👤 Xiao-Wei Chen

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2981
Articles
1996
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Also published as: Ai-Qun Chen, Aiping Chen, Alex Chen, Alex F Chen, Alice P Chen, Alice Y Chen, Alice Ye A Chen, Allen Menglin Chen, Alon Chen, Alvin Chen, An Chen, Andrew Chen, Anqi Chen, Aoshuang Chen, Aozhou Chen, B Chen, B-S Chen, Baihua Chen, Ban Chen, Bang Chen, Bang-dang Chen, Bao-Bao Chen, Bao-Fu Chen, Bao-Sheng Chen, Bao-Ying Chen, Baofeng Chen, Baojiu Chen, Baolin Chen, Baosheng Chen, Baoxiang Chen, Beidong Chen, Beijian Chen, Ben-Kuen Chen, Benjamin Chen, Benjamin Jieming Chen, Benjamin P C Chen, Beth L Chen, Bihong T Chen, Bin Chen, Bing Chen, Bing-Bing Chen, Bing-Feng Chen, Bing-Huei Chen, Bingdi Chen, Bingqian Chen, Bingqing Chen, Bingyu Chen, Binlong Chen, Binzhen Chen, Bo Chen, Bo-Fang Chen, Bo-Jun Chen, Bo-Rui Chen, Bo-Sheng Chen, Bohe Chen, Bohong Chen, Bosong Chen, Bowang Chen, Bowei Chen, Bowen Chen, Boyu Chen, Brian Chen, C Chen, C Y Chen, C Z Chen, C-Y Chen, Cai-Long Chen, Caihong Chen, Can Chen, Cancan Chen, Canrong Chen, Canyu Chen, Caressa Chen, Carl Pc Chen, Carol Chen, Carol X-Q Chen, Catherine Qing Chen, Ceshi Chen, Chan Chen, Chang Chen, Chang-Lan Chen, Chang-Zheng Chen, Changjie Chen, Changya Chen, Changyan Chen, Chanjuan Chen, Chao Chen, Chao-Jung Chen, Chao-Wei Chen, Chaochao Chen, Chaojin Chen, Chaoli Chen, Chaoping Chen, Chaoqun Chen, Chaoran Chen, Chaoyi Chen, Chaoyue Chen, Chen Chen, Chen-Mei Chen, Chen-Sheng Chen, Chen-Yu Chen, Cheng Chen, Cheng-Fong Chen, Cheng-Sheng Chen, Cheng-Yi Chen, Cheng-Yu Chen, Chengchuan Chen, Chengchun Chen, Chengde Chen, Chengsheng Chen, Chengwei Chen, Chenyang Chen, Chi Chen, Chi-Chien Chen, Chi-Hua Chen, Chi-Long Chen, Chi-Yu Chen, Chi-Yuan Chen, Chi-Yun Chen, Chian-Feng Chen, Chider Chen, Chien-Hsiun Chen, Chien-Jen Chen, Chien-Lun Chen, Chien-Ting Chen, Chien-Yu Chen, Chih-Chieh Chen, Chih-Mei Chen, Chih-Ping Chen, Chih-Ta Chen, Chih-Wei Chen, Chih-Yi Chen, Chin-Chuan Chen, Ching Kit Chen, Ching-Hsuan Chen, Ching-Jung Chen, Ching-Wen Chen, Ching-Yi Chen, Ching-Yu Chen, Chiqi Chen, Chiung Mei Chen, Chiung-Mei Chen, Chixiang Chen, Chong Chen, Chongyang Chen, Christina Y Chen, Christina Yingxian Chen, Christopher S Chen, Chu Chen, Chu-Huang Chen, Chuanbing Chen, Chuannan Chen, Chuanzhi Chen, Chuck T Chen, Chueh-Tan Chen, Chujie Chen, Chun Chen, Chun-An Chen, Chun-Chi Chen, Chun-Fa Chen, Chun-Han Chen, Chun-Houh Chen, Chun-Wei Chen, Chun-Yuan Chen, Chung-Hao Chen, Chung-Hsing Chen, Chung-Hung Chen, Chung-Jen Chen, Chung-Yung Chen, Chunhai Chen, Chunhua Chen, Chunji Chen, Chunjie Chen, Chunlin Chen, Chunnuan Chen, Chunxiu Chen, Chuo Chen, Chuyu Chen, Cindi Chen, Constance Chen, Cuicui Chen, Cuie Chen, Cuilan Chen, Cuimin Chen, Cuncun Chen, D F Chen, D M Chen, D-F Chen, D. Chen, Dafang Chen, Daijie Chen, Daiwen Chen, Daiyu Chen, Dake Chen, Dali Chen, Dan Chen, Dan-Dan Chen, Dandan Chen, Danlei Chen, Danli Chen, Danmei Chen, Danna Chen, Danni Chen, Danxia Chen, Danxiang Chen, Danyang Chen, Danyu Chen, Daoyuan Chen, Dapeng Chen, Dawei Chen, Defang Chen, Dejuan Chen, Delong Chen, Denghui Chen, Dengpeng Chen, Deqian Chen, Dexi Chen, Dexiang Chen, Dexiong Chen, Deying Chen, Deyu Chen, Di Chen, Di-Long Chen, Dian Chen, Dianke Chen, Ding Chen, Diyun Chen, Dong Chen, Dong-Mei Chen, Dong-Yi Chen, Dongli Chen, Donglong Chen, Dongquan Chen, Dongrong Chen, Dongsheng Chen, Dongxue Chen, Dongyan Chen, Dongyin Chen, Du-Qun Chen, Duan-Yu Chen, Duo Chen, Duo-Xue Chen, Duoting Chen, E S Chen, Eleanor Y Chen, Elizabeth H Chen, Elizabeth S Chen, Elizabeth Suchi Chen, Emily Chen, En-Qiang Chen, Erbao Chen, Erfei Chen, Erqu Chen, Erzhen Chen, Everett H Chen, F Chen, F-K Chen, Fa Chen, Fa-Xi Chen, Fahui Chen, Fan Chen, Fang Chen, Fang-Pei Chen, Fang-Yu Chen, Fang-Zhi Chen, Fang-Zhou Chen, Fangfang Chen, Fangli Chen, Fangyan Chen, Fangyuan Chen, Faye H Chen, Fei Chen, Fei Xavier Chen, Feifan Chen, Feifeng Chen, Feilong Chen, Feixue Chen, Feiyang Chen, Feiyu Chen, Feiyue Chen, Feng Chen, Feng-Jung Chen, Feng-Ling Chen, Fenghua Chen, Fengju Chen, Fengling Chen, Fengming Chen, Fengrong Chen, Fengwu Chen, Fengyang Chen, Fred K Chen, Fu Chen, Fu-Shou Chen, Fumei Chen, Fusheng Chen, Fuxiang Chen, Gang Chen, Gao B Chen, Gao Chen, Gao-Feng Chen, Gaoyang Chen, Gaoyu Chen, Gaozhi Chen, Gary Chen, Gary K Chen, Ge Chen, Gen-Der Chen, Geng Chen, Gengsheng Chen, Ginny I Chen, Gong Chen, Gongbo Chen, Gonghai Chen, Gonglie Chen, Guan-Wei Chen, Guang Chen, Guang-Chao Chen, Guang-Yu Chen, Guangchun Chen, Guanghao Chen, Guanghong Chen, Guangjie Chen, Guangju Chen, Guangliang Chen, Guanglong Chen, Guangnan Chen, Guangping Chen, Guangquan Chen, Guangyao Chen, Guangyi Chen, Guangyong Chen, Guanjie Chen, Guanren Chen, Guanyu Chen, Guanzheng Chen, Gui Mei Chen, Gui-Hai Chen, Gui-Lai Chen, Guihao Chen, Guiqian Chen, Guiquan Chen, Guiying Chen, Guo Chen, Guo-Chong Chen, Guo-Jun Chen, Guo-Rong Chen, Guo-qing Chen, Guochao Chen, Guochong Chen, Guofang Chen, Guohong Chen, Guohua Chen, Guojun Chen, Guoliang Chen, Guopu Chen, Guoshun Chen, Guoxun Chen, Guozhong Chen, Guozhou Chen, H Chen, H Q Chen, H T Chen, Hai-Ning Chen, Haibing Chen, Haibo Chen, Haide Chen, Haifeng Chen, Haijiao Chen, Haimin Chen, Haiming Chen, Haining Chen, Haiqin Chen, Haiquan Chen, Haitao Chen, Haiyan Chen, Haiyang Chen, Haiyi Chen, Haiying Chen, Haiyu Chen, Haiyun Chen, Han Chen, Han-Bin Chen, Han-Chun Chen, Han-Hsiang Chen, Han-Min Chen, Hanbei Chen, Hang Chen, Hangang Chen, Hanjing Chen, Hanlin Chen, Hanqing Chen, Hanwen Chen, Hanxi Chen, Hanyong Chen, Hao Chen, Hao Yu Chen, Hao-Zhu Chen, Haobo Chen, Haodong Chen, Haojie Chen, Haoran Chen, Haotai Chen, Haotian Chen, Haoting Chen, Haoyun Chen, Haozhu Chen, Harn-Shen Chen, Haw-Wen Chen, He-Ping Chen, Hebing Chen, Hegang Chen, Hehe Chen, Hekai Chen, Heng Chen, Heng-Sheng Chen, Heng-Yu Chen, Hengsan Chen, Hengsheng Chen, Hengyu Chen, Heni Chen, Herbert Chen, Hetian Chen, Heye Chen, Hong Chen, Hong Yang Chen, Hong-Sheng Chen, Hongbin Chen, Hongbo Chen, Hongen Chen, Honghai Chen, Honghui Chen, Honglei Chen, Hongli Chen, Hongmei Chen, Hongmin Chen, Hongmou Chen, Hongqi Chen, Hongqiao Chen, Hongshan Chen, Hongxiang Chen, Hongxing Chen, Hongxu Chen, Hongyan Chen, Hongyu Chen, Hongyue Chen, Hongzhi Chen, Hou-Tsung Chen, Hou-Zao Chen, Hsi-Hsien Chen, Hsiang-Wen Chen, Hsiao-Jou Cortina Chen, Hsiao-Tan Chen, Hsiao-Wang Chen, Hsiao-Yun Chen, Hsin-Han Chen, Hsin-Hong Chen, Hsin-Hung Chen, Hsin-Yi Chen, Hsiu-Wen Chen, Hsuan-Yu Chen, Hsueh-Fen Chen, Hu Chen, Hua Chen, Hua-Pu Chen, Huachen Chen, Huafei Chen, Huaiyong Chen, Hualan Chen, Huali Chen, Hualin Chen, Huan Chen, Huan-Xin Chen, Huanchun Chen, Huang Chen, Huang-Pin Chen, Huangtao Chen, Huanhua Chen, Huanhuan Chen, Huanxiong Chen, Huaping Chen, Huapu Chen, Huaqiu Chen, Huatao Chen, Huaxin Chen, Huayu Chen, Huei-Rong Chen, Huei-Yan Chen, Huey-Miin Chen, Hui Chen, Hui Mei Chen, Hui-Chun Chen, Hui-Fen Chen, Hui-Jye Chen, Hui-Ru Chen, Hui-Wen Chen, Hui-Xiong Chen, Hui-Zhao Chen, Huichao Chen, Huijia Chen, Huijiao Chen, Huijie Chen, Huimei Chen, Huimin Chen, Huiqin Chen, Huiqun Chen, Huiru Chen, Huishan Chen, Huixi Chen, Huixian Chen, Huizhi Chen, Hung-Chang Chen, Hung-Chi Chen, Hung-Chun Chen, Hung-Po Chen, Hung-Sheng Chen, I-Chun Chen, I-M Chen, Ida Y-D Chen, Irwin Chen, Ivy Xiaoying Chen, J Chen, Jacinda Chen, Jack Chen, Jake Y Chen, Jason A Chen, Jeanne Chen, Jen-Hau Chen, Jen-Sue Chen, Jennifer F Chen, Jenny Chen, Jeremy J W Chen, Ji-ling Chen, Jia Chen, Jia Min Chen, Jia Wei Chen, Jia-De Chen, Jia-Feng Chen, Jia-Lin Chen, Jia-Mei Chen, Jia-Shun Chen, Jiabing Chen, Jiacai Chen, Jiacheng Chen, Jiade Chen, Jiahao Chen, Jiahua Chen, Jiahui Chen, Jiajia Chen, Jiajing Chen, Jiajun Chen, Jiakang Chen, Jiale Chen, Jiali Chen, Jialing Chen, Jiamiao Chen, Jiamin Chen, Jian Chen, Jian-Guo Chen, Jian-Hua Chen, Jian-Jun Chen, Jian-Kang Chen, Jian-Min Chen, Jian-Qiao Chen, Jian-Qing Chen, Jianan Chen, Jianfei Chen, Jiang Chen, Jiang Ye Chen, Jiang-hua Chen, Jianghua Chen, Jiangxia Chen, Jianhua Chen, Jianhui Chen, Jiani Chen, Jianjun Chen, Jiankui Chen, Jianlin Chen, Jianmin Chen, Jianping Chen, Jianshan Chen, Jiansu Chen, Jianxiong Chen, Jianzhong Chen, Jianzhou Chen, Jiao Chen, Jiao-Jiao Chen, Jiaohua Chen, Jiaping Chen, Jiaqi Chen, Jiaqing Chen, Jiaren Chen, Jiarou Chen, Jiawei Chen, Jiawen Chen, Jiaxin Chen, Jiaxu Chen, Jiaxuan Chen, Jiayao Chen, Jiaye Chen, Jiayi Chen, Jiayuan Chen, Jichong Chen, Jie Chen, Jie-Hua Chen, Jiejian Chen, Jiemei Chen, Jien-Jiun Chen, Jihai Chen, Jijun Chen, Jimei Chen, Jin Chen, Jin-An Chen, Jin-Ran Chen, Jin-Shuen Chen, Jin-Wu Chen, Jin-Xia Chen, Jina Chen, Jinbo Chen, Jindong Chen, Jing Chen, Jing-Hsien Chen, Jing-Wen Chen, Jing-Xian Chen, Jing-Yuan Chen, Jing-Zhou Chen, Jingde Chen, Jinghua Chen, Jingjing Chen, Jingli Chen, Jinglin Chen, Jingming Chen, Jingnan Chen, Jingqing Chen, Jingshen Chen, Jingteng Chen, Jinguo Chen, Jingxuan Chen, Jingyao Chen, Jingyi Chen, Jingyuan Chen, Jingzhao Chen, Jingzhou Chen, Jinhao Chen, Jinhuang Chen, Jinli Chen, Jinlun Chen, Jinquan Chen, Jinsong Chen, Jintian Chen, Jinxuan Chen, Jinyan Chen, Jinyong Chen, Jion Chen, Jiong Chen, Jiongyu Chen, Jishun Chen, Jiu-Chiuan Chen, Jiujiu Chen, Jiwei Chen, Jiyan Chen, Jiyuan Chen, Jonathan Chen, Joy J Chen, Juan Chen, Juan-Juan Chen, Juanjuan Chen, Juei-Suei Chen, Juhai Chen, Jui-Chang Chen, Jui-Yu Chen, Jun Chen, Jun-Long Chen, Junchen Chen, Junfei Chen, Jung-Sheng Chen, Junhong Chen, Junhui Chen, Junjie Chen, Junling Chen, Junmin Chen, Junming Chen, Junpan Chen, Junpeng Chen, Junqi Chen, Junqin Chen, Junsheng Chen, Junshi Chen, Junyang Chen, Junyi Chen, Junyu Chen, K C Chen, Kai Chen, Kai-En Chen, Kai-Ming Chen, Kai-Ting Chen, Kai-Yang Chen, Kaifu Chen, Kaijian Chen, Kailang Chen, Kaili Chen, Kaina Chen, Kaiquan Chen, Kan Chen, Kang Chen, Kang-Hua Chen, Kangyong Chen, Kangzhen Chen, Katharine Y Chen, Katherine C Chen, Ke Chen, Kecai Chen, Kehua Chen, Kehui Chen, Kelin Chen, Ken Chen, Kenneth L Chen, Keping Chen, Kequan Chen, Kevin Chen, Kewei Chen, Kexin Chen, Keyan Chen, Keyang Chen, Keying Chen, Keyu Chen, Keyuan Chen, Kuan-Jen Chen, Kuan-Ling Chen, Kuan-Ting Chen, Kuan-Yu Chen, Kuangyang Chen, Kuey Chu Chen, Kui Chen, Kun Chen, Kun-Chieh Chen, Kunmei Chen, Kunpeng Chen, L B Chen, L F Chen, Lan Chen, Lang Chen, Lankai Chen, Lanlan Chen, Lanmei Chen, Le Chen, Le Qi Chen, Lei Chen, Lei-Chin Chen, Lei-Lei Chen, Leijie Chen, Lena W Chen, Leqi Chen, Letian Chen, Lexia Chen, Li Chen, Li Jia Chen, Li-Chieh Chen, Li-Hsien Chen, Li-Hsin Chen, Li-Hua Chen, Li-Jhen Chen, Li-Juan Chen, Li-Mien Chen, Li-Nan Chen, Li-Tzong Chen, Li-Zhen Chen, Li-hong Chen, Lian Chen, Lianfeng Chen, Liang Chen, Liang-Kung Chen, Liangkai Chen, Liangsheng Chen, Liangwan Chen, Lianmin Chen, Liaobin Chen, Lichang Chen, Lichun Chen, Lidian Chen, Lie Chen, Liechun Chen, Lifang Chen, Lifen Chen, Lifeng Chen, Ligang Chen, Lihong Chen, Lihua Chen, Lijin Chen, Lijuan Chen, Lili Chen, Limei Chen, Limin Chen, Liming Chen, Lin Chen, Lina Chen, Linbo Chen, Ling Chen, Ling-Yan Chen, Lingfeng Chen, Lingjun Chen, Lingli Chen, Lingxia Chen, Lingxue Chen, Lingyi Chen, Linjie Chen, Linlin Chen, Linna Chen, Linxi Chen, Linyi Chen, Liping Chen, Liqiang Chen, Liugui Chen, Liujun Chen, Liutao Chen, Lixia Chen, Lixian Chen, Liyun Chen, Lizhen Chen, Lizhu Chen, Lo-Yun Chen, Long Chen, Long-Jiang Chen, Longqing Chen, Longyun Chen, Lu Chen, Lu Hua Chen, Lu-Biao Chen, Lu-Zhu Chen, Lulu Chen, Luming Chen, Luyi Chen, Luzhu Chen, M Chen, M L Chen, Man Chen, Man-Hua Chen, Mao Chen, Mao-Yuan Chen, Maochong Chen, Maorong Chen, Marcus Y Chen, Mark I-Cheng Chen, Max Jl Chen, Mechi Chen, Mei Chen, Mei-Chi Chen, Mei-Chih Chen, Mei-Hsiu Chen, Mei-Hua Chen, Mei-Jie Chen, Mei-Ling Chen, Mei-Ru Chen, Meilan Chen, Meilin Chen, Meiling Chen, Meimei Chen, Meiting Chen, Meiyang Chen, Meiyu Chen, Meizhen Chen, Meng Chen, Meng Xuan Chen, Meng-Lin Chen, Meng-Ping Chen, Mengdi Chen, Menglan Chen, Mengling Chen, Mengping Chen, Mengqing Chen, Mengting Chen, Mengxia Chen, Mengyan Chen, Mengying Chen, Mian-Mian Chen, Miao Chen, Miao-Der Chen, Miao-Hsueh Chen, Miao-Yu Chen, Miaomiao Chen, Miaoran Chen, Michael C Chen, Michelle Chen, Mien-Cheng Chen, Min Chen, Min-Hsuan Chen, Min-Hu Chen, Min-Jie Chen, Ming Chen, Ming-Fong Chen, Ming-Han Chen, Ming-Hong Chen, Ming-Huang Chen, Ming-Huei Chen, Ming-Yu Chen, Mingcong Chen, Mingfeng Chen, Minghong Chen, Minghua Chen, Minglang Chen, Mingling Chen, Mingmei Chen, Mingxia Chen, Mingxing Chen, Mingyang Chen, Mingyi Chen, Mingyue Chen, Minjian Chen, Minjiang Chen, Minjie Chen, Minyan Chen, Mo Chen, Mu-Hong Chen, Muh-Shy Chen, Mulan Chen, Mystie X Chen, Na Chen, Naifei Chen, Naisong Chen, Nan Chen, Ni Chen, Nian-Ping Chen, Ning Chen, Ning-Bo Chen, Ning-Hung Chen, Ning-Yuan Chen, Ningbo Chen, Ningning Chen, Nuan Chen, On Chen, Ou Chen, Ouyang Chen, P P Chen, Pan Chen, Paul Chih-Hsueh Chen, Pei Chen, Pei-Chen Chen, Pei-Chun Chen, Pei-Lung Chen, Pei-Yi Chen, Pei-Yin Chen, Pei-zhan Chen, Peihong Chen, Peipei Chen, Peiqin Chen, Peixian Chen, Peiyou Chen, Peiyu Chen, Peize Chen, Peizhan Chen, Peng Chen, Peng-Cheng Chen, Pengxiang Chen, Ping Chen, Ping-Chung Chen, Ping-Kun Chen, Pingguo Chen, Po-Han Chen, Po-Ju Chen, Po-Min Chen, Po-See Chen, Po-Sheng Chen, Po-Yu Chen, Qi Chen, Qi-An Chen, Qian Chen, Qianbo Chen, Qianfen Chen, Qiang Chen, Qiangpu Chen, Qiankun Chen, Qianling Chen, Qianming Chen, Qianping Chen, Qianqian Chen, Qianxue Chen, Qianyi Chen, Qianyu Chen, Qianyun Chen, Qianzhi Chen, Qiao Chen, Qiao-Yi Chen, Qiaoli Chen, Qiaoling Chen, Qichen Chen, Qifang Chen, Qihui Chen, Qili Chen, Qinfen Chen, Qing Chen, Qing-Hui Chen, Qing-Juan Chen, Qing-Wei Chen, Qingao Chen, Qingchao Chen, Qingchuan Chen, Qingguang Chen, Qinghao Chen, Qinghua Chen, Qingjiang Chen, Qingjie Chen, Qingliang Chen, Qingmei Chen, Qingqing Chen, Qingqiu Chen, Qingshi Chen, Qingxing Chen, Qingyang Chen, Qingyi Chen, Qinian Chen, Qinsheng Chen, Qinying Chen, Qiong Chen, Qiongyun Chen, Qiqi Chen, Qitong Chen, Qiu Jing Chen, Qiu-Jing Chen, Qiu-Sheng Chen, Qiuchi Chen, Qiuhong Chen, Qiujing Chen, Qiuli Chen, Qiuwen Chen, Qiuxia Chen, Qiuxiang Chen, Qiuxuan Chen, Qiuyun Chen, Qiwei Chen, Qixian Chen, Qu Chen, Quan Chen, Quanjiao Chen, Quanwei Chen, Qunxiang Chen, R Chen, Ran Chen, Ranyun Chen, Ray-Jade Chen, Ren-Hui Chen, Renjin Chen, Renwei Chen, Renyu Chen, Robert Chen, Roger Chen, Rong Chen, Rong-Hua Chen, Rongfang Chen, Rongfeng Chen, Rongrong Chen, Rongsheng Chen, Rongyuan Chen, Roufen Chen, Rouxi Chen, Ru Chen, Rucheng Chen, Ruey-Hwa Chen, Rui Chen, Rui-Fang Chen, Rui-Min Chen, Rui-Pei Chen, Rui-Zhen Chen, Ruiai Chen, Ruibing Chen, Ruijing Chen, Ruijuan Chen, Ruilin Chen, Ruimin Chen, Ruiming Chen, Ruiqi Chen, Ruisen Chen, Ruixiang Chen, Ruixue Chen, Ruiying Chen, Rujun Chen, Runfeng Chen, Runsen Chen, Runsheng Chen, Ruofan Chen, Ruohong Chen, Ruonan Chen, Ruoyan Chen, Ruoying Chen, S Chen, S N Chen, S Pl Chen, S-D Chen, Sai Chen, San-Yuan Chen, Sean Chen, Sen Chen, Shali Chen, Shan Chen, Shanchun Chen, Shang-Chih Chen, Shang-Hung Chen, Shangduo Chen, Shangsi Chen, Shangwu Chen, Shangzhong Chen, Shanshan Chen, Shanyuan Chen, Shao-Ke Chen, Shao-Peng Chen, Shao-Wei Chen, Shao-Yu Chen, Shao-long Chen, Shaofei Chen, Shaohong Chen, Shaohua Chen, Shaokang Chen, Shaokun Chen, Shaoliang Chen, Shaotao Chen, Shaoxing Chen, Shaoze Chen, Shasha Chen, She Chen, Shen Chen, Shen-Ming Chen, Sheng Chen, Sheng-Xi Chen, Sheng-Yi Chen, Shengdi Chen, Shenghui Chen, Shenglan Chen, Shengnan Chen, Shengpan Chen, Shengyu Chen, Shengzhi Chen, Shi Chen, Shi-Qing Chen, Shi-Sheng Chen, Shi-Yi Chen, Shi-You Chen, Shibo Chen, Shih-Jen Chen, Shih-Pin Chen, Shih-Yin Chen, Shih-Yu Chen, Shilan Chen, Shiming Chen, Shin-Wen Chen, Shin-Yu Chen, Shipeng Chen, Shiqian Chen, Shiqun Chen, Shirui Chen, Shiuhwei Chen, Shiwei Chen, Shixuan Chen, Shiyan Chen, Shiyao Chen, Shiyi Chen, Shiyu Chen, Shou-Tung Chen, Shoudeng Chen, Shoujun Chen, Shouzhen Chen, Shu Chen, Shu-Fen Chen, Shu-Gang Chen, Shu-Hua Chen, Shu-Jen Chen, Shuai Chen, Shuai-Bing Chen, Shuai-Ming Chen, Shuaijie Chen, Shuaijun Chen, Shuaiyin Chen, Shuaiyu Chen, Shuang Chen, Shuangfeng Chen, Shuanghui Chen, Shuchun Chen, Shuen-Ei Chen, Shufang Chen, Shufeng Chen, Shuhai Chen, Shuhong Chen, Shuhuang Chen, Shuhui Chen, Shujuan Chen, Shuliang Chen, Shuming Chen, Shunde Chen, Shuntai Chen, Shunyou Chen, Shuo Chen, Shuo-Bin Chen, Shuoni Chen, Shuqin Chen, Shuqiu Chen, Shuting Chen, Shuwen Chen, Shuyi Chen, Shuying Chen, Si Chen, Si-Ru Chen, Si-Yuan Chen, Si-Yue Chen, Si-guo Chen, Sien-Tsong Chen, Sifeng Chen, Sihui Chen, Sijia Chen, Sijuan Chen, Sili Chen, Silian Chen, Siping Chen, Siqi Chen, Siqin Chen, Sisi Chen, Siteng Chen, Siting Chen, Siyi Chen, Siyu Chen, Siyu S Chen, Siyuan Chen, Siyue Chen, Size Chen, Song Chen, Song-Mei Chen, Songfeng Chen, Suet N Chen, Suet Nee Chen, Sufang Chen, Suipeng Chen, Sulian Chen, Suming Chen, Sun Chen, Sung-Fang Chen, Suning Chen, Sunny Chen, Sy-Jou Chen, Syue-Ting Chen, Szu-Chi Chen, Szu-Chia Chen, Szu-Chieh Chen, Szu-Han Chen, Szu-Yun Chen, T Chen, Tai-Heng Chen, Tai-Tzung Chen, Tailai Chen, Tan-Huan Chen, Tan-Zhou Chen, Tania Chen, Tao Chen, Tian Chen, Tianfeng Chen, Tianhang Chen, Tianhong Chen, Tianhua Chen, Tianpeng Chen, Tianran Chen, Tianrui Chen, Tiantian Chen, Tianzhen Chen, Tielin Chen, Tien-Hsing Chen, Ting Chen, Ting-Huan Chen, Ting-Tao Chen, Ting-Ting Chen, Tingen Chen, Tingtao Chen, Tingting Chen, Tom Wei-Wu Chen, Tong Chen, Tongsheng Chen, Tse-Ching Chen, Tse-Wei Chen, TsungYen Chen, Tuantuan Chen, Tzu-An Chen, Tzu-Chieh Chen, Tzu-Ju Chen, Tzu-Ting Chen, Tzu-Yu Chen, Tzy-Yen Chen, Valerie Chen, W Chen, Wai Chen, Wan Jun Chen, Wan-Tzu Chen, Wan-Yan Chen, Wan-Yi Chen, Wanbiao Chen, Wanjia Chen, Wanjun Chen, Wanling Chen, Wantao Chen, Wanting Chen, Wanyin Chen, Wei Chen, Wei J Chen, Wei Ning Chen, Wei-Cheng Chen, Wei-Cong Chen, Wei-Fei Chen, Wei-Hao Chen, Wei-Hui Chen, Wei-Kai Chen, Wei-Kung Chen, Wei-Lun Chen, Wei-Min Chen, Wei-Peng Chen, Wei-Ting Chen, Wei-Wei Chen, Wei-Yu Chen, Wei-xian Chen, Weibo Chen, Weican Chen, Weichan Chen, Weicong Chen, Weihao Chen, Weihong Chen, Weihua Chen, Weijia Chen, Weijie Chen, Weili Chen, Weilun Chen, Weina Chen, Weineng Chen, Weiping Chen, Weiqin Chen, Weiqing Chen, Weirui Chen, Weisan Chen, Weitao Chen, Weitian Chen, Weiwei Chen, Weixian Chen, Weixin Chen, Weiyi Chen, Weiyong Chen, Wen Chen, Wen-Chau Chen, Wen-Jie Chen, Wen-Pin Chen, Wen-Qi Chen, Wen-Tsung Chen, Wen-Yi Chen, Wenbiao Chen, Wenbing Chen, Wenfan Chen, Wenfang Chen, Wenhao Chen, Wenhua Chen, Wenjie Chen, Wenjun Chen, Wenlong Chen, Wenqin Chen, Wensheng Chen, Wenshuo Chen, Wentao Chen, Wenting Chen, Wentong Chen, Wenwen Chen, Wenwu Chen, Wenxi Chen, Wenxing Chen, Wenxu Chen, Willian Tzu-Liang Chen, Wu-Jun Chen, Wu-Xian Chen, Wuyan Chen, X Chen, X R Chen, X Steven Chen, Xi Chen, Xia Chen, Xia-Fei Chen, Xiaguang Chen, Xiameng Chen, Xian Chen, Xian-Kai Chen, Xianbo Chen, Xiancheng Chen, Xianfeng Chen, Xiang Chen, Xiang-Bin Chen, Xiang-Mei Chen, XiangFan Chen, Xiangding Chen, Xiangjun Chen, Xiangli Chen, Xiangliu Chen, Xiangmei Chen, Xiangna Chen, Xiangning Chen, Xiangqiu Chen, Xiangyu Chen, Xiankai Chen, Xianmei Chen, Xianqiang Chen, Xianxiong Chen, Xianyue Chen, Xianze Chen, Xianzhen Chen, Xiao Chen, Xiao-Chen Chen, Xiao-Hui Chen, Xiao-Jun Chen, Xiao-Lin Chen, Xiao-Qing Chen, Xiao-Quan Chen, Xiao-Yang Chen, Xiao-Ying Chen, Xiao-chun Chen, Xiao-he Chen, Xiao-ping Chen, Xiaobin Chen, Xiaobo Chen, Xiaochang Chen, Xiaochun Chen, Xiaodong Chen, Xiaofang Chen, Xiaofen Chen, Xiaofeng Chen, Xiaohan Chen, Xiaohong Chen, Xiaohua Chen, Xiaohui Chen, Xiaojiang S Chen, Xiaojie Chen, Xiaojing Chen, Xiaojuan Chen, Xiaojun Chen, Xiaokai Chen, Xiaolan Chen, Xiaole L Chen, Xiaolei Chen, Xiaoli Chen, Xiaolin Chen, Xiaoling Chen, Xiaolong Chen, Xiaolu Chen, Xiaomeng Chen, Xiaomin Chen, Xiaona Chen, Xiaonan Chen, Xiaopeng Chen, Xiaoping Chen, Xiaoqian Chen, Xiaoqing Chen, Xiaorong Chen, Xiaoshan Chen, Xiaotao Chen, Xiaoting Chen, Xiaowan Chen, Xiaowei Chen, Xiaowen Chen, Xiaoxiang Chen, Xiaoxiao Chen, Xiaoyan Chen, Xiaoyang Chen, Xiaoyin Chen, Xiaoyong Chen, Xiaoyu Chen, Xiaoyuan Chen, Xiaoyun Chen, Xiatian Chen, Xihui Chen, Xijun Chen, Xikun Chen, Ximei Chen, Xin Chen, Xin-Jie Chen, Xin-Ming Chen, Xin-Qi Chen, Xinan Chen, Xing Chen, Xing-Lin Chen, Xing-Long Chen, Xing-Zhen Chen, Xingdong Chen, Xinghai Chen, Xingxing Chen, Xingyi Chen, Xingyong Chen, Xingyu Chen, Xinji Chen, Xinlin Chen, Xinpu Chen, Xinqiao Chen, Xinwei Chen, Xinyan Chen, Xinyang Chen, Xinyi Chen, Xinyu Chen, Xinyuan Chen, Xinyue Chen, Xinzhuo Chen, Xiong Chen, Xiqun Chen, Xiu Chen, Xiu-Juan Chen, Xiuhui Chen, Xiujuan Chen, Xiuli Chen, Xiuping Chen, Xiuxiu Chen, Xiuyan Chen, Xixi Chen, Xiyao Chen, Xiyu Chen, Xu Chen, Xuan Chen, Xuancai Chen, Xuanjing Chen, Xuanli Chen, Xuanmao Chen, Xuanwei Chen, Xuanxu Chen, Xuanyi Chen, Xue Chen, Xue-Mei Chen, Xue-Qing Chen, Xue-Xin Chen, Xue-Yan Chen, Xue-Ying Chen, XueShu Chen, Xuechun Chen, Xuefei Chen, Xuehua Chen, Xuejiao Chen, Xuejun Chen, Xueli Chen, Xueling Chen, Xuemei Chen, Xuemin Chen, Xueqin Chen, Xueqing Chen, Xuerong Chen, Xuesong Chen, Xueting Chen, Xueyan Chen, Xueying Chen, Xufeng Chen, Xuhui Chen, Xujia Chen, Xun Chen, Xuxiang Chen, Xuxin Chen, Xuzhuo Chen, Y Chen, Y D I Chen, Y Eugene Chen, Y M Chen, Y P Chen, Y S Chen, Y U Chen, Y-D I Chen, Y-D Ida Chen, Ya Chen, Ya-Chun Chen, Ya-Nan Chen, Ya-Peng Chen, Ya-Ting Chen, Ya-xi Chen, Yafang Chen, Yafei Chen, Yahong Chen, Yajie Chen, Yajing Chen, Yajun Chen, Yalan Chen, Yali Chen, Yan Chen, Yan Jie Chen, Yan Q Chen, Yan-Gui Chen, Yan-Jun Chen, Yan-Ming Chen, Yan-Qiong Chen, Yan-yan Chen, Yanan Chen, Yananlan Chen, Yanbin Chen, Yanfei Chen, Yanfen Chen, Yang Chen, Yang-Ching Chen, Yang-Yang Chen, Yangchao Chen, Yanghui Chen, Yangxin Chen, Yanhan Chen, Yanhua Chen, Yanjie Chen, Yanjing Chen, Yanli Chen, Yanlin Chen, Yanling Chen, Yanming Chen, Yann-Jang Chen, Yanping Chen, Yanqiu Chen, Yanrong Chen, Yanru Chen, Yanting Chen, Yanyan Chen, Yanyun Chen, Yanzhu Chen, Yanzi Chen, Yao Chen, Yao-Shen Chen, Yaodong Chen, Yaosheng Chen, Yaowu Chen, Yau-Hung Chen, Yaxi Chen, Yayun Chen, Yazhuo Chen, Ye Chen, Ye-Guang Chen, Yeh Chen, Yelin Chen, Yen-Chang Chen, Yen-Chen Chen, Yen-Cheng Chen, Yen-Ching Chen, Yen-Fu Chen, Yen-Hao Chen, Yen-Hsieh Chen, Yen-Jen Chen, Yen-Ju Chen, Yen-Lin Chen, Yen-Ling Chen, Yen-Ni Chen, Yen-Rong Chen, Yen-Teen Chen, Yewei Chen, Yi Chen, Yi Feng Chen, Yi-Bing Chen, Yi-Chun Chen, Yi-Chung Chen, Yi-Fei Chen, Yi-Guang Chen, Yi-Han Chen, Yi-Hau Chen, Yi-Heng Chen, Yi-Hong Chen, Yi-Hsuan Chen, Yi-Hui Chen, Yi-Jen Chen, Yi-Lin Chen, Yi-Ru Chen, Yi-Ting Chen, Yi-Wen Chen, Yi-Yung Chen, YiChung Chen, YiPing Chen, Yian Chen, Yibing Chen, Yibo Chen, Yidan Chen, Yiding Chen, Yidong Chen, Yiduo Chen, Yifa Chen, Yifan Chen, Yifang Chen, Yifei Chen, Yih-Chieh Chen, Yihao Chen, Yihong Chen, Yii-Der Chen, Yii-Der I Chen, Yii-Derr Chen, Yii-der Ida Chen, Yijiang Chen, Yijun Chen, Yike Chen, Yilan Chen, Yilei Chen, Yili Chen, Yilin Chen, Yiming Chen, Yin-Huai Chen, Ying Chen, Ying-Cheng Chen, Ying-Hsiang Chen, Ying-Jie Chen, Ying-Jung Chen, Ying-Lan Chen, Ying-Ying Chen, Yingchun Chen, Yingcong Chen, Yinghui Chen, Yingji Chen, Yingjie Chen, Yinglian Chen, Yingting Chen, Yingxi Chen, Yingying Chen, Yingyu Chen, Yinjuan Chen, Yintong Chen, Yinwei Chen, Yinzhu Chen, Yiru Chen, Yishan Chen, Yisheng Chen, Yitong Chen, Yixin Chen, Yiyin Chen, Yiyun Chen, Yizhi Chen, Yong Chen, Yong-Jun Chen, Yong-Ping Chen, Yong-Syuan Chen, Yong-Zhong Chen, YongPing Chen, Yongbin Chen, Yongfa Chen, Yongfang Chen, Yongheng Chen, Yonghui Chen, Yongke Chen, Yonglu Chen, Yongmei Chen, Yongming Chen, Yongning Chen, Yongqi Chen, Yongshen Chen, Yongshuo Chen, Yongxing Chen, Yongxun Chen, You-Ming Chen, You-Xin Chen, You-Yue Chen, Youhu Chen, Youjia Chen, Youmeng Chen, Youran Chen, Youwei Chen, Yu Chen, Yu-Bing Chen, Yu-Cheng Chen, Yu-Chi Chen, Yu-Chia Chen, Yu-Chuan Chen, Yu-Fan Chen, Yu-Fen Chen, Yu-Fu Chen, Yu-Gen Chen, Yu-Han Chen, Yu-Hui Chen, Yu-Ling Chen, Yu-Ming Chen, Yu-Pei Chen, Yu-San Chen, Yu-Si Chen, Yu-Ting Chen, Yu-Tung Chen, Yu-Xia Chen, Yu-Xin Chen, Yu-Yang Chen, Yu-Ying Chen, Yuan Chen, Yuan-Hua Chen, Yuan-Shen Chen, Yuan-Tsong Chen, Yuan-Yuan Chen, Yuan-Zhen Chen, Yuanbin Chen, Yuanhao Chen, Yuanjia Chen, Yuanjian Chen, Yuanli Chen, Yuanqi Chen, Yuanwei Chen, Yuanwen Chen, Yuanyu Chen, Yuanyuan Chen, Yubin Chen, Yucheng Chen, Yue Chen, Yue-Lai Chen, Yuebing Chen, Yueh-Peng Chen, Yuelei Chen, Yuewen Chen, Yuewu Chen, Yuexin Chen, Yuexuan Chen, Yufei Chen, Yufeng Chen, Yuh-Lien Chen, Yuh-Ling Chen, Yuh-Min Chen, Yuhan Chen, Yuhang Chen, Yuhao Chen, Yuhong Chen, Yuhui Chen, Yujie Chen, Yule Chen, Yuli Chen, Yulian Chen, Yulin Chen, Yuling Chen, Yulong Chen, Yulu Chen, Yumei Chen, Yun Chen, Yun-Ju Chen, Yun-Tzu Chen, Yun-Yu Chen, Yundai Chen, Yunfei Chen, Yunfeng Chen, Yung-Hsiang Chen, Yung-Wu Chen, Yunjia Chen, Yunlin Chen, Yunn-Yi Chen, Yunqin Chen, Yunshun Chen, Yunwei Chen, Yunyun Chen, Yunzhong Chen, Yunzhu Chen, Yupei Chen, Yupeng Chen, Yuping Chen, Yuqi Chen, Yuqin Chen, Yuqing Chen, Yuquan Chen, Yurong Chen, Yushan Chen, Yusheng Chen, Yusi Chen, Yuting Chen, Yutong Chen, Yuxi Chen, Yuxian Chen, Yuxiang Chen, Yuxin Chen, Yuxing Chen, Yuyan Chen, Yuyang Chen, Yuyao Chen, Z Chen, Zan Chen, Zaozao Chen, Ze-Hui Chen, Ze-Xu Chen, Zechuan Chen, Zemin Chen, Zetian Chen, Zexiao Chen, Zeyu Chen, Zhanfei Chen, Zhang-Liang Chen, Zhang-Yuan Chen, Zhangcheng Chen, Zhanghua Chen, Zhangliang Chen, Zhanglin Chen, Zhangxin Chen, Zhanjuan Chen, Zhao Chen, Zhao-Xia Chen, ZhaoHui Chen, Zhaojun Chen, Zhaoli Chen, Zhaolin Chen, Zhaoran Chen, Zhaowei Chen, Zhaoyao Chen, Zhe Chen, Zhe-Ling Chen, Zhe-Sheng Chen, Zhe-Yu Chen, Zhebin Chen, Zhehui Chen, Zhelin Chen, Zhen Bouman Chen, Zhen Chen, Zhen-Hua Chen, Zhen-Yu Chen, Zhencong Chen, Zhenfeng Chen, Zheng Chen, Zheng-Zhen Chen, Zhenghong Chen, Zhengjun Chen, Zhengling Chen, Zhengming Chen, Zhenguo Chen, Zhengwei Chen, Zhengzhi Chen, Zhenlei Chen, Zhenyi Chen, Zhenyue Chen, Zheping Chen, Zheren Chen, Zhesheng Chen, Zheyi Chen, Zhezhe Chen, Zhi Bin Chen, Zhi Chen, Zhi-Hao Chen, Zhi-bin Chen, Zhi-zhe Chen, Zhiang Chen, Zhichuan Chen, Zhifeng Chen, Zhigang Chen, Zhigeng Chen, Zhiguo Chen, Zhihai Chen, Zhihang Chen, Zhihao Chen, Zhiheng Chen, Zhihong Chen, Zhijian Chen, Zhijian J Chen, Zhijing Chen, Zhijun Chen, Zhimin Chen, Zhinan Chen, Zhiping Chen, Zhiqiang Chen, Zhiquan Chen, Zhishi Chen, Zhitao Chen, Zhiting Chen, Zhiwei Chen, Zhixin Chen, Zhixuan Chen, Zhixue Chen, Zhiyong Chen, Zhiyu Chen, Zhiyuan Chen, Zhiyun Chen, Zhizhong Chen, Zhong Chen, Zhongbo Chen, Zhonghua Chen, Zhongjian Chen, Zhongliang Chen, Zhongxiu Chen, Zhongzhu Chen, Zhou Chen, Zhouji Chen, Zhouliang Chen, Zhoulong Chen, Zhouqing Chen, Zhuchu Chen, Zhujun Chen, Zhuo Chen, Zhuo-Yuan Chen, ZhuoYu Chen, Zhuohui Chen, Zhuojia Chen, Zi-Jiang Chen, Zi-Qing Chen, Zi-Yang Chen, Zi-Yue Chen, Zi-Yun Chen, Zian Chen, Zifan Chen, Zihan Chen, Zihang Chen, Zihao Chen, Zihe Chen, Zihua Chen, Zijie Chen, Zike Chen, Zilin Chen, Zilong Chen, Ziming Chen, Zinan Chen, Ziqi Chen, Ziqing Chen, Zitao Chen, Zixi Chen, Zixin Chen, Zixuan Chen, Ziying Chen, Ziyuan Chen, Zoe Chen, Zongming E Chen, Zongnan Chen, Zongyou Chen, Zongzheng Chen, Zugen Chen, Zuolong Chen
articles
Xiaodong Li, Yaning Fu, Yalan Luo +3 more · 2025 · Redox biology · Elsevier · added 2026-04-24
Glioblastoma is the most aggressive form of primary brain tumor, characterized with poor prognosis and resistance to conventional therapies. Increasing evidence points to oxidative stress and redox dy Show more
Glioblastoma is the most aggressive form of primary brain tumor, characterized with poor prognosis and resistance to conventional therapies. Increasing evidence points to oxidative stress and redox dysregulation as important contributors to glioblastoma progression. Previously, chloride intracellular channel protein 4 (CLIC4), a redox-sensitive protein, has been implicated in cancer biology. However, its roles in glioblastoma remain poorly understood. Here, we found that CLIC4 expression is upregulated in glioblastoma tissues and cell lines, and is positively correlated with tumor malignancy and poor survival outcomes in patients with glioblastoma. Gene silencing of CLIC4 significantly reduces glioblastoma cell viability, migration, and proliferation in vitro and suppress tumor growth in vivo. Mechanistically, CLIC4 appears to maintain redox homeostasis by regulating mitochondrial functions, including membrane potential, mass, ROS production, and the activity of complexes III and IV. Moreover, a G-quadruplex (G4) structure located in CLIC4 promoter region is related to CLIC4 upregulation by oxidative stress in glioblastoma. This G4 structure can be readily oxidized to a parallel conformation, thereby enhancing its binding with DHX36 protein to promote gene transcription. Collectively, these findings position CLIC4 as a pivotal modulator of oxidative stress in glioblastoma and a potential target for developing therapeutic approaches for the treatment of glioblastoma. Show less
📄 PDF DOI: 10.1016/j.redox.2025.103917
DHX36
Sihua Xu, Yiyuan Xiao, Chaoyu Xu +6 more · 2025 · BMJ open sport & exercise medicine · added 2026-04-24
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health issue due to its high prevalence, yet the impact of accelerometer-measured physical activity on clinical outcomes re Show more
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health issue due to its high prevalence, yet the impact of accelerometer-measured physical activity on clinical outcomes remains unclear. This study aims to examine the associations of physical activity with the risk of liver cirrhosis, cancer, cardiovascular disease (CVD) incidence and mortality. 32 681 MASLD participants with accelerometer-derived physical activity data from the UK Biobank were analysed. Physical activity intensity was categorised into light (LPA), moderate (MPA) and vigorous (VPA) intensity. Cox proportional hazard and acceleration failure models were employed to assess associations between physical activity duration and outcomes. During a median follow-up of 7.5-7.9 years, 1883 deaths, 151 liver cirrhosis, 3312 cancers and 6657 CVD events were recorded. Physical activity, regardless of intensity, was consistently associated with a reduced risk of liver cirrhosis, CVD and all-cause mortality. Compared with non-MASLD individuals, our analysis indicates that longer duration of physical activity, specifically >1945 min/week of LPA or >383 min/week of MPA may theoretically eliminate the excess risk of mortality associated with MASLD. Among MASLD individuals, longer physical activity duration, regardless of intensity, was associated with reduced risks of liver cirrhosis and mortality. MPA and VPA were associated with lower CVD risk, while VPA was associated with reduced cancer risk, highlighting the potential benefits of increasing the intensity and duration of physical activity in MASLD management. Show less
📄 PDF DOI: 10.1136/bmjsem-2025-002702
LPA
Mimi Li, Lichao Ye, Chunnuan Chen · 2025 · Scientific reports · Nature · added 2026-04-24
Despite the well-established association between the apolipoprotein B/apolipoprotein A1 (apoB/apoA1) ratio and ischemic stroke, its specific relationship with the underlying vascular pathologies contr Show more
Despite the well-established association between the apolipoprotein B/apolipoprotein A1 (apoB/apoA1) ratio and ischemic stroke, its specific relationship with the underlying vascular pathologies contributing to stroke remains poorly understood. This study aims to investigate the association between the apoB/apoA1 ratio and intracranial or extracranial atherosclerosis. We enrolled 408 patients with acute ischemic stroke who had never been treated with statins or fibrates. Based on the images from computed tomography angiography (CTA), the patients were categorized into four groups: intracranial atherosclerosis stenosis (ICAS, n = 136), extracranial carotid atherosclerosis stenosis (ECAS, n = 45), combined intracranial and extracranial atherosclerosis stenosis (COAS, n = 73), and non-cerebral atherosclerosis stenosis (NCAS, n = 154). Demographic characteristics, clinical factors, and serum lipid levels were collected and then compared across groups. The apoB/apoA1 ratio was significantly higher in patients with ICAS, ECAS and COAS compared to those in the NCAS group. Multivariable logistic regression analysis demonstrated that the ApoB/ApoA1 ratio was independently associated with ICAS, but not with ECAS. ROC curve analysis showed that the ApoB/ApoA1 ratio had a good diagnostic ability for ICAS, with an area under the curve (AUC) of 0.764, an optimal cut-off value of 0.8122, a sensitivity of 81.3%, and a specificity of 59.8%. An higher apoB/apoA1 ratio is associated with ICAS in ischemic stroke patients. Show less
📄 PDF DOI: 10.1038/s41598-025-97625-9
APOB
Eun-Gyung Lee, Lesley Leong, Sunny Chen +2 more · 2025 · International journal of molecular sciences · MDPI · added 2026-04-24
The ε4 allele of the apolipoprotein E (
📄 PDF DOI: 10.3390/ijms27010302
APOE
Xue Chen, Lili Yuan, Xiaoli Ma +8 more · 2025 · Annals of hematology · Springer · added 2026-04-24
Myeloid/lymphoid neoplasms with tyrosine kinase gene fusions (MLN-TK) are rare hematologic malignancies characterized by recurrent kinase rearrangements, including
📄 PDF DOI: 10.1007/s00277-025-06494-9
FGFR1
Youjia Qiu, Bingyi Song, Ziqian Yin +7 more · 2025 · European stroke journal · SAGE Publications · added 2026-04-24
Different serum lipid and lipid-lowering agents are reported to be related to the occurrence of intracerebral aneurysm (IA). However, the causal relationship between them requires further investigatio Show more
Different serum lipid and lipid-lowering agents are reported to be related to the occurrence of intracerebral aneurysm (IA). However, the causal relationship between them requires further investigation. Mendelian randomization (MR) analysis was performed on IA and its subtypes by using instrumental variants associated with six serum lipids, 249 lipid metabolic traits, and 10 lipid-lowering agents that were extracted from the largest genome-wide association study. Phenome-wide MR analyses were conducted to identify potential phenotypes associated with significant lipid-lowering agents. After multiple comparison adjustments ( This study not only supports that serum lipids (TG and HDL-C) are associated with IA but also confirms the positive effect and absence of safety concerns of intervening Show less
no PDF DOI: 10.1177/23969873241265019
CETP
Haokang Feng, Zhixue Chen, Jianang Li +13 more · 2025 · iScience · Elsevier · added 2026-04-24
Pancreatic cancer (PC), characterized by the absence of effective biomarkers and therapies, remains highly fatal. Data regarding the correlations between PC risk and individual plasma proteome known f Show more
Pancreatic cancer (PC), characterized by the absence of effective biomarkers and therapies, remains highly fatal. Data regarding the correlations between PC risk and individual plasma proteome known for minimally invasive biomarkers are scarce. Here, we analyzed 1,345 human plasma proteins using proteome-wide association studies, identifying 78 proteins significantly associated with PC risk. Of these, four proteins (ROR1, FN1, APOA5, and ABO) showed the most substantial causal link to PC, confirmed through Mendelian randomization and colocalization analyses. Data from two clinical cohorts further demonstrated that FN1 and ABO were notably overexpressed in both blood and tumor samples from PC patients, compared to healthy controls or para-tumor tissues. Additionally, elevated FN1 and ABO levels correlated with shorter median survival in patients. Multiple drugs targeting FN1 or ROR1 are available or in clinical trials. These findings suggest that plasma protein FN1 associated with PC holds potential as both prognostic biomarkers and therapeutic targets. Show less
📄 PDF DOI: 10.1016/j.isci.2024.111693
APOA5
Na Liu, Hongli Zeng, Xiangsheng Cai +6 more · 2025 · Frontiers in genetics · Frontiers · added 2026-04-24
To investigate the association between polymorphisms of the A case-control study was conducted, enrolling 100 HTG patients and 100 age-matched controls with normal triglyceride levels from the physica Show more
To investigate the association between polymorphisms of the A case-control study was conducted, enrolling 100 HTG patients and 100 age-matched controls with normal triglyceride levels from the physical examination cohort at Guangzhou 11th People's Hospital (January-December 2023) The observation group showed significant differences in genotype frequencies of Show less
📄 PDF DOI: 10.3389/fgene.2025.1654501
APOA5
Xueyi Sun, Shaolei Geng, Zeyuan Wang +1 more · 2025 · Human mutation · added 2026-04-24
Sepsis arises from a dysregulated host response to infection, leading to multiorgan inflammatory injury. Early diagnosis and treatment necessitate the identification of reliable immune biomarkers. Thi Show more
Sepsis arises from a dysregulated host response to infection, leading to multiorgan inflammatory injury. Early diagnosis and treatment necessitate the identification of reliable immune biomarkers. This study investigated the relationship between aging, immunity, and sepsis by analyzing six human aging-related gene sets (656 genes). We identified 16 aging-related differentially expressed genes (DEGs) in sepsis. Among these, ATP11B, RBBP7, DOCK10, and NUP160 demonstrated the strongest connectivity with other genes and exhibited significant predictive power. Functional enrichment analysis (GO and KEGG) revealed distinct signaling pathway profiles between high-risk and low-risk sepsis groups (stratified based on risk scores). These dysregulated pathways, associated with multiple immune cells, were primarily linked to transcriptional dysregulation in cellular processes and cancer-related pathways. Experimental validation assays corroborated the roles of ATP11B and RBBP7. Collectively, our bioinformatic and experimental findings indicate that ATP11B, RBBP7, DOCK10, and NUP160 are implicated in the pathogenesis and progression of sepsis. But their potential for sepsis biomarkers still requires further verification. Show less
no PDF DOI: 10.1155/humu/9789556
NUP160
Yicun Li, Yun Wu, Xiaolian Li +4 more · 2025 · Scientific reports · Nature · added 2026-04-24
Head and neck squamous cell carcinoma (HNSCC) poses a global health challenge. The management of HNSCC is complicated by the difficulty in detecting occult lymph node metastases, leading to dilemmas i Show more
Head and neck squamous cell carcinoma (HNSCC) poses a global health challenge. The management of HNSCC is complicated by the difficulty in detecting occult lymph node metastases, leading to dilemmas in elective neck dissection decisions, which will impair patients' quality of life without improving survival for nodal negative patients. We conducted a comparative analysis of the clinical features, genomic alterations, gene expression and methylation, tumor microenvironment and cellular states between the clinically N0 and pathologically N0 (cN0-pN0) patients and occult lymph node metastatic patients. Patients with occult lymph node metastases typically present with more poorly differentiated primary tumors and higher rates of angiolymphatic and perineural invasion. We identified a distinctive genomic mutation spectrum in the primary tumors of patients with occult metastases, notably in genes such as NSD1, ARHGAP15 and SMARCA4. A whole-genome DNA hypomethylation and altered gene expression profiles are identified in occult lymph node metastatic patients. Analysis of the tumor microenvironment revealed an enrichment of CARNS1 + NK cells and CBX1 + tumor cells in occult metastatic patients. In conclusion, patients with occult lymph node metastases exhibit distinct molecular and clinical features compared with cN0-pN0 patients. Show less
📄 PDF DOI: 10.1038/s41598-025-10320-7
CBX1
Wenjie Luo, Yubin Chen, Cheng Fang +2 more · 2025 · Journal of receptor and signal transduction research · Taylor & Francis · added 2026-04-24
Atherosclerosis is characterized by persistent inflammatory condition, leading to various cardiovascular complications. Foam cell formation, resulting from macrophage uptake of oxidized low-density li Show more
Atherosclerosis is characterized by persistent inflammatory condition, leading to various cardiovascular complications. Foam cell formation, resulting from macrophage uptake of oxidized low-density lipoprotein (ox-LDL), contributes significantly to atherosclerosis progression. This study was designed to investigate the involvement of bispecific phosphatase-6 (DUSP6) and its potential regulatory mechanisms in foam cell formation and atherosclerosis. We employed THP-1 cells to induce foam cell formation. The lipid droplet accumulation, cholesterol content, tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6 levels were evaluated using Oil Red O staining, cholesterol assay, ELISA, and qRT-PCR techniques. We investigated DUSP6 ubiquitination via immunoprecipitation and western blot (WB) analysis. A bioinformatics approach identified FBXL14 as a potential E3 ligase involved in DUSP6 ubiquitination, further confirmed by siRNA and overexpression experiments. The impact of FBXL14 on the NRF2 signaling pathway was assessed using WB analysis. DUSP6 interference suppressed foam cell formation and inflammatory factor secretion. Upon ox-LDL treatment, DUSP6 underwent deubiquitylation, with FBXL14 emerging as the candidate E3 ligase. FBXL14 overexpression induced DUSP6 ubiquitination, leading to the NRF2 signaling pathway activation. It counteracted with DUSP6 overexpression on foam cell formation and inflammation. In ApoE-/- mice, sh-DUSP6 adenovirus injection mitigated atherosclerotic lesion progression and improved the lipid profile, with increased the proteins expression of NQO1, HO-1, and NRF2 in aortic tissue. DUSP6 and FBXL14 play vital roles in modulating foam cell formation and inflammatory responses in atherosclerosis. Targeting these molecules could offer therapeutic potential in attenuating atherosclerosis-related complications. Not applicable. Show less
no PDF DOI: 10.1080/10799893.2025.2466689
DUSP6
Yu Ding, Haoyang Ling, Xiuyan Chen +6 more · 2025 · Medicine · added 2026-04-24
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any m Show more
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any means to prevent several risks associated with MI. Blood and urine tests are frequently employed in clinical examinations to detect cardiovascular diseases at an early stage. Mendelian randomization (MR) is commonly employed to explore disease-trait relationships and uncover therapeutic targets. Our goal was to explore the genetic links between 35 blood and urine biomarkers and MI. Blood and urine biomarker MR correlations with MI risk were studied. In version R10, the UK Biobank and Finnish databases included blood and urine marker data and MI data (26,060 cases and 343,079 controls). We performed bidirectional 2-sample MR with 4 methods: inverse variance weighted, MR-Egger, weighted median, and weighted mode. Final causal associations were determined by inverse variance weighted. Sensitivity analyses (heterogeneity, pleiotropy) were conducted. MR-PRESSO and PhenoScanner were used to exclude invalid instruments. We used multivariate MR to filter the most important genes without including other positive genes. To identify positive gene pathways and gene networks that cause MI, we employed GeneMANIA for gene prediction. The findings revealed a positive genetic association between the 8 blood and urine biomarker levels and an elevated risk of MI. There are apolipoprotein B (APOB), glycated hemoglobin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, sex hormone-binding globulin, triglycerides, and urate. Moreover, APOB, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol selectively affect MI through the rejection of other positive gene stems. Finally, APOB and numerous genes strongly impact MI development. APOB collaborates with related genes to regulate plasma lipoprotein particle levels, sterol homeostasis, organization, lipid homeostasis, and remodeling in MI. Our research further reveals the causal relationship between MI and blood/urine biomarkers, providing a new perspective for the prevention, diagnosis, and treatment of MI. Blood and urine marker tests can subsequently be conducted based on these results to detect MI and study the underlying mechanisms linking these metabolites to MI. Show less
no PDF DOI: 10.1097/MD.0000000000046146
APOB
Aili Toyli, Anjum Shaik, Chen Zhao +3 more · 2025 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Cardiovascular disease (CVD) and Alzheimer's disease (AD) are leading causes of death and disability worldwide, and recent research has increasingly illuminated a complex, bidirectional relationship b Show more
Cardiovascular disease (CVD) and Alzheimer's disease (AD) are leading causes of death and disability worldwide, and recent research has increasingly illuminated a complex, bidirectional relationship between the two. This review synthesizes epidemiological, mechanistic, imaging, and genetic evidence linking CVD and AD through the heart-brain axis-a network of interrelated physiological and demographic pathways. We detail how cerebral hypoperfusion, inflammation, blood-brain barrier dysfunction, imbalance of the autonomic nervous system, and systemic amyloidosis contribute to shared neurodegenerative and cardiovascular outcomes. Multi-organ imaging studies, including MRI and PET, reveal that dysfunction of the cardiovascular system correlates with brain atrophy, white matter lesions, glymphatic impairment, and accumulation of AD-related proteinopathies. Genetic analyses further support overlapping risk architectures, particularly involving APOE and loci associated with lipid metabolism, vascular integrity, and inflammation. Age and sex are critical modifiers, with midlife CVD exerting the strongest influence on later cognitive decline, and sex-specific physiological responses shaping disease susceptibility. Finally, we explore how modifiable lifestyle factors, pharmacologic interventions, and precision medicine approaches targeting inflammatory and vascular pathways can jointly reduce the burden of both CVD and AD. Multidisciplinary collaboration to understand the interconnected biology of the heart and brain is essential for advancing integrated prevention and treatment strategies in aging populations. Show less
📄 PDF DOI: 10.3389/fcvm.2025.1685461
APOE
Vinod Tiwari, Byungchang Jin, Olivia Sun +9 more · 2025 · Nature metabolism · Nature · added 2026-04-24
Citrin deficiency (CD) is caused by the inactivation of SLC25A13, a mitochondrial membrane protein required to move electrons from cytosolic NADH to the mitochondrial matrix in hepatocytes. People wit Show more
Citrin deficiency (CD) is caused by the inactivation of SLC25A13, a mitochondrial membrane protein required to move electrons from cytosolic NADH to the mitochondrial matrix in hepatocytes. People with CD do not like sweets. Here we show that SLC25A13 loss causes the accumulation of glycerol-3-phosphate (G3P), which activates the carbohydrate response element-binding protein (ChREBP) to transcribe FGF21, which acts in the brain to restrain intake of sweets and alcohol and to transcribe key genes driving lipogenesis. Mouse and human data suggest that G3P-ChREBP is a mechanistic component of the Randle Cycle that contributes to metabolic-dysfunction-associated steatotic liver disease and forms part of a system that communicates metabolic states from the liver to the brain in a manner that alters food and alcohol choices. The data provide a framework for understanding FGF21 induction in varied conditions, suggest ways to develop FGF21-inducing drugs and suggest potential drug candidates for lean metabolic-dysfunction-associated steatotic liver disease and support of urea cycle function in CD. Show less
📄 PDF DOI: 10.1038/s42255-025-01399-3
MLXIPL
Mubalake Abudoureyimu, Ni Sun, Weiwei Chen +3 more · 2025 · International journal of immunopathology and pharmacology · SAGE Publications · added 2026-04-24
This study aimed to investigate whether the dysregulation of Aurora-A is involved in lenvatinib resistance in hepatocellular carcinoma. Bioinformatics tools and drug sensitivity assays were used to in Show more
This study aimed to investigate whether the dysregulation of Aurora-A is involved in lenvatinib resistance in hepatocellular carcinoma. Bioinformatics tools and drug sensitivity assays were used to investigate the association between Aurora-A expression level and lenvatinib resistance in hepatocellular carcinoma cell lines. Cell function experiments had performed after treatment with lenvatinib and/or a selective Aurora-A inhibitor (MLN-8237). CircRNA microarray, RIP, RNA pull-down, and dual-luciferace reporter assay were performed to identify the downstream molecular mechanism of Aurora-A dysregulation. Aurora-A expression was positively correlated with lenvatinib resistance in hepatocellular carcinoma cells. The Aurora-A selective inhibitor MLN-8237, in combination with lenvatinib, synergistically inhibited hepatocellular carcinoma cell proliferation in vitro and vivo, suggesting the Aurora-A might be a potential therapeutic target for lenvatinib resistance. Mechanistically, Aurora-A induced FGFR1 expression through the hsa-circ-0058046/miR-424-5p/FGFR1 axis. Aurora-A promotes lenvatinib resistance through hsa-circ-0058046/miR-424-5p/FGFR1 axis in hepatocellular carcinoma cells. The simultaneous inhibition of FGFR1 by the Aurora-A inhibitor MLN-8237 and lenvatinib overcame lenvatinib resistance in hepatocellular carcinoma cells. Collectively, our findings indicate that Aurora-A promotes lenvatinib resistance through the hsa-circ-0058046/miR-424-5p/FGFR1 axis in hepatocellular carcinoma (HCC) cells. These results suggest that Aurora-A may serve as a therapeutic target for HCC patients exhibiting lenvatinib resistance. Furthermore, the combination of lenvatinib and MLN-8237 shows potential for clinical trials aimed at overcoming lenvatinib resistance. Show less
📄 PDF DOI: 10.1177/03946320251316692
FGFR1
Hui Yan, Rui Wang, Suryavathi Viswanadhapalli +35 more · 2025 · Science advances · Science · added 2026-04-24
B cells express many protein ligands, yet their regulatory functions are incompletely understood. We profiled ligand expression across murine B sublineage cells, including those activated by defined r Show more
B cells express many protein ligands, yet their regulatory functions are incompletely understood. We profiled ligand expression across murine B sublineage cells, including those activated by defined receptor signals, and assessed their regulatory capacities and specificities through in silico analysis of ligand-receptor interactions. Consequently, we identified a B cell subset that expressed cytokine interleukin-27 (IL-27) and chemokine CXCL10. Through the IL-27-IL-27 receptor interaction, these IL-27/CXCL10-producing B cells targeted CD40-activated B cells in vitro and, upon induction by immunization and viral infection, optimized antibody responses and antiviral immunity in vivo. Also present in breast cancer tumors and retained there through CXCL10-CXCR3 interaction-mediated self-targeting, these cells promoted B cell PD-L1 expression and immune evasion. Mechanistically, Show less
📄 PDF DOI: 10.1126/sciadv.adx9917
IL27
Aron A Shoara, Sladjana Slavkovic, Miguel A D Neves +7 more · 2025 · The Journal of biological chemistry · Elsevier · added 2026-04-24
Apolipoprotein A-IV (apoA-IV) is an abundant lipid-binding protein in blood plasma. We previously reported that apoA-IV, as an endogenous inhibitor, competitively binds platelet αIIbβ3 integrin from i Show more
Apolipoprotein A-IV (apoA-IV) is an abundant lipid-binding protein in blood plasma. We previously reported that apoA-IV, as an endogenous inhibitor, competitively binds platelet αIIbβ3 integrin from its N-terminal residues, reducing the potential risk of thrombosis. This study aims to investigate how the apoA-IV Show less
📄 PDF DOI: 10.1016/j.jbc.2025.108392
APOA4
Xinning Dong, Jing Xu, Kejun Du +3 more · 2025 · Neuroreport · added 2026-04-24
This study aimed to examine reticulon 4 (RTN4), neurite outgrowth inhibitor protein expression that changes in high-altitude traumatic brain injury (HA-TBI) and affects on blood-brain barrier's (BBB) Show more
This study aimed to examine reticulon 4 (RTN4), neurite outgrowth inhibitor protein expression that changes in high-altitude traumatic brain injury (HA-TBI) and affects on blood-brain barrier's (BBB) function. C57BL/6J 6-8-week-old male mice were used for TBI model induction and randomized into the normal altitude group and the 5000-m high-altitude (HA) group, each group was divided into control (C) and 8h/12h/24h/48h-TBI according to different times post-TBI. Brain water content (BWC) and modified Neurological Severity Score were measured, RTN4 and autophagy-related indexes (Beclin1, LC3B, and SQSTM1/p62) were detected by western blot, immunofluorescence technique, and PCR in peri-injury cortical tissues. The expression of NgR1, Lingo-1, TROY, P75, PirB, S1PR2, and RhoA receptors' downstream of RTN4 was detected by PCR. HA-TBI caused increased neurological deficits including motor, sensory, balance and reflex deficits, increased BWC, earlier peak RTN4 expression and a longer duration of high expression in peri-injury cortical tissues, and enhanced levels of Beclin1, LC3B, and SQSTM1/p62 to varying degrees. Concurrently, the transcription of S1PR2 and PirB, the main signaling molecules downstream of RTN4, was significantly increased. In HA-TBI's early stages, the increased RTN4 may regulate enhanced autophagic initiation and impaired autolysosome degradation in vascular endothelial cells via S1PR2 receptor activation, thereby reducing BBB function. This suggests that autophagy could be a new target using RTN4 intervention as a clinical HA-TBI mechanism. Show less
no PDF DOI: 10.1097/WNR.0000000000002122
LINGO1
Yun Zhang, Huaqiu Chen, Yijia Feng +14 more · 2025 · Nature aging · Nature · added 2026-04-24
Individuals with type 2 diabetes mellitus have an increased risk of developing Alzheimer's disease (AD). GLP-1 receptor agonists (GLP-1RAs) are used for glycemic control in diabetes and show potential Show more
Individuals with type 2 diabetes mellitus have an increased risk of developing Alzheimer's disease (AD). GLP-1 receptor agonists (GLP-1RAs) are used for glycemic control in diabetes and show potential neuroprotective properties, but their effects on AD and the underlying mechanisms are not well understood. Here we demonstrate that GLP-1RAs can alleviate AD-related phenotypes by activating 5' AMP-activated protein kinase (AMPK) signaling. We found that plasma GLP-1 levels were decreased in AD model mice and negatively correlated with amyloid-beta (Aβ) load in patients with AD. Enhancing GLP-1 signaling through GLP-1RAs increased CaMKK2-AMPK signaling, which subsequently reduced BACE1-mediated cleavage of amyloid precursor protein (APP) and Aβ generation. GLP-1RAs also increased AMPK activity in microglia, inhibiting neuroinflammation and promoting Aβ phagocytosis. Consequently, GLP-1RAs inhibited plaque formation and improved memory deficits in AD model mice. Our findings indicate that AMPK activation mediates the effects of GLP-1RAs on AD, highlighting the therapeutic potential of GLP-1RAs for the treatment of AD. Show less
📄 PDF DOI: 10.1038/s43587-025-00869-3
BACE1
Lin Chen, Mingzhu Xu, Yan Zhu +1 more · 2025 · American journal of translational research · added 2026-04-24
To identify risk factors for heart failure (HF) within one year after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS) and to develop a predictive nomogram model Show more
To identify risk factors for heart failure (HF) within one year after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS) and to develop a predictive nomogram model. A retrospective analysis was performed on 492 patients with ACS treated at Suzhou Municipal Hospital between January 2020 and October 2023. Patients were divided into the HF group and the non-HF group according to the occurrence of HF within one year after PCI. 70% of the cases were randomly assigned to the training set and 30% to the validation set. Univariate and multivariate logistic regression analyses were conducted to screen independent predictors, and a nomogram model was subsequently established. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Among the 492 patients, the incidence of HF within one year after PCI was 26.42% (n = 130). Logistic regression identified type 2 diabetes mellitus (T2DM), left ventricular ejection fraction (LVEF), lipoprotein(a) [LP(a)], B-type natriuretic peptide (BNP), and high-sensitivity C-reactive protein (Hs-CRP) as independent predictors of HF, with odds ratios of 5.756, 0.904, 1.427, 1.012, and 1.666, respectively (all P < 0.05). The model demonstrated excellent discrimination, with areas under the ROC curve of 0.946 in the training set and 0.958 in the validation set. DCA indicated that the model provided greater net clinical benefit than the "treat-all" or "treat-none" strategies, and its predictive performance surpassed that of each individual factor (P < 0.05). The nomogram model incorporating T2DM, LVEF, LP(a), BNP and Hs-CRP provides an effective tool for predicting HF risk within one year after PCI in patients with ACS, offering valuable guidance for early clinical identification and risk stratification of high-risk individuals. Show less
no PDF DOI: 10.62347/DTOE6334
LPA
Roshni Jaffery, Yuhang Zhao, Sarfraz Ahmed +11 more · 2025 · bioRxiv : the preprint server for biology · Cold Spring Harbor Laboratory · added 2026-04-24
Mutations in the Leucine-rich repeat kinase 2 ( We investigated the levels of soluble immune regulators in the serum (n=651) and cerebrospinal fluid (CSF, n=129) of In this extensive discovery cohort, Show more
Mutations in the Leucine-rich repeat kinase 2 ( We investigated the levels of soluble immune regulators in the serum (n=651) and cerebrospinal fluid (CSF, n=129) of In this extensive discovery cohort, we identified several elevated serum immune regulatory factors associated with This study highlights distinct immune profiles associated with LRRK2 mutations and PD in the periphery and CNS. Serum levels of SDF-1alpha and TNF-RII were elevated in LRRK2 mutation carriers, while CSF immune markers were reduced. In PD, irrespective of LRRK2 status, reduced CSF inflammatory analytes and weak serum signals were observed. These results provide insight into immune dysregulation linked to LRRK2 mutations. If replicable in independent datasets, they offer potential avenues for biomarker and therapeutic exploration. Show less
📄 PDF DOI: 10.1101/2025.03.20.644460
IL27
Lifang Chen, Wei Zhang, Huan Chen +11 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
Histone deacetylase 3 (HDAC3) is an epigenetic modifying enzyme closely linked to the development of atherosclerosis. Endothelial inflammation is a critical factor in atherosclerosis. However, the rol Show more
Histone deacetylase 3 (HDAC3) is an epigenetic modifying enzyme closely linked to the development of atherosclerosis. Endothelial inflammation is a critical factor in atherosclerosis. However, the role of HDAC3 in mediating epigenetic modifications and regulating endothelial inflammation in atherosclerosis remains unclear. This study aims to investigate the impact of HDAC3 on endothelial inflammation and its contribution to atherosclerosis. Firstly, single-cell transcriptomic analysis identified elevated expression of HDAC3 and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) in inflammatory endothelial cells of atherosclerotic plaques in symptomatic patients. Endothelial-specific knockout HDAC3 in an apolipoprotein E knockout (ApoE Show less
📄 PDF DOI: 10.1038/s41418-025-01620-6
APOE
Yasuaki Uemoto, Chang-Ching A Lin, Bingnan Wang +10 more · 2025 · Cancer letters · Elsevier · added 2026-04-24
no PDF DOI: 10.1016/j.canlet.2025.217782
FGFR1
Muhammad Umar, Liping Tong, Hongting Jin +2 more · 2025 · Genes & diseases · Elsevier · added 2026-04-24
Clubfoot, medically termed congenital talipes equinovarus (CTEV), is a prevalent musculoskeletal birth defect, affecting approximately 0.3% of all live births. This serious congenital anomaly results Show more
Clubfoot, medically termed congenital talipes equinovarus (CTEV), is a prevalent musculoskeletal birth defect, affecting approximately 0.3% of all live births. This serious congenital anomaly results from structural abnormalities in the foot and lower leg, leading to abnormal positioning of the ankle and foot joints. This review provides a comprehensive overview of the causative factors associated with CTEV and evaluates current therapeutic approaches. Although variations in genes encoding contractile proteins of skeletal myofibers have been proposed as contributors to the etiology of CTEV, no definitive candidate genes have been conclusively linked to increased risk. However, genes such as Show less
📄 PDF DOI: 10.1016/j.gendis.2025.101690
AXIN1
Lishan Zeng, Xin Chen, Kai Kang +12 more · 2025 · Cardiovascular research · Oxford University Press · added 2026-04-24
Effective therapeutic drugs for calcific aortic valve disease (CAVD) are lacking, although its incidence has been increasing over the past decade and is predicted to continue rising in the future. Thi Show more
Effective therapeutic drugs for calcific aortic valve disease (CAVD) are lacking, although its incidence has been increasing over the past decade and is predicted to continue rising in the future. This study aimed to explore the role and potential mechanisms of liver X receptor α (LXRα) in CAVD, which offers a promising approach for treating CAVD. Osteogenic stimulation was performed following which a substantial downregulation of LXRα was observed in human calcific aortic valves and valvular interstitial cells. Further functional investigations revealed that silencing LXRα exacerbated calcification both in vitro and in vivo. We showed that LXRα suppressed the protein kinase R-like endoplasmic reticulum kinase/eukaryotic initiation factor 2/activating transcription factor 4 pathway, which controls endoplasmic reticulum stress (ERS) and promotes osteogenic differentiation, thereby slowing the course of CAVD. Our research offers fresh perspectives on how LXRα controls the pathophysiology of CAVD via regulating ERS. The findings suggest that targeting LXRα is a potential treatment strategy for treating aortic valve calcification. Show less
no PDF DOI: 10.1093/cvr/cvaf044
NR1H3
Haoran Guo, Junjie Chen · 2025 · Discover oncology · Springer · added 2026-04-24
Growth Factor Receptor-Binding Protein 10 (GRB10) is an adaptor protein implicated in tyrosine kinase signaling, yet its pan-cancer role and clinical impact remain incompletely characterized. This stu Show more
Growth Factor Receptor-Binding Protein 10 (GRB10) is an adaptor protein implicated in tyrosine kinase signaling, yet its pan-cancer role and clinical impact remain incompletely characterized. This study aims to define the pan-cancer landscape of GRB10 dysregulation and its clinical implications for prognosis and immunotherapy response. Multi-omics analysis of 33 TCGA cancers and validation in GEO cohorts assessed GRB10 expression, prognostic impact (Cox regression, Kaplan-Meier), functional enrichment (Gene Set Enrichment Analysis, Gene Set Variation Analysis), immune correlates (Spearman with immune genes, ESTIMATE/CIBERSORT/EPIC/TIMER/xCell infiltration), tumor mutational burden (TMB), and immunotherapy predictive power. In the TCGA pan-cancer cohort, GRB10 was significantly elevated in 11 cancer types (CHOL, COAD, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, STAD, THCA) and downregulated in 4 (BLCA, BRCA, CESC, UCEC). Aberrant GRB10 expression was strongly associated with adverse prognosis in LGG, CESC, COAD, and LUAD for OS, DSS, and PFI, a finding validated externally in GEO colon cancer cohorts. Conversely, it served as a protective factor in KIRC. Functionally, GRB10 was implicated in epithelial-mesenchymal transition (EMT), evidenced by positive correlations with EMT pathway scores and key regulators (ZEB1, ZEB2, SNAI1, SNAI2). GRB10 expression robustly correlated with an immunosuppressive tumor microenvironment (TME), evidenced by negative enrichment of immune response pathways (e.g., IFN-α/γ) and complex associations with immune-related genes. Immune infiltration analysis revealed consistent positive correlations with CD4⁺ memory-resting T cells and negative correlations with CD4⁺ effector memory T cells across most cancers. Critically, high GRB10 expression predicted significantly shorter survival and poorer response rates in multiple immunotherapy-treated cohorts (urothelial carcinoma, melanoma, gastric cancer). GRB10 also showed significant associations with TMB in several cancers, and its protein interaction network was enriched in PI3K-Akt, FoxO, and Rap1 signaling pathways. Our pan-cancer analysis establishes GRB10 as a key facilitator of tumor progression, linked to EMT and an immunosuppressive microenvironment, and nominates it as a novel biomarker for adverse prognosis and resistance to immune checkpoint blockade therapy. The online version contains supplementary material available at 10.1007/s12672-025-04333-x. Show less
no PDF DOI: 10.1007/s12672-025-04333-x
SNAI1
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
Zhengliang Li, Xiaokai Chen, Juan Wang +6 more · 2025 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
To investigate the risk factors associated with coronary heart disease (CHD) in patients with metabolic-associated fatty liver disease (MAFLD) and develop a nomogram prediction model. This study inclu Show more
To investigate the risk factors associated with coronary heart disease (CHD) in patients with metabolic-associated fatty liver disease (MAFLD) and develop a nomogram prediction model. This study included 394 patients with MAFLD who underwent coronary angiography at The Affiliated Hospital of Qingdao University between December 2019 and December 2024. The study cohort was divided in a 7:3 ratio into training and validation sets comprising 277 and 117 cases, respectively. The training group was further divided into the MAFLD-only ( Of the 394 MAFLD cases, 313 had CHD-related complications. Of the 277 patients in the training set, 220 had CHD, and of the 117 patients in the validation set, 93 had CHD. LASSO regression analysis revealed that the following variables were associated with the risk of CHD: sex, lipoprotein(a) (Lp[a]), low-density lipoprotein cholesterol, white blood cell count (WBC), glycated triglyceride-glucose index (TyG), and atherosclerosis index (AIP). Multivariate logistic regression analysis revealed that sex, Lp(a), WBC, TyG, and AIP were independent risk factors for CHD in MAFLD cases. A nomogram was constructed and an ROC curve was plotted, based on which the optimal cutoff value was determined as 0.698. The area under the curve of the nomogram in the training and validation cohorts was 0.860 (95% CI = 0.807-0.913) and 0.843 (95% CI = 0.757-0.929), respectively. Calibration curves for CHD risk probability showed good agreement between the nomogram's predicted probabilities and the observed event rates. DCA demonstrated the net clinical benefit of the constructed nomogram. Sex, Lp(a), WBC, TyG, and AIP emerged as independent risk factors for CHD in patients with MAFLD and the nomogram prediction model constructed using these factors could effectively predict CHD occurrence. Show less
📄 PDF DOI: 10.3389/fcvm.2025.1652321
LPA
Zijian Wang, Radek Zenkl, Latifa Greche +33 more · 2025 · Plant phenomics (Washington, D.C.) · Elsevier · added 2026-04-24
Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection an Show more
Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 ​%. However, the precision for stems with 54 ​% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies. Show less
📄 PDF DOI: 10.1016/j.plaphe.2025.100084
LPA
Xinruo Zhang, Jennifer A Brody, Mariaelisa Graff +122 more · 2025 · Nature communications · Nature · added 2026-04-24
Xinruo Zhang, Jennifer A Brody, Mariaelisa Graff, Heather M Highland, Nathalie Chami, Hanfei Xu, Zhe Wang, Kendra R Ferrier, Geetha Chittoor, Navya Shilpa Josyula, Mariah Meyer, Shreyash Gupta, Xihao Li, Zilin Li, Matthew A Allison, Diane M Becker, Lawrence F Bielak, Joshua C Bis, Meher Preethi Boorgula, Donald W Bowden, Jai G Broome, Erin J Buth, Christopher S Carlson, Kyong-Mi Chang, Sameer Chavan, Yen-Feng Chiu, Lee-Ming Chuang, Matthew P Conomos, Dawn L DeMeo, Mengmeng Du, Ravindranath Duggirala, Celeste Eng, Alison E Fohner, Barry I Freedman, Melanie E Garrett, Xiuqing Guo, Chris Haiman, Benjamin D Heavner, Bertha Hidalgo, James E Hixson, Yuk-Lam Ho, Brian D Hobbs, Donglei Hu, Qin Hui, Chii-Min Hwu, Rebecca D Jackson, Deepti Jain, Rita R Kalyani, Sharon L R Kardia, Tanika N Kelly, Ethan M Lange, Michael LeNoir, Changwei Li, Loic Le Marchand, Merry-Lynn N McDonald, Caitlin P McHugh, Alanna C Morrison, Take Naseri, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Jeffrey O'Connell, Christopher J O'Donnell, Nicholette D Palmer, James S Pankow, James A Perry, Ulrike Peters, Michael H Preuss, D C Rao, Elizabeth A Regan, Sefuiva M Reupena, Dan M Roden, Jose Rodriguez-Santana, Colleen M Sitlani, Jennifer A Smith, Hemant K Tiwari, Ramachandran S Vasan, Zeyuan Wang, Daniel E Weeks, Jennifer Wessel, Kerri L Wiggins, Lynne R Wilkens, Peter W F Wilson, Lisa R Yanek, Zachary T Yoneda, Wei Zhao, Sebastian Zöllner, Donna K Arnett, Allison E Ashley-Koch, Kathleen C Barnes, John Blangero, Eric Boerwinkle, Esteban G Burchard, April P Carson, Daniel I Chasman, Yii-der Ida Chen, Joanne E Curran, Myriam Fornage, Victor R Gordeuk, Jiang He, Susan R Heckbert, Lifang Hou, Marguerite R Irvin, Charles Kooperberg, Ryan L Minster, Braxton D Mitchell, Mehdi Nouraie, Bruce M Psaty, Laura M Raffield, Alexander P Reiner, Stephen S Rich, Jerome I Rotter, M Benjamin Shoemaker, Nicholas L Smith, Kent D Taylor, Marilyn J Telen, Scott T Weiss, Yingze Zhang, Nancy Heard-Costa, Yan V Sun, Xihong Lin, L Adrienne Cupples, Leslie A Lange, Ching-Ti Liu, Ruth J F Loos, Kari E North, Anne E Justice Show less
Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data fr Show more
Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10 Show less
no PDF DOI: 10.1038/s41467-025-58420-2
POC5