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The softness of tumour-cell-derived microparticles regulates their drug-delivery efficiency (vol 3, pg 729, 2019)

NATURE BIOmedical ENGINEERING 2021年第5期5卷 481-481页

作者： Liang, Qingle Bie, Nana Yong, Tuying Tang, Ke Shi, Xiaolong Wei, Zhaohan Jia, Haibo Zhang, Xiaoqiong Zhao, Haiyan Huang, Wei Gan, Lu Huang, Bo Yang, Xiangliang National Engineering Research Center for Nanomedicine College of Life Science and Technology Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Bioinorganic Chemistry and Materia Medica Huazhong University of Science and Technology Wuhan China Department of Biochemistry and Molecular Biology Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Immunology and National Key Laboratory of Medical Molecular Biology Institute of Basic Medical Sciences Chinese Academy of Medical Sciences Peking Union Medical College Beijing China Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Artificial Intelligent and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Molecular Biophysics of Ministry of Education College of Life Science and Technology Huazhong University of Science and Technology Wuhan China

A Correction to this paper has been published: https://***/10.1038/s41551-021-00694-0.

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Apolipoprotein A-Ⅳ and its derived peptide, T55-121,improve glycemic control and increase energy expenditure

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Life Metabolism 2024年第4期 42-53页

作者： Zhen Cao Lei Lei Ziyun Zhou Shimeng Xu Linlin Wang Weikang Gong Qi Zhang Bin Pan Gaoxin Zhang Quan Yuan Liujuan Cui Min Zheng Tao Xu You Wang Shuyan Zhang Pingsheng Liu National Laboratory of Biomacromolecules CAS Center for Excellence in Biomacromolecules Institute of Biophysics Chinese Academy of Sciences University of Chinese Academy of Sciences The State Key Laboratory of Bioactive Substance and Function of Natural Medicines Institute of Materia Medica Chinese Academy of Medical Sciences and Peking Union Medical College Luxembourg Centre for Systems Biomedicine University of Luxembourg Guangzhou Regenerative Medicine and Health Guangdong Laboratory Bioland Laboratory Department of Computer Science School of Computing National University of Singapore School of Basic Medical Sciences Southwest Medical University The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious Diseases Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases The First Affiliated Hospital College of Medicine Zhejiang University Beijing Key Laboratory of Emerging Infectious Diseases Institute of Infectious Diseases Beijing Ditan Hospital Capital Medical University Beijing Institute of Infectious Diseases National Center for Infectious Diseases Beijing Ditan Hospital Capital Medical University National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases

It is crucial to understand the glucose control within our bodies. Bariatric/metabolic surgeries, including laparoscopic sleeve gastrectomy(LSG) and Roux-en-Y gastric bypass(RYGB), provide an avenue for exploring the potential key factors involved in maintaining glucose homeostasis since these surgeries have shown promising results in improving glycemic control among patients with severe type 2 diabetes(T2D). For the first time, a markedly altered population of serum proteins in patients after LSG was discovered and analyzed through proteomics. Apolipoprotein A-Ⅳ(apoA-Ⅳ) was revealed to be increased dramatically in diabetic obese patients following LSG, and a similar effect was observed in patients after RYGB surgery. Moreover, recombinant apoA-Ⅳ protein treatment was proven to enhance insulin secretion in isolated human islets. These results showed that apoA-Ⅳ may play a crucial role in glycemic control in humans, potentially through enhancing insulin secretion in human islets. ApoA-Ⅳ was further shown to enhance energy expenditure and improve glucose tolerance in diabetic rodents, through stimulating glucose-dependent insulin secretion in pancreatic β cells, partially via Gαs-coupled GPCR/cAMP(G protein-coupled receptor/cyclic adenosine monophosphate) ***, T55-121, truncated peptide 55-121 of apoA-Ⅳ, was discovered to mediate the function of apoA-Ⅳ. These collective findings contribute to our understanding of the relationship between apoA-Ⅳ and glycemic control, highlighting its potential as a biomarker or therapeutic target in managing and improving glucose regulation.

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Heartbeat Detection and Rate Estimation from Ballistocardiograms using the Gated Recurrent Unit Network

Heartbeat Detection and Rate Estimation from Ballistocardiog...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Dong Hai Chao Chen Ruhan Yi Shuiping Gou Bo Yu Su Changzhe Jiao Marjorie Skubic Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University IBM Research EECS Dept. University of Missouri Division of Sleep and Circadian Disorders Harvard Medical School Brigham and Women Hospital

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Inspired by the application of recurrent neural networks (RNNs) to image recognition, in this paper, we propose a heartbeat detection framework based on the Gated Recurrent Unit (GRU) network. In this contribution, the heartbeat detection task from ballistocardiogram (BCG) signals was modeled as a classification problem where the segments of BCG signals were formulated as images fed into the GRU network for feature extraction. The proposed framework has advantages in fusion of multi-channel BCG signals and effective extraction of the temporal and waveform characteristics of the heartbeat signal, thereby enhancing heart rate estimation accuracy. In laboratory collected BCG data, the proposed method achieved the best heart rate estimation results compared to previous algorithms.

关键词： Heart beat Feature extraction Monitoring Estimation Heart rate variability Training

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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DEEP RECURSIVE EMBEDDING FOR HIGH-DIMENSIONAL DATA

arXiv

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arXiv 2021年

作者： Zhou, Zixia Zu, Xinrui Wang, Yuanyuan Lelieveldt, Boudewijn P.F. Tao, Qian The Department of Electronic Engineering Fudan University Shanghai200433 China University of Twente Drienerlolaan 5 Enschede7522 NB Netherlands Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai200032 China The Division of Image Processing Department of Radiology Leiden University Medical Center Albinusdreef 2 Leiden2333 ZA Netherlands The Department of Imaging Physics Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands

Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this paper, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by well-established objectives such as Kullback-Leibler (KL) divergence minimization. We further propose a recursive strategy, called deep recursive embedding (DRE), to make use of the latent data representations for boosted embedding performance. We exemplify the flexibility of DRE by different architectures and loss functions, and benchmarked our method against the two most popular embedding methods, namely, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). The proposed DRE method can map out-of-sample data and scale to extremely large datasets. Experiments on a range of public datasets demonstrated improved embedding performance in terms of local and global structure preservation, compared with other state-of-the-art embedding methods. © 2021, CC BY-NC-SA.

关键词： Deep neural networks

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Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

arXiv

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arXiv 2021年

作者： Qin, Yulei Gu, Yun Zhang, Hanxiao Yang, Jie Wang, Lihui Wang, Zhexin Yao, Feng Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China CREATIS INSA Lyon CNRS UMR 5220 INSERM U1206 Université de Lyon Villeurbanne69621 France Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang550025 China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai200025 China

Objectives: To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. Materials and Methods: The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings 3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. Computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (χ2) testing were performed to assess the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. Results: For the benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, χ2=105.04) and (OR=7.06, χ2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, χ2=159.02) and (OR=2.29, χ2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Conclusion: Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers. © 2021, CC BY.

关键词： Computerized tomography

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A Robust On-Road Vehicle Detection and Tracking Method Based on Monocular Vision 12th

A Robust On-Road Vehicle Detection and Tracking Method Based...

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12th International Conference on Genetic and Evolutionary computing, ICGEC 2018

作者： Xiao, Ling Zhang, Yongjun Liu, Jun Zhao, Yong Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University Shenzhen China

ISBN: (纸本)9789811358401

In this paper, we propose a new framework for vehicle detection and tracking. Multi-features are used in the vehicle detection algorithm, which can be divided into two main steps: generation of candidates using features such as the shadow and vertical edge, and verification of the candidates using HOG and SVM. In the vehicle tracking algorithm, the RGB model and orientation histogram are used to represent the object feature, and the mean shift is employed to search the mode of the potential object rapidly in a neighborhood frame, which obtains the preliminary tracking results. Then, we use ORB feature matching and correction methods to adjust the preliminary tracking results. The improved Mean-Shift tracking results and the ORB correction results are then fused by linear weighted, which obtains the final results of the tracking. Experimental results demonstrate that the proposed approach is robust and validate in complicated real scenes. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Tracking (position)

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Cross-chain mapping blockchain: scalable data management in massive IoT networks

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Digital Communications and Networks 2024年

作者： Wenjian Hu Yao Yu Xin Hao Phee Lep Yeoh Lei Guo Yonghui Li School of Computer Science and Engineering Northeastern University Shenyang 110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang 110819 China School of Information Technology Deakin University Waurn Ponds VIC 3216 Australia School of Science Technology and Engineering University of the Sunshine Coast Sippy Downs QLD 4556 Australia School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Electrical and Computer Engineering University of Sydney Sydney NSW 2006 Australia

We propose a Cross-Chain Mapping Blockchain (CCMB) for scalable data management in massive Internet of Things (IoT) networks. Specifically, CCMB aims to improve the scalability of securely storing, tracing, and transmitting IoT behavior and reputation data based on our proposed cross-mapped Behavior Chain (BChain) and Reputation Chain (RChain). To improve off-chain IoT data storage scalability, we show that our lightweight CCMB architecture efficiently utilizes available fog-cloud resources. The scalability of on-chain IoT data tracing is enhanced using our Mapping Smart Contract (MSC) and cross-chain mapping design to perform rapid Reputation-to-Behavior (R2B) traceability queries between BChain and RChain blocks. To maximize off-chain to on-chain throughput, we optimize the CCMB block settings and producers based on a general Poisson Point Process (PPP) network model. The constrained optimization problem is formulated as a Markov Decision Process (MDP), and solved using a dual-network Deep Reinforcement Learning (DRL) algorithm. Simulation results validate CCMB's scalability advantages in storage, traceability, and throughput. In specific massive IoT scenarios, CCMB can reduce the storage footprint by 50% and traceability query time by 90%, while improving system throughput by 55% compared to existing benchmarks.

关键词： Blockchain Cross-chain mappings Data management Internet of things Deep reinforcement learning

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Cell-type composition analysis of scRNA-seq data with deep convolution neural network

Research Square

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Research Square 2020年

作者： Ma, He Fang, Zhihao Liu, Zongbin Chen, Yan College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China College of Engineering Peking University Beijing China Shenzhen Zigzag Biotechnology Ltd Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Background: With the rapid development of single-cell RNA sequencing (scRNA-seq), more large-scale single-cell sequencing data has been generated. Due to the continuous increase of single-cell sequencing data, the analysis of cell-type composition from single-cell transcriptomics has also to face huge challenges. Since the emergence of scRNA-seq technology, the size of sequencing datasets has grown more than 1 million times in just over a decade. Meanwhile, as more gene markers are discovered, the data dimension of single-cell sequencing becomes higher. All of these put forward more stringent requirements on data dimensionality reduction and clustering algorithms. Under the constraints of practical factors such as occurrence of noise and dropouts and the limitation of overhead, it is also required an effective and efficient method that can obtain accurate analysis results in a very short time, and has a competitive algorithm stability. Results: We present scCAE, an effective and efficient method based on convolution autoencoder that can accurately and rapidly analyze cell-type composition from single-cell transcriptomics datasets. Our method achieved the best results in the data sets that simulate the cell differentiation process among existing methods, which achieved the ARI of 69.64% and 68.83% at 10 and 25 clusters tasks. And, in the case of different dropouts, our method also works well. When the sparsity level of data metric is 71%, scCAE can achieved the ARI of 45.29%, which is the highest of the existing methods. In terms of algorithm overhead, our method has also achieved good results by comparing with several existing methods. It takes less time than most methods and takes up much less memory than other algorithms based neural networks. Conclusions: Our method, scCAE, has more accurate and reasonable results in the analysis of cell-types composition. And, because of the design of imputer, it can deal with a large number of dropouts in the data matrix. Becau

关键词： Convolution

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UNet-ESPC-Cascaded Super-Resolution Reconstruction in Spectral CT

UNet-ESPC-Cascaded Super-Resolution Reconstruction in Spectr...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Zhouxia Li Xiaoni Wang Lihui Wang Wen Ji Miaomiao Zhang Yuemin Zhu Feng Yang School of Computer and Information Technology Beijing Jiaotong University Beijing China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Information Engineering Capital Normal University Beijing China INSA Lyon CNRS Inserm Univ Lyon Lyon France National Library of Medicine National Institute of Health Bethesda MD USA

ISBN: (数字)9781728144801

ISBN: (纸本)9781728144818

Spectral CT based on photon counting detectors is a promising imaging modality since it provides the possibility of both obtaining CT images from multi-energy bins with a single X-ray exposure and allowing low-dose imaging. However, the image quality such as spatial resolution reconstructed from multiple energy bins is degraded because of the use of narrow energy bins in spectral CT. We propose to use deep learning methods for super-resolution reconstruction of spectral CT images. To this end, we introduce an UNet-ESPC-cascaded model and perform a patch-based training to obtain the optimal parameters of the model. Experimental results on physical phantom datasets demonstrated that our deep learning based reconstruction method can reduce the F form error between the reconstructed super-resolution CT image and the ground truth, by 11.6% and 5.66% with respect to respectively bilinear-interpolation-based reconstruction and iterative back projection methods. Our method achieves best results with a patch size of 20 and a stride of 15.

关键词： image reconstruction Computed tomography Superresolution Interpolation Energy resolution Deep learning Training

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