Recent Developments in Metallic Degradable Micromotors for Biomedical and Environmental Remediation Applications

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.

GPa-level pressure-induced enhanced corrosion resistance in TiZrTaNbSn biomedical high-entropy alloy

TiZrTaNb-based high-entropy alloys(HEAs)are research frontier of biomedical materials due to their high hardness,good yield strength,excellent wear resistance and corrosion resistance.Sn,as an essential trace element in the human body that plays a significant role in physiological process.It has stable chemical properties and a low elastic modulus.In this study,a new material,TiZrTaNbSn HEAs,was proposed as a potential biomedical alloy.The Ti_(35)Zr_(25)Ta_(15)Nb_(15)Sn_(10)biomedical high-entropy alloys(BHEAs)were successfully prepared through an arc melting furnace and then remelted using a German high-temperature and high-pressure apparatus under GPa-level(4 GPa and 7 GPa).The precipitation behavior of the needle-like HCP-Zr_(5)Sn_(3)phase that precipitates discontinuously at the grain boundary was successfully controlled.The phase constitution,microstructure,and corrosion resistance of the alloy were studied.The results show that the needle-like HCP-Zr_(5)Sn_(3)phase is eliminated and the(Zr,Sn)-rich nano-precipitated phase is precipitated in the microstructure under high pressure,which leads to the narrowing of grain boundaries and consequently improves the corrosion resistance of the alloy.In addition,the formation mechanisms of(Zr,Sn)-rich nanoprecipitates in BHEAs were discussed.More Zr and Sn dissolve in the matrix due to the effect of high pressure,during the cooling process,they precipitate to form a(Zr,Sn)-rich nano-precipitated phase.

Hydrogen sulfide responsive nanoplatforms: Novel gas responsive drug delivery carriers for biomedical applications

Hydrogen sulfide(H_(2)S)is a toxic,essential gas used in various biological and physical processes and has been the subject of many targeted studies on its role as a new gas transmitter.These studies have mainly focused on the production and pharmacological side effects caused by H_(2)S.Therefore,effective strategies to remove H_(2)S has become a key research topic.Furthermore,the development of novel nanoplatforms has provided new tools for the targeted removal of H_(2)S.This paper was performed to review the association between H_(2)S anddisease,relatedH_(2)S inhibitory drugs,aswell as H_(2)S responsive nanoplatforms(HRNs).This review first analyzed the role of H_(2)S in multiple tissues and conditions.Second,common drugs used to eliminate H_(2)S,as well as their potential for combination with anticancer agents,were summarized.Not only the existing studies on HRNs,but also the inhibition H_(2)S combined with different therapeutic methods were both sorted out in this review.Furthermore,this review provided in-depth analysis of the potential of HRNs about treatment or detection in detail.Finally,potential challenges of HRNs were proposed.This study demonstrates the excellent potential of HRNs for biomedical applications.

Renewable Polymers in Biomedical Applications:From the Bench to the Market

Polymers from renewable resources have been used for a long time in biomedical applications and found an irreplaceable role in some of them.Their uses have been increasing because of their attractive properties,contributing to the improvement of life quality,mainly in drug release systems and in regenerative medicine.Formulations using natural polymer,nano and microscale particles preparation,composites,blends and chemical modification strategies have been used to improve their properties for clinical application.Although many studies have been carried out with these natural polymers,the way to reach the market is long and only very few of them become commercially available.Vegetable cellulose,bacterial cellulose,chitosan,poly(lactic acid)and starch can be found among the most studied polymers for biological applications,some with several derivatives already established in the market,and others with potential for such.In this scenario this work aims to describe the properties and potential of these renewable polymers for biomedical applications,the routes from the bench to the market,and the perspectives for future developments.

A Method of Generating Semi-Experimental Biomedical Datasets

This paper proposed a method to generate semi-experimental biomedical datasets based on full-wave simulation software.The system noise such as antenna port couplings is fully considered in the proposed datasets,which is more realistic than synthetical datasets.In this paper,datasets containing different shapes are constructed based on the relative permittivities of human tissues.Then,a back-propagation scheme is used to obtain the rough reconstructions,which will be fed into a U-net convolutional neural network(CNN)to recover the high-resolution images.Numerical results show that the network trained on the datasets generated by the proposed method can obtain satisfying reconstruction results and is promising to be applied in real-time biomedical imaging.

Biobanks and biomarkers:Their current and future role in biomedical research

The importance and utility of biobanks has increased exponentially since their inception and creation.Initially used as part of translational research,they now contribute over 40%of data for all cancer research papers in the United States of America and play a crucial role in all aspects of healthcare.Multiple classification systems exist but a simplified approach is to either classify as population-based or disease-oriented entities.Whilst historically publicly funded institutions,there has been a significant increase in industry funded entities across the world which has changed the dynamic of biobanks offering new possibilities but also new challenges.Biobanks face legal questions over data sharing and intellectual property as well as ethical and sustainability questions particularly as the world attempts to move to a low-carbon economy.International collaboration is required to address some of these challenges but this in itself is fraught with complexity and difficulty.This review will examine the current utility of biobanks in the modern healthcare setting as well as the current and future challenges these vital institutions face.

The Sustainable Development Pathway of the Biomedical Industry Based on Environmental,Social,and Governance(ESG)Concepts

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.

Additive manufacturing of promising heterostructure for biomedical applications

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.

Additive manufacturing of sustainable biomaterials for biomedical applications

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.

Anticorrosive and antibacterial smart integrated strategy for biomedical magnesium

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.

Data analysis guidelines for single‑cell RNA‑seq in biomedical studies and clinical applications

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.

CRISPR/Cas9 systems:Delivery technologies and biomedical applications

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.

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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%.

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

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.

Graph Ranked Clustering Based Biomedical Text Summarization Using Top k Similarity

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.

IOT Assisted Biomedical Monitoring Sensors for Healthcare in Human

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%.

Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

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.

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

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.

Hospital Stakeholders’ Perception on Environmental Effects Related to Biomedical Waste in Togo’s University Hospitals (UHC) in 2021

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.

Overview and progress of X-nuclei magnetic resonance imaging in biomedical studies

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.