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Intelligent Deep Learning Based disease diagnosis Using Biomedical Tongue Images

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Computers, Materials & Continua 2022年第3期70卷 5667-5681页

作者： V.Thanikachalam S.Shanthi K.Kalirajan Sayed Abdel-Khalek Mohamed Omri Lotfi M.Ladhar Sri Sivasubramaniya Nadar College of Engineering Chennai603110India Department of Information Technology Department of Electronics and Communication EngineeringCARE College of EngineeringTiruchirappalli620009India Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Mathematics Department Faculty of ScienceSohag UniversitySohagEgypt Deanship of Scientific Research King Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

The rapid development of biomedical imaging modalities led to its wide application in disease ***-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ***,physicians examine the characteristics of tongue prior to *** this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is *** model can reduce the physical harm made to the *** tongue image analytical methodologies have been proposed ***,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis *** this motivation,the current research article designs an Intelligent DL-baseddisease diagnosis method using Biomedical Tongue Images called IDLDD-BTI *** proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal ***,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)*** last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color *** design of OGSO algorithm for SqueezeNet model shows the novelty of the *** assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several *** resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

关键词： Biomedical images image processing tongue color image deep learning squeezenet disease diagnosis

The application of CRISPR-Cas in disease diagnosis and treatment

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中国科学:化学英文版 2023年第10期66卷 2734-2742页

作者： Zhiying Wang Yin Liu Fuling Zhou Yafen Wang Xiang Zhou School of Public Health Wuhan UniversityWuhan 430071China College of Chemistry and Molecular Sciences Wuhan UniversityWuhan 430072China Taikang Center for Life and Medical Sciences Wuhan UniversityWuhan 430071China Cross Research Institute of Zhongnan Hospital Wuhan UniversityWuhan 430071China Department of Hematology of Zhongnan Hospital Wuhan UniversityWuhan 430071China Department of Chemistry College of SciencesHuazhong Agricultural UniversityWuhan 430070China

Clustered regularly interspaced short palindromic repeat(CRISPR)has been gaining much attention in the modern medical field and has been widely used for the diagnosis and treatment of diseases in recent *** this review,we will introduce the application of CRISPR in disease diagnosis and treatment,including its use in detecting pathogens,gene mutations,and genetic diseases,as well as its application in gene therapy for single-gene diseases,cancer,viral infectious diseases,and cardiovascular ***,we will discuss the potential future directions and challenges of CRISPR in the diagnosis and treatment of diseases,and provide a thorough overview of the ways in which CRISPR is used for diagnosing and treating diseases.

关键词： CRISPR application disease diagnosis disease treatment

Potential use of a dried saliva spot(DSS)in therapeutic drug monitoring and disease diagnosis

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Journal of Pharmaceutical Analysis 2022年第6期12卷 815-823页

作者： Yu Han Xi-Ling Li Minghui Zhang Jing Wang Su Zeng Jun Zhe Min Key Laboratory of Natural Medicines of the Changbai Mountain Ministry of EducationDepartment of Pharmaceutical AnalysisCollege of PharmacyYanbian UniversityYanjiJilin133002China Institute of Drug Metabolism and Pharmaceutical Analysis Zhejiang Province Key Laboratory of Anti-Cancer Drug ResearchCollege of Pharmaceutical SciencesZhejiang UniversityHangzhou310058China

In recent years,scientific researchers have increasingly become interested in noninvasive sampling methods for therapeutic drug monitoring and disease *** a result,dried saliva spot(DSS),which is a sampling technique for collecting dried saliva samples,has been widely used as an alternative matrix to serum for the detection of target *** the DSS method with a highly sensitive detection instrument improves the efficiency of the preparation and analysis of biological ***,dried blood spots,dried plasma spots,and dried matrix spots,which are similar to those of the DSS method,are *** with alternative biological fluids used in dried spot methods,including serum,tears,urine,and plasma,saliva has the advantage of convenience in terms of sample collection from children or persons with *** review aims to provide integral strategies and guidelines for dried spot methods to analyze biological samples by illustrating several dried spot ***,we summarize recent advancements in DSS methods from June 2014 to March 2021 and discuss the advantages and disadvantages of the key aspects of this method,including sample preparation and method ***,we outline the challenges and prospects of such methods in practical applications.

关键词： Human saliva Dried saliva spot Therapeutic drug monitoring disease diagnosis

Intelligent disease diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

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Computers, Materials & Continua 2022年第4期71卷 1189-1203页

作者： Wafaa Alsaggaf Felwa Abukhodair Amani Tariq Jamal Sayed Abdel-Khalek Romany F.Mansour Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics and Statistics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly *** use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud ***,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT *** the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision *** this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC *** proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease *** addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic *** COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN *** order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI *** experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.

关键词： Intelligent models healthcare systems disease diagnosis internet of things cloud computing clustering deep learning

Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular disease diagnosis

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Computers, Materials & Continua 2022年第4期71卷 255-268页

作者： R.Krishnaswamy B.Sivakumar B.Viswanathan Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India School of Computing SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Factually of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia

Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.

关键词： Biomedical signals ecg disease diagnosis artificial intelligence parameter tuning gru model

Advances of nanoparticles as drug delivery systems for disease diagnosis and treatment

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Chinese Chemical Letters 2023年第2期34卷 65-88页

作者： Rui Liu Cong Luo Zhiqing Pang Jinming Zhang Shaobo Ruan Meiying Wu Lei Wang Tao Sun Nan Li Liang Han Jinjin Shi Yuanyu Huang Weisheng Guo Shaojun Peng Wenhu Zhou Huile Gao 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 610041China Department of Pharmaceutics Wuya College of InnovationShenyang Pharmaceutical UniversityShenyang 110016China Department of Pharmaceutics School of PharmacyFudan UniversityKey Laboratory of Smart Drug DeliveryMinistry of EducationShanghai 200433China State Key Laboratory of Southwestern Chinese Medicine Resources Pharmacy SchoolChengdu University of Traditional Chinese MedicineChengdu 611130China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China School of Pharmaceutical Sciences(Shenzhen) Sun Yat-sen UniversityShenzhen 518107China School of Pharmaceutical Sciences Zhengzhou UniversityKey Laboratory of Targeting Therapy and Diagnosis for Critical DiseasesZhengzhou 450001China Tianjin Key Laboratory of Drug Delivery&High-Efficiency School of Pharmaceutical Science and TechnologyTianjin UniversityTianjin 300072China Jiangsu Key Laboratory of Neuropsychiatric diseases and College of Pharmaceutical Sciences Soochow UniversitySuzhou 215123China Advanced Research Institute of Multidisciplinary Science School of Life ScienceKey Laboratory of Molecular Medicine and BiotherapyKey Laboratory of Medical Molecule Science and Pharmaceutics EngineeringBeijing Institute of TechnologyBeijing 100081China Radiology Center Key Laboratory of Molecular Target&Clinical PharmacologySchool of Pharmaceutical Sciences&The Second Affiliated HospitalGuangzhou Medical UniversityGuangzhou 510260China Zhuhai Institute of Translational Medicine Zhuhai People's Hospital(Zhuhai hospital affiliated with Jinan University)Zhuhai 519000China Xiangya School of Pharmaceutical Sciences Central South UniversityChangsha 410013China

Decades have passed since the first nanoparticles-base medicine was approved for human cancer treatment, and the research and development of nanoparticles for drug delivery are always ***, the significant advances complicate nanoparticles’ branches, including liposomes, solid lipid nanoparticles, inorganic nanoparticles, micelles, nanovaccines and nano-antibodies, etc. These nanoparticles show numerous capabilities in treatment and diagnosis of stubborn diseases like cancer and neurodegenerative diseases, emerging as novel drug carriers or therapeutic agents in future. In this review, the complicated branches of nanoparticles are classified and summarized, with their property and functions concluded. Besides, there are also some delivery strategies that make nanoparticles smarter and more efficient in drug delivery, and frontiers in these strategies are also summarized in this review. Except these excellent works in newly-produced drug delivery nanoparticles, some points of view and future expectations are made in the end.

关键词： Nanoparticles Drug delivery Nanomaterials Delivery strategies Specific delivery disease diagnosis disease treatment

Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis disease diagnosis and Classification

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Computers, Materials & Continua 2022年第8期72卷 2859-2875页

作者： Areej A.Malibari Siwar Ben Haj Hassine Abdelwahed Motwakel Manar Ahmed Hamza Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayilAsirSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Atherosclerosis diagnosis is an inarticulate and complicated cognitive *** on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical *** the medical diagnostic outcomes need to be prompt and accurate,the recently developed artificial intelligence(AI)and deep learning(DL)models have received considerable attention among research *** study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis disease diagnosis and Classification(MDL-BADDC)*** proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing,feature selection,classification,and parameter ***,the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer(QOBMO)based feature selection ***,the deep stacked autoencoder(DSAE)based classification model is designed for the detection and classification of atherosclerosis ***,the krill herd algorithm(KHA)based parameter tuning technique is applied to properly adjust the parameter *** order to showcase the enhanced classification performance of the MDL-BADDC technique,a wide range of simulations take place on three benchmarks biomedical *** comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.

关键词： Atherosclerosis disease biomedical data data classification machine learning disease diagnosis deep learning

Simulated Annealing with Deep Learning Based Tongue Image Analysis for Heart disease diagnosis

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Intelligent Automation & Soft Computing 2023年第7期37卷 111-126页

作者： S.Sivasubramaniam S.P.Balamurugan Department of Computer and Information Science Annamalai UniversityChidambaram608002India

Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine,for example,traditional Chinese medicine(TCM),Japanese traditional herbal medicine,and traditional Korean medicine(TKM).The diagnosis procedure is mainly based on the expert’s knowledge depending upon the visual inspec-tion comprising color,substance,coating,form,and motion of the *** conventional tongue diagnosis has limitations since the procedure is inconsistent and ***,computer-aided tongue analyses have a greater potential to present objective and more consistent health *** manuscript introduces a novel Simulated Annealing with Transfer Learning based Tongue Image Analysis for disease diagnosis(SADTL-TIADD)*** presented SADTL-TIADD model initially pre-processes the tongue image to improve the ***,the presented SADTL-TIADD technique employed an EfficientNet-based feature extractor to generate useful feature *** turn,the SA with the ELM model enhances classification efficiency for disease detection and *** design of SA-based parameter tuning for heart disease diagnosis shows the novelty of the work.A wide-ranging set of simulations was performed to ensure the improved performance of the SADTL-TIADD *** experimental outcomes highlighted the superior of the presented SADTL-TIADD system over the compared methods with maximum accuracy of 99.30%.

关键词： Tongue color images disease diagnosis transfer learning simulated annealing machine learning

In vivo Self-assembled Peptide Nanoprobes for disease diagnosis

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Chemical Research in Chinese Universities 2021年第4期37卷 855-869页

作者： YANG Jia ZHENG Rui AN Hongwei WANG Hao CAS Center for Excellence in Nanoscience CAS Key Laboratory for Biomedical Effects of Nanomaterials and NanosafetyNational Center for Nanoscience and Technology(NCNST)Beijing100190P.R.China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences(UCAS)Beijing100049P.R.China School of Future Technology University of Chinese Academy of Sciences(UCAS)Beijing100049P.R.China School of Nanoscience and Technology University of Chinese Academy of Sciences(UCAS)Beijing100049P.R.China

In vivo imaging is creating great opportunities for disease diagnosis as a research tool. Probes are usually used to observe physiological structures in vivo clearly. Recent progresses of nanoprobes are important for the generation of high resolution and high contrast images required by accurate and precision disease diagnosis. In vivo self-assembled peptide(SAP) nanoprobes are playing major roles in in vivo imaging by modularity of design, high imaging contrast, response to the location of the lesion, and long-time retention in the lesion. And the response to lesion and long-term retention in there can enhance imaging sensitivity and specificity of in vivo SAP nanoprobes. Therefore, in vivo SAP nanoprobes are simple ancillary contrast entities to optimize the imaging effect. In this review, the recent progress of in vivo SAP nanoprobes for in vivo imaging, from molecular design of peptides to biomedical and clinical applications including disease diagnosis and disease-related molecular imaging is systematically summarized. We evaluate their ability, including sensitivity and specificity to provide relevant information under preoperative and during surgery circumstances and critically their likelihood to be clinically translated. Finally, a brief outlook on remaining challenges and potential directions for future research in this area is presented.

关键词： In vivo self-assembly Nanoprobe Peptide disease diagnosis