👤 Chujie Chen

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2981
Articles
1996
Name variants
Also published as: Ai-Qun Chen, Aiping Chen, Alex Chen, Alex F Chen, Alice P Chen, Alice Y Chen, Alice Ye A Chen, Allen Menglin Chen, Alon Chen, Alvin Chen, An Chen, Andrew Chen, Anqi Chen, Aoshuang Chen, Aozhou Chen, B Chen, B-S Chen, Baihua Chen, Ban Chen, Bang Chen, Bang-dang Chen, Bao-Bao Chen, Bao-Fu Chen, Bao-Sheng Chen, Bao-Ying Chen, Baofeng Chen, Baojiu Chen, Baolin Chen, Baosheng Chen, Baoxiang Chen, Beidong Chen, Beijian Chen, Ben-Kuen Chen, Benjamin Chen, Benjamin Jieming Chen, Benjamin P C Chen, Beth L Chen, Bihong T Chen, Bin Chen, Bing Chen, Bing-Bing Chen, Bing-Feng Chen, Bing-Huei Chen, Bingdi Chen, Bingqian Chen, Bingqing Chen, Bingyu Chen, Binlong Chen, Binzhen Chen, Bo Chen, Bo-Fang Chen, Bo-Jun Chen, Bo-Rui Chen, Bo-Sheng Chen, Bohe Chen, Bohong Chen, Bosong Chen, Bowang Chen, Bowei Chen, Bowen Chen, Boyu Chen, Brian Chen, C Chen, C Y Chen, C Z Chen, C-Y Chen, Cai-Long Chen, Caihong Chen, Can Chen, Cancan Chen, Canrong Chen, Canyu Chen, Caressa Chen, Carl Pc Chen, Carol Chen, Carol X-Q Chen, Catherine Qing Chen, Ceshi Chen, Chan Chen, Chang Chen, Chang-Lan Chen, Chang-Zheng Chen, Changjie Chen, Changya Chen, Changyan Chen, Chanjuan Chen, Chao Chen, Chao-Jung Chen, Chao-Wei Chen, Chaochao Chen, Chaojin Chen, Chaoli Chen, Chaoping Chen, Chaoqun Chen, Chaoran Chen, Chaoyi Chen, Chaoyue Chen, Chen Chen, Chen-Mei Chen, Chen-Sheng Chen, Chen-Yu Chen, Cheng Chen, Cheng-Fong Chen, Cheng-Sheng Chen, Cheng-Yi Chen, Cheng-Yu Chen, Chengchuan Chen, Chengchun Chen, Chengde Chen, Chengsheng Chen, Chengwei Chen, Chenyang Chen, Chi Chen, Chi-Chien Chen, Chi-Hua Chen, Chi-Long Chen, Chi-Yu Chen, Chi-Yuan Chen, Chi-Yun Chen, Chian-Feng Chen, Chider Chen, Chien-Hsiun Chen, Chien-Jen Chen, Chien-Lun Chen, Chien-Ting Chen, Chien-Yu Chen, Chih-Chieh Chen, Chih-Mei Chen, Chih-Ping Chen, Chih-Ta Chen, Chih-Wei Chen, Chih-Yi Chen, Chin-Chuan Chen, Ching Kit Chen, Ching-Hsuan Chen, Ching-Jung Chen, Ching-Wen Chen, Ching-Yi Chen, Ching-Yu Chen, Chiqi Chen, Chiung Mei Chen, Chiung-Mei Chen, Chixiang Chen, Chong Chen, Chongyang Chen, Christina Y Chen, Christina Yingxian Chen, Christopher S Chen, Chu Chen, Chu-Huang Chen, Chuanbing Chen, Chuannan Chen, Chuanzhi Chen, Chuck T Chen, Chueh-Tan Chen, Chun Chen, Chun-An Chen, Chun-Chi Chen, Chun-Fa Chen, Chun-Han Chen, Chun-Houh Chen, Chun-Wei Chen, Chun-Yuan Chen, Chung-Hao Chen, Chung-Hsing Chen, Chung-Hung Chen, Chung-Jen Chen, Chung-Yung Chen, Chunhai Chen, Chunhua Chen, Chunji Chen, Chunjie Chen, Chunlin Chen, Chunnuan Chen, Chunxiu Chen, Chuo Chen, Chuyu Chen, Cindi Chen, Constance Chen, Cuicui Chen, Cuie Chen, Cuilan Chen, Cuimin Chen, Cuncun Chen, D F Chen, D M Chen, D-F Chen, D. Chen, Dafang Chen, Daijie Chen, Daiwen Chen, Daiyu Chen, Dake Chen, Dali Chen, Dan Chen, Dan-Dan Chen, Dandan Chen, Danlei Chen, Danli Chen, Danmei Chen, Danna Chen, Danni Chen, Danxia Chen, Danxiang Chen, Danyang Chen, Danyu Chen, Daoyuan Chen, Dapeng Chen, Dawei Chen, Defang Chen, Dejuan Chen, Delong Chen, Denghui Chen, Dengpeng Chen, Deqian Chen, Dexi Chen, Dexiang Chen, Dexiong Chen, Deying Chen, Deyu Chen, Di Chen, Di-Long Chen, Dian Chen, Dianke Chen, Ding Chen, Diyun Chen, Dong Chen, Dong-Mei Chen, Dong-Yi Chen, Dongli Chen, Donglong Chen, Dongquan Chen, Dongrong Chen, Dongsheng Chen, Dongxue Chen, Dongyan Chen, Dongyin Chen, Du-Qun Chen, Duan-Yu Chen, Duo Chen, Duo-Xue Chen, Duoting Chen, E S Chen, Eleanor Y Chen, Elizabeth H Chen, Elizabeth S Chen, Elizabeth Suchi Chen, Emily Chen, En-Qiang Chen, Erbao Chen, Erfei Chen, Erqu Chen, Erzhen Chen, Everett H Chen, F Chen, F-K Chen, Fa Chen, Fa-Xi Chen, Fahui Chen, Fan Chen, Fang Chen, Fang-Pei Chen, Fang-Yu Chen, Fang-Zhi Chen, Fang-Zhou Chen, Fangfang Chen, Fangli Chen, Fangyan Chen, Fangyuan Chen, Faye H Chen, Fei Chen, Fei Xavier Chen, Feifan Chen, Feifeng Chen, Feilong Chen, Feixue Chen, Feiyang Chen, Feiyu Chen, Feiyue Chen, Feng Chen, Feng-Jung Chen, Feng-Ling Chen, Fenghua Chen, Fengju Chen, Fengling Chen, Fengming Chen, Fengrong Chen, Fengwu Chen, Fengyang Chen, Fred K Chen, Fu Chen, Fu-Shou Chen, Fumei Chen, Fusheng Chen, Fuxiang Chen, Gang Chen, Gao B Chen, Gao Chen, Gao-Feng Chen, Gaoyang Chen, Gaoyu Chen, Gaozhi Chen, Gary Chen, Gary K Chen, Ge Chen, Gen-Der Chen, Geng Chen, Gengsheng Chen, Ginny I Chen, Gong Chen, Gongbo Chen, Gonghai Chen, Gonglie Chen, Guan-Wei Chen, Guang Chen, Guang-Chao Chen, Guang-Yu Chen, Guangchun Chen, Guanghao Chen, Guanghong Chen, Guangjie Chen, Guangju Chen, Guangliang Chen, Guanglong Chen, Guangnan Chen, Guangping Chen, Guangquan Chen, Guangyao Chen, Guangyi Chen, Guangyong Chen, Guanjie Chen, Guanren Chen, Guanyu Chen, Guanzheng Chen, Gui Mei Chen, Gui-Hai Chen, Gui-Lai Chen, Guihao Chen, Guiqian Chen, Guiquan Chen, Guiying Chen, Guo Chen, Guo-Chong Chen, Guo-Jun Chen, Guo-Rong Chen, Guo-qing Chen, Guochao Chen, Guochong Chen, Guofang Chen, Guohong Chen, Guohua Chen, Guojun Chen, Guoliang Chen, Guopu Chen, Guoshun Chen, Guoxun Chen, Guozhong Chen, Guozhou Chen, H Chen, H Q Chen, H T Chen, Hai-Ning Chen, Haibing Chen, Haibo Chen, Haide Chen, Haifeng Chen, Haijiao Chen, Haimin Chen, Haiming Chen, Haining Chen, Haiqin Chen, Haiquan Chen, Haitao Chen, Haiyan Chen, Haiyang Chen, Haiyi Chen, Haiying Chen, Haiyu Chen, Haiyun Chen, Han Chen, Han-Bin Chen, Han-Chun Chen, Han-Hsiang Chen, Han-Min Chen, Hanbei Chen, Hang Chen, Hangang Chen, Hanjing Chen, Hanlin Chen, Hanqing Chen, Hanwen Chen, Hanxi Chen, Hanyong Chen, Hao Chen, Hao Yu Chen, Hao-Zhu Chen, Haobo Chen, Haodong Chen, Haojie Chen, Haoran Chen, Haotai Chen, Haotian Chen, Haoting Chen, Haoyun Chen, Haozhu Chen, Harn-Shen Chen, Haw-Wen Chen, He-Ping Chen, Hebing Chen, Hegang Chen, Hehe Chen, Hekai Chen, Heng Chen, Heng-Sheng Chen, Heng-Yu Chen, Hengsan Chen, Hengsheng Chen, Hengyu Chen, Heni Chen, Herbert Chen, Hetian Chen, Heye Chen, Hong Chen, Hong Yang Chen, Hong-Sheng Chen, Hongbin Chen, Hongbo Chen, Hongen Chen, Honghai Chen, Honghui Chen, Honglei Chen, Hongli Chen, Hongmei Chen, Hongmin Chen, Hongmou Chen, Hongqi Chen, Hongqiao Chen, Hongshan Chen, Hongxiang Chen, Hongxing Chen, Hongxu Chen, Hongyan Chen, Hongyu Chen, Hongyue Chen, Hongzhi Chen, Hou-Tsung Chen, Hou-Zao Chen, Hsi-Hsien Chen, Hsiang-Wen Chen, Hsiao-Jou Cortina Chen, Hsiao-Tan Chen, Hsiao-Wang Chen, Hsiao-Yun Chen, Hsin-Han Chen, Hsin-Hong Chen, Hsin-Hung Chen, Hsin-Yi Chen, Hsiu-Wen Chen, Hsuan-Yu Chen, Hsueh-Fen Chen, Hu Chen, Hua Chen, Hua-Pu Chen, Huachen Chen, Huafei Chen, Huaiyong Chen, Hualan Chen, Huali Chen, Hualin Chen, Huan Chen, Huan-Xin Chen, Huanchun Chen, Huang Chen, Huang-Pin Chen, Huangtao Chen, Huanhua Chen, Huanhuan Chen, Huanxiong Chen, Huaping Chen, Huapu Chen, Huaqiu Chen, Huatao Chen, Huaxin Chen, Huayu Chen, Huei-Rong Chen, Huei-Yan Chen, Huey-Miin Chen, Hui Chen, Hui Mei Chen, Hui-Chun Chen, Hui-Fen Chen, Hui-Jye Chen, Hui-Ru Chen, Hui-Wen Chen, Hui-Xiong Chen, Hui-Zhao Chen, Huichao Chen, Huijia Chen, Huijiao Chen, Huijie Chen, Huimei Chen, Huimin Chen, Huiqin Chen, Huiqun Chen, Huiru Chen, Huishan Chen, Huixi Chen, Huixian Chen, Huizhi Chen, Hung-Chang Chen, Hung-Chi Chen, Hung-Chun Chen, Hung-Po Chen, Hung-Sheng Chen, I-Chun Chen, I-M Chen, Ida Y-D Chen, Irwin Chen, Ivy Xiaoying Chen, J Chen, Jacinda Chen, Jack Chen, Jake Y Chen, Jason A Chen, Jeanne Chen, Jen-Hau Chen, Jen-Sue Chen, Jennifer F Chen, Jenny Chen, Jeremy J W Chen, Ji-ling Chen, Jia Chen, Jia Min Chen, Jia Wei Chen, Jia-De Chen, Jia-Feng Chen, Jia-Lin Chen, Jia-Mei Chen, Jia-Shun Chen, Jiabing Chen, Jiacai Chen, Jiacheng Chen, Jiade Chen, Jiahao Chen, Jiahua Chen, Jiahui Chen, Jiajia Chen, Jiajing Chen, Jiajun Chen, Jiakang Chen, Jiale Chen, Jiali Chen, Jialing Chen, Jiamiao Chen, Jiamin Chen, Jian Chen, Jian-Guo Chen, Jian-Hua Chen, Jian-Jun Chen, Jian-Kang Chen, Jian-Min Chen, Jian-Qiao Chen, Jian-Qing Chen, Jianan Chen, Jianfei Chen, Jiang Chen, Jiang Ye Chen, Jiang-hua Chen, Jianghua Chen, Jiangxia Chen, Jianhua Chen, Jianhui Chen, Jiani Chen, Jianjun Chen, Jiankui Chen, Jianlin Chen, Jianmin Chen, Jianping Chen, Jianshan Chen, Jiansu Chen, Jianxiong Chen, Jianzhong Chen, Jianzhou Chen, Jiao Chen, Jiao-Jiao Chen, Jiaohua Chen, Jiaping Chen, Jiaqi Chen, Jiaqing Chen, Jiaren Chen, Jiarou Chen, Jiawei Chen, Jiawen Chen, Jiaxin Chen, Jiaxu Chen, Jiaxuan Chen, Jiayao Chen, Jiaye Chen, Jiayi Chen, Jiayuan Chen, Jichong Chen, Jie Chen, Jie-Hua Chen, Jiejian Chen, Jiemei Chen, Jien-Jiun Chen, Jihai Chen, Jijun Chen, Jimei Chen, Jin Chen, Jin-An Chen, Jin-Ran Chen, Jin-Shuen Chen, Jin-Wu Chen, Jin-Xia Chen, Jina Chen, Jinbo Chen, Jindong Chen, Jing Chen, Jing-Hsien Chen, Jing-Wen Chen, Jing-Xian Chen, Jing-Yuan Chen, Jing-Zhou Chen, Jingde Chen, Jinghua Chen, Jingjing Chen, Jingli Chen, Jinglin Chen, Jingming Chen, Jingnan Chen, Jingqing Chen, Jingshen Chen, Jingteng Chen, Jinguo Chen, Jingxuan Chen, Jingyao Chen, Jingyi Chen, Jingyuan Chen, Jingzhao Chen, Jingzhou Chen, Jinhao Chen, Jinhuang Chen, Jinli Chen, Jinlun Chen, Jinquan Chen, Jinsong Chen, Jintian Chen, Jinxuan Chen, Jinyan Chen, Jinyong Chen, Jion Chen, Jiong Chen, Jiongyu Chen, Jishun Chen, Jiu-Chiuan Chen, Jiujiu Chen, Jiwei Chen, Jiyan Chen, Jiyuan Chen, Jonathan Chen, Joy J Chen, Juan Chen, Juan-Juan Chen, Juanjuan Chen, Juei-Suei Chen, Juhai Chen, Jui-Chang Chen, Jui-Yu Chen, Jun Chen, Jun-Long Chen, Junchen Chen, Junfei Chen, Jung-Sheng Chen, Junhong Chen, Junhui Chen, Junjie Chen, Junling Chen, Junmin Chen, Junming Chen, Junpan Chen, Junpeng Chen, Junqi Chen, Junqin Chen, Junsheng Chen, Junshi Chen, Junyang Chen, Junyi Chen, Junyu Chen, K C Chen, Kai Chen, Kai-En Chen, Kai-Ming Chen, Kai-Ting Chen, Kai-Yang Chen, Kaifu Chen, Kaijian Chen, Kailang Chen, Kaili Chen, Kaina Chen, Kaiquan Chen, Kan Chen, Kang Chen, Kang-Hua Chen, Kangyong Chen, Kangzhen Chen, Katharine Y Chen, Katherine C Chen, Ke Chen, Kecai Chen, Kehua Chen, Kehui Chen, Kelin Chen, Ken Chen, Kenneth L Chen, Keping Chen, Kequan Chen, Kevin Chen, Kewei Chen, Kexin Chen, Keyan Chen, Keyang Chen, Keying Chen, Keyu Chen, Keyuan Chen, Kuan-Jen Chen, Kuan-Ling Chen, Kuan-Ting Chen, Kuan-Yu Chen, Kuangyang Chen, Kuey Chu Chen, Kui Chen, Kun Chen, Kun-Chieh Chen, Kunmei Chen, Kunpeng Chen, L B Chen, L F Chen, Lan Chen, Lang Chen, Lankai Chen, Lanlan Chen, Lanmei Chen, Le Chen, Le Qi Chen, Lei Chen, Lei-Chin Chen, Lei-Lei Chen, Leijie Chen, Lena W Chen, Leqi Chen, Letian Chen, Lexia Chen, Li Chen, Li Jia Chen, Li-Chieh Chen, Li-Hsien Chen, Li-Hsin Chen, Li-Hua Chen, Li-Jhen Chen, Li-Juan Chen, Li-Mien Chen, Li-Nan Chen, Li-Tzong Chen, Li-Zhen Chen, Li-hong Chen, Lian Chen, Lianfeng Chen, Liang Chen, Liang-Kung Chen, Liangkai Chen, Liangsheng Chen, Liangwan Chen, Lianmin Chen, Liaobin Chen, Lichang Chen, Lichun Chen, Lidian Chen, Lie Chen, Liechun Chen, Lifang Chen, Lifen Chen, Lifeng Chen, Ligang Chen, Lihong Chen, Lihua Chen, Lijin Chen, Lijuan Chen, Lili Chen, Limei Chen, Limin Chen, Liming Chen, Lin Chen, Lina Chen, Linbo Chen, Ling Chen, Ling-Yan Chen, Lingfeng Chen, Lingjun Chen, Lingli Chen, Lingxia Chen, Lingxue Chen, Lingyi Chen, Linjie Chen, Linlin Chen, Linna Chen, Linxi Chen, Linyi Chen, Liping Chen, Liqiang Chen, Liugui Chen, Liujun Chen, Liutao Chen, Lixia Chen, Lixian Chen, Liyun Chen, Lizhen Chen, Lizhu Chen, Lo-Yun Chen, Long Chen, Long-Jiang Chen, Longqing Chen, Longyun Chen, Lu Chen, Lu Hua Chen, Lu-Biao Chen, Lu-Zhu Chen, Lulu Chen, Luming Chen, Luyi Chen, Luzhu Chen, M Chen, M L Chen, Man Chen, Man-Hua Chen, Mao Chen, Mao-Yuan Chen, Maochong Chen, Maorong Chen, Marcus Y Chen, Mark I-Cheng Chen, Max Jl Chen, Mechi Chen, Mei Chen, Mei-Chi Chen, Mei-Chih Chen, Mei-Hsiu Chen, Mei-Hua Chen, Mei-Jie Chen, Mei-Ling Chen, Mei-Ru Chen, Meilan Chen, Meilin Chen, Meiling Chen, Meimei Chen, Meiting Chen, Meiyang Chen, Meiyu Chen, Meizhen Chen, Meng Chen, Meng Xuan Chen, Meng-Lin Chen, Meng-Ping Chen, Mengdi Chen, Menglan Chen, Mengling Chen, Mengping Chen, Mengqing Chen, Mengting Chen, Mengxia Chen, Mengyan Chen, Mengying Chen, Mian-Mian Chen, Miao Chen, Miao-Der Chen, Miao-Hsueh Chen, Miao-Yu Chen, Miaomiao Chen, Miaoran Chen, Michael C Chen, Michelle Chen, Mien-Cheng Chen, Min Chen, Min-Hsuan Chen, Min-Hu Chen, Min-Jie Chen, Ming Chen, Ming-Fong Chen, Ming-Han Chen, Ming-Hong Chen, Ming-Huang Chen, Ming-Huei Chen, Ming-Yu Chen, Mingcong Chen, Mingfeng Chen, Minghong Chen, Minghua Chen, Minglang Chen, Mingling Chen, Mingmei Chen, Mingxia Chen, Mingxing Chen, Mingyang Chen, Mingyi Chen, Mingyue Chen, Minjian Chen, Minjiang Chen, Minjie Chen, Minyan Chen, Mo Chen, Mu-Hong Chen, Muh-Shy Chen, Mulan Chen, Mystie X Chen, Na Chen, Naifei Chen, Naisong Chen, Nan Chen, Ni Chen, Nian-Ping Chen, Ning Chen, Ning-Bo Chen, Ning-Hung Chen, Ning-Yuan Chen, Ningbo Chen, Ningning Chen, Nuan Chen, On Chen, Ou Chen, Ouyang Chen, P P Chen, Pan Chen, Paul Chih-Hsueh Chen, Pei Chen, Pei-Chen Chen, Pei-Chun Chen, Pei-Lung Chen, Pei-Yi Chen, Pei-Yin Chen, Pei-zhan Chen, Peihong Chen, Peipei Chen, Peiqin Chen, Peixian Chen, Peiyou Chen, Peiyu Chen, Peize Chen, Peizhan Chen, Peng Chen, Peng-Cheng Chen, Pengxiang Chen, Ping Chen, Ping-Chung Chen, Ping-Kun Chen, Pingguo Chen, Po-Han Chen, Po-Ju Chen, Po-Min Chen, Po-See Chen, Po-Sheng Chen, Po-Yu Chen, Qi Chen, Qi-An Chen, Qian Chen, Qianbo Chen, Qianfen Chen, Qiang Chen, Qiangpu Chen, Qiankun Chen, Qianling Chen, Qianming Chen, Qianping Chen, Qianqian Chen, Qianxue Chen, Qianyi Chen, Qianyu Chen, Qianyun Chen, Qianzhi Chen, Qiao Chen, Qiao-Yi Chen, Qiaoli Chen, Qiaoling Chen, Qichen Chen, Qifang Chen, Qihui Chen, Qili Chen, Qinfen Chen, Qing Chen, Qing-Hui Chen, Qing-Juan Chen, Qing-Wei Chen, Qingao Chen, Qingchao Chen, Qingchuan Chen, Qingguang Chen, Qinghao Chen, Qinghua Chen, Qingjiang Chen, Qingjie Chen, Qingliang Chen, Qingmei Chen, Qingqing Chen, Qingqiu Chen, Qingshi Chen, Qingxing Chen, Qingyang Chen, Qingyi Chen, Qinian Chen, Qinsheng Chen, Qinying Chen, Qiong Chen, Qiongyun Chen, Qiqi Chen, Qitong Chen, Qiu Jing Chen, Qiu-Jing Chen, Qiu-Sheng Chen, Qiuchi Chen, Qiuhong Chen, Qiujing Chen, Qiuli Chen, Qiuwen Chen, Qiuxia Chen, Qiuxiang Chen, Qiuxuan Chen, Qiuyun Chen, Qiwei Chen, Qixian Chen, Qu Chen, Quan Chen, Quanjiao Chen, Quanwei Chen, Qunxiang Chen, R Chen, Ran Chen, Ranyun Chen, Ray-Jade Chen, Ren-Hui Chen, Renjin Chen, Renwei Chen, Renyu Chen, Robert Chen, Roger Chen, Rong Chen, Rong-Hua Chen, Rongfang Chen, Rongfeng Chen, Rongrong Chen, Rongsheng Chen, Rongyuan Chen, Roufen Chen, Rouxi Chen, Ru Chen, Rucheng Chen, Ruey-Hwa Chen, Rui Chen, Rui-Fang Chen, Rui-Min Chen, Rui-Pei Chen, Rui-Zhen Chen, Ruiai Chen, Ruibing Chen, Ruijing Chen, Ruijuan Chen, Ruilin Chen, Ruimin Chen, Ruiming Chen, Ruiqi Chen, Ruisen Chen, Ruixiang Chen, Ruixue Chen, Ruiying Chen, Rujun Chen, Runfeng Chen, Runsen Chen, Runsheng Chen, Ruofan Chen, Ruohong Chen, Ruonan Chen, Ruoyan Chen, Ruoying Chen, S Chen, S N Chen, S Pl Chen, S-D Chen, Sai Chen, San-Yuan Chen, Sean Chen, Sen Chen, Shali Chen, Shan Chen, Shanchun Chen, Shang-Chih Chen, Shang-Hung Chen, Shangduo Chen, Shangsi Chen, Shangwu Chen, Shangzhong Chen, Shanshan Chen, Shanyuan Chen, Shao-Ke Chen, Shao-Peng Chen, Shao-Wei Chen, Shao-Yu Chen, Shao-long Chen, Shaofei Chen, Shaohong Chen, Shaohua Chen, Shaokang Chen, Shaokun Chen, Shaoliang Chen, Shaotao Chen, Shaoxing Chen, Shaoze Chen, Shasha Chen, She Chen, Shen Chen, Shen-Ming Chen, Sheng Chen, Sheng-Xi Chen, Sheng-Yi Chen, Shengdi Chen, Shenghui Chen, Shenglan Chen, Shengnan Chen, Shengpan Chen, Shengyu Chen, Shengzhi Chen, Shi Chen, Shi-Qing Chen, Shi-Sheng Chen, Shi-Yi Chen, Shi-You Chen, Shibo Chen, Shih-Jen Chen, Shih-Pin Chen, Shih-Yin Chen, Shih-Yu Chen, Shilan Chen, Shiming Chen, Shin-Wen Chen, Shin-Yu Chen, Shipeng Chen, Shiqian Chen, Shiqun Chen, Shirui Chen, Shiuhwei Chen, Shiwei Chen, Shixuan Chen, Shiyan Chen, Shiyao Chen, Shiyi Chen, Shiyu Chen, Shou-Tung Chen, Shoudeng Chen, Shoujun Chen, Shouzhen Chen, Shu Chen, Shu-Fen Chen, Shu-Gang Chen, Shu-Hua Chen, Shu-Jen Chen, Shuai Chen, Shuai-Bing Chen, Shuai-Ming Chen, Shuaijie Chen, Shuaijun Chen, Shuaiyin Chen, Shuaiyu Chen, Shuang Chen, Shuangfeng Chen, Shuanghui Chen, Shuchun Chen, Shuen-Ei Chen, Shufang Chen, Shufeng Chen, Shuhai Chen, Shuhong Chen, Shuhuang Chen, Shuhui Chen, Shujuan Chen, Shuliang Chen, Shuming Chen, Shunde Chen, Shuntai Chen, Shunyou Chen, Shuo Chen, Shuo-Bin Chen, Shuoni Chen, Shuqin Chen, Shuqiu Chen, Shuting Chen, Shuwen Chen, Shuyi Chen, Shuying Chen, Si Chen, Si-Ru Chen, Si-Yuan Chen, Si-Yue Chen, Si-guo Chen, Sien-Tsong Chen, Sifeng Chen, Sihui Chen, Sijia Chen, Sijuan Chen, Sili Chen, Silian Chen, Siping Chen, Siqi Chen, Siqin Chen, Sisi Chen, Siteng Chen, Siting Chen, Siyi Chen, Siyu Chen, Siyu S Chen, Siyuan Chen, Siyue Chen, Size Chen, Song Chen, Song-Mei Chen, Songfeng Chen, Suet N Chen, Suet Nee Chen, Sufang Chen, Suipeng Chen, Sulian Chen, Suming Chen, Sun Chen, Sung-Fang Chen, Suning Chen, Sunny Chen, Sy-Jou Chen, Syue-Ting Chen, Szu-Chi Chen, Szu-Chia Chen, Szu-Chieh Chen, Szu-Han Chen, Szu-Yun Chen, T Chen, Tai-Heng Chen, Tai-Tzung Chen, Tailai Chen, Tan-Huan Chen, Tan-Zhou Chen, Tania Chen, Tao Chen, Tian Chen, Tianfeng Chen, Tianhang Chen, Tianhong Chen, Tianhua Chen, Tianpeng Chen, Tianran Chen, Tianrui Chen, Tiantian Chen, Tianzhen Chen, Tielin Chen, Tien-Hsing Chen, Ting Chen, Ting-Huan Chen, Ting-Tao Chen, Ting-Ting Chen, Tingen Chen, Tingtao Chen, Tingting Chen, Tom Wei-Wu Chen, Tong Chen, Tongsheng Chen, Tse-Ching Chen, Tse-Wei Chen, TsungYen Chen, Tuantuan Chen, Tzu-An Chen, Tzu-Chieh Chen, Tzu-Ju Chen, Tzu-Ting Chen, Tzu-Yu Chen, Tzy-Yen Chen, Valerie Chen, W Chen, Wai Chen, Wan Jun Chen, Wan-Tzu Chen, Wan-Yan Chen, Wan-Yi Chen, Wanbiao Chen, Wanjia Chen, Wanjun Chen, Wanling Chen, Wantao Chen, Wanting Chen, Wanyin Chen, Wei Chen, Wei J Chen, Wei Ning Chen, Wei-Cheng Chen, Wei-Cong Chen, Wei-Fei Chen, Wei-Hao Chen, Wei-Hui Chen, Wei-Kai Chen, Wei-Kung Chen, Wei-Lun Chen, Wei-Min Chen, Wei-Peng Chen, Wei-Ting Chen, Wei-Wei Chen, Wei-Yu Chen, Wei-xian Chen, Weibo Chen, Weican Chen, Weichan Chen, Weicong Chen, Weihao Chen, Weihong Chen, Weihua Chen, Weijia Chen, Weijie Chen, Weili Chen, Weilun Chen, Weina Chen, Weineng Chen, Weiping Chen, Weiqin Chen, Weiqing Chen, Weirui Chen, Weisan Chen, Weitao Chen, Weitian Chen, Weiwei Chen, Weixian Chen, Weixin Chen, Weiyi Chen, Weiyong Chen, Wen Chen, Wen-Chau Chen, Wen-Jie Chen, Wen-Pin Chen, Wen-Qi Chen, Wen-Tsung Chen, Wen-Yi Chen, Wenbiao Chen, Wenbing Chen, Wenfan Chen, Wenfang Chen, Wenhao Chen, Wenhua Chen, Wenjie Chen, Wenjun Chen, Wenlong Chen, Wenqin Chen, Wensheng Chen, Wenshuo Chen, Wentao Chen, Wenting Chen, Wentong Chen, Wenwen Chen, Wenwu Chen, Wenxi Chen, Wenxing Chen, Wenxu Chen, Willian Tzu-Liang Chen, Wu-Jun Chen, Wu-Xian Chen, Wuyan Chen, X Chen, X R Chen, X Steven Chen, Xi Chen, Xia Chen, Xia-Fei Chen, Xiaguang Chen, Xiameng Chen, Xian Chen, Xian-Kai Chen, Xianbo Chen, Xiancheng Chen, Xianfeng Chen, Xiang Chen, Xiang-Bin Chen, Xiang-Mei Chen, XiangFan Chen, Xiangding Chen, Xiangjun Chen, Xiangli Chen, Xiangliu Chen, Xiangmei Chen, Xiangna Chen, Xiangning Chen, Xiangqiu Chen, Xiangyu Chen, Xiankai Chen, Xianmei Chen, Xianqiang Chen, Xianxiong Chen, Xianyue Chen, Xianze Chen, Xianzhen Chen, Xiao Chen, Xiao-Chen Chen, Xiao-Hui Chen, Xiao-Jun Chen, Xiao-Lin Chen, Xiao-Qing Chen, Xiao-Quan Chen, Xiao-Wei Chen, Xiao-Yang Chen, Xiao-Ying Chen, Xiao-chun Chen, Xiao-he Chen, Xiao-ping Chen, Xiaobin Chen, Xiaobo Chen, Xiaochang Chen, Xiaochun Chen, Xiaodong Chen, Xiaofang Chen, Xiaofen Chen, Xiaofeng Chen, Xiaohan Chen, Xiaohong Chen, Xiaohua Chen, Xiaohui Chen, Xiaojiang S Chen, Xiaojie Chen, Xiaojing Chen, Xiaojuan Chen, Xiaojun Chen, Xiaokai Chen, Xiaolan Chen, Xiaole L Chen, Xiaolei Chen, Xiaoli Chen, Xiaolin Chen, Xiaoling Chen, Xiaolong Chen, Xiaolu Chen, Xiaomeng Chen, Xiaomin Chen, Xiaona Chen, Xiaonan Chen, Xiaopeng Chen, Xiaoping Chen, Xiaoqian Chen, Xiaoqing Chen, Xiaorong Chen, Xiaoshan Chen, Xiaotao Chen, Xiaoting Chen, Xiaowan Chen, Xiaowei Chen, Xiaowen Chen, Xiaoxiang Chen, Xiaoxiao Chen, Xiaoyan Chen, Xiaoyang Chen, Xiaoyin Chen, Xiaoyong Chen, Xiaoyu Chen, Xiaoyuan Chen, Xiaoyun Chen, Xiatian Chen, Xihui Chen, Xijun Chen, Xikun Chen, Ximei Chen, Xin Chen, Xin-Jie Chen, Xin-Ming Chen, Xin-Qi Chen, Xinan Chen, Xing Chen, Xing-Lin Chen, Xing-Long Chen, Xing-Zhen Chen, Xingdong Chen, Xinghai Chen, Xingxing Chen, Xingyi Chen, Xingyong Chen, Xingyu Chen, Xinji Chen, Xinlin Chen, Xinpu Chen, Xinqiao Chen, Xinwei Chen, Xinyan Chen, Xinyang Chen, Xinyi Chen, Xinyu Chen, Xinyuan Chen, Xinyue Chen, Xinzhuo Chen, Xiong Chen, Xiqun Chen, Xiu Chen, Xiu-Juan Chen, Xiuhui Chen, Xiujuan Chen, Xiuli Chen, Xiuping Chen, Xiuxiu Chen, Xiuyan Chen, Xixi Chen, Xiyao Chen, Xiyu Chen, Xu Chen, Xuan Chen, Xuancai Chen, Xuanjing Chen, Xuanli Chen, Xuanmao Chen, Xuanwei Chen, Xuanxu Chen, Xuanyi Chen, Xue Chen, Xue-Mei Chen, Xue-Qing Chen, Xue-Xin Chen, Xue-Yan Chen, Xue-Ying Chen, XueShu Chen, Xuechun Chen, Xuefei Chen, Xuehua Chen, Xuejiao Chen, Xuejun Chen, Xueli Chen, Xueling Chen, Xuemei Chen, Xuemin Chen, Xueqin Chen, Xueqing Chen, Xuerong Chen, Xuesong Chen, Xueting Chen, Xueyan Chen, Xueying Chen, Xufeng Chen, Xuhui Chen, Xujia Chen, Xun Chen, Xuxiang Chen, Xuxin Chen, Xuzhuo Chen, Y Chen, Y D I Chen, Y Eugene Chen, Y M Chen, Y P Chen, Y S Chen, Y U Chen, Y-D I Chen, Y-D Ida Chen, Ya Chen, Ya-Chun Chen, Ya-Nan Chen, Ya-Peng Chen, Ya-Ting Chen, Ya-xi Chen, Yafang Chen, Yafei Chen, Yahong Chen, Yajie Chen, Yajing Chen, Yajun Chen, Yalan Chen, Yali Chen, Yan Chen, Yan Jie Chen, Yan Q Chen, Yan-Gui Chen, Yan-Jun Chen, Yan-Ming Chen, Yan-Qiong Chen, Yan-yan Chen, Yanan Chen, Yananlan Chen, Yanbin Chen, Yanfei Chen, Yanfen Chen, Yang Chen, Yang-Ching Chen, Yang-Yang Chen, Yangchao Chen, Yanghui Chen, Yangxin Chen, Yanhan Chen, Yanhua Chen, Yanjie Chen, Yanjing Chen, Yanli Chen, Yanlin Chen, Yanling Chen, Yanming Chen, Yann-Jang Chen, Yanping Chen, Yanqiu Chen, Yanrong Chen, Yanru Chen, Yanting Chen, Yanyan Chen, Yanyun Chen, Yanzhu Chen, Yanzi Chen, Yao Chen, Yao-Shen Chen, Yaodong Chen, Yaosheng Chen, Yaowu Chen, Yau-Hung Chen, Yaxi Chen, Yayun Chen, Yazhuo Chen, Ye Chen, Ye-Guang Chen, Yeh Chen, Yelin Chen, Yen-Chang Chen, Yen-Chen Chen, Yen-Cheng Chen, Yen-Ching Chen, Yen-Fu Chen, Yen-Hao Chen, Yen-Hsieh Chen, Yen-Jen Chen, Yen-Ju Chen, Yen-Lin Chen, Yen-Ling Chen, Yen-Ni Chen, Yen-Rong Chen, Yen-Teen Chen, Yewei Chen, Yi Chen, Yi Feng Chen, Yi-Bing Chen, Yi-Chun Chen, Yi-Chung Chen, Yi-Fei Chen, Yi-Guang Chen, Yi-Han Chen, Yi-Hau Chen, Yi-Heng Chen, Yi-Hong Chen, Yi-Hsuan Chen, Yi-Hui Chen, Yi-Jen Chen, Yi-Lin Chen, Yi-Ru Chen, Yi-Ting Chen, Yi-Wen Chen, Yi-Yung Chen, YiChung Chen, YiPing Chen, Yian Chen, Yibing Chen, Yibo Chen, Yidan Chen, Yiding Chen, Yidong Chen, Yiduo Chen, Yifa Chen, Yifan Chen, Yifang Chen, Yifei Chen, Yih-Chieh Chen, Yihao Chen, Yihong Chen, Yii-Der Chen, Yii-Der I Chen, Yii-Derr Chen, Yii-der Ida Chen, Yijiang Chen, Yijun Chen, Yike Chen, Yilan Chen, Yilei Chen, Yili Chen, Yilin Chen, Yiming Chen, Yin-Huai Chen, Ying Chen, Ying-Cheng Chen, Ying-Hsiang Chen, Ying-Jie Chen, Ying-Jung Chen, Ying-Lan Chen, Ying-Ying Chen, Yingchun Chen, Yingcong Chen, Yinghui Chen, Yingji Chen, Yingjie Chen, Yinglian Chen, Yingting Chen, Yingxi Chen, Yingying Chen, Yingyu Chen, Yinjuan Chen, Yintong Chen, Yinwei Chen, Yinzhu Chen, Yiru Chen, Yishan Chen, Yisheng Chen, Yitong Chen, Yixin Chen, Yiyin Chen, Yiyun Chen, Yizhi Chen, Yong Chen, Yong-Jun Chen, Yong-Ping Chen, Yong-Syuan Chen, Yong-Zhong Chen, YongPing Chen, Yongbin Chen, Yongfa Chen, Yongfang Chen, Yongheng Chen, Yonghui Chen, Yongke Chen, Yonglu Chen, Yongmei Chen, Yongming Chen, Yongning Chen, Yongqi Chen, Yongshen Chen, Yongshuo Chen, Yongxing Chen, Yongxun Chen, You-Ming Chen, You-Xin Chen, You-Yue Chen, Youhu Chen, Youjia Chen, Youmeng Chen, Youran Chen, Youwei Chen, Yu Chen, Yu-Bing Chen, Yu-Cheng Chen, Yu-Chi Chen, Yu-Chia Chen, Yu-Chuan Chen, Yu-Fan Chen, Yu-Fen Chen, Yu-Fu Chen, Yu-Gen Chen, Yu-Han Chen, Yu-Hui Chen, Yu-Ling Chen, Yu-Ming Chen, Yu-Pei Chen, Yu-San Chen, Yu-Si Chen, Yu-Ting Chen, Yu-Tung Chen, Yu-Xia Chen, Yu-Xin Chen, Yu-Yang Chen, Yu-Ying Chen, Yuan Chen, Yuan-Hua Chen, Yuan-Shen Chen, Yuan-Tsong Chen, Yuan-Yuan Chen, Yuan-Zhen Chen, Yuanbin Chen, Yuanhao Chen, Yuanjia Chen, Yuanjian Chen, Yuanli Chen, Yuanqi Chen, Yuanwei Chen, Yuanwen Chen, Yuanyu Chen, Yuanyuan Chen, Yubin Chen, Yucheng Chen, Yue Chen, Yue-Lai Chen, Yuebing Chen, Yueh-Peng Chen, Yuelei Chen, Yuewen Chen, Yuewu Chen, Yuexin Chen, Yuexuan Chen, Yufei Chen, Yufeng Chen, Yuh-Lien Chen, Yuh-Ling Chen, Yuh-Min Chen, Yuhan Chen, Yuhang Chen, Yuhao Chen, Yuhong Chen, Yuhui Chen, Yujie Chen, Yule Chen, Yuli Chen, Yulian Chen, Yulin Chen, Yuling Chen, Yulong Chen, Yulu Chen, Yumei Chen, Yun Chen, Yun-Ju Chen, Yun-Tzu Chen, Yun-Yu Chen, Yundai Chen, Yunfei Chen, Yunfeng Chen, Yung-Hsiang Chen, Yung-Wu Chen, Yunjia Chen, Yunlin Chen, Yunn-Yi Chen, Yunqin Chen, Yunshun Chen, Yunwei Chen, Yunyun Chen, Yunzhong Chen, Yunzhu Chen, Yupei Chen, Yupeng Chen, Yuping Chen, Yuqi Chen, Yuqin Chen, Yuqing Chen, Yuquan Chen, Yurong Chen, Yushan Chen, Yusheng Chen, Yusi Chen, Yuting Chen, Yutong Chen, Yuxi Chen, Yuxian Chen, Yuxiang Chen, Yuxin Chen, Yuxing Chen, Yuyan Chen, Yuyang Chen, Yuyao Chen, Z Chen, Zan Chen, Zaozao Chen, Ze-Hui Chen, Ze-Xu Chen, Zechuan Chen, Zemin Chen, Zetian Chen, Zexiao Chen, Zeyu Chen, Zhanfei Chen, Zhang-Liang Chen, Zhang-Yuan Chen, Zhangcheng Chen, Zhanghua Chen, Zhangliang Chen, Zhanglin Chen, Zhangxin Chen, Zhanjuan Chen, Zhao Chen, Zhao-Xia Chen, ZhaoHui Chen, Zhaojun Chen, Zhaoli Chen, Zhaolin Chen, Zhaoran Chen, Zhaowei Chen, Zhaoyao Chen, Zhe Chen, Zhe-Ling Chen, Zhe-Sheng Chen, Zhe-Yu Chen, Zhebin Chen, Zhehui Chen, Zhelin Chen, Zhen Bouman Chen, Zhen Chen, Zhen-Hua Chen, Zhen-Yu Chen, Zhencong Chen, Zhenfeng Chen, Zheng Chen, Zheng-Zhen Chen, Zhenghong Chen, Zhengjun Chen, Zhengling Chen, Zhengming Chen, Zhenguo Chen, Zhengwei Chen, Zhengzhi Chen, Zhenlei Chen, Zhenyi Chen, Zhenyue Chen, Zheping Chen, Zheren Chen, Zhesheng Chen, Zheyi Chen, Zhezhe Chen, Zhi Bin Chen, Zhi Chen, Zhi-Hao Chen, Zhi-bin Chen, Zhi-zhe Chen, Zhiang Chen, Zhichuan Chen, Zhifeng Chen, Zhigang Chen, Zhigeng Chen, Zhiguo Chen, Zhihai Chen, Zhihang Chen, Zhihao Chen, Zhiheng Chen, Zhihong Chen, Zhijian Chen, Zhijian J Chen, Zhijing Chen, Zhijun Chen, Zhimin Chen, Zhinan Chen, Zhiping Chen, Zhiqiang Chen, Zhiquan Chen, Zhishi Chen, Zhitao Chen, Zhiting Chen, Zhiwei Chen, Zhixin Chen, Zhixuan Chen, Zhixue Chen, Zhiyong Chen, Zhiyu Chen, Zhiyuan Chen, Zhiyun Chen, Zhizhong Chen, Zhong Chen, Zhongbo Chen, Zhonghua Chen, Zhongjian Chen, Zhongliang Chen, Zhongxiu Chen, Zhongzhu Chen, Zhou Chen, Zhouji Chen, Zhouliang Chen, Zhoulong Chen, Zhouqing Chen, Zhuchu Chen, Zhujun Chen, Zhuo Chen, Zhuo-Yuan Chen, ZhuoYu Chen, Zhuohui Chen, Zhuojia Chen, Zi-Jiang Chen, Zi-Qing Chen, Zi-Yang Chen, Zi-Yue Chen, Zi-Yun Chen, Zian Chen, Zifan Chen, Zihan Chen, Zihang Chen, Zihao Chen, Zihe Chen, Zihua Chen, Zijie Chen, Zike Chen, Zilin Chen, Zilong Chen, Ziming Chen, Zinan Chen, Ziqi Chen, Ziqing Chen, Zitao Chen, Zixi Chen, Zixin Chen, Zixuan Chen, Ziying Chen, Ziyuan Chen, Zoe Chen, Zongming E Chen, Zongnan Chen, Zongyou Chen, Zongzheng Chen, Zugen Chen, Zuolong Chen
articles
Wenxiang Hu, Biying Zhu, Na He +15 more · 2025 · Research square · added 2026-04-24
Genome-wide association studies (GWAS) have identified nearly 100 loci associated with metabolic dysfunction-associated steatotic liver disease (MASLD), but the molecular functions of these variant al Show more
Genome-wide association studies (GWAS) have identified nearly 100 loci associated with metabolic dysfunction-associated steatotic liver disease (MASLD), but the molecular functions of these variant alleles remain elusive, particularly when they occur in non-coding regions. Here we profiled the chromatin accessibility landscape of liver nuclei from MASLD individuals, and demonstrated these accessible genomic sites were bound by cell type-specific transcription factors (TFs) and enriched for MASLD risk variants, highlighting lineage- and disease state-specific regulation. Using a massively parallel reporter assay (MPRA), we identified hundreds of differential activity variants (DAVs) that operate in a cell type-specific manner or in a stimulus-dependent context by disrupting liver pathogenesis-associated transcriptional regulatory network. Integrative analyses combining liver eQTLs, chromatin looping, and single-cell CRISPRi screening linked these DAVs to functional target genes. Notably, we demonstrated that DAVs located near Show less
📄 PDF DOI: 10.21203/rs.3.rs-6984670/v1
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Na Liu, Hongli Zeng, Xiangsheng Cai +6 more · 2025 · Frontiers in genetics · Frontiers · added 2026-04-24
To investigate the association between polymorphisms of the A case-control study was conducted, enrolling 100 HTG patients and 100 age-matched controls with normal triglyceride levels from the physica Show more
To investigate the association between polymorphisms of the A case-control study was conducted, enrolling 100 HTG patients and 100 age-matched controls with normal triglyceride levels from the physical examination cohort at Guangzhou 11th People's Hospital (January-December 2023) The observation group showed significant differences in genotype frequencies of Show less
📄 PDF DOI: 10.3389/fgene.2025.1654501
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Yaozhong Liu, Huilun Wang, Minzhi Yu +19 more · 2025 · Circulation · added 2026-04-24
Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease with no effective pharmacological treatments. The causal role of triglycerides (TGs) in AAA development remains unclear and contr Show more
Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease with no effective pharmacological treatments. The causal role of triglycerides (TGs) in AAA development remains unclear and controversial. Mendelian randomization was applied to assess causal relationships between lipoproteins, circulating proteins, metabolites, and the risk of AAA. To test the hypothesis that elevated plasma TG levels accelerate AAA development, we used Mendelian randomization analyses integrating genetic, proteomic, and metabolomic data identified causal relationships between elevated TG-rich lipoproteins, TG metabolism-related proteins/metabolites, and AAA risk. In the angiotensin II infusion AAA model, most These findings identify hypertriglyceridemia as a key contributor to AAA pathogenesis and suggest that targeting TG-rich lipoproteins may be a promising therapeutic strategy for AAA. Show less
📄 PDF DOI: 10.1161/CIRCULATIONAHA.125.074737
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Lingyan Li, Xingjie Wu, Qianqian Guo +9 more · 2025 · Journal of pharmaceutical analysis · Elsevier · added 2026-04-24
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, over Show more
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, overshadowing its influence on the biological effects of formulations. In this study, we reevaluated the delivery effect of CH using widely applied lipid microspheres (LM) as a model DDS. We conducted comprehensive investigations into the impact of CH on the distribution, cell uptake, and protein corona (PC) of LM at sites of cardiovascular inflammatory injury. The results demonstrated that moderate CH promoted the accumulation of LM at inflamed cardiac and vascular sites without exacerbating damage while partially mitigating pathological damage. Then, the slow cellular uptake rate observed for CH@LM contributed to a prolonged duration of drug efficacy. Network pharmacology and molecular docking analyses revealed that CH depended on LM and exerted its biological effects by modulating peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in vascular endothelial cells and estrogen receptor alpha (ERα) protein levels in myocardial cells, thereby enhancing LM uptake at cardiovascular inflammation sites. Proteomics analysis unveiled a serum adsorption pattern for CH@LM under inflammatory conditions showing significant adsorption with CH metabolism-related apolipoprotein family members such as apolipoprotein A-V (Apoa5); this may be a major contributing factor to their prolonged circulation Show less
📄 PDF DOI: 10.1016/j.jpha.2024.101182
APOA5
Yi Wen, Hongxia Li, Sydney Smith +9 more · 2025 · Journal of clinical lipidology · Elsevier · added 2026-04-24
Cholesteryl ester transfer protein (CETP) mediates the exchange of triglycerides (TG) from apolipoprotein B (ApoB)-containing lipoproteins to high-density lipoproteins (HDL) and the reciprocal exchang Show more
Cholesteryl ester transfer protein (CETP) mediates the exchange of triglycerides (TG) from apolipoprotein B (ApoB)-containing lipoproteins to high-density lipoproteins (HDL) and the reciprocal exchange of cholesterol (C) from HDL to ApoB-containing lipoproteins. CETP inhibition increases HDL-C and decreases low-density lipoprotein cholesterol (LDL-C) while modestly decreasing TG. Considering that CETP inhibitors block removal of TG from TG-rich lipoproteins (TRL), it is interesting that CETP inhibition decreases TG concentrations. TG levels are largely regulated by lipoprotein lipase (LPL), the enzyme primarily responsible for hydrolyzing TG. The angiopoietin-like 3/8 complex (ANGPTL3/8) is the most potent circulating LPL inhibitor, while the TG-lowering apolipoprotein A5 (ApoA5) acts by suppressing ANGPTL3/8-mediated LPL inhibition. To better understand CETP biology, we studied the effects of CETP overexpression and CETP inhibition on the levels of ANGPTL3/8 and ApoA5 in circulation using dedicated immunoassays. CETP-overexpressing transgenic mice had increased TG and normal ANGPTL3/8 levels but manifested dramatically reduced ApoA5 concentrations. Administration of the CETP inhibitor evacetrapib had no effect on ANGPTL3/8 levels in CETP-overexpressing mice or in humans. However, evacetrapib administration increased ApoA5 concentrations in both species. In human subjects, evacetrapib treatment increased circulating ApoA5 levels in the late-stage ACCELERATE and ACCENTUATE studies by 160.1% and 204.7%, respectively. Our results uncover a previously unrecognized link between CETP and ApoA5 by showing that CETP overexpression reduces ApoA5 levels while CETP inhibition increases ApoA5 concentrations. Show less
no PDF DOI: 10.1016/j.jacl.2025.06.008
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Eugene Lin, Yu-Ting Yan, Mu-Hong Chen +3 more · 2025 · Nature communications · Nature · added 2026-04-24
This pioneering genome-wide association study examined surrogate markers for insulin resistance (IR) in 147,880 Taiwanese individuals using data from the Taiwan Biobank. The study focused on two IR su Show more
This pioneering genome-wide association study examined surrogate markers for insulin resistance (IR) in 147,880 Taiwanese individuals using data from the Taiwan Biobank. The study focused on two IR surrogate markers: the triglyceride to high-density lipoprotein cholesterol (TG:HDL-C) ratio and the TyG index (the product of fasting plasma glucose and triglycerides). We identified genome-wide significance loci within four gene clusters: GCKR, MLXIPL, APOA5, and APOC1, uncovering 197 genes associated with IR. Transcriptome-wide association analysis revealed significant associations between these clusters and TyG, primarily in adipose tissue. Gene ontology analysis highlighted pathways related to Alzheimer's disease, glucose homeostasis, insulin resistance, and lipoprotein dynamics. The study identified sex-specific genes associated with TyG. Polygenic risk score analysis linked both IR markers to gout and hyperlipidemia. Our findings elucidate the complex relationships between IR surrogate markers, genetic predisposition, and disease phenotypes in the Taiwanese population, contributing valuable insights to the field of metabolic research. Show less
📄 PDF DOI: 10.1038/s41467-025-58506-x
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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
no PDF
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
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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
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Yaozhong Liu, Huilun Wang, Minzhi Yu +19 more · 2025 · medRxiv : the preprint server for health sciences · Cold Spring Harbor Laboratory · added 2026-04-24
Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease without effective medications. This study integrated genetic, proteomic, and metabolomic data to identify causation between incre Show more
Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease without effective medications. This study integrated genetic, proteomic, and metabolomic data to identify causation between increased triglyceride (TG)-rich lipoproteins and AAA risk. Three hypertriglyceridemia mouse models were employed to test the hypothesis that increased plasma TG concentrations accelerate AAA development and rupture. In the angiotensin II-infusion AAA model, most Show less
no PDF DOI: 10.1101/2024.08.07.24311621
APOA5
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
Chao Zhao, Nuan Wang, Di Shi +3 more · 2025 · Lipids · Wiley · added 2026-04-24
Ischemic stroke is frequently associated with symptomatic intracranial atherosclerotic stenosis (sICAS), is a leading cause of global disability and mortality. Current guidelines recommend dual antipl Show more
Ischemic stroke is frequently associated with symptomatic intracranial atherosclerotic stenosis (sICAS), is a leading cause of global disability and mortality. Current guidelines recommend dual antiplatelet and intensive statin therapies. Proprotein convertase subtilisin 9/kexin type 9 (PCSK9) inhibitors have emerged as a potent lipid-lowering therapy, potentially influenced by genetic variations, particularly in the CYP2C19 gene. This study at Xuzhou Central Hospital from January 2021 to December 2023 included 151 patients divided into a statin group (n = 73) and a PCSK9 inhibitor (PCSK9i) group (n = 78). It evaluated lipid profiles, inflammatory markers, neurological function, and clinical outcomes over a 180-day follow-up period, with additional analysis stratified by CYP2C19 genotype. The PCSK9i group demonstrated significant improvements in lipid parameters compared to the statin group, including greater reductions in low-density lipoprotein cholesterol (LDL-C) (p = 0.008), total cholesterol (TC) (p < 0.001), and triacylglycerols (TAG) (p = 0.041), along with apolipoprotein A1 (ApoA1) and apolipoprotein B (ApoB) (both p < 0.001). Inflammatory markers, particularly interleukin-6 (IL-6), significantly reduced in the PCSK9i group (p < 0.001). In the PCSK9i group, CYP2C19 rapid metabolizers achieved greater reductions in LDL-C (p = 0.021), ApoB (p = 0.003), and IL-6 levels (p = 0.041) compared to slow metabolizers. Post-treatment modified Rankin Scale (mRS) scores were significantly lower in rapid metabolizers compared to slow metabolizers (p = 0.018), though clinical events occurred infrequently in both subgroups. This study demonstrates that PCSK9 inhibitor therapy combined with statins provides enhanced lipid-lowering and anti-inflammatory effects compared to statin monotherapy in sICAS patients. While the CYP2C19 genotype may influence specific treatment responses, particularly lipid parameters, its impact on clinical outcomes requires further investigation. Show less
no PDF DOI: 10.1002/lipd.70018
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Lili Qiao, Jiameng Miao, Weixuan Du +5 more · 2025 · Frontiers in clinical diabetes and healthcare · Frontiers · added 2026-04-24
Diabetes mellitus and dyslipidemia are major risk factors for atherosclerosis. Hypoechoic plaques, which indicate vulnerable or unstable plaques, may rupture and lead to ischemic stroke, cognitive imp Show more
Diabetes mellitus and dyslipidemia are major risk factors for atherosclerosis. Hypoechoic plaques, which indicate vulnerable or unstable plaques, may rupture and lead to ischemic stroke, cognitive impairment, increased adverse cardiac events, and even death. This study aimed to investigate the correlation between plasma lipid levels and the characteristics of atherosclerotic plaques in adult patients with type 2 diabetes mellitus. A retrospective analysis was conducted on adult patients with type 2 mellitus who were hospitalized in the Department of Endocrinology at Affiliated Hospital of Hebei University between January 2017 and December 2021.Patients were categorized into two groups based on arterial ultrasound results. Statistical analyses were performed to compare plasma lipid levels and plaque characteristics across the groups. 1) Statistically significant differences were observed among the two groups in terms of gender, hypertension, age, duration of diabetes mellitus, plaque location, triglycerides (TG),total cholesterol (TC), Apolipoprotein A1 (Apo A1),very-low-density lipoprotein (VLDL), VLDL/apolipoprotein B(ApoB), high-density lipoprotein cholesterol (HDL)/ApoA1 ( In clinical practice, the characteristics of atherosclerotic plaques and lipid profiles should be jointly evaluated to guide targeted treatment and effectively reduce the risk of atherosclerotic cardiovascular disease. Show less
📄 PDF DOI: 10.3389/fcdhc.2025.1688715
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Tao Zhang, Siyu Yang, Haijun Jiang +7 more · 2025 · ZooKeys · added 2026-04-24
The genus
📄 PDF DOI: 10.3897/zookeys.1262.164459
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Yu Ding, Haoyang Ling, Xiuyan Chen +6 more · 2025 · Medicine · added 2026-04-24
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any m Show more
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any means to prevent several risks associated with MI. Blood and urine tests are frequently employed in clinical examinations to detect cardiovascular diseases at an early stage. Mendelian randomization (MR) is commonly employed to explore disease-trait relationships and uncover therapeutic targets. Our goal was to explore the genetic links between 35 blood and urine biomarkers and MI. Blood and urine biomarker MR correlations with MI risk were studied. In version R10, the UK Biobank and Finnish databases included blood and urine marker data and MI data (26,060 cases and 343,079 controls). We performed bidirectional 2-sample MR with 4 methods: inverse variance weighted, MR-Egger, weighted median, and weighted mode. Final causal associations were determined by inverse variance weighted. Sensitivity analyses (heterogeneity, pleiotropy) were conducted. MR-PRESSO and PhenoScanner were used to exclude invalid instruments. We used multivariate MR to filter the most important genes without including other positive genes. To identify positive gene pathways and gene networks that cause MI, we employed GeneMANIA for gene prediction. The findings revealed a positive genetic association between the 8 blood and urine biomarker levels and an elevated risk of MI. There are apolipoprotein B (APOB), glycated hemoglobin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, sex hormone-binding globulin, triglycerides, and urate. Moreover, APOB, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol selectively affect MI through the rejection of other positive gene stems. Finally, APOB and numerous genes strongly impact MI development. APOB collaborates with related genes to regulate plasma lipoprotein particle levels, sterol homeostasis, organization, lipid homeostasis, and remodeling in MI. Our research further reveals the causal relationship between MI and blood/urine biomarkers, providing a new perspective for the prevention, diagnosis, and treatment of MI. Blood and urine marker tests can subsequently be conducted based on these results to detect MI and study the underlying mechanisms linking these metabolites to MI. Show less
no PDF DOI: 10.1097/MD.0000000000046146
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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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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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Petnamnueng Dettipponpong, Mei-Ying Sin, Yu-Hui Chen +5 more · 2025 · Journal of thermal biology · Elsevier · added 2026-04-24
By various assessments, the previous study has unequivocally concluded functional apoB and MTTP (microsomal triglyceride transfer protein) for VLDL production in chicken ovaries. The present study sou Show more
By various assessments, the previous study has unequivocally concluded functional apoB and MTTP (microsomal triglyceride transfer protein) for VLDL production in chicken ovaries. The present study sought to use whole tissue culture to define the role of VLDL secretion by small yellow follicles (SYFs) along their development under normal and heat stress (HS) conditions. Under thermoneutral conditions (39 °C), chicken SYFs increased MTTP activity, apoB expression and VLDL secretion, while underwent cell apoptosis along the time course. Despite relieved ER stress and protein ubiquitinylation, inhibition of VLDL secretion by Lomitapide and Mipomersen greatly increased triglyceride accumulation, impaired estradiol production and cell proliferation, and accelerated cell apoptosis in accordance with upregulated caspase 3/7 activity, JNK activation, protein carbonylation, and MDA accumulation. Exposure to HS at 44 °C boosted cell apoptosis in a duration-dependent manner. Acute HS for 3 h enhanced VLDL secretion, impaired estradiol production and cell proliferation, and promoted IL-1b production, oxidative damages, and cell apoptosis, whereas except MDA content and cell proliferation, the detrimental effects were halted after 13 h recovery. Lomitapide and Mipomersen augmented lipid accumulation, oxidative stress, inflammatory response, and exacerbated transient impairment of estradiol secretion and cell proliferation in SYFs under 3 h HS and after recovery, but failed to rescue cell viability despite relieved ER and proteostatic stress. In conclusion, routine secretion of VLDL by SYFs serves as an intrinsic mechanism to sustain cell viability and functions to support the whole program required for follicle development, while under HS, this mechanism provisionally rescues steroidogenesis and cell proliferation. Show less
no PDF DOI: 10.1016/j.jtherbio.2025.104298
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Qing-Wu Wu, Shi-Li Gu, Yang-Yang Chen +4 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Postmenopausal women are at elevated risk for osteoporosis and dysregulated lipid metabolism. While the relationship between conventional lipid markers and bone mineral density (BMD) remains controver Show more
Postmenopausal women are at elevated risk for osteoporosis and dysregulated lipid metabolism. While the relationship between conventional lipid markers and bone mineral density (BMD) remains controversial, the association between apolipoprotein B-100 (ApoB-100) (an established independent predictor of atherosclerosis) and bone metabolism in postmenopausal women remains poorly understood. This study investigated the relationship between ApoB-100 and lumbar BMD in postmenopausal women, with specific focus on potential inflammatory and platelet-mediated pathways. We conducted a cross-sectional study of 1,429 postmenopausal women who underwent health screening at the First Affiliated Hospital of Xinxiang Medical University between January 2022 and December 2024. ApoB-100 levels were measured by immunoturbidimetry, and lumbar BMD was assessed using low-dose chest CT imaging. Participants were stratified into tertiles based on ApoB-100 levels. We employed univariate and multivariate regression analyses to evaluate the relationship between lumbar BMD and ApoB-100. Generalized additive models with smooth curve fitting were used to characterize the linear relationship. Subgroup analyses assessed the consistency of associations across different populations, while mediation models quantified the intermediary roles of the neutrophil-to-lymphocyte ratio (NLR) and platelet count. After multivariate adjustment, ApoB-100 demonstrated a significant independent negative correlation with lumbar BMD (β=-6.37, 95%CI: -9.26 to -3.49). This association was more pronounced in women younger than 60 years (β=-10.18, 95%CI: -13.94 to -6.42), those with BMI≥28kg/m² (β=-10.73, 95%CI: -15.31 to -0.86), and those without hypertension (β=-7.3, 95%CI: -10.42 to -4.19). Mediation analysis revealed that NLR accounted for 8.17% of the negative association between ApoB-100 and lumbar BMD, while platelet count showed a suppressive indirect association (20.60%). ApoB-100 exhibits an independent negative association with lumbar BMD in postmenopausal women, partially mediated through inflammatory and platelet pathways. These findings support the potential utility of ApoB-100 as a biomarker for osteoporosis risk assessment in postmenopausal women, particularly within specific high-risk subgroups. Show less
📄 PDF DOI: 10.3389/fendo.2025.1667161
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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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Jing Jin, Yu Lei, Jia Zheng +7 more · 2025 · Lipids in health and disease · BioMed Central · added 2026-04-24
Among individuals diagnosed with type 2 diabetes mellitus (T2DM), an abnormal accumulation of visceral fat heightens the cardiovascular risk (CVR), and the major reason for death for these people is a Show more
Among individuals diagnosed with type 2 diabetes mellitus (T2DM), an abnormal accumulation of visceral fat heightens the cardiovascular risk (CVR), and the major reason for death for these people is atherosclerotic cardiovascular disease (ASCVD). This study aimed to gain further insights into the longitudinal relationship between CVR and visceral fat area (VFA) in patients with T2DM, and to compare the predictive performance of additional abdominal obesity measures and VFA for changes in CVR. This prospective cohort study included 316 patients with T2DM who were followed up for more than one year, and VFA was measured by the bioimpedance method. This study investigated the prospective association between a VFA percentage change (∆VFA, %) and CVR, and evaluated the potential nonlinear relationships between ∆VFA (%) and the increase 10-year ASCVD risk. Furthermore, the area under the pooled curve (AUC) was contrasted for both ∆VFA (%) and other abdominal obesity indices. The excessive VFA loss group showed lower low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), total cholesterol (TC), triglyceride-glucose index, LDL-C/HDL-C, brachial-ankle pulse wave velocity, 10-year ASCVD risk, atherogenic index of plasma, TC/HDL-C, and apolipoproteins B/apolipoproteins A-1 than the VFA gain group (all β [Formula: see text] 0, HR [Formula: see text] 1, all P [Formula: see text] 0.05) after covariate controlling. VFA reduction of more than 14.82% led to a reduction in the stated risk. Moreover, ∆VFA (%) demonstrated superior predictive value for changes in ASCVD risk, with an AUC of 0.585 (95% CI: 0.513-0.656), compared to other obesity indices. Excessive VFA reduction improved 10-year ASCVD risk in patients diagnosed with T2DM. VFA was a more effective predictor of 10-year ASCVD risk changes than other abdominal obesity measures. This investigation has been registered with the Chinese Clinical Trial Registry (ChiCTR2400086569). Show less
📄 PDF DOI: 10.1186/s12944-025-02711-6
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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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Xuan Bai, Dingzi Zhou, Jing Luo +14 more · 2025 · Medicine · added 2026-04-24
Lipid metabolism abnormalities and inflammation have been implicated in gallstone disease (GSD) development, but the causal relationships and potential mediation effects among lipid metabolites, infla Show more
Lipid metabolism abnormalities and inflammation have been implicated in gallstone disease (GSD) development, but the causal relationships and potential mediation effects among lipid metabolites, inflammatory factors, and GSD remain unclear. The aim of this study is to explore the causal relationships among these 3 factors. This study employed 2-sample Mendelian Randomization (TSMR) and 2-step MR to investigate the causal relationships and potential mediation effects among 91 inflammatory factors, 6 lipid metabolism-related molecules (HDL-C, LDL-C, TG, total cholesterol, ApoA1, and ApoB), and GSD. We opted for 4 distinct MR analysis methods including inverse variance weighted method, weighted median method, MR-Egger regression method and MR-PRESSO analysis. Sensitivity analyses included MR-Egger intercept tests, Cochran's Q statistic, Steiger tests, and leave-one-out analyses. Product of coefficients method was used to estimate mediation proportion. TSMR analysis revealed that every 1-unit increase in low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB), the risk of GSD decreased by 16.5%, 10.2%, 8.4%, and 13.1%, respectively. Inflammatory factors such as Natural killer cell receptor 2B4 (CD244), Macrophage colony-stimulating factor 1 (CSF-1), and interleukin-18 receptor 1 (IL-18R1) were identified as risk factors for GSD, while Fibroblast growth factor 19 levels (FGF19), Interleukin-1-alpha levels (IL-1α), and Interleukin-8 levels (IL-8) were found to be protective. Mediation analysis through 2-step MR identified potential pathways involving ApoA1--IL-8--GSD (P = .084) and IL-1α--ApoB--GSD (P = .117). This study provides robust evidence of causal links between specific lipid metabolites and GSD, as well as suggestive causal associations for several inflammatory factors. However, mediation analysis did not support significant roles for lipids or inflammatory factors as mediators in GSD pathogenesis. Future research could be further pursued in areas such as drug target intervention and mechanistic studies. Show less
no PDF DOI: 10.1097/MD.0000000000044704
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Pu Jiang, Liangyu Liu, Lixian Chen +2 more · 2025 · Pharmaceuticals (Basel, Switzerland) · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/ph18091280
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Marijana Vujkovic, David E Kaplan, Jonas Ghouse +73 more · 2025 · medRxiv : the preprint server for health sciences · Cold Spring Harbor Laboratory · added 2026-04-24
Cirrhosis and hepatocellular carcinoma (HCC) are long-term complications of chronic liver disease (CLD). In this large multi-ancestry genome-wide association study of all-cause cirrhosis (35,481 cases Show more
Cirrhosis and hepatocellular carcinoma (HCC) are long-term complications of chronic liver disease (CLD). In this large multi-ancestry genome-wide association study of all-cause cirrhosis (35,481 cases, 2.36M controls) and HCC (6,680 cases, 1.76M controls), we identified 27 loci associated with cirrhosis (10 novel) and 11 with HCC (three novel). Three novel cirrhosis loci were replicated in independent cohorts (e.g. Show less
📄 PDF DOI: 10.1101/2025.09.16.25335186
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Sihua Huang, Yan Lan, Cheng Zheng +3 more · 2025 · BMC neurology · BioMed Central · added 2026-04-24
As inflammatory processes may be involved in the pathogenesis of diabetic distal sensorimotor polyneuropathy (DSPN), the first aim of the present study was to determine the clinical characteristics of Show more
As inflammatory processes may be involved in the pathogenesis of diabetic distal sensorimotor polyneuropathy (DSPN), the first aim of the present study was to determine the clinical characteristics of type 2 diabetes mellitus (T2DM) with distal sensorimotor polyneuorpathy (DSPN). Next goal was to investigate inflammatory biomarkers, insulin-like growth factor- 1 and lipid profile in these patients. Finally, we aimed to compare the renal function in these patients. In a cross-sectional study, we included 160 patients diagnosed with T2DM. The control group was included 22 non-diabetic healthy subjects (HC). The patients with diabetes were divided into four groups, absent (n = 74), mild (n = 38), moderate (n = 24), and severe (n = 24) using a nomogram based on the MNSI features for a DSPN severity grading probability. Patients with moderate and severe DSPN were a little older and had longer duration of diabetes compared to patients with absent and mild DSPNS (p < 0.05). Serum levels of interferon-gamma (INF-γ), interleukin (IL)-1β, IL-4, IL- 6 levels in patients with severe DSPN were significantly higher than HC, absent, mild and moderate of DSPN (p < 0.05). The circulating levels of insulin-like growth factor-1 (IGF-1) were significantly lower in patients with severe DSPN (p < 0.05) compared to absent, mild and moderate of DSPN and HC. Diabetic patients with moderate DSPN showed increased circulating levels of TC, LDL-C, APOB (p < 0.05) compared to HC and patients with absent, mild and severe DSPN. Moreover, APO-A1/APOB was significantly lower in patients with diabetes compared to HC. In addition, patients with severe DSPN showed increased Cystatin C (p < 0.05) compared to HC and absent, mild, and moderate DSPN. Multivariate ordered logistic regression analysis showed that the levels of IL-6 (OR = 3.166, 95%CI 1.461-6.860, p = 0.003, IL-1β(OR = 1.148, 95%CI 1.070-2.232; p = 0.000), TC (OR = 1.174, 95%CI 1.011-1.364; p = 0.035), LDL-C (OR = 1.246, 95%CI 1.098-3.618; p = 0.003), Cystatin C (OR = 1.867, 95%CI 1.245-3.434; p = 0.004), ages (OR = 1.043, 95%CI 1.009-1.078; p = 0.012), and duration of diabetes (OR = 1.157, 95%CI 1.049-1.277; p = 0.004) were positively associated with increasing the odds ration of DSPN in T2DM. Conversely, the level of IGF-1 (OR = 0.922, 95%CI 0.961-0.982; p = 0.000) and ratio of APO-A1/APOB (OR = 0.212, 95%CI 0.078-0.567; p = 0.002) were significantly associated with decreasing the odds ratio of DSPN in T2DM. The levels of inflammatory biomarkers such as INF-γ, IL-1β, IL-4, IL- 6 were increased in patients with severe DSPN in T2DM. Ages, duration of diabetes as well as high circulating levels of IL-6, IL-1β, TC, LDL-C and Cystatin C were positively associated with DSPN in T2DM. Conversely, the level of IGF-1 and the ratio of APOA1/APOB were independent protective factors for DSPN in T2DM. Our results emphasize the importance of addressing issues related to inflammatory biomarkers, lipids and early impaired renal function in T2DM with DSPN, as these may be of potential relevance for deteriorating DSPN. Show less
📄 PDF DOI: 10.1186/s12883-025-04379-y
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Daibao Peng, Fei Chen, Haixuan Sun +1 more · 2025 · Scientific reports · Nature · added 2026-04-24
Thrombosis is a life-threatening complication in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. This study aims to conduct a statistical analysis of the incidence of blood clo Show more
Thrombosis is a life-threatening complication in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. This study aims to conduct a statistical analysis of the incidence of blood clots and lipid concentrations, and to examine the networks of oxylipins in hospitalised patients with SARS-CoV-2. Serum samples of 1731 hospitalised patients with SARS-COV-2 were used to measure six lipid parameters: total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A (apoA), and apolipoprotein B (apoB). Additionally, the lipid profiles and oxidative lipidomics characteristics were examined via liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-MS/MS) in SARS-COV-2-positive patients with and without thrombosis. The mortality rate in the SARS-COV-2 thrombosis group was significantly higher at 29.6% compared to the SARS-COV-2 non-thrombosis group at 12.1% (P < 0.0001). The levels of the lipid parameters were closely associated with both thrombosis and SARS-COV-2 severity. Patients with SARS-COV-2 admitted to the hospital exhibited significant changes in oxidative lipid metabolites, specifically in the arachidonic acid (ARA) and docosahexaenoic acid (DHA) classes, compared with those in the control group. Among the thrombus group, 28 oxidative lipid metabolites were found to be differentially expressed compared to the non-thrombus group, and with the most notable variations observed in 20-hydroxyPGF2α and 14(15)-EpETE. Enrichment analysis using KEGG revealed that differential oxidized lipid metabolites mainly concentrated in the ARA and serotonergic synapses metabolism signaling pathway. Our findings indicate a close association between lipid mediators and both SARS-COV-2 and thrombi. Specifically, ARA and serotonergic synapses metabolism signaling pathway may be an important pathogenic factor for thrombosis caused by SARS-COV-2. Furthermore, 20-hydroxyPGF2α and 14(15)-EpETE show promise as potential biomarkers for SARS-CoV-2-induced thrombosis. Show less
📄 PDF DOI: 10.1038/s41598-025-17722-7
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Qin Jiang, Tao Yang, Hao Yang +9 more · 2025 · Biomolecules · MDPI · added 2026-04-24
(1) Objective: This study aimed to systematically elucidate the molecular mechanisms by which gypenosides (GP), a major active component of
📄 PDF DOI: 10.3390/biom15081205
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Chengrong Wu, Qian Pu, Yalin Zou +5 more · 2025 · Scientific reports · Nature · added 2026-04-24
Calcific aortic valve stenosis (CAVS) is steadily rising worldwide with no effective pharmacological agents available. Observational studies implicated dyslipidaemia as a risk factor for CAVS. Whether Show more
Calcific aortic valve stenosis (CAVS) is steadily rising worldwide with no effective pharmacological agents available. Observational studies implicated dyslipidaemia as a risk factor for CAVS. Whether dyslipidaemia is causative for CAVS and the therapeutic potential of different lipid-modifying drug targets for CAVS treatment remains unclear. We appraised the relationship of genetically-proxied lipid traits and 12 lipid-modifying drug targets with CAVS risk using Mendelian randomization (MR). Genetic variants associated with lipid traits and variants in genes encoding lipid-modifying drug targets were retrieved from GLGC. Summary-level data for CAVS were obtained from the TARGET consortium and FinnGen. Validation analyses were performed using genetic instruments retrieved from liver-derived gene expression and circulation plasma levels of targets. Colocalisation and mediation analyses were performed to evaluate the robustness of our findings and explore potential mediators (i.e., lipoprotein a (Lp(a)), body mass index, apolipoprotein B (ApoB)). The MR analyses supported that total cholesterol and LDL-cholesterol level were independent causal risk factors. The drug-target MR analysis suggested that genetic mimicry of PCSK9 inhibition should reduce CAVS risk (OR = 0.63, 95% CI = 0.56-0.70), which was corroborated by colocalisation analysis. Secondary analyses supported a genetically proxied effect of liver-specific PCSK9 expression (OR = 0.94 per SD reduction in PCSK9 expression, 95% CI = 0.88-1.00) and circulating plasma levels of PCSK9 (OR = 0.86 per SD reduction in PCSK9 protein, 95% CI = 0.83-0.88) on CAVS risk. ApoB and Lp(a) mediated 55.9% and 4.5%, respectively, of the total effect of PCSK9 on CAVS risk. Multiple sensitivity analyses supported this observation. Our study supports total cholesterol, LDL-cholesterol as a causal factor for CAVS, and genetically proxied inhibition of PCSK9 may reduced its risk. Show less
📄 PDF DOI: 10.1038/s41598-025-15525-4
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Wenhui Wu, Chengcheng Wang, Tao Zhang +12 more · 2025 · Journal of ethnopharmacology · Elsevier · added 2026-04-24
In Traditional Chinese Medicine (TCM), dampness is a pathogenic factor arising from impaired production and transportation of bodily fluids. While Fuling Zexie decoction (FLZXD) has demonstrated thera Show more
In Traditional Chinese Medicine (TCM), dampness is a pathogenic factor arising from impaired production and transportation of bodily fluids. While Fuling Zexie decoction (FLZXD) has demonstrated therapeutic efficacy in dampness constitution (DC) treatment, the material basis underlying its constitutional modulatory effects remains unclear. This study proposes objective indicators for the differentiation and therapeutic evaluation of DC and elucidates the material basis of FLZXD in DC treatment. Serum exosome proteomic profiling was conducted across two independent cohorts to identify DC-related indicators and assess the therapeutic efficacy of FLZXD in DC-associated hyperlipidemia (DC-hyperlipidemia). The bioactive compounds of FLZXD were prioritized through a comprehensive analysis of patent documentation and network pharmacology, with subsequent validation of DC-related targets using enzyme-linked immunosorbent assay (ELISA). Proteomic analysis of serum exosomes revealed signatures that differentiate individuals with a balanced constitution (BC) from those with DC. The differentially expressed proteins (DEPs) were enriched predominantly in pathways related to the complement cascade and cardiovascular diseases. FLZXD demonstrated therapeutic efficacy against DC-hyperlipidemia, as evidenced by the reversal of DEPs expression following treatment, which was supported by the patentable findings and network pharmacology analysis. Through experimental validation and pharmacological evidence, the active herbs of FLZXD (Fuling, Zexie and Baizhu, collectively referred to as FZB) were identified, and a total of 73 putative therapeutic targets involved in the dampness-resolving effects of FZB were revealed. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment further confirmed that FLZXD exerts its anti-dampness effects primarily through regulation of the complement and coagulation cascades. Among eight candidate indicators specifically associated with DC, four proteins were validated via ELISA, indicating potential utility for the differentiation of DC. The sensitivity (%), specificity (%), fold change (FC), p-value, and area under the curve (AUC) for each indicator were as follows: apolipoprotein B-100 (APOB) (100.00, 80.00, 0.63, 0.0051, 0.94), complement factor H-related protein 1 (CFHR1) (90.00, 100.00, 0.55, 0.0001, 0.98), alpha-1-acid glycoprotein 1 (ORM1) (100.00, 80.00, 0.71, 0.0043, 0.92), and pigment epithelium-derived factor (SERPINF1) (90.00, 70.00, 0.66, 0.0002, 0.87). The integrative approach, combining proteomic profiling, network pharmacology analysis, and clinical validation, establishes an integrative approach for research on TCM constitutions. This approach provides (1) molecular insights into the differentiation of DC, (2) a foundation for mechanism-based, targeted therapeutic strategies, and (3) enhanced patient stratification to support personalized treatment approaches. Show less
no PDF DOI: 10.1016/j.jep.2025.120353
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