👤 Xiao-Wei Chen

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2981
Articles
1996
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Also published as: Ai-Qun Chen, Aiping Chen, Alex Chen, Alex F Chen, Alice P Chen, Alice Y Chen, Alice Ye A Chen, Allen Menglin Chen, Alon Chen, Alvin Chen, An Chen, Andrew Chen, Anqi Chen, Aoshuang Chen, Aozhou Chen, B Chen, B-S Chen, Baihua Chen, Ban Chen, Bang Chen, Bang-dang Chen, Bao-Bao Chen, Bao-Fu Chen, Bao-Sheng Chen, Bao-Ying Chen, Baofeng Chen, Baojiu Chen, Baolin Chen, Baosheng Chen, Baoxiang Chen, Beidong Chen, Beijian Chen, Ben-Kuen Chen, Benjamin Chen, Benjamin Jieming Chen, Benjamin P C Chen, Beth L Chen, Bihong T Chen, Bin Chen, Bing Chen, Bing-Bing Chen, Bing-Feng Chen, Bing-Huei Chen, Bingdi Chen, Bingqian Chen, Bingqing Chen, Bingyu Chen, Binlong Chen, Binzhen Chen, Bo Chen, Bo-Fang Chen, Bo-Jun Chen, Bo-Rui Chen, Bo-Sheng Chen, Bohe Chen, Bohong Chen, Bosong Chen, Bowang Chen, Bowei Chen, Bowen Chen, Boyu Chen, Brian Chen, C Chen, C Y Chen, C Z Chen, C-Y Chen, Cai-Long Chen, Caihong Chen, Can Chen, Cancan Chen, Canrong Chen, Canyu Chen, Caressa Chen, Carl Pc Chen, Carol Chen, Carol X-Q Chen, Catherine Qing Chen, Ceshi Chen, Chan Chen, Chang Chen, Chang-Lan Chen, Chang-Zheng Chen, Changjie Chen, Changya Chen, Changyan Chen, Chanjuan Chen, Chao Chen, Chao-Jung Chen, Chao-Wei Chen, Chaochao Chen, Chaojin Chen, Chaoli Chen, Chaoping Chen, Chaoqun Chen, Chaoran Chen, Chaoyi Chen, Chaoyue Chen, Chen Chen, Chen-Mei Chen, Chen-Sheng Chen, Chen-Yu Chen, Cheng Chen, Cheng-Fong Chen, Cheng-Sheng Chen, Cheng-Yi Chen, Cheng-Yu Chen, Chengchuan Chen, Chengchun Chen, Chengde Chen, Chengsheng Chen, Chengwei Chen, Chenyang Chen, Chi Chen, Chi-Chien Chen, Chi-Hua Chen, Chi-Long Chen, Chi-Yu Chen, Chi-Yuan Chen, Chi-Yun Chen, Chian-Feng Chen, Chider Chen, Chien-Hsiun Chen, Chien-Jen Chen, Chien-Lun Chen, Chien-Ting Chen, Chien-Yu Chen, Chih-Chieh Chen, Chih-Mei Chen, Chih-Ping Chen, Chih-Ta Chen, Chih-Wei Chen, Chih-Yi Chen, Chin-Chuan Chen, Ching Kit Chen, Ching-Hsuan Chen, Ching-Jung Chen, Ching-Wen Chen, Ching-Yi Chen, Ching-Yu Chen, Chiqi Chen, Chiung Mei Chen, Chiung-Mei Chen, Chixiang Chen, Chong Chen, Chongyang Chen, Christina Y Chen, Christina Yingxian Chen, Christopher S Chen, Chu Chen, Chu-Huang Chen, Chuanbing Chen, Chuannan Chen, Chuanzhi Chen, Chuck T Chen, Chueh-Tan Chen, Chujie Chen, Chun Chen, Chun-An Chen, Chun-Chi Chen, Chun-Fa Chen, Chun-Han Chen, Chun-Houh Chen, Chun-Wei Chen, Chun-Yuan Chen, Chung-Hao Chen, Chung-Hsing Chen, Chung-Hung Chen, Chung-Jen Chen, Chung-Yung Chen, Chunhai Chen, Chunhua Chen, Chunji Chen, Chunjie Chen, Chunlin Chen, Chunnuan Chen, Chunxiu Chen, Chuo Chen, Chuyu Chen, Cindi Chen, Constance Chen, Cuicui Chen, Cuie Chen, Cuilan Chen, Cuimin Chen, Cuncun Chen, D F Chen, D M Chen, D-F Chen, D. Chen, Dafang Chen, Daijie Chen, Daiwen Chen, Daiyu Chen, Dake Chen, Dali Chen, Dan Chen, Dan-Dan Chen, Dandan Chen, Danlei Chen, Danli Chen, Danmei Chen, Danna Chen, Danni Chen, Danxia Chen, Danxiang Chen, Danyang Chen, Danyu Chen, Daoyuan Chen, Dapeng Chen, Dawei Chen, Defang Chen, Dejuan Chen, Delong Chen, Denghui Chen, Dengpeng Chen, Deqian Chen, Dexi Chen, Dexiang Chen, Dexiong Chen, Deying Chen, Deyu Chen, Di Chen, Di-Long Chen, Dian Chen, Dianke Chen, Ding Chen, Diyun Chen, Dong Chen, Dong-Mei Chen, Dong-Yi Chen, Dongli Chen, Donglong Chen, Dongquan Chen, Dongrong Chen, Dongsheng Chen, Dongxue Chen, Dongyan Chen, Dongyin Chen, Du-Qun Chen, Duan-Yu Chen, Duo Chen, Duo-Xue Chen, Duoting Chen, E S Chen, Eleanor Y Chen, Elizabeth H Chen, Elizabeth S Chen, Elizabeth Suchi Chen, Emily Chen, En-Qiang Chen, Erbao Chen, Erfei Chen, Erqu Chen, Erzhen Chen, Everett H Chen, F Chen, F-K Chen, Fa Chen, Fa-Xi Chen, Fahui Chen, Fan Chen, Fang Chen, Fang-Pei Chen, Fang-Yu Chen, Fang-Zhi Chen, Fang-Zhou Chen, Fangfang Chen, Fangli Chen, Fangyan Chen, Fangyuan Chen, Faye H Chen, Fei Chen, Fei Xavier Chen, Feifan Chen, Feifeng Chen, Feilong Chen, Feixue Chen, Feiyang Chen, Feiyu Chen, Feiyue Chen, Feng Chen, Feng-Jung Chen, Feng-Ling Chen, Fenghua Chen, Fengju Chen, Fengling Chen, Fengming Chen, Fengrong Chen, Fengwu Chen, Fengyang Chen, Fred K Chen, Fu Chen, Fu-Shou Chen, Fumei Chen, Fusheng Chen, Fuxiang Chen, Gang Chen, Gao B Chen, Gao Chen, Gao-Feng Chen, Gaoyang Chen, Gaoyu Chen, Gaozhi Chen, Gary Chen, Gary K Chen, Ge Chen, Gen-Der Chen, Geng Chen, Gengsheng Chen, Ginny I Chen, Gong Chen, Gongbo Chen, Gonghai Chen, Gonglie Chen, Guan-Wei Chen, Guang Chen, Guang-Chao Chen, Guang-Yu Chen, Guangchun Chen, Guanghao Chen, Guanghong Chen, Guangjie Chen, Guangju Chen, Guangliang Chen, Guanglong Chen, Guangnan Chen, Guangping Chen, Guangquan Chen, Guangyao Chen, Guangyi Chen, Guangyong Chen, Guanjie Chen, Guanren Chen, Guanyu Chen, Guanzheng Chen, Gui Mei Chen, Gui-Hai Chen, Gui-Lai Chen, Guihao Chen, Guiqian Chen, Guiquan Chen, Guiying Chen, Guo Chen, Guo-Chong Chen, Guo-Jun Chen, Guo-Rong Chen, Guo-qing Chen, Guochao Chen, Guochong Chen, Guofang Chen, Guohong Chen, Guohua Chen, Guojun Chen, Guoliang Chen, Guopu Chen, Guoshun Chen, Guoxun Chen, Guozhong Chen, Guozhou Chen, H Chen, H Q Chen, H T Chen, Hai-Ning Chen, Haibing Chen, Haibo Chen, Haide Chen, Haifeng Chen, Haijiao Chen, Haimin Chen, Haiming Chen, Haining Chen, Haiqin Chen, Haiquan Chen, Haitao Chen, Haiyan Chen, Haiyang Chen, Haiyi Chen, Haiying Chen, Haiyu Chen, Haiyun Chen, Han Chen, Han-Bin Chen, Han-Chun Chen, Han-Hsiang Chen, Han-Min Chen, Hanbei Chen, Hang Chen, Hangang Chen, Hanjing Chen, Hanlin Chen, Hanqing Chen, Hanwen Chen, Hanxi Chen, Hanyong Chen, Hao Chen, Hao Yu Chen, Hao-Zhu Chen, Haobo Chen, Haodong Chen, Haojie Chen, Haoran Chen, Haotai Chen, Haotian Chen, Haoting Chen, Haoyun Chen, Haozhu Chen, Harn-Shen Chen, Haw-Wen Chen, He-Ping Chen, Hebing Chen, Hegang Chen, Hehe Chen, Hekai Chen, Heng Chen, Heng-Sheng Chen, Heng-Yu Chen, Hengsan Chen, Hengsheng Chen, Hengyu Chen, Heni Chen, Herbert Chen, Hetian Chen, Heye Chen, Hong Chen, Hong Yang Chen, Hong-Sheng Chen, Hongbin Chen, Hongbo Chen, Hongen Chen, Honghai Chen, Honghui Chen, Honglei Chen, Hongli Chen, Hongmei Chen, Hongmin Chen, Hongmou Chen, Hongqi Chen, Hongqiao Chen, Hongshan Chen, Hongxiang Chen, Hongxing Chen, Hongxu Chen, Hongyan Chen, Hongyu Chen, Hongyue Chen, Hongzhi Chen, Hou-Tsung Chen, Hou-Zao Chen, Hsi-Hsien Chen, Hsiang-Wen Chen, Hsiao-Jou Cortina Chen, Hsiao-Tan Chen, Hsiao-Wang Chen, Hsiao-Yun Chen, Hsin-Han Chen, Hsin-Hong Chen, Hsin-Hung Chen, Hsin-Yi Chen, Hsiu-Wen Chen, Hsuan-Yu Chen, Hsueh-Fen Chen, Hu Chen, Hua Chen, Hua-Pu Chen, Huachen Chen, Huafei Chen, Huaiyong Chen, Hualan Chen, Huali Chen, Hualin Chen, Huan Chen, Huan-Xin Chen, Huanchun Chen, Huang Chen, Huang-Pin Chen, Huangtao Chen, Huanhua Chen, Huanhuan Chen, Huanxiong Chen, Huaping Chen, Huapu Chen, Huaqiu Chen, Huatao Chen, Huaxin Chen, Huayu Chen, Huei-Rong Chen, Huei-Yan Chen, Huey-Miin Chen, Hui Chen, Hui Mei Chen, Hui-Chun Chen, Hui-Fen Chen, Hui-Jye Chen, Hui-Ru Chen, Hui-Wen Chen, Hui-Xiong Chen, Hui-Zhao Chen, Huichao Chen, Huijia Chen, Huijiao Chen, Huijie Chen, Huimei Chen, Huimin Chen, Huiqin Chen, Huiqun Chen, Huiru Chen, Huishan Chen, Huixi Chen, Huixian Chen, Huizhi Chen, Hung-Chang Chen, Hung-Chi Chen, Hung-Chun Chen, Hung-Po Chen, Hung-Sheng Chen, I-Chun Chen, I-M Chen, Ida Y-D Chen, Irwin Chen, Ivy Xiaoying Chen, J Chen, Jacinda Chen, Jack Chen, Jake Y Chen, Jason A Chen, Jeanne Chen, Jen-Hau Chen, Jen-Sue Chen, Jennifer F Chen, Jenny Chen, Jeremy J W Chen, Ji-ling Chen, Jia Chen, Jia Min Chen, Jia Wei Chen, Jia-De Chen, Jia-Feng Chen, Jia-Lin Chen, Jia-Mei Chen, Jia-Shun Chen, Jiabing Chen, Jiacai Chen, Jiacheng Chen, Jiade Chen, Jiahao Chen, Jiahua Chen, Jiahui Chen, Jiajia Chen, Jiajing Chen, Jiajun Chen, Jiakang Chen, Jiale Chen, Jiali Chen, Jialing Chen, Jiamiao Chen, Jiamin Chen, Jian Chen, Jian-Guo Chen, Jian-Hua Chen, Jian-Jun Chen, Jian-Kang Chen, Jian-Min Chen, Jian-Qiao Chen, Jian-Qing Chen, Jianan Chen, Jianfei Chen, Jiang Chen, Jiang Ye Chen, Jiang-hua Chen, Jianghua Chen, Jiangxia Chen, Jianhua Chen, Jianhui Chen, Jiani Chen, Jianjun Chen, Jiankui Chen, Jianlin Chen, Jianmin Chen, Jianping Chen, Jianshan Chen, Jiansu Chen, Jianxiong Chen, Jianzhong Chen, Jianzhou Chen, Jiao Chen, Jiao-Jiao Chen, Jiaohua Chen, Jiaping Chen, Jiaqi Chen, Jiaqing Chen, Jiaren Chen, Jiarou Chen, Jiawei Chen, Jiawen Chen, Jiaxin Chen, Jiaxu Chen, Jiaxuan Chen, Jiayao Chen, Jiaye Chen, Jiayi Chen, Jiayuan Chen, Jichong Chen, Jie Chen, Jie-Hua Chen, Jiejian Chen, Jiemei Chen, Jien-Jiun Chen, Jihai Chen, Jijun Chen, Jimei Chen, Jin Chen, Jin-An Chen, Jin-Ran Chen, Jin-Shuen Chen, Jin-Wu Chen, Jin-Xia Chen, Jina Chen, Jinbo Chen, Jindong Chen, Jing Chen, Jing-Hsien Chen, Jing-Wen Chen, Jing-Xian Chen, Jing-Yuan Chen, Jing-Zhou Chen, Jingde Chen, Jinghua Chen, Jingjing Chen, Jingli Chen, Jinglin Chen, Jingming Chen, Jingnan Chen, Jingqing Chen, Jingshen Chen, Jingteng Chen, Jinguo Chen, Jingxuan Chen, Jingyao Chen, Jingyi Chen, Jingyuan Chen, Jingzhao Chen, Jingzhou Chen, Jinhao Chen, Jinhuang Chen, Jinli Chen, Jinlun Chen, Jinquan Chen, Jinsong Chen, Jintian Chen, Jinxuan Chen, Jinyan Chen, Jinyong Chen, Jion Chen, Jiong Chen, Jiongyu Chen, Jishun Chen, Jiu-Chiuan Chen, Jiujiu Chen, Jiwei Chen, Jiyan Chen, Jiyuan Chen, Jonathan Chen, Joy J Chen, Juan Chen, Juan-Juan Chen, Juanjuan Chen, Juei-Suei Chen, Juhai Chen, Jui-Chang Chen, Jui-Yu Chen, Jun Chen, Jun-Long Chen, Junchen Chen, Junfei Chen, Jung-Sheng Chen, Junhong Chen, Junhui Chen, Junjie Chen, Junling Chen, Junmin Chen, Junming Chen, Junpan Chen, Junpeng Chen, Junqi Chen, Junqin Chen, Junsheng Chen, Junshi Chen, Junyang Chen, Junyi Chen, Junyu Chen, K C Chen, Kai Chen, Kai-En Chen, Kai-Ming Chen, Kai-Ting Chen, Kai-Yang Chen, Kaifu Chen, Kaijian Chen, Kailang Chen, Kaili Chen, Kaina Chen, Kaiquan Chen, Kan Chen, Kang Chen, Kang-Hua Chen, Kangyong Chen, Kangzhen Chen, Katharine Y Chen, Katherine C Chen, Ke Chen, Kecai Chen, Kehua Chen, Kehui Chen, Kelin Chen, Ken Chen, Kenneth L Chen, Keping Chen, Kequan Chen, Kevin Chen, Kewei Chen, Kexin Chen, Keyan Chen, Keyang Chen, Keying Chen, Keyu Chen, Keyuan Chen, Kuan-Jen Chen, Kuan-Ling Chen, Kuan-Ting Chen, Kuan-Yu Chen, Kuangyang Chen, Kuey Chu Chen, Kui Chen, Kun Chen, Kun-Chieh Chen, Kunmei Chen, Kunpeng Chen, L B Chen, L F Chen, Lan Chen, Lang Chen, Lankai Chen, Lanlan Chen, Lanmei Chen, Le Chen, Le Qi Chen, Lei Chen, Lei-Chin Chen, Lei-Lei Chen, Leijie Chen, Lena W Chen, Leqi Chen, Letian Chen, Lexia Chen, Li Chen, Li Jia Chen, Li-Chieh Chen, Li-Hsien Chen, Li-Hsin Chen, Li-Hua Chen, Li-Jhen Chen, Li-Juan Chen, Li-Mien Chen, Li-Nan Chen, Li-Tzong Chen, Li-Zhen Chen, Li-hong Chen, Lian Chen, Lianfeng Chen, Liang Chen, Liang-Kung Chen, Liangkai Chen, Liangsheng Chen, Liangwan Chen, Lianmin Chen, Liaobin Chen, Lichang Chen, Lichun Chen, Lidian Chen, Lie Chen, Liechun Chen, Lifang Chen, Lifen Chen, Lifeng Chen, Ligang Chen, Lihong Chen, Lihua Chen, Lijin Chen, Lijuan Chen, Lili Chen, Limei Chen, Limin Chen, Liming Chen, Lin Chen, Lina Chen, Linbo Chen, Ling Chen, Ling-Yan Chen, Lingfeng Chen, Lingjun Chen, Lingli Chen, Lingxia Chen, Lingxue Chen, Lingyi Chen, Linjie Chen, Linlin Chen, Linna Chen, Linxi Chen, Linyi Chen, Liping Chen, Liqiang Chen, Liugui Chen, Liujun Chen, Liutao Chen, Lixia Chen, Lixian Chen, Liyun Chen, Lizhen Chen, Lizhu Chen, Lo-Yun Chen, Long Chen, Long-Jiang Chen, Longqing Chen, Longyun Chen, Lu Chen, Lu Hua Chen, Lu-Biao Chen, Lu-Zhu Chen, Lulu Chen, Luming Chen, Luyi Chen, Luzhu Chen, M Chen, M L Chen, Man Chen, Man-Hua Chen, Mao Chen, Mao-Yuan Chen, Maochong Chen, Maorong Chen, Marcus Y Chen, Mark I-Cheng Chen, Max Jl Chen, Mechi Chen, Mei Chen, Mei-Chi Chen, Mei-Chih Chen, Mei-Hsiu Chen, Mei-Hua Chen, Mei-Jie Chen, Mei-Ling Chen, Mei-Ru Chen, Meilan Chen, Meilin Chen, Meiling Chen, Meimei Chen, Meiting Chen, Meiyang Chen, Meiyu Chen, Meizhen Chen, Meng Chen, Meng Xuan Chen, Meng-Lin Chen, Meng-Ping Chen, Mengdi Chen, Menglan Chen, Mengling Chen, Mengping Chen, Mengqing Chen, Mengting Chen, Mengxia Chen, Mengyan Chen, Mengying Chen, Mian-Mian Chen, Miao Chen, Miao-Der Chen, Miao-Hsueh Chen, Miao-Yu Chen, Miaomiao Chen, Miaoran Chen, Michael C Chen, Michelle Chen, Mien-Cheng Chen, Min Chen, Min-Hsuan Chen, Min-Hu Chen, Min-Jie Chen, Ming Chen, Ming-Fong Chen, Ming-Han Chen, Ming-Hong Chen, Ming-Huang Chen, Ming-Huei Chen, Ming-Yu Chen, Mingcong Chen, Mingfeng Chen, Minghong Chen, Minghua Chen, Minglang Chen, Mingling Chen, Mingmei Chen, Mingxia Chen, Mingxing Chen, Mingyang Chen, Mingyi Chen, Mingyue Chen, Minjian Chen, Minjiang Chen, Minjie Chen, Minyan Chen, Mo Chen, Mu-Hong Chen, Muh-Shy Chen, Mulan Chen, Mystie X Chen, Na Chen, Naifei Chen, Naisong Chen, Nan Chen, Ni Chen, Nian-Ping Chen, Ning Chen, Ning-Bo Chen, Ning-Hung Chen, Ning-Yuan Chen, Ningbo Chen, Ningning Chen, Nuan Chen, On Chen, Ou Chen, Ouyang Chen, P P Chen, Pan Chen, Paul Chih-Hsueh Chen, Pei Chen, Pei-Chen Chen, Pei-Chun Chen, Pei-Lung Chen, Pei-Yi Chen, Pei-Yin Chen, Pei-zhan Chen, Peihong Chen, Peipei Chen, Peiqin Chen, Peixian Chen, Peiyou Chen, Peiyu Chen, Peize Chen, Peizhan Chen, Peng Chen, Peng-Cheng Chen, Pengxiang Chen, Ping Chen, Ping-Chung Chen, Ping-Kun Chen, Pingguo Chen, Po-Han Chen, Po-Ju Chen, Po-Min Chen, Po-See Chen, Po-Sheng Chen, Po-Yu Chen, Qi Chen, Qi-An Chen, Qian Chen, Qianbo Chen, Qianfen Chen, Qiang Chen, Qiangpu Chen, Qiankun Chen, Qianling Chen, Qianming Chen, Qianping Chen, Qianqian Chen, Qianxue Chen, Qianyi Chen, Qianyu Chen, Qianyun Chen, Qianzhi Chen, Qiao Chen, Qiao-Yi Chen, Qiaoli Chen, Qiaoling Chen, Qichen Chen, Qifang Chen, Qihui Chen, Qili Chen, Qinfen Chen, Qing Chen, Qing-Hui Chen, Qing-Juan Chen, Qing-Wei Chen, Qingao Chen, Qingchao Chen, Qingchuan Chen, Qingguang Chen, Qinghao Chen, Qinghua Chen, Qingjiang Chen, Qingjie Chen, Qingliang Chen, Qingmei Chen, Qingqing Chen, Qingqiu Chen, Qingshi Chen, Qingxing Chen, Qingyang Chen, Qingyi Chen, Qinian Chen, Qinsheng Chen, Qinying Chen, Qiong Chen, Qiongyun Chen, Qiqi Chen, Qitong Chen, Qiu Jing Chen, Qiu-Jing Chen, Qiu-Sheng Chen, Qiuchi Chen, Qiuhong Chen, Qiujing Chen, Qiuli Chen, Qiuwen Chen, Qiuxia Chen, Qiuxiang Chen, Qiuxuan Chen, Qiuyun Chen, Qiwei Chen, Qixian Chen, Qu Chen, Quan Chen, Quanjiao Chen, Quanwei Chen, Qunxiang Chen, R Chen, Ran Chen, Ranyun Chen, Ray-Jade Chen, Ren-Hui Chen, Renjin Chen, Renwei Chen, Renyu Chen, Robert Chen, Roger Chen, Rong Chen, Rong-Hua Chen, Rongfang Chen, Rongfeng Chen, Rongrong Chen, Rongsheng Chen, Rongyuan Chen, Roufen Chen, Rouxi Chen, Ru Chen, Rucheng Chen, Ruey-Hwa Chen, Rui Chen, Rui-Fang Chen, Rui-Min Chen, Rui-Pei Chen, Rui-Zhen Chen, Ruiai Chen, Ruibing Chen, Ruijing Chen, Ruijuan Chen, Ruilin Chen, Ruimin Chen, Ruiming Chen, Ruiqi Chen, Ruisen Chen, Ruixiang Chen, Ruixue Chen, Ruiying Chen, Rujun Chen, Runfeng Chen, Runsen Chen, Runsheng Chen, Ruofan Chen, Ruohong Chen, Ruonan Chen, Ruoyan Chen, Ruoying Chen, S Chen, S N Chen, S Pl Chen, S-D Chen, Sai Chen, San-Yuan Chen, Sean Chen, Sen Chen, Shali Chen, Shan Chen, Shanchun Chen, Shang-Chih Chen, Shang-Hung Chen, Shangduo Chen, Shangsi Chen, Shangwu Chen, Shangzhong Chen, Shanshan Chen, Shanyuan Chen, Shao-Ke Chen, Shao-Peng Chen, Shao-Wei Chen, Shao-Yu Chen, Shao-long Chen, Shaofei Chen, Shaohong Chen, Shaohua Chen, Shaokang Chen, Shaokun Chen, Shaoliang Chen, Shaotao Chen, Shaoxing Chen, Shaoze Chen, Shasha Chen, She Chen, Shen Chen, Shen-Ming Chen, Sheng Chen, Sheng-Xi Chen, Sheng-Yi Chen, Shengdi Chen, Shenghui Chen, Shenglan Chen, Shengnan Chen, Shengpan Chen, Shengyu Chen, Shengzhi Chen, Shi Chen, Shi-Qing Chen, Shi-Sheng Chen, Shi-Yi Chen, Shi-You Chen, Shibo Chen, Shih-Jen Chen, Shih-Pin Chen, Shih-Yin Chen, Shih-Yu Chen, Shilan Chen, Shiming Chen, Shin-Wen Chen, Shin-Yu Chen, Shipeng Chen, Shiqian Chen, Shiqun Chen, Shirui Chen, Shiuhwei Chen, Shiwei Chen, Shixuan Chen, Shiyan Chen, Shiyao Chen, Shiyi Chen, Shiyu Chen, Shou-Tung Chen, Shoudeng Chen, Shoujun Chen, Shouzhen Chen, Shu Chen, Shu-Fen Chen, Shu-Gang Chen, Shu-Hua Chen, Shu-Jen Chen, Shuai Chen, Shuai-Bing Chen, Shuai-Ming Chen, Shuaijie Chen, Shuaijun Chen, Shuaiyin Chen, Shuaiyu Chen, Shuang Chen, Shuangfeng Chen, Shuanghui Chen, Shuchun Chen, Shuen-Ei Chen, Shufang Chen, Shufeng Chen, Shuhai Chen, Shuhong Chen, Shuhuang Chen, Shuhui Chen, Shujuan Chen, Shuliang Chen, Shuming Chen, Shunde Chen, Shuntai Chen, Shunyou Chen, Shuo Chen, Shuo-Bin Chen, Shuoni Chen, Shuqin Chen, Shuqiu Chen, Shuting Chen, Shuwen Chen, Shuyi Chen, Shuying Chen, Si Chen, Si-Ru Chen, Si-Yuan Chen, Si-Yue Chen, Si-guo Chen, Sien-Tsong Chen, Sifeng Chen, Sihui Chen, Sijia Chen, Sijuan Chen, Sili Chen, Silian Chen, Siping Chen, Siqi Chen, Siqin Chen, Sisi Chen, Siteng Chen, Siting Chen, Siyi Chen, Siyu Chen, Siyu S Chen, Siyuan Chen, Siyue Chen, Size Chen, Song Chen, Song-Mei Chen, Songfeng Chen, Suet N Chen, Suet Nee Chen, Sufang Chen, Suipeng Chen, Sulian Chen, Suming Chen, Sun Chen, Sung-Fang Chen, Suning Chen, Sunny Chen, Sy-Jou Chen, Syue-Ting Chen, Szu-Chi Chen, Szu-Chia Chen, Szu-Chieh Chen, Szu-Han Chen, Szu-Yun Chen, T Chen, Tai-Heng Chen, Tai-Tzung Chen, Tailai Chen, Tan-Huan Chen, Tan-Zhou Chen, Tania Chen, Tao Chen, Tian Chen, Tianfeng Chen, Tianhang Chen, Tianhong Chen, Tianhua Chen, Tianpeng Chen, Tianran Chen, Tianrui Chen, Tiantian Chen, Tianzhen Chen, Tielin Chen, Tien-Hsing Chen, Ting Chen, Ting-Huan Chen, Ting-Tao Chen, Ting-Ting Chen, Tingen Chen, Tingtao Chen, Tingting Chen, Tom Wei-Wu Chen, Tong Chen, Tongsheng Chen, Tse-Ching Chen, Tse-Wei Chen, TsungYen Chen, Tuantuan Chen, Tzu-An Chen, Tzu-Chieh Chen, Tzu-Ju Chen, Tzu-Ting Chen, Tzu-Yu Chen, Tzy-Yen Chen, Valerie Chen, W Chen, Wai Chen, Wan Jun Chen, Wan-Tzu Chen, Wan-Yan Chen, Wan-Yi Chen, Wanbiao Chen, Wanjia Chen, Wanjun Chen, Wanling Chen, Wantao Chen, Wanting Chen, Wanyin Chen, Wei Chen, Wei J Chen, Wei Ning Chen, Wei-Cheng Chen, Wei-Cong Chen, Wei-Fei Chen, Wei-Hao Chen, Wei-Hui Chen, Wei-Kai Chen, Wei-Kung Chen, Wei-Lun Chen, Wei-Min Chen, Wei-Peng Chen, Wei-Ting Chen, Wei-Wei Chen, Wei-Yu Chen, Wei-xian Chen, Weibo Chen, Weican Chen, Weichan Chen, Weicong Chen, Weihao Chen, Weihong Chen, Weihua Chen, Weijia Chen, Weijie Chen, Weili Chen, Weilun Chen, Weina Chen, Weineng Chen, Weiping Chen, Weiqin Chen, Weiqing Chen, Weirui Chen, Weisan Chen, Weitao Chen, Weitian Chen, Weiwei Chen, Weixian Chen, Weixin Chen, Weiyi Chen, Weiyong Chen, Wen Chen, Wen-Chau Chen, Wen-Jie Chen, Wen-Pin Chen, Wen-Qi Chen, Wen-Tsung Chen, Wen-Yi Chen, Wenbiao Chen, Wenbing Chen, Wenfan Chen, Wenfang Chen, Wenhao Chen, Wenhua Chen, Wenjie Chen, Wenjun Chen, Wenlong Chen, Wenqin Chen, Wensheng Chen, Wenshuo Chen, Wentao Chen, Wenting Chen, Wentong Chen, Wenwen Chen, Wenwu Chen, Wenxi Chen, Wenxing Chen, Wenxu Chen, Willian Tzu-Liang Chen, Wu-Jun Chen, Wu-Xian Chen, Wuyan Chen, X Chen, X R Chen, X Steven Chen, Xi Chen, Xia Chen, Xia-Fei Chen, Xiaguang Chen, Xiameng Chen, Xian Chen, Xian-Kai Chen, Xianbo Chen, Xiancheng Chen, Xianfeng Chen, Xiang Chen, Xiang-Bin Chen, Xiang-Mei Chen, XiangFan Chen, Xiangding Chen, Xiangjun Chen, Xiangli Chen, Xiangliu Chen, Xiangmei Chen, Xiangna Chen, Xiangning Chen, Xiangqiu Chen, Xiangyu Chen, Xiankai Chen, Xianmei Chen, Xianqiang Chen, Xianxiong Chen, Xianyue Chen, Xianze Chen, Xianzhen Chen, Xiao Chen, Xiao-Chen Chen, Xiao-Hui Chen, Xiao-Jun Chen, Xiao-Lin Chen, Xiao-Qing Chen, Xiao-Quan Chen, Xiao-Yang Chen, Xiao-Ying Chen, Xiao-chun Chen, Xiao-he Chen, Xiao-ping Chen, Xiaobin Chen, Xiaobo Chen, Xiaochang Chen, Xiaochun Chen, Xiaodong Chen, Xiaofang Chen, Xiaofen Chen, Xiaofeng Chen, Xiaohan Chen, Xiaohong Chen, Xiaohua Chen, Xiaohui Chen, Xiaojiang S Chen, Xiaojie Chen, Xiaojing Chen, Xiaojuan Chen, Xiaojun Chen, Xiaokai Chen, Xiaolan Chen, Xiaole L Chen, Xiaolei Chen, Xiaoli Chen, Xiaolin Chen, Xiaoling Chen, Xiaolong Chen, Xiaolu Chen, Xiaomeng Chen, Xiaomin Chen, Xiaona Chen, Xiaonan Chen, Xiaopeng Chen, Xiaoping Chen, Xiaoqian Chen, Xiaoqing Chen, Xiaorong Chen, Xiaoshan Chen, Xiaotao Chen, Xiaoting Chen, Xiaowan Chen, Xiaowei Chen, Xiaowen Chen, Xiaoxiang Chen, Xiaoxiao Chen, Xiaoyan Chen, Xiaoyang Chen, Xiaoyin Chen, Xiaoyong Chen, Xiaoyu Chen, Xiaoyuan Chen, Xiaoyun Chen, Xiatian Chen, Xihui Chen, Xijun Chen, Xikun Chen, Ximei Chen, Xin Chen, Xin-Jie Chen, Xin-Ming Chen, Xin-Qi Chen, Xinan Chen, Xing Chen, Xing-Lin Chen, Xing-Long Chen, Xing-Zhen Chen, Xingdong Chen, Xinghai Chen, Xingxing Chen, Xingyi Chen, Xingyong Chen, Xingyu Chen, Xinji Chen, Xinlin Chen, Xinpu Chen, Xinqiao Chen, Xinwei Chen, Xinyan Chen, Xinyang Chen, Xinyi Chen, Xinyu Chen, Xinyuan Chen, Xinyue Chen, Xinzhuo Chen, Xiong Chen, Xiqun Chen, Xiu Chen, Xiu-Juan Chen, Xiuhui Chen, Xiujuan Chen, Xiuli Chen, Xiuping Chen, Xiuxiu Chen, Xiuyan Chen, Xixi Chen, Xiyao Chen, Xiyu Chen, Xu Chen, Xuan Chen, Xuancai Chen, Xuanjing Chen, Xuanli Chen, Xuanmao Chen, Xuanwei Chen, Xuanxu Chen, Xuanyi Chen, Xue Chen, Xue-Mei Chen, Xue-Qing Chen, Xue-Xin Chen, Xue-Yan Chen, Xue-Ying Chen, XueShu Chen, Xuechun Chen, Xuefei Chen, Xuehua Chen, Xuejiao Chen, Xuejun Chen, Xueli Chen, Xueling Chen, Xuemei Chen, Xuemin Chen, Xueqin Chen, Xueqing Chen, Xuerong Chen, Xuesong Chen, Xueting Chen, Xueyan Chen, Xueying Chen, Xufeng Chen, Xuhui Chen, Xujia Chen, Xun Chen, Xuxiang Chen, Xuxin Chen, Xuzhuo Chen, Y Chen, Y D I Chen, Y Eugene Chen, Y M Chen, Y P Chen, Y S Chen, Y U Chen, Y-D I Chen, Y-D Ida Chen, Ya Chen, Ya-Chun Chen, Ya-Nan Chen, Ya-Peng Chen, Ya-Ting Chen, Ya-xi Chen, Yafang Chen, Yafei Chen, Yahong Chen, Yajie Chen, Yajing Chen, Yajun Chen, Yalan Chen, Yali Chen, Yan Chen, Yan Jie Chen, Yan Q Chen, Yan-Gui Chen, Yan-Jun Chen, Yan-Ming Chen, Yan-Qiong Chen, Yan-yan Chen, Yanan Chen, Yananlan Chen, Yanbin Chen, Yanfei Chen, Yanfen Chen, Yang Chen, Yang-Ching Chen, Yang-Yang Chen, Yangchao Chen, Yanghui Chen, Yangxin Chen, Yanhan Chen, Yanhua Chen, Yanjie Chen, Yanjing Chen, Yanli Chen, Yanlin Chen, Yanling Chen, Yanming Chen, Yann-Jang Chen, Yanping Chen, Yanqiu Chen, Yanrong Chen, Yanru Chen, Yanting Chen, Yanyan Chen, Yanyun Chen, Yanzhu Chen, Yanzi Chen, Yao Chen, Yao-Shen Chen, Yaodong Chen, Yaosheng Chen, Yaowu Chen, Yau-Hung Chen, Yaxi Chen, Yayun Chen, Yazhuo Chen, Ye Chen, Ye-Guang Chen, Yeh Chen, Yelin Chen, Yen-Chang Chen, Yen-Chen Chen, Yen-Cheng Chen, Yen-Ching Chen, Yen-Fu Chen, Yen-Hao Chen, Yen-Hsieh Chen, Yen-Jen Chen, Yen-Ju Chen, Yen-Lin Chen, Yen-Ling Chen, Yen-Ni Chen, Yen-Rong Chen, Yen-Teen Chen, Yewei Chen, Yi Chen, Yi Feng Chen, Yi-Bing Chen, Yi-Chun Chen, Yi-Chung Chen, Yi-Fei Chen, Yi-Guang Chen, Yi-Han Chen, Yi-Hau Chen, Yi-Heng Chen, Yi-Hong Chen, Yi-Hsuan Chen, Yi-Hui Chen, Yi-Jen Chen, Yi-Lin Chen, Yi-Ru Chen, Yi-Ting Chen, Yi-Wen Chen, Yi-Yung Chen, YiChung Chen, YiPing Chen, Yian Chen, Yibing Chen, Yibo Chen, Yidan Chen, Yiding Chen, Yidong Chen, Yiduo Chen, Yifa Chen, Yifan Chen, Yifang Chen, Yifei Chen, Yih-Chieh Chen, Yihao Chen, Yihong Chen, Yii-Der Chen, Yii-Der I Chen, Yii-Derr Chen, Yii-der Ida Chen, Yijiang Chen, Yijun Chen, Yike Chen, Yilan Chen, Yilei Chen, Yili Chen, Yilin Chen, Yiming Chen, Yin-Huai Chen, Ying Chen, Ying-Cheng Chen, Ying-Hsiang Chen, Ying-Jie Chen, Ying-Jung Chen, Ying-Lan Chen, Ying-Ying Chen, Yingchun Chen, Yingcong Chen, Yinghui Chen, Yingji Chen, Yingjie Chen, Yinglian Chen, Yingting Chen, Yingxi Chen, Yingying Chen, Yingyu Chen, Yinjuan Chen, Yintong Chen, Yinwei Chen, Yinzhu Chen, Yiru Chen, Yishan Chen, Yisheng Chen, Yitong Chen, Yixin Chen, Yiyin Chen, Yiyun Chen, Yizhi Chen, Yong Chen, Yong-Jun Chen, Yong-Ping Chen, Yong-Syuan Chen, Yong-Zhong Chen, YongPing Chen, Yongbin Chen, Yongfa Chen, Yongfang Chen, Yongheng Chen, Yonghui Chen, Yongke Chen, Yonglu Chen, Yongmei Chen, Yongming Chen, Yongning Chen, Yongqi Chen, Yongshen Chen, Yongshuo Chen, Yongxing Chen, Yongxun Chen, You-Ming Chen, You-Xin Chen, You-Yue Chen, Youhu Chen, Youjia Chen, Youmeng Chen, Youran Chen, Youwei Chen, Yu Chen, Yu-Bing Chen, Yu-Cheng Chen, Yu-Chi Chen, Yu-Chia Chen, Yu-Chuan Chen, Yu-Fan Chen, Yu-Fen Chen, Yu-Fu Chen, Yu-Gen Chen, Yu-Han Chen, Yu-Hui Chen, Yu-Ling Chen, Yu-Ming Chen, Yu-Pei Chen, Yu-San Chen, Yu-Si Chen, Yu-Ting Chen, Yu-Tung Chen, Yu-Xia Chen, Yu-Xin Chen, Yu-Yang Chen, Yu-Ying Chen, Yuan Chen, Yuan-Hua Chen, Yuan-Shen Chen, Yuan-Tsong Chen, Yuan-Yuan Chen, Yuan-Zhen Chen, Yuanbin Chen, Yuanhao Chen, Yuanjia Chen, Yuanjian Chen, Yuanli Chen, Yuanqi Chen, Yuanwei Chen, Yuanwen Chen, Yuanyu Chen, Yuanyuan Chen, Yubin Chen, Yucheng Chen, Yue Chen, Yue-Lai Chen, Yuebing Chen, Yueh-Peng Chen, Yuelei Chen, Yuewen Chen, Yuewu Chen, Yuexin Chen, Yuexuan Chen, Yufei Chen, Yufeng Chen, Yuh-Lien Chen, Yuh-Ling Chen, Yuh-Min Chen, Yuhan Chen, Yuhang Chen, Yuhao Chen, Yuhong Chen, Yuhui Chen, Yujie Chen, Yule Chen, Yuli Chen, Yulian Chen, Yulin Chen, Yuling Chen, Yulong Chen, Yulu Chen, Yumei Chen, Yun Chen, Yun-Ju Chen, Yun-Tzu Chen, Yun-Yu Chen, Yundai Chen, Yunfei Chen, Yunfeng Chen, Yung-Hsiang Chen, Yung-Wu Chen, Yunjia Chen, Yunlin Chen, Yunn-Yi Chen, Yunqin Chen, Yunshun Chen, Yunwei Chen, Yunyun Chen, Yunzhong Chen, Yunzhu Chen, Yupei Chen, Yupeng Chen, Yuping Chen, Yuqi Chen, Yuqin Chen, Yuqing Chen, Yuquan Chen, Yurong Chen, Yushan Chen, Yusheng Chen, Yusi Chen, Yuting Chen, Yutong Chen, Yuxi Chen, Yuxian Chen, Yuxiang Chen, Yuxin Chen, Yuxing Chen, Yuyan Chen, Yuyang Chen, Yuyao Chen, Z Chen, Zan Chen, Zaozao Chen, Ze-Hui Chen, Ze-Xu Chen, Zechuan Chen, Zemin Chen, Zetian Chen, Zexiao Chen, Zeyu Chen, Zhanfei Chen, Zhang-Liang Chen, Zhang-Yuan Chen, Zhangcheng Chen, Zhanghua Chen, Zhangliang Chen, Zhanglin Chen, Zhangxin Chen, Zhanjuan Chen, Zhao Chen, Zhao-Xia Chen, ZhaoHui Chen, Zhaojun Chen, Zhaoli Chen, Zhaolin Chen, Zhaoran Chen, Zhaowei Chen, Zhaoyao Chen, Zhe Chen, Zhe-Ling Chen, Zhe-Sheng Chen, Zhe-Yu Chen, Zhebin Chen, Zhehui Chen, Zhelin Chen, Zhen Bouman Chen, Zhen Chen, Zhen-Hua Chen, Zhen-Yu Chen, Zhencong Chen, Zhenfeng Chen, Zheng Chen, Zheng-Zhen Chen, Zhenghong Chen, Zhengjun Chen, Zhengling Chen, Zhengming Chen, Zhenguo Chen, Zhengwei Chen, Zhengzhi Chen, Zhenlei Chen, Zhenyi Chen, Zhenyue Chen, Zheping Chen, Zheren Chen, Zhesheng Chen, Zheyi Chen, Zhezhe Chen, Zhi Bin Chen, Zhi Chen, Zhi-Hao Chen, Zhi-bin Chen, Zhi-zhe Chen, Zhiang Chen, Zhichuan Chen, Zhifeng Chen, Zhigang Chen, Zhigeng Chen, Zhiguo Chen, Zhihai Chen, Zhihang Chen, Zhihao Chen, Zhiheng Chen, Zhihong Chen, Zhijian Chen, Zhijian J Chen, Zhijing Chen, Zhijun Chen, Zhimin Chen, Zhinan Chen, Zhiping Chen, Zhiqiang Chen, Zhiquan Chen, Zhishi Chen, Zhitao Chen, Zhiting Chen, Zhiwei Chen, Zhixin Chen, Zhixuan Chen, Zhixue Chen, Zhiyong Chen, Zhiyu Chen, Zhiyuan Chen, Zhiyun Chen, Zhizhong Chen, Zhong Chen, Zhongbo Chen, Zhonghua Chen, Zhongjian Chen, Zhongliang Chen, Zhongxiu Chen, Zhongzhu Chen, Zhou Chen, Zhouji Chen, Zhouliang Chen, Zhoulong Chen, Zhouqing Chen, Zhuchu Chen, Zhujun Chen, Zhuo Chen, Zhuo-Yuan Chen, ZhuoYu Chen, Zhuohui Chen, Zhuojia Chen, Zi-Jiang Chen, Zi-Qing Chen, Zi-Yang Chen, Zi-Yue Chen, Zi-Yun Chen, Zian Chen, Zifan Chen, Zihan Chen, Zihang Chen, Zihao Chen, Zihe Chen, Zihua Chen, Zijie Chen, Zike Chen, Zilin Chen, Zilong Chen, Ziming Chen, Zinan Chen, Ziqi Chen, Ziqing Chen, Zitao Chen, Zixi Chen, Zixin Chen, Zixuan Chen, Ziying Chen, Ziyuan Chen, Zoe Chen, Zongming E Chen, Zongnan Chen, Zongyou Chen, Zongzheng Chen, Zugen Chen, Zuolong Chen
articles
Xinchao Guan, Tao Liu, Sili Chen +4 more · 2026 · The Journal of biological chemistry · Elsevier · added 2026-04-24
Fusion genes are pivotal drivers of tumorigenesis, often generating oncogenic chimeric RNAs and fusion circular RNAs. However, the mechanisms by which these transcripts synergistically contribute to c Show more
Fusion genes are pivotal drivers of tumorigenesis, often generating oncogenic chimeric RNAs and fusion circular RNAs. However, the mechanisms by which these transcripts synergistically contribute to cancer progression remain poorly understood. Here, we identified a lung cancer-specific chimeric RNA KANSL1-ARL17A (chKANSARL) and its circular variant fusion circular RNA KANSL1-ARL17 A (F-circKA), both derived from the fusion gene KANSARL. Functional assays revealed that overexpression of either chKANSARL or F-circKA significantly enhanced lung cancer cell proliferation, migration, and invasion, while their knockdown suppressed these malignant phenotypes. In vivo experiments demonstrated that chKANSARL overexpression accelerated tumor growth in immunodeficient mice. Notably, coexpression experiments uncovered a synergistic regulatory interaction between F-circKA and chKANSARL, amplifying oncogenic effects. Mechanistically, miRNA sequencing and dual-luciferase assays revealed that F-circKA acts as a molecular sponge for miR-6860, thereby derepressing chKANSARL expression. Rescue experiments further validated this regulatory axis, wherein miR-6860 inhibition reversed the tumor-suppressive effects of F-circKA knockdown. Collectively, our study identifies and characterizes a novel F-circKA/miR-6860/chKANSARL regulatory axis, revealing how dual transcriptional outputs from the KANSARL fusion gene can synergistically drive lung cancer progression. These findings highlight a previously unrecognized layer of cooperative regulation between linear and circular fusion RNAs in oncogenesis and provide a new framework for understanding fusion gene-mediated tumorigenesis. Show less
📄 PDF DOI: 10.1016/j.jbc.2026.111170
KANSL1
Yanwei Yin, Xiaorong Chen, Chongzeng Bi +1 more · 2026 · Acta psychologica · Elsevier · added 2026-04-24
This study, adopting a person-centered approach and using network analysis, explores latent subtypes of Junzi personality among college students and their links to Receptiveness to Opposing Views, off Show more
This study, adopting a person-centered approach and using network analysis, explores latent subtypes of Junzi personality among college students and their links to Receptiveness to Opposing Views, offering empirical backing for the ancient Chinese idea of "Junzi harmonize yet remain distinct." Traditional variable-centered methods often fail to fully expose the underlying typological structure due to the possible heterogeneous combinations in Junzi personality dimensions. Thus, a person-centered latent profile analysis (LPA) was used to pinpoint typical personality trait patterns. With 1116 college students as participants, the study employed the Junzi Personality Questionnaire Based on Confucian Thought and the Receptiveness to Opposing Views Scale. LPA identified three personality types: The Moderate Type (50%), The Daring-Aggressive Type (15%), and The Virtuously-Accomplished Type (35%). Regression analysis showed significant correlations between gender, age, and personality type, with The Virtuously-Accomplished Type scoring notably higher in Receptiveness to Opposing Views. Network analysis further revealed distinct differences in the network structures of Receptiveness to Opposing Views among the three types: The Moderate Type centered on "derogation of opponents," "refraining from what should not be done," and "respectfulness and propriety"; The Daring-Aggressive Type focused on "conversancy with righteousness and cherishment of benign rule," "derogation of opponents," and "respectfulness and propriety"; while The Virtuously-Accomplished Type highlighted "negative emotions" and "wisdom, benevolence, and courage," with "taboo issues" at the periphery in all datasets. The findings uncover the heterogeneity of Junzi personality and its varied associations with Receptiveness to Opposing Views, providing insights for understanding harmonious interactions in diverse settings. Show less
no PDF DOI: 10.1016/j.actpsy.2026.106577
LPA
Li He, Wen-Wen Yu, Hao-Tian Zheng +4 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Hemodialysis, as one of the main alternative treatment methods for end-stage renal disease, has received much attention in recent years. Due to the particularity of hemodialysis treatment, patients ha Show more
Hemodialysis, as one of the main alternative treatment methods for end-stage renal disease, has received much attention in recent years. Due to the particularity of hemodialysis treatment, patients have a relatively high risk of infection during the treatment process. Hemodialysis nurses, who are the main executors of the treatment operations and have the most contact with patients, have a close relationship with the infection risk of patients. The level of their hospital infection prevention and control literacy is closely related to the infection risk of patients. To explore the current level of knowledge, attitudes, and practices (KAP) of hospital infection prevention and control among haemodialysis nurses in the Sichuan Province, China, and identified their potential categories. This provided evidence-based recommendations for improving infection control management in hemodialysis departments. A cross-sectional study was conducted From July 15 to August 15, 2025 using a convenience sampling method to survey 470 hemodialysis nurses from 78 hospitals in Sichuan Province. Participants were licensed nurses with over 3 months of hemodialysis experience. Data were collected using the A total of 460 valid questionnaires were collected, with an effective response rate of 97.87%. The average scores for knowledge, attitudes, and practices related to hospital infection prevention and control among haemodialysis nurses were 4.67 ± 0.43, 4.59 ± 0.43, and 4.74 ± 0.34, respectively. Three latent profile models were constructed, with the two-class model identified as the optimal solution, which were defined as the "Low KAP Group" (25.9%) and "High KAP Group" (74.1%). Logistic regression analysis revealed that sex, responsibility for infection control, hospital level, annual number of infection control training sessions, organizational support, and work engagement were significant influencing factors ( The KAP level of haemodialysis nurses in hospital infection prevention and control was relatively high. Hospital managers should tailor supportive work environments on the basis of the individual characteristics and work engagement of haemodialysis nurses to improve the KAP level of nosocomial infection prevention and control among haemodialysis nurses. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1734891
LPA
Dehao Yang, Shiyue Wang, Yangguang Lu +8 more · 2026 · Alzheimer's research & therapy · BioMed Central · added 2026-04-24
The clinical interpretation of Alzheimer's disease (AD) is frequently complicated by the prevalence of missense variants designated as being of uncertain significance within associated genes. Conventi Show more
The clinical interpretation of Alzheimer's disease (AD) is frequently complicated by the prevalence of missense variants designated as being of uncertain significance within associated genes. Conventional computational prediction tools often overlook disease-specific pathophysiological contexts and lack pertinence and interpretability. Therefore, the present study aimed to develop a novel, interpretable framework for predicting the pathogenicity of AD missense variants by integrating transcriptomic and proteomic data enrichment patterns with machine learning methods. A cross-sectional variant-level analysis was performed using publicly available databases. Missense variants in APOE, APP, PSEN1, PSEN2, SORL1, and TREM2 reported in AD patients were retrieved from Alzforum and compared with missense variants from individuals without neurological diseases, as cataloged in the gnomAD v2.1.1 non-neuro subset. Variants were annotated with tissue-specific expression, secondary structure, relative solvent accessibility, and other functional features using tools like AlphaFold. Enrichment of specific features was assessed with Fisher's exact tests with Bonferroni correction for multiple comparisons. Given that PSEN1 showed the strongest enrichment signals, six machine-learning algorithms were trained on PSEN1 variants to distinguish AD-associated variants from gnomAD variants, using a 10 × 5 nested cross-validation scheme. External validation was conducted using PSEN1 missense variants from ClinVar annotated as pathogenic/likely pathogenic or benign/likely benign. Model performance was compared with SIFT and PolyPhen-2, and interpretability was evaluated by feature ablation and SHapley Additive exPlanations analyses. AD-associated variants exhibited statistically significant enrichment within some transcriptomic or proteomic features, with PSEN1 contributing significantly to the enrichment observed across these features. Random forest and gradient boosting models achieved high performance in the internal training dataset and maintained high recall in the external validation dataset, outperforming SIFT and approaching the performance of PolyPhen-2. Relative solvent accessibility was the most discriminative individual feature, while regional and topological features provided complementary discriminative power. This integrative, multi-omics framework links disease-specific enrichment patterns with interpretable gene-level machine learning for AD missense variants. The results highlight the importance of expression level, structural context, etc. for PSEN1 variant pathogenicity and may help prioritize variants for functional studies. Further validation in additional genes and independent cohorts is warranted prior to any clinical application. Show less
📄 PDF DOI: 10.1186/s13195-025-01950-0
APOE
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
Wei Xia, Nan Shi, Yongjing Lai +12 more · 2026 · Nature communications · Nature · added 2026-04-24
Rodents are widely used in immunology but do not always recapitulate human immune functions. The tree shrew (Tupaia belangeri) is phylogenetically closer to primates than rodents and may help bridge t Show more
Rodents are widely used in immunology but do not always recapitulate human immune functions. The tree shrew (Tupaia belangeri) is phylogenetically closer to primates than rodents and may help bridge this gap, yet its immune system has not been comprehensively characterised at single-cell resolution. Here, we present a single-cell transcriptomic atlas of the tree shrew immune system, profiling 39 cell types across 12 tissues. We uncover human-like tonsillar structures and two transcriptionally distinct splenic macrophage subsets: an NR1H3 Show less
no PDF DOI: 10.1038/s41467-026-71218-0
NR1H3
Meimei Chen, Ruina Huang, Zhaoyang Yang · 2026 · Nan fang yi ke da xue xue bao = Journal of Southern Medical University · added 2026-04-24
To investigate the causal relationship between inflammatory proteins and Alzheimer's disease (AD) and the mediating role of plasma metabolites therein. Using Mendelian mandomization (MR) methods and p Show more
To investigate the causal relationship between inflammatory proteins and Alzheimer's disease (AD) and the mediating role of plasma metabolites therein. Using Mendelian mandomization (MR) methods and publicly available genome-wide association study (GWAS) data, we selected 91 single nucleotide polymorphisms (SNPs) that were strongly linked to inflammatory proteins without reverse causality with AD as the outcome. A bidirectional two-sample MR analysis was performed. Inflammatory proteins with causal links to AD were identified via inverse variance weighted (IVW) analysis. A mediation MR analysis was then performed using 1400 plasma metabolites to assess their mediating role in this causal pathway. The preliminary bidirectional MR analysis identified 3 inflammatory proteins that had a potential positive causal association with AD without reverse causality: Axin-1, C-X-C motif chemokine ligand 11 (CXCL11), and interleukin-12β (IL-12β). Elevated levels of Axin-1 were positively causally associated with AD risk (OR=1.082, 95% This study reveals how specific inflammatory proteins influence AD risk via plasma metabolites and provides genetic evidence for inflammatory-metabolic interactions in AD to facilitate the identification of potential biomarkers and targets for early detection and intervention of AD. Show less
no PDF DOI: 10.12122/j.issn.1673-4254.2026.02.05
AXIN1
Chao-Yun Cheng, Yih-Jer Wu, Chih-Fan Yeh +25 more · 2026 · Journal of the Formosan Medical Association = Taiwan yi zhi · Elsevier · added 2026-04-24
Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic Show more
Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve disease (CAVD). Structurally composed of a low-density lipoprotein (LDL)-like particle covalently linked to apolipoprotein(a) [apo(a)], Lp(a) exhibits unique atherogenic, thrombogenic, and inflammatory properties, largely due to its role as a carrier of oxidized phospholipids (OxPL). Plasma Lp(a) concentrations are predominantly determined by the number of kringle IV type 2 (KIV-2) repeats in the LPA gene, with minimal influence from lifestyle or environmental factors. Despite substantial evidence linking elevated Lp(a) to cardiovascular risk, clinical testing remains underutilized, especially in East Asian countries. In Taiwan, although population-level Lp(a) concentrations are comparatively low, a significant subset exceeds risk thresholds, with local studies confirming its prognostic value in coronary artery disease and ischemic stroke. Barriers, including limited physician awareness, implementation barriers, and therapeutic nihilism, contribute to its under-recognition. This review highlights the molecular features of Lp(a), its pathogenesis of cardiovascular disorders, epidemiology, and current barriers and future advances in diagnostic testing, with a particular focus on implications for cardiovascular risk management in Taiwan. Show less
no PDF DOI: 10.1016/j.jfma.2026.03.073
LPA
Rong Chen, Qixin Xiao, Gesang Pingcuo +3 more · 2026 · Acta psychologica · Elsevier · added 2026-04-24
Previous research has suggested that high levels of internet use are associated with lower levels of physical activity. However, recent studies have yielded mixed findings. First, we aim to explore th Show more
Previous research has suggested that high levels of internet use are associated with lower levels of physical activity. However, recent studies have yielded mixed findings. First, we aim to explore the prevalence of internet addiction and sedentary behavior among college students. Second, we examine the relationship between sedentary behavior and body composition. Additionally, we employ latent profile analysis (LPA) to identify subgroups of internet addiction profiles and to explore the associations between these latent profiles and sedentary behavior. This cross-sectional study examined the relationship between sedentary behavior, internet addiction, and body composition among 369 Chinese college students. Sedentary behavior was assessed via self-reported sitting time, internet addiction was measured using a standardized questionnaire, and body composition was evaluated with the InBody 120 device. LPA, an individual-centered method, was used to identify homogeneous subgroups of internet addiction. 42.3 % of students exhibited internet addiction and 72.6 % reported ≥6 h of daily sitting. LPA revealed two distinct profiles of internet addiction-"Regular" (57.2 %) and "Internet addiction" (42.8 %)-highlighting its heterogeneous nature. The findings suggest that age (p = 0.296), gender (p = 0.304), and sedentary time (p = 0.954) may not be the primary factors contributing to these profiles. Policymakers and campus health programs should tailor interventions to distinct internet addiction subgroups. Further research is needed to examine psychological, behavioral, and social contributors, as well as long-term effects. Show less
no PDF DOI: 10.1016/j.actpsy.2025.106027
LPA
Xia Li, Fengling Yang, Xingyu Chen +2 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
This study employs latent profile analysis (LPA) to identify potential categories of nurse burnout and to analyze differences in characteristics and influencing factors across burnout categories. From Show more
This study employs latent profile analysis (LPA) to identify potential categories of nurse burnout and to analyze differences in characteristics and influencing factors across burnout categories. From June to August 2025, a mixed sampling approach combining convenience and snowball sampling was used to recruit nurses from hospitals of varying levels in Southwest China. Three tools were used for data collection: A self-designed routine information questionnaire, Maslach Burnout Inventory-General Survey (MBI-GS) and Practice Environment Scale of the Nursing Work Index (PES-NWI), LPA identifies potential categories of nurses' professional burnout and uses multivariate logistic regression analysis to explore the factors associated with these categories. This study comprised a total of 809 participants. LPA identified four distinct latent classes of nursing burnout: Class 1, low-burnout-high-efficacy (11.5%); Class 2, mild-burnout-unfulfilled (33.9%); Class 3, moderate-burnout-exhausted (44.6%); and Class 4, severe-burnout-dysfunctional (10.0%). Multivariate logistic regression analysis showed that age, years of work experience, hospital level, nurses' participation in hospital management, nursing quality standards, staffing and resource adequacy, and medical care cooperation are significant predictors of burnout among nurses ( Nurse burnout in southwest China is mainly moderate to severe and exhibits distinctive characteristics. It is recommended to implement personalized interventions tailored to the specific characteristics of nurses' professional burnout to alleviate the situation. Particular attention should be given to nurses with fewer than five years of experience by providing enhanced job support and psychological assistance to help them navigate critical periods of professional burnout. These measures aim to safeguard nurses' physical and mental health, improving the overall quality of nursing, and promoting the healthy development of global medical care. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1764970
LPA
Gary Chen, Adrienne Sexton · 2026 · Patient education and counseling · Elsevier · added 2026-04-24
This scoping review aims to map the experiences and outcomes of patients and their families undergoing genetic testing and counseling regarding dementia to inform future research directions and clinic Show more
This scoping review aims to map the experiences and outcomes of patients and their families undergoing genetic testing and counseling regarding dementia to inform future research directions and clinical practice. Rigorous scoping review methodology was followed. Ovid Medline, Embase, PsycINFO, and CINAHL were searched with keywords and MeSH terms related to "genetic testing", "genetic counseling", "dementia", "decision making", and "patient outcomes" for peer-reviewed studies with adult participants published over the last ten years. Thirty-six articles met inclusion criteria. Narrative synthesis organized findings into temporal categories including motivations for genetic testing, experiences during the testing/counseling process, and outcomes after testing. Common motivators included reducing uncertainty, reproductive planning, life planning, and the prospect of a treatment becoming available in the future. A lack of current treatments and fear that knowledge of genetic risk would be difficult to cope with were common barriers to testing. Patient-centered communication improved satisfaction. Genetic testing was generally psychologically well tolerated, and a wide range of practical responses were reported including changes to lifestyle, diet, advanced care and financial planning, and engaging in clinical trials. This review maps the experiences and outcomes of genetic testing or counseling for people with or at potentially increased genetic risk of dementia. Genetic testing and counseling for directly causal dementia genes and APOE genotype appears well tolerated but long-term outcome data is lacking. Motivations, concerns and perceived benefits of knowing genetic results vary depending on personal, familial and cultural viewpoints. Genetic counseling can help patients and families prepare, reduce decisional regret, and adapt to results. Motivations varied, and a patient-centered approach addressing both information and psychological aspects improves satisfaction. Future longitudinal research should ascertain ways to support individuals from a wide range of demographics with understanding and adjusting to genetic risk information regarding dementia. Show less
no PDF DOI: 10.1016/j.pec.2025.109424
APOE
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
Xi Cheng, Shuzhen Zhao, Mingyi Du +4 more · 2026 · Cytokine · Elsevier · added 2026-04-24
Angiopoietin-like 4 (ANGPTL4) is a hepatokine involved in metabolism and inflammation and has been implicated in oncogenesis, yet its relationship with cancer risk in humans remains unclear. We analyz Show more
Angiopoietin-like 4 (ANGPTL4) is a hepatokine involved in metabolism and inflammation and has been implicated in oncogenesis, yet its relationship with cancer risk in humans remains unclear. We analyzed 35,716 cancer-free UK Biobank participants with baseline plasma ANGPTL4. Multivariable Cox models and restricted cubic splines assessed associations with 24 site-specific incident cancers; bidirectional two-sample Mendelian randomization (MR) evaluated causality. During a median follow-up of 12.5 years, 9304 incident cancer cases occurred. Compared with the lowest quartile (Q1), the higher quartiles (Q2, Q3, and Q4) of ANGPTL4 levels were significantly associated with the risks of ten cancers, including cancers of the bladder, breast, cervix uteri, colorectum/anus, esophagus, kidney, liver, mesothelial/soft tissues, multiple myeloma, and ovary (hazard ratios ranging from 1.02 to 3.98). Risks generally increased across ANGPTL4 quartiles, and spline analyses supported approximately linear dose-response patterns. Adding ANGPTL4 to an age-sex model improved discrimination across several sites (ΔC-index 0-0.071), with statistical significance observed only for breast cancer. Associations were directionally consistent but heterogeneous by age, sex, and BMI. Forward MR provided no evidence that genetically proxied ANGPTL4 causally increases cancer risk. In reverse MR, genetic liability to liver cancer showed a nominal positive association with circulating ANGPTL4, suggesting ANGPTL4 may be elevated as part of tumor-related biology. Higher circulating ANGPTL4 is associated with increased risk of multiple cancers, with sex-and tissue-specific heterogeneity. Although MR does not support a universal causal role, ANGPTL4 remains a promising pan-cancer biomarker for risk stratification and early prevention. Show less
no PDF DOI: 10.1016/j.cyto.2025.157089
ANGPTL4
Weiwei Xiang, Hua Ke, Xiaojia Song +10 more · 2026 · BMC women's health · BioMed Central · added 2026-04-24
This study aims to examine the health characteristics of female sex workers (FSWs) in entertainment venues and to investigate the relationship between these characteristics and sleep quality. This stu Show more
This study aims to examine the health characteristics of female sex workers (FSWs) in entertainment venues and to investigate the relationship between these characteristics and sleep quality. This study employed a cross-sectional design and was conducted from January to April 2024 in Wuhan, China. Participants were FSWs recruited through snowball sampling from entertainment venues, including hotels, restaurants, nightclubs, karaoke bars and dance halls. Data were collected via structured questionnaires covering sociodemographic information, work experience, psychological stress, health status, sleep quality and circadian rhythms. Latent profile analysis (LPA) was employed to identify health characteristic profiles among FSWs, and multivariate logistic regression was used to examine the associations between these profiles and sleep quality. Among the 1,036 FSWs surveyed, 45.1% had poor sleep quality. LPA classified FSWs’ health characteristics into three profiles: the high overall functioning group, the lower physical–emotional functioning group and the lower psychosocial functioning group. Multivariate logistic regression analysis showed that FSWs in the lower physical–emotional functioning group had higher odds of poor sleep quality (OR = 2.184) compared with those in the high overall functioning group. FSWs in the lower psychosocial functioning group had substantially higher odds of poor sleep quality (OR = 7.755) than that in the high overall functioning group. FSWs demonstrate substantial heterogeneity in health characteristics and exhibit lower overall sleep quality compared with the general population. Psychological and physiological factors are major influencing factors for their sleep quality, suggesting the importance of prioritising mental and physical health in this population. Show less
📄 PDF DOI: 10.1186/s12905-026-04346-w
LPA
Zhongshan Cheng, Sung-Liang Yu, Chih-Hsiang Yu +19 more · 2026 · Scientific reports · Nature · added 2026-04-24
The international consensus classification or the World Health Organization classifications underrepresented driver alterations enriched in pediatric acute myeloid leukemia (AML). To address this, we Show more
The international consensus classification or the World Health Organization classifications underrepresented driver alterations enriched in pediatric acute myeloid leukemia (AML). To address this, we retrospectively characterized the genomic landscape of 105 pediatric patients with AML of East Asian ancestry using transcriptome and whole-exome sequencing (WES). In addition to the common recurrent fusions such as RUNX1::RUNX1T1 and CBFB::MYH11, we identified rearrangements involving KMT2A, NUP98, GLIS, as well as FLT3 and UBTF tandem duplications. The median somatic mutation rate in AML was 0.97 per megabase, as estimated by WES. Frequently mutated pathways included signaling: 68.6% (72/105), transcription: 37.1% (39/105), epigenetic regulation: 26.7% (28/105), cohesin: 7.6% (8/105), RNA binding: 3.8% (4/105), and protein modification: 5.7% (6/105). When analyzed together, high-risk genetic subtypes including GLISr, UBTF tandem duplications, PICALM::MLLT10, and HOXr were significantly associated with poorer 5 year overall survival (OS) in multivariable analysis (p-value = 0.037). Although FLT3 internal tandem duplications were significantly associated with inferior 5 year OS in univariable analysis, this effect was not significant in multivariable analysis (p-value = 0.382). Patients with RUNX1 mutations had inferior 5 year OS in multivariable analysis (p-value = 0.009). These findings suggest specific genomic alterations that may refine risk stratification and guide future therapeutic protocols in Taiwanese pediatric patients with AML. Show less
📄 PDF DOI: 10.1038/s41598-025-34152-7
MLLT10
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
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
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
Yu-Wei Liu, Chi-Jen Wu, Kai-Fu Chang +16 more · 2026 · Journal of Cancer · added 2026-04-24
Obstructive sleep apnea (OSA) is characterized by recurrent intermittent hypoxia (IH) and has been increasingly associated with lung cancer incidence and mortality. However, how IH-related biological Show more
Obstructive sleep apnea (OSA) is characterized by recurrent intermittent hypoxia (IH) and has been increasingly associated with lung cancer incidence and mortality. However, how IH-related biological programs relate to immune remodeling, stemness-associated phenotypes, and therapeutic resistance in lung cancer remains incompletely understood. We integrated single-cell RNA sequencing data from IH-exposed murine lung tissues (GSE301350) with bulk transcriptomic datasets from TCGA-LUAD and GSE31210 to examine hypoxia-associated cellular and transcriptional patterns. Stemness was quantified using CytoTRACE and transcriptome-based stemness scoring, and its associations with immune infiltration, immune checkpoint expression, TIDE scores, predicted drug sensitivity, and immunotherapy response were evaluated. A stemness-based prognostic model was constructed using LASSO Cox regression and validated in independent cohorts. Single-cell analysis revealed marked immune remodeling under intermittent hypoxia (IH), including expansion of effector T cells, and monocytes/macrophages, populations alongside reduced B cells and dendritic cells. In human LUAD cohorts, stemness-high tumors were associated with mitochondrial and metabolic stress-related transcriptional programs, and increased expression of immune checkpoint genes (PD-1, PD-L1, CTLA4, LAG3). Elevated stemness scores correlated with higher TIDE scores, poorer overall survival, and reduced predicted responsiveness to immunotherapy. LASSO modeling identified a six-gene stemness signature (EIF5A, MELTF, SEMA3C, CPS1, TCN1, SELENOK), that consistently stratified patients into high- and low-risk groups across TCGA and GSE31210 cohorts. Multivariate Cox regression confirmed the risk score as an independent prognostic factor. Drug sensitivity analyses further suggested that stemness-high tumors may exhibit increased susceptibility to selected kinase inhibitors (Dasatinib, A-770041) and metabolic modulators (Phenformin, Salubrinal). OSA-associated IH is linked to stemness-associated transcriptional plasticity, immune suppression, and adverse clinical outcomes in lung cancer. The identified stemness-based gene signature provides a robust prognostic biomarker and highlights potential therapeutic vulnerabilities, supporting integrative strategies that combine stemness and immune -targeted approaches with immunotherapy in OSA-associated lung cancer. Show less
📄 PDF DOI: 10.7150/jca.126708
CPS1
Jun An, Sihui Wu, Kexin Guo +4 more · 2026 · American journal of medical genetics. Part A · Wiley · added 2026-04-24
Molecular genetic testing was performed on a fetus with ectrodactyly of the right foot to clarify the pathogenic cause and provide evidence for prenatal counseling. Trio whole-exome sequencing (trio-W Show more
Molecular genetic testing was performed on a fetus with ectrodactyly of the right foot to clarify the pathogenic cause and provide evidence for prenatal counseling. Trio whole-exome sequencing (trio-WES) was performed on the fetus and his parents to identify the underlying genetic cause. Candidate variants were validated by Sanger sequencing, and their molecular effects were analyzed through minigene assays. Trio-WES identified a novel heterozygous variant (c.1977+1G>C) in FGFR1, which is consistent with FGFR1-related Hartsfield syndrome (HS; OMIM#615465). Sanger sequencing confirmed that this variant was de novo. The minigene assay revealed that all variants (c.1977+1G>C, c.1977+1G>A, and c.1977+1G>T) at the splice site generated two aberrant splicing events: (1) complete retention of intron 14, leading to a frameshift and premature termination codon; and (2) skipping of exon 14, causing an in-frame deletion of 41 amino acids. These events collectively impaired the function of the FGFR1 protein's tyrosine kinase domain. To our knowledge, prenatal reports of FGFR1-related HS remain extremely limited, and this is the first molecularly confirmed prenatal diagnosis of HS in China. The findings not only expand the mutational spectrum of HS but also provide genetic counseling and reproductive guidance for this family. Show less
no PDF DOI: 10.1002/ajmg.a.64226
FGFR1
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
Ying Hou, Xin Zhang, Xia Sun +4 more · 2026 · Arteriosclerosis, thrombosis, and vascular biology · added 2026-04-24
Lipid-lowering therapy is a cornerstone in the treatment of atherosclerotic cardiovascular diseases. Although some lipid-lowering drugs have demonstrated positive effects in patients with atherosclero Show more
Lipid-lowering therapy is a cornerstone in the treatment of atherosclerotic cardiovascular diseases. Although some lipid-lowering drugs have demonstrated positive effects in patients with atherosclerotic cardiovascular diseases, their effects are limited in those with homozygous familial hypercholesterolemia. It is essential to seek new lipid-lowering targets. YAP (Yes-associated protein) may be involved in lipid metabolism in the liver; therefore, we investigated the function of hepatocyte YAP in hyperlipidemia and atherosclerosis. Hyperlipidemia models were generated in apoE knockout (apoE High-cholesterol diet-fed apoE Taken together, our findings revealed a novel role for the YAP-TEAD4-ANGPTL3 axis in lipid metabolism independent of LDLR. Inhibition of hepatocyte YAP may be an effective lipid-lowering strategy for homozygous familial hypercholesterolemia. Show less
no PDF DOI: 10.1161/ATVBAHA.125.324122
APOE
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
Ya Wang, Jinyi Fu, Jingyi Zhan +7 more · 2026 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Atherosclerosis (AS) is a central pathological driver underlying most cardiovascular diseases. Gut microbiota and related metabolites participate in regulating atherosclerosis. Fifty C57BL/6J ApoE Ath Show more
Atherosclerosis (AS) is a central pathological driver underlying most cardiovascular diseases. Gut microbiota and related metabolites participate in regulating atherosclerosis. Fifty C57BL/6J ApoE Atherosclerotic plaques accumulated in the aorta and aortic sinus after HFD, while statin and high-dose GP alleviated this burden. TC, TG, LDL-C, MCP-1, MCP-3 and IL-2 showed significant increase after HFD, while statin and GP decreased LDL-C, MCP-1 and MCP-3. The goblet cells, ZO-1 and Occludin decreased after HFD, while statin and GP increased them, indicating that the intestinal barrier integrity was improved. Additionally, the composition of gut microbiota was modulated by GP. Some candidate taxa were identified, such as This study suggests that GP is beneficial for alleviating atherosclerosis in HFD-induced ApoE Show less
📄 PDF DOI: 10.3389/fcvm.2026.1773819
APOE
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
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
Peijun Tian, Renying Zou, Linhong Song +7 more · 2026 · Food & function · Royal Society of Chemistry · added 2026-04-24
Correction for 'Ingestion of
📄 PDF DOI: 10.1039/d6fo90015a
BDNF correction ingestion
Vichuda Charoensaensuk, Bor-Ren Huang, Shiang-Suo Huang +10 more · 2026 · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · Elsevier · added 2026-04-24
Lumbrokinase belongs to a group of fibrinolytic enzymes, particularly tissue plasminogen activator (tPA), which can facilitate the proteolytic maturation of brain-derived neurotrophic factor (BDNF). D Show more
Lumbrokinase belongs to a group of fibrinolytic enzymes, particularly tissue plasminogen activator (tPA), which can facilitate the proteolytic maturation of brain-derived neurotrophic factor (BDNF). Drugs administered via oral or intravenous routes are often metabolized in the liver or kidneys, and these delivery methods for brain-targeted therapies must overcome the natural barriers of the central nervous system (CNS). Intranasal drug delivery via the nose-to-brain route has emerged as a promising approach to bypass these barriers, enhance drug penetration into the brain, and minimize exposure to peripheral organs. In this study, we demonstrate that intranasally administered lumbrokinase successfully reached the brain. Behaviorally, lumbrokinase significantly improved chronic social defeat stress (CSDS)-induced social avoidance and cognitive impairments. At the molecular level, CSDS increased hippocampal precursor BDNF (proBDNF) expression and reduced mature BDNF (mBDNF) compared with control mice. Importantly, lumbrokinase treatment promoted the expression of tPA and plasmin, thereby restoring the proBDNF/mBDNF balance in the hippocampus and reversing stress-induced maladaptive behaviors. Additionally, lumbrokinase increased TrkB, PSD95, and enhanced phosphorylation of PI3K, AKT, and mTOR in the hippocampus, indicating improved synaptic signaling and plasticity. In conclusion, this study demonstrates that intranasal delivery enables lumbrokinase to reach the brain effectively, providing robust therapeutic benefits against CSDS-induced behavioral and cognitive deficits. Enhancing plasmin-mediated BDNF maturation through non-invasive intranasal enzyme delivery may represent a promising approach for treating stress-related mood disorders. Show less
no PDF DOI: 10.1016/j.biopha.2026.119024
BDNF bdnf cns fibrinolytic enzymes intranasal drug delivery lumbrokinase neurotrophic factor tissue plasminogen activator
Tao Ding, Jing Zhang, Xue Jiang +1 more · 2026 · International journal of psychiatry in medicine · SAGE Publications · added 2026-04-24
ObjectiveTo evaluate the effects of a combined psychological and functional exercise intervention on emotion, quality of life, and brain-derived neurotrophic factor (BDNF) levels in patients with Park Show more
ObjectiveTo evaluate the effects of a combined psychological and functional exercise intervention on emotion, quality of life, and brain-derived neurotrophic factor (BDNF) levels in patients with Parkinson's disease (PD).MethodsIn this randomized controlled trial, 172 patients with PD were randomly assigned into 2 groups with 86 patients in each group. The control group received routine care, while the intervention group received a 12-week intervention combining psychological support with functional exercise in addition to routine care. Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Parkinson's Disease Questionnaire-39 (PDQ-39), Barthel Index, Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), and serum BDNF levels were assessed before and after the intervention. Adherence rates were also determined for each group. Spearman correlation analysis was used to examine associations between changes in BDNF (ΔBDNF) and changes in HAMA (ΔHAMA) and HAMD (ΔHAMD) scores.ResultsAt the end of the 12-week clinical trial, the intervention group demonstrated significantly lower HAMA, HAMD, PDQ-39, and MDS-UPDRS scores ( Show less
no PDF DOI: 10.1177/00912174261422307
BDNF brain-derived neurotrophic factor exercise neurology neuroscience parkinson's disease psychology rehabilitation
Yong Chen, Yanchao Zhang, Shen Rui +3 more · 2026 · iScience · Elsevier · added 2026-04-24
Atherosclerosis (AS), a chronic inflammatory disorder initiated by vascular endothelial dysfunction (ED), is prominently triggered by hemodynamic low-shear stress (LSS). Interferon regulatory factor 6 Show more
Atherosclerosis (AS), a chronic inflammatory disorder initiated by vascular endothelial dysfunction (ED), is prominently triggered by hemodynamic low-shear stress (LSS). Interferon regulatory factor 6 (IRF6) is a transcription factor that regulates the inflammatory response following injury. In this work, the LSS-induced AS model was induced by the partial ligation of the left carotid artery in high-fat diet-fed ApoE Show less
📄 PDF DOI: 10.1016/j.isci.2026.115127
APOE