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Journal of Medicine, Surgery, and Public Health 2024年 3卷 100135-100135页

作者： Moustaq Karim Khan Rony Daifallah M. Alrazeeni Fazila Akter Latifun Nesa Dipak Chandra Das Muhammad Join Uddin Jeni Begum Most. Tahmina Khatun Md. Abdun Noor Sumon Ahmad Sabren Mukta Tanha Tuli Rani Deb Mst. Rina Parvin Master of Public Health Bangladesh Open University Gazipur Bangladesh Action Research for Public Health Development in Bangladesh Dhaka Bangladesh Prince Sultan Bin Abdul Aziz College for Emergency Medical Services King Saud University Riyadh Saudi Arabia Dhaka Nursing College affiliated with the University of Dhaka Bangladesh Department of Health and Functioning Western Norway University of Applied Sciences Norway Master’s of Child Health Nursing National Institute of Advanced Nursing Education and Research Mugda Dhaka Bangladesh Master of Social Science in Sociology & Anthropology Shanto-Mariam University of Creative Technology Dhaka Bangladesh Master of Public Health RTM Al-Kabir Technical University Sylhet Bangladesh Master of Public Health Leading university Sylhet Bangladesh Rajshahi Medical College Hospital Rajshahi Bangladesh Master of Public Health Daffodil International University Dhaka Bangladesh School of Medical Sciences Shahjalal University of Science and Technology Sylhet Bangladesh Applied Nutrition and Food Technology Islamic University Kushtia Bangladesh Bangabandhu Sheikh Mujib Medical University Dhaka Bangladesh Armed Forces Nursing Service Major at Bangladesh Army (AFNS Officer) Combined Military Hospital Dhaka Bangladesh

Nursing, a cornerstone of healthcare, is a profession characterized by its dedication to patient well-being. However, the demanding nature of nursing often takes a toll on work-life balance. This commentary investigates how artificial intelligence (AI) could significantly impact the healthcare sector, particularly by enhancing the work-life balance of nurses. It highlights how AI can greatly lessen administrative tasks, improve clinical decision-making, and support remote patient monitoring, ultimately helping nurses achieve a more balanced work-life dynamic. The advancement of AI in healthcare presents a strong opportunity to improve nurses' work-life balance. Our comprehensive conceptual framework illustrates how AI can transform nursing practice, offering nurses newfound efficiency and flexibility. By responsibly integrating AI technologies, healthcare institutions can empower nurses to excel in their roles while enjoying a more sustainable work-life equilibrium. This commentary serves as a roadmap for embracing the potential of AI, not as a replacement for nurses, but as a valuable ally in fostering a better future for both nurses and the patients they serve.

关键词： Artificial intelligence Work-life balance Nurses Administrative tasks Monitoring revolution

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Asymmetric Anion Zinc Salt Derived Solid Electrolyte Interphase Enabled Long-Lifespan Aqueous Zinc Bromine Batteries

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Angewandte Chemie 2024年第11期136卷

作者： Shengmei Chen Shimei Li Longtao Ma Yiran Ying Zhuoxi Wu Prof. Haitao Huang Prof. Chunyi Zhi Department of Materials Science and Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong 999077 P. R. China Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE) 999077 Shatin NT HKSAR China School of Materials Science and Engineering Guangdong Provincial Key Laboratory of Advanced Energy Storage Materials South China University of Technology Guangzhou 510641 P. R. China Department of Applied Physics and Research Institute for Smart Energy The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong 999077 P. R. China Centre for Functional Photonics City University of Hong Kong Kowloon Hong Kong 999077 China Hong Kong Institute for Advanced Study City University of Hong Kong Kowloon Hong Kong 999077 China Centre for Advanced Nuclear Safety and Sustainable Development City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong 999077 China

Organic additives with high-reduction potentials are generally applied in aqueous electrolytes to stabilize the Zn anode, while compromise safety and environmental compatibility. Highly concentrated water-in-salt electrolytes have been proposed to realize the high reversibility of Zn plating/stripping; however, their high cost and viscosity hinder their practical applications. Therefore, exploring low-concentration Zn salts, that can be used directly to stabilize Zn anodes, is of primary importance. Herein, we developed an asymmetric anion group, bi(difluoromethanesulfonyl)(trifluoromethanesulfonyl)imide (DFTFSI - )-based novel zinc salt, Zn(DFTFSI) 2 , to obtain a high ionic conductivity and a highly stable dendrite-free Zn anode. Experimental tests and theoretical calculations verified that DFTFSI − in the Zn 2+ solvation sheath and inner Helmholtz plane would be preferentially reduced to construct layer-structured SEI films, inhibiting hydrogen evolution and side reactions. Consequently, the Zn Zn symmetric cell with 1M Zn(DFTFSI) 2 aqueous electrolyte delivers an ultralong cycle life for >2500 h outperforming many other conventional Zn salt electrolytes. The Zn Br 2 battery also exhibits a long lifespan over 1200 cycles at ~99.8 % Coulombic efficiency with a high capacity retention of 92.5 %. Furthermore, this outstanding performance translates well to a high-areal-capacity Zn Br 2 battery (~5.6 mAh ⋅ cm -2 ), cycling over 320 cycles with 95.3 % initial capacity retained.

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The bulk van der Waals layered magnet CrSBr is a quasi-1D material

arXiv

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arXiv 2022年

作者： Klein, Julian Pingault, Benjamin Florian, Matthias Heißenbüttel, Marie-Christin Steinhoff, Alexander Song, Zhigang Torres, Kierstin Dirnberger, Florian Curtis, Jonathan B. Weile, Mads Penn, Aubrey Deilmann, Thorsten Dana, Rami Bushati, Rezlind Quan, Jiamin Luxa, Jan Sofer, Zdenek Alù, Andrea Menon, Vinod M. Wurstbauer, Ursula Rohlfing, Michael Narang, Prineha Lončar, Marko Ross, Frances M. Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States QuTech Delft University of Technology Delft2600 GA Netherlands Department of Electrical and Computer Engineering Department of Physics University of Michigan Ann ArborMI48109 United States Institut für Festkörpertheorie Westfälische Wilhelms-Universität Münster Münster48149 Germany Institut für Theoretische Physik Universität Bremen P.O. Box 330 440 Bremen28334 Germany Bremen Center for Computational Materials Science University of Bremen Bremen28359 Germany Department of Physics City College of New York New YorkNY10031 United States College of Letters and Science UCLA Los AngelesCA90095 United States Department of Physics Technical University of Denmark Kgs. LyngbyDK-2800 Denmark MIT.nano Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics The Graduate Center City University of New York New YorkNY10016 United States Photonics Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 Prague 6166 28 Czech Republic Institute of Physics Center for Nanotechnology University of Münster Münster48149 Germany

Correlated quantum phenomena in one-dimensional (1D) systems that exhibit competing electronic and magnetic order are of strong interest for studying fundamental interactions and excitations, such as Tomonaga-Luttinger liquids and topological orders and defects with properties completely different from the quasiparticles expected in their higher-dimensional counterparts. However, clean 1D electronic systems are difficult to realize experimentally, particularly magnetically ordered systems. Here, we show that the van der Waals layered magnetic semiconductor CrSBr behaves like a quasi-1D material embedded in a magnetically ordered environment. The strong 1D electronic character originates from the Cr-S chains and the combination of weak interlayer hybridization and anisotropy in effective mass and dielectric screening with an effective electron mass ratio of meX/meY ∼ 50. This extreme anisotropy experimentally manifests in strong electron-phonon and exciton-phonon interactions, a Peierls-like structural instability and a Fano resonance from a van Hove singularity of similar strength of metallic carbon nanotubes. Moreover, due to the reduced dimensionality and interlayer coupling, CrSBr hosts spectrally narrow (1 meV) excitons of high binding energy and oscillator strength that inherit the 1D character. Overall, CrSBr is best understood as a stack of weakly hybridized monolayers and appears to be an experimentally attractive candidate for the study of exotic exciton and 1D correlated many-body physics in the presence of magnetic order. Copyright © 2022, The Authors. All rights reserved.

关键词： Excitons

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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Retraction notice to “Prediction of the interaction between HIV viruses and Human Serum Albumin (HSA) molecules using an equilibrium dynamics simulation program for application in bio medical science” [J. Mol. Liq. 318 (2020) 113989]

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Journal of Molecular Liquids 2025年 430卷

作者： Aliakbar Karimipour Arash Karimipour Nitasha Adavoodi Jolfaei Maboud Hekmatifar Davood Toghraie Roozbeh Sabetvand Sara Rostami Institute of Research and Development Duy Tan University Da Nang 550000 Vietnam Department of Mechanical Engineering Najafabad Branch Islamic Azad University Najafabad Iran PES College of Pharmacy 50 Feet Road Hanumanth Nagar BSK 1 Stage Bangaluru Karnataka 560050 India Department of Mechanical Engineering Khomeinishahr Branch Islamic Azad University Khomeinishahr Iran Department of Energy Engineering and Physics Faculty of Condensed Matter Physics Amirkabir University of Technology Tehran Iran Laboratory of Magnetism and Magnetic Materials Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Vietnam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Vietnam

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Novel Amorphous Nitride-Halide Solid Electrolytes with Enhanced Performance for All-Solid-State Batteries

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Angewandte Chemie 2024年第3期137卷

作者： Bolong Hong Dr. Lei Gao Dr. Pengfei Nan Yuhang Li Prof. Ming Liu Prof. Ruqiang Zou Prof. Jun Gu Prof. Qiang Xu Prof. Jinlong Zhu Prof. Songbai Han Shenzhen Key Laboratory of Solid-State Batteries Guangdong Provincial Key Laboratory of Energy Materials for Electric Power Guangdong-Hong Kong-Macao Joint Laboratory for Photonic Thermal-Electrical Energy Materials and Devices Institute of Major Scientific Facilities for New Materials Southern University of Science and Technology 518055 Shenzhen P. R. China Department of Physics Southern University of Science and Technology 518055 Shenzhen P. R. China College of Semiconductors (National Graduate College for Engineers) Southern University of Science and Technology 518055 Shenzhen P. R. China School of Advanced Materials Peking University Shenzhen Graduate School 518055 Shenzhen P. R. China School of Materials Science and Engineering Peking University 100871 Beijing P. R. China Information Materials and Intelligent Sensing Laboratory of Anhui Province Leibniz International Joint Research Center of Materials Sciences of Anhui Province Institutes of Physical Science and Information Technology Anhui University 230601 Hefei P. R. China Shenzhen Geim Graphene Center Tsinghua University Shenzhen International Graduate School 518055 Shenzhen P. R. China Department of Chemistry Southern University of Science and Technology 518055 Shenzhen P. R. China Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM) Department of Chemistry Department of Materials Science and Engineering SUSTech-Kyoto University Advanced Energy Materials Joint Innovation Laboratory (SKAEM-JIL) Key University Laboratory of Highly Efficient Utilization of Solar Energy and Sustainable Development of Guangdong Southern University of Science and Technology 518055 Shenzhen P. R. China Center for Neutron Scattering and Advanced Light Sources (CNALS) Dongguan University of Technology 523808 Dongguan P. R. China National Center for Applied Mathematics Shenzhen (NCAMS) 518055 Shenzhen P. R. China

Solid electrolytes (SEs) in all-solid-state batteries (ASSBs) are garnering considerable attention for their potential applications in next-generation energy storage systems. Amorphous SEs with dual-anion hold great promise for achieving favorable performance, such as high ionic conductivity and good compatibility with electrodes within ASSBs. Here, we discover a family of amorphous nitride-halide SEs, Li 3 x MCl y N x (M=Ta or La, 1≤3 x ≤1.4, y=5 or 3), which can achieve ionic conductivities up to 7.34 mS cm −1 at 30 °C. The amorphous properties and local structures are investigated using powder X-ray diffraction, cryogenic transmission electron microscopy, and atomic pair distribution function analysis. Impressively, ASSBs employing amorphous Li 3 x TaCl 5 N x have demonstrated good performance at high rates and charging voltages, as well as at low temperature.

关键词： amorphous solid electrolyte all-solid-state batteries

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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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Erratum:A Search for Solar Axions and Anomalous Neutrino Magnetic Moment with the Complete PandaX-Ⅱ Data[***.38(2021)011301]

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Chinese Physics Letters 2021年第10期38卷 71-72页

作者： Xiaopeng Zhou Xinning Zeng Xuyang Ning Abdusalam Abdukerim Wei Chen Xun Chen Yunhua Chen Chen Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Di Huang Yan Huang Yanlin Huang Zhou Huang Xiangdong Ji Yonglin Ju Shuaijie Li Huaxuan Liu Jianglai Liu † Xiaoying Lu Wenbo Ma Yugang Ma Yajun Mao Yue Meng Kaixiang Ni Jinhua Ning Xiangxiang Ren Changsong Shang Guofang Shen Lin Si Andi Tan Anqing Wang Hongwei Wang Meng Wang Qiuhong Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Mengmeng Wu Shiyong Wu Weihao Wu Jingkai Xia Mengjiao Xiao Pengwei Xie Binbin Yan Jijun Yang Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Dan Zhang Tao Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou INPAC School of Physics and AstronomyShanghai Jiao Tong Universityand Shanghai Key Laboratory for Particle Physics and CosmologyShanghai 200240China School of Physics Beihang UniversityBeijing 100191China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213China Yalong River Hydropower Development Company Ltd.288 Shuanglin RoadChengdu 610051China School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghai 200240China School of Physics Nankai UniversityTianjin 300071China Key Laboratory of Nuclear Physics and Ion-Beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China School of Physics Peking UniversityBeijing 100871China International Research Center for Nuclei and Particles in the Cosmos&Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing 100191China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Physics University of MarylandCollege ParkMaryland 20742USA School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physics and Key Laboratory of Particle Physics and Particle Irradiation(MOE) Shandong UniversityJinan 250100China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Center for High Energy Physics Peking UniversityBeijing 100871China

In our published letter,[1]we have identified a minor error in Figs.2 and ***,we have 2111 events in these two plots with 646,249,382,and 834 events in Run 9(20.0 ton·day),Run 10(19.4 ton·day),Run 11–1(24.2 ton·day),and Run 11–2(37.1 ton·day).The mistake is due to an incorrect application of a small energy non-linearity(known as the BLS non-linearity)in making plots,but has no impact to the final *** 2 and 3 are now updated in this note.

关键词： letter error

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Discovery and affinity maturation of antibody fragments from an unfavorably enriched phage display selection by deep sequencing and machine learning

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Journal of Bioscience and Bioengineering 2025年

作者： Sakiya Kawada Yoichi Kurumida Tomoyuki Ito Thuy Duong Nguyen Hafumi Nishi Hikaru Nakazawa Yutaka Saito Tomoshi Kameda Koji Tsuda Mitsuo Umetsu Department of Biomolecular Engineering Graduate School of Engineering Tohoku University 6-6-11 Aoba Aramaki Aoba-ku Sendai 980-8579 Japan Department of Data Science School of Frontier Engineering Kitasato University 1-15-1 Kitazato Minami-ku Sagamihara Kanagawa 252-0373 Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) 2-4-7 Aomi Koto-ku Tokyo 135-0064 Japan Department of Applied Information Sciences Graduate School of Information Sciences Tohoku University 6-3-09 Aoba Aramaki Aoba-ku Sendai 980-8579 Japan Tohoku Medical Megabank Organization Tohoku University 2-1 Seiryo-machi Aoba-ku Sendai 980-8573 Japan Faculty of Core Research Ochanomizu University 2-1-1 Ohtsuka Bunkyo-ku Tokyo 112-8610 Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8561 Japan Center for Advanced Intelligence Project RIKEN 1-4-1 Nihombashi Chuo-ku Tokyo 103-0027 Japan Research and Services Division of Materials Data and Integrated Systems National Institute for Materials Science 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan

Phage display selection has been used for directed evolution of antibody fragments. However, variants with binding affinity cannot be always identified due to undesirable enrichment of target-unrelated variants in the biopanning process. Here, our goal was to obtain functional variants by deep sequencing and machine learning from a phage display library where functional variants were not appropriately enriched. Deep sequencing of the previously biopanned pools revealed that amplification bias might have prevented the enrichment of target-binding phages. We performed a sequence similarity search based on the deep sequencing analysis so that the influence of bias was decreased, leading to discovery of a variant with binding affinity, which could not be discovered by a conventional screening method alone. We applied machine learning to the deep sequencing data; the machine learning proposed effective mutations for increasing affinity, allowing us to identify a variant with improved affinity (EC 50 = 3.46 μM). In summary, we present the possibility of obtaining functional variants even from unfavorably enriched phage libraries by using deep sequencing and machine learning.

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Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care

Journal of Medicine, Surgery, and Public Health

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Journal of Medicine, Surgery, and Public Health 2024年 3卷 100113-100113页

作者： Rony, Moustaq Karim Khan Akter, Khadiza Debnath, Mitun Rahman, Md Moshiur Johra, Fateha tuj Akter, Fazila Chandra Das, Dipak Mondal, Sujit Mousumi Das Uddin, Muhammad Join Rina Parvin, Mst Master of Public Health Bangladesh Open University Dhaka Bangladesh Master of Public Health Daffodil International University Dhaka Bangladesh Master of Public Health National Institute of Preventive and Social Medicine Bangladesh Department of Biotechnology and Genetic engineering University of development alternative Dhaka Bangladesh Masters in Disaster Management University of Dhaka Dhaka Bangladesh Dhaka Nursing College affiliated with the University of Dhaka Bangladesh Department of Health and Functioning Western Norway University of Applied Sciences Norway Master of Social Science in Sociology & Anthropology Shanto-Mariam University of Creative Technology Dhaka Bangladesh Master of Science in Nursing National Institute of Advanced Nursing Education and Research Mugda Dhaka Bangladesh Master of Public Health Leading University Sylhet Bangladesh Master of Public Health RTM Al-Kabir Technical University Sylhet Bangladesh School of Medical Sciences Shahjalal University of Science and Technology Bangladesh Major at Bangladesh Army (AFNS officer) Combined Military Hospital Dhaka Bangladesh

AbstractThe primary objective of this commentary was to identify the strengths and weaknesses of AI technologies, uncover opportunities for improvement, and recognize potential threats that could impede their successful implementation in nursing care. This commentary involved constructing a SWOT matrix to analyze AI adoption, identifying internal strengths and weaknesses, and external opportunities and threats. The analysis revealed several strengths of AI adoption in nursing care, including enhanced data analysis capabilities, improved patient monitoring, and increased efficiency in routine tasks. However, weaknesses such as the high initial costs of implementation and concerns about data security were identified. Opportunities included the potential for AI to reduce healthcare costs and improve patient outcomes. Nonetheless, threats such as resistance to technological change and ethical dilemmas related to AI decision-making processes were recognized as potential barriers to successful adoption. This article sheds light on the intricate landscape of AI adoption in nursing care. While AI brings forth substantial strengths, it simultaneously poses challenges that healthcare systems should confront. To fully harness AI's potential, healthcare organizations should thoughtfully deliberate on the identified weaknesses and threats, actively seeking avenues for seamless integration. In this concerted effort, the healthcare industry is poised to unlock the transformative capabilities of AI, elevating nursing care standards, and ultimately, advancing patient outcomes.

关键词： Artificial intelligence Nursing care Clinical decision-making Patient care Diagnosis

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