👤 Yansong Liu

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3184
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 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, 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

A latent profile analysis of health characteristics in relation to sleep among female sex workers in entertainment venues in Wuhan, China.

Weiwei Xiang, Hua Ke, Xiaojia Song +10 more · 2026 · BMC women's health · BioMed Central · added 2026-04-24
This study aims to examine the health characteristics of female sex workers (FSWs) in entertainment venues and to investigate the relationship between these characteristics and sleep quality. This stu Show more
This study aims to examine the health characteristics of female sex workers (FSWs) in entertainment venues and to investigate the relationship between these characteristics and sleep quality. This study employed a cross-sectional design and was conducted from January to April 2024 in Wuhan, China. Participants were FSWs recruited through snowball sampling from entertainment venues, including hotels, restaurants, nightclubs, karaoke bars and dance halls. Data were collected via structured questionnaires covering sociodemographic information, work experience, psychological stress, health status, sleep quality and circadian rhythms. Latent profile analysis (LPA) was employed to identify health characteristic profiles among FSWs, and multivariate logistic regression was used to examine the associations between these profiles and sleep quality. Among the 1,036 FSWs surveyed, 45.1% had poor sleep quality. LPA classified FSWs’ health characteristics into three profiles: the high overall functioning group, the lower physical–emotional functioning group and the lower psychosocial functioning group. Multivariate logistic regression analysis showed that FSWs in the lower physical–emotional functioning group had higher odds of poor sleep quality (OR = 2.184) compared with those in the high overall functioning group. FSWs in the lower psychosocial functioning group had substantially higher odds of poor sleep quality (OR = 7.755) than that in the high overall functioning group. FSWs demonstrate substantial heterogeneity in health characteristics and exhibit lower overall sleep quality compared with the general population. Psychological and physiological factors are major influencing factors for their sleep quality, suggesting the importance of prioritising mental and physical health in this population. Show less
📄 PDF DOI: 10.1186/s12905-026-04346-w
LPA

Association between device-measured physical activity and aortic aneurysm risk: a prospective cohort study of 93165 UK biobank participants.

Shaohua Yan, Changyan Zhu, Yuqiu Hu +6 more · 2026 · Nutrition, metabolism, and cardiovascular diseases : NMCD · Elsevier · added 2026-04-24
Aortic aneurysm (AA) is a life-threatening vascular disease with high fatality upon rupture. While physical activity (PA) reduces cardiovascular risk, its role in AA prevention remains uncertain, part Show more
Aortic aneurysm (AA) is a life-threatening vascular disease with high fatality upon rupture. While physical activity (PA) reduces cardiovascular risk, its role in AA prevention remains uncertain, particularly when assessed objectively. We analyzed 93,165 UK Biobank participants (56% women; median age 57 years) with valid 7-day wrist-worn accelerometer data. PA was categorized as light (LPA), moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA). Diagnosed AA was ascertained through linked hospital, death, and primary care records. Cox models estimated hazard ratios (HRs) for AA across quartiles and per-standard deviation (SD) increments, with adjustment for demographic, lifestyle, and cardiometabolic factors. Over a median 7.9-year follow-up, 499 clinically recorded AA cases occurred. Higher accelerometer-measured PA was inversely associated with AA risk. Per-SD increments in total PA, MPA, VPA, and MVPA corresponded to 17%, 22%, 19%, and 23% lower risks, respectively. Compared with the lowest quartile, the highest MVPA quartile had a 44% lower AA risk (HR = 0.56, 95% CI 0.42-0.76). Subtype analyses revealed stronger protective effects for abdominal aortic aneurysm (AAA) than thoracic aortic aneurysm (TAA), while LPA was not significantly associated. These findings demonstrate that higher levels of accelerometer-measured MVPA are robustly associated with a decreased risk of clinically detected AA in a dose-dependent manner. The associations were particularly pronounced for AAA. This study provides objective evidence supporting the potential benefits of MVPA for aortic health. Show less
no PDF DOI: 10.1016/j.numecd.2026.104715
LPA

Altered Amyloid-β42 and BACE-1 Proteins in Plasma Astrocyte-Derived Exosomes in Hypertensive Patients With Cerebral Microbleeds.

Xiaoxiao Liu, Yuanyuan Liu, Ran Yao +6 more · 2026 · American journal of hypertension · Oxford University Press · added 2026-04-24
Cerebral microbleeds (CMBs) have been found to promote Alzheimer's disease (AD) progression. Hypertension (HTN) is one of the major etiological factors for CMBs and an important risk factor for AD. Ho Show more
Cerebral microbleeds (CMBs) have been found to promote Alzheimer's disease (AD) progression. Hypertension (HTN) is one of the major etiological factors for CMBs and an important risk factor for AD. However, the association between HTN-related CMBs and AD pathology remains undetermined. This study aims to identify the relationship between HTN-related CMBs and amyloid-β 42 (Aβ42) and β-site amyloid precursor protein cleaving enzyme 1 (BACE-1) levels in plasma astrocyte-derived exosomes (ADEs). In total, 88 HTN participants including 30 with deep/infratentorial (D/I) CMBs, 30 with mixed CMBs, and 28 without CMBs were analyzed. Susceptibility-weighted imaging was performed to assess the location, presence, and number of CMBs. ELISA kits for BACE-1 and Aβ42 were employed to evaluate the levels of astrocyte-derived exosomal proteins. The results indicated that plasma ADE levels of Aβ42 were reduced in the HTN + D/I CMBs and HTN + Mixed CMBs groups relative to the HTN-CMBs group. Furthermore, the plasma ADE levels of Aβ42 were significantly associated with CMBs in patients with HTN. However, no significant differences were found in the plasma ADE levels of BACE-1 among the HTN + D/I CMBs, HTN + Mixed CMBs, and HTN-CMBs groups. The study revealed that reduced plasma ADE levels of Aβ42 were significantly associated with CMBs in HTN patients. This finding suggests a potential link between HTN-related CMBs and AD-related amyloid-β pathology, offering novel insights into the mechanisms by which HTN-related CMBs promote AD progression. Show less
no PDF DOI: 10.1093/ajh/hpaf158
BACE1

Endoplasmic Reticulum Stress in the Keratinocytes Contributes to Chronic Itch by Activation of Lipocalin 2/MC4R/TRPV1 Pathway.

Wei Ge, Yu Feng, Li Zhang +9 more · 2026 · Neuroscience bulletin · Springer · added 2026-04-24
Endoplasmic reticulum (ER) stress plays a significant role in chronic pain, but its potential involvement in chronic itch remains largely unexplored and poorly understood. In the current study, we inv Show more
Endoplasmic reticulum (ER) stress plays a significant role in chronic pain, but its potential involvement in chronic itch remains largely unexplored and poorly understood. In the current study, we investigated whether ER stress signaling in keratinocytes contributes to the pathogenesis of chronic itch. Our behavioral tests showed that the ER stress inhibitor 4-PBA attenuated itch-related behaviors in both acute and chronic itching mouse models, and reduced compound 48/80 and serotonin-induced activity of dorsal root ganglion (DRG) neurons. qPCR and western blotting revealed that the ER stress-related proteins and Lipocalin-2 (LCN2) were significantly elevated in the affected skin under chronic itch conditions and in cultured keratinocyte HaCaT cells and mice skin keratinocytes. The ELISA test showed that the level of LCN2 increased significantly in plasma but not in DRG tissue, from both acetone-ether-water (AEW) induced dry skin and imiquimod (IMQ) induced psoriasis model mice. Current clamp recording demonstrated that LCN2 induced hyperexcitability in dorsal root ganglia neurons, which could be abolished by HS024, the inhibitor of melanocortin receptor 4 (MC4R). In addition, pharmacological inhibition of transient receptor potential vanilloid 1 (TRPV1) or TRPV1 knockout blocked LCN2-induced hyperexcitability in DRG neurons. In conclusion, this study demonstrated that keratinocyte ER stress is involved in chronic itch genesis by releasing LCN2, which sensitized primary sensory neurons via TRPV1. These findings suggested that inhibition of ER stress in keratinocytes could be a promising therapeutic strategy for treating chronic itch. Show less
📄 PDF DOI: 10.1007/s12264-026-01600-x
MC4R

Bioinspired scaffold recapitulating chondrogenic ontogeny and microenvironment for functional cartilage regeneration.

Tianyu Yu, Xun Sun, Yang Liu +13 more · 2026 · Bioactive materials · Elsevier · added 2026-04-24
Focal articular cartilage defects often progress to osteoarthritis, imposing a substantial global health burden. Current neglect of cartilage developmental regulation and cartilage microenvironment co Show more
Focal articular cartilage defects often progress to osteoarthritis, imposing a substantial global health burden. Current neglect of cartilage developmental regulation and cartilage microenvironment compromises therapeutic efficacy. We developed an innovation CE-SKP/CPH/P2G3 scaffold which effectively repairs focal cartilage defects and emulates native cartilage ontogeny: the superficial CE-SKP hydrogel layer recruits SMSCs and promotes chondrogenesis; the middle CPH hydrogel layer induces chondrocyte hypertrophic calcification, forming cartilage calcified layer; and the basal P2G3 nanofiber membrane isolates subchondral cells, enforcing a top-down developmental sequence and preserving a localized hypoxic niche. Show less
📄 PDF DOI: 10.1016/j.bioactmat.2025.11.041
FGFR1

LncRNA SNHG5 promotes macrophage lipid accumulation and aggravates atherosclerosis by targeting the miR-216a-5p/HDAC3/ABCA1 axis.

Xian Liu, Hui-Hui Wang, Xin-Yu Lan +6 more · 2026 · Biochimica et biophysica acta. Molecular and cell biology of lipids · Elsevier · added 2026-04-24
Long noncoding RNA small nucleolar RNA host gene 5 (SNHG5) has been implicated in cell death, glucose homeostasis, and tumor progression, yet its role in atherosclerosis (AS) remains unclear. In this Show more
Long noncoding RNA small nucleolar RNA host gene 5 (SNHG5) has been implicated in cell death, glucose homeostasis, and tumor progression, yet its role in atherosclerosis (AS) remains unclear. In this study, SNHG5 expression was markedly elevated in aortic tissues of high-fat diet-fed apoE Show less
no PDF DOI: 10.1016/j.bbalip.2026.159738
APOE

Neural circuitry remodeling in chronic pain and depression comorbidity: Toward an emotion-perception integration network model.

Min Ma, Yue Zhang, Zhenjiao Liu +3 more · 2026 · Brain research bulletin · Elsevier · added 2026-04-24
Chronic pain (CP) and major depressive disorder (MDD) are highly disabling global diseases, and their high comorbidity creates a bidirectional vicious cycle, significantly exacerbating functional impa Show more
Chronic pain (CP) and major depressive disorder (MDD) are highly disabling global diseases, and their high comorbidity creates a bidirectional vicious cycle, significantly exacerbating functional impairment and treatment resistance. Multidisciplinary evidence suggests that the comorbid nature arises from deep functional coupling and neural network remodeling between the sensory-pain and emotional systems, rather than merely a symptom overlap. Neuroimaging, animal models, and neuromodulation studies demonstrate that key brain regions, including the prefrontal cortex (PFC), anterior cingulate cortex (ACC), amygdala, hippocampus, insula, and reward system, show consistent abnormalities in the comorbid state, creating a cross-brain network that jointly regulates pain, emotion, and cognition. This paper systematically reviews the central structures, neural circuits, and neurotransmitter regulatory mechanisms of CP-MDD comorbidity and proposes an integrated emotion-perception coupling network model. We highlight the mechanisms and translational potential of multi-pathway intervention strategies, with a focus on neuromodulation techniques (rTMS, tDCS), combined with ketamine, BDNF modulators, and anti-inflammatory drugs. Additionally, it is emphasized that future research must integrate multimodal imaging, multi-omics data, and computational modeling to establish a mechanism-driven personalized stratification system. With the support of high spatiotemporal resolution brain connectomics technology, this will facilitate the transition from a 'symptom control' to a 'mechanism repair' paradigm in treating comorbidities. Show less
no PDF DOI: 10.1016/j.brainresbull.2026.111784
BDNF chronic pain depression emotion perception neural circuitry neural network neuroimaging neuromodulation

Latent Profile and Influencing Factor Analysis of Childbirth Readiness Among Women in Their Third Trimester of Pregnancy: A Cross-Sectional Study.

Cailing Liu, Yueyuan He, Xue Yang +5 more · 2026 · International journal of women's health · added 2026-04-24
This study aimed to assess the childbirth readiness of women in their third trimester of pregnancy and to identify distinct readiness profiles using latent profile analysis (LPA). Additionally, it exp Show more
This study aimed to assess the childbirth readiness of women in their third trimester of pregnancy and to identify distinct readiness profiles using latent profile analysis (LPA). Additionally, it explored the factors influencing childbirth readiness in order to guide targeted interventions for improved maternal and neonatal outcomes. A cross-sectional study was conducted among women in their third trimester of pregnancy between May and November 2024. Eligible participants completed a general information questionnaire, the Childbirth Readiness Scale (CRS), the Childbirth Attitude Questionnaire (CAQ), and the Perceived Social Support Scale (PSSS). LPA identified three groups with distinct childbirth readiness levels: "Low Readiness - Childbirth Knowledge Deficit" (37.9%), "Moderate Readiness - Good Lifestyle Habits" (47.9%), and "High Readiness - Rich Health Knowledge" (14.2%). In addition, gestational age, previous childbirth history, adverse pregnancy outcomes, childbirth attitudes, and social support had different influences on women in different latent profiles of childbirth readiness. There was significant heterogeneity in childbirth readiness among women in their third trimester. Women with lower readiness-especially in childbirth knowledge-would greatly benefit from targeted educational programs, whereas those with moderate readiness levels would find enhanced emotional and psychological support most advantageous. These findings support the implementation of profile-based, personalized prenatal care strategies to improve childbirth preparedness and optimize maternal and neonatal outcomes. Show less
📄 PDF DOI: 10.2147/IJWH.S574855
LPA

Exosome Cargo Molecules and NLRP3/BDNF: Clinical and Preclinical Evidence for Acupuncture-Mediated Spinal Cord Injury Recovery.

Yongliang Wang, Jian Zhang, Jinsheng Liu +3 more · 2026 · International journal of general medicine · added 2026-04-24
Validate the clinical utility of exosome cargo (miRNAs/proteins) and NLRP3/BDNF as key regulatory molecules for acupuncture-mediated spinal cord injury (SCI) recovery. From the establishment of the da Show more
Validate the clinical utility of exosome cargo (miRNAs/proteins) and NLRP3/BDNF as key regulatory molecules for acupuncture-mediated spinal cord injury (SCI) recovery. From the establishment of the database to May 2025, a literature search was conducted on PubMed, and Embase, using keywords ["exosome cargo" or "exosome"], ["acupuncture" or "acupuncture and moxibustion" or "electroacupuncture" or "EA"], ["spinal cord injury" or "SCI"], ["immune regulation"], ["inflammatory reaction"], ["neuroregeneration" or "nerve"]. Including peer-reviewed studies on human/animal models, articles that do not meet the requirements are excluded. Preclinically, MSC-exosomal miR-145-5p suppressed TLR4/NF-κB signaling, reducing spinal IL-1β by 47% in SD rats. Schwann cell-exosomal MFG-E8 activated SOCS3/STAT3, increasing M2 macrophage CD206 by 63% and raising rat BBB scores by 3.8 points; Treg-exosomal miR-2861 upregulated tight junction proteins (occludin/ZO-1) to repair the blood-spinal cord barrier. Acupuncture (EA at GV14/GV4) upregulated spinal BDNF by 72% and NGF by 58% via Wnt/β-catenin, while EA at GV6/GV9 downregulated NLRP3 by 42-58% and TNF-α by 35-47%. Clinically, EA at EX-B2 increased ASIA scores by 3.2±1.1 points (Guo et al). Besides, 5x/week EA improved ASIA vs 3x/week (+6.4 points). EA+exercise reduced MAS by 1.6-2.9 points, with outcomes correlated to peripheral NLRP3 reduction, BDNF elevation, and MBI/WISCIII increases. Exosome cargo (miR-145-5p/MFG-E8) and NLRP3/BDNF are key regulatory molecules underlying acupuncture-mediated SCI recovery. However, limitations (small RCT samples, heterogeneous acupuncture protocols, unstandardized exosome isolation) hinder translation. Future work should focus on standardized biomarker detection, exosome engineering, and large-scale clinical trials. Show less
📄 PDF DOI: 10.2147/IJGM.S595567
BDNF

Effects of

Xitian Wang, Chunhua Zhang, Xiaocong Liu +5 more · 2026 · Journal of Alzheimer's disease : JAD · SAGE Publications · added 2026-04-24
BackgroundMild cognitive impairment (MCI) confers an increased risk of Alzheimer's disease (AD). The apolipoprotein E (
no PDF DOI: 10.1177/13872877261435429
APOE

Associations between eHealth literacy and 24-hour movement behaviors in older adults: the mediating and moderating roles of self-efficacy.

Ning Su, Jiayu Hu, Borui Shang +4 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24
Older adults increasingly rely on digital health resources, yet evidence regarding the relationship between eHealth literacy (eHL) and 24-hour movement behaviors (24-HMB), including physical activity Show more
Older adults increasingly rely on digital health resources, yet evidence regarding the relationship between eHealth literacy (eHL) and 24-hour movement behaviors (24-HMB), including physical activity (PA), sedentary behavior (SB), and sleep, remains underexplored. This study examined the associations between eHL and 24-HMB in Chinese older adults and examined self-efficacy as a potential mediator and moderator. Using a convenience sampling approach, 564 community-dwelling older adults (aged 60-74 years) were recruited from four urban Chinese cities via an online survey. A total of 553 valid cases were retained for analyses. eHL was assessed using the eHealth Literacy Scale-Web 3.0, and self-efficacy was assessed using a validated Self-Efficacy Scale. PA and SB were assessed objectively using ActiGraph GT3X+ accelerometers over three consecutive days (two weekdays and one weekend day). Sleep duration was derived from accelerometer-based estimates anchored by daily sleep logs. Multiple linear regression analyses were conducted to examine associations, and mediation and moderation were tested using PROCESS macro (Model 4 and Model 1, respectively), adjusting for age, sex, and education. After adjustment for covariates ( In this cross-sectional, urban, device-using sample of older adults, higher eHL was associated with a more favorable 24-HMB profile, particularly higher LPA and lower SB, while associations with sleep duration were weaker. Self-efficacy showed modest indirect associations consistent with partial mediation for PA and SB and also acted as a moderator of several associations. Given the observational design and modest effect sizes, findings should be interpreted cautiously and require confirmation in longitudinal or experimental studies with more representative sampling and improved sleep assessment. Show less
📄 PDF DOI: 10.3389/fmed.2026.1746861
LPA

The Role of Lipoprotein(a) in Cardiovascular Risk Stratification: Integrating Low-density Lipoprotein Cholesterol and Polygenic Risk Scores.

Lei Liu, Huihui Ma, Senwen Yang +6 more · 2026 · The American journal of cardiology · Elsevier · added 2026-04-24
High-density lipoprotein(a) (Lp(a)) is a well-established independent risk factor for atherosclerotic cardiovascular diseases (ASCVD). However, the interaction between Lp(a), low-density lipoprotein c Show more
High-density lipoprotein(a) (Lp(a)) is a well-established independent risk factor for atherosclerotic cardiovascular diseases (ASCVD). However, the interaction between Lp(a), low-density lipoprotein cholesterol (LDL-C), and polygenic risk score (PRS) in cardiovascular diseases has been the subject of relatively limited research. The present study included a total of 346,751 participants from the UK Biobank. According to the guideline of Lp(a), the study subjects were divided into 3 groups: the first group was <75 mmol/L (n = 272,643), the second group was 75 to 125 mmol/L (n = 35,792), and the third group was >125 mmol/L (n = 38,316). Elevated Lp(a) levels were associated with a progressively increased risk of overall cardiovascular events (CVEs), including ischemic stroke (IS), coronary heart disease (CHD), angina pectoris, and myocardial infarction (MI). In contrast, the risks of atrial fibrillation (AF) and heart failure (HF) decreased with higher Lp(a) levels. Additive interaction analyses revealed significant synergistic effects between Lp(a) and LDL-C for CHD (relative excess risk interaction [RERI] = 0.081, attributable proportion of interaction [AP] = 0.046, synergy index [SI] = 1.117), angina pectoris (RERI = 0.112, AP = 0.055, SI = 1.121), and MI (RERI = 0.183, AP = 0.079, SI = 1.161), with MI showing the strongest synergy. Incorporating PRS further amplified these effects, and the RERI (CHD: RERI = 0.721; angina pectoris: RERI = 0.781; MI: RERI = 1.318) and SI (CHD: SI = 2.218; angina pectoris: SI = 1.97; MI: SI = 2.326) were significantly higher than those of the interaction model containing only Lp(a) and LDL-C. In conclusion, Lp(a) and LDL-C show a significant synergistic effect in ASCVD, and this effect is more prominent in individuals with a higher PRS, suggesting that dual lipid management should be strengthened for such populations. While AF and HF may require alternative risk factor management. Show less
no PDF DOI: 10.1016/j.amjcard.2025.09.012
LPA

RT-ICI therapy induces a distal immunometabolic axis that shapes systemic macrophage polarization and enhances local T cell immunity.

Yizhi Ge, Haitao Liu, Jiayi Shen +4 more · 2026 · Cell communication and signaling : CCS · BioMed Central · added 2026-04-24
Colorectal cancer (CRC) liver metastases remain refractory to immunotherapy due to a profoundly immunosuppressive tumor microenvironment. Here, we conducted a prospective clinical study enrolling 18 p Show more
Colorectal cancer (CRC) liver metastases remain refractory to immunotherapy due to a profoundly immunosuppressive tumor microenvironment. Here, we conducted a prospective clinical study enrolling 18 patients with microsatellite-stable CRC liver metastases treated with high-dose radiotherapy (RT) followed by anti–PD-1 immune checkpoint inhibitors (RT–ICI). Integrative analysis of single-cell RNA-sequencing, spatial transcriptomics, and peripheral immune profiling revealed that RT–ICI therapy reprograms both tumor-intrinsic and immune compartments. RT triggered the emergence of an APOA2⁺ tumor cell state characterized by enhanced lipid metabolic activity and transient elevation of circulating HDL. This metabolic reprogramming, in turn, promoted systemic activation of CETP⁺ M2-like macrophages, a population marked by high LXR/RXR transcriptional activity and enriched expression of immunosuppressive and lipid-processing genes. Despite their expansion, CETP⁺ macrophages localized preferentially to non-irradiated tumor regions, suggesting a distal immunometabolic effect driven by HDL-mediated signaling. Concurrently, combination therapy expanded GZMB⁺ effector T cells and induced a novel population of inflammatory–toxic T cells (IT_T), which exhibited high cytotoxicity and spatial co-localization with CXCL10⁺ macrophages. Ligand–receptor analysis and pseudotime modeling revealed that irradiated tumor cells acted as “in situ vaccines” by enhancing MHC–TCR interactions and promoting T cell differentiation along non-exhausted cytotoxic lineages. Together, these findings reveal a dual mechanism by which RT–ICI therapy enhances local anti-tumor immunity while modulating systemic lipid metabolism and macrophage polarization, offering insights for combinatorial immunotherapy design in immunologically “cold” tumors. The online version contains supplementary material available at 10.1186/s12964-026-02689-3. Show less
📄 PDF DOI: 10.1186/s12964-026-02689-3
CETP

Cryo-EM structures of Oryza sativa MRP5 reveal a phytate accumulation mechanism in plant vacuoles.

Jiaqi Zuo, Jie Zhang, Ying Tang +10 more · 2026 · The Plant cell · Oxford University Press · added 2026-04-24
Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties Show more
Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties make cytosolic accumulation cytotoxic, necessitating its sequestration into vacuoles for safe storage. Here, we present the cryo-EM structures of the rice vacuolar phytate transporter, OsMRP5, captured in distinct functional states. These structures reveal the molecular basis of OsMRP5 function as an ATP-binding cassette (ABC) transporter. OsMRP5 employs a specialized substrate-recognition mechanism, uniquely adapted to bind the fully hydrophilic InsP6 through extensive electrostatic and hydrogen-bonding interactions within two distinct, highly polar binding sites in its central cavity. A distinctive electropositive tunnel, positioned above the central cavity, forms a continuous pathway connecting the InsP6-binding pocket to the vacuolar export site. This tunnel likely generates an electrostatic attraction that facilitates the movement of the highly anionic InsP6 through the transporter. By mapping mutations from low-phytic acid (lpa) crop variants onto the OsMRP5 structures, we pinpoint their conserved locations critical for transporter function and validate their impact experimentally. These results reveal how OsMRP5 recognizes and transports the highly charged InsP6 molecules into vacuoles, providing a molecular framework for targeted manipulation of this agriculturally important transporter. Show less
no PDF DOI: 10.1093/plcell/koag088
LPA

DPP-4 inhibitors for preventing post-stroke cognitive impairment in diabetic patients with acute ischemic stroke: a retrospective cohort study.

Hongran Fu, Jianfang Liu, Jie Wu +1 more · 2026 · American journal of translational research · added 2026-04-24
To evaluate the preventive effect of dipeptidyl peptidase-4 inhibitors (DPP-4i) on post-stroke cognitive impairment (PSCI) in patients with type 2 diabetes mellitus (T2DM) and concurrent acute ischemi Show more
To evaluate the preventive effect of dipeptidyl peptidase-4 inhibitors (DPP-4i) on post-stroke cognitive impairment (PSCI) in patients with type 2 diabetes mellitus (T2DM) and concurrent acute ischemic stroke (AIS). A retrospective cohort study was conducted on 236 patients with T2DM+AIS recruited from April 2021 to October 2024. Patients were grouped based on DPP-4i use: an observation group (107 cases) with DPP-4i therapy and a control group (129 cases) without. Patients' baseline demographics, clinical features, laboratory indices, and follow-up data were extracted from the electronic medical record system. The primary outcome measure was the incidence of PSCI, defined as a Montreal Cognitive Assessment Scale (MoCA) score <26 at six months after AIS. Secondary outcomes included inflammatory cytokines, oxidative stress markers, neuroprotective factors (BDNF), glycemic metabolism indicators, and life quality [Barthel Index (BI), Functional Independence Measure (FIM), and Instrumental Activities of Daily Living (IADL)]. At 6 months after AIS, the incidence of PSCI was significantly lower in the observation group than in the control group (P<0.05). Furthermore, inflammatory and oxidative stress marker levels were decreased whereas BDNF level was significantly elevated in the observation group compared to the control group (all P<0.05). According to the quality-of-life assessment, patients receiving DPP-4i had higher BI, FIM, and IADL scores (P<0.05), along with a lower all-cause readmission rate (P<0.05). Subgroup analysis indicated that different DPP-4i types (e.g., sitagliptin, saxagliptin) had consistent cognitive protective effects (P>0.05). DPP-4i can lower PSCI risk in T2DM+AIS patients. Its mechanism involves multi-dimensional effects like anti-inflammation, anti-oxidation, insulin sensitivity enhancement, and neuroprotection. Show less
no PDF DOI: 10.62347/PLKN4994
BDNF cognitive impairment diabetes dpd-4 inhibitors ischemic stroke post-stroke cognitive impairment stroke type 2 diabetes

Lecanemab treatment for mild alzheimer's disease with high risk of cerebral hemorrhage: a case report.

Qi Tian, Mengqi Liu, Fuxin Zhong +2 more · 2026 · BMC neurology · BioMed Central · added 2026-04-24
Lecanemab has been approved for the treatment of mild cognitive impairment due to Alzheimer's disease (AD) and mild AD dementia based on the efficacy in slowing cognitive decline and preliminary safet Show more
Lecanemab has been approved for the treatment of mild cognitive impairment due to Alzheimer's disease (AD) and mild AD dementia based on the efficacy in slowing cognitive decline and preliminary safety data from the phase Ⅲ Clarity AD trial. However, this trial excluded patients with high risk of cerebral hemorrhage, such as individuals with intracranial aneurysms or > 4 microhemorrhages. A 70-year-old male with mild AD, intracranial aneurysm, microhemorrhages, and APOE ε3/ε4 genotype received lecanemab after multidisciplinary evaluation and informed consent. Over six months of intensive monitoring, cognitive function stabilized with no deterioration, daily activities were preserved, microhemorrhages remained stable (with one new small lesion noted at 3 months), and no aneurysm rupture or severe adverse events (including amyloid-related imaging abnormalities) occurred. This case suggests that, despite hemorrhage risks, lecanemab may have a manageable risk-benefit profile in selected real-world AD patients under intensive monitoring and multidisciplinary care, with its application beyond clinical trial criteria requiring more nuanced and individualized consideration. Show less
📄 PDF DOI: 10.1186/s12883-025-04581-y
APOE

TrkB promotes the neuronal secretion of soluble Siglec-2 (CD22) to mitigate microglial activation and alleviate depression-like behaviors in male mice.

Xin Shi, Shi-Zhong Cai, Jin-Long Chai +5 more · 2026 · Molecular psychiatry · Nature · added 2026-04-24
Microglia-neuron contacts have been shown to regulate neural network activity through the formation and elimination of synapses. The pathogenesis of major depressive disorder is accompanied by a decli Show more
Microglia-neuron contacts have been shown to regulate neural network activity through the formation and elimination of synapses. The pathogenesis of major depressive disorder is accompanied by a decline in brain-derived neurotrophic factor (BDNF) signaling, associated with increased microglia activity that disrupts cognitive function. The actions of both typical and rapid-acting antidepressant drugs, which have been shown to increase BDNF signaling through the tropomyosin receptor kinase B (TrkB) receptor, decrease microglia activation and the levels of pro-inflammatory cytokines. Examining the link between BDNF signaling and the microglial pro-inflammatory response, we demonstrate that TrkB signaling elicits the neuronal secretion of CD22 (Siglec-2), a sialic acid-binding immunoglobulin-type lectin, to inhibit microglial activation and alleviate depression-like symptoms. In a male chronic mild stress (CMS) mouse model of depression decreased expression of the postsynaptic scaffolding protein PSD-95 and Gαi1/3 were found to compromise TrkB signaling leading to reduced CD22 levels in hippocampal tissue. Restoration of TrkB-Gαi1/3-Akt signaling with dSyn3, a peptidomimetic compound targeting the PDZ3 domain of PSD-95, enhanced CD22 expression to inhibit microglial activation, promote dendritic spine formation and rapidly mitigate depression-like symptoms. Furthermore, hippocampal overexpression of CD22 in neurons was sufficient to reduce microglial activation and depressive-like behaviors in male CMS mice. S-ketamine, a rapid-acting antidepressant, increased CD22 expression to mitigate depression-like symptoms. While neuronal knockdown of CD22 in the hippocampus did not significantly impair the rapid (within 4 h) antidepressant effects typically observed with S-ketamine or dSyn3 administration, strikingly, knockdown of CD22 attenuated the long-acting (within 3 days) antidepressant effects of S-ketamine or dSyn3, as evidenced by sustained immobility in the TST (tail suspension test) and FST (forced swim test), and a lack of improvement in sucrose preference. In contrast, a single dose of fluoxetine failed to increase CD22 expression or inhibit microglia activity. These results suggest that rapidly-acting anti-depressant drugs enhance TrkB-induced neuronal expression and secretion of CD22 to promote the homeostatic state of microglia required for antidepressant actions. In male depression mice, dSyn3 facilitates BDNF-induced TrkB-PSD-95-Gαi1/3 complex formation to increase Akt-mTOR activation as well as synaptic and spine density in the hippocampus. TrkB signaling increases CD22 expression and secretion from neurons blocking microglial activation in the hippocampal region of male CMS mice. Show less
📄 PDF DOI: 10.1038/s41380-026-03575-7
BDNF

ROS-responsive self-assembly of baicalin-peptide as a "Molecular Latch" to break the endothelial-to-mesenchymal transition-inflammation loop in atherosclerotic plaques.

Zien Lin, Zhiye Wu, Lisha Li +9 more · 2026 · Journal of controlled release : official journal of the Controlled Release Society · Elsevier · added 2026-04-24
Atherosclerotic plaque rupture, driven by a vicious pathological cycle between endothelial-to-mesenchymal transition (EndMT) and chronic inflammation, represents a major therapeutic challenge in cardi Show more
Atherosclerotic plaque rupture, driven by a vicious pathological cycle between endothelial-to-mesenchymal transition (EndMT) and chronic inflammation, represents a major therapeutic challenge in cardiovascular disease. Current clinical strategies, including statins and antiplatelet agents, fail to disrupt the EndMT-inflammation axis, while conventional TGF-β pathway inhibitors-critical for EndMT regulation-exhibit narrow therapeutic windows and systemic toxicity owing to the pleiotropic nature of TGF-β signaling. Here, we reported VRBPC, a VCAM-1-targeting, reactive oxygen species (ROS)-responsive baicalin-peptide conjugate that undergoes in situ self-assembly within atherosclerotic plaques to form a "molecular latch" that breaks the EndMT-inflammation loop. Upon VCAM-1-mediated endocytosis into activated endothelial cells, VRBPC responds to elevated ROS levels in the plaque microenvironment, triggering localized self-assembly that enhances baicalin retention and promotes its competitive binding to HSP90-a critical chaperone for TGF-β receptor stabilization. This mechanism inhibits Smad2/3 phosphorylation, reverses EndMT, and simultaneously suppresses inflammatory responses in macrophages. In vitro, VRBPC effectively restored endothelial phenotype, reduced aberrant migration, and diminished foam cell formation alongside pro-inflammatory cytokine secretion. In ApoE Show less
no PDF DOI: 10.1016/j.jconrel.2026.114821
APOE

[Influences of aged male parents on learning ability of offspring in SD rats and intervention effect of Wuzi Yanzong Pills based on miR-34a-5p/SIRT1 signaling pathway].

Zi-Hao Liu, Min Xiao, Xiao-Cui Jiang +4 more · 2026 · Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica · added 2026-04-24
This study aims to investigate the effects of aged male parents on the learning ability of offspring and the intervention effect of Wuzi Yanzong Pills based on the microRNA-34a-5p(miR-34a-5p)/silent i Show more
This study aims to investigate the effects of aged male parents on the learning ability of offspring and the intervention effect of Wuzi Yanzong Pills based on the microRNA-34a-5p(miR-34a-5p)/silent information regulator 1(SIRT1) signaling pathway. Thirty-two SD male rats of 15 months old were randomized into aged model, model+high-dose(8 g·kg~(-1)) Wuzi Yanzong Pills, model+low-dose(2 g·kg~(-1)) Wuzi Yanzong Pills, and model+vitamin C(100 mg·kg~(-1)) groups(n=8). In addition, 8 SD male rats of 3 months old were selected as the control group. Rats in treatment groups were fed the diets containing different doses of Wuzi Yanzong Pills or vitamin C, and the control and model groups received a regular diet for 12 weeks. After 5 days of co-caging with 3-month-old female mice, the fertilization rate was recorded. An automated sperm analyzer was used to examine the sperm motility and count, and the testicular spermatogenesis was assessed by hematoxylin-eosin staining. The senescence cells in the testicular tissue was detected by β-galactosidase staining, and miR-34a-5p expression was quantified via qPCR. The litter size was counted, and the body mass and body length were measured on days 1 and 30 to assess offspring development. For the offspring of 30 days old, their learning ability was examined via Morris water maze, and Nissl staining was employed to count hippocampal neurons. The miR-34a-5p expression in the hippocampal tissue of the offspring was determined by qPCR, and the protein levels of brain-derived neurotrophic factor(BDNF) and SIRT1 were determined by Western blot. Compared with the control group, the model group exhibited reductions in fertility rate, litter size, and sperm motility and count, as well as impaired testicular spermatogenesis(P<0.01). In addition, the model group showed increased senescence cells in testicular and epididymal tissue, accompanied by elevated miR-34a-5p expression in sperms. The 30-day-old offspring showed slow growth, reduced hippocampal neurons, up-regulated miR-34a-5p expression, and down-regulated protein levels of SIRT1 and BDNF in the hippocampus(P<0.01), along with impaired learning and memory performance(P<0.01). Compared with the model group, both high-dose Wuzi Yanzong Pills and vitamin C improved the fertilization rate, litter size, sperm motility, sperm count, and testicular spermatogenesis(P<0.05). The 30-day-old offspring in the two groups showed accelerated growth and development, increased hippocampal neurons, and elevated BDNF protein level in the hippocampus(P<0.05), along with enhanced learning and memory capabilities(P<0.05). Compared with the vitamin C group, the high-dose Wuzi Yanzong Pills group exhibited accelerated offspring growth(P<0.05), increases in fertilization rate and litter size(P<0.05), and improved learning and memory abilities(P<0.05). These findings indicate that Wuzi Yanzong Pills can improve testicular spermatogenesis and sperm quality in aged rats, thereby enhancing offspring's learning and memory performance. Specifically, Wuzi Yanzong Pills regulate miR-34a-5p expression to delay spermatogenic cell senescence in the testicular tissue and improve the offspring's cognitive function by miR-34a-5p mediated intergenerational transmission. Show less
no PDF DOI: 10.19540/j.cnki.cjcmm.20250916.801
BDNF intervention learning ability microrna mir-34a-5p rats signaling pathway sirt1

Ultra-low-input rG4-seq reveals the RNA G-quadruplex regulome in gene expression and genome integrity.

Tie-Gang Meng, Wei Yue, Chao Li +14 more · 2026 · Nucleic acids research · Oxford University Press · added 2026-04-24
RNA G-quadruplexes (rG4s), formed through guanine self-recognition into stacked tetrads, serve as critical regulators of gene expression, yet their comprehensive mapping and dynamic regulation in phys Show more
RNA G-quadruplexes (rG4s), formed through guanine self-recognition into stacked tetrads, serve as critical regulators of gene expression, yet their comprehensive mapping and dynamic regulation in physiological contexts remain technically challenging. Here, we develop Ultra-low-input rG4-seq (ULI-rG4-seq), enabling precise rG4 detection enabling precise rG4 detection with ∼140 bp resolution in samples as small as 100 oocytes, and reveal notable enrichment of rG4s near crucial regulatory regions, particularly transcription start sites and end sites. This technological advance, combined with Trim-away or oocyte-specific knockout of DHX36 (also known as G4R1 or RHAU), an rG4-specific helicase, reveals acute and chronic loss of DHX36 leads to opposing effects on rG4 levels. This observation extends beyond the traditional view of helicases as unwinding enzymes and suggests sophisticated cellular mechanisms maintaining RNA structural homeostasis. Through integrated analysis of rG4 landscapes and DHX36-binding profiles, we demonstrate coordination between cytoplasmic rG4 regulation and nuclear gene expression, revealing how RNA structure dynamics orchestrate RNA stability and translation, thereby influencing transcriptional elongation, genome stability, and alternative splicing. Finally, we show that deletion of DHX36 resulted in decreased oocyte quality, premature ovarian failure and complete female infertility due to transcriptional defects and genome instability related to R-loop accumulation. These technological and conceptual advances not only deepen our understanding of RNA-based regulation but also open new therapeutic possibilities for diseases involving RNA structure. Show less
📄 PDF DOI: 10.1093/nar/gkag040
DHX36

APM⁺ macrophages associated with plaque vulnerability via MIF-CD74 signaling: a multi-omics study.

Xiang Xu, Yuanze Li, Siqi Xiang +3 more · 2026 · Human genomics · BioMed Central · added 2026-04-24
Atherosclerosis (AS) is a chronic vascular disease and the principal cause leading to ischemic cardiomyopathy (ICM). It involves complex metabolic dysregulation beyond the resolution of single-omics. Show more
Atherosclerosis (AS) is a chronic vascular disease and the principal cause leading to ischemic cardiomyopathy (ICM). It involves complex metabolic dysregulation beyond the resolution of single-omics. Emerging evidence implicates arginine-proline metabolism (APM) in driving inflammation and impairing efferocytosis, yet the cellular basis of plaque instability remains elusive. We employed a five-stage analytical framework. First, metabolomic profiling revealed shared pathways between AS and ICM. Second, single-cell RNA sequencing identified APM-enriched macrophage subtypes in both diseases. Pseudotime analysis, Scissor algorithm, and cell-cell communication analyses linked these subtypes to APM signaling, stroke prognosis, and key ligand-receptor interactions. Third, cNMF and unsupervised clustering defined APM-related gene signatures in macrophages, validated by survival analysis. Fourth, spatial transcriptomics confirmed their spatial distribution and colocalization within unstable plaques. Finally, key biomarkers were validated in atherosclerotic lesions using ApoE Metabolomic profiling revealed APM as a shared dysregulated pathway in AS and ICM. We identified a macrophage subset (SPP1⁺ macrophages and mono-macrophages), termed APM_high macrophages, enriched in the fibrous cap and characterized by elevated collagenase activity, heightened inflammation, and disrupted cholesterol homeostasis. Spatial and cell-cell communication analyses revealed strong interactions with dendritic cells via the MIF-(CD74 + CXCR4) axis, potentially contributing to plaque destabilization. Transcriptomic clustering uncovered a high-APM plaque subtype associated with worse ischemic outcomes. Six diagnostic biomarkers were identified through machine learning and validated across multiple cohorts and in ApoE In summary, our study decodes the metabolic basis of inflammation shared between AS and ICM, suggesting an APM_high macrophage-centered regulatory axis across multiple omics layers. This work advances our understanding of the cardio-metabolic axis and suggests new avenues for targeted therapy. Show less
📄 PDF DOI: 10.1186/s40246-025-00869-9
APOE

Effects of dietary N-carbamylglutamate (NCG) supplementation on arginine biosynthesis, metabolism, digestion and mTOR signaling pathway gene expression in Eriocheir sinensis.

Zhiqiang Liu, Jianyang Sun, Aoya Shi +7 more · 2026 · Comparative biochemistry and physiology. Part B, Biochemistry & molecular biology · Elsevier · added 2026-04-24
N-carbamylglutamate (NCG) is an activator of arginine biosynthesis, but its specific role in crustaceans remains poorly understood. This study aimed to investigate the effects of NCG on arginine biosy Show more
N-carbamylglutamate (NCG) is an activator of arginine biosynthesis, but its specific role in crustaceans remains poorly understood. This study aimed to investigate the effects of NCG on arginine biosynthesis capacity, metabolism, digestion, and the gene expression of the mTOR signaling pathway in Eriocheir sinensis. In Experiment 1, hepatopancreas was cultured in vitro with NCG medium (0, 65, 75, and 85 mg/L NCG). In Experiment 2, crabs were fed either regular feed or NCG feed (content: 302.96 ± 4.07 mg/kg) for 14 days. In Experiment 1, NCG significantly upregulated pyrroline-5-carboxylate synthase (p5cs) gene expression (P < 0.05), an enzyme that is related to arginine biosynthesis. Similarly, dietary NCG upregulated p5cs expression and significantly increased the activities of carbamoyl-phosphate synthase-1 (CPS-1) and P5CS in the hepatopancreas and intestine (P < 0.05). Metabolomics analysis indicated that NCG altered the metabolic profile of the hepatopancreas, promoting cholesterol metabolism, and arginine and proline metabolism. In the intestine, trypsin and α-amylase activities were significantly elevated (P < 0.05). NCG also altered the composition of intestinal microflora, with an increase in Proteobacteria and in the ratio of Firmicutes to Bacteroidota. Additionally, NCG increased the content of signaling molecule nitric oxide (NO) and upregulated the expression of genes in the mTOR signaling pathway (P < 0.05). In conclusion, NCG supplementation enhanced arginine biosynthesis capacity, stimulated intestinal enzymatic activities, and upregulated mTOR signaling pathway gene expression in Eriocheir sinensis, indicating the potential for improved metabolism and digestion. Show less
no PDF DOI: 10.1016/j.cbpb.2025.111178
CPS1

Rs12516 A allele elevates the risk of colorectal cancer via functional CeRNA network of BDNF-AS, miR-4704-5p and BRCA1.

Daxu Liu, Zhijun Fan, Teng Zhang +5 more · 2026 · BMC cancer · BioMed Central · added 2026-04-24
DNA double-strand break repair has emerged as a vital pathway to repair DNA damage seriously related to the risk of colorectal cancer (CRC). To explore valid susceptible biomarkers of CRC via investig Show more
DNA double-strand break repair has emerged as a vital pathway to repair DNA damage seriously related to the risk of colorectal cancer (CRC). To explore valid susceptible biomarkers of CRC via investigating the association of single nucleotide polymorphisms in DSBR genes with CRC risk, seven polymorphisms located in 3'-untranslated regions of DSBR genes including RAD51 rs11852786, RAD51B rs963917, BRCA1 rs12516 and rs8176318, BRCA2 rs15869, XRCC4 rs2035990 and XRCC5 rs2440 were detected and analyzed in a CRC case-control study (cases (202) and also controls (202)). The PolymiRTs and miRSNP database were used to predict the microRNAs that can bind to 3'UTR SNPs. Since long non-coding RNA as a miRNA "sponge" played the role of competing endogenous RNA, DAVID database was used to find the lncRNAs that can bind to the candidate miRNA seed sequences. BRCA1 rs12516 minor A allele was found to be linked with a higher risk of CRC than its major G allele (OR = 2.716, 95%CI: 1.394-5.292, P = 0.003). The stratified analyses demonstrated rs12516 AA genotype with a more elevated risk of CRC in male (OR = 3.089, 95% CI:1.315 ~ 7.255) or age > 50 population (OR = 3.318, 95%CI:1.571 ~ 7.006) than its GG genotype. BRCA1 rs12516 A allele created a novel miR-4704-5p binding target, and there was a negative correlation between miR-4704-5p and BRCA1 expression (r =-0.7199, P = 0.0440). Based on the theory of ceRNA network, it was predicted that lncRNA BDNF-AS can competitively bind to miR-4704-5p, whose expression was exhibited to be negatively correlated with BDNF-AS (r=-0.3481, P = 0.0375). On the contrary, BDNF-AS expression showed a positive correlation with BRCA1 mRNA level in colorectal tissue carrying rs12516 of A allele (adjacent tissue: r = 0.7269, P = 0.0411; cancer tissue: r = 0.7134, P = 0.0469). ROC curve showed both BDNF-AS (AUC = 0.651, P = 0.0277) and miR-4704-5p (AUC = 0.7215, P = 0.0012) can distinguish CRC tissues from their adjacent tissues. BRCA1 rs12516 is characterized as a potential biomarker associated with CRC risk, via a possible functional ceRNA network of BDNF-AS, miR-4704-5p and BRCA1. The interaction of a lower expression of BDNF-AS, a higher expression of miR-4704-5p and rs12516 A allele could together increase the risk of colorectal cancer. Show less
📄 PDF DOI: 10.1186/s12885-025-14692-x
BDNF

Bidirectional association between immune-mediated diseases and major depressive disorder: evidence from cohort, genome-wide pleiotropic, and experimental studies.

Xiaohua Chen, Huan Liu, Yurong Liu +16 more · 2026 · Molecular psychiatry · Nature · added 2026-04-24
Although immune-mediated diseases (IMDs) and major depressive disorder (MDD) commonly co-occur, the bidirectional relationship between them remains to be fully elucidated. Using data from the prospect Show more
Although immune-mediated diseases (IMDs) and major depressive disorder (MDD) commonly co-occur, the bidirectional relationship between them remains to be fully elucidated. Using data from the prospective UK Biobank cohort, we evaluated the bidirectional associations by time-varying Cox proportional hazards regression models and assessed shared genetic architecture using genome-wide association study summary statistics. Additionally, we employed collagen-induced arthritis (CIA) and chronic social defeat stress (CSDS) mouse models to investigate the relationship between rheumatoid arthritis (RA) and depression. Over 5,226,841 person-years of follow-up, 23,534 incident MDD cases were identified. The presence of any IMD was associated with higher MDD risk (hazard ratio [HR]: 1.95; 95% CI: 1.89-2.01). Conversely, 59,742 incident cases of IMD were documented. MDD was associated with increased IMD risk (HR: 1.47; 95% CI: 1.40-1.54). We observed significant global genetic correlations between IMDs and MDD (r Show less
📄 PDF DOI: 10.1038/s41380-026-03459-w
BDNF

Paeonol alleviates atherosclerosis by inhibiting CD8

Yulong Yang, Ting Zhang, Lishun Dong +4 more · 2026 · Journal of ethnopharmacology · Elsevier · added 2026-04-24
Moutan Cortex, a traditional Chinese medicine, has been used to treat cardiovascular diseases. Paeonol (Pae), a key bioactive compound, is responsible for its anti-atherosclerotic effects. Although CD Show more
Moutan Cortex, a traditional Chinese medicine, has been used to treat cardiovascular diseases. Paeonol (Pae), a key bioactive compound, is responsible for its anti-atherosclerotic effects. Although CD8 We investigated whether Pae inhibits atherosclerosis by targeting the spleen tyrosine kinase (SYK)/nuclear factor of activated T-cells c1 (NFATc1) pathway, thereby reducing CD8 High-fat diet-fed apolipoprotein E-deficient (ApoE Pae attenuated plaque formation and T-cell activation in ApoE SYK in CD8 Show less
no PDF DOI: 10.1016/j.jep.2026.121462
APOE

Simulated closed-loop magnetic stimulation promotes function recovery and axonal regeneration in spinal cord injury.

Lechi Zhang, Zhihang Xiao, Chunya Xia +6 more · 2026 · Communications biology · Nature · added 2026-04-24
Spinal cord injury (SCI) represents significant central nervous system trauma and has consistently been a focal point of research in the domain of neural regeneration and repair. Currently, there is n Show more
Spinal cord injury (SCI) represents significant central nervous system trauma and has consistently been a focal point of research in the domain of neural regeneration and repair. Currently, there is no effective treatment available. Various modalities of magnetic stimulation have emerged for recovery from spinal cord injuries; however, the underlying mechanisms remain unclear, significantly hindering the application of magnetic stimulation technologies in treating such injuries. This study aims to elucidate these relevant mechanisms by establishing a simulated closed-loop magnetic stimulation system. In this study, we established a right hemisection model at T8 in mice and administered continuous simulated closed-loop magnetic stimulation targeting the left motor cortex and right L5 nerve root over six weeks. We subsequently utilized a spinal cord dorsal hemisection model to examine regeneration of the corticospinal tract (CST). Motor-evoked potential assessments and calcium imaging techniques were employed to explore neural circuit repair. Additionally, we integrated transcriptomics, proteomics, and metabolomics approaches to investigate related mechanisms. The findings indicate that simulated closed-loop magnetic stimulation effectively restores motor function in the hind limbs, promotes the regeneration of corticospinal tracts in mice with spinal cord injuries, and facilitates the reconstruction of sensorimotor circuits and functions within the spinal cord. Simulated closed-loop magnetic stimulation significantly enhances axonal regeneration of the CST following SCI. This effect may be mediated through the activation of the AMPK-CREB-BDNF signaling pathway, which promotes neurotrophic factor secretion and subsequently induces nerve axon regeneration. This study suggests that simulated closed-loop magnetic stimulation represents a promising therapeutic approach for the treatment for impaired gait following SCI. Show less
no PDF DOI: 10.1038/s42003-026-09848-9
BDNF axonal regeneration central nervous system function recovery magnetic stimulation neural regeneration spinal cord injury trauma

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

Mapping comorbid depression and anxiety in Southwest China's ethnic minorities areas: A population-based latent profile and network analysis of 58,739 individuals.

Yongmei Wu, Wenjing Xia, Yang Yang +18 more · 2026 · Journal of affective disorders · Elsevier · added 2026-04-24
Anxiety and depression are highly comorbid mental health disorders with heterogeneous symptom patterns and poorly understood transdiagnostic mechanisms. This study aims to characterize latent subgroup Show more
Anxiety and depression are highly comorbid mental health disorders with heterogeneous symptom patterns and poorly understood transdiagnostic mechanisms. This study aims to characterize latent subgroups, risk factors, and symptom-level interactions underlying depression-anxiety comorbidity across adolescents and adults in multi-ethnic Southwest China. The study included a total of 41,394 adolescents (aged 9-19) and 17,345 adults (aged 18-80). Adolescents were recruited using multistage stratified cluster sampling, whereas adults were recruited by convenience sampling. All participants completed a self-designed sociodemographic questionnaire, the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7). Latent profile analysis identified subgroups, logistic regression analyzed risk/protective factors, and network analysis mapped symptom interactions and bridge nodes. This study found that three adolescent profiles emerged: high (11.66 %), moderate (31.95 %), and low/no depression-anxiety (56.39 %). Adults were classified into low/no comorbidity (90.63 %) and comorbid depression-anxiety (9.37 %). Risk factors for adolescents included female gender (OR = 2.77, 95 %CI: 2.55-3.00; OR = 1.59, 95 %CI: 1.52-1.67), higher grade levels (OR = 3.45, 95 %CI: 3.10-3.84; OR = 3.56, 95 %CI: 3.33-3.80), smoking (OR = 1.72, 95 %CI: 1.51-1.96; OR = 1.28, 95 %CI: 1.17-1.41),drinking (OR = 2.45, 95 %CI: 2.23-2.70; OR = 1.66, 95 %CI: 1.55-1.77), family instability (OR = 1.16, 95 %CI: 1.02-1.31; OR = 1.33, 95 %CI: 1.14-1.56) and "other" ethnic minority (OR = 1.15, 95 %CI: 1.04-1.26). For adults, female gender(OR = 1.68; 95 %CI: 1.44-1.97), living alone(OR = 1.37; 95 %CI: 1.14-1.65), poor self-rated health (OR = 0.13, 95 %CI: 0.11-0.15), and Dai ethnicity (OR = 0.70, 95 %CI: 0.49-0.96) predicted comorbidity. Network analysis revealed distinct bridge symptoms: adolescents in the high depression-anxiety group had five symptoms: depressed or sad mood (phq2), psychomotor agitation/retardation (phq8), nervousness or anxiety (gad1), restlessness (gad5), and irritable (gad6); however, adults with comorbidity had one symptom: afraid something will happen (gad7). This study identified three patterns of depression-anxiety comorbidity in adolescents and two in adults. Efforts should prioritize adolescents from "other" ethnic minorities, strengthening family and peer support, as well as smoking and drinking interventions for adolescents, and addressing social isolation, physical health, and catastrophizing cognition in adults may mitigate the comorbidity burden. Show less
no PDF DOI: 10.1016/j.jad.2025.121112
LPA

Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling.

Zhenyan Wu, Xue Jiang, Yu Xin +3 more · 2026 · BMJ open · added 2026-04-24
To investigate the association between quantitative retinal vascular parameters and coronary artery disease (CAD) and to evaluate the efficacy of a retinal phenotype-based diagnostic model as a non-in Show more
To investigate the association between quantitative retinal vascular parameters and coronary artery disease (CAD) and to evaluate the efficacy of a retinal phenotype-based diagnostic model as a non-invasive tool for early CAD screening. A retrospective cross-sectional study. A single-centre study conducted at the Cardiovascular Center of Beijing Tongren Hospital, Capital Medical University, China, between January and October 2024. 417 patients with suspected angina undergoing their first coronary angiography (CAG) were enrolled. Inclusion criteria were age >18 years and high-quality fundus photography within 24 hours pre-CAG. Major exclusions were prior coronary interventions, severe systemic/valvular heart diseases and ocular conditions impairing retinal vascular visualisation. The primary outcome was the association between quantitative retinal vascular parameters and the presence of CAD (defined as ≥50% stenosis). Secondary outcomes included the diagnostic performance area under the receiver operating characteristic curve (AUROC) of three predictive models: one based on quantitative retinal vascular parameters alone, one based on traditional risk factors and a combined model integrating both retinal and clinical variables. This study enrolled 417 patients undergoing initial CAG. Compared with non-CAD controls (n=190), patients with CAD (n=227) had higher prevalence of hypertension, dyslipidaemia and diabetes, along with elevated levels of fasting blood glucose, lipoprotein(a) (Lp(a)), triglyceride (TG) and glycated haemoglobin (HbA1c) (all p<0.05). Quantitative fundus analysis revealed that multiple retinal vascular parameters were independently associated with CAD after multivariable adjustment, including fractal dimension (FD), vessel density (VD) and specific zonal measures of vessel diameter and tortuosity (all p<0.05). Multivariable logistic regression incorporating both fundus and clinical variables identified the following independent predictors of CAD: a decrease in FD (OR=0.26, 95% CI 0.16 to 0.41, p<0.01), reduced optic disc long-to-short axis ratio (OR=0.04, 95% CI 0.004 to 0.46, p=0.01) and optic disc-to-macula distance (OR=0.91, 95% CI 0.86 to 0.97, p<0.01), male sex, dyslipidaemia and elevated levels of Lp(a), TG, low-density lipoprotein cholesterol and HbA1c (all p<0.05). The final diagnostic model achieved an AUROC of 0.802 (95% CI 0.76 to 0.845), with a sensitivity of 0.797 and a specificity of 0.679 at the optimal cut-off. Internal validation via bootstrap resampling (1000 iterations) confirmed the robustness of the identified predictors. Our findings, derived from an artificial intelligence-based fully automated quantitative retinal vascular parameters measurement method, revealed that multiple quantitative fundus parameters-including FD, VD and other morphological parameters were significantly associated with CAD risk. The CAD diagnostic model we developed demonstrates strong performance and high interpretability, making it suitable for early CAD screening and diagnosis. Show less
📄 PDF DOI: 10.1136/bmjopen-2025-106135
LPA

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