👤 Siteng Chen

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2981
Articles
1996
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Also published as: Ai-Qun Chen, Aiping Chen, Alex Chen, Alex F Chen, Alice P Chen, Alice Y Chen, Alice Ye A Chen, Allen Menglin Chen, Alon Chen, Alvin Chen, An Chen, Andrew Chen, Anqi Chen, Aoshuang Chen, Aozhou Chen, B Chen, B-S Chen, Baihua Chen, Ban Chen, Bang Chen, Bang-dang Chen, Bao-Bao Chen, Bao-Fu Chen, Bao-Sheng Chen, Bao-Ying Chen, Baofeng Chen, Baojiu Chen, Baolin Chen, Baosheng Chen, Baoxiang Chen, Beidong Chen, Beijian Chen, Ben-Kuen Chen, Benjamin Chen, Benjamin Jieming Chen, Benjamin P C Chen, Beth L Chen, Bihong T Chen, Bin Chen, Bing Chen, Bing-Bing Chen, Bing-Feng Chen, Bing-Huei Chen, Bingdi Chen, Bingqian Chen, Bingqing Chen, Bingyu Chen, Binlong Chen, Binzhen Chen, Bo Chen, Bo-Fang Chen, Bo-Jun Chen, Bo-Rui Chen, Bo-Sheng Chen, Bohe Chen, Bohong Chen, Bosong Chen, Bowang Chen, Bowei Chen, Bowen Chen, Boyu Chen, Brian Chen, C Chen, C Y Chen, C Z Chen, C-Y Chen, Cai-Long Chen, Caihong Chen, Can Chen, Cancan Chen, Canrong Chen, Canyu Chen, Caressa Chen, Carl Pc Chen, Carol Chen, Carol X-Q Chen, Catherine Qing Chen, Ceshi Chen, Chan Chen, Chang Chen, Chang-Lan Chen, Chang-Zheng Chen, Changjie Chen, Changya Chen, Changyan Chen, Chanjuan Chen, Chao Chen, Chao-Jung Chen, Chao-Wei Chen, Chaochao Chen, Chaojin Chen, Chaoli Chen, Chaoping Chen, Chaoqun Chen, Chaoran Chen, Chaoyi Chen, Chaoyue Chen, Chen Chen, Chen-Mei Chen, Chen-Sheng Chen, Chen-Yu Chen, Cheng Chen, Cheng-Fong Chen, Cheng-Sheng Chen, Cheng-Yi Chen, Cheng-Yu Chen, Chengchuan Chen, Chengchun Chen, Chengde Chen, Chengsheng Chen, Chengwei Chen, Chenyang Chen, Chi Chen, Chi-Chien Chen, Chi-Hua Chen, Chi-Long Chen, Chi-Yu Chen, Chi-Yuan Chen, Chi-Yun Chen, Chian-Feng Chen, Chider Chen, Chien-Hsiun Chen, Chien-Jen Chen, Chien-Lun Chen, Chien-Ting Chen, Chien-Yu Chen, Chih-Chieh Chen, Chih-Mei Chen, Chih-Ping Chen, Chih-Ta Chen, Chih-Wei Chen, Chih-Yi Chen, Chin-Chuan Chen, Ching Kit Chen, Ching-Hsuan Chen, Ching-Jung Chen, Ching-Wen Chen, Ching-Yi Chen, Ching-Yu Chen, Chiqi Chen, Chiung Mei Chen, Chiung-Mei Chen, Chixiang Chen, Chong Chen, Chongyang Chen, Christina Y Chen, Christina Yingxian Chen, Christopher S Chen, Chu Chen, Chu-Huang Chen, Chuanbing Chen, Chuannan Chen, Chuanzhi Chen, Chuck T Chen, Chueh-Tan Chen, Chujie Chen, Chun Chen, Chun-An Chen, Chun-Chi Chen, Chun-Fa Chen, Chun-Han Chen, Chun-Houh Chen, Chun-Wei Chen, Chun-Yuan Chen, Chung-Hao Chen, Chung-Hsing Chen, Chung-Hung Chen, Chung-Jen Chen, Chung-Yung Chen, Chunhai Chen, Chunhua Chen, Chunji Chen, Chunjie Chen, Chunlin Chen, Chunnuan Chen, Chunxiu Chen, Chuo Chen, Chuyu Chen, Cindi Chen, Constance Chen, Cuicui Chen, Cuie Chen, Cuilan Chen, Cuimin Chen, Cuncun Chen, D F Chen, D M Chen, D-F Chen, D. Chen, Dafang Chen, Daijie Chen, Daiwen Chen, Daiyu Chen, Dake Chen, Dali Chen, Dan Chen, Dan-Dan Chen, Dandan Chen, Danlei Chen, Danli Chen, Danmei Chen, Danna Chen, Danni Chen, Danxia Chen, Danxiang Chen, Danyang Chen, Danyu Chen, Daoyuan Chen, Dapeng Chen, Dawei Chen, Defang Chen, Dejuan Chen, Delong Chen, Denghui Chen, Dengpeng Chen, Deqian Chen, Dexi Chen, Dexiang Chen, Dexiong Chen, Deying Chen, Deyu Chen, Di Chen, Di-Long Chen, Dian Chen, Dianke Chen, Ding Chen, Diyun Chen, Dong Chen, Dong-Mei Chen, Dong-Yi Chen, Dongli Chen, Donglong Chen, Dongquan Chen, Dongrong Chen, Dongsheng Chen, Dongxue Chen, Dongyan Chen, Dongyin Chen, Du-Qun Chen, Duan-Yu Chen, Duo Chen, Duo-Xue Chen, Duoting Chen, E S Chen, Eleanor Y Chen, Elizabeth H Chen, Elizabeth S Chen, Elizabeth Suchi Chen, Emily Chen, En-Qiang Chen, Erbao Chen, Erfei Chen, Erqu Chen, Erzhen Chen, Everett H Chen, F Chen, F-K Chen, Fa Chen, Fa-Xi Chen, Fahui Chen, Fan Chen, Fang Chen, Fang-Pei Chen, Fang-Yu Chen, Fang-Zhi Chen, Fang-Zhou Chen, Fangfang Chen, Fangli Chen, Fangyan Chen, Fangyuan Chen, Faye H Chen, Fei Chen, Fei Xavier Chen, Feifan Chen, Feifeng Chen, Feilong Chen, Feixue Chen, Feiyang Chen, Feiyu Chen, Feiyue Chen, Feng Chen, Feng-Jung Chen, Feng-Ling Chen, Fenghua Chen, Fengju Chen, Fengling Chen, Fengming Chen, Fengrong Chen, Fengwu Chen, Fengyang Chen, Fred K Chen, Fu Chen, Fu-Shou Chen, Fumei Chen, Fusheng Chen, Fuxiang Chen, Gang Chen, Gao B Chen, Gao Chen, Gao-Feng Chen, Gaoyang Chen, Gaoyu Chen, Gaozhi Chen, Gary Chen, Gary K Chen, Ge Chen, Gen-Der Chen, Geng Chen, Gengsheng Chen, Ginny I Chen, Gong Chen, Gongbo Chen, Gonghai Chen, Gonglie Chen, Guan-Wei Chen, Guang Chen, Guang-Chao Chen, Guang-Yu Chen, Guangchun Chen, Guanghao Chen, Guanghong Chen, Guangjie Chen, Guangju Chen, Guangliang Chen, Guanglong Chen, Guangnan Chen, Guangping Chen, Guangquan Chen, Guangyao Chen, Guangyi Chen, Guangyong Chen, Guanjie Chen, Guanren Chen, Guanyu Chen, Guanzheng Chen, Gui Mei Chen, Gui-Hai Chen, Gui-Lai Chen, Guihao Chen, Guiqian Chen, Guiquan Chen, Guiying Chen, Guo Chen, Guo-Chong Chen, Guo-Jun Chen, Guo-Rong Chen, Guo-qing Chen, Guochao Chen, Guochong Chen, Guofang Chen, Guohong Chen, Guohua Chen, Guojun Chen, Guoliang Chen, Guopu Chen, Guoshun Chen, Guoxun Chen, Guozhong Chen, Guozhou Chen, H Chen, H Q Chen, H T Chen, Hai-Ning Chen, Haibing Chen, Haibo Chen, Haide Chen, Haifeng Chen, Haijiao Chen, Haimin Chen, Haiming Chen, Haining Chen, Haiqin Chen, Haiquan Chen, Haitao Chen, Haiyan Chen, Haiyang Chen, Haiyi Chen, Haiying Chen, Haiyu Chen, Haiyun Chen, Han Chen, Han-Bin Chen, Han-Chun Chen, Han-Hsiang Chen, Han-Min Chen, Hanbei Chen, Hang Chen, Hangang Chen, Hanjing Chen, Hanlin Chen, Hanqing Chen, Hanwen Chen, Hanxi Chen, Hanyong Chen, Hao Chen, Hao Yu Chen, Hao-Zhu Chen, Haobo Chen, Haodong Chen, Haojie Chen, Haoran Chen, Haotai Chen, Haotian Chen, Haoting Chen, Haoyun Chen, Haozhu Chen, Harn-Shen Chen, Haw-Wen Chen, He-Ping Chen, Hebing Chen, Hegang Chen, Hehe Chen, Hekai Chen, Heng Chen, Heng-Sheng Chen, Heng-Yu Chen, Hengsan Chen, Hengsheng Chen, Hengyu Chen, Heni Chen, Herbert Chen, Hetian Chen, Heye Chen, Hong Chen, Hong Yang Chen, Hong-Sheng Chen, Hongbin Chen, Hongbo Chen, Hongen Chen, Honghai Chen, Honghui Chen, Honglei Chen, Hongli Chen, Hongmei Chen, Hongmin Chen, Hongmou Chen, Hongqi Chen, Hongqiao Chen, Hongshan Chen, Hongxiang Chen, Hongxing Chen, Hongxu Chen, Hongyan Chen, Hongyu Chen, Hongyue Chen, Hongzhi Chen, Hou-Tsung Chen, Hou-Zao Chen, Hsi-Hsien Chen, Hsiang-Wen Chen, Hsiao-Jou Cortina Chen, Hsiao-Tan Chen, Hsiao-Wang Chen, Hsiao-Yun Chen, Hsin-Han Chen, Hsin-Hong Chen, Hsin-Hung Chen, Hsin-Yi Chen, Hsiu-Wen Chen, Hsuan-Yu Chen, Hsueh-Fen Chen, Hu Chen, Hua Chen, Hua-Pu Chen, Huachen Chen, Huafei Chen, Huaiyong Chen, Hualan Chen, Huali Chen, Hualin Chen, Huan Chen, Huan-Xin Chen, Huanchun Chen, Huang Chen, Huang-Pin Chen, Huangtao Chen, Huanhua Chen, Huanhuan Chen, Huanxiong Chen, Huaping Chen, Huapu Chen, Huaqiu Chen, Huatao Chen, Huaxin Chen, Huayu Chen, Huei-Rong Chen, Huei-Yan Chen, Huey-Miin Chen, Hui Chen, Hui Mei Chen, Hui-Chun Chen, Hui-Fen Chen, Hui-Jye Chen, Hui-Ru Chen, Hui-Wen Chen, Hui-Xiong Chen, Hui-Zhao Chen, Huichao Chen, Huijia Chen, Huijiao Chen, Huijie Chen, Huimei Chen, Huimin Chen, Huiqin Chen, Huiqun Chen, Huiru Chen, Huishan Chen, Huixi Chen, Huixian Chen, Huizhi Chen, Hung-Chang Chen, Hung-Chi Chen, Hung-Chun Chen, Hung-Po Chen, Hung-Sheng Chen, I-Chun Chen, I-M Chen, Ida Y-D Chen, Irwin Chen, Ivy Xiaoying Chen, J Chen, Jacinda Chen, Jack Chen, Jake Y Chen, Jason A Chen, Jeanne Chen, Jen-Hau Chen, Jen-Sue Chen, Jennifer F Chen, Jenny Chen, Jeremy J W Chen, Ji-ling Chen, Jia Chen, Jia Min Chen, Jia Wei Chen, Jia-De Chen, Jia-Feng Chen, Jia-Lin Chen, Jia-Mei Chen, Jia-Shun Chen, Jiabing Chen, Jiacai Chen, Jiacheng Chen, Jiade Chen, Jiahao Chen, Jiahua Chen, Jiahui Chen, Jiajia Chen, Jiajing Chen, Jiajun Chen, Jiakang Chen, Jiale Chen, Jiali Chen, Jialing Chen, Jiamiao Chen, Jiamin Chen, Jian Chen, Jian-Guo Chen, Jian-Hua Chen, Jian-Jun Chen, Jian-Kang Chen, Jian-Min Chen, Jian-Qiao Chen, Jian-Qing Chen, Jianan Chen, Jianfei Chen, Jiang Chen, Jiang Ye Chen, Jiang-hua Chen, Jianghua Chen, Jiangxia Chen, Jianhua Chen, Jianhui Chen, Jiani Chen, Jianjun Chen, Jiankui Chen, Jianlin Chen, Jianmin Chen, Jianping Chen, Jianshan Chen, Jiansu Chen, Jianxiong Chen, Jianzhong Chen, Jianzhou Chen, Jiao Chen, Jiao-Jiao Chen, Jiaohua Chen, Jiaping Chen, Jiaqi Chen, Jiaqing Chen, Jiaren Chen, Jiarou Chen, Jiawei Chen, Jiawen Chen, Jiaxin Chen, Jiaxu Chen, Jiaxuan Chen, Jiayao Chen, Jiaye Chen, Jiayi Chen, Jiayuan Chen, Jichong Chen, Jie Chen, Jie-Hua Chen, Jiejian Chen, Jiemei Chen, Jien-Jiun Chen, Jihai Chen, Jijun Chen, Jimei Chen, Jin Chen, Jin-An Chen, Jin-Ran Chen, Jin-Shuen Chen, Jin-Wu Chen, Jin-Xia Chen, Jina Chen, Jinbo Chen, Jindong Chen, Jing Chen, Jing-Hsien Chen, Jing-Wen Chen, Jing-Xian Chen, Jing-Yuan Chen, Jing-Zhou Chen, Jingde Chen, Jinghua Chen, Jingjing Chen, Jingli Chen, Jinglin Chen, Jingming Chen, Jingnan Chen, Jingqing Chen, Jingshen Chen, Jingteng Chen, Jinguo Chen, Jingxuan Chen, Jingyao Chen, Jingyi Chen, Jingyuan Chen, Jingzhao Chen, Jingzhou Chen, Jinhao Chen, Jinhuang Chen, Jinli Chen, Jinlun Chen, Jinquan Chen, Jinsong Chen, Jintian Chen, Jinxuan Chen, Jinyan Chen, Jinyong Chen, Jion Chen, Jiong Chen, Jiongyu Chen, Jishun Chen, Jiu-Chiuan Chen, Jiujiu Chen, Jiwei Chen, Jiyan Chen, Jiyuan Chen, Jonathan Chen, Joy J Chen, Juan Chen, Juan-Juan Chen, Juanjuan Chen, Juei-Suei Chen, Juhai Chen, Jui-Chang Chen, Jui-Yu Chen, Jun Chen, Jun-Long Chen, Junchen Chen, Junfei Chen, Jung-Sheng Chen, Junhong Chen, Junhui Chen, Junjie Chen, Junling Chen, Junmin Chen, Junming Chen, Junpan Chen, Junpeng Chen, Junqi Chen, Junqin Chen, Junsheng Chen, Junshi Chen, Junyang Chen, Junyi Chen, Junyu Chen, K C Chen, Kai Chen, Kai-En Chen, Kai-Ming Chen, Kai-Ting Chen, Kai-Yang Chen, Kaifu Chen, Kaijian Chen, Kailang Chen, Kaili Chen, Kaina Chen, Kaiquan Chen, Kan Chen, Kang Chen, Kang-Hua Chen, Kangyong Chen, Kangzhen Chen, Katharine Y Chen, Katherine C Chen, Ke Chen, Kecai Chen, Kehua Chen, Kehui Chen, Kelin Chen, Ken Chen, Kenneth L Chen, Keping Chen, Kequan Chen, Kevin Chen, Kewei Chen, Kexin Chen, Keyan Chen, Keyang Chen, Keying Chen, Keyu Chen, Keyuan Chen, Kuan-Jen Chen, Kuan-Ling Chen, Kuan-Ting Chen, Kuan-Yu Chen, Kuangyang Chen, Kuey Chu Chen, Kui Chen, Kun Chen, Kun-Chieh Chen, Kunmei Chen, Kunpeng Chen, L B Chen, L F Chen, Lan Chen, Lang Chen, Lankai Chen, Lanlan Chen, Lanmei Chen, Le Chen, Le Qi Chen, Lei Chen, Lei-Chin Chen, Lei-Lei Chen, Leijie Chen, Lena W Chen, Leqi Chen, Letian Chen, Lexia Chen, Li Chen, Li Jia Chen, Li-Chieh Chen, Li-Hsien Chen, Li-Hsin Chen, Li-Hua Chen, Li-Jhen Chen, Li-Juan Chen, Li-Mien Chen, Li-Nan Chen, Li-Tzong Chen, Li-Zhen Chen, Li-hong Chen, Lian Chen, Lianfeng Chen, Liang Chen, Liang-Kung Chen, Liangkai Chen, Liangsheng Chen, Liangwan Chen, Lianmin Chen, Liaobin Chen, Lichang Chen, Lichun Chen, Lidian Chen, Lie Chen, Liechun Chen, Lifang Chen, Lifen Chen, Lifeng Chen, Ligang Chen, Lihong Chen, Lihua Chen, Lijin Chen, Lijuan Chen, Lili Chen, Limei Chen, Limin Chen, Liming Chen, Lin Chen, Lina Chen, Linbo Chen, Ling Chen, Ling-Yan Chen, Lingfeng Chen, Lingjun Chen, Lingli Chen, Lingxia Chen, Lingxue Chen, Lingyi Chen, Linjie Chen, Linlin Chen, Linna Chen, Linxi Chen, Linyi Chen, Liping Chen, Liqiang Chen, Liugui Chen, Liujun Chen, Liutao Chen, Lixia Chen, Lixian Chen, Liyun Chen, Lizhen Chen, Lizhu Chen, Lo-Yun Chen, Long Chen, Long-Jiang Chen, Longqing Chen, Longyun Chen, Lu Chen, Lu Hua Chen, Lu-Biao Chen, Lu-Zhu Chen, Lulu Chen, Luming Chen, Luyi Chen, Luzhu Chen, M Chen, M L Chen, Man Chen, Man-Hua Chen, Mao Chen, Mao-Yuan Chen, Maochong Chen, Maorong Chen, Marcus Y Chen, Mark I-Cheng Chen, Max Jl Chen, Mechi Chen, Mei Chen, Mei-Chi Chen, Mei-Chih Chen, Mei-Hsiu Chen, Mei-Hua Chen, Mei-Jie Chen, Mei-Ling Chen, Mei-Ru Chen, Meilan Chen, Meilin Chen, Meiling Chen, Meimei Chen, Meiting Chen, Meiyang Chen, Meiyu Chen, Meizhen Chen, Meng Chen, Meng Xuan Chen, Meng-Lin Chen, Meng-Ping Chen, Mengdi Chen, Menglan Chen, Mengling Chen, Mengping Chen, Mengqing Chen, Mengting Chen, Mengxia Chen, Mengyan Chen, Mengying Chen, Mian-Mian Chen, Miao Chen, Miao-Der Chen, Miao-Hsueh Chen, Miao-Yu Chen, Miaomiao Chen, Miaoran Chen, Michael C Chen, Michelle Chen, Mien-Cheng Chen, Min Chen, Min-Hsuan Chen, Min-Hu Chen, Min-Jie Chen, Ming Chen, Ming-Fong Chen, Ming-Han Chen, Ming-Hong Chen, Ming-Huang Chen, Ming-Huei Chen, Ming-Yu Chen, Mingcong Chen, Mingfeng Chen, Minghong Chen, Minghua Chen, Minglang Chen, Mingling Chen, Mingmei Chen, Mingxia Chen, Mingxing Chen, Mingyang Chen, Mingyi Chen, Mingyue Chen, Minjian Chen, Minjiang Chen, Minjie Chen, Minyan Chen, Mo Chen, Mu-Hong Chen, Muh-Shy Chen, Mulan Chen, Mystie X Chen, Na Chen, Naifei Chen, Naisong Chen, Nan Chen, Ni Chen, Nian-Ping Chen, Ning Chen, Ning-Bo Chen, Ning-Hung Chen, Ning-Yuan Chen, Ningbo Chen, Ningning Chen, Nuan Chen, On Chen, Ou Chen, Ouyang Chen, P P Chen, Pan Chen, Paul Chih-Hsueh Chen, Pei Chen, Pei-Chen Chen, Pei-Chun Chen, Pei-Lung Chen, Pei-Yi Chen, Pei-Yin Chen, Pei-zhan Chen, Peihong Chen, Peipei Chen, Peiqin Chen, Peixian Chen, Peiyou Chen, Peiyu Chen, Peize Chen, Peizhan Chen, Peng Chen, Peng-Cheng Chen, Pengxiang Chen, Ping Chen, Ping-Chung Chen, Ping-Kun Chen, Pingguo Chen, Po-Han Chen, Po-Ju Chen, Po-Min Chen, Po-See Chen, Po-Sheng Chen, Po-Yu Chen, Qi Chen, Qi-An Chen, Qian Chen, Qianbo Chen, Qianfen Chen, Qiang Chen, Qiangpu Chen, Qiankun Chen, Qianling Chen, Qianming Chen, Qianping Chen, Qianqian Chen, Qianxue Chen, Qianyi Chen, Qianyu Chen, Qianyun Chen, Qianzhi Chen, Qiao Chen, Qiao-Yi Chen, Qiaoli Chen, Qiaoling Chen, Qichen Chen, Qifang Chen, Qihui Chen, Qili Chen, Qinfen Chen, Qing Chen, Qing-Hui Chen, Qing-Juan Chen, Qing-Wei Chen, Qingao Chen, Qingchao Chen, Qingchuan Chen, Qingguang Chen, Qinghao Chen, Qinghua Chen, Qingjiang Chen, Qingjie Chen, Qingliang Chen, Qingmei Chen, Qingqing Chen, Qingqiu Chen, Qingshi Chen, Qingxing Chen, Qingyang Chen, Qingyi Chen, Qinian Chen, Qinsheng Chen, Qinying Chen, Qiong Chen, Qiongyun Chen, Qiqi Chen, Qitong Chen, Qiu Jing Chen, Qiu-Jing Chen, Qiu-Sheng Chen, Qiuchi Chen, Qiuhong Chen, Qiujing Chen, Qiuli Chen, Qiuwen Chen, Qiuxia Chen, Qiuxiang Chen, Qiuxuan Chen, Qiuyun Chen, Qiwei Chen, Qixian Chen, Qu Chen, Quan Chen, Quanjiao Chen, Quanwei Chen, Qunxiang Chen, R Chen, Ran Chen, Ranyun Chen, Ray-Jade Chen, Ren-Hui Chen, Renjin Chen, Renwei Chen, Renyu Chen, Robert Chen, Roger Chen, Rong Chen, Rong-Hua Chen, Rongfang Chen, Rongfeng Chen, Rongrong Chen, Rongsheng Chen, Rongyuan Chen, Roufen Chen, Rouxi Chen, Ru Chen, Rucheng Chen, Ruey-Hwa Chen, Rui Chen, Rui-Fang Chen, Rui-Min Chen, Rui-Pei Chen, Rui-Zhen Chen, Ruiai Chen, Ruibing Chen, Ruijing Chen, Ruijuan Chen, Ruilin Chen, Ruimin Chen, Ruiming Chen, Ruiqi Chen, Ruisen Chen, Ruixiang Chen, Ruixue Chen, Ruiying Chen, Rujun Chen, Runfeng Chen, Runsen Chen, Runsheng Chen, Ruofan Chen, Ruohong Chen, Ruonan Chen, Ruoyan Chen, Ruoying Chen, S Chen, S N Chen, S Pl Chen, S-D Chen, Sai Chen, San-Yuan Chen, Sean Chen, Sen Chen, Shali Chen, Shan Chen, Shanchun Chen, Shang-Chih Chen, Shang-Hung Chen, Shangduo Chen, Shangsi Chen, Shangwu Chen, Shangzhong Chen, Shanshan Chen, Shanyuan Chen, Shao-Ke Chen, Shao-Peng Chen, Shao-Wei Chen, Shao-Yu Chen, Shao-long Chen, Shaofei Chen, Shaohong Chen, Shaohua Chen, Shaokang Chen, Shaokun Chen, Shaoliang Chen, Shaotao Chen, Shaoxing Chen, Shaoze Chen, Shasha Chen, She Chen, Shen Chen, Shen-Ming Chen, Sheng Chen, Sheng-Xi Chen, Sheng-Yi Chen, Shengdi Chen, Shenghui Chen, Shenglan Chen, Shengnan Chen, Shengpan Chen, Shengyu Chen, Shengzhi Chen, Shi Chen, Shi-Qing Chen, Shi-Sheng Chen, Shi-Yi Chen, Shi-You Chen, Shibo Chen, Shih-Jen Chen, Shih-Pin Chen, Shih-Yin Chen, Shih-Yu Chen, Shilan Chen, Shiming Chen, Shin-Wen Chen, Shin-Yu Chen, Shipeng Chen, Shiqian Chen, Shiqun Chen, Shirui Chen, Shiuhwei Chen, Shiwei Chen, Shixuan Chen, Shiyan Chen, Shiyao Chen, Shiyi Chen, Shiyu Chen, Shou-Tung Chen, Shoudeng Chen, Shoujun Chen, Shouzhen Chen, Shu Chen, Shu-Fen Chen, Shu-Gang Chen, Shu-Hua Chen, Shu-Jen Chen, Shuai Chen, Shuai-Bing Chen, Shuai-Ming Chen, Shuaijie Chen, Shuaijun Chen, Shuaiyin Chen, Shuaiyu Chen, Shuang Chen, Shuangfeng Chen, Shuanghui Chen, Shuchun Chen, Shuen-Ei Chen, Shufang Chen, Shufeng Chen, Shuhai Chen, Shuhong Chen, Shuhuang Chen, Shuhui Chen, Shujuan Chen, Shuliang Chen, Shuming Chen, Shunde Chen, Shuntai Chen, Shunyou Chen, Shuo Chen, Shuo-Bin Chen, Shuoni Chen, Shuqin Chen, Shuqiu Chen, Shuting Chen, Shuwen Chen, Shuyi Chen, Shuying Chen, Si Chen, Si-Ru Chen, Si-Yuan Chen, Si-Yue Chen, Si-guo Chen, Sien-Tsong Chen, Sifeng Chen, Sihui Chen, Sijia Chen, Sijuan Chen, Sili Chen, Silian Chen, Siping Chen, Siqi Chen, Siqin Chen, Sisi Chen, 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

Bidirectional association between immune-mediated diseases and major depressive disorder: evidence from cohort, genome-wide pleiotropic, and experimental studies.

Xiaohua Chen, Huan Liu, Yurong Liu +16 more · 2026 · Molecular psychiatry · Nature · added 2026-04-24
Although immune-mediated diseases (IMDs) and major depressive disorder (MDD) commonly co-occur, the bidirectional relationship between them remains to be fully elucidated. Using data from the prospect Show more
Although immune-mediated diseases (IMDs) and major depressive disorder (MDD) commonly co-occur, the bidirectional relationship between them remains to be fully elucidated. Using data from the prospective UK Biobank cohort, we evaluated the bidirectional associations by time-varying Cox proportional hazards regression models and assessed shared genetic architecture using genome-wide association study summary statistics. Additionally, we employed collagen-induced arthritis (CIA) and chronic social defeat stress (CSDS) mouse models to investigate the relationship between rheumatoid arthritis (RA) and depression. Over 5,226,841 person-years of follow-up, 23,534 incident MDD cases were identified. The presence of any IMD was associated with higher MDD risk (hazard ratio [HR]: 1.95; 95% CI: 1.89-2.01). Conversely, 59,742 incident cases of IMD were documented. MDD was associated with increased IMD risk (HR: 1.47; 95% CI: 1.40-1.54). We observed significant global genetic correlations between IMDs and MDD (r Show less
📄 PDF DOI: 10.1038/s41380-026-03459-w
BDNF

Spatially diffuse cAMP signalling with oppositely biased GLP-1 receptor agonists in β-cells despite differences in receptor localisation.

Shiqian Chen, Carolina B Lobato, Carissa Wong +13 more · 2026 · Molecular metabolism · Elsevier · added 2026-04-24
Internalisation of G protein-coupled receptors (GPCRs) can contribute to altered cellular responses by directing signalling from non-canonical locations, such as endosomes. If signalling processes are Show more
Internalisation of G protein-coupled receptors (GPCRs) can contribute to altered cellular responses by directing signalling from non-canonical locations, such as endosomes. If signalling processes are locally constrained, active receptors in different subcellular locations could produce different downstream effects. This phenomenon may be relevant to the optimal targeting of the glucagon-like peptide-1 receptor (GLP-1R), a type 2 diabetes and obesity target GPCR for which several ligands with varying internalisation tendency have been discovered. To investigate, we compared the signalling localisation effects of two prototypical GLP-1RAs with opposite signal bias and effects on GLP-1R trafficking: exendin-asp3 (ExD3), a full agonist that drives rapid internalisation, and exendin-phe1 (ExF1), which shows much slower internalisation. After using bioorthogonal labelling and fluorescent agonist conjugates to verify the divergent trafficking patterns of ExF1 and ExD3 in β-cell lines and primary pancreatic islets, we used live cell biosensors to monitor signalling at different subcellular locations. This revealed that cAMP/PKA/ERK signalling in β-cells is in fact distributed widely across the cell over short- (<5 min) and medium-term (up to 60 min) stimulation at pharmacological (>10 pM) concentrations, with no major differences in signal localisation that could be linked to internalised versus cell surface-bound GLP-1R. Moreover, washout experiments highlighted that, whilst fast-internalising ExD3 shows much greater accumulation and binding to GLP-1R in endosomes than slow-internalising ExF1, it is a rather inefficient driver of both cAMP production in β-cells and insulin secretion from perfused rat pancreata. These data provide a greater understanding of the cellular effects of biased GLP-1R agonism. Show less
📄 PDF DOI: 10.1016/j.molmet.2025.102304
GIPR

Bioecological attributes of learning adaptation among nursing freshmen during pandemic-induced isolation: a latent profile analysis.

Huanhuan Huang, Yetao Luo, Qi Huang +5 more · 2026 · BMC nursing · BioMed Central · added 2026-04-24
The COVID-19 pandemic has significantly disrupted educational style, potentially affecting the learning adaptation of nursing freshmen who are integral to the future nursing workforce. This study aime Show more
The COVID-19 pandemic has significantly disrupted educational style, potentially affecting the learning adaptation of nursing freshmen who are integral to the future nursing workforce. This study aimed to identify distinct subgroups of nursing freshmen based on their bioecological attributes related to learning adaptation during the pandemic. A multicenter, cross-sectional study was conducted of 1170 first-year nursing students from six higher education institutions in China. Learning adaptation, resilience, parental attachment, interaction anxiety, and mobile phone addiction, were investigated. Latent Profile Analysis (LPA) was utilized to identify distinct profiles. Descriptive statistics indicated a positive level of learning adaptation among participants, with an overall mean score of 3.51 ± 0.57. LPA revealed four distinct profiles: 'Struggling Learners' (5.47%), 'Moderate Engagers' (70.60%), 'Adaptable Strivers' (18.29%), and 'Optimal Adapters' (5.64%), which demonstrated significant differences in adaptation, resilience, parental attachment, interaction anxiety, and mobile phone addiction tendencies (P < 0.05). The study's findings emphasize the heterogeneity in learning adaptation among nursing freshmen and the importance of considering bioecological attributes when developing educational interventions during crisis. Recognizing these profiles can guide the development of targeted strategies to enhance student adaptation and academic achievement. Show less
📄 PDF DOI: 10.1186/s12912-025-04261-9
LPA

Exploring the correlation between ferroptosis-associated biomarkers and MRI-derived parameters in liver fibrosis associated with metabolic dysfunction-associated steatotic liver disease.

Meihua Yang, Qian Xu, Fangyan Li +9 more · 2026 · Clinical and experimental medicine · Springer · added 2026-04-24
The global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, and the accurate, non-invasive assessment of liver fibrosis remains an important clinical c Show more
The global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, and the accurate, non-invasive assessment of liver fibrosis remains an important clinical challenge. This study aimed to identify ferroptosis biomarkers associated with MASLD-related liver fibrosis progression, explore their potential biological links with MRI-derived parameters, and provide new clues for developing non-invasive diagnostic strategies for ferroptosis. A MASLD-related liver fibrosis model was established using 30 Sprague-Dawley (SD) rats. Hub differentially expressed ferroptosis-related genes (DE-FRGs) were identified through the integration of weighted gene co-expression network analysis (WGCNA), differential expression analysis, and LASSO regression. The role of ferroptosis in MASLD was evaluated using transmission electron microscopy (TEM) and measurements of glutathione (GSH) and Fe²⁺ content. T2*, R2*, and proton density fat fraction (PDFF) were obtained through magnetic resonance imaging (MRI) and were analyzed for correlations with hub DE-FRGs and Fe²⁺ levels. A total of eight hub DE-FRGs were identified: Pck2, Idh2, Nr1d1, Fads1, Sat1, Abhd12, Got1, and Srebf1. Enrichment analyses revealed that these hub DE-FRGs were predominantly implicated in carbohydrate response, amino acid biosynthesis, insulin resistance, and the AMPK signaling pathway. TEM and biochemical markers analyses demonstrated an association between MASLD-related liver fibrosis and ferroptosis. MRI‑derived parameters were significantly correlated with Fe²⁺ levels and the expression of hub DE-FRGs. This study preliminarily identified hub DE-FRGs associated with liver fibrosis in MASLD and their signaling pathways, verified indirect indicators related to ferroptosis, and proposed their potential correlation with MRI-derived parameters. Show less
📄 PDF DOI: 10.1007/s10238-025-02034-x
FADS1

Exploration of the co-regulatory mechanism of calorie restriction and endurance exercise on elderly skeletal muscle and its potential intervention targets.

Donghui Zhu, Xiuxiu Chen · 2026 · Experimental gerontology · Elsevier · added 2026-04-24
This study aims to explore the shared transcriptomic features of caloric restriction (CR) and endurance exercise in skeletal muscle among older adults. As age increases, muscle atrophy gradually becom Show more
This study aims to explore the shared transcriptomic features of caloric restriction (CR) and endurance exercise in skeletal muscle among older adults. As age increases, muscle atrophy gradually becomes a common issue of functional decline in the elderly. Utilizing bioinformatics analysis, this research identified 101 overlapping differentially expressed genes (DEGs) involved in both CR and endurance exercise. These genes are primarily enriched in key biological pathways related to longevity, Apelin signaling, AMPK signaling, FoxO signaling, and cGMP-PKG signaling pathways. Additionally, we identified 10 key genes (such as LPL, PPARGC1A, and IGF1), 4 transcription factors (FOXC1, POU2F2, GATA2, and STAT3), and 4 microRNAs (miR-155-5p, miR-124-3p, miR-1-3p, and miR-16-5p) interacting with these genes. Drug-gene interaction analysis identified carotuximab as a compound with potential relevance for future investigation in the context of muscle aging. These findings provide new insights into the molecular mechanisms underlying muscle functional decline in the elderly and propose potential targets and drugs for intervention development. Show less
no PDF DOI: 10.1016/j.exger.2026.113083
LPL

Hypertrophic cardiomyopathy: comprehensive insights into pathogenic genes and genotype-phenotype associations.

Luwen Hao, Xin Chen, Bo Qin · 2026 · Frontiers in cell and developmental biology · Frontiers · added 2026-04-24
Hypertrophic cardiomyopathy (HCM) is a genetically heterogeneous cardiac disorder characterized by unexplained left ventricular hypertrophy and represents a leading cause of morbidity and sudden cardi Show more
Hypertrophic cardiomyopathy (HCM) is a genetically heterogeneous cardiac disorder characterized by unexplained left ventricular hypertrophy and represents a leading cause of morbidity and sudden cardiac death, particularly in young adults and athletes. Early studies focused on morphological features, but advances in molecular genetics have shifted emphasis toward genetic diagnosis, mechanistic insights, and family-based management. Pathogenic variants in sarcomeric genes, especially Show less
📄 PDF DOI: 10.3389/fcell.2026.1741252
MYBPC3

Inverted U-Shaped Dose-Response Curve of the Anti-Atherosclerotic Effect of Protocatechuic Acid in Both Male and Female C57BL/6J Mice.

Shasha Zhu, Qiuhui Xu, Yihan Wang +4 more · 2026 · Molecular nutrition & food research · Wiley · added 2026-04-24
Dietary protocatechuic acid (PCA) inhibits atherosclerosis development in male ApoE-/- mice. However, its anti-atherosclerotic property in genetically unmodified (wild-type) male or female mice remain Show more
Dietary protocatechuic acid (PCA) inhibits atherosclerosis development in male ApoE-/- mice. However, its anti-atherosclerotic property in genetically unmodified (wild-type) male or female mice remains unknown.Five-week-old C57BL/6J mice (half males and females) were divided into negative (fed a chow diet), positive (fed an atherogenic diet), or 5, 25, 50, 100, or 200 mg/kg BW/d of PCA (fed an atherogenic diet) groups. Oral gavage with PCA between 25-100 mg/kg BW/d for 25 weeks significantly attenuated atherogenic diet-induced plaque formation in a dose-dependent manner, whereas the anti-atherosclerotic efficiency of 200 mg/kg BW/d of PCA was comparable with that of 50 mg/kg BW/d. PCA did not affect serum lipids (total triglyceride, total cholesterol, HDL cholesterol), pro-inflammatory cytokines (tumor necrosis factor alpha, IL-1b, IL-6), oxidized LDL, and total antioxidant capacity, and acetylcholine or sodium nitroprusside-induced aortic relaxation. Instead, PCA (≥25 mg/kg BW/d) reduced macrophage accumulation and content of tumor necrosis factor alpha, superoxide, and 4-hydroxynonenal within plaques, and inhibited monocyte adhesion to aortic endothelium in both male and female mice.PCA inhibits early atherosclerosis formation in both male and female C57BL/6J mice with a "U-shaped" dose-response relationship, possibly by reducing inflammation burden and oxidative stress within atherosclerotic plaques. Show less
no PDF DOI: 10.1002/mnfr.70447
APOE

Study on the characteristics and influencing factors of learned helplessness in breast cancer patients undergoing chemotherapy: A latent class analysis.

Xiaoxiao Li, Yanyan Jiao, Zhongqiang Guo +4 more · 2026 · Acta psychologica · Elsevier · added 2026-04-24
This study employed a latent profile analysis (LPA) to identify distinct subgroups of learned helplessness among Chinese breast cancer chemotherapy patients and examined influencing factors. Through c Show more
This study employed a latent profile analysis (LPA) to identify distinct subgroups of learned helplessness among Chinese breast cancer chemotherapy patients and examined influencing factors. Through convenience sampling, 260 breast cancer chemotherapy patients aged 18-74 years from a tertiary hospital in Henan Province were recruited between May 2024 and January 2025. Data were collected using a general demographic questionnaire, the Learned Helplessness Scale, the Brief Illness Perception Questionnaire, the Social Support Rating Scale, and the General Self-Efficacy Scale. An LPA was applied to classify learned helplessness patterns, followed by a multivariate logistic regression to determine the influencing factors. The latent profile analysis revealed three distinct profiles of learned helplessness among breast cancer patients undergoing chemotherapy: a "low helplessness-low hopelessness stable profile" (17.0%), a "moderate helplessness-moderate hopelessness fluctuating profile" (52.0%), and a "high helplessness-high hopelessness profile" (31.0%). The multivariable logistic regression revealed that age range 18-44 years, low monthly household income per capita, fatigue, and illness perception were significantly associated with the "high helplessness-high hopelessness profile" (P < 0.05). Conversely, the age range 45-59 years was significantly associated with the "moderate helplessness-moderate hopelessness fluctuating profile" (P < 0.001). Furthermore, experiencing ≤2 chemotherapy-related side effects, a higher level of perceived social support, and greater self-efficacy were significant predictors of membership in the "low helplessness-low hopelessness profile" (P < 0.05). Breast cancer chemotherapy patients were categorized into three distinct subgroups, which were influenced by age, income, fatigue, treatment side effects, illness perception, self-efficacy, and social support. Show less
no PDF DOI: 10.1016/j.actpsy.2026.106392
LPA

Decoding calcific aortic valve disease: superior predictive power of time-weighted lipoprotein(a).

Dan Jiang, Yi-Ling Liu, Jian Liu +7 more · 2026 · Lipids in health and disease · BioMed Central · added 2026-04-24
Calcific aortic valve disease (CAVD) is a cardiovascular disease closely associated with aging. The role of lipoprotein(a) [Lp(a)] has attracted considerable attention in recent years. However, limite Show more
Calcific aortic valve disease (CAVD) is a cardiovascular disease closely associated with aging. The role of lipoprotein(a) [Lp(a)] has attracted considerable attention in recent years. However, limited research has simultaneously explored the relationships between Lp(a), age, and CAVD. This study sought to assess the relationship linking Lp(a), time-weighted Lp(a), and CAVD. A total of 5,156 inpatients with comprehensive clinical data were recruited for this study. The associations of Lp(a) and time-weighted Lp(a) with CAVD were examined via multivariate logistic regression analysis, alongside the application of restricted cubic spline analysis. The diagnostic utility of Lp(a) and time-weighted Lp(a) for CAVD was assessed by constructing receiver operating characteristic (ROC) curves. CAVD prevalence rose with age, whereas the rate of increase diminished with advancing age. The average Lp(a) level in the young populations with CAVD was more than twice that in the No-CAVD group, particularly among those aged 55 years or younger. The prevalence of CAVD in non-elderly populations was markedly 2–4 fold greater in the higher Lp(a) group (> 30 mg/dL) than in the lower Lp(a) group (≤ 30 mg/dL). Multivariate adjusted odds ratios ‌(ORs) for CAVD increased with advancing Lp(a) or age. Time-weighted Lp(a), which takes into account both age and Lp(a), was more strongly linked to elevated CAVD risk than Lp(a) alone. Time-weighted Lp(a) enhanced the diagnostic value of CAVD, improving both sensitivity and specificity. The risk of CAVD is strongly associated with both age and elevated Lp(a) levels. Time-weighted Lp(a), which integrates these factors, serves as a superior indicator that better captures cumulative long-term Lp(a) variation and yields stronger CAVD risk stratification. The online version contains supplementary material available at 10.1186/s12944-026-02884-8. Show less
📄 PDF DOI: 10.1186/s12944-026-02884-8
LPA

Identification of biomarkers associated with endoplasmic reticulum stress-related cell death in osteoporosis based on bulk and single-cell transcriptomic analyses and experimental validation.

Yifeng Xia, Zhongyu Peng, Lingrui Zhao +6 more · 2026 · Scientific reports · Nature · added 2026-04-24
Osteoporosis (OP) is a metabolic bone disease characterized by low bone mineral density (BMD), and its pathogenesis involves endoplasmic reticulum (ER) stress-related cell death. This study aimed to i Show more
Osteoporosis (OP) is a metabolic bone disease characterized by low bone mineral density (BMD), and its pathogenesis involves endoplasmic reticulum (ER) stress-related cell death. This study aimed to identify diagnostic biomarkers associated with ER stress-related cell death in OP and explore their underlying mechanisms. The training dataset (GSE56815), validation dataset (GSE56814), and single-cell RNA sequencing (scRNA-seq) dataset (GSE147287) were downloaded. Differentially expressed genes (DEGs) between OP patients and controls were identified. Candidate genes were obtained by intersecting DEGs with ER stress-related genes and programmed cell death (PCD)-related genes. Machine learning was used to screen intersection genes, and biomarkers were determined via expression level analysis. Gene set enrichment analysis (GSEA), immune cell infiltration analysis, drug prediction and molecular docking, scRNA-seq analysis, key cell screening, cell communication analysis, and pseudotime analysis were performed. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) were further conducted. A total of 28 candidate genes were obtained by intersection. CAMKK2 and DAPK3 were confirmed as biomarkers, and were consistently down-regulated in both datasets and verified by RT-qPCR. GSEA analysis revealed that biomarkers were enriched in cytokine-cytokine receptor interaction. Correlations between biomarkers and activated dendritic cells were found via immune cell infiltration analysis. Preliminary computational analyses indicated that drugs including calcitriol and danazol may potentially interact with the biomarkers in a stable manner. Bone marrow-derived mesenchymal stem cells (BM-MSCs) were identified as potential key cells via scRNA-seq analysis. Complex interactions involving BM-MSCs, such as ANGPTL4-CDH11 mediating BM-MSC self-communication, were revealed by cell communication analysis. Dynamic expression of biomarkers during BM-MSC differentiation was shown by pseudotime analysis: CAMKK2 fluctuated with differentiation stages, while DAPK3 shifted from high to low then high expression. CAMKK2 and DAPK3 were confirmed as diagnostic biomarkers for OP, providing insights into OP diagnosis and potential therapeutic targets. Show less
📄 PDF DOI: 10.1038/s41598-026-43744-w
ANGPTL4

Clinical characteristics and APOE variant spectrum in Chinese patients with lipoprotein glomerulopathy.

Mengshi Li, Yang Li, Lei Jiang +7 more · 2026 · Chinese medical journal · added 2026-04-24
📄 PDF DOI: 10.1097/CM9.0000000000003978
APOE

Genetic association between PCSK9 and coronary artery calcification mediated by inflammatory cytokines.

Weijian Wang, Jiangping Ye, Xinyi Hu +3 more · 2026 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Coronary artery calcification (CAC), a hallmark of coronary atherosclerosis, links closely to dysregulated lipid metabolism and chronic inflammation. Proprotein convertase subtilisin/kexin type 9 (PCS Show more
Coronary artery calcification (CAC), a hallmark of coronary atherosclerosis, links closely to dysregulated lipid metabolism and chronic inflammation. Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors exert potent lipid-lowering and anti-inflammatory effects, holding translational potential for vascular calcification intervention. However, evidence on PCSK9 inhibition's impact on vascular calcification remains inconsistent. Here, we combined genetic causal analysis with First, we used two-sample Mendelian randomization (MR) and multivariable Mendelian randomization to identify lipid profiles genetically associated with coronary artery calcification. Subsequently, we investigated the value of the PCSK9 gene as a potential therapeutic target for CAC through drug target MR and colocalization analysis, and screened for potential inflammatory mediators via Mediation MR analyses. Following the completion of the aforementioned analyses, we verified the beneficial effect of PCSK9 inhibitors on delaying vascular calcification through animal experiments and cell experiments. MR analysis revealed that genetic proxies for apolipoprotein B (ApoB) (OR=1.64; 95%CI: 1.42-1.90; Inhibition of PCSK9 may effectively slow the progression of coronary artery calcification, with inflammatory mediators such as FGF23 playing key regulatory roles in this process. Show less
📄 PDF DOI: 10.3389/fcvm.2026.1767013
APOB

[Construction of a membrane-anchored bifunctional growth factor mimetic and its application in organoid culture].

Yufeng Chen, Shaoxiong Jiang, Qingchan Xu +6 more · 2026 · Sheng wu gong cheng xue bao = Chinese journal of biotechnology · added 2026-04-24
Currently, organoids emerges as novel
no PDF DOI: 10.13345/j.cjb.250807
FGFR1

A Significant Correlation Between Lipoprotein(a) Levels and Peripheral Arterial Disease in Patients With Type 2 Diabetes Mellitus: A Retrospective Study.

Chao Chen, Fang Lv · 2026 · British journal of hospital medicine (London, England : 2005) · added 2026-04-24
Lipoprotein(a) [Lp(a)] is recognized as a cardiovascular risk indicator; however, its connection to peripheral arterial disease (PAD) in individuals with type 2 diabetes mellitus (T2DM) is not well es Show more
Lipoprotein(a) [Lp(a)] is recognized as a cardiovascular risk indicator; however, its connection to peripheral arterial disease (PAD) in individuals with type 2 diabetes mellitus (T2DM) is not well established. This research seeks to explore how Lp(a) concentrations relate to the occurrence of PAD in T2DM patients. A retrospective analysis was conducted on 590 patients diagnosed with T2DM who were admitted to Hefei First People's Hospital from January 2022 to August 2024. Participants were grouped into tertiles according to their Lp(a) levels. The diagnosis of PAD was made using the ankle-brachial index (ABI), with an ABI <0.9 considered indicative of PAD. The association between Lp(a) concentrations and PAD was examined using multivariate logistic regression models, subgroup analyses, receiver operating characteristic (ROC) curves, and restricted cubic spline (RCS) plotting. Compared to lower Lp(a) levels, the group with higher Lp(a) levels exhibited a higher prevalence of PAD ( A significant correlation was observed between elevated Lp(a) levels and an increased risk of PAD in patients with T2DM. Show less
no PDF DOI: 10.31083/BJHM50381
LPA

Transcriptomic Analysis of High and Low Lipid Droplet Deposition Subpopulations of Chicken Preadipocytes Based on SSC Sorting.

Boyu Wang, Yantao Li, Yake Wang +4 more · 2026 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
Fat deposition plays a crucial role in regulating the production performance and meat quality of broilers. Although the heterogeneity of mammalian adipocytes has been extensively studied, research on Show more
Fat deposition plays a crucial role in regulating the production performance and meat quality of broilers. Although the heterogeneity of mammalian adipocytes has been extensively studied, research on the molecular mechanisms underlying differences in lipid droplet accumulation in avian adipocytes remains limited. This study confirmed a significant positive correlation (R Show less
📄 PDF DOI: 10.3390/ani16060885
LPL

Fermented red quinoa (FRQ) effectively mitigates chronic alcohol-induced cognitive impairment and hepatic steatosis through multi-mechanistic pathways.

Zi-Han Lin, Zhaohui Wang, FenFen Wei +5 more · 2026 · Food research international (Ottawa, Ont.) · Elsevier · added 2026-04-24
Long-term alcohol consumption drives systemic damage through metabolites such as acetaldehyde, which trigger oxidative stress, inflammation, and gut dysbiosis. This study evaluated the protective effe Show more
Long-term alcohol consumption drives systemic damage through metabolites such as acetaldehyde, which trigger oxidative stress, inflammation, and gut dysbiosis. This study evaluated the protective effects of fermented red quinoa (FRQ) in an alcohol-exposed mouse model, with a focus on cognitive function. Male C57BL/6J mice were randomized into three groups for a 28-day study: a normal control, an alcohol-treated group gavaged with ethanol (1 mL/100 g·BW), and a group receiving the same ethanol dose co-administered with FRQ powder (human equivalent dose: 9 g/60 kg·BW). Our results demonstrated that fermentation with Lactobacillus kisonensis significantly increased the content of phenolic compounds (e.g., quercetin and veratric acid) in FRQ. FRQ intervention improved cognitive function, ameliorated synaptic structural impairment and blood-brain barrier disruption, and attenuated hepatic steatosis. The protective mechanisms involved three pathways: 1) The specific phenolic compounds in FRQ promoted alcohol metabolism by regulating ADH/ALDH activity, leading to reduced acetaldehyde levels. As a primary initiating pathway, this metabolic enhancement dominantly attenuated subsequent oxidative stress and inflammation, mitigating injury in the liver, brain, and colon. 2) It directly modulated AP-1 subunits (ΔFOSB/JUND), restored BDNF, and rebalanced the glutamate/GABA systems. 3) It regulated the gut-liver-brain axis by remodeling the gut microbiota (e.g., enriching butyrate-producing Butyricicoccus), reinforcing intestinal barrier integrity, and thereby suppressing systemic LPS translocation and inflammation. In conclusion, FRQ mitigates alcohol-induced cognitive and hepatic damage via multiple mechanisms, highlighting its promise as an integrative dietary intervention. Show less
no PDF DOI: 10.1016/j.foodres.2026.118547
BDNF alcohol consumption alcohol-induced cognitive impairment cognitive function fermented food gut dysbiosis hepatic steatosis inflammation

Effects of GABAAR modulators CL218872 and MRK-016 on neural repair and synaptic plasticity in mice with Intracerebral hemorrhage.

Tingting Chen, Hongxia He, Fei Huang +3 more · 2026 · PloS one · PLOS · added 2026-04-24
Intracerebral hemorrhage (ICH) is a devastating condition characterized by rapid onset, high rates of disability and mortality, and prolonged recovery. Dysregulated γ-aminobutyric acid type A receptor Show more
Intracerebral hemorrhage (ICH) is a devastating condition characterized by rapid onset, high rates of disability and mortality, and prolonged recovery. Dysregulated γ-aminobutyric acid type A receptor (GABAAR) signaling contributes to ICH-induced neurotoxicity, presenting a promising therapeutic target. To assess the neurorestorative effects of the GABAAR α1-selective partial positive allosteric modulator (PAM) CL218872 and the α5-selective negative allosteric modulator (NAM) MRK-016 on synaptic plasticity and neural repair following ICH. An ICH mouse model was constructed using collagenase IV, and ICH mice were administered the GABAAR modulators CL218872 or MRK-016. Differences in inflammation and neurological deficit score were compared between different groups of mice. Morphologic and functional changes in mouse neuronal cells were next determined by Nissl and Golgi-Cox staining. Synaptic structural changes in ICH mice were visualized by transmission electron microscopy, and changes in synaptic plasticity-related molecules were quantified to assess the effects of GABAAR modulators on synapses in ICH mice. Treatment with CL218872 resulted in a reduction in hemorrhage and improved neurobehavioral outcomes in ICH mice. Additionally, CL218872 mitigated inflammation by downregulating phospho-p65, IL-6 and TNF-α expression. Histological analysis revealed an increase in neuronal density, preservation of cell morphology, and enhanced synaptic connectivity following CL218872 treatment. Furthermore, synaptic structure was restored, and there was an upregulation of brain-derived neurotrophic factor (BDNF), growth-associated protein-43 (GAP-43), postsynaptic density protein 95 (PSD-95), and synaptophysin in ICH mice. However, treatment with MRK-016 yielded the opposite result. The GABAAR α1-selective PAM CL218872 exerts neuroprotective and neurorestorative effects in ICH, suggesting its therapeutic potential for ICH management. Show less
📄 PDF DOI: 10.1371/journal.pone.0345025
BDNF

Endothelial JMJD1C drives pathological ocular neovascularization by activating SREBF2-dependent cholesterol biosynthesis.

Yang Yu, Zhangyu Liu, Jiayu Huang +6 more · 2026 · Free radical biology & medicine · Elsevier · added 2026-04-24
Pathological ocular neovascularization is closely linked to aberrant histone modifications, yet the underlying molecular mechanisms remain incompletely defined. This study investigates the role of the Show more
Pathological ocular neovascularization is closely linked to aberrant histone modifications, yet the underlying molecular mechanisms remain incompletely defined. This study investigates the role of the histone demethylase JMJD1C and its encoding gene Jmjd1c in driving pathological angiogenesis and evaluates its therapeutic potential in ocular proliferative vascular diseases. Jmjd1c expression was examined in mouse models of ocular neovascularization and in endothelial cells (ECs) using immunostaining, qRT-PCR, and Western blotting. The pro-angiogenic functions of JMJD1C were assessed through EdU incorporation, Transwell migration, tube-formation, and spheroid-sprouting assays in vitro, as well as retinal flat-mount isolectin-B4 staining and H&E staining in vivo. RNA sequencing, immunostaining, qPCR, Western blotting, and ChIP-qPCR were employed to dissect the molecular mechanisms by which JMJD1C regulates pathological angiogenesis. Endothelial-specific deletion of Jmjd1c markedly reduced pathological neovascularization in both oxygen-induced retinopathy (OIR) and laser-induced choroidal neovascularization (CNV) models. Loss of JMJD1C impaired endothelial cell proliferation, migration, tube formation, and sprouting angiogenesis. Mechanistically, Jmjd1c deletion suppressed Srebf2 transcription and cholesterol biosynthesis by increasing repressive H3K9me2 histone marks in endothelial cells. Pharmacological inhibition of JMJD1C similarly attenuated neovascularization in wild-type mice. JMJD1C acts as a key regulator of pathological ocular angiogenesis through histone demethylation-mediated control of endothelial cholesterol biosynthesis. These findings establish JMJD1C and the Jmjd1c-Srebf2 regulatory axis as promising therapeutic targets for ocular vascular diseases. Show less
no PDF DOI: 10.1016/j.freeradbiomed.2026.01.024
JMJD1C

Retigabine ameliorates CRS-induced depressive-like behaviors and cognitive impairment by inhibiting endoplasmic reticulum stress-mediated apoptosis.

Jing Zhang, Yi-Heng Li, Jin-Jing Zhang +4 more · 2026 · Brain research bulletin · Elsevier · added 2026-04-24
Retigabine (RTG) shows notable neuroprotective efficacy in multiple brain injury models; however, its interplay with endoplasmic reticulum stress (ERS) is poorly understood. This study was designed to Show more
Retigabine (RTG) shows notable neuroprotective efficacy in multiple brain injury models; however, its interplay with endoplasmic reticulum stress (ERS) is poorly understood. This study was designed to explore the therapeutic potential of RTG against CRS-induced depression-like behaviors and cognitive deficits in mice and to uncover the associated molecular mechanisms. A depression-like and cognitive impairment model was established in C57BL/6 male mice using chronic restraint stress (CRS). Six-week-old C57BL/6 male mice were randomly assigned to the following groups: control (Con), model (CRS), RTG (10 mg/kg), XE-991 (2 mg/kg) or tunicamycin (Tm, 2 mg/kg). Behavioral tests were conducted to assess depression-like behaviors and cognitive function. Hippocampal neuronal morphology was examined by H&E and immunofluorescence staining, while changes in endoplasmic reticulum stress (ERS)-related signaling pathways were analyzed by Western blot. Retigabine treatment reduced hippocampal neuronal damage and the expression of ERS-related factors (GRP78, CHOP) and the pro-apoptotic factor BAX in CRS-induced mice, while it increased the levels of BDNF. These effects were antagonized by XE-991 and the ERS agonist tunicamycin (Tm). Retigabine may alleviate CRS-induced depressive-like behaviors and cognitive impairment by inhibiting ERS-mediated apoptosis, suggesting its potential as a novel therapeutic strategy for depression. Show less
no PDF DOI: 10.1016/j.brainresbull.2026.111798
BDNF apoptosis behaviors cognitive impairment depression endoplasmic reticulum stress neuroprotection stress

Mapping comorbid depression and anxiety in Southwest China's ethnic minorities areas: A population-based latent profile and network analysis of 58,739 individuals.

Yongmei Wu, Wenjing Xia, Yang Yang +18 more · 2026 · Journal of affective disorders · Elsevier · added 2026-04-24
Anxiety and depression are highly comorbid mental health disorders with heterogeneous symptom patterns and poorly understood transdiagnostic mechanisms. This study aims to characterize latent subgroup Show more
Anxiety and depression are highly comorbid mental health disorders with heterogeneous symptom patterns and poorly understood transdiagnostic mechanisms. This study aims to characterize latent subgroups, risk factors, and symptom-level interactions underlying depression-anxiety comorbidity across adolescents and adults in multi-ethnic Southwest China. The study included a total of 41,394 adolescents (aged 9-19) and 17,345 adults (aged 18-80). Adolescents were recruited using multistage stratified cluster sampling, whereas adults were recruited by convenience sampling. All participants completed a self-designed sociodemographic questionnaire, the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7). Latent profile analysis identified subgroups, logistic regression analyzed risk/protective factors, and network analysis mapped symptom interactions and bridge nodes. This study found that three adolescent profiles emerged: high (11.66 %), moderate (31.95 %), and low/no depression-anxiety (56.39 %). Adults were classified into low/no comorbidity (90.63 %) and comorbid depression-anxiety (9.37 %). Risk factors for adolescents included female gender (OR = 2.77, 95 %CI: 2.55-3.00; OR = 1.59, 95 %CI: 1.52-1.67), higher grade levels (OR = 3.45, 95 %CI: 3.10-3.84; OR = 3.56, 95 %CI: 3.33-3.80), smoking (OR = 1.72, 95 %CI: 1.51-1.96; OR = 1.28, 95 %CI: 1.17-1.41),drinking (OR = 2.45, 95 %CI: 2.23-2.70; OR = 1.66, 95 %CI: 1.55-1.77), family instability (OR = 1.16, 95 %CI: 1.02-1.31; OR = 1.33, 95 %CI: 1.14-1.56) and "other" ethnic minority (OR = 1.15, 95 %CI: 1.04-1.26). For adults, female gender(OR = 1.68; 95 %CI: 1.44-1.97), living alone(OR = 1.37; 95 %CI: 1.14-1.65), poor self-rated health (OR = 0.13, 95 %CI: 0.11-0.15), and Dai ethnicity (OR = 0.70, 95 %CI: 0.49-0.96) predicted comorbidity. Network analysis revealed distinct bridge symptoms: adolescents in the high depression-anxiety group had five symptoms: depressed or sad mood (phq2), psychomotor agitation/retardation (phq8), nervousness or anxiety (gad1), restlessness (gad5), and irritable (gad6); however, adults with comorbidity had one symptom: afraid something will happen (gad7). This study identified three patterns of depression-anxiety comorbidity in adolescents and two in adults. Efforts should prioritize adolescents from "other" ethnic minorities, strengthening family and peer support, as well as smoking and drinking interventions for adolescents, and addressing social isolation, physical health, and catastrophizing cognition in adults may mitigate the comorbidity burden. Show less
no PDF DOI: 10.1016/j.jad.2025.121112
LPA

High-intensity intermittent training promotes adipose tissue browning via the IL-27/p38 MAPK-PGC-1α signaling pathway in diet-induced obese rats.

Chunlong Wang, Yulong Hu, Junfei Chen +1 more · 2026 · Frontiers in physiology · Frontiers · added 2026-04-24
This study investigated the effects of high-intensity intermittent training (HIIT) Forty male Sprague-Dawley rats were randomly divided into two groups: standard diet (C, n = 10) and high-fat diet (HF Show more
This study investigated the effects of high-intensity intermittent training (HIIT) Forty male Sprague-Dawley rats were randomly divided into two groups: standard diet (C, n = 10) and high-fat diet (HFD, n = 30). After 8 weeks of HFD feeding, 24 obese rats were further randomised into three subgroups: HFD (H, n = 8), HFD + moderate-intensity training (HMT, n = 8), and HFD + HIIT (HHT, n = 8). The HMT and HHT groups underwent 8 week training interventions (six sessions/week). The HMT protocol included a 10 min warm-up (treadmill speed: 10 m/min), a 40 min moderate-intensity aerobic phase (60%-70% of maximum speed), and a 10 min recovery (10 m/min). The HHT protocol featured 10 min warm-up and recovery phases (10 m/min), with 40 min of alternating treadmill training: 3 min at 50% maximum speed followed by 3 min at 90% maximum speed. No significant differences in body weight were observed between the HHT and HMT groups. HHT rats displayed significantly lower plasma triglyceride levels than H and HMT rats. Compared with HMT, HHT reduced adipose mass and adipocyte size and increased mitochondrial succinate dehydrogenase and cytochrome c oxidase (COX) activities in adipose tissue. However, HHT rats displayed lower COX activity in visceral white adipose tissue than HMT rats. Training upregulated browning-related genes and uncoupling protein 1 (UCP1) in adipose tissue, with stronger effects in HHT than in HMT. Plasma and adipose tissue IL-27 levels, as well as p38 MAPK-PGC-1α signalling pathway activation, were significantly elevated in both training groups, with greater increases in HHT. HIIT promotes adipose tissue browning by activating the IL-27 signalling pathway and ameliorates obesity-associated metabolic disorders more effectively than MAIT, supporting its potential as a therapeutic strategy for obesity. Show less
📄 PDF DOI: 10.3389/fphys.2026.1745363
IL27

Phytochemical profiling and molecular interactions of Parishins a and C as potent AChE inhibitors from red Gastrodia elata.

Ming Chen, Yuchi Zhang, Jingying Xu +7 more · 2026 · Biophysical chemistry · Elsevier · added 2026-04-24
Current in vitro enzyme inhibition assays often involve subjective data analysis based on the researcher's experience. In this study, we developed a multi-dimensional quantitative integration platform Show more
Current in vitro enzyme inhibition assays often involve subjective data analysis based on the researcher's experience. In this study, we developed a multi-dimensional quantitative integration platform (MDQIP) that uses a model to objectively calculate and rank compound activities, addressing the limitations of traditional "experience-driven" evaluations, accelerates the screening and evaluation of potential AChE inhibitors from Red Gastrodia elata, offering a more efficient approach to drug discovery. Ultrafiltration-LC screening identified parishin A as having the most stable binding, with binding degree and recovery rates of 98.85% and 99.39%, respectively. Molecular docking revealed that parishins A and C were the strongest AChE inhibitors, exhibiting stable binding through hydrogen bonds, π-alkyl, and π-π interactions. Molecular dynamics simulations confirmed the stability of these compounds, with binding energies of -82.65 ± 4.24 and - 80.69 ± 4.19 kcal/mol. Enzyme kinetics showed that parishins A and C are mixed-type inhibitors, with IC Show less
no PDF DOI: 10.1016/j.bpc.2026.107617
BACE1

Large-Scaled Proteomics Analysis for Glaucoma: Integrated Genetics, Multi-Omics, UK Biobank and Therapeutic Analysis.

Jianming Xu, Yuelan Gao, Jun Yu +6 more · 2026 · Investigative ophthalmology & visual science · added 2026-04-24
To identify plasma proteins associated with glaucoma and assess the translational potential of key proteins as both biomarkers and therapeutic targets. Genome-wide association study data were obtained Show more
To identify plasma proteins associated with glaucoma and assess the translational potential of key proteins as both biomarkers and therapeutic targets. Genome-wide association study data were obtained from the UK Biobank Pharma Proteomics Project, FinnGen, and the Million Veteran Program. We used a four-stage analytical framework: Stage 1 applied Mendelian randomization and Bayesian colocalization to evaluate associations between 2923 plasma proteins and glaucoma; Stage 2 used summary-based Mendelian randomization to explore transcriptomic and epigenomic associations of the identified proteins with glaucoma risk; Stage 3 involved a prospective association analysis of protein levels and incident glaucoma in the UK Biobank cohort, including 40,170 glaucoma-free participants; and Stage 4 systematically evaluated the druggability of the prioritized protein targets. We identified 26 plasma proteins with putative causal associations with glaucoma, six of which were novel: COL24A1, KAZALD1, EBAG9, CSNK1D, AZI2, and AXIN1. COL24A1 (odds ratio [OR] = 0.85; 95% confidence interval [CI], 0.80-0.90; PFDR < 0.001; PP.H4 = 0.95) and EFEMP1 (OR = 0.88; 95% CI, 0.83-0.92; PFDR < 0.001; PP.H4 = 0.98) emerged as the most compelling candidates. To further elucidate the regulatory mechanisms, multiomics analyses indicated that epigenetic modifications and alternative splicing events affecting these genes were associated with elevated glaucoma risk. Notably, EFEMP1 was significantly associated with glaucoma incidence in the prospective cohort analysis (fully adjusted Cox model: hazard ratio = 1.61; 95% CI, 1.29-2.00; PFDR = 0.002), demonstrating strong predictive performance (C-index = 0.811, area under the curve = 0.806) and representing a promising therapeutic target. Our findings provide new insights into the proteomic basis of glaucoma and highlight promising opportunities for developing targeted therapies. Show less
📄 PDF DOI: 10.1167/iovs.67.2.4
AXIN1

FADS1 contributes to anesthesia/surgery-induced cognitive impairment by aggravating omega-6 fatty acid metabolic disruption in aged mice.

Zheping Chen, Qianqian Wu, Jiahui Ma +9 more · 2026 · Journal of neuroinflammation · BioMed Central · added 2026-04-24
Perioperative neurocognitive disorder (PND) is one of the most prevalent neurological complications in elderly surgical patients. Dysregulated lipid metabolism is a hallmark of aging and is strongly a Show more
Perioperative neurocognitive disorder (PND) is one of the most prevalent neurological complications in elderly surgical patients. Dysregulated lipid metabolism is a hallmark of aging and is strongly associated with cognitive dysfunction. This study aimed to investigate whether ω-6 polyunsaturated fatty acid (PUFA) metabolism contribute to PND and examined whether fatty acid desaturase 1 (FADS1) represents a key regulatory link between fatty acid metabolism and PND in aged mice. An anesthesia/surgery-induced cognitive dysfunction model was established Anesthesia/surgery significantly upregulated hippocampal FADS1 expression (1.91-fold [0.37] vs. 1.00-fold [0.43]; These findings highlight anesthesia/surgery could disrupt ω-6 PUFA metabolism, notably activating the PGD The online version contains supplementary material available at 10.1186/s12974-025-03678-y. Show less
📄 PDF DOI: 10.1186/s12974-025-03678-y
FADS1

NRF1 Induces ApoEhigh Cancer-Associated Fibroblasts to Promote Stemness of Renal Cell Carcinoma.

Ying Zhang, Zhouting Tuo, Yuan Lin +10 more · 2026 · Cancer research · added 2026-04-24
Cancer-associated fibroblasts (CAF) are abundant stromal cells in the tumor microenvironment (TME) that play a vital role in promoting tumor progression and drug resistance. The mechanisms regulating Show more
Cancer-associated fibroblasts (CAF) are abundant stromal cells in the tumor microenvironment (TME) that play a vital role in promoting tumor progression and drug resistance. The mechanisms regulating heterogeneity of CAFs in renal cell carcinoma (RCC) could represent potential targets for reprogramming the TME. In this study, we conducted single-cell RNA sequence and flow cytometry analyses that identified a CAF subset overexpressing apolipoprotein E (ApoE), which was correlated with poor survival in patients with RCC. Mechanistically, NRF1 activation in CAFs induced formation of ApoEhigh CAFs and secretion of NRG1. ApoEhigh CAFs potentiated stemness properties in the surrounding RCC cells by secreting NRG1 and subsequently activating the HER2/NF-κB pathway. Interfering with NRG1 expression or inhibiting NF-κB signaling reduced ApoEhigh CAF-induced stemness of RCC cells. Furthermore, neutralizing NRG1 enhanced the efficacy of sunitinib in RCC models in vivo. Together, these findings highlight targeting the tumor-promoting functions of ApoEhigh CAFs as a promising approach for treating advanced RCC. NRF1 drives formation of ApoEhigh cancer-associated fibroblasts that secrete NRG1 to stimulate stemness of renal cell carcinoma, revealing a stromal-mediated mechanism that can be inhibited to improve treatment of advanced kidney cancer. Show less
no PDF DOI: 10.1158/0008-5472.CAN-25-0959
APOE

Allyl isothiocyanate ameliorates metabolic dysfunction-associated steatotic liver disease

Ting Gao, Kang-Peng Zhong, Jun-Zhuo Wang +2 more · 2026 · World journal of gastroenterology · added 2026-04-24
Prior studies indicate that allyl isothiocyanate (AITC) alleviates metabolic dysfunction-associated steatotic liver disease (MASLD). The vitamin D receptor (VDR) is known to exert protective effects i Show more
Prior studies indicate that allyl isothiocyanate (AITC) alleviates metabolic dysfunction-associated steatotic liver disease (MASLD). The vitamin D receptor (VDR) is known to exert protective effects in MASLD; however, whether AITC alleviates MASLD through VDR remains unclear. To clarify the function and underlying mechanisms of AITC in MASLD AML-12 cells were exposed to 300 μM palmitate acid (PA) for 24 hours to establish an To establish an AITC provides a robust molecular basis for improving MASLD by activating hepatic VDR and driving the downstream HNF-4α/MTTP/ApoB signaling pathway. This pathway reduces hepatic lipid accumulation, promotes FA β-oxidation, and improves insulin resistance, establishing AITC as a promising treatment for MASLD. Show less
📄 PDF DOI: 10.3748/wjg.v32.i4.113647
APOB

Targeting FGFR signaling overcomes therapeutic resistance and immune evasion in oncogenic PIK3CA-driven serous-like endometrial cancer.

Xin Cheng, Changli Qian, Erica Holdridge +18 more · 2026 · bioRxiv : the preprint server for biology · added 2026-04-24
Serous endometrial cancer (SEC) is an aggressive subtype of endometrial cancer (EC) with poor prognosis and limited treatment options. Here, we developed a clinically relevant, immunocompetent serous- Show more
Serous endometrial cancer (SEC) is an aggressive subtype of endometrial cancer (EC) with poor prognosis and limited treatment options. Here, we developed a clinically relevant, immunocompetent serous-like mouse model incorporating oncogenic Show less
no PDF DOI: 10.64898/2026.02.16.706009
FGFR1

The miR-2110/

ThanhLoan Tran, Zhong-Yu Wang, Pei-Shan Li +10 more · 2026 · Biochemistry and biophysics reports · Elsevier · added 2026-04-24
Coronary heart disease (CHD) is driven by endothelial dysfunction and chronic vascular inflammation. hsa-miR-2110 (miR-2110) has been associated with adverse cardiovascular outcomes, but its mechanist Show more
Coronary heart disease (CHD) is driven by endothelial dysfunction and chronic vascular inflammation. hsa-miR-2110 (miR-2110) has been associated with adverse cardiovascular outcomes, but its mechanistic role in CHD remains unclear. In this study, miR-2110 expression was quantified in peripheral blood from CHD patients and healthy controls. Functional effects were assessed in EA.hy926 endothelial cells following lentiviral overexpression of miR-2110. The target gene Show less
📄 PDF DOI: 10.1016/j.bbrep.2026.102508
APOE

Scutellarin-eluting stents attenuate coronary restenosis by inhibiting the abnormal proliferation and migration of vascular smooth muscle cells through suppression of NF-κB signaling.

Li Zhang, Yuting Wang, Wei Min Gao +8 more · 2026 · Phytomedicine : international journal of phytotherapy and phytopharmacology · Elsevier · added 2026-04-24
Coronary restenosis remains a major challenge following percutaneous coronary intervention (PCI), necessitating the development of effective stent-eluting drugs. Previous studies indicate that scutell Show more
Coronary restenosis remains a major challenge following percutaneous coronary intervention (PCI), necessitating the development of effective stent-eluting drugs. Previous studies indicate that scutellarin protects vascular endothelial cells and exhibits anti-thrombotic and anti-platelet effects. Notably, our prior research demonstrated that scutellarin specifically counteracts oxidative stress-driven endothelial dysfunction, a key initiating event in restenosis. This combined evidence strongly suggests its potential against in-stent restenosis (ISR). Therefore, this study explores the efficacy of scutellarin in preventing ISR after PCI. We investigated scutellarin, derived from Erigeron breviscapus, for its potential to prevent ISR following PCI. The efficacy and mechanism of scutellarin were evaluated using both in vivo and in vitro models. An experimental atherosclerosis model was established in APOE In APOE This study establishes the efficacy of scutellarin in mitigating ISR using two complementary in vivo models. Scutellarin-eluting stents in atherosclerotic minipigs overcome translational barriers through full interventional simulation. Furthermore, scutellarin inhibits VSMCs proliferation, migration and promotes autophagy-coordinated apoptosis by the coordinated downregulation of both the Pl3K/AKT and lKKs/NF-κB cascades.These findings highlight scutellarin as a promising candidate for next-generation bioactive stent coatings, bridging phytopharmacology and precision interventional cardiology. Show less
no PDF DOI: 10.1016/j.phymed.2026.157948
APOE

Heterogeneity in the co-occurrence of internalizing and externalizing behavior problems among college students: Results of latent profile analysis and latent transition analysis.

Ting Li, Ke Chen · 2026 · Journal of affective disorders · Elsevier · added 2026-04-24
Internalizing and externalizing behavior problems co-occur frequently and there is heterogeneity in the co-occurrence of such symptoms; however, few studies have explored this heterogeneity and its de Show more
Internalizing and externalizing behavior problems co-occur frequently and there is heterogeneity in the co-occurrence of such symptoms; however, few studies have explored this heterogeneity and its developmental mechanisms from a person-centered perspective. The primary aim of this study is to employ Latent Profile Analysis (LPA) and Latent Transition Analysis (LTA)-person-centered statistical approaches-to explore this underlying heterogeneity, uncover its dynamic developmental trajectories, and further examine the key factors that influence class membership and transitions. A sample of 2232 Chinese college students from three universities in Chongqing was assessed at two time points spaced ten months apart. Latent Profile Analysis (LPA) and Latent Transition Analysis (LTA) were conducted on measures of internalizing and externalizing problems. LPA revealed three distinct profiles for both internalizing problems ("Low-Risk/Well-Adapted", "Moderate-Risk/Affective-Distress", "High-Risk/Comorbid") and externalizing problems ("Well-Adapted", "Adaptation Difficulties", "Maladaptive") at T1, with similar structures at T2. LTA indicated high stability for the low- and high-risk internalizing profiles, but significant fluidity in the middle, with nearly half of the moderate-risk group transitioning to the high-risk profile. For externalizing problems, there was a pronounced shift toward the "Maladaptive" profile over time. Negative parental rearing and PWU were significant risk factors for adverse transitions, while positive parenting, self-transcendence values, and objective social support served as protective factors. Co-occurring internalizing and externalizing problems among Chinese college students are heterogeneous and dynamic. The moderate-risk group represents a critical target for early intervention. Modifiable ecological factors across family, individual, and technological domains significantly predict longitudinal trajectories, informing targeted prevention strategies. Show less
no PDF DOI: 10.1016/j.jad.2025.120957
LPA