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Frailty and Other Factors Associated With Early Outcomes in Middle-to Older Age Trauma Patients: A Prospective Cohort Study

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American Journal of Geriatric Psychiatry 2024年第2期32卷 244-255页

作者： Yeh, Tian-Shin Kang, Jiunn-Horng Littlejohns, Thomas J. Wu, Chia-Chieh Chen, Jin-Hua Piravej, Krisna Chiu, Wen-Ta Lam, Carlos Department of Physical Medicine and Rehabilitation School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Physical Medicine and Rehabilitation Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Epidemiology and Nutrition Harvard T. H. Chan School of Public Health Harvard University Boston MA United States Department of Physical Medicine and Rehabilitation National Taiwan University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation College of Medicine National Taiwan University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Nuffield Department of Population Health University of Oxford Oxford United Kingdom Emergency Department Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Emergency School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei Taiwan Institutional Research Center Office of Data Science Taipei Medical University Taipei Taiwan Biostatistics Center Department of Medical Research Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok Thailand Department of Chula Neuroscience Center King Chulalongkorn Memorial Hospital Bangkok Thailand Graduate Institute of Injury Prevention and Control College of Public Health Taipei Medical University Taipei Taiwan AHMC Health System Alhambra CA United States

Objectives: To prospectively investigate associations of frailty and other predictor variables with functional recovery and health outcomes in middle-aged and older patients with trauma. Design: Single-center prospective cohort study. Setting: Emergency department of Wan Fang Hospital in Taiwan. Participants: Trauma patients aged 45 and older. Measurements: Frailty was assessed with the Clinical Frailty Scale (CFS). Injury mechanisms, pre-existing diseases, and fracture locations were recorded at baseline. The primary outcome was functional recovery assessed using the Barthel Index (BI). Secondary outcomes were new care needs, unscheduled return visits, and falls 3 months postinjury. Results: A total of 588 participants were included in the final analysis. For every one-point increase in the CFS, the multivariable-adjusted odds ratio (OR, 95% confidence interval [CI]) of failure to retain the preinjury BI was 1.34 (1.16–1.55);associations were consistent across levels of age and injury severities. Significant joint associations of frailty and age with poor functional recovery were observed. CFS was also associated with new care needs (OR for every one-point increase, 1.36, 95% CI, 1.17–1.58), unscheduled return visits (OR 1.26, 95% CI, 1.04–1.51), and falls (OR 1.23, 95% CI, 1.01–1.51). Other variables associated with failure to retain preinjury BI included road traffic accident and presence of hip fracture. Conclusion: Frailty was significantly associated with poor functional and health outcomes regardless of injury severity in middle-aged and older patients with trauma. Injury mechanisms and fracture locations were also significant predictors of functional recovery postinjury. © 2023 American Association for Geriatric Psychiatry

关键词： Clinical Frailty Scale Frailty functional recovery injury middle-to older age

Conditional Generative Modeling for Amorphous Multi-Element Materials

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

Factors that affect the technological transition of firms toward the industry 4.0 technologies

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arXiv 2022年

作者： Kim, Seung Hwan Jeon, Jeong hwan Aridi, Anwar Jun, Bogang Technology Management Economics and Policy Program Seoul National University Seoul Korea Republic of Department of Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology Ulsan Korea Republic of Finance Competitiveness and Innovation Global Practice Europe and Central Asia Unit The World Bank Group Department of Economics Inha University Korea Republic of Department of Data Science Inha University Korea Republic of Research Center for Small Businesses Ecosystem Inha University Korea Republic of

This research aims to identify factors that affect the technological transition of firms toward industry 4.0 technologies (I4Ts) focusing on firm capabilities and policy impact using relatedness and complexity measures. For the analysis, a unique dataset of Korean manufacturing firms’ patent and their financial and market information was used. Following the Principle of Relatedness, which is a recently shaped empirical principle in the field of economic complexity, economic geography, and regional studies, we build a technology space and then trace each firm’s footprint on the space. Using the technology space of firms, we can identify firms that successfully develop a new industry 4.0 technology and examine whether their accumulated capabilities in their previous technology domains positively affect their technological diversification and which factors play a critical role in their transition towards industry 4.0. In addition, by combining data on whether the firms received government support on R&D activities, we further analyzed the role of government policy in supporting firms’ knowledge activity in new industry 4.0 technologies. We found that firms with higher related technologies and more government support are more likely to enter new I4Ts. We expect our research to inform policymakers who aim to diversify firms’ technological capabilities into I4Ts. © 2022, CC BY.

关键词： Industry 4.0

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Graph AI in Medicine

arXiv 2023年

作者： Johnson, Ruth Li, Michelle M. Noori, Ayush Queen, Owen Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Berkowitz Family Living Laboratory Harvard Medical School BostonMA02115 United States Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Harvard College CambridgeMA02138 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters or minimal to no re-training. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions. © 2023, CC BY.

关键词： Knowledge graph

Fractal geometry predicts dynamic differences in structural and functional connectomes

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arXiv 2025年

作者： Rădulescu, Anca Kaslik, Eva Fikl, Alexandru Nakuci, Johan Muldoon, Sarah Anderson, Michael Department of Mathematics SUNY New Paltz United States Department of Computer Science West University of Timișoara Romania Institute for Advanced Environmental Research West University of Timișoara Romania U.S. ARMY DEVCOM Army Research Laboratory United States Department of Mathematics Institute for Artificial Intelligence and Data Science Neuroscience Program University of Buffalo SUNY United States Department of Data Analytics Georgia Tech United States

Understanding the intricate architecture of brain networks and its connection to brain function is essential for deciphering the underlying principles of cognition and disease. While, traditional graph-theoretical measures have been widely used to characterize these networks, they often fail to fully capture the emergent properties of large-scale neural dynamics. Here, we introduce an alternative approach to quantify brain networks that is rooted in complex dynamics, fractal geometry, and asymptotic analysis. We apply these concepts to brain connectomes and demonstrate how quadratic iterations and geometric properties of Mandelbrot-like sets can provide novel insights into structural and functional network dynamics. Our findings reveal fundamental distinctions between structural (positive) and functional (signed) connectomes, such as the shift of cusp orientation and the variability in equi-M set geometry. Notably, structural connectomes exhibit more robust, predictable features, while functional connectomes show increased variability for non-trivial tasks. We further demonstrate that traditional graph-theoretical measures, when applied separately to the positive and negative sub-networks of functional connectomes, fail to fully capture their dynamic complexity. Instead, size and shape-based invariants of the equi-M set effectively differentiate between rest and emotional task states, which highlights their potential as superior markers of emergent network dynamics. These results suggest that incorporating fractal-based methods into network neuroscience provides a powerful tool for understanding how information flows in natural systems beyond static connectivity measures, while maintaining their simplicity. © 2025, CC BY.

关键词： Fractal dimension

Cholesterol allosteric modulation of the oxytocin receptor

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Biophysical Journal 2025年第12期124卷 1930-1943页

作者： Marlow, Brennica Vogel, Alexander Kuenze, Georg Pankonin, Maik Reinhardt, Franziska Stadler, Peter F. Hildebrand, Peter W. Meiler, Jens Center for Structural Biology Vanderbilt University Nashville TN United States Institute for Drug Discovery Medical Faculty Leipzig University Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Leipzig University Leipzig Germany Interdisciplinary Center for Bioinformatics Leipzig University Leipzig Germany Chemical and Physical Biology Program Vanderbilt University Nashville TN United States Department of Chemistry Vanderbilt University Nashville TN United States Institute of Medical Physics and Biophysics Medical Faculty Leipzig University Leipzig Germany Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Leipzig Germany Max-Planck-Institute for Mathematics in the Sciences Leipzig Germany Institute of Theoretical Chemistry University of Vienna Wien Austria Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Institute New Mexico Santa Fe Mexico

G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric cross talk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our molecular dynamics approach, also coincide with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand-mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration. © 2025 Biophysical Society

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Channel-Annotated Deep Learning for Enhanced Interpretability in EEG-Based Seizure Detection

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SSRN

SSRN 2024年

作者： Wong, Sheng Fung Simmons, Anj Rivera-Villicana, Jessica Barnett, Scott Sivathamboo, Shobi Perucca, Piero Ge, Zongyuan Kwan, Patrick Kuhlmann, Levin O’Brien, Terence J. Applied Artificial Intelligence Institute Deakin University BurwoodVIC Australia School of Computing Technologies RMIT University Australia Department of Neuroscience Central Clinical School Monash University MelbourneVIC Australia Department of Neurology Alfred Health MelbourneVIC Australia Department of Medicine The Royal Melbourne Hospital The University of Melbourne ParkvilleVIC Australia Department of Neurology The Royal Melbourne Hospital ParkvilleVIC Australia Epilepsy Research Centre Department of Medicine Austin Health The University of Melbourne HeidelbergVIC Australia Bladin-Berkovic Comprehensive Epilepsy Program Austin Health HeidelbergVIC Australia Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia eResearch Centre

Currently, electroencephalogram (EEG) provides critical data to support a diagnosis of epilepsy through the identification of seizure events. The review process is undertaken by clinicians or EEG specialists and is labour-intensive, especially for longterm EEG recordings. Deep learning (DL) has been proposed to automate and expedite the seizure review and annotation process, providing superior performance when compared to traditional machine learning (ML) methods. However, DL algorithms lack interpretability which is a crucial factor for clinical adoption. Consequently, the "black-box" nature of these DL algorithms limits the transparency of these algorithms, preventing clinicians from having knowledge of how the predictions are derived. In this study, we propose a novel two-block seizure detection algorithm that leverages the channel-annotation of the EEG recordings in the TUH EEG Seizure Corpus (TUSZ) based on the likelihood of seizure activities on each channel. This method allows direct interpretation of the EEG segment without requiring any further interpretability or Explainable artificial intelligence (XAI) methods during the prediction phase. Further, we adopted an interpretable method for explaining decisions made by the seizure detection algorithms, identifying channels that influence the final predictions. This DL approach utilizing CNN, transformer and MLP achieved an AUC of 0.92 for the seizure detection task, comparable with other state-of-the-art algorithms. Our algorithm was further validated on a separate continuous EEG dataset achieving an AUC of 0.82. Additionally, we also evaluated the reliability and efficacy of our XAI method on predicted seizure events, achieving a sensitivity of 0.59 in accurately localizing channels with seizure activities. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

Diffusion-Stego: Training-free diffusion generative steganography via message projection

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Information sciences 2025年 718卷

作者： Daegyu Kim Chaehun Shin Jooyoung Choi Dahuin Jung Sungroh Yoon Data Science and AI Laboratory Electrical and Computer Engineering Seoul National University Seoul 08826 Republic of Korea School of Computer Science and Engineering Soongsil University Seoul 06978 Republic of Korea Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul 08826 Republic of Korea AIIS ASRI INMC and ISRC Seoul National University Seoul 08826 Republic of Korea

Generative steganography is the process of hiding secret messages in generated images instead of cover images. Existing studies on generative steganography use GAN or Flow models to obtain high hiding message capacity and anti-detection ability over cover images. However, they create relatively unrealistic stego images because of the inherent limitations of generative models. We propose Diffusion-Stego, a generative steganography approach based on diffusion models that outperform other generative models in image generation. Diffusion-Stego projects secret messages into the latent noise of diffusion models and generates stego images with an iterative denoising process. Since the naive hiding of secret messages into noise boosts visual degradation and decreases extracted message accuracy, we introduce message projection, which hides messages into noise space while addressing these issues. We suggest three options for message projection to adjust the trade-off between extracted message accuracy, anti-detection ability, and image quality. Diffusion-Stego is a training-free approach, so we can apply it to pre-trained diffusion models that generate high-quality images, or even large-scale text-to-image models, such as Stable diffusion. Diffusion-Stego achieved a high capacity of messages as well as high quality that makes it challenging to distinguish from real images in the PNG format.

关键词： Diffusion models Generative models Generative steganography

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基于大语言模型的糖尿病管理:潜力与展望

science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance