On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th Nation...On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.展开更多
Magnesium and its related materials have potential applications in the automotive sector for weight reduction,in energy storage technologies such as batteries and hydrogen storage,and in biomedical field due to their ...Magnesium and its related materials have potential applications in the automotive sector for weight reduction,in energy storage technologies such as batteries and hydrogen storage,and in biomedical field due to their biodegradability.In comparison,the researches on the latter ones are currently receiving more and more interests.This paper explores recent research advancements in Mg-based materials in these fields especially within recent 4 years in Germany.展开更多
As a vectorial property,polarization encodes high-dimensional information of light.Polarization-based imaging can characterize detailed structural features of biomedical samples label-freely.However,compared with othe...As a vectorial property,polarization encodes high-dimensional information of light.Polarization-based imaging can characterize detailed structural features of biomedical samples label-freely.However,compared with other fundamental properties of light,such as intensity,wavelength and phase,polarization has a shorter application history in biomedicine,because of the requirement for both advanced polarization optical components and computational approaches,which can be achieved nowadays with the fast theoretical and hardware development.展开更多
Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in relat...Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.展开更多
In this review,we propose a comprehensive overview of additive manufacturing(AM)technologies and design possibilities in manufacturing metamaterials for various applications in the biomedical field,of which many are i...In this review,we propose a comprehensive overview of additive manufacturing(AM)technologies and design possibilities in manufacturing metamaterials for various applications in the biomedical field,of which many are inspired by nature itself.It describes how new AM technologies(e.g.continuous liquid interface production and multiphoton polymerization,etc)and recent developments in more mature AM technologies(e.g.powder bed fusion,stereolithography,and extrusion-based bioprinting(EBB),etc)lead to more precise,efficient,and personalized biomedical components.EBB is a revolutionary topic creating intricate models with remarkable mechanical compatibility of metamaterials,for instance,stress elimination for tissue engineering and regenerative medicine,negative or zero Poisson’s ratio.By exploiting the designs of porous structures(e.g.truss,triply periodic minimal surface,plant/animal-inspired,and functionally graded lattices,etc),AM-made bioactive bone implants,artificial tissues,and organs are made for tissue replacement.The material palette of the AM metamaterials has high diversity nowadays,ranging from alloys and metals(e.g.cobalt-chromium alloys and titanium,etc)to polymers(e.g.biodegradable polycaprolactone and polymethyl methacrylate,etc),which could be even integrated within bioactive ceramics.These advancements are driving the progress of the biomedical field,improving human health and quality of life.展开更多
The second near-infrared window(NIR-II,900-1880 nm)overcomes critical limitations of visible(360-760 nm)and NIR-I(760-900 nm)imaging—including restricted penetration depth,low signal-to-back⁃ground ratio,and tissue a...The second near-infrared window(NIR-II,900-1880 nm)overcomes critical limitations of visible(360-760 nm)and NIR-I(760-900 nm)imaging—including restricted penetration depth,low signal-to-back⁃ground ratio,and tissue autofluorescence—establishing its pivotal role for in vivo deep-tissue bioimaging.With exponential growth in NIR-II photodiagnosis and phototherapy research over the past decade,bibliometric analy⁃sis is essential to map the evolving landscape and guide strategic priorities.We systematically analyzed 2,491 NIR-II-related publications(2009-2023)from the Web of Science Core Collection,employing scientometric tools for distinct analytical purposes:(a)VOSviewer,SCImago Graphica,and Gephi for co-authorship and co-occur⁃rence network mapping;(b)the R bibliometrix package for tracking field evolution and identifying high-impact publications/journals.The search retrieved 2491 studies from 359 journals originating from 54 countries.The country with the most published articles is China.Chinese institutions drive>60%of publications,with Stanford University(USA)and Nanyang Technological University(Singapore)ranked as the top two institutions by re⁃search quality.International cooperation is becoming increasingly frequent.Fan Quli,Tang Benzhong and Dai Hongjie are the top 3 productive authors in this field.Keyword evolution identifies"photodynamic therapy"and"immunotherapy"as pivotal future directions.We summarize the most cited literatures and NIR-II imaging clini⁃cal trials.This study delineates the NIR-II research trajectory,highlighting China's leadership,intensifying glob⁃al collaboration,and interdisciplinary convergence.Future efforts should prioritize the novel NIR-II probe devel⁃opment for NIR-II imaging and clinical translation of photodynamic/immunotherapy combinational platforms.展开更多
Natural fibers,as a typical renewable and biodegradable material,have shown great potential for many applications(e.g.,catalysis,hydrogel,biomedicine)in recent years.Recently,the growing importance of natural fibers i...Natural fibers,as a typical renewable and biodegradable material,have shown great potential for many applications(e.g.,catalysis,hydrogel,biomedicine)in recent years.Recently,the growing importance of natural fibers in these photo-driven applications is reflected by the increasing number of publications.The utilization of renewable materials in photo-driven applications not only contributes to mitigating the energy crisis but also facilitates the transition of society toward a low-carbon economy,thus enabling harmonious coexistence between humans and the environment within the context of sustainable development.This paper provides an overview of the recent advances of natural fibers which acted as substrates or precursors to construct an efficient system of light utilization.The different chemical properties and pretreatment methods of cellulose affect its performance in final photo-driven applications,including solar-driven water purification,photocatalysis,and photothermal biomedical applications.Nevertheless,current research rarely conducts a comprehensive comparisonof themfromabroadperspective.As a whole,this review first reveals the different structural advantages as well as thematching degree between natural fibers(bacterial cellulose,plant cellulose,and animal fiber)and three typical photo-driven applications.Besides,new strategies for optimizing the utilization of natural fibers are an important subject under the background of low-carbon and circular economy.Finally,some suggestions and prospects are put forward for the limitations and research prospects of natural fibers in photo-driven applications,which provides a new idea for the synthesis of renewable functional materials.展开更多
A much more sustainable,cost effective and very flexible process for manufacturing critical fibres based on ultra high molecular weight polyethylene(UHMWPE)is being launched by the UK’s Fibre Extrusion Technologies(F...A much more sustainable,cost effective and very flexible process for manufacturing critical fibres based on ultra high molecular weight polyethylene(UHMWPE)is being launched by the UK’s Fibre Extrusion Technologies(FET).展开更多
This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We syste...This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.展开更多
Additive manufacturing has emerged as a transformative technology for producing biomedical metals and implants,offering the potential to revolutionize patient care and treatment outcomes.This article reviews the recen...Additive manufacturing has emerged as a transformative technology for producing biomedical metals and implants,offering the potential to revolutionize patient care and treatment outcomes.This article reviews the recent advances in additive manufacturing(AM)of biomedical metal implants,especially load-bearing biomedical alloys,biodegradable alloys,novel metals,and 4D printing,whose properties are systematically assessed to facilitate material selection for specific medical applications.The applications of the most cutting-edge artificial intelligence in AM and surface functional modification are also presented.This article also explores the application of AM in various medical specialties,such as orthopedics,dentistry,cardiology,and neurosurgery,demonstrating its potential to solve complex clinical challenges and advance patient-centered healthcare solutions.Furthermore,it highlights the critical roles of AM in shaping the future of medical implant manufacturing.The optimistic outlook on the bright future of AM in medical metals delivers personalized,high-performance medical implants that improve patient treatment outcomes and well-being.展开更多
Biodegradable magnesium(Mg)-based metals can undergo spontaneous corrosion and full degradation in the human body,releasing magnesium ions,hydroxides,and hydrogen.Mg and its alloys have shown preliminary success as an...Biodegradable magnesium(Mg)-based metals can undergo spontaneous corrosion and full degradation in the human body,releasing magnesium ions,hydroxides,and hydrogen.Mg and its alloys have shown preliminary success as an implantable biomaterial.Current research on biodegradable Mg-based metals addresses clinical challenges,including material design and preparation,property enhancement,and exploring relevant biological functions.This review provides a comprehensive overview of the biomedical applications of Mg-based implants across eight fields:cardiovascular,orthopedics,stomatology,general surgery,neurosurgery,fat metabolism,and other potential areas,building upon previously published work.The challenges and prospects of biodegradable Mg-based implants in these application fields are discussed.展开更多
Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert p...Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert performance.This survey reviews the principal model families as convolutional,recurrent,generative,reinforcement,autoencoder,and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation,classification,reconstruction,and anomaly detection.A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust,context-aware predictions.To support clinical adoption,we outline post-hoc explainability techniques(Grad-CAM,SHAP,LIME)and describe emerging intrinsically interpretable designs that expose decision logic to end users.Regulatory guidance from the U.S.FDA,the European Medicines Agency,and the EU AI Act is summarised,linking transparency and lifecycle-monitoring requirements to concrete development practices.Remaining challenges as data imbalance,computational cost,privacy constraints,and cross-domain generalization are discussed alongside promising solutions such as federated learning,uncertainty quantification,and lightweight 3-D architectures.The article therefore offers researchers,clinicians,and policymakers a concise,practice-oriented roadmap for deploying trustworthy deep-learning systems in healthcare.展开更多
Background:Diabetic retinopathy remains one of the leading causes of vision impairment globally and poses diagnostic challenges due to the complexity of clinical imaging data and variability in disease progression.In ...Background:Diabetic retinopathy remains one of the leading causes of vision impairment globally and poses diagnostic challenges due to the complexity of clinical imaging data and variability in disease progression.In this study,we propose an innovative methodology that integrates artificial intelligence and quantum computing to enhance the early detection and clinical management of diabetic retinopathy.Methods:We developed a hybrid model combining machine learning algorithms with simulated quantum circuits to classify retinal images and associated clinical data.Anonymized datasets were used,and deep inductive transfer techniques were applied to improve diagnostic precision and generalizability.Results:The proposed model achieved a classification accuracy of 94.6%,significantly reducing diagnostic time and improving the prioritization of high-risk cases compared to conventional methods.The hybrid approach demonstrated superior performance in processing speed and accuracy for complex clinical scenarios.Conclusion:This study highlights the potential of combining AI and quantum computing to revolutionize the diagnosis of diabetic retinopathy.The proposed model provides a scalable and efficient solution for clinical environments,enabling faster and more accurate decision-making in ophthalmic care.展开更多
The field of photocatalysis has witnessed a significant advancement in the development of bioinspired and biomimetic photocatalysts for various biomedical applications,including drug delivery,tissue engineering,cancer...The field of photocatalysis has witnessed a significant advancement in the development of bioinspired and biomimetic photocatalysts for various biomedical applications,including drug delivery,tissue engineering,cancer therapy,and bioimaging.Nature has evolved efficient light-harvesting systems and energy conversion mechanisms,which serve as a benchmark for researchers.However,reproducing such complexity and harnessing it for biomedical applications is a daunting task.It requires a comprehensive understanding of the underlying biological processes and the ability to replicate them synthetically.By utilizing light energy,these photocatalysts can trigger specific chemical reactions,leading to targeted drug release,enhanced tissue regeneration,and precise imaging of biological structures.In this context,addressing the stability,long-term performance,scalability,and costeffectiveness of these materials is crucial for their widespread implementation in biomedical applications.While challenges such as complexity and stability persist,their advantages such as targeted drug delivery and personalized medicine make them a fascinating area of research.The purpose of this review is to provide a comprehensive analysis and evaluation of existing research,highlighting the advancements,current challenges,advantages,limitations,and future prospects of bioinspired and biomimetic photocatalysts in biomedicine.展开更多
Designing advanced hydrogels with controlled mechanical properties,drug delivery manner and multifunctional properties will be beneficial for biomedical applications.However,the further development of hydrogel is limi...Designing advanced hydrogels with controlled mechanical properties,drug delivery manner and multifunctional properties will be beneficial for biomedical applications.However,the further development of hydrogel is limited due to its poor mechanical property and structural diversity.Hydrogels combined with polymeric micelles to obtain micelle-hydrogel composites have been designed for synergistic enhancement of each original properties.Incorporation polymeric micelles into hydrogel networks can not only enhance the mechanical property of hydrogel,but also expand the functionality of hydrogel.Recent advances in polymeric micelle-hydrogel composites are herein reviewed with a focus on three typical micelle incorporation methods.In this review,we will also highlight some emerging biomedical applications in developing micelle-hydrogel composite with multiple functionalities.In addition,further development and application prospects of the micelle-hydrogels composites have also been addressed.展开更多
Welcome to the 4th volume of Biomedical Engineering Communications the first issue of 2025!Biomedical engineering is a rapidly evolving field that combines engineering principles with medical and biological sciences t...Welcome to the 4th volume of Biomedical Engineering Communications the first issue of 2025!Biomedical engineering is a rapidly evolving field that combines engineering principles with medical and biological sciences to create innovative healthcare technologies.Biomedical engineering brings an interdisciplinary,problem-solving approach to bioengineering,biology and medicine.This interdisciplinary field is essential for developing advanced medical devices,diagnostic tools,and therapeutic solutions that enhance patient care and improve health outcomes.It allows them to develop technologies and systems that directly contribute to diagnosing,treating and preventing diseases.展开更多
Introduction Since the 21st century,the biomedical field has gained increasing attention.The biomedical field mainly encompasses biology,materials science,pharmacology,and drug delivery,etc.These areas hold significan...Introduction Since the 21st century,the biomedical field has gained increasing attention.The biomedical field mainly encompasses biology,materials science,pharmacology,and drug delivery,etc.These areas hold significant importance for human society in terms of health protection,disease diagnosis and treatment,via medical technology innovation and drug development.Consequently,scientists place great emphasis on research in this domain.It must be noted that the research process in biomedicine mainly includes topic selection,experimentation,analysis,and summary.Among these,topic selection is a critical step that affects the entire process.This topic selection not only clarifies the direction and objectives of the study but also provides a clear framework for subsequent research,thereby ensuring scientific rigor and effectiveness while laying a solid foundation for the result analysis.Thus,how to approach topic selection is a crucial issue that requires careful consideration.展开更多
Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional a...Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.展开更多
Compared to conventional thermomechanical processing,additive manufacturing offers the advantage of producing a strong <100> texture in β-Ti alloys for a low elastic modulus.Further reducing the elastic modulus...Compared to conventional thermomechanical processing,additive manufacturing offers the advantage of producing a strong <100> texture in β-Ti alloys for a low elastic modulus.Further reducing the elastic modulus of these additively manufactured alloys to values closer to that of bone tissue would be beneficial for practical applications.In this work,a β-type Ti-24Nb-4Zr-8Sn alloy was fabricated via electron beam melting to investigate its microstructure and mechanical properties.The results indicate that samples built vertically relative to the build orientation can achieve a dynamic elastic modulus of 39.5 GPa,which is about 15 GPa lower than those of the most additively manufactured titanium alloys.This is coupled with a high strength-to-modulus ratio of 1.7% and an elongation exceeding 30%.These favorable properties are attributed to a strong <100> texture of the β-phase matrix combined with an electron-to-atom ratio of 4.15,which is close to the elastic stability limit of the bodycentered cubic crystal.Aided by its orientation-dependent elastic moduli of single crystals,a model was established to evaluate the elastic anisotropy of the as-manufactured samples and to get a method to further reduce the elastic modulus.These results would be helpful for additively manufactured titanium alloys to further reduce elastic modulus and improve biomechanical compatibility.展开更多
As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate...As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.展开更多
文摘On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.
文摘Magnesium and its related materials have potential applications in the automotive sector for weight reduction,in energy storage technologies such as batteries and hydrogen storage,and in biomedical field due to their biodegradability.In comparison,the researches on the latter ones are currently receiving more and more interests.This paper explores recent research advancements in Mg-based materials in these fields especially within recent 4 years in Germany.
文摘As a vectorial property,polarization encodes high-dimensional information of light.Polarization-based imaging can characterize detailed structural features of biomedical samples label-freely.However,compared with other fundamental properties of light,such as intensity,wavelength and phase,polarization has a shorter application history in biomedicine,because of the requirement for both advanced polarization optical components and computational approaches,which can be achieved nowadays with the fast theoretical and hardware development.
基金supported by the National Natural Science Foundation of China(61902095).
文摘Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
基金sponsored by the Science and Technology Program of Hubei Province,China(2022EHB020,2023BBB096)support provided by Centre of the Excellence in Production Research(XPRES)at KTH。
文摘In this review,we propose a comprehensive overview of additive manufacturing(AM)technologies and design possibilities in manufacturing metamaterials for various applications in the biomedical field,of which many are inspired by nature itself.It describes how new AM technologies(e.g.continuous liquid interface production and multiphoton polymerization,etc)and recent developments in more mature AM technologies(e.g.powder bed fusion,stereolithography,and extrusion-based bioprinting(EBB),etc)lead to more precise,efficient,and personalized biomedical components.EBB is a revolutionary topic creating intricate models with remarkable mechanical compatibility of metamaterials,for instance,stress elimination for tissue engineering and regenerative medicine,negative or zero Poisson’s ratio.By exploiting the designs of porous structures(e.g.truss,triply periodic minimal surface,plant/animal-inspired,and functionally graded lattices,etc),AM-made bioactive bone implants,artificial tissues,and organs are made for tissue replacement.The material palette of the AM metamaterials has high diversity nowadays,ranging from alloys and metals(e.g.cobalt-chromium alloys and titanium,etc)to polymers(e.g.biodegradable polycaprolactone and polymethyl methacrylate,etc),which could be even integrated within bioactive ceramics.These advancements are driving the progress of the biomedical field,improving human health and quality of life.
基金Supported by National Natural Science Foundation of China(81874059 and 82102105)the Natural Science Foundation of Zhejiang Province(LQ22H160017)the China Postdoctoral Science Foundation(2021M702825).
文摘The second near-infrared window(NIR-II,900-1880 nm)overcomes critical limitations of visible(360-760 nm)and NIR-I(760-900 nm)imaging—including restricted penetration depth,low signal-to-back⁃ground ratio,and tissue autofluorescence—establishing its pivotal role for in vivo deep-tissue bioimaging.With exponential growth in NIR-II photodiagnosis and phototherapy research over the past decade,bibliometric analy⁃sis is essential to map the evolving landscape and guide strategic priorities.We systematically analyzed 2,491 NIR-II-related publications(2009-2023)from the Web of Science Core Collection,employing scientometric tools for distinct analytical purposes:(a)VOSviewer,SCImago Graphica,and Gephi for co-authorship and co-occur⁃rence network mapping;(b)the R bibliometrix package for tracking field evolution and identifying high-impact publications/journals.The search retrieved 2491 studies from 359 journals originating from 54 countries.The country with the most published articles is China.Chinese institutions drive>60%of publications,with Stanford University(USA)and Nanyang Technological University(Singapore)ranked as the top two institutions by re⁃search quality.International cooperation is becoming increasingly frequent.Fan Quli,Tang Benzhong and Dai Hongjie are the top 3 productive authors in this field.Keyword evolution identifies"photodynamic therapy"and"immunotherapy"as pivotal future directions.We summarize the most cited literatures and NIR-II imaging clini⁃cal trials.This study delineates the NIR-II research trajectory,highlighting China's leadership,intensifying glob⁃al collaboration,and interdisciplinary convergence.Future efforts should prioritize the novel NIR-II probe devel⁃opment for NIR-II imaging and clinical translation of photodynamic/immunotherapy combinational platforms.
基金supported by the Jiangsu Province Key Laboratory of Fine Petrochemical Engineering(No.KF2204).
文摘Natural fibers,as a typical renewable and biodegradable material,have shown great potential for many applications(e.g.,catalysis,hydrogel,biomedicine)in recent years.Recently,the growing importance of natural fibers in these photo-driven applications is reflected by the increasing number of publications.The utilization of renewable materials in photo-driven applications not only contributes to mitigating the energy crisis but also facilitates the transition of society toward a low-carbon economy,thus enabling harmonious coexistence between humans and the environment within the context of sustainable development.This paper provides an overview of the recent advances of natural fibers which acted as substrates or precursors to construct an efficient system of light utilization.The different chemical properties and pretreatment methods of cellulose affect its performance in final photo-driven applications,including solar-driven water purification,photocatalysis,and photothermal biomedical applications.Nevertheless,current research rarely conducts a comprehensive comparisonof themfromabroadperspective.As a whole,this review first reveals the different structural advantages as well as thematching degree between natural fibers(bacterial cellulose,plant cellulose,and animal fiber)and three typical photo-driven applications.Besides,new strategies for optimizing the utilization of natural fibers are an important subject under the background of low-carbon and circular economy.Finally,some suggestions and prospects are put forward for the limitations and research prospects of natural fibers in photo-driven applications,which provides a new idea for the synthesis of renewable functional materials.
文摘A much more sustainable,cost effective and very flexible process for manufacturing critical fibres based on ultra high molecular weight polyethylene(UHMWPE)is being launched by the UK’s Fibre Extrusion Technologies(FET).
基金The Natural Sciences and Engineering Research Council of Canada(NSERC)funded this review study.
文摘This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.
基金the financial supports from the ECU industrial Grant(No.G1006320)ECU DVC strategic research support fund(Grant Number 23965)National Natural Science Foundation of China under Grant Nos.52404382,52274387 and 52311530772。
文摘Additive manufacturing has emerged as a transformative technology for producing biomedical metals and implants,offering the potential to revolutionize patient care and treatment outcomes.This article reviews the recent advances in additive manufacturing(AM)of biomedical metal implants,especially load-bearing biomedical alloys,biodegradable alloys,novel metals,and 4D printing,whose properties are systematically assessed to facilitate material selection for specific medical applications.The applications of the most cutting-edge artificial intelligence in AM and surface functional modification are also presented.This article also explores the application of AM in various medical specialties,such as orthopedics,dentistry,cardiology,and neurosurgery,demonstrating its potential to solve complex clinical challenges and advance patient-centered healthcare solutions.Furthermore,it highlights the critical roles of AM in shaping the future of medical implant manufacturing.The optimistic outlook on the bright future of AM in medical metals delivers personalized,high-performance medical implants that improve patient treatment outcomes and well-being.
基金supported by grants from the Fundamental Research Funds for the Central Universities(No.2232024D-34 and No 2232023A-10)the National Natural Science Foundation of China(No.52201300)+4 种基金the National Key R&D Program of China(No.2023YFC2416800)the Shanghai Pujiang Program(No.23PJ1400500 and No 23PJ1400600)the State Key Laboratory for Modification of Chemical Fibers and Polymer Materials,Major/key program(No.23M1060280)the Science and Technology Project of Jiangsu Province(No.BE2022758)the Medicine-Engineering Interdisciplinary Project of Shanghai Xuhui District Dental Center(No.SHXYFYG202305).
文摘Biodegradable magnesium(Mg)-based metals can undergo spontaneous corrosion and full degradation in the human body,releasing magnesium ions,hydroxides,and hydrogen.Mg and its alloys have shown preliminary success as an implantable biomaterial.Current research on biodegradable Mg-based metals addresses clinical challenges,including material design and preparation,property enhancement,and exploring relevant biological functions.This review provides a comprehensive overview of the biomedical applications of Mg-based implants across eight fields:cardiovascular,orthopedics,stomatology,general surgery,neurosurgery,fat metabolism,and other potential areas,building upon previously published work.The challenges and prospects of biodegradable Mg-based implants in these application fields are discussed.
基金supported by the Science Committee of the Ministry of Higher Education and Science of the Republic of Kazakhstan within the framework of grant AP23489899“Applying Deep Learning and Neuroimaging Methods for Brain Stroke Diagnosis”.
文摘Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing,enabling automated lesion detection,physiological monitoring,and therapy planning with accuracy that rivals expert performance.This survey reviews the principal model families as convolutional,recurrent,generative,reinforcement,autoencoder,and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation,classification,reconstruction,and anomaly detection.A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust,context-aware predictions.To support clinical adoption,we outline post-hoc explainability techniques(Grad-CAM,SHAP,LIME)and describe emerging intrinsically interpretable designs that expose decision logic to end users.Regulatory guidance from the U.S.FDA,the European Medicines Agency,and the EU AI Act is summarised,linking transparency and lifecycle-monitoring requirements to concrete development practices.Remaining challenges as data imbalance,computational cost,privacy constraints,and cross-domain generalization are discussed alongside promising solutions such as federated learning,uncertainty quantification,and lightweight 3-D architectures.The article therefore offers researchers,clinicians,and policymakers a concise,practice-oriented roadmap for deploying trustworthy deep-learning systems in healthcare.
文摘Background:Diabetic retinopathy remains one of the leading causes of vision impairment globally and poses diagnostic challenges due to the complexity of clinical imaging data and variability in disease progression.In this study,we propose an innovative methodology that integrates artificial intelligence and quantum computing to enhance the early detection and clinical management of diabetic retinopathy.Methods:We developed a hybrid model combining machine learning algorithms with simulated quantum circuits to classify retinal images and associated clinical data.Anonymized datasets were used,and deep inductive transfer techniques were applied to improve diagnostic precision and generalizability.Results:The proposed model achieved a classification accuracy of 94.6%,significantly reducing diagnostic time and improving the prioritization of high-risk cases compared to conventional methods.The hybrid approach demonstrated superior performance in processing speed and accuracy for complex clinical scenarios.Conclusion:This study highlights the potential of combining AI and quantum computing to revolutionize the diagnosis of diabetic retinopathy.The proposed model provides a scalable and efficient solution for clinical environments,enabling faster and more accurate decision-making in ophthalmic care.
文摘The field of photocatalysis has witnessed a significant advancement in the development of bioinspired and biomimetic photocatalysts for various biomedical applications,including drug delivery,tissue engineering,cancer therapy,and bioimaging.Nature has evolved efficient light-harvesting systems and energy conversion mechanisms,which serve as a benchmark for researchers.However,reproducing such complexity and harnessing it for biomedical applications is a daunting task.It requires a comprehensive understanding of the underlying biological processes and the ability to replicate them synthetically.By utilizing light energy,these photocatalysts can trigger specific chemical reactions,leading to targeted drug release,enhanced tissue regeneration,and precise imaging of biological structures.In this context,addressing the stability,long-term performance,scalability,and costeffectiveness of these materials is crucial for their widespread implementation in biomedical applications.While challenges such as complexity and stability persist,their advantages such as targeted drug delivery and personalized medicine make them a fascinating area of research.The purpose of this review is to provide a comprehensive analysis and evaluation of existing research,highlighting the advancements,current challenges,advantages,limitations,and future prospects of bioinspired and biomimetic photocatalysts in biomedicine.
基金the Natural Science Basic Research Program of Shaanxi Province(No.2023-JC-YB-101)the Basic Science Research Program of Shaanxi Basic Sciences Institute(Chemistry,Biology)(No.22JHQ079)National Natural Science Foundation of China(No.82272150).
文摘Designing advanced hydrogels with controlled mechanical properties,drug delivery manner and multifunctional properties will be beneficial for biomedical applications.However,the further development of hydrogel is limited due to its poor mechanical property and structural diversity.Hydrogels combined with polymeric micelles to obtain micelle-hydrogel composites have been designed for synergistic enhancement of each original properties.Incorporation polymeric micelles into hydrogel networks can not only enhance the mechanical property of hydrogel,but also expand the functionality of hydrogel.Recent advances in polymeric micelle-hydrogel composites are herein reviewed with a focus on three typical micelle incorporation methods.In this review,we will also highlight some emerging biomedical applications in developing micelle-hydrogel composite with multiple functionalities.In addition,further development and application prospects of the micelle-hydrogels composites have also been addressed.
文摘Welcome to the 4th volume of Biomedical Engineering Communications the first issue of 2025!Biomedical engineering is a rapidly evolving field that combines engineering principles with medical and biological sciences to create innovative healthcare technologies.Biomedical engineering brings an interdisciplinary,problem-solving approach to bioengineering,biology and medicine.This interdisciplinary field is essential for developing advanced medical devices,diagnostic tools,and therapeutic solutions that enhance patient care and improve health outcomes.It allows them to develop technologies and systems that directly contribute to diagnosing,treating and preventing diseases.
文摘Introduction Since the 21st century,the biomedical field has gained increasing attention.The biomedical field mainly encompasses biology,materials science,pharmacology,and drug delivery,etc.These areas hold significant importance for human society in terms of health protection,disease diagnosis and treatment,via medical technology innovation and drug development.Consequently,scientists place great emphasis on research in this domain.It must be noted that the research process in biomedicine mainly includes topic selection,experimentation,analysis,and summary.Among these,topic selection is a critical step that affects the entire process.This topic selection not only clarifies the direction and objectives of the study but also provides a clear framework for subsequent research,thereby ensuring scientific rigor and effectiveness while laying a solid foundation for the result analysis.Thus,how to approach topic selection is a crucial issue that requires careful consideration.
基金Open Access funding provided by the National Institutes of Health(NIH)The funding for this project was provided by NCATS Intramural Fund.
文摘Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.
基金financially supported by the National Natural Science Foundation of China(Nos.U2341259,52401254 and 52321001)the National Key Research and Development Program of China(No.2023YFC2412600)+1 种基金the China Postdoctoral Science Foundation(No.2024M753297 and GZC20241759)the Liaoning Outstanding Youth Foundation(No.2024JH3/50100015)
文摘Compared to conventional thermomechanical processing,additive manufacturing offers the advantage of producing a strong <100> texture in β-Ti alloys for a low elastic modulus.Further reducing the elastic modulus of these additively manufactured alloys to values closer to that of bone tissue would be beneficial for practical applications.In this work,a β-type Ti-24Nb-4Zr-8Sn alloy was fabricated via electron beam melting to investigate its microstructure and mechanical properties.The results indicate that samples built vertically relative to the build orientation can achieve a dynamic elastic modulus of 39.5 GPa,which is about 15 GPa lower than those of the most additively manufactured titanium alloys.This is coupled with a high strength-to-modulus ratio of 1.7% and an elongation exceeding 30%.These favorable properties are attributed to a strong <100> texture of the β-phase matrix combined with an electron-to-atom ratio of 4.15,which is close to the elastic stability limit of the bodycentered cubic crystal.Aided by its orientation-dependent elastic moduli of single crystals,a model was established to evaluate the elastic anisotropy of the as-manufactured samples and to get a method to further reduce the elastic modulus.These results would be helpful for additively manufactured titanium alloys to further reduce elastic modulus and improve biomechanical compatibility.
基金supported by the China Fundamental Research Funds for the Central Universities(No.2662022XXYJ001,2662022JC004,2662023XXPY005)。
文摘As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.
