检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Ozyoruk, Kutsev Bengisu Can, Sermet Gokceler, Guliz Irem Başak, Kayhan Demir, Derya Serin, Gurdeniz Hacisalihoglu, Uguray Payam Kurtuluş, Emirhan Darbaz, Berkan Lu, Ming Y. Chen, Tiffany Y. Williamson, Drew F.K. Yilmaz, Funda Mahmood, Faisal Turan, Mehmet Institute of Biomedical Engineering Bogazici University Istanbul Turkey Uk Biocentre Tilbrook United Kingdom Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and Mit CambradgeMA United States Saǧlik Bilimleri University Kartal Dr.Lütfi Kirdar City Hospital Department of Pathology Istanbul Turkey Faculty of Medicine Department of Pathology Ege University Izmir Turkey Istanbul Yeni Yuzyil University Medical Faculty Gaziosmanpasa Hospital Pathology Department Turkey

Frozen sectioning (FS) is the preparation method of choice for microscopic evaluation of tissues during surgical operations. The high speed of procedure allows pathologists to rapidly assess the key microscopic features, such as tumor margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation. However, FS is prone to introducing many misleading artificial structures (histological artefacts), such as nuclear ice crystals, compression, and cutting artefacts, hindering timely and accurate diagnostic judgement of the pathologist. Additional training and prolonged experience is often required to make highly effective and time critical diagnosis on frozen sections. On the other hand, the gold standard tissue preparation technique of formalin-fixation and paraffin-embedding (FFPE) provides significantly superior image quality, but is a very time-consuming process (12-48 hours), making it unsuitable for intra-operative use. In this paper, we propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes. AI-FFPE rectifies FS artefacts with the guidance of an attention-mechanism that puts a particular emphasis on artefacts while utilising a self-regularization mechanism established between FS input image and synthesized FFPE-style image that preserves clinically relevant features. As a result, AI-FFPE method successfully generates FFPE-style images without significantly extending tissue processing time and consequently improves diagnostic accuracy. We demonstrate the efficacy of AI-FFPE on lung and brain frozen sections using a variety of different qualitative and quantitative metrics including visual Turing tests from 20 board certified pathologists. Additionally, we demonstrate that deep models trained on FFPE tissue can adapt to frozen sections which a much higher efficacy whe

关键词： Surgery

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A hypoxia-activated copper ion nanoexchanger for cancer immunotherapy by in situ precise production of immunogenic cell death inducer

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Chinese Chemical Letters 2025年

作者： Xinlu Zhang Yongxin Liu Huan Li Shutong Chen Guocheng Wang Xu Zhang Chen Cao Xiaoyuan Chen Sheng Wang School of Life Sciences Faculty of Medicine Tianjin University Tianjin 300072 China Department of Diagnostic Radiology Yong Loo Lin School of Medicine National University of Singapore Singapore 119074 Singapore Department of Chemical and Biomolecular Engineering College of Design and Engineering National University of Singapore Singapore 117575 Singapore Department of Biomedical Engineering College of Design and Engineering National University of Singapore Singapore 117575 Singapore Department of Pharmacy and Pharmaceutical Sciences Faculty of Science National University of Singapore Singapore 117544 Singapore Clinical Imaging Research Centre Centre for Translational Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore Nanomedicine Translational Research Program Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore Theranostics Center of Excellence (TCE) Yong Loo Lin School of Medicine National University of Singapore Singapore 138667 Singapore Institute of Molecular and Cell Biology Agency for Science Technology and Research (A*STAR) Singapore 138673 Singapore

In situ therapeutic agent production strategy is promising to overcome the drawbacks of direct drug delivery. Hypoxia provides a great target for precise treatment of tumor. Here we report a copper ion competition-based nanoparticle (NP) for hypoxia-activated formation of diethyldithiocarbamate (DTC)-copper complex, an immunogenic cell death (ICD) inducer. The NP is composed of an amphiphilic hypoxia-responsive DTC precursor and a fluorescence quenched copper ion-chelated squaric acid. In hypoxic tumor cells, the azobenzene linker in DTC precursor can be cleaved through bioreduction, leading to DTC release and subsequent copper ion exchange between DTC and squaric acid. Simultaneous formation of toxic DTC-copper complexes and fluorescence recovery will allow for visualization of in situ therapeutic agents production. Furthermore, the DTC-copper complexes can induce ICD and promote cytotoxic T lymphocyte infiltration for cancer immunotherapy. This study not only provides a promising hypoxia-activated nanomedicine for precision cancer therapy, but also a visualization strategy for evaluating the treatment process.

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Simulation and computational analysis of fire sprinkle bulb activation with variation of liquid and wall thickness

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AIP Conference Proceedings 2020年第1期2314卷

作者： Alfian Pramudita Putra Erik Jan Westerman Akif Rahmatillah 1Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia 2Department of Biomedical Engineering Faculty of Science and Engineering University of Groningen Groningen The Netherlands

Fire sprinkler is a safety system that is active when fire is detected. It consists of a bulb that holds the water flow and made of glass with liquid inside. The increase of temperature in its environment would expand the volume of the liquid and break the glass which leads to water spray to extinguish the fire. This study aims to model and simulate the fire sprinkler bulb and optimize several parameters related to its activation including the thickness of the bulb, type of liquid and the ratio of air and liquid inside the bulb. The modeling and simulation was performed in COMSOL Multiphysics with variation of wall thickness and liquid inside the bulb. The result showed that the best parameter was the liquid of glycerol, a height of 17.5 mm and a glass thickness of 0.1 mm, this resulted in the air to liquid ratio of 0.0042428 and breaking time of 7.2400s. This result was the fastest breaking time towards several simulations. For future study, several parameters need to be considered in the simulation to minimize the gap between the simulation and the actual condition, such as room condition, and water pressure.

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Exploring the effect of image enhancement techniques on COVID‐19 detection using chest X‐rays images

arXiv

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arXiv 2020年

作者： Rahman, Tawsifur Khandakar, Amith Qiblawey, Yazan Tahir, Anas Kiranyaz, Serkan Kashem, Saad Bin Abul Islam, Mohammad Tariqul Maadeed, Somaya Al Zughaier, Susu M. Khan, Muhammad Salman Chowdhury, Muhammad E.H. Department of Biomedical Physics & Technology University of Dhaka Dhaka 1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Robotics and Advanced Computing Qatar Armed Forces‐Academic Bridge Program Qatar Foundation Doha24404 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine Biomedical and Pharmaceutical Research Unit QU Health Qatar University Doha2713 Qatar University of Engineering and Technology Peshawar Pakistan

The use of computer-aided diagnosis in the reliable and fast detection of corona virus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. CXR images are sometimes of poor quality and so image enhancement techniques can help the machine learning models to extract valuable discriminating features from the image. Numerous works have been reported on COVID-19 detection from smaller set of original X-ray images. However, the effect of image enhancement in COVID-19 detection was not reported in the literature. This paper explores the effect of various popular image enhancement techniques and states the © 2020, CC BY.

关键词： Image enhancement

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Dietary carbohydrates alter immune-modulatory functionalities and DNA inversions in Bacteroides thetaiotaomicron

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Nature communications 2025年第1期16卷 4938页

作者： Noa Gal-Mandelbaum Shaqed Carasso Alon Kedem Tamar Ziv Roni Keshet-David Roberto Abboud Rawan Zaatry Tal Gefen Naama Geva-Zatorsky Department of Cell Biology and Cancer Science Rappaport Faculty of Medicine and Research Institute Rappaport Technion Integrated Cancer Center (RTICC) Technion-Israel Institute of Technology Haifa Israel. Smoler Proteomics Center Lokey Interdisciplinary Center for Life Sciences & Engineering Technion-Israel Institute of Technology Haifa Israel. Department of Cell Biology and Cancer Science Rappaport Faculty of Medicine and Research Institute Rappaport Technion Integrated Cancer Center (RTICC) Technion-Israel Institute of Technology Haifa Israel. naamagvz@***. CIFAR Humans & the Microbiome program CIFAR Toronto Canada. naamagvz@***.

The gut bacteria environment is highly dynamic. Environmental conditions were shown to affect microbial composition. Yet, their influences on bacterial functionality (e.g., immune-modulation activity) are mostly overlooked. Distinct strains of the same species, and even the same bacterial strain, may have different effects on the immune system depending on their growth environment. Therefore, studying the functionality of strains under different conditions is crucial. We analyzed functional alterations in the gut symbiont Bacteroides thetaiotaomicron (B. theta) under different dietary components consumption in humans, upon white sugar consumption in mice, and in response to 190 different carbon sources in vitro. Dietary alterations affected the orientation of phase variable regions in B. theta in humans, in vivo, and in vitro, and altered B. theta's proteome and immune-modulatory functionality. Studying the effects of dietary components on the immune-modulatory functionalities of key members of the gut microbiota will allow for personalized dietary recommendations.

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Simulating Cardiac Fluid Dynamics in the Human Heart

arXiv

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arXiv 2023年

作者： Davey, Marshall Puelz, Charles Rossi, Simone Smith, Margaret Anne Wells, David R. Sturgeon, Gregory M. Segars, W. Paul Vavalle, John P. Peskin, Charles S. Griffith, Boyce E. University of North Carolina Chapel HillNC United States Department of Pediatrics-Cardiology Baylor College of Medicine Texas Children’s Hospital HoustonTX United States Department of Mathematics University North Carolina Chapel HillNC United States Department of Radiology Duke University Medical Center DurhamNC United States Division of Cardiology Department of Medicine University of North Carolina School of Medicine Chapel HillNC United States Courant Institute of Mathematical Sciences New York University New YorkNY United States Departments of Mathematics and Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina School of Medicine Chapel HillNC United States McAllister Heart Institute University of North Carolina School of Medicine Chapel HillNC United States

Cardiac fluid dynamics fundamentally involves interactions between complex blood flows and the structural deformations of the muscular heart walls and the thin, flexible valve leaflets. There has been longstanding scientific, engineering, and medical interest in creating mathematical models of the heart that capture, explain, and predict these fluid-structure interactions. However, existing computational models that account for interactions among the blood, the actively contracting myocardium, and the cardiac valves are limited in their abilities to predict valve performance, resolve fine-scale flow features, or use realistic descriptions of tissue biomechanics. Here we introduce and benchmark a comprehensive mathematical model of cardiac fluid dynamics in the human heart. A unique feature of our model is that it incorporates biomechanically detailed descriptions of all major cardiac structures that are calibrated using tensile tests of human tissue specimens to reflect the heart’s microstructure. Further, it is the first fluid-structure interaction model of the heart that provides anatomically and physiologically detailed representations of all four cardiac valves. We demonstrate that this integrative model generates physiologic dynamics, including realistic pressure-volume loops that automatically capture isovolumetric contraction and relaxation, and predicts fine-scale flow features. None of these outputs are prescribed;instead, they emerge from interactions within our comprehensive description of cardiac physiology. Such models can serve as tools for predicting the impacts of medical devices or clinical interventions. They also can serve as platforms for mechanistic studies of cardiac pathophysiology and dysfunction, including congenital defects, cardiomyopathies, and heart failure, that are difficult or impossible to perform in patients. Copyright © 2023, The Authors. All rights reserved.

关键词： Heart

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The doctor will polygraph you now

Npj health systems

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Npj health systems 2024年第1期1卷 1页

作者： James Anibal Jasmine Gunkel Shaheen Awan Hannah Huth Hang Nguyen Tram Le Jean-Christophe Bélisle-Pipon Micah Boyer Lindsey Hazen Yael Bensoussan David Clifton Bradford Wood Center for Interventional Oncology Clinical Center National Institutes of Health (NIH) Bethesda MD USA. Computational Health Informatics Lab Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford UK. Department of Bioethics National Institutes of Health (NIH) Bethesda MD USA. Department of Communication Sciences & Disorders University of Central Florida Orlando FL USA. Global Infectious Disease Program Georgetown University Washington DC USA. College of Engineering University of South Florida Tampa FL USA. Faculty of Health Sciences Simon Fraser University Burnaby BC Canada. USF Health Voice Center Department of Otolaryngology-Head & Neck Surgery University of South Florida Tampa FL USA.

Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors that could be reasonably understood from patient-reported information. This raises novel ethical concerns about respect, privacy, and control over patient data. Ethical concerns surrounding clinical AI systems for social behavior verification can be divided into two main categories: (1) the potential for inaccuracies/biases within such systems, and (2) the impact on trust in patient-provider relationships with the introduction of automated AI systems for "fact-checking", particularly in cases where the data/models may contradict the patient. Additionally, this report simulated the misuse of a verification system using patient voice samples and identified a potential LLM bias against patient-reported information in favor of multi-dimensional data and the outputs of other AI methods (i.e., "AI self-trust"). Finally, recommendations were presented for mitigating the risk that AI verification methods will cause harm to patients or undermine the purpose of the healthcare system.

关键词： Ethics Machine learning Science, technology and society

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Effect modification by cardiovascular and metabolic disease onset on long-term PM2.5 exposure and mortality: a nationwide cohort study

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Scientific reports 2025年第1期15卷 19570页

作者： Hyemin Jang Jinah Park Eun Soo Lee Dongmug Kang Jeongmin Moon Insung Song Seoyeong Ahn Ayoung Kim Cinoo Kang Jieun Oh Dohoon Kwon Jieun Min Ejin Kim Michelle L Bell Ho Kim Whanhee Lee Department of Public Health Sciences Graduate School of Public health Seoul National University Seoul South Korea. Department of Occupational and Environmental Medicine Pusan National University Yangsan Hospital Yangsan South Korea. Department of Preventive and Occupational & Environmental Medicine School of Medicine Pusan National University Yangsan South Korea. School of Biomedical Convergence Engineering College of Information and Biomedical Engineering Pusan National University Yangsan South Korea. Department of Environmental Medicine College of Medicine Ewha Womans University Seoul South Korea. Institute of Health and Environment Graduate School of Public Health Seoul National University Seoul South Korea. School of the Environment Yale University New Haven CT USA. Interdisciplinary Program in Precision Public Health Department of Public Health Sciences Graduate School of Korea University Seoul South Korea. Department of Public Health Sciences Graduate School of Public health Seoul National University Seoul South Korea. hokim@snu.ac.kr. School of Biomedical Convergence Engineering College of Information and Biomedical Engineering Pusan National University Yangsan South Korea. whanhee.lee@pusan.ac.kr. Research and Management Center for Health Risk of Particulate Matter Seoul South Korea. whanhee.lee@pusan.ac.kr.

Cardiovascular and metabolic diseases-specifically myocardial infarction, congestive heart failure, cerebrovascular disease, peripheral vascular disease, renal disease, and liver disease-are major public health concerns worldwide. However, studies examining how these diseases modify the relationship between long-term PM exposure and mortality remain scarce. Using a Cox regression model with a population-representative cohort from South Korea, we estimated the modifying effects of six major cardiovascular and metabolic diseases on PM-mortality association in two cohorts: (1) individuals without underlying diseases (NoUD) and (2) those with only hypertension and/or diabetes (HTN/DM). The interaction between PM and each disease onset was used to estimate effect modification, with results presented as relative hazard ratios (RHRs) per 10 µg/m increase in PM. Among 183,834 subjects, non-accidental deaths occurred in 18.0% of the NoUD (N = 134,584) and 25.7% of the HTN/DM (N = 49,250). In the NoUD cohort, renal disease (RHR: 1.58; 95% CI: 1.27-1.97), myocardial infarction (1.41; 1.15-1.73), and liver disease (1.40; 1.25-1.57) significantly modified the effect of PM on mortality. In the HTN/DM cohort, renal disease (1.74; 1.43-2.12), myocardial infarction (1.62; 1.29-2.05), and cerebrovascular disease (1.32; 1.17-1.51) showed higher RHRs. Our findings highlight the importance of consistent and preemptive care for renal disease and myocardial infarction and provide evidence for target-specific interventions to reduce the risk of PM on mortality.

关键词： Cardiovascular disease Metabolic disease Mortality Myocardial infarction Particulate matter Renal disease

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On the Lagrangian-Eulerian Coupling in the Immersed Finite Element/Difference Method

arXiv

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arXiv 2021年

作者： Lee, Jae H. Griffith, Boyce E. Department of Mathematics University of North Carolina Chapel HillNC United States Department of Mechanical Engineering Johns Hopkins University BaltimoreMD United States Institute for Computational Medicine Johns Hopkins University BaltimoreMD United States Departments of Mathematics Applied Physical Sciences and Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina School of Medicine Chapel HillNC United States McAllister Heart Institute University of North Carolina School of Medicine Chapel HillNC United States

The immersed boundary (IB) method is a non-body conforming approach to fluid-structure interaction (FSI) that uses an Eulerian description of the momentum, viscosity, and incompressibility of a coupled fluid-structure system and a Lagrangian description of the deformations, stresses, and resultant forces of the immersed structure. Integral transforms with Dirac delta function kernels couple the Eulerian and Lagrangian variables, and in practice, discretizations of these integral transforms use regularized delta function kernels. Many different kernel functions have been proposed, but prior numerical work investigating the impact of the choice of kernel function on the accuracy of the methodology has often been limited to simplified test cases or Stokes flow conditions that may not reflect the method’s performance in applications, particularly at intermediate-to-high Reynolds numbers, or under different loading conditions. This work systematically studies the effect of the choice of regularized delta function in several fluid-structure interaction benchmark tests using the immersed finite element/difference (IFED) method, which is an extension of the IB method that uses a finite element structural discretizations combined with a Cartesian grid finite difference method for the incompressible Navier-Stokes equations. Whereas the conventional IB method spreads forces from the nodes of the structural mesh and interpolates velocities to those nodes, the IFED formulation evaluates the regularized delta function on a collection of interaction points that can be chosen to be denser than the nodes of the Lagrangian mesh. This opens the possibility of using structural discretizations with wide node spacings that would produce gaps in the Eulerian force in nodally coupled schemes (e.g., if the node spacing is comparable to or broader than the support of the regularized delta function). Earlier work with this methodology suggested that such coarse structural meshes can yield imp

关键词： Fluid structure interaction

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Radiomics and deep learning characterisation of liver malignancies in CT images - A systematic review

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Computers in biology and medicine 2025年 194卷 110491页

作者： Bappah Suleiman Yahaya Noor Diyana Osman Noor Khairiah A Karim Gokula Kumar Appalanaido Iza Sazanita Isa Department of Biomedical Imaging Advanced Medical and Dental Institute Universiti Sains Malaysia Kepala Batas Pulau Penang 13200 Malaysia Department of Radiography and Radiation Sciences Faculty of Allied Health Sciences Federal University of Health Sciences Azare Nigeria. Advanced Management of Liver Malignancies Research Program Advanced Medical and Dental Institute Universiti Sains Malaysia Penang Malaysia. Electronic address: noordiyana@usm.my. Advanced Management of Liver Malignancies Research Program Advanced Medical and Dental Institute Universiti Sains Malaysia Penang Malaysia. Faculty of Electrical Engineering Universiti Teknologi MARA Penang Campus Permatang Pauh Pulau Pinang 13500 Malaysia.

BACKGROUND:Computed tomography (CT) has been widely used as an effective tool for liver imaging due to its high spatial resolution, and ability to differentiate tissue densities, which contributing to comprehensive image analysis. Recent advancements in artificial intelligence (AI) promoted the role of Machine Learning (ML) in managing liver cancers by predicting or classifying tumours using mathematical algorithms. Deep learning (DL), a subset of ML, expanded these capabilities through convolutional neural networks (CNN) that analyse large data automatically. This review examines methods, achievements, limitations, and performance outcomes of ML-based radiomics and DL models for liver malignancies from CT imaging. METHOD:A systematic search for full-text articles in English on CT radiomics and DL in liver cancer analysis was conducted in PubMed, Scopus, Science Citation Index, and Cochrane Library databases between 2020 and 2024 using the keywords; machine learning, radiomics, deep learning, computed tomography, liver cancer and associated MESH terms. PRISMA guidelines were used to identify and screen studies for inclusion. RESULTS:A total of 49 studies were included consisting of 17 Radiomics, 24 DL, and 8 combined DL/Radiomics studies. Radiomics has been predominantly utilised for predictive analysis, while DL has been extensively applied to automatic liver and tumour segmentation with a surge of a recent increase in studies integrating both techniques. CONCLUSION:Despite the growing popularity of DL methods, classical radiomics models are still relevant and often preferred over DL methods when performance is similar, due to lower computational and data needs. Performance of models keep improving, but challenges like data scarcity and lack of standardised protocols persists.

关键词： Computed tomography Deep learning Liver cancer Machine learning Radiomics Segmentation

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