检索结果-内蒙古大学图书馆

Discovery of a Novel Androgen Receptor Antagonist Manifesting Evidence to Disrupt the Dimerization of the Ligand-Binding Domain via Attenuating the Hydrogen-Bonding Network Between the Two Monomers

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JOURNAL OF MEDICINAL CHEMISTRY 2021年第23期64卷 17221-17238页

作者： Fu, Weitao Zhang, Minkui Liao, Jianing Tang, Qing Lei, Yixuan Gong, Zhou Shan, Luhu Duan, Mojie Chai, Xin Pang, Jinping Tang, Chun wang, xuwen Xu, Xiaohong Li, Dan Sheng, Rong Hou, Tingjun Zhejiang Univ Coll Pharmaceut Sci Hangzhou 310058 Zhejiang Peoples R China Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol Natl Ctr Magnet Resonance WuhanState Key Lab Mag Wuhan Inst Phys & MathCAS Key Lab Magnet Resonan Wuhan 430071 Hubei Peoples R China Univ Chinese Acad Sci Zhejiang Canc Hosp Inst Canc Res & Basic Med Sci Canc HospChinese Acad Sci Hangzhou 310022 Zhejiang Peoples R China Chinese Acad Sci Natl Ctr Magnet Resonance Wuhan Wuhan Inst Phys & Math Key Lab Magnet Resonance Biol SystState Key Lab Wuhan 430071 Hubei Peoples R China Peking Univ Coll Chem & Mol Engn Beijing Natl Lab Mol Sci Beijing 100871 Peoples R China Peking Univ Peking Tsinghua Ctr Life Sci Beijing 100871 Peoples R China

Androgen receptor (AR) has proved to be a vital drug target for treating prostate cancer. Here, we reported the discovery of a novel AR antagonist 92 targeting the AR ligand-binding pocket, but distinct from the marketed drug enzalutamide (Enz), 92 demonstrated inhibition on the AR ligand-binding domain (LBD) dimerization, which is a novel mechanism reported for the first time. First, a novel hit (26, IC50 = 5.57 mu M) was identified through virtual screening based on a theoretical AR LBD dimer bound with the Enz model. Then, guided by molecular modeling, 92 was discovered with 32.7-fold improved AR antagonistic activity (IC50 = 0.17 mu M). Besides showing high bioactivity and safety, 92 can inhibit AR nuclear translocation. Furthermore, 92 inhibited the formation of the AR LBD dimer, possibly through attenuating the hydrogen-bonding network between the two monomers. This interesting finding would pave the way for the discovery of a new class of AR antagonists.

关键词： Antagonists Monomers Oligomers Assays Dimerization

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Discovery of Novel GR Ligands toward Druggable GR Antagonist Conformations Identified by MD Simulations and Markov State Model Analysis

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ADVANCED SCIENCE 2022年第3期9卷 e2102435页

作者： Hu, Xueping Pang, Jinping Zhang, Jintu Shen, Chao Chai, Xin wang, Ercheng Chen, Haiyi wang, xuwen Duan, Mojie Fu, Weitao Xu, Lei Kang, Yu Li, Dan Xia, Hongguang Hou, Tingjun Zhejiang Univ Coll Pharmaceut Sci Innovat Inst Artificial Intelligence Med Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ State Key Lab CAD&CG Hangzhou 310058 Zhejiang Peoples R China Chinese Acad Sci Key Lab magnet Resonance Biol Syst State Key Lab Magnet Resonance & Atom & Mol Phys Natl Ctr Magnet Resonance WuhanWuhan Inst Phys & Wuhan 430071 Hubei Peoples R China Jiangsu Univ Technol Inst Bioinformat & Med Engn Sch Elect & Informat Engn Changzhou 213001 Jiangsu Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Dept Biochem Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Res Ctr Clin Pharm Hangzhou 310058 Zhejiang Peoples R China

Binding of different ligands to glucocorticoid receptor (GR) may induce different conformational changes and even trigger completely opposite biological functions. To understand the allosteric communication within the GR ligand binding domain, the folding pathway of helix 12 (H12) induced by the binding of the agonist dexamethasone (DEX), antagonist RU486, and modulator AZD9567 are explored by molecular dynamics simulations and Markov state model analysis. The ligands can regulate the volume of the activation function-2 through the residues Phe737 and Gln738. Without ligand or with agonist binding, H12 swings from inward to outward to visit different folding positions. However, the binding of RU486 or AZD9567 perturbs the structural state, and the passive antagonist state appears more stable. Structure-based virtual screening and in vitro bioassays are used to discover novel GR ligands that bias the conformation equilibria toward the passive antagonist state. HP-19 exhibits the best anti-inflammatory activity (IC50 = 0.041 +/- 0.011 mu m) in nuclear factor-kappa B signaling pathway, which is comparable to that of DEX. HP-19 also does not induce adverse effect-related transactivation functions of GR. The novel ligands discovered here may serve as promising starting points for the development of GR modulators.

关键词： glucocorticoid ligand binding domain Markov state model nuclear receptor virtual screening

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基于3种时间序列模型的九龙江河流库区藻华预测

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环境科学学报 2022年第11期42卷 172-183页

作者：任树顺高萌王煦雯余镒琦陈纪新陈能汪厦门大学环境与生态学院福建省海陆界面生态环境重点实验室厦门361102 厦门大学近海海洋环境科学国家重点实验室厦门361102 罗德岛大学海洋学研究生院纳拉甘西特美国罗德岛028824 中国海洋大学环境科学与工程学院青岛266100

湖库富营养化和有害藻华是全球性生态环境问题,藻华预测与早期预警是保障湖库水源地供水安全的关键技术.如何基于高频水生态在线监测数据进行藻华的实时动态预测成为水生态管理领域的重大需求.本研究以福建省九龙江江东库区(水源地)为例... 详细信息

湖库富营养化和有害藻华是全球性生态环境问题,藻华预测与早期预警是保障湖库水源地供水安全的关键技术.如何基于高频水生态在线监测数据进行藻华的实时动态预测成为水生态管理领域的重大需求.本研究以福建省九龙江江东库区(水源地)为例,利用3年连续观测的逐时平均总叶绿素a浓度数据,对比研究了SARIMA、Prophet和LSTM(长短期记忆神经网络)3种时间序列模型在藻华(日平均叶绿素a大于15μg·L^(-1))预测方面的效果.结果表明:(1)时间序列模型要求参数少,灵活性强,能清晰反映水质特征和未来变化趋势,可弥补传统藻类监测预警方法的局限性;(2)基于深度学习框架的LSTM模型,具有独特的迭代优化算法,对藻类非线性变化特征的识别和预测能力较强,其总叶绿素a逐日预测和7日预测效果均显著优于SARIMA模型和Prophet模型;(3)输入数据长度会在一定程度上影响模型预测效果,最优的输入数据时间长度为7 d;输入数据频率对预测效果也有影响,在预测非藻华日时,小时数据的预测效果优于日频率数据;在预测藻华日时,两种频率数据无显著差异,但日频率数据能更准确识别藻华日特征.总结起来,基于LSTM模型实现总叶绿素a浓度的短期预测,可为九龙江河流库区藻华早期预警和供水安全保障提供技术支撑.

关键词：藻华预测模型人工神经网络高频监测叶绿素a 九龙江

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JOINTS RELATION INFERENCE NETWORK FOR SKELETON-BASED ACTION RECOGNITION 26

JOINTS RELATION INFERENCE NETWORK FOR SKELETON-BASED ACTION ...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Ye, Fanfan Tang, Huiming wang, xuwen Liang, Xiao Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9781538662496

Recently, the graph convolutional networks based methods have achieved remarkable performance in skeleton-based action recognition. However, current methods do not make full use of the topology of the skeleton graph, because they set and fixed the relation of skeleton joints or adjacency matrix for all input samples manually. In order to solve the problem, we design an end-to-end architecture consisting of joints relation inference network (JRIN) and skeleton graph convolutional network (SGCN). JRIN can aggregate spatial-temporal feature of every two joints globally, then infer the optimal relation between every two joints automatically. These relations of joints is quantified as the optimal adjacency matrices. Then SGCN will take the optimal matrices to do the final action recognition. During training, special initialization and alternate training strategies are proposed to optimize the model. Our method achieves very competitive performance on widely used NTU-RGB+D and Kinetics datasets.

关键词： Action Recognition Relation Inference Graph Convolutional Network Skeleton

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A Computational Framework Towards Medical Image Explanation 7th

A Computational Framework Towards Medical Image Explanation

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AIME 7th Joint International Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care (KR4HC/ProHealth) / 1st Workshop on Transparent, Explainable and Affective AI in Medical Systems (TEAAM)

作者： wang, xuwen Zhang, Yu Guo, Zhen Li, Jiao Chinese Acad Med Sci & Peking Union Med Coll Inst Med Informat & Lib Beijing 100020 Peoples R China Genertec Universal Med Grp Co Ltd Beijing 100000 Peoples R China

ISBN: (纸本)9783030374464;9783030374457

In this paper, a unified computational framework towards medical image explanation is proposed to promote the ability of computers on understanding and interpreting medical images. Four complementary modules are included, such as the construction of Medical Image-Text Joint Embedding (MITE) based on large-scale medical images and related texts;a Medical Image Semantic Association (MISA) mechanism based on the MITE multimodal knowledge representation;a Hierarchical Medical Image Caption (HMIC) module that is visually understandable to radiologists;and a language-independent medical imaging report generation prototype system by integrating the HMIC and transfer learning method. As an initial study of automatic medical image explanation, preliminary experiments were carried out to verify the feasibility of the proposed framework, including the extraction of large scale medical image-text pairs, semantic concept detection from medical images, and automatic medical imaging reports generation. However, there is still a great challenge to produce medical image interpretations clinically usable, and further research is needed to empower machines explaining medical images like a human being.

关键词： Medical image explanation Medical Image-Text Joint Embedding Medical Image Semantic Association Hierarchical Medical Image Caption Medical imaging reports generation

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Novel androgen receptor antagonist identified by structure-based virtual screening, structural optimization, and biological evaluation

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EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY 2020年 192卷 112156-000页

作者： Tang, Qin Fu, Weitao Zhang, Minkui wang, Ercheng Shan, Lvhu Chai, Xin Pang, Jinping wang, xuwen Xu, Xiaohong Xu, Lei Li, Dan Sheng, Rong Hou, Tingjun Zhejiang Univ Coll Pharmaceut Sci Hangzhou 310058 Zhejiang Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Zhejiang Canc Hosp Inst Canc Res & Basic Med SciCanc Hosp Hangzhou 310022 Zhejiang Peoples R China Jiangsu Univ Technol Sch Elect & Informat Engn Inst Bioinformat & Med Engn Changzhou 213001 Jiangsu Peoples R China

Androgen receptor (AR) plays important roles in the development of prostate cancer (PCa), and therefore it has been regarded as the most important therapeutic target for both hormone-sensitive prostate cancer (HSPC) and advanced PCa. In this study, a novel hit (C18) with IC50 of 2.4 mu M against AR transcriptional activity in LNCaP cell was identified through structure-based virtual screening based on molecular docking and free energy calculations. The structure-activity relationship analysis and structural optimization of C18 resulted in the discovery of a structural analogue (AT2), a more potent AR antagonist with 16-fold improved anti-AR potency. Further assays indicated that AT2 was capable of effectively inhibiting the transcriptional function of AR and blocking the nuclear translocation of AR like the second-generation AR antagonists. The antagonists discovered in this study may be served as the promising lead compounds for the development of AR-driven PCa therapeutics. (c) 2020 Elsevier Masson SAS. All rights reserved.

关键词： Prostate cancer Androgen receptor Structure-based virtual screening Antagonist Molecular docking

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真性小眼球PRSS56基因突变与临床特征研究

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眼科 2025年第1期34卷 27-33页

作者：陶靖郝洁沈人娟傅语潇李姣王序文首都医科大学附属北京同仁医院北京同仁眼科中心眼科学与视觉科学北京市重点实验室北京100730 中国医学科学院/北京协和医学院医学信息研究所北京100020

目的研究中国真性小眼球患者PRSS56基因突变的遗传和临床特征,揭示基因型与表型之间的相关性。设计回顾性病例系列。研究对象2003-2023年在北京同仁医院眼科就诊并随访的40个真性小眼球家系。方法回顾对患者和家系成员进行的详细眼科检... 详细信息

目的研究中国真性小眼球患者PRSS56基因突变的遗传和临床特征,揭示基因型与表型之间的相关性。设计回顾性病例系列。研究对象2003-2023年在北京同仁医院眼科就诊并随访的40个真性小眼球家系。方法回顾对患者和家系成员进行的详细眼科检查资料。对40个家系中有血样的51例患者及42例家系成员采用全外显子组测序(WES)分析PRSS56基因编码区和相邻内含子区,并进行生物信息学分析和致病性预测。基因型与表型的发生率统计分析采用SPSS27.0和R 4.4.1软件进行。主要指标眼部检查结果、突变基因型、闭角型青光眼(ACG)及葡萄膜渗漏综合征(UES)并发症的发生情况。结果在这40个真性小眼球家系中,检测出15个与PRSS56基因突变相关的家系(其中先证者15例,患病家属5例),检出率为37.50%(15/40)。共发现16种PRSS56基因突变,包括3种新突变[c.494dupG(***167Gly)、c.340dupG(***115Gly)、c.407C>T(***136Val)]和13种已报道的突变。其中c.1066dupC突变频率为53.33%(8/15),是本队列中最常见的突变。PRSS56基因突变的真性小眼球呈现视力低下、眼轴短、前房浅、视盘拥挤、视网膜血管扩张迂曲、黄斑区视网膜皱褶等临床特征。首诊年龄>40岁组的平均巩膜厚度更厚,平均中央前房深度更浅,发生ACG和UES的比例更高(P均<0.05)。c.1066dupC高频突变携带组呈现眼前节更短的趋势。结论本研究阐述了中国真性小眼球PRSS56基因突变与临床特征及其相关性,扩展了PRSS56基因的突变谱,加深了真性小眼球基因型和表型关联的认识,为该病的精确诊疗和预后管理奠定了更加完善的理论基础。

关键词：真性小眼球 PRSS56基因突变

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Medical Image Labelling and Semantic Understanding for Clinical Applications 1

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10th International Conference of the CLEF-Association (CLEF)

作者： wang, xuwen Guo, Zhen Zhang, Yu Li, Jiao Chinese Acad Med Sci & Peking Union Med Coll Inst Med Informat Med Lib Beijing 100020 Peoples R China

ISBN: (数字)9783030285777

ISBN: (纸本)9783030285777;9783030285760

Semantic concept detection contributes to machine understanding and learning from medical images;it also plays an important role in image reading and image-assisted diagnosis. In this study, the problem of detecting high-frequency concepts from medical images was transformed into a multilabel classification task. The transfer learning method based on convolutional neural networks (CNNs) was used to recognize high-frequency medical concepts. The image retrieval-based topic modelling method was used to obtain the semantically related concepts from images similar to the given medical images. Our group participated in the concept detection subtasks that were launched by ImageCLEFcaption 2018 and ImageCLEFmed Caption 2019. In the 2018 task, the CNN-based transfer learning method achieved an F1 score of 0.0928, while the retrieval-based topic model achieved an F1 score of 0.0907. Although the latter method recalled some low-frequency concepts, it heavily depended on the image retrieval results. For the latter 2019 task, we proposed body part-based pre-classification strategies and achieved an F1 score of 0.2235. The results indicated that the transfer learning-based multi-label classification method was more robust in high-frequency concept detection across different data sets, but there is still much room for improvement in large-scale open semantic concept detection research.

关键词： Concept detection Transfer learning Multi-label classification Pre-classification Medical image retrieval

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Medical Data Mining Course Development in Postgraduate Medical Education: Web-Based Survey and Case Study

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JMIR MEDICAL EDUCATION 2021年第4期7卷 e24027页

作者： Yang, Lin Zheng, Si Xu, Xiaowei Sun, Yueping wang, xuwen Li, Jiao Chinese Acad Med Sci & Peking Union Med Coll Inst Med Informat & Lib 3 Yabao Rd Beijing 100020 Peoples R China

Background: Medical postgraduates' demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important. Objective: This study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school. Methods: We developed a medical data mining course called "Practical Techniques of Medical Data Mining" for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms-Rain Classroom, Tencent Meeting, and WeChat-were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience. Results: In total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students' characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an onlin

关键词： medical data mining course development online teaching postgraduate medical education

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Importance of a crystalline water network in docking-based virtual screening: a case study of BRD4

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PHYSICAL CHEMISTRY CHEMICAL PHYSICS 2019年第45期21卷 25276-25289页

作者： Zhong, Haiyang wang, Zhe wang, xuwen Liu, Hui Li, Dan Liu, Huanxiang Yao, Xiaojun Hou, Tingjun Lanzhou Univ State Key Lab Appl Organ Chem Lanzhou 730000 Peoples R China Lanzhou Univ Dept Chem Lanzhou 730000 Peoples R China Zhejiang Univ Coll Pharmaceut Sci Hangzhou 310058 Zhejiang Peoples R China Lanzhou Univ Sch Pharm Lanzhou 730000 Peoples R China Macau Univ Sci & Technol Macau Inst Appl Res Med & Hlth State Key Lab Qual Res Chinese Med Taipa Macao Peoples R China

As a member of the bromodomain and extra terminal domain (BET) protein family, bromodomain-containing protein 4 (BRD4) is an epigenetic reader and can recognize acetylated lysine residues in histones. BRD4 has been regarded as an essential drug target for cancers, inflammatory diseases and acute heart failure, and therefore the discovery of potent BRD4 inhibitors with novel scaffolds is highly desirable. In this study, the crystalline water molecules in BRD4 involved in ligand binding were analyzed first, and the simulation results suggest that several conserved crystalline water molecules are quite essential to keep the stability of the crystalline water network and therefore they need to be reserved in structure-based drug design. Then, a docking-based virtual screening workflow with the consideration of the conserved crystalline water network in the binding pocket was utilized to identify the potential inhibitors of BRD4. The in vitro fluorescence resonance energy transfer (HTRF) binding assay illustrates that 4 hits have good inhibitory activity against BRD4 in the micromolar regime, including three compounds with IC50 values below 5 mu M and one below 1 mu M (0.37 mu M). The structural analysis demonstrates that three active compounds possess novel scaffolds. Moreover, the interaction patterns between the hits and BRD4 were characterized by molecular dynamics simulations and binding free energy calculations, and then several suggestions for the further optimization of these hits were proposed.

关键词： Proteins

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