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Near-Infrared Aggregation-Induced Emission Luminogens for In Vivo Theranostics of Alzheimer's Disease

Angewandte Chemie 2022年第2期135卷

作者： Dr. Tianfu Zhang Xiaoyu Chen Dr. Congmin Yuan Prof. Xiaobin Pang Ping Shangguan Yisheng Liu Lulu Han Prof. Jianwei Sun Dr. Jacky W. Y. Lam Dr. Yang Liu Dr. Jiefei Wang Prof. Bingyang Shi Prof. Ben Zhong Tang Department of Chemistry the Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction Division of Life Science and State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China Contribution: Conceptualization (lead) Methodology (lead) Writing - original draft (lead) Writing - review & editing (lead) Henan-Macquarie University Joint Centre for Biomedical Innovation School of Life Sciences Henan University 475004 Kaifeng China Contribution: Methodology (equal) Contribution: Methodology (supporting) Software (supporting) Henan Key Laboratory of Brain Targeted Bio-nanomedicine School of Life Sciences & School of Pharmacy Henan University 475004 Kaifeng China Contribution: Methodology (supporting) Contribution: Writing - review & editing (supporting) Contribution: Supervision (supporting) Writing - review & editing (supporting) Contribution: Funding acquisition (supporting) Methodology (supporting) Supervision (equal) Writing - review & editing (equal) Centre for motor neuron disease Macquarie Medical School Faculty of Medicine & Health Sciences Macquarie University Sydney NSW Australia Contribution: Funding acquisition (equal) Supervision (equal) Writing - review & editing (equal) School of Science and Engineering Shenzhen Institute of Aggregate Science and Technology The Chinese University of Hong Kong 518172 Shenzhen Guangdong China Contribution: Funding acquisition (lead) Project administration (lead) Supervision (lead) Writing - review & editing (equal)

Optimized theranostic strategies for Alzheimer's disease (AD) remain almost absent from bench to clinic. Current probes and drugs attempting to prevent β-amyloid (Aβ) fibrosis encounter failures due to the blood–brain barrier (BBB) penetration challenge and blind intervention time window. Herein, we design a near-infrared (NIR) aggregation-induced emission (AIE) probe, DNTPH, via balanced hydrophobicity-hydrophilicity strategy. DNTPH binds selectively to Aβ fibrils with a high signal-to-noise ratio. In vivo imaging revealed its excellent BBB permeability and long-term tracking ability with high-performance AD diagnosis. Remarkably, DNTPH exhibits a strong inhibitory effect on Aβ fibrosis and promotes fibril disassembly, thereby attenuating Aβ-induced neurotoxicity. DNTPH treatment significantly reduced Aβ plaques and rescued learning deficits in AD mice. Thus, DNTPH serves as the first AIE in vivo theranostic agent for real-time NIR imaging of Aβ plaques and AD therapy simultaneously.

关键词： Aggregation-Induced Emission Alzheimer's Disease Near-Infrared Theranostic β-Amyloid

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

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arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 — Ex vivo imaging: added value and acquisition

arXiv

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arXiv 2022年

作者： Schilling, Kurt G. Grussu, Francesco Ianus, Andrada Hansen, Brian Howard, Amy F.D. Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Allan Johnson, G. Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Re

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio (SNR) and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY.

关键词： Image acquisition

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Expert consensus on the diagnosis and treatment of solid tumors with BRAF mutations

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The Innovation 2024年第6期5卷 100-116页

作者： Wenxian Wang Bin Lian Chunwei Xu Qian Wang Ziming Li Nan Zheng Aijun Liu Jinpu Yu Wenzhao Zhong Zhijie Wang Yongchang Zhang Jingjing Liu Shirong Zhang Xiuyu Cai Anwen Liu Wen Li Lili Mao Ping Zhan Hongbing Liu Tangfeng Lv Liyun Miao Lingfeng Min Yu Chen Jingping Yuan Feng Wang Zhansheng Jiang Gen Lin Long Huang Xingxiang Pu Rongbo Lin Weifeng Liu Chuangzhou Rao Dongqing Lv Zongyang Yu Xiaoyan Li Chuanhao Tang Chengzhi Zhou Junping Zhang Junli Xue Hui Guo Qian Chu Rui Meng Xuewen Liu Jingxun Wu Rui Zhang Jin Zhou Zhengfei Zhu Yongheng Li Hong Qiu Fan Xia Yuanyuan Lu Xiaofeng Chen Jian Feng Rui Ge Enyong Dai Yu Han Weiwei Pan Fei Pang Xin Huang Meizhen Hu Qing Hao Kai Wang Fan Wu Binbin Song Bingwei Xu Liping Wang Youcai Zhu Li Lin Yanru Xie Xinqing Lin Jing Cai Ling Xu Jisheng Li Xiaodong Jiao Kainan Li Jia Wei Huijing Feng Lin Wang Yingying Du Wang Yao Xuefei Shi Xiaomin Niu Dongmei Yuan Yanwen Yao Jianhui Huang Yue Feng Yinbin Zhang Pingli Sun Hong Wang Mingxiang Ye Dong Wang Zhaofeng Wang Yue Hao Zhen Wang Bin Wan Donglai Lv Shengjie Yang Jin Kang Jiatao Zhang Chao Zhang Wenfeng Li Jianfei Fu Lizhi Wu Shijie Lan Juanjuan Ou Lin Shi Zhanqiang Zhai Yina Wang Bihui Li Zhang Zhang Ke Wang Xuelei Ma Zhongwu Li Zhefeng Liu Nong Yang Lin Wu Huijuan Wang Gu Jin Guansong Wang Jiandong Wang Hubing Shi Meiyu Fang Yong Fang Yuan Li Xiaojia Wang Jing Chen Yiping Zhang Xixu Zhu Yi Shen Shenglin Ma Biyun Wang Yong Song Zhengbo Song Wenfeng Fang Yuanzhi Lu Lu Si Department of Chemotherapy Chinese Academy of Sciences University Cancer Hospital(Zhejiang Cancer Hospital)HangzhouZhejiang 310022P.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Department of Melanoma and SarcomaPeking University Cancer Hospital&InstituteBeijing 100142P.R.China Institute of Cancer and Basic Medicine(ICBM) Chinese Academy of SciencesHangzhouZhejiang 310022P.R.China Department of Respiratory Medicine Affiliated Jinling HospitalMedical School of Nanjing UniversityNanjingJiangsu 210002P.R.China Department of Respiratory Medicine Affiliated Hospital of Nanjing University of Chinese MedicineJiangsu Province Hospital of Chinese MedicineNanjingJiangsu 210029P.R.China Department of Shanghai Lung Cancer Center Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200030P.R.China Beijing Key Laboratory of Mental Disorders National Clinical Research Center for Mental Disorders&National Center for Mental DisordersBeijing Anding HospitalCapital Medical UniversityBeijing 200030China Advanced Innovation Center for Human Brain Protection Capital Medical UniversityBeijing 200030China Senior Department of Pathology the 7th Medical Center of PLA General HospitalBeijing 100700P.R.China Department of Cancer Molecular Diagnostics Core Tianjin Medical University Cancer Institute and HospitalTianjin 300060P.R.China Guangdong Lung Cancer Institute Guangdong Provincial Laboratory of Translational Medicine in Lung CancerGuangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesSchool of MedicineGuangzhouGuangdong 510080P.R.China State Key Laboratory of Molecular Oncology Department of Medical OncologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021P.R.China Department of Medical Oncology Lung Cancer and Gastrointestinal UnitHunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School

The BRAF gene is an important signaling molecule in human cells that is involved in the regulation of cell growth,differentiation,and *** the BRAF gene mutates,it can lead to abnormal activation of the signaling pathway,which promotes cell proliferation,inhibits cell apoptosis,and ultimately contributes to the occurrence and development of *** mutations are widely present in various cancers,including malignant melanoma,thyroid cancer,colorectal cancer,non-small cell lung cancer,and hairy cell leukemia,among *** is an important target for the treatment of various solid tumors,and targeted combination therapies,represented by BRAF inhibitors,have become one of the main treatment modalities for a variety of BRAF-mutation-positive solid tumors.

关键词： BRAF diagnosis treatment

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Target-specificity of different amyrin subunits in impeding HCV influx mechanism inside the human cells considering the quantum tunnel profiles and molecular strings of the CD81 receptor: a combined in silico and in vivo study

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In silico pharmacology 2023年第1期11卷 8页

作者： Anika Jabin Mohammad Fahim Uddin Salauddin Al Azad Ashfaque Rahman Fawzia Tabassum Pritthy Sarker A K M Helal Morshed Samiur Rahman Fatima Fairuz Raisa Musfiqur Rahman Sakib Abeer Hasan Olive Tabassum Islam Ramisha Tahsin Shahlaa Zernaz Ahmed Partha Biswas Mst Umme Habiba Mahbuba Siddiquy Maryam Jafary Department of Biochemistry and Microbiology North South University Dhaka 1229 Bangladesh. College of Material Science and Engineering Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang People's Republic of China. Key Laboratory of Industrial Biotechnology Ministry of Education School of Biotechnology Jiangnan University Wuxi 214122 Jiangsu Province People's Republic of China. Department of Genetic Engineering and Biotechnology Shahjalal University of Science and Technology Sylhet 3114 Bangladesh. Pathology and Pathophysiology Major Academy of Medical Science Zhengzhou University Zhengzhou City 450001 Henan Province People's Republic of China. Department of Electrical and Electronic Engineering Brac University Dhaka 1212 Bangladesh. Department of Pharmacy Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj 8100 Bangladesh. Department of Pharmacy East West University Dhaka 1212 Bangladesh. Department of Computer Science and Engineering East West University Dhaka 1212 Bangladesh. Department of Pharmaceutical Sciences North South University Dhaka 1229 Bangladesh. Department of Genetic Engineering and Biotechnology Jashore University of Science and Technology Jashore 7408 Bangladesh. Data Science Research Unit RPG Interface Lab Jashore 7400 Bangladesh. State Key Laboratory of Food Science and Technology Jiangnan University Wuxi 214122 Jiangsu Province People's Republic of China. Division of Food Safety and Hygiene Department of Environmental Health Engineering School of Public Health Tehran University of Medical Sciences Tehran *** Iran.

HCV is a hepatotropic RNA virus recognized for its frequent virulence and fatality worldwide. Despite many vaccine development programs underway, researchers are on a quest for natural bioactive compounds due to their multivalent efficiencies against viral infections, considering which the current research aimed to figure out the target-specificity and therapeutic potentiality of α, β, and δ subunits of amyrin, as novel bioactive components against the HCV influx mechanism. Initially, the novelty of amyrin subunits was conducted from 203 pharmacophores, comparing their in-silico pharmacokinetic and pharmacodynamic profiles. Besides, the best active site of CD81 was determined following the quantum tunneling algorithm. The molecular dynamic simulation was conducted (100 ns) following the molecular docking steps to reveal the parameters- RMSD (Å); Cα; RMSF (Å); MolSA (Å); Rg (nm); PSA (Å); SASA (Å), and the MM-GBSA dG binding scores. Besides, molecular strings of CD81, along with the co-expressed genes, were classified, as responsible for encoding CD81-mediated protein clusters during HCV infection, resulting in the potentiality of amyrins as targeted prophylactics in HCV infection. Finally, in vivo profiling of the oxidative stress marker, liver-specific enzymes, and antioxidant markers was conducted in the DMN-induced mice model, where -amyrin scored the most significant values in all aspects.

关键词： CD81 receptor Co-expressed genes behind HCV invasion DMN-induced hepatic fibrosis HCV infection In-silico pharmacokinetics and pharmacodynamics Target-specificity of amyrins

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Correction: Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder

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BMC medicine 2023年第1期21卷 113页

作者： Shile Qi Vince D Calhoun Daoqiang Zhang Jeremy Miller Zhi-De Deng Katherine L Narr Yvette Sheline Shawn M McClintock Rongtao Jiang Xiao Yang Joel Upston Tom Jones Jing Sui Christopher C Abbott College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. shile.qi@***. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA. College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. Department of Psychiatry University of New Mexico Albuquerque NM USA. Noninvasive Neuromodulation Unit Experimental Therapeutics & Pathophysiology Branch National Institute of Mental Health Bethesda MD USA. Departments of Neurology Psychiatry and Biobehavioral Sciences University of California Los Angeles CA USA. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Division of Psychology Department of Psychiatry UT Southwestern Medical Center Dallas TX USA. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. Huaxi Brain Research Center West China Hospital of Sichuan University Chengdu China. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. jsui@***. Department of Psychiatry University of New Mexico Albuquerque NM USA. cabbott@salud.unm.edu.

The original article [1] mistakenly omitted panel (g) from Figure 1; the panel has since been re-inserted.

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Author Correction: Mapping global lake dynamics reveals the emerging roles of small lakes

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Nature communications 2022年第1期13卷 6337页

作者： Xuehui Pi Qiuqi Luo Lian Feng Yang Xu Jing Tang Xiuyu Liang Enze Ma Ran Cheng Rasmus Fensholt Martin Brandt Xiaobin Cai Luke Gibson Junguo Liu Chunmiao Zheng Weifeng Li Brett A Bryan School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China. Department of Urban Planning and Design The University of Hong Kong Hong Kong SAR China. Urban Systems Institute The University of Hong Kong Hong Kong SAR China. School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China. fengl@***. Department of Geosciences and Natural Resource Management University of Copenhagen Copenhagen Denmark. Department of Biology University of Copenhagen Copenhagen Denmark. Department of Physical Geography and Ecosystem Science Lund University Lund Sweden. Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China. Institute of Geodesy and Geophysics Chinese Academy of Sciences Wuhan China. School of Water Conservancy North China University of Water Resources and Electric Power 450046 Zhengzhou China. EIT Institute for Advanced Study Ningbo China. Institute for Climate and Carbon Neutrality The University of Hong Kong Hong Kong SAR China. Centre for Integrative Ecology School of Life and Environmental Sciences Deakin University Burwood Victoria Australia.

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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FOUR NOVEL SUSCEPTIBILITY VARIANTS IDENTIFIED IN CHINESE ARE ASSOCIATED WITH SPORADIC ALZHEIMER’S DISEASE

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Alzheimer's & Dementia 2017年第7期13卷 P189-P191页

作者： Jianping Jia Xianbo Zuo Ling Wei Cuibai Wei Kai Wang Luxi Shen Fangyu Li Wei Qin Yi Tang Dantao Peng Lan Tan Benyan Luo Qihao Guo Yong Ji Muni Tang Yanjiang Wang Yifeng Du Jiewen Zhang Junjian Zhang Qiumin Qu Longfei Jia Center of Alzheimer's Disease Beijing Institute for Brain Disorders Beijing China Xuan Wu Hospital Capital Medical University Beijing China Neurodegenerative Laboratory of Ministry of Education of the People’s Republic of China Beijing China Beijing Key Laboratory of Geriatric Cognitive Disorders Beijing China First Affiliated Hospital of Anhui Medical University Hefei China State Key Laboratory Incubation Base of Dermatology Ministry of National Science and Technology Hefei China China-Japan Friendship Hospital Beijing China Qingdao Municipal Hospital School of Medicine Qingdao University Qingdao China First Affiliated Hospital Zhejiang University Hangzhou China Huashan Hospital Fudan University Shanghai China Tianjin Huanhu Hospital Tianjin China Guangzhou Huiai Hospital Affiliated Hospital of Guangzhou Medical College Guangzhou China Daping Hospital Third Military Medical University Chongqing China Shandong Provincial Hospital Shandong University Jinan China Henan Provincial People's Hospital Zhengzhou China Zhongnan Hospital Wuhan University Wuhan China First Affiliated Hospital of Xi'an Jiaotong University Xi'an China Henry Ford Hospital Detroit MI USA

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