👤 Qibing Li

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Also published as: A Li, Ai-Jun Li, Ai-Qin Li, Ailing Li, Aimin Li, Aixin Li, Alexander H Li, Alexander Li, Amy Li, An-Qi Li, AnHai Li, Anan Li, Andrew C Li, Ang Li, Anna Fen-Yau Li, Annie Li, Anqi Li, Anyao Li, Ao Li, Aowen Li, Aoxi Li, Audrey Li, Bai-Qiang Li, Baichuan Li, Baiqiang Li, Baixing Li, Baizhou Li, Bang-Yan Li, Bao Li, Bao-Shan Li, Baoguang Li, Baoguo Li, Baohong Li, Baohua Li, Baolin Li, Baoqi Li, Baoqing Li, Baosheng Li, Baoting Li, Bei Li, Bei-Bei Li, Beibei Li, Beixu Li, Ben Li, Ben-Shang Li, Benyi Li, Biao Li, Bichun Li, Bin Li, Bin-Kui Li, Binbin Li, Bing Li, Bing-Heng Li, Bing-Hui Li, Bing-Mei Li, Bingbing Li, Binghu Li, Binghua Li, Bingjie Li, Bingjue Li, Bingkun Li, Binglan Li, Bingong Li, Bingshan Li, Bingsheng Li, Bingsong Li, Bingxin Li, Binjun Li, Binkui Li, Binru Li, Binxing Li, Biyu Li, Bizhi Li, Bo Li, BoWen Li, Bohao Li, Bohua Li, Bolun Li, Boru Li, Botao Li, Boxuan Li, Boya Li, Boyang Li, Bugao Li, C H Li, C Li, C X Li, C Y Li, Caesar Z Li, Cai Li, Cai-Hong Li, Caihong Li, Caili Li, Caixia Li, Caiyu Li, Caiyun Li, Can Li, Cang Li, Caolong Li, Chang Li, Chang-Da Li, Chang-Ping Li, Chang-Sheng Li, Chang-Yan Li, Chang-hai Li, Changcheng Li, Changgui Li, Changhong Li, Changhui Li, Changjiang Li, Changkai Li, Changqing Li, Changwei Li, Changxian Li, Changyan Li, Changyu Li, Changzheng Li, Chanjuan Li, Chanyuan Li, Chao Bo Li, Chao Li, Chaochen Li, Chaojie Li, Chaonan Li, Chaoqian Li, Chaowei Li, Chaoying Li, Chen Li, Chen-Chen Li, Chen-Lu Li, Chen-Xi Li, Chenfeng Li, Cheng Li, Cheng-Lin Li, Cheng-Tian Li, Cheng-Wei Li, Chengbin Li, Chengcheng Li, Chenghao Li, Chenghong Li, Chengjian Li, Chengjun Li, Chenglan Li, Chenglong Li, Chengnan Li, Chengping Li, Chengqian Li, Chengquan Li, Chengsi Li, Chenguang Li, Chengwen Li, Chengxin Li, Chengyun Li, Chenhao Li, Chenjie Li, Chenli Li, Chenlin Li, Chenlong Li, Chenlu Li, Chenmeng Li, Chenrui Li, Chensheng Li, Chenwen Li, Chenxi Li, Chenxiao Li, Chenxin Li, Chenxuan Li, Chenyang Li, Chenyao Li, Chenyu Li, Cheung Li, Chi-Ming Li, Chi-Yuan Li, Chia Li, Chia-Yang Li, Chien-Feng Li, Chien-Hsiu Li, Chien-Te Li, Chih-Chi Li, Chitao Li, Chiyang Li, Chong Li, Chongyang Li, Chongyi Li, Chris Li, Chu-Qiao Li, Chuan F Li, Chuan Li, Chuan-Hai Li, Chuan-Yun Li, Chuanbao Li, Chuanfang Li, Chuang Li, Chuangpeng Li, Chuanning Li, Chuanyin Li, Chumei Li, Chun Li, Chun-Bo Li, Chun-Lai Li, Chun-Mei Li, Chun-Quan Li, Chun-Xiao Li, Chun-Xu Li, Chung-Hao Li, Chung-I Li, Chunhong Li, Chunhui Li, Chunjie Li, Chunjun Li, Chunlan Li, Chunlian Li, Chunliang Li, Chunlin Li, Chunmei Li, Chunmiao Li, Chunqing Li, Chunqiong Li, Chunshan Li, Chunsheng Li, Chunting Li, Chunxia Li, Chunxiao Li, Chunxing Li, Chunxue Li, Chunya Li, Chunyan Li, Chunyi Li, Chunying Li, Chunyu Li, Chunzhu Li, Chuzhong Li, Cien Li, Cong Li, Congcong Li, Congfa Li, Conghui Li, Congjiao Li, Conglin Li, Congxin Li, Congye Li, Cui Li, Cui-lan Li, Cuicui Li, Cuiguang Li, Cuilan Li, Cuiling Li, Cun Li, Cunxi Li, Cyril Li, D C Li, Da Li, Da-Hong Li, Da-Jin Li, Da-Lei Li, Da-wei Li, DaZhuang Li, Dacheng Li, Dai Li, Daiyue Li, Dalei Li, Dali Li, Dalin Li, Dan C Li, Dan Li, Dan-Dan Li, Dan-Ni Li, Dandan Li, Daniel Tian Li, Danjie Li, Danni Li, Danxi Li, Danyang Li, Daoyuan Li, Dapei Li, Dawei Li, Dayong Li, Dazhi Li, De-Jun Li, De-Tao Li, Dechao Li, Defa Li, Defeng Li, Defu Li, Dehai Li, Deheng Li, Dehua Li, Dejun Li, Demin Li, Deming Li, Dengfeng Li, Dengke Li, Dengxiong Li, Deqiang Li, Desen Li, Desheng Li, Dexiong Li, Deyu Li, Dezhi Li, Di Li, Di-Jie Li, Dianjie Li, Dijie Li, Ding Li, Ding Yang Li, Ding-Biao Li, Ding-Jian Li, Dingchen Li, Dingshan Li, Diyan Li, Dong Li, Dong Sheng Li, Dong-Jie Li, Dong-Ling Li, Dong-Run Li, Dong-Yun Li, Dong-fei Li, Dongbiao Li, Dongdong Li, Dongfang Li, Dongfeng Li, Donghe Li, Donghua Li, Dongliang Li, Dongmei Li, Dongmin Li, Dongnan Li, Dongtao Li, Dongyang Li, Dongye Li, Duan Li, Duanbin Li, Duanxiang Li, Dujuan Li, Duo Li, Duoyun Li, Ellen Li, En Li, En-Min Li, Enhao Li, Enhong Li, Enxiao Li, F Li, Fa-Hong Li, Fa-Hui Li, Fadi Li, Fan Li, Fang Li, Fangqi Li, Fangyan Li, Fangyong Li, Fangyuan Li, Fangzhou Li, Fei Li, Fei-Lin Li, Fei-feng Li, Feifei Li, Feilong Li, Fen Li, Feng Li, Feng-Feng Li, Fengfeng Li, Fengjuan Li, Fengli Li, Fengqi Li, Fengqiao Li, Fengqing Li, Fengxia Li, Fengxiang Li, Fengyi Li, Fengyuan Li, Fu-Rong Li, Fugen Li, Fuhai Li, Fujun Li, Fulun Li, Fuping Li, Fusheng Li, Fuyu Li, Fuyuan Li, G Li, G-P Li, Gaijie Li, Gaizhen Li, Gaizhi Li, Gan Li, Gang Li, Ganggang Li, Gao-Fei Li, Gaoyuan Li, Ge Li, Gen Li, Gen-Lin Li, Gerard Li, Gong-Hua Li, Gongda Li, Guanbin Li, Guandu Li, Guang Li, Guang Y Li, Guang-Li Li, Guang-Xi Li, Guangda Li, Guangdi Li, Guanghua Li, Guanghui Li, Guangjin Li, Guangli Li, Guanglu Li, Guanglve Li, Guangming Li, Guangping Li, Guangpu Li, Guangqiang Li, Guangquan Li, Guangwen Li, Guangxi Li, Guangxiao Li, Guangyan Li, Guangzhao Li, Guangzhen Li, Guannan Li, Guanqiao Li, Guanyu Li, Gui Lin Li, Gui-Bo Li, Gui-Hua Li, Gui-Rong Li, Gui-xing Li, Guigang Li, Guihua Li, Guilan Li, Guisen Li, Guixia Li, Guixin Li, Guiyang Li, Guiying Li, Guiyuan Li, Guo Li, Guo-Chun Li, Guo-Jian Li, Guo-Li Li, Guo-Ping Li, Guo-Qiang Li, Guobin Li, Guoge Li, Guohong Li, Guohua Li, Guohui Li, Guojin Li, Guojun Li, Guoli Li, Guoping Li, Guoqin Li, Guoqing Li, Guowei Li, Guoxi Li, Guoxiang Li, Guoxing Li, Guoyan Li, Guoyin Li, H J Li, H Li, H-F Li, H-H Li, H-J Li, Hai Li, Hai-Yun Li, Haibin Li, Haibo Li, Haifeng Li, Haihong Li, Haihua Li, Haijun Li, Hailong Li, Haimin Li, Haiming Li, Hainan Li, Haipeng Li, Hairong Li, Haitao Li, Haitong Li, Haixia Li, Haiyan Li, Haiyang Li, Haiying Li, Haiyu Li, Han Li, Han-Bing Li, Han-Bo Li, Han-Ni Li, Han-Ru Li, Han-Wei Li, Hanbin Li, Hanbing Li, Hanbo Li, Handong Li, Hang Li, Hangwen Li, Hanjun Li, Hankun Li, Hanlu Li, Hanmei Li, Hanqi Li, Hanqin Li, Hansen Li, Hanting Li, Hanxiao Li, Hanxue Li, Hao Li, Hao-Fei Li, Haojing Li, Haolong Li, Haomiao Li, Haoqi Li, Haoran Li, Haotong Li, Haoxian Li, Haoyu Li, Haying Li, He Li, He-Zhen Li, Hecheng Li, Hegen Li, Hehua Li, Heng Li, Heng-Zhen Li, Hengguo Li, Hengtong Li, Hengyu Li, Hening Li, Hewei Li, Hexin Li, Heying Li, Hong Li, Hong-Chun Li, Hong-Lan Li, Hong-Lian Li, Hong-Mei Li, Hong-Tao Li, Hong-Wen Li, Hong-Yan Li, Hong-Yu Li, Hong-Zheng Li, Hongbo Li, Hongchang Li, Hongde Li, Honggang Li, Hongguo Li, Honghua Li, Honghui Li, Hongjia Li, Hongjiang Li, Hongjuan Li, Honglei Li, Hongli Li, Honglian Li, Hongliang Li, Honglin Li, Hongling Li, Honglong Li, Hongmei Li, Hongmin Li, Hongming Li, Hongqin Li, Hongquan Li, Hongru Li, Hongsen Li, Hongwei Li, Hongxia Li, Hongxin Li, Hongxing Li, Hongxue Li, Hongyan Li, Hongye Li, Hongyi Li, Hongyu Li, Hongyun Li, Hongzhe K Li, Hongzheng Li, Hongzhi Li, Hsiao-Fen Li, Hsiao-Hui Li, Hsin-Hua Li, Hsin-Yun Li, Hu Li, Hua Li, Hua-Zhong Li, Huabin Li, Huafang Li, Huafu Li, Huaixing Li, Huaiyuan Li, Hualian Li, Hualing Li, Huamao Li, Huan Li, Huanan Li, Huang Li, Huangbao Li, Huangyuan Li, Huanhuan Li, Huanjun Li, Huanqing Li, Huanqiu Li, Huaping Li, Huashun Li, Huawei Li, Huayao Li, Huayin Li, Huaying Li, Hui Li, Hui-Jun Li, Hui-Long Li, Hui-Ping Li, Huibo Li, Huifang Li, Huifeng Li, Huihuang Li, Huihui Li, Huijie Li, Huijuan Li, Huijun Li, Huilan Li, Huili Li, Huiliang Li, Huilin Li, Huilong Li, Huimin Li, Huiping Li, Huiqin Li, Huiqing Li, Huiqiong Li, Huiting Li, Huixia Li, Huixue Li, Huiying Li, Huiyou Li, Huiyuan Li, Huizi Li, Hujie Li, Hulun Li, Hung Li, Hung-Yuan Li, Ivan Li, J Li, J T Li, Jason Li, Jen-Ming Li, Jenny J Li, Ji Li, Ji Xia Li, Ji-Cheng Li, Ji-Feng Li, Ji-Liang Li, Ji-Lin Li, Ji-Min Li, Jia Li, Jia Li Li, Jia-Da Li, Jia-Huan Li, Jia-Peng Li, Jia-Ru Li, Jia-Xin Li, Jiabei Li, Jiachen Li, Jiacheng Li, Jiafang Li, Jiafei Li, Jiahao Li, Jiahui Li, Jiajia Li, Jiajie Li, Jiajing Li, Jiajun Li, Jiajv Li, Jiali Li, Jialin Li, Jialing Li, Jialun Li, Jiaming Li, Jian Li, Jian'an Li, Jian-Jun Li, Jian-Mei Li, Jian-Qiang Li, Jian-Shuang Li, Jianan Li, Jianang Li, Jianbin Li, Jianbo Li, Jianchun Li, Jiandong Li, Jianfang Li, Jianfeng Li, Jiang Li, Jiangan Li, Jiangbo Li, Jiangchao Li, Jiangfeng Li, Jianglin Li, Jianglong Li, Jiangtao Li, Jiangui Li, Jianguo Li, Jiangxia Li, Jiangya Li, Jianhai Li, Jianhua Li, Jiani Li, Jianing Li, Jianliang Li, Jianlin Li, Jianmin Li, Jiannan Li, Jianping Li, Jianrong Li, Jianrui Li, Jiansheng Li, Jianshuang Li, Jianwei Li, Jianxin Li, Jianxiong Li, Jianye Li, Jianyi Li, Jianyong Li, Jianyu Li, Jianzhong Li, Jiao Li, Jiao-Jiao Li, Jiaomei Li, Jiaping Li, Jiaqi Li, Jiawei Li, Jiaxi Li, Jiaxin Li, Jiaxuan Li, Jiayan Li, Jiayang Li, Jiayi Li, Jiaying Li, Jiayu Li, Jiayuan Li, Jiazhou Li, Jicheng Li, Jie Li, Jie-Pin Li, Jie-Shou Li, Jiehan Li, Jiejia Li, Jiejie Li, Jiejing Li, Jieming Li, Jiequn Li, Jieshou Li, Jiexi Li, Jiexin Li, Jiezhen Li, Jifang Li, Jihua Li, Jin Li, Jin-Jiang Li, Jin-Liang Li, Jin-Long Li, Jin-Mei Li, Jin-Ping Li, Jin-Qiu Li, Jin-Wei Li, Jin-Xiu Li, Jinchen Li, Jinfang Li, Jinfeng Li, Jing Li, Jing-Jing Li, Jing-Ming Li, Jing-Yao Li, Jing-Yi Li, Jing-gao Li, Jingcheng Li, Jingchun Li, Jingfeng Li, Jinghao Li, Jinghui Li, Jingjing Li, Jingke Li, Jinglin Li, Jingmei Li, Jingming Li, Jingping Li, Jingqi Li, Jingshang Li, Jingshu Li, Jingtong Li, Jingui Li, Jingwen Li, Jingxia Li, Jingxiang Li, Jingxin Li, Jingya Li, Jingyi Li, Jingyong Li, Jingyu Li, Jingyun Li, Jinhua Li, Jinhui Li, Jinjie Li, Jinku Li, Jinlan Li, Jinliang Li, Jinlin Li, Jinman Li, Jinming Li, Jinping Li, Jinsong Li, Jinwei Li, Jinxia Li, Jinxin Li, Jinzhi Li, Jiong Li, Jiong-Ming Li, Jipeng Li, Jiqing Li, Jisen Li, Jisheng Li, Jiuke Li, Jiuyi Li, Jiwei Li, Jiwen Li, Jixi Li, Jixuan Li, Jiyang Li, Jiyuan Li, John Zhong Li, Jonathan Z Li, Joyce Li, Ju-Rong Li, Juan Li, Juan-Juan Li, Juanjuan Li, Juanling Li, Juanni Li, Jufang Li, Julia Li, Jun Li, Jun Z Li, Jun-Cheng Li, Jun-Jie Li, Jun-Ling Li, Jun-Ru Li, Jun-Yan Li, Jun-Ying Li, JunBo Li, Junfeng Li, Junhong Li, Junhui Li, Junjie Li, Junjun Li, Junming Li, Junping Li, Junqin Li, Junru Li, Junsheng Li, Juntong Li, Junxian Li, Junxin Li, Junxu Li, Junya Li, Junyi Li, Junying Li, Justin Li, Jutang Li, Juxue Li, K-L Li, Ka Li, Ka Wan Li, Kai Li, Kai-Wen Li, Kaibin Li, Kaibo Li, Kaifeng Li, Kailong Li, Kaimi Li, Kainan Li, Kaiwei Li, Kaixin Li, Kaiyi Li, Kaiyuan Li, Kang Li, Kangli Li, Kangyuan Li, Karen Li, Kathy H Li, Kawah Li, Ke Li, KeZhong Li, Keanning Li, Kecheng Li, Kechun Li, Keguo Li, Kejuan Li, Keke Li, Kening Li, Kenli Li, Kenneth Kai Wang Li, Keqing Li, Keshen Li, Keying Li, Keyuan Li, Kezhen Li, Kongdong Li, Kuan Li, Kui Li, Kuiliang Li, Kun Li, Kun-Peng Li, Kun-Ping Li, Kun-Xin Li, Kunlin Li, Kunlong Li, Kunlun Li, Kunpeng Li, L I Li, L K Li, L Li, L P Li, L-Y Li, Lai K Li, Laiqing Li, Lamei Li, Lan Li, Lan-Juan Li, Lan-Lan Li, Lanfang Li, Lang Li, Lanjuan Li, Lanlan Li, Lanzhou Li, Le Li, Le-Le Li, Le-Ying Li, Lei Li, Leilei Li, Leipeng Li, Letai Li, Leyao Li, Li Li, Li-Min Li, Li-Na Li, Lian Li, Lianbing Li, Liang Li, Liangdong Li, Liangji Li, Liangkui Li, Liangqian Li, Lianhong Li, Lianjian Li, Lianyong Li, Liao-Yuan Li, Lieyou Li, Liguo Li, Lihong Li, Lihua Li, Lijia Li, Lijuan Li, Lijun Li, Lili Li, Liliang Li, Liling Li, Liming Li, Lin Li, Lin-Feng Li, Linchuan Li, Linfeng Li, Ling Li, Ling-Jie Li, Ling-Ling Li, Ling-Zhi Li, Lingjiang Li, Lingjie Li, Lingjun Li, Lingling Li, Lingxi Li, Lingyan Li, Lingyi Li, Lingzhi Li, Linhong Li, Linke Li, Linlin Li, Linqi Li, Linqing Li, Linsheng Li, Linting Li, Linxin Li, Linyan Li, Linying Li, Lipeng Li, Liping Li, Liqin Li, Liqun Li, Lirong Li, Lisha Li, Litao Li, Liuzheng Li, Liwei Li, Lixi Li, Lixia Li, Lixiang Li, Liyan Li, Long Li, Long Shan Li, Long-Yan Li, Longhui Li, Longxuan Li, Longyu Li, Lu Li, Lu-Yun Li, Lucia M Li, Lucy Li, Luhan Li, Lujiao Li, Lujie Li, Lulu Li, Luquan Li, Luxuan Li, Luyao Li, Luying Li, M D Li, M Li, M V Li, M-J Li, Man Li, Man-Xiang Li, Man-Zhi Li, Mangmang Li, Manjiang Li, Manna Li, Manru Li, Manxia Li, Mao Li, Maogui Li, Maolin Li, Maoquan Li, Maosheng Li, Marilyn Li, Mei Li, Mei-Lan Li, Mei-Ya Li, Mei-Zhen Li, Meifang Li, Meifen Li, Meijia Li, Meilan Li, Meiqing Li, Meitao Li, Meiting Li, Meiyan Li, Meiying Li, Meiyue Li, Meizi Li, Melody M H Li, Meng Li, Meng-Hua Li, Meng-Jun Li, Meng-Meng Li, Meng-Miao Li, Meng-Yang Li, Meng-Yao Li, Meng-Yue Li, MengGe Li, Mengfan Li, Menghua Li, Mengjiao Li, Mengjuan Li, Mengling Li, Menglu Li, Mengmeng Li, Mengqing Li, Mengqiu Li, Mengsen Li, Mengshi Li, Mengxi Li, Mengxia Li, Mengxuan Li, Mengyang Li, Mengyao Li, Mengying Li, Mengyuan Li, Mengyun Li, Mengze Li, Mi Li, Mian Li, Miao Li, Miao X Li, Miaoxin Li, Michelle Li, Mimi Li, Min Li, Min-Dian Li, Min-Rui Li, Min-jun Li, Minerva X Li, Ming D Li, Ming Li, Ming V Li, Ming Xing Li, Ming Zhou Li, Ming-Han Li, Ming-Hao Li, Ming-Jiang Li, Ming-Kai Li, Ming-Qing Li, Ming-Wei Li, Ming-Xing Li, Ming-Yang Li, Mingdan Li, Mingfang Li, Mingfei Li, Minghao Li, Minghua Li, Minghui Li, Mingjiang Li, Mingjie Li, Mingjun Li, Mingke Li, Mingkun Li, Mingli Li, Minglong Li, Minglun Li, Mingna Li, Mingqiang Li, Mingquan Li, Mingrui Li, Mingwei Li, Mingxi Li, Mingxia Li, Mingxing Li, Mingxu Li, Mingxuan Li, Mingyang Li, Mingyao Li, Mingyue Li, Mingzhe Li, Mingzhou Li, Minhui Li, Minle Li, Minmin Li, Minqi Li, Minyue Li, Minze Li, Minzhe Li, Miyang Li, Mo Li, Mohan Li, Monica M Li, Moyi Li, Mufan Li, Mulin Jun Li, Muzi Li, N Li, Na Li, Naishi Li, Nan Li, Nan-Nan Li, Nana Li, Nanjun Li, Nanlong Li, Nanxing Li, Nanzhen Li, Ni Li, Nianfu Li, Nianyu Li, Nien Li, Nien-Chen Li, Nien-Chi Li, Ning Li, Ningyan Li, Ningyang Li, Niu Li, Nuomin Li, O Li, P H Li, P Li, Pan Li, Panlong Li, Panyuan Li, Pei Li, Pei-Lin Li, Pei-Qin Li, Pei-Shan Li, Pei-Ying Li, Pei-Zhi Li, PeiQi Li, Peibo Li, Peifen Li, Peifeng Li, Peihong Li, Peihua Li, Peilin Li, Peilong Li, Peining Li, Peipei Li, Peiqin Li, Peiran Li, Peiwu Li, Peixin Li, Peiyu Li, Peiyuan Li, Peiyun Li, Peng Li, Peng Peng Li, Peng-li Li, Pengcui Li, Penghui Li, Pengjie Li, Pengju Li, Pengsong Li, Pengyang Li, Pengyu Li, Pengyun Li, Pik Yi Li, Pilong Li, Pindong Li, Ping Li, Ping'an Li, Pinghua Li, Pingping Li, Pu Li, Pu-Yu Li, Q Li, Qi Li, Qi-Fu Li, Qi-Jing Li, Qian Li, Qian-Qian Li, Qiang Li, Qiang-Ming Li, Qiankun Li, Qianqian Li, Qiao Li, Qiao-Xin Li, Qiaolian Li, Qiaoqiao Li, Qifang Li, Qihang Li, Qihua Li, Qiji Li, Qijun Li, Qilan Li, Qilong Li, Qin Li, Qiner Li, Qing Li, Qing Run Li, Qing-Chang Li, Qing-Fang Li, Qing-Min Li, Qing-Wei Li, Qingchao Li, Qingfang Li, Qingfeng Li, Qinggang Li, Qinghe Li, Qinghong Li, Qinghua Li, Qingjie Li, Qinglan Li, Qingli Li, Qinglin Li, Qingling Li, Qingqin S Li, Qingrun Li, Qingshang Li, Qingsheng Li, Qingxian Li, Qingyang Li, Qingyu Li, Qingyuan Li, Qingyun Li, Qinqin Li, Qinrui Li, Qintong Li, Qiong Li, Qionghua Li, Qipei Li, Qiqiong Li, Qiu Li, Qiufeng Li, Qiuhong Li, Qiusheng Li, Qiuxuan Li, Qiuya Li, Qiuyan Li, Qiwei Li, Qiyong Li, Qizhai Li, Quan Li, Quan-Zhong Li, Quanpeng Li, Quanshun Li, Quanzhang Li, Qun Li, R H L Li, R Li, Ran Li, Ranchang Li, Ranran Li, Ranwei Li, Ren Li, Ren-Ke Li, Rena Li, Roger Li, Ronald Li, Rong Li, Rong-Bing Li, Ronggui Li, Rongkai Li, Rongling Li, Rongqing Li, Rongsong Li, Rongxia Li, Rongyao Li, Rosa J W Li, Ru Li, Ru-Hao Li, Rui Li, Rui-Fang Li, Rui-Han Li, Rui-Jún Eveline Li, Ruibing Li, Ruidong Li, Ruifang Li, Ruihuan Li, Ruijia Li, Ruijin Li, Ruikai Li, Ruitong Li, Ruiwen Li, Ruixi Li, Ruixia Li, Ruixue Li, Ruiyang Li, Rujia Li, Rulin Li, Rumei Li, Runbing Li, Runwen Li, Runzhao Li, Runzhen Li, Runzhi Li, Ruobing Li, Ruolin Li, Ruonan Li, Ruotai Li, Ruotian Li, Ruotong Li, Ruyi Li, Ruyue Li, S A Li, S E Li, S L Li, S Li, S S Li, S-C Li, Sai Li, Saijuan Li, Sainan Li, San-Feng Li, Sanqiang Li, Senlin Li, Senmao Li, Sha Li, Sha-Sha Li, Shan Li, Shan-Shan Li, Shangjia Li, Shanglai Li, Shangming Li, Shanhang Li, Shanpeng Li, Shanshan Li, Shanyi Li, Shao-Dan Li, Shaobin Li, Shaodan Li, Shaofei Li, Shaoguang Li, Shaojian Li, Shaojing Li, Shaoliang Li, Shaomin Li, Shaoqi Li, Shaoyong Li, Shasha Li, Shawn S C Li, Shawn Shun-Cheng Li, Shen Li, Sheng Li, Sheng-Fu Li, Sheng-Jie Li, Sheng-Qing Li, Sheng-Tien Li, Shengbiao Li, Shengbin Li, Shengchao A Li, Shenghao Li, Shengjie Li, Shengli Li, Shengliang Li, Shengsheng Li, Shengwen Li, Shengxian Li, Shengxu Li, Shengze Li, Sherly X Li, Shi Li, Shi-Fang Li, Shi-Guang Li, Shi-Hong Li, Shi-Ying Li, Shibao Li, Shibo Li, Shichao Li, Shigang Li, Shihao Li, Shiheng Li, Shihong Li, Shijie Li, Shijun Li, Shikang Li, Shilan Li, Shili Li, Shiliang Li, Shilin Li, Shilun Li, Shiqi Li, Shiquan Li, Shisheng Li, Shishi Li, Shitao Li, Shiya Li, Shiyan Li, Shiyang Li, Shiyi Li, Shiying Li, Shiyu Li, Shiyue Li, Shiyun Li, Shu Li, Shu-Fang Li, Shu-Fen Li, Shu-Feng Li, Shu-Hong Li, Shu-Qi Li, Shu-Xin Li, Shuai Li, Shuaicheng Li, Shuang Li, Shuang-Ling Li, Shuangding Li, Shuangfei Li, Shuanglong Li, Shuangmei Li, Shuangshuang Li, Shuangxiu Li, Shubo Li, Shude Li, Shufen Li, Shugang Li, Shuguang Li, Shuhao Li, Shuhua Li, Shuhui Li, Shujiao Li, Shujie Li, Shujin Li, Shujing Li, Shulin Li, Shun Li, Shunhua Li, Shunle Li, Shunqin Li, Shunqing Li, Shunwang Li, Shuo Li, Shupeng Li, Shuqiang Li, Shuwei Li, Shuwen Li, Shuying Li, Shuyu D Li, Shuyu Dan Li, Shuyuan Li, Shuyue Li, Si Li, Si-Wei Li, Si-Xing Li, Si-Ying Li, Si-Yuan Li, Sibing Li, Sichen Li, Sichong Li, Side Li, Siguang Li, Sijie Li, Simin Li, Siming Li, Sin-Lun Li, Siqi Li, Sitao Li, Siting Li, Siwen Li, Siyi Li, Siyu Li, Siyue Li, Song Li, Song-Chao Li, Songhan Li, Songlin Li, Songtao Li, Songyu Li, Songyun Li, Stephen Li, Su Li, SuYun Li, Suchun Li, Suheng Li, Suhong Li, Suiyan Li, Sujing Li, Suk-Yee Li, Sumei Li, Sunan Li, Sung-Chou Li, Supeng Li, Suping Li, Suran Li, Suwei Li, Suwen Li, Suyan Li, T Li, Taibo Li, Taiwen Li, Taixu Li, Tao Li, Taoyingnan Li, Teng Li, Tengyan Li, Thomas Li, Tian Li, Tian-Yi Li, Tian-chang Li, Tian-wang Li, Tianchang Li, Tiandong Li, Tianfeng Li, Tiange Li, Tianjiao Li, Tianjun Li, Tianming Li, Tiansen Li, Tiantian Li, Tianxiang Li, Tianyao Li, Tianye Li, Tianyi Li, Tianyou Li, Tie Li, Tiegang Li, Tiehua Li, Tiewei Li, Timmy Li, Ting Li, Tingguang Li, Tinghao Li, Tinghua Li, Tingsong Li, Tingting Li, Tong Li, Tong-Ruei Li, Tongyao Li, Tongzheng Li, Tsai-Kun Li, Tuojian Li, Tuoping Li, Vivian Li, Vivian S W Li, W H Li, W J Li, W Li, W W Li, W Y Li, W-B Li, Wan Jie Li, Wan Li, Wan-Hong Li, Wan-Shan Li, Wan-Xin Li, Wang Li, Wanling Li, Wanni Li, Wanqian Li, Wanru Li, Wanshi Li, Wanshun Li, Wanting Li, Wanwan Li, Wanxin Li, Wanyan Li, Wanyi Li, Wei Li, Wei-Bo Li, Wei-Dong Li, Wei-Jun Li, Wei-Li Li, Wei-Ming Li, Wei-Na Li, Wei-Ping Li, Wei-Qin Li, Wei-Yang Li, Weidong Li, Weifeng Li, Weiguang Li, Weiguo Li, Weihai Li, Weiheng Li, Weihua Li, Weijian Li, Weijie Li, Weijun Li, Weike Li, Weiling Li, Weimin Li, Weina Li, Weining Li, Weiping Li, Weiqin Li, Weirong Li, Weisong Li, Weiyang Li, Weiye Li, Weiyong Li, Weizu Li, Wen Lan Li, Wen Li, Wen-Chao Li, Wen-Jie Li, Wen-Ting Li, Wen-Wen Li, Wen-Xi Li, Wen-Xing Li, Wen-Ya Li, Wen-Ying Li, Wen-juan Li, Wenbo Li, Wenchao Li, Wende Li, Wendeng Li, Wenfang Li, Wenfeng Li, Wenge Li, Wenguo Li, Wenhao Li, Wenhong Li, Wenhua Li, Wenhui Li, Wenjia Li, Wenjian Li, Wenjie Li, Wenjing Li, Wenjuan Li, Wenjun Li, Wenke Li, Wenlei Li, Wenli Li, Wenlong Li, Wenming Li, Wenqi Li, Wenqiang Li, Wenqing Li, Wenqun Li, Wenrui Li, Wensheng Li, Wentao Li, Wenwen Li, Wenxi Li, Wenxia Li, Wenxiang Li, Wenxin Li, Wenxiu Li, Wenxue Li, Wenyan Li, Wenyang Li, Wenyi Li, Wenying Li, Wenyong Li, Wenyu Li, Wenzhe Li, Wenzhuo Li, Wu-Jun Li, Wuguo Li, Wulan Li, Wuyan Li, X B Li, X L Li, X Li, X Y Li, X-H Li, X-L Li, Xi Li, Xi-Hai Li, Xi-Xi Li, Xia Li, Xian Li, Xiancheng Li, Xiang Li, Xiang-Dong Li, Xiang-Jun Li, Xiang-Ping Li, Xiang-Yu Li, Xiangcheng Li, Xiangchun Li, Xiangdong Li, Xiangfei Li, Xiangjun Li, Xiangling Li, Xianglong Li, Xiangnan Li, Xiangpan Li, Xiangping Li, Xiangqi Li, Xiangrui Li, Xiangwei Li, Xiangyan Li, Xiangyang Li, Xiangyun Li, Xiangzhe Li, Xiankai Li, Xiankun Li, Xianlin Li, Xianlong Li, Xianlu Li, Xianlun Li, Xianrui Li, Xianyong Li, Xiao Li, Xiao-Cheng Li, Xiao-Dong Li, Xiao-Feng Li, Xiao-Gang Li, Xiao-Guang Li, Xiao-Hong Li, Xiao-Hui Li, Xiao-Jiao Li, Xiao-Jing Li, Xiao-Jun Li, Xiao-Kang Li, Xiao-Li Li, Xiao-Lin Li, Xiao-Long Li, Xiao-Min Li, Xiao-Na Li, Xiao-Qiang Li, Xiao-Qin Li, Xiao-Qiu Li, Xiao-Sa Li, Xiao-Tong Li, Xiao-Yao Li, Xiao-Yun Li, Xiao-kun Li, Xiao-mei Li, Xiao-xu Li, Xiao-yu Li, XiaoQiu Li, Xiaobai Li, Xiaobin Li, Xiaobing Li, Xiaobo Li, Xiaochen Li, Xiaochun Li, Xiaocun Li, Xiaodong Li, Xiaofang Li, Xiaofei Li, Xiaofeng Li, Xiaoguang Li, Xiaohan Li, Xiaoheng Li, Xiaohong Li, Xiaohu Li, Xiaohua Li, Xiaohuan Li, Xiaohui Li, Xiaojiao Li, Xiaojiaoyang Li, Xiaojing Li, Xiaoju Li, Xiaojuan Li, Xiaokun Li, Xiaolei Li, Xiaoli Li, Xiaolian Li, Xiaoliang Li, Xiaolin Li, Xiaoling Li, Xiaolong Li, Xiaoman Li, Xiaomei Li, Xiaomeng Li, Xiaomin Li, Xiaoming Li, Xiaona Li, Xiaonan Li, Xiaoning Li, Xiaopeng Li, Xiaoping Li, Xiaoqi Li, Xiaoqiang Li, Xiaoqin Li, Xiaoqing Li, Xiaoqiong Li, Xiaoquan Li, Xiaoran Li, Xiaorong Li, Xiaotian Li, Xiaoting Li, Xiaotong Li, Xiaowei Li, Xiaoxia Li, Xiaoxiao Li, Xiaoxiong Li, Xiaoxuan Li, Xiaoya Li, Xiaoyan Li, Xiaoyao Li, Xiaoyi Li, Xiaoying Li, Xiaoyong Li, Xiaoyu Li, Xiaoyuan Li, Xiaoyun Li, Xiaozhao Li, Xiaozhen Li, Xiaozheng Li, Xiatian Li, Xiawei Li, Xiaxia Li, Xiayu Li, Xidan Li, Xihao Li, Xihe Li, Xijing Li, Xikun Li, Xiliang Li, Ximei Li, Xin Li, Xin-Chang Li, Xin-Jian Li, Xin-Ping Li, Xin-Tao Li, Xin-Ya Li, Xin-Yu Li, Xin-Yue Li, Xin-Zhu Li, Xinbin Li, Xing Li, Xing-Wang Li, Xingchen Li, Xingcheng Li, Xingfang Li, Xinghuan Li, Xinghui Li, Xingli Li, Xinglong Li, Xingwang Li, Xingxing Li, Xingya Li, Xingye Li, Xingyu Li, Xingyuan Li, Xinhai Li, Xinhua Li, Xinhui Li, Xining Li, Xinjia Li, Xinjian Li, Xinke Li, Xinle Li, Xinli Li, Xinlin Li, Xinmei Li, Xinmiao Li, Xinmin Li, Xinming Li, Xinpeng Li, Xinping Li, Xinrong Li, Xinrui Li, Xinsheng Li, Xinwei Li, Xinxin Li, Xinxiu Li, Xinyan Li, Xinyang Li, Xinyao Li, Xinye Li, Xinyi Li, Xinyu Li, Xinyuan Li, Xinzhi Li, Xinzhong Li, Xiong Bing Li, Xiong Li, Xiongfeng Li, Xionghao Li, Xionghui Li, Xiu-Ling Li, Xiucui Li, Xiufeng Li, Xiujuan Li, Xiuli Li, Xiuling Li, Xiumei Li, Xiuqi Li, Xiurong Li, Xiushen Li, Xiushi Li, Xiuzhen Li, Xixi Li, Xiying Li, Xiyue Li, Xiyun Li, Xu Li, Xu-Bo Li, Xu-Wei Li, Xu-Zhao Li, Xuan Li, Xuan-Ling Li, Xuanfei Li, Xuanxuan Li, Xuanzheng Li, Xudong Li, Xue Cheng Li, Xue Li, Xue-Er Li, Xue-Fei Li, Xue-Hua Li, Xue-Lian Li, Xue-Min Li, Xue-Nan Li, Xue-Peng Li, Xue-Yan Li, Xue-Ying Li, Xue-jing Li, Xue-zhi Li, Xuebiao Li, Xueer Li, Xuefei Li, Xuefeng Li, Xuehua Li, Xuejie Li, Xuejun Li, Xuekun Li, Xuelian Li, Xuelin Li, Xueling Li, Xuemei Li, Xuemin Li, Xuening Li, Xuepeng Li, Xueqin Li, Xueren Li, Xueshan Li, Xuesong Li, Xueting Li, Xuewang Li, Xuewei Li, Xuewen Li, Xueyang Li, Xueyi Li, Xueying Li, Xuezhong Li, Xuhang Li, Xuhong Li, Xuhua Li, Xujun Li, Xun Li, Xunjia Li, Xuri Li, Xutong Li, Xuyi Li, Xuze Li, Y H Li, Y L Li, Y Li, Y M Li, Y X Li, Y-Y Li, Ya Li, Ya-Feng Li, Ya-Ge Li, Ya-Jun Li, Ya-Li Li, Ya-Pei Li, Ya-Qiang Li, Ya-Ting Li, Ya-Zhou Li, YaJie Li, Yadong Li, Yahui Li, Yajiao Li, Yajing Li, Yajuan Li, Yajun Li, Yakui Li, Yalan Li, Yali Li, Yalin Li, Yan Bing Li, Yan Li, Yan Ning Li, Yan-Chun Li, Yan-Guang Li, Yan-Hong Li, Yan-Hua Li, Yan-Li Li, Yan-Nan Li, Yan-Xue Li, Yan-Yan Li, Yan-Yu Li, Yanan Li, Yanbin Li, Yanbing Li, Yanbo Li, Yanchang Li, Yanchuan Li, Yanchun Li, Yandong Li, Yanfeng Li, Yang Li, Yangxue Li, Yangyang Li, Yanhui Li, Yani Li, Yanjiao Li, Yanjie Li, Yanjing Li, Yanjun Li, Yanli Li, Yanlin Li, Yanling Li, Yanlong Li, Yanmei Li, Yanmin Li, Yanming Li, Yanni Li, Yanping Li, Yanqing Li, Yansen Li, Yanshu Li, Yansong Li, Yantao Li, Yanwei Li, Yanwu Li, Yanxi Li, Yanxiang Li, Yanxin Li, Yanyan Li, Yanying Li, Yanze Li, Yanzhong Li, Yao Li, Yaobo Li, Yaochen Li, Yaodong Li, Yaofu Li, Yaojia Li, Yaokun Li, Yaoqi Li, Yaoyao Li, Yaqi Li, Yaqiang Li, Yaqiao Li, Yaqin Li, Yaqing Li, Yaqiong Li, Yarong Li, Yawei Li, Yaxi Li, Yaxian Li, Yaxiong Li, Yaxuan Li, Yaying Li, Yayu Li, Yazhou Li, Ye Li, Yehong Li, Yeshan Li, Yetian Li, Yi Li, Yi-Heng Li, Yi-Ling Li, Yi-Ning Li, Yi-Shuan J Li, Yi-Ting Li, Yi-Wen Li, Yi-Yang Li, Yi-Ying Li, Yi-Yun Li, YiPing Li, YiQing Li, Yibo Li, Yiche Li, Yicun Li, Yifan Li, Yifei Li, Yifeng Li, Yige Li, Yihan Li, Yihao Li, Yiheng Li, Yihong Li, Yijian Li, Yijie Li, Yijing Li, Yiju Li, Yikang Li, Yike Li, Yilang Li, Yiliang Li, Yilong Li, Yimei Li, Yimeng Li, Yiming Li, Yin Li, Yinan Li, Ying Li, Ying-Bo Li, Ying-Lan Li, Ying-Qin Li, Ying-Qing Li, Ying-na Li, Yinggao Li, Yinghao Li, Yinghua Li, Yinghui Li, Yingjian Li, Yingjie Li, Yingjun Li, Yinglin Li, Yingnan Li, Yingpu Li, Yingqin Li, Yingrui Li, Yingshuo Li, Yingxi Li, Yingxia Li, Yingyi Li, Yingying Li, Yinhao Li, Yining Li, Yinliang Li, Yinxiong Li, Yinyan Li, Yinzhen Li, Yipeng Li, Yiqiang Li, Yirun Li, Yitong Li, Yiwei Li, Yiwen Li, Yixi Li, Yixiang Li, Yixiao Li, Yixin Li, Yixing Li, Yixuan Li, Yixue Li, Yiyang Li, Yizhe Li, Yong Li, Yong-Jian Li, Yong-Jun Li, Yong-Liang Li, Yongchao Li, Yonghao Li, Yonghe Li, Yongjia Li, Yongjiang Li, Yongjin Li, Yongjing Li, Yongjun Li, Yongkai Li, Yongle Li, Yongli Li, Yongmei Li, Yongnan Li, Yongpeng Li, Yongping Li, Yongqi Li, Yongqiang Li, Yongqiu Li, Yongsen Li, Yongsheng Li, Yongting Li, Yongxiang Li, Yongxin Li, Yongxue Li, Yongze Li, Yongzhe Li, Yongzhen Li, Yongzheng Li, You Li, You Ran Li, You-Mei Li, Youchen Li, Youjun Li, Youming Li, Youran Li, Yousheng Li, Youwei Li, Yu Li, Yu-Cheng Li, Yu-Chia Li, Yu-Hang Li, Yu-Hao Li, Yu-He Li, Yu-Hui Li, Yu-I Li, Yu-Jin Li, Yu-Jui Li, Yu-Kun Li, Yu-Lin Li, Yu-Sheng Li, Yu-Xiang Li, Yu-Ye Li, Yu-Ying Li, Yu-quan Li, Yuan Hao Li, Yuan Li, Yuan-Hai Li, Yuan-Jing Li, Yuan-Tao Li, Yuan-Yuan Li, Yuan-hao Li, Yuanchang Li, Yuanchuang Li, Yuancong Li, Yuandong Li, Yuanfang Li, Yuanfei Li, Yuanhao Li, Yuanhe Li, Yuanheng Li, Yuanhong Li, Yuanhua Li, Yuanjing Li, Yuanmei Li, Yuanyou Li, Yuanyuan Li, Yuanze Li, Yubin Li, Yubo Li, Yuchan Li, Yuchao Li, Yucheng Li, Yuchuan Li, Yuchun Li, Yudong Li, Yue Li, Yue-Chun Li, Yue-Jia Li, Yue-Ming Li, Yue-Rui Li, Yue-Ting Li, Yue-Ying Li, YueQiang Li, Yuefei Li, Yuefeng Li, Yueguo Li, Yuehua Li, Yuemei Li, Yueping Li, Yueqi Li, Yueting Li, Yuezheng Li, Yufan Li, Yufen Li, Yufeng Li, Yuguang Li, Yuhan Li, Yuhang Li, Yuhong Li, Yuhua Li, Yuhuang Li, Yuhui Li, Yujie Li, Yujun Li, Yukun Li, Yuli Li, Yulin Li, Yuling Li, Yulong Li, Yumao Li, Yumei Li, Yumiao Li, Yumin Li, Yun Li, Yun-Da Li, Yun-Lin Li, Yun-Peng Li, Yun-tian Li, Yuna Li, Yunan Li, Yunchu Li, Yunfeng Li, Yunjiu Li, Yunlong Li, Yunlun Li, Yunman Li, Yunmin Li, Yunpeng Li, Yunqi Li, Yunrui Li, Yunshen Li, Yunsheng Li, Yunting Li, Yunxi Li, Yunxiao Li, Yunxu Li, Yunyun Li, Yunze Li, Yuping Li, Yuqi Li, Yuqian Li, Yuqing Li, Yuqiu Li, Yuquan Li, Yushan Li, Yutang Li, Yutian Li, Yuting Li, Yutong Li, Yuwei Li, Yuxi Li, Yuxiang Li, Yuxin Li, Yuxiu Li, Yuxuan Li, Yuyan Li, Yuying Li, Yuyun Li, Yuzhe Li, Yvonne Li, Z Li, Z-H Li, Zaibo Li, Ze Li, Ze-An Li, Zecai Li, Zechuan Li, Zehan Li, Zehua Li, Zejian Li, Zemin Li, Zengyang Li, Zequn Li, Zesong Li, Zexu Li, Zeyu Li, Zeyuan Li, Zezhi Li, Zhan Li, Zhandong Li, Zhang Li, Zhanjun Li, Zhankui Li, Zhanquan Li, Zhantao Li, Zhao Li, Zhao-Cong Li, Zhao-Yang Li, Zhaobing Li, Zhaohan Li, Zhaojin Li, Zhaoliang Li, Zhaolun Li, Zhaoping Li, Zhaosha Li, Zhaoshui Li, Zhaoyong Li, Zhe Li, Zhehui Li, Zhen Li, Zhen-Hua Li, Zhen-Jia Li, Zhen-Li Li, Zhen-Xi Li, Zhen-Yu Li, Zhen-Yuan Li, Zhenbei Li, Zhencheng Li, Zhencong Li, Zhenfei Li, Zhenfen Li, Zheng Li, Zheng-Dao Li, Zhengda Li, Zhenghao Li, Zhenghui Li, Zhengjie Li, Zhengliang Li, Zhenglong Li, Zhengnan Li, Zhengpeng Li, Zhengrui Li, Zhenguang Li, Zhengwei Li, Zhengyang Li, Zhengyao Li, Zhengying Li, Zhengyu Li, Zhenhao Li, Zhenhua Li, Zhenhui Li, Zhenjia Li, Zhenjun Li, Zhenli Li, Zhenlu Li, Zhenming Li, Zhenshu Li, Zhenyan Li, Zhenyu Li, Zhenzhe Li, Zhenzhou Li, Zheyun Li, Zhi Li, Zhi-Bin Li, Zhi-Gang Li, Zhi-Jian Li, Zhi-Peng Li, Zhi-Wei Li, Zhi-Xing Li, Zhi-Yong Li, Zhi-Yuan Li, Zhi-qiang Li, Zhibin Li, Zhichao Li, Zhifan Li, Zhifei Li, Zhigang Li, Zhigao Li, Zhihao Li, Zhihong Li, Zhihua Li, Zhihui Li, Zhijia Li, Zhijie Li, Zhijun Li, Zhilei Li, Zhimei Li, Zhiming Li, Zhipeng Li, Zhiping Li, Zhiqiang Li, Zhiqiong Li, Zhiquan Li, Zhirong Li, Zhisheng Li, Zhiwei Li, Zhixiong Li, Zhixuan Li, Zhiyang Li, Zhiyi Li, Zhiyong Li, Zhiyu Li, Zhiyuan Li, Zhizhong Li, Zhizong Li, Zhong Li, Zhong-Xin Li, Zhongcai Li, Zhongding Li, Zhonggen Li, Zhonghua Li, Zhongjie Li, Zhonglian Li, Zhonglin Li, Zhongwen Li, Zhongxia Li, Zhongxian Li, Zhongxuan Li, Zhongyu Li, Zhongzhe Li, Zhou Li, Zhouhua Li, Zhouxiang Li, Zhu Li, Zhuang Li, Zhuangzhuang Li, Zhuanjian Li, Zhuo Li, Zhuo-Rong Li, Zhuoran Li, Zhuorong Li, Zi-Zhan Li, Zichao Li, Zihai Li, Zihan Li, Zihao Li, 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articles
Xiao-Jie Yang, Jiang Li, Jing-Yuan Chen +6 more · 2025 · Sheng li xue bao : [Acta physiologica Sinica] · added 2026-04-24
The current study aimed to clarify the roles of apolipoprotein A5 (ApoA5) and milk fat globule-epidermal growth factor 8 (Mfge8) in regulating myocardial lipid deposition and the regulatory relationsh Show more
The current study aimed to clarify the roles of apolipoprotein A5 (ApoA5) and milk fat globule-epidermal growth factor 8 (Mfge8) in regulating myocardial lipid deposition and the regulatory relationship between them. The serum levels of ApoA5 and Mfge8 in obese and healthy people were compared, and the obesity mouse model induced by the high-fat diet (HFD) was established. In addition, primary cardiomyocytes were purified and identified from the hearts of suckling mice. The 0.8 mmol/L sodium palmitate treatment was used to establish the lipid deposition cardiomyocyte model Show less
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APOA5
Haokang Feng, Zhixue Chen, Jianang Li +13 more · 2025 · iScience · Elsevier · added 2026-04-24
Pancreatic cancer (PC), characterized by the absence of effective biomarkers and therapies, remains highly fatal. Data regarding the correlations between PC risk and individual plasma proteome known f Show more
Pancreatic cancer (PC), characterized by the absence of effective biomarkers and therapies, remains highly fatal. Data regarding the correlations between PC risk and individual plasma proteome known for minimally invasive biomarkers are scarce. Here, we analyzed 1,345 human plasma proteins using proteome-wide association studies, identifying 78 proteins significantly associated with PC risk. Of these, four proteins (ROR1, FN1, APOA5, and ABO) showed the most substantial causal link to PC, confirmed through Mendelian randomization and colocalization analyses. Data from two clinical cohorts further demonstrated that FN1 and ABO were notably overexpressed in both blood and tumor samples from PC patients, compared to healthy controls or para-tumor tissues. Additionally, elevated FN1 and ABO levels correlated with shorter median survival in patients. Multiple drugs targeting FN1 or ROR1 are available or in clinical trials. These findings suggest that plasma protein FN1 associated with PC holds potential as both prognostic biomarkers and therapeutic targets. Show less
📄 PDF DOI: 10.1016/j.isci.2024.111693
APOA5
Liqin Ji, Yisen Shangguan, Chen Chen +6 more · 2025 · Antioxidants (Basel, Switzerland) · MDPI · added 2026-04-24
To investigate the effect of tannic acid (TA) on the growth, disease resistance, and intestinal health of Chinese soft-shelled turtles, individual turtles were fed with 0 g/kg (CG), 0.5 g/kg, 1 g/kg, Show more
To investigate the effect of tannic acid (TA) on the growth, disease resistance, and intestinal health of Chinese soft-shelled turtles, individual turtles were fed with 0 g/kg (CG), 0.5 g/kg, 1 g/kg, 2 g/kg, and 4 g/kg TA diets for 98 days. Afterwards, the turtles' disease resistance was tested using Show less
📄 PDF DOI: 10.3390/antiox14010112
APOA5
Sotirios Tsimikas, Henry N Ginsberg, Veronica J Alexander +5 more · 2025 · Journal of clinical lipidology · Elsevier · added 2026-04-24
Familial chylomicronemia syndrome (FCS) is diagnosed by genetic or nongenetic criteria. To assess responses to treatment of apolipoprotein (apo)C-III, triglycerides, and pancreatitis events in patient Show more
Familial chylomicronemia syndrome (FCS) is diagnosed by genetic or nongenetic criteria. To assess responses to treatment of apolipoprotein (apo)C-III, triglycerides, and pancreatitis events in patients with FCS-based diagnostic methods. APPROACH enrolled 66 patients with FCS randomized to volanesorsen or placebo for 12 months. In 50 participants, genetic confirmation of FCS was based on the presence of pathogenic bi-allelic variants in LPL, APOC2, APOA5, GPIHBP1, or LMF1 genes. In 16 participants without a genetic diagnosis, FCS was diagnosed using clinical criteria and postheparin lipoprotein lipase activity ≤20% of normal. Plasma levels of apoC-III, triglycerides and related variables were measured at 3, 6, and 12 months. No significant differences were present in mean apoC-III reductions with volanesorsen at 3, 6, or 12 months in patients with FCS diagnosed either genetically or nongenetically. In contrast, the triglyceride reductions were statistically less robust in patients with genetic diagnosis at each timepoint, with mean (95% CI) percent reduction in triglycerides of -68.7% (-78.7, -58.6) vs -84.0% (-99.4, -68.6), P = .014 at Month 3; -58.2% (-78.1, -38.2) vs -84.5% (-122.4, -46.7), P = .009 at Month 6; and -35.6% (-57.7, -13.4) vs. -69.0% (-105.0, -33.1), P = .005 at Month 12. Patients with a genetic diagnosis had significantly lower response rates for achieved triglycerides <500 mg/dL, <750 mg/dL, <880 mg/dL and <1000 mg/dL than patients with a nongenetic diagnosis. All 5 episodes of acute pancreatitis occurred in patients with a genetic diagnosis. For a similar reduction in apoC-III in response to volanesorsen, triglyceride reduction is attenuated in patients with genetically vs nongenetically diagnosed FCS. Show less
no PDF DOI: 10.1016/j.jacl.2024.12.018
APOA5
Shuo Yang, Jinfeng Li, Hongli Zeng +7 more · 2025 · Journal of medical biochemistry · added 2026-04-24
To explore the correlation between different traditional Chinese medicine (TCM) constitution types and apolipoprotein B (ApoB) in patients with hyperuricemia (HUA) and to investigate the relationships Show more
To explore the correlation between different traditional Chinese medicine (TCM) constitution types and apolipoprotein B (ApoB) in patients with hyperuricemia (HUA) and to investigate the relationships between TCM constitutions, uric acid levels, and various cardiovascular risk factors. A cross-sectional study involving 683 patients diagnosed with HUA was conducted. Patients' TCM constitutions were classified using the standardise "Classification and Determination of TCM Constitution" questionnaire. Serum uric acid (UA), lipid profiles, ApoB, and homocysteine (Hcy) levels were measured. Among 683 HUA patients, phlegm-dampness (22.99% ) and damp-heat constitution (20.06% ) were the most common TCM constitution types. UA, ApoB, and Hcy levels in patients with phlegm-damp constitution were significantly higher than those in other constitutions (P< 0.05). UA levels were negatively correlated with HDL-C (r=-0.472, P= 0.027) and positively correlated with ApoB (r= 0.618, P= 0.012) and Hcy (r= 0.492, P= 0.018). Phlegm-damp and damp-heat constitutions are the most common TCM constitution types in HUA patients and are associated with higher levels of UA, ApoB, and Hcy. These constitutional types are independently associated with increased cardiovascular risk. Show less
📄 PDF DOI: 10.5937/jomb0-57755
APOB
Ziyi Pan, Xuewen Li, Dongsheng Wu +3 more · 2025 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
Lipid overaccumulation in the liver predisposes ducks to metabolic disorders. The molecular mechanism of oleic acid (OA)-induced hepatic steatosis in ducks is not fully elucidated. A cellular model of Show more
Lipid overaccumulation in the liver predisposes ducks to metabolic disorders. The molecular mechanism of oleic acid (OA)-induced hepatic steatosis in ducks is not fully elucidated. A cellular model of steatosis was established by treating primary duck hepatocytes with OA. Transcriptome sequencing was performed to identify key signaling pathways and candidate genes. The role of Apolipoprotein A1 (APOA1) was investigated through overexpression and knockdown experiments. Intracellular triglycerides (TGs) were quantified commercially; lipid droplets were visualized by Oil Red O staining. Intracellular TG accumulation was induced by OA treatment in a dose-dependent manner. Through transcriptome analysis, 1045 differentially expressed genes (DEGs) were identified, with APOA1 being recognized as a key candidate within the peroxisome proliferator-activated receptor (PPAR) signaling pathway. The content of TGs and lipid droplets was increased by APOA1 overexpression, whereas these effects were suppressed by APOA1 knockdown. The expression of acetyl-CoA carboxylase alpha (ACACA) and fatty acid synthase (FASN) was upregulated by APOA1. Conversely, the expression of carnitine O-palmitoyltransferase 1 (CPT1), acyl-CoA oxidase 1 (ACOX1), and apolipoprotein B (APOB) was downregulated. This study demonstrates that OA upregulates APOA1, suggesting the involvement of the PPAR pathway and providing a theoretical basis for modulating hepatic fat deposition. Show less
📄 PDF DOI: 10.3390/ani15243603
APOB
Yuxuan Tao, Chenglong Yao, Runjia Liu +4 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Chronic heart failure (CHF) is frequently complicated by depression, which worsens prognosis but remains underdiagnosed due to symptom overlap and a lack of objective screening tools. Although biomark Show more
Chronic heart failure (CHF) is frequently complicated by depression, which worsens prognosis but remains underdiagnosed due to symptom overlap and a lack of objective screening tools. Although biomarkers reflecting lipid metabolism, insulin resistance, inflammation, and neuro-immuno-endocrine imbalance have been implicated in both CHF and depression, their predictive value for psychiatric outcomes in CHF patients is unclear. This study aimed to develop and validate interpretable machine learning (ML) models for predicting depression risk in CHF patients via the use of clinical and biomarker data. We retrospectively enrolled 3, 110 CHF patients admitted between January 2015 and December 2024 at Guang'anmen Hospital. Demographic, clinical, and laboratory indicators, including apolipoprotein B (ApoB), the triglyceride-glucose (TyG) index, and a novel glycated TyG (gTyG) index, were collected. Logistic regression and restricted cubic spline analyses were used to assess dose-response associations between biomarkers and depression. Eight ML algorithms were trained and evaluated, with model interpretability assessed via SHapley Additive exPlanation (SHAP). Among the 3, 110 patients, 37.3% had comorbid depression. Elevated ApoB and gTyG indices were strongly associated with depression risk in both the unadjusted and fully adjusted models (ApoB Q4 vs. Q1: OR 5.41, 95% CI 3.72-7.87; gTyG Q4 vs. Q1: OR 2.88, 95% CI 1.88-4.41; both P < 0.001), demonstrating clear nonlinear dose-response relationships. The TyG index was associated with depression in the crude analyses but lost significance after adjustment. Among the ML models, the RF model achieved the best performance (AUC 0.933 in training, accuracy 0.814, sensitivity 0.939). SHAP analysis revealed that the ApoB and gTyG indices were the most influential predictors. A user-friendly web application was developed for individualized risk prediction. This study demonstrated that the ApoB and gTyG index are robust biomarkers for predicting depression risk in CHF patients. The RF model provided the highest predictive accuracy and interpretability, highlighting its potential utility for early risk stratification and targeted intervention. The incorporation of these biomarkers into routine clinical practice may facilitate timely identification and management of depression in CHF patients, ultimately improving patient outcomes. Show less
📄 PDF DOI: 10.3389/fendo.2025.1737713
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Sichong Yang, Dan Mu, Xiaoting Li · 2025 · Scientific reports · Nature · added 2026-04-24
To analyze the potential therapeutic value and mechanism of luteolin in age-related macular degeneration (AMD) using network pharmacology and cellular experiments. SHD-compound targets were retrieved Show more
To analyze the potential therapeutic value and mechanism of luteolin in age-related macular degeneration (AMD) using network pharmacology and cellular experiments. SHD-compound targets were retrieved from the TCMSP database, while AMD-related targets were extracted from OMIM and DisGeNET databases. Overlapping targets were identified via Venny 2.1. A PPI network was constructed using the STRING database, followed by functional enrichment analysis of overlapping targets via Metascape. Pharmacological networks were mapped using Cytoscape. For cellular experiments, the optimal concentration of luteolin was determined by CCK-8 assay. Human umbilical vein endothelial cells (HUVECs) were divided into: Control group (Without any intervention), Model group (VEGF165-induced model), and Treatment group (VEGF165-induced + luteolin). Angiogenesis was evaluated via scratch, transwell migration, invasion, and tube formation assays. VEGFA protein expression was assessed by Western blot. We identified 157 SHD-compound targets and 87 AMD-related targets, yielding 6 overlapping targets (ESR1, PON1, SOD1, APOB, VEGFA, IL6). PPI networks and enrichment analysis revealed that luteolin in SHD may inhibit AMD neovascularization via VEGFA signaling pathways. The concentration of luteolin (25 µmol/L) used in the experiments was selected based on the dose-response results. In vitro assays showed the Treatment group exhibited: significantly reduced horizontal migration (scratch assay, p < 0.05), decreased vertical migration (transwell assay, p < 0.05), suppressed invasion (p < 0.05), and inhibited tube formation (p < 0.05). Western blot confirmed reduced VEGFA expression in the treatment group (p < 0.05). Luteolin alleviates angiogenesis in HUVECs by inhibiting VEGFA expression, highlighting its potential as a therapeutic candidate for neovascular AMD. Show less
📄 PDF DOI: 10.1038/s41598-025-33839-1
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Jia-Xuan Zhang, Zhi-Qiang Huang, Jian-Ming Yang +2 more · 2025 · Neuropsychiatric disease and treatment · added 2026-04-24
To assess the predictive ability of baseline serum apolipoprotein B (ApoB) and the ratio of ApoB to apolipoprotein A1 (ApoB/ApoA1 ratio) for dyslipidemia risk in patients receiving second-generation a Show more
To assess the predictive ability of baseline serum apolipoprotein B (ApoB) and the ratio of ApoB to apolipoprotein A1 (ApoB/ApoA1 ratio) for dyslipidemia risk in patients receiving second-generation antipsychotics (SGAs). Medical records of patients hospitalized between March 2019 and March 2025 were retrospectively reviewed. The optimal cut-off points for baseline serum ApoB levels and the ApoB/ApoA1 ratio were identified using a maximally selected log-rank statistic analysis. Multivariable Cox proportional hazards models estimated hazard ratios (HRs) with 95% confidence intervals (95% CIs). The Kaplan-Meier method with Log rank testing was used to compare the cumulative incidence of dyslipidemia between groups defined by these cut-off points. Of 311 enrolled patients, 33 (10.6%) lacking baseline ApoA1 measurements were excluded from ApoB/ApoA1 ratio analyses. The optimal cut-off points were 0.70 g/L for baseline ApoB and 0.45 for the ApoB/ApoA1 ratio. Multivariable Cox proportional hazards models, fully adjusted for covariates, demonstrated significantly elevated dyslipidemia risk for patients exceeding these thresholds vs low-risk groups: adjusted HR 2.98 (95% CI: 2.05-4.32, p < 0.001) for high ApoB and 3.17 (95% CI: 1.62-6.22, p = 0.001) for high ApoB/ApoA1 ratio. Continuous analysis showed each 0.1 g/L ApoB increase conferred a 34% higher risk (adjusted HR 1.34, 95% CI: 1.21-1.48, p < 0.001), while each 0.1-unit ApoB/ApoA1 ratio increase conferred a 20% higher risk (adjusted HR 1.20, 95% CI: 1.10-1.30, p < 0.001). Kaplan-Meier curves confirmed significantly higher cumulative dyslipidemia incidence in high vs low groups for both markers (Log rank test, both p < 0.001). Baseline serum ApoB levels and the ApoB/ApoA1 ratio are valuable risk markers for dyslipidemia in patients treated with SGAs. Show less
📄 PDF DOI: 10.2147/NDT.S564450
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Ting He, Jinbo Zhao, Ling Hou +2 more · 2025 · International journal of general medicine · added 2026-04-24
Coronary heart disease (CHD) has a significant co-morbid association with chronic kidney disease (CKD), but identification tools for the risk of concomitant CKD in patients with CHD are still lacking. Show more
Coronary heart disease (CHD) has a significant co-morbid association with chronic kidney disease (CKD), but identification tools for the risk of concomitant CKD in patients with CHD are still lacking. The purpose of this research was to construct machine learning (ML) models for identifying undetected CKD in CHD patients. 1786 CHD patients undergoing coronary intervention were retrospectively included. Lasso regression and multifactor logistic regression were used to screen feature variables. Five ML models, ie, logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost), were constructed. Participants were divided into the training set and validation set in a 7:3 ratio. The evaluation metrics included the area under the curve, calibration curve, and decision curve. Totally, 1786 CHD patients were enrolled and split into training (70%) and validation (30%) sets. The prevalence of CKD was 21.8% (390/1786). Multivariate logistic regression analysis showed that men, advanced age, hypertension, diabetes mellitus, history of atrial fibrillation (AF), high Gensini, low hemoglobin, low plateletcrit (PCT), high triglycerides (TG), high lipoprotein(a) (Lp(a)), hyperkalemia, high uric acid to albumin ratio (UAR), high systemic inflammation response index (SIRI), low lymphocyte to monocyte ratio (LMR), and high apolipoprotein B to apolipoprotein A1 (ApoB/ApoA1) ratio were the key clinical and laboratory test indicators of CKD. The XGBoost model performed optimally in the validation set (AUC=0.909, 95% CI 0.881 -0.937). SHapley Additive explanation analysis identified UAR, hypertension, Gensini score, age, and SIRI as the top 5 key features. The XGBoost model constructed on routine clinical data was effective in identifying CKD risk in CHD patients, with UAR as a novel strong predictor. Decision curve analysis confirmed the clinical utility of the model, indicating that it may be used to guide decisions for enhanced monitoring and early intervention over a wide range of risk thresholds. Show less
📄 PDF DOI: 10.2147/IJGM.S558568
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Binbin Gong, Xike Mao, Guoxiang Li +4 more · 2025 · European journal of medical research · BioMed Central · added 2026-04-24
The objective of this study was to assess the correlation between the ApoB/ApoA ratio and the recurrence of kidney stones in a Chinese adult population. We collected electronic records of patients wit Show more
The objective of this study was to assess the correlation between the ApoB/ApoA ratio and the recurrence of kidney stones in a Chinese adult population. We collected electronic records of patients with kidney stones who underwent surgical treatment at our hospital from March 2016 to March 2022. These patients were followed up and categorized into groups based on the recurrence of kidney stones. Parameters related to routine blood and biochemical tests, as well as the history of hypertension and diabetes mellitus, were gathered. Multiple imputation was applied for missing data. Subsequently, differences between the recurrence and non-recurrence groups were assessed using the chi-square test, independent samples t test, or Wilcoxon rank sum test. Logistic regression analysis, subgroup analysis, and propensity-matched analysis were conducted to evaluate the relationship between the ApoB/ApoA ratio and kidney stone recurrence. The study included a total of 923 participants aged > 18 years, among whom 296 experienced kidney stone recurrence during the follow-up period. An elevated ApoB/ApoA ratio was identified as a risk factor for kidney stone recurrence (adjusted OR = 2.48, 95% CI 1.04, 5.92). Propensity-matched analyses further supported the association, showing that elevated ApoB/ApoA ratios were linked to a higher risk of renal stone recurrence (OR = 3.37, 95% CI 1.24-9.17). The dose-response curve illustrated a positive linear correlation between the ApoB/ApoA ratio and the risk of kidney stone recurrence. Increased ApoB/ApoA ratios are positively correlated with the risk of kidney stone recurrence. This association remains significant, although a causal relationship cannot be definitively established. Show less
📄 PDF DOI: 10.1186/s40001-025-03396-4
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Ya-Ting Chen, Jing Sui, Yu Yang +16 more · 2025 · BMC medicine · BioMed Central · added 2026-04-24
Pentadecanoic acid (PEA), an odd-chain fatty acid derived from diet by the gut microbiome, has garnered increasing attention for its systemic health-promoting properties. Its potential role in bladder Show more
Pentadecanoic acid (PEA), an odd-chain fatty acid derived from diet by the gut microbiome, has garnered increasing attention for its systemic health-promoting properties. Its potential role in bladder cancer (BC) occurrence and invasion, however, remains unclear. Large-scale cohorts' analyses were performed to assess the association between dietary PEA and BC occurrence and invasion. In vitro and in vivo experiments, including EJ and T24 BC cell assays and a BBN-induced mouse model, were conducted to experimentally assess the impact of PEA on BC. Serum proteomics, gut microbiome, and targeted fecal lipidomics analyses were employed to explore the underlying mechanisms. Dietary PEA was negatively associated with BC occurrence and invasion in cohort analyses. PEA suppressed EJ and T24 BC cell migration, invasion, and proliferation, while inhibiting BC development in a BBN-induced mouse model. In vivo serum proteomics identified differentially expressed lipid-related proteins (e.g., Apoe and Apob) following PEA treatment, implicating its modulation of lipid metabolism pathways. Considering the essential role of the gut-bladder axis, the gut microbiome analysis exhibited that PEA markedly altered bacteria (e.g., g_Alistipes) and fungi (e.g., o_Erysiphales, g_Teberdinia, and g_Gibberella), with concomitant lipid metabolism changes. Furthermore, targeted fecal lipidomics demonstrated the shifts in key lipids, such as phosphatidylethanolamines (PE) involved in essential lipid clusters, suggesting regulation by gut microbiome linked to BC development. Collectively, our findings demonstrate that PEA mitigates BC by reshaping the gut microbiome and modulating lipid metabolism, providing new insights into its molecular and therapeutic potential. Show less
📄 PDF DOI: 10.1186/s12916-025-04554-5
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Kai Zhao, Yubo Zhao, Zhening Yan +1 more · 2025 · Medicine · added 2026-04-24
There were some evidences to suggest the correlation between circulating lipid levels and cholecystitis, but no evidence had been indicated the causal relationship between lipid-lowering drugs and cho Show more
There were some evidences to suggest the correlation between circulating lipid levels and cholecystitis, but no evidence had been indicated the causal relationship between lipid-lowering drugs and cholecystitis. To investigate this, we employed drug target Mendelian randomization (MR), summary-data-based MR (SMR), and genetic colocalization analyses to assess the association between lipid-lowering drugs and cholecystitis. In this study, we used 2 sets of genetic tools to proxy lipid-lowering drugs: elevated high-density lipoprotein cholesterol (CETP), decreased low-density lipoprotein cholesterol (LDLR, HMGCR, NPC1L1, PCSK9, APOB, and ABCG5/ABCG8), and decreased triglycerides (LPL, PPARA, ANGPTL3, and APOC3); the expression quantitative trait locus of target genes from the eQTLGen consortium and Genotype-Tissue Expression project V8. Then, the causal effects of these lipid-lowering drugs genetic proxies on cholecystitis were estimated using a variety of MR, SMR, and colocalization as sensitivity analyses. Collectively, in the MR results, we found that the significant causal effects between genetically proxied ABCG5/ABCG8 enhancement and HMGCR inhibitors were associated with a reduced risk of cholecystitis. The results of SMR and heterogeneity in dependent instruments tests indicated that the expression of ABCG5/ABCG8 and HMGCR in multiple tissues were associated with cholecystitis. In conclusion, our study provides genetic evidence demonstrating a causal relationship between the enhancement of ABCG5/ABCG8 gene proxies and the use of HMGCR inhibitors with a reduced risk of cholecystitis. These findings support the potential reuse of lipid-lowering drugs in patients with cholecystitis and could inform the development of effective treatment strategies for this population in clinical practice. Show less
📄 PDF DOI: 10.1097/MD.0000000000046000
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Xuehao Cui, Chao Sun, Dejia Wen +2 more · 2025 · Global heart · added 2026-04-24
Cardiovascular diseases (CVDs) are the leading global cause of mortality and disability, with prevalence increasing due to aging and risk factors like obesity and hypertension. The retina, rich in mic Show more
Cardiovascular diseases (CVDs) are the leading global cause of mortality and disability, with prevalence increasing due to aging and risk factors like obesity and hypertension. The retina, rich in microvasculature, provides a unique opportunity to investigate microvascular dysfunction linked to CVDs and other systemic vascular diseases. This study used a multifaceted approach to assess the genetic correlation and causal relationship between retinal characteristics and CVDs. Linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR) analyses were conducted using genome-wide association study (GWAS) data from the UK Biobank and FinnGen datasets. A cross-sectional study was also conducted to validate the findings, collecting optical coherence tomography (OCT) images from 124 eyes (89 with CVDs and 35 healthy controls). A prediction model is based on least absolute shrinkage and selection operator (LASSO) regression to assess the risk of CVD. Using LDSC and two-sample MR, we found genetic evidence consistent with a causal effect whereby genetically proxied thinner retinal nerve fiber layer (RNFL) was associated with higher risks of hypertension and myocardial infarction (MI), while genetically proxied thicker photoreceptor inner segment/outer segment (PR-IS/OS) was associated with coronary heart disease and MI (false discovery rate [FDR] thresholds as reported). Genetically proxied thinner retinal pigment epithelium (RPE) showed an inverse association with stroke risk. Several circulating biomarkers-including lipoprotein(a) [Lp(a)], low-density lipoprotein cholesterol (LDL-C), and ApoB-exhibited MR evidence of association with multiple CVDs. In a cross-sectional cohort, retinal layer differences and their relationships with lipids were directionally consistent with the genetic findings. Retinal structural traits measured by OCT-particularly RNFL, PR-IS/OS, and RPE thickness-are best interpreted as non-invasive markers that reflect systemic vascular biology. Our MR analyses support shared etiologic pathways between retinal microstructure and CVDs rather than implying that retinal damage clinically causes cardiovascular events. Findings warrant validation in larger and more diverse populations and should not be considered definitive proof of causality. Show less
📄 PDF DOI: 10.5334/gh.1493
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Lijia Zhao, Jie Meng, Jingjing Li +5 more · 2025 · Nutrition reviews · Oxford University Press · added 2026-04-24
Dipeptidyl peptidase-4 inhibitors (DPP-4i) serve as an incretin-based hypoglycemic class for the treatment of type 2 diabetes (T2D). DPP-4i have been reported to produce a pleiotropic effect on lipid Show more
Dipeptidyl peptidase-4 inhibitors (DPP-4i) serve as an incretin-based hypoglycemic class for the treatment of type 2 diabetes (T2D). DPP-4i have been reported to produce a pleiotropic effect on lipid profiles in addition to regulation of glucose homeostasis. The aim of this systematic review and meta-analysis was to quantitatively evaluate the impact of DPP-4i on lipid parameters in patients with T2D. PubMed, Embase, and The Cochrane Library were systematically searched for randomized controlled trials. Trials were identified if changes in lipid parameters, including low-density-lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), high-density-lipoprotein cholesterol (HDL-C), non-HDL-C, and apolipoprotein B (ApoB) were reported. A total of 95 publications were identified. DPP-4i significantly reduced levels of LDL-C (-3.48 mg/dL; 95% CI, -4.77 to -2.20; I2 = 70%, P < .00001), TC (-2.59 mg/dL; 95% CI, -3.88 to -1.29; I2 = 73%, P < .0001), TG (-5.39 mg/dL; 95% CI, -8.04 to -2.75; I2 = 77%, P < .0001), and non-HDL-C (-6.27 mg/dL; 95% CI, -10.94 to -1.60; I2 = 53%, P = .008). No significant effect was found on HDL-C (-0.32 mg/dL; 95% CI, -1.19 to 0.55; I2 = 97%, P = .47) and ApoB (-0.88 mg/dL; 95% CI, -3.36 to 1.60; I2 = 36%, P = .49) during DPP-4i treatment. DDP-4i significantly improved lipid parameters including LDL-C, TC, TG, and non-HDL-C in patients with T2D. This underscores the potential cardiovascular benefits of DPP-4i and their role in improving diabetes-related outcomes. PROSPERO registration no. CRD42020175999. Show less
no PDF DOI: 10.1093/nutrit/nuaf209
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Ran Li, Xuelian Ruan, Mingxing Chen +6 more · 2025 · Annals of clinical and laboratory science · added 2026-04-24
Biochemical items play a significant role in clinical decision-making, so this study aims to evaluate the performance of different biochemical platforms. We collected 1,524 serum samples that were cen Show more
Biochemical items play a significant role in clinical decision-making, so this study aims to evaluate the performance of different biochemical platforms. We collected 1,524 serum samples that were centrifuged, and plasma was analyzed for HDL-C, LDL-C, Apo A1, Apo B, PA, and Fs-CRP with the Mindray BS2000M and Roche Cobas 8000 platforms. The results were evaluated by a non-parametric two-related sample test, Passing-Bablok regression analysis, Weighted Least Square analysis (WLS), and Bland-Altman analysis according to CLSI EP09-A3, EP5-A2, and EP15-A3. Between the two systems, there were statistically significant differences in the average bias of LDL-C, Apo A1, Apo B, PA, and Fs-CRP ( These findings suggest that the two platforms have good correlation and consistency in high-concentration medical decision levels in HDL-C, LDL-C, Apo A1, Apo B, and Fs-CRP, and all levels of PA in the two platforms are interchangeable and can replace each other. Show less
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Qi Liu, Qian Du, Xiaolu Yuan +4 more · 2025 · Diabetes, metabolic syndrome and obesity : targets and therapy · added 2026-04-24
To establish a short-term high-fat/high-cholesterol (HFHC) diet-induced Metabolic dysfunction-associated steatotic liver disease (MASLD) mouse model, and evaluate the effects of rapamycin (RaPa) and c Show more
To establish a short-term high-fat/high-cholesterol (HFHC) diet-induced Metabolic dysfunction-associated steatotic liver disease (MASLD) mouse model, and evaluate the effects of rapamycin (RaPa) and chloroquine (CQ) on this model to explore their therapeutic potential and side effects. An early MASLD mouse model was constructed via short-term HFHC diet feeding. Model mice were intraperitoneally injected with RaPa or CQ. Drug effects were analyzed on body weight, liver weight, lipid metabolism-related genes (APOB, FASN, PLIN2), inflammatory factors (IL-6, IL-10), and fibrosis markers (LOX, Col-1α-1, CCL2, TGFβ1, PDGFRβ, α-SMA) at mRNA and protein levels. RaPa ameliorated body weight and liver weight in early MASLD mice, downregulated FASN and PLIN2 expression, upregulated IL-10 mRNA levels, and alleviated hepatic steatosis, but induced metabolic disorders such as Insulin resistance and hyperlipidemia. In contrast, CQ promoted FASN and PLIN2 expression, exacerbated hepatic steatosis, reduced IL-10 mRNA levels, and upregulated fibrosis-related markers (LOX, TGFβ1, PDGFRβ, α-SMA) at both mRNA and protein levels, thereby driving MASLD progression to liver fibrosis. Notably, CQ improved metabolic abnormalities in model mice, including obesity, hyperlipidemia, and Insulin resistance. RaPa and CQ exhibit dual effects on early MASLD: RaPa alleviates hepatic steatosis but exacerbates metabolic disorders, whereas CQ improves metabolic abnormalities but accelerates liver fibrosis. This paradox highlights the need to balance metabolic regulation and liver injury prevention in MASLD treatment, providing critical experimental insights for targeted drug development. Show less
📄 PDF DOI: 10.2147/DMSO.S539555
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Jingshu Li, Xuanyi Du, Rui Zhang +7 more · 2025 · Scientific reports · Nature · added 2026-04-24
End-stage renal disease (ESRD) is associated with high morbidity and mortality. Identifying patients with stage 4 chronic kidney disease (CKD) at risk of short-term progression to ESRD remains challen Show more
End-stage renal disease (ESRD) is associated with high morbidity and mortality. Identifying patients with stage 4 chronic kidney disease (CKD) at risk of short-term progression to ESRD remains challenging. Accurate prediction can improve advanced care planning and patient outcomes. This study aimed to develop and validate a machine learning (ML) model for predicting progression within 25 weeks (approximately six months) of ESRD in patients with stage 4 CKD. Electronic health records (EHRs) of patients with stage 4 CKD were analyzed. Nine ML models including Ridge regression (Ridge), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were used to predict short-term progression to ESRD within 25 weeks. The models were trained and externally validated using the data of 346 and 105 patients. Of the 451 patients with stage 4 CKD, 219 developed ESRD. Among the evaluated models, XGBoost demonstrated the best overall performance. In the internal validation, it achieved an area under the curve (AUC) of 0.93, an accuracy of 0.90, and an F1 score of 0.89. In the external validation, XGBoost maintained the highest AUC (0.85), accuracy (0.79), and F1 score (0.79), along with the highest average precision (0.89) and a low log-loss (0.48), indicating strong discriminative ability and good generalizability. The top predictive features included high-density lipoprotein cholesterol, Alb, Cys C, ApoB, FGB, Bun, Neutrophil, and Total cholesterol. This study demonstrated the feasibility of ML for assessing ESRD prognosis based on easily accessible clinical features. XGBoost demonstrated superior performance in both internal and external validation, suggesting its potential for future patient screening. Show less
📄 PDF DOI: 10.1038/s41598-025-23037-4
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Yale Tang, Chao Wang, Luxuan Li +5 more · 2025 · Biomolecules · MDPI · added 2026-04-24
This study aimed to investigate whether knockout of the
📄 PDF DOI: 10.3390/biom15101454
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Fangbo Hu, Rongjie Wu, Xu Zhao +5 more · 2025 · Translational stroke research · Springer · added 2026-04-24
Mendelian randomization studies have identified that apolipoprotein B (ApoB) is the primary genetic determinant of ischemic stroke, rather than other lipid markers. However, its association with recur Show more
Mendelian randomization studies have identified that apolipoprotein B (ApoB) is the primary genetic determinant of ischemic stroke, rather than other lipid markers. However, its association with recurrent non-cardioembolic acute ischemic stroke (NCAIS) remains unclear. This study aimed to investigate this association. This study recruited 578 patients with acute ischemic stroke, excluding those with cardiogenic embolism. After a 3-year follow-up, a total of 428 patients completed the prospective cohort study. A Cox regression model was used to evaluate the association between ApoB levels at admission and the recurrence rate. Additionally, a nested case-control study was conducted by comparing blood samples collected at the time of recurrence from recurrent patients with those from non-recurrent patients. Binary logistic regression and ROC analysis were used to assess the association between serum ApoB, low-density lipoprotein cholesterol (LDL-C), and recurrent stroke at the time of recurrence. The Cox regression model demonstrated that ApoB levels at admission were independently associated with an increased risk of recurrent NCAIS (HR=6.697; 95%CI 2.581-17.374, P < 0.001). Recurrent stroke patients had significantly higher serum ApoB levels at admission than non-recurrent ones [0.85 g/L (IQR 0.21) vs. 0.63 g/L (IQR 0.15)]. In ROC analysis, ApoB (AUC = 0.732) showed a greater discriminatory ability for recurrent stroke than LDL-C (AUC = 0.685). Higher serum ApoB levels increased the risk of recurrence in patients with NCAIS, and ApoB demonstrated better discriminatory ability than LDL-C after therapy. These findings suggest that routine ApoB measurement may help improve secondary stroke risk assessment. Show less
📄 PDF DOI: 10.1007/s12975-025-01367-9
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Jinglu Yu, Zimeng Pan, Miao Sun +6 more · 2025 · Expert review of molecular diagnostics · Taylor & Francis · added 2026-04-24
To construct a nomogram for predicting metabolic syndrome (MetS) in women with polycystic ovary syndrome. In this retrospective study, we analyzed clinical and biochemical data from 859 Chinese women Show more
To construct a nomogram for predicting metabolic syndrome (MetS) in women with polycystic ovary syndrome. In this retrospective study, we analyzed clinical and biochemical data from 859 Chinese women diagnosed with PCOS. Univariable logistic regression and forward stepwise logistic regression were employed to identify independent predictors of MetS. A predictive nomogram was developed that integrates age, acne status, body mass index (BMI), fasting insulin levels (FINS), and the ApoB/ApoA ratio. The model's discriminative performance, calibration accuracy, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration curves accompanied by Brier scores, Hosmer - Lemeshow tests, decision curve analysis (DCA), and clinical impact curves (CIC). Internal validation was conducted through bootstrap resampling over 1,000 iterations. The nomogram exhibited strong discriminative capability with an AUC of 0.874 (95% CI: 0.850-0.899), surpassing BMI alone which had an AUC of 0.824 ( The proposed nomogram accurately predicts MetS risk in PCOS patients, supporting early identification and individualized management. Show less
no PDF DOI: 10.1080/14737159.2025.2579046
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Wei Jia, Huimin Wang, Ting Feng +5 more · 2025 · Foods (Basel, Switzerland) · MDPI · added 2026-04-24
FVPB1, a novel heteropolysaccharide, was extracted from the
📄 PDF DOI: 10.3390/foods14193452
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Fangfang Wang, Dong You, Xiaoye Niu +4 more · 2025 · Cardiovascular diabetology · BioMed Central · added 2026-04-24
Plozasiran (VSA001, ARO-APOC3) is an RNA interference therapy that targets Apolipoprotein C3 (APOC3), a key regulator of lipoprotein metabolism. The study aimed at assessing the safety, tolerability, Show more
Plozasiran (VSA001, ARO-APOC3) is an RNA interference therapy that targets Apolipoprotein C3 (APOC3), a key regulator of lipoprotein metabolism. The study aimed at assessing the safety, tolerability, pharmacokinetics (PK), and pharmacodynamic (PD) profiles of plozasiran in Chinese healthy volunteers (HVs). In this double-blind, placebo-controlled, phase I clinical study, a total of 24 Chinese adult HVs received single subcutaneous (SC) injection of 25 mg, 50 mg plozasiran or placebo on day 1. Safety, tolerability, PK and PD profiles were accessed during a follow-up period of 85 days. Eighteen HVs received plozasiran (25 mg: n = 9; 50 mg: n = 9) and 6 HVs received placebo. Plozasiran was well tolerated in Chinese HVs. No death, no severe adverse events or treatment-emergent adverse events (TEAEs) leading to discontinuation were observed. TEAEs were reported in 9 of 18 HVs from plozasiran group and in 1 of 6 HVs from placebo group. All TEAEs were transient and recovered autonomously, except for 2 subjects with 4 TEAEs from plozasiran group needed concomitant medications. After SC injection, plozasiran was rapidly absorbed and quickly eliminated in the plasma. Maximum geomean serum concentration was 102 ng/mL (CV%:36.4%) and 216 ng/mL (58.1%) for 25 mg and 50 mg group, respectively. The median T Plozasiran at 25 and 50 mg was well tolerated with acceptable safety profile in Chinese HVs. Safety, PK and PD profiles observed in the present study were consistent with the data reported from clinical studies conducted outside China. Show less
📄 PDF DOI: 10.1186/s12933-025-02929-9
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Huixia Wang, Wenli Li, Yijia Tao +3 more · 2025 · BMC veterinary research · BioMed Central · added 2026-04-24
Neonatal piglets possess lysosome-rich foetal-type enterocytes that facilitate uptake and intracellular processing of maternally provided nutrients. However, the role of lysosomes in early-life growth Show more
Neonatal piglets possess lysosome-rich foetal-type enterocytes that facilitate uptake and intracellular processing of maternally provided nutrients. However, the role of lysosomes in early-life growth and intestinal maturation remains unclear. Therefore, this study was conducted to determine the role of lysosomes in the development of neonatal intestine in piglets. For 1-day-old neonatal piglets, a total of 12 piglets (Duroc × (Landrace × Large Yorkshire)) were divided into 2 groups using a split-litter design. To initiate malfunction in lysosomes, newborn piglets were subjected to oral gavage with imipramine (25 mg/kg bodyweight) once daily for 7 days. For 21-day-old piglets, a total of 12 piglets were divided into two groups, and each group received the same treatment as described above. Piglets receiving imipramine demonstrated significantly stunted growth at 7 days of age, but not at 27 days. By postnatal day 7, the foetal-type enterocytes of untreated piglets were restricted in the mid to upper ileal villus and contained several large lysosomal vacuoles. In contrast, marked changes in ileal morphological and histological structure were observed following imipramine treatment, as evidenced by reduced degree of vacuolation, decreased lysosomal count, as well as pronounced mitochondrial swelling; however, no vacuolated enterocytes were found in 27-day-old piglets. Furthermore, signaling pathways associated with lipid transport and metabolism were significantly enriched, and the related hub genes were identified by bioinformatic analysis after imipramine administration. These findings were further confirmed by biochemical analysis demonstrating that serum levels of total cholesterol (TC) and apolipoprotein A1 (ApoA1) were significantly increased while serum ApoB was decreased in 7-day-old piglets receiving imipramine treatment. Additionally, there was an opposite trend in levels of ApoA1and ApoB in ileal mucosa compared to serum. These results demonstrate that lysosome dysfunction induced by imipramine resulted in significant growth retardation, pronounced morphological and ultrastructural alterations in ileal enterocytes, along with disrupted lipid metabolism in early postnatal piglets; however, no such effect was observed in 27-day-old piglets. These findings enhance understanding of lysosomal functions and intestinal maturation in neonatal piglets. Show less
📄 PDF DOI: 10.1186/s12917-025-05063-6
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Hanyu Wang, Robert Clarke, Christiana Kartsonaki +12 more · 2025 · European heart journal open · Oxford University Press · added 2026-04-24
Little is known about the importance of blood lipids for risk of myocardial infarction (MI) in Chinese vs. European populations. We compared the associations with MI of apolioprotein B (ApoB) vs. low- Show more
Little is known about the importance of blood lipids for risk of myocardial infarction (MI) in Chinese vs. European populations. We compared the associations with MI of apolioprotein B (ApoB) vs. low-density lipoprotein cholesterol (LDL-C) and remnant-cholesterol (remnant-C) vs. triglycerides in the China Kadoorie Biobank (CKB) and UK Biobank (UKB). Plasma levels of LDL-C, high-density lipoprotein-cholesterol (HDL-C), apolipoprotein B (ApoB), apolipoprotein A1 (ApoA1), non-HDL-C, remnant-C, LDL-C/ApoB, and HDL-C/ApoA1 ratios were measured in a nested case-control study of MI (948 cases, 6101 controls) in CKB and a prospective study (5344 cases in 279 989 participants) in UKB. Associations of lipids with MI were assessed using logistic regression in CKB and Cox regression in UKB after adjustment for confounders and correction for regression dilution. The mean levels of LDL-C were about 30% lower in CKB than in UKB [2.3 (0.6) vs. 3.7 (0.8) mmol/L], but mean levels of HDL-C were comparable [1.3 (0.3) vs. 1.5 (0.4) mmol/L], as were those for triglycerides [1.8 (1.1) vs. 1.7 (1.1) mmol/L]. While the rate ratios (RRs) of MI for 1 SD higher usual levels of LDL-C in Chinese were about half those in Europeans (1.27; 1.13-1.44 vs. 1.55; 1.49-1.61), the corresponding RRs for ApoB or non-HDL with MI were comparable between Chinese and Europeans. The findings reinforce current guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) in China that advocate initiation of statin treatment in individuals at high-risk of ASCVD rather than high levels of LDL-C. Show less
📄 PDF DOI: 10.1093/ehjopen/oeaf119
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Xue Zhang, Kuanlu Fan, Jiaxin Li · 2025 · Archives of medical science : AMS · added 2026-04-24
Lipid metabolism is pivotal in diabetic retinopathy (DR) development. Nevertheless, the relationship between lipid-lowering drugs and the risk of DR remains a topic of debate. This study employed Mend Show more
Lipid metabolism is pivotal in diabetic retinopathy (DR) development. Nevertheless, the relationship between lipid-lowering drugs and the risk of DR remains a topic of debate. This study employed Mendelian randomization (MR) to investigate the potential effects of pharmacological lipid-lowering targets on DR and to clarify the causal association between blood lipid characteristics and DR. The data comprised genetic variations related to lipid traits and genetic variations associated with lipid-lowering drug targets obtained from the Global Lipid Consortium. Total DR, non-proliferative DR (NPDR), and proliferative DR (PDR) were sourced from the Finnish R9 database. Lipid-lowering drug targets were tested using inverse variance-weighted MR (IVW-MR) and statistics-based MR (SMR). Colocalization and mediation analysis were conducted to validate the results and explore potential mediating factors. A reduced risk of total DR and NPDR was associated with genetically improved 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) (OR = 0.62; 95% CI: 0.46-0.83; This Mendelian randomization study suggests that abnormalities in triglyceride (TG) levels serve as a pathogenic element in DR. Of the nine lipid-lowering drug targets assessed, HMGCR and APOB have emerged as potential promising targets for managing NPDR. These findings underscore the importance of controlling both BMI and HbA Show less
📄 PDF DOI: 10.5114/aoms/199622
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Helen Williams, Habib Francis, Jasmin Huang +4 more · 2025 · Atherosclerosis plus · Elsevier · added 2026-04-24
Familial Hypercholesterolaemia (FH) is characterised by high cholesterol and premature cardiovascular disease. While hypercholesterolaemia and inflammation are both key drivers in the formation of ath Show more
Familial Hypercholesterolaemia (FH) is characterised by high cholesterol and premature cardiovascular disease. While hypercholesterolaemia and inflammation are both key drivers in the formation of atherosclerotic plaques, inflammation remains understudied in FH. Inflammatory (M1) macrophages contribute to plaque destabilisation and macrophage precursors, monocytes, can be skewed towards an inflammatory state. Aims: Determine; whether monocytes of FH individuals are inflammatory, if they readily form inflammatory macrophages, and whether this remains so in statin-treated individuals. Blood samples were collected from people with FH (statin-treated and untreated) and healthy controls. Lipid profile was obtained and monocyte inflammatory marker expression was determined by whole blood flow cytometry. Monocytes were cultured with autologous serum and resultant macrophage profile determined by flow cytometry. Total cholesterol and low-density lipoprotein cholesterol (LDL-C) were higher in the Untreated-FH group compared to the Treated-FH group and controls. In both Treated-FH and Untreated-FH groups, monocytes were inflammatory with high CD86 (M1). The ratio of inflammatory/anti-inflammatory markers (CD86/CD163) significantly correlated with LDL-C and ApoB/ApoA1 ratio across the cohort, indicating the high LDL-C of FH may promote an inflammatory monocyte profile. Monocyte-derived-macrophages from (Treated) FH individuals also had a more inflammatory profile (CD86 and CD86/CD163). Overall, monocytes show inflammatory skewing in FH individuals, even those with moderately-reduced cholesterol levels. These monocytes readily become inflammatory macrophages. This, along with subsequent inflammatory macrophage formation, could contribute to plaque destabilisation and downstream clinical events. This supports inflammatory monocyte targeting as a potential approach to reduce residual risk in FH individuals. Show less
📄 PDF DOI: 10.1016/j.athplu.2025.09.002
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Alexa Canchola, Keyuan Li, Kunpeng Chen +12 more · 2025 · ACS nano · ACS Publications · added 2026-04-24
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokine Show more
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. Yet systematic analyses are hampered by the absence of standardized, richly annotated data sets. Here, we introduce the Protein Corona Database (PC-DB), which compiles data from 83 studies (2000-2024) and integrates 817 NP formulations with quantitative profiles of 2497 adsorbed proteins. The PC-DB exposes pronounced heterogeneity in NP materials (metal 28.8%, silica 22.8%, lipid-based 14.8%), surface modifications, sizes (1-1400 nm), and ζ-potentials (-70 to +70 mV). Subsequent meta-analysis shows that silica, polystyrene, and lipid-based NPs smaller than 100 nm with moderately negative to neutral ζ-potentials preferentially bind the lipoproteins APOE and APOB-100, which are linked to receptor-mediated uptake and enhanced delivery efficiency. In contrast, metal and metal-oxide NPs carrying highly negative surface charge enrich complement component C3, indicating a greater likelihood of immune recognition and clearance. Interpretable machine learning models (LightGBM and XGBoost; ROC-AUC > 0.85) confirm NP size, ζ-potential, and incubation time as the most influential predictors of protein adsorption. These results delineate how physicochemical parameters dictate PC composition and illustrate the power of predictive modeling to guide rational NP design. Show less
📄 PDF DOI: 10.1021/acsnano.5c08608
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Yuanyuan Wang, Dachuan Guo, Youzhi Wang +2 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
[This corrects the article DOI: 10.3389/fendo.2025.1542190.].
📄 PDF DOI: 10.3389/fendo.2025.1699149
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Yuanyuan Wang, Dachuan Guo, Youzhi Wang +2 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Low-density lipoprotein cholesterol (LDL-C) has now been the primary target for lipid-lowering therapy in the European and US guidelines for the management of dyslipidemia, with increasing interest in Show more
Low-density lipoprotein cholesterol (LDL-C) has now been the primary target for lipid-lowering therapy in the European and US guidelines for the management of dyslipidemia, with increasing interest in apolipoprotein B (ApoB) as a secondary target. The relationship between ApoB and the severity of acute myocardial infarction as well as residual risk still needs to be further determined. Coronary atherosclerosis occurs as a result of a complex set of factors, and there is a strong relationship between insulin resistance and cardiovascular disease. In contrast, there are limited studies on the relationship between TyG index (triglyceride glucose index), an indicator of insulin resistance, and cardiovascular disease. The purpose of this study was to investigate the value of ApoB and TyG index in assessing the severity of myocardial infarction and predicting prognosis. This study included 712 participants with acute myocardial infarction for a 5-year follow-up. Spearman correlation analysis and generalized linear model analysis were used to assess the correlation between ApoB and the severity of coronary atherosclerosis. Risk regression analysis was used to assess the correlation between ApoB and residual risk in patients with acute myocardial infarction, and the C-statistic, net reclassification index (NRI), and integrated discriminant improvement index (IDI) were further calculated to assess the predictive value of ApoB for residual risk after myocardial infarction. Categorizing apoB, LDL-C, and TyG indices according to tertiles, higher levels of ApoB were significantly associated with the severity of coronary artery stenosis in patients with acute myocardial infarction ( ApoB is an independent risk factor for major adverse cardiovascular events (MACE) following myocardial infarction. Elevated ApoB levels are more advantageous than elevated LDL-C levels in assessing the severity of coronary artery stenosis in myocardial infarction patients and predicting residual risk after myocardial infarction. Therefore, in patients with acute myocardial infarction, ApoB can be considered to guide further intensive treatment. However, the TyG index did not demonstrate a significant advantage in predicting cardiovascular residual risk in this study. Show less
📄 PDF DOI: 10.3389/fendo.2025.1542190
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