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您的检索式：作者名="ZHU Zaiming"
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| 1 | New estertin derivatives based on trivacant keggin-type ^9- cluster 显示文摘 | ZHANG Lancui XUE Han ZHU Zaiming | 2010 | Inorganic Chemistry Communications2010，13，5： | 1 |
| 2 | New Estertin Derivatives Based on Trivacant Keggin-Type ^9- Cluster 显示文摘 | Zhang Lancui Xue Han Zhu Zaiming | 2010 | lnorg Chem Commun2010，13，5： | 1 |
| 3 | Neddylation inhibitor MLN4924 suppresses cilia formation by modulating AKT1显示文摘The primary cilium is a microtubule-based sensory organelle.The molecular mechanism that regulates ciliary dynamics remains elusive.Here,we report an unexpected finding that MLN4924,a small molecule inhibitor of NEDD8-activating enzyme(NAE),blocks primary ciliary formation by inhibiting synthesis/assembly and promoting disassembly.This is mainly mediated by MLN4924-induced phosphorylation of AKT1 at Ser473 under serum-starved,ciliary-promoting conditions.Indeed,pharmaceutical inhibition(by MK2206)or genetic depletion(via siRNA)of AKT1 rescues MLN4924 effect,indicating its causai role.Interestingly,pAKT 1-Ser473 activity regulates both ciliary synthesis/assembly and disassembly in a MLN4924 dependent manner,whereas pAKT-Thr308 determines the ciliary length in MLN4924-independent but VHL-dependent manner.Finally,MLN4924 inhibits mouse hair regrowth,a process requires ciliogenesis?Collectively,our study dem on strates an unexpected role of a neddylation inhibitor in regulation of ciliogenesis via AKT1,and pro?vides a proof-of-concept for potential utility of MLN4924 in the treatment of human diseases associated with abnormal ciliogenesis. | Hongmei Mao Zaiming Tang Hua Li Bo Sun Mingjia Tan Shaohua Fan Yuan Zhu Yi Sun | 2019 | Protein & Cell2019，10，10： | 1 |
| 4 | Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists显示文摘In recent years,with the development of artificial intelligence,especially deep learning technology,researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical practice.However,because of the complexity and flexibility of the deep learning algorithms,these researches have great variability on model building,validation process,performance description and results interpretation.The lack of a reliable,consistent,standardized design protocol has,to a certain extent,affected the progress of clinical translation and technology development of computer aided detection systems.After reviewing a large number of literatures and extensive discussion with domestic experts,this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular diseases.With further research and application expansion,this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools. | Longjiang Zhang Zhao Shi Min Chen Yingmin Chen Jingliang Cheng Li Fan Nan Hong Wenxiao Jia Guihua Jiang Shenghong Ju Xiaogang Li Xiuli Li Changhong Liang Weihua Liao Shiyuan Liu Zaiming Lu Lin Ma Ke Ren Pengfei Rong Bin Song Gang Sun Rongpin Wang Zhibo Wen Haibo Xu Kai Xu Fuhua Yan Yizhou Yu Yunfei Zha Fandong Zhang Minwen Zheng Zhen Zhou Wenzhen Zhu Guangming Lu Zhengyu Jin on behalf of Chinese Association of Radiologists | 2022 | Intelligent Medicine2022，2，4： | 0 |
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