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Recent advances of antibody drug conjugates for clinical applications

Acta Pharmaceutica Sinica B 2020年第9期10卷 1589-1600页

作者： Pengxuan Zhao Yuebao Zhang Wenqing Li Christopher Jeanty Guangya Xiang Yizhou Dong Division of Pharmaceutics&Pharmacology College of Pharmacythe Ohio State UniversityColumbusOH 43210USA School of Pharmacy Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Biomedical Engineering the Ohio State UniversityColumbusOH 43210USA The Center for Clinical and Translational Science the Ohio State UniversityColumbusOH 43210USA The Comprehensive Cancer Center the Ohio State UniversityColumbusOH 43210USA Dorothy M.Davis Heart&Lung Research Institute the Ohio State UniversityColumbusOH 43210USA Department of Radiation Oncology the Ohio State UniversityColumbusOH 43210USA Neuroscience Program College of Medicine&College of Arts and Sciencesthe Ohio State UniversityColumbusOH 43210USA

Antibody drug conjugates(ADCs)normally compose of a humanized antibody and small molecular drug via a chemical *** decades of preclinical and clinical studies,a series of ADCs have been widely used for treating specific tumor types in the clinic such as brentuximab vedotin(Adcetris?)for relapsed Hodgkin’s lymphoma and systemic anaplastic large cell lymphoma,gemtuzumab ozogamicin(Mylotarg?)for acute myeloid leukemia,ado-trastuzumab emtansine(Kadcyla?)for HER2-positive metastatic breast cancer,inotuzumab ozogamicin(Besponsa?)and most recently polatuzumab vedotin-piiq(Polivy?)for B cell *** than eighty ADCs have been investigated in different clinical stages from approximately six hundred clinical trials to *** review summarizes the key elements of ADCs and highlights recent advances of ADCs,as well as important lessons learned from clinical data,and future directions.

关键词： Antibody drug conjugates Antibody Cytotoxic agents Linker Clinical application

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Effects of spatial uniformity of continuous and pulse laser irradiation on the microstructure and mechanical properties of Ti-6Al-4V in powder bed fusion

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materials Today Communications 2025年 46卷

作者： Seunghwan Moon Seungmuk Ji Yoonseok Ko Min-Su Lee Tea-Sung Jun Dong-Ik Kim Jong-Souk Yeo School of Integrated Technology College of Computing Yonsei University Seoul 03722 Republic of Korea BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul 03722 Republic of Korea Center for Energy Materials Research Korea Institute of Science and Technology Seoul 02792 Republic of Korea Department of Mechanical Engineering Incheon National University Incheon 22012 Republic of Korea

Powder bed fusion of metals with laser beam (PBF-LB/M) using Ti-6Al-4V (Ti64) has enabled delicate fabrication of biocompatible medical implants. This research focuses on how the spatial uniformity of laser irradiation affects material under continuous wave (CW) and pulse width modulated (PWM) laser conditions, using a self-developed PBF-LB/M system. The volumetric energy density (VED) was used as a reference parameter in order to accurately compare the effects of CW and PWM lasers, but limitations of VED, which does not include spatial and kinetic factors, which are reinforced in the PWM laser process, were also considered. Results revealed that the uniform distribution of energy irradiation across the entire region brought similar porosities and Vickers hardness tendencies for CW and PWM laser processes. In contrast, the spatially nonuniform energy transfer resulted in a significantly higher porosity in the specimen fabricated using the PWM laser than that produced using the CW laser at the same VED, reducing compressive yield strength. The electron backscattered diffraction analysis showed no significant differences in the average grain size, phase distribution, and misorientation angle of the samples produced using CW and PWM lasers under the same VED. However, sharper grains with an aspect ratio of less than 0.3 were 7 % higher in specimens produced using the PWM laser. This study highlights the possibility of controlling the microstructure and mechanical properties in a wider range when using the PWM laser with spatially nonuniform irradiation.

关键词： Vickers hardness

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Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Research Square

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Research Square 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, Di Zhang, Shuo Mura, Cameron Bourne, Philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Mechanical microenvironments of living cells: a critical frontier in mechanobiology

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Acta Mechanica Sinica 2019年第2期35卷 265-269页

作者： Guoyou Huang Feng Xu Guy M. Genin Tian Jian Lu The Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology Xi’an Jiaotong University Xi’an 710049 China Bioinspired Engineering and Biomechanics Center (BEBC) Xi’an Jiaotong University Xi’an 710049 China Department of Mechanical Engineering &Materials Science Washington University in St. Louis St. Louis 63130 MO USA NSF Science and Technology Center for Engineering Mechanobiology Washington University in St. Louis St. Louis 63130 MO USA State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing 210016 China State Key Laboratory for Strength and Vibration of Mechanical Structures Xi'an Jiaotong University Xi'an 710049 China

The fields of biomechanics and mechanobiology have long been predicated on the premise that mechanics governs cell behavior. However, over the past few years, a growing body of evidence has suggested that the mechanical environment very close to cells–the cell microenvironment–plays the most important role in determining what a cell feels and how it responds to tissue-level stimuli. To complicate matters further, cells can actively manipulate their microenvironments through pathways of recursive mechanobiological feedback. Harnessing this recursive behavior to understand and control cell physiology and pathophysiology is a critical frontier in the field of mechanobiology. Recent results suggest that the key to opening this scientific frontier to investigation and engineering application is understanding a different frontier: the physical frontier that cells face when probing their mechanical microenvironments.

关键词： Mechanical microenvironments living cells mechanobiology

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Heavy metals concentration in mangrove tissues and associated sediments and seawater from the north coast of Persian Gulf, Iran: Ecological and health risk assessment

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Environmental Nanotechnology, Monitoring and Management 2021年 15卷

作者： Rezaei, Marzieh Kafaei, Raheleh Mahmoodi, Marzieh Sanati, Ali Mohammad Vakilabadi, Dariush Ranjbar Arfaeinia, Hossein Dobaradaran, Sina Sorial, George A. Ramavandi, Bahman Boffito, Daria Camilla Department of Environmental Health Engineering Faculty of Health and Nutrition Bushehr University of Medical Sciences Bushehr Iran Student Research Committee School of Public Health and Safety Shahid Beheshti University of Medical Sciences Tehran Iran Department of Biostatistics Faculty of Health and Nutrition Bushehr University of Medical Sciences Bushehr Iran Department of Environmental Science Persian Gulf Research Institute Persian Gulf University Bushehr Iran Systems Environmental Health and Energy Research Center The Persian Gulf Biomedical Sciences Research Institute Bushehr University of Medical Sciences Bushehr Iran Environmental Engineering Program Department of Chemical and Environmental Engineering College of Engineering and Applied Science University of Cincinnati Cincinnati 45221-0012 OH United States Department of Chemical Engineering Polytechnique Montréal C.P. 6079 Succ CV Montréal H3C3A7 QC Canada

We evaluated the concentration of copper (Cu), zinc (Zn), mercury (Hg), nickel (Ni), chromium (Cr), vanadium (V), and cobalt (Co) in the mangrove tissues (leaf and root) and adjusted sediments, as well as seawater samples in the north coast of the Persian Gulf from three counties (Mahshahr, Bord Khun, Asaluyeh). Assaluyeh and Mahshahr were selected because of their proximity to the oil and petrochemical industries and Bord Khun was considered for being away from pollution sources. The metal detected in highest concentration in mangrove leaves and roots was Cu (Asaluyeh: 1.507 μg/g and 1.198 μg/g, Bord Khun: 1.812 and 1.756 μg/g, and Mahshahr: 0.880 and 1.617 μg/g, for leaves and roots, respectively). For sediments, Cr (among the metals) had the highest concentration (1.325 μg/g) in Asaluyeh, whereas in Bord Khun and Mahshahr Ni was predominant: 2.542 and 2.237 μg/g, respectively. For seawater, Cu was the most abundant metal and Hg was the one with the lowest concentration. The bio-concentration factor for Zn and Cu revealed that mangroves in the study areas have suitable potential for phytoremediation of these two metals. Translocation factors from the roots to leaves also indicated higher transportability for Zn and Cu through the plant tissues, compared to the other metals. Ecological indexes quantified the contamination of sediments and revealed a low level of heavy metal accumulation in sediments of the north of the Persian Gulf. Human risk hazards appeared low in all the areas under study. Gas and petrochemical activities can be considered as a potential source of the discharge of heavy metals into the aquatic environment. © 2021 Elsevier B.V.

关键词： Asaluyeh Bord Khun Heavy metals distribution Mahshahr Mangrove ecosystem Pollution assessment

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Tuning extracellular fluid viscosity to enhance transfection efficiency

Nature Chemical Engineering

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Nature Chemical engineering 2024年 576-587页

作者： Jingyao Ma Yining Zhu Jiayuan Kong Di Yu Wu Han Toh Milun Jain Qin Ni Zhuoxu Ge Jinghan Lin Joseph Choy Leonardo Cheng Konstantinos Konstantopoulos Maximilian F. Konig Sean X. Sun Hai-Quan Mao Institute for NanoBioTechnology Johns Hopkins University Baltimore MD USA Department of Materials Science and Engineering Johns Hopkins University Baltimore MD USA Translational Tissue Engineering Center Johns Hopkins University School of Medicine Baltimore MD USA Department of Biomedical Engineering Johns Hopkins University School of Medicine Baltimore MD USA Department of Computer Science Johns Hopkins University Baltimore MD USA Department of Biology Johns Hopkins University Baltimore MD USA Department of Mechanical Engineering Johns Hopkins University Baltimore MD USA Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore MD USA Department of Oncology The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore MD USA Division of Rheumatology Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA Ludwig Center for Cancer Genetics and Therapeutics Johns Hopkins University School of Medicine Baltimore MD USA Center for Cell Dynamics School of Medicine Johns Hopkins University Baltimore MD USA

Gene therapies and cellular programming rely on effective cell transfection. Despite continuous advancements in carrier development and transfection techniques to enhance efficiency, the biophysical parameter of extracellular fluid viscosity has been largely overlooked. Here we report a substantial impact of culture media viscosity on transfection efficiency of several delivery vehicles, including lipid nanoparticles, polyplexes, adeno-associated vectors and lentiviral vectors across a range of cell types. We observed substantially increased transfection efficiencies for lipid nanoparticles and polyplexes when the media viscosity matched that of biological fluids (2.0–4.0 centipoise (cP)). This enhancement correlates with higher levels of cellular uptake and improved endosomal escape. Moreover, cells cultured in optimized viscosity conditions exhibit a different profile of uptake pathways compared with those cultured at the standard viscosity of 0.8 cP. This discovery highlights the critical role of media viscosity in the transfection process and provides an additional method to optimize gene delivery and cell programming processes, potentially reducing production costs and increasing the accessibility of gene and cell therapies.

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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVID-19 anti-infective polypharmacology

arXiv

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arXiv 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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Correction: Biomass derived nitrogen functionalized carbon nanodots for nanomolar determination of levofloxacin in pharmaceutical and water samples

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Mikrochimica acta 2023年第7期190卷 269页

作者： Velusamy Arul Natarajan Sampathkumar Sindhuja Kotteeswaran Ponnusamy Arul Ahmed Muteb Aljuwayid Mohamed A Habila Mani Govindasamy Department of Chemistry Sri Eshwar College of Engineering (Autonomous) Coimbatore 641 202 Tamil Nadu India. kvarulchem6@***. Department of Chemistry Korea University 145 Anam-Ro Seongbuk-Gu Seoul South Korea. kvarulchem6@***. Department of Chemistry SSM College of Arts and Science Dindigul 624002 Tamil Nadu India. International PhD Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan. Department of Chemical Engineering and Biotechnology Institute of Biochemical and Biomedical Engineering National Taipei University of Technology Taipei 106 Taiwan ROC. vpsarul@***. Department of Chemistry College of Science King Saud University P. O. Box 2455 Riyadh 11451 Saudi Arabia. International PhD Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan. govindasamy420700@***. Research Center for Intelligence Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan. govindasamy420700@***. Department of Research and Innovation Saveetha School of Engineering SIMATS 602105 Chennai India. govindasamy420700@***.

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EMPRESS. X. Spatially resolved mass-metallicity relation in extremely metal-poor galaxies: Evidence of episodic star-formation fueled by a metal-poor gas infall

arXiv

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arXiv 2024年

作者： Nakajima, Kimihiko Ouchi, Masami Isobe, Yuki Xu, Yi Ozaki, Shinobu Nagao, Tohru Inoue, Akio K. Rauch, Michael Kusakabe, Haruka Onodera, Masato Nishigaki, Moka Ono, Yoshiaki Sugahara, Yuma Hattori, Takashi Hirai, Yutaka Hashimoto, Takuya Kim, Ji Hoon Moriya, Takashi J. Yanagisawa, Hiroto Aoyama, Shohei Fujimoto, Seiji Fukushima, Hajime Fukushima, Keita Harikane, Yuichi Hatano, Shun Hayashi, Kohei Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Motohara, Kentaro Murai, Kai Nagamine, Kentaro Nakane, Minami Saito, Tomoki Sasaki, Rin Shibuya, Takatoshi Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Yajima, Hidenobu Zhang, Yechi National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Osawa 2-21-1 Mitaka Tokyo181-8588 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Cavendish Laboratory University of Cambridge 19 JJ Thomson Avenue CambridgeCB3 0HE United Kingdom Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Research Center for Space and Cosmic Evolution Ehime University Bunkyo-cho 2-5 Ehime Matsuyama790-8577 Japan Department of Physics School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Observatoire de Genève Université de Genève 51 Chemin de Pégase Versoix1290 Switzerland 650 North Aohoku Place HiloHI96720 United States Department of Physics and Astronomy University of Notre Dame 225 Nieuwland Science Hall Notre DameIN46556 United States Astronomical Institute Tohoku University 6-3 Aoba Aramaki Aoba-ku Miyagi Sendai980-8578 Japan Division of Physics Faculty of Pure and Applied Sciences University of Tsukuba Ibaraki Tsukuba305-8571 Japan Faculty of Pure and Applied Sciences University of Tsukuba Ibaraki Tsukuba305-8571 Japan Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Cent

Using the Subaru/FOCAS IFU capability, we examine the spatially resolved relationships between gas-phase metallicity, stellar mass, and star-formation rate surface densities (Σ★ and ΣSFR, respectively) in extremely metal-poor galaxies (EMPGs) in the local universe. Our analysis includes 24 EMPGs, comprising 9,177 spaxels, which span a unique parameter space of local metallicity (12 + log(O/H) 6.9 to 7.9) and stellar mass surface density (Σ★ ∼ 105 to 107 Mo kpc−2), extending beyond the range of existing large integral-field spectroscopic surveys. Through spatially resolved emission line diagnostics based on the [N ii] BPT-diagram, we verify the absence of evolved active galactic nuclei in these EMPGs. Our findings reveal that, while the resolved mass-metallicity relation exhibits significant scatter in the low-mass regime, this scatter is closely correlated with local star-formation surface density. Specifically, metallicity decreases as ΣSFR increases for a given Σ★. Notably, half of the EMPGs show a distinct metal-poor horizontal branch on the resolved mass-metallicity relation. This feature typically appears at the peak clump with the highest Σ★ and ΣSFR and is surrounded by a relatively metal-enriched ambient region. These findings support a scenario in which metal-poor gas infall fuels episodic star formation in EMPGs, consistent with the kinematic properties observed in these systems. In addition, we identify four EMPGs with exceptionally low central metallicities (12 + log(O/H) 7.2), which display only a metal-poor clump without a surrounding metal-rich region. This suggests that such ultra-low metallicity EMPGs, at less than a few percent of the solar metallicity, may serve as valuable analogs for galaxies in the early stages of galaxy evolution. Copyright © 2024, The Authors. All rights reserved.

关键词： Spectroscopic analysis

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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