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Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

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Light(Science & Applications) 2022年第7期11卷 1595-1606页

作者： Geon Kim Daewoong Ahn Minhee Kang Jinho Park DongHun Ryu YoungJu Jo Jinyeop Song Jea Sung Ryu Gunho Choi Hyun Jung Chung Kyuseok Kim Doo Ryeon Chung In Young Yoo Hee Jae Huh Hyun-seok Min Nam Yong Lee YongKeun Park Department of Physics Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea KAIST Institute for Health Science and Technology KAISTDaejeon 34141Republic of Korea Tomocube Inc. Daejeon 34109Republic of Korea Smart Healthcare&Device Research Center Samsung Medical CenterSungkyunkwan University School of MedicineSeoul 06351Republic of Korea Graduate School of Nanoscience and Technology Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Biological Sciences Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Emergency Medicine Bundang CHA HospitalSeongnam-siGyeonggi-Do 13496Korea Division of Infectious Diseases Department of Internal MedicineSamsung Medical CenterSungkyunkwan University School of MedicineSeoul 06351Republic of Korea Department of Laboratory Medicine Seoul St.Mary's HospitalCollege of MedicineThe Catholic University of KoreaSeoul 06591Republic of Korea Department of Laboratory Medicine and Genetics Samsung Medical CenterSungkyunkwan University School of MedicineSeoul 06351Republic of Korea Department of Applied Physics Stanford UniversityStanfordCA 94305USA Department of Physics Massachusetts Institute of TechnologyCambridgeMA 02139USA

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections.Microbial infections are a major healthcare issue worldwide,as these widespread diseases often develop into deadly symptoms.While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection,this effective treatment is difficult to practice.The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification,which includes time-consuming sample growth.Here,we propose a microscopy-based framework that identifies the pathogen from single to few cells.Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network.We demonstrate the identification of 19 bacterial species that cause bloodstream infections,achieving an accuracy of 82.5%from an individual bacterial cell or cluster.This performance,comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample,underpins the effectiveness of our framework in clinical applications.Furthermore,our accuracy increases with multiple measurements,reaching 99.9%with seven different measurements of cells or clusters.We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

关键词： artificial neural exploit

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artificial enzymes:learning from nature and beyond

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Science China(Life Sciences) 2023年第2期66卷 421-422页

作者： Jinghong Li Department of Chemistry Key Laboratory of Bioorganic Phosphorus Chemistry&Chemical BiologyTsinghua UniversityBeijing 100084China

As Professor Ronald Breslow,a pioneer of artificial Enzymes and Biomimetic Chemistry,wrote in artificial Enzymes,artificial enzymes are part of the world of Biomimetic Chemistry,which especially refers to accelerating reactions imitating/similar to natural catalytic processes,with a context not limited to the substances of biology(Breslow,2005).

关键词： catalytic beyond artificial

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Intelligent Medicine

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Intelligent Medicine 2024年第1期4卷 58-64页

Aim&Scopes Intelligent Medicine is an open access,peer-reviewed journal sponsored and owned by the Chinese Medical Association and designated to publish high-quality original research and review articles from the interdisciplinary areas concerning the theory,algorithms,and practice of internet technology,artificial intelligence(Al),big data science,and medical information in the clinical medicine,biomedicine,and public health.

关键词： artificial Intelligent internet

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人工智能能耗惊人

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Engineering 2024年第1期32卷 7-9页

作者： Mitch Leslie Senior Technology Writer

In the month of August 2023,more than 180 million users logged on to ChatGPT(Fig.1)[1],the famous artificial intelligence(AI)chatbot[2].Developed by San Francisco,CA,USA-based OpenAI,ChatGPT can write prose in the style of Ernest Hemingway,generate computer code,help jobseekers craft their resumes,furnish reasonably accurate medical diagnoses,and perform a range of other sophisticated tasks[2-5].People who sign up for the free version of ChatGPT-as well as anyone else accessing the many Al-enhanced search engines and programs that are now available-do not have to pay for the assistance.But it is not actually free.

关键词：人工智能 computer artificial

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Nanograded artificial nacre with efficient energy dissipation

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The Innovation 2023年第6期4卷 76-82页

作者： Yu-Feng Meng Cheng-Xin Yu Li-Chuan Zhou Li-Mei Shang Bo Yang Qing-Yue Wang Xiang-Sen Meng Li-Bo Mao Shu-Hong Yu Department of Chemistr New Comerstone Science LaboratoryInstitute of Biomimetic Materials&ChemistryAnhui Engineering Laboratory of Biomimetic MaterialsDivision of Nanomaterials&ChemistryHefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei 230026China Key Laboratory of Materials Physics Anhui Key Laboratory of Nanomaterials and NanotechnologyCAS Center for Excellence in NanoscienceInstitute of Solid State PhysicsChinese Academy of SciencesHefei 230031China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Mechanical Engineering Hefei University of TechnologyHefei 230009China Institute of Innovative Materials Department of Materials Science and EngineeringDepatment of ChemistrySouthem University of Science and TechnologyShenzhen 518055China

The renowned mechanical performance of biological ceramics can beattributed to their hierarchical structures,wherein structural features atthe nanoscale play a crucial role.However,nanoscale features,such asnanogradients,have rarely been incorporated in biomimetic ceramicsbecause of the challenges in simultaneously controlling the materialstructure at multiple length scales.Here,we report the fabrication of artificial nacre with graphene oxide nanogradients in its aragonite plateletsthrough a matrix-directed mineralization method.The gradients areformed via the spontaneous accumulation of graphene oxide nanosheetson the surface of the platelets during the mineralization process,whichthen induces a lateral residual stress field in the platelets.Nanoindentation tests and mercury intrusion porosimetry demonstrate that the material's energy dissipation is enhanced both intrinsically and extrinsicallythrough the compressive stress near the platelet surface.The energydissipation density reaches 0.159±0.007 nJ/μm^(3),and the toughnessamplification is superior to that of the most advanced cer amics.Numer-ical simulations also agree with the finding that the stress field not ablycontributes to the overall energy dissipation.This work demonstratesthat the energy dissipation of biomimetic ceramics can be furtherincreased by integrating design principles spanning multiple scales.This strategy can be readily extended to the combinations of other struc-tural models for the design and fabrication of structural ceramics withcustomized and optimized performance.

关键词： ceramics artificial energy

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Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders—a comprehensive review

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International Journal of Oral Science 2023年第4期15卷 575-587页

作者： Mayank Shrivastava Liang Ye Adams School of Dentistry University of North CarolinaChapel HillNCUSA Department of Rehabilitation Medicine University of Minnesota Medical SchoolMinneapolisMNUSA

Chronic Painful Temporomandibular Disorders(TMD)are challenging to diagnose and manage due to their complexity and lack of understanding of brain mechanism.In the past few decades’neural mechanisms of pain regulation and perception have been clarified by neuroimaging research.Advances in the neuroimaging have bridged the gap between brain activity and the subjective experience of pain.Neuroimaging has also made strides toward separating the neural mechanisms underlying the chronic painful TMD.Recently,artificial Intelligence(AI)is transforming various sectors by automating tasks that previously required humans’intelligence to complete.AI has started to contribute to the recognition,assessment,and understanding of painful TMD.The application of AI and neuroimaging in understanding the pathophysiology and diagnosis of chronic painful TMD are still in its early stages.The objective of the present review is to identify the contemporary neuroimaging approaches such as structural,functional,and molecular techniques that have been used to investigate the brain of chronic painful TMD individuals.Furthermore,this review guides practitioners on relevant aspects of AI and how AI and neuroimaging methods can revolutionize our understanding on the mechanisms of painful TMD and aid in both diagnosis and management to enhance patient outcomes.

关键词： diagnosis artificial separating

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Insights into artificial intelligence in clinical oncology:opportunities and challenges

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Science China(Life Sciences) 2022年第3期65卷 643-647页

作者： Kai Chen Hanwei Li Zhanpeng Pan Zhuo Wu Erwei Song Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China Breast Tumor Center Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China artificial Intelligence Lab Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China Department of Radiology Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China Fountain-Valley Institute for Life Sciences Guangzhou Institute of Biomedicine and HealthChinese Academy of SciencesGuangzhou 510530China Bioland Laboratory Guangzhou 510005China

Cancer continues to be a major public health issue worldwide.Given the complexity of the etiology and pathophysiology of cancer,challenges always exist at every step of cancer management,including early screening,diagnosis,treatment selection,and surveillance.Emerging novel technologies hold promise in addressing a wide range of healthcare problems.artificial intelligence(AI),which is capable of learning of features from given datasets.

关键词： diagnosis Cancer artificial

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Clinical practice guideline for body composition assessment based on upper abdominal magnetic resonance images annotated using artificial intelligence

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Chinese Medical Journal 2022年第6期135卷 631-633页

作者： Han Lv Mengyi Li Zhenchang Wang Dawei Yang Hui Xu Juan Li Yang Liu Di Cao Yawen Liu Xinru Wu He Jin Peng Zhang Liqin Zhao Rixing Bai Yunlong Yue Bin Li Nengwei Zhang Mingzhu Zou Jinghai Song Weibin Yu Pin Zhang Weijun Tang Qiyuan Yao Liheng Liu Hui Yang Zhenghan Yang Zhongtao Zhang National Clinical Research Center for Digestive Diseases in Beijing Friendship Hospital,Capital Medical University Beijing Quality Control and Improvement Center of Medical Imaging National Health Commission Capacity Building and Continuing Education Center,Chinese College of Metabolic and Bariatric Surgeons Beijing Quality Control and Improvement Center of Metabolic and Bariatric Surgery Department of Radiology Beijing Friendship HospitalCapital Medical UniversityBeijing 100050China Department of General Surgery Beijing Friendship HospitalCapital Medical University&National Clinical Research Center for Digestive DiseasesBeijing 100050China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Department of Radiology Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China Department of General Surgery Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China Department of MR Beijing Shijitan HospitalCapital Medical University/Peking UniversityNinth Clinical Medical CollegeBeijing 100038China Department of General Surgery Beijing Shijitan HospitalCapital Medical University/Peking UniversityNinth Clinical Medical CollegeBeijing 100038China Department of Radiology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of General Surgery Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Radiology Shanghai Jiao Tong University Affiliated Sixth People's HospitalShanghai 200233China Department of Bariatric and Metabolic Surgery Shanghai Jiao Tong University Affiliated Sixth People's HospitalShanghai 200233China Department of Radiology Huashan HospitalFudan UniversityShanghai 200040China Center for Obesity and Metabolic Surgery Huashan HospitalFudan UniversityShanghai 200040China Department of Radiology Zhongshan HospitalFudan UniversityShanghai 200032China Department of Radiology The Second Affiliated Hospital of Shandong First Medical UniversityTai'anShandong 271000China 不详

Introduction Upper abdominal magnetic resonance(MR)imaging is appropriate for body composition analysis.111 Especially for individuals with obesity,it is of great value to quantify the hepatic proton density fat fraction(PDFF)and the amount of abdominal adipose tissue during clinical evaluation and for research on obesity-related risks.Analytical results may be used to determine the optimal choice of surgical procedure and evaluate treatment outcomes.Multiple artificial intelligence(Al)algorithms and systems have been developed for the automated measurement of body composition.The basis of Al development and application is to have uniform standards for clinical data acquisition and management.The uneven quality ofMR images is one ofthe major obstacles to Al system development and analytical results.A standardized process of MR scanning and clinical data management is urgently needed.

关键词： abdominal obesity artificial

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Physical artificial intelligence(PAI):the next-generation artificial intelligence

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信息与电子工程前沿:英文版 2023年第8期24卷 1231-1238页

作者： Yingbo LI Zhao LI Yucong DUAN Anamaria-Beatrice SPULBER School of Computer Science and Technology Hainan UniversityHaikou570228China Zhejiang Lab Hangzhou311121China

artificial intelligence(AI)has been a driving force for innovation and social progress in various domains(Pan,2017).However,most of its industrial applications have focused on the signal processing domain,which relies... 详细信息

关键词： artificial domains intelligence

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Strategy evaluation and optimization with an artificial society toward a Pareto optimum

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The Innovation 2022年第5期3卷 33-35页

作者： Zhengqiu Zhu Bin Chen Hailiang Chen Sihang Qiu Changjun Fan Yong Zhao Runkang Guo Chuan Ai Zhong Liu Zhiming Zhao Liqun Fang Xin Lu College of Systems Engineering National University of Defense TechnologyChangsha 410073China Hunan Institute of Advanced Technology Changsha 410073China Research Group of Multiscale Networked Systems University of Amsterdam(UvA)1099 Amsterdamthe Netherlands State Key Laboratory of Pathogen and Biosecurity Beijing Institute of Microbiology and EpidemiologyBeijing 100071China Department of Global Public Health Karolinska Institutet17177 StockholmSweden

Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty,unreliable predictions,and poor decision-making.To address this problem,we propose a universal computational experiment framework with a fine-grained artificial society that is integrated with data-based models.The purpose of the framework is to evaluate the consequences of various combinations of strategies geared towards reaching a Pareto optimum with regards to efficacy versus costs.As an example,by modeling coronavirus disease 2019 mitigation,we show that Pareto frontier nations could achieve better economic growth and more effective epidemic control through the analysis of real-world data.Our work suggests that a nation’s intervention strategy could be optimized based on the measures adopted by Pareto frontier nations through large-scale computational experiments.Our solution has been validated for epidemic control,and it can be generalized to other urban issues as well.

关键词： artificial optimization optimum

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