👤 Guo-Chong 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-Jun Chen, Guo-Rong Chen, Guo-qing Chen, Guochao Chen, Guochong Chen, Guofang Chen, Guohong Chen, Guohua Chen, Guojun Chen, Guoliang Chen, Guopu Chen, Guoshun Chen, Guoxun Chen, Guozhong Chen, Guozhou Chen, H Chen, H Q Chen, H T Chen, Hai-Ning Chen, Haibing Chen, Haibo Chen, Haide Chen, Haifeng Chen, Haijiao Chen, Haimin Chen, Haiming Chen, Haining Chen, Haiqin Chen, Haiquan Chen, Haitao Chen, Haiyan Chen, Haiyang Chen, Haiyi Chen, Haiying Chen, Haiyu Chen, Haiyun Chen, Han Chen, Han-Bin Chen, Han-Chun Chen, Han-Hsiang Chen, Han-Min Chen, Hanbei Chen, Hang Chen, Hangang Chen, Hanjing Chen, Hanlin Chen, Hanqing Chen, Hanwen Chen, Hanxi Chen, Hanyong Chen, Hao Chen, Hao Yu Chen, Hao-Zhu Chen, Haobo Chen, Haodong Chen, Haojie Chen, Haoran Chen, Haotai Chen, Haotian Chen, Haoting Chen, Haoyun Chen, Haozhu Chen, Harn-Shen Chen, Haw-Wen Chen, He-Ping Chen, Hebing Chen, Hegang Chen, Hehe Chen, Hekai Chen, Heng Chen, Heng-Sheng Chen, Heng-Yu Chen, Hengsan Chen, Hengsheng Chen, Hengyu Chen, Heni Chen, Herbert Chen, Hetian Chen, Heye Chen, Hong Chen, Hong Yang Chen, Hong-Sheng Chen, Hongbin Chen, Hongbo Chen, Hongen Chen, Honghai Chen, Honghui Chen, Honglei Chen, Hongli Chen, Hongmei Chen, Hongmin Chen, Hongmou Chen, Hongqi Chen, Hongqiao Chen, Hongshan Chen, Hongxiang Chen, Hongxing Chen, Hongxu Chen, Hongyan Chen, Hongyu Chen, Hongyue Chen, Hongzhi Chen, Hou-Tsung Chen, Hou-Zao Chen, Hsi-Hsien Chen, Hsiang-Wen Chen, Hsiao-Jou Cortina Chen, Hsiao-Tan Chen, Hsiao-Wang Chen, Hsiao-Yun Chen, Hsin-Han Chen, Hsin-Hong Chen, Hsin-Hung Chen, Hsin-Yi Chen, Hsiu-Wen Chen, Hsuan-Yu Chen, Hsueh-Fen Chen, Hu Chen, Hua Chen, Hua-Pu Chen, Huachen Chen, Huafei Chen, Huaiyong Chen, Hualan Chen, Huali Chen, Hualin Chen, Huan Chen, Huan-Xin Chen, Huanchun Chen, Huang Chen, Huang-Pin Chen, Huangtao Chen, Huanhua Chen, Huanhuan Chen, Huanxiong Chen, Huaping Chen, Huapu Chen, Huaqiu Chen, Huatao Chen, Huaxin Chen, Huayu Chen, Huei-Rong Chen, Huei-Yan Chen, Huey-Miin Chen, Hui Chen, Hui Mei Chen, Hui-Chun Chen, Hui-Fen Chen, Hui-Jye Chen, Hui-Ru Chen, Hui-Wen Chen, Hui-Xiong Chen, Hui-Zhao Chen, Huichao Chen, Huijia Chen, Huijiao Chen, Huijie Chen, Huimei Chen, Huimin Chen, Huiqin Chen, Huiqun Chen, Huiru Chen, Huishan Chen, Huixi Chen, Huixian Chen, Huizhi Chen, Hung-Chang Chen, Hung-Chi Chen, Hung-Chun Chen, Hung-Po Chen, Hung-Sheng Chen, I-Chun Chen, I-M Chen, Ida Y-D Chen, Irwin Chen, Ivy Xiaoying Chen, J Chen, Jacinda Chen, Jack Chen, Jake Y Chen, Jason A Chen, Jeanne Chen, Jen-Hau Chen, Jen-Sue Chen, Jennifer F Chen, Jenny Chen, Jeremy J W Chen, Ji-ling Chen, Jia Chen, Jia Min Chen, Jia Wei Chen, Jia-De Chen, Jia-Feng Chen, Jia-Lin Chen, Jia-Mei Chen, Jia-Shun Chen, Jiabing Chen, Jiacai Chen, Jiacheng Chen, Jiade Chen, Jiahao Chen, Jiahua Chen, Jiahui Chen, Jiajia Chen, Jiajing Chen, Jiajun Chen, Jiakang Chen, Jiale Chen, Jiali Chen, Jialing Chen, Jiamiao Chen, Jiamin Chen, Jian Chen, Jian-Guo Chen, Jian-Hua Chen, Jian-Jun Chen, Jian-Kang Chen, Jian-Min Chen, Jian-Qiao Chen, Jian-Qing Chen, Jianan Chen, Jianfei Chen, Jiang Chen, Jiang Ye Chen, Jiang-hua Chen, Jianghua Chen, Jiangxia Chen, Jianhua Chen, Jianhui Chen, Jiani Chen, Jianjun Chen, Jiankui Chen, Jianlin Chen, Jianmin Chen, Jianping Chen, Jianshan Chen, Jiansu Chen, Jianxiong Chen, Jianzhong Chen, Jianzhou Chen, Jiao Chen, Jiao-Jiao Chen, Jiaohua Chen, Jiaping Chen, Jiaqi Chen, Jiaqing Chen, Jiaren Chen, Jiarou Chen, Jiawei Chen, Jiawen Chen, Jiaxin Chen, Jiaxu Chen, Jiaxuan Chen, Jiayao Chen, Jiaye Chen, Jiayi Chen, Jiayuan Chen, Jichong Chen, Jie Chen, Jie-Hua Chen, Jiejian Chen, Jiemei Chen, Jien-Jiun Chen, Jihai Chen, Jijun Chen, Jimei Chen, Jin Chen, Jin-An Chen, Jin-Ran Chen, Jin-Shuen Chen, Jin-Wu Chen, Jin-Xia Chen, Jina Chen, Jinbo Chen, Jindong Chen, Jing Chen, Jing-Hsien Chen, Jing-Wen Chen, Jing-Xian Chen, Jing-Yuan Chen, Jing-Zhou Chen, Jingde Chen, Jinghua Chen, Jingjing Chen, Jingli Chen, Jinglin Chen, Jingming Chen, Jingnan Chen, Jingqing Chen, Jingshen Chen, Jingteng Chen, Jinguo Chen, Jingxuan Chen, Jingyao Chen, Jingyi Chen, Jingyuan Chen, Jingzhao Chen, Jingzhou Chen, Jinhao Chen, Jinhuang Chen, Jinli Chen, Jinlun Chen, Jinquan Chen, Jinsong Chen, Jintian Chen, Jinxuan Chen, Jinyan Chen, Jinyong Chen, Jion Chen, Jiong Chen, Jiongyu Chen, Jishun Chen, Jiu-Chiuan Chen, Jiujiu Chen, Jiwei Chen, Jiyan Chen, Jiyuan Chen, Jonathan Chen, Joy J Chen, Juan Chen, Juan-Juan Chen, Juanjuan Chen, Juei-Suei Chen, Juhai Chen, Jui-Chang Chen, Jui-Yu Chen, Jun Chen, Jun-Long Chen, Junchen Chen, Junfei Chen, Jung-Sheng Chen, Junhong Chen, Junhui Chen, Junjie Chen, Junling Chen, Junmin Chen, Junming Chen, Junpan Chen, Junpeng Chen, Junqi Chen, Junqin Chen, Junsheng Chen, Junshi Chen, Junyang Chen, Junyi Chen, Junyu Chen, K C Chen, Kai Chen, Kai-En Chen, Kai-Ming Chen, Kai-Ting Chen, Kai-Yang Chen, Kaifu Chen, Kaijian Chen, Kailang Chen, Kaili Chen, Kaina Chen, Kaiquan Chen, Kan Chen, Kang Chen, Kang-Hua Chen, Kangyong Chen, Kangzhen Chen, Katharine Y Chen, Katherine C Chen, Ke Chen, Kecai Chen, Kehua Chen, Kehui Chen, Kelin Chen, Ken Chen, Kenneth L Chen, Keping Chen, Kequan Chen, Kevin Chen, Kewei Chen, Kexin Chen, Keyan Chen, Keyang Chen, Keying Chen, Keyu Chen, Keyuan Chen, Kuan-Jen Chen, Kuan-Ling Chen, Kuan-Ting Chen, Kuan-Yu Chen, Kuangyang Chen, Kuey Chu Chen, Kui Chen, Kun Chen, Kun-Chieh Chen, Kunmei Chen, Kunpeng Chen, L B Chen, L F Chen, Lan Chen, Lang Chen, Lankai Chen, Lanlan Chen, Lanmei Chen, Le Chen, Le Qi Chen, Lei Chen, Lei-Chin Chen, Lei-Lei Chen, Leijie Chen, Lena W Chen, Leqi Chen, Letian Chen, Lexia Chen, Li Chen, Li Jia Chen, Li-Chieh Chen, Li-Hsien Chen, Li-Hsin Chen, Li-Hua Chen, Li-Jhen Chen, Li-Juan Chen, Li-Mien Chen, Li-Nan Chen, Li-Tzong Chen, Li-Zhen Chen, Li-hong Chen, Lian Chen, Lianfeng Chen, Liang Chen, Liang-Kung Chen, Liangkai Chen, Liangsheng Chen, Liangwan Chen, Lianmin Chen, Liaobin Chen, Lichang Chen, Lichun Chen, Lidian Chen, Lie Chen, Liechun Chen, Lifang Chen, Lifen Chen, Lifeng Chen, Ligang Chen, Lihong Chen, Lihua Chen, Lijin Chen, Lijuan Chen, Lili Chen, Limei Chen, Limin Chen, Liming Chen, Lin Chen, Lina Chen, Linbo Chen, Ling Chen, Ling-Yan Chen, Lingfeng Chen, Lingjun Chen, Lingli Chen, Lingxia Chen, Lingxue Chen, Lingyi Chen, Linjie Chen, Linlin Chen, Linna Chen, Linxi Chen, Linyi Chen, Liping Chen, Liqiang Chen, Liugui Chen, Liujun Chen, Liutao Chen, Lixia Chen, Lixian Chen, Liyun Chen, Lizhen Chen, Lizhu Chen, Lo-Yun Chen, Long Chen, Long-Jiang Chen, Longqing Chen, Longyun Chen, Lu Chen, Lu Hua Chen, Lu-Biao Chen, Lu-Zhu Chen, Lulu Chen, Luming Chen, Luyi Chen, Luzhu Chen, M Chen, M L Chen, Man Chen, Man-Hua Chen, Mao Chen, Mao-Yuan Chen, Maochong Chen, Maorong Chen, Marcus Y Chen, Mark I-Cheng Chen, Max Jl Chen, Mechi Chen, Mei Chen, Mei-Chi Chen, Mei-Chih Chen, Mei-Hsiu Chen, Mei-Hua Chen, Mei-Jie Chen, Mei-Ling Chen, Mei-Ru Chen, Meilan Chen, Meilin Chen, Meiling Chen, Meimei Chen, Meiting Chen, Meiyang Chen, Meiyu Chen, Meizhen Chen, Meng Chen, Meng Xuan Chen, Meng-Lin Chen, Meng-Ping Chen, Mengdi Chen, Menglan Chen, Mengling Chen, Mengping Chen, Mengqing Chen, Mengting Chen, Mengxia Chen, Mengyan Chen, Mengying Chen, Mian-Mian Chen, Miao Chen, Miao-Der Chen, Miao-Hsueh Chen, Miao-Yu Chen, Miaomiao Chen, Miaoran Chen, Michael C Chen, Michelle Chen, Mien-Cheng Chen, Min Chen, Min-Hsuan Chen, Min-Hu Chen, Min-Jie Chen, Ming Chen, Ming-Fong Chen, Ming-Han Chen, Ming-Hong Chen, Ming-Huang Chen, Ming-Huei Chen, Ming-Yu Chen, Mingcong Chen, Mingfeng Chen, Minghong Chen, Minghua Chen, Minglang Chen, Mingling Chen, Mingmei Chen, Mingxia Chen, Mingxing Chen, Mingyang Chen, Mingyi Chen, Mingyue Chen, Minjian Chen, Minjiang Chen, Minjie Chen, Minyan Chen, Mo Chen, Mu-Hong Chen, Muh-Shy Chen, Mulan Chen, Mystie X Chen, Na Chen, Naifei Chen, Naisong Chen, Nan Chen, Ni Chen, Nian-Ping Chen, Ning Chen, Ning-Bo Chen, Ning-Hung Chen, Ning-Yuan Chen, Ningbo Chen, Ningning Chen, Nuan Chen, On Chen, Ou Chen, Ouyang Chen, P P Chen, Pan Chen, Paul Chih-Hsueh Chen, Pei Chen, Pei-Chen Chen, Pei-Chun Chen, Pei-Lung Chen, Pei-Yi Chen, Pei-Yin Chen, Pei-zhan Chen, Peihong Chen, Peipei Chen, Peiqin Chen, Peixian Chen, Peiyou Chen, Peiyu Chen, Peize Chen, Peizhan Chen, Peng Chen, Peng-Cheng Chen, Pengxiang Chen, Ping Chen, Ping-Chung Chen, Ping-Kun Chen, Pingguo Chen, Po-Han Chen, Po-Ju Chen, Po-Min Chen, Po-See Chen, Po-Sheng Chen, Po-Yu Chen, Qi Chen, Qi-An Chen, Qian Chen, Qianbo Chen, Qianfen Chen, Qiang Chen, Qiangpu Chen, Qiankun Chen, Qianling Chen, Qianming Chen, Qianping Chen, Qianqian Chen, Qianxue Chen, Qianyi Chen, Qianyu Chen, Qianyun Chen, Qianzhi Chen, Qiao Chen, Qiao-Yi Chen, Qiaoli Chen, Qiaoling Chen, Qichen Chen, Qifang Chen, Qihui Chen, Qili Chen, Qinfen Chen, Qing Chen, Qing-Hui Chen, Qing-Juan Chen, Qing-Wei Chen, Qingao Chen, Qingchao Chen, Qingchuan Chen, Qingguang Chen, Qinghao Chen, Qinghua Chen, Qingjiang Chen, Qingjie Chen, Qingliang Chen, Qingmei Chen, Qingqing Chen, Qingqiu Chen, Qingshi Chen, Qingxing Chen, Qingyang Chen, Qingyi Chen, Qinian Chen, Qinsheng Chen, Qinying Chen, Qiong Chen, Qiongyun Chen, Qiqi Chen, Qitong Chen, Qiu Jing Chen, Qiu-Jing Chen, Qiu-Sheng Chen, Qiuchi Chen, Qiuhong Chen, Qiujing Chen, Qiuli Chen, Qiuwen Chen, Qiuxia Chen, Qiuxiang Chen, Qiuxuan Chen, Qiuyun Chen, Qiwei Chen, Qixian Chen, Qu Chen, Quan Chen, Quanjiao Chen, Quanwei Chen, Qunxiang Chen, R Chen, Ran Chen, Ranyun Chen, Ray-Jade Chen, Ren-Hui Chen, Renjin Chen, Renwei Chen, Renyu Chen, Robert Chen, Roger Chen, Rong Chen, Rong-Hua Chen, Rongfang Chen, Rongfeng Chen, Rongrong Chen, Rongsheng Chen, Rongyuan Chen, Roufen Chen, Rouxi Chen, Ru Chen, Rucheng Chen, Ruey-Hwa Chen, Rui Chen, Rui-Fang Chen, Rui-Min Chen, Rui-Pei Chen, Rui-Zhen Chen, Ruiai Chen, Ruibing Chen, Ruijing Chen, Ruijuan Chen, Ruilin Chen, Ruimin Chen, Ruiming Chen, Ruiqi Chen, Ruisen Chen, Ruixiang Chen, Ruixue Chen, Ruiying Chen, Rujun Chen, Runfeng Chen, Runsen Chen, Runsheng Chen, Ruofan Chen, Ruohong Chen, Ruonan Chen, Ruoyan Chen, Ruoying Chen, S Chen, S N Chen, S Pl Chen, S-D Chen, Sai Chen, San-Yuan Chen, Sean Chen, Sen Chen, Shali Chen, Shan Chen, Shanchun Chen, Shang-Chih Chen, Shang-Hung Chen, Shangduo Chen, Shangsi Chen, Shangwu Chen, Shangzhong Chen, Shanshan Chen, Shanyuan Chen, Shao-Ke Chen, Shao-Peng Chen, Shao-Wei Chen, Shao-Yu Chen, Shao-long Chen, Shaofei Chen, Shaohong Chen, Shaohua Chen, Shaokang Chen, Shaokun Chen, Shaoliang Chen, Shaotao Chen, Shaoxing Chen, Shaoze Chen, Shasha Chen, She Chen, Shen Chen, Shen-Ming Chen, Sheng Chen, Sheng-Xi Chen, Sheng-Yi Chen, Shengdi Chen, Shenghui Chen, Shenglan Chen, Shengnan Chen, Shengpan Chen, Shengyu Chen, Shengzhi Chen, Shi Chen, Shi-Qing Chen, Shi-Sheng Chen, Shi-Yi Chen, Shi-You Chen, Shibo Chen, Shih-Jen Chen, Shih-Pin Chen, Shih-Yin Chen, Shih-Yu Chen, Shilan Chen, Shiming Chen, Shin-Wen Chen, Shin-Yu Chen, Shipeng Chen, Shiqian Chen, Shiqun Chen, Shirui Chen, Shiuhwei Chen, Shiwei Chen, Shixuan Chen, Shiyan Chen, Shiyao Chen, Shiyi Chen, Shiyu Chen, Shou-Tung Chen, Shoudeng Chen, Shoujun Chen, Shouzhen Chen, Shu Chen, Shu-Fen Chen, Shu-Gang Chen, Shu-Hua Chen, Shu-Jen Chen, Shuai Chen, Shuai-Bing Chen, Shuai-Ming Chen, Shuaijie Chen, Shuaijun Chen, Shuaiyin Chen, Shuaiyu Chen, Shuang Chen, Shuangfeng Chen, Shuanghui Chen, Shuchun Chen, Shuen-Ei Chen, Shufang Chen, Shufeng Chen, Shuhai Chen, Shuhong Chen, Shuhuang Chen, Shuhui Chen, Shujuan Chen, Shuliang Chen, Shuming Chen, Shunde Chen, Shuntai Chen, Shunyou Chen, Shuo Chen, Shuo-Bin Chen, Shuoni Chen, Shuqin Chen, Shuqiu Chen, Shuting Chen, Shuwen Chen, Shuyi Chen, Shuying Chen, Si Chen, Si-Ru Chen, Si-Yuan Chen, Si-Yue Chen, Si-guo Chen, Sien-Tsong Chen, Sifeng Chen, Sihui Chen, Sijia Chen, Sijuan Chen, Sili Chen, Silian Chen, Siping Chen, Siqi Chen, Siqin Chen, Sisi Chen, Siteng Chen, Siting Chen, Siyi Chen, Siyu Chen, Siyu S Chen, Siyuan Chen, Siyue Chen, Size Chen, Song Chen, Song-Mei Chen, Songfeng Chen, Suet N Chen, Suet Nee Chen, Sufang Chen, Suipeng Chen, Sulian Chen, Suming Chen, Sun Chen, Sung-Fang Chen, Suning Chen, Sunny Chen, Sy-Jou Chen, Syue-Ting Chen, Szu-Chi Chen, Szu-Chia Chen, Szu-Chieh Chen, Szu-Han Chen, Szu-Yun Chen, T Chen, Tai-Heng Chen, Tai-Tzung Chen, Tailai Chen, Tan-Huan Chen, Tan-Zhou Chen, Tania Chen, Tao Chen, Tian Chen, Tianfeng Chen, Tianhang Chen, Tianhong Chen, Tianhua Chen, Tianpeng Chen, Tianran Chen, Tianrui Chen, Tiantian Chen, Tianzhen Chen, Tielin Chen, Tien-Hsing Chen, Ting Chen, Ting-Huan Chen, Ting-Tao Chen, Ting-Ting Chen, Tingen Chen, Tingtao Chen, Tingting Chen, Tom Wei-Wu Chen, Tong Chen, Tongsheng Chen, Tse-Ching Chen, Tse-Wei Chen, TsungYen Chen, Tuantuan Chen, Tzu-An Chen, Tzu-Chieh Chen, Tzu-Ju Chen, Tzu-Ting Chen, Tzu-Yu Chen, Tzy-Yen Chen, Valerie Chen, W Chen, Wai Chen, Wan Jun Chen, Wan-Tzu Chen, Wan-Yan Chen, Wan-Yi Chen, Wanbiao Chen, Wanjia Chen, Wanjun Chen, Wanling Chen, Wantao Chen, Wanting Chen, Wanyin Chen, Wei Chen, Wei J Chen, Wei Ning Chen, Wei-Cheng Chen, Wei-Cong Chen, Wei-Fei Chen, Wei-Hao Chen, Wei-Hui Chen, Wei-Kai Chen, Wei-Kung Chen, Wei-Lun Chen, Wei-Min Chen, Wei-Peng Chen, Wei-Ting Chen, Wei-Wei Chen, Wei-Yu Chen, Wei-xian Chen, Weibo Chen, Weican Chen, Weichan Chen, Weicong Chen, Weihao Chen, Weihong Chen, Weihua Chen, Weijia Chen, Weijie Chen, Weili Chen, Weilun Chen, Weina Chen, Weineng Chen, Weiping Chen, Weiqin Chen, Weiqing Chen, Weirui Chen, Weisan Chen, Weitao Chen, Weitian Chen, Weiwei Chen, Weixian Chen, Weixin Chen, Weiyi Chen, Weiyong Chen, Wen Chen, Wen-Chau Chen, Wen-Jie Chen, Wen-Pin Chen, Wen-Qi Chen, Wen-Tsung Chen, Wen-Yi Chen, Wenbiao Chen, Wenbing Chen, Wenfan Chen, Wenfang Chen, Wenhao Chen, Wenhua Chen, Wenjie Chen, Wenjun Chen, Wenlong Chen, Wenqin Chen, Wensheng Chen, Wenshuo Chen, Wentao Chen, Wenting Chen, Wentong Chen, Wenwen Chen, Wenwu Chen, Wenxi Chen, Wenxing Chen, Wenxu Chen, Willian Tzu-Liang Chen, Wu-Jun Chen, Wu-Xian Chen, Wuyan Chen, X Chen, X R Chen, X Steven Chen, Xi Chen, Xia Chen, Xia-Fei Chen, Xiaguang Chen, Xiameng Chen, Xian Chen, Xian-Kai Chen, Xianbo Chen, Xiancheng Chen, Xianfeng Chen, Xiang Chen, Xiang-Bin Chen, Xiang-Mei Chen, XiangFan Chen, Xiangding Chen, Xiangjun Chen, Xiangli Chen, Xiangliu Chen, Xiangmei Chen, Xiangna Chen, Xiangning Chen, Xiangqiu Chen, Xiangyu Chen, Xiankai Chen, Xianmei Chen, Xianqiang Chen, Xianxiong Chen, Xianyue Chen, Xianze Chen, Xianzhen Chen, Xiao Chen, Xiao-Chen Chen, Xiao-Hui Chen, Xiao-Jun Chen, Xiao-Lin Chen, Xiao-Qing Chen, Xiao-Quan Chen, Xiao-Wei Chen, Xiao-Yang Chen, Xiao-Ying Chen, Xiao-chun Chen, Xiao-he Chen, Xiao-ping Chen, Xiaobin Chen, Xiaobo Chen, Xiaochang Chen, Xiaochun Chen, Xiaodong Chen, Xiaofang Chen, Xiaofen Chen, Xiaofeng Chen, Xiaohan Chen, Xiaohong Chen, Xiaohua Chen, Xiaohui Chen, Xiaojiang S Chen, Xiaojie Chen, Xiaojing Chen, Xiaojuan Chen, Xiaojun Chen, Xiaokai Chen, Xiaolan Chen, Xiaole L Chen, Xiaolei Chen, Xiaoli Chen, Xiaolin Chen, Xiaoling Chen, Xiaolong Chen, Xiaolu Chen, Xiaomeng Chen, Xiaomin Chen, Xiaona Chen, Xiaonan Chen, Xiaopeng Chen, Xiaoping Chen, Xiaoqian Chen, Xiaoqing Chen, Xiaorong Chen, Xiaoshan Chen, Xiaotao Chen, Xiaoting Chen, Xiaowan Chen, Xiaowei Chen, Xiaowen Chen, Xiaoxiang Chen, Xiaoxiao Chen, Xiaoyan Chen, Xiaoyang Chen, Xiaoyin Chen, Xiaoyong Chen, Xiaoyu Chen, Xiaoyuan Chen, Xiaoyun Chen, Xiatian Chen, Xihui Chen, Xijun Chen, Xikun Chen, Ximei Chen, Xin Chen, Xin-Jie Chen, Xin-Ming Chen, Xin-Qi Chen, Xinan Chen, Xing Chen, Xing-Lin Chen, Xing-Long Chen, Xing-Zhen Chen, Xingdong Chen, Xinghai Chen, Xingxing Chen, Xingyi Chen, Xingyong Chen, Xingyu Chen, Xinji Chen, Xinlin Chen, Xinpu Chen, Xinqiao Chen, Xinwei Chen, Xinyan Chen, Xinyang Chen, Xinyi Chen, Xinyu Chen, Xinyuan Chen, Xinyue Chen, Xinzhuo Chen, Xiong Chen, Xiqun Chen, Xiu Chen, Xiu-Juan Chen, Xiuhui Chen, Xiujuan Chen, Xiuli Chen, Xiuping Chen, Xiuxiu Chen, Xiuyan Chen, Xixi Chen, Xiyao Chen, Xiyu Chen, Xu Chen, Xuan Chen, Xuancai Chen, Xuanjing Chen, Xuanli Chen, Xuanmao Chen, Xuanwei Chen, Xuanxu Chen, Xuanyi Chen, Xue Chen, Xue-Mei Chen, Xue-Qing Chen, Xue-Xin Chen, Xue-Yan Chen, Xue-Ying Chen, XueShu Chen, Xuechun Chen, Xuefei Chen, Xuehua Chen, Xuejiao Chen, Xuejun Chen, Xueli Chen, Xueling Chen, Xuemei Chen, Xuemin Chen, Xueqin Chen, Xueqing Chen, Xuerong Chen, Xuesong Chen, Xueting Chen, Xueyan Chen, Xueying Chen, Xufeng Chen, Xuhui Chen, Xujia Chen, Xun Chen, Xuxiang Chen, Xuxin Chen, Xuzhuo Chen, Y Chen, Y D I Chen, Y Eugene Chen, Y M Chen, Y P Chen, Y S Chen, Y U Chen, Y-D I Chen, Y-D Ida Chen, Ya Chen, Ya-Chun Chen, Ya-Nan Chen, Ya-Peng Chen, Ya-Ting Chen, Ya-xi Chen, Yafang Chen, Yafei Chen, Yahong Chen, Yajie Chen, Yajing Chen, Yajun Chen, Yalan Chen, Yali Chen, Yan Chen, Yan Jie Chen, Yan Q Chen, Yan-Gui Chen, Yan-Jun Chen, Yan-Ming Chen, Yan-Qiong Chen, Yan-yan Chen, Yanan Chen, Yananlan Chen, Yanbin Chen, Yanfei Chen, Yanfen Chen, Yang Chen, Yang-Ching Chen, Yang-Yang Chen, Yangchao Chen, Yanghui Chen, Yangxin Chen, Yanhan Chen, Yanhua Chen, Yanjie Chen, Yanjing Chen, Yanli Chen, Yanlin Chen, Yanling Chen, Yanming Chen, Yann-Jang Chen, Yanping Chen, Yanqiu Chen, Yanrong Chen, Yanru Chen, Yanting Chen, Yanyan Chen, Yanyun Chen, Yanzhu Chen, Yanzi Chen, Yao Chen, Yao-Shen Chen, Yaodong Chen, Yaosheng Chen, Yaowu Chen, Yau-Hung Chen, Yaxi Chen, Yayun Chen, Yazhuo Chen, Ye Chen, Ye-Guang Chen, Yeh Chen, Yelin Chen, Yen-Chang Chen, Yen-Chen Chen, Yen-Cheng Chen, Yen-Ching Chen, Yen-Fu Chen, Yen-Hao Chen, Yen-Hsieh Chen, Yen-Jen Chen, Yen-Ju Chen, Yen-Lin Chen, Yen-Ling Chen, Yen-Ni Chen, Yen-Rong Chen, Yen-Teen Chen, Yewei Chen, Yi Chen, Yi Feng Chen, Yi-Bing Chen, Yi-Chun Chen, Yi-Chung Chen, Yi-Fei Chen, Yi-Guang Chen, Yi-Han Chen, Yi-Hau Chen, Yi-Heng Chen, Yi-Hong Chen, Yi-Hsuan Chen, Yi-Hui Chen, Yi-Jen Chen, Yi-Lin Chen, Yi-Ru Chen, Yi-Ting Chen, Yi-Wen Chen, Yi-Yung Chen, YiChung Chen, YiPing Chen, Yian Chen, Yibing Chen, Yibo Chen, Yidan Chen, Yiding Chen, Yidong Chen, Yiduo Chen, Yifa Chen, Yifan Chen, Yifang Chen, Yifei Chen, Yih-Chieh Chen, Yihao Chen, Yihong Chen, Yii-Der Chen, Yii-Der I Chen, Yii-Derr Chen, Yii-der Ida Chen, Yijiang Chen, Yijun Chen, Yike Chen, Yilan Chen, Yilei Chen, Yili Chen, Yilin Chen, Yiming Chen, Yin-Huai Chen, Ying Chen, Ying-Cheng Chen, Ying-Hsiang Chen, Ying-Jie Chen, Ying-Jung Chen, Ying-Lan Chen, Ying-Ying Chen, Yingchun Chen, Yingcong Chen, Yinghui Chen, Yingji Chen, Yingjie Chen, Yinglian Chen, Yingting Chen, Yingxi Chen, Yingying Chen, Yingyu Chen, Yinjuan Chen, Yintong Chen, Yinwei Chen, Yinzhu Chen, Yiru Chen, Yishan Chen, Yisheng Chen, Yitong Chen, Yixin Chen, Yiyin Chen, Yiyun Chen, Yizhi Chen, Yong Chen, Yong-Jun Chen, Yong-Ping Chen, Yong-Syuan Chen, Yong-Zhong Chen, YongPing Chen, Yongbin Chen, Yongfa Chen, Yongfang Chen, Yongheng Chen, Yonghui Chen, Yongke Chen, Yonglu Chen, Yongmei Chen, Yongming Chen, Yongning Chen, Yongqi Chen, Yongshen Chen, Yongshuo Chen, Yongxing Chen, Yongxun Chen, You-Ming Chen, You-Xin Chen, You-Yue Chen, Youhu Chen, Youjia Chen, Youmeng Chen, Youran Chen, Youwei Chen, Yu Chen, Yu-Bing Chen, Yu-Cheng Chen, Yu-Chi Chen, Yu-Chia Chen, Yu-Chuan Chen, Yu-Fan Chen, Yu-Fen Chen, Yu-Fu Chen, Yu-Gen Chen, Yu-Han Chen, Yu-Hui Chen, Yu-Ling Chen, Yu-Ming Chen, Yu-Pei Chen, Yu-San Chen, Yu-Si Chen, Yu-Ting Chen, Yu-Tung Chen, Yu-Xia Chen, Yu-Xin Chen, Yu-Yang Chen, Yu-Ying Chen, Yuan Chen, Yuan-Hua Chen, Yuan-Shen Chen, Yuan-Tsong Chen, Yuan-Yuan Chen, Yuan-Zhen Chen, Yuanbin Chen, Yuanhao Chen, Yuanjia Chen, Yuanjian Chen, Yuanli Chen, Yuanqi Chen, Yuanwei Chen, Yuanwen Chen, Yuanyu Chen, Yuanyuan Chen, Yubin Chen, Yucheng Chen, Yue Chen, Yue-Lai Chen, Yuebing Chen, Yueh-Peng Chen, Yuelei Chen, Yuewen Chen, Yuewu Chen, Yuexin Chen, Yuexuan Chen, Yufei Chen, Yufeng Chen, Yuh-Lien Chen, Yuh-Ling Chen, Yuh-Min Chen, Yuhan Chen, Yuhang Chen, Yuhao Chen, Yuhong Chen, Yuhui Chen, Yujie Chen, Yule Chen, Yuli Chen, Yulian Chen, Yulin Chen, Yuling Chen, Yulong Chen, Yulu Chen, Yumei Chen, Yun Chen, Yun-Ju Chen, Yun-Tzu Chen, Yun-Yu Chen, Yundai Chen, Yunfei Chen, Yunfeng Chen, Yung-Hsiang Chen, Yung-Wu Chen, Yunjia Chen, Yunlin Chen, Yunn-Yi Chen, Yunqin Chen, Yunshun Chen, Yunwei Chen, Yunyun Chen, Yunzhong Chen, Yunzhu Chen, Yupei Chen, Yupeng Chen, Yuping Chen, Yuqi Chen, Yuqin Chen, Yuqing Chen, Yuquan Chen, Yurong Chen, Yushan Chen, Yusheng Chen, Yusi Chen, Yuting Chen, Yutong Chen, Yuxi Chen, Yuxian Chen, Yuxiang Chen, Yuxin Chen, Yuxing Chen, Yuyan Chen, Yuyang Chen, Yuyao Chen, Z Chen, Zan Chen, Zaozao Chen, Ze-Hui Chen, Ze-Xu Chen, Zechuan Chen, Zemin Chen, Zetian Chen, Zexiao Chen, Zeyu Chen, Zhanfei Chen, Zhang-Liang Chen, Zhang-Yuan Chen, Zhangcheng Chen, Zhanghua Chen, Zhangliang Chen, Zhanglin Chen, Zhangxin Chen, Zhanjuan Chen, Zhao Chen, Zhao-Xia Chen, ZhaoHui Chen, Zhaojun Chen, Zhaoli Chen, Zhaolin Chen, Zhaoran Chen, Zhaowei Chen, Zhaoyao Chen, Zhe Chen, Zhe-Ling Chen, Zhe-Sheng Chen, Zhe-Yu Chen, Zhebin Chen, Zhehui Chen, Zhelin Chen, Zhen Bouman Chen, Zhen Chen, Zhen-Hua Chen, Zhen-Yu Chen, Zhencong Chen, Zhenfeng Chen, Zheng Chen, Zheng-Zhen Chen, Zhenghong Chen, Zhengjun Chen, Zhengling Chen, Zhengming Chen, Zhenguo Chen, Zhengwei Chen, Zhengzhi Chen, Zhenlei Chen, Zhenyi Chen, Zhenyue Chen, Zheping Chen, Zheren Chen, Zhesheng Chen, Zheyi Chen, Zhezhe Chen, Zhi Bin Chen, Zhi Chen, Zhi-Hao Chen, Zhi-bin Chen, Zhi-zhe Chen, Zhiang Chen, Zhichuan Chen, Zhifeng Chen, Zhigang Chen, Zhigeng Chen, Zhiguo Chen, Zhihai Chen, Zhihang Chen, Zhihao Chen, Zhiheng Chen, Zhihong Chen, Zhijian Chen, Zhijian J Chen, Zhijing Chen, Zhijun Chen, Zhimin Chen, Zhinan Chen, Zhiping Chen, Zhiqiang Chen, Zhiquan Chen, Zhishi Chen, Zhitao Chen, Zhiting Chen, Zhiwei Chen, Zhixin Chen, Zhixuan Chen, Zhixue Chen, Zhiyong Chen, Zhiyu Chen, Zhiyuan Chen, Zhiyun Chen, Zhizhong Chen, Zhong Chen, Zhongbo Chen, Zhonghua Chen, Zhongjian Chen, Zhongliang Chen, Zhongxiu Chen, Zhongzhu Chen, Zhou Chen, Zhouji Chen, Zhouliang Chen, Zhoulong Chen, Zhouqing Chen, Zhuchu Chen, Zhujun Chen, Zhuo Chen, Zhuo-Yuan Chen, ZhuoYu Chen, Zhuohui Chen, Zhuojia Chen, Zi-Jiang Chen, Zi-Qing Chen, Zi-Yang Chen, Zi-Yue Chen, Zi-Yun Chen, Zian Chen, Zifan Chen, Zihan Chen, Zihang Chen, Zihao Chen, Zihe Chen, Zihua Chen, Zijie Chen, Zike Chen, Zilin Chen, Zilong Chen, Ziming Chen, Zinan Chen, Ziqi Chen, Ziqing Chen, Zitao Chen, Zixi Chen, Zixin Chen, Zixuan Chen, Ziying Chen, Ziyuan Chen, Zoe Chen, Zongming E Chen, Zongnan Chen, Zongyou Chen, Zongzheng Chen, Zugen Chen, Zuolong Chen
articles
Yafei Chen, Baoqin Huang, Hong Liang +8 more · 2024 · The Science of the total environment · Elsevier · added 2026-04-24
Organophosphate esters (OPEs) exposure could affect offspring health. However, the underlying mechanisms are not well documented. Based on a birth cohort study, we aimed to investigate the association Show more
Organophosphate esters (OPEs) exposure could affect offspring health. However, the underlying mechanisms are not well documented. Based on a birth cohort study, we aimed to investigate the associations among gestational OPEs exposure, placental DNA methylation levels of peroxisome proliferator-activated receptor (PPAR) signaling pathway-related genes, and fetal growth. We measured the concentrations of eight OPE metabolites in maternal urine samples and neonatal anthropometric measurements in 733 mother-child pairs. In 327 placental samples, we assessed the DNA methylation levels of 14 genes which were involved in the PPARs signaling pathway and expressed in placenta. Multiple linear regression models were used to examine the associations of OPEs exposure with placental DNA methylation, and of OPEs and placental DNA methylation with neonatal anthropometric measurements. Causal mediation analyses were conducted to examine the potential mediating role of placental DNA methylation in the pathway between OPEs exposure and fetal growth. We observed a general pattern of OPEs exposure being associated with hypermethylation of candidate genes, with statistically significant associations identified for several OPEs with RXRA, ACAA1, ACADL, ACADM, PLTP, and NR1H3 methylation. Further, gestational exposure to BCIPP, DPP, BBOEP, ∑NCl-OPEs, and ∑OPEs tended to be associated with lower anthropometric measurements, with more significant associations observed on arm circumference, and abdominal and back skinfold thickness. Notably, RXRA, ACAA1, ACOX1, CPT2, ACADM, and NR1H3 methylation tended to be associated with lower neonatal anthropometric measurements, especially for abdominal and back skinfold thickness. Moreover, mediation analyses showed that 19.42 % of the total effect of DPP on the back skinfold thickness was mediated by changes in RXRA methylation, and there was a significant indirect effect of RXRA methylation. Gestational OPEs exposure could disrupt the placental DNA methylation levels of PPAR signaling pathway-related genes, which might contribute to the effect of OPEs on fetal growth. Show less
no PDF DOI: 10.1016/j.scitotenv.2024.174569
NR1H3
Shuqi Wang, Haina Gao, Mengmeng Zhang +1 more · 2024 · Diabetes, metabolic syndrome and obesity : targets and therapy · added 2026-04-24
To explore the relationship between vitamin D (VitD) deficiency and the apolipoprotein B/apolipoprotein A1 (apo B/A1) in type 2 diabetes mellitus (T2DM) patients. This was a retrospective study that l Show more
To explore the relationship between vitamin D (VitD) deficiency and the apolipoprotein B/apolipoprotein A1 (apo B/A1) in type 2 diabetes mellitus (T2DM) patients. This was a retrospective study that lasted 2 years and 6 months, collecting information and laboratory data from 784 patients with T2DM. Patients were divided into VitD deficiency group (n = 433) and non-VitD deficiency group (n = 351) based on VitD levels. Calculated apo B/A1 ratio, and patients were further divided into high-apo B/A1 group (n = 392) and low-apo B/A1 group (n = 392) based on the median of the apo B/A1. All data were analyzed using Prism 8.0.1 and R version 4.3.1 software. Apo B/A1 levels of T2DM patients combined with VitD deficiency was significantly higher than that of non-VitD deficiency patients, and the VitD levels of patients with high apo B/A1 was significantly lower than that patients with low apo B/A1 (all P<0.001). Spearman correlation analysis showed that VitD levels were negatively correlated with apo B/A1 (r=-0.238, P<0.001). Multiple linear regression analysis revealed after adjusting other factors, VitD levels were significantly negatively associated with apo B/A1 (β=-0.123, P=0.001). Binary logistic regression analysis showed apoB/A1 was an independent risk factor for VitD deficiency in T2DM patients. Restrictive cubic spline indicated a significant linear relationship between apoB/A1 and VitD deficiency (P general trend <0.0001, P nonlinear = 0.0896), after stratification of gender, the results showed that apo B/A1 was more susceptible to VitD deficiency in female patients. The receiver operating characteristic (ROC) curve analysis showed that the area under the curve, sensitivity and specificity of the apo B/A1 for VitD deficiency were 0.654, 66.3% and 59.8%, respectively. The apo B/A1 was significantly negatively associated with VitD levels and an independent risk factor for VitD deficiency in patients with T2DM. Show less
📄 PDF DOI: 10.2147/DMSO.S465391
APOB
Yuan Wang, Ineza Karambizi Sandrine, Li Ma +9 more · 2024 · Cell death & disease · Nature · added 2026-04-24
no PDF DOI: 10.1038/s41419-024-07183-7
TNKS1BP1
Xingkai An, Shanshan Zhao, Jie Fang +10 more · 2024 · Frontiers in neurology · Frontiers · added 2026-04-24
Migraine is a common primary headache that has a significant impact on patients' quality of life. The co-occurrence of migraine and depression is frequent, resulting in more complex symptoms and a poo Show more
Migraine is a common primary headache that has a significant impact on patients' quality of life. The co-occurrence of migraine and depression is frequent, resulting in more complex symptoms and a poorer prognosis. The evidence suggests that depression and migraine comorbidity share a polygenic genetic background. The aim of this study is to identify related genetic variants that contribute to genetic susceptibility to migraine with and without depression in a Chinese cohort. In this case-control study, 263 individuals with migraines and 223 race-matched controls were included. Eight genetic polymorphism loci selected from the GWAS were genotyped using Sequenom's MALDI-TOF iPLEX platform. In univariate analysis, The study indicates that there is an association between Show less
📄 PDF DOI: 10.3389/fneur.2024.1418529
ANKDD1B

[

Z Ke, Z Huang, R He +4 more · 2024 · Nan fang yi ke da xue xue bao = Journal of Southern Medical University · added 2026-04-24
To investigate the role of high-mobility group AT-hook 2 (HMGA2) in osteogenic differentiation of adipose-derived mesenchymal stem cells (ADSCs) and the effect of Bioinformatics studies using the GEO Show more
To investigate the role of high-mobility group AT-hook 2 (HMGA2) in osteogenic differentiation of adipose-derived mesenchymal stem cells (ADSCs) and the effect of Bioinformatics studies using the GEO database and Rstudio software identified HMGA2 as a key factor in adipogenic-osteogenic differentiation balance of ADSCs. The protein-protein interaction network of HMGA2 in osteogenic differentiation was mapped using String and visualized with Cytoscape to predict the downstream targets of HMGA2. Primary mouse ADSCs (mADSCs) were transfected with GEO database analysis showed that HMGA2 is a crucial regulator of osteogenic differentiation in ADSCs, and Show less
no PDF DOI: 10.12122/j.issn.1673-4254.2024.07.02
SNAI1
Hao Chen, Guimin Hou, Tian Lan +5 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Hepatocellular carcinoma (HCC) is among the most prevalent digestive system malignancies and is associated with a poor prognosis. Necroptosis, a form of regulated death mediated by death receptors, ex Show more
Hepatocellular carcinoma (HCC) is among the most prevalent digestive system malignancies and is associated with a poor prognosis. Necroptosis, a form of regulated death mediated by death receptors, exhibits characteristics of both necrosis and apoptosis. Long non-coding RNAs (lncRNAs) have been identified as crucial regulators in tumor necroptosis. This study aims to identify the necroptosis-related lncRNAs (np-lncRNA) in HCC and investigate their relationships with prognosis. The RNA-sequencing data, along with clinicopathological and survival information of HCC patients were sourced from The Cancer Genome Atlas (TCGA) database. The np-lncRNAs were analyzed to assess their potential in predicting HCC prognosis. Prognostic signatures related to necroptosis were constructed using stepwise multivariate Cox regression analysis. The prognosis of patients was compared using Kaplan-Meier (KM) analysis. The accuracy of the prognostic signature was evaluated using Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Quantitative real-time polymerase chain reaction(qPCR) was employed to validate the lncRNAs expression levels of lncRNAs among samples from an independent cohort. The np-lncRNAs ZFPM2-AS1, AC099850.3, BACE1-AS, KDM4A-AS1 and MKLN1-AS were identified as potential prognostic biomarkers. The prognostic signature constructed from these np-lncRNAs achieved an Area Under the Curve (AUC) of 0.773. Based on the risk score derived from the signature, patients were divided into two groups, with the high-risk group exhibiting poorer overall survival. Gene Set Enrichment Analysis (GSEA) revealed significantly different between the low risk and high risk groups in tumor-related pathways (such as mTOR, MAPK and p53 signaling pathways) and immune-related functions (like T cell receptor signaling pathway and natural killer cell mediated cytotoxicity). The increased expression of np-lncRNAs was confirmed in another independent HCC cohort. This signature offers a dependable method for forecasting the prognosis of HCC patients. Our findings indicate a subset of np-lncRNA biomarkers that could be utilized for prognosis prediction and personalized treatment strategies of HCC patients. Show less
📄 PDF DOI: 10.1016/j.heliyon.2024.e37403
BACE1
Ling Zeng, Zhikuan Yang, Wei Pan +5 more · 2024 · Journal of inflammation research · added 2026-04-24
In this study, we aimed to investigate the relationship between the intraocular levels of inflammatory factors and myopia-related retinal vascular and neuronal degeneration. One hundred and forty-seve Show more
In this study, we aimed to investigate the relationship between the intraocular levels of inflammatory factors and myopia-related retinal vascular and neuronal degeneration. One hundred and forty-seven patients with Implantable Collamer Lens (ICL) implantation were enrolled and all participants received comprehensive ophthalmic examination. About 100~150 ul of aqueous humor was collected immediately before ICL surgery. The levels of inflammatory factors including Aggrecan, April, BAFF, CCL5, CD163, Chi3l1, gp130, IL-6Rα, IL-8, IL-10, IL-11, IL-12, IL-19, IL-27, IL-28A, IL-34, IFN-β, IFN-γ, MMP-1, MMP-2, MMP-3 and PTX3 in the aqueous humor were measured using the Luminex Multiplexing system. Results showed that aqueous humor levels of pro-inflammatory factors Chi3l1, IL-6Rα, IL-8, IL-12, IL-27, inflammation-related cytokines April, BAFF and IL-34 progressively increased from the progression of myopic retinopathy. Conversely, the aqueous levels of IL-11 and Aggrecan gradually decreased from the progression of myopic retinopathy. Correlation analysis showed that the intraocular levels of Chi3l1, IL-6Rα, IL-8, IL-27 and BAFF were negatively correlated with retinal vascular density. The intraocular level of IL-6Rα was negatively correlated with retinal neuronal thickness. Protein-Protein Interaction (PPI) analysis revealed that Chi3l1 and Aggrecan were the upstream cytokines that affect IL-10 and IL-8 in the pathological myopic eyes. KEGG pathway analysis showed that cytokine-cytokine receptor interaction, JAK-STAT signaling pathway, rheumatoid arthritis, and chagas disease were influenced by these altered inflammatory factors (adjusted p-value<0.001). The production of inflammatory factors in the eyes of individuals with high myopia and pathological myopia was altered, and the elevated levels of intraocular pro-inflammatory factors such as Chi3l1, IL-6Rα, and IL-8 were closely associated with myopia-related retinal microvascular and neurodegeneration. Show less
📄 PDF DOI: 10.2147/JIR.S484338
IL27
Chia-Hsuan Cheng, Hiromi Yatsuda, Han-Hsiang Chen +3 more · 2024 · Sensors (Basel, Switzerland) · MDPI · added 2026-04-24
Cardiovascular disease (CVD) represents the leading cause of death worldwide. For individuals at elevated risk for cardiovascular disease, early detection and monitoring of lipid status is imperative. Show more
Cardiovascular disease (CVD) represents the leading cause of death worldwide. For individuals at elevated risk for cardiovascular disease, early detection and monitoring of lipid status is imperative. The majority of lipid measurements conducted in hospital settings employ optical detection, which necessitates the use of relatively large-sized detection machines. It is, therefore, necessary to develop point-of-care testing (POCT) for lipoprotein in order to monitor CVD. To enhance the management and surveillance of CVD, this study sought to develop a POCT approach for apolipoprotein B (ApoB) utilizing a shear horizontal surface acoustic wave (SH-SAW) platform to assess the risk of heart disease. The platform employs a reflective SH-SAW sensor to reduce the sensor size and enhance the phase-shifted signals. In this study, the platform was utilized to monitor the impact of a weekly almond and oat milk or statins intervention on alterations in CVD risk. The SH-SAW ApoB test exhibited a linear range of 0 to 212 mg/dL, and a coefficient correlation (R) of 0.9912. Following a four-week intervention period, both the almond and oat milk intervention (-23.3%, Show less
📄 PDF DOI: 10.3390/s24206517
APOB
Maodou Xu, Yaoyao Zhang, Yang Zhang +3 more · 2024 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
The fat deposition of different adipose tissues is widely recognized as correlated, with distinct effects on meat quality traits and reproductive performance in poultry. In this study, we utilized lip Show more
The fat deposition of different adipose tissues is widely recognized as correlated, with distinct effects on meat quality traits and reproductive performance in poultry. In this study, we utilized lipidomics and transcriptomics analyses to investigate the heterogeneity and regulators of intramuscular fat (IMF), abdominal fat (AF), and subcutaneous fat (SF) in geese. Lipidomic profiling revealed 165, 129, and 77 differential lipid molecules (DLMs) between AF vs. IMF, SF vs. IMF, and SF vs. AF, respectively, with 47 common DLMs identified between AF vs. IMF and SF vs. IMF. Transcriptomic analysis identified 3369, 5758, and 131 differentially expressed genes (DEGs) between AF vs. IMF, SF vs. IMF, and SF vs. AF, respectively, with 2510 common DEGs identified between AF vs. IMF and SF vs. IMF. The KEGG results indicate that DLMs were predominantly enriched in glycerophospholipid and glycerolipid metabolism pathways, while DEGs were primarily enriched in metabolic pathways. Pearson correlation analysis identified Show less
📄 PDF DOI: 10.3390/ani14131990
LPL
Mengyun Qiao, Haitao Yang, Li Liu +9 more · 2024 · Toxics · MDPI · added 2026-04-24
Long-term exposure to lead (Pb) can result in chronic damage to the body through accumulation in the central nervous system (CNS) leading to neurodegenerative diseases, such as Alzheimer's disease (AD Show more
Long-term exposure to lead (Pb) can result in chronic damage to the body through accumulation in the central nervous system (CNS) leading to neurodegenerative diseases, such as Alzheimer's disease (AD). This study delves into the intricate role of miR-671/CDR1as regulation in the etiology of AD-like lesions triggered by chronic Pb exposure in adult mice. To emulate the chronic effects of Pb, we established a rodent model spanning 10 months of controlled Pb administration, dividing 52 C57BL/6J mice into groups receiving varying concentrations of Pb (1, 2, or 4 g/L) alongside an unexposed control. Blood Pb levels were monitored using serum samples to ensure accurate dosing and to correlate with observed toxicological outcomes. Utilizing the Morris water maze, a robust behavioral assay for assessing cognitive functions, we documented a dose-dependent decline in learning and memory capabilities among the Pb-exposed mice. Histopathological examination of the hippocampal tissue revealed tell-tale signs of AD-like neurodegeneration, characterized by the accumulation of amyloid plaques and neurofibrillary tangles. At the molecular level, a significant upregulation of AD-associated genes, namely amyloid precursor protein (APP), β-secretase 1 (BACE1), and tau, was observed in the hippocampal tissue of Pb-exposed mice. This was accompanied by a corresponding surge in the protein levels of APP, BACE1, amyloid-β (Aβ), and phosphorylated tau (p-tau), further implicating Pb in the dysregulation of these key AD markers. The expression of CDR1as, a long non-coding RNA implicated in AD pathogenesis, was found to be suppressed in Pb-exposed mice. This observation suggests a potential mechanistic link between Pb-induced neurotoxicity and the dysregulation of the CDR1as/miR-671 axis, which warrants further investigation. Moreover, our study identified a dose-dependent alteration in the intracellular and extracellular levels of the transcription factor nuclear factor-kappa B (NF-κB). This finding implicates Pb in the modulation of NF-κB signaling, a pathway that plays a pivotal role in neuroinflammation and neurodegeneration. In conclusion, our findings underscored the deleterious effects of Pb exposure on the CNS, leading to the development of AD-like pathology. The observed modulation of NF-κB signaling and miR-671/CDR1as regulation provides a plausible mechanistic framework for understanding the neurotoxic effects of Pb and its potential contribution to AD pathogenesis. Show less
📄 PDF DOI: 10.3390/toxics12060410
BACE1
Xiyu Chen, Yang Huang, Shuo Yang +5 more · 2024 · Biosensors & bioelectronics · Elsevier · added 2026-04-24
An in-situ nanozyme signal tag combined with a DNA-mediated universal antibody-oriented strategy was proposed to establish a high-performance immunosensing platform for Alzheimer's disease (AD)-relate Show more
An in-situ nanozyme signal tag combined with a DNA-mediated universal antibody-oriented strategy was proposed to establish a high-performance immunosensing platform for Alzheimer's disease (AD)-related biomarker detection. Briefly, a Zr-based metal-organic framework (MOF) with peroxidase (POD)-like activity was synthesized to encapsulating the electroactive molecule methylene blue (MB), and subsequently modified with a layer of gold nanoparticles on its surface. This led to the creation of double POD-like activity nanozymes surrounding the MB molecule to form a nanozyme signal tag. A large number of hydroxyl radicals were generated by the nanozyme signal tag with the help of H Show less
no PDF DOI: 10.1016/j.bios.2024.116738
BACE1
Shen-Xi Ouyang, Jia-Hui Zhu, Qi Cao +13 more · 2024 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Drug-induced liver injury (DILI) is a significant global health issue that poses high mortality and morbidity risks. One commonly observed cause of DILI is acetaminophen (APAP) overdose. GSDME is an e Show more
Drug-induced liver injury (DILI) is a significant global health issue that poses high mortality and morbidity risks. One commonly observed cause of DILI is acetaminophen (APAP) overdose. GSDME is an effector protein that induces non-canonical pyroptosis. In this study, the activation of GSDME, but not GSDMD, in the liver tissue of mice and patients with APAP-DILI is reported. Knockout of GSDME, rather than GSDMD, in mice protected them from APAP-DILI. Mice with hepatocyte-specific rescue of GSDME reproduced APAP-induced liver injury. Furthermore, alterations in the immune cell pools observed in APAP-induced DILI, such as the replacement of TIM4 Show less
📄 PDF DOI: 10.1002/advs.202305715
CPS1
D. Yan, D. Chen, H.-J. Im · 2024 · Journal of cellular biochemistry · Wiley · added 2026-04-24
D. Yan, D. Chen, and H.-J. Im, "Fibroblast Growth Factor-2 Promotes Catabolism via FGFR1-Ras-Raf-MEK1/2-ERK1/2 Axis That Coordinates With the PKCδ Pathway in Human Articular Chondrocytes," Journal of Show more
D. Yan, D. Chen, and H.-J. Im, "Fibroblast Growth Factor-2 Promotes Catabolism via FGFR1-Ras-Raf-MEK1/2-ERK1/2 Axis That Coordinates With the PKCδ Pathway in Human Articular Chondrocytes," Journal of Cellular Biochemistry 113, no. 9 (2012): 2856-2865, https://doi.org/10.1002/jcb.24160. The above article, published online on 5 April 2012 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Christian Behl; and Wiley Periodicals LLC. The retraction has occurred due to concerns related to the data presented in the article raised by the Office of Research Compliance at Rush University Medical Center following an investigation jointly conducted by Rush University and the Jesse Brown Veterans Affairs Medical Center (JBVAMC). Specifically, image elements of the experimental data in Figures 2, 4 A and 5 C were found to have been used by the same author(s) for publication elsewhere in a different scientific context. The corresponding author, Dr. Hee-Jeong Im Sampen, has been informed of the decision to retract but did not agree with it, as she is confident that any errors in the publication do not impact the reliability of the paper's findings. She also advised the editors that she stands ready to cooperate fully to make any necessary corrections. However, the article is retracted as the editors lost trust in the accuracy of the data and consider the conclusions invalid. Show less
no PDF DOI: 10.1002/jcb.30665
FGFR1
Yunjiang Zheng, Qianyi Chen, Lei Cao +3 more · 2024 · Clinical laboratory · added 2026-04-24
In this study, we aimed to identify the hub genes responsible for increased vascular endothelial cell permeability. We applied the weighted Gene Expression Omnibus (GEO) database to mine dataset GSE17 Show more
In this study, we aimed to identify the hub genes responsible for increased vascular endothelial cell permeability. We applied the weighted Gene Expression Omnibus (GEO) database to mine dataset GSE178331 and ob-tained the most relevant high-throughput sequenced genes for an increased permeability of vascular endothelial cells due to inflammation. We constructed two weighted gene co-expression network analysis (WGCNA) networks, and the differential expression of high-throughput sequenced genes related to endothelial cell permeability were screened from the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differential genes. Their degree values were obtained from the topological properties of protein-protein interaction (PPI) networks of differential genes, and the hub genes associated with an increased endothelial cell permeability were analyzed. Reverse transcription-polymerase chain reaction (RT-PCR) and western blotting techniques were used to detect the presence of these hub genes in TNF-α induced mRNA and the protein expression in endothelial cells. In total, 1,475 differential genes were mainly enriched in the cell adhesion and TNF-α signaling pathway. With TNF-α inducing an increase in the endothelial cell permeability and significantly increasing mRNA and protein expression levels, we identified three hub genes, namely PTGS2, ICAM1, and SNAI1. There was a significant difference in the high-dose TNF-α group and in the low-dose TNF-α group compared to the control group, in the endothelial cell permeability experiment (p = 0.008 vs. p = 0.02). Measurement of mRNA and protein levels of PTGS2, ICAM1, and SNAI1 by western blotting analysis showed that there was a significant impact on TNF-α and that there was a significant dose-dependent relationship (p < 0.05 vs. p < 0.01). The three hub genes identified through bioinformatics analyses in the present study may serve as biomarkers of increased vascular endothelial cell permeability. The findings offer valuable insights into the progress and mechanism of vascular endothelial cell permeability. Show less
no PDF DOI: 10.7754/Clin.Lab.2024.231125
SNAI1
Heike Schönherr, Pelin Ayaz, Alexander M Taylor +26 more · 2024 · Proceedings of the National Academy of Sciences of the United States of America · National Academy of Sciences · added 2026-04-24
Fibroblast growth factor receptor (FGFR) kinase inhibitors have been shown to be effective in the treatment of intrahepatic cholangiocarcinoma and other advanced solid tumors harboring
📄 PDF DOI: 10.1073/pnas.2317756121
FGFR1
I-Weng Yen, Shin-Yu Lin, Ming-Wei Lin +12 more · 2024 · Clinica chimica acta; international journal of clinical chemistry · Elsevier · added 2026-04-24
Large-for-gestational-age (LGA) neonates have increased risk of adverse pregnancy outcomes and adult metabolic diseases. We aimed to investigate the relationship between plasma angiopoietin-like prote Show more
Large-for-gestational-age (LGA) neonates have increased risk of adverse pregnancy outcomes and adult metabolic diseases. We aimed to investigate the relationship between plasma angiopoietin-like protein 4 (ANGPTL4), a protein involved in lipid and glucose metabolism during pregnancy, placental function, growth factors, and the risk of LGA. We conducted a prospective cohort study and recruited women with singleton pregnancies at the National Taiwan University Hospital between 2013 and 2018. First trimester maternal plasma ANGPTL4 concentrations were measured. Among 353 pregnant women recruited, the LGA group had higher first trimester plasma ANGPTL4 concentrations than the appropriate-for-gestational-age group. Plasma ANGPTL4 was associated with hemoglobin A1c, post-load plasma glucose, plasma triglyceride, plasma free fatty acid concentrations, plasma growth hormone variant (GH-V), and birth weight, but was not associated with cord blood growth factors. After adjusting for age, body mass index, hemoglobin A1c, and plasma triglyceride concentrations, plasma ANGPTL4 concentrations were significantly associated with LGA risk, and its predictive performance, as measured by the area under the receiver operating characteristic curve, outperformed traditional risk factors for LGA. Plasma ANGPTL4 is associated with glucose and lipid metabolism during pregnancy, plasma GH-V, and birth weight, and is an early biomarker for predicting the risk of LGA. Show less
no PDF DOI: 10.1016/j.cca.2024.117775
ANGPTL4
Yajing Shen, Jiajun Chen, Jinyu Wu +6 more · 2024 · Cancer prevention research (Philadelphia, Pa.) · added 2026-04-24
The purpose of this study was to identify biomarkers associated with hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) and to develop a new combination with good diagnostic performance. Show more
The purpose of this study was to identify biomarkers associated with hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) and to develop a new combination with good diagnostic performance. This study was divided into four phases: discovery, verification, validation, and modeling. A total of four candidate tumor-associated autoantibodies (TAAb; anti-ZIC2, anti-PCNA, anti-CDC37L1, and anti-DUSP6) were identified by human proteome microarray (52 samples) and bioinformatics analysis. Subsequently, these candidate TAAbs were further confirmed by indirect ELISA with two testing cohorts (120 samples for verification and 663 samples for validation). The AUC for these four TAAbs to identify patients with HBV-HCC from chronic hepatitis B (CHB) patients ranged from 0.693 to 0.739. Finally, a diagnostic panel with three TAAbs (anti-ZIC2, anti-CDC37L1, and anti-DUSP6) was developed. This panel showed superior diagnostic efficiency in identifying early HBV-HCC compared with alpha-fetoprotein (AFP), with an AUC of 0.834 [95% confidence interval (CI), 0.772-0.897] for this panel and 0.727 (95% CI, 0.642-0.812) for AFP (P = 0.0359). In addition, the AUC for this panel to identify AFP-negative patients with HBV-HCC was 0.796 (95% CI, 0.734-0.858), with a sensitivity of 52.4% and a specificity of 89.0%. Importantly, the panel in combination with AFP significantly increased the positive rate for early HBV-HCC to 84.1% (P = 0.005) and for late HBV-HCC to 96.3% (P < 0.001). Our findings suggest that AFP and the autoantibody panel may be independent but complementary serologic biomarkers for HBV-HCC detection. We developed a robust diagnostic panel for identifying patients with HBV-HCC from patients with CHB. This autoantibody panel provided superior diagnostic performance for HBV-HCC at an early stage and/or with negative AFP results. Our findings suggest that AFP and the autoantibody panel may be independent but complementary biomarkers for HBV-HCC detection. Show less
no PDF DOI: 10.1158/1940-6207.CAPR-23-0311
DUSP6
Yunjia Chen, Ender Karaca, Nathaniel H Robin +6 more · 2024 · Genetics in medicine : official journal of the American College of Medical Genetics · Elsevier · added 2026-04-24
Multiple studies suggest an association between DLG2 and neurodevelopmental disorders and indicate the haploinsufficiency of this gene; however, few cases have been thoroughly described. We performed Show more
Multiple studies suggest an association between DLG2 and neurodevelopmental disorders and indicate the haploinsufficiency of this gene; however, few cases have been thoroughly described. We performed additional studies to confirm this clinical association and DLG2 haploinsufficiency. Chromosomal microarray analysis was performed on 11,107 patients at the Cytogenetics Laboratory at the University of Alabama at Birmingham. The Database of Genomic Variants-Gold Standard Variants and the Genome Aggregation Database were selected for the association analysis. Fifty-nine patients from the literature and DECIPHER, all having DLG2 intragenic deletions, were included for comprehensive analysis of the distribution of these deletions. A total of 13 patients with DLG2 intragenic deletions, from 10 families in our cohort, were identified. Nine of 10 probands presented with clinical features of neurodevelopmental disorders. Congenital anomalies and dysmorphism were common in our cohort of patients. Association analysis showed that the frequency of DLG2 deletions in our cohort is significantly higher than those in the Database of Genomic Variants-Gold Standard Variants and the Genome Aggregation Database. Most of DLG2 intragenic deletions identified in 69 unrelated patients from our cohort, the literature, and DECIPHER map to the 5' region of the gene, with a hotspot centered around HPin7, exon 8, and HPin8. Our findings reinforce the link between DLG2 intragenic deletions and neurodevelopmental disorders, strongly support the haploinsufficiency of this gene, and indicate that these deletions might also have an association with congenital anomalies and dysmorphism. Show less
no PDF DOI: 10.1016/j.gim.2023.101010
DLG2
Xiaolu Chen, Yajiao Huang, Ban Chen +3 more · 2024 · European journal of medicinal chemistry · Elsevier · added 2026-04-24
Recently, FGFR4 has become a hot target for the treatment of cancer owing to its important role in cellular physiological processes. FGFR4 has been validated to be closely related to the occurrence of Show more
Recently, FGFR4 has become a hot target for the treatment of cancer owing to its important role in cellular physiological processes. FGFR4 has been validated to be closely related to the occurrence of cancers, such as hepatocellular carcinoma, rhabdomyosarcoma, breast cancer and colorectal cancer. Hence, the development of FGFR4 small-molecule inhibitors is essential to further understanding the functions of FGFR4 in cancer and the treatment of FGFR4-dependent diseases. Given the particular structures of FGFR1-4, the development of FGFR4 selective inhibitors presents significant challenges. The non-conserved Cys552 in the hinge region of the FGFR4 complex becomes the key to the selectivity of FGFR4 and FGFR1/2/3 inhibitors. In this review, we systematically introduce the close relationship between FGFR4 and cancer, and conduct an in-depth analysis of the developing methodology, binding mechanism, kinase selectivity, pharmacokinetic characteristics of FGFR4 selectivity inhibitors, and their application in clinical research. Show less
no PDF DOI: 10.1016/j.ejmech.2023.115947
FGFR1
Zhiping Zhang, Xueluo Zhang, Huiqin Xue +10 more · 2024 · Molecular genetics & genomic medicine · Wiley · added 2026-04-24
Congenital myasthenic syndrome is a heterogeneous group of inherited neuromuscular transmission disorders. Variants in RAPSN are a common cause of CMS, accounting for approximately 14%-27% of all CMS Show more
Congenital myasthenic syndrome is a heterogeneous group of inherited neuromuscular transmission disorders. Variants in RAPSN are a common cause of CMS, accounting for approximately 14%-27% of all CMS cases. Whether preimplantation genetic testing for monogenic disease (PGT-M) could be used to prevent the potential birth of CMS-affected children is unclear. Application of WES (whole-exome sequencing) for carrier testing and guidance for the PGT-M in the absence of a genetically characterized index patient as well as assisted reproductive technology were employed to prevent the occurrence of birth defects in subsequent pregnancy. The clinical phenotypes of stillborn fetuses were also assessed. The family carried two likely pathogenic variants in RAPSN(NM₀₀₅₀₅₅.5): c.133G>A (p.V45M) and c.280G>A (p.E94K). And the potential birth of CMS-affected child was successfully prevented, allowing the family to have offspring devoid of disease-associated variants and exhibiting a normal phenotype. This report constitutes the first documented case of achieving a CMS-free offspring through PGT-M in a CMS-affected family. By broadening the known variant spectrum of RAPSN in the Chinese population, our findings underscore the feasibility and effectiveness of PGT-M for preventing CMS, offering valuable insights for similarly affected families. Show less
no PDF DOI: 10.1002/mgg3.2409
RAPSN
Shuai Fan, Yuxin Chen, Wenyu Wang +7 more · 2024 · Current issues in molecular biology · MDPI · added 2026-04-24
FGFR1 is a key member of the fibroblast growth factor receptor family, mediating critical signaling pathways such as RAS-MAPK and PI3K-AKT. which are integral to regulating essential cellular processe Show more
FGFR1 is a key member of the fibroblast growth factor receptor family, mediating critical signaling pathways such as RAS-MAPK and PI3K-AKT. which are integral to regulating essential cellular processes, including proliferation, differentiation, and survival. Alterations in FGFR1 can lead to constitutive activation of signaling pathways that drive oncogenesis by promoting uncontrolled cell division, inhibiting apoptosis, and enhancing the metastatic potential of cancer cells. This article reviews the activation mechanisms and signaling pathways of FGFR1 and provides a detailed exposition of the types of FGFR1 aberration. Furthermore, we have compiled a comprehensive overview of current therapies targeting FGFR1 aberration in cancer, aiming to offer new perspectives for future cancer treatments by focusing on drugs that address specific FGFR1 alterations. Show less
📄 PDF DOI: 10.3390/cimb46110783
FGFR1
Ran Zhao, Fanxiang Yin, Mangaladoss Fredimoses +12 more · 2024 · Phytomedicine : international journal of phytotherapy and phytopharmacology · Elsevier · added 2026-04-24
Colorectal cancer (CRC) continues to be a major global health challenge, ranking as a top cause of cancer-related mortality. Alarmingly, the five-year survival rate for CRC patients hovers around a me Show more
Colorectal cancer (CRC) continues to be a major global health challenge, ranking as a top cause of cancer-related mortality. Alarmingly, the five-year survival rate for CRC patients hovers around a mere 10-30 %. The disruption of fibroblast growth factor receptor (FGFRs) signaling pathways is significantly implicated in the onset and advancement of CRC, presenting a promising target for therapeutic intervention in CRC management. Further investigation is essential to comprehensively elucidate FGFR1's function in CRC and to create potent therapies that specifically target FGFR1. This study aims to demonstrate the oncogenic role of FGFR1 in colorectal cancer and to explore the potential of β,β-dimethylacrylalkannin (β,β-DMAA) as a therapeutic option to inhibit FGFR1. In this research, we employed a comprehensive suite of techniques including tissue array, kinase profiling, computational docking, knockdown assay to predict and explore the inhibitor of FGFR1. Furthermore, we utilized kinase assay, pull-down, cell proliferation tests, and Patient derived xenograft (PDX) mouse models to further investigate a novel FGFR1 inhibitor and its impact on the growth of CRC. In our research, we discovered that FGFR1 protein is markedly upregulated in colorectal cancer tissues, suggesting a significant role in regulating cellular proliferation, particularly in patients with colorectal cancer. Furthermore, we conducted a computational docking, kinase profiling analysis, simulation and identified that β,β-DMAA could directly bind with FGFR1 within ATP binding pocket domain. Cell-based assays confirmed that β,β-DMAA effectively inhibited the proliferation of colon cancer cells and also triggered cell cycle arrest, apoptosis, and altered FGFR1-mediated signaling pathways. Moreover, β,β-DMAA effectively attenuated the development of PDX tumors in mice that were FGFR1-positive, with no notable toxicity observed. In summary, our study highlights the pivotal role of FGFR1 in colorectal cancer, suggesting that inhibiting FGFR1 activity could be a promising strategy for therapeutic intervention. We present strong evidence that targeting FGFR1 with β,β-DMAA is a viable approach for the management of colorectal cancer. Given its low toxicity and high efficacy, β,β-DMAA, as an FGFR1 inhibitor, warrants further investigation in clinical settings for the treatment of FGFR1-positive tumors. Show less
no PDF DOI: 10.1016/j.phymed.2024.155612
FGFR1
Junqi Liao, Yuan Zhu, Aimei Zhang +12 more · 2024 · Diabetes, metabolic syndrome and obesity : targets and therapy · added 2026-04-24
The relationship between insulin resistance-related indices and the outcomes of acute ischemic stroke (AIS) is still unclear. This study aimed to explore the association between the Apo B/Apo A-1 rati Show more
The relationship between insulin resistance-related indices and the outcomes of acute ischemic stroke (AIS) is still unclear. This study aimed to explore the association between the Apo B/Apo A-1 ratio and the Prognostic Nutritional Index (PNI) with the 90-day outcomes of AIS. A total of 2011 AIS patients with a 3-month follow-up were enrolled in the present study from January 2017 to July 2021. Multivariate logistic regression modeling was performed to analyze the relationship between Apo B/Apo A-1 ratio, PNI, and AIS poor outcomes. The mediating effect between the three was analyzed using the Bootstrap method with PNI as the mediating variable. Among the 2011 included AIS patients, 20.3% had a poor outcome. Patients were categorized according to quartiles of Apo B/Apo A-1 ratio and PNI. Multivariate logistic regression revealed that the fourth Apo B/Apo A-1 ratio quartile had poorer outcomes than the first quartile (OR 1.75,95%CL 1.21-2.53, P=0.003), and the fourth PNI quartile exhibited a lower risk of poor outcomes than the first quartile (OR 0.40, 95%CL 0.27-0.61, P<0.001). PNI displayed a significant partially mediating effect (21.4%) between the Apo B/Apo A-1 ratio and poor AIS outcomes. The Apo B/Apo A-1 ratio is a risk factor for poor AIS outcomes, whereas PNI acts as a protective factor. The association between the ApoB/ApoA-1 ratio and poor AIS outcomes was partially mediated by PNI. Show less
📄 PDF DOI: 10.2147/DMSO.S473385
APOB
Chenming Zhang, Yunfeng Ma, Wenbang Liu +5 more · 2024 · Reproductive toxicology (Elmsford, N.Y.) · Elsevier · added 2026-04-24
This study replicated a mouse model of sperm DNA damage induced by benzo(a)pyrene (BaP), and the transcriptomic and proteomic features of the model were examined to clarify the pathways related to BaP Show more
This study replicated a mouse model of sperm DNA damage induced by benzo(a)pyrene (BaP), and the transcriptomic and proteomic features of the model were examined to clarify the pathways related to BaP-induced damage to sperm DNA. Male mice in the BaP group were subjected to BaP at a dosage of 100 mg/kg/d or an equivalent quantity of saline solution in the control group for 60 days. Subsequently, the DNA fragmentation index (DFI) in sperm was assessed using a sperm chromatin structure assay (SCSA). RNA-seq and data-independent acquisition (DIA) were used to identify the mRNA and protein expression patterns in the testis. The sperm DFI significantly increased in the BaP group. Compared to the control group, the BaP group exhibited differential expression of 240 genes (referred to as DEGs) and 616 proteins (referred to as DEPs). These molecules included Aldh1a1, Cyb5r3, Fads1, Oxsm, Rcn3, and Prss45. Pathways in cancer, the PI3K-Akt signaling pathway, metabolic pathways, and the MAPK signaling pathway were the primary areas where these genes showed enrichment. BaP can damage the DNA of sperm and affect metabolism, the PI3K-Akt pathway, and pathways associated with cancer signaling. Show less
no PDF DOI: 10.1016/j.reprotox.2024.108596
FADS1
Chen Chen, Qingxiang Liu, Jianfei Wang +7 more · 2024 · Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · Elsevier · added 2026-04-24
Early evaluation and intervention for post-stroke cognitive impairment are crucial for improving the prognosis of acute ischemic stroke. The search for specific diagnostic markers and feasible therape Show more
Early evaluation and intervention for post-stroke cognitive impairment are crucial for improving the prognosis of acute ischemic stroke. The search for specific diagnostic markers and feasible therapeutic targets is extremely urgent.The characteristics of circular RNAs make them promising candidates. To screen circular RNAs as novel biomarkers and therapeutic targets for post-stroke cognitive impairment in large-artery atherosclerosis anterior circulation cerebral infarction patients. In this prospective observational study, patients with first-ever large-artery atherosclerosis anterior circulation cerebral infarction were recruited. The Montreal Cognitive Assessment was used to assess the cognitive statuses of patients. Venous blood samples were collected on the seventh day after stroke onset. A circRNA microarray was used to identify differentially expressed circular RNAs in the discovery cohort (four patients with post-stroke cognitive impairment and four patients with post-stroke cognitive normal characteristics), and validation was performed in the validation cohorts (45 patients with post-stroke cognitive impairment and 30 patients with post-stroke cognitive normal characteristics) using quantitative real-time polymerase chain reaction. Receiver operating characteristic curves of the validated circular RNAs and the NIHSS score were constructed, and the area under the curve, sensitivity, and specificity were calculated. Correlation analysis was performed to explore the relationship between the copy number of circular RNAs and the cognitive status. The functions of the differentially expressed circular RNAs were predicted using bioinformatics analysis. CircRNA microarray analysis revealed 189 human circular RNAs (152 upregulated and 37 downregulated) that were differentially expressed in the plasma samples of patients with post-stroke cognitive impairment and PSCN characteristics. The expression of hsa_circ₀₀₈₉₇₆₃, hsa_circ₀₀₆₄₆₄₄, and hsa_circ₀₀₈₉₇₆₂ was validated using quantitative real-time polymerase chain reaction. The area under the curve, sensitivity, and specificity of hsa_circ₀₀₈₉₇₆₂ in post-stroke cognitive impairment diagnosis were 0.993, 97.8%, and 96.7%, respectively, and the correlation coefficient between hsa_circ₀₀₈₉₇₆₂ expression and the Montreal Cognitive Assessment score was -0.693 (p < 0.001), which made it an ideal biomarker. Bioinformatic analysis revealed that the targeted mRNAs of the three circular RNAs were enriched in pathologically related signaling pathways of post-stroke cognitive impairment, such as the MAPK and PI3K-Akt signaling pathways. Based on the circRNA-miRNA-mRNA network, the three circular RNAs play a crucial role in numerous pathological processes of acute ischemic stroke and post-stroke cognitive impairment by sponging miRNAs such as MiR-335, MiR-424, and MiR-670. By building the protein-protein interaction network, we identified cluster 1 according to the MCODE score; cluster 1 was composed of ERBB4, FGFR1, CACNA2D1, NRG1, PPP2R5E, CACNB4, CACNB2, CCND1, NTRK2, and PTCH. Hsa_circ₀₀₈₉₇₆₂, hsa_circ₀₀₆₄₆₄₄, and hsa_circ₀₀₈₉₇₆₃ are potential novel biomarkers and focal points for exploring intervention targets in post-stroke cognitive impairment of large-artery atherosclerosis anterior circulation cerebral infarction patients. ChiCTR2000035074. Show less
no PDF DOI: 10.1016/j.jstrokecerebrovasdis.2024.107945
FGFR1

Fas

Ritu Bohat, Xiaofang Liang, Yanping Chen +21 more · 2024 · Clinical immunology (Orlando, Fla.) · Elsevier · added 2026-04-24
Sle1 and Fas
📄 PDF DOI: 10.1016/j.clim.2023.109874
IL27
Jourdan K Ewoldt, Miranda C Wang, Micheal A McLellan +15 more · 2024 · Science advances · Science · added 2026-04-24
Hypertrophic cardiomyopathy (HCM) is characterized by thickening of the left ventricular wall, diastolic dysfunction, and fibrosis, and is associated with mutations in genes encoding sarcomere protein Show more
Hypertrophic cardiomyopathy (HCM) is characterized by thickening of the left ventricular wall, diastolic dysfunction, and fibrosis, and is associated with mutations in genes encoding sarcomere proteins. While in vitro studies have used human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to study HCM, these models have not examined the multicellular interactions involved in fibrosis. Using engineered cardiac microtissues (CMTs) composed of HCM-causing Show less
📄 PDF DOI: 10.1126/sciadv.adi6927
MYBPC3
Huimei Chen, Ran You, Jing Guo +14 more · 2024 · Journal of the American Society of Nephrology : JASN · added 2026-04-24
WWP2 expression is elevated in the tubulointerstitium of fibrotic kidneys and contributes to CKD pathogenesis and progression. WWP2 uncouples the profibrotic activation and cell proliferation in renal Show more
WWP2 expression is elevated in the tubulointerstitium of fibrotic kidneys and contributes to CKD pathogenesis and progression. WWP2 uncouples the profibrotic activation and cell proliferation in renal myofibroblasts. WWP2 controls mitochondrial respiration in renal myofibroblasts through the metabolic regulator peroxisome proliferator-activated receptor gamma coactivator 1-alpha. Renal fibrosis is a common pathologic end point in CKD that is challenging to reverse, and myofibroblasts are responsible for the accumulation of a fibrillar collagen–rich extracellular matrix. Recent studies have unveiled myofibroblasts' diversity in proliferative and fibrotic characteristics, which are linked to different metabolic states. We previously demonstrated the regulation of extracellular matrix genes and tissue fibrosis by WWP2, a multifunctional E3 ubiquitin–protein ligase. Here, we investigate WWP2 in renal fibrosis and in the metabolic reprograming of myofibroblasts in CKD. We used kidney samples from patients with CKD and The tubulointerstitial expression of WWP2 was associated with fibrotic progression in patients with CKD and in murine kidney disease models. WWP2 deficiency promoted myofibroblast proliferation and halted profibrotic activation, reducing the severity of renal fibrosis WWP2 regulates the metabolic reprogramming of profibrotic myofibroblasts by a WWP2-PGC-1 Show less
no PDF DOI: 10.1681/ASN.0000000000000328
WWP2
Yuxuan Wang, Dewei Zeng, Limin Wei +10 more · 2024 · BMC veterinary research · BioMed Central · added 2026-04-24
Reducing production costs while producing high-quality livestock and poultry products is an ongoing concern in the livestock industry. The addition of oil to livestock and poultry diets can enhance fe Show more
Reducing production costs while producing high-quality livestock and poultry products is an ongoing concern in the livestock industry. The addition of oil to livestock and poultry diets can enhance feed palatability and improve growth performance. Emulsifiers can be used as potential feed supplements to improve dietary energy utilization and maintain the efficient productivity of broilers. Therefore, further investigation is warranted to evaluate whether dietary emulsifier supplementation can improve the efficiency of fat utilization in the diet of yellow-feathered broilers. In the present study, the effects of adding emulsifier to the diet on lipid metabolism and the performance of yellow-feathered broilers were tested. A total of 240 yellow-feasted broilers (21-day-old) were randomly divided into 4 groups (6 replicates per group, 10 broilers per replicate, half male and half female within each replicate). The groups were as follows: the control group (fed with basal diet), the group fed with basal diet supplemented with 500 mg/kg emulsifier, the group fed with a reduced oil diet (reduced by 1%) supplemented with 500 mg/kg emulsifier, and the group fed with a reduced oil diet supplemented with 500 mg/kg emulsifier. The trial lasted for 42 days, during which the average daily feed intake, average daily gain, and feed-to-gain ratio were measured. Additionally, the expression levels of lipid metabolism-related genes in the liver, abdominal fat and each intestinal segment were assessed. The results showed that compared with the basal diet group, (1) The average daily gain of the basal diet + 500 mg/kg emulsifier group significantly increased (P < 0.05), and the half-even-chamber rate was significantly increased (P < 0.05); (2) The mRNA expression levels of Cd36, Dgat2, Apob, Fatp4, Fabp2, and Mttp in the small intestine were significantly increased (P < 0.05). (3) Furthermore, liver TG content significantly decreased (P < 0.05), and the mRNA expression level of Fasn in liver was significantly decreased (P < 0.05), while the expression of Apob, Lpl, Cpt-1, and Pparα significantly increased (P < 0.05). (4) The mRNA expression levels of Lpl and Fatp4 in adipose tissue were significantly increased (P < 0.05), while the expression of Atgl was significantly decreased (P < 0.05). (5) Compared with the reduced oil diet group, the half-evading rate and abdominal fat rate of broilers in the reduced oil diet + 500 mg/kg emulsifier group were significantly increased (P < 0.05), and the serum level of LDL-C increased significantly (P < 0.05)0.6) The mRNA expression levels of Cd36, Fatp4, Dgat2, Apob, and Mttp in the small intestine were significantly increased (P < 0.05). 7) The mRNA expression levels of Fasn and Acc were significantly decreased in the liver (P < 0.05), while the mRNA expression levels of Lpin1, Dgat2, Apob, Lpl, Cpt-1, and Pparα were significantly increased (P < 0.05). These results suggest that dietary emulsifier can enhance the fat utilization efficiency of broilers by increasing the small intestinal fatty acid uptake capacity, inhibiting hepatic fatty acid synthesis and promoting hepatic TG synthesis and transport capacity. This study provides valuable insights for the potential use of emulsifier supplementation to improve the performance of broiler chickens. Show less
📄 PDF DOI: 10.1186/s12917-024-04095-8
LPL
Lixuan Huang, Ying Sun, Chao Luo +5 more · 2024 · Frontiers in pharmacology · Frontiers · added 2026-04-24
Schizophrenia significantly impacts cognitive and behavioral functions and is primarily treated with second-generation antipsychotics (SGAs) such as olanzapine. Despite their efficacy, these drugs are Show more
Schizophrenia significantly impacts cognitive and behavioral functions and is primarily treated with second-generation antipsychotics (SGAs) such as olanzapine. Despite their efficacy, these drugs are linked to serious metabolic side effects which can diminish patient compliance, worsen psychiatric symptoms and increase cardiovascular disease risk. This study explores the hypothesis that SGAs affect the molecular determinants of synaptic plasticity and brain activity, particularly focusing on the lateral septum (LS) and its interactions within hypothalamic circuits that regulate feeding and energy expenditure. Utilizing functional ultrasound imaging, RNA sequencing, and weighted gene co-expression network analysis, we identified significant alterations in the functional connection between the hypothalamus and LS, along with changes in gene expression in the LS of mice following prolonged olanzapine exposure. Our analysis revealed a module closely linked to increases in body weight and adiposity, featuring genes primarily involved in lipid metabolism pathways, notably Show less
📄 PDF DOI: 10.3389/fphar.2024.1419098
APOC3