Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation.Metal-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fu...Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation.Metal-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen bubbles.This novel feature has projected degradable metallic micromotors for active in vivo drug delivery applications.In addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and performance.Its propulsion efficiency and life span govern the overall performance of a degradable metallic micromotor.Here we review the design and recent advancements of metallic degradable micromotors.Furthermore,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical applications.Moreover,we discuss micromotors’efficacy in detecting and destroying environmental pollutants.Finally,we address the limitations and future research directions of degradable metallic micromotors.展开更多
There is a growing global awareness of environmental,social,and governance(ESG)concerns.The biopharmaceutical industry is an important field that affects human health and well-being,and its sustainable development is ...There is a growing global awareness of environmental,social,and governance(ESG)concerns.The biopharmaceutical industry is an important field that affects human health and well-being,and its sustainable development is now the industry’s focus.Based on the current state of the green development of China’s biopharmaceutical industry,the article proposes suggestions and paths for promoting the industry to better fulfill its social responsibilities and protect the environment while pursuing economic benefits.By doing so,the industry can make a greater contribution to global public health and become an important factor in promoting human health and social prosperity.展开更多
Biopolymers are promising environmentally benign materials applicable in multifarious applications.They are especially favorable in implantable biomedical devices thanks to their excellent unique properties,including ...Biopolymers are promising environmentally benign materials applicable in multifarious applications.They are especially favorable in implantable biomedical devices thanks to their excellent unique properties,including bioactivity,renewability,bioresorbability,biocompatibility,biodegradability and hydrophilicity.Additive manufacturing(AM)is a flexible and intricate manufacturing technology,which is widely used to fabricate biopolymer-based customized products and structures for advanced healthcare systems.Three-dimensional(3D)printing of these sustainable materials is applied in functional clinical settings including wound dressing,drug delivery systems,medical implants and tissue engineering.The present review highlights recent advancements in different types of biopolymers,such as proteins and polysaccharides,which are employed to develop different biomedical products by using extrusion,vat polymerization,laser and inkjet 3D printing techniques in addition to normal bioprinting and four-dimensional(4D)bioprinting techniques.It also incorporates the influence of nanoparticles on the biological and mechanical performances of 3D-printed tissue scaffolds,and addresses current challenges as well as future developments of environmentally friendly polymeric materials manufactured through the AMtechniques.Ideally,there is a need for more focused research on the adequate blending of these biodegradable biopolymers for achieving useful results in targeted biomedical areas.We envision that biopolymer-based 3D-printed composites have the potential to revolutionize the biomedical sector in the near future.展开更多
Biomedical magnesium is an ideal material for hard tissue repair and replacement.However,its rapid degradation and infection after implantation significantly hindersclinical applications.To overcome these two critical...Biomedical magnesium is an ideal material for hard tissue repair and replacement.However,its rapid degradation and infection after implantation significantly hindersclinical applications.To overcome these two critical drawbacks,we describe an integrated strategybased on the changes in pH and Mg^(2+)triggered by magnesiumdegradation.This system can simultaneously offer anticorrosion and antibacterial activity.First,nanoengineered peptide-grafted hyperbranched polymers(NPGHPs)with excellent antibacterial activity were introduced to sodium alginate(SA)to construct a sensitive NPGHPs/SA hydrogel.The swelling degree,responsiveness,and antibacterial activity were then investigated,indicating that the system can perform dual stimulation of pH and Mg^(2+)with controllable antimicrobial properties.Furthermore,an intelligent platform was constructed by coating hydrogels on magnesium with polydopamine as the transition layer.The alkaline environment generated by the corrosion of magnesium reduces the swelling degree of the coatingso that the liquid is unfavorable for contacting the substrate,thus exhibiting superior corrosion resistance.Antibacterial testing shows that the material can effectively fight against bacteria,while hemolytic and cytotoxicity testing suggest that it is highly biocompatible.Thus,this work realizes the smart integration of anticorrosion and antibacterial properties of biomedical magnesium,thereby providing broader prospects for the use of magnesium.展开更多
As a new generation of materials/structures,heterostructure is characterized by heterogeneous zones with dramatically different mechanical,physical or chemical properties.This endows heterostructure with unique interf...As a new generation of materials/structures,heterostructure is characterized by heterogeneous zones with dramatically different mechanical,physical or chemical properties.This endows heterostructure with unique interfaces,robust architectures,and synergistic effects,making it a promising option as advanced biomaterials for the highly variable anatomy and complex functionalities of individual patients.However,the main challenges of developing heterostructure lie in the control of crystal/phase evolution and the distribution/fraction of components and structures.In recent years,additive manufacturing techniques have attracted increasing attention in developing heterostructure due to the unique flexibility in tailored structures and synthetic multimaterials.This review focuses on the additive manufacturing of heterostructure for biomedical applications.The structural features and functional mechanisms of heterostructure are summarized.The typical material systems of heterostructure,mainly including metals,polymers,ceramics,and their composites,are presented.And the resulting synergistic effects on multiple properties are also systematically discussed in terms of mechanical,biocompatible,biodegradable,antibacterial,biosensitive and magnetostrictive properties.Next,this work outlines the research progress of additive manufacturing employed in developing heterostructure from the aspects of advantages,processes,properties,and applications.This review also highlights the prospective utilization of heterostructure in biomedical fields,with particular attention to bioscaffolds,vasculatures,biosensors and biodetections.Finally,future research directions and breakthroughs of heterostructure are prospected with focus on their more prospective applications in infection prevention and drug delivery.展开更多
The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategie...The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies.With the expansion of capacity for high-throughput scRNA-seq,including clinical samples,the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field.Here,we review the workflow for typical scRNA-seq data analysis,covering raw data processing and quality control,basic data analysis applicable for almost all scRNA-seq data sets,and advanced data analysis that should be tailored to specific scientific questions.While summarizing the current methods for each analysis step,we also provide an online repository of software and wrapped-up scripts to support the implementation.Recommendations and caveats are pointed out for some specific analysis tasks and approaches.We hope this resource will be helpful to researchers engaging with scRNA-seq,in particular for emerging clinical applications.展开更多
The emergence of the clustered regularly interspaced short palindromic repeats(CRISPR)/CRISPR-associated protein 9(Cas9)genome-editing system has brought about a significant revolution in the realm of managing human d...The emergence of the clustered regularly interspaced short palindromic repeats(CRISPR)/CRISPR-associated protein 9(Cas9)genome-editing system has brought about a significant revolution in the realm of managing human diseases,establishing animal models,and so on.To fully harness the potential of this potent gene-editing tool,ensuring efficient and secure delivery to the target site is paramount.Consequently,developing effective delivery methods for the CRISPR/Cas9 system has become a critical area of research.In this review,we present a comprehensive outline of delivery strategies and discuss their biomedical applications in the CRISPR/Cas9 system.We also provide an indepth analysis of physical,viral vector,and non-viral vector delivery strategies,including plasmid-,mRNA-and protein-based approach.In addition,we illustrate the biomedical applications of the CRISPR/Cas9 system.This review highlights the key factors affecting the delivery process and the current challenges facing the CRISPR/Cas9 system,while also delineating future directions and prospects that could inspire innovative delivery strategies.This review aims to provide new insights and ideas for advancing CRISPR/Cas9-based delivery strategies and to facilitate breakthroughs in biomedical research and therapeutic applications.展开更多
With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-base...With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.展开更多
Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using...Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic images.The recent advances in deep learning(DL)models find useful to examine the medical image and make proper decisions.In this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is presented.The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma.The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage.Besides,the k-means clustering technique is applied for the image segmentation process.In addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction process.Lastly,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification process.The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects.The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.展开更多
Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort.Evaluating and selecting the most informati...Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort.Evaluating and selecting the most informative sentences from biomedical articles is always challenging.This study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and information.The research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization model.The input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between them.The proposed framework utilizes the top k similarity technique in a combination of UMLS and a sampled probability-based clustering method which aids in unearthing relevant meanings of the biomedical domain-specific word vectors and finding the best possible associations between crucial sentences.The quality of the framework is assessed via different parameters like information retention,coverage,readability,cohesion,and ROUGE scores in clustering and non-clustering modes.The significant benefits of the suggested technique are capturing crucial biomedical information with increased coverage and reasonable memory consumption.The configurable settings of combined parameters reduce execution time,enhance memory utilization,and extract relevant information outperforming other biomedical baseline models.An improvement of 17%is achieved when the proposed model is checked against similar biomedical text summarizers.展开更多
The Internet of Things(IoT)is a concept that refers to the deployment of Internet Protocol(IP)address sensors in health care systems to monitor patients’health.It has the ability to access the Internet and collect da...The Internet of Things(IoT)is a concept that refers to the deployment of Internet Protocol(IP)address sensors in health care systems to monitor patients’health.It has the ability to access the Internet and collect data from sensors.Automated decisions are made after evaluating the information of illness people records.Patients’health and well-being can be monitored through IoT medical devices.It is possible to trace the origins of biological,medical equipment and processes.Human reliability is a major concern in user activity and fitness trackers in day-to-day activities.The fundamental challenge is to measure the efficiency of the human system accurately.Aim to maintain tabs on the well-being of humans;this paper recommends the use of wireless body area networks(WBANs)and artificial neural networks(ANN)to create an IoT-based healthcare framework for hospital information systems(IoT-HF-HIS).Our evaluation system uses a server to estimate how much computing power is needed for modeling,and simulations of the framework have been done using data rate and latency requirements are implementing the energy-aware technology presented in this paper.The proposed framework implements several hospital information system case studies by building a time-saving simulation environment.As the world’s population ages,more and more people suffer from physical and emotional ailments.Using the recommended strategy regularly has been proven user-friendly,reliable,and cost-effective,with an overall performance of 95.2%.展开更多
The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification M...The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG technique.The major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical signals.The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach.Moreover,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)approach.Finally,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classification efficiency.In order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.展开更多
Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in healthcare.Biome-dical data is usually examined by physicians for decision making ...Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in healthcare.Biome-dical data is usually examined by physicians for decision making process in patient treatment.Since manual diagnosis is a tedious and time consuming task,numerous automated models,using Artificial Intelligence(AI)techniques,have been presented so far.With this motivation,the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI(BDC-CMBOAI)technique.The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data.Besides,the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection(CMBO-FS)technique to derive a useful subset of features.In addition,Ridge Regression(RR)model is also utilized as a classifier to identify the existence of disease.The novelty of the current work is its designing of CMBO-FS model for data classification.Moreover,CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy.The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.展开更多
Introduction: Given its effects, hospital waste is an environmental concern and a threat to health personnel, users of health services and neighboring populations. Our objective was to assess the perception of health ...Introduction: Given its effects, hospital waste is an environmental concern and a threat to health personnel, users of health services and neighboring populations. Our objective was to assess the perception of health care stakeholders on the environmental effects related to biomedical waste produced in Teaching Hospitals (CHU) in Togo in 2021. Methods: This was a cross-sectional study held from June 24 to August 28, 2021. It targeted three university hospitals, 340 health care providers and services selected by a probabilistic method with a simple random technique in 25 services, 72 directors, deputy directors, supervisors and heads of services, 27 collection and incineration agents selected by a non-probabilistic method with a reasoned choice technique, 44 patients and attendants and 36 householders of neighboring residents selected by a non-probabilistic method with an accidental choice technique. Variables such as the spreading of disease vectors, soil, air and water contamination, the presence of unpleasant odors and unsightly living conditions were assessed. Results: According to the respondents, biomedical waste causes the proliferation of vectors (55.3%), an unsightly environment inside the hospital (47.1%), and unpleasant odors (61.2%). Incineration operations disturb hospital residents (52.8%), according to the householders of the residents. During observation, we note deposits of waste that have not been destroyed and wastewater flowing in some places. Conclusion: Biomedical waste in Togo’s university hospitals generates environmental effects and therefore potentially high risks for human health. Improving their management should be a concern for all hospital actors.展开更多
Proton nuclear(^(1)H)is the observed nucleus on which most magnetic resonance imaging(MRI)applications depend.Most traditional^(1)H MRI can provide structural and functional information about organisms,while various n...Proton nuclear(^(1)H)is the observed nucleus on which most magnetic resonance imaging(MRI)applications depend.Most traditional^(1)H MRI can provide structural and functional information about organisms,while various non-proton nuclei(X-nuclei)MRI can provide more metabolic information.However,due to the relatively poor signal-to-noise ratio(SNR)of X-nuclei MRI,their applications are quite rare compared to^(1)H.Benefit from the rapid developments of MRI hardware and software technologies,X-nuclei MRI has recently attracted increasing interests in biomedical research.This review firstly introduces some current methods to improve the SNR of X-nuclei MRI.Secondly,this review describes biomedical applications of X-nuclei MRI,especially focusing on the current use of X-nuclei(^(13)C,^(17)O,^(19)F,^(23)Na and^(31)P)MRI to study related diseases in different organs,including the brain,liver,kidney,heart and bone.Finally,perspectives studies on X-nuclei imaging and its potential applications are described in biomedical research.展开更多
BACKGROUND There are few if any life-span population-based studies of psychiatric disorderassociated biomedical and biophysical disorders and diseases(morbidity).AIM To scope the present state of research regarding th...BACKGROUND There are few if any life-span population-based studies of psychiatric disorderassociated biomedical and biophysical disorders and diseases(morbidity).AIM To scope the present state of research regarding the biomedical and biophysical morbidity associated with affective and mental disorder in epidemiological samples,and to examine the life-span relationship between affective disorders and biomedical/biophysical disorders to illustrate a novel approach employing the odds ratio to represent the intensity of biomedical and biophysical morbidity associated in time in a population.METHODS A repeatable systematic literature search of PubMed was represented in summary.Additionally,a regional population-based dataset was constructed and analyzed to represent the age-and sex-specific diagnoses(International Classification of Diseases Version 9,ICD-9)for those with and without affective disorder.The analysis presents a novel index of the relative age-specific frequency of life-span biomedical and biophysical diagnoses associated with affective disorder.RESULTS The volume of biomedical and biophysical morbidity associated with mental disorder literature has increased,yet few studies measure comprehensive temporal hyper-morbidity(over-representation of diseases over time,either before or after the index diagnostic event)in populations.Further,there have been only a few population-based studies examining the morbidity associated with affective disorder and only one that examines the full diagnostic range of lifespan morbidity.Substantial differences arose between males and females with more females than males having greater frequencies of diagnoses.The age-specific distributions of the maximum proportional diagnosis frequency ratios for each sex illustrate the greatest diagnosis-specific differences when comparing the biomedical and biophysical diagnoses of those with and without affective disorder when the same diagnosis was represented in each grouping at the same age.CONCLUSION Clinical research needs to focus on more than one or two comorbid biomedical or biophysical disorders at a time.Comprehensive population-based examination of the lifespan biomedical and biophysical multi-morbidity associated with affective disorder has the potential to directly inform clinical practice.Representing the proportional ratios of age-specific frequency of diagnoses for the full range of ICD-9 diagnoses is a novel analytical model.Diagnostic frequency appears a viable representation of a given disease state,such as affective disorder.Fortunately,the WPA has developed a global education section to better understand the biomedical and biophysical morbidity associated with all psychiatric disorders.This has been identified by the WPA as the psychiatric practice challenge of the 21st century.展开更多
Materials exhibiting auxetic properties have a negative Poisson’s ratio, which intrigued researchers to understand the behavior of auxetic structure. Several researchers focused on the different auxetic cell designs,...Materials exhibiting auxetic properties have a negative Poisson’s ratio, which intrigued researchers to understand the behavior of auxetic structure. Several researchers focused on the different auxetic cell designs, while others focused on the auxetic applications. With the advance of additive manufacturing methods, computer-aided design and finite element analysis in recent decades, auxetics have been explored. One of the interesting applications is in the field of biomedical devices or implants, especially for certain natural biomedical organs such as tissues, certain ligaments that have auxetic properties. This paper is an overview of auxetic design approaches and biomedical applications.展开更多
This study presents a comprehensive physicochemical analysis of neem plant leaf extracts with a focus on their potential applications in pharmaceutical and biomedical contexts. Utilizing the soxhlet extraction method ...This study presents a comprehensive physicochemical analysis of neem plant leaf extracts with a focus on their potential applications in pharmaceutical and biomedical contexts. Utilizing the soxhlet extraction method with n-hexane as the solvent, the study investigated the quantitative and qualitative composition of neem leaf extracts in reference to concentrations. The results revealed a diverse array of compounds, including cyanogenic glycoside, cardiac glycoside, tannin, steroids, phytate, flavone, oxalate, rutin, lunamarin, catechin, spatein, naringin, resveratrol, kaempferol, flavonones, epicatechin, and epihedrine, with notable concentrations. Further analyses indicated shared physicochemical properties, such as carboxyl and hydroxyl groups. Qualitative assessments affirmed the presence of flavonoid and phenolic compounds, while FTIR analysis confirmed the existence of carboxyl and hydroxyl groups. These findings emphasize the potential use of neem leaves as pharmaceutical raw materials due to their antioxidant-rich content. Additionally, the study explored the density, viscosity, saponification value, and foaming power of neem leaf extracts, providing insights into their industrial applicability. GC-MS analyses highlighted the presence of significant chemical compounds, with potential therapeutic implications. Mineral analysis demonstrated essential elements for human and animal nutrition. This study underscores neem plant leaves’ multifaceted potential across pharmaceutical, herbal medicine, cosmetic, and functional food sectors. It lays a solid foundation for further research into the specific health benefits, offering valuable insights for harnessing neem leaves’ potential in innovative products and treatments.展开更多
Objective To explore the influence of new drug R&D investment and sales expense on the performance of biomedical enterprises.Methods The financial statements of 76 listed biomedical enterprises for 5 consecutive y...Objective To explore the influence of new drug R&D investment and sales expense on the performance of biomedical enterprises.Methods The financial statements of 76 listed biomedical enterprises for 5 consecutive years were selected,and the data were modeled to study the effect of R&D investment and sales expense on the performance of biomedical enterprises by using financial indicators as tools and statistical methods of multiple linear regression.Results and Conclusion Under the premise that the weak related factors such as enterprise scale,life cycle and asset-liability ratio are set as unrelated variables,the R&D investment intensity of biomedical enterprises is negatively correlated with the current performance,which also shows that the R&D of biomedical enterprises has the characteristics of high risk.Besides,the influence of early R&D investment is delayed.However,the sales expense of leading biomedical enterprises with large scales have higher proportion.Meanwhile the greater sales expense of the same enterprise in different periods,the better the enterprise performance is.Biomedical enterprises should consider their own development stage to develop more patented drugs.Besides,they must formulate plans for allocating reasonable sales personnel and cost expense to ensure that enterprises can obtain better benefits.展开更多
基金the National Convergence Research of Scientific Challenges through the National Research Foundation of Korea(NRF)the DGIST R&D Program(No.2021M3F7A1082275 and 23-CoE-BT-02)funded by the Ministry of Science and ICT.
文摘Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation.Metal-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen bubbles.This novel feature has projected degradable metallic micromotors for active in vivo drug delivery applications.In addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and performance.Its propulsion efficiency and life span govern the overall performance of a degradable metallic micromotor.Here we review the design and recent advancements of metallic degradable micromotors.Furthermore,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical applications.Moreover,we discuss micromotors’efficacy in detecting and destroying environmental pollutants.Finally,we address the limitations and future research directions of degradable metallic micromotors.
文摘There is a growing global awareness of environmental,social,and governance(ESG)concerns.The biopharmaceutical industry is an important field that affects human health and well-being,and its sustainable development is now the industry’s focus.Based on the current state of the green development of China’s biopharmaceutical industry,the article proposes suggestions and paths for promoting the industry to better fulfill its social responsibilities and protect the environment while pursuing economic benefits.By doing so,the industry can make a greater contribution to global public health and become an important factor in promoting human health and social prosperity.
文摘Biopolymers are promising environmentally benign materials applicable in multifarious applications.They are especially favorable in implantable biomedical devices thanks to their excellent unique properties,including bioactivity,renewability,bioresorbability,biocompatibility,biodegradability and hydrophilicity.Additive manufacturing(AM)is a flexible and intricate manufacturing technology,which is widely used to fabricate biopolymer-based customized products and structures for advanced healthcare systems.Three-dimensional(3D)printing of these sustainable materials is applied in functional clinical settings including wound dressing,drug delivery systems,medical implants and tissue engineering.The present review highlights recent advancements in different types of biopolymers,such as proteins and polysaccharides,which are employed to develop different biomedical products by using extrusion,vat polymerization,laser and inkjet 3D printing techniques in addition to normal bioprinting and four-dimensional(4D)bioprinting techniques.It also incorporates the influence of nanoparticles on the biological and mechanical performances of 3D-printed tissue scaffolds,and addresses current challenges as well as future developments of environmentally friendly polymeric materials manufactured through the AMtechniques.Ideally,there is a need for more focused research on the adequate blending of these biodegradable biopolymers for achieving useful results in targeted biomedical areas.We envision that biopolymer-based 3D-printed composites have the potential to revolutionize the biomedical sector in the near future.
基金This work was financially supported by the National Natural Science Foundation of China(no.51671179,51971014)the Excellent teacher ability improvement project(E1E40308).
文摘Biomedical magnesium is an ideal material for hard tissue repair and replacement.However,its rapid degradation and infection after implantation significantly hindersclinical applications.To overcome these two critical drawbacks,we describe an integrated strategybased on the changes in pH and Mg^(2+)triggered by magnesiumdegradation.This system can simultaneously offer anticorrosion and antibacterial activity.First,nanoengineered peptide-grafted hyperbranched polymers(NPGHPs)with excellent antibacterial activity were introduced to sodium alginate(SA)to construct a sensitive NPGHPs/SA hydrogel.The swelling degree,responsiveness,and antibacterial activity were then investigated,indicating that the system can perform dual stimulation of pH and Mg^(2+)with controllable antimicrobial properties.Furthermore,an intelligent platform was constructed by coating hydrogels on magnesium with polydopamine as the transition layer.The alkaline environment generated by the corrosion of magnesium reduces the swelling degree of the coatingso that the liquid is unfavorable for contacting the substrate,thus exhibiting superior corrosion resistance.Antibacterial testing shows that the material can effectively fight against bacteria,while hemolytic and cytotoxicity testing suggest that it is highly biocompatible.Thus,this work realizes the smart integration of anticorrosion and antibacterial properties of biomedical magnesium,thereby providing broader prospects for the use of magnesium.
基金The Natural Science Foundation of China(51935014,52275395,82072084)Hunan Provincial Natural Science Foundation of China(2020JJ3047)+4 种基金Central South University Innovation-Driven Research Programme(2023CXQD023)JiangXi Provincial Natural Science Foundation of China(20224ACB204013)Technology Innovation Platform Project of Shenzhen Institute of Information Technology 2020(PT2020E002)Guangdong Province Precision Manufacturing and Intelligent Production Education Integration Innovation Platform(2022CJPT019)The Project of State Key Laboratory of Precision Manufacturing for Extreme Service Performance。
文摘As a new generation of materials/structures,heterostructure is characterized by heterogeneous zones with dramatically different mechanical,physical or chemical properties.This endows heterostructure with unique interfaces,robust architectures,and synergistic effects,making it a promising option as advanced biomaterials for the highly variable anatomy and complex functionalities of individual patients.However,the main challenges of developing heterostructure lie in the control of crystal/phase evolution and the distribution/fraction of components and structures.In recent years,additive manufacturing techniques have attracted increasing attention in developing heterostructure due to the unique flexibility in tailored structures and synthetic multimaterials.This review focuses on the additive manufacturing of heterostructure for biomedical applications.The structural features and functional mechanisms of heterostructure are summarized.The typical material systems of heterostructure,mainly including metals,polymers,ceramics,and their composites,are presented.And the resulting synergistic effects on multiple properties are also systematically discussed in terms of mechanical,biocompatible,biodegradable,antibacterial,biosensitive and magnetostrictive properties.Next,this work outlines the research progress of additive manufacturing employed in developing heterostructure from the aspects of advantages,processes,properties,and applications.This review also highlights the prospective utilization of heterostructure in biomedical fields,with particular attention to bioscaffolds,vasculatures,biosensors and biodetections.Finally,future research directions and breakthroughs of heterostructure are prospected with focus on their more prospective applications in infection prevention and drug delivery.
基金suppor ted by the National Key Research and Development Program of China (2022YFC2702502)the National Natural Science Foundation of China (32170742, 31970646, and 32060152)+7 种基金the Start Fund for Specially Appointed Professor of Jiangsu ProvinceHainan Province Science and Technology Special Fund (ZDYF2021SHFZ051)the Natural Science Foundation of Hainan Province (820MS053)the Start Fund for High-level Talents of Nanjing Medical University (NMUR2020009)the Marshal Initiative Funding of Hainan Medical University (JBGS202103)the Hainan Province Clinical Medical Center (QWYH202175)the Bioinformatics for Major Diseases Science Innovation Group of Hainan Medical Universitythe Shenzhen Science and Technology Program (JCYJ20210324140407021)
文摘The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies.With the expansion of capacity for high-throughput scRNA-seq,including clinical samples,the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field.Here,we review the workflow for typical scRNA-seq data analysis,covering raw data processing and quality control,basic data analysis applicable for almost all scRNA-seq data sets,and advanced data analysis that should be tailored to specific scientific questions.While summarizing the current methods for each analysis step,we also provide an online repository of software and wrapped-up scripts to support the implementation.Recommendations and caveats are pointed out for some specific analysis tasks and approaches.We hope this resource will be helpful to researchers engaging with scRNA-seq,in particular for emerging clinical applications.
基金supported by the National Natural Science Foundation of China[32271464]the Hunan Provincial Natural Science Foundation for Distinguished Young Scholars[2022JJ10086]+4 种基金the Innovation-Driven Project of Central South University[2020CX048]the Joint Fund of the Hunan Provincial Natural Science Foundation and the Hunan Medical Products Adminstration[2023JJ60501]the Natural Science Foundation of Changsha[kq2202131]the Postgraduate Innovation Project of Central South University[2021zzts0977,2022ZZTS0980]the Hunan Provincial Innovation Foundation for Postgraduate[CX20210340,CX20220372].
文摘The emergence of the clustered regularly interspaced short palindromic repeats(CRISPR)/CRISPR-associated protein 9(Cas9)genome-editing system has brought about a significant revolution in the realm of managing human diseases,establishing animal models,and so on.To fully harness the potential of this potent gene-editing tool,ensuring efficient and secure delivery to the target site is paramount.Consequently,developing effective delivery methods for the CRISPR/Cas9 system has become a critical area of research.In this review,we present a comprehensive outline of delivery strategies and discuss their biomedical applications in the CRISPR/Cas9 system.We also provide an indepth analysis of physical,viral vector,and non-viral vector delivery strategies,including plasmid-,mRNA-and protein-based approach.In addition,we illustrate the biomedical applications of the CRISPR/Cas9 system.This review highlights the key factors affecting the delivery process and the current challenges facing the CRISPR/Cas9 system,while also delineating future directions and prospects that could inspire innovative delivery strategies.This review aims to provide new insights and ideas for advancing CRISPR/Cas9-based delivery strategies and to facilitate breakthroughs in biomedical research and therapeutic applications.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under Grant Number(71/43)Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R203)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR29).
文摘With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.
基金the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 1/80/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R191)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic images.The recent advances in deep learning(DL)models find useful to examine the medical image and make proper decisions.In this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is presented.The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma.The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage.Besides,the k-means clustering technique is applied for the image segmentation process.In addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction process.Lastly,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification process.The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects.The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.
文摘Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort.Evaluating and selecting the most informative sentences from biomedical articles is always challenging.This study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and information.The research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization model.The input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between them.The proposed framework utilizes the top k similarity technique in a combination of UMLS and a sampled probability-based clustering method which aids in unearthing relevant meanings of the biomedical domain-specific word vectors and finding the best possible associations between crucial sentences.The quality of the framework is assessed via different parameters like information retention,coverage,readability,cohesion,and ROUGE scores in clustering and non-clustering modes.The significant benefits of the suggested technique are capturing crucial biomedical information with increased coverage and reasonable memory consumption.The configurable settings of combined parameters reduce execution time,enhance memory utilization,and extract relevant information outperforming other biomedical baseline models.An improvement of 17%is achieved when the proposed model is checked against similar biomedical text summarizers.
文摘The Internet of Things(IoT)is a concept that refers to the deployment of Internet Protocol(IP)address sensors in health care systems to monitor patients’health.It has the ability to access the Internet and collect data from sensors.Automated decisions are made after evaluating the information of illness people records.Patients’health and well-being can be monitored through IoT medical devices.It is possible to trace the origins of biological,medical equipment and processes.Human reliability is a major concern in user activity and fitness trackers in day-to-day activities.The fundamental challenge is to measure the efficiency of the human system accurately.Aim to maintain tabs on the well-being of humans;this paper recommends the use of wireless body area networks(WBANs)and artificial neural networks(ANN)to create an IoT-based healthcare framework for hospital information systems(IoT-HF-HIS).Our evaluation system uses a server to estimate how much computing power is needed for modeling,and simulations of the framework have been done using data rate and latency requirements are implementing the energy-aware technology presented in this paper.The proposed framework implements several hospital information system case studies by building a time-saving simulation environment.As the world’s population ages,more and more people suffer from physical and emotional ailments.Using the recommended strategy regularly has been proven user-friendly,reliable,and cost-effective,with an overall performance of 95.2%.
基金Taif University Researchers Supporting Project Number(TURSP-2020/161)Taif University,Taif,Saudi Arabia.
文摘The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG technique.The major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical signals.The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach.Moreover,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)approach.Finally,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classification efficiency.In order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R203)Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR03.
文摘Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in healthcare.Biome-dical data is usually examined by physicians for decision making process in patient treatment.Since manual diagnosis is a tedious and time consuming task,numerous automated models,using Artificial Intelligence(AI)techniques,have been presented so far.With this motivation,the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI(BDC-CMBOAI)technique.The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data.Besides,the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection(CMBO-FS)technique to derive a useful subset of features.In addition,Ridge Regression(RR)model is also utilized as a classifier to identify the existence of disease.The novelty of the current work is its designing of CMBO-FS model for data classification.Moreover,CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy.The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.
文摘Introduction: Given its effects, hospital waste is an environmental concern and a threat to health personnel, users of health services and neighboring populations. Our objective was to assess the perception of health care stakeholders on the environmental effects related to biomedical waste produced in Teaching Hospitals (CHU) in Togo in 2021. Methods: This was a cross-sectional study held from June 24 to August 28, 2021. It targeted three university hospitals, 340 health care providers and services selected by a probabilistic method with a simple random technique in 25 services, 72 directors, deputy directors, supervisors and heads of services, 27 collection and incineration agents selected by a non-probabilistic method with a reasoned choice technique, 44 patients and attendants and 36 householders of neighboring residents selected by a non-probabilistic method with an accidental choice technique. Variables such as the spreading of disease vectors, soil, air and water contamination, the presence of unpleasant odors and unsightly living conditions were assessed. Results: According to the respondents, biomedical waste causes the proliferation of vectors (55.3%), an unsightly environment inside the hospital (47.1%), and unpleasant odors (61.2%). Incineration operations disturb hospital residents (52.8%), according to the householders of the residents. During observation, we note deposits of waste that have not been destroyed and wastewater flowing in some places. Conclusion: Biomedical waste in Togo’s university hospitals generates environmental effects and therefore potentially high risks for human health. Improving their management should be a concern for all hospital actors.
基金supported by Chinese Academy of Sciences MRI Technology Alliance under Grant 2020GZ1003.
文摘Proton nuclear(^(1)H)is the observed nucleus on which most magnetic resonance imaging(MRI)applications depend.Most traditional^(1)H MRI can provide structural and functional information about organisms,while various non-proton nuclei(X-nuclei)MRI can provide more metabolic information.However,due to the relatively poor signal-to-noise ratio(SNR)of X-nuclei MRI,their applications are quite rare compared to^(1)H.Benefit from the rapid developments of MRI hardware and software technologies,X-nuclei MRI has recently attracted increasing interests in biomedical research.This review firstly introduces some current methods to improve the SNR of X-nuclei MRI.Secondly,this review describes biomedical applications of X-nuclei MRI,especially focusing on the current use of X-nuclei(^(13)C,^(17)O,^(19)F,^(23)Na and^(31)P)MRI to study related diseases in different organs,including the brain,liver,kidney,heart and bone.Finally,perspectives studies on X-nuclei imaging and its potential applications are described in biomedical research.
文摘BACKGROUND There are few if any life-span population-based studies of psychiatric disorderassociated biomedical and biophysical disorders and diseases(morbidity).AIM To scope the present state of research regarding the biomedical and biophysical morbidity associated with affective and mental disorder in epidemiological samples,and to examine the life-span relationship between affective disorders and biomedical/biophysical disorders to illustrate a novel approach employing the odds ratio to represent the intensity of biomedical and biophysical morbidity associated in time in a population.METHODS A repeatable systematic literature search of PubMed was represented in summary.Additionally,a regional population-based dataset was constructed and analyzed to represent the age-and sex-specific diagnoses(International Classification of Diseases Version 9,ICD-9)for those with and without affective disorder.The analysis presents a novel index of the relative age-specific frequency of life-span biomedical and biophysical diagnoses associated with affective disorder.RESULTS The volume of biomedical and biophysical morbidity associated with mental disorder literature has increased,yet few studies measure comprehensive temporal hyper-morbidity(over-representation of diseases over time,either before or after the index diagnostic event)in populations.Further,there have been only a few population-based studies examining the morbidity associated with affective disorder and only one that examines the full diagnostic range of lifespan morbidity.Substantial differences arose between males and females with more females than males having greater frequencies of diagnoses.The age-specific distributions of the maximum proportional diagnosis frequency ratios for each sex illustrate the greatest diagnosis-specific differences when comparing the biomedical and biophysical diagnoses of those with and without affective disorder when the same diagnosis was represented in each grouping at the same age.CONCLUSION Clinical research needs to focus on more than one or two comorbid biomedical or biophysical disorders at a time.Comprehensive population-based examination of the lifespan biomedical and biophysical multi-morbidity associated with affective disorder has the potential to directly inform clinical practice.Representing the proportional ratios of age-specific frequency of diagnoses for the full range of ICD-9 diagnoses is a novel analytical model.Diagnostic frequency appears a viable representation of a given disease state,such as affective disorder.Fortunately,the WPA has developed a global education section to better understand the biomedical and biophysical morbidity associated with all psychiatric disorders.This has been identified by the WPA as the psychiatric practice challenge of the 21st century.
文摘Materials exhibiting auxetic properties have a negative Poisson’s ratio, which intrigued researchers to understand the behavior of auxetic structure. Several researchers focused on the different auxetic cell designs, while others focused on the auxetic applications. With the advance of additive manufacturing methods, computer-aided design and finite element analysis in recent decades, auxetics have been explored. One of the interesting applications is in the field of biomedical devices or implants, especially for certain natural biomedical organs such as tissues, certain ligaments that have auxetic properties. This paper is an overview of auxetic design approaches and biomedical applications.
文摘This study presents a comprehensive physicochemical analysis of neem plant leaf extracts with a focus on their potential applications in pharmaceutical and biomedical contexts. Utilizing the soxhlet extraction method with n-hexane as the solvent, the study investigated the quantitative and qualitative composition of neem leaf extracts in reference to concentrations. The results revealed a diverse array of compounds, including cyanogenic glycoside, cardiac glycoside, tannin, steroids, phytate, flavone, oxalate, rutin, lunamarin, catechin, spatein, naringin, resveratrol, kaempferol, flavonones, epicatechin, and epihedrine, with notable concentrations. Further analyses indicated shared physicochemical properties, such as carboxyl and hydroxyl groups. Qualitative assessments affirmed the presence of flavonoid and phenolic compounds, while FTIR analysis confirmed the existence of carboxyl and hydroxyl groups. These findings emphasize the potential use of neem leaves as pharmaceutical raw materials due to their antioxidant-rich content. Additionally, the study explored the density, viscosity, saponification value, and foaming power of neem leaf extracts, providing insights into their industrial applicability. GC-MS analyses highlighted the presence of significant chemical compounds, with potential therapeutic implications. Mineral analysis demonstrated essential elements for human and animal nutrition. This study underscores neem plant leaves’ multifaceted potential across pharmaceutical, herbal medicine, cosmetic, and functional food sectors. It lays a solid foundation for further research into the specific health benefits, offering valuable insights for harnessing neem leaves’ potential in innovative products and treatments.
文摘Objective To explore the influence of new drug R&D investment and sales expense on the performance of biomedical enterprises.Methods The financial statements of 76 listed biomedical enterprises for 5 consecutive years were selected,and the data were modeled to study the effect of R&D investment and sales expense on the performance of biomedical enterprises by using financial indicators as tools and statistical methods of multiple linear regression.Results and Conclusion Under the premise that the weak related factors such as enterprise scale,life cycle and asset-liability ratio are set as unrelated variables,the R&D investment intensity of biomedical enterprises is negatively correlated with the current performance,which also shows that the R&D of biomedical enterprises has the characteristics of high risk.Besides,the influence of early R&D investment is delayed.However,the sales expense of leading biomedical enterprises with large scales have higher proportion.Meanwhile the greater sales expense of the same enterprise in different periods,the better the enterprise performance is.Biomedical enterprises should consider their own development stage to develop more patented drugs.Besides,they must formulate plans for allocating reasonable sales personnel and cost expense to ensure that enterprises can obtain better benefits.
