👤 Xiufen Liu

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3182
Articles
1983
Name variants
Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Chih Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Huazhen Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen 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, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, Yanyan Liu, Yanyao Liu, Yanying Liu, Yanyun Liu, Yao Liu, Yao-Hui Liu, Yaobo Liu, Yaoquan Liu, Yaou Liu, Yaowen Liu, Yaoyao Liu, Yaozhong Liu, Yaping Liu, Yaqiong Liu, Yarong Liu, Yaru Liu, Yating Liu, Yaxin Liu, Ye Liu, Ye-Dan Liu, Yehai Liu, Yen-Chen Liu, Yen-Chun Liu, Yen-Nien Liu, Yeqing Liu, Yi Liu, Yi-Chang Liu, Yi-Chien Liu, Yi-Han Liu, Yi-Hung Liu, Yi-Jia Liu, Yi-Ling Liu, Yi-Meng Liu, Yi-Ming Liu, Yi-Yun Liu, Yi-Zhang Liu, YiRan Liu, Yibin Liu, Yibing Liu, Yicun Liu, Yidan Liu, Yidong Liu, Yifan Liu, Yifu Liu, Yihao Liu, Yiheng Liu, Yihui Liu, Yijing Liu, Yilei Liu, Yili Liu, Yilin Liu, Yimei Liu, Yiming Liu, Yin Liu, Yin-Ping Liu, Yinchu Liu, Yinfang Liu, Ying Liu, Ying Poi Liu, Yingchun Liu, Yinghua Liu, Yinghuan Liu, Yinghui Liu, Yingjun Liu, Yingli Liu, Yingwei Liu, Yingxia Liu, Yingyan Liu, Yingyi Liu, Yingying Liu, Yingzi Liu, Yinhe Liu, Yinhui Liu, Yining Liu, Yinjiang Liu, Yinping Liu, Yinuo Liu, Yiping Liu, Yiqing Liu, Yitian Liu, Yiting Liu, Yitong Liu, Yiwei Liu, Yiwen Liu, Yixiang Liu, Yixiao Liu, Yixuan Liu, Yiyang Liu, Yiyi Liu, Yiyuan Liu, Yiyun Liu, Yizhi Liu, Yizhuo Liu, Yong Liu, Yong Mei Liu, Yong-Chao Liu, Yong-Hong Liu, Yong-Jian Liu, Yong-Jun Liu, Yong-Tai Liu, Yong-da Liu, Yongchao Liu, Yonggang Liu, Yonggao Liu, Yonghong Liu, Yonghua Liu, Yongjian Liu, Yongjie Liu, Yongjun Liu, Yongli Liu, Yongmei Liu, Yongming Liu, Yongqiang Liu, Yongshuo Liu, Yongtai Liu, Yongtao Liu, Yongtong Liu, Yongxiao Liu, Yongyue Liu, You Liu, You-ping Liu, Youan Liu, Youbin Liu, Youdong Liu, Youhan Liu, Youlian Liu, Youwen Liu, Yu Liu, Yu Xuan Liu, Yu-Chen Liu, Yu-Ching Liu, Yu-Hui Liu, Yu-Li Liu, Yu-Lin Liu, Yu-Peng Liu, Yu-Wei Liu, Yu-Zhang Liu, YuHeng Liu, Yuan Liu, Yuan-Bo Liu, Yuan-Jie Liu, Yuan-Tao Liu, YuanHua Liu, Yuanchu Liu, Yuanfa Liu, Yuanhang Liu, Yuanhui Liu, Yuanjia Liu, Yuanjiao Liu, Yuanjun Liu, Yuanliang Liu, Yuantao Liu, Yuantong Liu, Yuanxiang Liu, Yuanxin Liu, Yuanxing Liu, Yuanying Liu, Yuanyuan Liu, Yubin Liu, Yuchen Liu, Yue Liu, Yuecheng Liu, Yuefang Liu, Yuehong Liu, Yueli Liu, Yueping Liu, Yuetong Liu, Yuexi Liu, Yuexin Liu, Yuexing Liu, Yueyang Liu, Yueyun Liu, Yufan Liu, Yufei Liu, Yufeng Liu, Yuhao Liu, Yuhe Liu, Yujia Liu, Yujiang Liu, Yujie Liu, Yujun Liu, Yulan Liu, Yuling Liu, Yulong Liu, Yumei Liu, Yumiao Liu, Yun Liu, Yun-Cai Liu, Yun-Qiang Liu, Yun-Ru Liu, Yun-Zi Liu, Yunfen Liu, Yunfeng Liu, Yuning Liu, Yunjie Liu, Yunlong Liu, Yunqi Liu, Yunqiang Liu, Yuntao Liu, Yunuan Liu, Yunuo Liu, Yunxia Liu, Yunyun Liu, Yuping Liu, Yupu Liu, Yuqi Liu, Yuqiang Liu, Yuqing Liu, Yurong Liu, Yuru Liu, Yusen Liu, Yutao Liu, Yutian Liu, Yuting Liu, Yutong Liu, Yuwei Liu, Yuxi Liu, Yuxia Liu, Yuxiang Liu, Yuxin Liu, Yuxuan Liu, Yuyan Liu, Yuyi Liu, Yuyu Liu, Yuyuan Liu, Yuzhen Liu, Yv-Xuan Liu, Z H Liu, Z Q Liu, Z Z Liu, Zaiqiang Liu, Zan Liu, Zaoqu Liu, Ze Liu, Zefeng Liu, Zekun Liu, Zeming Liu, Zengfu Liu, Zeyu Liu, Zezhou Liu, Zhangyu Liu, Zhangyuan Liu, Zhansheng Liu, Zhao Liu, Zhaoguo Liu, Zhaoli Liu, Zhaorui Liu, Zhaotian Liu, Zhaoxiang Liu, Zhaoxun Liu, Zhaoyang Liu, Zhe Liu, Zhekai Liu, Zheliang Liu, Zhen Liu, Zhen-Lin Liu, Zhendong Liu, Zhenfang Liu, Zhenfeng Liu, Zheng Liu, Zheng-Hong Liu, Zheng-Yu Liu, ZhengYi Liu, Zhengbing Liu, Zhengchuang Liu, Zhengdong Liu, Zhenghao Liu, Zhengkun Liu, Zhengtang Liu, Zhengting Liu, Zhenguo Liu, Zhengxia Liu, Zhengye Liu, Zhenhai Liu, Zhenhao Liu, Zhenhua Liu, Zhenjiang Liu, Zhenjiao Liu, Zhenjie Liu, Zhenkui Liu, Zhenlei Liu, Zhenmi Liu, Zhenming Liu, Zhenna Liu, Zhenqian Liu, Zhenqiu Liu, Zhenwei Liu, Zhenxing Liu, Zhenxiu Liu, Zhenzhen Liu, Zhenzhu Liu, Zhi Liu, Zhi Y Liu, Zhi-Fen Liu, Zhi-Guo Liu, Zhi-Jie Liu, Zhi-Kai Liu, Zhi-Ping Liu, Zhi-Ren Liu, Zhi-Wen Liu, Zhi-Ying Liu, Zhicheng Liu, Zhifang Liu, Zhigang Liu, Zhiguo Liu, Zhihan Liu, Zhihao Liu, Zhihong Liu, Zhihua Liu, Zhihui Liu, Zhijia Liu, Zhijie Liu, Zhikui Liu, Zhili Liu, Zhiming Liu, Zhipeng Liu, Zhiping Liu, Zhiqian Liu, Zhiqiang Liu, Zhiru Liu, Zhirui Liu, Zhishuo Liu, Zhitao Liu, Zhiteng Liu, Zhiwei Liu, Zhixiang Liu, Zhixue Liu, Zhiyan Liu, Zhiying Liu, Zhiyong Liu, Zhiyuan Liu, Zhong Liu, Zhong Wu Liu, Zhong-Hua Liu, Zhong-Min Liu, Zhong-Qiu Liu, Zhong-Wu Liu, Zhong-Ying Liu, Zhongchun Liu, Zhongguo Liu, Zhonghua Liu, Zhongjian Liu, Zhongjuan Liu, Zhongmin Liu, Zhongqi Liu, Zhongqiu Liu, Zhongwei Liu, Zhongyu Liu, Zhongyue Liu, Zhongzhong Liu, Zhou Liu, Zhou-di Liu, Zhu Liu, Zhuangjun Liu, Zhuanhua Liu, Zhuo Liu, Zhuoyuan Liu, Zi Hao Liu, Zi-Hao Liu, Zi-Lun Liu, Zi-Ye Liu, Zi-wen Liu, Zichuan Liu, Zihang Liu, Zihao Liu, Zihe Liu, Ziheng Liu, Zijia Liu, Zijian Liu, Zijing J Liu, Zimeng Liu, Ziqian Liu, Ziqin Liu, Ziteng Liu, Zitian Liu, Ziwei Liu, Zixi Liu, Zixuan Liu, Ziyang Liu, Ziying Liu, Ziyou Liu, Ziyuan Liu, Ziyue Liu, Zong-Chao Liu, Zong-Yuan Liu, Zonghua Liu, Zongjun Liu, Zongtao Liu, Zongxiang Liu, Zu-Guo Liu, Zuguo Liu, Zuohua Liu, Zuojin Liu, Zuolu Liu, Zuyi Liu, Zuyun Liu
articles

Maternal exposure to urban particulate matter induces cardiac developmental toxicity in zebrafish offspring by disrupting mitochondrial homeostasis.

Shiqian Liu, Ruiyang Ding, Linyuan Huang +4 more · 2026 · Journal of advanced research · Elsevier · added 2026-04-24
Urban particulate matter (UPM) is a major air pollutant affecting public health, with maternal exposure potentially leading to cardiac developmental disorders in offspring. However, the exact mechanis Show more
Urban particulate matter (UPM) is a major air pollutant affecting public health, with maternal exposure potentially leading to cardiac developmental disorders in offspring. However, the exact mechanisms underlying the intergenerational effects of UPM remain unclear. This study aimed to investigate the molecular mechanisms involved in cardiac developmental defects caused by maternal UPM exposure in offspring zebrafish. Female zebrafish were exposed to UPM for 21 days to examine intergenerational effects. The results indicated that maternal zebrafish in the exposed group exhibited ovarian damage and a reduced number of embryos and fertilization rates. Zebrafish offspring exhibited abnormal cardiac development, including pericardial edema and pathological heart injury. Mechanistically, transcriptomic analysis of the offspring indicated that UPM exposure induced significant modifications in the mitochondrial biogenesis pathway, with altered expression of mitochondrial function-related genes. Maternal UPM exposure impaired respiration in zebrafish embryos and increased angiopoietin-like 4 (ANGPTL4) expression in offspring hearts. In vitro, Angptl4 knockdown alleviated UPM-induced mitochondrial membrane potential reduction and mitochondrial reactive oxygen species overproduction in cardiomyocytes, whereas Angptl4 overexpression exacerbated UPM-induced mitochondrial toxicity. These findings show that maternal UPM exposure disrupts mitochondrial homeostasis by upregulating ANGPTL4 expression, leading to abnormal cardiac development in zebrafish offspring. Show less
📄 PDF DOI: 10.1016/j.jare.2025.05.041
ANGPTL4

Predictive value of serum apolipoprotein panel (ApoA1 / ApoA2 / ApoA4) as a biomarker for individual radiosensitivity.

Na Huang, Heming Wang, Xiao Li +8 more · 2026 · Lipids in health and disease · BioMed Central · added 2026-04-24
Significant interindividual variability in radiosensitivity poses a major challenge to conventional radiation protection and radiotherapy. Current prediction strategies relying on DNA damage or genomi Show more
Significant interindividual variability in radiosensitivity poses a major challenge to conventional radiation protection and radiotherapy. Current prediction strategies relying on DNA damage or genomic analysis have inherent limitations, underscoring the need for minimally invasive serum biomarkers. While serum apolipoproteins are crucial regulators of lipid transport, metabolism, and cellular stress response, their role as biomarkers for radiosensitivity remains largely unexplored. A 7.3 Gy ⁶⁰Co γ-ray whole-body irradiation mouse model (with training and independent validation cohorts) was established to assess individual radiosensitivity. Pre-irradiation peripheral serum samples underwent high-throughput proteomics analysis to identify differential proteins (DEPs) linked to 30-day post-irradiation survival. KEGG and GO enrichment analyses were conducted to characterize DEP-associated pathways. An XGBoost machine learning model was built using candidate biomarkers, with SHAP analysis to define their predictive contributions; Cox proportional hazards and Pearson correlation analyses were applied to evaluate survival associations. DIA-based proteomics identified 580 DEPs in the training cohort and 449 in the validation cohort. KEGG and GO enrichment analyses confirmed that these DEPs were predominantly enriched in the cholesterol metabolism and reverse cholesterol transport pathways. The predictive model based on an apolipoprotein panel (ApoA1/ApoA2/ApoA4), established using the XGBoost algorithm, exhibited exceptional performance in the training cohort (AUC = 1) and maintained robust generalizability in an independent validation cohort (AUC = 0.833). Compared with non-survivors, survivors exhibited significantly elevated serum levels of ApoA1 and ApoA2 but markedly reduced levels of ApoA4. Cox proportional hazards regression analysis established ApoA1 and ApoA2 as independent protective factors, whereas high ApoA4 expression was an adverse prognostic indicator. Notably, ApoA4 levels also demonstrated a strong negative correlation with post-irradiation survival time. The serum apolipoprotein profile (ApoA1/ApoA2/ApoA4) serves not only as a promising minimally invasive biomarker for predicting individual radiosensitivity in mice but also reveals a critical link between the cholesterol metabolic pathway and radiation response. This finding lays a theoretical foundation for translating predictive, cholesterol metabolism-related biomarkers to support radiation response assessments. Given the limitations of animal models, subsequent studies are required to validate the clinical applicability of this panel in human cohorts, with the aim of offering an effective tool for personalized radiation protection and precise radiotherapy. The online version contains supplementary material available at 10.1186/s12944-026-02868-8. Show less
📄 PDF DOI: 10.1186/s12944-026-02868-8
APOA4

Multilayer assembled MFGM mimetics improve digestive fate of human milk structural lipids.

Yehui Liang, Ruize Pan, Nian Liu +4 more · 2026 · Food research international (Ottawa, Ont.) · Elsevier · added 2026-04-24
Current infant formulas lack the native multilayer structure of breast milk fat globule membrane (MFGM), impacting lipid digestion. In this study, the inner layer material and concentration of the bio Show more
Current infant formulas lack the native multilayer structure of breast milk fat globule membrane (MFGM), impacting lipid digestion. In this study, the inner layer material and concentration of the biomimetic fat globule membrane were optimized by comparing particle size, Zeta-potential and interface protein load. It was found that compared with sodium caseinate (CN) and whey protein (WP), when the lactoferrin (LF) concentration was 2 %, the particle size was lower (277.85 ± 6.15 nm) and Zeta-potential value was higher (19.67 ± 1.27 mv). Using milk phospholipid (MPL) as the outer layer material, when the MPL concentration was 2 %, the emulsion had a smaller particle size (291.33 ± 1.15 nm) and a better stability (10.22 ± 0.62 %). Therefore, the biomimetic multilayer membrane was constructed by electrostatic layer-by-layer deposition of 2 % LF and 2 % MPL. Combining Fluorescence and Fourier transform infrared spectroscopy (FTIR), the interaction between LF and MPL molecules in the LF-MPL multilayer structure is primarily a spontaneous, endothermic process driven by hydrophobic forces, exhibited superior stability (except thermal stability) than LF monolayer membrane. The results of in vitro digestion showed that compared with LF, WP and WP-MPL emulsions, LF-MPL emulsions had the highest free fatty acid (FFA) release rate of 69.97 %. LF-MPL enhanced gastric stability and promoted intestinal lipolysis and improved the degree of lipid digestion. In addition, LF-MPL promoted the absorption and utilization of triglyceride (TAG) in cells and animals, and secretion and upregulated lipid absorption genes (FATP4, DGAT1, APOB, APOA4, MTTP). These findings demonstrate that biomimetic LF-MPL multilayers improve lipid digestion, absorption, and bioavailability, providing a theoretical basis for designing more breast milk-like infant formulas. Show less
no PDF DOI: 10.1016/j.foodres.2025.118055
APOA4

Proteomic Profiling of Plasma Extracellular Vesicles Combined with Multivariate Modeling Identified Potential Biomarkers for Distinguishing Benign Pulmonary Nodules from Early-Stage Lung Adenocarcinoma.

Shujun Liu, Yating Ma, Bo Sun +3 more · 2026 · Journal of proteome research · ACS Publications · added 2026-04-24
Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and is difficult to distinguish from benign pulmonary nodules (BPNs), particularly at early stages. Extracellular vesicles (EVs) re Show more
Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and is difficult to distinguish from benign pulmonary nodules (BPNs), particularly at early stages. Extracellular vesicles (EVs) represent a promising source of biomarkers for the diagnosis of malignant pulmonary nodules. This study aimed to identify robust and clinically relevant EV-based protein biomarkers via isolation with EXODUS, a system that enables efficient direct capture of plasma EVs, followed by data-independent acquisition mass spectrometry (DIA-MS) for in-depth proteomic profiling. A total of 1383 proteins were identified from the plasma EVs obtained from 25 individuals (10 BPN and 15 early stage LUAD), while dysregulated protein signatures were revealed through differential expression analysis. Machine learning algorithms incorporating demographic variables, imaging features, EV protein profiles, and conventional tumor markers were applied to select diagnostic candidates. Random forest analysis revealed two upregulated proteins, NTN3 and APOA4, as promising biomarkers. Subsequently, their diagnostic performance and net clinical benefits were validated in an independent EV cohort (6 LUAD and 6 BPN) using ELISAs and decision curve analysis. In summary, we present an integrated pipeline that combines EXODUS-based isolation, DIA-MS, and machine learning to detect markers from plasma EVs for distinguishing early stage lung cancer from benign nodules. Show less
no PDF DOI: 10.1021/acs.jproteome.5c00610
APOA4

Functional analysis and identification of miRNAs associated with lipid metabolism from milk-derived exosomes.

Xin Lu, Tianyu Deng, Yue Liu +4 more · 2026 · Journal of animal science and biotechnology · BioMed Central · added 2026-04-24
Exosomes are crucial mediators of intercellular communication. As a key component of milk, milk-derived exosomes are abundant in genetic cargo, particularly microRNAs (miRNAs), indicating their potent Show more
Exosomes are crucial mediators of intercellular communication. As a key component of milk, milk-derived exosomes are abundant in genetic cargo, particularly microRNAs (miRNAs), indicating their potential role in regulating mammary gland physiology. Therefore, this study aimed to investigate the specificity of miRNAs in milk-derived exosomes and their regulatory roles in lipid synthesis in bovine mammary epithelial cells (BMECs). Based on 17,838 DHI records showing a significantly higher milk fat percentage (MFP) in late lactation (4.24% ± 1.07%), 10 high- (5.96% ± 0.26%, HMF) and 10 low-MFP (1.68% ± 0.23%, LMF) cows were selected during this stage for milk-derived exosome isolation and miRNA profiling. Exosomes isolated via differential ultracentrifugation were verified as 50-150 nm vesicles expressing CD9, CD81, and TSG101. miRNA sequencing identified 1,320 differentially expressed miRNAs (496 upregulated and 824 downregulated) between the HMF_EXO and LMF_EXO groups. Uptake assays confirmed that BMECs internalized these exosomes, and qRT-PCR validation showed that miR-423-5p and miR-125b were significantly upregulated and downregulated in HMF_EXO- and LMF_EXO-treated BMECs, respectively. Functionally, exosomal miR-423-5p promoted intracellular lipid accumulation and TG synthesis in BMECs by targeting APOA5, whereas miR-125b inhibited lipolysis and fatty acid oxidation by repressing SLC27A1. This study demonstrates that milk-derived exosomal miRNAs represent a novel mechanism for regulating milk fat synthesis. Specifically, miR-423-5p and miR-125b directly modulated lipid metabolism in BMECs via the miR-423-5p/APOA5 and miR-125b/SLC27A1 pathways. These findings provide new insights into the molecular regulation of milk fat synthesis and highlight the importance of exosome-mediated intercellular communication in the lactating mammary gland. Show less
📄 PDF DOI: 10.1186/s40104-025-01331-5
APOA5

Chrono-Specific Calcium Intervention Disrupts Hepatic Lipid Metabolism via the PER1-PPARα Axis.

Haoyu Wang, Jinling Yu, Fei Liang +5 more · 2026 · Journal of the American Nutrition Association · Taylor & Francis · added 2026-04-24
Controversies exist regarding the effects of calcium supplementation on lipid metabolism, and the time-specific effects and underlying mechanisms remain unclear. This study aims to elucidate the diffe Show more
Controversies exist regarding the effects of calcium supplementation on lipid metabolism, and the time-specific effects and underlying mechanisms remain unclear. This study aims to elucidate the differential impacts of calcium intervention at different times (morning/evening) on hepatic lipid metabolism and the molecular mechanisms involved. Forty female CD-1 (ICR) mice were randomly divided into four groups: Morning Control Group (MCN), Morning Calcium Intervention Group (MCI, intragastric administration of calcium carbonate at 08:00), Evening Control Group (ECN), and Evening Calcium Intervention Group (ECI, intragastric administration of calcium carbonate at 20:00). Mice were fed a normal calcium or low-calcium diet for 10 wk. Morning calcium intervention (MCI) in mice significantly increased serum and hepatic total cholesterol (TC), triglyceride (TG), and low-density lipoprotein (LDL) levels, and induced lipid droplet deposition and swelling in hepatocytes. Transcriptome and validation experiments showed upregulated hepatic PER1 expression in the MCI group, while PPARα and its downstream lipid metabolism genes (CPT1A, APOA5) were downregulated. In HepG2 cells, nighttime calcium incubation (NC) significantly increased intracellular TG and LDL contents, upregulated PER1 expression, and inhibited PPARα, CPT1A, and APOA5 expressions. Knocking down PER1 reversed the abnormal gene expression and lipid-elevating effects in the NC group. Collectively, our findings demonstrate that the circadian timing of calcium intake critically regulates hepatic lipid homeostasis Show less
no PDF DOI: 10.1080/27697061.2025.2557251
APOA5

Subtype-specific associations between serum lipid profiles and disease severity in patients with amyotrophic lateral sclerosis.

Lu Cao, Gang Chen, Jing Zhou +5 more · 2026 · Biomedical reports · added 2026-04-24
Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disorder. Notably, the differences in lipid metabolism between bulbar- and limb-onset subtypes of ALS remain unclear, particula Show more
Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disorder. Notably, the differences in lipid metabolism between bulbar- and limb-onset subtypes of ALS remain unclear, particularly in non-Western populations. The present study investigated serum lipid profiles in a Chinese cohort of patients with ALS to explore their associations with disease severity and clinical subtypes. A retrospective, cross-sectional study was conducted, involving 158 patients with ALS and 62 matched healthy controls. Serum lipid parameters, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), small dense LDL cholesterol (sdLDL-c), apolipoprotein A-1 (ApoA1), apolipoprotein B (ApoB) and the TG/HDL ratio, were compared between the groups. Correlation analyses and multivariable linear regression models incorporating phenotype x lipid interaction terms were conducted after adjusting for age, sex, body mass index and disease duration. Patients with ALS exhibited significantly higher TC, TG, LDL, sdLDL-c, ApoA1, ApoB and TG/HDL ratios than controls. Subtype-specific analyses revealed different associations; in bulbar-onset ALS, higher sdLDL-c and TG/HDL ratios were associated with better functional status, whereas higher HDL and ApoA1 levels were negatively correlated with functional status. By contrast, in limb-onset ALS, higher sdLDL-c and ApoB levels were associated with worse function. Interaction analyses confirmed significant phenotype modification for sdLDL-c, TG/HDL ratio, HDL and ApoA1. These results suggest that lipid-severity relationships in ALS vary by subtype, indicating metabolic heterogeneity across phenotypes and supporting the potential of specific lipid parameters as exploratory markers for disease monitoring. Show less
📄 PDF DOI: 10.3892/br.2026.2141
APOB

Multiomics Mendelian Randomization of GWAS, eQTL, and pQTL Combined With Drug Analysis to Predict Drug Targets in Myocardial Infarction.

Didi Yuan, Lian Hu, Yanqing Huang +4 more · 2026 · Journal of cardiovascular pharmacology and therapeutics · SAGE Publications · added 2026-04-24
Despite significant advances in the management of myocardial infarction (MI), therapeutic options targeting upstream pathogenic mechanisms remain scarce. This study introduces a novel multiomics-to-dr Show more
Despite significant advances in the management of myocardial infarction (MI), therapeutic options targeting upstream pathogenic mechanisms remain scarce. This study introduces a novel multiomics-to-drug discovery framework to identify and validate causal therapeutic targets for MI. We conducted a systematic two-sample Mendelian randomization (MR) analysis integrating expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) data from the IEU OpenGWAS database, with replication in the UK Biobank cohort. Causal inference was rigorously validated using HEIDI heterogeneity tests, Bayesian colocalization, bidirectional MR, and multivariate MR (MVMR) to account for potential confounders. Downstream applications were explored via protein-protein interaction (PPI) network analysis, phenome-wide association studies (PheWAS), and molecular docking simulations. Initial screening identified four candidate genes (BMP1, APOB, FABP2, and ALDH2) associated with MI risk in both discovery and replication cohorts. However, only BMP1 demonstrated consistent causal effects at both transcriptional and proteomic levels, passing all sensitivity analyses with no evidence of horizontal pleiotropy in PheWAS. Colocalization and bidirectional MR further confirmed BMP1 as a robust, independent causal driver of MI. Molecular docking revealed that UK-383367, a selective BMP1 inhibitor, exhibits high binding affinity to the BMP1 active site. While BMP1 is traditionally associated with extracellular matrix remodeling, this study provides the first genetic evidence establishing it as an independent causal risk factor for MI, distinct from conventional traits such as hypertension. By bridging causal genetic inference with structure-based drug prediction, we propose BMP1 inhibition, specifically via agents like UK-383367, as a promising therapeutic strategy to mitigate MI-related pathological remodeling. Show less
no PDF DOI: 10.1177/10742484261440344
APOB

Association of uric acid-to-HDL cholesterol ratio and coronary atherosclerotic heart disease in patients with and without CKD.

Chang-Hao Sun, Xin-Yu Zhu, Zhi-Long Wang +5 more · 2026 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
The ratio of uric acid to high-density lipoprotein cholesterol (UHR) is a novel comprehensive indicator related to dyslipidemia. However, the association between UHR and coronary artery disease (CAD) Show more
The ratio of uric acid to high-density lipoprotein cholesterol (UHR) is a novel comprehensive indicator related to dyslipidemia. However, the association between UHR and coronary artery disease (CAD) risk in patients with chronic kidney disease (CKD) remains unclear. After matching based on age and gender propensity scores, 2124 subjects were included and divided into the CKD group (708 cases) and the non-CKD group (1416 cases). The predictive performance of UHR for CAD was evaluated by the area under the curve (AUC), and the independent association between UHR and the risk of CAD onset was analyzed using a multivariate logistic regression model. The correlation and dose-response relationship between the ratio of uric acid to high-density lipoprotein cholesterol (UHR) and the risk of CAD were analyzed using LOESS fitting and restricted cubic spline (RCS) analysis. After matching, the multiple lipid-related indices (Triglycerides (TG), Remnant Cholesterol (RC), Atherogenic Index (AI), Atherogenic Index of Plasma (AIP), Triglyceride Glucose Index (TyG), Lipoprotein Composite Index (LCI), Triglyceride to High-Density Lipoprotein Cholesterol Ratio (TG/HDL-C), Total Cholesterol to High-Density Lipoprotein Cholesterol Ratio (TC/HDL-C), Low-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio (LDL-C/HDL-C), UHR) in the CKD group were significantly higher than those in the non-CKD group. The AUC analysis showed that HDL-C, AIP, TG/HDL-C, and UHR had strong predictive performance in the overall cohort and the non-CKD group, while in the CKD group, HDL-C, AI, and TC/HDL-C are better predictive indicators. After adjusting for all confounding factors, multivariate regression analysis revealed that HDL-C, apolipoprotein A-1 (APOA-1), and the APOA-1/APOB ratio were independent protective factors for CAD in the entire cohort. Among them, the protective effect of HDL-C was the most stable (overall population aOR = 0.26, 95% CI: 0.17-0.39, p < 0.001), and it was significantly in both the CKD (aOR = 0.18, 95% CI: 0.09-0.40, p < 0.001) and non-CKD subgroups (aOR = 0.31, 95% CI: 0.18-0.52, p < 0.001). In CKD, UHR is significantly correlated with CAD (aOR = 6.23, 95% CI: 1.89-20.60, p = 0.003), and the association was more significant in the non-CKD group (aOR = 15.15, 95% CI: 4.20-54.72, p < 0.001). CKD status significantly modified the association between UHR and CAD (P for interaction = 0.015). LOESS fitting suggested that UHR was positively correlated with the probability of CAD occurrence (the correlation was more significant at low UHR, and it slowed down when UHR > 0.5, r = 0.2, p < 0.001), and negatively correlated with eGFR (r = -0.38, p < 0.001). RCS analysis confirmed a significant nonlinear association between UHR and CAD (overall P < 0.001, nonlinear P = 0.002), and the risk of CAD increased when UHR was > 0.41 in CKD patients. UHR is an independent risk factor for coronary heart disease, with higher adjusted OR values and more significant independent risk effects in non-CKD populations. Show less
no PDF DOI: 10.1186/s12872-026-05838-1
APOB

Five lipid-inflammation composite indices and obesity-related metabolic disorders, cardiovascular and renal diseases A multi-state trajectory analysis using data from the UK Biobank cohort.

Ran He, Qikai Luo, Taian Jin +5 more · 2026 · Diabetes research and clinical practice · Elsevier · added 2026-04-24
Biomarkers that capture the dynamic transition from obesity to metabolic dysfunction and subsequent cardiorenal disease remain insufficient. This study evaluated stage-specific associations of lipid-i Show more
Biomarkers that capture the dynamic transition from obesity to metabolic dysfunction and subsequent cardiorenal disease remain insufficient. This study evaluated stage-specific associations of lipid-inflammation indices across this continuum. We included 109,442 obese adults (UK Biobank) across four stages, obesity (Stage 1), metabolic disorders (Stage 2), cardiorenal disease (Stage 3), and death (Stage 4). Five baseline indices (ApoB/A1-CRP, RCII, NHR, lymphocyte-to-HDL-C, monocyte-to-HDL-C) were evaluated. Markov multistate models were used to estimate transition-specific risks, with Cox regression and restricted cubic spline (RCS) analyses as complementary approaches. During a median follow-up of 15.73 years, 11.14% of participants progressed from Stage 1 to 2, and 25.88% from Stage 2 to 3. In fully adjusted model, ApoB/A1-CRP (HR, 1.07, 95% CI, 1.00-1.14, P = 0.048) and RCII (HR, 1.08, 95% CI, 1.01-1.15, P = 0.017) were significantly associated with Stage 2 to 3 progression. Upon Stage 3 stratification, NHR was primarily associated with mortality following cardiorenal disease onset. RCS analyses indicated significant non-linear associations for ApoB/A1-CRP, RCII, and NHR. RCII demonstrates robustness in sensitivity analysis. RCII is independently associated with the progression from metabolic disorders to cardiorenal diseases in obesity. It may serve as a clinically biomarker for early risk stratification. Show less
no PDF DOI: 10.1016/j.diabres.2026.113234
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Comparative efficacy and safety of novel Lp(a)-lowering therapies for ASCVD prevention: A network meta-analysis.

An-Xin Wu, Xing-Jin Wang, Chen Zhao +5 more · 2026 · Pharmacological research · Elsevier · added 2026-04-24
Elevated lipoprotein(a) [Lp(a)] is a genetically determined and independent risk factor for atherosclerotic cardiovascular disease (ASCVD) that is largely resistant to conventional lipid-lowering ther Show more
Elevated lipoprotein(a) [Lp(a)] is a genetically determined and independent risk factor for atherosclerotic cardiovascular disease (ASCVD) that is largely resistant to conventional lipid-lowering therapies. Novel Lp(a)-targeted agents, including small interfering RNA (siRNA), antisense oligonucleotides (ASO), and the oral small-molecule inhibitor muvalaplin, have shown potent efficacy in early trials. We conducted a systematic review and network meta-analysis to comprehensively compare their efficacy and safety. A total of 25 randomized controlled trials (RCTs) involving 7715 participants were included, evaluating six siRNA agents, four ASO agents, and one small-molecule inhibitor. The primary outcome was percentage change from baseline in Lp(a). Secondary outcomes included absolute change in Lp(a), percentage changes in apolipoprotein B (apoB) and low-density lipoprotein cholesterol (LDL-C), and adverse events. SiRNA therapies achieved the greatest Lp(a) reductions (olpasiran: mean difference [MD] -92.1%, 95% CI -100.1 to -84.0%; zerlasiran: -80.6%, 95% CI -87.7 to -73.5%), followed by muvalaplin (-76.8%, 95% CI -90.3 to -63.2%) and ASO therapy (pelacarsen: -54.2%, 95% CI -72.2 to -36.2%; all P < 0.001). Most agents achieved absolute Lp(a) reductions exceeding 105 nmol/L, suggesting clinically meaningful benefit. Baseline Lp(a) levels significantly modified treatment response (P < 0.001), and concomitant proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor use further enhanced LDL-C reduction (P = 0.024). All therapies were well tolerated, with injection-site reactions most frequent for injectables, while muvalaplin was well tolerated. These findings indicate that targeted Lp(a)-lowering therapies substantially reduce circulating Lp(a), with siRNA showing the greatest potency and muvalaplin offering a convenient oral alternative for personalized ASCVD risk reduction. Show less
no PDF DOI: 10.1016/j.phrs.2026.108178
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Exosome-mediated cholesterol flow drives scoliosis progression via promoting the spinal cartilage-bone positive feedback.

Min Zuo, Haixia Xu, Yuying Yang +7 more · 2026 · Communications biology · Nature · added 2026-04-24
Adolescent Idiopathic Scoliosis (AIS) is the most common form of spinal deformity among adolescents. To explore its etiology of progression and scoliosis-modifying drugs, chondrocytic senescence was c Show more
Adolescent Idiopathic Scoliosis (AIS) is the most common form of spinal deformity among adolescents. To explore its etiology of progression and scoliosis-modifying drugs, chondrocytic senescence was confirmed in AIS facet joint cartilage by analyzing clinical specimen. Furthermore, through 4D/480 label-free proteomics analysis, we identified an exosome-mediated positive feedback loop during scoliosis progression, which driving the elevation of cholesterol flow between spinal cartilage and vertebra. To further investigate the pathological significance of the loop in vivo, high-cholesterol flow was reconstructed in C57BL/6 J mice by injecting with recombinant adeno-associated virus rAAV9-Runx2-HMGCR. Our results confirmed the important role of the positive feedback loop in the development of scoliosis. Meanwhile, Avasimibe or/and Corylin were used to delay the scoliosis progression by targeting the key exosomal proteins APOB (Apolipoprotein B-100) or/and HSP90β (Heat Shock Protein 90-beta). This research extends the etiology of scoliosis progression and provides an alternative perspective for scoliosis non-surgical treatment. Show less
📄 PDF DOI: 10.1038/s42003-026-09960-w
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Excess apolipoprotein B predicts long-term survival in statin-treated CAD irrespective of achieved LDL-C levels: a pooled cohort analysis.

Wei Pan, Xiaozhao Lu, Ziwei Zhou +14 more · 2026 · Lipids in health and disease · BioMed Central · added 2026-04-24
Residual cardiovascular risk persists in statin-treated patients with coronary artery disease (CAD), even when low-density lipoprotein cholesterol (LDL-C) targets are met. Excess apolipoprotein B (apo Show more
Residual cardiovascular risk persists in statin-treated patients with coronary artery disease (CAD), even when low-density lipoprotein cholesterol (LDL-C) targets are met. Excess apolipoprotein B (apoB), defined as measured apoB minus LDL-C-predicted apoB, may capture atherogenic particle burden beyond LDL-C, but its prognostic value for long-term mortality in secondary prevention remains uncertain. We conducted a pooled analysis of two nationwide Chinese cohorts (CIN-II and RED-CARPET) comprising 68,616 statin-treated CAD patients. Excess apoB was calculated using an internal reference population (triglycerides ≤ 1.0 mmol/L). Associations with all-cause and cardiovascular mortality were assessed using multivariable Cox models, with adjustment for clinical covariates including nutritional status. External validation was performed in 13,702 participants from the UK Biobank. Over a median follow-up of 5.2 years, 10,835 deaths occurred (5,090 cardiovascular). Each 1-standard deviation (15.4 mg/dL) increase in excess apoB was associated with a 12% higher risk of all-cause mortality (adjusted hazard ratio [aHR] 1.12, 95% CI 1.06-1.18) and a 24% higher risk of cardiovascular mortality (aHR 1.24, 95% CI 1.15-1.34). Patients in the highest excess apoB quartile (≥ 11.5 mg/dL) had significantly worse survival. Notably, these associations persisted consistently across all achieved LDL-C strata (< 2.0 to > 4.0 mmol/L). These findings were robustly confirmed in the external validation cohort. Excess apoB is an independent predictor of long-term mortality in statin-treated CAD patients, even among those with well-controlled LDL-C. Its incorporation into risk assessment could improve prognostic stratification and guide personalized management in secondary prevention. CIN-II: ClinicalTrials.gov, NCT05050877 (Retrospectively registered, 21 September 2021); RED-CARPET: Chinese Clinical Trial Registry, ChiCTR2000039901 (Prospectively registered, 14 November 2020). The UK Biobank study is covered by generic ethical approval from the NHS National Research Ethics Service (Ref: 99231). Show less
no PDF DOI: 10.1186/s12944-026-02928-z
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Efficacy and safety of lipoprotein(a)-targeted therapeutics: a systematic review and network meta-analysis.

Jiaqiang Hu, Jun Wang, Haixia Zhang +4 more · 2026 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Lipoprotein(a)-targeted therapies are emerging approaches for lowering lipoprotein(a) [lp(a)]. We conducted a systematic review and network meta-analysis to evaluate the efficacy and safety of lipopro Show more
Lipoprotein(a)-targeted therapies are emerging approaches for lowering lipoprotein(a) [lp(a)]. We conducted a systematic review and network meta-analysis to evaluate the efficacy and safety of lipoprotein(a)-targeted therapies in patients. We searched PubMed, Embase, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) up to May 6, 2025, for randomized controlled trials (RCTs) with intervention duration of at least 12 weeks. The primary outcomes were percentage and absolute changes in Lp(a). Secondary outcomes included changes in low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (apoB), and safety outcomes including adverse events (AEs), serious adverse events (SAEs), and injection-site reactions. A frequentist framework network meta- analysis was performed. Nine studies involving 1,432 participants were included. All six Lp(a)-targeted therapies significantly reduced Lp(a) levels. Compared with placebo, Olpasiran was the most effective therapy for both percentage [mean difference: -92.06, 95% (-109.80; -74.32), Lp(a)-targeted therapies achieved substantial reductions in Lp(a). Olpasiran was the most effective agent in lowering Lp(a) levels. These therapies also improved LDL-C and apoB. The majority of Lp(a)-targeted therapies demonstrate generally favorable safety profiles; However, injection-site reactions, particularly with Zerlasiran, warrant careful consideration. https://www.crd.york.ac.uk/PROSPERO/view/CRD420251069288, PROSPERO CRD420251069288. Show less
📄 PDF DOI: 10.3389/fcvm.2026.1758366
APOB

Effect of triazine thiols on steady-state mRNA levels in iPSC-derived hepatocytes.

Carla Martinez-Morant, Jui-Tung Liu, Yu-Lin Jiang +2 more · 2026 · microPublication biology · added 2026-04-24
We previously reported that triazine thiols reduce apolipoprotein B (ApoB) secretion from human iPSC-derived hepatocytes (HLCs) and from humanized mice. To determine whether these compounds affected h Show more
We previously reported that triazine thiols reduce apolipoprotein B (ApoB) secretion from human iPSC-derived hepatocytes (HLCs) and from humanized mice. To determine whether these compounds affected hepatocyte mRNA levels, we performed bulk RNA sequencing of HLCs treated with the triazine thiol DL-1 or with vehicle (DMSO) for 24 hours. Analyses revealed that in triazine thiol-treated cells, 145 mRNAs were reduced and 37 increased by ≥ 2-fold.  Several mRNAs encoding cysteine-rich metallothionines were upregulated, implying that HLCs respond to treatment by mounting a protective response through metal buffering. Show less
📄 PDF DOI: 10.17912/micropub.biology.002062
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Integrated transcriptomic, proteomic, and metabolomic analyses reveal molecular mechanisms underlying clutch length differences in goose ovaries.

Hechuan Wang, Yunuo Liu, Ke Jiang +6 more · 2026 · Poultry science · Elsevier · added 2026-04-24
Clutch length is a key determinant of reproductive efficiency in geese and strongly positively correlates with egg production. We recorded daily egg production in 280 individually housed Zi geese, cal Show more
Clutch length is a key determinant of reproductive efficiency in geese and strongly positively correlates with egg production. We recorded daily egg production in 280 individually housed Zi geese, calculated clutch-related indices, and selected 12 geese to form long-clutch (LC) and short-clutch (SC) groups for ovarian transcriptomic, proteomic, and metabolomic analyses. The results showed that egg number, large clutch length, large clutch number, average clutch length, and average clutch number were significantly higher in LC than in SC groups (P < 0.0001). Transcriptomic analysis identified 885 differentially expressed genes enriched in oocyte development and ovarian steroidogenesis, with APOB, PLA2G4C, MMP2, MMP9, and NOBOX as key genes; proteomic analysis identified 437 differentially abundant proteins enriched in arachidonic acid metabolism and mitophagy, with CXCL12, RARB, and MAD2L1 as key proteins; and metabolomic analysis identified 35 differentially abundant metabolites enriched in glycolysis/gluconeogenesis, with lactic acid, guanidinoacetic acid, and 3-hydroxybutyrylcarnitine as key metabolites. Integration of multi-omics datasets highlighted a lactate-associated cross-omics signature supported by YWHAZ at the protein level and by the lactate transporter SLC16A3. Collectively, these findings deepen our understanding of the molecular basis underlying clutch-length variation in goose ovaries and highlight candidate genes, proteins, and metabolites for future functional validation. Show less
📄 PDF DOI: 10.1016/j.psj.2026.106731
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CLCC1 governs ER bilayer equilibration to maintain lipid homeostasis.

Lingzhi Wu, Jianqin Wang, Yawei Wang +20 more · 2026 · Nature · Nature · added 2026-04-24
Orchestration of lipid production, storage and mobilization is vital for cellular and systemic homeostasis
📄 PDF DOI: 10.1038/s41586-026-10161-y
APOB

Association of apolipoprotein E gene polymorphisms with risk of coronary artery disease in a Han Chinese population at middle and high altitude in China.

Fanrong Zeng, Xinyi Zhang, Meng Zhang +6 more · 2026 · Frontiers in endocrinology · Frontiers · added 2026-04-24
This study investigated the impact of This retrospective case-control study involved 628 CAD patients and 628 matched controls without CAD. ApoE genotyping was conducted using PCR-chip technology, and Show more
This study investigated the impact of This retrospective case-control study involved 628 CAD patients and 628 matched controls without CAD. ApoE genotyping was conducted using PCR-chip technology, and genotype and allele frequencies were compared between groups. Multivariate logistic regression analyzed the link between ApoE polymorphisms and CAD risk in populations at middle and high altitudes. The data revealed significant differences in These findings validated that the Show less
📄 PDF DOI: 10.3389/fendo.2026.1765770
APOB

Structure-corona-function engineering of micelles enables rapid hepatic bioactivation of clopidogrel for emergency antiplatelet therapy.

Qinying Chen, Dali Chen, Zhihao Liu +12 more · 2026 · Journal of controlled release : official journal of the Controlled Release Society · Elsevier · added 2026-04-24
Rapid platelet inhibition is essential for effective management during emergency percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS). However, the oral dosage form Show more
Rapid platelet inhibition is essential for effective management during emergency percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS). However, the oral dosage form of clopidogrel (CLP) commonly used in clinical practice shows a delayed onset due to gastrointestinal absorption, first-pass metabolism, and the requirement for hepatic cytochrome P450 (CYP450)-mediated bioactivation, which limits its applications in urgent scenarios and complicating post-PCI bleeding management. To address these challenges, we developed an intravenous micellar formulation (CLP/PM) using FDA-approved mPEG-PLA copolymers to promote rapid hepatic exposure and metabolic activation. By tuning the PLA chain length, micellar core density and PEG conformation were modulated, thereby influencing protein corona (PC) formation and liver-affinity interactions. Proteomic profiling revealed that micelles with intermediate PLA length selectively recruited liver-affinity apolipoproteins (ApoM, ApoH, ApoA1, and ApoB), which are known ligands of LDLR and SR-BI, while minimizing adsorption of inflammatory and opsonization proteins. The optimized CLP/PM (3.9 k) exhibited a hepatotropic-like PC that was associated with hepatocyte-enriched uptake in primary liver cell analyses. In vivo biodistribution showed rapid liver-level signal, and pharmacokinetic studies supported enhanced CYP450-mediated activation, achieving a higher C Show less
no PDF DOI: 10.1016/j.jconrel.2026.114727
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Transcriptomic and functional evidence reveals dual-module immune response of human-nasal staphylococcus aureus in Koi Carp (Cyprinus carpio).

Mei Li, Zeqing Xu, Jiarui Zeng +6 more · 2026 · International journal of medical microbiology : IJMM · Elsevier · added 2026-04-24
Staphylococcus aureus is a significant pathogen that poses a threat to both human and animal health. Its pathogenicity in humans has been extensively studied, however, the signaling pathways and key g Show more
Staphylococcus aureus is a significant pathogen that poses a threat to both human and animal health. Its pathogenicity in humans has been extensively studied, however, the signaling pathways and key genes in Koi Carp responding to S. aureus from human rhinitis remain unclear. In this study, we established an intraperitoneal infection model in koi carp (Cyprinus carpio) using an S. aureus isolate from patients with rhinitis and integrated RNA-seq, qPCR, and ELISA to dissect the host response. Our findings reveal a dual-module immune evasion strategy employed by S. aureus in koi carp. Module I: The pathogen down-regulated the entire complement coagulation cascade (C3, C9, CFH, F7/9/10) and apolipoprotein-mediated opsonins (APOA1, APOB, APOC1/2), thereby crippling innate clearance. Module II: The host mounted a restricted but potent counter-response, characterized by type I IFN signalling (gvin1, MHC-I), NK/T-cell co-stimulation (CD244, SLAMF5), and the selective induction of IL-8 and IL-1β, while IL-6, IL-10, and TNF-α remained unchanged. Functionally, serum superoxide dismutase (SOD), catalase (CAT), and lysozyme (LZM) activities surged, confirming an oxidative burst, whereas splenic CD22R protein decreased, indicating B-cell disinhibition. These results establish a molecular basis for understanding the interaction between human-derived S. aureus and the immune system of aquatic organisms. Show less
no PDF DOI: 10.1016/j.ijmm.2026.151707
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Association of Hyperbilirubinemia with Lipid Profile and Lipid-Related Diseases: A Large Community-Based Cohort Study.

Borong Yu, Yuhe Liu, Wenqian Wu +3 more · 2026 · Journal of clinical medicine · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/jcm15020455
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Association of residual lipoprotein-cholesterol and atherogenic index of plasma with in-hospital major adverse cardiovascular and cerebrovascular events after percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction.

Ni An, Hailong Lu, Tian Liu +1 more · 2026 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
In recent years, non-traditional lipid indices have emerged as significant predictors for cardiovascular events following emergency percutaneous coronary intervention (PCI) for ST-segment elevation my Show more
In recent years, non-traditional lipid indices have emerged as significant predictors for cardiovascular events following emergency percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI). However, the relationship of residual lipoprotein-cholesterol (RLP-C) and atherogenic index of plasma (AIP) with in-hospital outcomes, especially their predictive value for major adverse cardiovascular and cerebrovascular events (MACCEs) after PCI in STEMI patients, remains underexplored and warrants further investigation. This retrospective cohort study included 526 STEMI patients who underwent emergency PCI between January 2023 and August 2024. We collected baseline demographic, clinical, and laboratory data. RLP-C and AIP were calculated from lipid profiles obtained before PCI. Independent predictors of in-hospital MACCEs were identified using multivariate logistic regression, and model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis. Among 526 STEMI patients receiving PCI, 92 (17.49%) developed in-hospital MACCEs. Multivariate analysis identified RLP-C (OR = 3.97, 95%CI: 1.71–9.21; RLP-C and AIP are independent predictors of in-hospital MACCEs following PCI in STEMI patients. Combined assessment of these indices improves risk stratification and may facilitate early targeted interventions to improve outcomes. The online version contains supplementary material available at 10.1186/s12872-026-05555-9. Show less
📄 PDF DOI: 10.1186/s12872-026-05555-9
APOB

Wash-Free and Simultaneous Imaging Assay of PCSK9 and ApoB Proteins for Efficient Screening and Efficacy Evaluation of the PCSK9 Inhibitors.

Ruyu Yang, Xinmei Liu, Xinning Gai +4 more · 2026 · Analytical chemistry · ACS Publications · added 2026-04-24
Dyslipidemia is a major risk factor for atherosclerotic cardiovascular disease and poses serious health risks to humans. Apolipoprotein B (ApoB) is a comprehensive lipid-lowering efficacy marker, whil Show more
Dyslipidemia is a major risk factor for atherosclerotic cardiovascular disease and poses serious health risks to humans. Apolipoprotein B (ApoB) is a comprehensive lipid-lowering efficacy marker, while proprotein convertase subtilisin/kexin type 9 (PCSK9) is a crucial lipid-lowing target. The combination of PCSK9 and ApoB protein detection is helpful for screening PCSK9 inhibitors and providing synchronous feedback on the lipid-lowering efficacy. Addressing the limitations of traditional quantitative methods such as complicated procedures, lengthy workflows, low sensitivity, and challenges in the simultaneous detection of multiple biomarkers, a novel dual-component fluorescence immunoassay was developed, based on stimulus-responsive hollow mesoporous nanoparticles, noninterfering dyes, and DNA-labeled antibodies. It effectively enabled the concurrent quantification of PCSK9 and ApoB within a single testing process, eliminating the need for washing steps during the immunoreaction. When integrated with a paper chip, the system enabled imaging readout with a maximum throughput of 49 tests h Show less
no PDF DOI: 10.1021/acs.analchem.5c06359
APOB

FGF1 ameliorates hepatic steatosis through acute activation of the unfolded protein response and VLDL production.

Tim van Zutphen, Dicky Struik, Weilin Liu +8 more · 2026 · JHEP reports : innovation in hepatology · Elsevier · added 2026-04-24
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a serious chronic liver disease with limited therapeutic options. Fibroblast growth factor (FGF) analogs show promising therapeutic Show more
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a serious chronic liver disease with limited therapeutic options. Fibroblast growth factor (FGF) analogs show promising therapeutic benefits for MASLD, yet the underlying mechanisms remain incompletely understood. Here, we studied the mechanism underlying the anti-steatotic properties of FGF1, the prototype member of the FGF family. The effect of FGF1 was studied in human and rodent hepatocytes and in obese mouse models exhibiting acute or chronic endoplasmic reticulum (ER) stress characteristic of MASLD. Metabolic analysis and proteomics were applied to evaluate liver physiology, ER stress and signaling. We show that FGF1 reduces hepatic triglyceride (TG) levels in obese mice (51%, These results define ER stress-dependent modulation of VLDL secretion as a mechanism underlying the anti-steatotic activity of FGF1. Targeting the FGF-UPR pathway may thus have therapeutic potential for treating MASLD. Fibroblast growth factors show therapeutic potential in both preclinical models and clinical trials for treating metabolic dysfunction-associated steatotic liver disease, a highly prevalent condition with limited treatment options. Identifying the mechanisms underlying their anti-steatotic effects may accelerate clinical development. Our finding that triglyceride secretion is the major driver of the anti-steatotic action of FGF1, together with the involvement of an adaptive unfolded protein response, provides deeper insight into the therapeutic potential of this pathway. These results also highlight possible implications for liver physiology and for the circulating lipoprotein profile, with relevance for both efficacy and safety considerations. Show less
📄 PDF DOI: 10.1016/j.jhepr.2025.101660
APOB

Identification of differentially expressed genes associated with tracheal injury recovery in a rabbit model of septic shock.

Pei Zhang, Huaihai Lu, Xuze Li +6 more · 2026 · BMC medical genomics · BioMed Central · added 2026-04-24
Sepsis is a syndrome caused by the host's inflammatory response to an infection with an unknown mechanism. This study aimed to identify differentially expressed genes (DEGs) potentially involved in th Show more
Sepsis is a syndrome caused by the host's inflammatory response to an infection with an unknown mechanism. This study aimed to identify differentially expressed genes (DEGs) potentially involved in the development and recovery of tracheal injury from septic shock. Nine New Zealand white rabbits were randomized to control (CON), septic shock model (SS), and septic shock norepinephrine treatment (SSNE) groups (each group n = 3). The SS and SSNE groups were injected with lipopolysaccharide to induce septic shock. The SSNE group was administered Ringer lactate with norepinephrine to maintain normal blood pressure. All animals underwent cuffed endotracheal intubation for 2 h. The injured tracheal segment was harvested. RNA sequencing was performed to identify the DEGs, followed by bioinformatics analysis, and pathological staining (both HE and Masson) was performed for pathological evaluation. Bioinformatics analysis included principal component analysis (PCA), gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) network construction. Key findings were validated by qRT-PCR and immunohistochemistry. We obtained 124 upregulated and 28 downregulated DEGs in SS vs. CON groups, along with 60 upregulated and 178 downregulated DEGs in SSNE vs. SS groups. The pathological score showed that trachea tissue in the SS group had the highest score. The protein-protein interaction (PPI) prediction identified APOB and CD36 as the hub genes. The molecular experiments further confirmed that at mRNA and protein levels, APOB was significantly upregulated, while CD36 was significantly downregulated. Subsequent qRT-PCR and immunohistochemical analyses confirmed that APOB expression was significantly upregulated while CD36 was downregulated in the septic shock group, a trend partially reversed by norepinephrine treatment. Our study results suggest that APOB and CD36 may be involved in the pathogenesis of tracheal injury recovery in septic shock patients treated with NE. Not applicable. Show less
📄 PDF DOI: 10.1186/s12920-025-02304-3
APOB

Screening of the key single nucleotide polymorphisms in type 2 diabetes mellitus complicated with lower extremity arterial disease by machine learning.

Xinyi Li, Aige Yang, Xiao Liu +2 more · 2026 · Journal of hypertension · added 2026-04-24
Diabetic lower extremity arterial disease (LEAD) is a manifestation of diabetic lower extremity vascular complications. This study aimed to screen the key single nucleotide polymorphism (SNP) gene sig Show more
Diabetic lower extremity arterial disease (LEAD) is a manifestation of diabetic lower extremity vascular complications. This study aimed to screen the key single nucleotide polymorphism (SNP) gene signature in patients with type 2 diabetes mellitus (T2DM) and LEAD. A total of 147 patients with T2DM complicated by LEAD and 144 patients with T2DM without LEAD were enrolled for transcriptome sequencing. The Plink software was used to preprocess the data. Five machine learning methods were adopted to build the SNP diagnosis models. The receiver operating characteristic (ROC) curve was used to quantify the predicted probabilities of the model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the cluster Profiler package. Finally, regression statistical analysis was used to correlate the key SNPs with clinical information and biochemical indicators. A total of 24 SNPs were retained and 10 SNPs were risk allele genes. Nine SNPs (rs7412, rs1800629, rs699947, rs3918242, rs668, rs1800470, rs1800449, rs1800469, and rs1024611) were identified as the key SNPs sites. GO and KEGG pathway analyses revealed that these genes are mainly enriched in fluid shear stress and atherosclerosis. Finally, rs1800449 was associated with low-density lipoprotein cholesterol (LDL-C). With high density lipoprotein cholesterol (HDL-C), related site was rs1024611. The sites associated with total cholesterol (CHOL) were rs1800449 and rs7412.The site associated with apolipoprotein B (APOB) and apolipoprotein A1 (APOA1) were rs1800470 and rs1800469. This study authenticated nine SNPs for the diagnosis of T2DM patients with LEAD, which will be of great significance in the development of diagnostic molecular biomarkers for T2DM patients. Show less
no PDF DOI: 10.1097/HJH.0000000000004164
APOB

Apolipoprotein C3 drives adverse cardiac remodeling in ischemic heart failure.

Yufei Han, Yixue Zhao, Zihao Zhou +8 more · 2026 · BMC medicine · BioMed Central · added 2026-04-24
Ischemic heart failure (IHF) is one of the leading causes of death in the world. Plasma apolipoprotein C3 (ApoC3) levels are significantly elevated in patients with heart failure and positively associ Show more
Ischemic heart failure (IHF) is one of the leading causes of death in the world. Plasma apolipoprotein C3 (ApoC3) levels are significantly elevated in patients with heart failure and positively associated with the incidence of ischemic heart disease (IHD). However, the causal association between ApoC3 and IHD development is unclear. ApoC3 expression changes were assessed in plasma from IHF patients/healthy donors and cardiac tissue from rodent models. 10-week-old male human ApoC3 transgenic (ApoC3 Overexpression of human ApoC3 in ApoC3 ApoC3 overexpression could activate cardiac TLR2/NF-κB to trigger the inflammation, oxidation, and apoptosis pathways, finally aggravating IHF in mice. Inactivation of ApoC3 could significantly alleviate IHF in hamsters. Show less
no PDF DOI: 10.1186/s12916-026-04855-3
APOC3

Apolipoprotein C3 exacerbates vascular calcification by promoting ferroptosis via the TLR2/AMPK pathway in vascular smooth muscle cells.

Zihao Zhou, Jinxuan Chen, Huan Wang +16 more · 2026 · Redox biology · Elsevier · added 2026-04-24
Vascular calcification (VC) is prevalent in patients with chronic renal failure (CRF), and it is closely related to the morbidity and mortality of cardiovascular diseases; however, no medical treatmen Show more
Vascular calcification (VC) is prevalent in patients with chronic renal failure (CRF), and it is closely related to the morbidity and mortality of cardiovascular diseases; however, no medical treatments are available for this condition. Recent clinical studies have shown that plasma apolipoprotein C3 (ApoC3) levels are positively correlated with VC. However, whether ApoC3 is involved in VC remains unclear. Sections of calcified renal arteries from CRF patients were immunostained to measure calcium deposition and ApoC3 expression. VC was induced in ApoC3 transgenic (Tg) and knockout (KO) mice by both 5/6 nephrectomy and vitamin D ApoC3 expression levels were increased in calcified arteries from mice and patients with CRF. ApoC3 overexpression exacerbated calcium deposition in the calcified aortas from Tg mice in vivo, and in calcified aortic rings of Tg mice ex vivo and VSMCs infected by adenovirus of ApoC3 in vitro. Consistently with these findings, ApoC3 deficiency alleviated these effects. Furthermore, ApoC3 overexpression increased ferroptosis in calcified aortas and VSMCs, whereas ApoC3 deficiency suppressed ferroptosis. Further investigation revealed that ApoC3 inhibited the AMPK/NRF2 signaling pathway through toll-like receptor 2 (TLR2) in calcified VSMCs, downregulated the expression of solute carrier family 7 member 11 (SLC7A11) and glutathione peroxidase 4 (GPX4), subsequently increased lipid peroxidation and promoted ferroptosis, ultimately exacerbating calcification in the VSMCs. Furthermore, we found that knockdown of ApoC3 by siRNA remarkably attenuated calcification of renal arterial rings in humans. We demonstrated that ApoC3 exacerbated VC and increased the osteogenic transdifferentiation in VSMCs by increasing ferroptosis. ApoC3 might be a potential target for VC treatment. Show less
📄 PDF DOI: 10.1016/j.redox.2026.104088
APOC3

Interplay between intra-pancreatic fat deposition, exchangeable apolipoproteins, and lipoprotein subclasses.

Yutong Liu, Juyeon Ko, Loren Skudder-Hill +3 more · 2026 · Nutrition, metabolism, and cardiovascular diseases : NMCD · Elsevier · added 2026-04-24
Exchangeable apolipoproteins, including apolipoprotein C-II (apo C-II), apolipoprotein C-III (apo C-III), and apolipoprotein E (apo E), play central roles in the modulation of cardiovascular disease ( Show more
Exchangeable apolipoproteins, including apolipoprotein C-II (apo C-II), apolipoprotein C-III (apo C-III), and apolipoprotein E (apo E), play central roles in the modulation of cardiovascular disease (CVD) risk by readily transferring between anti-atherogenic high-density lipoprotein (HDL) and pro-atherogenic triglyceride-rich lipoproteins (TRL). High intra-pancreatic fat deposition (IPFD) has also emerged as a novel risk factor for CVD. This study aimed to investigate the associations of apo C-II, apo C-III, and apo E with IPFD, as well as with TRL and HDL subclasses. Abdominal magnetic resonance imaging at 3.0 T was used to quantify IPFD. Plasma levels of apo C-II, apo C-III, and apo E were measured. TRL and HDL subclasses were analysed, with TRL categorised into very-low-density lipoprotein (VLDL) and intermediate-density lipoprotein (IDL) subclasses (IDL-C, IDL-B, and IDL-A), and HDL into HDL-large, HDL-intermediate, and HDL-small subclasses. Univariate and multivariate linear regression analyses were performed to assess these associations. A total of 128 individuals were analysed. IPFD showed a significant inverse association with both apo C-II and apo C-III, consistent across all statistical models. In the most adjusted model, each unit increase in IPFD was associated with a 0.36-unit decrease in apo C-II (p = 0.001) and a 0.31-unit decrease in apo C-III (p = 0.004). Furthermore, apo C-II and apo C-III were significantly and inversely associated with all IDL subclasses (p < 0.02), but not with VLDL, across all models. No statistically significant association between apo E and IPFD or any IDL subclass was observed in the most adjusted model. Apo C-II and apo C-III, but not apo E, contribute to the previously observed positive relationship between IPFD and IDL. Show less
no PDF DOI: 10.1016/j.numecd.2025.104280
APOC3

Cell-type-aware transcriptome-wide association studies identify 91 independent risk genes for Alzheimer's disease dementia.

Qiang Liu, Randy L Parrish, Shizhen Tang +7 more · 2026 · Communications biology · Nature · added 2026-04-24
Most existing transcriptome wide association studies (TWASs) of Alzheimer's Disease (AD) dementia only use bulk RNA-seq data and a single statistical method. Here, we utilize an omnibus TWAS (TWAS-O) Show more
Most existing transcriptome wide association studies (TWASs) of Alzheimer's Disease (AD) dementia only use bulk RNA-seq data and a single statistical method. Here, we utilize an omnibus TWAS (TWAS-O) pipeline that leverages multiple complementary statistical methods to integrate the snRNA-seq dataset (n = 415) of the dorsolateral prefrontal cortex (DLPFC) and the latest GWAS data of AD dementia. We fine-map TWAS risk genes by gene-based conditional analysis and conducted validation analyses by the analogous omnibus proteome-wide association studies (PWAS-O) using bulk proteomics data of DLPFC (n = 716). We identify 223 unique cell-type-aware TWAS risk genes from 350 associations across six major brain cell-types, including 91 fine-mapped independent associations, 11 of which are novel. By PWAS-O, we identify 21 significant PWAS risk genes, including 13 independent associations, which validated 31.9% independent cell-type-aware TWAS associations. By protein-protein interaction network analyses, our novel cell-type-aware TWAS findings are linked to established AD risk genes such as APOE, BIN1, and MAPT. Show less
no PDF DOI: 10.1038/s42003-026-10030-4
APOE