👤 Xingwei Wu

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Also published as: Jiake Wu, Ming-Jiuan Wu, Siying Wu, Yijian Wu, Fong-Li Wu, Chih-Chung Wu, Jin'en Wu, Zixiang Wu, D P Wu, Zhongwei Wu, Haiping Wu, Geyan Wu, Qi-Zhu Wu, Jianjin Wu, Su Wu, Shwu-Yuan Wu, Xiaodi Wu, Kuen-Phon Wu, Changxin Wu, Guofeng Wu, Zhiping Wu, Xiaojun Wu, Qibing Wu, Xiaoting Wu, Cheng-Hsin Wu, Junhua Wu, Wenze Wu, Hong Wu, Yandi Wu, Zhong Wu, An-Chih Wu, Jianhui Wu, Xiaoke Wu, Zhenguo Wu, Jason H Y Wu, Bing-Bing Wu, Selena Meiyun Wu, Yi-Mi Wu, M Wu, Hui-Mei Wu, Danni Wu, Sijie Wu, Minqing Wu, Geng-ze Wu, Kun Wu, Cheng-Hua Wu, Shaofei Wu, Zhaoyang Wu, Qihan Wu, R Ryanne Wu, Kunling Wu, Hao Wu, Mingxuan Wu, Pei Wu, Wendy Wu, Yukang Wu, Douglas C Wu, Jingtao Wu, Guizhen Wu, Zhangjie Wu, Lili Wu, Jianwu Wu, Min-Jiao Wu, Biaoliang Wu, Huan Wu, Shengxi Wu, Fei-Fei Wu, Peih-Shan Wu, Yu-Yuan Wu, Guoqing Wu, Pei-Yu Wu, Lun-Gang Wu, Jing Wu, Geting Wu, Dongzhe Wu, G Wu, Junlong Wu, Jia-Jun Wu, Jiangyue Wu, Muzhou Wu, Junzhu Wu, Ray-Chin Wu, Jian-Qiu Wu, T Wu, Jianxiong Wu, Liping Wu, Haiwei Wu, Guoping Wu, Yong-Hao Wu, Jin-hua Wu, Yi Wu, You Wu, Chongming Wu, Qunzheng Wu, Xudong Wu, Liqiang Wu, Cuiling Wu, Kunfang Wu, Bian Wu, Limeng Wu, Jason Wu, Zhibing Wu, Shuying Wu, Caihong Wu, Naqiong Wu, Joseph C Wu, Huating Wu, Tianhao Wu, Zhi-Hong Wu, Congying Wu, Gaojun Wu, Dongping Wu, Chiao-En Wu, Li Wu, Yihang Wu, Shaoxuan Wu, Haixia Wu, Gen Wu, Fanchang Wu, Xiaorong Wu, Jiahao Wu, Mingjie Wu, Mei Wu, Jiapei Wu, Lingqian Wu, Jia Wu, Fangge Wu, Sen-Chao Wu, Yanhui Wu, Zhiqiang Wu, Sarah Wu, Shugeng Wu, Xuanqin Wu, Dongmei Wu, Caiwen Wu, Jiangdong Wu, Junjing Wu, Guihua Wu, Meini Wu, Yingbiao Wu, Rui Wu, Hua-Yu Wu, Bifeng Wu, Jingwan Wu, Lingling Wu, Junzheng Wu, Xinmiao Wu, Yi-Fang Wu, Yuyi Wu, Yixuan Wu, Qinglin Wu, Leilei Wu, Bin Wu, Tianqi Wu, Shiya Wu, Hui-Chen Wu, Jian Wu, Sijun Wu, Yiwen Wu, Cong Wu, Feng Wu, Xi-Ze Wu, Qiuji Wu, Alexander T H Wu, Semon Wu, Qinan Wu, Lai Man Natalie Wu, Zhuokai Wu, Ran Wu, Panyun Wu, Kui Wu, Yumei Wu, Xinrui Wu, Biwei Wu, Yueling Wu, Xing Wu, Jiayi Wu, Hua Wu, Yuen-Jung Wu, Bingjie Wu, Xiaoliang Wu, Matthew A Wu, Jin Wu, Juanjuan Wu, Qiuhong Wu, Hongfu Wu, Xiaoming Wu, Ming-Sian Wu, Ronghua Wu, Junduo Wu, Dandan Wu, Yuliang Wu, Ming-Shiang Wu, Ying-Ying Wu, Chaoling Wu, Guang-Liang Wu, De Wu, Yihua Wu, Yuanyuan Wu, Tsung-Jui Wu, Yulian Wu, Han Wu, Lipeng Wu, Zhihao Wu, Jiexi Wu, Anna H Wu, Qiu Wu, Yaqin Wu, Huazhen Wu, Shengru Wu, Chieh-Lin Stanley Wu, Xiaoqian Wu, Xiahui Wu, Jianli Wu, Yun-Wen Wu, Jian-Yi Wu, Qiuya Wu, Tsai-Kun Wu, Xinyin Wu, Guoyao Wu, Zhenfeng Wu, Guoli Wu, J W Wu, Bill X Wu, Zujun Wu, Jianliang Wu, Yuanshun Wu, Ling-Ying Wu, Zeng-An Wu, Jianrong Wu, Xue Wu, Ke Wu, Cheng-Yang Wu, Mengxue Wu, Jinghong Wu, Rongrong Wu, Ruolan Wu, Rong Wu, Kevin Zl Wu, Xiaohong Wu, Run Wu, Zaihao Wu, Chaowei Wu, Yu-Ke Wu, Xinjing Wu, Anyue Wu, Wanxia Wu, Yun Wu, Xuan Wu, Meili Wu, Shu Wu, Yi-No Wu, Chao-Liang Wu, Chengwei Wu, Y-W Wu, Pensee Wu, Zhao-Bo Wu, Guangxian Wu, Xiao Wu, Juanli Wu, Xinlei Wu, Changjie Wu, Sai Wu, Jiawei Wu, Yujuan Wu, Haoze Wu, Renlv Wu, Yipeng Wu, Xiaoyang Wu, Yuh-Lin Wu, Yu'e Wu, An-Hua Wu, Dan-Chun Wu, Meng-Chao Wu, Yuanhao Wu, Jer-Yuarn Wu, Qian-Yan Wu, Guangyan Wu, Huisheng Wu, Huijuan Wu, Shuting Wu, Long-Jun Wu, Alice Ying-Jung Wu, Xiru Wu, Zhenfang Wu, Lidi Wu, Yetong Wu, Disheng Wu, Linmei Wu, Huiwen Wu, Zhenzhou Wu, Yuhong Wu, Liang Wu, Liyan Wu, Kuan-Li Wu, Pei-Ting Wu, Xiao-Jin Wu, Lifeng Wu, Terence Wu, Shujuan Wu, Gang Wu, Xue-Mei Wu, Szu-Hsien Wu, Yan-ling Wu, Xiaokang Wu, Lingyan Wu, Yih-Jer Wu, Xinghua Wu, Chunfu Wu, Yingxia Wu, Rongling Wu, Xifeng Wu, Jinhua Wu, Sihan Wu, Ming-Yue Wu, Shiyang Wu, K D Wu, Jinmei Wu, Luyan Wu, Shin-Long Wu, Zhipeng Wu, Shuai Wu, Guangzhen Wu, Zhixiang Wu, Longting Wu, Zhengsheng Wu, Xiaoqiong Wu, Yaoxing Wu, Yuqin Wu, Yudan Wu, Zoe Wu, Hongting Wu, Chi-Jen Wu, R Wu, Zhongqiu Wu, Meina Wu, Anke Wu, Dengying Wu, Cheng-Jang Wu, Hsi-Chin Wu, Shufang Wu, Yongjiang Wu, Yuan-de Wu, Sihui Wu, Qi Wu, Wenhui Wu, Fenfang Wu, K S Wu, Jianzhi Wu, Nana Wu, Lin-Han Wu, Zhen Wu, Jinjun Wu, Chen-Lu Wu, Jing-Fang Wu, Haiyan Wu, Yihui Wu, Qiqing Wu, Zhengzhi Wu, Dai-Chao Wu, Zhenyan Wu, Wen-Jeng Wu, Guanming Wu, Yongqun Wu, Sean M Wu, Hei-Man Wu, Su-Hui Wu, Diana H Wu, Ben J Wu, Pingxian Wu, Chew-Wun Wu, Yillin Wu, Xiaobing Wu, Jiang-Bo Wu, Jerry Wu, Siming Wu, Zijun Wu, Daqing Wu, Yu-Hsuan Wu, Lichao Wu, Zhimin Wu, Qijing Wu, Daxian Wu, Zhaoyi Wu, Z Wu, Tong Wu, Cheng-Chun Wu, Shusheng Wu, Tracy Wu, D Wu, Ting-Ting Wu, Xiao-Yan Wu, Lan Wu, J Wu, Changchen Wu, Qi-Fang Wu, Changwei Wu, Liangyan Wu, Liufeng Wu, Kan Wu, Eugenia Wu, Mingming Wu, Xiaolong Wu, Chunru Wu, Zhaofei Wu, Shenhao Wu, Li-Peng Wu, Yuna Wu, Minna Wu, Justin Che-Yuen Wu, Buling Wu, Chengyu Wu, Wutian Wu, Yuwei Wu, Guixin Wu, Haijing Wu, Hei Man Wu, Qiuchen Wu, Xiao-Hui Wu, Junfei Wu, Wenda Wu, Xiaofeng Wu, Linyu Wu, Yung-Fu Wu, Mengbo Wu, Zhenling Wu, Maoqing Wu, Zuping Wu, Chun-Chieh Wu, Julian Wu, Binbin Wu, Xiaohui Wu, Qian Wu, Xinchun Wu, Shuisheng Wu, Linxiang Wu, Xueqing Wu, Bo Wu, Moxin Wu, Xiao-Cheng Wu, Anzhou Wu, Shuyi Wu, Jiahui Wu, Meiqin Wu, Shihao Wu, Jer-Yuan Wu, Wen-Shu Wu, Wudelehu Wu, Ruonan Wu, Song Wu, Yulin Wu, De-Fu Wu, Hongyu Wu, Yurong Wu, Zixuan Wu, Shih-Ying Wu, Chih-Hsing Wu, Chengrong Wu, Yinghao Wu, Yuanzhao Wu, Wenjie Wu, Baochuan Wu, Ziliang Wu, Liuting Wu, Chia-Ling Wu, Y Q Wu, Man Wu, Na Wu, Wutain Wu, Chenyang Wu, Jinyu Wu, Selwin K Wu, Ping Wu, Lorna Wu, D I Wu, Yi-Cheng Wu, Jianzhong Wu, Xiaoyun Wu, Zhourui Wu, Li-Jun Wu, Xinhe Wu, Zhi-Wei Wu, Yinan Wu, Xinyan Wu, Xin Wu, Ting-Feng Wu, Yawei Wu, Shixin Wu, Hong-Mei Wu, Xiaojin Wu, Jiarui Wu, Yiqun Wu, Tsung-Teh Wu, Qi-Nian Wu, Ju Wu, Kai-Yue Wu, Xi-Chen Wu, Pengjie Wu, Zhe Wu, Shaoping Wu, Zhou Wu, Han-Jie Wu, Weijie Wu, Haijiang Wu, Hongfei Wu, Xiaojie Wu, Yi-Ying Wu, Zhentian Wu, Ze Wu, Kai-Hong Wu, Yuting Wu, Minyao Wu, Xueyan Wu, Feifei Wu, Shinan Wu, Yonghui Wu, Haoxuan Wu, Yanzhi Wu, Yiyi Wu, Dong Wu, Guohao Wu, Wenjing Wu, Shibo Wu, Wenqian Wu, Tian Wu, Hai-Yan Wu, Tiantian Wu, Chong Wu, Hongxian Wu, Daoyuan Wu, Zongfu Wu, Ling Wu, Yuxiang Wu, Xilong Wu, Yuyu Wu, Zong-Jia Wu, Fengming Wu, Huijian Wu, Guorong Wu, Chuanhong Wu, Choufei Wu, Chi-Chung Wu, Junfang Wu, Xiaoqing Wu, Ling-Fei Wu, Xinyang Wu, Xiaomin Wu, Yili Wu, Hong-Fu Wu, Shao-Ming Wu, Thomas D Wu, Lizhen Wu, Yuanming Wu, Hsien-Ming Wu, Jian Hui Wu, Litong Wu, Yuxian Wu, Weihua Wu, Lei Wu, C Wu, Wei Wu, Yu-E Wu, Qiulian Wu, Mei-Hwan Wu, Yuexiu Wu, Shaoze Wu, Zilong Wu, Chi-Hao Wu, Baojin Wu, Chao Wu, Yao Wu, Ya Wu, Do-Bo Wu, Wenjun Wu, Zhongren Wu, Nini Wu, Michael C Wu, Ning Wu, Jie Wu, Ming J Wu, Yi-Syuan Wu, Limei Wu, Zhenzhen Wu, Wen-Chieh Wu, Tianwen Wu, Junfeng Wu, Yunhua Wu, Shunan Wu, Junqi Wu, Jianing Wu, Honglin Wu, Maureen Wu, Yexiang Wu, Yan-Hua Wu, Mengjun Wu, Y H Wu, Mingxing Wu, Liuying Wu, Suhua Wu, Xiaomeng Wu, Shyh-Jong Wu, Tung-Ho Wu, Hongliang Wu, Wenxian Wu, Xuekun Wu, Ed Xuekui Wu, Wenqiang Wu, Chuang Wu, Jingyi Wu, Duojiao Wu, Xueyuan Wu, Ji-Zhou Wu, Lianqian Wu, Gaige Wu, Qing-Qian Wu, Haihu Wu, Xiushan Wu, Xueyao Wu, Tingchun Wu, Yafei Wu, Lingxi Wu, R-J Wu, Weidong Wu, Re-Wen Wu, Zhidan Wu, Peiyao Wu, Xuemei Wu, Chen Wu, Yiting Wu, Kerui Wu, Lihong Wu, Shiqi Wu, Liren Wu, Xiuhua Wu, Beili Wu, Yongqi Wu, Ruihong Wu, Huini Wu, Lingyun Wu, Guang-Long Wu, Po-Chang Wu, Qinghua Wu, Wenxue Wu, Ru-Zi Wu, Wenlin Wu, Changjing Wu, Xiexing Wu, J Y Wu, Jianping Wu, Guanggeng Wu, Zhichong Wu, W J Wu, Di Wu, Shaoyu Wu, Xiaotong Wu, Junyong Wu, Hui Wu, Hongyan Wu, Shengde Wu, Mengyuan Wu, Yutong Wu, Zheming Wu, Yiping Wu, Guiping Wu, Wen-Hui Wu, Dapeng Wu, Bing Wu, Wen-Sheng Wu, Yunpeng Wu, Li-Ling Wu, Xiao-Yuan Wu, Baiyan Wu, Qiu-Li Wu, Ying Wu, Xiao-Ye Wu, Da-Hua Wu, Hsing-Chieh Wu, Hui-Xuan Wu, Chieh-Jen Wu, Pengning Wu, Sichen Wu, S F Wu, Mengying Wu, Jia-En Wu, Ming-Der Wu, Weida Wu, Qi-Jun Wu, Guo-Chao Wu, Zhenyong Wu, Qi-Biao Wu, Yangfeng Wu, Lijie Wu, Zhiye Wu, Jihui Wu, JieQian Wu, Zhengliang L Wu, Qianqian Wu, Jingyun Wu, Xiaoman Wu, Ruohao Wu, Yiyang Wu, Zhengfeng Wu, Xiao-Jun Wu, Lizi Wu, Qiang Wu, Riping Wu, J-Z Wu, Guangjie Wu, Pengfei Wu, Jundong Wu, Beier Wu, Jianying Wu, Meng-Ling Wu, Lingxiang Wu, Jamie L Y Wu, Keija Wu, Xilin Wu, Yanhua Wu, An-Li Wu, Yi-Ming Wu, Chengbiao Wu, Huanghui Wu, Dong-Feng Wu, Kunsheng Wu, Zhengcan Wu, Yuxin Wu, Kun-Rong Wu, Dong-Fang Wu, Guanxian Wu, Sensen Wu, Guifen Wu, Yifeng Wu, Pin Wu, Tzu-Chun Wu, Qingping Wu, Mian Wu, R M Wu, S J Wu, Senquan Wu, Haisu Wu, Jingjing Wu, Cheng Wu, Meng Wu, Geping Wu, Yu Wu, Yumin Wu, Xia Wu, Xian-Run Wu, William Ka Kei Wu, Juan Wu, Pei-Ei Wu, Meng-Hsun Wu, Yingying Wu, S M Wu, Xiangwei Wu, Guangrun Wu, Liuxin Wu, Yangyu Wu, Jia-Hui Wu, Jin-Zhen Wu, S L Wu, Shaohuan Wu, June K Wu, Yanli Wu, Haishan Wu, H Wu, Zhou-Ming Wu, Deqing Wu, Tao Wu, Dong-Bo Wu, Binxin Wu, Yalan Wu, Xiangxin Wu, Xueji Wu, Hongxi Wu, Zhonghui Wu, Jiaxi Wu, Tianzhi Wu, Meiqi Wu, Weiwei Wu, Yan-Jun Wu, Lijuan Wu, Tingqin Wu, Jianming Wu, P L Wu, Yih-Ru Wu, Lanlan Wu, Jianjun Wu, Jianguang Wu, An-Xin Wu, Xingjie Wu, Jianzhang Wu, Xianan Wu, Wei-Ping Wu, Haoan Wu, Fang-Tzu Wu, Zhongjun Wu, Wenwen Wu, Xi Wu, Teng Wu, Xiaoling Wu, Mengjuan Wu, Wen Wu, Yifan Wu, Yang Wu, Qianhu Wu, Shenyue Wu, Wu-Tian Wu, Qianwen Wu, Ye Wu, Lixing Wu, Gui-Qin Wu, Grace F Wu, Xing-Ping Wu, Ming Wu, Lisha Wu, Yanchuan Wu, Siqi Wu, Yuming Wu, Yuan Wu, Yu-Ting Wu, I H Wu, Hailong Wu, Minghua Wu, B Wu, Zhenlong Wu, Fang Wu, Guanzhong Wu, Liqun Wu, Guifu Wu, Chris Y Wu, Zhikang Wu, Qi-Yong Wu, Qingshi Wu, Zhao-Yang Wu, Man-Jing Wu, Chih-Ching Wu, Jun Wu, Jinhui Wu, Jincheng Wu, Linhong Wu, Hung-Tsung Wu, Tangchun Wu, Xinglong Wu, Zhen-Yang Wu, Ma Wu, Dongyan Wu, Yin Wu, Jiu-Lin Wu, Yong Wu, Yan Wu, Weizhen Wu, Fanggeng Wu, Dishan Wu, Changyu Wu, Yue Wu, Yi-Long Wu, Ge-ru Wu, Jinqiao Wu, Zhongyang Wu, Jing-Wen Wu, Lifang Wu, Sheng-Li Wu, Jia-Wei Wu, Songfen Wu, Yihan Wu, Kebang Wu, Wenyong Wu, Cai-Qin Wu, Yilong Wu, Yanan Wu, Hsiu-Chuan Wu, Xueqian Wu, Yen-Wen Wu, Paul W Wu, Ying-Ting Wu, Xing-De Wu, Yucan Wu, Mingfu Wu, Na-Qiong Wu, Linzhi Wu, Jinze Wu, Xuhan Wu, H J Wu, Ruize Wu, Dirong Wu, Chung-Yi Wu, Yaohong Wu, Jianyi Wu, Jugang Wu, Jiao Wu, Liang-Huan Wu, Xueling Wu, Ruying Wu, Gen Sheng Wu, Zhaoyuan Wu, Shiwen Wu, Andong Wu, Yu-Ling Wu, Hsan-Au Wu, Jia-Qi Wu, Yanting Wu, Xihai Wu, Lulu Wu, Xuxian Wu, Xiaomei Wu, Jingyue Wu, Shuihua Wu, Ren Wu, S Wu, Yupeng Wu, Haoming Wu, Samuel M Wu, Fan Wu, Yuesheng Wu, Yihe Wu, Tiange Wu, Shuang Wu, Jiayu Wu, Chia-Lung Wu, Yaojiong Wu, Shengnan Wu, Zhuoze Wu, Y Wu, Y Y Wu, Zimu Wu, Depei Wu, Yi-Hua Wu, Haiyun Wu, Yanyan Wu, Min Wu, Wenjuan Wu, Guangxi Wu, Jinfeng Wu, Junjie Wu, Yawen Wu, Pinglian Wu, Hui-Hui Wu, Xunwei Wu, Xuefeng Wu, Constance Wu, Depeng Wu, Dianqing Wu, Qibiao Wu, Nan Wu, Hao-Tian Wu, Hanyu Wu, Xiaojiang Wu, Cheng-Jun Wu, San-pin Wu, Xiaofan Wu, Xiwei Wu, Shi-Xin Wu, Shao-Guo Wu, Sunyi Wu, Yueheng Wu, Chengqian Wu, Kuixian Wu, Guanyi Wu, Xin-Xi Wu, Qiuxia Wu, Danhong Wu, He Wu, Zhong-Jun Wu, Siyi Wu, Xiangsheng Wu, Kaili Wu, Liting Wu, Lanxiang Wu, Ping-Hsun Wu, Zheng Wu, Wen-Ling Wu, Jiang-Nan Wu, Huanlin Wu, Yongfei Wu, Catherine A Wu, Leslie Wu, Shuo Wu, Peng-Fei Wu, Cho-Kai Wu, Meng-Han Wu, Hon-Yen Wu, Anguo Wu, Yuguang Philip Wu, Hai-Yin Wu, Yicheng Wu, Xiaolang Wu, Yujie Wu, Qing Wu, V C Wu, Haomin Wu, Xingdong Wu, Hengyu Wu, Jiang Wu, Chengxi Wu, Xiaoli Wu, Junyi Wu, William K K Wu, Ling-qian Wu, Chun Wu, Lesley Wu, Niting Wu, Jiayuan Wu, Xueying Wu, Yingning Wu, S-F Wu, David Wu, Mei-Na Wu, Joshua L Wu, Jin-Shang Wu, Guanzhao Wu, Jianqiang Wu, Runda Wu, Li-Hsien Wu, June-Hsieh Wu, Rongjie Wu, Huazhang Wu, Huanwen Wu, Xiu-Zhi Wu, Yanran Wu, Xianfeng Wu, Weibin Wu, Xuanshuang Wu, G X Wu, Yan Yan Wu, Runpei Wu, Jiaqi Wu, Chien-Ting Wu, Li-Na Wu, Qinfeng Wu, Chia-Chang Wu, Yueming Wu, Siyu Wu, Renhai Wu, Baojian Wu, Yi-Xia Wu, Wei-Yin Wu, Renrong Wu, C-H Wu, Chuan-Ling Wu, Xinran Wu, Fengying Wu, Qiuliang Wu, Guanhui Wu, Jinjie Wu, Wei-Chi Wu, Wei-Xun Wu, Meng-Na Wu, Lin Wu, Wan-Fu Wu, Jiajing Wu, Colin Chih-Chien Wu, Yajie Wu, Qiaowei Wu, Yaru Wu, Xue-Yan Wu, Xiaoping Wu, Weijun Wu, Mengchao Wu, Boquan Wu, Chunyan Wu, Zelai Wu, Pei-Wen Wu, Guojun Wu, Yichen Wu, Ming-Tao Wu, Hsueh-Erh Wu, Guang-Bo Wu, Chia-Zhen Wu, Kay L H Wu, Zhi-Yong Wu, Yong-Hong Wu, Anping Wu, Jiahang Wu, Xiaobin Wu, Ching-Yi Wu, Linzhen Wu, Xiaoxing Wu, Haidong Wu, Zhen-Qi Wu, Mark N Wu, Xianpei Wu, Jianmin Wu, Guanrong Wu, Yanchun Wu, Dongsheng Wu, An-Dong Wu, Ren-Chin Wu, Yuchen Wu, Mengna Wu, Zhuanbin Wu, Lijun Wu, Yanjing Wu, Haodi Wu, Lun Wu, Si-Jia Wu, Yongfa Wu, Hai-Ping Wu, Ximei Wu, Xiangping Wu, Wenyu Wu, L-F Wu, Yixia Wu, Haiying Wu, Yiran Wu, Yanhong Wu, Xiayin Wu, Yali Wu, Yushun Wu, Qitian Wu, Qin Wu, Xiaofu Wu, Jiamei Wu, Xiaoyong Wu, Qiong Wu, Xiaoying Wu, Wujun Wu, N Wu, Peiyi Wu, Yongmei Wu, Xiaojing Wu, Yizhou Wu, Dan Wu, Wen-Qiang Wu, Anshi Wu, Junqing Wu, Xiao-Yang Wu, Zhaoxia Wu, Liyang Wu, Hongke Wu, Mengqiu Wu, Peng Wu, Haibin Wu, Ding Lan Wu, Yingzhi Wu, Lecheng Wu, Kejia Wu, Anyi Wu, Junshu Wu, Jianxin Wu, Deguang Wu, Jiaxuan Wu, W Wu, Justin C Y Wu, Jiong Wu, Yu-Chih Wu, Qinglan Wu, Xinyi Wu, Diana Wu, Xuefen Wu, Zhongluan Wu, Yanqiong Wu, Shengming Wu, Jian-Lin Wu, Donglin Wu, Daren Wu, Lintao Wu, Xiaodong Wu, Chang-Jiun Wu, Chunshuai Wu, Irene X Y Wu, Yaping Wu, Yangna Wu, Xiping Wu, Zongheng Wu, Chia-Chen Wu, Wenyi Wu, Yansheng Wu, Aimin Wu, Shaojun Wu, Caisheng Wu, Zhongchan Wu, Xu Wu, Yaohua Wu, Fei Wu, Yibo Wu, Qinyi Wu, Zhengyu Wu, Yadi Wu, Hang Wu, L Wu, Mingjun Wu, Yuetong Wu, Wen-Juan Wu, Guangming Wu, Lingzhi Wu, Tingting Wu, Zhong-Yan Wu, Zhuzhu Wu, Yuanbing Wu, Cuiyan Wu, Baoqin Wu, Colin O Wu, Shuyan Wu, Hongmei Wu, Guangsen Wu, Xiaolin Wu, An Guo Wu, Kailang Wu, Chien-Sheng Wu, Chun-Hua Wu, Wenqi Wu, Jemma X Wu, Quanhui Wu, Qing-Wu Wu, Yanxiang Wu, Jiajin Wu, Yuan Kai Wu, Qiao Wu
articles
Haoyang Sun, Zhaoxu Lu, Jin Guo +10 more · 2026 · Child: care, health and development · Blackwell Publishing · added 2026-04-24
Speed capability is critical for early childhood development, but troubling patterns are emerging in the motor fitness of Chinese preschoolers (3-6 years). This study investigated how compositional 24 Show more
Speed capability is critical for early childhood development, but troubling patterns are emerging in the motor fitness of Chinese preschoolers (3-6 years). This study investigated how compositional 24-h movement behaviours (sleep, sedentary behaviour [SB], light physical activity [LPA] and moderate-to-vigorous physical activity [MVPA]) relate to speed capability. Via compositional data analysis and isotemporal substitution modelling, we assessed relationships between 24-h movement behaviours (sleep, SB, LPA and MVPA) and speed capability in 275 preschoolers (mean age 4.98 ± 0.76 years). Participants completed 20-m sprint tests and 7-day accelerometry. Time-reallocation effects were quantified through pairwise behavioural substitutions (5- to 30-min durations), with all models adjusted for age, sex and BMI z scores (z-BMI). Higher relative MVPA time significantly predicted faster sprint times (β = -1.302, p < 0.001), while higher LPA predicted slower times (β = 1.570, p = 0.003). Reallocating 15 min from sleep, SB or LPA to MVPA reduced sprint times by 0.176, 0.201 and 0.385 s, respectively (all p < 0.05). Conversely, reallocating MVPA to other behaviours worsened performance. The effects exhibited asymmetry: displacing time away from MVPA impaired speed capability to a greater extent than equivalent gains in MVPA time improved it. MVPA is the strongest positive predictor of speed capability in preschoolers. Optimizing 24-h movement patterns by reallocating time from LPA or SB to MVPA is associated with enhanced speed performance, supporting targeted interventions for early childhood development. Show less
no PDF DOI: 10.1111/cch.70218
LPA
Leah M Schumacher, Yin Wu, J Graham Thomas +6 more · 2026 · International journal of obesity (2005) · Nature · added 2026-04-24
This study used compositional data techniques that address the interdependence of 24-h movement behaviors (sleep, sedentary behavior [SB], light-intensity physical activity [LPA], moderate-to-vigorous Show more
This study used compositional data techniques that address the interdependence of 24-h movement behaviors (sleep, sedentary behavior [SB], light-intensity physical activity [LPA], moderate-to-vigorous intensity physical activity [MVPA]) to examine: (1) how patients undergoing metabolic bariatric surgery (MBS) allocate time among these behaviors before MBS, and (2) whether overall time-use composition and modeled reallocation patterns relate to early weight loss after MBS. Participants wore an accelerometer 24 h/day for 10 days before MBS to measure time in sleep, SB, LPA, and MVPA. Isotemporal substitution models estimated differences in 6-month post-MBS percentage total weight loss (%TWL) associated with reallocations of these pre-surgery movement behaviors. Forty-five participants provided valid data. Pre-MBS time-use composition was associated with %TWL (23.8 ± 5.1%; F = 2.66, p = 0.047). Reallocating 15-60 SB or LPA minutes/day to MVPA was estimated to relate to 0.9-3.5% greater %TWL. Reallocating 15-30 MVPA minutes/day to SB or LPA was estimated to relate to 1.4-5.0% less %TWL (all comparisons p < 0.05). Other reallocations were non-significant. In conclusion, modeled shifts in time from SB or LPA to MVPA and vice versa were associated with estimated increases or decreases in early post-surgical weight loss, respectively. Experimental research is needed to clarify causal relationships and inform interventions to improve MBS outcomes. Show less
📄 PDF DOI: 10.1038/s41366-025-01983-3
LPA
Luomeng Qian, Zhiguang Fu, Ping Chen +11 more · 2026 · International journal of biological sciences · added 2026-04-24
📄 PDF DOI: 10.7150/ijbs.125483
LPA
Xinyu Li, Siwu Tian, Zeng Yu +2 more · 2026 · International journal of orthopaedic and trauma nursing · Elsevier · added 2026-04-24
A health-promoting lifestyle involves increasing health awareness and actively adopting healthier habits. For women with osteopenia, becoming more aware of osteoporosis prevention and taking positive Show more
A health-promoting lifestyle involves increasing health awareness and actively adopting healthier habits. For women with osteopenia, becoming more aware of osteoporosis prevention and taking positive preventive actions can effectively improve health outcomes. This study employed latent profile analysis (LPA) to assess the potential categories of healthy lifestyle promotion for women at high risk of primary osteoporosis. It aimed to identify high-risk subgroups, analyze differences and influencing factors among these groups, and offer evidence-based guidance for clinical nursing practice. From December 2024 to July 2025, women were recruited using convenience sampling from endocrine outpatient departments and physical examination centers at two Grade A tertiary hospitals in Guiyang City. Data collection followed the planned time frame, and only eligible samples were included. Latent profile analysis was performed with Mplus 8.3, and univariate and multiple logistic regression analyses were conducted using SPSS 27.0. A total of 340 valid questionnaires were analyzed. Participants were categorized into three latent profiles: the low self-management-ineffective health behaviors group (28.8 %), the moderate self-management-average health behaviors group (45.3 %), and the high self-management-favorable health behaviors group (25.9 %). These findings highlight disparities in the adoption of healthy lifestyles among women at high risk of primary osteoporosis. In clinical practice, nurses help patients with low health management recognize and overcome cognitive biases, use healthcare resources appropriately, and understand the importance of bone health. For patients with moderate health management, the can suggest exercise in addition to calcium supplementation. For those with high self-management, nurses can support their social networks to help maintain healthy behaviors over time. Show less
no PDF DOI: 10.1016/j.ijotn.2025.101251
LPA
Zhaoxu Lu, Jin Guo, Yihua Bao +13 more · 2026 · International journal of obesity (2005) · Nature · added 2026-04-24
To use compositional data analysis to examine the associations of daily movement behaviors with body composition, and to predict changes in body composition after reallocating time among behaviors in Show more
To use compositional data analysis to examine the associations of daily movement behaviors with body composition, and to predict changes in body composition after reallocating time among behaviors in preschool-aged children. 268 preschoolers were included in the cross-sectional study. An accelerometer was used to assess sedentary behavior (SB), light and moderate-to-vigorous physical activity (LPA and MVPA). A parental report was used to collect sleep time. Bioelectrical impedance analysis was employed to assess body composition. Compositional linear regression analysis was employed to explore how daily movement behaviors were associated with body composition. Compositional isotemporal substitution analysis was employed to estimate changes in body composition after reallocating time among behaviors. 24-h movement behaviors composition significantly predicted fat-free mass index (FFMI), soft lean mass index (SLMI), and skeletal muscle mass index (SMMI), but not fat mass index, percent body fat, and bone mineral content index. The compositional isotemporal substitution analyses consistently showed that increasing MVPA at the expenses of SB was positively associated with FFMI (+0.328 kg/m The findings highlight the importance of MVPA in improving preschoolers' body composition. Increasing MVPA at the expenses of SB may be a strategy to improve body composition in preschoolers. Show less
📄 PDF DOI: 10.1038/s41366-025-01939-7
LPA
Yanmeng Pan, Xingyu Yang, Mian Wu +1 more · 2026 · Journal of affective disorders · Elsevier · added 2026-04-24
Childhood trauma is a well-established risk factor for poor clinical outcomes in bipolar disorder (BD), yet most studies have relied on cumulative trauma scores, potentially overlooking heterogeneity Show more
Childhood trauma is a well-established risk factor for poor clinical outcomes in bipolar disorder (BD), yet most studies have relied on cumulative trauma scores, potentially overlooking heterogeneity in trauma exposure and its differential impact on psychopathology. This study employed latent profile analysis (LPA) to identify distinct subtypes of childhood trauma based on the Childhood Trauma Questionnaire (CTQ) among 725 individuals with BD in a Chinese clinical sample. Differences across trauma profiles were examined in relation to demographic features, psychiatric symptoms (anxiety, depression, mania), and suicidal ideation (Beck Scale for Suicide Ideation, BSSI). A four-class solution was identified, and the relationship with mental health outcomes was analyzed. Class 4 group, characterized by the most severe emotional abuse and physical neglect, along with the lowest emotional neglect, reported the highest levels of anxiety (HAMA), depression (HAMD), and suicidal ideation (BSSI). In contrast, manic symptoms (YMRS) were present across all groups but did not differ significantly between trauma profiles. Logistic regression indicated that emotional abuse was the strongest predictor of trauma class membership. Distinct trauma profiles in BD are differentially associated with symptom severity and suicide risk. These findings highlight the clinical value of moving beyond cumulative trauma scores to identify trauma-specific subtypes. Early identification of high-risk trauma configurations may inform personalized assessment and intervention strategies for individuals with BD. Show less
no PDF DOI: 10.1016/j.jad.2025.120490
LPA
Xin Bai, Zhe Wu, Lin Lu +9 more · 2026 · European radiology · Springer · added 2026-04-24
To develop a deep-learning model for segmenting and classifying adrenal nodules as either lipid-poor adenoma (LPA) or nodular hyperplasia (NH) on contrast-enhanced computed tomography (CECT) images. T Show more
To develop a deep-learning model for segmenting and classifying adrenal nodules as either lipid-poor adenoma (LPA) or nodular hyperplasia (NH) on contrast-enhanced computed tomography (CECT) images. This retrospective dual-center study included 164 patients (median age 51.0 years; 93 females) with pathologically confirmed LPA or NH. The model was trained on 128 patients from the internal center and validated on 36 external cases. Radiologists annotated adrenal glands and nodules on 1-mm portal-venous phase CT images. We proposed Mamba-USeg, a novel state-space models (SSMs)-based multi-class segmentation method that performs simultaneous segmentation and classification. Performance was evaluated using the mean Dice similarity coefficient (mDSC) for segmentation and sensitivity/specificity for classification, with comparisons made against MultiResUNet and CPFNet. From per-slice segmentation, the model yielded an mDSC of 0.855 for the adrenal gland; for nodule segmentation, it achieved mDSCs of 0.869 (LPA) and 0.863 (NH), significantly outperforming two previous models-MultiResUNet (LPA, p < 0.001; NH, p = 0.014) and CPFNet (LPA, p = 0.003; NH, p = 0.023). Classification performance from per slice demonstrated sensitivity of 95.3% (95% confidence interval [CI] 91.3-96.6%) and specificity of 92.7% (95% CI: 91.9-93.6%) for LPA, and sensitivity of 94.2% (95% CI: 89.7-97.7%) and specificity of 91.5% (95% CI: 90.4-92.4%) for NH. The classification accuracy for patients from external sources was 91.7% (95% CI: 76.8-98.9%). The proposed multi-class segmentation model can accurately segment and differentiate between LPA and NH on CECT images, demonstrating superior performance to existing methods. Question Accurate differentiation between LPA and NH on imaging remains clinically challenging yet critically important for guiding appropriate treatment approaches. Findings Mamba-Useg, a multi-class segmentation model utilizing pixel-level analysis and majority voting strategies, can accurately segment and classify adrenal nodules as LPA or NH. Clinical relevance The proposed multi-class segmentation model can simultaneously segment and classify adrenal nodules, outperforming previous models in accuracy; it significantly aids clinical decision-making and thereby reduces unnecessary surgeries in adrenal hyperplasia patients. Show less
📄 PDF DOI: 10.1007/s00330-025-12007-z
LPA
Guangming Li, Yi Jin, Xiaowei Yuan +4 more · 2026 · Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · Elsevier · added 2026-04-24
Dibutyl phthalate (DBP) is a widely distributed endocrine-disrupting chemical with potential carcinogenic properties, yet its role in head and neck squamous cell carcinoma (HNSC) remains unclear. Here Show more
Dibutyl phthalate (DBP) is a widely distributed endocrine-disrupting chemical with potential carcinogenic properties, yet its role in head and neck squamous cell carcinoma (HNSC) remains unclear. Here, we applied an integrative framework combining network toxicology, Mendelian randomization (MR), multi-omics analyses, molecular docking, molecular dynamics simulations, and in vitro experiments to elucidate the mechanisms underlying DBP-associated HNSC. Lipoprotein lipase (LPL) was identified as the sole overlapping gene between DBP-related targets and HNSC-associated genes. MR analysis supported a potential causal relationship between LPL and HNSC susceptibility. Expression profiling demonstrated tissue- and cell type-specific patterns of LPL and its dysregulation in HNSC, with associations to tumor stage and prognosis. Genomic analyses revealed that LPL alterations were infrequent and mainly driven by copy number loss. LPL expression positively correlated with immune and stromal infiltration. Enrichment analyses implicated immune regulation and PI3K-AKT signaling. Molecular simulations showed stable DBP-LPL binding. Functionally, DBP promoted SCC9 proliferation and reduced LPL expression, and was associated with transcriptional changes in PI3K-AKT-mTOR-related genes, whereas LPL restoration mitigated these effects. These findings reveal a novel DBP-LPL axis in HNSC. Show less
no PDF DOI: 10.1016/j.fct.2026.116091
LPL
Qing Cui, Gang Wu, Qianyun Chen +4 more · 2026 · Genomics · Elsevier · added 2026-04-24
The fat mass and obesity-associated (FTO) gene, though widely studied in human obesity and livestock lipid accumulation, remains poorly understood in bovine adipogenesis. This study investigated its r Show more
The fat mass and obesity-associated (FTO) gene, though widely studied in human obesity and livestock lipid accumulation, remains poorly understood in bovine adipogenesis. This study investigated its role in bovine adipocytes via overexpression, given its high expression in Guanling cattle adipose tissue. Results demonstrated that FTO significantly increased triglyceride content, adiponectin secretion, and lipid droplet accumulation (P < 0.01). It also upregulated key adipogenic markers (PPARγ, C/EBPβ, FABP4, LPL; P < 0.05). Transcriptomic analysis revealed that FTO promotes adipocyte differentiation and lipogenesis through regulating multiple lipid metabolic pathways. These findings reveal that FTO positively regulates bovine adipocyte differentiation by modulating lipid metabolic networks, thereby filling a critical gap in the understanding of FTO-mediated lipid metabolism in ruminants. Show less
no PDF DOI: 10.1016/j.ygeno.2026.111233
LPL
Michael J Stein, Hansjörg Baurecht, Patricia Bohmann +13 more · 2026 · Communications medicine · Nature · added 2026-04-24
Moderate-to-vigorous physical activity (MVPA) is inversely associated with risks of cancer, cardiovascular diseases (CVD), type 2 diabetes (T2D), and their co-occurrence, defined as multimorbidity; ho Show more
Moderate-to-vigorous physical activity (MVPA) is inversely associated with risks of cancer, cardiovascular diseases (CVD), type 2 diabetes (T2D), and their co-occurrence, defined as multimorbidity; however, the underlying biological pathways remain unclear. In 33,806 UK Biobank participants with 2911 measured blood proteins, a proteomic signature of MVPA was derived with linear and LASSO regressions. Multivariable Cox models, adjusted for MVPA, estimated prospective associations with cancer, CVD, T2D, and multimorbidity. We show that after multiple testing corrections, 220 proteins are retained in the MVPA signature. Proteins related to food intake, metabolism, and cell growth (e.g., LEP, MSTN) are inversely associated, while those involved in immune cell migration and musculoskeletal integrity (e.g., integrins, COMP) are positively associated with MVPA. Several proteins positively associated with MVPA are inversely associated with disease risk (e.g., integrins, CLEC4A for cancer; LPL, LEP for T2D), while proteins negatively associated with MVPA are positively associated with disease risk (e.g., CD38, TGFA for CVD). The proteomic signature score is inversely associated with cancer risk (hazard ratio per interquartile range: 0.87; 95% confidence interval: 0.78, 0.96) and T2D (0.66; 0.60, 0.72). For multimorbidity, proteins inversely related to MVPA align with expected risk patterns (e.g., GGT1, HR: 1.32; 95% CI: 1.12, 1.57), but the proteomic signature score is not associated. This study identifies several proteins associated with MVPA that are also associated with cancer, CVD, T2D, and the multimorbidity of these conditions. Further studies investigating the causal nature of these associations are welcome. Show less
📄 PDF DOI: 10.1038/s43856-026-01514-9
LPL
Uriel L Jean-Baptiste, Simcha R Singh, Ming J Wu +3 more · 2026 · Protein science : a publication of the Protein Society · Wiley · added 2026-04-24
Endothelial lipase (EL) is a key regulator of high-density lipoprotein (HDL) metabolism. Many aspects of EL function remain incompletely understood due to challenges in purifying active EL. This study Show more
Endothelial lipase (EL) is a key regulator of high-density lipoprotein (HDL) metabolism. Many aspects of EL function remain incompletely understood due to challenges in purifying active EL. This study identifies apolipoprotein J (ApoJ) as a novel chaperone for EL, crucial for its solubility and activity. Using an optimized purification protocol that yields active EL, we discovered that ApoJ consistently co-purifies with EL, maintaining its activity. We further show that knocking down ApoJ decreases the activity of EL. We demonstrate that ApoJ interacts with EL via its hydrophobic lid and tryptophan loop regions, and that mutating these regions abolishes the effect of ApoJ on the solubility and activity of EL. We show that ApoJ, EL, and ApoA1 (the defining lipoprotein of HDL particles) colocalize in HDL particles in mouse plasma. However, we find that ApoJ is not a direct carrier for EL to HDL particles. Instead, our data suggest that ApoJ primarily serves to enhance EL activity through its role as a chaperone, even when incorporated into lipid substrates. Our findings suggest a model in which ApoJ protects EL in plasma and enhances its hydrolysis of lipoprotein substrates. We propose that ApoJ is an accessory protein for EL, analogous to GPIHBP1 for LPL and co-lipase for PL. Further study of the interaction between EL and ApoJ will promote a better understanding of HDL metabolism. Show less
📄 PDF DOI: 10.1002/pro.70518
LPL
Yun He, Yaoyao Liu, Junwen Ouyang +6 more · 2026 · Pharmaceuticals (Basel, Switzerland) · MDPI · added 2026-04-24
📄 PDF DOI: 10.3390/ph19020285
LPL
Z Meng, Y Liu, W Yang +4 more · 2026 · Animal : an international journal of animal bioscience · Elsevier · added 2026-04-24
Backfat thickness, a key selection trait in pig-breeding programmes, has traditionally been measured as a homogeneous layer. However, backfat is anatomically structured into three distinct layers, and Show more
Backfat thickness, a key selection trait in pig-breeding programmes, has traditionally been measured as a homogeneous layer. However, backfat is anatomically structured into three distinct layers, and each layer likely contributes differently to carcass quality. In addition, previous studies have shown that the deposition of the third layer of backfat is phenotypically correlated with intramuscular fat (IMF). Therefore, targeted selection for specific backfat layers, particularly the third layer, represents a potential strategy to increase IMF content while maintaining a high lean meat percentage. However, the genetic architecture of these distinct porcine backfat layers remains poorly understood. The aim of this study was to estimate the genetic parameters and identify key candidate genes underlying the three backfat layers. We collected B-mode ultrasound images from 561 Landrace pigs to measure individual layer thickness, followed by DNA extraction, genotyping, genetic parameter estimation, and a genome-wide association study (GWAS). Our measurements showed that the first layer of backfat (FBF) is the thickest, followed by the second (SBF) and the third (TBF) layers. Genetic parameter estimation yielded heritability estimates of 0.37, 0.42, 0.38, 0.34, 0.32, 0.24, and 0.21 for total backfat (BF), FBF, FBF/BF, SBF, SBF/BF, TBF, and TBF/BF, respectively. Through integrated analysis of GWAS, Bayesian fine-mapping, and gene annotation, we identified 15 non-redundant candidate genes associated with different backfat layers. These included two genes (SOAT1 and ACBD6) shared by BF and SBF, LPL for BF and FBF, and CAND1 for TBF and TBF/BF. Additionally, SERPINA12 and SERPINA6 were associated with BF; PRKAG1 and PRDM16 with FBF; EPRS1 and SLC39A10 with FBF/BF; PTGES and CRAT with SBF; and ACLY, CAVIN1, and PDZRN3 with SBF/BF. Our results indicate that each layer is governed by a distinct set of genes, which advances our understanding of the genetic basis of backfat layers in pigs. Show less
no PDF DOI: 10.1016/j.animal.2026.101764
LPL
Hui Jiang, Ming-Hui Geng, Yue-Mei Zhan +7 more · 2026 · Hereditas · BioMed Central · added 2026-04-24
The primary renal complication of diabetes mellitus is diabetic kidney disease (DKD). The precise pathogenic mechanisms of DKD remain poorly elucidated. The aim of this study was to identify potential Show more
The primary renal complication of diabetes mellitus is diabetic kidney disease (DKD). The precise pathogenic mechanisms of DKD remain poorly elucidated. The aim of this study was to identify potential energy metabolism-related genes associated with DKD. The GSE30529 and GSE30528 datasets were retrieved from the Gene Expression Omnibus, and energy metabolism-related genes were obtained from the GeneCards database. Differentially expressed genes (DEGs) between DKD and control groups were analyzed. The biological functions and signaling pathways of these DEGs were evaluated using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). The diagnostic performance of hub genes for DKD was assessed using receiver operating characteristic (ROC) curve analysis. Expression levels of five significant energy metabolism-related genes were validated through immunohistochemistry. The Nephroseq V5 tool was used to evaluate gene expression in DKD and to determine correlations between gene expression and renal function in patients with DKD. A total of 17 energy metabolism-related DEGs were identified. Five hub genes-ALB, IGF1, CD36, LPL, and UCP2-were identified. Among these, CD36 and LPL demonstrated relatively high diagnostic accuracy for DKD. The findings suggest that CD36, IGF1, LPL, and UCP2 may serve as potential biomarkers for DKD. The genes CD36, IGF1, LPL, and UCP2 represent potential energy metabolism-related biomarkers with possible applications in the diagnosis and treatment of DKD. Show less
📄 PDF DOI: 10.1186/s41065-026-00632-7
LPL
Gayatri Arani, Amit Arora, Shuai Yang +21 more · 2026 · Medicine and science in sports and exercise · added 2026-04-24
Physical activity (PA) and sedentary behavior (SB) are associated with many diseases, including Alzheimer's disease and all-cause dementia. However, the specific biological mechanisms through which PA Show more
Physical activity (PA) and sedentary behavior (SB) are associated with many diseases, including Alzheimer's disease and all-cause dementia. However, the specific biological mechanisms through which PA protects against disease are not entirely understood. This study aims to address this gap, with a specific focus on all-cause dementia. We first assessed the conventional observational associations of three self-reported and three device-based PA/SB measures with circulating levels of 2,911 plasma proteins measured in the UK Biobank (n max =39,160) and assessed functional enrichment of identified proteins. We then used bi-directional Mendelian randomization (MR) to further evaluate the evidence for causal relationships of PA/SB with protein levels. Finally, we performed mediation analyses to identify proteins that may mediate the relationship of PA with incident all-cause dementia. Our findings revealed 41 proteins consistently associated with all PA measures and 1,027 proteins associated with at least one PA measure. Both conventional observational and MR study designs converged on proteins that appear to increase as a result of PA, including integrins such as ITGAV and ITGAM, as well as MXRA8, CLEC4A, CLEC4M, LPL, and ADGRG2; on proteins that appear to decrease as a result of PA such as LEP, INHBC, CLMP, PTGDS, ADM, OGN, and PI3; and on proteins that are more responsive to high-intensity PA, such as CA14, CA6, CA4, KIT, and ANGPT2. Functional enrichment analyses revealed processes such as cell-matrix adhesion, integrin-mediated signaling, and collagen binding. Finally, GDF15, ITGAV, ITGAM, ITGA11, HPGDS, GFAP, ADM, AHNAK, and DPP4 were among 21 unique proteins found to mediate the relationship of PA with all-cause dementia, implicating processes such as synaptic plasticity, neurogenesis, and inflammation. Our results provide insights into how PA affects biological processes and protects against dementia, and provide avenues for future research into the health-promoting effects of PA. Show less
no PDF DOI: 10.1249/MSS.0000000000003948
LPL
Yang Xiao, Xiaoqin Li, Shenghao Li +4 more · 2026 · Aquaculture nutrition · added 2026-04-24
This study evaluated the feasibility of replacing soybean lecithin (SBL) with lysophospholipids (LYLs) in the diet of Pacific white shrimp,
📄 PDF DOI: 10.1155/anu/6301061
LPL
Suhua Wu, Juan Peng, Xiaodong Wang +11 more · 2026 · FASEB journal : official publication of the Federation of American Societies for Experimental Biology · added 2026-04-24
Obesity has become a global epidemic and a major contributor to the development of Type 2 diabetes (T2D) through the promotion of insulin resistance. Emerging evidence has shown that GPX4 expression i Show more
Obesity has become a global epidemic and a major contributor to the development of Type 2 diabetes (T2D) through the promotion of insulin resistance. Emerging evidence has shown that GPX4 expression is reduced in macrophages under hyperglycemic conditions; however, the involvement of macrophage-specific GPX4 in obesity-associated insulin resistance remains unclear. We generated macrophage-specific Gpx4 knockout (Gpx4 Show less
📄 PDF DOI: 10.1096/fj.202503596R
LPL
Zhongshan Cheng, Sung-Liang Yu, Chih-Hsiang Yu +19 more · 2026 · Scientific reports · Nature · added 2026-04-24
The international consensus classification or the World Health Organization classifications underrepresented driver alterations enriched in pediatric acute myeloid leukemia (AML). To address this, we Show more
The international consensus classification or the World Health Organization classifications underrepresented driver alterations enriched in pediatric acute myeloid leukemia (AML). To address this, we retrospectively characterized the genomic landscape of 105 pediatric patients with AML of East Asian ancestry using transcriptome and whole-exome sequencing (WES). In addition to the common recurrent fusions such as RUNX1::RUNX1T1 and CBFB::MYH11, we identified rearrangements involving KMT2A, NUP98, GLIS, as well as FLT3 and UBTF tandem duplications. The median somatic mutation rate in AML was 0.97 per megabase, as estimated by WES. Frequently mutated pathways included signaling: 68.6% (72/105), transcription: 37.1% (39/105), epigenetic regulation: 26.7% (28/105), cohesin: 7.6% (8/105), RNA binding: 3.8% (4/105), and protein modification: 5.7% (6/105). When analyzed together, high-risk genetic subtypes including GLISr, UBTF tandem duplications, PICALM::MLLT10, and HOXr were significantly associated with poorer 5 year overall survival (OS) in multivariable analysis (p-value = 0.037). Although FLT3 internal tandem duplications were significantly associated with inferior 5 year OS in univariable analysis, this effect was not significant in multivariable analysis (p-value = 0.382). Patients with RUNX1 mutations had inferior 5 year OS in multivariable analysis (p-value = 0.009). These findings suggest specific genomic alterations that may refine risk stratification and guide future therapeutic protocols in Taiwanese pediatric patients with AML. Show less
📄 PDF DOI: 10.1038/s41598-025-34152-7
MLLT10
Ruohao Wu, Wenting Tang, Yu Li +5 more · 2026 · Genes & diseases · Elsevier · added 2026-04-24
📄 PDF DOI: 10.1016/j.gendis.2025.101970
MLXIPL
Ziwei Hu, Jiahui Pang, Xinli Liu +13 more · 2026 · CNS neuroscience & therapeutics · Wiley · added 2026-04-24
Neuropathic pain (NP), a chronic disorder caused by somatosensory nervous system lesions, severely impairs the quality of life. Microglial metabolic reprogramming and neuroinflammation drive NP progre Show more
Neuropathic pain (NP), a chronic disorder caused by somatosensory nervous system lesions, severely impairs the quality of life. Microglial metabolic reprogramming and neuroinflammation drive NP progression. Although ChREBP (key metabolic regulator) protects against NP, its specific mechanisms remain unclear. NP rat model was established via spared nerve injury (SNI) surgery, and mechanical allodynia was evaluated using Von Frey tests. ChREBP expression in microglia was detected through immunofluorescence, RT-qPCR, and western blot. Functional studies involved ChREBP knockdown/overexpression to assess effects on microglial polarization, neuroinflammation, neuronal excitability, pain behaviors, and fatty acid metabolism. Mechanisms were explored via dual-luciferase reporter and chromatin immunoprecipitation assays. Mechanical pain thresholds were significantly decreased on the ipsilateral side after SNI. ChREBP was upregulated in SDH microglia after SNI and in LPS-stimulated microglia in vitro. ChREBP knockdown inhibited anti-inflammatory microglial polarization, exacerbated neuroinflammation, and aggravated pain. Conversely, ChREBP overexpression promoted the anti-inflammatory phenotype, suppressed neuroinflammation, and alleviated pain. ChREBP enhanced microglial fatty acid oxidation and energy metabolism. Mechanistically, ChREBP bound to the TFBS1 site on the PGC-1α promoter to activate its transcription. PGC-1α overexpression rescued the impairments caused by ChREBP knockdown, including reduced fatty acid oxidation, suppressed anti-inflammatory polarization, elevated inflammatory factors, and increased neuronal excitability. The protective effects of ChREBP were attenuated by the fatty acid oxidation inhibitor Etomoxir. ChREBP alleviates NP by enhancing microglial fatty acid oxidation and anti-inflammatory phenotype via PGC-1α transcriptional activation, revealing a novel metabolic-immune axis for potential NP therapy. Show less
📄 PDF DOI: 10.1002/cns.70744
MLXIPL
Catherine A Wu, Matthew A Wu, Shane R Zhao +5 more · 2026 · Stem cell research · Elsevier · added 2026-04-24
Hypertrophic cardiomyopathy (HCM) is a prevalent inherited cardiac disorder characterized by left ventricular hypertrophy and contractile dysfunction. Mutations in sarcomeric genes, particularly cardi Show more
Hypertrophic cardiomyopathy (HCM) is a prevalent inherited cardiac disorder characterized by left ventricular hypertrophy and contractile dysfunction. Mutations in sarcomeric genes, particularly cardiac myosin-binding protein C (MYBPC3), are a leading cause of HCM. Here, we generated two induced pluripotent stem cell (iPSC) lines from peripheral blood mononuclear cells of patients carrying distinct MYBPC3 mutations (c.2490dupT and c.1800delA). Both lines displayed normal morphology, stable karyotypes, robust expression of pluripotency markers, and trilineage differentiation potential. These patient-specific iPSC lines provide a valuable platform for modeling MYBPC3-associated HCM and enable mechanistic and therapeutic studies of inherited cardiac disease. Show less
no PDF DOI: 10.1016/j.scr.2026.103982
MYBPC3
Rifat Nowshin Raka, Zhongwei Zhang, Junsong Xiao +1 more · 2026 · Computers in biology and medicine · Elsevier · added 2026-04-24
Neurodegenerative and psychiatric disorders share overlapping molecular mechanisms, including neuroinflammation, oxidative stress, and neurotransmitter dysregulation. Essential oils from Lavandula ang Show more
Neurodegenerative and psychiatric disorders share overlapping molecular mechanisms, including neuroinflammation, oxidative stress, and neurotransmitter dysregulation. Essential oils from Lavandula angustifolia (TLEO) and Rosa rugosa (PREO) contain neuroactive compounds with therapeutic potential, but their mechanisms remain poorly defined. This study aimed to elucidate the shared and distinct molecular targets and pathways of TLEO and PREO using a multi-scale computational strategy. Compounds identified by GC-MS were evaluated through ADMET profiling, target prediction, and disease-target intersection analysis. Enrichment, network, docking, and dynamics analyses were performed on shared protein-coding targets between essential oils and twelve brain disorders, including seven neurodegenerative conditions (Alzheimer's disease, amyotrophic lateral sclerosis, Friedreich ataxia, Huntington's disease, Lewy body disease, Parkinson's disease, spinal muscular atrophy) and five psychiatric disorders (autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia). A total of 110 compounds yielded 252 common targets, with CHRM2 (GPCR) and NR1H3 (non-GPCR) identified as key hubs. Docking suggested strong binding affinities for caryophyllene oxide at CHRM2 (-7.3 kcal/mol) and α-himachalene at NR1H3 (-8.5 kcal/mol). Molecular dynamics simulations confirmed stable, compact complexes with low RMSD and SASA values. MM/PBSA free energy calculations quantitatively validated these interactions, revealing favorable binding energetics driven predominantly by van der Waals and hydrophobic contributions, consistent with the terpenoid chemical profiles. Functional enrichment highlighted involvement in cholinergic signaling, lipid metabolism, and inflammatory regulation. This study demonstrates that PREO and TLEO can modulate multiple targets relevant to brain disorders through both GPCR and non-GPCR mechanisms. These findings provide a computationally inferred mechanistic framework for the potential neuroprotective synergy of these oils and highlight essential oil-derived compounds as promising leads for further experimental investigation. Show less
no PDF DOI: 10.1016/j.compbiomed.2026.111681
NR1H3
Ying Yang, Xiang Li, Dan-Li Tang +5 more · 2026 · International journal of molecular sciences · MDPI · added 2026-04-24
This study aimed to systematically elucidate the antihyperlipidemic mechanism of paeoniflorin, and we adopted an integrated multi-omics strategy to screen the key molecular targets and regulatory path Show more
This study aimed to systematically elucidate the antihyperlipidemic mechanism of paeoniflorin, and we adopted an integrated multi-omics strategy to screen the key molecular targets and regulatory pathways involved in its action, followed by experimental validation to verify the potential regulatory effects of paeoniflorin on the screened targets and metabolic processes. Rats with high-fat diet-induced hyperlipidemia received paeoniflorin treatment. Liver histopathology was evaluated using hematoxylin-eosin and Oil Red O staining. Serum levels of total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, total bile acids, activated partial thromboplastin time, prothrombin time, thrombin time, and fibrinogen were measured using a biochemical analyzer. Integrated multi-omics analyses were performed to investigate paeoniflorin's lipid-lowering mechanism. Critical pathways and targets identified were validated using Western blotting. Paeoniflorin alleviated pathological liver damage in hyperlipidemic rats and improved blood lipid levels, coagulation function, and liver function markers. Multi-omics analyses verified that paeoniflorin downregulated the expression of TREM-1, TLR4, NF-κB, TNF-α, and IL-1β, thereby alleviating hepatic inflammation. Paeoniflorin also upregulated the expression of low-density lipoprotein receptors (LDLR), liver X receptor alpha (LXRα), and ATP-binding cassette subfamily G member 1 (ABCG1), while downregulating proprotein convertase subtilisin/kexin type 9 (PCSK9) expression, contributing to balanced cholesterol metabolism. Paeoniflorin normalized glycerophospholipid and branched-chain amino acid metabolism, which correlated with reduced inflammation and improved cholesterol metabolism. Paeoniflorin ameliorates hyperlipidemia through multitarget mechanisms, potentially by suppressing the TREM-1-TLR4-NF-κB signaling pathway to reduce inflammation and by regulating cholesterol metabolism via the PCSK9-LDLR and LXRα-ABCG1 pathways. Show less
no PDF DOI: 10.3390/ijms27073039
NR1H3
Mingyi Du, Huangbo Yuan, Tianhao Wu +6 more · 2026 · Science advances · Science · added 2026-04-24
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a globally prevalent disease, yet its genetic architecture remains incompletely characterized. We integrated genome-wide association Show more
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a globally prevalent disease, yet its genetic architecture remains incompletely characterized. We integrated genome-wide association study data from multiple cohorts totaling nearly 3 million individuals of European ancestry and applied cross-trait genomic modeling of hepatic fat and seven cardiometabolic traits to construct an MASLD-specific polygenic architecture. We identified 128 risk variants across 100 loci and prioritized 55 effector genes, including established (e.g., Show less
no PDF DOI: 10.1126/sciadv.aeb5665
NRXN3
Jishi Ye, Yu Ding, Ruolan Wu +6 more · 2026 · International journal of surgery (London, England) · added 2026-04-24
Methotrexate (MTX) is a widely used chemotherapy drug, but its neurotoxicity can lead to cognitive impairments, particularly through effects on hippocampal function. Nevertheless, the underlying molec Show more
Methotrexate (MTX) is a widely used chemotherapy drug, but its neurotoxicity can lead to cognitive impairments, particularly through effects on hippocampal function. Nevertheless, the underlying molecular mechanisms are not fully understood. Deciphering MTX-induced cognitive impairment-linked molecular mechanisms in cells of the hippocampus could uncover novel therapeutic targets. In this study, we established a mouse model of cognitive impairment induced by the chemotherapy drug MTX. We applied single-nucleus RNA sequencing (snRNA-seq) to analyze the transcriptomic alterations in hippocampal cells of mice following MTX treatment, with a focus on neuron-specific gene expression changes. MTX chemotherapy led to a decrease in excitatory neurons but an increase in inhibitory neurons, altering the excitatory-inhibitory balance of neural networks and thus mediate cognitive dysfunction. Furthermore, MTX significantly disrupted the transcriptional regulatory network and potential trajectory of GABAergic neurons. It enhanced the Nrg1-Erbb4 pathway while attenuating the Nrxn3-Lrrtm4 pathway, destabilizing trans-synaptic signaling and causing abnormalities in excitatory and inhibitory synaptic functions. These disruptions may ultimately lead to neural network imbalance and cognitive dysfunction. This study highlights the specific effects of MTX chemotherapy on hippocampal cellular function and provides valuable insights into the molecular mechanisms underlying cognitive deficits and potential therapeutic targets. Show less
no PDF DOI: 10.1097/JS9.0000000000003519
NRXN3
Qitian Wu, Xiaoqing Wang, Qiming Mu +7 more · 2026 · Animal microbiome · BioMed Central · added 2026-04-24
This study aims to elucidate the regulatory mechanisms of host genetics on the porcine gut microbiota and their subsequent impact on the feed conversion ratio (FCR). While initial genome-wide associat Show more
This study aims to elucidate the regulatory mechanisms of host genetics on the porcine gut microbiota and their subsequent impact on the feed conversion ratio (FCR). While initial genome-wide association studies (GWAS) did not identify significant SNPs directly associated with FCR, we investigated the gut microbiota as a potential intermediate phenotype influencing feed efficiency. Nonmetric multidimensional scaling (NMDS) based on Bray–Curtis distances demonstrated a distinct separation in microbial community structure between the high-feed conversion ratio (HFCR) and low-feed conversion ratio (LFCR) groups (stress = 0.19), suggesting a link between FCR and gut microbial composition. Furthermore, a significant, albeit weak, negative correlation was observed between the genomic relatedness matrices and microbial Bray‒Curtis dissimilarity ( The online version contains supplementary material available at 10.1186/s42523-026-00527-y. Show less
no PDF DOI: 10.1186/s42523-026-00527-y
PATJ
Xiaochen Qi, Guandu Li, Yuanxin Liu +8 more · 2026 · iScience · Elsevier · added 2026-04-24
Autophagy supports clear cell renal cell carcinoma (ccRCC) progression, yet its upstream regulatory mechanisms remain to be fully defined. Integrating bulk, single-cell, and spatial transcriptomics, w Show more
Autophagy supports clear cell renal cell carcinoma (ccRCC) progression, yet its upstream regulatory mechanisms remain to be fully defined. Integrating bulk, single-cell, and spatial transcriptomics, we identify a regulatory axis wherein the transcription factor ZBED6 activates the expression of the autophagy-initiating kinase PIK3C3 via the repression of IGF2, thereby driving pro-tumorigenic autophagy. Spatial analysis confirms the co-localization of ZBED6 and PIK3C3 in tumor tissues. Using genes associated with this axis, we develop a six-gene prognostic signature that stratifies patients with distinct survival outcomes and differential responses to immunotherapy and targeted therapy. Functional assays show that ZBED6 promotes ccRCC cell proliferation, migration, and invasion. This work elucidates a pathway governing autophagy in ccRCC and provides a framework for prognostic assessment and precision therapy. Show less
no PDF DOI: 10.1016/j.isci.2026.114952
PIK3C3
Na Li, Xiaohua Li, Xianxiu Qiu +7 more · 2026 · Autophagy · Taylor & Francis · added 2026-04-24
The mammalian class III phosphatidylinositol-3-kinase complex (PtdIns3K) forms two biochemically and functionally distinct subcomplexes including the ATG14-containing complex I (PtdIns3K-C1) and the U Show more
The mammalian class III phosphatidylinositol-3-kinase complex (PtdIns3K) forms two biochemically and functionally distinct subcomplexes including the ATG14-containing complex I (PtdIns3K-C1) and the UVRAG-containing complex II (PtdIns3K-C2). Both subcomplexes adopt a V-shaped architecture with a BECN1-ATG14 or UVRAG adaptor arm and a PIK3R4/VPS15-PIK3C3/VPS34 catalytic arm. NRBF2 is a pro-autophagic modulator that specifically associates with PtdIns3K-C1 to enhance its kinase activity and promotes macroautophagy/autophagy. How NRBF2 exerts such a positive effect is not fully understood. Here we report that NRBF2 binds to PIK3R4/VPS15 with moderate affinity through a conserved site on its N-terminal MIT domain. The NRBF2-PIK3R4/VPS15 interaction is incompatible with the UVRAG-containing PtdIns3K-C2 because the C2 domain of UVRAG outcompetes NRBF2 for PIK3R4/VPS15 binding. Our crystal structure of the NRBF2 coiled-coil (CC) domain reveals a symmetric homodimer with multiple hydrophobic pairings at the CC interface, which is in distinct contrast to the asymmetric dimer observed in the yeast ortholog Atg38. Mutations in the CC domain that rendered NRBF2 monomeric led to weakened binding to PIK3R4/VPS15 and only partial rescue of autophagy deficiency in Show less
no PDF DOI: 10.1080/15548627.2025.2580438
PIK3C3
Liang-Huan Wu, Yueh-Hsiung Kuo, Fan-Li Lin +9 more · 2026 · Experimental eye research · Elsevier · added 2026-04-24
Retinal ischemia-reperfusion (I/R) injury is a key pathological feature of acute glaucoma that induces oxidative stress, inflammation, and retinal glial activation, ultimately leading to retinal degen Show more
Retinal ischemia-reperfusion (I/R) injury is a key pathological feature of acute glaucoma that induces oxidative stress, inflammation, and retinal glial activation, ultimately leading to retinal degeneration and neuronal dysfunction. This study evaluated the therapeutic potential of 3,4-dihydroxybenzalacetone (DBA) in protecting against I/R-induced retinal damage. DBA was tested in LPS-stimulated BV-2 microglia, in TNFα- or tBHP-treated rMC-1 Müller glial cells, and in a rat model of retinal I/R injury. In vitro assays demonstrated that DBA suppressed oxidative and inflammatory responses in microglia by reducing ROS, NO, IL-6, iNOS, and COX-2 levels. In Müller cells, DBA activated the NRF2/HO-1 pathway under oxidative stress and attenuated TNFα-induced upregulation of MMP-9 and MCP-1. Signaling analysis revealed that DBA inhibited the phosphorylation of p65 and STAT3 in both glial cell types, with additional ERK inhibition observed specifically in Müller cells. In vivo, DBA preserved retinal electrophysiological activity, as evidenced by maintained a- and b-wave responses, and reduced the expression of MMP-9, GFAP, and CD68 in the retina. These findings indicate that DBA confers partial retinal protection by modulating multiple glial-related signaling pathways and suggest its potential as a multi-target therapeutic agent for retinal neurodegenerative diseases. Show less
no PDF DOI: 10.1016/j.exer.2025.110762
RMC1
Jo-Yu Lin, Tien-Huang Lin, Yuan-Li Huang +9 more · 2026 · Cells · MDPI · added 2026-04-24
Prostate cancer (PCa) is the most general cancer in men and is often linked with distant metastasis in its later stages. The caffeic acid (CA) derivative, N-(4-methoxyphenyl)methylcaffeamide (MPMCA), Show more
Prostate cancer (PCa) is the most general cancer in men and is often linked with distant metastasis in its later stages. The caffeic acid (CA) derivative, N-(4-methoxyphenyl)methylcaffeamide (MPMCA), demonstrates superior liver-protective effects compared to CA. Nevertheless, the functions of MPMCA on prostate cancer metastasis remain unclear. Here, we demonstrate that MPMCA blocks migration and invasion in prostate cancer cells without affecting cell viability. By suppressing the production of mesenchymal markers Vimentin, N-cadherin and β-catenin and upregulating the production of the epithelial marker Zonula Occludens-1 (ZO-1), MPMCA also controls Epithelial-Mesenchymal Transition (EMT). The Phosphoinositide 3-kinase (PI3K), Protein kinase B (AKT) and mechanistic target of rapamycin (mTOR) pathway has been documented to regulate MPMCA-inhibited cell motility. Transfection with Snail and Slug cDNA reverses MPMCA's suppression of EMT, migration, and invasion in prostate cancer cells. Importantly, our in vivo data indicates that MPMCA reduces Snail and Slug expression and prostate cancer metastasis. Our evidence suggests that MPMCA is a novel therapeutic candidate for treating metastatic prostate cancer. Show less
no PDF DOI: 10.3390/cells15050454
SNAI1