👤 Ding 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-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, Jinjian 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, 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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
Pang Yao, Andri Iona, Alfred Pozarickij +26 more · 2024 · Diabetes care · added 2026-04-24
Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for Show more
Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D). We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D. Overall, 33 proteins were significantly associated (false discovery rate <0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D. Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D. Show less
📄 PDF DOI: 10.2337/dc23-2145
LPL
Takao Kimura, Kazuya Miyashita, Isamu Fukamachi +12 more · 2024 · Journal of lipid research · Elsevier · added 2026-04-24
To support in vivo and in vitro studies of intravascular triglyceride metabolism in mice, we created rat monoclonal antibodies (mAbs) against mouse LPL. Two mAbs, mAbs 23A1 and 31A5, were used to deve Show more
To support in vivo and in vitro studies of intravascular triglyceride metabolism in mice, we created rat monoclonal antibodies (mAbs) against mouse LPL. Two mAbs, mAbs 23A1 and 31A5, were used to develop a sandwich ELISA for mouse LPL. The detection of mouse LPL by the ELISA was linear in concentrations ranging from 0.31 ng/ml to 20 ng/ml. The sensitivity of the ELISA made it possible to quantify LPL in serum and in both pre-heparin and post-heparin plasma samples (including in grossly lipemic samples). LPL mass and activity levels in the post-heparin plasma were lower in Gpihbp1 Show less
📄 PDF DOI: 10.1016/j.jlr.2024.100532
LPL
Yue Zhang, Yan Zhai, Xinxin Wei +5 more · 2024 · Meat science · Elsevier · added 2026-04-24
This study investigated how lipid metabolism in the longissimus thoracis is influenced by the diet supplemented with grape seed procyanidins (GSPs) in growing-finishing pigs. Forty-eight crossbred pig Show more
This study investigated how lipid metabolism in the longissimus thoracis is influenced by the diet supplemented with grape seed procyanidins (GSPs) in growing-finishing pigs. Forty-eight crossbred pigs were randomly assigned to four groups, each receiving a basal diet, or basal diet added with 150, 200, and 250 mg/kg GSPs. Transcriptomics and metabolomics were employed to explore differential gene and metabolite regulation. The expression of key lipid metabolism-related genes was tested via qRT-PCR, and the lipid and fatty acid composition of the longissimus thoracis were determined. Dietary GSPs at different concentrations upregulated lipoprotein lipase (LPL), which is involved in lipolysis, and significantly increased the mRNA expression levels of carnitine palmitoyltransferase-1B (CPT1B) and cluster of differentiation 36 (CD36), implicated in transmembrane transport of fatty acids. Dietary supplementation of GSPs at 200 or 250 mg/kg markedly reduced total cholesterol and triglyceride content in longissimus thoracis. Dietary GSPs significantly decreased the contents of low-density lipoprotein cholesterol and saturated fatty acids, while increasing unsaturated fatty acids. In conclusion, GSPs may regulate lipid metabolism, reducing cholesterol level, and improving fatty acid composition in the longissimus thoracis of growing-finishing pigs. Our findings provide evidence for the beneficial effects of GSPs as pig feed additives for improving lipid composition. Show less
no PDF DOI: 10.1016/j.meatsci.2024.109504
LPL
Guang Wang, Pei Li, Si-Wei Su +4 more · 2024 · Aging · Impact Journals · added 2026-04-24
Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic condition with painful bladder. At present, the pathogenesis of IC/BPS is still unknown. Quercetin (QCT) is a kind of natural flavonoi Show more
Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic condition with painful bladder. At present, the pathogenesis of IC/BPS is still unknown. Quercetin (QCT) is a kind of natural flavonoid with wide sources and multiple biological activities. The purpose of this study was to explore the effects of QCT on mRNA expression and related regulatory signal pathways in IC model rats. LL-37 was used to induce the IC/BPS model rats. 20 mg/kg QCT was injected intraperitoneally into IC/BPS rats. ELISA, HE, Masson and TB staining were used to evaluate the level of inflammation and pathology. The concentration of QCT in rats was detected by HPLC. The mRNA sequencing was used to detect the differentially expressed (DE) mRNA in each group. The over-expression experiment of Lpl was carried out in IC/BPS model rats. QCT treatment significantly decreased the level of MPO, IL-1β, IL-6 and TNF-α induced by LL-37 in rats, and alleviated bladder injury and mast cell degranulation. There were significant differences in mRNA sequencing data between groups, and the hub gene Lpl were screened by Cytohubba. The expression of Lpl was downregulated in IC/BPS rats. QCT intervention promoted Lpl expression. Overexpression of Lpl reduced the bladder injury induced by LL-37, increased GAG level and decreased the expression of MPO, IL-1β, IL-6 and TNF-α. In this study, we provided the DE mRNA in IC/BPS rats treated with QCT, the signaling pathways for DE enrichment, screened out the hub genes, and revealed that Lpl overexpression alleviated IC/BPS model rats. Show less
📄 PDF DOI: 10.18632/aging.205682
LPL
Fanxiong Wang, Yuzhu Sha, Xiu Liu +10 more · 2024 · Foods (Basel, Switzerland) · MDPI · added 2026-04-24
The intestinal microbiota of ruminants is an important factor affecting animal production and health. Research on the association mechanism between the intestinal microbiota and meat quality of rumina Show more
The intestinal microbiota of ruminants is an important factor affecting animal production and health. Research on the association mechanism between the intestinal microbiota and meat quality of ruminants will play a positive role in understanding the formation mechanism of meat quality in ruminants and improving production efficiency. In this study, the fatty acid composition and content, expression of related genes, and structural characteristics of the ileum microbiota of ewes of Tibetan sheep at different ages (4 months, 1.5 years, 3.5 years, and 6 years) were detected and analyzed. The results revealed significant differences in fatty acid composition and content in the muscle of Tibetan sheep at different ages ( Show less
📄 PDF DOI: 10.3390/foods13050679
LPL
Yuan-Zheng Zhu, Jian-Kun Liu, Xue-Er Li +7 more · 2024 · The journals of gerontology. Series A, Biological sciences and medical sciences · Oxford University Press · added 2026-04-24
Advanced age is an independent risk factor for coronary artery disease (CAD), the leading global cause of mortality. Senescent vascular cells in the atherosclerotic plaques exhibit senescence-associat Show more
Advanced age is an independent risk factor for coronary artery disease (CAD), the leading global cause of mortality. Senescent vascular cells in the atherosclerotic plaques exhibit senescence-associated secretory phenotype (SASP). How SASP contributes to atherosclerosis and CAD, however, remains unclear. Here, we integrated RNA-array datasets of senescent human coronary arterial endothelial cells (HCAECs) and aortic smooth muscle cells (HASMCs) as well as genome-wide association data for CAD. We identified 26 genes from HCAECs and 6 genes from HASMCs related to SASP and CAD in both in-house and published datasets. Of which, Cystatin C (CST3), a CAD susceptibility gene, was found to be expressed in both HCAECs and HASMCs, thus, it was prioritized for further investigation. We demonstrated it was significantly elevated in senescent vascular cells, aged arteries, and early atherosclerosis. In vitro experiments showed that CST3 enhances the monocyte-endothelial cell adhesion. Additionally, ligand-receptor pairing analyses revealed two important pathways, COL4A1-ITGA1 and LPL-LRP1 pathways, linked to the critical processes in the development of atherosclerosis, including cell adhesion, inflammation response, extracellular matrix organization, and lipid metabolism. We further demonstrated a reduced monocyte-endothelial cell adhesion following the knockdown of COL4A1 or ITGA1 and a significantly increased expression of COL4A1, ITGA1, and LPL in arterial intima of aged mice and ApoE-/- mice. Our findings demonstrate that vascular cell-derived SASP proteins increase the CAD susceptibility and identify CST3 functionally contributing to atherosclerosis. Show less
no PDF DOI: 10.1093/gerona/glae070
LPL
Weipeng Hu, Yiming Yang, Haifeng Hu · 2024 · IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · IEEE · added 2026-04-24
Remarkable success of the existing Near-InfraRed and VISible (NIR-VIS) approaches owes to sufficient labeled training data. However, collecting and tagging data from different domains is a time-consum Show more
Remarkable success of the existing Near-InfraRed and VISible (NIR-VIS) approaches owes to sufficient labeled training data. However, collecting and tagging data from different domains is a time-consuming and expensive task. In this paper, we tackle the NIR-VIS face recognition problem in a semi-supervised manner, termed as semi-supervised NIR-VIS Heterogeneous Face Recognition (NIR-VIS-sHFR). To cope with this problem, we propose a novel pseudo Label association and Prototype-based invariant Learning (LPL), consisting of three key components, i.e., Cross-domain pseudo Label Association (CLA), Intra-domain Compact Representation learning (ICR), and Prototype-based Inter-domain Invariant learning (PII). Firstly, the CLA iteratively builds inter-domain association graphs for pseudo-label association, subsequently facilitating cross-domain model development based on the generated pseudo-labels. Furthermore, the ICR is proposed to achieve the separation of in-domain features from different clusters and the aggregation of features from the same cluster, by performing cluster adaptation learning with prototype-based initialization. Finally, with the cross-domain pseudo-label training data produced by CLA, the PII explores potential domain-invariant and identity-related features, which employs cross-domain prototypes with identity-associated momentum updating to effectively guide inter-domain instances learning. The semi-supervised LPL method achieves comparable performance to recent supervised learning methods on multiple challenging NIR-VIS datasets, which demonstrates that the LPL is capable of learning robust cross-domain representations even without identity label information. Show less
no PDF DOI: 10.1109/TIP.2024.3364530
LPL
Hao Wu, Tianyu Lou, Mingxia Pan +13 more · 2024 · Journal of ethnopharmacology · Elsevier · added 2026-04-24
Nonalcoholic steatohepatitis (NASH) is a prominent cause of liver-related death that poses a threat to global health and is characterized by severe hepatic steatosis, lobular inflammation, and balloon Show more
Nonalcoholic steatohepatitis (NASH) is a prominent cause of liver-related death that poses a threat to global health and is characterized by severe hepatic steatosis, lobular inflammation, and ballooning degeneration. To date, no Food and Drug Administration-approved medicine is commercially available. The Chaihu Guizhi Ganjiang Decoction (CGGD) shows potential curative effects on regulation of blood lipids and blood glucose, mitigation of organism inflammation, and amelioration of hepatic function. However, the overall regulatory mechanisms underlying its effects on NASH remain unclear. This study aimed to investigate the efficiency of CGGD on methionine- and choline-deficient (MCD)-induced NASH and unravel its underlying mechanisms. A NASH model of SD rats was established using an MCD diet for 8 weeks, and the efficacy of CGGD was evaluated based on hepatic lipid accumulation, inflammatory response, and fibrosis. The effects of CGGD on the intestinal barrier, metabolic profile, and differentially expressed genes (DEGs) profile were analyzed by integrating gut microbiota, metabolomics, and transcriptome sequencing to elucidate its mechanisms of action. In MCD-induced NASH rats, pathological staining demonstrated that CGGD alleviated lipid accumulation, inflammatory cell infiltration, and fibrosis in the hepatic tissue. After CGGD administration, liver index, liver weight, serum alanine aminotransferase (ALT), and aspartate aminotransferase (AST) contents, liver triglycerides (TG), and free fatty acids (FFAs) were decreased, meanwhile, it down-regulated the level of proinflammatory mediators (TNF-α, IL-6, IL-1β, MCP-1), and up-regulated the level of anti-inflammatory factors (IL-4, IL-10), and the expression of liver fibrosis markers TGFβ, Acta2, Col1a1 and Col1a2 were weakened. Mechanistically, CGGD treatment altered the diversity of intestinal flora, as evidenced by the depletion of Allobaculum, Blautia, norank_f_Erysipelotrichaceae, and enrichment of the probiotic genera Roseburia, Lactobacillus, Lachnoclostridium, etc. The colonic histopathological results indicated that the gut barrier damage recovered in the CGGD treatment group, and the expression levels of colonic short-chain fatty acids (SCFAs)-specific receptors FFAR2, FFAR3, and tight junction (TJs) proteins ZO-1, Occludin, Claudin-1 were increased compared with those in the model group. Further metabolomic and transcriptomic analyses suggested that CGGD mitigated the lipotoxicity caused by glycerophospholipid and eicosanoid metabolism disorders by decreasing the levels of PLA2G4A, LPCAT1, COX2, and LOX5. In addition, CGGD could activate the inhibitory lipotoxic transcription factor PPARα, regulate the proteins of FABP1, APOC2, APOA2, and LPL to promote fatty acid catabolism, and suppress the TLR4/MyD88/NFκB pathway to attenuate NASH. Our study demonstrated that CGGD improved steatosis, inflammation, and fibrosis on NASH through enhancing intestinal barrier integrity and alleviating PPARα mediated lipotoxicity, which makes it an attractive candidate for potential new strategies for NASH prevention and treatment. Show less
no PDF DOI: 10.1016/j.jep.2024.117841
LPL
Lu Yang, Jingchang Ma, Yitian Liu +4 more · 2024 · Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology · added 2026-04-24
Objective To observe the expression of adhesion molecule CD226 on the small intestinal group 3 innate lymphoid cells (ILC3) in mice. Methods The bioinformatics was used to analyze the expression of CD Show more
Objective To observe the expression of adhesion molecule CD226 on the small intestinal group 3 innate lymphoid cells (ILC3) in mice. Methods The bioinformatics was used to analyze the expression of CD226 on murine ILCs. Small intestinal mucosal lamina propria lymphocytes (LPL) were isolated from wild-type C57BL/6J mice, and the expression of CD226 on ILC1 and ILC3 was detected by flow cytometry. A mouse model of dextran sulfate sodium (DSS)-induced colitis was constructed to observe the changes in the expression of CD226 on ILC3. Results Both ILC1 and ILC3 in the mice small intestine expressed CD226 molecules; the proportion of ILC3 was reduced, while the expression level of CD226 on ILC3 was increased in the colitis model. Conclusion CD226 is expressed on the small intestines of mice, and although the proportion of ILC3 decreases in the DSS-induced colitis, the expression of CD226 on ILC3 increases. Show less
no PDF
LPL
Yongsheng Ma, Qitai Lin, Wenming Yang +10 more · 2024 · Orthopaedic surgery · Blackwell Publishing · added 2026-04-24
The current clinical pulse lavage technique for flushing fresh osteochondral allografts (OCAs) to remove immunogenic elements from the subchondral bone is ineffective. This study aimed to identify the Show more
The current clinical pulse lavage technique for flushing fresh osteochondral allografts (OCAs) to remove immunogenic elements from the subchondral bone is ineffective. This study aimed to identify the optimal method for removing immunogenic elements from OCAs. We examined five methods for the physical removal of immunogenic elements from OCAs from the femoral condyle of porcine knees. We distributed the OCAs randomly into the following seven groups: (1) control, (2) saline, (3) ultrasound, (4) vortex vibration (VV), (5) low-pulse lavage (LPL), (6) high-pulse lavage (HPL), and (7) high-speed centrifugation (HSC). OCAs were evaluated using weight measurement, micro-computed tomography (micro-CT), macroscopic and histological evaluation, DNA quantification, and chondrocyte activity testing. Additionally, the subchondral bone was zoned to assess the bone marrow and nucleated cell contents. One-way ANOVA and paired two-tailed Student's t-test are used for statistical analysis. Histological evaluation and DNA quantification showed no significant reduction in marrow elements compared to the control group after the OCAs were treated with saline, ultrasound, or VV treatments; however, there was a significant reduction in marrow elements after LPL, HPL, and HSC treatments. Furthermore, HSC more effectively reduced the marrow elements of OCAs in the middle and deep zones compared with LPL (p < 0.0001) and HPL (p < 0.0001). Macroscopic evaluation revealed a significant reduction in blood, lipid, and marrow elements in the subchondral bone after HSC. Micro-CT, histological analyses, and chondrocyte viability results showed that HSC did not damage the subchondral bone and cartilage; however, LPL and HPL may damage the subchondral bone. HSC may play an important role in decreasing immunogenicity and therefore potentially increasing the success of OCA transplantation. Show less
📄 PDF DOI: 10.1111/os.13991
LPL
Yuhao Ma, Ganxian Cai, Jianfei Chen +6 more · 2024 · BMC genomics · BioMed Central · added 2026-04-24
Dorper and Tan sheep are renowned for their rapid growth and exceptional meat quality, respectively. Previous research has provided evidence of the impact of gut microbiota on breed characteristics. T Show more
Dorper and Tan sheep are renowned for their rapid growth and exceptional meat quality, respectively. Previous research has provided evidence of the impact of gut microbiota on breed characteristics. The precise correlation between the gastrointestinal tract and peripheral organs in each breed is still unclear. Investigating the metabolic network of the intestinal organ has the potential to improve animal growth performance and enhance economic benefits through the regulation of intestinal metabolites. In this study, we identified the growth advantage of Dorper sheep and the high fat content of Tan sheep. A transcriptome study of the brain, liver, skeletal muscle, and intestinal tissues of both breeds revealed 3,750 differentially expressed genes (DEGs). The genes PPARGC1A, LPL, and PHGDH were found to be highly expressed in Doper, resulting in the up-regulation of pathways related to lipid oxidation, glycerophospholipid metabolism, and amino acid anabolism. Tan sheep highly express the BSEP, LDLR, and ACHE genes, which up-regulate the pathways involved in bile transport and cholesterol homeostasis. Hindgut content analysis identified 200 differentially accumulated metabolites (DAMs). Purines, pyrimidines, bile acids, and fatty acid substances were more abundant in Dorper sheep. Based on combined gene and metabolite analyses, we have identified glycine, serine, and threonine metabolism, tryptophan metabolism, bile secretion, cholesterol metabolism, and neuroactive ligand-receptor interaction as key factors contributing to the differences among the breeds. This study indicates that different breeds of sheep exhibit unique breed characteristics through various physiological regulatory methods. Dorper sheep upregulate metabolic signals related to glycine, serine, and threonine, resulting in an increase in purine and pyrimidine substances. This, in turn, promotes the synthesis of amino acids and facilitates body development, resulting in a faster rate of weight gain. Tan sheep accelerate bile transport, reduce bile accumulation in the intestine, and upregulate cholesterol homeostasis signals in skeletal muscles. This promotes the accumulation of peripheral and intramuscular fat, resulting in improved meat quality. This work adopts a joint analysis method of multi-tissue transcriptome and gut metabolome, providing a successful case for analyzing the mechanisms underlying the formation of various traits. Show less
📄 PDF DOI: 10.1186/s12864-023-09870-9
LPL
Yongkang Wang, Qiankun Li, Lunjun Qu +6 more · 2024 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Long-persistent luminescent (LPL) materials have attracted considerable research interest due to their extensive applications and outstanding afterglow performance. However, the performance of red LPL Show more
Long-persistent luminescent (LPL) materials have attracted considerable research interest due to their extensive applications and outstanding afterglow performance. However, the performance of red LPL materials lags behind that of green and blue materials. Therefore, it is crucial to explore novel red LPL materials. This study introduces a straightforward and viable strategy for organic-inorganic hybrids, wherein the organic ligand 1,3,6,8-Tetrakis(4-carboxyphenyl)pyrene (TCPP) is coordinated to the surface of a red persistent phosphor Sr Show less
📄 PDF DOI: 10.1002/advs.202306942
LPL
Meng Wang, Tao Wei, Chaoji Yu +7 more · 2024 · Molecular neurobiology · Springer · added 2026-04-24
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia worldwide. Dysregulation of various metabolism pathways may mediate the development of AD pat Show more
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia worldwide. Dysregulation of various metabolism pathways may mediate the development of AD pathology and cognitive dysfunction. Variants of triggering receptor expressed on myeloid cells-2 (TREM2) are known to increase the risk of developing AD. TREM2 plays a role in AD development by maintaining cellular energy and biosynthesis, but the precise mechanism through which it accomplishes this is unknown. Metabolomic analysis of hippocampal tissue from APP/PS1 and APP/PS1-TREM2 knockout (KO) mice found that TREM2 KO was associated with abnormalities in several metabolism pathways, and the effect was particularly pronounced in lipid metabolism and glucose metabolism pathways. Consistently, transcriptomic analysis of these mice determined that most differentially expressed genes were involved in energy metabolism pathways. We screened seven differentially expressed genes in APP/PS1-TREM2 KO mice that may influence AD development by altering energy metabolism. Integrative analysis of the metabolomic and transcriptomic profiles showed that TREM2 may regulate lipid metabolism and sphingolipid metabolism by affecting lipoprotein lipase (LPL) expression, thereby influencing AD progression. Our results prompt further studies of the interactions among TREM2, LPL, glucolipid metabolism, and sphingolipid metabolism in AD to identify new diagnostic and treatment strategies. Show less
📄 PDF DOI: 10.1007/s12035-023-03840-8
LPL
Dong Jun Lim, Hee Joo Yang, Mi Young Lee +4 more · 2024 · Journal of cutaneous pathology · Blackwell Publishing · added 2026-04-24
Lymphoplasmacytic lymphoma (LPL) is a rare variant of non-Hodgkin lymphoma, accounting for <1% of cases. Skin involvement in LPL is quite rare-accounting for approximately 5% of extramedullary disease Show more
Lymphoplasmacytic lymphoma (LPL) is a rare variant of non-Hodgkin lymphoma, accounting for <1% of cases. Skin involvement in LPL is quite rare-accounting for approximately 5% of extramedullary disease-and includes a variety of clinical morphologies, such as erythematous-to-violaceous plaques, violaceous nodules or tumors, and ulceration at various anatomical sites. Herein, we report the case of a 45-year-old Korean woman who presented with generalized erythematous indurated plaques and pendulous skin growths, which were asymptomatic, with marked diffuse infiltration of lymphocytes and plasma cells in the dermis. Immunohistochemical studies revealed that the lymphoid cells expressed CD3, CD79a, and cytoplasmic IgG, but lacked CD10 and IgM. Moreover, kappa light chain restriction and monoclonal immunoglobulin heavy chain gene rearrangement were observed. Upon further workup, lymphoma involvement was reported in multiple lymph nodes, including those in the cervical and axillary regions. This case shows a unique form of cutaneous LPL clinically presenting as acquired cutis laxa, emphasizing the dermatologists' need to be vigilant for variant forms of this disease. Show less
no PDF DOI: 10.1111/cup.14551
LPL
Xingwei Wu, Zhenguo Wu, Zehang Xie +5 more · 2024 · International immunopharmacology · Elsevier · added 2026-04-24
Lung adenocarcinoma (LUAD) is the most common and aggressive cancer with a high incidence. N1-specific pseudouridine methyltransferase (EMG1), a highly conserved nucleolus protein, plays an important Show more
Lung adenocarcinoma (LUAD) is the most common and aggressive cancer with a high incidence. N1-specific pseudouridine methyltransferase (EMG1), a highly conserved nucleolus protein, plays an important role in the biological development of ribosomes. However, the role of EMG1 in the progression of LUAD is still unclear. The expression of EMG1 in LUAD cells, and LUAD tissues, and adjacent noncancerous tissues was quantified using real-time polymerase chain reaction (PCR) and western blotting. The roles of EMG1 in LUAD cell proliferation, migration, invasion and tumorigenicity were explored in vitro and in vivo. Western blot analysis to underlying molecular mechanism of EMG1 regulating the biological function of LUAD. EMG1 expression and its impact on tumor prognosis were analyzed using a range of databases including GEPIA, UALCAN, cBioPortal, LinkedOmics, and Kaplan-Meier Plotter. EMG1 expression was elevated in LUAD patients compared to normal tissues, and EMG1 expression was strongly correlated with prognosis in LUAD patients. EMG1 expression correlated with age, gender, N stage, T stage, and pathologic stage. EMG1 expression was strongly positively correlated with MRPL51, PHB2, SNRPG, ATP5MD, and TPI1, and strongly negatively correlated with MACF1, DOCK9, RAPGEF2, SYNJ1, and KIDINS220, the major enrichment pathways for EMG1 and related genes include Cell cycle, DNA Replication and Pathways in cancer signaling pathways. EMG1 expression level was significantly increased in LUAD cell lines and tissues. Knockdown of EMG1 could inhibit LUAD cell proliferation, migration, invasion, and tumorigenicity. Besides, EMG1 overexpression could promote LUAD cell proliferation, migration, and invasion. High expression of EMG1 predicts poor prognosis in LUAD patients, and EMG1 may play an oncogenic role in the tumor microenvironment by participating in the infiltration of LUAD immune cells. EMG1 regulated various functions in LUAD by directly mediating Akt/mTOR/p70s6k signaling pathways activation. The results suggest that EMG1 may be a novel biomarker for assessing prognosis and immune cell infiltration in LUAD. Show less
no PDF DOI: 10.1016/j.intimp.2024.112553
MACF1
Wuxia Qiu, Xiao Lin, Shaoqing Yang +8 more · 2024 · Genes & diseases · Elsevier · added 2026-04-24
📄 PDF DOI: 10.1016/j.gendis.2023.101103
MACF1
Ning Yang, Ningzhi Zhang, Guojing Lu +3 more · 2024 · Scientific reports · Nature · added 2026-04-24
RNA-binding proteins (RBPs) contribute to the pathogenesis of proliferative diabetic retinopathy (PDR) by regulating gene expression through alternative splicing events (ASEs). However, the RBPs diffe Show more
RNA-binding proteins (RBPs) contribute to the pathogenesis of proliferative diabetic retinopathy (PDR) by regulating gene expression through alternative splicing events (ASEs). However, the RBPs differentially expressed in PDR and the underlying mechanisms remain unclear. Thus, this study aimed to identify the differentially expressed genes in the neovascular membranes (NVM) and retinas of patients with PDR. The public transcriptome dataset GSE102485 was downloaded from the Gene Expression Omnibus database, and samples of PDR and normal retinas were analyzed. A mouse model of oxygen-induced retinopathy was used to confirm the results. The top 20 RBPs were screened for co-expression with alternative splicing genes (ASGs). A total of 403 RBPs were abnormally expressed in the NVM and retina samples. Functional analysis demonstrated that the ASGs were enriched in cell cycle pathways. Cell cycle-associated ASEs and an RBP-AS regulatory network, including 15 RBPs and their regulated ASGs, were extracted. Splicing factor proline/glutamine rich (SFPQ), microtubule-associated protein 1 B (MAP1B), heat-shock protein 90-alpha (HSP90AA1), microtubule-actin crosslinking factor 1 (MACF1), and CyclinH (CCNH) expression remarkably differed in the mouse model. This study provides novel insights into the RBP-AS interaction network in PDR and for developing screening and treatment options to prevent diabetic retinopathy-related blindness. Show less
📄 PDF DOI: 10.1038/s41598-024-57516-x
MACF1
Qiang He, Jie Wang, Jingjing Li +1 more · 2024 · Pharmaceuticals (Basel, Switzerland) · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/ph17121634
MAP2K5
Ji-Nuo Wang, Bangcheng Ye, Fei Cheng +6 more · 2024 · Hematology (Amsterdam, Netherlands) · Taylor & Francis · added 2026-04-24
Six patients with Patients with
no PDF DOI: 10.1080/16078454.2024.2423324
MLLT10
David Teachey, Haley Newman, Shawn Lee +41 more · 2024 · Research square · added 2026-04-24
The influence of genetic ancestry on biology, survival outcomes, and risk stratification in T-cell Acute Lymphoblastic Leukemia (T-ALL) has not been explored. Genetic ancestry was genomically-derived Show more
The influence of genetic ancestry on biology, survival outcomes, and risk stratification in T-cell Acute Lymphoblastic Leukemia (T-ALL) has not been explored. Genetic ancestry was genomically-derived from DNA-based single nucleotide polymorphisms in children and young adults with T-ALL treated on Children's Oncology Group trial AALL0434. We determined associations of genetic ancestry, leukemia genomics and survival outcomes; co-primary outcomes were genomic subtype, pathway alteration, overall survival (OS), and event-free survival (EFS). Among 1309 patients, T-ALL molecular subtypes varied significantly by genetic ancestry, including increased frequency of genomically defined ETP-like, MLLT10, and BCL11B-activated subtypes in patients of African ancestry. In multivariable Cox models adjusting for high-risk subtype and pathways, patients of Admixed American ancestry had superior 5-year EFS/OS compared with European; EFS/OS for patients of African and European ancestry were similar. The prognostic value of five commonly altered T-ALL genes varied by ancestry - including Show less
no PDF DOI: 10.21203/rs.3.rs-4858231/v1
MLLT10
Haimin Sun, Yongmei Zhu, Jianfeng Li +4 more · 2024 · EJHaem · Wiley · added 2026-04-24
The
📄 PDF DOI: 10.1002/jha2.922
MLLT10
Rong Wang, Yue Wu, Ruicong Xue +7 more · 2024 · British journal of haematology · Blackwell Publishing · added 2026-04-24
no PDF DOI: 10.1111/bjh.19522
MLLT10
Yongge Luo, Lei Yang, Han Wu +4 more · 2024 · Biomolecules · MDPI · added 2026-04-24
The relationship between type 2 diabetes mellitus (T2DM) and colorectal cancer (CRC) has long been extensively recognized, but their crosstalk mechanisms based on gene regulation remain elusive. In ou Show more
The relationship between type 2 diabetes mellitus (T2DM) and colorectal cancer (CRC) has long been extensively recognized, but their crosstalk mechanisms based on gene regulation remain elusive. In our study, for the first time, bulk RNA-seq and single-cell RNA-seq data were used to explore the shared molecular mechanisms between T2DM and CRC. Moreover, Connectivity Map and molecular docking were employed to determine potential drugs targeting the candidate targets. Eight genes ( Show less
📄 PDF DOI: 10.3390/biom14060693
MLXIPL
Han Liu, Xiao Bao, Hao Shi +7 more · 2024 · Molecular genetics and genomics : MGG · Springer · added 2026-04-24
Given the high morbidity, mortality, and hereditary risk of cardiovascular diseases (CVDs), their prevention and control have garnered widespread attention and remain central to clinical research. Thi Show more
Given the high morbidity, mortality, and hereditary risk of cardiovascular diseases (CVDs), their prevention and control have garnered widespread attention and remain central to clinical research. This study aims to assess the feasibility and necessity of haplotyping-based preimplantation genetic testing for the prevention of inherited CVD. A total of 15 preimplantation genetic testing for monogenic defect (PGT-M) cycles were performed in 12 CVD families from January 2016 to July 2022. All couples were affected by CVDs and carried specific causative genes (including MYH7, MYBPC3, TTN, TPM1, LMNA, KCNQ1, FBN1 and LDLR). Among the 10 couples with adequate genetic pedigree information, we utilized the karyomapping assay to obtain single-nucleotide polymorphisms (SNPs) allele data. For the 2 couples who had no reference in their family, we used single sperm next-generation sequencing (NGS) to realize haplotype construction. Linkage analysis was performed to deduce embryonic genotype, and aneuploidy was screened simultaneously. Prenatal diagnostic testing via amniocentesis at 18-22 weeks of gestation was performed to verify the genetic conditions of transferred embryos. In total, 120 embryos were examined in this study, and the results showed that only 26.7% (32/120) were mutation-free and euploid-confirmed embryos. Additionally, for female CVD patients, we convened a multidisciplinary team (MDT) to advise the couple on their fertility concerns and management measures during pregnancy and delivery. With our cooperation, 10 couples successfully obtained healthy babies not carrying the pathogenic mutations. The results of prenatal diagnostics were consistent with the results of PGT-M. Our study demonstrates that PGT-M based on haplotype analysis is reliable and necessary for the prevention of inherited CVDs. It also highlights the important value of multidisciplinary collaboration for CVD prevention and treatment. Inherited cardiovascular diseases (CVDs) present as a huge challenge for modern medical and health systems. Hundreds of genetic variants have been reported to cause CVD and the number of people with the disease is enormous and still on the rise globally. Here we recruited twelve couples suffering from inherited CVD and provided them with effective pre-implantation genetic testing for monogenic defect (PGT-M) strategy to avoid the occurrence of genetic defects in the offspring. Specifically, after embryo biopsy, we utilized karyomapping assay (for 10 couples with a family history) or next-generation sequencing (NGS) (for 2 couples having no reference in their pedigree) to obtain single-nucleotide polymorphisms (SNPs) allele data and then performed linkage analysis to deduce embryonic genotype. A total of 120 embryos from 15 PGT-M cycles were examined and 12 variants in 8 genes linked to inherited CVD were identified. Thirty-two mutation-free and euploid confirmed embryos were considered suitable for embryo transfer. Besides, for female CVD patients, we called up a multidisciplinary team (MDT) advising the couple on their fertility concerns and management measures of pregnancy and delivery. With our cooperation, 10 couples successfully obtain healthy babies not carrying the pathogenic mutations. Our study further validated the reliability of PGT-M utilizing linkage analysis as a means to prevent the transmission of genetic disorders to future generations, and offered valuable insights for multidisciplinary clinical practices on CVD. Show less
📄 PDF DOI: 10.1007/s00438-024-02208-4
MYBPC3
Shuo Wu, Ping Yang, Zilong Geng +11 more · 2024 · Cell research · Nature · added 2026-04-24
📄 PDF DOI: 10.1038/s41422-024-00930-7
MYBPC3
Haoran Yang, Anna Zhao, Yuxiang Chen +3 more · 2024 · BMC oral health · BioMed Central · added 2026-04-24
Periodontitis is a chronic inflammatory condition triggered by immune system malfunction. Mitochondrial extracellular vesicles (MitoEVs) are a group of highly heterogeneous extracellular vesicles (EVs Show more
Periodontitis is a chronic inflammatory condition triggered by immune system malfunction. Mitochondrial extracellular vesicles (MitoEVs) are a group of highly heterogeneous extracellular vesicles (EVs) enriched in mitochondrial fractions. The objective of this research was to examine the correlation between MitoEVs and the immune microenvironment of periodontitis. Data from MitoCarta 3.0, GeneCards, and GEO databases were utilized to identify differentially expressed MitoEV-related genes (MERGs) and conduct functional enrichment and pathway analyses. The random forest and LASSO algorithms were employed to identify hub MERGs. Infiltration levels of immune cells in periodontitis and healthy groups were estimated using the CIBERSORT algorithm, and phenotypic subgroups of periodontitis based on hub MERG expression levels were explored using a consensus clustering method. A total of 44 differentially expressed MERGs were identified. The random forest and LASSO algorithms identified 9 hub MERGs (BCL2L11, GLDC, CYP24A1, COQ2, MTPAP, NIPSNAP3A, FAM162A, MYO19, and NDUFS1). ROC curve analysis showed that the hub gene and logistic regression model presented excellent diagnostic and discriminating abilities. Immune infiltration and consensus clustering analysis indicated that hub MERGs were highly correlated with various types of immune cells, and there were significant differences in immune cells and hub MERGs among different periodontitis subtypes. The periodontitis classification model based on MERGs shows excellent performance and can offer novel perspectives into the pathogenesis of periodontitis. The high correlation between MERGs and various immune cells and the significant differences between immune cells and MERGs in different periodontitis subtypes can clarify the regulatory roles of MitoEVs in the immune microenvironment of periodontitis. Future research should focus on elucidating the functional mechanisms of hub MERGs and exploring potential therapeutic interventions based on these findings. Show less
no PDF DOI: 10.1186/s12903-024-03912-8
MYO19
Lu-Yang Zhang, Yun-Hui Chu, Yun-Fan You +12 more · 2024 · Journal of the American Heart Association · added 2026-04-24
Stroke is a leading cause of death worldwide, with a lack of effective treatments for improving the prognosis. The aim of the present study was to identify novel therapeutic targets for functional out Show more
Stroke is a leading cause of death worldwide, with a lack of effective treatments for improving the prognosis. The aim of the present study was to identify novel therapeutic targets for functional outcome after ischemic stroke . Cis-expression quantitative trait loci data for druggable genes were used as instrumental variables. The primary outcome was the modified Rankin Scale score at 3 months after ischemic stroke, evaluated as a dichotomous variable (3-6 versus 0-2) and also as an ordinal variable. Drug target Mendelian randomization, Steiger filtering analysis, and colocalization analysis were performed. Additionally, phenome-wide Mendelian randomization analysis was performed to identify the safety of the drug target genes at the genetic level. Among >2600 druggable genes, genetically predicted expression of 16 genes ( The present study revealed 4 candidate therapeutic targets for improving functional outcome after ischemic stroke, while the underlying mechanisms need further investigation. Show less
no PDF DOI: 10.1161/JAHA.124.034749
NR1H3
Jun Yang, Shitian Zou, Zeyou Qiu +11 more · 2024 · eLife · added 2026-04-24
Quiescence (G0) maintenance and exit are crucial for tissue homeostasis and regeneration in mammals. Here, we show that methyl-CpG binding protein 2 (Mecp2) expression is cell cycle-dependent and nega Show more
Quiescence (G0) maintenance and exit are crucial for tissue homeostasis and regeneration in mammals. Here, we show that methyl-CpG binding protein 2 (Mecp2) expression is cell cycle-dependent and negatively regulates quiescence exit in cultured cells and in an injury-induced liver regeneration mouse model. Specifically, acute reduction of Mecp2 is required for efficient quiescence exit as deletion of Mecp2 accelerates, while overexpression of Mecp2 delays quiescence exit, and forced expression of Mecp2 after Mecp2 conditional knockout rescues cell cycle reentry. The E3 ligase Nedd4 mediates the ubiquitination and degradation of Mecp2, and thus facilitates quiescence exit. A genome-wide study uncovered the dual role of Mecp2 in preventing quiescence exit by transcriptionally activating metabolic genes while repressing proliferation-associated genes. Particularly disruption of two nuclear receptors, Show less
no PDF DOI: 10.7554/eLife.89912
NR1H3
Mingdao Mu, Haoyu Sun, Shuyan Geng +8 more · 2024 · Molecular brain · BioMed Central · added 2026-04-24
Neurexin-3 (Nrxn3) has been genetically associated with obesity, but the underlying neural mechanisms remain poorly understood. This study aimed to investigate the role of Nrxn3 in the paraventricular Show more
Neurexin-3 (Nrxn3) has been genetically associated with obesity, but the underlying neural mechanisms remain poorly understood. This study aimed to investigate the role of Nrxn3 in the paraventricular nucleus of the hypothalamus (PVN) in regulating energy balance and glucose homeostasis. We found that Nrxn3 expression in the PVN was upregulated in response to metabolic stressors, including cold exposure and fasting. Using Cre-loxP technology, we selectively ablated Nrxn3 in CaMKIIα-expressing neurons of the PVN in male mice. This genetic manipulation resulted in marked weight gain attributable to increased adiposity and impaired glucose tolerance, without affecting food intake. Our findings identify PVN CaMKIIα-expressing neurons as a critical locus where Nrxn3 modulates energy balance by regulating adipogenesis and glucose metabolism, independently of appetite. These results reveal a novel neural mechanism potentially linking Nrxn3 dysfunction to obesity pathogenesis, suggesting that targeting PVN Nrxn3-dependent neural pathways may inform new therapeutic approaches for obesity prevention and treatment. Show less
no PDF DOI: 10.1186/s13041-024-01124-3
NRXN3
Guangzhao Li, Xiaowang Niu, Xiang Li +3 more · 2024 · Cellular and molecular biology (Noisy-le-Grand, France) · added 2026-04-24
This study aimed to identify and validate a 9-gene signature for predicting overall survival (OS) in glioma patients. Analysis of multiple gene expression datasets led to the identification of 135 can Show more
This study aimed to identify and validate a 9-gene signature for predicting overall survival (OS) in glioma patients. Analysis of multiple gene expression datasets led to the identification of 135 candidate genes associated with OS in glioma patients. Further analysis revealed that IGFBP2, PBK, NRXN3, TGIF1, DNAJA4, and LGALS3BP were identified as risk factors for OS, while ENAH, PPP2R2C, and SPHKAP were found to be protective factors. Multifaceted validation using different databases confirmed their differential expression patterns in glioma tissues compared to normal brain tissue. By utilizing LASSO regression and multivariate Cox regression analysis, a risk score was developed based on the expression levels of the 9 crucial genes. The risk score showed a significant correlation with OS in both training and validation cohorts and yielded superior predictive accuracy compared to individual gene expression. Moreover, a predictive nomogram incorporating the risk score, WHO grade, age, IDH mutation, and 1p/19q co-deletion was constructed and validated, which exhibited high predictive capabilities for survival rates at different time points. Enrichment analysis revealed the involvement of extracellular matrix-related pathways and immune system signaling in glioma prognosis. Furthermore, the risk score showed a strong correlation with immune cell infiltration and immune checkpoint expression, suggesting its potential role in the tumor immune microenvironment. In conclusion, our study provides a robust 9-gene signature and a predictive nomogram for evaluating the prognosis of glioma patients, offering valuable insights into personalized treatment strategies. Show less
no PDF DOI: 10.14715/cmb/2024.70.1.18
NRXN3