👤 Mei-Hua 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-Jie Chen, Mei-Ling Chen, Mei-Ru Chen, Meilan Chen, Meilin Chen, Meiling Chen, Meimei Chen, Meiting Chen, Meiyang Chen, Meiyu Chen, Meizhen Chen, Meng Chen, Meng Xuan Chen, Meng-Lin Chen, Meng-Ping Chen, Mengdi Chen, Menglan Chen, Mengling Chen, Mengping Chen, Mengqing Chen, Mengting Chen, Mengxia Chen, Mengyan Chen, Mengying Chen, Mian-Mian Chen, Miao Chen, Miao-Der Chen, Miao-Hsueh Chen, Miao-Yu Chen, Miaomiao Chen, Miaoran Chen, Michael C Chen, Michelle Chen, Mien-Cheng Chen, Min Chen, Min-Hsuan Chen, Min-Hu Chen, Min-Jie Chen, Ming Chen, Ming-Fong Chen, Ming-Han Chen, Ming-Hong Chen, Ming-Huang Chen, Ming-Huei Chen, Ming-Yu Chen, Mingcong Chen, Mingfeng Chen, Minghong Chen, Minghua Chen, Minglang Chen, Mingling Chen, Mingmei Chen, Mingxia Chen, Mingxing Chen, Mingyang Chen, Mingyi Chen, Mingyue Chen, Minjian Chen, Minjiang Chen, Minjie Chen, Minyan Chen, Mo Chen, Mu-Hong Chen, Muh-Shy Chen, Mulan Chen, Mystie X Chen, Na Chen, Naifei Chen, Naisong Chen, Nan Chen, Ni Chen, Nian-Ping Chen, Ning Chen, Ning-Bo Chen, Ning-Hung Chen, Ning-Yuan Chen, Ningbo Chen, Ningning Chen, Nuan Chen, On Chen, Ou Chen, Ouyang Chen, P P Chen, Pan Chen, Paul Chih-Hsueh Chen, Pei Chen, Pei-Chen Chen, Pei-Chun Chen, Pei-Lung Chen, Pei-Yi Chen, Pei-Yin Chen, Pei-zhan Chen, Peihong Chen, Peipei Chen, Peiqin Chen, Peixian Chen, Peiyou Chen, Peiyu Chen, Peize Chen, Peizhan Chen, Peng Chen, Peng-Cheng Chen, Pengxiang Chen, Ping Chen, Ping-Chung Chen, Ping-Kun Chen, Pingguo Chen, Po-Han Chen, Po-Ju Chen, Po-Min Chen, Po-See Chen, Po-Sheng Chen, Po-Yu Chen, Qi Chen, Qi-An Chen, Qian Chen, Qianbo Chen, Qianfen Chen, Qiang Chen, Qiangpu Chen, Qiankun Chen, Qianling Chen, Qianming Chen, Qianping Chen, Qianqian Chen, Qianxue Chen, Qianyi Chen, Qianyu Chen, Qianyun Chen, Qianzhi Chen, Qiao Chen, Qiao-Yi Chen, Qiaoli Chen, Qiaoling Chen, Qichen Chen, Qifang Chen, Qihui Chen, Qili Chen, Qinfen Chen, Qing Chen, Qing-Hui Chen, Qing-Juan Chen, Qing-Wei Chen, Qingao Chen, Qingchao Chen, Qingchuan Chen, Qingguang Chen, Qinghao Chen, Qinghua Chen, Qingjiang Chen, Qingjie Chen, Qingliang Chen, Qingmei Chen, Qingqing Chen, Qingqiu Chen, Qingshi Chen, Qingxing Chen, Qingyang Chen, Qingyi Chen, Qinian Chen, Qinsheng Chen, Qinying Chen, Qiong Chen, Qiongyun Chen, Qiqi Chen, Qitong Chen, Qiu Jing Chen, Qiu-Jing Chen, Qiu-Sheng Chen, Qiuchi Chen, Qiuhong Chen, Qiujing Chen, Qiuli Chen, Qiuwen Chen, Qiuxia Chen, Qiuxiang Chen, Qiuxuan Chen, Qiuyun Chen, Qiwei Chen, Qixian Chen, Qu Chen, Quan Chen, Quanjiao Chen, Quanwei Chen, Qunxiang Chen, R Chen, Ran Chen, Ranyun Chen, Ray-Jade Chen, Ren-Hui Chen, Renjin Chen, Renwei Chen, Renyu Chen, Robert Chen, Roger Chen, Rong Chen, Rong-Hua Chen, Rongfang Chen, Rongfeng Chen, Rongrong Chen, Rongsheng Chen, Rongyuan Chen, Roufen Chen, Rouxi Chen, Ru Chen, Rucheng Chen, Ruey-Hwa Chen, Rui Chen, Rui-Fang Chen, Rui-Min Chen, Rui-Pei Chen, Rui-Zhen Chen, Ruiai Chen, Ruibing Chen, Ruijing Chen, Ruijuan Chen, Ruilin Chen, Ruimin Chen, Ruiming Chen, Ruiqi Chen, Ruisen Chen, Ruixiang Chen, Ruixue Chen, Ruiying Chen, Rujun Chen, Runfeng Chen, Runsen Chen, Runsheng Chen, Ruofan Chen, Ruohong Chen, Ruonan Chen, Ruoyan Chen, Ruoying Chen, S Chen, S N Chen, S Pl Chen, S-D Chen, Sai Chen, San-Yuan Chen, Sean Chen, Sen Chen, Shali Chen, Shan Chen, Shanchun Chen, Shang-Chih Chen, Shang-Hung Chen, Shangduo Chen, Shangsi Chen, Shangwu Chen, Shangzhong Chen, Shanshan Chen, Shanyuan Chen, Shao-Ke Chen, Shao-Peng Chen, Shao-Wei Chen, Shao-Yu Chen, Shao-long Chen, Shaofei Chen, Shaohong Chen, Shaohua Chen, Shaokang Chen, Shaokun Chen, Shaoliang Chen, Shaotao Chen, Shaoxing Chen, Shaoze Chen, Shasha Chen, She Chen, Shen Chen, Shen-Ming Chen, Sheng Chen, Sheng-Xi Chen, Sheng-Yi Chen, Shengdi Chen, Shenghui Chen, Shenglan Chen, Shengnan Chen, Shengpan Chen, Shengyu Chen, Shengzhi Chen, Shi Chen, Shi-Qing Chen, Shi-Sheng Chen, Shi-Yi Chen, Shi-You Chen, Shibo Chen, Shih-Jen Chen, Shih-Pin Chen, Shih-Yin Chen, Shih-Yu Chen, Shilan Chen, Shiming Chen, Shin-Wen Chen, Shin-Yu Chen, Shipeng Chen, Shiqian Chen, Shiqun Chen, Shirui Chen, Shiuhwei Chen, Shiwei Chen, Shixuan Chen, Shiyan Chen, Shiyao Chen, Shiyi Chen, Shiyu Chen, Shou-Tung Chen, Shoudeng Chen, Shoujun Chen, Shouzhen Chen, Shu Chen, Shu-Fen Chen, Shu-Gang Chen, Shu-Hua Chen, Shu-Jen Chen, Shuai Chen, Shuai-Bing Chen, Shuai-Ming Chen, Shuaijie Chen, Shuaijun Chen, Shuaiyin Chen, Shuaiyu Chen, Shuang Chen, Shuangfeng Chen, Shuanghui Chen, Shuchun Chen, Shuen-Ei Chen, Shufang Chen, Shufeng Chen, Shuhai Chen, Shuhong Chen, Shuhuang Chen, Shuhui Chen, Shujuan Chen, Shuliang Chen, Shuming Chen, Shunde Chen, Shuntai Chen, Shunyou Chen, Shuo Chen, Shuo-Bin Chen, Shuoni Chen, Shuqin Chen, Shuqiu Chen, Shuting Chen, Shuwen Chen, Shuyi Chen, Shuying Chen, Si Chen, Si-Ru Chen, Si-Yuan Chen, Si-Yue Chen, Si-guo Chen, Sien-Tsong Chen, Sifeng Chen, Sihui Chen, Sijia Chen, Sijuan Chen, Sili Chen, Silian Chen, Siping Chen, Siqi Chen, Siqin Chen, Sisi Chen, Siteng Chen, Siting Chen, Siyi Chen, Siyu Chen, Siyu S Chen, Siyuan Chen, Siyue Chen, Size Chen, Song Chen, Song-Mei Chen, Songfeng Chen, Suet N Chen, Suet Nee Chen, Sufang Chen, Suipeng Chen, Sulian Chen, Suming Chen, Sun Chen, Sung-Fang Chen, Suning Chen, Sunny Chen, Sy-Jou Chen, Syue-Ting Chen, Szu-Chi Chen, Szu-Chia Chen, Szu-Chieh Chen, Szu-Han Chen, Szu-Yun Chen, T Chen, Tai-Heng Chen, Tai-Tzung Chen, Tailai Chen, Tan-Huan Chen, Tan-Zhou Chen, Tania Chen, Tao Chen, Tian Chen, Tianfeng Chen, Tianhang Chen, Tianhong Chen, Tianhua Chen, Tianpeng Chen, Tianran Chen, Tianrui Chen, Tiantian Chen, Tianzhen Chen, Tielin Chen, Tien-Hsing Chen, Ting Chen, Ting-Huan Chen, Ting-Tao Chen, Ting-Ting Chen, Tingen Chen, Tingtao Chen, Tingting Chen, Tom Wei-Wu Chen, Tong Chen, Tongsheng Chen, Tse-Ching Chen, Tse-Wei Chen, TsungYen Chen, Tuantuan Chen, Tzu-An Chen, Tzu-Chieh Chen, Tzu-Ju Chen, Tzu-Ting Chen, Tzu-Yu Chen, Tzy-Yen Chen, Valerie Chen, W Chen, Wai Chen, Wan Jun Chen, Wan-Tzu Chen, Wan-Yan Chen, Wan-Yi Chen, Wanbiao Chen, Wanjia Chen, Wanjun Chen, Wanling Chen, Wantao Chen, Wanting Chen, Wanyin Chen, Wei Chen, Wei J Chen, Wei Ning Chen, Wei-Cheng Chen, Wei-Cong Chen, Wei-Fei Chen, Wei-Hao Chen, Wei-Hui Chen, Wei-Kai Chen, Wei-Kung Chen, Wei-Lun Chen, Wei-Min Chen, Wei-Peng Chen, Wei-Ting Chen, Wei-Wei Chen, Wei-Yu Chen, Wei-xian Chen, Weibo Chen, Weican Chen, Weichan Chen, Weicong Chen, Weihao Chen, Weihong Chen, Weihua Chen, Weijia Chen, Weijie Chen, Weili Chen, Weilun Chen, Weina Chen, Weineng Chen, Weiping Chen, Weiqin Chen, Weiqing Chen, Weirui Chen, Weisan Chen, Weitao Chen, Weitian Chen, Weiwei Chen, Weixian Chen, Weixin Chen, Weiyi Chen, Weiyong Chen, Wen Chen, Wen-Chau Chen, Wen-Jie Chen, Wen-Pin Chen, Wen-Qi Chen, Wen-Tsung Chen, Wen-Yi Chen, Wenbiao Chen, Wenbing Chen, Wenfan Chen, Wenfang Chen, Wenhao Chen, Wenhua Chen, Wenjie Chen, Wenjun Chen, Wenlong Chen, Wenqin Chen, Wensheng Chen, Wenshuo Chen, Wentao Chen, Wenting Chen, Wentong Chen, Wenwen Chen, Wenwu Chen, Wenxi Chen, Wenxing Chen, Wenxu Chen, Willian Tzu-Liang Chen, Wu-Jun Chen, Wu-Xian Chen, Wuyan Chen, X Chen, X R Chen, X Steven Chen, Xi Chen, Xia Chen, Xia-Fei Chen, Xiaguang Chen, Xiameng Chen, Xian Chen, Xian-Kai Chen, Xianbo Chen, Xiancheng Chen, Xianfeng Chen, Xiang Chen, Xiang-Bin Chen, Xiang-Mei Chen, XiangFan Chen, Xiangding Chen, Xiangjun Chen, Xiangli Chen, Xiangliu Chen, Xiangmei Chen, Xiangna Chen, Xiangning Chen, Xiangqiu Chen, Xiangyu Chen, Xiankai Chen, Xianmei Chen, Xianqiang Chen, Xianxiong Chen, Xianyue Chen, Xianze Chen, Xianzhen Chen, Xiao Chen, Xiao-Chen Chen, Xiao-Hui Chen, Xiao-Jun Chen, Xiao-Lin Chen, Xiao-Qing Chen, Xiao-Quan Chen, Xiao-Wei Chen, Xiao-Yang Chen, Xiao-Ying Chen, Xiao-chun Chen, Xiao-he Chen, Xiao-ping Chen, Xiaobin Chen, Xiaobo Chen, Xiaochang Chen, Xiaochun Chen, Xiaodong Chen, Xiaofang Chen, Xiaofen Chen, Xiaofeng Chen, Xiaohan Chen, Xiaohong Chen, Xiaohua Chen, Xiaohui Chen, Xiaojiang S Chen, Xiaojie Chen, Xiaojing Chen, Xiaojuan Chen, Xiaojun Chen, Xiaokai Chen, Xiaolan Chen, Xiaole L Chen, Xiaolei Chen, Xiaoli Chen, Xiaolin Chen, Xiaoling Chen, Xiaolong Chen, Xiaolu Chen, Xiaomeng Chen, Xiaomin Chen, Xiaona Chen, Xiaonan Chen, Xiaopeng Chen, Xiaoping Chen, Xiaoqian Chen, Xiaoqing Chen, Xiaorong Chen, Xiaoshan Chen, Xiaotao Chen, Xiaoting Chen, Xiaowan Chen, Xiaowei Chen, Xiaowen Chen, Xiaoxiang Chen, Xiaoxiao Chen, Xiaoyan Chen, Xiaoyang Chen, Xiaoyin Chen, Xiaoyong Chen, Xiaoyu Chen, Xiaoyuan Chen, Xiaoyun Chen, Xiatian Chen, Xihui Chen, Xijun Chen, Xikun Chen, Ximei Chen, Xin Chen, Xin-Jie Chen, Xin-Ming Chen, Xin-Qi Chen, Xinan Chen, Xing Chen, Xing-Lin Chen, Xing-Long Chen, Xing-Zhen Chen, Xingdong Chen, Xinghai Chen, Xingxing Chen, Xingyi Chen, Xingyong Chen, Xingyu Chen, Xinji Chen, Xinlin Chen, Xinpu Chen, Xinqiao Chen, Xinwei Chen, Xinyan Chen, Xinyang Chen, Xinyi Chen, Xinyu Chen, Xinyuan Chen, Xinyue Chen, Xinzhuo Chen, Xiong Chen, Xiqun Chen, Xiu Chen, Xiu-Juan Chen, Xiuhui Chen, Xiujuan Chen, Xiuli Chen, Xiuping Chen, Xiuxiu Chen, Xiuyan Chen, Xixi Chen, Xiyao Chen, Xiyu Chen, Xu Chen, Xuan Chen, Xuancai Chen, Xuanjing Chen, Xuanli Chen, Xuanmao Chen, Xuanwei Chen, Xuanxu Chen, Xuanyi Chen, Xue Chen, Xue-Mei Chen, Xue-Qing Chen, Xue-Xin Chen, Xue-Yan Chen, Xue-Ying Chen, XueShu Chen, Xuechun Chen, Xuefei Chen, Xuehua Chen, Xuejiao Chen, Xuejun Chen, Xueli Chen, Xueling Chen, Xuemei Chen, Xuemin Chen, Xueqin Chen, Xueqing Chen, Xuerong Chen, Xuesong Chen, Xueting Chen, Xueyan Chen, Xueying Chen, Xufeng Chen, Xuhui Chen, Xujia Chen, Xun Chen, Xuxiang Chen, Xuxin Chen, Xuzhuo Chen, Y Chen, Y D I Chen, Y Eugene Chen, Y M Chen, Y P Chen, Y S Chen, Y U Chen, Y-D I Chen, Y-D Ida Chen, Ya Chen, Ya-Chun Chen, Ya-Nan Chen, Ya-Peng Chen, Ya-Ting Chen, Ya-xi Chen, Yafang Chen, Yafei Chen, Yahong Chen, Yajie Chen, Yajing Chen, Yajun Chen, Yalan Chen, Yali Chen, Yan Chen, Yan Jie Chen, Yan Q Chen, Yan-Gui Chen, Yan-Jun Chen, Yan-Ming Chen, Yan-Qiong Chen, Yan-yan Chen, Yanan Chen, Yananlan Chen, Yanbin Chen, Yanfei Chen, Yanfen Chen, Yang Chen, Yang-Ching Chen, Yang-Yang Chen, Yangchao Chen, Yanghui Chen, Yangxin Chen, Yanhan Chen, Yanhua Chen, Yanjie Chen, Yanjing Chen, Yanli Chen, Yanlin Chen, Yanling Chen, Yanming Chen, Yann-Jang Chen, Yanping Chen, Yanqiu Chen, Yanrong Chen, Yanru Chen, Yanting Chen, Yanyan Chen, Yanyun Chen, Yanzhu Chen, Yanzi Chen, Yao Chen, Yao-Shen Chen, Yaodong Chen, Yaosheng Chen, Yaowu Chen, Yau-Hung Chen, Yaxi Chen, Yayun Chen, Yazhuo Chen, Ye Chen, Ye-Guang Chen, Yeh Chen, Yelin Chen, Yen-Chang Chen, Yen-Chen Chen, Yen-Cheng Chen, Yen-Ching Chen, Yen-Fu Chen, Yen-Hao Chen, Yen-Hsieh Chen, Yen-Jen Chen, Yen-Ju Chen, Yen-Lin Chen, Yen-Ling Chen, Yen-Ni Chen, Yen-Rong Chen, Yen-Teen Chen, Yewei Chen, Yi Chen, Yi Feng Chen, Yi-Bing Chen, Yi-Chun Chen, Yi-Chung Chen, Yi-Fei Chen, Yi-Guang Chen, Yi-Han Chen, Yi-Hau Chen, Yi-Heng Chen, Yi-Hong Chen, Yi-Hsuan Chen, Yi-Hui Chen, Yi-Jen Chen, Yi-Lin Chen, Yi-Ru Chen, Yi-Ting Chen, Yi-Wen Chen, Yi-Yung Chen, YiChung Chen, YiPing Chen, Yian Chen, Yibing Chen, Yibo Chen, Yidan Chen, Yiding Chen, Yidong Chen, Yiduo Chen, Yifa Chen, Yifan Chen, Yifang Chen, Yifei Chen, Yih-Chieh Chen, Yihao Chen, Yihong Chen, Yii-Der Chen, Yii-Der I Chen, Yii-Derr Chen, Yii-der Ida Chen, Yijiang Chen, Yijun Chen, Yike Chen, Yilan Chen, Yilei Chen, Yili Chen, Yilin Chen, Yiming Chen, Yin-Huai Chen, Ying Chen, Ying-Cheng Chen, Ying-Hsiang Chen, Ying-Jie Chen, Ying-Jung Chen, Ying-Lan Chen, Ying-Ying Chen, Yingchun Chen, Yingcong Chen, Yinghui Chen, Yingji Chen, Yingjie Chen, Yinglian Chen, Yingting Chen, Yingxi Chen, Yingying Chen, Yingyu Chen, Yinjuan Chen, Yintong Chen, Yinwei Chen, Yinzhu Chen, Yiru Chen, Yishan Chen, Yisheng Chen, Yitong Chen, Yixin Chen, Yiyin Chen, Yiyun Chen, Yizhi Chen, Yong Chen, Yong-Jun Chen, Yong-Ping Chen, Yong-Syuan Chen, Yong-Zhong Chen, YongPing Chen, Yongbin Chen, Yongfa Chen, Yongfang Chen, Yongheng Chen, Yonghui Chen, Yongke Chen, Yonglu Chen, Yongmei Chen, Yongming Chen, Yongning Chen, Yongqi Chen, Yongshen Chen, Yongshuo Chen, Yongxing Chen, Yongxun Chen, You-Ming Chen, You-Xin Chen, You-Yue Chen, Youhu Chen, Youjia Chen, Youmeng Chen, Youran Chen, Youwei Chen, Yu Chen, Yu-Bing Chen, Yu-Cheng Chen, Yu-Chi Chen, Yu-Chia Chen, Yu-Chuan Chen, Yu-Fan Chen, Yu-Fen Chen, Yu-Fu Chen, Yu-Gen Chen, Yu-Han Chen, Yu-Hui Chen, Yu-Ling Chen, Yu-Ming Chen, Yu-Pei Chen, Yu-San Chen, Yu-Si Chen, Yu-Ting Chen, Yu-Tung Chen, Yu-Xia Chen, Yu-Xin Chen, Yu-Yang Chen, Yu-Ying Chen, Yuan Chen, Yuan-Hua Chen, Yuan-Shen Chen, Yuan-Tsong Chen, Yuan-Yuan Chen, Yuan-Zhen Chen, Yuanbin Chen, Yuanhao Chen, Yuanjia Chen, Yuanjian Chen, Yuanli Chen, Yuanqi Chen, Yuanwei Chen, Yuanwen Chen, Yuanyu Chen, Yuanyuan Chen, Yubin Chen, Yucheng Chen, Yue Chen, Yue-Lai Chen, Yuebing Chen, Yueh-Peng Chen, Yuelei Chen, Yuewen Chen, Yuewu Chen, Yuexin Chen, Yuexuan Chen, Yufei Chen, Yufeng Chen, Yuh-Lien Chen, Yuh-Ling Chen, Yuh-Min Chen, Yuhan Chen, Yuhang Chen, Yuhao Chen, Yuhong Chen, Yuhui Chen, Yujie Chen, Yule Chen, Yuli Chen, Yulian Chen, Yulin Chen, Yuling Chen, Yulong Chen, Yulu Chen, Yumei Chen, Yun Chen, Yun-Ju Chen, Yun-Tzu Chen, Yun-Yu Chen, Yundai Chen, Yunfei Chen, Yunfeng Chen, Yung-Hsiang Chen, Yung-Wu Chen, Yunjia Chen, Yunlin Chen, Yunn-Yi Chen, Yunqin Chen, Yunshun Chen, Yunwei Chen, Yunyun Chen, Yunzhong Chen, Yunzhu Chen, Yupei Chen, Yupeng Chen, Yuping Chen, Yuqi Chen, Yuqin Chen, Yuqing Chen, Yuquan Chen, Yurong Chen, Yushan Chen, Yusheng Chen, Yusi Chen, Yuting Chen, Yutong Chen, Yuxi Chen, Yuxian Chen, Yuxiang Chen, Yuxin Chen, Yuxing Chen, Yuyan Chen, Yuyang Chen, Yuyao Chen, Z Chen, Zan Chen, Zaozao Chen, Ze-Hui Chen, Ze-Xu Chen, Zechuan Chen, Zemin Chen, Zetian Chen, Zexiao Chen, Zeyu Chen, Zhanfei Chen, Zhang-Liang Chen, Zhang-Yuan Chen, Zhangcheng Chen, Zhanghua Chen, Zhangliang Chen, Zhanglin Chen, Zhangxin Chen, Zhanjuan Chen, Zhao Chen, Zhao-Xia Chen, ZhaoHui Chen, Zhaojun Chen, Zhaoli Chen, Zhaolin Chen, Zhaoran Chen, Zhaowei Chen, Zhaoyao Chen, Zhe Chen, Zhe-Ling Chen, Zhe-Sheng Chen, Zhe-Yu Chen, Zhebin Chen, Zhehui Chen, Zhelin Chen, Zhen Bouman Chen, Zhen Chen, Zhen-Hua Chen, Zhen-Yu Chen, Zhencong Chen, Zhenfeng Chen, Zheng Chen, Zheng-Zhen Chen, Zhenghong Chen, Zhengjun Chen, Zhengling Chen, Zhengming Chen, Zhenguo Chen, Zhengwei Chen, Zhengzhi Chen, Zhenlei Chen, Zhenyi Chen, Zhenyue Chen, Zheping Chen, Zheren Chen, Zhesheng Chen, Zheyi Chen, Zhezhe Chen, Zhi Bin Chen, Zhi Chen, Zhi-Hao Chen, Zhi-bin Chen, Zhi-zhe Chen, Zhiang Chen, Zhichuan Chen, Zhifeng Chen, Zhigang Chen, Zhigeng Chen, Zhiguo Chen, Zhihai Chen, Zhihang Chen, Zhihao Chen, Zhiheng Chen, Zhihong Chen, Zhijian Chen, Zhijian J Chen, Zhijing Chen, Zhijun Chen, Zhimin Chen, Zhinan Chen, Zhiping Chen, Zhiqiang Chen, Zhiquan Chen, Zhishi Chen, Zhitao Chen, Zhiting Chen, Zhiwei Chen, Zhixin Chen, Zhixuan Chen, Zhixue Chen, Zhiyong Chen, Zhiyu Chen, Zhiyuan Chen, Zhiyun Chen, Zhizhong Chen, Zhong Chen, Zhongbo Chen, Zhonghua Chen, Zhongjian Chen, Zhongliang Chen, Zhongxiu Chen, Zhongzhu Chen, Zhou Chen, Zhouji Chen, Zhouliang Chen, Zhoulong Chen, Zhouqing Chen, Zhuchu Chen, Zhujun Chen, Zhuo Chen, Zhuo-Yuan Chen, ZhuoYu Chen, Zhuohui Chen, Zhuojia Chen, Zi-Jiang Chen, Zi-Qing Chen, Zi-Yang Chen, Zi-Yue Chen, Zi-Yun Chen, Zian Chen, Zifan Chen, Zihan Chen, Zihang Chen, Zihao Chen, Zihe Chen, Zihua Chen, Zijie Chen, Zike Chen, Zilin Chen, Zilong Chen, Ziming Chen, Zinan Chen, Ziqi Chen, Ziqing Chen, Zitao Chen, Zixi Chen, Zixin Chen, Zixuan Chen, Ziying Chen, Ziyuan Chen, Zoe Chen, Zongming E Chen, Zongnan Chen, Zongyou Chen, Zongzheng Chen, Zugen Chen, Zuolong Chen
articles
Liqin Ji, Qing Shi, Chen Chen +6 more · 2025 · Biology · MDPI · added 2026-04-24
The Chinese soft-shelled turtle (
📄 PDF DOI: 10.3390/biology14010055
HSD17B12
Yu-Fan Chen, Chien-Wei Lee, Yi-Shuan J Li +8 more · 2025 · Experimental & molecular medicine · Nature · added 2026-04-24
Macrophages play a crucial role in coordinating the skeletal muscle repair response, but their phenotypic diversity and the transition of specialized subsets to resolution-phase macrophages remain poo Show more
Macrophages play a crucial role in coordinating the skeletal muscle repair response, but their phenotypic diversity and the transition of specialized subsets to resolution-phase macrophages remain poorly understood. Here, to address this issue, we induced injury and performed single-cell RNA sequencing on individual cells in skeletal muscle at different time points. Our analysis revealed a distinct macrophage subset that expressed high levels of Gpnmb and that coexpressed critical factors involved in macrophage-mediated muscle regeneration, including Igf1, Mertk and Nr1h3. Gpnmb gene knockout inhibited macrophage-mediated efferocytosis and impaired skeletal muscle regeneration. Functional studies demonstrated that GPNMB acts directly on muscle cells in vitro and improves muscle regeneration in vivo. These findings provide a comprehensive transcriptomic atlas of macrophages during muscle injury, highlighting the key role of the GPNMB macrophage subset in regenerative processes. Our findings suggest that modulating GPNMB signaling in macrophages may represent a promising avenue for future research into therapeutic strategies for enhancing skeletal muscle regeneration. Show less
no PDF DOI: 10.1038/s12276-025-01467-4
NR1H3
Dongli Chen, Hong Zhang, Yuqi Xiu +5 more · 2025 · Frontiers in psychiatry · Frontiers · added 2026-04-24
Stroke is a leading cause of mortality and disability globally, with post-stroke depression and post-stroke anxiety being common and significant complications that hinder recovery and adversely affect Show more
Stroke is a leading cause of mortality and disability globally, with post-stroke depression and post-stroke anxiety being common and significant complications that hinder recovery and adversely affect quality of life. Although these conditions frequently co-occur, their heterogeneity remains poorly understood. This study integrates the Health Ecology Model (HEM) and employs Latent Profile Analysis (LPA) to identify distinct psychological profiles of depression and anxiety among patients with acute ischemic stroke (AIS), as well as to investigate their multilevel determinants. Patients with AIS from a tertiary hospital in Guangdong Province, China, from January to November 2024 were included. Within one week of stroke onset, the data of sociodemographic, clinical characteristics, swallowing function, stroke severity, activities of daily living, resilience and social support were collected according to the HEM guidelines. The Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7 were used to assess the depression and anxiety symptoms of the patients three months after stroke onset. LPA was employed to identify distinct psychological profiles, and variables with a A total of 551 patients with AIS were included in the study, 49 were lost to follow-up or withdrew, resulting in a final analytic sample of 502 participants (91.11%). Three distinct psychological profiles were identified: no depression-anxiety (67.93%), high-risk depression-anxiety (21.12%) and major depression-anxiety (10.95%). In the multivariate analysis, the results indicated that occupation (OR = 0.61, 95% CI [0.40-0.93]), National Institutes of Health Stroke Scale (NIHSS, OR = 1.60, 95% CI [1.06-2.42]), Barthel Index (BI, OR = 1.67, 95% CI [1.27-2.19]) and hypertension (OR = 2.37, 95% CI [1.29-4.35]) were independent predictors of the high-risk depression-anxiety profile, while NIHSS (OR = 2.33, 95% CI [1.42-3.85]), BI (OR = 2.65, 95% CI [1.62-4.35]) and resilience (OR = 0.92, 95% CI [0.87-0.98]) were significantly associated with the major depression-anxiety profile. This study reveals significant heterogeneity in psychological distress among AIS survivors. Key predictors of post-stroke emotional comorbidity include occupation, hypertension, stroke severity, activities of daily living and low resilience. Early identification of high-risk individuals can significantly enhance screening and intervention strategies, particularly by focusing on symptoms such as anhedonia and nervousness. Future research should focus on longitudinal designs and objective biomarkers to better understand the mechanisms behind post-stroke emotional comorbidity. Show less
📄 PDF DOI: 10.3389/fpsyt.2025.1651116
LPA
Yanyan Xu, Xiangtong Ye, Yanfeng Du +8 more · 2025 · Acta pharmaceutica Sinica. B · Elsevier · added 2026-04-24
Alzheimer's disease (AD), characterized by
📄 PDF DOI: 10.1016/j.apsb.2025.02.035
BACE1
Shuang Cheng, Peng Shu, Jie Chen +3 more · 2025 · Journal of multidisciplinary healthcare · added 2026-04-24
Early identification of individuals with low advance care planning (ACP) engagement remains a critical component of clinical care. However, we know little about the heterogeneity of ACP engagement at Show more
Early identification of individuals with low advance care planning (ACP) engagement remains a critical component of clinical care. However, we know little about the heterogeneity of ACP engagement at the individual level. This study identified latent subgroups of ACP engagement using latent profile analysis (LPA), and explored their associations with death attitudes. This study recruited 302 end-stage renal disease (ESRD) patients undergoing dialysis. Data included sociodemographic characteristics, the Advance Care Planning Engagement Survey (ACPES; Chinese version), and the Death Attitude Profile-Revised (DAP-R). Based on multidimensional indicators, LPA was employed to identify distinct ACP engagement profiles. Model fit and classification quality in LPA were evaluated based on class sizes and entropy values. All analyses were completed in SPSS 26.0 and Mplus 8.3, with R3STEP and BCH methods employed to uncover underlying patterns and relationships. Among dialysis-dependent ESRD patients, ACP engagement was categorized into two latent profiles: a "low-ACP Engagement" profile (n = 162, 53.6%) and a "high-ACP Engagement" profile (n = 140, 46.4%), with good classification quality (entropy = 0.909). The profile membership was significantly associated with dialysis vintage, and educational level (both This study identifies two distinct ACP engagement profiles among dialysis-dependent ESRD patients. Findings emphasize the need for tailored interventions, particularly for patients with shorter dialysis vintage and lower education level, and highlight the role of death attitudes in shaping ACP engagement. These findings should be interpreted with caution due to the cross-sectional design and single-center setting. Show less
📄 PDF DOI: 10.2147/JMDH.S555057
LPA
Chengrong Wu, Qian Pu, Yalin Zou +5 more · 2025 · Scientific reports · Nature · added 2026-04-24
Calcific aortic valve stenosis (CAVS) is steadily rising worldwide with no effective pharmacological agents available. Observational studies implicated dyslipidaemia as a risk factor for CAVS. Whether Show more
Calcific aortic valve stenosis (CAVS) is steadily rising worldwide with no effective pharmacological agents available. Observational studies implicated dyslipidaemia as a risk factor for CAVS. Whether dyslipidaemia is causative for CAVS and the therapeutic potential of different lipid-modifying drug targets for CAVS treatment remains unclear. We appraised the relationship of genetically-proxied lipid traits and 12 lipid-modifying drug targets with CAVS risk using Mendelian randomization (MR). Genetic variants associated with lipid traits and variants in genes encoding lipid-modifying drug targets were retrieved from GLGC. Summary-level data for CAVS were obtained from the TARGET consortium and FinnGen. Validation analyses were performed using genetic instruments retrieved from liver-derived gene expression and circulation plasma levels of targets. Colocalisation and mediation analyses were performed to evaluate the robustness of our findings and explore potential mediators (i.e., lipoprotein a (Lp(a)), body mass index, apolipoprotein B (ApoB)). The MR analyses supported that total cholesterol and LDL-cholesterol level were independent causal risk factors. The drug-target MR analysis suggested that genetic mimicry of PCSK9 inhibition should reduce CAVS risk (OR = 0.63, 95% CI = 0.56-0.70), which was corroborated by colocalisation analysis. Secondary analyses supported a genetically proxied effect of liver-specific PCSK9 expression (OR = 0.94 per SD reduction in PCSK9 expression, 95% CI = 0.88-1.00) and circulating plasma levels of PCSK9 (OR = 0.86 per SD reduction in PCSK9 protein, 95% CI = 0.83-0.88) on CAVS risk. ApoB and Lp(a) mediated 55.9% and 4.5%, respectively, of the total effect of PCSK9 on CAVS risk. Multiple sensitivity analyses supported this observation. Our study supports total cholesterol, LDL-cholesterol as a causal factor for CAVS, and genetically proxied inhibition of PCSK9 may reduced its risk. Show less
📄 PDF DOI: 10.1038/s41598-025-15525-4
APOB
Anyu Zeng, Hongmin Chen, Tianqi Luo +13 more · 2025 · Molecular cancer · BioMed Central · added 2026-04-24
Osteosarcoma demonstrates limited responsiveness to PD-1 blockade, largely due to its immunosuppressive tumor microenvironment (TME). The specific mechanisms by which cancer-associated fibroblasts (CA Show more
Osteosarcoma demonstrates limited responsiveness to PD-1 blockade, largely due to its immunosuppressive tumor microenvironment (TME). The specific mechanisms by which cancer-associated fibroblasts (CAFs) contribute to immunosuppression in osteosarcoma are not fully understood. We performed single-cell RNA sequencing (scRNA-seq) on osteosarcoma tissues from patients treated with neoadjuvant chemotherapy and anti-PD-1 therapy to investigate the tumor microenvironment. Cellular composition, gene expression programs, and signaling pathways were analyzed. Functional assays, pull-down and PLA-flow binding validation, and in vivo mouse models were used to dissect the mechanisms by which CAF-derived factors influence CD8⁺ T cell function and contribute to immunotherapy response. We identified a subpopulation of CD36⁺ CAFs, characterized by adaptive uptake of oxidized low-density lipoprotein (OxLDL) and activation of the PPARG-FABP4 axis. This metabolic program promoted ANGPTL4 secretion, which bound integrin on CD8⁺ T cells and activated the JAK2-STAT3 pathway, leading to T cell exhaustion and impaired effector function. In vivo, administration of VitE effectively scavenged OxLDL, reprogrammed the TME, enhanced CD8⁺ T cell infiltration, and synergized with PD-1 blockade to improve tumor control. CD36⁺ CAFs drive immunosuppressive metabolic reprogramming via the OxLDL-PPARG-ANGPTL4 axis, promoting CD8⁺ T cell exhaustion and resistance to immunotherapy in osteosarcoma. Targeting this pathway with VitE alleviated CAF-mediated immune suppression and enhanced PD-1 blockade responses in preclinical models, providing a rationale for metabolism-based combinatorial strategies in osteosarcoma. Show less
📄 PDF DOI: 10.1186/s12943-025-02516-2
ANGPTL4
Baolong Wang, Peiyou Chen, Zhihao Jia +1 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24
The purpose of this study is to explore the effect of physical activity on the executive function of 5-6-year-old children and to provide a theoretical and empirical basis for further research on impr Show more
The purpose of this study is to explore the effect of physical activity on the executive function of 5-6-year-old children and to provide a theoretical and empirical basis for further research on improvements in the executive function of children caused by physical activity. A total of 170 children (5-6 years old) from several kindergartens were selected via multistage stratified sampling. All the children wore 7-day accelerometers (ActiGraph GT3X) to measure their daily physical activities. Parents completed the preschool children's executive function questionnaire (BRIEF-P) to assess their daily executive function. (1) The total duration of physical activity (TPA) was 110.84 ± 22.52 min/day, the duration of low-intensity physical activity (LPA) was 36.23 ± 7.53 min/day, and the duration of medium- and high-intensity physical activity (MVPA) was 74.55 ± 16.77 min/day. A total of 82.6% of the children reached the recommended amount of MVPA. (2) After adjusting for body mass index (BMI), parents' highest educational background and parents' total monthly income, MVPA was negatively correlated with children's total executive function score ( Physical activity can improve the executive function of children aged 5-6 years to some extent. MVPA can improve children's executive function and subdomains, and there is a correlation between boys' physical activity and executive function. Show less
📄 PDF DOI: 10.3389/fpsyg.2025.1651806
LPA
J Xia, X H Hu, Y Zhao +4 more · 2025 · Zhonghua nei ke za zhi · added 2026-04-24
A retrospective analysis of clinical data of 8 patients with PICALM::MLLT10 (P/M) fusion gene-positive acute myeloid leukemia (AML) diagnosed by transcriptome sequencing (RNA-seq) at the First Affilia Show more
A retrospective analysis of clinical data of 8 patients with PICALM::MLLT10 (P/M) fusion gene-positive acute myeloid leukemia (AML) diagnosed by transcriptome sequencing (RNA-seq) at the First Affiliated Hospital of Soochow University from June 2017 to March 2023 was performed. Laboratory findings and treatment status were analyzed, and survival analysis was performed using the Kaplan-Meier method. The 8 patients included 5 males and 3 females, aged 16-35 years, with a median age of 27 years. The platelet count of patients was normal, and 3 patients had mild to moderate anemia. Extramedullary infiltration was present in all patients with clinical manifestations, including 5 patients with mediastinal masses, 2 patients with hepatosplenomegaly, 1 patient with central nervous system leukemia, and 1 patient with cervical lymph node enlargement. Karyotypical analysis revealed 7 patients with an abnormal karyotype, including 6 cases of complex karyotypes. Of these, 4 patients harbored the t(10;11) translocation. The complete remission rate of induction chemotherapy in the patients was 7/8, and 2 patients experienced early recurrence. All patients subsequently underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT), The follow-up period ranged from 86 to 812 days, with a median of 330 days. Among the 8 patients, 3 survived and 5 died due to recurrence. Relapse and death only occurred in the P/M fusion gene-positive patients after transplantation. The overall survival rate at 1 year after transplantation was 37.5%. P/M Show less
no PDF DOI: 10.3760/cma.j.cn112138-20240913-00577
MLLT10
Shenlong Mo, Zhenying Hu, Huaiyi Zhu +5 more · 2025 · Toxins · MDPI · added 2026-04-24
2-Amino-14,16-dimethyloctadecan-3-ol (AOD) is commonly found in foods contaminated with
📄 PDF DOI: 10.3390/toxins17080413
FADS3
Siqi Chen, Ziliang Hu, Mingyue Zhao +4 more · 2025 · Journal of proteomics · Elsevier · added 2026-04-24
Inflammation is a complex factor in the pathogenesis of intracranial aneurysms (IA), but its specific cellular inflammatory factors remain uncertain. We collected two cohorts and measured the represen Show more
Inflammation is a complex factor in the pathogenesis of intracranial aneurysms (IA), but its specific cellular inflammatory factors remain uncertain. We collected two cohorts and measured the representation of vascular inflammation-related proteins using the Olink CVD II Vascular Inflammation Panel. We subsequently validated our findings using ELISA and RT-qPCR. Our proteomic analysis identified 11 vascular inflammation-related markers that were significantly differentially represented between the IA and control groups. These markers were implicated in leukocyte migration, immune response, triglyceride and lipoprotein metabolism, acute phase response, T cell regulation, and several key biological pathways, including PPAR, HIF-1, cytokine-cytokine interactions, and PI3K-AKT signaling. Further validation with ELISA and RT-qPCR confirmed the differential representation of IL6, PTX3, LPL, and OLR1 between the two groups. Notably, a combination marker incorporating these four factors demonstrated high diagnostic potential for the early detection of IA. Our study has identified a set of informative biomarkers (IL6, PTX3, LPL, and OLR1) that could be valuable for the early diagnosis of IA. Importantly, this is the first report of significantly elevated OLR1 representation in the plasma of IA patients. Further investigation into the role of OLR1 in the pathogenesis of IA is warranted. SIGNIFICANCE: This study significantly advances our understanding of the molecular mechanisms underlying intracranial aneurysm (IA) pathogenesis. By identifying a panel of novel biomarkers, including the previously unreported elevated expression of OLR1 in IA patients, we provide crucial insights into the inflammatory processes involved in aneurysm formation and development. These findings have important clinical implications, as the identified biomarkers could serve as valuable tools for early diagnosis and potentially targeted therapeutic interventions. Furthermore, the study highlights the complex interplay of inflammatory pathways in IA, suggesting that a multi-faceted approach may be necessary for effective management. Show less
no PDF DOI: 10.1016/j.jprot.2025.105374
LPL
Shuai Tian, Jing Han, Zhaomin Zhang +3 more · 2025 · European journal of applied physiology · Springer · added 2026-04-24
High-intensity exercise promotes visceral adipose tissue (VAT) breakdown in females via the hypothalamic ERα pathway, and exogenous lactate infusion combined with aerobic training (AT) mimics this eff Show more
High-intensity exercise promotes visceral adipose tissue (VAT) breakdown in females via the hypothalamic ERα pathway, and exogenous lactate infusion combined with aerobic training (AT) mimics this effect. However, whether lactate administration can independently mediate hypothalamic plasticity and VAT catabolism as a standalone nutritional strategy remains unexplored. Firstly, using a two-factor design (Lactate × AT) in female SD rats, we showed that long-term exogenous lactate infusion independently induced co-expression of Estrogen receptor α (ERα) and Brain-derived neurotrophic factor (BDNF) in the ventromedial hypothalamus (VMH) and elevated local field potential spectral power in specific bands. These neural adaptations were accompanied by increased resting metabolic rate, enhanced fat oxidation, and enhanced lipolysis, thereby preventing excessive VAT accumulation induced by a high-fat diet. Furthermore, pharmacological inhibition confirmed that Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-α (PGC-1α) acts as a co-upstream signal of ERα and BDNF mediating this process. Our findings reveal that standalone lactate administration induces functional plasticity and metabolic reprogramming through the VMH PGC-1α-ERα pathway, independent of exercise, and effectively suppresses pathological VAT accumulation in female rats. This study identifies potential nutritional interventions and mechanistic targets for preventing female-centered obesity. Show less
📄 PDF DOI: 10.1007/s00421-025-06097-2
BDNF
Jia Zhang, Song Bin Huang, Dan Ni Peng +3 more · 2025 · Frontiers in psychology · Frontiers · added 2026-04-24
This study aimed to identify heterogeneous patterns of medical coping modes (MCM) and to examine the moderating role of social support in the relationship between these patterns and social disability Show more
This study aimed to identify heterogeneous patterns of medical coping modes (MCM) and to examine the moderating role of social support in the relationship between these patterns and social disability in young and middle-aged patients after percutaneous coronary intervention (PCI). A cross-sectional study was conducted among 129 post-PCI patients from a single center in China. Participants completed the Medical Coping Modes Questionnaire (MCMQ), the Social Support Rating Scale (SSRS), and the Social Disability Screening Schedule (SDSS). Latent profile analysis (LPA) was used to identify distinct coping patterns. The moderation effect of social support was tested using the Johnson-Neyman technique. Two distinct coping profiles were identified via LPA: "Adaptive Copers" (55.1%), characterized by higher confrontation and lower avoidance/resignation, and "Maladaptive Copers" (44.9%), showing the opposite pattern. A counterintuitive finding emerged, with the Maladaptive Copers reporting significantly lower social disability scores. Furthermore, beyond this profile differentiation, social support demonstrated a significant U-shaped moderating effect in the coping-disability relationship. Its moderating role was statistically significant only at very low (<39.884) and very high (>52.924) levels of support. This study reveals two key findings: first, post-PCI patients are heterogeneous in coping, comprising adaptive and maladaptive subgroups; second, the impact of these coping styles on social disability is non-linearly moderated by social support. Clinicians should assess both coping profiles and social support levels to tailor interventions effectively. Show less
📄 PDF DOI: 10.3389/fpsyg.2025.1731898
LPA
Yu Liao, Mingchao Wang, Fuli Qin +2 more · 2025 · Frontiers in pharmacology · Frontiers · added 2026-04-24
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically Show more
Evidence of the benefits of cordycepin (Cpn) for treating obesity is accumulating, but detailed knowledge of its therapeutic targets and mechanisms remains limited. This study aimed to systematically identify Cpn's therapeutic targets and pathways in Western diet (WD)-induced obesity using integrated network pharmacology, transcriptomics, and experimental validation. A Western diet (WD)-induced mice model was used to evaluate the effectiveness of Cpn in ameliorating obesity. A network pharmacology analysis was then employed to identify the potential anti-obesity targets of Cpn. GO functional enrichment and KEGG pathway analysis were performed to elucidate the potential functions of the identified targets, followed by constructing a protein-protein interaction network to screen the core targets. Meanwhile, quantitative transcriptomics was conducted to validate and broaden the network pharmacology findings. Finally, molecular docking and quantitative real-time PCR assay were used for the core target validation. Cpn treatment effectively alleviated obesity-related symptoms in WD-induced mice. The metabolic pathway, insulin signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, lipid and atherosclerosis pathway, and core targets including CPS1, HRAS, MAPK14, PAH, ALDOB, AKT1, GSK3B, HSP90AA1, BHMT2, EGFR, CASP3, MAT1A, APOM, APOA2, APOC3, and APOA1 are involved in regulating the therapeutic effect of Cpn. This study comprehensively uncovers the potential mechanism of Cpn against obesity based on network pharmacology and quantitative transcriptomics, which provides evidence for revealing the pathogenesis of obesity, suggesting that Cpn is a possible lead compound for anti-obesity treatment. Show less
📄 PDF DOI: 10.3389/fphar.2025.1571480
APOC3
Yulong Fu, Canran Gao, Hailing Zhang +7 more · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Injectable hydrogel implants represent a promising therapeutic approach for ischemic heart failure; but their efficacy is often limited by low bioactivity, poor durability, and inadequate injection te Show more
Injectable hydrogel implants represent a promising therapeutic approach for ischemic heart failure; but their efficacy is often limited by low bioactivity, poor durability, and inadequate injection techniques. Herein, a unique hydrogel incorporating extracellular matrix from fish swim bladder (FSB-ECM), which has distinct advantages over mammalian derived ECM, such as low antigenicity, bioactivity, and source safety, is developed. It consists of collagen, glycoproteins, and proteoglycans, including 13 proteins common in the myocardial matrix and three specific proteins: HSPG, Col12a1, and vWF. This hydrogel enhances cardiac cell adhesion and stretching while promoting angiogenesis and M2 macrophage polarization. In addition, its storage modulus (G') increases over time, reaching about 1000 Pa after 5 min, which facilitates transcatheter delivery and in situ gelling. Furthermore, this hydrogel provides sustained support for cardiac contractions, exhibiting superior longevity. In a rat model of ischemic heart failure, the ejection fraction significantly improves with FSB-ECM treatment, accompanied by increased angiogenesis, reduced inflammation, and decreased infarct size. Finally, RNA sequencing combined with in vitro assays identifies ANGPTL4 as a key protein involved in mediating the effects of FSB-ECM treatment. Overall, this new injectable hydrogel based on FSB-ECM is suitable for transcatheter delivery and possesses remarkable reparative capabilities for treating heart failure. Show less
📄 PDF DOI: 10.1002/advs.202500036
ANGPTL4
Shuenn-Nan Chiu, Jyh-Ming Jimmy Juang, Wei-Chieh Tseng +7 more · 2025 · Heart rhythm O2 · Elsevier · added 2026-04-24
Sudden cardiac arrest (SCA) is a leading cause of death in pediatric hypertrophic cardiomyopathy (HCM). The study sought to analyze the clinical and genetic characteristics of pediatric HCM and assess Show more
Sudden cardiac arrest (SCA) is a leading cause of death in pediatric hypertrophic cardiomyopathy (HCM). The study sought to analyze the clinical and genetic characteristics of pediatric HCM and assess the applicability of current SCA risk prediction models. We enrolled individuals diagnosed as HCM before 20 years of age, between 2000 and 2020, excluding those secondary to hemodynamic causes and those associated with genetic syndromes other than RASopathies. Among 91 patients (31 female, 60 male), SCA occurred in 13 (14.3%) patients, with 6 (46%) cases presenting as the initial symptom. These 6 patients were older and had lower left ventricular mass In pediatric HCM, SCA is notably associated with sarcomere gene pathogenic variants. While newer risk scoring systems, if incorporated with genetic information, effectively predict SCA in this Asia cohort, a challenge remains: nearly half of SCA cases present as the initial clinical manifestation. Show less
📄 PDF DOI: 10.1016/j.hroo.2025.03.022
MYBPC3
Xian Chen, Hui Wang, Qianqian Li +4 more · 2025 · Discover oncology · Springer · added 2026-04-24
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether c Show more
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether causal relationships exist among these associations remains unclear, as traditional observational studies are susceptible to confounding factors. To evaluate causal relationships between kidney cancer, kidney fibrosis, and inflammatory factors using Mendelian randomization, and explore tumor microenvironment heterogeneity through single-cell analysis. Based on large-scale GWAS data, bidirectional Mendelian randomization analysis was performed to assess causal relationships between kidney cancer and kidney fibrosis, using MR Egger, inverse variance weighted (IVW), and weighted mode methods. Causal associations between kidney cancer and inflammatory factors including Axin-1, C-C motif chemokine 28, and interleukin-10 receptor subunit were analyzed. Single-cell RNA sequencing data from the GEO database (GSM4819725) was integrated for tumor microenvironment analysis. Bidirectional Mendelian randomization analysis revealed no significant causal relationship between kidney cancer and kidney fibrosis [kidney cancer→kidney fibrosis: IVW OR=0.992(95%CI: 0.913-1.077, P=0.842); kidney fibrosis→kidney cancer: IVW OR=0.922(95%CI: 0.824-1.030, P=0.151)]. However, significant positive causal associations were identified between kidney cancer and multiple inflammatory factors: Axin-1 levels [OR=1.448(95%CI: 1.107-1.894, P=0.007)], C-C motif chemokine 28 [OR=1.287(95%CI: 1.076-1.540, P=0.006)], and interleukin-10 receptor subunit [OR=1.135(95%CI: 1.032-1.248, P=0.009)]. Sensitivity analyses confirmed the robustness of results. Single-cell analysis revealed cellular heterogeneity in the tumor microenvironment, including various cell types such as immune cells, T cells, and NK cells, with pseudotime analysis demonstrating cell differentiation trajectories and dynamic gene expression changes. Mendelian randomization analysis provides genetic evidence for causal relationships between kidney cancer and inflammatory factors, while excluding direct causal associations between kidney cancer and kidney fibrosis. Show less
📄 PDF DOI: 10.1007/s12672-025-03343-z
AXIN1
Yongting Li, Xiaolong Chen, Tingting Wang +3 more · 2025 · Brain sciences · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/brainsci15121339
BDNF
Ping Wang, Liping Zhu, Kecai Chen +6 more · 2025 · Ecotoxicology and environmental safety · Elsevier · added 2026-04-24
Oxidative deterioration of fish oil in aquafeeds poses a significant challenge to fish health and aquaculture sustainability, making it crucial to mitigate this issue through healthy and green nutriti Show more
Oxidative deterioration of fish oil in aquafeeds poses a significant challenge to fish health and aquaculture sustainability, making it crucial to mitigate this issue through healthy and green nutritional strategies. This study examined the potential of stevia chlorogenic acid (SCGA), a bioactive byproduct of stevia processing, to alleviate intestinal injury, gut microbiota dysbiosis, and lipid metabolism disorders induced by oxidized fish oil in turbot. Four diets with equal nitrogen and lipid contents were formulated: a control diet (PC) containing 5 % fresh fish oil, an oxidized fish oil diet (OFO) comprising 5 % oxidized fish oil, and two additional OFO diets supplemented with 200 mg/kg (OFO200) or 400 mg/kg (OFO400) of SCGA. Each dietary treatment was randomly assigned to three replicates, each containing 40 fish weighing approximately 16.99 ± 0.01 g, and administered over a 10-week period. Fish fed the OFO diet exhibited significantly compromised growth performance, as indicated by decreased WGR and SGR, along with reduced serum immune indices (IgM, C3, and C4) and lipid parameters (TC, HDL, LDL), and elevated serum D-LA levels (P < 0.05). Moreover, dietary OFO markedly suppressed antioxidant enzyme activities (serum SOD; intestinal SOD, GSH-Px, and CAT) and elevated MDA concentrations (P < 0.05). Additionally, OFO reduced intestinal expression of tight junction-associated genes (Claudin-4, Claudin-7, Occludin) while increasing expression levels of MLCK, Keap1, inflammatory mediators (IL-6, IL-1β, TNF-α2, NF-κB, IFN-γ), and Caspase7 (P < 0.05). Notably, the TLR signaling pathway-related genes were upregulated, accompanied by pronounced shifts in gut microbiota composition (P < 0.05). In hepatic tissue, lipogenesis-associated genes (FAS, ACC) were significantly increased, while key genes involved in lipid transport and β-oxidation (CD36, LPL, ACOX1, PPARγ) exhibited reduced expression (P < 0.05). Dietary supplementation with 200 and 400 mg/kg SCGA effectively mitigated these detrimental impacts. SCGA restored growth performance, serum immune parameters, and antioxidant enzyme activities to levels comparable to the PC group. It also normalized gene expression related to intestinal barrier function, inflammation, apoptosis, and hepatic lipid metabolism. Furthermore, SCGA supplementation modulated gut microbiota structure by increasing beneficial genera and decreasing potential pathogens. In conclusion, SCGA effectively improves growth performance, alleviates OFO-induced intestinal injury and microbial dysbiosis, and regulates lipid metabolism in turbot. These findings provide theoretical insights and technical support for the application of SCGA in aquaculture. Show less
no PDF DOI: 10.1016/j.ecoenv.2025.119321
LPL
Sihua Xu, Yiyuan Xiao, Chaoyu Xu +6 more · 2025 · BMJ open sport & exercise medicine · added 2026-04-24
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health issue due to its high prevalence, yet the impact of accelerometer-measured physical activity on clinical outcomes re Show more
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health issue due to its high prevalence, yet the impact of accelerometer-measured physical activity on clinical outcomes remains unclear. This study aims to examine the associations of physical activity with the risk of liver cirrhosis, cancer, cardiovascular disease (CVD) incidence and mortality. 32 681 MASLD participants with accelerometer-derived physical activity data from the UK Biobank were analysed. Physical activity intensity was categorised into light (LPA), moderate (MPA) and vigorous (VPA) intensity. Cox proportional hazard and acceleration failure models were employed to assess associations between physical activity duration and outcomes. During a median follow-up of 7.5-7.9 years, 1883 deaths, 151 liver cirrhosis, 3312 cancers and 6657 CVD events were recorded. Physical activity, regardless of intensity, was consistently associated with a reduced risk of liver cirrhosis, CVD and all-cause mortality. Compared with non-MASLD individuals, our analysis indicates that longer duration of physical activity, specifically >1945 min/week of LPA or >383 min/week of MPA may theoretically eliminate the excess risk of mortality associated with MASLD. Among MASLD individuals, longer physical activity duration, regardless of intensity, was associated with reduced risks of liver cirrhosis and mortality. MPA and VPA were associated with lower CVD risk, while VPA was associated with reduced cancer risk, highlighting the potential benefits of increasing the intensity and duration of physical activity in MASLD management. Show less
📄 PDF DOI: 10.1136/bmjsem-2025-002702
LPA
Xiansong Fang, Xiaoyun Wen, Ya Hou +3 more · 2025 · Journal of biochemical and molecular toxicology · Wiley · added 2026-04-24
Breast cancer has seriously affected women's physical and mental health. This investigation aims at screening differentially expressed genes (DEGs) in breast cancer and illuminating the potential biol Show more
Breast cancer has seriously affected women's physical and mental health. This investigation aims at screening differentially expressed genes (DEGs) in breast cancer and illuminating the potential biological functions of Leiomodin 1 (LMOD1) and its behind mechanisms against breast cancer. The common DEGs (co-DEGs) between the GSE22820 and GSE29431 data sets and pivotal genes were screened out using bioinformatics methods. The biological roles of LMOD1 overexpression on malignant phenotypes were validated by functional assays and the impact on fatty acid synthesis was also elucidated in breast cancer cell lines. Additionally, colivelin, a STAT3 activator, was applied for further investigating the role of LMOD1 on the JAK2/STAT3 pathway in vitro. A total of 208 co-DEGs and 5 focal genes were screened through bioinformatics analysis, and 5 focal genes were downregulated in breast cancer cell lines. LMOD1 overexpression retarded proliferative, migratory, invasive capabilities of breast cancer cells. LMOD1 overexpression suppressed fatty acid synthesis. Furthermore, the inhibitory effects on malignant phenotypes of breast cancer cells with LMOD1 overexpression were partially abolished after colivelin treatment. Additionally, LMOD1 could impede fatty acid synthesis in breast cancer cells. Our study highlighted LMOD1 exerted as a tumor-suppressive role in breast cancer, which was correlated with restraining the JAK2/STAT3 pathway activation. Show less
no PDF DOI: 10.1002/jbt.70092
LMOD1
Jun Qiao, Lei Jiang, Liuyang Cai +14 more · 2025 · Nature communications · Nature · added 2026-04-24
The extensive co-occurrence of cardiovascular diseases (CVDs), as evidenced by epidemiological studies, is supported by positive genetic correlations identified in comprehensive genetic investigations Show more
The extensive co-occurrence of cardiovascular diseases (CVDs), as evidenced by epidemiological studies, is supported by positive genetic correlations identified in comprehensive genetic investigations, suggesting a shared genetic basis. However, the precise genetic mechanisms underlying these associations remain elusive. By assessing genetic correlations, genetic overlap, and causal connections, we aim to shed light on common genetic underpinnings among major CVDs. Employing multi-trait analysis, we pursue diverse strategies to unveil shared genetic elements, encompassing SNPs, genes, gene sets, and functional categories with pleiotropic implications. Our study systematically quantifies genetic overlap beyond genome-wide genetic correlations across CVDs, while identifying a putative causal relationship between coronary artery disease (CAD) and heart failure (HF). We then pinpointed 38 genomic loci with pleiotropic influence across CVDs, of which the most influential pleiotropic locus is located at the LPA gene. Notably, 12 loci present high evidence of multi-trait colocalization and display congruent directional effects. Examination of genes and gene sets linked to these loci unveiled robust associations with circulatory system development processes. Intriguingly, distinct patterns predominantly driven by atrial fibrillation, coronary artery disease, and venous thromboembolism underscore the significant disparities between clinically defined CVD classifications and underlying shared biological mechanisms, according to functional annotation findings. Show less
📄 PDF DOI: 10.1038/s41467-025-62419-0
LPA
Yuping Huang, Junguang Liao, Panpan Shen +7 more · 2025 · JCI insight · added 2026-04-24
Cranial neural crest cells (CNCs) play a critical role in craniofacial bone morphogenesis, engaging in intricate interactions with various molecular signals to ensure proper development, yet the molec Show more
Cranial neural crest cells (CNCs) play a critical role in craniofacial bone morphogenesis, engaging in intricate interactions with various molecular signals to ensure proper development, yet the molecular scaffolds coordinating these processes remain incompletely defined. Here, we identify neurofibromin 2 (Nf2) as a critical regulator to direct CNC-derived skull morphogenesis. Genetic ablation of Nf2 in murine CNCs causes severe craniofacial anomalies, featuring declined proliferation and increased apoptosis in osteoprogenitors, impaired type I collagen biosynthesis and trafficking, and aberrant osteogenic mineralization. Mechanistically, we uncover that Nf2 serves as a molecular linker that individually interacts with FGF receptor 1 (FGFR1) and Akt through spatially segregated phosphor-sites, and structural modeling and mutagenesis identified Ser10 and Thr230 as essential residues, with Thr230 mutation selectively ablating Akt binding while preserving FGFR1 association. Strikingly, Akt inhibition phenocopied Nf2 deficiency, reducing collagen production and Nf2 phosphorylation, whereas phospho-mimetic Nf2 (T230D) rescued CNC-derived osteogenic defects in Nf2-mutant animals. Our findings underscore the physiological significance of Nf2 as a phosphorylation-operated scaffold licensing the FGFR1/AKT axis to regulate collagen type I biogenesis and trafficking, ensuring normal CNC-derived osteogenesis and craniofacial bone development, thus exposing the Nf2/FGFR1/AKT signaling axis as a therapeutic target and promising advancements in treatment of craniofacial anomalies. Show less
📄 PDF DOI: 10.1172/jci.insight.191112
FGFR1
Mimi Li, Lichao Ye, Chunnuan Chen · 2025 · Scientific reports · Nature · added 2026-04-24
Despite the well-established association between the apolipoprotein B/apolipoprotein A1 (apoB/apoA1) ratio and ischemic stroke, its specific relationship with the underlying vascular pathologies contr Show more
Despite the well-established association between the apolipoprotein B/apolipoprotein A1 (apoB/apoA1) ratio and ischemic stroke, its specific relationship with the underlying vascular pathologies contributing to stroke remains poorly understood. This study aims to investigate the association between the apoB/apoA1 ratio and intracranial or extracranial atherosclerosis. We enrolled 408 patients with acute ischemic stroke who had never been treated with statins or fibrates. Based on the images from computed tomography angiography (CTA), the patients were categorized into four groups: intracranial atherosclerosis stenosis (ICAS, n = 136), extracranial carotid atherosclerosis stenosis (ECAS, n = 45), combined intracranial and extracranial atherosclerosis stenosis (COAS, n = 73), and non-cerebral atherosclerosis stenosis (NCAS, n = 154). Demographic characteristics, clinical factors, and serum lipid levels were collected and then compared across groups. The apoB/apoA1 ratio was significantly higher in patients with ICAS, ECAS and COAS compared to those in the NCAS group. Multivariable logistic regression analysis demonstrated that the ApoB/ApoA1 ratio was independently associated with ICAS, but not with ECAS. ROC curve analysis showed that the ApoB/ApoA1 ratio had a good diagnostic ability for ICAS, with an area under the curve (AUC) of 0.764, an optimal cut-off value of 0.8122, a sensitivity of 81.3%, and a specificity of 59.8%. An higher apoB/apoA1 ratio is associated with ICAS in ischemic stroke patients. Show less
📄 PDF DOI: 10.1038/s41598-025-97625-9
APOB
Robert Chen, Ben Omega Petrazzini, Áine Duffy +4 more · 2025 · Genome biology · BioMed Central · added 2026-04-24
Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been Show more
Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been limited by phenotyping challenges and small sample sizes. We test associations of rare and ultra-rare coding variants with proton density fat fraction (PDFF) and MASLD case-control status in 736,010 participants of diverse ancestries from the UK Biobank, All of Us, and BioMe and performed a trans-ancestral meta-analysis. We then developed models to accurately predict PDFF and MASLD status in the UK Biobank and tested associations with these predicted phenotypes to increase statistical power. The trans-ancestral meta-analysis with PDFF and MASLD case-control status identifies two single variants and two gene-level associations in APOB, CDH5, MYCBP2, and XAB2. Association testing with predicted phenotypes, which replicates more known genetic variants from GWAS than true phenotypes, identifies 16 single variants and 11 gene-level associations implicating 23 additional genes. Two variants were polymorphic only among African ancestry participants and several associations showed significant heterogeneity in ancestry and sex-stratified analyses. In total, we identified 27 genes, of which 3 are monogenic causes of steatosis (APOB, G6PC1, PPARG), 4 were previously associated with MASLD (APOB, APOC3, INSR, PPARG), and 23 had supporting clinical, experimental, and/or genetic evidence. Our results suggest that trans-ancestral association analyses can identify ancestry-specific rare and ultra-rare coding variants in MASLD pathogenesis. Furthermore, we demonstrate the utility of machine learning in genetic investigations of difficult-to-phenotype diseases in trans-ancestral biobanks. Show less
📄 PDF DOI: 10.1186/s13059-025-03518-5
APOB
Min Wang, Chong Xu, Xiaoshan Du +7 more · 2025 · Molecular therapy. Nucleic acids · Elsevier · added 2026-04-24
Ischemic stroke (IS) is a major cause of disability and mortality, but its genetic basis remains poorly understood. This study integrates data from three large-scale genome-wide association studies (G Show more
Ischemic stroke (IS) is a major cause of disability and mortality, but its genetic basis remains poorly understood. This study integrates data from three large-scale genome-wide association studies (GWASs), the GWAS Catalog, MEGASTROKE, and Open GWAS, to identify novel genetic loci linked to IS. Our meta-analysis revealed 124 new IS-associated loci, with enrichment in genes involved in cerebrovascular function, inflammation, and metabolism. Candidate genes like Show less
📄 PDF DOI: 10.1016/j.omtn.2025.102633
HSD17B12
Xiaotao Jiang, Hui Wu, Ning Yan +14 more · 2025 · Research (Washington, D.C.) · added 2026-04-24
The development of an immunosuppressive microenvironment is a critical factor in stomach carcinogenesis. Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) serve a pivotal function in medi Show more
The development of an immunosuppressive microenvironment is a critical factor in stomach carcinogenesis. Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) serve a pivotal function in mediating immune suppression. However, the precise mechanisms underlying PMN-MDSCs infiltration into the tumor immune microenvironment (TIME) and their immunosuppressive functions remain poorly understood. In this investigation, we observed that PMN-MDSCs were up-regulated during stomach carcinogenesis, with gastric cancer (GC) cells secreting CCL26 to promote the infiltration of PMN-MDSCs into the TIME via the CX3CR1 receptor. The infiltrating CX3CR1 Show less
no PDF DOI: 10.34133/research.1002
SNAI1
Xiong Guo, Chong Huang, Ling Zhang +18 more · 2025 · Circulation · added 2026-04-24
Heart failure with preserved ejection fraction (HFpEF) has become the most prevalent type of heart failure, but effective treatments are lacking. Cardiac lymphatics play a crucial role in maintaining Show more
Heart failure with preserved ejection fraction (HFpEF) has become the most prevalent type of heart failure, but effective treatments are lacking. Cardiac lymphatics play a crucial role in maintaining heart health by draining fluids and immune cells. However, their involvement in HFpEF remains largely unexplored. We examined cardiac lymphatic alterations in mice with HFpEF with comorbid obesity and hypertension, and in heart tissues from patients with HFpEF. Using genetically engineered mouse models and various cellular and molecular techniques, we investigated the role of cardiac lymphatics in HFpEF and the underlying mechanisms. In mice with HFpEF, cardiac lymphatics displayed substantial structural and functional anomalies, including decreased lymphatic endothelial cell (LEC) density, vessel fragmentation, reduced branch connections, and impaired capacity to drain fluids and immune cells. LEC numbers and marker expression levels were also decreased in heart tissues from patients with HFpEF. Stimulating lymphangiogenesis with an adeno-associated virus expressing an engineered variant of vascular endothelial growth factor C (VEGFC Our study provides evidence that cardiac lymphatic disruption, driven by impaired BCAA catabolism in LECs, is a key factor contributing to HFpEF. These findings unravel the crucial role of BCAA catabolism in modulating lymphatic biology, and suggest that preserving cardiac lymphatic integrity may present a novel therapeutic strategy for HFpEF. Show less
📄 PDF DOI: 10.1161/CIRCULATIONAHA.124.071741
BCKDK
Xiumeng Hua, Zhe Sun, Congrui Wang +9 more · 2025 · Journal of the American Heart Association · added 2026-04-24
Hypertrophic cardiomyopathy (HCM), characterized by ventricular hypertrophy and fibrosis, frequently progresses to heart failure. Although metabolic dysregulation is implicated in HCM pathophysiology, Show more
Hypertrophic cardiomyopathy (HCM), characterized by ventricular hypertrophy and fibrosis, frequently progresses to heart failure. Although metabolic dysregulation is implicated in HCM pathophysiology, the role of PDK4 (pyruvate dehydrogenase kinase 4), a key regulator of cardiac glucose and fatty acid oxidation, in HCM-related heart failure remains unknown. Single-nucleus RNA sequencing was performed to analyze gene expression in patients with HCM (n=12), categorized into the following groups: normal, reduced, and heart failure. We validated our findings in additional cohorts of patients undergoing septal resection or heart transplantation. Cardiac-specific Single-nucleus RNA sequencing identified distinct cardiomyocyte clusters, with cardiomyocyte cluster 4 ( Our findings highlight metabolic disturbance, specifically PDK4-driven suppression of glucose oxidation, as crucial in HCM progression to heart failure. PDK4 represents a promising therapeutic target for preventing or treating heart failure in patients with HCM. Show less
📄 PDF DOI: 10.1161/JAHA.125.041401
MYBPC3
Megan E Capozzi, David Bouslov, Ashot Sargsyan +21 more · 2025 · The Journal of clinical investigation · added 2026-04-24
The incretin peptides glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptors coordinate β cell secretion that is proportional to nutrient intake. This effect permits consis Show more
The incretin peptides glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptors coordinate β cell secretion that is proportional to nutrient intake. This effect permits consistent and restricted glucose excursions across a range of carbohydrate intake. The canonical signaling downstream of ligand-activated incretin receptors involves coupling to Gαs protein and generation of intracellular cAMP. However, recent reports have highlighted the importance of additional signaling nodes engaged by incretin receptors, including other G proteins and β-arrestin proteins. Here, the importance of Gαs signaling was tested in mice with conditional, postdevelopmental β cell deletion of Gnas (encoding Gαs) under physiological and pharmacological conditions. Deletion of Gαs/cAMP signaling induced immediate and profound hyperglycemia that responded minimally to incretin receptor agonists, a sulfonylurea, or bethanechol. While islet area and insulin content were not affected in Gnasβcell-/-, perifusion of isolated islets demonstrated impaired responses to glucose, incretins, acetylcholine, and IBMX In the absence of Gαs, incretin-stimulated insulin secretion was impaired but not absent, with some contribution from Gαq signaling. Collectively, these findings validate a central role for cAMP in mediating incretin signaling, but also demonstrate broad impairment of insulin secretion in the absence of Gαs that causes both fasting hyperglycemia and glucose intolerance. Show less
📄 PDF DOI: 10.1172/JCI183741
GIPR