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Antirheumatic drugs and the risk of nonalcoholic fatty liver disease in patients with rheumatoid arthritis: A nationwide, population-based cohort study

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International Journal of Rheumatic Diseases 2024年第1期27卷 e15003-e15003页

作者： Meng, Chia-Chu Chen, Der-Yuan Chen, Yi-Hsing Huang, Wen-Nan Chen, Hsin-Hua Division of Allergy Immunology and Rheumatology Taichung Veterans General Hospital Taichung Taiwan Program in Translational Medicine Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung Taiwan Rheumatology and Immunology Center China Medical University Hospital Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Faculty of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan College of Business and Management Ling Tung University Taichung Taiwan Division of General Medicine Department of Medicine Taichung Veterans General Hospital Taichung Taiwan Program in Translational Medicine and Rong Hsing Research Center for Translational Medi-cine National Chung Hsing University Taichung Taiwan Big Data Center National Chung Hsing University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan

Objectives: To assess the association between antirheumatic drugs and of the risk of nonalcoholic fatty liver disease (NAFLD) in a nationwide rheumatoid arthritis (RA) cohort. Methods: Using claim data from the 2000–2020 National Health Insurance Research database, we identified 21 457 incident patients with RA from 2002 to 2020 without prior liver diseases. A time-varying multivariable Cox regression model was applied to estimate for the association of NAFLD with the use of antirheumatic drugs after adjusting potential confounders, show as adjusted hazard ratios (aHRs) with 95% confidence interval (CIs). Subgroup analyses were conducted based on age-, sex-, and obesity-related comorbidities. Results: Multivariable time-dependent Cox regression analyses showed that defined daily dose (DDD) of NSAID (aHR, 1.03;95% CI: 1.02–1.05) and prednisolone equivalent dose >5 mg/day (aHR, 2.39;95% CI: 1.85–3.09) were risk factors of NAFLD in patients with RA, while prednisolone equivalent dose ≤5 mg/day (aHR of 0.53;95% CI: 0.40–0.71) and HCQ use (aHR of 0.75;95% CI: 0.60–0.93) were associated with a decreased risk of NAFLD. In addition, a history of hospitalizations, number of outpatient visits, age, male, and leflunomide use were associated with the development of NAFLD in some subgroups. Conclusion: This study reveals that NSAID use and prednisolone equivalent dose >5 mg/day were associated with an increased risk of NAFLD in patients with RA, while the use of HCQ and prednisolone equivalent dose ≤5 mg/day decreased the risk of NAFLD. © 2023 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

关键词： incidence medication non-alcoholic fatty liver disease rheumatoid arthritis

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3D Liver and Tumor Segmentation in CT Scans with Robust Self-Paced Learning

3D Liver and Tumor Segmentation in CT Scans with Robust Self...

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International Conference on Information Science and Control engineering (ICISCE)

作者： Obed Tettey Nartey Guowu Yang Sarpong Kwadwo Asare Jinzhao Wu Dorothy Araba Yakoba Agyapong Lady Nadia Frempong Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Mathematics and Physics Guangxi University for Nationalities Nanning China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana

Liver segmentation is a challenging problem where fully supervised deep learning models require large amounts of voxel-wise labels, which are usually laborious, expensive, and time consuming to obtain. However, massive non-annotated 3D CT volumes are usually easily accessible. Most deep learning based works to address semi-supervised CT medical image segmentation problems have mostly been graph-based or adversarial-based. In sharp contrast, this work investigates the potential of a semi-supervised learning method; novel self-training framework utilizing self-paced learning strategy to generate and select pseudo-labels from easy-to-transfer(well represented classes) to hard-to-transfer(less represented classes) samples. The proposed scheme introduces an effective pseudo-label generation and selection algorithm to generate highly confident pseudo-labels, jointly learn and optimize a model which can be solved by alternatively generating pseudo labels on unlabeled target data and re-training the model with the generated labels. Further, we also propose a novel class balancing framework to avoid the gradual eminence of easy-to-transfer discriminative features on pseudo-label generation utilizing class-wise normalization of confidence scores. Comprehensive experiments conducted on publicly available datasets show that the proposed method outperformed other state-of-the-art semi-supervised and some fully supervised methods on the segmentation results of tumors and achieved very competitive performance for liver segmentation.

关键词： Training Deep learning Image segmentation Information science Three-dimensional displays Computed tomography Liver

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Abnormal Scanning Patterns Based on Eye Movement Entropy in Early Psychosis

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2024年

作者： Zhang, Dan Ma, Chunyan Xu, Lihua Liu, Xu Cui, Huiru Wei, Yanyan Zheng, Wensi Hong, Yawen Xie, Yuou Qian, Zhenying Hu, Yegang Tang, Yingying Li, Chunbo Liu, Zhi Chen, Tao Liu, Haichun Zhang, Tianhong Wang, Jijun Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine People's Republic of China Shanghai China First Clinical Medical College of Nanjing Medical University People's Republic of China Nanjing China Shanghai Institute for Advanced Communication and Data Science Shanghai University People's Republic of China Shanghai China School of Communication and Information Engineering Shanghai University People's Republic of China Shanghai China Labor and Worklife Program Harvard University Cambridge Massachusetts United States Big Data Research Laboratory University of Waterloo Waterloo ON Canada Niacin (Shanghai) Technology Co. Ltd. People's Republic of China Shanghai China Department of Automation Shanghai Jiao Tong University People's Republic of China Shanghai China Shanghai Mental Health Center Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai Jiaotong University School of Medicine People's Republic of China Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences People's Republic of China Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University People's Republic of China Shanghai China

Background: Restricted scan path mode is hypothesized to explain abnormal scanning patterns in patients with schizophrenia. Here, we calculated entropy scores (drawing on gaze data to measure the statistical randomness of eye movements) to quantify how strategical and random participants were when processing image stimuli. Methods: Eighty-six patients with first-episode schizophrenia (FES), 124 individuals at clinical high risk (CHR) for psychosis, and 115 healthy control participants (HCs) completed an eye-tracking examination while freely viewing 35 static images (each presented for 10 seconds) and cognitive assessments. We compared group differences in the overall entropy score, as well as entropy scores under various conditions. We also investigated the correlations between entropy scores and symptoms and cognitive function. Results: Increased overall entropy scores were noted in the FES and CHR groups compared with the HC group, and these differences were already apparent within 0 to 2.5 seconds. In addition, the CHR group exhibited higher entropy than the HC group when viewing low-meaning images. Moreover, the entropy within 0 to 2.5 seconds showed significant correlations with negative symptoms in the FES group, attention/vigilance scores in the CHR group, and speed of processing and attention/vigilance scores across all 3 groups. Conclusions: The results indicate that individuals with FES and those at CHR scanned pictures more randomly and less strategically than HCs. These patterns also correlated with clinical symptoms and neurocognition. The current study highlights the potential of the eye movement entropy measure as a neurophysiological marker for early psychosis. © 2024 Society of Biological Psychiatry

关键词： Biomarker Clinical high risk for psychosis Cognitive impairments Early psychosis Eye movement Schizophrenia

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Comparative Genome Analysis of Three Halobacillus Strains Isolated From Saline Environments Reveal Potential Salt Tolerance and Algicidal Mechanisms

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Environmental microbiology reports 2025年第3期17卷 e70121页

作者： Saru Gurung Chang-Muk Lee Hang-Yeon Weon So-Ra Han Tae-Jin Oh Department of Life Science and Biochemical Engineering SunMoon University Asan Republic of Korea. Bio Big Data-Based Chungnam Smart Clean Research Leader Training Program SunMoon University Asan Republic of Korea. Agricultural Microbiology Division National Institute of Agricultural Sciences Rural Development Administration Jeonju Republic of Korea. Technology Services Division National Institute of Agricultural Sciences Rural Development Administration Jeonju Republic of Korea. Genome-Based BioIT Convergence Institute Asan Republic of Korea. Department of Pharmaceutical Engineering and Biotechnology SunMoon University Asan Republic of Korea.

Harmful algal blooms (HABs) pose a significant global threat to water ecosystems, prompting extensive research into their inhibition and control strategies. This study presents genomic and bioinformatic analyses to investigate the algicidal potential and elucidate the survival mechanisms in harsh conditions of newly identified Halobacillus species three strains (SSTM10-2, SSBR10-3, and SSHM10-5) isolated from saline environments. Moreover, genomic and bioinformatic analyses were conducted to elucidate their survival mechanisms in harsh conditions. Moreover, comparative genomic analysis revealed a diverse set of orthologous genes, with a core genome primarily associated with metabolism and information processing. Pangenome analysis highlighted accessory and unique genes potentially involved in environmental adaptation and stress response. Functional annotation using KEGG pathways identified genes linked to xenobiotic compound degradation, stress tolerance, and salt adaptation. Additionally, the study elucidated potential mechanisms underlying algicidal activity, implicating Carbohydrate-Active enZYmes (CAZymes), cytochrome P450 oxidases (CYP), and quorum sensing (QS) systems. Finally, analysis of KEGG pathways related to microcystin degradation suggested the strains' capacity to mitigate HABs. Thus, this research enhances understanding of the genomic diversity, phylogeny, and functional characteristics of Halobacillus species, offering insights into their ecological roles and potential applications in biotechnology and environmental management.

关键词： Halobacillus sp. algicidal activity comparative genome analysis halotolerance stress response

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Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation

arXiv

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arXiv 2025年

作者： Chen, Samuel Yen-Chi Liu, Chen-Yu Chen, Kuan-Cheng Huang, Wei-Jia Chang, Yen-Jui Huang, Wei-Hao Wells Fargo New YorkNY United States Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom Research Institute Taipei Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan Jij inc. Tokyo Japan

The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational quantum circuits (VQCs), also known as quantum neural networks (QNNs), have shown promise both empirically and theoretically. However, their broader adoption is hindered by reliance on quantum hardware during inference. Hardware imperfections and limited access to quantum devices pose practical challenges. To address this, the Quantum-Train (QT) framework leverages the exponential scaling of quantum amplitudes to generate classical neural network parameters, enabling inference without quantum hardware and achieving significant parameter compression. Yet, designing effective quantum circuit architectures for such quantum-enhanced neural programmers remains non-trivial and often requires expertise in quantum information science. In this paper, we propose an automated solution using differentiable optimization. Our method jointly optimizes both conventional circuit parameters and architectural parameters in an end-to-end manner via automatic differentiation. We evaluate the proposed framework on classification, time-series prediction, and reinforcement learning tasks. Simulation results show that our method matches or outperforms manually designed QNN architectures. This work offers a scalable and automated pathway for designing QNNs that can generate classical neural network parameters across diverse applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Comment on: Incidence and Risk Factors of Mental Illnesses Among Patients with Systemic Autoimmune Rheumatic Diseases: An 18-year population-based study: Reply

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Rheumatology (Oxford, England) 2025年第3期64卷 1563-1564页

作者： Wei-Min Chu Wen-Cheng Chao Der-Yuan Chen Wei-Li Ho Hsin-Hua Chen Department of Family Medicine Taichung Veterans General Hospital Taichung Taiwan. School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan. Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan. Geriatrics and Gerontology Research Center College of Medicine National Chung Hsing University Taichung Taiwan. School of Medicine Chung Shan Medical University Taichung Taiwan. Department of Critical Care Medicine Taichung Veterans General Hospital Taichung Taiwan. Big Data Center National Chung Hsing University Taichung Taiwan. Department of Automatic Control Engineering Feng Chia University Taichung Taiwan. Deparment of Rheumatology and Immunology China Medical University Hospital Taichung Taiwan. College of Medicine China Medical University Taichung Taiwan. Division of Allergy Immunology and Rheumatology Chiayi Branch Taichung Veterans General Hospital Chiayi Taiwan. Division of General Medicine Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan. Division of Allergy Immunology and Rheumatology Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan. Ph.D Program in Translational Medicine College of Life Sciences Chung Hsing University Taichung Taiwan.

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DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models

arXiv

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arXiv 2021年

作者： Lu, Denghui Jiang, Wanrun Chen, Yixiao Zhang, Linfeng Jia, Weile Wang, Han Chen, Mohan HEDPS CAPT College of Engineering Peking University Beijing100871 China Songshan Lake Materials Laboratory Guangdong Dongguan523808 China Institute of Physics Chinese Academy of Sciences Beijing100190 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Beijing Institute of Big Data Research Beijing100871 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China

Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning based PES model. This scheme, we call DP Compress, is an efficient post-processing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster, and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online. Copyright © 2021, The Authors. All rights reserved.

关键词： Molecular dynamics

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TOP: An Efficient Two-Levels of Positive Resampling Framework for Class Imbalanced data

TOP: An Efficient Two-Levels of Positive Resampling Framewor...

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big data, Cloud Computing, data Science & engineering (BCD)

作者： Nathaniel Netirungroj Eakasit Pacharawongsakda Big Data Engineering program Dhurakij Pundit University Bangkok Thailand

In real-world applications such as fraud detection, target class values have an unequal size. This problem is called class-imbalanced data. Many strategies have been proposed to deal with this situation. Most of them focused on changing the data characteristics. For example, adjusting class distribution is one of the most popular approaches for this matter. In this work, we proposed TOP (TwO-levels of Positive resampling framework), an alternative framework to resolve such a problem. Our technique exploits DBSCAN mechanism and other resampling algorithms in order to maximize classification performance. It is able to dynamically draw two boundaries that represent similarity level between consideration positive and other instances. Many possible resampling techniques such as undersampling or over-sampling are allowed to perform inside those areas. We benchmarked TOP with three types of resampling techniques including over-sampling, down-sampling, and hybrid sampling by training eleven machine learning algorithms on fifteen datasets. As a result, our technique outperformed other techniques in several evaluation metrics.

关键词： Measurement Radio frequency Clustering algorithms Machine learning algorithms Machine learning big data Feature extraction

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Comment on: The risk of major adverse cardiovascular events in patients with systemic sclerosis: a nationwide, population-based cohort study. Reply

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Rheumatology (Oxford, England) 2024年第8期63卷 e234-e235页

作者： Tsai-Hung Yen Hsin-Hua Chen Division of Allergy Immunology and Rheumatology Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan. Division of General Medicine Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan. Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan. Big Data Center National Chung Hsing University Taichung Taiwan. Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan. PhD Program in Translational Medicine and Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung Taiwan.

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基于大语言模型的糖尿病管理:潜力与展望

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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

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