheimer's disease (AD). Key AD biomarkers, in particular amyloid-β (Aβ) and tau, in CSF are strongly associat Show more
Cerebrospinal fluid (CSF) proteomics offers insights into molecular changes in aging and Alzheimer's disease (AD). Key AD biomarkers, in particular amyloid-β (Aβ) and tau, in CSF are strongly associated with Show less
📄 PDF DOI: 10.21203/rs.3.rs-8605807/v1
APOE

Levosimendan inhibits HIV-1 infection in myeloid cells in the RIOK1-dependent manner.

Jinshan He, Jie Ma, Youngmin Park +10 more · 2026 · bioRxiv : the preprint server for biology · added 2026-04-24
Despite of the highly potent antiretroviral therapies, HIV-1 establishes persistent infection and causes chronic inflammation in AIDS patients. Beyond CD4+ T cells, HIV-1 infects myeloid cells, includ Show more
Despite of the highly potent antiretroviral therapies, HIV-1 establishes persistent infection and causes chronic inflammation in AIDS patients. Beyond CD4+ T cells, HIV-1 infects myeloid cells, including circulating monocytes and tissue-resident macrophages, and integrates with host genomes to form stable viral reservoirs. To achieve a functional HIV cure, latency-promoting agents (LPAs) have been developed for the "block-and-lock" strategy to reinforce deep HIV-1 latency and permanently silence proviruses. However, most LPAs have been tested mainly in CD4 Show less
no PDF DOI: 10.64898/2026.04.08.717218
LPA

Fujian Tablets promote corticospinal tract remodeling and improve motor function in MCAO rats by regulating BDNF shearing enzyme-mediated proBDNF/mBDNF balance.

Shang Gao, Rui Su, Jie Gao +7 more · 2026 · Journal of ethnopharmacology · Elsevier · added 2026-04-24
Fujian Tablets (FJT), a traditional Chinese medicinal (TCM) preparation, has been clinically used in the rehabilitation of neurological disorders related to ischemic brain injury in the context of TCM Show more
Fujian Tablets (FJT), a traditional Chinese medicinal (TCM) preparation, has been clinically used in the rehabilitation of neurological disorders related to ischemic brain injury in the context of TCM theory. However, its molecular mechanism underlying the promotion of post-ischemic stroke motor function recovery, especially via regulating corticospinal tract (CST) remodeling-a key structure for motor control-remains unelucidated. This study aimed to investigate the effect of FJT on CST remodeling in the denervated hemisphere and motor function recovery in middle cerebral artery occlusion (MCAO) rats, and to explore its potential mechanism by focusing on the balance between precursor brain-derived neurotrophic factor (proBDNF) and mature BDNF (mBDNF), which is tightly regulated by BDNF-cleaving enzymes (Pcsk1 and Furin). The MCAO rat model was established using the intraluminal filament method. Model rats were randomly divided into four groups: MCAO model group, FJT low-dose group, FJT medium-dose group, and FJT high-dose group. Motor function was evaluated by Catwalk gait analysis (assessing average speed, step length, and standing time). CST remodeling and conduction efficiency were determined via biotinylated dextran amine (BDA) neural tracing and motor evoked potential (MEP) detection, respectively. The mRNA and protein expressions of BDNF, cleaving enzymes (Pcsk1, Furin), and related receptors (TrkB, p75NTR, Sortilin) in brain tissues were measured using quantitative real-time polymerase chain reaction (RT-qPCR) and Western blot. BDNF silencing experiment was performed to verify the role of BDNF in FJT-induced effects. Additionally, in vitro neuronal culture was used to observe the effects of FJT, exogenous mBDNF, and Pcsk1/Furin inhibitors on neuronal growth. Compared with the MCAO model group, medium-dose FJT exhibited the most significant therapeutic effects. Specifically, FJT notably improved gait parameters increasing average speed from 20.77 mm/s (MCAO) to 25.71 mm/s (FJT) and step length by approximately 21.14 %. Furthermore, FJT enhanced MEP conduction efficiency and promoted CST remodeling, characterized by a 5.26 % increase in BDA-positive nerve fibers and elevated growth-associated protein 43 (GAP43) expression in the denervated hemisphere. At the molecular level, FJT upregulated the mRNA and protein expressions of Pcsk1 and Furin, increased the levels of BDNF and its functional receptor TrkB, and downregulated the expressions of proBDNF-preferring receptors p75NTR and Sortilin, ultimately shifting the proBDNF/mBDNF ratio toward mBDNF dominance. BDNF silencing significantly attenuated these improvements, reversing FJT-induced motor recovery and CST remodeling. In vitro, FJT-promoted neuronal growth was mimicked by exogenous mBDNF but reversed by Pcsk1/Furin inhibitors. Compared with the MCAO model group, medium-dose FJT exhibited the most significant therapeutic effects. Specifically, FJT notably improved gait parameters, increasing the average speed from 20.77 mm/s (MCAO) to 25.71 mm/s (FJT) and step length by approximately 21.14 %. Furthermore, FJT enhanced MEP conduction efficiency and promoted CST remodeling, characterized by a 5.26% increase in BDA-positive nerve fibers and elevated Growth-Associated Protein 43 (GAP43) expression in the denervated hemisphere. At the molecular level, FJT upregulated the mRNA and protein expressions of Pcsk1 and Furin, increased the levels of BDNF and its functional receptor TrkB, and downregulated the expressions of proBDNF-preferring receptors p75NTR and Sortilin, ultimately shifting the proBDNF/mBDNF ratio toward mBDNF dominance. BDNF silencing significantly attenuated these improvements, reversing FJT-induced motor recovery and CST remodeling. In vitro, FJT-promoted neuronal growth was mimicked by exogenous mBDNF but reversed by Pcsk1/Furin inhibitors. Show less
no PDF DOI: 10.1016/j.jep.2026.121235
BDNF bdnf corticospinal tract ischemic brain injury motor function neurological disorders stroke recovery traditional chinese medicine

Interplay of accelerated biological aging, lifestyle factors, and genetic susceptibility in cognitive function: a community study.

Tianpei Ma, Xin Chen, Qingwen Zhao +19 more · 2026 · The journals of gerontology. Series A, Biological sciences and medical sciences · Oxford University Press · added 2026-04-24
Cognitive impairment is a significant health concern in aging populations, but the interplay between biological aging, lifestyle factors, and genetic susceptibility remains unclear. This study examine Show more
Cognitive impairment is a significant health concern in aging populations, but the interplay between biological aging, lifestyle factors, and genetic susceptibility remains unclear. This study examined whether accelerated biological aging is associated with cognitive impairment, whether lifestyle modifies this association, and how genetic background influences these relationships in Chinese older adults. In this cross-sectional study (2022-2023), 7033 participants from southwestern China were included. Accelerated biological aging was calculated as the residual difference between biological age (based on 10 biomarkers) and chronological age. Lifestyle was assessed via a composite index (smoking, alcohol, physical activity, diet, sleep). Cognitive function was measured using the Chinese Mini-Mental State Examination (C-MMSE), and genetic risk was evaluated through polygenic scores and APOE ε4 status. Linear and logistic regression models assessed associations between accelerated aging and cognition. Accelerated biological aging was associated with lower MMSE scores ( β = -0.243, 95% CI: -0.354, -0.133) and higher cognitive impairment prevalence (OR = 1.098, 95% CI: 1.040, 1.158). An unhealthy lifestyle exacerbated cognitive impairment in biologically older individuals (RERI = 0.25). Those with both accelerated aging and unhealthy lifestyle had the lowest MMSE scores ( β = -1.424, 95% CI: -1.846, -1.003) and highest odds of cognitive impairment (OR = 1.467, 95% CI: 1.194, 1.803). These effects were consistent across all genetic background subgroups. Accelerated aging was associated with lower cognitive function, especially in individuals with unhealthy lifestyles, regardless of genetic susceptibility. This highlights lifestyle modification as a potential intervention target for aging-related cognitive impairment. Show less
no PDF DOI: 10.1093/gerona/glaf277
APOE

Proposed Role of Circadian Clock Genes in Pathogenesis of HCC: Molecular Subtyping and Characterization.

Zhikui Lu, Yi Zhou, Jian Luo +2 more · 2026 · Biomedicines · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/biomedicines14030645
AXIN1

Aging, matrix metalloproteinase imaging, and survival prospects in aortic aneurysm.

Mean Ghim, Onur Varli, Azmi A Ahmad +11 more · 2026 · bioRxiv : the preprint server for biology · added 2026-04-24
Age is a risk factor for aortic aneurysm (AA), and different segments of the aorta exhibit varying susceptibilities to aneurysm. The specific factors that contribute to the higher incidence of AA and Show more
Age is a risk factor for aortic aneurysm (AA), and different segments of the aorta exhibit varying susceptibilities to aneurysm. The specific factors that contribute to the higher incidence of AA and its complications with aging remain unclear. Matrix metalloproteinases (MMPs) are elevated in AA. However, the connection between aging, aortic MMP activity, and the increased prevalence of AA and its complications has not been systematically evaluated. This study leveraged MMP-targeted molecular imaging to investigate how aging affects aortic MMP expression and activity, as well as aneurysm development and survival. AA development and animal survival were monitored for 28 days after Angiotensin (Ang)-II infusion in 8-10-week-old (young) and >51-week-old (old) Old animals' survival to 28 days was significantly lower than that of young Ang-II-infused Aging is associated with increased MMP activity along the aorta and worse AA survival. MMP-targeted molecular imaging can inform the aneurysm survival prospects. Selective MMP inhibitors and tracers may help prevent and track aneurysm growth, dissection, and rupture. Show less
📄 PDF DOI: 10.64898/2026.01.02.697375
APOE

Yin-Yang 1/Neural Precursor Cell-Expressed Developmentally Downregulated 4-Like Axis Suppresses Mer Tyrosine Kinase-Mediated Macrophage Efferocytosis to Exacerbate Atherosclerosis Via Triggering Pyroptosis.

Qiang Liu, Zaihua Cheng, Tao Wu +2 more · 2026 · Journal of the American Heart Association · added 2026-04-24
Atherosclerosis is considered as a major contributor for cardiovascular disease with high morbidity and mortality globally. However, the cross-talk between efferocytosis and inflammation in atheroscle Show more
Atherosclerosis is considered as a major contributor for cardiovascular disease with high morbidity and mortality globally. However, the cross-talk between efferocytosis and inflammation in atherosclerosis remains elusive. ApoE (apolipoprotein E) YY1 and NEDD4L were upregulated, but MerTK was downregulated in the arteries of ApoE Our findings demonstrated that YY1 positively regulated NEDD4L to modulate MerTK-mediated efferocytosis and activate NLRP3-mediated inflammation and pyroptosis, thus exacerbating atherosclerosis. Show less
📄 PDF DOI: 10.1161/JAHA.124.039855
APOE

Mendelian randomization and colocalization reveal potential causal effects of average daily gain on carcass composition and reproductive traits in pigs.

Wondossen Ayalew, Guangzhen Li, Yuqiang Liu +6 more · 2026 · Journal of animal science and biotechnology · BioMed Central · added 2026-04-24
Selective breeding has substantially improved productive and reproductive traits in pigs. Yet, these traits are biologically interconnected, and selection for one often affects others in unintended wa Show more
Selective breeding has substantially improved productive and reproductive traits in pigs. Yet, these traits are biologically interconnected, and selection for one often affects others in unintended ways. While genome-wide association studies (GWAS) have uncovered many loci linked to these traits, they provide limited insight into causal mechanisms. Mendelian randomization (MR) provides a robust framework for inferring causality and identifying shared genetic determinants. Here, we integrated MR, colocalization, and functional genomics to investigate the biological links between growth, carcass composition, and reproduction in pigs. Using average daily gain (ADG) as the exposure, MR revealed potentially significant causal effects (P < 0.05) of ADG on carcass composition traits, including backfat thickness (BFT: Our findings suggest a shared genetic architecture and provide potential evidence of a causal influence of ADG on carcass composition and reproductive traits in pigs. This integrative framework supports the development of multi-trait breeding strategies that enhance productivity while managing inherent trade-offs in regulating complex traits. Show less
📄 PDF DOI: 10.1186/s40104-026-01363-5
MC4R

Single-cell and transcriptomic profiling reveal stemness-driven immune evasion in obstructive sleep apnea (OSA) associated lung cancer.

Yu-Wei Liu, Chi-Jen Wu, Kai-Fu Chang +16 more · 2026 · Journal of Cancer · added 2026-04-24
Obstructive sleep apnea (OSA) is characterized by recurrent intermittent hypoxia (IH) and has been increasingly associated with lung cancer incidence and mortality. However, how IH-related biological Show more
Obstructive sleep apnea (OSA) is characterized by recurrent intermittent hypoxia (IH) and has been increasingly associated with lung cancer incidence and mortality. However, how IH-related biological programs relate to immune remodeling, stemness-associated phenotypes, and therapeutic resistance in lung cancer remains incompletely understood. We integrated single-cell RNA sequencing data from IH-exposed murine lung tissues (GSE301350) with bulk transcriptomic datasets from TCGA-LUAD and GSE31210 to examine hypoxia-associated cellular and transcriptional patterns. Stemness was quantified using CytoTRACE and transcriptome-based stemness scoring, and its associations with immune infiltration, immune checkpoint expression, TIDE scores, predicted drug sensitivity, and immunotherapy response were evaluated. A stemness-based prognostic model was constructed using LASSO Cox regression and validated in independent cohorts. Single-cell analysis revealed marked immune remodeling under intermittent hypoxia (IH), including expansion of effector T cells, and monocytes/macrophages, populations alongside reduced B cells and dendritic cells. In human LUAD cohorts, stemness-high tumors were associated with mitochondrial and metabolic stress-related transcriptional programs, and increased expression of immune checkpoint genes (PD-1, PD-L1, CTLA4, LAG3). Elevated stemness scores correlated with higher TIDE scores, poorer overall survival, and reduced predicted responsiveness to immunotherapy. LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts. Multivariate Cox regression confirmed the risk score as an independent prognostic factor. Drug sensitivity analyses further suggested that stemness-high tumors may exhibit increased susceptibility to selected kinase inhibitors (Dasatinib, A-770041) and metabolic modulators (Phenformin, Salubrinal). OSA-associated IH is linked to stemness-associated transcriptional plasticity, immune suppression, and adverse clinical outcomes in lung cancer. The identified stemness-based gene signature provides a robust prognostic biomarker and highlights potential therapeutic vulnerabilities, supporting integrative strategies that combine stemness and immune -targeted approaches with immunotherapy in OSA-associated lung cancer. Show less
📄 PDF DOI: 10.7150/jca.126708
CPS1

Cross-Platform and cross-species lipidomic profiling identifies promising biomarkers for adolescent major depressive disorder.

Yao Gao, Tao Dong, Ancha Baranova +9 more · 2026 · Molecular psychiatry · Nature · added 2026-04-24
Major depressive disorder (MDD) in adolescents is a critical public health concern, yet objective diagnostic biomarkers remain lacking. We conducted an integrative lipidomics study across human cohort Show more
Major depressive disorder (MDD) in adolescents is a critical public health concern, yet objective diagnostic biomarkers remain lacking. We conducted an integrative lipidomics study across human cohorts and a chronic unpredictable mild stress (CUMS) rat model. Targeted UPLC-MS/MS profiling was applied to a training cohort (95 MDD, 40 controls), and untargeted UPLC-HRMS profiling to an independent cohort (56 MDD, 37 controls). Candidate biomarkers were identified using univariate tests, partial least squares discriminant analysis, and three feature-selection methods (Boruta, LASSO, RFE), with predictive performance evaluated by cross-validation and external replication. Translational relevance was examined in CUMS rats through behavioral assays and lipidomic profiling of serum and brain tissues. Pathway enrichment and regression models explored metabolic context and clinical associations. In the training cohort, we found that 244 lipids were significantly altered, highlighting altered glycerophospholipid, glycerolipid, and sphingolipid metabolism. A 29-lipid panel achieved 90.4% cross-validation accuracy, while a reduced 7-lipid subset reached 94.8%. In the validation cohort, an 8-lipid panel achieved 71.2% accuracy, and a minimal 2-lipid set-LPA(18:2) and SPH(d16:1)-reached 72.1%. Cross-species analysis confirmed consistent downregulation of SPH(d16:1) in serum of both humans and rats, and of LPC(0:0/16:0) specifically in the rat prefrontal cortex. Regression analyses linked sex, age, and anxiety severity to lipid alterations. This cross-platform, cross-species study identifies reproducible lipid signatures of adolescent MDD, highlights SPH(d16:1) and LPC(0:0/16:0) as translational biomarkers, and implicates glycerophospholipid metabolism in MDD pathophysiology, providing a foundation for biomarker-guided diagnostics and therapeutics. Show less
📄 PDF DOI: 10.1038/s41380-026-03486-7
LPA