πŸ‘€ Mian-Mian 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, 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
Chun-Heng Kuo, Shu-Huei Wang, Hsien-Chia Juan +5 more Β· 2024 Β· BioFactors (Oxford, England) Β· Wiley Β· added 2026-04-24
Angiopoietin-like protein 4 (ANGPTL4) is a secretory glycoprotein involved in regulating glucose homeostasis in non-pregnant subjects. However, its role in glucose metabolism during pregnancy and the Show more
Angiopoietin-like protein 4 (ANGPTL4) is a secretory glycoprotein involved in regulating glucose homeostasis in non-pregnant subjects. However, its role in glucose metabolism during pregnancy and the pathophysiology of gestational diabetes mellitus (GDM) remains elusive. Thus, this study aimed to clarify the relationship between ANGPTL4 and GDM and investigate the pathophysiology of placental ANGPTL4 in glucose metabolism. We investigated this issue using blood and placenta samples in 957 pregnant women, the human 3A-sub-E trophoblast cell line, and the L6 skeletal muscle cell line. We found that ANGPTL4 expression in the placenta was higher in obese pregnant women than in lean controls. Palmitic acid significantly induced ANGPTL4 expression in trophoblast cells in a dose-response manner. ANGPTL4 overexpression in trophoblast cells resulted in endoplasmic reticulum (ER) stress, which stimulated the expression and secretion of growth hormone-variant (GH2) but not human placental lactogen. In L6 skeletal muscle cells, soluble ANGPTL4 suppressed insulin-mediated glucose uptake through the epidermal growth factor receptor (EGFR)/extracellular signal-regulated kinases 1/2 (ERK 1/2) pathways. In pregnant women, plasma ANGPTL4 concentrations in the first trimester predicted the incidence of GDM and were positively associated with BMI, plasma triglyceride, and plasma GH2 in the first trimester. However, they were negatively associated with insulin sensitivity index ISI Show less
no PDF DOI: 10.1002/biof.2076
ANGPTL4
Danhua Ma, Jijun Chen, Yuyuan Shi +4 more Β· 2024 Β· Scientific reports Β· Nature Β· added 2026-04-24
In this study, we aimed to study the role of TCONS₀₀₀₀₆₀₉₁ in the pathogenesis of oral squamous cellular carcinoma (OSCC) transformed from oral lichen planus (OLP). This study recruited 108 OSCC pati Show more
In this study, we aimed to study the role of TCONS₀₀₀₀₆₀₉₁ in the pathogenesis of oral squamous cellular carcinoma (OSCC) transformed from oral lichen planus (OLP). This study recruited 108 OSCC patients which transformed from OLP as the OSCC group and 102 OLP patients with no sign of OSCC as the Control group. ROC curves were plotted to measure the diagnostic values of TCONS₀₀₀₀₆₀₉₁, miR-153, miR-370 and let-7g, and the changes in gene expressions were measured by RT-qPCR. Sequence analysis and luciferase assays were performed to analyze the molecular relationships among these genes. Cell proliferation and apoptosis were observed via MTT and FCM. TCONS₀₀₀₀₆₀₉₁ exhibited a better diagnosis value for OSCC transformed from OLP. OSCC group showed increased TCONS₀₀₀₀₆₀₉₁ expression and decreased expressions of miR-153, miR-370 and let-7g. The levels of SNAI1, IRS and HMGA2 was all significantly increased in OSCC patients. And TCONS₀₀₀₀₆₀₉₁ was found to sponge miR-153, miR-370 and let-7g, while these miRNAs were respectively found to targe SNAI1, IRS and HMGA2. The elevated TCONS₀₀₀₀₆₀₉₁ suppressed the expressions of miR-153, miR-370 and let-7g, leading to the increased expression of SNAI1, IRS and HMGA2. Also, promoted cell proliferation and suppressed apoptosis were observed upon the over-expression of TCONS₀₀₀₀₆₀₉₁. This study demonstrated that the expressions of miR-153, miR-370 and let-7g were down-regulated by the highly expressed TCONS₀₀₀₀₆₀₉₁ in OSCC patients, which accordingly up-regulated the expressions of SNAI1, IRS and HMGA2, resulting in the promoted cell proliferation and suppressed cell apoptosis. Show less
no PDF DOI: 10.1038/s41598-024-60310-4
SNAI1
Peng Wang, Shuqi Yang, Changcheng Li +4 more Β· 2024 Β· Experimental lung research Β· Taylor & Francis Β· added 2026-04-24
To observe the dynamic changes in monocyte subsets during septic lung injury and to assess the anti-inflammatory role of the sulfotransferase homolog 2 (ST2) receptor. Dynamic changes of monocyte subs Show more
To observe the dynamic changes in monocyte subsets during septic lung injury and to assess the anti-inflammatory role of the sulfotransferase homolog 2 (ST2) receptor. Dynamic changes of monocyte subsets from patients with septic lung injury and mice post-cecal ligation and puncture (CLP) were monitored. ST2 receptors on mice monocytes and concentrations of IL-33, IL-1Ξ², IL-12, and IL-27 from peripheral blood or culture supernatant were detected. CD14 Changes in monocyte subsets expressing the ST2 receptor play an important role in septic lung injury by modulating inflammatory cytokine secretion. Show less
no PDF DOI: 10.1080/01902148.2024.2398989
IL27
Colette A Abbey, Camille L Duran, Zhishi Chen +8 more Β· 2024 Β· Arteriosclerosis, thrombosis, and vascular biology Β· added 2026-04-24
New blood vessel formation requires endothelial cells to transition from a quiescent to an invasive phenotype. Transcriptional changes are vital for this switch, but a comprehensive genome-wide approa Show more
New blood vessel formation requires endothelial cells to transition from a quiescent to an invasive phenotype. Transcriptional changes are vital for this switch, but a comprehensive genome-wide approach focused exclusively on endothelial cell sprout initiation has not been reported. Using a model of human endothelial cell sprout initiation, we developed a protocol to physically separate cells that initiate the process of new blood vessel formation (invading cells) from noninvading cells. We used this model to perform multiple transcriptomics analyses from independent donors to monitor endothelial gene expression changes. Single-cell population analyses, single-cell cluster analyses, and bulk RNA sequencing revealed common transcriptomic changes associated with invading cells. We also found that collagenase digestion used to isolate single cells upregulated the Fos proto-oncogene transcription factor. Exclusion of Fos proto-oncogene expressing cells revealed a gene signature consistent with activation of signal transduction, morphogenesis, and immune responses. Many of the genes were previously shown to regulate angiogenesis and included multiple tip cell markers. Upregulation of SNAI1 (snail family transcriptional repressor 1), PTGS2 (prostaglandin synthase 2), and JUNB (JunB proto-oncogene) protein expression was confirmed in invading cells, and silencing JunB and SNAI1 significantly reduced invasion responses. Separate studies investigated rounding 3, also known as RhoE, which has not yet been implicated in angiogenesis. Silencing rounding 3 reduced endothelial invasion distance as well as filopodia length, fitting with a pathfinding role for rounding 3 via regulation of filopodial extensions. Analysis of in vivo retinal angiogenesis in Validation of multiple genes, including rounding 3, revealed a functional role for this gene signature early in the angiogenic process. This study expands the list of genes associated with the acquisition of a tip cell phenotype during endothelial cell sprout initiation. Show less
no PDF DOI: 10.1161/ATVBAHA.123.320599
SNAI1
Junming Huang, BoWen Li, Huangwei Wei +4 more Β· 2024 Β· Scientific reports Β· Nature Β· added 2026-04-24
Parkinson's disease (PD) is a progressive neurodegenerative disease whose etiology is attributed to development of Lewy bodies and degeneration of dopaminergic neurons in the substantia nigra (SN). Cu Show more
Parkinson's disease (PD) is a progressive neurodegenerative disease whose etiology is attributed to development of Lewy bodies and degeneration of dopaminergic neurons in the substantia nigra (SN). Currently, there are no definitive diagnostic indicators for PD. In this study, we aimed to identify potential diagnostic biomarkers for PD and analyzed the impact of immune cell infiltrations on disease pathogenesis. The PD expression profile data for human SN tissue, GSE7621, GSE20141, GSE20159, GSE20163 and GSE20164 were downloaded from the Gene Expression Omnibus (GEO) database for use in the training model. After normalization and merging, we identified differentially expressed genes (DEGs) using the Robust rank aggregation (RRA) analysis. Simultaneously, DEGs after batch correction were identified. Gene interactions were determined through venn Diagram analysis. Functional analyses and protein-protein interaction (PPI) networks were used to the identify hub genes, which were visualized through Cytoscape. A Lasso Cox regression model was employed to identify the potential diagnostic genes. The GSE20292 dataset was used for validation. The proportion of infiltrating immune cells in the samples were determined via the CIBERSORT method. Sixty-two DEGs were screened in this study. They were found to be enriched in nerve conduction, dopamine (DA) metabolism, and DA biosynthesis Gene Ontology (GO) terms. The PPI network and Lasso Cox regression analysis revealed seven potential diagnostic genes, namely SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1, and AGTR1, were subsequently validated in peripheral blood samples obtained from healthy control (HC) and PD patients, as well as in the GSE20292 dataset. The results revealed the exceptional sensitivity and specificity of these genes in PD diagnosis and monitoring. Moreover, PD patients exhibited a higher number of plasma cells, compared to HC individuals. The SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1, and AGTR1 are potential diagnostic biomarkers for PD. Our findings also reveal the essential roles of immune cell infiltration in both disease onset and trajectory. Show less
πŸ“„ PDF DOI: 10.1038/s41598-024-52276-0
AXIN1
Xinyue Ming, Shirui Chen, Huijuan Li +3 more Β· 2024 Β· Cellular signalling Β· Elsevier Β· added 2026-04-24
This study aimed to investigate the effects of hepatic microRNA-122 (miR-122) on Sortilin-mediated apolipoprotein B100 (apoB-100) secretion, and on aortic lipid deposition and atherosclerosis (AS) les Show more
This study aimed to investigate the effects of hepatic microRNA-122 (miR-122) on Sortilin-mediated apolipoprotein B100 (apoB-100) secretion, and on aortic lipid deposition and atherosclerosis (AS) lesions and to clarify the antiatherosclerotic mechanism of 6-methylcoumarin (6-MC) via the modulation of miR-122. Bioinformatics analysis revealed that miR-122 was putatively overexpressed in a liver-specific manner and was downregulated in steatotic livers. miR-122 was shown to suppress the expression of Sortilin by complementarily pairing to the 3'-untranslated region (3'-UTR) of Sortilin mRNA via bioinformatics and dual-luciferase reporter assays, impeding Sortilin-mediated apoB-100 secretion from HepG2 cells. Administration of 6-MC significantly upregulated hepatocellular miR-122 levels, reducing Sortilin expression and apoB-100 secretion in HepG2 cells. The miR-122 mimic vigorously enhanced 6-MC-depressed Sortilin expression, while miR-122 inhibitor repealed the inhibitory effect of 6-MC on Sortilin expression to some extent in HepG2 cells. After internal intervention with the miR-122 precursor, and 6-MC supplementation alone or in combination with the miR-122 sponge led to the reduction in blood triglyceride (TG) levels, low-density lipoprotein-cholesterol (LDL-C) and apoB-100 and a reduction in aortic lipid deposition and AS lesions in apolipoprotein E-deficient (ApoE Show less
no PDF DOI: 10.1016/j.cellsig.2024.111384
APOB
Ling Zeng, Zhikuan Yang, Wei Pan +5 more Β· 2024 Β· Journal of inflammation research Β· 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-6RΞ±, IL-8, IL-10, IL-11, IL-12, IL-19, IL-27, IL-28A, IL-34, IFN-Ξ², IFN-Ξ³, 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-6RΞ±, 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-6RΞ±, IL-8, IL-27 and BAFF were negatively correlated with retinal vascular density. The intraocular level of IL-6RΞ± 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-6RΞ±, and IL-8 were closely associated with myopia-related retinal microvascular and neurodegeneration. Show less
πŸ“„ PDF DOI: 10.2147/JIR.S484338
IL27
Qianmeng Lin, Shuyan Dai, Lingzhi Qu +5 more Β· 2024 Β· Communications chemistry Β· Nature Β· added 2026-04-24
Acquired drug resistance poses a challenge for single-target FGFR inhibitors, leading to the development of dual- or multi-target FGFR inhibitors. Sulfatinib is a multi-target kinase inhibitor for tre Show more
Acquired drug resistance poses a challenge for single-target FGFR inhibitors, leading to the development of dual- or multi-target FGFR inhibitors. Sulfatinib is a multi-target kinase inhibitor for treating neuroendocrine tumors, selectively targeting FGFR1/CSF-1R. To elucidate the molecular mechanisms behind its binding and kinase selectivity, we determined the crystal structures of sulfatinib with FGFR1/CSF-1R. The results reveal common structural features and distinct conformational adaptability of sulfatinib in response to FGFR1/CSF-1R binding. Further biochemical and structural analyses disclose sensitivity of sulfatinib to FGFR/CSF-1R gatekeeper mutations. The insensitivity of sulfatinib to FGFR gatekeeper mutations highlights the indispensable interactions with the hydrophobic pocket for FGFR selectivity, whereas the rotatory flexibility may enable sulfatinib to overcome CSF-1R Show less
πŸ“„ PDF DOI: 10.1038/s42004-023-01084-0
FGFR1
Yu-Chen Liu, Sheng-Yi Chen, Ying-Ying Chen +3 more Β· 2024 Β· International journal of biological macromolecules Β· Elsevier Β· added 2026-04-24
Patients may find it challenging to accept several FDA-approved drugs for Alzheimer's disease (AD) treatment due to their unaffordable prices and side effects. Despite the known antioxidant, anti-infl Show more
Patients may find it challenging to accept several FDA-approved drugs for Alzheimer's disease (AD) treatment due to their unaffordable prices and side effects. Despite the known antioxidant, anti-inflammatory, and microbiota-regulating effects of common buckwheat (Fagopyrum esculentum) polysaccharides (FEP), their specific role in preventing AD has not been determined. Here, this study investigated the preventive effects of FEP on AD development in AlCl Show less
no PDF DOI: 10.1016/j.ijbiomac.2024.133898
BACE1
Hao Meng, Zhiying Liao, Yanting Ji +15 more Β· 2024 Β· Signal transduction and targeted therapy Β· Nature Β· added 2026-04-24
The angiotensin-converting enzyme 2 (ACE2) is a primary cell surface viral binding receptor for SARS-CoV-2, so finding new regulatory molecules to modulate ACE2 expression levels is a promising strate Show more
The angiotensin-converting enzyme 2 (ACE2) is a primary cell surface viral binding receptor for SARS-CoV-2, so finding new regulatory molecules to modulate ACE2 expression levels is a promising strategy against COVID-19. In the current study, we utilized islet organoids derived from human embryonic stem cells (hESCs), animal models and COVID-19 patients to discover that fibroblast growth factor 7 (FGF7) enhances ACE2 expression within the islets, facilitating SARS-CoV-2 infection and resulting in impaired insulin secretion. Using hESC-derived islet organoids, we demonstrated that FGF7 interacts with FGF receptor 2 (FGFR2) and FGFR1 to upregulate ACE2 expression predominantly in Ξ² cells. This upregulation increases both insulin secretion and susceptibility of Ξ² cells to SARS-CoV-2 infection. Inhibiting FGFR counteracts the FGF7-induced ACE2 upregulation, subsequently reducing viral infection and replication in the islets. Furthermore, retrospective clinical data revealed that diabetic patients with severe COVID-19 symptoms exhibited elevated serum FGF7 levels compared to those with mild symptoms. Finally, animal experiments indicated that SARS-CoV-2 infection increased pancreatic FGF7 levels, resulting in a reduction of insulin concentrations in situ. Taken together, our research offers a potential regulatory strategy for ACE2 by controlling FGF7, thereby protecting islets from SARS-CoV-2 infection and preventing the progression of diabetes in the context of COVID-19. Show less
πŸ“„ PDF DOI: 10.1038/s41392-024-01790-8
FGFR1
Huibin Huang, Juan Li, Tianhua Chen +5 more Β· 2024 Β· Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology Β· Taylor & Francis Β· added 2026-04-24
To analyse changes in lipid levels during the development of intrahepatic cholestasis of pregnancy (ICP) and identify new biomarkers for predicting ICP. A retrospective case-control study was conducte Show more
To analyse changes in lipid levels during the development of intrahepatic cholestasis of pregnancy (ICP) and identify new biomarkers for predicting ICP. A retrospective case-control study was conducted to analyse 473 pregnant women who underwent regular prenatal examinations and delivered at the Women and Children's Hospital, School of Medicine, Xiamen University, between June 2020 and June 2023, including 269 normal pregnancy controls and 204 pregnant women with cholestasis. Patients with ICP with gestational diabetes mellitus (GDM) have lower high-density lipoprotein (HDL) levels than in those without GDM. Total bile acid (TBA) levels were significantly higher in pregnant women with GDM than those without. The apolipoprotein A (APOA) level was lower in patients with ICP and hypothyroidism than those without hypothyroidism. TBA levels were significantly higher in pregnant women with hypothyroidism than those without. Triglyceride (TG) levels were significantly higher in patients with preeclampsia (PE) than those without. HDL and APOA levels were lower in women with ICP complicated by preterm delivery than those with normal delivery. The AUC (area under the curve) of the differential diagnosis of cholestasis of pregnancy for the APOA/APOB (apolipoprotein B) ratio was 0.727, with a sensitivity of 85.9% and specificity of 47.5%. The results suggested that dyslipidaemia is associated with an increased risk of ICP and its complications. The timely detection of blood lipid and bile acid levels can assist in the diagnosis of ICP and effectively prevent ICP and other complications. Show less
no PDF DOI: 10.1080/01443615.2024.2369929
APOB
Fangli Zhou, Yun Ding, Tao Chen +16 more Β· 2024 Β· European journal of endocrinology Β· Oxford University Press Β· added 2026-04-24
Primary aldosteronism (PA), a significant cause of secondary hypertension affecting ∼10% of patients with severe hypertension, exacerbates cardiovascular, and cerebrovascular complications even after Show more
Primary aldosteronism (PA), a significant cause of secondary hypertension affecting ∼10% of patients with severe hypertension, exacerbates cardiovascular, and cerebrovascular complications even after blood pressure control. PA is categorized into two main subtypes: unilateral aldosterone-producing adenomas (APA) and bilateral hyperaldosteronism (BHA), each requiring distinct treatment approaches. Accurate subtype classification is crucial for selecting the most effective treatment. The goal of this study was to develop novel blood-based proteomic biomarkers to differentiate between APA and BHA subtypes in patients with PA. Five subtyping differential protein biomarker candidates (APOC3, CD56, CHGA, KRT5, and AZGP1) were identified through targeted proteomic profiling of plasma. The subtyping efficiency of these biomarkers was assessed at both the tissue gene expression and blood protein expression levels. To explore the underlying biology of APA and BHA, significant differential pathways were investigated. The five-protein panel proved highly effective in distinguishing APA from BHA in both tissue and blood samples. By integrating these five protein biomarkers with aldosterone and renin, our blood-based predictive methods achieved remarkable receiver operating characteristic (ROC) area under the ROC curves of 0.986 (95% CI: 0.963-1.000) for differentiating essential hypertension from PA, and 0.922 (95% CI: 0.846-0.998) for subtyping APA versus BHA. These outcomes surpass the performance of the existing Kobayashi score subtyping system. Furthermore, the study validated differential pathways associated with the pathophysiology of PA, aligning with current scientific knowledge and opening new avenues for advancing PA care. The new blood-based biomarkers for PA subtyping hold the potential to significantly enhance clinical utility and advance the practice of PA care. Show less
no PDF DOI: 10.1093/ejendo/lvae148
APOC3
Lulu Sun, Qilu Zhang, Mengyao Shi +9 more Β· 2024 Β· Journal of the American Heart Association Β· added 2026-04-24
The association of lipid-lowering drug targets and their gene variants with cardiovascular diseases has been previously clarified. However, the relationship between gene variants of lipid-lowering dru Show more
The association of lipid-lowering drug targets and their gene variants with cardiovascular diseases has been previously clarified. However, the relationship between gene variants of lipid-lowering drug targets and the adverse prognosis of ischemic stroke patients remains unclear. Multiple single-nucleotide polymorphisms associated with 6 lipid-lowering drug targets were genotyped for patients with ischemic stroke. The primary outcome was death or major disability within 2 years after ischemic stroke. Genetic risk scoreΒ was constructed from significant single-nucleotide polymorphismsΒ identified via additive models, which was calculated by multiplying the number of risk alleles at each locus by the corresponding beta coefficient and then summing the products. The rs2006760-C of the rs2006760-C of Show less
πŸ“„ PDF DOI: 10.1161/JAHA.124.036544
CETP
Baotong Zhang, Mingcheng Liu, Fengyi Mai +13 more Β· 2024 Β· The Journal of clinical investigation Β· added 2026-04-24
Inactivation of phosphatase and tensin homolog (PTEN) is prevalent in human prostate cancer and causes high-grade adenocarcinoma with a long latency. Cancer-associated fibroblasts (CAFs) play a pivota Show more
Inactivation of phosphatase and tensin homolog (PTEN) is prevalent in human prostate cancer and causes high-grade adenocarcinoma with a long latency. Cancer-associated fibroblasts (CAFs) play a pivotal role in tumor progression, but it remains elusive whether and how PTEN-deficient prostate cancers reprogram CAFs to overcome the barriers for tumor progression. Here, we report that PTEN deficiency induced KrΓΌppel-like factor 5 (KLF5) acetylation and that interruption of KLF5 acetylation orchestrated intricate interactions between cancer cells and CAFs that enhance FGF receptor 1 (FGFR1) signaling and promote tumor growth. Deacetylated KLF5 promoted tumor cells to secrete TNF-Ξ±, which stimulated inflammatory CAFs to release FGF9. CX3CR1 inhibition blocked FGFR1 activation triggered by FGF9 and sensitized PTEN-deficient prostate cancer to the AKT inhibitor capivasertib. This study reveals the role of KLF5 acetylation in reprogramming CAFs and provides a rationale for combined therapies using inhibitors of AKT and CX3CR1. Show less
πŸ“„ PDF DOI: 10.1172/JCI175949
FGFR1
Yifei Chen, Yujia Jing, Liangyu Hu +4 more Β· 2024 Β· International journal of molecular sciences Β· MDPI Β· added 2026-04-24
The core clock gene
πŸ“„ PDF DOI: 10.3390/ijms25189785
FADS1
Aylwin Ming Wee Lim, Evan Unit Lim, Pei-Lung Chen +1 more Β· 2024 Β· iScience Β· Elsevier Β· added 2026-04-24
Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on i Show more
Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on individuals from UK Biobank to reveal endotypes. Five MetS subgroups were identified: Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycemic. For all of the endotypes, we identified the corresponding cardiometabolic traits and their associations with clinical outcomes. Genome-wide association studies (GWASs) were conducted to identify associated genotypic traits. We then determined endotype-specific genotypic traits and constructed polygenic risk score (PRS) models specific to each endotype. GWAS of each MetS clusters revealed different genotypic traits. C1 GWAS revealed novel findings of Show less
πŸ“„ PDF DOI: 10.1016/j.isci.2024.109815
MYBPC3
Lingang Dai, Dongwei An, Jiajin Huang +7 more Β· 2024 Β· International journal of biological macromolecules Β· Elsevier Β· added 2026-04-24
The kidding traits of goats are an important index of production. However, the molecular regulatory mechanisms of kidding traits in goats have not been fully elucidated. This study aimed to investigat Show more
The kidding traits of goats are an important index of production. However, the molecular regulatory mechanisms of kidding traits in goats have not been fully elucidated. This study aimed to investigate the molecular regulatory network of kidding traits in goats. Multi-omics revealed the enrichment of 10 signaling pathways, with fatty acid biosynthesis, biosynthesis of unsaturated fatty acids, and steroid hormone biosynthesis pathways being closely related to reproduction. Interestingly, the key rate-limiting enzymes, fatty acid synthase (FASN), stearoyl-CoA desaturase 5 (SCD5), fatty acid desaturase 1 (FADS1), 3Ξ²-hydroxysteroid dehydrogenase/isomerase (3BHSD), and steroidogenic acute regulatory protein (STAR) enriched in these pathways regulate changes in reproduction-related metabolites. In interference experiments, it was observed that suppressing these key rate-limiting enzymes inhibited the expression of CYP19A1, ESR2, and FSHR. Furthermore, interference inhibited granulosa cell proliferation, caused cell cycle arrest, and promoted apoptosis. Thus, these results suggest that the specific markers of nanny goats with multiple kids are the key rate-limiting enzymes FASN, SCD5, FADS1, 3BHSD, and STAR. These findings may greatly enhance the understanding of regulatory mechanisms that govern goat parturition. Show less
no PDF DOI: 10.1016/j.ijbiomac.2024.136737
FADS1
Yan Q Chen, Ye Yang, Eugene Y Zhen +18 more Β· 2024 Β· Proceedings of the National Academy of Sciences of the United States of America Β· National Academy of Sciences Β· added 2026-04-24
Apolipoprotein AV (APOA5) lowers plasma triglyceride (TG) levels by binding to the angiopoietin-like protein 3/8 complex (ANGPTL3/8) and suppressing its capacity to inhibit lipoprotein lipase (LPL) ca Show more
Apolipoprotein AV (APOA5) lowers plasma triglyceride (TG) levels by binding to the angiopoietin-like protein 3/8 complex (ANGPTL3/8) and suppressing its capacity to inhibit lipoprotein lipase (LPL) catalytic activity and its ability to detach LPL from binding sites within capillaries. However, the sequences in APOA5 that are required for suppressing ANGPTL3/8 activity have never been defined. A clue to the identity of those sequences was the presence of severe hypertriglyceridemia in two patients harboring an Show less
πŸ“„ PDF DOI: 10.1073/pnas.2322332121
APOA5
Yanbing Han, Qingqing Mo, Zhuangzhuang Ma +8 more Β· 2024 Β· Nano letters Β· ACS Publications Β· added 2026-04-24
Long-persistent luminescence (LPL) materials have attracted intensive attention due to their fascinating emission after excitation. However, current LPL materials typically depend on external doping t Show more
Long-persistent luminescence (LPL) materials have attracted intensive attention due to their fascinating emission after excitation. However, current LPL materials typically depend on external doping to introduce traps or emitting centers, resulting in a complex synthesis and controllability. For the first time, we develop another category of undoped LPL materials based on antimonate CaSb Show less
no PDF DOI: 10.1021/acs.nanolett.4c04471
LPL
Xiong Gao, Wei Luo, Liyuan Qu +14 more Β· 2024 Β· European journal of preventive cardiology Β· Oxford University Press Β· added 2026-04-24
The lack of effective pharmacotherapies for aortic aneurysms (AA) is a persistent clinical challenge. Lipid metabolism plays an essential role in AA. However, the impact of lipid-lowering drugs on AA Show more
The lack of effective pharmacotherapies for aortic aneurysms (AA) is a persistent clinical challenge. Lipid metabolism plays an essential role in AA. However, the impact of lipid-lowering drugs on AA remains controversial. The study aimed to investigate the genetic association between lipid-lowering drugs and AA. Our research used publicly available data on genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) studies. Genetic instruments, specifically eQTLs related to drug-target genes and SNPs (single nucleotide polymorphisms) located near or within the drug-target loci associated with low-density lipoprotein cholesterol (LDL-C), have been served as proxies for lipid-lowering medications. Drug-Target Mendelian Randomization (MR) study is used to determine the causal association between lipid-lowering drugs and different types of AA. The MR analysis revealed that higher expression of HMGCR (3-hydroxy-3-methylglutaryl coenzyme A reductase) was associated with increased risk of AA (OR = 1.58, 95% CI = 1.20-2.09, P = 1.20 Γ— 10-03) and larger lumen size (aortic maximum area: OR = 1.28, 95% CI = 1.13-1.46, P = 1.48 Γ— 10-04; aortic minimum area: OR = 1.26, 95% CI = 1.21-1.42, P = 1.78 Γ— 10-04). PCSK9 (proprotein convertase subtilisin/kexin type 9) and CETP (cholesteryl ester transfer protein) show a suggestive relationship with AA (PCSK9: OR = 1.34, 95% CI = 1.10-1.63, P = 3.07 Γ— 10-03; CETP: OR = 1.38, 95% CI = 1.06-1.80, P = 1.47 Γ— 10-02). No evidence to support genetically mediated NPC1L1 (Niemann-Pick C1-Like 1) and LDLR (low-density lipoprotein cholesterol receptor) are associated with AA. This study provides causal evidence for the genetic association between lipid-lowering drugs and AA. Higher gene expression of HMGCR, PCSK9, and CETP increases AA risk. Furthermore, HMGCR inhibitors may link with smaller aortic lumen size. Show less
no PDF DOI: 10.1093/eurjpc/zwae044
CETP
Yingduo Yu, Qigu Yao, Deying Chen +6 more Β· 2024 Β· Stem cell research & therapy Β· BioMed Central Β· added 2026-04-24
The metabolic patterns of human placental-derived mesenchymal stem cell (hP-MSC) treatment for primary sclerosing cholangitis (PSC) remain unclear, and therapeutic effects significantly vary due to in Show more
The metabolic patterns of human placental-derived mesenchymal stem cell (hP-MSC) treatment for primary sclerosing cholangitis (PSC) remain unclear, and therapeutic effects significantly vary due to individual differences. Therefore, it is crucial to investigate the serological response to hP-MSC transplantation through small molecular metabolites and identify easily detectable markers for efficacy evaluation. Using Mdr2 Collectively, the results of the liver histology, serum liver enzyme levels, and inflammatory factors supported the therapeutic efficacy of hP-MSC treatment. Based on significant differences, 41 differentially expressed metabolites were initially identified; these were enriched in bile acid, lipid, and hydroxyproline metabolism. After treatment, bile acid transport was accelerated, whereas bile acid production was reduced; unsaturated fatty acid synthesis was upregulated overall, with increased FADS2 and elongase expression and enhanced fatty acid Ξ²-oxidation; hepatic proline 4-hydroxylase expression was decreased, leading to reduced hydroxyproline production. Correlation analysis of liver enzymes and metabolites, combined with time trends, identified eight potential biomarkers: 2-aminomuconate semialdehyde, L-1-pyrroline-3-hydroxy-5-carboxylic acid, L-isoglutamine, and maleamic acid were more abundant in model mice but decreased after hP-MSC treatment. Conversely, 15-methylpalmitic, eicosenoic, nonadecanoic, and octadecanoic acids were less abundant in model mice but increased after hP-MSC treatment. This study revealed metabolic regulatory changes in PSC model mice after hP-MSC treatment and identified eight promising biomarkers, providing preclinical evidence to support therapeutic applications of hP-MSC. Show less
πŸ“„ PDF DOI: 10.1186/s13287-024-03967-y
FADS1
Caibo Ning, Meng Jin, Yimin Cai +28 more Β· 2024 Β· BMC medicine Β· BioMed Central Β· added 2026-04-24
The hippocampus, with its complex subfields, is linked to numerous neuropsychiatric traits. While most research has focused on its global structure or a few specific subfields, a comprehensive analysi Show more
The hippocampus, with its complex subfields, is linked to numerous neuropsychiatric traits. While most research has focused on its global structure or a few specific subfields, a comprehensive analysis of hippocampal substructures and their genetic correlations across a wide range of neuropsychiatric traits remains underexplored. Given the hippocampus's high heritability, considering hippocampal and subfield volumes (HASV) as endophenotypes for neuropsychiatric conditions is essential. We analyzed MRI-derived volumetric data of hippocampal and subfield structures from 41,525 UK Biobank participants. Genome-wide association studies (GWAS) on 24 HASV traits were conducted, followed by genetic correlation, overlap, and Mendelian randomization (MR) analyses with 10 common neuropsychiatric traits. Polygenic risk scores (PRS) based on HASV traits were also evaluated for predicting these traits. Our analysis identified 352 independent genetic variants surpassing a significance threshold of 2.1 × 10 These findings highlight the extensive distribution of pleiotropic genetic determinants between HASVs and neuropsychiatric traits. Moreover, they suggest a significant potential for effectively managing and intervening in these diseases during their early stages. Show less
πŸ“„ PDF DOI: 10.1186/s12916-024-03682-8
KANSL1
Zekun Xin, Lijun Dong, Guojun Chen +1 more Β· 2024 Β· Asian journal of surgery Β· Elsevier Β· added 2026-04-24
no PDF DOI: 10.1016/j.asjsur.2024.11.010
FADS3
Chen Chen, Vanessa G Lee Β· 2024 Β· Attention, perception & psychophysics Β· added 2026-04-24
Attention is tuned towards locations that frequently contain a visual search target (location probability learning; LPL). Peripheral vision, covering a larger field than the fovea, often receives info Show more
Attention is tuned towards locations that frequently contain a visual search target (location probability learning; LPL). Peripheral vision, covering a larger field than the fovea, often receives information about the target. Yet what is the role of peripheral vision in attentional learning? Using gaze-contingent eye tracking, we examined the impact of simulated peripheral vision loss on location probability learning. Participants searched for a target T among distractor Ls. Unbeknownst to them, the T appeared disproportionately often in one quadrant. Participants searched with either intact vision or "tunnel vision," restricting the visible search items to the central 6.7ΒΊ (in diameter) of the current gaze. When trained with tunnel vision, participants in Experiment 1 acquired LPL, but only if they became explicitly aware of the target's location probability. The unaware participants were not faster finding the target in high-probability than in low-probability locations. When trained with intact vision, participants in Experiment 2 successfully acquired LPL, regardless of whether they were aware of the target's location probability. Thus, whereas explicit learning may proceed with central vision alone, implicit LPL is strengthened by peripheral vision. Consistent with Guided Search (Wolfe, 2021), peripheral vision supports a nonselective pathway to guide visual search. Show less
πŸ“„ PDF DOI: 10.3758/s13414-023-02808-z
LPL
Jianmin Zhu, Liting Yang, Jing Xia +6 more Β· 2024 Β· Transplantation Β· added 2026-04-24
Stimulation of myeloid-derived suppressor cell (MDSC) formation represents a potential curative therapeutic approach for graft-versus-host disease (GVHD), which significantly impacts the prognosis of Show more
Stimulation of myeloid-derived suppressor cell (MDSC) formation represents a potential curative therapeutic approach for graft-versus-host disease (GVHD), which significantly impacts the prognosis of allogeneic hematopoietic stem cell transplantation. However, the lack of an effective strategy for inducing MDSC production in vivo has hindered their clinical application. In our previous study, MDSC expansion was observed in interleukin (IL)-27-treated mice. In this study, we overexpressed exogenous IL-27 in mice using a recombinant adeno-associated virus vector to investigate its therapeutic and exacerbating effects in murine GVHD models. In our study, we demonstrated that exogenous administration of IL-27 significantly suppressed GVHD development in a mouse model. We found that IL-27 treatment indirectly inhibited the proliferation and activation of donor T cells by rapidly expanding recipient and donor myeloid cells, which act as MDSCs after irradiation or under inflammatory conditions, rather than through regulatory T-cell expansion. Additionally, IL-27 stimulated MDSC expansion by enhancing granulocyte-monocyte progenitor generation. Notably, we verified that IL-27 signaling in donor T cells exerted an antagonistic effect on GVHD prevention and treatment. Further investigation revealed that combination therapy involving IL-27 and T-cell depletion exhibited remarkable preventive effects on GVHD in both mouse and xenogeneic GVHD models. Collectively, these findings suggest that IL-27 promotes MDSC generation to reduce the incidence of GVHD, whereas targeted activation of IL-27 signaling in myeloid progenitors or its combination with T-cell depletion represents a potential strategy for GVHD therapy. Show less
no PDF DOI: 10.1097/TP.0000000000005069
IL27
Bo-Yi Pan, Chen-Sheng Chen, Fang-Yu Chen +1 more Β· 2024 Β· International journal of molecular sciences Β· MDPI Β· added 2026-04-24
Apolipoprotein C3 (APOC3) plays a critical role in regulating triglyceride levels and serves as a key predictor of cardiovascular disease (CVD) risk, particularly in patients with diabetes. While APOC Show more
Apolipoprotein C3 (APOC3) plays a critical role in regulating triglyceride levels and serves as a key predictor of cardiovascular disease (CVD) risk, particularly in patients with diabetes. While APOC3 is known to inhibit lipoprotein lipase, recent findings reveal its broader influence across lipoprotein metabolism, where it modulates the structure and function of various lipoproteins. Therefore, this review examines the complex metabolic cycle of APOC3, emphasizing the impact of APOC3-containing lipoproteins on human metabolism, particularly in patients with diabetes. Notably, APOC3 affects triglyceride-rich lipoproteins and causes structural changes in high-, very low-, intermediate-, and low-density lipoproteins, thereby increasing CVD risk. Evidence suggests that elevated APOC3 levels-above the proposed safe range of 10-15 mg/dL-correlate with clinically significant CVD outcomes. Recognizing APOC3 as a promising biomarker for CVD, this review underscores the urgent need for high-throughput, clinically feasible methods to further investigate its role in lipoprotein physiology in both animal models and human studies. Additionally, we analyze the relationship between APOC3-related genes and lipoproteins, reinforcing the value of large-population studies to understand the impact of APOC3 on metabolic diseases. Ultimately, this review supports the development of therapeutic strategies targeting APOC3 reduction as a preventive approach for diabetes-related CVD. Show less
πŸ“„ PDF DOI: 10.3390/ijms252312759
APOC3
Haoran E, Lei Zhang, Zhenhua Yang +11 more Β· 2024 Β· Journal of experimental & clinical cancer research : CR Β· BioMed Central Β· added 2026-04-24
Thymic epithelial tumors (TETs) are infrequent malignancies that arise from the anterior mediastinum. Therapeutic options for TETs, especially thymic carcinoma (TC), remain relatively constrained. Thi Show more
Thymic epithelial tumors (TETs) are infrequent malignancies that arise from the anterior mediastinum. Therapeutic options for TETs, especially thymic carcinoma (TC), remain relatively constrained. This study aims to investigate the oncogenic hub gene and its underlying mechanisms in TETs, as well as to identify potential therapeutic targets. Weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEG) analysis were utilized to identify significant oncogenes using The Cancer Genome Atlas (TCGA) database. LASSO logistic regression analysis was performed to assess the association between hub genes and clinical parameters. The influence of the hub gene on promoting epithelial-mesenchymal transition (EMT), tumor progression, and regulating cancer stem cell-like properties was assessed both in vitro and in vivo. Single-cell RNA sequencing (scRNA-seq) was utilized to analyze the alterations in the tumor and its microenvironment following the administration of the hub gene's inhibitor. Multiplex immunohistochemistry (mIHC) was employed to validate the results. The potential mechanism was further elucidated through the utilization of Cleavage Under Targets and Tagmentation (CUT&Tag), RNA-sequencing, chromatin immunoprecipitation (ChIP), CUT&RUN, luciferase reporter assay, co-immunoprecipitation (Co-IP), mass spectrometry (MS) and phosphoproteomic assays. SNAI1 was identified as a hub transcription factor for TETs, and its positive correlation with the invasiveness of the disease was confirmed. Subsequent experiments revealed that the upregulation of SNAI1 augmented the migration, invasion, and EMT of TET cell lines. Furthermore, we observed that the overexpression of SNAI1 sustained cancer stem cell-like properties. ScRNA-seq demonstrated that the use of a SNAI1 inhibitor inhibited the transition of macrophages from M1 to M2 phenotype, a finding further validated by multiplex immunohistochemistry (mIHC). Phosphoinositide-3-kinase regulatory subunit 2 (PIK3R2) was identified as one of the downstream targets of SNAI1 through CUT&Tag and RNA-sequencing, a finding validated by ChIP-qPCR, CUT&RUN-qPCR, luciferase reporter and immunofluorescence assays. Co-IP, MS and phosphoproteomic assays further confirmed that PIK3R2 directly interacted with phosphorylated EphA2 (p-EphA2), facilitating downstream GSK3Ξ²/Ξ²-catenin signaling pathway. The tumorigenic role of SNAI1 through the PIK3R2/p-EphA2 axis was preliminarily validated in TETs. A potential therapeutic strategy for TETs may involve the inhibition of SNAI1. Show less
no PDF DOI: 10.1186/s13046-024-03243-0
SNAI1
Soo Heon Kwak, Shylaja Srinivasan, Ling Chen +15 more Β· 2024 Β· Nature metabolism Β· Nature Β· added 2026-04-24
The prevalence of youth-onset type 2 diabetes (T2D) and childhood obesity has been rising steadily
πŸ“„ PDF DOI: 10.1038/s42255-023-00970-0
MC4R
You-Wang Lu, Rong-Jing Dong, Lu-Hui Yang +6 more Β· 2024 Β· Scientific reports Β· Nature Β· added 2026-04-24
Leprosy and psoriasis rarely coexist, the specific molecular mechanisms underlying their mutual exclusion have not been extensively investigated. This study aimed to reveal the underlying mechanism re Show more
Leprosy and psoriasis rarely coexist, the specific molecular mechanisms underlying their mutual exclusion have not been extensively investigated. This study aimed to reveal the underlying mechanism responsible for the mutual exclusion between psoriasis and leprosy. We obtained leprosy and psoriasis data from ArrayExpress and GEO database. Differential expression analysis was conducted separately on the leprosy and psoriasis using DEseq2. Differentially expressed genes (DEGs) with opposite expression patterns in psoriasis and leprosy were identified, which could potentially involve in their mutual exclusion. Enrichment analysis was performed on these candidate mutually exclusive genes, and a protein-protein interaction (PPI) network was constructed to identify hub genes. The expression of these hub genes was further validated in an external dataset to obtain the critical mutually exclusive genes. Additionally, immune cell infiltration in psoriasis and leprosy was analyzed using single-sample gene set enrichment analysis (ssGSEA), and the correlation between critical mutually exclusive genes and immune cells was also examined. Finally, the expression pattern of critical mutually exclusive genes was evaluated in a single-cell transcriptome dataset. We identified 1098 DEGs in the leprosy dataset and 3839 DEGs in the psoriasis dataset. 48 candidate mutually exclusive genes were identified by taking the intersection. Enrichment analysis revealed that these genes were involved in cholesterol metabolism pathways. Through PPI network analysis, we identified APOE, CYP27A1, FADS1, and SOAT1 as hub genes. APOE, CYP27A1, and SOAT1 were subsequently validated as critical mutually exclusive genes on both internal and external datasets. Analysis of immune cell infiltration indicated higher abundance of 16 immune cell types in psoriasis and leprosy compared to normal controls. The abundance of 6 immune cell types in psoriasis and leprosy positively correlated with the expression levels of APOE and CYP27A1. Single-cell data analysis demonstrated that critical mutually exclusive genes were predominantly expressed in Schwann cells and fibroblasts. This study identified APOE, CYP27A1, and SOAT1 as critical mutually exclusive genes. Cholesterol metabolism pathway illustrated the possible mechanism of the inverse association of psoriasis and leprosy. The findings of this study provide a basis for identifying mechanisms and therapeutic targets for psoriasis. Show less
πŸ“„ PDF DOI: 10.1038/s41598-024-52783-0
FADS1
Yajing Shen, Jiajun Chen, Jinyu Wu +6 more Β· 2024 Β· Cancer prevention research (Philadelphia, Pa.) Β· 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