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arXiv 2024年

作者： Dawood, Moez Heavner, Ben Wheeler, Marsha M. Ungar, Rachel A. LoTempio, Jonathan Wiel, Laurens Berger, Seth Bernstein, Jonathan A. Chong, Jessica X. Délot, Emmanuèle C. Eichler, Evan E. Gibbs, Richard A. Lupski, James R. Shojaie, Ali Talkowski, Michael E. Wagner, Alex H. Wei, Chia-Lin Wellington, Christopher Wheeler, Matthew T. Carvalho, Claudia M.B. Gifford, Casey A. May, Susanne Miller, Danny E. Rehm, Heidi L. Sedlazeck, Fritz J. Vilain, Eric O'Donnell-Luria, Anne Posey, Jennifer E. Chadwick, Lisa H. Bamshad, Michael J. Montgomery, Stephen B. Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX United States Medical Scientist Training Program Baylor College of Medicine HoustonTX United States Department of Biostatistics University of Washington SeattleWA United States Department of Genetics School of Medicine Stanford University StanfordCA United States Department of Pathology School of Medicine Stanford University Stanford CA United States Stanford Center for Biomedical Ethics School of Medicine Stanford University Stanford CA United States Institute for Clinical and Translational Science University of California IrvineCA United States Division of Cardiovascular Medicine School of Medicine Stanford University Stanford CA United States Division of Genetics and Metabolism Children’s National Rare Disease Institute WashingtonDC United States Center for Genetic Medicine Research Children’s National Rare Disease Institute WashingtonDC United States Department of Genomics and Precision Medicine George Washington University WashingtonDC United States Department of Pediatrics School of Medicine Stanford University Stanford CA United States Department of Pediatrics Dvision of Genetic Medicine University of Washington SeattleWA United States Brotman Baty Institute for Precision Medicine University of Washington SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Howard Hughes Medical Institute University of Washington SeattleWA United States Department of Pediatrics Baylor College of Medicine HoustonTX United States Center for Genomic Medicine Massachusetts General Hospital BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard BostonMA United States Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA United States Stanley Center for

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://***/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics. © 2024, CC BY.

关键词： Genome

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用于糖尿病培训的大型语言模型:一项前瞻性研究（英文）

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Science Bulletin 2025年第6期 934-942页

作者：李灏萱江泽铧管洲榆包玉倩刘月星胡婷婷李佳佳刘茹涵吴量程棣纪宏伟王勇王亚星 Carol Y.Cheung 郑颖丰王继红李震吴卫兵 Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan 程景煜黄天荫李华婷蔡淳毛丽娟 Lee-Ling Lim 覃宇宗盛斌贾伟平 Shanghai University of Sport School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua Medicine Tsinghua University Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Department of Cardiology the Affiliated Hospital of Qingdao University CEMS NCMIS HCMS MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular Diseases Department of Renal Medicine Singapore General Hospital Sing Health-Duke Academic Medical Centre Department of Endocrinology Singapore General Hospital Department of Endocrinology Austin Health Department of Medicine The University of Melbourne(Austin Health) Australian Centre for Accelerating Diabetes Innovations(ACADI) The Univ

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models(LLMs)provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: Chat GPT-3.5, Chat GPT-4.0, Google Bard, Lla MA-7B, Lla MA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, Med GPT, Huatuo GPT, and Chinese Lla MA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians(PCPs) without and with the assistance of Chat GPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that Chat GPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, Lla MA-7B, and Lla MA2-7B. For the NCE-CPFC examination, Chat GPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, Med GPT, and Chat GPT-3.5 successfully passed, whereas Lla MA2-7B, Huatuo GPT,Chinese LLa MA2-7B, and Lla MA-7B failed. Chat GPT-4.0(84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

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Transverse ultrafast laser inscription in bulk silicon

arXiv

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arXiv 2021年

作者： Chambonneau, Maxime Blothe, M. Li, Q. Fedorov, V.Yu. Heuermann, T. Gebhardt, M. Gaida, C. Tertelmann, S. Sotier, F. Limpert, J. Tzortzakis, S. Nolte, S. Abbe Center of Photonics Institute of Applied Physics Friedrich-Schiller-University Jena Albert-Einstein-Strae 15 Jena07745 Germany Science Program Texas A and M University at Qatar P.O. Box 23874 Doha Qatar P. N. Lebedev Physical Institute of Russian Academy of Sciences 53 Leninskiy Prospekt Moscow119991 Russia Helmholtz-Institute Jena Frobelstieg 3 Jena07743 Germany Active Fiber Systems GmbH Ernst-Ruska-Ring 17 Jena07745 Germany Innolas Photonics GmbH Justus-von-Liebig-Ring 8 Krailling82152 Germany Center of Excellence in Photonics Fraunhofer Institute for Applied Optics and Precision Engineering Iof Albert-Einstein-Strae 7 Jena07745 Germany P.O. Box 1527 HeraklionGR-71110 Greece Materials Science and Technology Department University of Crete Heraklion71003 Greece

In-volume ultrafast laser direct writing of silicon is generally limited by strong nonlinear propagation effects preventing the initiation of modifications. By employing a triple-optimization procedure in the spectral, temporal and spatial domains, we demonstrate that modifications can be repeatably produced inside silicon. Our approach relies on irradiation at 2-μm wavelength with temporallydistorted femtosecond pulses. These pulses are focused in a way that spherical aberrations of different origins counterbalance, as predicted by point spread function analyses and in good agreement with nonlinear propagation simulations. We also establish the laws governing modification growth on a pulse-to-pulse basis, which allows us to demonstrate transverse inscription inside silicon with various line morphologies depending on the irradiation conditions. We finally show that the production of single-pulse repeatable modifications is a necessary condition for reliable transverse inscription inside silicon. © 2021, CC BY.

关键词： Silicon

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Transverse ultrafast laser inscription in bulk silicon

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Physical Review Research 2021年第4期3卷 043037-043037页

作者： M. Chambonneau M. Blothe Q. Li V. Yu. Fedorov T. Heuermann M. Gebhardt C. Gaida S. Tertelmann F. Sotier J. Limpert S. Tzortzakis S. Nolte Institute of Applied Physics Abbe Center of Photonics Friedrich Schiller University Jena Albert-Einstein-Straße 15 07745 Jena Germany Science Program Texas A&M University at Qatar P.O. Box 23874 Doha Qatar P. N. Lebedev Physical Institute of the Russian Academy of Sciences 53 Leninskiy Prospekt 119991 Moscow Russia Helmholtz-Institute Jena Fröbelstieg 3 07743 Jena Germany Active Fiber Systems GmbH Ernst-Ruska-Ring 17 07745 Jena Germany Innolas Photonics GmbH Justus-von-Liebig-Ring 8 82152 Krailling Germany Fraunhofer Institute for Applied Optics and Precision Engineering IOF Center of Excellence in Photonics Albert-Einstein-Straße 7 07745 Jena Germany Institute of Electronic Structure and Laser (IESL) Foundation for Research and Technology–Hellas (FORTH) P.O. Box 1527 GR-71110 Heraklion Greece Materials Science and Technology Department University of Crete 71003 Heraklion Greece

In-volume ultrafast laser direct writing of silicon is generally limited by strong nonlinear propagation effects preventing the production of modifications. By using advantageous spectral, temporal, and spatial conditions, we demonstrate that modifications can be repeatably produced inside silicon. Our approach relies on irradiation at ≈2 μm wavelength with temporally distorted femtosecond pulses. These pulses are focused in a way that spherical aberrations of different origins mutually balance, as predicted by point spread function analyses and in good agreement with nonlinear propagation simulations. We also establish the laws governing modification growth on a pulse-to-pulse basis, which allows us to demonstrate transverse inscription inside silicon with various line morphologies depending on the irradiation conditions. We finally show that the production of single-pulse repeatable modifications is a necessary condition for reliable transverse inscription inside silicon.

关键词： Classical optics Light-matter interaction Multiphoton or tunneling ionization & excitation Nonlinear optics Optical transient phenomena Optics & lasers Photonics Plasma ionization Ultrafast phenomena

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The Chemistry of Organic Contrast Agents in the NIR-II Window

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Angewandte Chemie 2021年第14期134卷

作者： Dr. Jing Mu Ming Xiao Dr. Yu Shi Xuewen Geng Dr. Hui Li Prof. Yuxin Yin Prof. Xiaoyuan Chen Institute of Precision Medicine Peking University Shenzhen Hospital Shenzhen 518036 China Department of Biology University of Rochester Rochester NY 14627 USA Departments of Diagnostic Radiology Surgery Chemical and Biomolecular Engineering and Biomedical Engineering Yong Loo Lin School of Medicine and Faculty of Engineering National University of Singapore Singapore 119074 Singapore Nanomedicine Translational Research Program NUS Center for Nanomedicine Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore Clinical Imaging Research Centre Centre for Translational Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore

Optical imaging, especially fluorescence and photoacoustic imaging, is a non-invasive imaging approach with high spatial and temporal resolution and high sensitivity, compared to positron emission tomography (PET) or magnetic resonance imaging (MRI). Due to the merits of imaging using the second near-infrared (NIR-II) window, like deeper penetration depth, high signal-to-noise ratio, high resolution, and low tissue damage, researchers have devoted great effort to developing contrast agents with NIR-II absorption or emission. In this Review, we summarize recently developed organic luminescent and photoacoustic materials, ranging from small molecules to conjugated polymers. Then, we systematically introduce engineering strategies and describe the imaging performance classified by the skeleton cores. Finally, we elucidate the challenges and prospects of these NIR-II organic dyes for potential clinical applications. We expect our summary can inspire researchers to expand the spectrum of NIR-II contrast agents for diverse bioapplications.

关键词： fluorescence imaging organic fluorophores photoacoustic imaging second near-infrared (NIR-II)

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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A mountable toilet system for personalized health monitoring via the analysis of excreta (vol 15, pg 561, 2020)

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NATURE BIOMEDICAL engineering 2020年第6期4卷 662-662页

作者： Park, Seung-min Won, Daeyoun D. Lee, Brian J. Escobedo, Diego Esteva, Andre Aalipour, Amin Ge, T. Jessie Kim, Jung Ha Suh, Susie Choi, Elliot H. Lozano, Alexander X. Yao, Chengyang Bodapati, Sunil Achterberg, Friso B. Kim, Jeesu Park, Hwan Choi, Youngjae Kim, Woo Jin Yu, Jung Ho Bhatt, Alexander M. Lee, Jong Kyun Spitler, Ryan Wang, Shan X. Gambhir, Sanjiv S. Department of Radiology Stanford University School of Medicine Stanford CA USA Molecular Imaging Program at Stanford Stanford University School of Medicine Stanford CA USA Department of Surgery Seoul Song Do Hospital Seoul Republic of Korea Cancer Immunology Laboratory Seoul Song Do Hospital Seoul Republic of Korea Department of Surgery Leiden University Medical Center Leiden the Netherlands Department of Creative IT Engineering Pohang University of Science and Technology (POSTECH) Pohang Republic of Korea Precision Health and Integrated Diagnostic Center (PHIND) Stanford University School of Medicine Palo Alto CA USA Department of Materials Science and Engineering Stanford University Stanford CA USA Department of Bioengineering Stanford University Stanford CA USA Canary Center at Stanford for Cancer Early Detection Stanford University School of Medicine Palo Alto CA USA Salesforce Research Palo Alto CA USA Department of Urology Stanford University School of Medicine Stanford CA USA Department of Pharmacology Case Western Reserve University School of Medicine Cleveland OH USA Faculty of Medicine University of Toronto Toronto Ontario Canada Department of Electrical Engineering Stanford University Stanford CA USA College of Medicine The Catholic University of Korea Seoul Republic of Korea

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Diagnosis

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Generation of the anti-Twist crowned helical gear by modifying the gear rotation angle in the hobbing process 2nd

Generation of the anti-Twist crowned helical gear by modifyi...

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2nd International Conference on Machining, Materials and Mechanical Technologies, IC3MT 2016

作者： Hsu, Ruei-Hung Wu, Yu-Ren Chen, Shih-Sheng Bachelor's Program in Precision System Design Feng Chia University Taiwan Department of Mechanical Engineering National Central University Taiwan

ISBN: (纸本)9783035711011

In the gear-hobbing process, the work gear tooth flank is usually longitudinally crowned by varying the center distance between the hob and the work gear. Without crossed angle compensation, however, this center distance variation produces a twisted tooth flank on the work gear. This paper therefore proposes a methodology to reduce this tooth flank twist and achieve anti-Twist in longitudinal crowning by modifying the gear rotation angle in the hobbing process which is practiced using a CNC hobbing machine with three synchronous axes. © 2017 Trans Tech Publications, Switzerland.

关键词： Helical gears

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Hypoxia-responsive circular RNA circAAGAB reduces breast cancer malignancy by activating p38 MAPK and sponging miR-378 h

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Cancer cell international 2023年第1期23卷 45页

作者： Kuan-Yi Lee Chia-Ming Liu Li-Han Chen Chien-Yueh Lee Tzu-Pin Lu Li-Ling Chuang Liang-Chuan Lai Graduate Institute of Physiology College of Medicine National Taiwan University Taipei Taiwan. Institute of Fisheries Science College of Life Science National Taiwan University Taipei Taiwan. Department of Life Science College of Life Science National Taiwan University Taipei Taiwan. Master Program for Biomedical Engineering College of Biomedical Engineering China Medical University Taichung Taiwan. Institute of Epidemiology and Preventive Medicine Department of Public Health National Taiwan University Taipei Taiwan. School of Physical Therapy and Graduate Institute of Rehabilitation Science College of Medicine Chang Gung University Taoyuan Taiwan. lchuang@gap.cgu.edu.tw. Department of Physical Medicine and Rehabilitation Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. lchuang@gap.cgu.edu.tw. Graduate Institute of Physiology College of Medicine National Taiwan University Taipei Taiwan. llai@ntu.edu.tw. Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan. llai@ntu.edu.tw.

BACKGROUND:Breast cancer is a prevalent disease in women, with high prevalence worldwide. The hypoxic microenvironment of solid tumors develops during the progress of carcinogenesis and leads to greater malignancy and treatment resistance. Recently, accumulating evidence indicates that non-coding RNAs, such as circular RNAs (circRNAs), play a pivotal role in altering cellular functions. However, the underlying mechanisms of circRNAs in breast cancer are still unclear. Therefore, the purpose of this study was to investigate the role of a tumor-suppressive circRNA, circAAGAB, in breast cancer by assuming down-regulation of circAAGAB under hypoxia and the properties of a tumor suppressor. METHODS:Firstly, circAAGAB was identified from expression profiling by next generation sequencing. Next, the stability of circAAGAB increased by interacting with the RNA binding protein FUS. Moreover, cellular and nuclear fractionation showed that most circAAGAB resided in the cytoplasm and that it up-regulated KIAA1522, NKX3-1, and JADE3 by sponging miR-378 h. Lastly, the functions of circAAGAB were explored by identifying its down-stream genes using Affymetrix microarrays and validated by in vitro assays. RESULTS:The results showed that circAAGAB reduced cell colony formation, cell migration, and signaling through p38 MAPK pathway, as well as increased radiosensitivity. CONCLUSION:These findings suggest that the oxygen-responsive circAAGAB acts as a tumor suppressor in breast cancer, and may contribute to the development of a more specific therapeutic regimen for breast cancer.

关键词： Breast cancer Hypoxia Radiosensitivity Tumor suppressor circAAGAB miRNA sponge p38 MAPK

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Supramolecular Polymerization-Induced Nanoassemblies for Self-Augmented Cascade Chemotherapy and Chemodynamic Therapy of Tumor

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Angewandte Chemie 2021年第32期133卷

作者： Dr. Kuikun Yang Dr. Guocan Yu Zhiqing Yang Ludan Yue Dr. Xiangjun Zhang Chen Sun Jianwen Wei Dr. Lang Rao Prof. Xiaoyuan Chen Prof. Ruibing Wang State Key Laboratory of Quality Research in Chinese Medicine Institute of Chinese Medical Science University of Macau Avenida da Universidade Taipa Macau China MoE Frontiers Science Center for Precision Oncology University of Macau Avenida da Universidade Taipa Macau China Key Lab of Organic Optoelectronics & Molecular Engineering Department of Chemistry Tsinghua University Beijing China Institute of Biomedical Health Technology and Engineering Shenzhen Bay Laboratory Shenzhen China Departments of Diagnostic Radiology Surgery Chemical and Biomolecular Engineering and Biomedical Engineering Yong Loo Lin School of Medicine and Faculty of Engineering National University of Singapore Singapore Singapore Clinical Imaging Research Centre Centre for Translational Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Nanomedicine Translational Research Program NUS Center for Nanomedicine Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore

The clinical application of chemodynamic therapy is impeded by the insufficient intracellular H 2 O 2 level in tumor tissues. Herein, we developed a supramolecular nanoparticle via a simple one-step supramolecular polymerization-induced self-assembly process using platinum (IV) complex-modified β-cyclodextrin-ferrocene conjugates as supramolecular monomers. The supramolecular nanoparticles could dissociate rapidly upon exposure to endogenous H 2 O 2 in the tumor and release hydroxyl radicals as well as platinum (IV) prodrugs in situ, which is reduced into cisplatin to significantly promote the generation of H 2 O 2 in the tumor tissue. Thus, the supramolecular nanomedicine overcomes the limitation of conventional chemodynamic therapy via the self-augmented cascade radical generation and drug release. In addition, dissociated supramolecular nanoparticles could be readily excreted from the body via renal clearance to effectively avoid systemic toxicity and ensure long term biocompatibility of the nanomedicine. This work may provide new insights on the design and development of novel supramolecular nanoassemblies for cascade chemo/chemodynamic therapy.

关键词： drug delivery self-amplified dissociation self-assembly supramolecular polymerization synergistic chemo/chemodynamic therapy

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