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第十四届全国免疫学学术大会

作者： Songrong Wang Rendong Hu Min Ma Xing You Haiyan Cui Yi He Damo Xu Zhibin Zhao Selmi Carlo Gershwin M.Eric Liang Li Zhexiong Lian Chronic Disease Laboratory School of Medicine South China University of Technology School of Biomedical Sciences and Engineering South China University of Technology Guangzhou International Campus Department of Respiratory and Critical Care Medicine The Third Affiliated Hospital of Southern Medical University Department of Rheumatology and Immunology The Third Affiliated Hospital Southern Medical University State Key Laboratory of Respiratory Disease for Allergy at Shenzhen University Shenzhen Key Laboratory of Allergy & Immunology Shenzhen University School of Medicine Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Rheumatology and Clinical Immunology Humanitas Clinical and Research Center-IRCCS Department of Biomedical Sciences Humanitas University Division of Rheumatology Allergy and Clinical Immunology University of California at Davis School of Medicine National Engineering Research Center for Tissue Restoration and Reconstruction South China University of Technology Key Laboratory of Biomedical Engineering of Guangdong Province South China University of Technology Key Laboratory of Biomedical Materials and Engineering of the Ministry of Education South China University of Technology

IL-10-producing B cells(B10) are involved in the prevention of autoimmunity and allergy but mechanisms remain poorly understood. We took advantage of the ovalbumin-induced asthma mouse model to demonstrate that the activity of FoxO1 is upregulated in lung B cells and correlates inversely with B10 cells while showing a decreased activity in ex vivoandin vitroinduced B10 cells. We further observed that FoxO1 deficiency leads to increased B10 cells. These observations have in vivoclinical evidence, as the B cell specific FoxO1 deficiency leads to reduced lung eosinophils and asthma remission in the mouse and there are reduced regulatory B cells and increased FoxO1 activity in B cells of patients with asthma. Single cell RNA-sequencing data demonstrated a negative correlation between the expression of Foxo1and Il-10in B cells from the mouse spleen and lung and the human lung. For biology mechanism, FoxO1 inhibits B10 cell differentiation through inhibiting the expression of Prdm1, which encodes Blimp-1, a transcription factor of B10 cells. Our experimental evidence in both murine and human asthma demonstrates that FoxO1 is a negative regulator of B10 cell differentiation via negatively regulating prdm1 and its expression in B cells contributes to promote allergic disease.

关键词： FoxO1 B10 Prdm1 Asthma IL-10

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A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imaging

arXiv

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arXiv 2024年

作者： Dong, Siyuan Cai, Zhuotong Hangel, Gilbert Bogner, Wolfgang Widhalm, Georg Huang, Yaqing Liang, Qinghao You, Chenyu Kumaragamage, Chathura Fulbright, Robert K. Mahajan, Amit Karbasi, Amin Onofrey, John A. de Graaf, Robin A. Duncan, James S. Department of Electrical Engineering Yale University New HavenCT United States Department of Biomedical Engineering Yale University New HavenCT United States Department of Biomedical Imaging and Image-Guided Therapy Highfield MR Center Medical University of Vienna Vienna Austria Department of Neurosurgery Medical University of Vienna Vienna Austria Department of Pathology Yale University New HavenCT United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases, cancers and diabetes. High spatial resolution MRSI is needed to characterize lesions, but in practice MRSI is acquired at low resolution due to time and sensitivity restrictions caused by the low metabolite concentrations. Therefore, there is an imperative need for a post-processing approach to generate high-resolution MRSI from low-resolution data that can be acquired fast and with high sensitivity. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but they still have limited capability to generate accurate and high-quality images. Recently, diffusion models have demonstrated superior learning capability than other generative models in various tasks, but sampling from diffusion models requires iterating through a large number of diffusion steps, which is time-consuming. This work introduces a Flow-based Truncated Denoising Diffusion Model (FTDDM) for super-resolution MRSI, which shortens the diffusion process by truncating the diffusion chain, and the truncated steps are estimated using a normalizing flow-based network. The network is conditioned on upscaling factors to enable multi-scale super-resolution. To train and evaluate the deep learning models, we developed a 1H-MRSI dataset acquired from 25 high-grade glioma patients. We demonstrate that FTDDM outperforms existing generative models while speeding up the sampling process by over 9-fold compared to the baseline diffusion model. Neuroradiologists’ evaluations confirmed the clinical advantages of our method, which also supports uncertainty estimation and sharpness adjustment extending its potential clinical applications. © 2024, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction

arXiv

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arXiv 2024年

作者： Du, Chenhe Lin, Xiyue Wu, Qing Tian, Xuanyu Su, Ying Luo, Zhe Zheng, Rui Chen, Yang Wei, Hongjiang Zhou, S. Kevin Yu, Jingyi Zhang, Yuyao The School of Information Science and Technology ShanghaiTech University Shanghai China The Department of Critical Care Medicine Zhongshan Hospital Fudan University Shanghai China The Laboratory of Image Science and Technology The School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China The School of Biomedical Engineering Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China

Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown promise in under-determined CT imaging reconstruction tasks. However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural Representation (DPER), an advanced unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. The two sub-problems are respectively addressed by INR reconstruction scheme and pre-trained score-based diffusion model. This combination first injects the implicit image local consistency prior from INR. Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM and LIDC), an in-house clinical COVID-19 dataset and a public raw projection dataset created by Mayo Clinic. The results show that our method outperforms the state-of-the-art reconstruction methods on in-domain datasets, while achieving significant performance improvements on out-of-domain (OOD) datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Defining Proximity Proteome of Histone Modifications by Antibody-mediated Protein A-APEX2 Labeling

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 87-100页

作者： Xinran Li Jiaqi Zhou Wenjuan Zhao Qing Wen Weijie Wang Huipai Peng Yuan Gao Kelly J.Bouchonville Steven M.Offer Kuiming Chan Zhiquan Wang Nan Li Haiyun Gan Shenzhen Key Laboratory of Synthetic Genomics Guangdong Provincial Key Laboratory of Synthetic GenomicsCAS Key Laboratory of Quantitative Engineering BiologyShenzhen Institute of Synthetic BiologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Cold Spring Harbor Laboratory Cold Spring HarborNY 11724USA Department of Molecular Pharmacology and Experimental Therapeutics Mayo ClinicRochesterMN 55905USA Mayo Clinic College of Medicine RochesterMN 55905USA Mayo Clinic Cancer Center RochesterMN 55905USA Department of Biomedical Sciences City University of Hong KongHong Kong Special Administrative Region 999077China Key Laboratory of Biochip Technology Biotech and Health CentreShenzhen Research Institute of City University of Hong KongShenzhen 518172China Division of Hematology Department of MedicineMayo ClinicRochesterMN 55905USA

Proximity labeling catalyzed by promiscuous enzymes,such as APEX2,has emerged as a powerful approach to characterize multiprotein complexes and protein-protein ***,current methods depend on the expression of exogenous fusion proteins and cannot be applied to identify proteins surrounding post-translationally modified *** address this limitation,we developed a new method to label proximal proteins of interest by antibody-mediated protein A-ascorbate peroxidase 2(pA-APEX2) labeling(AMAPEX).In this method,a modified protein is bound in situ by a specific antibody,which then tethers a pA-APEX2 fusion *** of APEX2 labels the nearby proteins with biotin;the biotinylated proteins are then purified using streptavidin beads and identified by mass *** demonstrated the utility of this approach by profiling the proximal proteins of histone modifications including H3 K27 me3,H3 K9 me3,H3 K4 me3,H4 K5 ac,and H4 K12 ac,as well as verifying the co-localization of these identified proteins with bait proteins by published ChIP-seq analysis and nucleosome ***,AMAPEX is an efficient method to identify proteins that are proximal to modified histones.

关键词： Proximity labeling Post-translationally AMAPEX Modified histone

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Clot Formation Under Physiological Flow Conditions Is Altered in Sickle Cell Disease As Measured By a Multiparameter Microfluidic System

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BLOOD 2017年 130卷 539-539页

作者： Ravindran, Abhijit Baek, John Myers, David R. Lam, Wilbur A. Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA Department of Pediatrics Aflac Cancer Center and Blood Disorders Service of Children's Healthcare of Atlanta Emory University School of Medicine Atlanta GA

539

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Tract Integrity Predicts Neurological Outcomes in Neonatal Hypoxic-Ischemic Encephalopathy

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Pediatrics & Neonatology 2025年

作者： Pei-Ling Tsai Yu-Chun Lo Yi-Shan Tsai Yea-Shwu Hwang Ting-Chun Lin Yao-Wen Liang Chen-En Huang Fu-Chun Hsu Yi-Fang Tu You-Yin Chen Institute of Allied Health Sciences College of Medicine National Cheng Kung University Tainan Taiwan PhD Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Medical Imaging National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Department of Occupational Therapy College of Medicine National Cheng Kung University Tainan Taiwan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei Taiwan College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Pediatrics National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Institute of Clinical Medicine College of Medicine National Cheng Kung University Tainan Taiwan Medical Device Innovation and Translation Center National Yang Ming Chiao Tung University Taipei Taiwan

Objective Early prognostication in infants with neonatal hypoxic-ischemic encephalopathy (HIE) is crucial for follow-up management. Alternations of microstructural integrity of neural fiber tracts in large-scale neural networks might predict neurological outcomes. Methods All consecutively-admitted patients with neonatal HIE from 2016 to 2020 were enrolled. Diffusion tensor imaging (DTI) tractography was performed at 4–7 days of age. Neurodevelopmental outcomes at 24 months of age were assessed with Bayley Scales of Infant Development, 3rd edition. Results A total of 62 HIE infants were enrolled, and 53 of these underwent DTI. Fractional anisotropy (FA) and mean diffusivity (MD) were measured to indicate the microstructural integrity of tracts, including the arcuate fasciculus, uncinate fasciculus, inferior fronto-occipital fasciculus, corticospinal tract (CST), and optic radiations (OR). Lower FA values of target tracts were associated with lower developmental scores at 24 months of age, particularly in the CST and OR (all P ≤ 0.003). The prognostic accuracy of FA values in CST surpassed that in the region of thalamus/basal ganglia (area under curves 0.839, 95% CI: 0.728–0.950 vs. 0.755, 95% CI: 0.583–0.928). Furthermore, the correlation between FA values in target tracts and developmental scores was most significant in infants with HIE stage III and this was not affected by therapeutic hypothermia. Conclusion Tract integrity in the CST and OR detected by DTI provides valuable insights for early prognostication in infants with HIE, regardless of therapeutic hypothermia.

关键词： neonatal hypoxic-ischemic encephalopathy diffusion tensor imaging tractography corticospinal tract optic radiations neurodevelopmental outcome

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Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images

arXiv

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arXiv 2017年

作者： Wang, Hongkai Zhou, Zongwei Li, Yingci Chen, Zhonghua Lu, Peiou Wang, Wenzhi Liu, Wanyu Yu, Lijuan Department of Biomedical Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology No. 2 Linggong Street Ganjingzi District Dalian Liaoning116024 China Department of Biomedical Informatics College of Health Solutions Arizona State University 13212 East Shea Boulevard ScottsdaleAZ85259 United States Center of PET/CT Affiliated Tumor Hospital of Harbin Medical University 150 Haping Road Nangang District Harbin Heilongjiang Province150081 China HIT-INSA Sino French Research Centre for Biomedical Imaging Harbin Institute of Technology Harbin Heilongjiang150001 China

Background: This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. Results: For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. Conclusions: The present study shows that the performance of CNN is not significantly different from the best classica methods and human doctors for classifying mediastinal lymph node metastasis

关键词： Computer aided diagnosis

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Repurposing design in bedrooms to improve home accessibility: task and motion analysis using the virtual reality environment

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2022年第1期66卷 368 - 372页

作者： Yue Luo Nicolas S Grimaldi Xiaojie Lu Sherry Ahrentzen Boyi Hu J. Crayton Pruitt Family Department of Biomedical Engineering University of Florida Gainesville Florida USA The Hong Kong University of Science and Technology (Guangzhou) Guangzhou Guangdong ChinaShimberg Center for Housing Studies University of Florida Gainesville Florida USA Shimberg Center for Housing Studies University of Florida Gainesville Florida USA

Functional disabilities, which have an association with a higher risk of falling in households, are common in the U.S. Individuals with functional limitations may benefit from accessible homes since they improve safety and reduce the risk of falling. Repurposing design is one strategy to improve home accessibility, by adapting home spaces and fixtures to serve a new purpose. This study aimed to propose a repurposing design in the bedroom area. The idea for a new bedroom repurposing design was proposed after analyzing a standard bedroom floor plan. The original standard bedroom layout and the redesigned bedroom layout were simulated in a virtual reality environment. To evaluate the accessibility of the new bedroom repurposing design, six participants (four females, 56.3 ± 14.1 years old) were recruited to perform two daily activities (i.e., relocating the robe & going to the bathroom) in both the original and redesigned bedrooms. The comparison of participants' task performance and trunk motion in both bedroom layouts revealed: (1) the sequences of actions required for two daily activities were similar between both bedroom layouts, (2) participants spent less time and walked for a shorter distance in the redesigned bedroom layout than in the original layout, (3) the motion smoothness when walking to the closet improved, whereas the RMS of trunk acceleration when opening the bathroom door decreased in the redesigned bedroom layout compared to the original layout.

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Physicochemical Properties, in Vitro Digestibility, and Intestine-Targeted Release Behavior of Starch–Steviol Glycoside Composite Hydrogels

SSRN

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SSRN 2023年

作者： Wang, Fan Ma, Rongrong Zhu, Jingling Zhan, Jinling Li, Jun Tian, Yaoqi State Key Laboratory of Food Science and Technology Jiangnan University Wuxi214122 China Department of Biomedical Engineering National University of Singapore 15 Kent Ridge Crescent Singapore119276 Singapore School of Food Science and Technology Jiangnan University Wuxi214122 China National University of Singapore 5 A Engineering Drive 1 Singapore117411 Singapore National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing Jiangnan University Wuxi214122 China State Key Laboratory of Food Science and Technology Jiangnan University China Department of Biomedical Engineering Faculty of Engineering National University of Singapore Singapore

Steviol glycosides possess Bola-form amphiphilic structure, which can solubilize hydrophobic phytochemicals and exert physical modification to the hydrophilic matrix. However, the effects of steviol glycosides on the sensory quality and nutritional value of starch hydrogel have not yet been reported. In this work, the physicochemical properties, in vitro digestibility, and release behavior of starch hydrogel in the present of steviol glycosides were investigated. The results show that the addition of steviol glycosides promoted the gelatinization and gelation of starch. The as-obtained hydrogels possessed soft texture, large volume, and high water holding capacity due to the reorganizations of molecular arrangement and microstructure induced by hydrogen bonding between starch chains and steviol glycosides, as evidenced by SEM, XRD and FTIR. The solubilization capacity of steviol glycosides could well integrate hydrophobic phytochemicals (e.g., curcumin) into starch hydrogel, endowing the latter with improved colour brilliance and nutritional value. The introduction of steviol glycosides hardly affected the digestibility of starch hydrogel, but it can promote the release efficiency of hydrophobic phytochemicals from hydrogel. Notably, this release behavior was pH dependent, which tends to target the alkaline intestine. The present study provides some theoretical and technical supports for the development of functional sugar-free starch hydrogel matrix. © 2023, The Authors. All rights reserved.

关键词： Hydrogels

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Neural Implicit Representation for Three-dimensional Ultrasound Carotid Surface Reconstruction using Unsigned Distance Function

Neural Implicit Representation for Three-dimensional Ultraso...

引用

IEEE Symposium (IUS) Ultrasonics

作者： Hongbo Chen Logiraj Kumaralingam Jiawen Li Kumaradevan Punithakumar Lawrence H Le Rui Zheng Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Alberta Canada School of Information Science and Technology ShanghaiTech University Shanghai China Department of Biomedical Engineering University of Alberta Edmonton Alberta Canada Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai China

Accurate 3D geometric shapes of carotid arteries are imperative for the three-dimensional (3D) ultrasound (US) imaging to clinically assess the carotid atherosclerosis (CA). However, the traditional surface reconstruction method, such as iso-surface (ISO-SURF), suffers from the image noise, voxel resolution and additional processing. In this paper, we introduce the neural implicit representation based on the deep learning network for the 3D surface reconstruction of media-adventitia boundary (MAB), plaque, and lumen-intima boundary (LIB) together. The unsigned distance functions are learned by the network to generate the mesh. For the validation, six volumes were simulated in carotid shape with MAB and LIB, and random noise was added around the boundaries. The results showed that experiments on six simulated volumes illustrated better performance than ISO-SURF, with 47%, 36%, and 55% decrease in Chamfer distance, average absolute distance, and Hausdorff distance, respectively. The visualization results from the CA clinical data revealed a smoother and more continuous geometric surface than ISO-SURF. The comparison result has shown the potential of the proposed method to examine vascular pathologies in the future.

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