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Targets capture by distributed active swarms via bio-inspired reinforcement learning

Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 210-221页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li Institute of Biomechanics and medical engineering Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua University Institute of Nuclear and New Energy technology Tsinghua University Shanghai Aerospace Control technology Institute Shanghai Key Laboratory of Aerospace Intelligent Control technology College of Automation engineering Nanjing University of Aeronautics and Astronautics

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common goals. Learning from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific functions. Here, we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement learning. We exploit collective milling structures of natural herds to cage the targets, and adopt a switching control policy inspired by sperms' chiral dynamics to optimize the trajectories of individuals, through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical, adaptive and scalable manner. There exists a critical swarm size, beyond which the excessive competition between agents would generate large mechanical forces, leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple targets. This work provides physical insights into distributed active swarms and could offer a multilevel, decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices, machine immunity, and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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Data-Driven Healthcare:The Role of Computational Methods in medical Innovation

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Computer Modeling in engineering & Sciences 2025年第1期142卷 1-48页

作者： Hariharasakthisudhan Ponnarengan Sivakumar Rajendran Vikas Khalkar Gunapriya Devarajan Logesh Kamaraj Department of Mechanical engineering Dr.Mahalingam College of Engineering and TechnologyPollachiCoimbatoreTamil Nadu642003India Department of Mechanical engineering JKKN College of Engineering and TechnologyKumarapalayamNamakkalTamil Nadu637209India Department of Mechanical engineering Gharda Institute of TechnologyLavelMaharashtra415708India Department of Electrical and Electronics engineering Sri Eshwar College of EngineeringCoimbatoreTamilnadu641202India Department of Mechanical engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India

The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical *** review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health *** findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and *** synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment ***,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease *** review emphasizes the need for interdisciplinary collaboration to further advance this *** research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and *** fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.

关键词： Computational models biomedical engineering bioinformatics machine learning wearable technology

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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Computer Modeling in engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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Nanocarrier-mediated siRNA delivery:a new approach for the treatment of traumatic brain injury-related Alzheimer's disease

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Neural Regeneration Research 2025年第9期20卷 2538-2555页

作者： Jie Jin Huajing Zhang Qianying Lu Linqiang Tian Sanqiao Yao Feng Lai Yangfan Liang Chuanchuan Liu Yujia Lu Sijia Tian Yanmei Zhao Wenjie Ren Institute of Disaster and Emergency Medicine Tianjin UniversityTianjinChina Key Laboratory for Disaster Medicine technology TianjinChina Henan medical Key Laboratory for Research of Trauma and Orthopedics The Third Affiliated Hospital of Xinxiang Medical UniversityXinxiangHenan ProvinceChina Clinical medical Center of Tissue engineering and Regeneration Xinxiang Medical UniversityXinxiangHenan ProvinceChina Institutes of Health Central Plain Xinxiang Medical UniversityXinxiangHenan ProvinceChina School of Public Health Xinxiang Medical UniversityXinxiangHenan ProvinceChina

Traumatic brain injury and Alzheimer's disease share pathological similarities,including neuronal loss,amyloid-βdeposition,tau hyperphosphorylation,blood-brain barrier dysfunction,neuroinflammation,and cognitive ***,traumatic brain injury can exacerbate Alzheimer's disease-like pathologies,potentially leading to the development of Alzheimer's *** offer a potential solution by facilitating the delive ry of small interfering RNAs across the blood-brain barrier for the targeted silencing of key pathological genes implicated in traumatic brain injury and Alzheimer's disease.U nlike traditional approaches to neuro regeneration,this is a molecula r-targeted strategy,thus avoiding non-specific drug *** review focuses on the use of nanocarrier systems for the efficient and precise delive ry of siRNAs,discussing the advantages,challenges,and future *** principle,siRNAs have the potential to target all genes and non-targetable protein s,holding significant promise for treating various *** the various therapeutic approaches currently available for neurological diseases,siRNA gene silencing can precisely"turn off"the expression of any gene at the genetic level,thus radically inhibiting disease progression;however,a significant challenge lies in delivering siRNAs across the blood-brain *** have received increasing attention as an innovative drug delive ry tool fo r the treatment of brain *** are considered a potential therapeutic strategy with the advantages of being able to cross the blood-brain barrier,targeted drug delivery,enhanced drug stability,and multifunctional *** use of nanoparticles to deliver specific modified siRNAs to the injured brain is gradually being recognized as a feasible and effective *** this strategy is still in the preclinical exploration stage,it is expected to achieve clinical translation in the future,creating a new field of molecular tar

关键词： blood-brain barrier cognitive dysfunction exosomes gene silencing immunotherapy inorganic nanomaterials liposome nanoparticles neuroinflammation polymer nanoparticles stem cells

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Passive activity enhances residual control ability in patients with complete spinal cord injury

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Neural Regeneration Research 2025年第8期20卷 2337-2347页

作者： Yanqing Xiao Mingming Gao Zejia He Jia Zheng Hongming Bai Jia-Sheng Rao Guiyun Song Wei Song Xiaoguang Li Beijing Key Laboratory for Biomaterials and Neural Regeneration Beijing Advanced Innovation Center for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijingChina Department of Rehabilitation Evaluation China Rehabilitation Research CenterBeijingChina Cardiac Center Beijing Children’s HospitalCapital Medical UniversityBeijingChina The State Key Laboratory of Virtual Reality technology and Systems Beihang UniversityBeijingChina Department of Rehabilitation engineering China Rehabilitation Science InstituteBeijingChina

Patients with complete spinal cord injury retain the potential for volitional muscle activity in muscles located below the spinal injury ***,because of prolonged inactivity,initial attempts to activate these muscles may not effectively engage any of the remaining neurons in the descending pathway.A previous study unexpectedly found that a brief clinical round of passive activity significantly increased volitional muscle activation,as measured by surface *** this study,we further explored the effect of passive activity on surface electromyographic signals during volitional control tasks among individuals with complete spinal cord *** patients with chronic complete thoracic spinal cord injury were *** electromyography data from eight major leg muscles were acquired and compared before and after the passive activity *** results indicated that the passive activity led to an increased number of activated volitional muscles and an increased frequency of *** the cumulative root mean square of surface electromyography amplitude for volitional control of movement showed a slight increase after passive activity,the difference was not statistically *** findings suggest that brief passive activity may enhance the ability to initiate volitional muscle activity during surface electromyography tasks and underscore the potential of passive activity for improving residual motor control among patients with motor complete spinal cord injury.

关键词： complete spinal cord injury cycle training epidural electrical stimulation motor training passive activity physiological state spinal cord circuit surface electromyography volitional control task

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Surfing the nano-wave:Emerging trends in cancer diagnostics and therapy

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Nano Research 2025年第1期18卷 520-544页

作者： Genping Fu Yuting Weng Yunfan Li Carl Ho Yiyu Fu Wenxuan Wang Songbin Fu Wenjing Sun Jie Lin Xiaoyu Fan Binbin Jia Mengdi Cai Linlin Li Laboratory of medical Genetics Harbin Medical UniversityHarbin 150081China Key Laboratory of Preservation of Human Genetics Resources and Disease Control in China(Harbin medical University) Ministry of EducationHarbin 150081China Beijing Key Laboratory for Optical Materials and Photonic Devices Department of ChemistryCapital Normal UniversityBeijing 100048China College of Materials and Chemical engineering Key Laboratory of Inorganic Nonmetallic Crystalline and Energy Conversion MaterialsChina Three Gorges UniversityYichang 443002China Washington University School of Medicine in St.Louis St.LouisMO 63110USA Ningbo Institute of Materials technology and engineering Chinese Academy of SciencesNingbo 315201China Aix-Marseille College Wuhan University of TechnologyWuhan 430070China

Facing the growing global challenge posed by cancer,the quest for more accurate and potent cancer diagnostic and therapeutic strategies continues to require a multidisciplinary integration *** review firstly summarizes various types of nanoparticles in cancer ***,it offers a comprehensive overview of signalenhancing techniques for visualizing in situ tumors,along with multimodal diagnostic methods for detecting *** for tumor therapy,cutting-edge drug delivery methods that can cross biological barriers and the pinpoint targeting of tumor lesions for precise medical intervention are *** the domain of therapeutic diagnostics,we elucidate the theoretical underpinnings and structural paradigms that underlie a spectrum of advanced diagnostic and therapeutic ***,we present a compendium of publications delineating the clinical applications of each nano-based theragnostic integration ***,this comprehensive review discusses the safety concerns pertaining to the clinical application of nanoparticles and proposes some strategic recommendations to enhance the precision and safety of theragnostic-guided,nanotechnology-based clinical practices.A deeper understanding of nanomaterials,together with intimate interdisciplinary collaborations,nano-wave will most probably guide human beings to win the battle against cancers.

关键词： nanoparticles drug delivery gastric cancer glioma glioblastoma

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Progress of research in the application of ultrasound technology for the treatment of Alzheimer’s disease

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Neural Regeneration Research 2025年第10期20卷 2823-2837页

作者： Qiuquan Cai Lianghui Meng Meina Quan Ling Wang Jing Ren Chenguang Zheng Jiajia Yang Dong Ming Academy of medical engineering and Translational Medicine Tianjin UniversityTianjinChina Innovation Center for Neurological Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin UniversityTianjinChina Tianjin Key Laboratory of Brain Science and Neural engineering Tianjin UniversityTianjinChina

Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.

关键词： Alzheimer’s disease blood-brain barrier drugs magnetic resonance imaging-guided focused ultrasound microbubbles scanning ultrasound ultrasound ultrasound stimulation

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Mechanism of inflammatory response and therapeutic effects of stem cells in ischemic stroke:current evidence and future perspectives

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Neural Regeneration Research 2025年第1期20卷 67-81页

作者： Yubo Wang Tingli Yuan Tianjie Lyu Ling Zhang Meng Wang Zhiying He Yongjun Wang Zixiao Li Vascular Neurology Department of NeurologyBeijing Tiantan HospitalCapital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalCapital Medical UniversityBeijingChina Shanghai engineering Research Center of Stem Cells Translational Medicine ShanghaiChina National Center for Healthcare Quality Management in Neurological Diseases Beijing Tiantan HospitalCapital Medical UniversityBeijingChina Institute for Regenerative Medicine Shanghai East HospitalSchool of Life Sciences and TechnologyTongji UniversityShanghaiChina Chinese Institute for Brain Research BeijingChina Research Unit of Artificial Intelligence in Cerebrovascular Disease Chinese Academy of Medical SciencesBeijingChina Beijing engineering Research Center of Digital Healthcare for Neurological Diseases BeijingChina

Ischemic stroke is a leading cause of death and disability worldwide,with an increasing trend and tendency for onset at a younger ***,in particular,bears a high burden of stroke *** recent years,the inflammatory response after stroke has become a research hotspot:understanding the role of inflammatory response in tissue damage and repair following ischemic stroke is an important direction for its *** review summarizes several major cells involved in the inflammatory response following ischemic stroke,including microglia,neutrophils,monocytes,lymphocytes,and ***,we have also highlighted the recent progress in various treatments for ischemic stroke,particularly in the field of stem cell ***,understanding the complex interactions between inflammation and ischemic stroke can provide valuable insights for developing treatment strategies and improving patient *** cell therapy may potentially become an important component of ischemic stroke treatment.

关键词： cell therapy immune cell inflammatory ischemic stroke stem cell

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Early identification of stroke through deep learning with multi-modal human speech and movement data

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Neural Regeneration Research 2025年第1期20卷 234-241页

作者： Zijun Ou Haitao Wang Bin Zhang Haobang Liang Bei Hu Longlong Ren Yanjuan Liu Yuhu Zhang Chengbo Dai Hejun Wu Weifeng Li Xin Li School of Computer Science and engineering Sun Yat-sen UniversityGuangzhouGuangdong ProvinceChina Department of Neurology Guangdong Neuroscience InstituteGuangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina Department of Emergency Medicine Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina

Early identification and treatment of stroke can greatly improve patient outcomes and quality of *** clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized *** this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute *** collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the *** compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and *** found that the findings of our deep learning model had a higher clinical value compared with the other ***,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech *** results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

关键词： artificial intelligence deep learning diagnosis early detection FAST screening stroke

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical engineering of Southern medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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