👤 R Chen

🔍 Search 📋 Browse 🏷️ Tags ❤️ Favourites ➕ Add 🧪 BiometalDB 🧬 Extraction
2981
Articles
1996
Name variants
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, 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, Chian-Feng 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-κB signaling activation in foam cells, leading to a marked downregulation of pro-inflammatory cytokine and disruption of inflammatory cascades. In ApoE Show less
📄 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
📄 PDF DOI: 10.1002/nop2.70373
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
📄 PDF DOI: 10.21037/tlcr-2025-894
LPA
Xinghua Wu, Kai Lin, Chen Gao +4 more · 2025 · European journal of histochemistry : EJH · added 2026-04-24
In recent years, accumulating evidence has highlighted the critical role of miR-627-5p in the occurrence and progression of various cancers. However, its specific role and mechanism in cervical cancer Show more
In recent years, accumulating evidence has highlighted the critical role of miR-627-5p in the occurrence and progression of various cancers. However, its specific role and mechanism in cervical cancer (CC) remain unclear. This study aimed to elucidate the mechanism by which miR-627-5p inhibits the malignant progression of CC and assess its potential clinical implications. In C33A cells, the mRNA expression levels of ANGPTL4 and miR-627-5p were analyzed using qRT-PCR. The miR-627-5p mimics and their control (miR-NC) were transfected into C33A cells to determine whether miR-627-5p directly regulates ANGPTL4 expression. A comprehensive suite of assays, including CCK-8, migration, transwell, flow cytometry, and Western blotting, was conducted to evaluate how miR-627-5p modulates the malignant biological behavior of CC cells. Rescue experiments were performed by overexpressing ANGPTL4. In C33A cells, miR-627-5p expression was reduced, whereas ANGPTL4 expression was elevated. Further analysis confirmed that miR-627-5p negatively regulates ANGPTL4 by directly targeting its 3'-UTR. Functional assays demonstrated that miR-627-5p inhibits proliferation, invasion, migration, and epithelial-mesenchymal transition (EMT) while promoting apoptosis and S-phase arrest in C33A cells, effects that were reversed by ANGPTL4 overexpression. These findings highlight the potential of miR-627-5p as both a biomarker and a therapeutic target for CC. By inhibiting EMT and regulating ANGPTL4 expression, miR-627-5p may provide a novel avenue for improving therapeutic strategies, particularly in advanced or metastatic CC. Moreover, miRNA-based therapies, supported by advanced delivery systems such as nanoparticle carriers, could enhance the stability and precision of miR-627-5p applications. This study lays the groundwork for future research integrating miR-627-5p into precision medicine approaches for CC treatment. Show less
📄 PDF DOI: 10.4081/ejh.2025.4161
ANGPTL4
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
📄 PDF DOI: 10.3390/nu17233648
LPL
Baoqi Li, Mingcong Xu, Bang An +8 more · 2025 · Materials horizons · Royal Society of Chemistry · added 2026-04-24
Dynamic responsive structural colored materials have drawn increased consideration in a wide range of applications, such as colorimetric sensors and high-safety tags. However, the sophisticated intera Show more
Dynamic responsive structural colored materials have drawn increased consideration in a wide range of applications, such as colorimetric sensors and high-safety tags. However, the sophisticated interactions among the individual responsive parts restrict the advanced design of multimodal responsive photonic materials. Inspired by stimuli-responsive color change in chameleon skin, a simple and effective photo-crosslinking strategy is proposed to construct hydroxypropyl cellulose (HPC) based hydrogels with multiple responsive structured colors. By controlling UV exposure time, the structural color of HPC hydrogels can be effectively controlled in a full-color spectrum. At the same time, HPC hydrogels showcase temperature and mechanical dual-responsive structural colors. In particular, the microstructure of HPC hydrogels undergoes a transition from the chiral nematic phase to the nematic phase under the action of external stretching, leading to a significant reflection of circularly polarized light (CPL) to linearly polarized light (LPL). Given the diverse responsiveness exhibited by HPC hydrogels and their unique structural transition properties under external forces, we have explored their potential applications as dynamic anti-counterfeiting labels and optical skins. This work reveals the great possibility of using structural colored cellulose hydrogels in multi-sensing and optical displays, opening up a new path for the exploration of next-generation flexible photonic devices. Show less
no PDF DOI: 10.1039/d4mh01646g
LPL
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 γ-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
📄 PDF DOI: 10.1167/iovs.66.6.6
FADS1
Xiaolan Chen, Jin You, Qin Ma +7 more · 2025 · Nature communications · Nature · added 2026-04-24
R-loop is a common chromatin feature consisting of a displaced single-stranded DNA and an RNA-DNA hybrid, and dysregulation of R-loop surveillance results in genomic and transcriptomic instability. Al Show more
R-loop is a common chromatin feature consisting of a displaced single-stranded DNA and an RNA-DNA hybrid, and dysregulation of R-loop surveillance results in genomic and transcriptomic instability. Although the RNA moiety of most R-loops originates from linear transcripts, circular RNAs (circRNAs), outputs from back-splicing, can also hybridize with the complementary strand of a DNA duplex. However, how circRNA-associated R-loops (ciR-loops) are monitored remains elusive. Here, we identify the DEAD-box RNA helicase Brr2 as an evolutionarily-conserved ciR-loop repressor with dual roles in inhibiting circRNA generation and resolving harmful ciR-loops. Accumulation of ciR-loops caused by loss-of-function of this dual-action factor induces antisense transcription and premature transcription termination for many genes and generates significant DNA damage, which further leads to a series of defects in DNA replication, cell division and cell proliferation. We propose that functional integration of multilayered regulation by a single protein can be an efficient double protection against genome instability. Show less
📄 PDF DOI: 10.1038/s41467-025-64174-8
DHX36
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
📄 PDF DOI: 10.1186/s12876-025-04004-9
APOB
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
📄 PDF DOI: 10.1177/00368504251352375
APOB
Sydney G Walker, Yan Q Chen, Kelli L Sylvers-Davie +13 more · 2025 · JCI insight · added 2026-04-24
Angiopoietin-like 3 (ANGPTL3) is a major regulator of lipoprotein metabolism. ANGPTL3 deficiency results in lower levels of triglycerides, LDL-cholesterol (LDL-C), and HDL-cholesterol (HDL-C), and may Show more
Angiopoietin-like 3 (ANGPTL3) is a major regulator of lipoprotein metabolism. ANGPTL3 deficiency results in lower levels of triglycerides, LDL-cholesterol (LDL-C), and HDL-cholesterol (HDL-C), and may protect from cardiovascular disease. ANGPTL3 oligomerizes with ANGPTL8 to inhibit lipoprotein lipase (LPL), the enzyme responsible for plasma triglyceride hydrolysis. Independently of ANGPTL8, oligomers of ANGPTL3 can inhibit endothelial lipase (EL), which regulates circulating HDL-C and LDL-C levels through the hydrolysis of lipoprotein phospholipids. The N-terminal region of ANGPTL3 is necessary for both oligomerization and lipase inhibition. However, our understanding of the specific residues that contribute to these functions is incomplete. In this study, we performed mutagenesis of the N-terminal region to identify residues important for EL inhibition and oligomerization. We also assessed the presence of different ANGPTL3 species in human plasma. We identified a motif important for lipase inhibition, and protein structure prediction suggested that this region interacted directly with EL. We also found that recombinant ANGPTL3 formed a homotrimer and was unable to inhibit EL activity when trimerization was disrupted. Surprisingly, we observed that human plasma contained more monomeric ANGPTL3 than trimeric ANGPTL3. An important implication of these findings is that previous correlations between circulating ANGPTL3 and circulating triglyceride-rich lipoproteins need to be revisited. Show less
📄 PDF DOI: 10.1172/jci.insight.197827
LPL
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
APOA5
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
Haotian Chen, Zhengye Liu, Hanze Du +7 more · 2025 · BMJ open gastroenterology · added 2026-04-24
Gallstone disease (GD) is a common gastrointestinal disorder with a significant genetic component. Despite known risk factors, the genetic basis of GD remains incompletely understood. We aimed to iden Show more
Gallstone disease (GD) is a common gastrointestinal disorder with a significant genetic component. Despite known risk factors, the genetic basis of GD remains incompletely understood. We aimed to identify novel genetic loci associated with GD, explore their clinical implications and investigate their therapeutic potential. We conducted a genome-wide association study from the UK Biobank followed by a meta-analysis, integrating summary statistics from the FinnGen R11, with further replication from Biobank Japan. Using systematic bioinformatic approaches, we performed gene prioritisation, colocalisation analysis, transcriptome-wide association study, Mendelian randomisations, cross-trait genetic correlations, phenome-wide association study, clinical investigations and gene-environment interactions by leveraging data from the FinnGen, Genotype-Tissue Expression project and Liver Cell Atlas single-cell transcriptomics data set. Our study highlighted novel susceptibility loci near candidate genes (ie, This study provides new insights into the genetic basis of GD and highlights the role of hepatocytes in GD pathogenesis. These findings have implications for the personalised prevention strategies and new therapeutic interventions in individuals predisposed to GD. Show less
📄 PDF DOI: 10.1136/bmjgast-2025-001976
FADS1
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
📄 PDF DOI: 10.1172/jci.insight.189503
ANGPTL4
Litong Qi, Hua Shen, Meng Chai +11 more · 2025 · Cardiovascular diabetology · BioMed Central · added 2026-04-24
This study evaluated the efficacy and safety of tafolecimab in patients with type 2 diabetes (T2D) and hypercholesterolemia by a post-hoc analysis of pooled data from three phase 3 trials. Data from u Show more
This study evaluated the efficacy and safety of tafolecimab in patients with type 2 diabetes (T2D) and hypercholesterolemia by a post-hoc analysis of pooled data from three phase 3 trials. Data from up to 12 weeks were analyzed to assess the effects of tafolecimab 450 mg every four weeks (Q4W) in patients with T2D and hypercholesterolemia. The primary endpoint was the percentage change in low-density lipoprotein cholesterol (LDL-C) levels from baseline to week 12. Secondary endpoints included the proportion of participants achieving LDL-C levels below 1.8 mmol/L at weeks 12, the proportion of patients achieving LDL-C ≥ 50% reduction and LDL-C < 1.4 mmol/L, as well as percentage changes from baseline to week 12 in non-high-density lipoprotein cholesterol (non-HDL-C), apolipoprotein B (apo B), lipoprotein(a) [Lp(a)], and triglyceride (TG) levels. The reduction in LDL-C from baseline was significantly greater in patients receiving tafolecimab than in those receiving placebo (estimated treatment difference: - 64.02%, 95% confidence interval: [- 68.08%, - 59.96%], P < 0.0001). The proportion of patients achieving a reduction of over 50% and an absolute LDL-C value below 1.4 mmol/L was significantly higher in the tafolecimab group than that in the placebo group (P < 0.0001). Furthermore, a significantly greater proportion of patients in the tafolecimab group achieved LDL-C levels below 1.8 mmol/L at week 12 compared to the placebo group (P < 0.0001). The tafolecimab group also showed significant reductions in TG, non-HDL-C, apo B, and Lp(a) from baseline to week 12 compared to the placebo group (all P < 0.001). The incidence of adverse events was generally similar between the two groups. Tafolecimab 450 mg Q4W demonstrated a superior lipid-lowering efficacy and favorable safety profile compared to placebo. This suggests it could be a promising new treatment option for Chinese patients with T2D and hypercholesterolemia. Show less
📄 PDF DOI: 10.1186/s12933-025-02816-3
APOB
Lin Ai, Yi Han, Ting Ge +14 more · 2025 · Acta pharmacologica Sinica · Nature · added 2026-04-24
Some individuals are more susceptible to developing or suffering from pain states than others. However, the brain mechanisms underlying the susceptibility to pain responses are unknown. In this study, Show more
Some individuals are more susceptible to developing or suffering from pain states than others. However, the brain mechanisms underlying the susceptibility to pain responses are unknown. In this study, we defined pain susceptibility by recapitulating inter-individual differences in pain responses in mice exposed to a paradigm of socially transferred allodynia (STA), and with a combination of chemogenetic, molecular, pharmacological and electrophysiological approaches, we identified GABA-ergic neurons in the dorsal raphe nucleus (DRN) as a cellular target for the development and maintenance of STA susceptibility. We showed that DRN GABA-ergic neurons were selectively activated in STA-susceptible mice when compared with the unsusceptible (resilient) or control mice. Chemogenetic activation of DRN GABA-ergic neurons promoted STA susceptibility; whereas inhibiting these neurons prevented the development of STA susceptibility and reversed established STA. In in vitro slice electrophysiological analysis, we demonstrated that melanocortin 4 receptor (MC4R) enriched in DRN GABA-ergic neurons was a molecular target for regulating pain susceptibility, possibly by affecting DRN GABA-ergic neuronal activity. These results establish the DRN GABA-ergic neurons as an essential target for controlling pain susceptibility, thus providing important information for developing conceptually innovative and more accurate analgesic strategies. Show less
no PDF DOI: 10.1038/s41401-025-01494-x
MC4R
Yifei Lu, Tian Xia, Yongjia Jin +8 more · 2025 · Pediatric surgery international · Springer · added 2026-04-24
Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma. FOXO1 fusion indicates poor prognosis and lead to dysregulation of transcriptioanal network. This study aims to investigate cli Show more
Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma. FOXO1 fusion indicates poor prognosis and lead to dysregulation of transcriptioanal network. This study aims to investigate clinical characteristics and therapeutic targets concerning FOXO1 fusion status. 65 pediatric RMS patients were enrolled. Clinical data were analyzed using Kaplan-Meier estimates and Cox regression. Surgically resected tumor tissues were subject to single-cell RNA sequencing (scRNA-seq). Patient-derived xenograft (PDX) was establish and dissociated to cells for high-throughput drug screening. Among the 65 patinets (36 patients with embryonal RMSs (ERMSs), 15 patients with alveolar RMSs (ARMSs) and 14 patients with other types of RMSs), 73.3% of ARMSs were defined as fusion positive (FP) while 6 ERMS (ERMS)s were also FP. Cox regression analysis identified FOXO1 fusion as a risk factor alone and combined with pathologic subtype, sex and age or metastasis status. scRNA-seq revealed distinct transcription factor networks between FP and FN RMS, showing up-regulated activity of OLIG2, NHLH1, SNAI1, TFF3 and other TFs related to neural development and differentiation. MAPK, PI3K-Akt, and mTOR pathways were enriched in FP-RMS tumor cells. High-throughput drug screening of PDX-derived cells identified sensitive drugs targeting FP-RMS specific signatures. AMG-337 was selected and validated for its anti-tumor effect. FOXO1 fusion status influences RMS clinical outcomes, including rare FP-ERMS cases. scRNA-seq combined with drug screening identified MET as a promising therapeutic target in FP-RMS. Show less
no PDF DOI: 10.1007/s00383-025-06241-1
SNAI1
Ziming Chen, Weiqiang Guo, Yahan Gao +6 more · 2025 · Anti-cancer agents in medicinal chemistry · Bentham Science · added 2026-04-24
Ursolic acid (UA) exhibits antitumor activity; however, its effects and mechanisms on triple-negative breast cancer (TNBC) cells are not well understood. The present study aimed to explore the anti- T Show more
Ursolic acid (UA) exhibits antitumor activity; however, its effects and mechanisms on triple-negative breast cancer (TNBC) cells are not well understood. The present study aimed to explore the anti- TNBC mechanisms of UA by network pharmacology and experimental validation. TNBC cell lines MDA-MB-231 and BT-549 cells were treated with UA. A CCK-8 assay was performed to detect cell growth, while flow cytometry assessed cell cycle arrest and apoptosis. The underlying mechanism and potential targets of UA for TNBC treatment were investigated by network pharmacology, including PharmMapper database, GO, KEGG enrichment, and PPI analysis. The protein expressions and phosphorylation levels of FGFR1, AKT, and ERK were measured by western blot. Pull-down assay, cellular thermal shift assay (CETSA), and molecular docking were used to analyze the interaction between UA and FGFR1. Xenograft models were established to examine the effect of UA on TNBC tumor growth. UA effectively reduced cell viability, induced apoptosis, and arrested cell cycle in TNBC cells. Moreover, UA significantly regulated the expression of Bcl-2 and Bax to induce apoptosis. The results of network pharmacology and western blot suggested that UA reduced FGFR1/AKT/ERK pathway. Furthermore, pull-down, CETSA, and molecular docking results revealed that UA directly bound to FGFR1. In the xenograft model, UA inhibited the growth by suppressing FGFR1. In this study, we employed network pharmacology and experimental approaches to elucidate the mechanism of UA on TNBC. The results demonstrated that UA targeted FGFR1 to inhibit TNBC via mediating FGFR1/AKT/ERK pathway. Our findings demonstrate that UA inhibits the FGFR1/AKT/ERK pathway by directly targeting FGFR1, thereby suppressing TNBC progression and supporting its potential as a therapeutic agent for TNBC treatment. Show less
no PDF DOI: 10.2174/0118715206379579250722053647
FGFR1
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
📄 PDF DOI: 10.31083/RCM42784
LPA
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
📄 PDF DOI: 10.1038/s41586-024-08510-w
IL27
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
Günther Silbernagel, Yan Q Chen, Hongxia Li +19 more · 2025 · Circulation · added 2026-04-24
ANGPTL3/4/8 (angiopoietin-like proteins 3, 4, and 8) are important regulators of LPL (lipoprotein lipase). ANGPTL8 forms complexes with ANGPTL3 and ANGPTL4. ANGPTL4/8 complex formation converts ANGPTL Show more
ANGPTL3/4/8 (angiopoietin-like proteins 3, 4, and 8) are important regulators of LPL (lipoprotein lipase). ANGPTL8 forms complexes with ANGPTL3 and ANGPTL4. ANGPTL4/8 complex formation converts ANGPTL4 from a furin substrate to a plasmin substrate, and both cleavages generate similar C-terminal domain-containing (CD)-ANGPTL4 fragments. Whereas several studies have investigated associations of free ANGPTL proteins with cardiovascular risk, there are no data describing associations of the complexes and CD-ANGPTL4 with outcomes or describing the effects of the complexes on LPL bound to GPIHBP1 (glycosylphosphatidylinositol HDL-binding protein 1). Recombinant protein assays were used to study ANGPTL protein and complex effects on GPIHBP1-LPL activity. ANGPTL3/8, ANGPTL3, ANGPTL4/8, and CD-ANGPTL4 were measured with dedicated immunoassays in 2394 LURIC (Ludwigshafen Risk and Cardiovascular Health) study participants undergoing coronary angiography and 6188 getABI study (German Epidemiological Trial on Ankle Brachial Index) participants undergoing ankle brachial index measurement. There was a follow-up for cardiovascular death with a median (interquartile range) duration of 9.80 (8.75-10.40) years in the LURIC study and 7.06 (7.00-7.14) years in the getABI study. ANGPTL3/8 potently inhibited GPIHBP1-LPL activity and showed positive associations with LDL-C (low-density lipoprotein cholesterol) and triglycerides (both ANGPTL3/8 potently inhibited GPIHBP1-LPL enzymatic activity, consistent with its positive association with serum lipids. However, ANGPTL3/8, LDL-C, and triglyceride levels were not associated with cardiovascular death in the LURIC and getABI cohorts. In contrast, concentrations of ANGPTL4/8 and particularly CD-ANGPTL4 were positively associated with inflammation, the prevalence of diabetes, and cardiovascular mortality. Show less
no PDF DOI: 10.1161/CIRCULATIONAHA.124.069272
ANGPTL4
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
📄 PDF DOI: 10.1101/2025.03.23.644666
GIPR
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 × 10 Show less
📄 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
📄 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
📄 PDF DOI: 10.1038/s41467-025-62419-0
LPA
Robert Chen, Ben Omega Petrazzini, Áine Duffy +4 more · 2025 · Genome biology · BioMed Central · added 2026-04-24
Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been Show more
Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been limited by phenotyping challenges and small sample sizes. We test associations of rare and ultra-rare coding variants with proton density fat fraction (PDFF) and MASLD case-control status in 736,010 participants of diverse ancestries from the UK Biobank, All of Us, and BioMe and performed a trans-ancestral meta-analysis. We then developed models to accurately predict PDFF and MASLD status in the UK Biobank and tested associations with these predicted phenotypes to increase statistical power. The trans-ancestral meta-analysis with PDFF and MASLD case-control status identifies two single variants and two gene-level associations in APOB, CDH5, MYCBP2, and XAB2. Association testing with predicted phenotypes, which replicates more known genetic variants from GWAS than true phenotypes, identifies 16 single variants and 11 gene-level associations implicating 23 additional genes. Two variants were polymorphic only among African ancestry participants and several associations showed significant heterogeneity in ancestry and sex-stratified analyses. In total, we identified 27 genes, of which 3 are monogenic causes of steatosis (APOB, G6PC1, PPARG), 4 were previously associated with MASLD (APOB, APOC3, INSR, PPARG), and 23 had supporting clinical, experimental, and/or genetic evidence. Our results suggest that trans-ancestral association analyses can identify ancestry-specific rare and ultra-rare coding variants in MASLD pathogenesis. Furthermore, we demonstrate the utility of machine learning in genetic investigations of difficult-to-phenotype diseases in trans-ancestral biobanks. Show less
📄 PDF DOI: 10.1186/s13059-025-03518-5
APOB
Zhenwei Dai, Shu Jing, Haiyan Hu +8 more · 2025 · Brain and behavior · Wiley · added 2026-04-24
Human papillomavirus (HPV) infection is a global public health issue, and HPV-related stigma can affect cervical cancer prevention. But no validated tools exist to assess HPV stigma in Chinese adult w Show more
Human papillomavirus (HPV) infection is a global public health issue, and HPV-related stigma can affect cervical cancer prevention. But no validated tools exist to assess HPV stigma in Chinese adult women infected with HPV. This study aimed to adapt and validate the HPVsStigma scale (HPV-SS) in the Chinese context. A cross-sectional study was conducted from December 2024 to February 2025 among 501 HPV-infected women in Shenzhen, China. The HPV-SS was adapted from a 12-item HIV stigma scale. Demographic characteristics, HPV-related variables, and data on mental health were collected. Factor analyses (FA) were used to assess the scale's factorial structure, reliability, and validity. The bi-factor model was used to determine the score-reporting method of the scale. Item response theory (IRT) was employed to assess the relationship between participants' stigma levels and scale scores. Latent profile analysis (LPA) was conducted to classify the participants with different HPV stigma characteristics and determine the optimal cut-off value for HPV-SS. FA showed that the 3-factor model (personalized stigma, public-disclosure concerns, and negative self-image) had the best fit among the nested models, with good reliability and validity. The bi-factor model analysis indicated that the total scale score was more meaningful than dimension scores. IRT analysis confirmed that higher HPV-SS scores represented higher stigma levels. LPA identified a 2-class model as optimal, and the optimal cut-off value of the scale for high HPV stigma was 35. This study validated the 12-item HPV-SS for Chinese women infected with HPV, with good reliability and validity. The scale can be used to evaluate HPV stigma levels, facilitating targeted interventions to improve cervical cancer prevention and the psychological well-being of affected women. Show less
📄 PDF DOI: 10.1002/brb3.71044
LPA