👤 Chian-Feng Chen

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2981
Articles
1996
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Also published as: Wen-Chau Chen, Jingzhao Chen, Dexi Chen, Haifeng Chen, Chung-Jen Chen, Bo-Jun Chen, Gao-Feng Chen, Changyan Chen, Weiwei Chen, Fenghua Chen, Xiaojiang S Chen, Xiu-Juan Chen, Jung-Sheng Chen, Xiao-Ying Chen, Chong Chen, Junyang Chen, YiPing Chen, Xiaohan Chen, Li-Zhen Chen, Jiujiu Chen, Shin-Wen Chen, Guangping Chen, Dapeng Chen, Ximei Chen, Renwei Chen, Jianfei Chen, Yulu Chen, Yu-Chi Chen, Jia-De Chen, Rongfang Chen, She Chen, Zetian Chen, Tianran Chen, Emily Chen, Baoxiang Chen, Ya-Chun Chen, Dongxue Chen, Wei-xian Chen, Danmei Chen, Ceshi Chen, Junling Chen, Xia Chen, Daoyuan Chen, Yongbin Chen, Chi-Yu Chen, Dian Chen, Xiuxiu Chen, Bo-Fang Chen, Fangyuan Chen, Jin-An Chen, Xiaojuan Chen, Zhuohui Chen, Junqi Chen, Lina Chen, Fangfang Chen, Hanwen Chen, Yilei Chen, Po-Han Chen, Xiaoxiang Chen, Jimei Chen, Guochong Chen, Yanyun Chen, Yifei Chen, Cheng-Yu Chen, Zi-Jiang Chen, Jiayuan Chen, Miaoran Chen, Junshi Chen, Yu-Ying Chen, Pengxiang Chen, Hui-Ru Chen, Yupeng Chen, Ida Y-D Chen, Xiaofeng Chen, Qiqi Chen, Shengnan Chen, Mao-Yuan Chen, Lizhu Chen, Weichan Chen, Xiang-Bin Chen, Hanxi Chen, Sulian Chen, Zoe Chen, Minghong Chen, Chi Chen, Yananlan Chen, Yanzhu Chen, Shiyi Chen, Ze-Xu Chen, Zhiheng Chen, Jia-Mei Chen, Shuqin Chen, Yi-Hau Chen, Danni Chen, Donglong Chen, Xiaomeng Chen, Yidong Chen, Keyu Chen, Hao Chen, Junmin Chen, Wenlong Chen, Yufei Chen, Wanbiao Chen, Mo Chen, Youjia Chen, Xin-Jie Chen, Lanlan Chen, Huapu Chen, Shuaiyin Chen, Jing-Hsien Chen, Hengsheng Chen, Bing-Bing Chen, Fa-Xi Chen, Zhiqiang Chen, Ming-Huang Chen, Liangkai Chen, Li-Jhen Chen, Zhi-Hao Chen, Yinzhu Chen, Guanghong Chen, Gaozhi Chen, Jiakang Chen, Yongke Chen, Guangquan Chen, Li-Hsien Chen, Yiduo Chen, Zongnan Chen, Jing Chen, Meilan Chen, Jin-Shuen Chen, Huanxiong Chen, Yann-Jang Chen, Guozhong Chen, Yu-Bing Chen, Xiaobin Chen, Catherine Qing Chen, Youhu Chen, Hui Mei Chen, L F Chen, Haiyang Chen, Ruilin Chen, Peng Chen, Kailang Chen, Chao Chen, Suipeng Chen, Zemin Chen, Jianlin Chen, Shang-Chih Chen, Yen-Hsieh Chen, Jia-Lin Chen, Chaojin Chen, Minglang Chen, Xiatian Chen, Zeyu Chen, Kang Chen, Mei-Chi Chen, Jihai Chen, Pei Chen, Defang Chen, Zhao Chen, Tianrui Chen, Tingtao Chen, Caressa Chen, Jiwei Chen, Xuerong Chen, Yizhi Chen, XueShu Chen, Mingyue Chen, Huichao Chen, Chun-Chi Chen, Xiaomin Chen, Hetian Chen, Yuxing Chen, Jie-Hua Chen, Chuck T Chen, Yuanjia Chen, Hong Chen, Jianxiong Chen, S Chen, D M Chen, Jiao-Jiao Chen, Gongbo Chen, Xufeng Chen, Xiao-Jun Chen, Harn-Shen Chen, Qiu Jing Chen, Tai-Heng Chen, Pei-Lung Chen, Kaifu Chen, Huang-Pin Chen, Tse-Wei Chen, Yanrong Chen, Xianfeng Chen, Chung-Yung Chen, Yuelei Chen, Qili Chen, Guanren Chen, TsungYen Chen, Yu-Si Chen, Junsheng Chen, Min-Jie Chen, Xin-Ming Chen, Jiabing Chen, Sili Chen, Qinying Chen, Yue Chen, Lin Chen, Xiaoli Chen, Zhuo Chen, Aoshuang Chen, Junyu Chen, Chunji Chen, Yian Chen, Shanchun Chen, Shuen-Ei Chen, Canrong Chen, Shih-Jen Chen, Yaowu Chen, Han Chen, Yih-Chieh Chen, Wei-Cong Chen, Yanfen Chen, Tao Chen, Huangtao Chen, Jingyi Chen, Sheng Chen, Jing-Wen Chen, Gao Chen, Lei-Lei Chen, Kecai Chen, Yao-Shen Chen, Haiyu Chen, W Chen, Xiaona Chen, Cheng-Sheng Chen, X R Chen, Shuangfeng Chen, Jingyuan Chen, Xinyuan Chen, Huanhuan Chen, Mengling Chen, Liang-Kung Chen, Ming-Huei Chen, Hongshan Chen, Cuncun Chen, Qingchao Chen, Yanzi Chen, Lingli Chen, Shiqian Chen, Liangwan Chen, Lexia Chen, Wei-Ting Chen, Zhencong Chen, Tzy-Yen Chen, Mingcong Chen, Honglei Chen, Yuyan Chen, Huachen Chen, Yu Chen, Li-Juan Chen, Aozhou Chen, Xinlin Chen, Wai Chen, Dake Chen, Bo-Sheng Chen, Meilin Chen, Kequan Chen, Hong Yang Chen, Yan Chen, Bowei Chen, Silian Chen, Jian Chen, Yongmei Chen, Ling Chen, Jinbo Chen, Yingxi Chen, Ge Chen, Max Jl Chen, C Z Chen, Weitao Chen, Xiaole L Chen, Yonglu Chen, Shih-Pin Chen, Jiani Chen, Huiru Chen, San-Yuan Chen, Bing Chen, Xiao-ping Chen, Feiyue Chen, Shuchun Chen, Zhaolin Chen, Qianxue Chen, Xiaoyang Chen, Bowang Chen, Yinghui Chen, Ting-Ting Chen, Xiao-Yang Chen, Chi-Yuan Chen, Zhi-zhe Chen, Ting-Tao Chen, Xiaoyun Chen, Min-Hsuan Chen, Kuan-Ting Chen, Yongheng Chen, Wenhao Chen, Shengyu Chen, Kai Chen, Yueh-Peng Chen, Guangju Chen, Minghua Chen, Hong-Sheng Chen, Qingmei Chen, Song-Mei Chen, Limei Chen, Yuqi Chen, Yuyang Chen, Yang-Ching Chen, Yu-Gen Chen, Peizhan Chen, Rucheng Chen, Jin-Xia Chen, Szu-Chieh Chen, Xiaojun Chen, Jialing Chen, Heni Chen, Yi Feng Chen, Sen Chen, Alice Ye A Chen, Wen Chen, Han-Chun Chen, Dawei Chen, Fangli Chen, Ai-Qun Chen, Zhaojun Chen, Gong Chen, Yishan Chen, Zhijing Chen, Qiuxuan Chen, Miao-Der Chen, Fengwu Chen, Weijie Chen, Weixin Chen, Mei-Ling Chen, Hung-Po Chen, Rui-Pei Chen, Nian-Ping Chen, Tielin Chen, Canyu Chen, Xiaotao Chen, Nan Chen, C Chen, Juanjuan Chen, Xinan Chen, Jiaping Chen, Xiao-Lin Chen, Jianping Chen, Yayun Chen, Le Qi Chen, Jen-Sue Chen, Mechi Chen, Miao-Yu Chen, Zhou Chen, Szu-Han Chen, Zhen Bouman Chen, Baihua Chen, Qingao Chen, Shao-Ke Chen, Feng Chen, Jiawen Chen, Lianmin Chen, Sifeng Chen, Mengxia Chen, Xueli Chen, Can Chen, Yibo Chen, Zinan Chen, Lei-Chin Chen, Carol Chen, Yanlin Chen, Zihang Chen, Zaozao Chen, Haiqin Chen, Lu Hua Chen, Zhiyuan Chen, Meiyu Chen, Du-Qun Chen, Keying Chen, Naifei Chen, Peixian Chen, Jin-Ran Chen, Yijun Chen, Yulin Chen, Fumei Chen, Zhanfei Chen, Zhe-Yu Chen, Xin-Qi Chen, Valerie Chen, Ru Chen, Mengqing Chen, Runsheng Chen, Tong Chen, Tan-Zhou Chen, Suet Nee Chen, Cuicui Chen, Yifan Chen, Tian Chen, XiangFan Chen, Lingyi Chen, Hsiao-Yun Chen, Kenneth L Chen, Ni Chen, Huishan Chen, Fang-Yu Chen, Ken Chen, Yongshen Chen, Qiong Chen, Mingfeng Chen, Shoudeng Chen, Qiao Chen, Qian Chen, Yuebing Chen, Xuehua Chen, Chang-Lan Chen, Min-Hu Chen, Hongbin Chen, Jingming Chen, Qing Chen, Yu-Fan Chen, Hao-Zhu Chen, Yunjia Chen, Zhongjian Chen, Mingyi Chen, Qianping Chen, Huaxin Chen, Dong-Mei Chen, Peize Chen, Leijie Chen, Ming-Yu Chen, Jiaxuan Chen, Xiao-chun Chen, Wei-Min Chen, Ruisen Chen, Xuanwei Chen, Guiquan Chen, Minyan Chen, Feng-Ling Chen, Yili Chen, Alvin Chen, Xiaodong Chen, Bohong Chen, Chih-Ping Chen, Xuanjing Chen, Shuhui Chen, Ming-Hong Chen, Tzu-Yu Chen, Brian Chen, Bowen Chen, Kai-En Chen, Szu-Chia Chen, Guangchun Chen, Fang Chen, Chuyu Chen, Haotian Chen, Xiaoting Chen, Shaoliang Chen, Chun-Houh Chen, Shali Chen, Yu-Cheng Chen, Zhijun Chen, B Chen, Yuan Chen, Zhanglin Chen, Chaoran Chen, Xing-Long Chen, Zhinan Chen, Yu-Hui Chen, Yuquan Chen, Andrew Chen, Fengming Chen, Guangyong Chen, Jun Chen, Wenshuo Chen, Yi-Guang Chen, Jing-Yuan Chen, Kuangyang Chen, Mingyang Chen, Shaofei Chen, Weicong Chen, Gonghai Chen, Di-Long Chen, Limin Chen, Jishun Chen, Yunfei Chen, Caihong Chen, Tongsheng Chen, Ligang Chen, Wenqin Chen, Shiyu Chen, Xiaoyong Chen, Christina Y Chen, Yushan Chen, Ginny I Chen, Guo-Jun Chen, Xianzhen Chen, Wanling Chen, Kuan-Jen Chen, Maorong Chen, Kaijian Chen, Erqu Chen, Shen Chen, Quan Chen, Zian Chen, Yi-Lin Chen, Juei-Suei Chen, Yi-Ting Chen, Huaiyong Chen, Minjian Chen, Qianzhi Chen, Jiahao Chen, Xikun Chen, Juan-Juan Chen, Xiaobo Chen, Tianzhen Chen, Ziming Chen, Qianbo Chen, Jindong Chen, Jiu-Chiuan Chen, Yinwei Chen, Carl Pc Chen, Li-Hsin Chen, Jenny Chen, Ruoyan Chen, Yanqiu Chen, Yen-Fu Chen, Haiyan Chen, Zhebin Chen, Si Chen, Jian-Qiao Chen, Yang-Yang Chen, Ningning Chen, Zhifeng Chen, Zhenyi Chen, Hangang Chen, Zihe Chen, Mengdi Chen, Zhichuan Chen, Xu Chen, Huixi Chen, Weitian Chen, Bao-Sheng Chen, Tien-Hsing Chen, Junchen Chen, Yan-yan Chen, Xiangning Chen, Sijia Chen, Xinyan Chen, Kuan-Yu Chen, Qunxiang Chen, Guangliang Chen, Bing-Huei Chen, Fei Xavier Chen, Zhangcheng Chen, Qianming Chen, Xianze Chen, Yanhua Chen, Qinghao Chen, Yanting Chen, Sijuan Chen, Chen-Mei Chen, Qiankun Chen, Jianan Chen, Rong Chen, Xiankai Chen, Kaina Chen, Gui-Hai Chen, Y-D Ida Chen, Quanjiao Chen, Shuang Chen, Lichang Chen, Xinyi Chen, Yong-Jun Chen, Zhaoli Chen, Chunnuan Chen, Jui-Chang Chen, Zhiang Chen, Weirui Chen, Zhenguo Chen, Jennifer F Chen, Zhiguo Chen, Kunmei Chen, Huan-Xin Chen, Mengyan Chen, Dongrong Chen, Siyue Chen, Xianyue Chen, Chien-Lun Chen, YiChung Chen, Guang Chen, Quanwei Chen, Zongming E Chen, Ting-Huan Chen, Michael C Chen, Jinli Chen, Beth L Chen, Yuh-Lien Chen, Peihong Chen, Qiaoling Chen, Jiale Chen, Shufeng Chen, Xiaowan Chen, Xian-Kai Chen, Ling-Yan Chen, Yen-Ling Chen, Guiying Chen, Guangyi Chen, Yuling Chen, Xiangqiu Chen, Haiquan Chen, Cuie Chen, Gui-Lai Chen, R Chen, Heng-Yu Chen, Yongxun Chen, Fuxiang Chen, Mingmei Chen, Hua-Pu Chen, Yulong Chen, Zhitao Chen, Guohua Chen, Cheng-Yi Chen, Hongxu Chen, Yuanhao Chen, Qichen Chen, Hualin Chen, Guo-Rong Chen, Rongsheng Chen, Xuesong Chen, Wei-Fei Chen, Bao-Bao Chen, Anqi Chen, Yi-Han Chen, Ying-Jung Chen, Jinhuang Chen, Guochao Chen, Lei Chen, S N Chen, Songfeng Chen, Chenyang Chen, Xing Chen, Letian Chen, Meng Xuan Chen, Xiang-Mei Chen, Xiaoyan Chen, Yi-Heng Chen, D F Chen, Bang Chen, Jiaxu Chen, Wei Chen, Sihui Chen, Shu-Hua Chen, I-M Chen, Xuxin Chen, Zhangxin Chen, Jin Chen, Yin-Huai Chen, Wuyan Chen, Bingqing Chen, Bao-Fu Chen, Zhen-Hua Chen, Dan Chen, Zhe-Sheng Chen, Ranyun Chen, Wanyin Chen, Xueyan Chen, Xiaoyu Chen, Tai-Tzung Chen, Xiaofang Chen, Yongxing Chen, Yanghui Chen, Hekai Chen, Yuanwei Chen, Liang Chen, Hui-Jye Chen, Chengchun Chen, Han-Bin Chen, Shuaijie Chen, Yibing Chen, Kehui Chen, Shuhai Chen, Xueling Chen, Ying-Jie Chen, Qingxing Chen, Fang-Zhi Chen, Mei-Hua Chen, Yutong Chen, Lixian Chen, Alex Chen, Qiuhong Chen, Qiuxia Chen, Liping Chen, Hou-Tsung Chen, Zhanghua Chen, Chun-Fa Chen, Benjamin P C Chen, Yewei Chen, Mu-Hong Chen, Jianshan Chen, Xiaguang Chen, Meiling Chen, Heng Chen, Ying-Hsiang Chen, Longyun Chen, Dengpeng Chen, Jichong Chen, Shixuan Chen, Liaobin Chen, Everett H Chen, ZhuoYu Chen, Qihui Chen, Zhiyong Chen, Nuan Chen, Hongmei Chen, Guiqian Chen, Yan Q Chen, Fengling Chen, Hung-Chang Chen, Zhenghong Chen, Chengsheng Chen, Hegang Chen, Huei-Yan Chen, Liutao Chen, Meng-Lin Chen, Xi Chen, Qing-Juan Chen, Linna Chen, Xiaojing Chen, Lang Chen, Gengsheng Chen, Fengrong Chen, Weilun Chen, Shi Chen, Wan-Yi Chen, On Chen, Yufeng Chen, Benjamin Chen, Hui-Zhao Chen, Bo-Rui Chen, Kangyong Chen, Ruixiang Chen, Weiyong Chen, Ning-Hung Chen, Meng-Ping Chen, Huimei Chen, Ying Chen, Kang-Hua Chen, Pei-zhan Chen, Liujun Chen, Hanqing Chen, Chengchuan Chen, Guojun Chen, Yongfa Chen, Li Chen, Mingling Chen, Jacinda Chen, Jinlun Chen, Kun Chen, Yi Chen, Chiung Mei Chen, Shaotao Chen, Tianhong Chen, Chanjuan Chen, Yuhao Chen, Huizhi Chen, Chung-Hsing Chen, Qiuchi Chen, Haoting Chen, Luzhu Chen, Huanhua Chen, Long Chen, Jiang-hua Chen, Kai-Yang Chen, Jing-Zhou Chen, Yong-Syuan Chen, Lifang Chen, Ruonan Chen, Meimei Chen, Qingchuan Chen, Liugui Chen, Shaokun Chen, Yi-Yung Chen, Jintian Chen, Xuhui Chen, Dongyan Chen, Huei-Rong Chen, Xianmei Chen, Jinyan Chen, Yuxi Chen, Qingqing Chen, Weibo Chen, Qiwei Chen, Mingxia Chen, Hongmin Chen, Jiahui Chen, Yen-Jen Chen, Zihan Chen, Guozhou Chen, Fei Chen, Zhiting Chen, Denghui Chen, Gary Chen, Hongli Chen, Jack Chen, Zhigang Chen, Lie Chen, Siyuan Chen, Haojie Chen, Qing-Wei Chen, Maochong Chen, Mei-Jie Chen, Haining Chen, Xing-Zhen Chen, Weiqing Chen, Huanchun Chen, C-Y Chen, Tzu-An Chen, Jen-Hau Chen, Xiaojie Chen, Dongquan Chen, Gao B Chen, Daijie Chen, Zixi Chen, Lingfeng Chen, Jiayi Chen, Zan Chen, Shuming Chen, Mei-Hsiu Chen, Xueqin Chen, Huan Chen, Xiaoqing Chen, Hui-Xiong Chen, Ruoying Chen, Deying Chen, Huixian Chen, Zhezhe Chen, Lu Chen, Xiaolong Chen, Si-Yue Chen, Xinwei Chen, Wentao Chen, Yucheng Chen, Jiajing Chen, Allen Menglin Chen, Chixiang Chen, Shiqun Chen, Wenwu Chen, Chin-Chuan Chen, Ningbo Chen, Hsin-Hung Chen, Shenglan Chen, Jia-Feng Chen, Changya Chen, ZhaoHui Chen, Guo Chen, Juhai Chen, Xiao-Quan Chen, Cuimin Chen, Yongshuo Chen, Sai Chen, Fengyang Chen, Siteng Chen, Hualan Chen, Lian Chen, Yuan-Hua Chen, Minjie Chen, Shiyan Chen, Z Chen, Zhengzhi Chen, Jonathan Chen, H Chen, You-Yue Chen, Shu-Gang Chen, Hsuan-Yu Chen, Hongyue Chen, Weiyi Chen, Jiaqi Chen, Chengde Chen, Shufang Chen, Ze-Hui Chen, Xiuping Chen, Zhuojia Chen, Zhouji Chen, Lidian Chen, Yilan Chen, Kuan-Ling Chen, Alon Chen, Zi-Yue Chen, Hongmou Chen, Fang-Zhou Chen, Jianzhou Chen, Wenbiao Chen, Yujie Chen, Zhijian Chen, Zhouqing Chen, Xiuhui Chen, Qingguang Chen, Hanbei Chen, Qianyu Chen, Mengping Chen, Yongqi Chen, Sheng-Yi Chen, Siqi Chen, Yelin Chen, Shirui Chen, Yuan-Tsong Chen, Dongyin Chen, Lingxue Chen, Long-Jiang Chen, Yunshun Chen, Yahong Chen, Yaosheng Chen, Zhonghua Chen, Jingyao Chen, Pei-Yin Chen, Fusheng Chen, Xiaokai Chen, Shuting Chen, Miao-Hsueh Chen, Y-D I Chen, Zijie Chen, Haozhu Chen, Haodong Chen, Xiong Chen, Wenxi Chen, Feng-Jung Chen, Shangwu Chen, Zhiping Chen, Zhang-Yuan Chen, Wentong Chen, Ou Chen, Ruiming Chen, Xiyu Chen, Shuqiu Chen, Xiaoling Chen, Ruimin Chen, Hsiao-Wang Chen, Dongli Chen, Haibo Chen, Yiyun Chen, Luming Chen, Wenting Chen, Chongyang Chen, Qingqiu Chen, Wen-Pin Chen, Yuhui Chen, Lingxia Chen, Jun-Long Chen, Xingyu Chen, Haotai Chen, Bang-dang Chen, Qiuwen Chen, Rui Chen, K C Chen, Zhixuan Chen, Gaoyu Chen, Yitong Chen, Tzu-Ju Chen, Jingqing Chen, Huiqun Chen, Runsen Chen, Michelle Chen, Hanyong Chen, Xiaolin Chen, Ke Chen, Yangchao Chen, Y D I Chen, Jinghua Chen, Jia Wei Chen, Man-Hua Chen, H T Chen, Zheyi Chen, Lihong Chen, Guangyao Chen, Rujun Chen, Ming-Fong Chen, Haiyun Chen, Dexiong Chen, Huiqin Chen, Ching Kit Chen, En-Qiang Chen, Wanjia Chen, Xiangliu Chen, Meiting Chen, Szu-Chi Chen, Yii-der Ida Chen, Jian-Hua Chen, Yanjie Chen, Yingying Chen, Paul Chih-Hsueh Chen, Si-Ru Chen, Mingxing Chen, Rui-Zhen Chen, Changjie Chen, Qu Chen, Yintong Chen, Jingde Chen, Mao Chen, Xinghai Chen, Mei-Chih Chen, Xueqing Chen, Chun-An Chen, Cheng Chen, Ruijing Chen, Huayu Chen, Yunqin Chen, Yan-Gui Chen, Ruibing Chen, Size Chen, Qi-An Chen, Yuan-Zhen Chen, J Chen, Heye Chen, T Chen, Junpeng Chen, Tan-Huan Chen, Shuaijun Chen, Hao Yu Chen, Fahui Chen, Lan Chen, Dong-Yi Chen, Xianqiang Chen, Shi-Sheng Chen, Qiao-Yi Chen, Pei-Chen Chen, Xueying Chen, Yi-Wen Chen, Guohong Chen, Zhiwei Chen, Zuolong Chen, Erfei Chen, Yuqing Chen, Zhenyue Chen, Qiongyun Chen, Jianghua Chen, Yingji Chen, Xiuli Chen, Xiaowei Chen, Hengyu Chen, Sheng-Xi Chen, Haiyi Chen, Shao-Peng Chen, Yi-Ru Chen, Zhaoran Chen, Xiuyan Chen, Jinsong Chen, Sunny Chen, Xiaolan Chen, S-D Chen, Ruofan Chen, Qiujing Chen, Yun Chen, Wei-Cheng Chen, Chun-Wei Chen, Liechun Chen, Lulu Chen, Hsiu-Wen Chen, Yanping Chen, Jiayao Chen, Xuejiao Chen, Guan-Wei Chen, Yusi Chen, Yijiang Chen, Chi-Hua Chen, Qixian Chen, Ziqing Chen, Peiyou Chen, Chunhai Chen, Zheren Chen, Qiuyun Chen, Xiaorong Chen, Chaoqun Chen, Dan-Dan Chen, Xuechun Chen, Yafang Chen, Mystie X Chen, Jina Chen, Wei-Kai Chen, Yule Chen, Bo Chen, Kaili Chen, Junqin Chen, Jia Min Chen, Chen Chen, Guoliang Chen, Xiaonan Chen, Guangjie Chen, Xiao Chen, Jeanne Chen, Danyang Chen, Minjiang Chen, Jiyuan Chen, Zheng-Zhen Chen, Shou-Tung Chen, Ouyang Chen, Xiu Chen, H Q Chen, Peiyu Chen, Yuh-Min Chen, Youmeng Chen, Shuoni Chen, Peiqin Chen, Xinji Chen, Chih-Ta Chen, Shang-Hung Chen, Robert Chen, Suet N Chen, Yun-Tzu Chen, Suming Chen, Ye Chen, Yao Chen, Yi-Fei Chen, Ruixue Chen, Tianhang Chen, Suning Chen, Jingnan Chen, Xiaohong Chen, Kun-Chieh Chen, Tuantuan Chen, Mei Chen, He-Ping Chen, Zhi Bin Chen, Yuewu Chen, Mengying Chen, Po-See Chen, Xue Chen, Jian-Jun Chen, Xiyao Chen, Jeremy J W Chen, Jiemei Chen, Daiwen Chen, Christina Yingxian Chen, Qinian Chen, Chih-Wei Chen, Wensheng Chen, Yingcong Chen, Zhishi Chen, Duo Chen, Jiansu Chen, Keping Chen, Min Chen, Yi-Hui Chen, Yun-Ju Chen, Gaoyang Chen, Renjin Chen, Kui Chen, Shuai-Ming Chen, Hui-Fen Chen, Zi-Yun Chen, Shao-Yu Chen, Meiyang Chen, Jiahua Chen, Zongyou Chen, Yen-Rong Chen, Huaping Chen, Yu-Xin Chen, Bohe Chen, Kehua Chen, Zilin Chen, Zhang-Liang Chen, Ziqi Chen, Yinglian Chen, Hui-Wen Chen, Peipei Chen, Baolin Chen, Zugen Chen, Kangzhen Chen, Yanhan Chen, Sung-Fang Chen, Zheping Chen, Zixuan Chen, Jiajia Chen, Yuanjian Chen, Lili Chen, Xiangli Chen, Ban Chen, Yuewen Chen, X Chen, Yan-Qiong Chen, Chider Chen, Yung-Hsiang Chen, Hanlin Chen, Xiangjun Chen, Haibing Chen, Le Chen, Xuan Chen, Xue-Ying Chen, Zexiao Chen, Chen-Yu Chen, Zhe-Ling Chen, Fan Chen, Hsin-Yi Chen, Feilong Chen, Zilong Chen, Yi-Jen Chen, Zhiyun Chen, Ning Chen, Wenxu Chen, Chuanbing Chen, Yaxi Chen, Yi-Hong Chen, Eleanor Y Chen, Yuexin Chen, Kexin Chen, Shoujun Chen, Yen-Ju Chen, Yu-Chuan Chen, Yen-Teen Chen, Bao-Ying Chen, Xiaopeng Chen, Danli Chen, Katharine Y Chen, Jingli Chen, Qianyi Chen, Zihua Chen, Ya-xi Chen, Xuanxu Chen, Chung-Hung Chen, Yajie Chen, Cindi Chen, Hua Chen, Shuliang Chen, Elizabeth H Chen, Gen-Der Chen, Bingyu Chen, Keyang Chen, Siyu S Chen, Xinpu Chen, Yau-Hung Chen, Hsueh-Fen Chen, Han-Hsiang Chen, Wei Ning Chen, Guopu Chen, Zhujun Chen, Yurong Chen, Yuxian Chen, Wanjun Chen, Qiu-Jing Chen, Qifang Chen, Yuhan Chen, Jingshen Chen, Zhongliang Chen, Ching-Hsuan Chen, Zhaoyao Chen, Yongning Chen, Marcus Y Chen, Ping Chen, Junfei Chen, Yung-Wu Chen, Xueting Chen, Yingchun Chen, Wan-Yan Chen, Yuxin Chen, Yisheng Chen, Chun-Yuan Chen, Yulian Chen, Yan-Jun Chen, Guoxun Chen, Ding Chen, Yu-Fen Chen, Jason A Chen, Shuyi Chen, Cuilan Chen, Ruijuan Chen, Kevin Chen, Xuanmao Chen, Shen-Ming Chen, Ya-Nan Chen, Sean Chen, Zhaowei Chen, Xixi Chen, Yu-Chia Chen, Xuemin Chen, Binlong Chen, Weina Chen, Xuemei Chen, Di Chen, P P Chen, Yubin Chen, Chunhua Chen, Li-Chieh Chen, Ping-Chung Chen, Zhihao Chen, Xinyang Chen, Chan Chen, Yan Jie Chen, Shi-Qing Chen, Ivy Xiaoying Chen, Ying-Cheng Chen, Jia-Shun Chen, Shao-Wei Chen, Aiping Chen, Dexiang Chen, Qianfen Chen, Hongyu Chen, Wei-Kung Chen, Danlei Chen, Hongen Chen, Shipeng Chen, Jake Y Chen, Dongsheng Chen, Chien-Ting Chen, Shouzhen Chen, Hehe Chen, Yu-Tung Chen, Yilin Chen, Joy J Chen, Zhong Chen, Zhenfeng Chen, Zhongzhu Chen, Feiyang Chen, Xingxing Chen, Keyan Chen, Huimin Chen, Guanyu Chen, D. Chen, Dianke Chen, Zhigeng Chen, Sien-Tsong Chen, Yii-Der Chen, Chi-Yun Chen, Beidong Chen, Wu-Xian Chen, Zhihang Chen, Yuanqi Chen, Jianhua Chen, Xian Chen, Xiangding Chen, Jingteng Chen, Shuaiyu Chen, Xue-Mei Chen, Yu-Han Chen, Hongqiao Chen, Weili Chen, Yunzhu Chen, Guo-qing Chen, Miao Chen, Zhi Chen, Junhui Chen, Jing-Xian Chen, Zhiquan Chen, Shuhuang Chen, Shaokang Chen, Irwin Chen, Xiang Chen, Chuo Chen, Siting Chen, Keyuan Chen, Xia-Fei Chen, Zhihai Chen, Yuanyu Chen, Po-Sheng Chen, Qingjiang Chen, Yi-Bing Chen, Rongrong Chen, Katherine C Chen, Shaoxing Chen, Lifen Chen, Luyi Chen, Sisi Chen, Ning-Bo Chen, Yihong Chen, Guanjie Chen, Li-Hua Chen, Xiao-Hui Chen, Ting Chen, Chun-Han Chen, Xuzhuo Chen, Junming Chen, Zheng Chen, Wen-Jie Chen, Bingdi Chen, Jiang Ye Chen, Yanbin Chen, Duoting Chen, Shunyou Chen, Shaohua Chen, Jien-Jiun Chen, Jiaohua Chen, Shaoze Chen, Yifang Chen, Chiqi Chen, Yen-Hao Chen, Rui-Fang Chen, Hung-Sheng Chen, Kuey Chu Chen, Y S Chen, Xijun Chen, Chaoyue Chen, Heng-Sheng Chen, Lianfeng Chen, Yen-Ching Chen, Yuhong Chen, Yixin Chen, Yuanli Chen, Cancan Chen, Yanming Chen, Yajun Chen, Chaoping Chen, F-K Chen, Menglan Chen, Zi-Yang Chen, Yongfang Chen, Hsin-Hong Chen, Hongyan Chen, Chao-Wei Chen, Jijun Chen, Xiaochun Chen, Yazhuo Chen, Zhixin Chen, YongPing Chen, Jui-Yu Chen, Mian-Mian Chen, Liqiang Chen, Y P Chen, D-F Chen, Jinhao Chen, Yanyan Chen, Chang-Zheng Chen, Shao-long Chen, Guoshun Chen, Lo-Yun Chen, Yen-Lin Chen, Bingqian Chen, Dafang Chen, Yi-Chung Chen, Liming Chen, Qiuli Chen, Shuying Chen, Chih-Mei Chen, Renyu Chen, Wei-Hao Chen, Lihua Chen, Hang Chen, Hai-Ning Chen, Hu Chen, Yu-Fu Chen, Yalan Chen, Wan-Tzu Chen, Benjamin Jieming Chen, Yingting Chen, Jiacai Chen, Ning-Yuan Chen, Shuo-Bin Chen, Yu-Ling Chen, Jian-Kang Chen, Hengsan Chen, Yu-Ting Chen, Y Chen, Qingjie Chen, Jiong Chen, Chaoyi Chen, Yunlin Chen, Gang Chen, Hui-Chun Chen, Li-Tzong Chen, Zhangliang Chen, Qiangpu Chen, Xianbo Chen, Jinxuan Chen, Hebing Chen, Ran Chen, Zhehui Chen, Carol X-Q Chen, Yuping Chen, Xiangyu Chen, Xinyu Chen, Qianyun Chen, Junyi Chen, B-S Chen, Zhesheng Chen, Man Chen, Dali Chen, Danyu Chen, Huijiao Chen, Naisong Chen, Qitong Chen, Chueh-Tan Chen, Kai-Ming Chen, Jiarou Chen, Huang Chen, Chunjie Chen, Weiping Chen, Po-Min Chen, Guang-Chao Chen, Danxia Chen, Youran Chen, Chuanzhi Chen, Peng-Cheng Chen, Wen-Tsung Chen, Linxi Chen, Si-guo Chen, Zike Chen, Zhiyu Chen, Wanting Chen, Jiangxia Chen, Wenhua Chen, Roufen Chen, Shi-You Chen, Fang-Pei Chen, Chu Chen, Feifeng Chen, Chunlin Chen, Yunwei Chen, Wenbing Chen, Xuejun Chen, Meizhen Chen, Li Jia Chen, Tianhua Chen, Xiangmei Chen, Kewei Chen, Yuh-Ling Chen, Dejuan Chen, Jiyan Chen, Xinzhuo Chen, Yue-Lai Chen, Hsiao-Jou Cortina Chen, Weiqin Chen, Huey-Miin Chen, Elizabeth Suchi Chen, Kai-Ting Chen, Lizhen Chen, Xiaowen Chen, Chien-Yu Chen, Lingjun Chen, Gonglie Chen, Jiao Chen, Zhuo-Yuan Chen, Wei-Peng Chen, Xiangna Chen, Jiade Chen, Lanmei Chen, Siyu Chen, Kunpeng Chen, Hung-Chi Chen, Jia Chen, Shuwen Chen, Siqin Chen, Zhenlei Chen, Wen-Yi Chen, Si-Yuan Chen, Yidan Chen, Tianfeng Chen, Fu Chen, Leqi Chen, Jiamiao Chen, Shasha Chen, Qingyi Chen, Ben-Kuen Chen, Haitao Chen, Qi Chen, Yihao Chen, Yunfeng Chen, Elizabeth S Chen, Yiming Chen, Youwei Chen, Lichun Chen, Yanfei Chen, Hongxing Chen, Muh-Shy Chen, Yingyu Chen, Weihong Chen, Ming Chen, Kelin Chen, Duan-Yu Chen, Shi-Yi Chen, Shih-Yu Chen, Yanling Chen, Shuanghui Chen, Ya Chen, Yusheng Chen, Yuting Chen, Shiming Chen, Xinqiao Chen, Hongbo Chen, Mien-Cheng Chen, Jiacheng Chen, Herbert Chen, Ji-ling Chen, Sun Chen, Chen-Sheng Chen, Na Chen, Chih-Yi Chen, Wenfang Chen, Yii-Der I Chen, Qinghua Chen, Shuai Chen, Hsi-Hsien Chen, F Chen, Guo-Chong Chen, Zhe Chen, Beijian Chen, Roger Chen, You-Ming Chen, Hongzhi Chen, Zhen-Yu Chen, Xianxiong Chen, Chang Chen, Chujie Chen, Chuannan Chen, Kan Chen, Lu-Biao Chen, Yupei Chen, Qiu-Sheng Chen, Shangduo Chen, Yuan-Yuan Chen, Yundai Chen, Binzhen Chen, Cai-Long Chen, Yen-Chen Chen, Xue-Xin Chen, Yanru Chen, Chunxiu Chen, Yifa Chen, Xingdong Chen, Ruey-Hwa Chen, Shangzhong Chen, Ching-Wen Chen, Danna Chen, Jingjing Chen, Yafei Chen, Dandan Chen, Pei-Yi Chen, Shan Chen, Guanghao Chen, Longqing Chen, Yen-Cheng Chen, Zhanjuan Chen, Jinguo Chen, Zhongxiu Chen, Rui-Min Chen, Shunde Chen, Xun Chen, Jianmin Chen, Linyi Chen, Ying-Ying Chen, Chien-Hsiun Chen, Li-Nan Chen, Yu-Ming Chen, Qianqian Chen, Xue-Yan Chen, Shengdi Chen, Huali Chen, Xinyue Chen, Ching-Yi Chen, Honghai Chen, Baosheng Chen, Pingguo Chen, Yike Chen, Yuxiang Chen, Qing-Hui Chen, Yuanwen Chen, Yongming Chen, Zongzheng Chen, Ruiying Chen, Huafei Chen, Tingen Chen, Zhouliang Chen, Shih-Yin Chen, Shanyuan Chen, Yiyin Chen, Feiyu Chen, Zitao Chen, Constance Chen, Zhoulong Chen, Haide Chen, Jiang Chen, Ray-Jade Chen, Shiuhwei Chen, Chih-Chieh Chen, Chaochao Chen, Lijuan Chen, Qianling Chen, Jian-Min Chen, Xihui Chen, Yuli Chen, Wu-Jun Chen, Diyun Chen, Alice P Chen, Jingxuan Chen, Chiung-Mei Chen, Shibo Chen, M L Chen, Lena W Chen, Xiujuan Chen, Christopher S Chen, Yeh Chen, Xingyong Chen, Feixue Chen, Boyu Chen, Weixian Chen, Tingting Chen, Bosong Chen, Junjie Chen, Han-Min Chen, Szu-Yun Chen, Qingliang Chen, Huatao Chen, Bin Chen, L B Chen, Xuanyi Chen, Chun Chen, Dong Chen, Yinjuan Chen, Jiejian Chen, Lu-Zhu Chen, Alex F Chen, Pei-Chun Chen, Chien-Jen Chen, Y M Chen, Xiao-Chen Chen, Tania Chen, Yang Chen, Yangxin Chen, Mark I-Cheng Chen, Haiming Chen, Shuo Chen, Yong Chen, Hsiao-Tan Chen, Erzhen Chen, Jiaye Chen, Fangyan Chen, Guanzheng Chen, Haoyun Chen, Jiongyu Chen, Baofeng Chen, Yuqin Chen, Juan Chen, Haobo Chen, Shuhong Chen, Fu-Shou Chen, Wei-Yu Chen, Haw-Wen Chen, Feifan Chen, Deqian Chen, Linlin Chen, Xiaoshan Chen, Hui Chen, Wenwen Chen, Yanli Chen, Yuexuan Chen, Xiaoyin Chen, Yen-Chang Chen, Tiantian Chen, Ruiai Chen, Alice Y Chen, Jinglin Chen, Zifan Chen, Wantao Chen, Shanshan Chen, Jianjun Chen, Xiaoyuan Chen, Xuefei Chen, Runfeng Chen, Weisan Chen, Guangnan Chen, Junpan Chen, An Chen, Lankai Chen, Yiding Chen, Tianpeng Chen, Ya-Ting Chen, Lijin Chen, Ching-Yu Chen, Y Eugene Chen, Guanglong Chen, Rongyuan Chen, Yali Chen, Yanan Chen, Liyun Chen, Shuai-Bing Chen, Zhixue Chen, Xiaolu Chen, Xiao-he Chen, Hongxiang Chen, Bing-Feng Chen, Gary K Chen, Xiaohui Chen, Jin-Wu Chen, Qiuxiang Chen, Huaqiu Chen, X Steven Chen, Xiaoqian Chen, Chao-Jung Chen, Zhengjun Chen, Yong-Ping Chen, Zhelin Chen, Xuancai Chen, Yi-Hsuan Chen, Daiyu Chen, Gui Mei Chen, Hongqi Chen, Zhizhong Chen, Mengting Chen, Guofang Chen, Jian-Guo Chen, Hou-Zao Chen, Yuyao Chen, Lixia Chen, Yu-Yang Chen, Zhengling Chen, Qinfen Chen, Jiajun Chen, Xue-Qing Chen, Shenghui Chen, Yii-Derr Chen, Linbo Chen, Yanjing Chen, S Pl Chen, Chi-Long Chen, Jiawei Chen, Rong-Hua Chen, Shu-Fen Chen, Yu-San Chen, Ying-Lan Chen, Xiaofen Chen, Weican Chen, Xin Chen, Yumei Chen, Ruohong Chen, You-Xin Chen, Tse-Ching Chen, Xiancheng Chen, Yu-Pei Chen, Weihao Chen, Baojiu Chen, Haimin Chen, Zhihong Chen, Jion Chen, Yi-Chun Chen, Ping-Kun Chen, Wan Jun Chen, Willian Tzu-Liang Chen, Qingshi Chen, Ren-Hui Chen, Weihua Chen, Hanjing Chen, Guihao Chen, Xiao-Qing Chen, Po-Yu Chen, Liangsheng Chen, Fred K Chen, Haiying Chen, Tzu-Chieh Chen, Wei J Chen, Zhen Chen, Shu Chen, Jie Chen, Chung-Hao Chen, Zi-Qing Chen, Yu-Xia Chen, Weijia Chen, Ming-Han Chen, Yaodong Chen, Yong-Zhong Chen, Jinquan Chen, Haijiao Chen, Tom Wei-Wu Chen, Jingzhou Chen, Ya-Peng Chen, Shiwei Chen, Xiqun Chen, Yingjie Chen, Wenjun Chen, Linjie Chen, Hung-Chun Chen, Xiaoping Chen, Haoran Chen, Qiang Chen, Sy-Jou Chen, Y U Chen, Weineng Chen, Li-hong Chen, Cheng-Fong Chen, Yajing Chen, Song Chen, Qiaoli Chen, Yiru Chen, Guang-Yu Chen, Zhi-bin Chen, Deyu Chen, C Y Chen, Junhong Chen, Yonghui Chen, Chaoli Chen, Syue-Ting Chen, Sufang Chen, I-Chun Chen, Shangsi Chen, Xiao-Wei Chen, Qinsheng Chen, Zhao-Xia Chen, Yun-Yu Chen, Chi-Chien Chen, Wenxing Chen, Meng Chen, Zixin Chen, Jianhui Chen, Yuanyuan Chen, Jiamin Chen, Wei-Wei Chen, Xingyi Chen, Yen-Ni Chen, Danxiang Chen, Po-Ju Chen, Mei-Ru Chen, Ziying Chen, E S Chen, Tailai Chen, Qingyang Chen, Miaomiao Chen, Shuntai Chen, Wei-Lun Chen, Xuanli Chen, Zhengwei Chen, Fengju Chen, Chengwei Chen, Xujia Chen, Faye H Chen, Xiaoxiao Chen, Shengpan Chen, Shin-Yu Chen, Shiyao Chen, Yuan-Shen Chen, Shengzhi Chen, Shaohong Chen, Ching-Jung Chen, Zihao Chen, Kaiquan Chen, Duo-Xue Chen, Xiaochang Chen, Siping Chen, Rongfeng Chen, Jiali Chen, Hsin-Han Chen, Xiaohua Chen, Delong Chen, Wenjie Chen, Huijia Chen, Yunn-Yi Chen, Siyi Chen, Zhengming Chen, Chu-Huang Chen, Zhuchu Chen, Yuanbin Chen, Jinyong Chen, Yunzhong Chen, Pan Chen, Bihong T Chen, Yunyun Chen, Shujuan Chen, M Chen, Mulan Chen, Jiaren Chen, Zechuan Chen, Jian-Qing Chen, Wei-Hui Chen, Lifeng Chen, Geng Chen, Yan-Ming Chen, Zhijian J Chen, Honghui Chen, Wenfan Chen, Zhongbo Chen, Rouxi Chen, Ye-Guang Chen, Zhimin Chen, Tzu-Ting Chen, Xiaolei Chen, Ziyuan Chen, Shilan Chen, Ruiqi Chen, Xiameng Chen, Huijie Chen, Jiankui Chen, Yuhang Chen, Jianzhong Chen, Wen-Qi Chen, Fa Chen, Shu-Jen Chen, Li-Mien Chen, Xing-Lin Chen, Xuxiang Chen, Erbao Chen, Jiaqing Chen, Hsiang-Wen Chen, Jiaxin Chen
articles
Qi Chen, Yuan-Shu Peng, Qian Zhong +11 more · 2025 · Journal of nanobiotechnology · BioMed Central · added 2026-04-24
Atherosclerosis (AS) is a chronic inflammatory disorder characterized by foam cell formation and persistent inflammation as central pathological drivers. Although colchicine (Col) exhibits potent anti Show more
Atherosclerosis (AS) is a chronic inflammatory disorder characterized by foam cell formation and persistent inflammation as central pathological drivers. Although colchicine (Col) exhibits potent anti-inflammatory activities, its clinical application is limited by a narrow therapeutic window. In the present study, we developed phosphatidylserine-exposing nanovesicles (Col@PSVs) that leverage the innate phagocytic capacity of macrophage-derived foam cells by presenting surface "eat-me" signals, thereby enabling targeted immune modulation. The synergistic collaboration between Col and PSVs allows low-dose Col to retain robust anti-inflammatory efficacy while mitigating dose-dependent toxicity. Mechanistically, Col@PSVs potently suppress CCR7-mediated NF-ÎşB signaling activation in foam cells, leading to a marked downregulation of pro-inflammatory cytokine and disruption of inflammatory cascades. In ApoE Show less
đź“„ PDF DOI: 10.1186/s12951-025-03840-x
APOE
Weiyan Tian, Xianjuan Gou, Mingdan Li +3 more · 2025 · Nursing open · Wiley · added 2026-04-24
This study aimed to analyse the latent profiles of moral sensitivity of nursing students and to explore the different types of influencing factors. A cross-sectional study. Convenience sampling method Show more
This study aimed to analyse the latent profiles of moral sensitivity of nursing students and to explore the different types of influencing factors. A cross-sectional study. Convenience sampling method was used to select nursing students from five hospitals in Zunyi City, Guizhou Province, from July to September 2024. The demographic characteristics questionnaire and the Chinese version of the Nursing Student Moral Sensitivity Scale (MSQ-ST) were used as survey tools. Latent profile analysis (LPA) was performed on the moral sensitivity of nursing students. Logistic regression was used to analyse the influencing factors of different profiles. A total of 805 nursing students completed the questionnaire, of which 787 were valid, with a validity rate of 97.76%. The results of latent profile analysis showed that the moral sensitivity of nursing students was divided into two latent profiles: "low moral sensitivity group" (18.68%) and "high moral sensitivity group" (81.32%), and the results of logistic regression analysis showed that the level of hospital, the length of internship and the frequency of training on moral education were the factors influencing the moral sensitivity of nursing students (p < 0.05). In this study, we have demonstrated that there are two categories of moral sensitivity in nursing students, and that demographic traits have an impact on moral sensitivity in nursing students. These findings may provide a valuable theoretical foundation for nursing educators in developing the moral awareness of nursing students. No patient or public contribution. Show less
đź“„ PDF DOI: 10.1002/nop2.70373
LPA
Dilin Xu, Jin Lu, Yanfang Yang +11 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
Calcific aortic valve disease (CAVD) is characterized by progressive leaflet thickening and calcification, with no available pharmacological treatments. Plasma proteins play a pivotal role in disease Show more
Calcific aortic valve disease (CAVD) is characterized by progressive leaflet thickening and calcification, with no available pharmacological treatments. Plasma proteins play a pivotal role in disease regulation. This study aimed to uncover novel therapeutic targets for CAVD using Mendelian randomization (MR) integrated with transcriptomic analysis. Protein quantitative trait loci (pQTL) from the deCODE and UK Biobank Pharma Proteomics Project (UKB-PPP) plasma protein databases were used as exposure data. The FinnGen cohort (9870 cases, 402,311 controls) served as the discovery set, while the TARGET cohort (13,765 cases, 640,102 controls) provided validation. MR and summary data-based Mendelian randomization (SMR) were employed to screen for potential causal targets of CAVD. Colocalization analysis was conducted to assess whether CAVD and target proteins shared common causal SNPs. Additional analyses included trancriptomic profiling at multiple RNA levels. Protein-level validation was conducted via Western blot and immunostaining. Six proteins (ANGPTL4, PCSK9, ITGAV, CTSB, GNPTG, and FURIN) with strong genetic colocalization were identified by MR and SMR analysis. Among these, cellular trancriptomic analysis revealed ANGPTL4 and ITGAV with significantly greater expression in osteogenic group, which was further validated in calcified aortic valves and osteogenic valvular interstitial cells in protein level. This study identified six causal proteins with strong genetic colocalization for CAVD, with ANGPTL4 and ITGAV emerging as the most promising targets for further investigation. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.119110
ANGPTL4
Guomei Yang, Luoquan Ao, Qing Zhao +10 more · 2025 · Cell communication and signaling : CCS · BioMed Central · added 2026-04-24
Sepsis, a life-threatening organ dysfunction caused by dysregulated host responses to infection, has emerged as a leading cause of mortality in ICU patients. Macrophages, crucial effector cells in inn Show more
Sepsis, a life-threatening organ dysfunction caused by dysregulated host responses to infection, has emerged as a leading cause of mortality in ICU patients. Macrophages, crucial effector cells in innate immunity, play pivotal regulatory roles in sepsis pathogenesis. While Programmed death-ligand 1 (PD-L1), a key immune checkpoint molecule, is traditionally believed to exert immunosuppressive effects through membrane anchoring, its involvement in macrophage polarization during sepsis remains unclear. This study investigated the spatial distribution of PD-L1 in macrophages and its regulatory effects on inflammatory responses during sepsis. This study investigated PD-L1’s regulatory role in macrophage polarization through RNA sequencing, Immunoprecipitation-mass spectrometry, molecular docking, and site-directed mutagenesis, with preliminary validation in C57BL/6 mice. Using GEO database analysis combined with qRT-PCR and Western blotting, we confirmed elevated PD-L1 expression in sepsis and M1-polarized macrophages. Laser scanning confocal microscopy demonstrated dual localization of PD-L1, appearing both on the plasma membrane and intracellularly within M1 macrophages. RNA sequencing revealed PD-L1’s promotion of M1 polarization through enhanced AIM2 expression in the NOD-like receptor pathway. Integrated analyses employing mass spectrometry, molecular docking, site-directed mutagenesis, and Western blotting demonstrated PD-L1 binding to AIM2, which augmented expression of downstream effector molecules (IL-18 and IFN-γ) and potentiated STAT1 activation. Silencing AIM2 by siRNA or IL-18 antagonism reversed PD-L1-induced M1 markers (IL-27, IL-6, iNOS/NO). PD-L1 was further shown to exacerbate pathological progression in septic mouse models. Our study demonstrated that sepsis-induced PD-L1 overexpression in macrophages exacerbates pathological progression by upregulating AIM2 expression, binding to AIM2 to enhance IL-18 production, which activates STAT1 to drive M1 polarization. The online version contains supplementary material available at 10.1186/s12964-025-02578-1. Show less
đź“„ PDF DOI: 10.1186/s12964-025-02578-1
IL27
Deying Liu, Jiaxin Li, Chan Xu +7 more · 2025 · Human molecular genetics · Oxford University Press · added 2026-04-24
Mutations in four genes encoding the outer ring complex of nuclear pore complexes (NPCs), NUP85, NUP107, NUP133 and NUP160, cause monogenic steroid-resistant nephrotic syndrome (SRNS). Knockout of NUP Show more
Mutations in four genes encoding the outer ring complex of nuclear pore complexes (NPCs), NUP85, NUP107, NUP133 and NUP160, cause monogenic steroid-resistant nephrotic syndrome (SRNS). Knockout of NUP85, NUP107, or NUP133 in immortalized human podocytes activates CDC42, an important effector of SRNS pathogenesis. However, it is unknown whether or not loss of NUP160 dysregulates CDC42 in the podocytes. Here, we generated a podocyte-specific Nup160 knockout mouse model with double-fluorescent (mT/mG) Cre reporter genes using CRISPR/Cas9 and Cre/loxP technologies. We investigated nephrotic syndrome-associated phenotypes in the Nup160podo-/- mice, and performed single-cell transcriptomic and proteomic analysis of glomerular suspension cells and cultured primary podocytes, respectively. The Nup160podo-/- mice exhibited progressive proteinuria and fusion of podocyte foot processes. We found decreased Cdc42 protein and normal Cdc42 transcriptional level in the podocytes of the Nup160podo-/- mice using analysis of single-cell transcriptomes and proteomes. We subsequently observed that Cdc42 protein decreased in both kidney tissues and cultured primary podocytes of the Nup160podo-/- mice, although Cdc42 mRNA levels were elevated in the cultured primary podocytes of the Nup160podo-/- mice. We also found that Cdc42 activity was significantly reduced in the cultured primary podocytes of the Nup160podo-/- mice. In conclusion, loss of Nup160 dysregulated Cdc42 in the podocytes of the Nup160podo-/- mice with proteinuria and fusion of podocyte foot processes. Our findings suggest that the dysregulation of CDC42 may contribute to the pathogenesis of SRNS in patients with mutations in NUP160. Show less
no PDF DOI: 10.1093/hmg/ddaf064
NUP160
Mimi Li, Lichao Ye, Chunnuan Chen · 2025 · Scientific reports · Nature · added 2026-04-24
Despite the well-established association between the apolipoprotein B/apolipoprotein A1 (apoB/apoA1) ratio and ischemic stroke, its specific relationship with the underlying vascular pathologies contr Show more
Despite the well-established association between the apolipoprotein B/apolipoprotein A1 (apoB/apoA1) ratio and ischemic stroke, its specific relationship with the underlying vascular pathologies contributing to stroke remains poorly understood. This study aims to investigate the association between the apoB/apoA1 ratio and intracranial or extracranial atherosclerosis. We enrolled 408 patients with acute ischemic stroke who had never been treated with statins or fibrates. Based on the images from computed tomography angiography (CTA), the patients were categorized into four groups: intracranial atherosclerosis stenosis (ICAS, n = 136), extracranial carotid atherosclerosis stenosis (ECAS, n = 45), combined intracranial and extracranial atherosclerosis stenosis (COAS, n = 73), and non-cerebral atherosclerosis stenosis (NCAS, n = 154). Demographic characteristics, clinical factors, and serum lipid levels were collected and then compared across groups. The apoB/apoA1 ratio was significantly higher in patients with ICAS, ECAS and COAS compared to those in the NCAS group. Multivariable logistic regression analysis demonstrated that the ApoB/ApoA1 ratio was independently associated with ICAS, but not with ECAS. ROC curve analysis showed that the ApoB/ApoA1 ratio had a good diagnostic ability for ICAS, with an area under the curve (AUC) of 0.764, an optimal cut-off value of 0.8122, a sensitivity of 81.3%, and a specificity of 59.8%. An higher apoB/apoA1 ratio is associated with ICAS in ischemic stroke patients. Show less
đź“„ PDF DOI: 10.1038/s41598-025-97625-9
APOB
Zihao Zhou, Yidan Zheng, Shiyan Hu +13 more · 2025 · Heart (British Cardiac Society) · added 2026-04-24
Calcific aortic stenosis (CAS) is frequently accompanied by systemic comorbidities, but their causal relationships and shared genetic architecture remain poorly defined. We aimed to map the multisyste Show more
Calcific aortic stenosis (CAS) is frequently accompanied by systemic comorbidities, but their causal relationships and shared genetic architecture remain poorly defined. We aimed to map the multisystem comorbidity network of CAS and clarify underlying genetic mechanisms. In 467 484 participants from the UK Biobank, observational and polygenic phenome-wide association studies evaluated associations between CAS and 1571 phenotypes, integrating disease-trajectory analyses to visualise temporal patterns. Associations replicated across observational and polygenic analyses were tested using two-sample Mendelian randomisation (MR) based on 22 CAS-related variants from FinnGen. Polygenic risk score (PRS) analyses excluding specific genes assessed their contributions, particularly LPA and plasma lipoprotein(a) (Lp(a)) levels. CAS was associated with higher risks of 42 cardiovascular and non-cardiovascular conditions, most prominently metabolic, endocrine, haematological and respiratory disorders. Temporal analyses showed that circulatory and metabolic diseases typically precede other comorbidities in CAS trajectories. MR findings were consistent with causal effects of CAS on multiple cardiovascular diseases, iron-deficiency anaemia, mental disorders and pleural effusion. When LPA variants were removed from the CAS PRS or plasma Lp(a) concentration was adjusted for, most associations lost significance, indicating a shared LPA/Lp(a)-mediated genetic pathway. CAS is embedded within a broad multisystem comorbidity network, driven largely by genetic variation at LPA and elevated Lp(a). These findings highlight pleiotropic mechanisms linking valvular calcification with systemic disease and support LPA-targeted therapies as a promising avenue for reducing the multisystem burden of CAS. Show less
no PDF DOI: 10.1136/heartjnl-2025-326058
LPA
Tong Li, Yang Zhang, Hong Hu +5 more · 2025 · Translational lung cancer research · added 2026-04-24
While most patients with stage I non-small cell lung cancer (NSCLC) remain recurrence-free after resection, some still develop recurrent disease. The surgical curative time window concept, defined as Show more
While most patients with stage I non-small cell lung cancer (NSCLC) remain recurrence-free after resection, some still develop recurrent disease. The surgical curative time window concept, defined as no recurrence through 5-year follow-up, helps identify potentially cured patients, yet predictive clinicopathologic features in stage I invasive NSCLC need clarification. This study sought to identify such features to enable risk-adapted surveillance. We analyzed a prospectively collected dataset of patients with stage I invasive NSCLC who underwent R0 resection between 2008 and 2015. Cox regression analysis was used to evaluate the association between clinicopathologic features and disease recurrence, aiming to identify independent prognostic factors. A total of 1,817 patients met the inclusion criteria. The 5-year cumulative incidence of recurrence was 14.6%. Female sex, tumor size ≤2 cm, lepidic-predominant adenocarcinoma (LPA) histologic type, presence of a ground-glass opacity (GGO) component, and solid component size ≤10 mm were identified as independent prognostic factors. A risk stratification system was subsequently developed, classifying patients into two groups: a low-risk group (with ≥4 factors; n=341) and an elevated-risk group (with <4 factors; n=1,476). Kaplan-Meier analysis revealed statistically significant differences in recurrence-free survival (RFS), overall survival (OS), and lung cancer-specific survival (LCSS) between the two groups (P<0.001). The low-risk group is considered to represent the population within the surgical curative time window. Patients with stage I invasive NSCLC who meet at least four of the following five criteria-female sex, tumor size ≤2 cm, solid component ≤10 mm, presence of a GGO component, and LPA histologic type-may be considered within the "surgical curative time window" and may therefore qualify for reduced surveillance intensity. Show less
đź“„ PDF DOI: 10.21037/tlcr-2025-894
LPA
Yv-Xuan Liu, Jing Chen, Chen Liu +4 more · 2025 · Science progress · SAGE Publications · added 2026-04-24
BackgroundAlthough abnormalities in circulating lipids and lipoproteins are associated with increased cancer risk, their specific impact on lung cancer progression and prognosis is still unclear. This Show more
BackgroundAlthough abnormalities in circulating lipids and lipoproteins are associated with increased cancer risk, their specific impact on lung cancer progression and prognosis is still unclear. This study retrospectively assessed the influence of preoperative lipid and lipoprotein levels on non-small cell lung cancer progression and prognosis, stratified by age.MethodsIn this retrospective study, we analyzed 849 patients to investigate the association between lipid markers and lung cancer progression, and examined postoperative prognosis in a subset of 222 patients. Data was analyzed using restricted cubic spline curves, Kaplan-Meier survival analysis, and Cox proportional hazards models.ResultsA significant nonlinear relationship was observed between total cholesterol (TC), high-density lipoprotein (HDL), ApoB, ApoAI, ApoE, and baseline tumor diameter (BSLD) (PTC = 0.025; PHDL < 0.001; PApoB = 0.037; PApoAI =0.001; PApoE < 0.001). In contrast, Lp(a) showed a significant linear relationship with BSLD (P = 0.002). The Cox regression analysis revealed that triglyceride (TG) (hazard ratio (HR) = 0.50, 95% confidence interval (CI): 0.28-0.92, P = 0.025) was significantly negatively associated with lung cancer mortality in patients under 58 years. For patients over 58 years, higher ApoB levels were linked to a reduced risk of lung cancer death (HR = 0.59, 95% CI: 0.36-0.97, P = 0.038).ConclusionThis study reveals a significant negative correlation between ApoAI and HDL levels with BSLD, while Lp(a) shows a positive correlation. In terms of long-term prognosis, high-serum ApoB are associated with a lower mortality risk in all lung cancer patients, and high-serum TG levels associated with reduced mortality risk in patients aged under 58 while high-serum TC levels associated with reduced mortality risk in patients over 58, with high Lp(a) levels indicating a greater risk of mortality in older patients. Show less
đź“„ PDF DOI: 10.1177/00368504251352375
APOB
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
APOB
Ruotong Li, Wenye Zhao, Jiaxin Zhang +7 more · 2025 · FASEB journal : official publication of the Federation of American Societies for Experimental Biology · added 2026-04-24
The global increase in muscle weakness poses a critical public health concern. Nutritional interventions that improve muscular function hold promise as a therapeutic potential. Vitamin A (VA) and its Show more
The global increase in muscle weakness poses a critical public health concern. Nutritional interventions that improve muscular function hold promise as a therapeutic potential. Vitamin A (VA) and its active metabolites have been implicated in muscle development and the transformation of muscle fiber types. However, conventional VA formulations are restricted by poor stability and low bioavailability. In this study, a stable Nano VA was utilized to systematically evaluate its effects on muscle development and exercise performance in mice, as well as to explore its underlying mechanisms. A total of 44 male C57BL/6J mice were randomly divided into four groups: (i) normal control (NC), (ii) 5 mg/kg Nano VA (5 NVA), (iii) 10 mg/kg Nano VA (10 NVA), and (iv) 10 mg/kg VA (10 VA). The 10 NVA group demonstrated significantly improved muscle strength and swimming endurance, compared with the NC group. Further examination suggested a significant increase in myofiber diameter, cross-sectional area, and the content of fast-twitch fibers. Additionally, Nano VA treatment improved glucose tolerance and insulin sensitivity. To elucidate the mechanism by which Nano VA enhances muscle locomotor ability, transcriptomics and metabolomics data identified 111 differentially expressed genes and 253 differential metabolites. Of these, Angptl4, Ppp1r3a, and Cyp26b1 were identified as candidate regulators of muscle development and myofiber type transformation. In conclusion, Nano VA regulates muscle development and promotes muscle fiber type conversion, thus improving muscle strength and endurance in mice. Moreover, Nano VA facilitates mitigating and improving myasthenia gravis-related conditions. Show less
no PDF DOI: 10.1096/fj.202501417RR
ANGPTL4
Chunbo Zhuang, Fangfang Cui, Jin Chen +3 more · 2025 · Biochimica et biophysica acta. Molecular basis of disease · Elsevier · added 2026-04-24
Excessive hepatic lipid accumulation is the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), yet its underlying mechanisms still not fully understood. In this study, we id Show more
Excessive hepatic lipid accumulation is the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), yet its underlying mechanisms still not fully understood. In this study, we identified RNA binding motif protein 39 (Rbm39) as a key modulator of hepatic lipid homeostasis during MASLD progression. To establish in vivo MASLD model, mice were fed either a high-fat diet (HFD) or a Gubra-Amylin NASH (GAN) diet. We employed adeno-associated virus to manipulate Rbm39 expression levels to assess its role in MASLD. Transcriptome analysis was conducted to pinpoint the genes targeted by Rbm39. Western blot, RT-PCR, dual-luciferase reporter gene assays, and alternative splicing analysis were utilized to delve into the molecular mechanisms. Our results showed that Rbm39 expression was notably decreased in the livers of MASLD mice. Knockdown of hepatic Rbm39 aggravated HFD-induced hepatic steatosis and GAN diet-induced MASH, along with a notable decrease in serum lipid levels. Conversely, overexpression of Rbm39 attenuated MASLD development and progression. RNA sequencing data analysis indicated that Rbm39 regulated the expression of apolipoprotein B (Apob) and fatty acid-binding protein 4 (Fabp4), both of which are crucial for lipid transport. Mechanistically, Rbm39 enhanced the transcription of Apob by upregulating hepatocyte nuclear factor 4α (Hnf4α), while it suppressed Fabp4 transcription by regulating alternative splicing of hypoxia inducible factor-1α (Hif-1α). These findings highlight the pivotal role of Rbm39 in maintaining hepatic lipid homeostasis and suggest its potential as a therapeutic target for MASLD. Show less
no PDF DOI: 10.1016/j.bbadis.2025.167815
APOB
Yangqi Zhao, Yi Dong, Qingqing Zheng +7 more · 2025 · Investigative ophthalmology & visual science · added 2026-04-24
Fatty acid desaturase 1 (FADS1) is significantly and specifically upregulated following diabetic corneal injury. However, its role in diabetic keratopathy remains unclear. This study aimed to investig Show more
Fatty acid desaturase 1 (FADS1) is significantly and specifically upregulated following diabetic corneal injury. However, its role in diabetic keratopathy remains unclear. This study aimed to investigate the impact of FADS1 on wound healing and functional recovery of the diabetic corneal epithelium and explore its potential mechanisms. Using high-glucose-induced corneal epithelial cells and a streptozotocin-induced type 1 diabetic mouse model, FADS1 expression was suppressed via FADS1 small interfering RNA (siRNA). Cell migration was assessed using scratch and transwell assays. Wound healing and functional recovery of the corneal epithelium were evaluated using sodium fluorescein staining, anterior segment optical coherence tomography, hematoxylin and eosin staining, and immunofluorescence staining. FADS1 knockdown promoted wound healing and functional recovery of the diabetic corneal epithelium both in vivo and in vitro. Suppression of FADS1 enhanced high-glucose-induced corneal epithelial cell migration, which was dependent on elevated levels of the upstream metabolite Îł-linolenic acid. This effect was mediated through the activation of the mitogen-activated protein kinase signaling pathway and the accumulation of autophagosomes. After diabetic corneal epithelial injury, FADS1 expression is specifically upregulated. Knockdown of FADS1 promotes wound healing and functional recovery, suggesting a novel therapeutic strategy for diabetic keratopathy. Show less
đź“„ PDF DOI: 10.1167/iovs.66.6.6
FADS1
Neng Wang, Yu Zheng, Shuai Tao +1 more · 2025 · BMC gastroenterology · BioMed Central · added 2026-04-24
This study aimed to elucidate the correlations among dyslipidemia, immune function, and clinical outcomes in patients with acute-on-chronic liver failure (ACLF), with particular emphasis on the clinic Show more
This study aimed to elucidate the correlations among dyslipidemia, immune function, and clinical outcomes in patients with acute-on-chronic liver failure (ACLF), with particular emphasis on the clinical significance of lipid metabolism and cellular immune parameters in hepatitis B virus-associated ACLF (HBV-ACLF). A retrospective analysis was conducted on 803 patients with HBV-ACLF admitted to the Shanghai Public Health Clinical Center from January 2014 to January 2024. Patients were stratified into deceased (n = 414) and survival (n = 389) groups based on clinical outcomes. Clinical baseline data, lipid metabolic indices, and cellular immune parameters were collected. The Spearman correlation coefficient was utilized to assess the correlation between lipid metabolic indices and cellular immune parameters, and a multivariate Cox proportional hazards model was applied to analyze risk factors for mortality. Compared to the survival group, lipid metabolism indices in the deceased group were significantly reduced (P < 0.05). Lipid metabolism indices, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (APOA1), apolipoprotein B (APOB), total cholesterol (TC), and triglycerides (TG), demonstrated significant negative correlations with the severity of liver failure (P < 0.05). Correlation analysis with lymphocyte subset counts revealed positive correlations between low-density lipoprotein, TG, TC, APOB, and CD3 + T cells, CD4 + T cells, CD8 + T cells, and CD45 + T cells (P < 0.05). APOA1 and HDL-C were positively correlated with B cells and NK cells (P < 0.05). TG and APOB showed significant negative correlations with the CD4/CD8 ratio (P < 0.05). Multivariate Cox analysis identified age, creatinine, total bilirubin, international normalized ratio (INR), hepatic encephalopathy, and hepatorenal syndrome as independent risk factors affecting the short-term prognosis of HBV-ACLF, while sodium, APOA1, and APOB were identified as independent protective factors for ACLF (HR = 0.984, 95% CI: 0.974-0.995, P < 0.001, HR = 0.267,95% CI: 0.120-0.596, P = 0.001, HR = 0.486, 95% CI: 0.282-0.838, P = 0.010). Patients with HBV-ACLF exhibit decreased levels of TC, TG, LDL-C, HDL-C, APOA1, and APOB. These alterations in serum lipid profiles are associated with immune dysfunction and disease progression in HBV-ACLF. Notably, APOA1 and APOB serve as protective factors against 90-day mortality in hospitalized ACLF patients. Further investigation is warranted to elucidate the relationship between lipid metabolism disturbances and peripheral immunity in ACLF. Show less
đź“„ PDF DOI: 10.1186/s12876-025-04004-9
APOB
Yang Zhang, Xuyang Yang, Su Zhang +10 more · 2025 · JCI insight · added 2026-04-24
The prognosis for colorectal cancer (CRC) patients with liver metastasis remains poor, and the molecular mechanisms driving CRC liver metastasis are not fully understood. Tumor-derived hypoxia-induced Show more
The prognosis for colorectal cancer (CRC) patients with liver metastasis remains poor, and the molecular mechanisms driving CRC liver metastasis are not fully understood. Tumor-derived hypoxia-induced extracellular vesicles have emerged as key players in inducing angiogenesis by transferring noncoding RNAs. However, the specific role of CRC-derived hypoxic extracellular vesicles (H-EVs) in regulating premetastatic microenvironment (PMN) formation by inducing angiogenesis remains unclear. Our study demonstrates that H-EVs induce angiogenesis and liver metastasis. Through microRNA microarray analysis, we identified a reduction in miR-6084 levels within H-EVs. We found that miR-6084 inhibited angiogenesis by being transferred to endothelial cells via EVs. In endothelial cells, miR-6084 directly targeted angiopoietin like 4 (ANGPTL4) mRNA, thereby suppressing angiogenesis through the ANGPTL4-mediated JAK2/STAT3 pathway. Furthermore, we uncovered that specificity protein 1 (SP1) acted as a transcription factor regulating miR-6084 transcription, while hypoxia-inducible factor 1A (HIF1A) decreased miR-6084 expression by promoting SP1 protein dephosphorylation and facilitating ubiquitin-proteasome degradation in SW620 cells. In clinical samples, we observed low expression of miR-6084 in plasma-derived EVs from CRC patients with liver metastasis. In summary, our findings suggest that CRC-derived H-EVs promote angiogenesis and liver metastasis through the HIF1A/SP1/miR-6084/ANGPTL4 axis. Additionally, miR-6084 holds promise as a diagnostic and prognostic biomarker for CRC liver metastasis. Show less
đź“„ PDF DOI: 10.1172/jci.insight.189503
ANGPTL4
Ying-Shuang Chang, Yu-Yu Kan, Tzu-Ning Chao +2 more · 2025 · Molecular neurobiology · Springer · added 2026-04-24
Insulin supply is the golden standard for type 1 diabetes mellitus (T1DM) therapy. Is there a drug-reduction application for reversing glucose metabolism disabled and diabetic neuropathy (DN), and is Show more
Insulin supply is the golden standard for type 1 diabetes mellitus (T1DM) therapy. Is there a drug-reduction application for reversing glucose metabolism disabled and diabetic neuropathy (DN), and is it suitable for the young and elderly populations? Reducing T1DM-associated DN, and maintaining glucose metabolism require using the anti-aging gene Klotho to regulate specific signaling cascades. This study applied five 16:8 intermittent fasting (16-h fasting, 8-h eating; 168if) protocols by different executing times to young and elderly diabetic mice to evaluate whether 168if is age-dependent and how it alters Klotho-related signaling molecules. Blood glucose levels were efficiently reduced when 168if was implemented in the early stage of T1DM onset (DNf group) of young and elderly mice. Another four groups failed to reduce blood sugar. However, the DNf protocol was unsuitable for diabetic elderly mice because it posed a higher mortality risk for this population. Young DNf mice exhibited reduced thermal hyperalgesia and mechanical allodynia and reversed Klotho downregulation and protein kinase C epsilon (PKCε) upregulation compared with DN mice. Furthermore, young DNf mice exhibited normalization of fibroblast growth factor receptor 1 (FGFR1) and nuclear factor κB (NF-κB) expression, which is involved in Klotho-related glucose metabolism and anti-inflammation. The expression densities of PKCε, Klotho, FGFR1, and NF-κB were linear to neuropathic manifestations. This study demonstrated the effectiveness of 168if application in the early stage of T1DM onset, a straightforward and convenient dietary control method, as a blood glucose control for achieving pharmaceutical reduction and relieving neuropathic pain in young T1DM patients. Show less
no PDF DOI: 10.1007/s12035-025-04849-x
FGFR1
Zhigang Lei, Yu Wu, Weijie Xue +15 more · 2025 · Hepatology (Baltimore, Md.) · added 2026-04-24
Disrupting liver immune homeostasis drives inflammation. Recent evidence shifts immunoregulatory focus to hepatocytes, though the mechanisms remain poorly defined. Forkhead box O1 (FoxO1) is a critica Show more
Disrupting liver immune homeostasis drives inflammation. Recent evidence shifts immunoregulatory focus to hepatocytes, though the mechanisms remain poorly defined. Forkhead box O1 (FoxO1) is a critical homeostasis regulator, but its function in liver immune homeostasis is unknown. We aimed to clarify the role of hepatocyte FoxO1 in liver immune homeostasis and inflammation. Human liver FoxO1 expression and its association with inflammation were analyzed in patients with various inflammation-related liver diseases. Hepatocyte-specific Foxo1 knockout (FoxO1 â–łhepa ) mice were established. Hepatocyte-specific gene interference was employed in alcoholic hepatitis and hepatic schistosomiasis murine models. Transcriptomic, single-cell RNA sequencing, and CUT&Tag analyses were performed to elucidate the underlying mechanisms. Hepatocyte FoxO1 levels in human inflammatory livers declined prevalently and were inversely correlated with inflammation and fibrosis. Around 15-18 weeks after birth, FoxO1 â–łhepa mice exhibited mild spontaneous hepatic inflammation with natural killer T (NKT) cell and neutrophil accumulation. NKT cell depletion in FoxO1 â–łhepa mice with alcoholic hepatitis or hepatic schistosomiasis (HS) significantly reduced neutrophil accumulation and protected against liver inflammation and damage. Mechanistically, FoxO1 promoted retinoic acid synthesis to induce hepatocyte CD1d expression, which is necessary for regulating NKT cell apoptosis. Innovatively, decreased JMJD1C expression in hepatocytes caused histone H3 lysine 9 (H3K9) dimethylation at the Foxo1 promoter, repressing its transcription and disrupting local immune homeostasis. Our findings uncover a hitherto unrecognized mechanism for hepatocyte-based control of liver inflammation, in which hepatocyte FoxO1 maintained by JMJD1C restrains local NKT cells and neutrophils via CD1d induction, providing promising targets for inflammatory liver diseases. Show less
no PDF DOI: 10.1097/HEP.0000000000001590
JMJD1C
Yu Liao, Mingchao Wang, Fuli Qin +2 more · 2025 · Frontiers in pharmacology · Frontiers · added 2026-04-24
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically Show more
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically identify Cpn's therapeutic targets and pathways in Western diet (WD)-induced obesity using integrated network pharmacology, transcriptomics, and experimental validation. A Western diet (WD)-induced mice model was used to evaluate the effectiveness of Cpn in ameliorating obesity. A network pharmacology analysis was then employed to identify the potential anti-obesity targets of Cpn. GO functional enrichment and KEGG pathway analysis were performed to elucidate the potential functions of the identified targets, followed by constructing a protein-protein interaction network to screen the core targets. Meanwhile, quantitative transcriptomics was conducted to validate and broaden the network pharmacology findings. Finally, molecular docking and quantitative real-time PCR assay were used for the core target validation. Cpn treatment effectively alleviated obesity-related symptoms in WD-induced mice. The metabolic pathway, insulin signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, lipid and atherosclerosis pathway, and core targets including CPS1, HRAS, MAPK14, PAH, ALDOB, AKT1, GSK3B, HSP90AA1, BHMT2, EGFR, CASP3, MAT1A, APOM, APOA2, APOC3, and APOA1 are involved in regulating the therapeutic effect of Cpn. This study comprehensively uncovers the potential mechanism of Cpn against obesity based on network pharmacology and quantitative transcriptomics, which provides evidence for revealing the pathogenesis of obesity, suggesting that Cpn is a possible lead compound for anti-obesity treatment. Show less
đź“„ PDF DOI: 10.3389/fphar.2025.1571480
APOC3
Jiayi Chen, Yongmei Wu, Jianhua He +5 more · 2025 · Nutrients · MDPI · added 2026-04-24
This experiment investigated the response of carcass composition, digestive function, hepatic lipid metabolism, intestinal microbiota, and serum metabolomics to excessive or restrictive dietary energy Show more
This experiment investigated the response of carcass composition, digestive function, hepatic lipid metabolism, intestinal microbiota, and serum metabolomics to excessive or restrictive dietary energy in Ningxiang pigs. A total of 36 Ningxiang pigs (210 ± 2 d, 43.26 ± 3.21 kg) were randomly assigned to three treatments (6 pens of 2 piglets each) and fed a control diet (CON, digestive energy (DE) 13.02 MJ/kg,), excessive energy diet (EE, 15.22 MJ/kg), and restrictive energy diet (RE, DE 10.84 MJ/kg), respectively. Results showed that EE significantly increased the apparent digestibility of crude protein and total energy ( The findings suggest RE had no obvious negative effect on carcass traits of Ningxiang pigs. Apart from exacerbated body fat deposition, EE promoted fat accumulation in the liver by up-regulating the expression of lipogenic genes. Dietary energy changes affect hepatic bile acid metabolism, which may be mediated through the glycerophospholipid metabolism pathway, as well as disturbances in the gut microbiota. Show less
đź“„ PDF DOI: 10.3390/nu17233648
LPL
Haoyu Deng, Wan Yi Liang, Leqi Chen +6 more · 2025 · Journal of lipid research · Elsevier · added 2026-04-24
Sepsis is the dysregulated immune response to an infection and is a leading cause of mortality. Low levels of high-density lipoprotein (HDL) cholesterol are associated with increased risk of death fro Show more
Sepsis is the dysregulated immune response to an infection and is a leading cause of mortality. Low levels of high-density lipoprotein (HDL) cholesterol are associated with increased risk of death from sepsis, and increasing levels of HDL by inhibition of cholesteryl ester transfer protein (CETP) has been shown to decrease mortality in mouse models of sepsis. The objective of this study was to investigate the cellular mechanisms by which CETP inhibition and HDL lead to improved survival during sepsis. We found that HDL inhibits lipopolysaccharide (LPS)-induced activation of IL-1β in a mouse model of sepsis. The activation of IL-1β was dependent on the activity of scavenger receptor class B type 1 (SR-B1), and knockdown of SR-B1 significantly attenuated LPS-induced production of IL-1β in macrophages. Additionally, we found that LPS-induced SR-B1 internalization occurs through the endosome-lysosome pathway, which is also likely responsible for LPS degradation in the macrophages. Furthermore, we revealed that raising HDL by CETP inhibition markedly enhanced HDL-mediated anti-inflammatory effects in response to LPS stimulation, and these effects were not due to CETP itself but rather were HDL-dependent. Finally, we show that pharmacological inhibition of CETP significantly improved endotoxemia-induced mortality by inhibiting IL-1β production in the liver and circulation after LPS injection. Pathologically, CETP inhibition attenuated LPS-induced diffuse alveolar damage and hepatocyte necrosis, which may contribute to the improved mortality in mice treated with the CETP inhibitor anacetrapib. Taken together, our findings uncover a cellular mechanism by which HDL attenuates LPS-induced pro-inflammatory response via SR-B1-mediated LPS degradation. Show less
đź“„ PDF DOI: 10.1016/j.jlr.2025.100858
CETP
Azad Mojahedi, On Chen, Hal A Skopicki +2 more · 2025 · Reviews in cardiovascular medicine · added 2026-04-24
Despite advancements in treatment, coronary artery disease (CAD) remains a significant global health concern. Although lipoprotein(a) [Lp(a)] is recognized as a crucial cardiovascular risk factor asso Show more
Despite advancements in treatment, coronary artery disease (CAD) remains a significant global health concern. Although lipoprotein(a) [Lp(a)] is recognized as a crucial cardiovascular risk factor associated with increased risk, the prognostic value of using Lp(a) levels in patients with acute coronary syndrome (ACS) who have undergone percutaneous coronary intervention (PCI) remains debatable. This review aimed to investigate the association between Lp(a) levels and recurrent ischemic events in patients with ACS undergoing PCI. This systematic review included studies with individuals aged ≥18 years diagnosed with ACS who underwent PCI and had Lp(a) measurements. The included studies were sourced from the PubMed database, with a focus on articles published between January 2020 and January 2025. Keywords related to Lp(a) and cardiovascular diseases were used in the search. Data extraction involved a review of titles and abstracts followed by quality assessment using the QUADAS-2 tool. The final analysis included 10 studies with a combined population of 20,896 patients from diverse regions, including Japan, India, Egypt, China, and South Korea. Key findings indicate that elevated Lp(a) levels are significantly associated with adverse cardiovascular outcomes, including myocardial infarction and mortality, both in hospital and during long-term follow-up. This review highlights Lp(a) as a critical biomarker for predicting recurrent cardiovascular events in ACS patients post-PCI. The consistent correlation between elevated Lp(a) levels and adverse outcomes underscores the necessity of routine monitoring and targeted management of Lp(a) to mitigate residual cardiovascular risk. Show less
đź“„ PDF DOI: 10.31083/RCM42784
LPA
Xiaodong Chen, Yizhuo Ma, Haiyang Liu +1 more · 2025 · Biochemical pharmacology · Elsevier · added 2026-04-24
USP10 is a critical deubiquitinating enzyme within the ubiquitin-specific protease family, playing multifaceted roles in cellular physiology and disease pathogenesis. Structurally composed of a G3BP1- Show more
USP10 is a critical deubiquitinating enzyme within the ubiquitin-specific protease family, playing multifaceted roles in cellular physiology and disease pathogenesis. Structurally composed of a G3BP1-interacting motif, a N-terminal domain (mediating most protein interactions), and a catalytic USP domain (residues 415-795, catalytic triad C424-H736-D751), USP10 regulates diverse cellular pathways by stabilizing key proteins through deubiquitination. It exhibits context-dependent functional duality, particularly in cancer: USP10 promotes tumorigenesis in various cancers (e.g., glioblastoma, esophageal, pancreatic, breast cancers) by stabilizing oncoproteins like CCND1, YAP1, HDAC7, and RUNX1, enhancing proliferation, metastasis, and immune evasion. Conversely, it suppresses tumors (e.g., NSCLC, CRC, thyroid cancer) by stabilizing tumor suppressors like p53, PTEN, and Axin1, inhibiting pathways such as Wnt/β-catenin. Beyond oncology, USP10 contributes to neurodegenerative diseases (neuroprotective in PD/ALS, neurotoxic in AD via Tau stabilization), viral immunity (inhibits SARS-CoV-2 infection), inflammatory responses, male reproduction, and metabolic/cardiovascular disorders. Its regulatory mechanisms include phosphorylation (e.g., by AMPK, AKT, ATM) controlling subcellular localization and activity, and ubiquitination via USP13. USP10's therapeutic significance drives inhibitor development (Spautin-1, D1, Wu-5, P22077, Parthenolide), though cross-reactivity within the USP family due to conserved catalytic domains remains a challenge. Novel strategies like PROTACs and engineered ubiquitin variants (UbVs) offer promise for future selective targeting of USP10 dysregulation in diverse diseases. A comprehensive understanding of its structure and context-specific functions is essential for exploiting its full therapeutic potential. Show less
no PDF DOI: 10.1016/j.bcp.2025.117251
AXIN1
Ruijun Sun, Yuchi Zhang, Jingying Xu +7 more · 2025 · Archiv der Pharmazie · Wiley · added 2026-04-24
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-se Show more
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-serving as promising reservoirs of such inhibitors. Nevertheless, comprehensive screening and mechanistic characterization of their inhibitory potential remain limited. This study sought to identify potent AChE inhibitors from YGE, investigate their mechanisms of action, and assess their therapeutic prospects for AD. Methodologically, an integrated approach was employed, combining ultrafiltration-liquid chromatography (UF-LC) for rapid inhibitor screening, molecular docking and dynamics simulations for mechanistic insight, two-stage high-speed countercurrent chromatography for compound isolation, enzyme kinetics to delineate inhibition modalities, and network pharmacology to uncover relevant AD-related targets. The findings identified seven active constituents with notable AChE inhibition, among which parishins A and G were obtained at high purity (98.26% and 97.26%, respectively) and exhibited mixed-type inhibition with low IC Show less
no PDF DOI: 10.1002/ardp.70174
BACE1
Béatrice Bréart, Katherine Williams, Stellanie Krimm +34 more · 2025 · Nature · Nature · added 2026-04-24
Although cytotoxic CD8
đź“„ PDF DOI: 10.1038/s41586-024-08510-w
IL27
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
đź“„ PDF DOI: 10.1161/CIRCULATIONAHA.125.074737
APOA5
Edin Muratspahić, David Feldman, David E Kim +43 more · 2025 · bioRxiv : the preprint server for biology · Cold Spring Harbor Laboratory · added 2026-04-24
G protein-coupled receptors (GPCRs) play key roles in physiology and are central targets for drug discovery and development, yet the design of protein agonists and antagonists has been challenging as Show more
G protein-coupled receptors (GPCRs) play key roles in physiology and are central targets for drug discovery and development, yet the design of protein agonists and antagonists has been challenging as GPCRs are integral membrane proteins and conformationally dynamic. Here we describe computational Show less
đź“„ PDF DOI: 10.1101/2025.03.23.644666
GIPR
Jun He, Brenda Cabrera-Mendoza, Dan Qiu +7 more · 2025 · medRxiv : the preprint server for health sciences · added 2026-04-24
While previous genome-wide association studies (GWAS) identified multiple risk loci for suicide ideation (SI) and suicide attempt (SA), there is still a limited understanding of the genetic predisposi Show more
While previous genome-wide association studies (GWAS) identified multiple risk loci for suicide ideation (SI) and suicide attempt (SA), there is still a limited understanding of the genetic predisposition underlying suicidal behaviors in diverse populations. This study aimed to conduct a large-scale investigation of the suicidality spectrum (SP) to generate new insights into its biology and epidemiology. Leveraging ancestrally diverse participants (SI N This study provides convergent genetic evidence for both shared and phenotype-specific components of suicidal behaviors and delineates their associated factors spanning from proximal clinical and behavioral traits to more distal social determinants. These findings refine our understanding of the etiology of suicidal behaviors and may inform targeted strategies for suicide prevention in both clinical and public health settings. Show less
no PDF DOI: 10.64898/2025.12.15.25342298
BRWD1
Susan Adanna Ihejirika, Alexandra Huong Chiang, Aryaman Singh +3 more · 2025 · HGG advances · Elsevier · added 2026-04-24
Fish oil supplements (FOS) are known to alter circulating levels of polyunsaturated fatty acids (PUFAs) but in a heterogeneous manner across individuals. These varied responses may result from unident Show more
Fish oil supplements (FOS) are known to alter circulating levels of polyunsaturated fatty acids (PUFAs) but in a heterogeneous manner across individuals. These varied responses may result from unidentified gene-FOS interactions. To identify genetic factors that interact with FOS to alter the circulating levels of PUFAs, we performed a multi-level genome-wide interaction study (GWIS) of FOS on 14 plasma measurements in 200,060 unrelated European-ancestry individuals from the UK Biobank. From our single-variant tests, we identified genome-wide significant interacting SNPs (p < 5 Ă— 10 Show less
đź“„ PDF DOI: 10.1016/j.xhgg.2025.100459
FADS1
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
đź“„ PDF DOI: 10.1186/s12883-025-04379-y
APOB
Jun Qiao, Lei Jiang, Liuyang Cai +14 more · 2025 · Nature communications · Nature · added 2026-04-24
The extensive co-occurrence of cardiovascular diseases (CVDs), as evidenced by epidemiological studies, is supported by positive genetic correlations identified in comprehensive genetic investigations Show more
The extensive co-occurrence of cardiovascular diseases (CVDs), as evidenced by epidemiological studies, is supported by positive genetic correlations identified in comprehensive genetic investigations, suggesting a shared genetic basis. However, the precise genetic mechanisms underlying these associations remain elusive. By assessing genetic correlations, genetic overlap, and causal connections, we aim to shed light on common genetic underpinnings among major CVDs. Employing multi-trait analysis, we pursue diverse strategies to unveil shared genetic elements, encompassing SNPs, genes, gene sets, and functional categories with pleiotropic implications. Our study systematically quantifies genetic overlap beyond genome-wide genetic correlations across CVDs, while identifying a putative causal relationship between coronary artery disease (CAD) and heart failure (HF). We then pinpointed 38 genomic loci with pleiotropic influence across CVDs, of which the most influential pleiotropic locus is located at the LPA gene. Notably, 12 loci present high evidence of multi-trait colocalization and display congruent directional effects. Examination of genes and gene sets linked to these loci unveiled robust associations with circulatory system development processes. Intriguingly, distinct patterns predominantly driven by atrial fibrillation, coronary artery disease, and venous thromboembolism underscore the significant disparities between clinically defined CVD classifications and underlying shared biological mechanisms, according to functional annotation findings. Show less
đź“„ PDF DOI: 10.1038/s41467-025-62419-0
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