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Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 319-332页

作者： Xiaoqing Zhang Xiao Wu Zunjie Xiao Lingxi Hu Zhongxi Qiu Qingyang Sun Risa Higashita Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhenChina Tomey Corporation NagoyaJapan Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina Singapore Eye Research Institute SingaporeSingapore

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

关键词： artificial intelligence deep learning deep neural networks image analysis image classification medical applications medical image processing

A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang Zhu Wensheng Zhang Xuebing Yang Bob Zhang School of Computer Science and Engineering Central South UniversityChangshaChina Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of Automation Chinese Academy of SciencesBeijingChina Department of Computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health systems.Deduplication eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple duplicates.Delta compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate blocks.In addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system throughput.Therefore,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these problems.The similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate attribute.Then,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ratios.Moreover,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system throughput.Experiments demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

Impact of mobile health and medical applications on clinical practice in gastroenterology

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World Journal of Gastroenterology 2020年第29期26卷 4182-4197页

作者： Sven Kernebeck Theresa S Busse Maximilian D Bottcher Jurgen Weitz Jan Ehlers Ulrich Bork Didactics Educational Research in Health Science Faculty of HealthWitten/Herdecke UniversityWitten 58455Germany Department of GI- Thoracic-and Vascular SurgeryDresden Technical UniversityUniversity Hospital DresdenDresden 01307Germany

Mobile health apps (MHAs) and medical apps (MAs) are becoming increasinglypopular as digital interventions in a wide range of health-related applications inalmost all sectors of healthcare. The surge in demand for digital medical solutionshas been accelerated by the need for new diagnostic and therapeutic methods inthe current coronavirus disease 2019 pandemic. This also applies to clinicalpractice in gastroenterology, which has, in many respects, undergone a recentdigital transformation with numerous consequences that will impact patients andhealth care professionals in the near future. MHAs and MAs are considered tohave great potential, especially for chronic diseases, as they can support the selfmanagementof patients in many ways. Despite the great potential associated withthe application of MHAs and MAs in gastroenterology and health care in general,there are numerous challenges to be met in the future, including both the ethicaland legal aspects of applying this technology. The aim of this article is to providean overview of the current status of MHA and MA use in the field ofgastroenterology, describe the future perspectives in this field and point out someof the challenges that need to be addressed.

关键词： Mobile health Health applications medical applications Technology Telemedicine Mobile applications Smartphone eHealth mHealth Digital biomarker Electronic health records

Advances in medical applications of Additive Manufacturing

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Engineering 2020年第11期6卷 1222-1231页

作者： Chunxu Li Dario Pisignano Yu Zhao Jiajia Xue Department of Orthopaedics Peking Union Medical College HospitalChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing J 00730China National Enterprise for Nanoscience and Nanotechnology Nanoscience InstituteNational Research CouncilPisa 1-56127Italy Department of Physics University of PisaPisa 1-56127Italy Beijing Laboratory of Biomedical Materials Beijing University of Chemical TechnologyBeijing 100029China

In the past few decades,additive manufacturing(AM)has been developed and applied as a cost-effective and versatile technique for the fabrication of geometrically complex objects in the medical industry.In this review,we discuss current advances of AM in medical applications for the generation of pharmaceuticals,medical implants,and medical devices.Oral and transdermal drugs can be fabricated by a variety of AM technologies.Different types of hard and soft clinical implants have also been realized by AM,with the goal of producing tissue-engineered constructs.In addition,medical devices used for diagnostics and treatment of various pathological conditions have been developed.The growing body of research on AM reveals its great potential in medical applications.The goal of this review is to highlight the usefulness and elucidate the current limitations of AM applications in the medical field.

关键词： Additive manufacturing medical applications Pharmaceutics medical implants medical devices

Development of a PVA Hydrogel for Bioprosthetic Heart Valve Stent and Other medical applications

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材料科学与工程学报 2000年第Z2期20卷 988-992页

作者： Z.F.Zhang Department of Chemical and Biochemical Engineering London Ontario University of Western Ontario The John Robarts Research InstituteLondon Ontario Department of Chemical and medical Biophysics LondonOntario Department of Chemical and Cardiology LondonOntario University of Western Ontario The John Robarts Research InstituteLondonOntario University of Western Ontario The John Robarts Research InstituteLondon Ontario The University Campus London Health Sciences Center London Ontario Canada

Polyvinyl alcohol (PVA) hydrogel (15%w/w) was processed by freezing and thawing from one through six thermal cycles to obtain wide range of mechanical properties. The materials were tested under uniaxial tension and results show that matching mechanical properties to that of porcine aortic root can be achieved. A fatigue machine for elastomer materials and tissues was built and fatigue test on PVA sample was carried out. The test results show that the PVA has good fatigue property. The stent prototype was designed and successfully made by mold casting method. Some potential medical applications are discussed.

关键词： PVA hydrogel Bioprosthetic heart valve stent design and fabrication tensile test fatigue medical applications

Electrochemical biomaterials for self-powered implantable“tissue batteries”:A tutorial review

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Nano Research 2023年第4期16卷 5447-5463页

作者： Manhui Zheng Xuechuan Wang Ouyang Yue Zhongxue Bai Boqiang Cui Xinhua Liu College of Bioresources Chemical and Materials Engineering Shaanxi University of Science&TechnologyXi'an 710021China Institute of Biomass&Functional Materials Shaanxi University of Science&TechnologyXi'an 710021China

Currently,due to improvements in living standards,people are paying more attention to all-around disease prevention and health care.Self-powered implantable“tissue batteries”integrated with electrochemical materials are essential for disease prevention,diagnosis,treatment,postoperative therapy,and healthcare applications.We propose and define new concepts of“tissue batteries”-self-powered tissue batteries(SPTBs)-are flexible self-powered implantable systems or platforms based on electroactive biomaterials,acting at the interface of biological tissue.Based on the electrical phenomenon of living organisms in life activities,there has been an increased attention to SPTBs for tissue repair promotion.SPTBs take advantages of both the preeminent biocompatibility of biomaterials and the promotion of time-honored electrical stimulation therapy for tissue recovery,which are very promising for human illness treatment.However,studies on clinical applications of SPTBs are impeded by a lack of comprehensive cognitive assessment of SPTBs.Herein,SPTBs for life and health applications are comprehensively reviewed.First,electrochemical materials and their across-the-board applications for several types of SPTBs are introduced and compared with regard to disease prevention,diagnosis,precision therapy,and personalized health monitoring.Then,the potential mechanisms for SPTBs for tissue repair promotion are discussed.Finally,the prospective challenges are summarized and recommendations for future research are provided.This review elucidates on the significance and versatility of SPTBs for various medical applications.

关键词： electrochemical biomaterials self-powered implantable tissue batteries medical applications

Associative learning mechanism for drug‐target interaction prediction

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1558-1577页

作者： Zhiqin Zhu Zheng Yao Guanqiu Qi Neal Mazur Pan Yang Baisen Cong College of Automation Chongqing University of Posts and TelecommunicationsChongqingChina Computer Information Systems Department State University of New York at Buffalo StateBuffaloNew YorkUSA Department of Cardiovascular Surgery Chongqing General HospitalUniversity of Chinese Academy of SciencesChongqingChina Emergency Department The Second Affiliated Hospital of Chongqing Medical UniversityChongqingChina Data Scientist Diagnostics Digital DH(Shanghai)Diagnostics Co.Ltd.Danaher CompanyShanghaiChina

As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.Exploring drug‐target interaction strength in terms of drug‐target affinity(DTA)is an emerging and effective research approach for drug development.However,it is challenging to model drug‐target interactions in a deep learning manner,and few studies provide interpretable analysis of models.This paper proposes a DTA prediction method(mutual transformer‐drug target affinity[MT‐DTA])with interactive learning and an autoencoder mechanism.The proposed MT‐DTA builds a variational autoencoders system with a cascade structure of the attention model and convolutional neural networks.It not only enhances the ability to capture the characteristic information of a single molecular sequence but also establishes the characteristic expression relationship for each substructure in a single molecular sequence.On this basis,a molecular information interaction module is constructed,which adds information interaction paths between molecular sequence pairs and complements the expression of correlations between molecular substructures.The performance of the proposed model was verified on two public benchmark datasets,KIBA and Davis,and the results confirm that the proposed model structure is effective in predicting DTA.Additionally,attention transformer models with different configurations can improve the feature expression of drug/protein molecules.The model performs better in correctly predicting interaction strengths compared with state‐of‐the‐art baselines.In addition,the diversity of drug/protein molecules can be better expressed than existing methods such as SeqGAN and Co‐VAE to generate more effective new drugs.The DTA value prediction module fuses the drug‐target pair interaction information to output the predicted value of DTA.Additionally,this paper theoretically proves that the proposed method maximises evidence lower bound for the joint distribution of the DTA prediction

关键词： deep learning medical applications

Pathways for Sustainable Utilization of Waste Chicken Eggshell

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Journal of Renewable Materials 2022年第8期10卷 2217-2246页

作者： Omojola Awogbemi Daramy Vandi Von Kallon Victor Sunday Aigbodion Department of Mechanical and Industrial Engineering Technology University of JohannesburgJohannesburgSouth Africa African Centre of Excellence ACESPED University of NigeriaNsukkaNigeria Faculty of Engineering and the Built Environment University of JohannesburgJohannesburgSouth Africa

Chicken eggshell is one of the most common wastes generated from households,restaurants and other food processing outlets.Waste Chicken Eggshells(WCES)also constitutes an environmental nuisance and ends up discarded at dumping site with no consideration of further usage.The main constituent of WCES is calcium carbonate from which calcium or calcium oxide can be extracted for various applications.This current effort reviews recently published literature on the diverse applications of WCES.The considered utilization avenues include catalysts for biofuel production,construction industry,wastewater purification,industrial sector,food industry,medical,and agricultural applications.The specific areas of application apart from the transesterification reactions include cement additives and replacement in concrete,asphalt binder,adsorbent of metals and dyes,production of hydroxyapatite,food supplement and fortification,dentistry,therapeutics,bone formation,drug delivery,poultry feeds as well as organic fertilizer.For most of the identified applications,the WCES is subjected to pretreatment and other modification techniques before utilization.The conversion of WCES to valuable products is a cost-effective,safe,environmentally friendly,non-toxic and viable means of waste disposal and utilization.More investigations are needed to further explore the benefits derivable from this bioresource.

关键词： Waste chicken eggshells catalysts food supplements medical applications waste utilization biofuel production

Synthesis,functionalization,and nanomedical applications of functional magnetic nanoparticles

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Chinese Chemical Letters 2018年第11期29卷 1601-1608页

作者： Zhou Chen Cao Wu Zhenfeng Zhang Wangping Wu Xuefeng Wang Zhiqiang Yu School of Mechanical and Power Engineering Nanjing Tech University International Laboratory for Insulation and Energy Efficiency Materials College of Materials Science and TechnologyNanjing University of Aeronautics and Astronautics Shandong Huaying New Material Co. Ltd. School of Mechanical Engineering Changzhou University Department of Obstetrics and Gynecology Zhujiang HospitalSouthern Medical University Guangdong Provincial Key Laboratory of New Drug Screening School of Pharmaceutical SciencesSouthern Medical University

Synthesis of magnetic nanoparticles （MNPs） is one of the most active research areas in advanced materials. MNPs that have magnetic properties and other functionalities have been demonstrated to show great promise in nanomedical applications. This review summarizes the current MNPs preparation, functionalization and stabilization methods. It also analyzes the detailed features of MNPs. And furthermore it highlights some actual case analyses of these MNPs for disease therapy, drug delivery, hyperthermia, bioseparation and bioimaging applications.

关键词： Magnetic nanoparticles Synthesis Functionalization medical applications Nanomedicine