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Zn/Sr dual ions-collagen co-assembly hydroxyapatite enhances bone regeneration through procedural osteo-immunomodulation and osteogenesis

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Bioactive Materials 2022年第4期7卷 195-206页

作者： Zhenyu Zhong Xiaodan Wu Yifan Wang Mengdie Li Yan Li XuLong Liu Xin Zhang Ziyang Lan Jianglin Wang Yingying Du Shengmin Zhang Advanced Biomaterials and Tissue Engineering Center Huazhong University of Science and TechnologyWuhan430074China Department of Biomedical Engineering Huazhong University of Science and TechnologyWuhan430074China Institute of Regulatory Science for Medical Devices Huazhong University of Science and TechnologyWuhan430074China Department of Biomedical Engineering The University of Texas at AustinAustinTX78712USA

The immune microenvironment induced by biomaterials played vital roles in bone ***(HA)and its ion-substituted derivates represent a large class of core inorganic materials for bone tissue *** ion substitution was proved to be a potent way to grant HA more biological functions,few studies focused on the immunomodulatory properties of ion-doped ***,to explore the potential osteoimmunomodulatory effects of ion-doped HA,zinc and strontium co-assembled into HA through a collagen template biomimetic way(ZnSr-Col-HA)was successfully *** was found that ZnSr-Col-HA could induce a favorable osteo-immune microenvironment by stimulating ***,ZnSr-Col-HA demonstrated a procedural promoting effect on osteogenic differentiation of bone marrow mesenchymal stem cells(BMSCs)in ***,the osteo-immune microenvironment acted as a dominant factor in promoting osteogenic gene expressions at the early stage through OSM signal *** the direct stimulating effects on BMSCs by Zn^(2+)/Sr^(2+) were more effectively at the later stage with Nfatc1/Maf and Wnt signals *** vivo study confirmed strong promoting effects of ZnSr-Col-HA on critical-sized cranial defect *** current study indicated that such a combined biomaterial design philosophy of dual ion-doping and biomimetic molecular co-assembly to endow HA applicable osteoimmunomodulatory characteristics might bring up a new cutting-edge concept for bone regeneration study.

关键词： Hydroxyapatite Zinc Strontium Biomimetic co-assembly Osteoimmunomodulation

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Decomposed linear dynamical systems (dLDS) for learning the latent components of neural dynamics

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 2962-2973页

作者： Noga Mudrik Yenho Chen Eva Yezerets Christopher J. Rozell Adam S. Charles Department of Biomedical Engineering Center for Imaging Science Kavli NDI Johns Hopkins University Baltimore MD Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA Department of Biomedical Engineering Center for Imaging Science Johns Hopkins University Baltimore MD School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA Department of Biomedical Engineering Center for Imaging Science Mathematical Institute for Data Science Kavli NDI Johns Hopkins University Baltimore MD

Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and behavior. Models of neural dynamics often focus on either low-dimensional projections of neural activity or on learning dynamical systems that explicitly relate to the neural state over time. We discuss how these two approaches are interrelated by considering dynamical systems as representative of flows on a low-dimensional manifold. Building on this concept, we propose a new decomposed dynamical system model that represents complex non-stationary and nonlinear dynamics of time series data as a sparse combination of simpler, more interpretable components. Our model is trained through a dictionary learning procedure, where we leverage recent results in tracking sparse vectors over time. The decomposed nature of the dynamics is more expressive than previous switched approaches for a given number of parameters and enables modeling of overlapping and non-stationary dynamics. In both continuous-time and discrete-time instructional examples, we demonstrate that our model effectively approximates the original system, learns efficient representations, and captures smooth transitions between dynamical modes. Furthermore, we highlight our model's ability to efficiently capture and demix population dynamics generated from multiple independent subnetworks, a task that is computationally impractical for switched models. Finally, we apply our model to neural "full brain" recordings of C. elegans data, illustrating a diversity of dynamics that is obscured when classified into discrete states.

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Non-Invasive, Cerebral Blood Volume Measurements During Sleep: Toward Naturalistic Measurements of Glymphatic Flux

Non-Invasive, Cerebral Blood Volume Measurements During Slee...

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Optical Tomography and Spectroscopy, OT and S 2024 - Part of Optica Biophotonics Congress: biomedical Optics

作者： Otic, Nikola Renna, Marco Lewis, Laura Franceschini, Maria Angela Robinson, Mitchell B. Department of Biomedical Engineering Boston University BostonMA United States Harvard Medical School MGH/HST Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Electrical Engineering and Computer Science Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States

The hemodynamic sources of glymphatic flow are typically measured using MRI. In this work, we demonstrate the feasibility of measuring cerebral hemodynamics through NIRS toward the goal of estimating glymphatic flux i... 详细信息

ISBN: (纸本)9781957171340

关键词： Hemodynamics

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A cloud service composition method using a fuzzy-based particle swarm optimization algorithm

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Multimedia Tools and Applications 2024年第19期83卷 56275-56302页

作者： Nazif, Habibeh Nassr, Mohammad Al-Khafaji, Hamza Mohammed Ridha Jafari Navimipour, Nima Unal, Mehmet Department of Mathematics Payame Noor University Tehran Iran Communication Technology Engineering Department Tartous University Tartus Syria Dpartment of Mathematics and Natural Sciences Gulf University for Science and Technology Mishref Campus Mubarak Al-Abdullah Kuwait Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Babil Hillah51001 Iraq Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul Turkey

In today's dynamic business landscape, organizations heavily rely on cloud computing to leverage the power of virtualization and resource sharing. Service composition plays a vital role in cloud computing, combining multiple cloud services to fulfill complex user requests. Service composition in cloud computing presents several challenges. These include service heterogeneity, dynamic service availability, QoS (Quality of Service) constraints, and scalability issues. Traditional approaches often struggle to handle these challenges efficiently, leading to suboptimal resource utilization and poor service performance. This work presents a fuzzy-based strategy for composing cloud services to overcome these obstacles. The fact that service composition is NP-hard has prompted the use of a range of metaheuristic algorithms in numerous papers. Therefore, Particle Swarm Optimization (PSO) has been applied in this paper to solve the problem. Implementing a fuzzy-based PSO for service composition requires defining the fuzzy membership functions and rules based on the specific service domain. Once the fuzzy logic components are established, they can be integrated into the PSO algorithm. The simulation results have shown the high efficiency of the proposed method in decreasing the latency, cost, and response time. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Fuzzy logic

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Stretchable nanogenerator with micronano hierarchical interfaces for selfpowered biometric authentication

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npj Flexible Electronics 2024年第1期8卷 55-64页

作者： Hai-Tao Deng Yi-Xuan Xia Yu-Chi Liu Beomjoon Kim Xiao-Sheng Zhang School of Integrated Circuit Science and Engineering University of Electronic Science and Technology of ChinaChengduChina CREMeB Institute of Industrial ScienceThe University of TokyoTokyoJapan Center for Complex Network Intelligence(CCNI) Tsinghua Laboratory of Brain and Intelligence(THBI)Department of Biomedical EngineeringTsinghua UniversityBeijingChina

Multifunctional integration and heterogeneous surface integration are two crucial challenges in the indepth use and development of triboelectric nanogenerators(TENGs)in the wearable electronic field.A promising solution is to develop stretchableTENGs(STENG),which have the potential for microenergy supply and self-powered *** key challenge is to endow its functional components with good deformability and realize heterogeneous surface ***,we proposed a fully stretchable,surface adaptable TENG using stretchable micro-nano hierarchical friction *** integrated microenenrgy supply and multifunctional self-powered sensing(i.e.,bend,force,and frequency sensing)abilities *** highly-linear sensing abilities endows the STENG with a good biometric *** an application,a STENG array based-stretchable wearable keyboard was *** identified dynamic keystroke motions of all users with a Support Vector Machine,with a high accuracy of 93.21%.Besides,the intruderswhowere not sampled were distinguished fromusers,with an accuracy of 81.50%.

关键词： hierarchical generator integration

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Near-infrared light switching nitric oxide nanogenerator with“linkage mechanism”for tumor targeting multimodal synergistic therapy

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Science China Chemistry 2023年第2期66卷 586-600页

作者： Wei Zhang Qize Xuan Qi Zhang Tong Wang Chen Wang Hui Li Chao Chen Ping Wang State Key Laboratory of Bioreactor Engineering Biomedical Nanotechnology CenterSchool of BiotechnologyEast China University of Science and TechnologyShanghai200237China Institute for Environmental Pollution and Health School of Environmental and Chemical EngineeringShanghai UniversityShanghai200444China Department of Bioproducts and Biosystems Engineering University of MinnesotaSt PaulMN55108USA

Gaseous therapy based on nitric oxide(NO),as a potential anti-tumor treatment strategy,has attracted great attention,but the targeted and controlled gas release in the tumor site still remains a *** addressing these difficulties,a near-infrared(NIR)light-triggered NO release nanogenerator with a“linkage mechanism”was designed on the basis of sodium nitroprussidedoped mesoporous Prussian blue nanoparticles,in which the outer structure was modified with p H-sensitive gatekeeper chitosan and tumor-targeting agent folic ***“linkage mechanism”can achieve precise release of NO under the control of photothermal effect at tumor site,which can couple photothermal therapy and gas therapy to address the premature release of gas during ***,the amount of released gas can be controlled by adjusting the irradiation time and laser ***,as-fabricated nanocomposites hold high photothermal conversion efficiency under NIR laser irradiation,resulting in the on-demand release of NO and chemotherapy *** released NO can inhibit the expression of hypoxiainducible factorα(HIF-1α)and alleviate the hypoxic tumor microenvironment,thereby enhancing the efficacy of ***,in vitro and in vivo experiments exhibited remarkable antitumor efficiency,and the synergistic gas/chemo/photothermal therapy of deep tumors was *** findings indicate an effective strategy to stimulate further the development of deep tumor therapy,which may provide new insights into other NO-related medical applications.

关键词： NO generator NIR-trigger NO release linkage mechanism gas therapy targeted synergistic therapy

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Heart Arrhythmia Classification Using Deep Learning: A Comparative Study 6

Heart Arrhythmia Classification Using Deep Learning: A Compa...

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6th Iraqi International Conference on engineering technology and its Applications, IICETA 2023

作者： Radi, Omar Alslatie, Mohammad Mustafa, Wan Azani Alquran, Hiam Badarneh, Alaa Mohammed, F.F. Alkhayyat, Ahmed Jordan University of Science and Technology Department of Biomedical Engineering Irbid Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman11855 Jordan Faculty of Electrical Engineering Technology Perlis Arau Malaysia Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Al-Zahraa University for Women Karbala Iraq The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350303339

Heart arrhythmia is an irregular heartbeat that causes heart problems. It can be classified by their seriousness into serious and non-serious arrhythmia. Mainly to diagnose heart arrhythmias, we use Electrocardiogram (ECG). In this paper, the authors compared three different models of classifiers: Convolutional Neural Network, Dense Neural Network and Long Short-Term Memory to classify cardiac arrhythmia into two types normal and abnormal, using the MIT-BIH database. The results show that CNN and DNN have the best result of the models with 99% accuracy while LSTM shows 60 accuracy percent. © 2023 IEEE.

关键词： Diseases

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Diffusing Coupling High-Frequency-Purifying Structure Feature Extraction for Brain Multimodal Registration

Diffusing Coupling High-Frequency-Purifying Structure Featur...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Gao, Fan He, Yuanbo Li, Shuai Hao, Aimin Cao, Desen Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beihang University Beijing Advanced Innovation Center for Biomedical Engineering Beijing China Chinese PLA General Hospital Department of Biomedical Engineering Beijing China

ISBN: (纸本)9798350337488

The core of medical image registration is the alignment of corresponding structures. However, in multimodal image registration, substantial differences in appearance (intensity distribution) of the images often compel the registration model to prioritize intensity information over structure information, resulting in low accuracy of registration. Therefore, the disentangling structure information from intensity information is vital to improve the registration effectiveness. To this end, we propose a diffusing coupling high-frequency-purifying structure feature extraction for brain multimodal registration. Specifically, the denoising diffusion probabilistic models (DDPM) is firstly utilized to extract complete feature information from images. Then, the discrete cosine transform (DCT) is applied to purify high-frequency structure information from the complete feature information for registration. Furthermore, structure consistency constraint (SCC) is introduced based on purified structure information to emphasize the core position of the structure in registration. Through comprehensive comparisons with traditional and learning-based methods on the multimodal brain MRI dataset, our method demonstrates superior accuracy and stability in brain multimodal registration. Our code is available at https://***/goghfan/DDNet. © 2023 IEEE.

关键词： denoising diffusion probabilistic models high frequency purifying multimodal registration structure consistency constraint

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for Computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Bacterial and cancerous cell membrane fused liposome coordinates with PD-L1 inhibitor for cancer immunotherapy

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Nano Research 2024年第9期17卷 8389-8401页

作者： Xianjin Luo Chenglong Li Zhaofei Guo Hairui Wang Penghui He Yuanhao Zhao Yi Lin Chunting He Yingying Hou Yongshun Zhang Guangsheng Du Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial TechnologyWest China School of PharmacySichuan UniversityChengdu 610064China Pharmaceutical Biotechnology Department of PharmacyLMU MunichButenandt Strasse 5-13Munich 81377Germany Department of Biomedical Engineering College of Future TechnologyPeking UniversityBeijing 100871China

Although tumor cell membranes with broad-spectrum antigens have been explored for cancer vaccines for decades,their relatively poor capacity to stimulate immune responses,especially cellular immune responses,has limited their ***,we presented a novel bacterial and cancerous cell membrane fusogenic liposome for co-delivering cell membrane-derived antigens and ***,a programmed death-ligand 1(PD-L1)inhibitor,JQ-1,was incorporated into the formulation to tackle the up-regulated PD-L1 expression of antigen-presenting cells(APCs)upon vaccination,thereby augmenting its anti-tumor *** fusogenic liposomes demonstrated significantly improved cellular uptake by APCs and effectively suppressed PD-L1 expression in bone marrow-derived dendritic cells(BMDCs)in *** subcutaneous vaccination,the nano-vaccines efficiently drained to the tumor-draining lymph nodes(TDLNs),and significantly inhibited PD-L1 expression of both dendritic cells(DCs)and macrophages within the TDLNs and *** a result,the liposomal vaccine induced robust innate and cellular immune responses and inhibited tumor growth in a colorectal carcinoma-burden mouse *** summary,the fabricated cell membrane-based fusogenic liposomes offer a safe,effective,and easily applicable strategy for tumor immunotherapy and hold potential for personalized cancer immunotherapy.

关键词： fusogenic liposome tumor cell membrane adjuvant programmed death-ligand 1(PD-L1)inhibitor tumor draining lymph nodes

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