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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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Blended Conjugated Host and Unconjugated Dopant Polymers Towards N-type All-Polymer Conductors and High-ZT Thermoelectrics

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Angewandte Chemie 2023年第23期135卷

作者： Jinfeng Han Yufeng Jiang Emma Tiernan Connor Ganley Yunjia Song Taein Lee Arlene Chiu Patty McGuiggan Nicholas Adams Paulette Clancy Thomas P Russell Patrick E. Hopkins Susanna M. Thon John D. Tovar Howard E. Katz Department of Materials Science and Engineering and Department of Chemistry Johns Hopkins University 3400 North Charles Street Baltimore MD 21218 USA Contribution: Conceptualization (equal) Investigation (lead) Methodology (lead) Writing - original draft (lead) Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA Contribution: Formal analysis (supporting) Methodology (supporting) Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville VA 22904 USA Contribution: Formal analysis (supporting) Methodology (supporting) Writing - original draft (supporting) Department of Chemical and Biomolecular Engineering Johns Hopkins University 3400 North Charles Street Baltimore MD 21218 USA Contribution: Formal analysis (supporting) Investigation (supporting) Writing - original draft (supporting) Contribution: Investigation (supporting) Methodology (supporting) Department of Electrical and Computer Engineering Johns Hopkins University 3400 North Charles Street Baltimore MD 21218 USA Contribution: Formal analysis (supporting) Funding acquisition (supporting) Investigation (supporting) Methodology (equal) Project administration (supporting) Supervision (equal) Writing - review & editing (supporting) Contribution: Formal analysis (supporting) Supervision (supporting) Department of Materials Science and Engineering University of Virginia Charlottesville VA 22904 USA Department of Physics University of Virginia Charlottesville VA 22904 USA Contribution: Conceptualization (supporting) Methodology (equal) Supervision (supporting) Writing - review & editing (supporting) Contribution: Formal analysis (supporting) Methodology (supporting) Supervision (supporting) Contribution: Investigation (supporting) Methodology (supporting) Supervision (supporting)

N-Type thermoelectrics typically consist of small molecule dopant+polymer host. Only a few polymer dopant+polymer host systems have been reported, and these have lower thermoelectric parameters. N-type polymers with high crystallinity and order are generally used for high-conductivity ( ) organic conductors. Few n-type polymers with only short-range lamellar stacking for high-conductivity materials have been reported. Here, we describe an n-type short-range lamellar-stacked all-polymer thermoelectric system with highest of 78 S −1 , power factor ( PF ) of 163 μW m −1 K −2 , and maximum Figure of merit ( ZT ) of 0.53 at room temperature with a dopant/host ratio of 75 wt%. The minor effect of polymer dopant on the molecular arrangement of conjugated polymer PDPIN at high ratios, high doping capability, high Seebeck coefficient ( S ) absolute values relative to , and atypical decreased thermal conductivity ( ) with increased doping ratio contribute to the promising performance.

关键词： All-Polymer Thermoelectrics Electrical Conductivity Power Factor Semiconducting Polymer ZT

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Calibration of Low-Cost Sensor Data for Ambient PM 2.5 Monitoring Across Urban and Rural Settings in South Central Uganda

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Atmospheric Pollution Research 2025年

作者： Sophia Le William Checkley Lauren Dudley Joseph Ssuuna Anthony Ndyanabo Engineer Bainomugisha Joel Ssematimba Richard Sserunjogi Deo Okedi Deo Okure Joseph Kagaayi Larry Chang Kirsten Koehler Laura Nicolaou Department of Environmental Health and Engineering Bloomberg School of Public Health Johns Hopkins University Baltimore MD 21205 USA Center for Global Non-Communicable Disease Research and Training Johns Hopkins University Baltimore MD 21287 USA Department of International Health Bloomberg School of Public Health Johns Hopkins University Baltimore MD 21205 USA Division of Pulmonary and Critical Care School of Medicine Johns Hopkins University Baltimore MD 21287 USA Rakai Health Sciences Program Kalisizo Uganda AirQo Department of Computer Science Makerere University Kampala Uganda Division of Infectious Diseases School of Medicine Johns Hopkins University Baltimore MD 21827 USA

Air pollution is a leading risk factor for the global burden of disease. There are limited data from Sub-Saharan Africa (SSA). This study addresses significant data gaps in air quality monitoring in South-Central Uganda. Using a network of 27 low-cost air quality monitors in the rural Rakai region and urban cities of Masaka and Kampala, we developed a locally tailored calibration model for ambient PM 2.5 concentrations. The model incorporates ambient temperature and relative humidity, demonstrating robust performance with an R 2 of 0.93 and a root mean squared error (RMSE) of 3.97 μg/m 3 . Mean PM 2.5 concentrations during the dry and wet seasons were 20.2 μg/m 3 and 11.8 μg/m 3 in rural areas, and 29.6 μg/m 3 and 18.1 μg/m 3 in urban areas, respectively. We observed diurnal variations, with PM 2.5 levels peaking in the early morning (7 AM to 9 AM) and late evening (6 PM to 10 PM), correlating with peak traffic hours. Low-cost sensors can enhance air quality research in regions like South-Central Uganda that lack air pollution data. Calibration tailored to local conditions can improve measurement accuracy of ambient PM 2.5 concentrations.

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Terahertz Cavity Phonon Polaritons in lead Telluride in the Deep-Strong Coupling Regime

arXiv

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arXiv 2025年

作者： Baydin, Andrey Manjappa, Manukumara Mishra, Sobhan Subhra Xu, Hongjing Doumani, Jacques Tay, Fuyang Kim, Dasom Rappl, Paulo H.O. Abramof, Eduardo Singh, Ranjan Hernandez, Felix G.G. Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley–Curl Institute Rice University HoustonTX77005 United States Rice Advanced Materials Institute Rice University HoustonTX77005 United States Instrumentation and Applied Physics Department Indian Institute of Science Bangalore560012 India Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University 639798 Singapore Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Group of Research and Development in Materials and Plasma National Institute for Space Research São José dos Campos São Paulo12201-970 Brazil Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Instituto de Física Universidade de São Paulo São Paulo05508-090 Brazil Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

lead telluride is an important thermoelectric material due to its large Seebeck coefficient combined with its unusually low thermal conductivity that is related to the strong anharmonicity of phonons in this material. Here, we have studied the resonant and nonperturbative coupling of transverse optical phonons in lead telluride with cavity photons inside small-mode-volume metallic metasurface cavities that have photonic modes with terahertz frequencies. We observed a giant vacuum Rabi splitting on the order of the bare phonon and cavity frequencies. Through terahertz time-domain spectroscopy experiments, we systematically studied the vacuum Rabi splitting as a function of sample thickness, temperature, and cavity length. Under the strongest light–matter coupling conditions, the strength of coupling exceeded the bare phonon and cavity frequencies, putting the system into the deep-strong coupling regime. These results demonstrate that this uniquely tunable platform is promising for realizing and understanding predicted cavity-vacuum-induced ferroelectric instabilities and exploring applications of light–matter coupling in the ultrastrong and deep-strong coupling regimes in quantum technology. Copyright © 2025, The Authors. All rights reserved.

关键词： Phonons

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Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers

Npj imaging

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Npj imaging 2024年第1期2卷 56页

作者： Karissa Chan Corinne Fischer Pejman Jabehdar Maralani Sandra E Black Alan R Moody April Khademi Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada. Institute for Biomedical Engineering Science Tech (iBEST) a Partnership between St. Michael's Hospital and Toronto Metropolitan University Toronto ON Canada. Keenan Research Center for Biomedical Science St. Michael's Hospital Unity Health Network Toronto ON Canada. Department of Medical Imaging University of Toronto Toronto ON Canada. Institute of Medical Science University of Toronto Toronto ON Canada. Horvitz Brain Sciences Research Program Sunnybrook Research Institute Toronto ON Canada. Division of Neurology Department of Medicine Sunnybrook Health Sciences Centre Toronto ON Canada. L.C. Campbell Cognitive Neurology Research Unit Sunnybrook Health Sciences Centre Toronto ON Canada. Vector Institute for Artificial Intelligence Toronto ON Canada.

This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAIR biomarkers. Intensity and texture biomarkers were extracted from FLAIR volumes of 379 atherosclerosis patients. K-Means clustering identified five homogeneous subgroups. The 15 most important biomarkers for subgroup differentiation, identified via Random Forest classification, were used to generate biomarker profiles. ANOVA tests showed age and white matter lesion volume were significantly ( < 0.05) different across subgroups, while Fisher's tests revealed significant ( < 0.05) differences in the prevalence of several vascular risk factors across subgroup. Based on biomarker and clinical profiles, Subgroup 4 was characterized with neurodegeneration unrelated to CVD, Subgroup 3 identified patients with high CVD risk requiring aggressive intervention, and Subgroups 1, 2, and 5 identified patients with varying levels of moderate risk, suitable for long-term lifestyle interventions. This study supports personalized treatment and risk stratification based on FLAIR biomarkers.

关键词： Brain imaging Image processing

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Bioelectrical Interfaces Beyond Cellular Excitability: Cancer, Aging, and Gene Expression Reprogramming

arXiv

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arXiv 2025年

作者： Cadinu, Paolo Burgess, Matthew Jones, Catarina Franco Luciani, Titouan Iarossi, Marzia Schröter, Manuel Nakatsuka, Nako Djamgoz, Mustafa B.A. Gonçalves, Gil Sanjuán-Alberte, Paola Mendes, Paula M. Rawson, Frankie J. Nair, Malavika Levin, Michael Moreddu, Rosalia Program in Cellular and Molecular Medicine Boston Children’s Hospital BostonMA02115 United States Department of Microbiology Blavatnik Institute Harvard Medical School BostonMA02115 United States Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford United Kingdom Department of Bioengineering Institute for Bioengineering and Biosciences Instituto Superior Técnico University of Lisbon Lisbon Portugal Associate Laboratory i4HB Institute for Health and Bioeconomy Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Neuroscience Institut Pasteur Paris75015 France Department of Biomedical Engineering Technion Israel Institute of Technology Haifa Israel ETH Zurich Department of Biosystems Science and Engineering Basel Switzerland Laboratory of Chemical Nanotechnology Neuro-X Institute EPFL Genève1202 Switzerland Department of Life Sciences Imperial College London United Kingdom Department of Mechanical Engineering University of Aveiro Aveiro3810-193 Portugal Guimarães4800-058 Portugal School of Chemical Engineering University of Birmingham Birmingham United Kingdom Bioelectronics Laboratory Division of Regenerative Medicine and Cellular Therapies School of Pharmacy Biodiscovery Institute University of Nottingham Nottingham United Kingdom Allen Discovery Centre Tufts University Tufts University MedfordMA United States Wyss Institute for Bioinspired Engineering Harvard University BostonMA United States School of Electronics and Computer Science University of Southampton Southampton United Kingdom Institute for Life Sciences University of Southampton Southampton United Kingdom

Bioelectrical interfaces represent a significant evolution in the intersection of nanotechnology and biophysics, oRering new strategies for probing and influencing cellular processes. These systems capitalize on the subtle but powerful electric fields within living matter, potentially enabling applications beyond cellular excitability, ranging from targeted cancer therapies to interventions in genetic mechanisms and aging. This perspective article envisions the translation, development and application of next-generation solid-state bioelectrical interfaces and their transformative impact across several critical areas of medical research. © 2025, CC BY.

关键词： Medical nanotechnology

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Two-dimensional charge order stabilized in clean polytype heterostructures

arXiv

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arXiv 2021年

作者： Sung, Suk Hyun Schnitzer, Noah Novakov, Steve El Baggari, Ismail Luo, Xiangpeng Gim, Jiseok Vu, Nguyen M. Li, Zidong Brintlinger, Todd H. Liu, Yu Lu, Wenjian Sun, Yuping Deotare, Parag B. Sun, Kai Zhao, Liuyan Kourkoutis, Lena F. Heron, John T. Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering Cornell University IthacaNY14853 United States Kavli Institute at Cornell for Nanoscale Science IthacaNY14853 United States Department of Physics University of Michigan Ann ArborMI48109 United States Department of Physics Cornell University IthacaNY14853 United States Rowland Institute at Harvard CambridgeMA02142 United States Electrical and Computer Engineering Department University of Michigan Ann ArborMI48109 United States Materials Science and Technology Division U.S. Naval Research Laboratory WashingtonDC20375 United States Key Laboratory of Materials Physics Institute of Solid State Physics Chinese Academy of Sciences Hefei230031 China High Magnetic Field Laboratory Chinese Academy of Sciences Hefei230031 China Collaborative Innovation Centre of Advanced Microstructures Nanjing University Nanjing210093 China Applied Physics Program University of Michigan Ann ArborMI48109 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States

Compelling evidence suggests distinct correlated electron behavior may exist only in clean 2D materials such as 1T-TaS2. Unfortunately, experiment and theory suggest that extrinsic disorder in free standing 2D layers disrupts correlation-driven quantum behavior. Here we demonstrate a route to realizing fragile 2D quantum states through endotaxial polytype engineering of van der Waals materials. The true isolation of 2D charge density waves (CDWs) between metallic layers stabilizes commensurate long-range order and lifts the coupling between neighboring CDW layers to restore mirror symmetries via interlayer CDW twinning. The twinned-commensurate charge density wave (tC-CDW) reported herein has a single metal–insulator phase transition at ~350 K as measured structurally and electronically. Fast in-situ transmission electron microscopy and scanned nanobeam diffraction map the formation of tC-CDWs. This work introduces endotaxial polytype engineering of van der Waals materials to access latent 2D ground states distinct from conventional 2D fabrication. © 2021, CC BY.

关键词： Charge density waves

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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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BTEX exposure and its body burden pose differential risks for asthma and its phenotypic clusters

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Allergy 2023年第7期78卷 2036-2040页

作者： Yuan-Ting Hsu Chao-Chien Wu Chin-Chou Wang Wen-Yu Chung Chau-Chyun Sheu Yi-Hsin Yang Ming-Yen Cheng Ruay-Sheng Lai Sum-Yee Leung Chi-Cheng Lin Yu-Feng Wei Ching-Hsiung Lin Sheng-Hao Lin Jeng-Yuan Hsu Wei-Chang Huang Chia-Cheng Tseng Yung-Fa Lai Meng-Hsuan Cheng Huang-Chi Chen Chih-Jen Yang Chian-Heng Su Chien-Jen Wang Shih-Chang Hsu Chih-Hsing Hung Chon-Lin Lee Ming-Shyan Huang Shau-Ku Huang National Institute of Environmental Health Sciences National Health Research Institutes Miaoli Taiwan. Department of Internal Medicine Division of Pulmonary and Critical Care Medicine Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan. Department of Public Health Kaohsiung Medical University Kaohsiung Taiwan. Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan. Department of Internal Medicine Division of Pulmonary and Critical Care Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan. Department of Internal Medicine School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan. National Institute of Cancer Research National Health Research Institutes Tainan Taiwan. Department of Mathematics Hong Kong Baptist University Hong Kong China. Division of Chest Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan. Department of Internal Medicine Chest Division Antai Medical Care Cooperation Antai Tian-Sheng Memorial Hospital Pingtung Taiwan. Department of Internal Medicine E-Da Cancer Hospital I-Shou University Kaohsiung Taiwan. Department of Internal Medicine Division of Chest Medicine Changhua Christian Hospital Changhua Taiwan. Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan. Department of Recreation and Holistic Wellness MingDao University Changhua Taiwan. Department of Medical Research Division of Clinical Research Taichung Veterans General Hospital Taichung Taiwan. Department of Internal Medicine Division of Chest Medicine Taichung Veterans General Hospital Taichung Taiwan. Department of Medical Technology Jen-Teh Junior College of Medicine Nursing and Management Miaoli Taiwan. School of Medicine Chung Shan Medical University Taichung Taiwan. Ph.D. Program in Translational Medicine National Chung Hsing Universi

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