Advances in Biomedical Applications of Hydrogels from Seaweed-Derived Sulfated Polysaccharides:Carrageenan,Fucoidan,and Ulvan

Sulfated polysaccharides extracted from seaweeds,including Carrageenan,Fucoidan and Ulvan,are crucial bioactive compounds known for their diverse beneficial properties,such as anti-inflammatory,antitumor,immunomodulatory,antiviral,and anticoagulant effects.These polysaccharides form hydrogels hold immense promise in biomedicine,particularly in tissue engineering,drug delivery systems and wound healing.This review comprehensively explores the sources and structural characteristics of the three important sulfated polysaccharides extracted from different algae species.It elucidates the gelation mechanisms of these polysaccharides into hydrogels.Furthermore,the biomedical applications of these three sulfated polysaccharide hydrogels in wound healing,drug delivery,and tissue engineering are discussed,highlighting their potential in the biomedicine.

Advances in Medical Applications of Additive Manufacturing

In the past few decades,additive manufacturing(AM)has been developed and applied as a cost-effective and versatile technique for the fabrication of geometrically complex objects in the medical industry.In this review,we discuss current advances of AM in medical applications for the generation of pharmaceuticals,medical implants,and medical devices.Oral and transdermal drugs can be fabricated by a variety of AM technologies.Different types of hard and soft clinical implants have also been realized by AM,with the goal of producing tissue-engineered constructs.In addition,medical devices used for diagnostics and treatment of various pathological conditions have been developed.The growing body of research on AM reveals its great potential in medical applications.The goal of this review is to highlight the usefulness and elucidate the current limitations of AM applications in the medical field.

Impact of mobile health and medical applications on clinical practice in gastroenterology

Mobile health apps (MHAs) and medical apps (MAs) are becoming increasinglypopular as digital interventions in a wide range of health-related applications inalmost all sectors of healthcare. The surge in demand for digital medical solutionshas been accelerated by the need for new diagnostic and therapeutic methods inthe current coronavirus disease 2019 pandemic. This also applies to clinicalpractice in gastroenterology, which has, in many respects, undergone a recentdigital transformation with numerous consequences that will impact patients andhealth care professionals in the near future. MHAs and MAs are considered tohave great potential, especially for chronic diseases, as they can support the selfmanagementof patients in many ways. Despite the great potential associated withthe application of MHAs and MAs in gastroenterology and health care in general,there are numerous challenges to be met in the future, including both the ethicaland legal aspects of applying this technology. The aim of this article is to providean overview of the current status of MHA and MA use in the field ofgastroenterology, describe the future perspectives in this field and point out someof the challenges that need to be addressed.

Millimeter waves in medical applications:status and prospects

Millimeter waves are electromagnetic waves with wavelengths of 1–10 mm,which have characteristics of high frequency and short wavelength.They have gradually and widely been used in engineering and medical fields.We have identified studies related to millimeter waves in the biomedical field and summarized the biological effects of millimeter waves and their current status in medical applications.Finally,the shortcomings of existing studies and future developments were analyzed and discussed,with the aim of providing a reference for further research and development of millimeter waves in the medical field.

Synthesis,functionalization,and nanomedical applications of functional magnetic nanoparticles

Synthesis of magnetic nanoparticles （MNPs） is one of the most active research areas in advanced materials. MNPs that have magnetic properties and other functionalities have been demonstrated to show great promise in nanomedical applications. This review summarizes the current MNPs preparation, functionalization and stabilization methods. It also analyzes the detailed features of MNPs. And furthermore it highlights some actual case analyses of these MNPs for disease therapy, drug delivery, hyperthermia, bioseparation and bioimaging applications.

Deep Learning for Image Segmentation: A Focus on Medical Imaging

Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical imaging. With the recent advances in deep learning (DL) and itsconfounding results in image segmentation, more attention has been drawnto its use in medical image segmentation. This article introduces a surveyof the state-of-the-art deep convolution neural network (CNN) models andmechanisms utilized in image segmentation. First, segmentation models arecategorized based on their model architecture and primary working principle.Then, CNN categories are described, and various models are discussed withineach category. Compared with other existing surveys, several applicationswith multiple architectural adaptations are discussed within each category.A comparative summary is included to give the reader insights into utilizedarchitectures in different applications and datasets. This study focuses onmedical image segmentation applications, where the most widely used architecturesare illustrated, and other promising models are suggested that haveproven their success in different domains. Finally, the present work discussescurrent limitations and solutions along with future trends in the field.

A Collaborative Medical Diagnosis System Without Sharing Patient Data

As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploiting its potential because the data usually sits in data silos,and privacy and security regulations restrict their access and use.To address these issues,we built a secured and explainable machine learning framework,called explainable federated XGBoost(EXPERTS),which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data.It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals.To study the performance,we evaluate our approach by real-world datasets,and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.

Ultra-Wideband Systems with Energy Harvesting Units for Sensors, 5G, IoT and Medical Systems

The high tech industrial revolution in the last fifty years depleted and ruined the planet natural resources. Energy harvesting is the main challenge in the research in green technologies. Compact wideband efficient antennas are crucial for energy harvesting portable sensors and systems. Small antennas have low efficiency. The efficiency of 5G, IoT communication and energy harvesting systems may be improved by using wideband efficient passive and active antennas. The system dynamic range may be improved by connecting amplifiers to the small antenna feed line. Ultra-wideband portable harvesting systems are presented in this paper. This paper presents new Ultra-Wideband energy harvesting system and antennas in frequencies ranging from 0.15 GHz to 18 GHz. Three wideband antennas cover the frequency range from 0.15 GHz to 18 GHz. A wideband metamaterial antenna with metallic strips covers the frequency range from 0.15 GHz to 0.42 GHz. The antenna bandwidth is around 75% for VSWR better than 2.3:1. A wideband slot antenna covers the frequency range from 0.4 GHz to 6.4 GHz. A wideband fractal notch antenna covers the frequency range from 6 GHz to 18 GHz. Printed passive and active notch and slot antennas are compact, low cost and have low volume. The active antennas may be employed in energy harvesting portable systems. The antennas and the harvesting system components may be assembled on the same, printed board. The printed notch and slot antennas bandwidth are from 75% to 100% for VSWR better than 3:1. The slot and notch antenna gain is around 3 dBi with efficiency higher than 90%. The antennas electrical parameters were computed in free space and near the human body. There is a good agreement between computed and measured results.

On Soft Pre-Rough Approximation Space with Applications in Decision Making

A soft,rough set model is a distinctive mathematical model that can be used to relate a variety of real-life data.In the present work,we introduce new concepts of rough set based on soft pre-lower and soft pre-upper approximation space.These concepts are soft pre-rough equality,soft pre-rough inclusion,soft pre-rough belonging,soft predefinability,soft pre-internal lower,and soft pre-external lower.We study the properties of these concepts.Finally,we use the soft pre-rough approximation to illustrate the importance of our method in decision-making for Chikungunya medical illnesses.In reality,the impact factors of Chikungunya’s medical infection were determined.Moreover,we develop two new algorithms to address Chikungunya virus issues.Our proposed approach is sensible and effective.

Biodegradable Protection for Medical Devices with Medical Drugs Controlled Separation

With the aim of creating biodegradable materials for medical devices clinical appointments with high hemocompatibility we have developed a new polymer product.The basis of this product is plasticized by polyethylene glycol bacterial copolymer of hydroxybutyrate and oxovalerate. A well-known antitbrombotic supplement--acetylsalicylic acid has been added to improve hemocompatibility in the polymer. The results of our studies showed a controlled prolonged separation of acetylsalicylic acid from polymeric material in the blood. We studied in vitro the dynamics of liberation of acetylsalicylic acid from polymeric coatings. It was shown that the concentration of polyethylene glycol and the thickness of the polymer layer can affect the rate of diffusion of acetylsalicylic acid from polymer films.

Potential and role of artificial intelligence in current medical healthcare

Artificial intelligence(AI)is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings.The application of AI technology and machine learning in medicine have allowed medical practitioners to provide patients with better quality of services;and current advancements have led to a dramatic change in the healthcare system.However,many efficient applications are still in their initial stages,which need further evaluations to improve and develop these applications.Clinicians must recognize and acclimate themselves with the developments in AI technology to improve their delivery of healthcare services;but for this to be possible,a significant revision of medical education is needed to provide future leaders with the required competencies.This article reviews the potential and limitations of AI in healthcare,as well as the current medical application trends including healthcare administration,clinical decision assistance,patient health monitoring,healthcare resource allocation,medical research,and public health policy development.Also,future possibilities for further clinical and scientific practice were also summarized.

An intelligent prediction model of epidemic characters based on multi-feature

The epidemic characters of Omicron(e.g.large-scale transmission)are significantly different from the initial variants of COVID-19.The data generated by large-scale transmission is important to predict the trend of epidemic characters.However,the re-sults of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission.In consequence,these inaccurate results have negative impacts on the process of the manufacturing and the service industry,for example,the production of masks and the recovery of the tourism industry.The authors have studied the epidemic characters in two ways,that is,investigation and prediction.First,a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters.Second,theβ-SEIDR model is established,where the population is classified as Susceptible,Exposed,Infected,Dead andβ-Recovered persons,to intelligently predict the epidemic characters of COVID-19.Note thatβ-Recovered persons denote that the Recovered persons may become Sus-ceptible persons with probabilityβ.The simulation results show that the model can accurately predict the epidemic characters.

A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health systems.Deduplication eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple duplicates.Delta compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate blocks.In addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system throughput.Therefore,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these problems.The similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate attribute.Then,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ratios.Moreover,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system throughput.Experiments demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

Transfer force perception skills to robot‐assisted laminectomy via imitation learning from human demonstrations

A comparative study of two force perception skill learning approaches for robot‐assisted spinal surgery,the impedance model method and the imitation learning(IL)method,is presented.The impedance model method develops separate models for the surgeon and patient,incorporating spring‐damper and bone‐grinding models.Expert surgeons'feature parameters are collected and mapped using support vector regression and image navi-gation techniques.The imitation learning approach utilises long short‐term memory networks(LSTM)and addresses accurate data labelling challenges with custom models.Experimental results demonstrate skill recognition rates of 63.61%-74.62%for the impedance model approach,relying on manual feature extraction.Conversely,the imitation learning approach achieves a force perception recognition rate of 91.06%,outperforming the impedance model on curved bone surfaces.The findings demonstrate the potential of imitation learning to enhance skill acquisition in robot‐assisted spinal surgery by eliminating the laborious process of manual feature extraction.

Application of Knitted Fabrics in Technical and Medical Textiles

Knitted fabrics and knitting technology play very important role on the fields of technical and medical textiles and their importance is ever greater. Experts estimate that their annual consumption is increasing by 3,8 % in average and it can reach about 24 million tons in 2010. Within this the consumption of each sector is increasing. Roughly one third of the world’s fibre consumption is used for production of technical textiles.The term "technical textiles" covers many fields of application that are mirrored in the terminology of Techtextil which is very much used generally when grouping these products. Techtextil differentiates 11 groups and knitted fabrics and products made by knitting technologies can be found in each of them.The lecture introduces such applications on many examples. We think that use of knitting technologies in the development of technical and medical textiles can help this sector to survive this difficult period of the European textile industry.

Demands and influencing factors of digital transition care service in discharged older patients:a cross-sectional study

Objective:To identify the group classification of discharged older adults’digital transition care demands and analyze its influencing factors.Methods:From July to August 2022,we used stratified random sampling to recruit older patients who were discharged between July 2021 and July 2022 from tertiary hospitals in Shanghai.We used latent profile analysis to classify the older patients into distinct groups based on their service demands:low,medium,and high.We use multiple logistic regression to explore the factors influencing the different demand levels.Results:The degree of discharged older patients’demand was classified as low(Category 1(C1),34.2%),medium(Category 2(C2),49.5%),high-demand levels(Category 3(C3),16.3%).Compared to those have C2,older adults in C1 are more likely to be male(Odds Ratio(OR)=2.81,P=0.02),have 2 chronic diseases(OR=3.91,P=0.03),and are less likely to be junior high and below(OR=0.09,P=0.00),hospitalized for 1–2 times in the past year(1 times:OR=0.19,P=0.07;2 times:OR=0.14,P=0.02),living with children(OR=0.32,P=0.05),have less insurance(OR=0.48,P=0.03),less understanding of digital transitional care(OR=0.47,P=0.01),have less eHealth literacy(OR=0.80,P=0.00),have less degree of importance attributed by family(OR=0.52,P=0.03);Compared to those have medium demand level,older adults in high demand level are more likely to have self and spouse as primary income(self:OR=26.35,P=0.00;spouse:OR=24.06,P=0.02),walking to the nearest health facility(self:6.74,P=0.03),have higher eHealth literacy(OR=1.88,P=0.00),degree of importance within the family(OR=5.19,P=0.01),higher self’s influence on medical decisions-making(OR=5.69.P=0.01).They are less likely to be in 60–79 years group(OR=0.00–0.37,P=0.00–0.03),Household Annual Income<5,000 CNY(OR=0.05,P=0.02).Conclusion:Digital transitional care demands of discharged older patients can be divided into three categories.Constructing a digital transitional care service system that aligns with the demands of discharged older patients is essential.Communication,care plan development,and follow-up are the most fundamental services.Additionally,it is essential to understand the characteristics of high-demand populations to provide tailored services and identify vulnerable populations from health and social perspectives to offer cost-effective transitional care services.

Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Pathways for Sustainable Utilization of Waste Chicken Eggshell

Chicken eggshell is one of the most common wastes generated from households,restaurants and other food processing outlets.Waste Chicken Eggshells(WCES)also constitutes an environmental nuisance and ends up discarded at dumping site with no consideration of further usage.The main constituent of WCES is calcium carbonate from which calcium or calcium oxide can be extracted for various applications.This current effort reviews recently published literature on the diverse applications of WCES.The considered utilization avenues include catalysts for biofuel production,construction industry,wastewater purification,industrial sector,food industry,medical,and agricultural applications.The specific areas of application apart from the transesterification reactions include cement additives and replacement in concrete,asphalt binder,adsorbent of metals and dyes,production of hydroxyapatite,food supplement and fortification,dentistry,therapeutics,bone formation,drug delivery,poultry feeds as well as organic fertilizer.For most of the identified applications,the WCES is subjected to pretreatment and other modification techniques before utilization.The conversion of WCES to valuable products is a cost-effective,safe,environmentally friendly,non-toxic and viable means of waste disposal and utilization.More investigations are needed to further explore the benefits derivable from this bioresource.