👤 Faye H 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, 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, 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
Qisheng Hu, Yongheng Zhang, Huawei Ming +5 more · 2025 · Medicine · added 2026-04-24
Periodontitis (PD) is a chronic inflammatory disease in which oxidative stress plays a crucial role in its progression. Mitophagy eliminates damaged mitochondria and alleviates oxidative stress; howev Show more
Periodontitis (PD) is a chronic inflammatory disease in which oxidative stress plays a crucial role in its progression. Mitophagy eliminates damaged mitochondria and alleviates oxidative stress; however, its specific regulatory mechanisms in PD remain unclear. This study utilized single-cell and bulk RNA sequencing data to identify core genes and investigate their potential roles. We utilized single-cell RNA sequencing data and applied 4 algorithms - area under the curve cell level enrichment, U-statistics-based single-cell signature scoring, single-sample gene set scoring, and AddModuleScore - to assess mitophagy activity and identify candidate genes. Subsequently, based on bulk RNA-seq data, 5 machine learning algorithms, including Least Absolute Shrinkage and Selection Operator Regression, random forest, Boruta, gradient boosting machine, and eXtreme Gradient Boosting, were employed to further screen core genes from the candidate gene set. Finally, immune infiltration analysis, cell communication analysis, and gene interaction network construction were integrated to systematically elucidate the regulatory mechanisms of core genes in the progression of PD. Single-cell RNA sequencing combined with multiple algorithms revealed significantly elevated mitophagy activity in PD tissues, particularly in monocytes/macrophages and endothelial cells. Additionally, we identified 4 core genes: BNIP3L, VPS13C, CTTN, and MAP1LC3B. BNIP3L and CTTN were downregulated in periodontitis, correlating negatively with disease prevalence, immune infiltration, and inflammatory pathways, whereas VPS13C and MAP1LC3B were upregulated, showing positive correlations. CellChat analysis highlighted monocytes/macrophages and endothelial cells with high core gene expression as key mediators of intercellular communication. This study identified BNIP3L, VPS13C, CTTN, and MAP1LC3B as core mitophagy-related genes associated with PD, and highlighted the pivotal roles of monocytes/macrophages and endothelial cells in disease progression. These findings provide new insights into the pathogenesis of PD and offer a theoretical foundation for mitophagy-targeted diagnosis, biomarker identification, and precision therapy. Show less
no PDF DOI: 10.1097/MD.0000000000044002
VPS13C
Yu Saito, Shuhai Chen, Tetsuya Ikemoto +4 more · 2025 · The journal of medical investigation : JMI · added 2026-04-24
Accelerating ammonium metabolism of hepatocyte like cells (HLCs) is critical for various functions of hepatocytes. The aim of the present study was to investigate whether Farnesoid X receptor (FXR) ag Show more
Accelerating ammonium metabolism of hepatocyte like cells (HLCs) is critical for various functions of hepatocytes. The aim of the present study was to investigate whether Farnesoid X receptor (FXR) agonist, obeticholic acid (OCA), accelerated ammonium metabolism of HLCs, which was derived from adipose derived mesenchymal stem cells (ADSCs). Human ADSCs were seed in flat bottom plate, then our differentiation protocol was used for 21 days. OCA treatment had been performed in Step3 for 10days. Then, 1) hepatic maturation, 2) urea cycle genes, 3) urea production, and 4) ammonium metabolism was compared depend on the presence or absence of OCA. HLCs had been successfully produced for 21 days. HLCs with OCA showed significantly higher mRNA expressions of AAT than those without OCA. HLCs with OCA showed significantly higher mRNA expressions of urea cycle genes such as SLC25A13, CPS1, and OTC. Urea production was also tended to be upregulated by OCA addition. HLCs with OCA showed significantly higher clearance of NH4Cl at 6hr and 24 hr after addition of NH4Cl. FXR agonist, OCA, accelerates ammonium metabolism of ADSCs derived HLCs. HLCs could be one of treatment options of hepatic encephalopathy of patients with liver failure or urea cycle disorder in the future. J. Med. Invest. 72 : 54-59, February, 2025. Show less
no PDF DOI: 10.2152/jmi.72.54
CPS1
Xian Chen, Hui Wang, Qianqian Li +4 more · 2025 · Discover oncology · Springer · added 2026-04-24
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether c Show more
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether causal relationships exist among these associations remains unclear, as traditional observational studies are susceptible to confounding factors. To evaluate causal relationships between kidney cancer, kidney fibrosis, and inflammatory factors using Mendelian randomization, and explore tumor microenvironment heterogeneity through single-cell analysis. Based on large-scale GWAS data, bidirectional Mendelian randomization analysis was performed to assess causal relationships between kidney cancer and kidney fibrosis, using MR Egger, inverse variance weighted (IVW), and weighted mode methods. Causal associations between kidney cancer and inflammatory factors including Axin-1, C-C motif chemokine 28, and interleukin-10 receptor subunit were analyzed. Single-cell RNA sequencing data from the GEO database (GSM4819725) was integrated for tumor microenvironment analysis. Bidirectional Mendelian randomization analysis revealed no significant causal relationship between kidney cancer and kidney fibrosis [kidney cancer→kidney fibrosis: IVW OR=0.992(95%CI: 0.913-1.077, P=0.842); kidney fibrosis→kidney cancer: IVW OR=0.922(95%CI: 0.824-1.030, P=0.151)]. However, significant positive causal associations were identified between kidney cancer and multiple inflammatory factors: Axin-1 levels [OR=1.448(95%CI: 1.107-1.894, P=0.007)], C-C motif chemokine 28 [OR=1.287(95%CI: 1.076-1.540, P=0.006)], and interleukin-10 receptor subunit [OR=1.135(95%CI: 1.032-1.248, P=0.009)]. Sensitivity analyses confirmed the robustness of results. Single-cell analysis revealed cellular heterogeneity in the tumor microenvironment, including various cell types such as immune cells, T cells, and NK cells, with pseudotime analysis demonstrating cell differentiation trajectories and dynamic gene expression changes. Mendelian randomization analysis provides genetic evidence for causal relationships between kidney cancer and inflammatory factors, while excluding direct causal associations between kidney cancer and kidney fibrosis. Show less
📄 PDF DOI: 10.1007/s12672-025-03343-z
AXIN1
Changlong Zhang, Yuxuan Li, Yang Wang +6 more · 2025 · Journal of advanced research · Elsevier · added 2026-04-24
Polycystic ovary syndrome (PCOS) is frequently accompanied with metabolic dysfunctions, yet the causal relationships between metabolic factors and PCOS remain to be conclusively established and etiolo Show more
Polycystic ovary syndrome (PCOS) is frequently accompanied with metabolic dysfunctions, yet the causal relationships between metabolic factors and PCOS remain to be conclusively established and etiology-based therapies are lacking. To comprehensively identify the metabolic causal factors and potential drug targets for PCOS. This genetic association study was conducted using bidirectional two-sample Mendelian Randomization (MR), multivariable MR (MVMR) and drug-target MR. Considering metabolic sexual dimorphism, female-specific genome-wide association studies (GWASs) for metabolic factors were obtained. To ensure the robustness of the findings, an additional independent PCOS GWAS dataset was utilized for replication. The PCOS cohort included 10,074 PCOS cases (mean age 28 to 45 years) and 103,164 controls (mean age 27 to 60 years) of European ancestry. All participants were female. Employing two-sample MR analysis, we found that genetically proxied body mass index (BMI) (OR = 3.40 [95 % CI, 2.65-4.36]), triglyceride (TG) (OR = 1.54 [95 % CI, 1.17-2.04]), low-density lipoprotein cholesterol (LDL-c) (OR = 1.37 [95 % CI, 1.07-1.76]), and type 2 diabetes (T2D) (OR = 1.24 [95 % CI, 1.09-1.41]) were significantly associated with an increased risk of PCOS, whereas genetically predicted high-density lipoprotein cholesterol (HDL-c) (OR = 0.61 [95 % CI, 0.47-0.80]) decreased the odds of PCOS. Stepwise MVMR established a hierarchy of interactions among these metabolic factors, identifying BMI and HDL-c as the most prominent causal factors. Notably, drug-target MR analysis identified incretin-based therapeutics, PCSK9 inhibitors, LPL gene therapy, sulfonylureas, and thiazolidinediones as potential therapeutics for PCOS. All these findings were validated in an independent dataset. This study offered insights into the roles of obesity, diabetes, and dyslipidemia in PCOS etiology and therapeutics, underscoring the necessity for managing metabolic health in women and paving the way for tailored therapeutic strategies for PCOS based on its metabolic underpinnings. Show less
📄 PDF DOI: 10.1016/j.jare.2024.10.038
LPL
Chunxiao Yang, Zhiqing Gao, Ruiming Tang +16 more · 2025 · British journal of cancer · Nature · added 2026-04-24
Activation of cancer-associated fibroblasts (CAFs) plays an important role in tumor metastasis. The purpose of this study is to investigate the role of POU6F2 in conversion of hepatic stellate cells ( Show more
Activation of cancer-associated fibroblasts (CAFs) plays an important role in tumor metastasis. The purpose of this study is to investigate the role of POU6F2 in conversion of hepatic stellate cells (HSCs) into CAFs in liver metastasis of gastric adenocarcinoma (GAC). POU6F2 expression was examined by real-time PCR, Western blot and immunohistochemical staining. The functional roles of POU6F2 in GAC liver metastasis were investigated both cellular experiments in vitro and in vivo using a mouse model of subcutaneous splenic injection. ChIP and ELISA assays were used to explore the underlying molecular mechanism of POU6F2 in liver metastasis of GAC. Here we reported that POU6F2 was upregulated in GAC tissue with liver metastasis, which predicted poor early liver metastasis. Upregulating POU6F2 promoted EMT, invasion and migration of GAC cells in vitro, and the liver metastasis of GAC cells in vivo. Mechanic investigation further revealed that upregulating POU6F2 promoted the invasion and metastasis of GAC by transcriptional upregulation of EMT-inducer SNAI1, and promoting the conversion of HSCs into CAFs dependent on transcriptional upregulation of IGF2-induced activation of PI3K/AKT signaling. Our findings uncover a novel dual mechanism by which POU6F2 promotes liver metastasis of GAC. Show less
no PDF DOI: 10.1038/s41416-025-03017-1
SNAI1
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
Huihui Shi, Lei Chen, Juan Huang +6 more · 2025 · Oncology research · added 2026-04-24
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide. This study aimed to identify key genes involved in HCC development and elucidate their molecular mech Show more
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide. This study aimed to identify key genes involved in HCC development and elucidate their molecular mechanisms, with a particular focus on mitochondrial function and apoptosis. Differential expression analyses were performed across three datasets-The Cancer Genome Atlas (TCGA)-Liver Hepatocellular Carcinoma (LIHC), GSE36076, and GSE95698-to identify overlapping differentially expressed genes (DEGs). A prognostic risk model was then constructed. Cysteine/serine-rich nuclear protein 1 ( A six-gene prognostic model was established, comprising downregulated genes ( Show less
no PDF DOI: 10.32604/or.2025.068737
POC5
Bingye Zhang, Yanli Ren, Yang Huo +2 more · 2025 · Inorganic chemistry · ACS Publications · added 2026-04-24
A series of long-persistent luminescence (LPL) phosphors Zn
no PDF DOI: 10.1021/acs.inorgchem.5c01203
LPL
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
Daibao Peng, Fei Chen, Haixuan Sun +1 more · 2025 · Scientific reports · Nature · added 2026-04-24
Thrombosis is a life-threatening complication in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. This study aims to conduct a statistical analysis of the incidence of blood clo Show more
Thrombosis is a life-threatening complication in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. This study aims to conduct a statistical analysis of the incidence of blood clots and lipid concentrations, and to examine the networks of oxylipins in hospitalised patients with SARS-CoV-2. Serum samples of 1731 hospitalised patients with SARS-COV-2 were used to measure six lipid parameters: total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A (apoA), and apolipoprotein B (apoB). Additionally, the lipid profiles and oxidative lipidomics characteristics were examined via liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-MS/MS) in SARS-COV-2-positive patients with and without thrombosis. The mortality rate in the SARS-COV-2 thrombosis group was significantly higher at 29.6% compared to the SARS-COV-2 non-thrombosis group at 12.1% (P < 0.0001). The levels of the lipid parameters were closely associated with both thrombosis and SARS-COV-2 severity. Patients with SARS-COV-2 admitted to the hospital exhibited significant changes in oxidative lipid metabolites, specifically in the arachidonic acid (ARA) and docosahexaenoic acid (DHA) classes, compared with those in the control group. Among the thrombus group, 28 oxidative lipid metabolites were found to be differentially expressed compared to the non-thrombus group, and with the most notable variations observed in 20-hydroxyPGF2α and 14(15)-EpETE. Enrichment analysis using KEGG revealed that differential oxidized lipid metabolites mainly concentrated in the ARA and serotonergic synapses metabolism signaling pathway. Our findings indicate a close association between lipid mediators and both SARS-COV-2 and thrombi. Specifically, ARA and serotonergic synapses metabolism signaling pathway may be an important pathogenic factor for thrombosis caused by SARS-COV-2. Furthermore, 20-hydroxyPGF2α and 14(15)-EpETE show promise as potential biomarkers for SARS-CoV-2-induced thrombosis. Show less
📄 PDF DOI: 10.1038/s41598-025-17722-7
APOB
Tao Yang, Hong Liu, Jian Chen · 2025 · Genes & genomics · Springer · added 2026-04-24
Osteoarthritis (OA) is a common progressive joint disorder marked by synovial inflammation, cartilage degeneration, the formation of osteophytes, though its underlying molecular mechanisms remain uncl Show more
Osteoarthritis (OA) is a common progressive joint disorder marked by synovial inflammation, cartilage degeneration, the formation of osteophytes, though its underlying molecular mechanisms remain unclear. This study integrated bioinformatics and experimental validation to identify key genes in OA synovium and their association with immune infiltration. Analysis of the GSE82107 dataset (10 OA, 7 controls) revealed 909 differentially expressed genes (525 upregulated, 384 downregulated). WGCNA identified the "midnightblue" module, and its intersection with DEGs yielded 122 genes enriched in cytokine-cytokine receptor interaction, JAK-STAT signaling, and autophagy pathways. Protein-protein interaction analysis highlighted FLT3LG, MC4R, CXCL10, CARTPT, and LHX2 as core genes (AUC 0.743-0.871). Immune infiltration analysis showed elevated M0 macrophages in OA, with CXCL10 showing a strong positive correlation with M1 macrophage infiltration (r = 0.74), and MC4R correlating with the presence of follicular helper T cells (r = 0.85). In vitro, OA-derived fibroblast-like synoviocytes exhibited CXCL10 upregulation, MC4R downregulation, and increased IL-6, IL-8, and TNF-α secretion, which were markedly reduced by CXCL10 knockdown or MC4R overexpression. Synovial tissue assays confirmed these expression patterns. CXCL10 and MC4R may represent promising diagnostic markers and therapeutic targets, offering new insights into OA immunopathogenesis and precision intervention. Show less
📄 PDF DOI: 10.1007/s13258-025-01679-y
MC4R
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
📄 PDF DOI: 10.1186/s12964-025-02578-1
IL27
Yongdi Zuo, Lei Chen, Manrong He +2 more · 2025 · Journal of clinical lipidology · Elsevier · added 2026-04-24
Chronic kidney disease (CKD) is a globally prevalent condition and still lacks effective specific medications. Metabolic dysregulation plays a crucial role in CKD. To Identify new potential targets fo Show more
Chronic kidney disease (CKD) is a globally prevalent condition and still lacks effective specific medications. Metabolic dysregulation plays a crucial role in CKD. To Identify new potential targets for CKD through metabolites and their regulatory genes. A total of 233 metabolites from the genome-wide association studies (GWAS) Catalog were utilized for Mendelian randomization (MR) with CKD. External validation was conducted from UK Biobank. Cis-eQTL of genes related to very low-density lipoprotein (VLDL) were selected for MR with CKD and metabolites. The total effect of the fatty acid desaturase 1 gene (FADS1) on CKD and metabolite-mediated effects were calculated. Bulk RNA-seq were used to validate FADS1 expression in the kidney tissues of patients with CKD. The cholesteryl esters to total lipids ratio in medium VLDL (odds ratio [OR] = 0.84; P.adj = .039) and total cholesterol to total lipids ratio in small VLDL (OR = 0.84; P.adj = .003) were protective factors for CKD, whereas the triglycerides to total lipids ratio in small VLDL (OR = 1.18; P.adj = .009) and the triglycerides to total lipids ratio in very small VLDL (OR = 1.1; P.adj < .001) were risk factors. They mediated the risk of CKD by FADS1 (OR = 1.1; P.adj = .001), and mediation effects of 21.17%, 10.43%, 23.52%, and 29.96%, respectively, were obtained. The differential expression of FADS1 was observed in the kidney tissues of patients with CKD. FADS1 is a risk factor for CKD and a novel therapeutic target. Four metabolites mediate the detrimental effect of FADS1 in CKD. Show less
no PDF DOI: 10.1016/j.jacl.2025.06.024
FADS1
Jingxuan Lian, Xiaohui Duan, Wenjie Chen +6 more · 2025 · Cell death discovery · Nature · added 2026-04-24
Gastrointestinal (GI) cancers exhibit aberrant lipid metabolism, yet the causal mechanisms remain elusive. Here, we integrated Mendelian randomization (MR) and multi-omics data to dissect metabolic dr Show more
Gastrointestinal (GI) cancers exhibit aberrant lipid metabolism, yet the causal mechanisms remain elusive. Here, we integrated Mendelian randomization (MR) and multi-omics data to dissect metabolic drivers of 20 GI diseases. Focusing on colorectal (CC) and esophageal cancer (EC), we identified five metabolites (e.g., 1,2-di-palmitoyl-sn-glycero-3-phosphocholine) and arachidonic acid ethyl ester as causal drivers. Summary-data-based MR and colocalization analysis (PP.H4 > 0.75) revealed FADS1 as a master regulator of these metabolites, with genetic variants exhibiting tissue-specific lipidomic effects. Functional validation using FADS1-knockout cell lines and mouse models demonstrated that FADS1 inhibition suppresses tumor cell proliferation, migration, and invasion while promoting apoptosis. In vivo, FADS1 deletion reduced chemically induced CC/EC tumor burden by 62-75%, accompanied by decreased Ki-67/MMP-9 expression and inflammatory infiltration. Mechanistically, FADS1 ablation disrupted lipid metabolism (reduced linoleic acid and arachidonic acid) and attenuated PI3K/AKT and MAPK signaling. Multi-omics integration further corroborated FADS1-mediated epigenetic regulation (e.g., mQTL-driven DNA methylation). This study establishes FADS1 as a pivotal orchestrator of GI carcinogenesis via metabolic reprogramming and signaling dysregulation, offering a compelling therapeutic target for precision oncology in CC and EC. Regulatory mechanisms of FADS1 in CC and EC. Show less
📄 PDF DOI: 10.1038/s41420-025-02768-3
FADS1
Shirui Jiang, Ailin Zhang, Jiegang Deng +5 more · 2025 · Frontiers in pediatrics · Frontiers · added 2026-04-24
Pediatric primary cardiomyopathies (PCMs) are rare diseases with complex causes and nonspecific treatment. The influence of electrolytes and amino acids (AAs) on cardiomyopathies has not been extensiv Show more
Pediatric primary cardiomyopathies (PCMs) are rare diseases with complex causes and nonspecific treatment. The influence of electrolytes and amino acids (AAs) on cardiomyopathies has not been extensively studied. This study aimed to explore clinical characteristics and the usage of electrolytes and AAs in children with PCMs. Children diagnosed with PCMs who had genetic test reports were included. Relevant information was collected and processed, and clinical characteristics and mutated genes were clarified. Gene databases were searched to explore related electrolytes and AAs in the treatment of PCMs. The effect of calcium was explored in children with DCM. Paired samples T tests and nonparametric Wilcoxon signed-rank tests were performed for comparison between before and after using calcium. In this study, 27 children with gene test results were enrolled to perform gene-related analysis. The median age was 2.5 years old. Mutated genes were collected, including pathogenic, likely pathogenic, uncertain significance, and other mutations. The most frequently mutated genes related to dilated cardiomyopathy (DCM) were For children with DCM, calcium supplements may be beneficial. AAs, including serine, cysteine, and arginine, could be used for supplementary treatment in children with DCM and HCM. Show less
📄 PDF DOI: 10.3389/fped.2025.1631632
MYBPC3
Linglong Liu, Xiaoping Fang, Xinbo Wang +8 more · 2025 · International journal of nursing studies advances · Elsevier · added 2026-04-24
Family caregivers ('carers') bear the highest care burden during the postoperative survivorship period of pancreatic cancer, given its poor prognosis. Most carers report unmet needs when taking on car Show more
Family caregivers ('carers') bear the highest care burden during the postoperative survivorship period of pancreatic cancer, given its poor prognosis. Most carers report unmet needs when taking on caregiving responsibilities during this period. Thoroughly investigating carers' needs is essential for helping families address practical care challenges. However, this important topic remains underexplored. To assess the need levels and identify need subgroups among carers of patients with pancreatic cancer 6 months after surgery and demographic predictors contributing to heterogeneity. Cross-sectional study. Participants were recruited from the pancreas centres of four tertiary A-level comprehensive hospitals in Jiangsu Province, China. 240 patients with pancreatic cancer and their carers ('dyads') participated in the survey. Carers completed the Comprehensive Needs Assessment Tool in Cancer for Carers, the Activities of Daily Living Scale for patients, and the General Demographic Information Questionnaire for dyads. Latent profile analysis (LPA) was used to categorise carers' needs. Non-parametric and chi-square tests were used to examine differences in need scores and sociodemographic characteristics among subgroups. Multiple logistic regression (MLR) was used to analyse sociodemographic impacts. Six months post-surgery, the total carers' need score was 41.83 ± 22.65 points, indicating a moderate level, with the highest needs reported for healthcare personnel, information and knowledge, and facilities and services. The LPA results revealed that carers were divided into five distinct subgroups based on differing levels of need across the domains assessed by the Comprehensive Needs Assessment Tool in Cancer for Carers, with proportions of 8.8 %, 22.5 %, 8.3 %, 55 %, and 5.4 %. Subgroup membership was predicted by four factors: carers' sex (odds ratio [OR]: 11.08, 95 % confidence interval [CI]: 1.64, 74.99, We have highlighted the complex individualised needs of carers of patients with pancreatic cancer. Through LPA and MLR, we identified distinct need subgroups and their predictors. Healthcare professionals may be able to improve dyads' health by tailoring support to each subgroup's specific needs and issues. Registration number: ChiCTR2400079415, registered 03/01/2024, first recruitment 04/02/2024. Show less
📄 PDF DOI: 10.1016/j.ijnsa.2025.100416
LPA
Hongyu Kuang, Dan Li, Yunlin Chen +7 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
Pathological cardiac hypertrophy is an independent risk factor for heart failure (HF). Early identification and timely treatment are crucial for significantly delaying the progression of HF. Targeted Show more
Pathological cardiac hypertrophy is an independent risk factor for heart failure (HF). Early identification and timely treatment are crucial for significantly delaying the progression of HF. Targeted amino acid metabolomics and RNA sequencing (RNA-seq) were combined to explore the underlying mechanism. In vitro, H9c2 cells were stimulated with angiotensin II (Ang II) or were incubated with extra valine after Ang II stimulation. The branched chain alpha-ketoate dehydrogenase kinase (Bckdk) inhibitor 3,6-dichlorobenzo[b]thiophene-2-carboxylic acid (BT2) and rapamycin were utilized to confirm the role of the mammalian target of rapamycin complex 1 (mTORC1) signaling pathway in this process. A significant accumulation of valine was detected within hypertrophic hearts from spontaneously hypertensive rats (SHR). When branched chain amino acid (BCAA) degradation was increased by BT2, the most pronounced decrease was observed in the valine level (Δ = 0.185 μmol/g, p < 0.001), and cardiac hypertrophy was ameliorated. The role of imbalanced mitochondrial quality control (MQC), including the suppression of mitophagy and excessive mitochondrial fission, was revealed in myocardial hypertrophy. In vitro, high concentrations of valine exacerbated cardiomyocyte hypertrophy stimulated by Any II, resulting in the accumulation of impaired mitochondria and respiratory chain dysfunction. BT2, rapamycin, and mitochondrial division inhibitor 1 (Mdivi-1) all ameliorated MQC imbalance, mitochondrial damage and oxidative stress in hypertensive models with high valine concentration. Valine exacerbated pathological cardiac hypertrophy by causing a MQC imbalance, probably as an early biomarker for cardiac hypertrophy under chronic hypertension. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.119216
BCKDK
Yingli Xu, Longkai Shi, Linlin Chen +2 more · 2025 · Scientific reports · Nature · added 2026-04-24
Little is known about the association between physical activity and the risk of pre-sarcopenic obesity (pre-SO) among adolescents. Hence, this study aimed to examine the association between physical a Show more
Little is known about the association between physical activity and the risk of pre-sarcopenic obesity (pre-SO) among adolescents. Hence, this study aimed to examine the association between physical activity and pre-SO in a sample of 2143 adolescents aged 12 to 18 years from Yinchuan, China. The pre-SO was defined by three criteria: low skeletal muscle mass adjusted by weight (SMM/W) combined with body mass index (BMI), fat mass percentage (FMP), and waist circumference (WC). After adjusting for age, smoking, drinking, sleep time, and high-fat food consumption, participants with high physical activity (HPA) had a lower risk of pre-SO compared to those with low physical activity (LPA) according to the obesity criteria of FMP (OR   0.63, 95% CI, 0.48-0.83, P < 0.05), and WC (OR 0.71, 95% CI, 0.52-0.96, P < 0.05). Additionally, restricted cubic spline models showed a linear dose-response association between total physical activity (TPA) and pre-SO no matter what obesity criteria were adopted (all P overall trend < 0.05, all P non-linear > 0.50). Subgroup analyses revealed that individuals with higher TPA levels exhibited a decreased risk of pre-SO in boys according to the obesity criteria of FMP, and WC. In conclusion, HPA is associated with a reduced risk of pre-SO in adolescents, especially among boys. Show less
📄 PDF DOI: 10.1038/s41598-025-28449-w
LPA
Zhijing Zhang, Di Wang, Riguang Zhong +6 more · 2025 · Cellular and molecular neurobiology · Springer · added 2026-04-24
Perioperative neurocognitive disorder (PND) is a common complication following thoracic surgery and often leading to poor outcomes. Despite ongoing research, effective treatments for late PND remain l Show more
Perioperative neurocognitive disorder (PND) is a common complication following thoracic surgery and often leading to poor outcomes. Despite ongoing research, effective treatments for late PND remain limited. Identifying reliable biomarkers for early diagnosis is, therefore, essential. A prospective cohort study was conducted with 60 elderly patients undergoing thoracic surgery. Serum samples were collected within 10 minutes prior to anesthesia and following extubation to measure adiponectin (APN), cyclic adenosine monophosphate (cAMP), protein kinase A (PKA), aquaporin-4 (AQP4) and brain-derived neurotrophic factor (BDNF). Among PND patients, serum APN, PKA, AQP4, and BDNF levels were markedly decreased compared with the normal group. While serum cAMP (HR = 1.087, p = 0.695, 95% CI [0.284-4.166]) and PKA (HR = 0.996, p = 0.09, 95% CI [0.491-0.947]) were not significantly correlated with PND, serum APN (HR = 0.307, 95% CI [0.113-0.835], p = 0.021), AQP4 (HR = 0.204, 95% CI [0.060-0.697], p = 0.011), and BDNF (HR = 0.382, 95% CI [0.177-0.823], p = 0.014) were protective factors against PND. ROC analysis demonstrated that APN (AUC = 0.68, 95% CI [0.51-0.87]), AQP4 (AUC = 0.73, 95% CI [0.59-0.87]), BDNF (AUC = 0.73, 95% CI [0.59-0.87]), and the model of combining those biomarkers (AUC = 0.91, 95% CI [0.83-0.99]) could predict PND. PND patients exhibited a lower protective stress response to surgical trauma. High serum APN, AQP4, and BDNF levels were independent protective factors for PND, and a combined model of these biomarkers showed predictive potential for PND. Show less
📄 PDF DOI: 10.1007/s10571-025-01636-z
BDNF
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
Kirk Habegger, Alejandra Tomas, Roger Chen +3 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
📄 PDF DOI: 10.3389/fendo.2025.1710843
GIPR
Binzhen Chen, Jia Liu, Yaoxin Zhang +10 more · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Multiple myeloma (MM) remains an incurable disease primarily due to the emergence of drug resistance, and the underlying mechanisms remain unclear. Extrachromosomal circular DNAs (eccDNAs) are prevale Show more
Multiple myeloma (MM) remains an incurable disease primarily due to the emergence of drug resistance, and the underlying mechanisms remain unclear. Extrachromosomal circular DNAs (eccDNAs) are prevalent in cancer genomes of both coding and non-coding regions. However, the role of non-coding eccDNA regions that serve as enhancers has been largely overlooked. Here, genome-wide profiling of serum eccDNAs from donors and MM patients who responded well or poorly to bortezomib-lenalidomide-dexamethasone (VRd) therapy is characterized. A high copy number of eccDNA ANKRD28 (eccANKRD28) predicts poor therapy response and prognosis but enhanced transcriptional activity. Established VRd-resistant MM cell lines exhibit a higher abundance of eccANKRD28, and CRISPR/Cas9-mediated elevation of eccANKRD28 desensitizes bortezomib and lenalidomide treatment both in vitro and in vivo. Integrated multi-omics analysis (H3K27ac ChIP-seq, scRNA-seq, scATAC-seq, CUT&Tag, et al.) identifies eccANKRD28 as an active enhancer involved in drug resistance driven by the key transcription factor, POU class 2 homeobox 2 (POU2F2). POU2F2 interacts with sequence-specific eccANKRD28 as well as RUNX1 and RUNX2 motifs to form the protein complex, which activates the promoter of oncogenes, including IRF4, JUNB, IKZF3, RUNX3, and BCL2. This study elucidates the potential transcriptional network of enhancer eccANKRD28 in MM drug resistance from a previously unrecognized epigenetic perspective. Show less
📄 PDF DOI: 10.1002/advs.202415695
ANKRD28
Hao Xiong, Ruiqi Liu, Keke Xu +7 more · 2025 · Journal of translational medicine · BioMed Central · added 2026-04-24
Cancer is one of the major diseases threatening human health in the world. According to the latest global cancer statistics from the International Agency for Research on Cancer (IARC), there were appr Show more
Cancer is one of the major diseases threatening human health in the world. According to the latest global cancer statistics from the International Agency for Research on Cancer (IARC), there were approximately 20 million new cancer cases and 10 million cancer deaths worldwide. Amidst this global health concern, branched chain amino acids have emerged as key players, playing an important role in the occurrence and development of cancer. In certain malignancies like colorectal cancer, the average level of BCAA in tumor tissues is twice that in normal tissues. BCAA metabolism is intricately associated with the progression of multiple tumors and is modulated by diverse enzymes, including BCAT, BCKDH, and BCKDK. The metabolism of BCAA involves multiple enzymes and biochemical processes via signaling pathways such as PI3K/AKT/mTOR and AMPK/mTOR, etc. In addition, mTOR inhibitors show potential value in cancer treatment by regulating the metabolism and signaling pathways of tumor cells, which provides a new direction for anticancer efforts. Simultaneously, BCAAs are closely associated with tumor immunity, including NK cells, CD4 Show less
📄 PDF DOI: 10.1186/s12967-025-06664-3
BCKDK
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
Xuan Tie, Zhiang Chen, Shulei Yao +6 more · 2025 · Frontiers in bioscience (Landmark edition) · added 2026-04-24
Primary membranous nephropathy (pMN) often progresses to end-stage renal disease (ESRD) in the absence of immunosuppressive therapy. The immunological mechanisms driving pMN progression remain insuffi Show more
Primary membranous nephropathy (pMN) often progresses to end-stage renal disease (ESRD) in the absence of immunosuppressive therapy. The immunological mechanisms driving pMN progression remain insufficiently understood. We developed a single-cell transcriptomic profile of peripheral blood mononuclear cells (PBMCs) from 11 newly-diagnosed pMN patients and 5 healthy donors. Through correlation analysis, we identified potential biomarkers for disease stratification and poor prognosis. Expression levels of several proinflammatory factors were significantly increased in patients compared to healthy donors, such as interleukins ( Our study provides insight into the immunological mechanism of pMN and identifies numerous biomarkers and signaling pathways as potential therapeutic targets for managing the progression of high-risk pMN. Show less
no PDF DOI: 10.31083/FBL36332
DHX36
Lingyan Li, Xingjie Wu, Qianqian Guo +9 more · 2025 · Journal of pharmaceutical analysis · Elsevier · added 2026-04-24
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, over Show more
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, overshadowing its influence on the biological effects of formulations. In this study, we reevaluated the delivery effect of CH using widely applied lipid microspheres (LM) as a model DDS. We conducted comprehensive investigations into the impact of CH on the distribution, cell uptake, and protein corona (PC) of LM at sites of cardiovascular inflammatory injury. The results demonstrated that moderate CH promoted the accumulation of LM at inflamed cardiac and vascular sites without exacerbating damage while partially mitigating pathological damage. Then, the slow cellular uptake rate observed for CH@LM contributed to a prolonged duration of drug efficacy. Network pharmacology and molecular docking analyses revealed that CH depended on LM and exerted its biological effects by modulating peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in vascular endothelial cells and estrogen receptor alpha (ERα) protein levels in myocardial cells, thereby enhancing LM uptake at cardiovascular inflammation sites. Proteomics analysis unveiled a serum adsorption pattern for CH@LM under inflammatory conditions showing significant adsorption with CH metabolism-related apolipoprotein family members such as apolipoprotein A-V (Apoa5); this may be a major contributing factor to their prolonged circulation Show less
📄 PDF DOI: 10.1016/j.jpha.2024.101182
APOA5
Fabricio Ferreira de Oliveira, Sandro Soares de Almeida, Elizabeth Suchi Chen +2 more · 2025 · Sao Paulo medical journal = Revista paulista de medicina · added 2026-04-24
Lipid profiles are largely determined by genetic variants, and lipid metabolism plays a crucial role in Alzheimer's disease. To investigate whether lipid profile variability in response to diverse sta Show more
Lipid profiles are largely determined by genetic variants, and lipid metabolism plays a crucial role in Alzheimer's disease. To investigate whether lipid profile variability in response to diverse statins could be affected by cholesterol metabolism-related genetic variants in Alzheimer's disease.. This prospective observational pharmacogenetic study was conducted at the Universidade Federal de São Paulo (Unifesp), Brazil. Consecutive outpatients were prospectively followed for lipid profile variations over one year, estimated by the associations between statin therapy and the following variants: rs2695121 (NR1H2), rs3846662 (HMGCR), rs11669576 (LDLR8), rs5930 (LDLR10), rs5882 and rs708272 (CETP), rs7412 and rs429358 (APOE), and ACE insertion/deletion polymorphism. All polymorphisms in the 189 patients were in Hardy-Weinberg equilibrium. Statins resulted in lower total cholesterol and LDL cholesterol levels, whereas the effects on HDL cholesterol varied according to the statin used. Atorvastatin resulted in lower triglyceride level variations than simvastatin. APOE-ε4 carriers showed a better response to atorvastatin in elevating HDL-cholesterol than APOE-ε4 non-carriers. Carriers of the ACE insertion allele had cumulatively lower total cholesterol and LDL-cholesterol levels, regardless of statin therapy, but lower triglyceride levels when using atorvastatin. Carriers of rs11669576-G had lower total cholesterol and LDL-cholesterol levels when using simvastatin, and lower total cholesterol and triglycerides when using atorvastatin. Concerning CETP haplotypes, carriers of rs5882-A and rs708272-A benefitted the most from statins, which lowered total cholesterol and increased HDL-cholesterol levels, and from atorvastatin lowering triglycerides; however, the effects of atorvastatin lowering total cholesterol and LDL-cholesterol were more pronounced for carriers of rs5882-GG/rs708272-GG. Lipid profile variations may be pharmacogenetically mediated in Alzheimer's disease, thus, confirming their high heritability. Show less
📄 PDF DOI: 10.1590/1516-3180.2024.0160.27112024
CETP
Yongting Li, Xiaolong Chen, Tingting Wang +3 more · 2025 · Brain sciences · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/brainsci15121339
BDNF
Xiaoju Liu, Congcong Li, Qingyin Meng +7 more · 2025 · ACS infectious diseases · ACS Publications · added 2026-04-24
Derazantinib (DZB), a pan-fibroblast growth factor receptor (FGFR) inhibitor, exhibits potent activity against FGFR1-3 kinases and has been clinically approved for antitumor therapy. However, its anti Show more
Derazantinib (DZB), a pan-fibroblast growth factor receptor (FGFR) inhibitor, exhibits potent activity against FGFR1-3 kinases and has been clinically approved for antitumor therapy. However, its antibacterial properties remain unknown. Here, we demonstrated that DZB displays broad-spectrum activity against Show less
no PDF DOI: 10.1021/acsinfecdis.4c01020
FGFR1
Yuting Li, Mingrui Wang, Na Zhang +3 more · 2025 · Ginekologia polska · added 2026-04-24
This study investigates the relationship between serum homocysteine, blood lipids, and perinatal outcomes in patients with diet-controlled gestational diabetes mellitus (GDM) and those with normal glu Show more
This study investigates the relationship between serum homocysteine, blood lipids, and perinatal outcomes in patients with diet-controlled gestational diabetes mellitus (GDM) and those with normal glucose tolerance (NGT). A prospective cohort of 150 diet-controlled GDM patients and 150 pregnant women with NGT, all delivering at our hospital, were selected based on predefined criteria. Data on demographics, physical parameters, and perinatal outcomes were compiled. Blood samples for fasting plasma glucose (FPG), homocysteine (Hcy), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), and apolipoprotein A1 (apoA1) were collected before delivery. GDM patients exhibited higher levels of FPG, Hcy, and the apoB/apoA1 ratio, but lower HDL-C and apoA1 levels compared to the NGT group. Adverse outcomes such as macrosomia, premature rupture of membranes, and postpartum hemorrhage were more prevalent in the GDM group. In GDM patients, neonatal birth weight positively correlated with FPG and TG levels. Stratified Hcy analysis in GDM showed no significant differences in perinatal outcomes. However, the third quartile of the apoB/apoA1 ratio had a lower incidence of macrosomia compared to the first quartile, and the second quartile showed a higher incidence of birth asphyxia. GDM patients demonstrated increased levels of Hcy, FPG, and the apoB/apoA1 ratio, correlating with more adverse perinatal outcomes than healthy pregnant individuals. The relationships between Hcy, lipids, and these outcomes remain inconclusive, highlighting the need for further research. Show less
no PDF DOI: 10.5603/gpl.101475
APOB