👤 Jinjian Yang

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Also published as: A Yang, A-Li Yang, Acong Yang, Ai-Lun Yang, Aige Yang, Airong Yang, Aiting Yang, Aizhen Yang, Albert C Yang, Alex J T Yang, An-Qi Yang, Andrew Yang, Angang Yang, Angela Wei Hong Yang, Anni Yang, Aram Yang, B Yang, Baigao Yang, Baixia Yang, Bangjia Yang, Bao Yang, Baofeng Yang, Baoli Yang, Baoxin Yang, Baoxue Yang, Bei Yang, Beibei Yang, Biao Yang, Bin Q Yang, Bin Yang, Bing Xiang Yang, Bing Yang, Bingyu Yang, Bo Yang, Bohui Yang, Boo-Keun Yang, Bowen Yang, Boya Yang, Burton B Yang, Byoung Chul Yang, Caimei Yang, Caixia Yang, Caixian Yang, Caixin Yang, Can Yang, Canchai Yang, Ce Yang, Celi Yang, Chan Mo Yang, Chan-Mo Yang, Chang Yang, Chang-Hao Yang, Changheng Yang, Changqing Yang, Changsheng Yang, Changwei Yang, Changyun Yang, Chanjuan Yang, Chao Yang, Chao-Yuh Yang, Chaobo Yang, Chaofei Yang, Chaogang Yang, Chaojie Yang, Chaolong Yang, Chaoping Yang, Chaoqin Yang, Chaoqun Yang, Chaowu Yang, Chaoyun Yang, Chaozhe Yang, Chen Die Yang, Chen Yang, Cheng Yang, Cheng-Gang Yang, Chengfang Yang, Chenghao Yang, Chengkai Yang, Chengkun Yang, Chengran Yang, Chenguang Yang, Chengyingjie Yang, Chengzhang Yang, Chensi Yang, Chensu Yang, Chenxi Yang, Chenyu Yang, Chenzi Yang, Chi Yang, Chia-Wei Yang, Chieh-Hsin Yang, Chien-Wen Yang, Chih-Hao Yang, Chih-Min Yang, Chih-Yu Yang, Chihyu Yang, Ching-Fen Yang, Ching-Wen Yang, Chongmeng Yang, Chuan He Yang, Chuan Yang, Chuanbin Yang, Chuang Yang, Chuanli Yang, Chuhu Yang, Chun Yang, Chun-Chun Yang, Chun-Mao Yang, Chun-Seok Yang, Chunbaixue Yang, Chung-Hsiang Yang, Chung-Shi Yang, Chung-Yi Yang, Chunhua Yang, Chunhui Yang, Chunjie Yang, Chunjun Yang, Chunlei Yang, Chunli Yang, Chunmao Yang, Chunping Yang, Chunqing Yang, Chunru Yang, Chunxiao Yang, Chunyan Yang, Chunyu Yang, Congyi Yang, Cui Yang, Cuiwei Yang, Cunming Yang, Dai-Qin Yang, Dan Yang, Dan-Dan Yang, Dan-Hui Yang, Dandan Yang, Danlu Yang, Danrong Yang, Danzhou Yang, Dapeng Yang, De-Hua Yang, De-Zhai Yang, Decao Yang, Defu Yang, Deguang Yang, Dehao Yang, Dehua Yang, Dejun Yang, Deli Yang, Dengfa Yang, Deok Chun Yang, Deshuang Yang, Di Yang, Dianqiang Yang, Ding Yang, Ding-I Yang, Diya Yang, Diyuan Yang, Dong Yang, Dong-Hua Yang, Dongfeng Yang, Dongjie Yang, Dongliang Yang, Dongmei Yang, Dongren Yang, Dongshan Yang, Dongwei Yang, Dongwen Yang, DuJiang Yang, Eddy S Yang, Edwin Yang, Ei-Wen Yang, Emily Yang, Enlu Yang, Enzhi Yang, Eric Yang, Eryan Yang, Ethan Yang, Eunho Yang, Fajun Yang, Fan Yang, Fang Yang, Fang-Ji Yang, Fang-Kun Yang, Fei Yang, Feilong Yang, Feiran Yang, Feixiang Yang, Fen Yang, Feng Yang, Feng-Ming Yang, Feng-Yun Yang, Fengjie Yang, Fengjiu Yang, Fengjuan Yang, Fenglian Yang, Fengling Yang, Fengping Yang, Fengying Yang, Fengyong Yang, Fu Yang, Fude Yang, Fuhe Yang, Fuhuang Yang, Fumin Yang, Fuquan Yang, Furong Yang, Fuxia Yang, Fuyao Yang, G Y Yang, G Yang, Gan Yang, Gang Yang, Gangyi Yang, Gao Yang, Gaohong Yang, Gaoxiang Yang, Ge Yang, Gong Yang, Gong-Li Yang, Grace H Y Yang, Guan Yang, Guang Yang, Guangdong Yang, Guangli Yang, Guangwei Yang, Guangyan Yang, Guanlin Yang, Gui-Zhi Yang, Guigang Yang, Guitao Yang, Guo Yang, Guo-Can Yang, Guobin Yang, Guofen Yang, Guojun Yang, Guokun Yang, Guoli Yang, Guomei Yang, Guoping Yang, Guoqi Yang, Guosheng Yang, Guotao Yang, Guowang Yang, Guowei Yang, H X Yang, H Yang, Hai Yang, Hai-Chun Yang, Haibo Yang, Haihong Yang, Haikun Yang, Hailei Yang, Hailing Yang, Haiming Yang, Haiping Yang, Haiqiang Yang, Haitao Yang, Haixia Yang, Haiyan Yang, Haiying Yang, Han Yang, Hanchen Yang, Handong Yang, Hang Yang, Hannah Yang, Hanseul Yang, Hanteng Yang, Hao Yang, Hao-Jan Yang, HaoXiang Yang, Haojie Yang, Haolan Yang, Haoqing Yang, Haoran Yang, Haoyu Yang, Harrison Hao Yang, Hee Joo Yang, Heng Yang, Hengwen Yang, Henry Yang, Heqi Yang, Heyi Yang, Heyun Yang, Hoe-Saeng Yang, Hong Yang, Hong-Fa Yang, Hong-Li Yang, HongMei Yang, Hongbing Yang, Hongbo Yang, Hongfa Yang, Honghong Yang, Hongjie Yang, Hongjun Yang, Hongli Yang, Hongling Yang, Hongqun Yang, Hongxia Yang, Hongxin Yang, Hongyan Yang, Hongyu Yang, Hongyuan Yang, Hongyue Yang, Howard H Yang, Howard Yang, Hsin-Chou Yang, Hsin-Jung Yang, Hsin-Sheng Yang, Hua Yang, Hua-Yuan Yang, Huabing Yang, Huafang Yang, Huaijie Yang, Huan Yang, Huanhuan Yang, Huanjie Yang, Huanming Yang, Huansheng Yang, Huanyi Yang, Huarong Yang, Huaxiao Yang, Huazhao Yang, Hui Yang, Hui-Ju Yang, Hui-Li Yang, Hui-Ting Yang, Hui-Yu Yang, Hui-Yun Yang, Huifang Yang, Huihui Yang, Huijia Yang, Huijie Yang, Huiping Yang, Huiran Yang, Huixia Yang, Huiyu Yang, Hung-Chih Yang, Hwai-I Yang, Hye Jeong Yang, Hyerim Yang, Hyun Suk Yang, Hyun-Sik Yang, Ill Yang, Ivana V Yang, J S Yang, J Yang, James Y Yang, Jaw-Ji Yang, Jee Sun Yang, Jenny J Yang, Jerry Yang, Ji Hye Yang, Ji Yang, Ji Yeong Yang, Ji-chun Yang, Jia Yang, Jia-Ling Yang, Jia-Ying Yang, Jiahong Yang, Jiahui Yang, Jiajia Yang, Jiakai Yang, Jiali Yang, Jialiang Yang, Jian Yang, Jian-Bo Yang, Jian-Jun Yang, Jian-Ming Yang, Jian-Ye Yang, JianHua Yang, JianJun Yang, Jianbo Yang, Jiang-Min Yang, Jiang-Yan Yang, Jianing Yang, Jianke Yang, Jianli Yang, Jianlou Yang, Jianmin Yang, Jianming Yang, Jianqi Yang, Jianwei Yang, Jianyu Yang, Jiao Yang, Jiarui Yang, Jiawei Yang, Jiaxin Yang, Jiayan Yang, Jiayi Yang, Jiaying Yang, Jiayue Yang, Jichun Yang, Jie Yang, Jie-Cheng Yang, Jie-Hong Yang, Jie-Kai Yang, Jiefeng Yang, Jiehong Yang, Jieping Yang, Jiexiang Yang, Jihong Yang, Jimin Yang, Jin Yang, Jin-Jian Yang, Jin-Kui Yang, Jin-gang Yang, Jin-ju Yang, Jinan Yang, Jinfeng Yang, Jing Yang, Jing-Quan Yang, Jing-Yu Yang, Jingang Yang, Jingfeng Yang, Jinggang Yang, Jinghua Yang, Jinghui Yang, Jingjing Yang, Jingmin Yang, Jingping Yang, Jingran Yang, Jingshi Yang, Jingwen Yang, Jingya Yang, Jingyan Yang, Jingyao Yang, Jingye Yang, Jingyu Yang, Jingyun Yang, Jingze Yang, Jinhua Yang, Jinhui Yang, Jinpeng Yang, Jinru Yang, Jinshan Yang, Jinsong Yang, Jinsung Yang, Jinwen Yang, Jinzhao Yang, Jiong Yang, Ju Dong Yang, Ju Young Yang, Juan Yang, Juesheng Yang, Jumei Yang, Jun J Yang, Jun Yang, Jun-Hua Yang, Jun-Xia Yang, Jun-Xing Yang, Junbo Yang, Jung Dug Yang, Jung Wook Yang, Jung-Ho Yang, Junhan Yang, Junjie Yang, Junlin Yang, Junlu Yang, Junping Yang, Juntao Yang, Junyao Yang, Junyi Yang, Kai Yang, Kai-Chien Yang, Kai-Chun Yang, Kaidi Yang, Kaifeng Yang, Kaijie Yang, Kaili Yang, Kailin Yang, Kaiwen Yang, Kang Yang, Kang Yi Yang, Kangning Yang, Karen Yang, Ke Yang, Keming Yang, Keping Yang, Kexin Yang, Kuang-Yao Yang, Kui Yang, Kun Yang, Kunao Yang, Kunqi Yang, Kunyu Yang, Kuo Tai Yang, L Yang, Lamei Yang, Lan Yang, Le Yang, Lei Yang, Lexin Yang, Leyi Yang, Li Chun Yang, Li Yang, Li-Kun Yang, Li-Qin Yang, Li-li Yang, LiMan Yang, Lian-he Yang, Liang Yang, Liang-Yo Yang, Liangbin Yang, Liangle Yang, Liangliang Yang, Lichao Yang, Lichuan Yang, Licong Yang, Liehao Yang, Lihong Yang, Lihua Yang, Lihuizi Yang, Lijia Yang, Lijie Yang, Lijuan Yang, Lijun Yang, Lili Yang, Lin Sheng Yang, Lin Yang, Lina Yang, Ling Ling Yang, Ling Yang, Lingfeng Yang, Lingling Yang, Lingzhi Yang, Linlin Yang, Linnan Yang, Linqing Yang, Linquan Yang, Lipeng Yang, Liping Yang, Liting Yang, Liu Yang, Liu-Kun Yang, LiuMing Yang, Liuliu Yang, Liwei Yang, Lixian Yang, Lixue Yang, Long In Yang, Long Yang, Long-Yan Yang, Longbao Yang, Longjun Yang, Longyan Yang, Lu M Yang, Lu Yang, Lu-Hui Yang, Lu-Kun Yang, Lu-Qin Yang, Luda Yang, Man Yang, Manqing Yang, Maojie Yang, Maoquan Yang, Mei Yang, Meichan Yang, Meihua Yang, Meili Yang, Meiting Yang, Meixiang Yang, Meiying Yang, Meng Yang, Menghan Yang, Menghua Yang, Mengjie Yang, Mengli Yang, Mengliu Yang, Mengmeng Yang, Mengsu Yang, Mengwei Yang, Mengying Yang, Miaomiao Yang, Mickey Yang, Min Hee Yang, Min Yang, Mina Yang, Ming Yang, Ming-Hui Yang, Ming-Yan Yang, Minghui Yang, Mingjia Yang, Mingjie Yang, Mingjun Yang, Mingli Yang, Mingqian Yang, Mingshi Yang, Mingyan Yang, Mingyu Yang, Minyi Yang, Misun Yang, Mu Yang, Muh-Hwa Yang, Na Yang, Nan Yang, Nana Yang, Nanfei Yang, Neil V Yang, Ni Yang, Ning Yang, Ningjie Yang, Ningli Yang, Pan Yang, Pan-Chyr Yang, Paul Yang, Peichang Yang, Peiran Yang, Peiyan Yang, Peiying Yang, Peiyuan Yang, Peizeng Yang, Peng Yang, Peng-Fei Yang, PengXiang Yang, Pengfei Yang, Penghui Yang, Pengwei Yang, Pengyu Yang, Phillip C Yang, Pin Yang, Ping Yang, Ping-Fen Yang, Pinghong Yang, Pu Yang, Q H Yang, Q Yang, Qi Yang, Qi-En Yang, Qian Yang, Qian-Jiao Yang, Qian-Li Yang, QianKun Yang, Qiang Yang, Qianhong Yang, Qianqian Yang, Qianru Yang, Qiaoli Yang, Qiaorong Yang, Qiaoyuan Yang, Qifan Yang, Qifeng Yang, Qiman Yang, Qimeng Yang, Qiming Yang, Qin Yang, Qinbo Yang, Qing Yang, Qing-Cheng Yang, Qingcheng Yang, Qinghu Yang, Qingkai Yang, Qinglin Yang, Qingling Yang, Qingmo Yang, Qingqing Yang, Qingtao Yang, Qingwu Yang, Qingya Yang, Qingyan Yang, Qingyi Yang, Qingyu Yang, Qingyuan Yang, Qiong Yang, Qiu Yang, Qiu-Yan Yang, Qiuhua Yang, Qiuhui Yang, Qiulan Yang, Qiuli Yang, Qiuxia Yang, Qiwei Yang, Qiwen Yang, Quan Yang, Quanjun Yang, Quanli Yang, Qun-Fang Yang, R Yang, Ran Yang, Ren-Zhi Yang, Renchi Yang, Renhua Yang, Renjun Yang, Renqiang Yang, Renzhi Yang, Ri-Yao Yang, Richard K Yang, Robert Yang, Rong Yang, Rongrong Yang, Rongxi Yang, Rongyuan Yang, Rongze Yang, Rui Xu Yang, Rui Yang, Rui-Xu Yang, Rui-Yi Yang, Ruicheng Yang, Ruifang Yang, Ruihua Yang, Ruilan Yang, Ruili Yang, Ruiqin Yang, Ruirui Yang, Ruiwei Yang, Rulai Yang, Ruming Yang, Run Yang, Runjun Yang, Runxu Yang, Runyu Yang, Runzhou Yang, Ruocong Yang, Ruoyun Yang, Ruyu Yang, S J Yang, Se-Ran Yang, Sen Yang, Senwen Yang, Seung Yun Yang, Seung-Jo Yang, Seung-Ok Yang, Shan Yang, Shangchen Yang, Shanghua Yang, Shangwen Yang, Shanzheng Yang, Shao-Hua Yang, Shaobin Yang, Shaohua Yang, Shaoling Yang, Shaoqi Yang, Shaoqing Yang, Sheng Sheng Yang, Sheng Yang, Sheng-Huei Yang, Sheng-Qian Yang, Sheng-Wu Yang, ShengHui Yang, Shenglin Yang, Shengnan Yang, Shengqian Yang, Shengyong Yang, Shengzhuang Yang, Shenhui Yang, Shi-Ming Yang, Shiaw-Der Yang, Shifeng Yang, Shigao Yang, Shijie Yang, Shiming Yang, Shipeng Yang, Shiping Yang, Shiu-Ju Yang, Shiyi Yang, Shizhong Yang, Shizhuo Yang, Shu Yang, ShuSheng Yang, Shuai Yang, Shuaibing Yang, Shuaini Yang, Shuang Yang, Shuangshuang Yang, Shucai Yang, Shufang Yang, Shuhua Yang, Shujuan Yang, Shujun Yang, Shulan Yang, Shulin Yang, Shuming Yang, Shun-Fa Yang, Shuo Yang, Shuofei Yang, Shuping Yang, Shuqi Yang, Shuquan Yang, Shurong Yang, Shushen Yang, Shuye Yang, Shuyu Yang, Si Yang, Si-Fu Yang, Sibao Yang, Sibo Yang, Sichong Yang, Sihui Yang, Sijia Yang, Siqi Yang, Sirui Yang, Sisi Yang, Sitao Yang, Siwen Yang, Siyi Yang, Siyu Yang, Sizhen Yang, Sizhu Yang, Song Yang, Song-na Yang, Songpeng Yang, Songye Yang, Soo Hyun Yang, Su Yang, Su-Geun Yang, Suhong Yang, Sujae Yang, Sujuan Yang, Suk-Kyun Yang, Sun Kyung Yang, Suwol Yang, Suxia Yang, Suyi Yang, Suyu Yang, Tai-Hui Yang, Tailai Yang, Tao Yang, Tengyun Yang, Thomas P Yang, Ti Yang, Tian Yang, Tianbao Yang, Tianfeng Yang, Tianjie Yang, Tianmin Yang, Tianpeng Yang, Tianqiong Yang, Tiantian Yang, Tianxin Yang, Tianyou Yang, Tianyu Yang, Tianze Yang, Tianzhong Yang, Ting Yang, Ting-Xian Yang, Tingting Yang, Tingyu Yang, Tong Yang, Tong Yi Yang, Tong-Xin Yang, Tonglin Yang, Tongren Yang, Tuanmin Yang, Ueng-Cheng Yang, W Yang, Wan-Chen Yang, Wan-Jung Yang, Wang Yang, Wannian Yang, Wei Qiang Yang, Wei Yang, Wei-Fa Yang, Wei-Xin Yang, Weidong Yang, Weiguang Yang, Weihan Yang, Weijian Yang, Weili Yang, Weimin Yang, Weiran Yang, Weiwei Yang, Weixian Yang, Weizhong Yang, Wen Yang, Wen Z Yang, Wen-Bin Yang, Wen-Chin Yang, Wen-He Yang, Wen-Hsuan Yang, Wen-Ming Yang, Wen-Wen Yang, Wen-Xiao Yang, WenKai Yang, Wenbo Yang, Wenchao Yang, Wending Yang, Wenfei Yang, Wenhong Yang, Wenhua Yang, Wenhui Yang, Wenjian Yang, Wenjie Yang, Wenjing Yang, Wenjuan Yang, Wenjun Yang, Wenli Yang, Wenlin Yang, Wenming Yang, Wenqin Yang, Wenshan Yang, Wentao Yang, Wenwen Yang, Wenwu Yang, Wenxin Yang, Wenxing Yang, Wenying Yang, Wenzhi Yang, Wenzhu Yang, William Yang, Woong-Suk Yang, Wu Yang, Wu-de Yang, X Yang, X-J Yang, Xi Yang, Xi-You Yang, Xia Yang, Xian Yang, Xiang Yang, Xiang-Hong Yang, Xiang-Jun Yang, Xianggui Yang, Xianghong Yang, Xiangliang Yang, Xiangling Yang, Xiangqiong Yang, Xiangxiang Yang, Xiangyu Yang, Xiao Yang, Xiao-Dong Yang, Xiao-Fang Yang, Xiao-Hong Yang, Xiao-Jie Yang, Xiao-Juan Yang, Xiao-Meng Yang, Xiao-Ming Yang, Xiao-Qian Yang, Xiao-Yan Yang, Xiao-Ying Yang, Xiao-Yu Yang, Xiao-guang Yang, XiaoYan Yang, Xiaoao Yang, Xiaobin Yang, Xiaobo Yang, Xiaochen Yang, Xiaodan Yang, Xiaodi Yang, Xiaodong Yang, Xiaofei Yang, Xiaofeng Yang, Xiaohao Yang, Xiaohe Yang, Xiaohong R Yang, Xiaohong Yang, Xiaohuang Yang, Xiaohui Yang, Xiaojian Yang, Xiaojie Yang, Xiaojing Yang, Xiaojuan Yang, Xiaojun Yang, Xiaoli Yang, Xiaolu Yang, Xiaomeng Yang, Xiaoming Yang, Xiaonan Yang, Xiaoping Yang, Xiaoqian Yang, Xiaoqin Yang, Xiaoqun Yang, Xiaorong Yang, Xiaoshan Yang, Xiaoshi Yang, Xiaosong Yang, Xiaotian Yang, Xiaotong Yang, Xiaowei Yang, Xiaowen Yang, Xiaoxiao Yang, Xiaoxin Yang, Xiaoxu Yang, Xiaoyao Yang, Xiaoyi Yang, Xiaoyong Yang, Xiaoyu Yang, Xiaoyun Yang, Xiaozhen Yang, Xifei Yang, Xiling Yang, Ximan Yang, Xin Yang, Xin-He Yang, Xin-Yu Yang, Xin-Zhuang Yang, Xing Yang, Xinghai Yang, Xinglong Yang, Xingmao Yang, Xingming Yang, Xingsheng Yang, Xingyu Yang, Xingyue Yang, Xingzhi Yang, Xinjing Yang, Xinming Yang, Xinpu Yang, Xinwang Yang, Xinxin Yang, Xinyan Yang, Xinyi Yang, Xinyu Yang, Xinyue Yang, Xiong Ling Yang, Xiru Yang, Xitong Yang, Xiu Hong Yang, Xiuhua Yang, Xiulin Yang, Xiuna Yang, Xiuqin Yang, Xiurong Yang, Xiuwei Yang, Xiwen Yang, Xiyue Yang, Xu Yang, Xuan Yang, Xue Yang, Xue-Feng Yang, Xue-Ping Yang, Xuecheng Yang, Xuehan Yang, Xuejing Yang, Xuejun Yang, Xueli Yang, Xuena Yang, Xueping Yang, Xuesong Yang, Xuhan Yang, Xuhui Yang, Xuping Yang, Xuyang Yang, Y C Yang, Y F Yang, Y L Yang, Y P Yang, Y Q Yang, Y Yang, Y-T Yang, Ya Yang, Ya-Chen Yang, Yadong Yang, Yafang Yang, Yajie Yang, Yalan Yang, Yali Yang, Yaming Yang, Yan Yang, Yan-Bei Yang, Yan-Ling Yang, Yanan Yang, Yanfang Yang, Yang Yang, Yangfan Yang, Yangyang Yang, Yanhui Yang, Yanjianxiong Yang, Yanling Yang, Yanmei Yang, Yanmin Yang, Yanping Yang, Yanru Yang, Yanting Yang, Yanyan Yang, Yanzhen Yang, Yaorui Yang, Yaping Yang, Yaqi Yang, Yaxi Yang, Ye Yang, Yefa Yang, Yefeng Yang, Yeqing Yang, Yexin Yang, Yi Yang, Yi-Chieh Yang, Yi-Fang Yang, Yi-Feng Yang, Yi-Liang Yang, Yi-Ping Yang, Yi-ning Yang, Yibing Yang, Yichen Yang, Yidong Yang, Yifan Yang, Yifang Yang, Yifei Yang, Yifeng Yang, Yihe Yang, Yijie Yang, Yilian Yang, Yimei Yang, Yimin Yang, Yiming Yang, Yimu Yang, Yin-Rong Yang, Yinfeng Yang, Ying Yang, Ying-Hua Yang, Ying-Ying Yang, Yingdi Yang, Yingjun Yang, Yingqing Yang, Yingrui Yang, Yingxia Yang, Yingyu Yang, Yinhua Yang, Yining Yang, Yinxi Yang, Yiping Yang, Yiting Yang, Yiyi Yang, Yiying Yang, Yong Yang, Yong-Yu Yang, Yongfeng Yang, Yongguang Yang, Yonghong Yang, Yonghui Yang, Yongjia Yang, Yongjie Yang, Yongkang Yang, Yongqiang Yang, Yongsan Yang, Yongxin Yang, Yongxing Yang, Yongzhong Yang, Yoon La Yang, Yoon Mee Yang, Youhua Yang, YoungSoon Yang, Yu Yang, Yu-Fan Yang, Yu-Feng Yang, Yu-Jie Yang, Yu-Shi Yang, Yu-Tao Yang, Yu-Ting Yang, Yuan Yang, Yuan-Han Yang, Yuan-Jian Yang, Yuanhao Yang, Yuanjin Yang, Yuanquan Yang, Yuanrong Yang, Yuanying Yang, Yuanzhang Yang, Yuanzhi Yang, Yuchen Yang, Yucheng Yang, Yue Yang, Yueh-Ning Yang, Yuejin Yang, Yuexiang Yang, Yueze Yang, Yufan Yang, Yuhan Yang, Yuhang Yang, Yuhua Yang, Yujie Yang, Yujing Yang, Yulin Yang, Yuling Yang, Yulong Yang, Yun Yang, YunKai Yang, Yunfan Yang, Yung-Li Yang, Yunhai Yang, Yunlong Yang, Yunmei Yang, Yunwen Yang, Yunyun Yang, Yunzhao Yang, Yupeng Yang, Yuqi Yang, Yuta Yang, Yutao Yang, Yuting Yang, Yutong Yang, Yuwei Yang, Yuxi Yang, Yuxing Yang, Yuxiu Yang, Yuyan Yang, Yuyao Yang, Yuying Yang, Z Yang, Zaibin Yang, Zaiming Yang, Zaiqing Yang, Zanhao Yang, Ze Yang, Zemin Yang, Zeng-Ming Yang, Zengqiang Yang, Zengqiao Yang, Zeyu Yang, Zhang Yang, Zhangping Yang, Zhanyi Yang, Zhao Yang, Zhao-Na Yang, Zhaojie Yang, Zhaoli Yang, Zhaoxin Yang, Zhaoyang Yang, Zhaoyi Yang, Zhehan Yang, Zheming Yang, Zhen Yang, Zheng Yang, Zheng-Fei Yang, Zheng-lin Yang, Zhenglin Yang, Zhengqian Yang, Zhengtao Yang, Zhenguo Yang, Zhengyan Yang, Zhengzheng Yang, Zhengzhong Yang, Zhenhua Yang, Zhenjun Yang, Zhenmei Yang, Zhenqi Yang, Zhenrong Yang, Zhenwei Yang, Zhenxing Yang, Zhenyun Yang, Zhenzhen Yang, Zheyu Yang, Zhi Yang, Zhi-Can Yang, Zhi-Hong Yang, Zhi-Jun Yang, Zhi-Min Yang, Zhi-Ming Yang, Zhi-Rui Yang, Zhibo Yang, Zhichao Yang, Zhifen Yang, Zhigang Yang, Zhihang Yang, Zhihong Yang, Zhikuan Yang, Zhikun Yang, Zhimin Yang, Zhiming Yang, Zhiqiang Yang, Zhitao Yang, Zhiwei Yang, Zhixin Yang, Zhiyan Yang, Zhiyong Yang, Zhiyou Yang, Zhiyuan Yang, Zhongan Yang, Zhongfang Yang, Zhonghua Yang, Zhonghui Yang, Zhongli Yang, Zhongshu Yang, Zhongzhou Yang, Zhou Yang, Zhuliang Yang, Zhuo Yang, Zhuoya Yang, Zhuoyu Yang, Zi F Yang, Zi Yang, Zi-Han Yang, Zi-Wei Yang, Zicong Yang, Zifeng Yang, Zihan Yang, Ziheng Yang, Zijiang Yang, Zishan Yang, Zixia Yang, Zixuan Yang, Ziying Yang, Ziyou Yang, Ziyu Yang, Zong-de Yang, Zongfang Yang, Zongyu Yang, Zunxian Yang, Zuozhen Yang
articles
Dongchi Cai, Jialin Ji, Chunhui Yang +1 more · 2025 · Oncology research · added 2026-04-24
Metabolic reprogramming involving branched-chain amino acids (BCAAs)-leucine, isoleucine, and valine-is increasingly recognized as pivotal in cancer progression, metastasis, and immune modulation. Thi Show more
Metabolic reprogramming involving branched-chain amino acids (BCAAs)-leucine, isoleucine, and valine-is increasingly recognized as pivotal in cancer progression, metastasis, and immune modulation. This review comprehensively explores how cancer cells rewire BCAA metabolism to enhance proliferation, survival, and therapy resistance. Tumors manipulate BCAA uptake and catabolism via high expression of transporters like L-type amino acid transporter 1 (LAT1) and enzymes including branched chain amino acid transaminase 1(BCAT1), branched chain amino acid transaminase 2 (BCAT2), branched-chain alpha-keto acid dehydrogenase (BCKDH), and branched chain alpha-keto acid dehydrogenase kinase (BCKDK). These alterations sustain energy production, biosynthesis, redox homeostasis, and oncogenic signaling (especially mammalian target of rapamycin complex 1 [mTORC1]). Crucially, tumor-driven BCAA depletion also shapes an immunosuppressive microenvironment, impairing anti-tumor immunity by limiting essential nutrients for T cells and natural killer (NK) cells. Innovative therapeutic strategies targeting BCAA pathways-ranging from selective small-molecule inhibitors (e.g., LAT1 and BCAT1/2) to dietary modulation-have shown promising preclinical and early clinical efficacy, highlighting their potential to exploit metabolic vulnerabilities in cancer cells while bolstering immune responses. By integrating multi-omics data and precision targeting approaches, this review underscores the translational significance of BCAA metabolic reprogramming, positioning it as a novel frontier in cancer treatment. Show less
📄 PDF DOI: 10.32604/or.2025.071152
BCKDK
Meng Wang, Zhao Liu, Shuxun Ren +16 more · 2025 · Theranostics · added 2026-04-24
📄 PDF DOI: 10.7150/thno.105894
BCKDK
Hao Wu, Jiajia Yang, Zixia Yang +8 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
The protein branched-chain ketoacid dehydrogenase kinase (BCKDK), which regulates the metabolism of branched-chain amino acids, has recently been implicated in tumor progression. However, the role of Show more
The protein branched-chain ketoacid dehydrogenase kinase (BCKDK), which regulates the metabolism of branched-chain amino acids, has recently been implicated in tumor progression. However, the role of BCKDK in lung cancer remains largely unexplored. In this study, we explored the mechanisms by which BCKDK influences lung cancer progression and contributes to drug resistance. By integrating single-cell RNA and bulk RNA sequencing data from lung cancer patients, we identified BCKDK as a novel gene related to malignant epithelial cells, involved in tumor initiation and associated with poor patient prognosis. Subsequently, through a series of molecular biology experiments, we demonstrated that BCKDK promotes aerobic glycolysis, Trametinib resistance, and tumor progression in lung cancer by upregulating MYC transcription. Mechanistically, BCKDK interacts with BCLAF1 to promote its phosphorylation at the serine 285 site. This modification facilitates BCLAF1 binding to the MYC promoter, thereby enhancing MYC transcription. Subsequently, elevated MYC levels upregulate hexokinase 2, promoting aerobic glycolysis and lung cancer progression. In addition, the elevated glycolysis product, lactate, promotes Trametinib resistance by upregulating the ABC transporters. Taken together, our data identify BCKDK as a novel regulator of aerobic glycolysis that promotes lung cancer progression and Trametinib resistance through the BCKDK/BCLAF1/MYC/HK2 axis. Targeting BCKDK in combination with Trametinib may offer a promising treatment for lung cancer. Graphical representation of the BCKDK/BCLAF1/MYC/HK2 axis and its role in Trametinib resistance and lung cancer progression. Created with BioRender.com. Show less
no PDF DOI: 10.1038/s41418-025-01531-6
BCKDK
Haiying Liu, Jiaqian Feng, Tingting Pan +10 more · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Homologous recombination repair (HRR) is crucial for maintaining genomic stability by repairing DNA damage. Despite its importance, HRR's role in cancer progression is not fully elucidated. Here, this Show more
Homologous recombination repair (HRR) is crucial for maintaining genomic stability by repairing DNA damage. Despite its importance, HRR's role in cancer progression is not fully elucidated. Here, this work shows that nuclear-localized branched-chain α-ketoacid dehydrogenase kinase (BCKDK) acts as a modulator of HRR, promoting cell resistance against DNA damage-inducing therapy in breast cancer. Mechanistically, this work demonstrates that BCKDK is localized in the nucleus and phosphorylates RNF8 at Ser157, preventing the ubiquitin-mediated degradation of RAD51, thereby facilitating HRR-mediated DNA repair under replication stress. Notably, aberrant expression of the BCKDK/p-RNF8/RAD51 axis correlates with breast cancer progression and poor patient survival. Furthermore, this work identifies a small molecule inhibitor of BCKDK, GSK180736A, that disrupts its HRR function and exhibits strong tumor suppression when combined with DNA damage-inducing drugs. Collectively, this study reveals a new role of BCKDK in regulating HRR, independent of its metabolic function, presenting it as a potential therapeutic target and predictive biomarker in breast cancer. Show less
📄 PDF DOI: 10.1002/advs.202416590
BCKDK
Yicun Li, Yun Wu, Xiaolian Li +4 more · 2025 · Scientific reports · Nature · added 2026-04-24
Head and neck squamous cell carcinoma (HNSCC) poses a global health challenge. The management of HNSCC is complicated by the difficulty in detecting occult lymph node metastases, leading to dilemmas i Show more
Head and neck squamous cell carcinoma (HNSCC) poses a global health challenge. The management of HNSCC is complicated by the difficulty in detecting occult lymph node metastases, leading to dilemmas in elective neck dissection decisions, which will impair patients' quality of life without improving survival for nodal negative patients. We conducted a comparative analysis of the clinical features, genomic alterations, gene expression and methylation, tumor microenvironment and cellular states between the clinically N0 and pathologically N0 (cN0-pN0) patients and occult lymph node metastatic patients. Patients with occult lymph node metastases typically present with more poorly differentiated primary tumors and higher rates of angiolymphatic and perineural invasion. We identified a distinctive genomic mutation spectrum in the primary tumors of patients with occult metastases, notably in genes such as NSD1, ARHGAP15 and SMARCA4. A whole-genome DNA hypomethylation and altered gene expression profiles are identified in occult lymph node metastatic patients. Analysis of the tumor microenvironment revealed an enrichment of CARNS1 + NK cells and CBX1 + tumor cells in occult metastatic patients. In conclusion, patients with occult lymph node metastases exhibit distinct molecular and clinical features compared with cN0-pN0 patients. Show less
📄 PDF DOI: 10.1038/s41598-025-10320-7
CBX1
Rongjia Wang, Xunde Dong, Xiuling Liu +5 more · 2025 · Computer methods and programs in biomedicine · Elsevier · added 2026-04-24
Cardiovascular diseases are one of the major health threats to humans. Researchers have proposed numerous deep learning-based methods for the automatic analysis of electrocardiogram (ECG), achieving e Show more
Cardiovascular diseases are one of the major health threats to humans. Researchers have proposed numerous deep learning-based methods for the automatic analysis of electrocardiogram (ECG), achieving encouraging results. However, many existing methods are limited to task-specific model training and require retraining or full fine-tuning when confronted with a new ECG classification task, thus lacking flexibility in clinical applications. In this study, we propose a Task-Adaptive Classification method for ECG (TAC-ECG) based on cross-modal contrastive learning and low-rank convolutional adapters. TAC-ECG comprises two main phases. In the first phase, inspired by the Contrastive Language-Image Pre-training, we design the Contrastive ECG-Text Pre-training (CETP) to pre-train a robust ECG encoder. In the second phase, the pre-trained ECG encoder is frozen and integrated with a lightweight plug-in, the Low-Rank Convolutional Adapter (LRC-Adapter), forming an extensible ECG classification model. The frozen encoder extracts more discriminative features from the ECG signal, while the LRC-Adapter enables task-specific adaptation. For diverse ECG classification tasks, TAC-ECG only requires training the LRC-Adapter. This mechanism enables TAC-ECG to efficiently perform different ECG classification tasks, significantly reducing resource consumption and deployment costs in multi-tasking scenarios compared to traditional fully fine-tuned methods. We conducted extensive experiments using six different network architectures as ECG encoders. Specifically, we performed ECG classification experiments on four datasets: CPSC2018, Cinc2017, PTB-XL, and Chapman, targeting 9-category, 3-category, 5-category, and 4-category classifications respectively. The TAC-ECG achieved highly competitive results using only approximately 3% of the trainable parameters and approximately 25% of the total parameters compared to the fully fine-tuned method. These results demonstrates the effectiveness and practicality of the TAC-ECG method. The TAC-ECG offers a flexible and efficient method for ECG classification, enabling rapid adaptation to diverse tasks and enhancing clinical diagnostic practicality. Show less
no PDF DOI: 10.1016/j.cmpb.2025.108918
CETP
Yongjian Xu, Bo Yang, Xie Jinyang +1 more · 2025 · Fish physiology and biochemistry · Springer · added 2026-04-24
Feed is very important for fish farming. The appropriate composition and proportion of feed ingredients can promote the growth of fish, maintain normal physiology and behavior, and even improve the re Show more
Feed is very important for fish farming. The appropriate composition and proportion of feed ingredients can promote the growth of fish, maintain normal physiology and behavior, and even improve the resistance ability to disease and stress, etc. The core of artificial compound feed (ACF) is the composition and proportion of lipid, protein, and carbohydrate, which are also the main nutritional components required by fish. Appropriate levels and ratios can promote fish growth and save costs, and the improper would affect the biological clock systems of fish, leading to metabolic abnormalities. This study explored the preparation of ACF for H. kuda. The composition and proportion of the three main nutrients in ACF were screened based on the synchronicity between six pairs of clock genes (Clock, Bmal1, Per1, Per2, Per3, Cry1, and Cry2) in the central and peripheral clock systems, as well as the expression of eight lipid-metabolism genes (Hmgcr, Mvk, Mvd, Lss, Fdps, Cetp, Scap, Srebp1, Srebp2) in the liver and their synergy with liver clock genes. The results showed that, based on several parameters such as gene expression cycle, relative expression level, and top phase appearance time, the best synergy between the central and peripheral circadian clock systems was observed in ACF with crude fat content of 8.80%, crude protein content more than 38.4%, and carbohydrate content of 23.5%. Based on the expression relationship between lipid metabolism genes and circadian clock genes in the liver, it was further clarified that the optimal levels of fat, protein, and carbohydrate were determined with 8.80%, 38.4%, and 23.5%, respectively. After 4 weeks of breeding validation, compared with frozen Mysis, the screened ACF fed for H. kuda showed significant advantages in body length specific growth rate (SGR Show less
📄 PDF DOI: 10.1007/s10695-025-01514-x
CETP
Chuang Yang, Yiyuan Sun, Yihan Li +1 more · 2025 · Environmental health and preventive medicine · added 2026-04-24
Cancer is a major public health concern, particularly among middle-aged and elderly populations, who are disproportionately affected by rising cancer incidence. Environmental pollution has been identi Show more
Cancer is a major public health concern, particularly among middle-aged and elderly populations, who are disproportionately affected by rising cancer incidence. Environmental pollution has been identified as a significant risk factor for cancer development. China's Carbon Emission Trading Policy (CETP), implemented in pilot regions since 2013, aims to reduce carbon emissions and improve air quality. This study evaluates the impact of CETP on pan-cancer incidence, with a focus on its effects on specific cancer types and vulnerable populations. This quasi-natural experiment utilized data from the China Health and Retirement Longitudinal Study (CHARLS) and environmental data from the China National Environmental Monitoring Center (2011-2018). A staggered difference-in-differences (DID) model was employed to estimate the impact of CETP on cancer incidence. Robustness tests, including parallel trend tests, placebo analysis, and entropy balancing, validated the findings. Subgroup analyses were performed to assess the policy's heterogeneous effects based on gender, Body Mass Index (BMI), and smoking status. CETP implementation significantly reduced the incidence of six cancer types: endometrial, cervical, gastric, esophageal, breast, and lung cancers. Overall, pan-cancer incidence significantly declined post-policy implementation (CETP × POST: -47.200, 95% CI: [-61.103, -33.296], p < 0.001). The policy demonstrated stronger effects in highly polluted areas and among individuals with poorer mental health. Subgroup analysis revealed that females, individuals with lower BMI, and non-smokers experienced more substantial benefits. CETP significantly reduces cancer incidence by improving environmental quality and influencing mental health, with particularly strong effects observed among high-risk populations. This study highlights the important role of environmental economic policies in mitigating cancer burden and promoting public health. Future research should further explore the long-term impacts of this policy and its applicability across different national and regional contexts. Show less
📄 PDF DOI: 10.1265/ehpm.24-00387
CETP
Zhaoyang Ye, Guangliang Bai, Ling Yang +7 more · 2025 · Microorganisms · MDPI · added 2026-04-24
Diabetes mellitus (DM) and tuberculosis (TB) are two global health challenges that significantly impact population health, with DM increasing susceptibility to TB infections. However, early risk predi Show more
Diabetes mellitus (DM) and tuberculosis (TB) are two global health challenges that significantly impact population health, with DM increasing susceptibility to TB infections. However, early risk prediction methods for DM patients complicated with TB (DM-TB) are lacking. This study mined transcriptome data of DM-TB patients from the GEO database (GSE181143 and GSE114192) and used differential analysis, weighted gene co-expression network analysis (WGCNA), intersecting immune databases, combined with ten machine learning algorithms, to identify immune biomarkers associated with DM-TB. An early alert model for DM-TB was constructed based on the identified core differentially expressed genes (DEGs) and validated through a prospective cohort study and reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) for gene expression levels. Furthermore, we performed a detailed immune status analysis of DM-TB patients using the CIBERSORT algorithm. We identified 1090 DEGs associated with DM-TB and further pinpointed CETP (cholesteryl ester transfer protein) (AUC = 0.804, CI: 0.744-0.864), TYROBP (TYRO protein tyrosine kinase binding protein) (AUC = 0.810, CI: 0.752-0.867), and SECTM1 (secreted and transmembrane protein 1) (AUC = 0.811, CI: 0.757-0.864) as immune-related biomarkers for DM-TB patients. An early alert model was developed based on these three genes (AUC = 0.86, CI: 0.813-0.907), with a sensitivity of 0.80829 and a specificity of 0.75758 at a Youden index of 0.56587. External validation using the GSE114192 dataset showed an AUC of 0.901 (CI: 0.847-0.955). Population cohort research and RT-qPCR verified the expression levels of these three genes, demonstrating consistency with trends seen in the training set. KEGG enrichment analysis revealed that NF-κB and MAPK signaling pathways play crucial roles in the DM-TB pathogenic mechanism, and immune infiltration analysis showed significant suppression of certain adaptive immune cells and activation of inflammatory cells in DM-TB patients. This study identified three potential immune-related biomarkers for DM-TB, and the constructed risk assessment model demonstrated significant predictive efficiency, providing an early screening strategy for DM-TB. Show less
📄 PDF DOI: 10.3390/microorganisms13040919
CETP
Jing Yang, Guoli Li, Shan Wang +6 more · 2025 · Biomedicines · MDPI · added 2026-04-24
Mounting evidence indicates that the short-chain fatty acid butyrate protects against obesity and associated comorbidities, partially through the induction of adipose tissue thermogenesis. However, th Show more
Mounting evidence indicates that the short-chain fatty acid butyrate protects against obesity and associated comorbidities, partially through the induction of adipose tissue thermogenesis. However, the effects of butyrate on white adipose tissue (WAT) browning and its molecular mechanism are still elusive. The objective of this study was to investigate butyrate-induced thermogenesis in white adipose tissue and its underlying mechanism. We studied the effects of butyrate on diet-induced obesity in the humanized APOE*3-Leiden.CETP transgenic mouse model and explored factors related to white adipose browning. Specifically, mice were challenged with a high-fat diet supplemented with butyrate. Adiposity was measured to assess obesity development. Energy metabolism was detected using an indirect calorimetry system. RNA-seq analysis was conducted to analyze the transcription landscape of WAT and responsible targets. Furthermore, the revealed molecular mechanism was verified in vitro. Butyrate alleviated high-fat diet-induced obesity and promoted energy expenditure accompanied by brown adipose tissue activation and WAT browning. Mechanistically, RNA-seq analysis revealed that butyrate downregulated HDAC9 in WAT. Additionally, butyrate decreased HDAC9 while increasing thermogenesis in vitro. Inhibition of HDAC9 with TMP269 promoted thermogenic gene expression, mimicking the effects of butyrate. Butyrate protects against diet-induced obesity accompanied by decreasing the expression of HDAC9 in white adipose tissue and inducing browning. This study reveals a new mechanism whereby butyrate activates adaptive thermogenesis and provides new insights for the development of weight-loss drugs targeting adipose HDAC9. Show less
📄 PDF DOI: 10.3390/biomedicines13020260
CETP
Wenhui Hu, Han Feng, Ying Liu +8 more · 2025 · Human vaccines & immunotherapeutics · Taylor & Francis · added 2026-04-24
Cholesteryl ester transfer protein (CETP) plays a key role in lipoprotein metabolism, and its activity has been linked to the risk of atherosclerosis (AS). CETP inhibitors, such as obicetrapib, repres Show more
Cholesteryl ester transfer protein (CETP) plays a key role in lipoprotein metabolism, and its activity has been linked to the risk of atherosclerosis (AS). CETP inhibitors, such as obicetrapib, represent a novel approach in immunotherapy to reduce the risk of atherosclerotic cardiovascular disease (ASCVD) by targeting lipid metabolism. In addition, CETP vaccines are being explored as a novel strategy for the prevention and treatment of ASCVD by inducing the body to produce antibodies against CETP, which is expected to reduce CETP activity, thereby increasing high-density lipoproteins (HDL) levels. This paper provides a comprehensive overview of the structure of CETP, the mechanisms of lipid transfer and the progress of immunotherapy in the last decade, which provides possible ideas for future development of novel drugs and optimization of immunization strategies. Show less
📄 PDF DOI: 10.1080/21645515.2025.2462466
CETP
Jiahao Liu, Hongqing Zhu, Ziying Wang +6 more · 2025 · IEEE journal of biomedical and health informatics · IEEE · added 2026-04-24
Detecting early ischemic lesions (EIL) in computed tomography (CT) images is crucial for reducing diagnostic time and minimizing neuron loss due to oxygen deprivation. This paper introduces DCTP-Net, Show more
Detecting early ischemic lesions (EIL) in computed tomography (CT) images is crucial for reducing diagnostic time and minimizing neuron loss due to oxygen deprivation. This paper introduces DCTP-Net, a dual-branch network for segmenting acute ischemic stroke lesions in CT images, consisting of a segmentation branch and a prompt-aware branch. The segmentation branch uses an encoder-decoder network as the backbone to identify lesions, where the encoder fuses CT image features with prompt features from the prompt-aware branch. To enhance semantic feature extraction and reduce the impact of cerebral structural details, we introduce a cross-collaboration dynamic connection (CCDC) module to link the encoder and decoder. The prompt-aware branch includes a learnable prompt (LP) block to incorporate cerebral prior knowledge, and the prompt-aware encoder (PAE) combines the LP block with multi-level features from the segmentation branch for more precise representation. Additionally, we propose a CLIP-enhance textual prompt (CETP) module that utilizes the CLIP text encoder to generate specialized convolutional parameters for the segmentation head. These parameters are tailored to the unique characteristics of each input image, improving segmentation performance. Qualitative and quantitative studies reveal that DCTP-Net outperforms the current state-of-the-art, IS-Net, with Dice score increases of 3.9% on AISD and 3.8% on ISLES2018, demonstrating its superiority in EIL segmentation. Show less
no PDF DOI: 10.1109/JBHI.2024.3471627
CETP
Kun Lian, Yilan Chen, Tianhu Lv +3 more · 2025 · Asian journal of psychiatry · Elsevier · added 2026-04-24
Schizophrenia (SCZ) is a major neurodevelopmental disorder that exhibits poor response to current therapeutic interventions. Dysregulation of glutamate metabolism (GM) has been strongly associated wit Show more
Schizophrenia (SCZ) is a major neurodevelopmental disorder that exhibits poor response to current therapeutic interventions. Dysregulation of glutamate metabolism (GM) has been strongly associated with the development of SCZ, through mechanisms involving NMDA receptor dysfunction and neuroimmune imbalance. This study utilized Mendelian randomization (MR) to explore the causal association between 1400 blood metabolites and SCZ. Differentially Expressed GM-related Genes (GMDEGs) were identified via GEO transcriptome data integration, and consensus clustering techniques were employed to delineate the molecular subtypes. Using the key GM genes, a diagnostic model was developed and combined with CIBERSORT and MCPcounter analyses to assess immune infiltration. Moreover, the Drug Signatures Database (DSigDB) was used to identify potential targeted drugs, with their binding stability verified through Molecular Docking (MD) and dynamics simulations. Mendelian randomization identified 23 SCZ-related plasma metabolites, with glutamate exhibiting the most significant effect (P < 2.72e-31). Further analysis uncovered 25 Differentially Expressed Genes (DEGs) involved in GM, among which ASL, SLC1A5, and CLN3 were validated as the core targets. Immunoassays demonstrated that these DEGs were involved in the regulation of neutrophil and T cell infiltration. SCZ was categorized into C1 and C2 subtypes based on the expression profiles of these three hub glutamate metabolism genes. A diagnostic model integrating ASL, SLC1A5, and CLN3 was developed, which could identify potential therapeutic agents like Tanespimycin with an AUC of 0.844. Moreover, MD experiments confirmed strong binding affinities between tanespimycin and SLC1A5 (-7.7812 kcal/mol), geldanamycin and SLC1A5 (-7.1142 kcal/mol), cyclosporin A and CLN3 (-7.3049 kcal/mol). Meanwhile, molecular dynamics simulations indicated stable binding interactions. This study demonstrates the potential causal association of GM-related genes in SCZ, developed a precise diagnostic model, and proposed novel targeted therapeutic strategies. Show less
no PDF DOI: 10.1016/j.ajp.2025.104724
CLN3
Yi Qian, Jia Peng, Weiguo Jin +7 more · 2025 · European journal of medical research · BioMed Central · added 2026-04-24
Mitochondrial unfolded protein response (UPR The data were sourced from the cancer genome atlas (TCGA) and GSE31210 dataset and MRGs were retrieved to identify those with prognostic relevance, which w Show more
Mitochondrial unfolded protein response (UPR The data were sourced from the cancer genome atlas (TCGA) and GSE31210 dataset and MRGs were retrieved to identify those with prognostic relevance, which were applied to recognize the molecular clusters in LUAD. The cluster-specific differentially expressed genes (DEGs) were identified for the functional enrichment analysis. The independent differentially expressed MRGs were sorted out to develop a risk model. Besides, the tumor immune microenvironment was analyzed using the ESTIMATE, TIMER, MCP-counter, and ssGSEA algorithms. The data were processed with Mutect2 to evaluate the genetic mutation landscape, while the IMvigor210 cohort and pRRophetic package were utilized to predict immunotherapeutic responses and drug sensitivity. Finally, in vitro validation was performed via quantitative real-time PCR (qRT-PCR), cell counting kit-8 (CCK-8), wound healing, and Transwell assays. Most MRGs were higher expressed in LUAD, and CREB binding protein (CREBBP), lysine demethylase 6B (KDM6B) and leucine rich pentatricopeptide repeat containing (LRPPRC) were the top 3 genes with mutation frequency. 8 MRGs were applied to identify 2 molecular clusters, with the worst prognosis seen in cluster C1. The clusters-specific DEGs were mainly enriched in cell proliferation-related pathways and the established risk model based on 4 hub genes (ANLN, FAM83A, CPS1 and KRT6A) showed satisfying efficacy in predicting the prognosis and was negatively correlated with most immune cells. Besides, the tumor mutation burden tended to be stronger in high risk group with high gene mutation frequency. In IMvigor210 cohort, higher RiskScore was seen in patients with progressive disease and stable disease and related to a worse survival. 3 drug candidates, including Roscovitine, Rapamycin and PHA.665752 were positively correlated with RiskScore. Besides, all 4 MRGs were highly expressed in LUAD cells and the silencing of ANLN repressed the LUAD cell proliferation, migration and invasion. The established 4-MRGs signature not only serves as a robust prognostic indicator but also highlights the significant involvement of mitochondrial unfolded protein response in shaping tumor microenvironment and influencing immunotherapy outcomes in LUAD. The 4 MRGs may contribute to the understanding on UPR Show less
📄 PDF DOI: 10.1186/s40001-025-03453-y
CPS1
Xuancheng Xie, Hongjie Fan, Mengyao Zheng +8 more · 2025 · International journal of biological macromolecules · Elsevier · added 2026-04-24
Dysregulation of hepatic lipid homeostasis constitutes a core pathogenic mechanism in metabolic dysfunction-associated fatty liver disease (MAFLD); however, the regulatory role of circular RNAs (circR Show more
Dysregulation of hepatic lipid homeostasis constitutes a core pathogenic mechanism in metabolic dysfunction-associated fatty liver disease (MAFLD); however, the regulatory role of circular RNAs (circRNAs) in this process remains unclear. In this study, hepatic circRNAs transcriptomic profiling of MAFLD patients identified circSETD2-generated from exons 16-18 of the SETD2 gene-as a stably expressed and significantly upregulated novel circRNA with a closed circular structure localized in the cytoplasm of MAFLD patient liver tissues. Silencing circSETD2 attenuated free fatty acid - induced lipid accumulation in vitro by reducing lipogenesis and enhancing fatty acid β-oxidation. In high fat diet - fed mice, hepatic circSETD2 silencing mitigated steatosis, improved liver function, and reversed dyslipidemia. Mechanistically, RNA pull-down coupled with LC-MS/MS identified carbamoyl phosphate synthetase 1 (CPS1) as a circSETD2-interacting protein, which was subsequently validated by RNA immunoprecipitation and fluorescence in situ hybridization. Pharmacological modulation of CPS1 enzymatic activity in circSETD2-silenced cells established its mediator role. Specifically, circSETD2 directly bound to CPS1, reducing its enzymatic activity and thereby exacerbating lipid metabolic disturbances and disease progression in MAFLD. In summary, circSETD2 drives MAFLD pathogenesis by impairing CPS1-mediated regulation of lipid homeostasis, positioning it as a promising prognostic biomarker and therapeutic target. Show less
no PDF DOI: 10.1016/j.ijbiomac.2025.148879
CPS1
Abudunaibi Wupuer, Xing Peng, Jie Wang +4 more · 2025 · The journals of gerontology. Series A, Biological sciences and medical sciences · Oxford University Press · added 2026-04-24
Frailty and sarcopenia are age-related conditions linked to mitochondrial dysfunction, but their causal mechanisms remain poorly defined. This study aimed to identify mitochondrial-related genes causa Show more
Frailty and sarcopenia are age-related conditions linked to mitochondrial dysfunction, but their causal mechanisms remain poorly defined. This study aimed to identify mitochondrial-related genes causally associated with frailty and sarcopenia using comprehensive multi-omics approaches. We performed summary-data-based Mendelian randomization using genome-wide association study summary statistics for the frailty index and sarcopenia-related traits. Quantitative trait loci data for DNA methylation, gene expression, and plasma protein abundance were analyzed across 1136 mitochondrial-related genes. Colocalization analysis was applied to confirm shared causal variants. For frailty, GRPEL1 showed tissue-specific associations at methylation and expression levels (protective in blood: β = -.15, false discovery rate (FDR) = 1.5e-02; adverse in brain/muscle), while LRPPRC demonstrated consistent protective effects across tissues (β = -.05 to -.13, PPH4 > 0.93). For sarcopenia-related traits, GATM was associated with appendicular lean mass (ALM) across all omics levels with opposing tissue effects (negative in blood: β = -.03, FDR = 1.9e-09; positive in muscle), and ETFDH showed positive associations with ALM (β = .03, FDR = 1.4e-06). Additional genes included CPS1 and MMAB for frailty, NTHL1 and MTCH2 for grip strength, and TOMM70, BNIP3, TUFM for walking pace. Complete regulatory pathways were identified for GRPEL1 and GATM, linking methylation to expression to phenotype. This multi-omics study identified distinct mitochondrial genetic signatures for frailty and sarcopenia, with key genes including GRPEL1, LRPPRC, GATM, ETFDH, and others showing tissue-specific causal associations. These findings advance understanding of mitochondrial mechanisms in age-related functional decline and identify multiple therapeutic targets. Show less
no PDF DOI: 10.1093/gerona/glaf234
CPS1
Xinyue Yang, Shufen Li, Yuqing Feng +3 more · 2025 · Carbohydrate polymers · Elsevier · added 2026-04-24
Metabolic associated fatty liver disease (MAFLD) is a globally recognized chronic metabolic disorder characterized by lipid metabolism abnormalities. Accumulating evidence indicates that exopolysaccha Show more
Metabolic associated fatty liver disease (MAFLD) is a globally recognized chronic metabolic disorder characterized by lipid metabolism abnormalities. Accumulating evidence indicates that exopolysaccharides (EPS) could modulate the gut microbiota structure and function to prevent and treat MAFLD. Herein, a novel EPS designated BVP1 was isolated from Bacillus velezensis CGMCC 24752. Structural analysis revealed that BVP1 is a neutral α-mannan consisting of a backbone of 1,2,6-linked α-D-Manp, with branches composed of T-linked α-D-Manp, 1,2-linked α-D-Manp, and 1,3-linked α-D-Manp. Animal experiments showed that BVP1 significantly alleviated hepatic steatosis, liver injury and inflammation, and enhanced antioxidant activity in MAFLD mice. Single-nucleus RNA sequencing analysis revealed that BVP1 could restore HFD-induced imbalances in liver sinusoidal endothelial cells, hepatic stellate cells, macrophages and Kupffer cells by upregulating the expression of the lipid degradation gene Cps1 and downregulating the expression of the lipid synthesis gene Acsl1 in these cell subpopulations. Interestingly, BVP1 reshaped the gut microbiota and fecal metabolite profile by enriching beneficial bacteria and associated metabolites including salicylic acid, spermidine, and 4-hydroxyphenyl acetate. Fecal microbiota transplantation experiments verified that the anti-MAFLD effects are mediated by the BVP1-modified gut microbiota. Our findings highlight the potential of BVP1 as a promising therapeutic agent for MAFLD treatment. Show less
no PDF DOI: 10.1016/j.carbpol.2025.124150
CPS1
Jingbo Ma, Xuejuan Zi, Shuo Wu +6 more · 2025 · Bioresource technology · Elsevier · added 2026-04-24
A meta-analysis was conducted to assess the effects of citric acid (CA) on silage fermentation, and then used whole-plant cassava silage as a model to explore the underlying microbiological mechanisms Show more
A meta-analysis was conducted to assess the effects of citric acid (CA) on silage fermentation, and then used whole-plant cassava silage as a model to explore the underlying microbiological mechanisms with metagenomic and metabolomic data. The meta-analysis revealed that CA supplementation increased the dry matter, crude protein, water-soluble carbohydrate, and lactic acid contents in silage, but decreased the pH, dry matter loss, and the contents of fiber, NH Show less
no PDF DOI: 10.1016/j.biortech.2025.133025
CPS1
Yanchao Luan, Chao Liang, Qingsong Han +3 more · 2025 · BMC cancer · BioMed Central · added 2026-04-24
Metabolic pathways are known to significantly impact the development and advancement of lung cancer. This study sought to establish a signature related to butyrate metabolism that is specifically link Show more
Metabolic pathways are known to significantly impact the development and advancement of lung cancer. This study sought to establish a signature related to butyrate metabolism that is specifically linked to lung adenocarcinoma (LUAD). For the purpose of identifying butyrate metabolism-related differentially expressed genes (BMR-DEGs) in the TCGA-LUAD dataset, we introduced transcriptome data. This was followed by the implementation of the univariate Cox and LASSO analyses in order to construct a LUAD gene signature. We performed a comprehensive analysis of gene function enrichment between the two populations at risk, thoroughly examined their immune microenvironment characteristics, and assessed the effectiveness of immunotherapy. Finally, the function of CDKN3 in LUAD was verified by in vitro experiments. Through a comprehensive analysis of the TCGA-LUAD dataset, 51 significant BMR-DEGs were confirmed. Subsequently, five characteristic genes, CPS1, ABCC2, CDKN3, SLC2A1, and IGFBP1 were identified to create prognostic features for butyrate metabolism related outcomes in LUAD. Cox regression analysis determined that the pathological T stage, tumor stage, and RiskScore could serve as independent prognostic indicators. Analysis of the abundance of 22 immune infiltrating cells revealed that 15 immune cell types exhibited substantial differences and were strongly associated with risk ratings and prognosis. An important correlation exists between risk ratings and immunological checkpoints, which can be utilized to forecast the efficacy of treatment. In the high-risk group, there was an upregulation of the expression of PD-L2, PD-L1, and PD-1. Additionally, the risk score showed a positive correlation with TIDE and Exclusion score, while showing a negative correlation with Dysfunction score. Furthermore, the IC We identify and validate a novel BMR-related prognostic signature comprising 5 DEGs for LUAD patients. Our data might provide a new molecular target for LUAD treatment. Show less
📄 PDF DOI: 10.1186/s12885-024-13409-w
CPS1
Chunli Wei, Dongmei Xu, Jingliang Cheng +9 more · 2025 · Scientific reports · Nature · added 2026-04-24
DHX36 is an ATP-dependent DNA/RNA helicase that unwinds the guanine-quadruplexes (G4s) of DNA or RNA and regulates their metabolism for key biological functions. Breast cancer is a malignant tumor and Show more
DHX36 is an ATP-dependent DNA/RNA helicase that unwinds the guanine-quadruplexes (G4s) of DNA or RNA and regulates their metabolism for key biological functions. Breast cancer is a malignant tumor and effective targeted therapy drugs are limited, even though chemotherapy is generally used. In this study, we found that overexpression of DHX36 promotes breast cancer cell growth, migration, and invasion in vitro, while knocking down or knocking out reversed in vitro and in vivo. Moreover, DHX36 was highly expressed in most clinical breast tumor tissues compared with the matched healthy tissues. Accordingly, higher DHX36 expression correlated with poor recurrence-free survival (RFS) in the patients of breast cancer. These results substantiate that DHX36 might be a diagnostic and prognostic biomarker and is a proto-oncogene that promotes the growth and metastasis of breast cancer. Thus, targeting DHX36-associated G4s in genes, particularly in proto-oncogenes, might be a novel anticancer strategy. Show less
📄 PDF DOI: 10.1038/s41598-025-30889-3
DHX36
Yohan Jung, Harmony Grainger, Shizhuo Yang +4 more · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
The 2002 movie
📄 PDF DOI: 10.3389/fimmu.2025.1632283
DHX36
Yi Liu, Hanyuan Liu, Chenchen Zhu +5 more · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
High-grade serous ovarian cancer (HGSOC) is the most lethal type of gynecological cancer, and platinum-resistance is a serious challenge in its treatment. Long non-coding RNAs (lncRNAs) play critical Show more
High-grade serous ovarian cancer (HGSOC) is the most lethal type of gynecological cancer, and platinum-resistance is a serious challenge in its treatment. Long non-coding RNAs (lncRNAs) play critical regulatory roles in the occurrence and development of cancers. Here, using RNA sequencing of tumor small extracellular vesicles (sEVs) from HGSOC patients, the lncRNA CATED is identified as significantly upregulated in both tumors and tumor-derived sEVs in platinum-resistant HGSOC, and low CATED levels correlate with good prognosis. Functionally, CATED enhances cisplatin resistance by promoting cell proliferation and inhibiting apoptosis in vitro and in vivo. These effects could be transferred via CATED-overexpressing sEVs from donor cells and HGSOC tumor sEVs. Mechanistically, CATED binds to and upregulates DHX36 via PIAS1-mediated SUMOylation at the K105 site, and elevated DHX36 levels increase downstream RAP1A protein levels by enhancing RAP1A mRNA translation, consequently activating the MAPK pathway to promote platinum-resistance in HGSOC. Antisense oligonucleotide mediated knockdown of CATED reverse platinum-resistance in sEV-transmitted mouse models via the DHX36-RAP1A-MAPK pathway. This study newly identifies a sEV-transmitted lncRNA CATED in driving HGSOC platinum-resistance and elucidates the mechanism it regulates the interacting protein through SUMOylation. These findings also provide a novel strategy for improving chemotherapy in HGSOC by targeting CATED. Show less
📄 PDF DOI: 10.1002/advs.202505963
DHX36
Jie Yang, Geng Qin, Baoying Huang +9 more · 2025 · National science review · Oxford University Press · added 2026-04-24
The Mpox virus (MPXV) has emerged as a formidable orthopoxvirus, posing an immense challenge to global public health. An understanding of the regulatory mechanisms of MPXV infection, replication and i Show more
The Mpox virus (MPXV) has emerged as a formidable orthopoxvirus, posing an immense challenge to global public health. An understanding of the regulatory mechanisms of MPXV infection, replication and immune evasion will benefit the development of novel antiviral strategies. Despite the involvement of G-quadruplexes (G4s) in modulating the infection and replication processes of multiple viruses, their roles in the MPXV life cycle remain largely unknown. Here, we found a highly conservative and stable G4 in MPXV that acts as a positive regulatory element for viral immunodominant protein expression. Furthermore, by screening 42 optically pure chiral metal complexes, we identified the Λ enantiomer of a pair of chiral helical compounds that can selectively target mRNA G4 and enhance expression of the 39-kDa core protein encoded by the MPXV Show less
📄 PDF DOI: 10.1093/nsr/nwae388
DHX36
Yuhang Li, Min Tan, Guang Yang +4 more · 2025 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
Avian leukosis (AL), a major vertically transmitted infectious disease, poses a significant challenge to the conservation and industrial development of indigenous chicken breeds in China. In this stud Show more
Avian leukosis (AL), a major vertically transmitted infectious disease, poses a significant challenge to the conservation and industrial development of indigenous chicken breeds in China. In this study, Chengkou mountain chickens were used as a model to systematically identify genetic markers associated with resistance to avian leukosis virus subgroup J (ALV-J) through a genome-wide association study (GWAS). Genomic DNA was extracted from 500 hens at 300 days of age, and cloacal swabs, plasma, and egg white samples were collected to assess the ALV-J infection status. A total of 325 ALV-positive (ALV+) and 175 ALV-negative (ALV-) individuals were identified. Based on 10× whole-genome resequencing and stringent quality control, 12,644,463 high-quality SNPs were obtained. GWAS revealed a significant enrichment of SNPs on chromosome 6 (Chr6), from which 218 SNPs significantly associated with ALV-J resistance and 49 candidate genes were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that many of these genes, including Show less
📄 PDF DOI: 10.3390/ani15101365
DLG2
HanYu Zhu, Zhaoyang Wu, Junxiao Wang +8 more · 2025 · Schizophrenia research · Elsevier · added 2026-04-24
Schizophrenia (SZ) is a severe mental disorder with high heritability. DLG2 encodes the postsynaptic scaffolding protein DLG2 (PSD93, Postsynaptic Density Protein 93), and its variants were associated Show more
Schizophrenia (SZ) is a severe mental disorder with high heritability. DLG2 encodes the postsynaptic scaffolding protein DLG2 (PSD93, Postsynaptic Density Protein 93), and its variants were associated with an increased risk of SZ. However, the role of DLG2 locus variation in SZ remains elusive. This study aims to investigate the association between DLG2 gene polymorphisms and SZ susceptibility and the relationship between DLG2 and altered brain function and clinical symptoms in SZ patients. Single nucleotide polymorphisms (SNPs) rs11607886 and rs7479949 were genotyped in 350 SZ patients and 407 healthy controls (HCs). 47 SZ patients and 79 HCs were genotyped into two groups: the risk A allele carrier group and the GG-pure group. Functional magnetic resonance imaging (fMRI) indices were further analyzed. Subsequently, data from different brain regions were correlated with clinical symptom assessment. DLG2 rs11607886 was significantly associated with SZ. Significant main effects were found in the ALFF and ReHo, especially for the left precuneus gyrus (PCu). A significant interaction between genotype and diagnosis had a significant effect on FC, which was increased between the left PCu and the right middle temporal gyrus in carriers of the A allele with SZ (r = -0.336, P The rs11607886 polymorphism in DLG2 may influence the pathogenesis of SZ and have potential effects on cognitive function. The present study emphasizes DLG2 as a candidate gene for SZ and suggests an important role for PCu in SZ. Show less
no PDF DOI: 10.1016/j.schres.2025.04.004
DLG2
Jingjing Qi, Qian Hu, Yang Xi +5 more · 2025 · Animal genetics · Blackwell Publishing · added 2026-04-24
The beak bean, found only in waterfowl and Galliformes, aids in foraging, self-defense and pecking hard objects. Its rich coloration results from prolonged evolutionary adaptation. This study analyzed Show more
The beak bean, found only in waterfowl and Galliformes, aids in foraging, self-defense and pecking hard objects. Its rich coloration results from prolonged evolutionary adaptation. This study analyzed beak bean phenotypes of duck at 10, 20, 30 and 40 days of age, revealing that the most common type is the black beak bean, characterized by melanin deposition on the beak surface. This study performed single nucleotide polymorphism (SNP)-based genome-wide association studies (GWASs) to investigate the genetic basis of beak bean color, identifying signals on chromosome 1. The copy number variation region-based GWAS revealed a consistent candidate region overlapping with the SNP-based GWAS signals, further supporting the importance of this genomic region. Locus zoom analysis further refined the candidate regions to 48.5-50.5 and 50.8-52.8 Mb. Functional enrichment analysis highlighted six candidate genes within these regions: KITLG, DUSP6, GALNT4, MGAT4C, ATP2B1 and NTS. Notably, KITLG and DUSP6, which are linked to melanin production, were identified as key candidate genes for beak bean color. Our finding revealed the genetic basis of the bean color traits for the first time in ducks, providing a theoretical foundation and technological framework for enhancing duck beak coloration. Show less
no PDF DOI: 10.1111/age.70040
DUSP6
Xiangxiang Yang, Xiaohan Sun, Zimeng Du +9 more · 2025 · Animal bioscience · added 2026-04-24
Dual-specificity protein phosphatase 6 (DUSP6), also known as mitogenactivated protein kinase phosphatase 3 (MKP-3), was considered as a functional candidate gene for white fat accumulation in mice. H Show more
Dual-specificity protein phosphatase 6 (DUSP6), also known as mitogenactivated protein kinase phosphatase 3 (MKP-3), was considered as a functional candidate gene for white fat accumulation in mice. However, the physiological function of the DUSP6 gene on white adipocyte adipogenesis in farm animals remains unknown. In this study, we aimed to clarify the effect of DUSP6 on porcine subcutaneous preadipocyte proliferation and differentiation. We first make clear that the patterns of DUSP6 expression is associated with fat contents in porcine fat deposition related tissues. Porcine subcutaneous preadipocytes were isolated and induced to differentiation. Small interfering RNAs were applied to deplete DUSP6. MTT assay, CCK-8 analysis, Oil Red O staining, triglyceride determination and reverse transcription quantitative polymerase chain reaction were applied to study the regulatory role of DUSP6 during adipocyte adipogenesis in pigs. We found that the expression levels of DUSP6 were significantly higher in backfat and longissimus dorsi tissues from fat-type pigs than in those from lean-type pigs. Consistently, the significantly induced expression of DUSP6 was also observed in differentiated adipocytes. In addition, knockdown of DUSP6 greatly inhibited preadipocytes proliferation, through the decreased cell viability and downregulated mRNA expressions of cell proliferation-associated genes, including PCNA, CDK1, CDK2. Furthermore, knockdown of DUSP6 significantly inhibited preadipocytes differentiation, as evidenced by markedly reduced lipid droplet formation, attenuated triglyceride accumulation and downregulated expression levels of adipogenic transcription masters (PPARγ, C/EBPβ, FASN and FABP4) in DUSP6 knockdown cells. Our results demonstrate that DUSP6 is required for white adipocyte adipogenesis in pigs. Show less
📄 PDF DOI: 10.5713/ab.25.0175
DUSP6
Mengxiao Zou, Dan Yang, Han Xu +1 more · 2025 · Autoimmunity · Taylor & Francis · added 2026-04-24
Studies have found that there is tertiary lymphoid structure (TLS) in IgA nephropathy (IgAN), and the existence of TLS has an impact on renal function, creatinine, and proteinuria in patients. We aim Show more
Studies have found that there is tertiary lymphoid structure (TLS) in IgA nephropathy (IgAN), and the existence of TLS has an impact on renal function, creatinine, and proteinuria in patients. We aim to explore the potential molecular mechanisms and therapeutic targets of TLS in IgA nephropathy by bioinformatics methods, hoping to provide treatment methods. The datasets GSE226840, GSE237120, and GSE116626 from the Gene Expression Omnibus (GEO) database were employed to investigate the potential therapeutic targets of TLS in IgAN. The R was used to obtain the differentially expressed genes (DEGs) of three datasets, and the Venny was used to intersect the above three parts of the DEGs to obtain the common DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on obtained genes using Metascape. Protein-Protein interaction (PPI) network was constructed. The intersection of the above common differential genes and IgAN differential genes was obtained by Venny tool. The Nephroseq platform was used to screen core genes and explore their relationship with clinical features. Meanwhile, CIBERSORT was utilized to further delve into the correlation between core genes and immune cells. 92 TLS-related genes and 486 IgAN related genes were obtained, and 6 common genes were obtained after crossing the two genes. The intersection genes were verified by Nephroseq, and CDKN1A, CD83, DUSP6, and CD48 were identified as core genes. At the same time, there were differences in the composition of immune cells between the disease group and the control group when the immune infiltration analysis was performed. And by further analyzing the correlation between core genes and immune cells, the study found that the four genes were positively correlated with T cells, B cells, plasma cells, and other immune cells. By exploring the relationship between core genes and clinical features, CDKN1A and DUSP6 were negatively correlated with Glomerular Filtration Rate (GFR) and positively correlated with proteinuria in IgAN patients. CD48 was negatively correlated with GFR and positively correlated with Blood Urea Nitrogen (BUN). The four genes highly associated with TLS and IgAN were screened using GEO database in study. And CDKN1A, CD83, DUSP6 and CD48 may provide potential therapeutic targets for the treatment of TLS in IgAN. At the same time, studies have found that T cells, B cells, and macrophages may be involved in the formation of TLS in IgAN. Show less
no PDF DOI: 10.1080/08916934.2025.2519285
DUSP6
Sufang Wang, Nu Zhang, Guolin Shi +3 more · 2025 · Life sciences in space research · Elsevier · added 2026-04-24
Long-term space missions are of growing research interest because of the space exploration. However, plenty of works focused on the impaired immune response, less attention has been paid to the activa Show more
Long-term space missions are of growing research interest because of the space exploration. However, plenty of works focused on the impaired immune response, less attention has been paid to the activation of immunosuppressive or anti-inflammatory function. The molecular mechanism of immune disorder induced by microgravity still needs investigation. Here, we used a random positioning machine to generate a simulated microgravity environment and evaluated its effects on mouse RAW 264.7 macrophage cell line. We used ATAC-seq and RNA-seq for revealing the mechanism at chromatin level and gene level. From ATAC-seq, we obtained an average of 75,700,675 paired-end clean reads for each library and the mapping rates averaged at 96.8 %. The number of differential accessible regions were 510 for increased peaks, 638 for decreased peaks. From RNA-seq, we obtained 278 differentially expressed genes, of which 104 were down-regulated and 174 were up-regulated genes. Through ATAC-seq and RNA-seq multi-omics analysis, we identified a group of 17 genes. Then we chose 6 up-regulated genes (CD83, CEBPD, CXCR5, DUSP6, SEMA4B, TNFRSF22) that related to immunosuppressive function for further confirmation. The qRT-PCR results were consistent with sequencing results, which indicated that simulated microgravity leads to the up-regulated expression of immunosuppressive genes of macrophages. Taken together, our results offered novel insights for understanding the brief principles and mechanisms of simulated microgravity induced immune dysfunction to macrophage. Show less
no PDF DOI: 10.1016/j.lssr.2025.03.002
DUSP6
Xiao Wang, Yinglin Yuan, Fen Pei +11 more · 2025 · Animals : an open access journal from MDPI · MDPI · added 2026-04-24
Heat stress (HS) severely significantly reduces milk yield and causes substantial economic losses of dairy cows. TMT-based proteomes and an untargeted metabolomics approach were used to conduct the pr Show more
Heat stress (HS) severely significantly reduces milk yield and causes substantial economic losses of dairy cows. TMT-based proteomes and an untargeted metabolomics approach were used to conduct the proteomics and metabolomics in heat-stressed (HS, Show less
📄 PDF DOI: 10.3390/ani15203049
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