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.

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.

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.

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.

Associative learning mechanism for drug‐target interaction prediction

As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.Exploring drug‐target interaction strength in terms of drug‐target affinity(DTA)is an emerging and effective research approach for drug development.However,it is challenging to model drug‐target interactions in a deep learning manner,and few studies provide interpretable analysis of models.This paper proposes a DTA prediction method(mutual transformer‐drug target affinity[MT‐DTA])with interactive learning and an autoencoder mechanism.The proposed MT‐DTA builds a variational autoencoders system with a cascade structure of the attention model and convolutional neural networks.It not only enhances the ability to capture the characteristic information of a single molecular sequence but also establishes the characteristic expression relationship for each substructure in a single molecular sequence.On this basis,a molecular information interaction module is constructed,which adds information interaction paths between molecular sequence pairs and complements the expression of correlations between molecular substructures.The performance of the proposed model was verified on two public benchmark datasets,KIBA and Davis,and the results confirm that the proposed model structure is effective in predicting DTA.Additionally,attention transformer models with different configurations can improve the feature expression of drug/protein molecules.The model performs better in correctly predicting interaction strengths compared with state‐of‐the‐art baselines.In addition,the diversity of drug/protein molecules can be better expressed than existing methods such as SeqGAN and Co‐VAE to generate more effective new drugs.The DTA value prediction module fuses the drug‐target pair interaction information to output the predicted value of DTA.Additionally,this paper theoretically proves that the proposed method maximises evidence lower bound for the joint distribution of the DTA prediction model,which enhances the consistency of the probability distribution between actual and predicted values.The source code of proposed method is available at https://github.com/Lamouryz/Code/tree/main/MT‐DTA.

Investigation of Tablet Defects by AI Based Terahertz Technology

Recently a lot of medical tablets with special packets in the global market are available. For the safety and purity of the tablet, we need to scan it by developed scanner technology, which should be not more expensive and easily available in the market. The THz technology is one of them. In the proposed work, we have tasted tablet images with the help of the THz super-resolution scanner, which is already available in our lab. The AI machine learning data concept has been investigated. Good resolution of images has been obtained. Furthermore, the challenging research problems are discussed. Finally, it summarizes the recent updates in terahertz technology for drug inspection and medical applications with potential research challenges.

Ring-Opening Polymerization of ε-Caprolactone Catalyzed by Diisopropylamido Bis(methylcyclopentadienyl) Erbium Tetrahydrofuranate Complex

Ring-opening polymerization of Ε-caprolactone was successfully carried out by diisopropylamido bis (methylcyclopentadienyl) erbium as the catalyst. It is found that organoerbium amide is a very active catalyst for Ε-caprolactone ring-opening polymerization. The activity increases with the increasing of polymerization temperature. Under certain conditions, the polymerization system shows a living characteristic.

A NEW TYPE OF MICROPUMP DRIVEN BY A LOW ELECTRIC VOLTAGE

In this paper,we propose a new prototype model of a micro pump using ICPF(Ionic Conducting Polymer Film)actuator as the servo actuator.This micro pump consists of two active one- way valves that make use of the same ICPF actuator.The overall size of this micro pump prototype is 12mm in diameter and 20mm in length.The actuating mechanism is as follows:(1)The ICPF actuator as the diaphragm is bent into anode side by application of electricity.Then the volume of the pump chamber increases,resulting in the inflow of liquid from the inlet to the chamber.(2)By changing the current direction,the volume of the pump chamber decreases,resulting in the liquid flow from the chamber to the outlet.(3)The ICPF actuator is put on a sine voltage,the micro pump provides liquid flow from the inlet to the outlet continuously.Characteristic of the micro pump is measured.The experimental results indicate that the micro pump has the satisfactory responses.

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.

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Coronaviruses are a well-known family of viruses that can infect humans or animals.Recently,the new coronavirus(COVID-19)has spread worldwide.All countries in the world are working hard to control the coronavirus disease.However,many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system,which leads to the mass spread of diseases.As a powerful tool,artificial intelligence(AI)has been successfully applied to solve various complex problems ranging from big data analysis to computer vision.In the process of epidemic control,many algorithms are proposed to solve problems in various fields of medical treatment,which is able to reduce the workload of the medical system.Due to excellent learning ability,AI has played an important role in drug development,epidemic forecast,and clinical diagnosis.This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.

The Interesting of Antifungal Effects of Novel In Vitro Fabrics of Stabilized ZnO Nanofluids

According to the extent of fungal infections, to be chronic these such diseases and recently the emerging issue of increased antibiotic resistance in fungal infections, most of scientists are going to find a proper way to replace antibacterial agent by significant semiconductor ZnO nanoparticles (NPs). They are well known to be one of the most important and special metal oxide nanoparticles in pharmaceutical against the most common fungi. ZnO nanoparticles were synthesized using sol-gel, hydrothermal and functionalized surface methods and formulated in water solutions as nanofluids. XRD, FTIR and SEM techniques and UV-Vis absorbance spectroscopy characterized their ZnO modified nanostructures. Also antimycotic potential according to generally tests such as: (MIC) minimum inhibitory concentration, (MFC) minimum fungicidal concentration and normally well diffusion method with standard strains fungi were performed. Among five common fungi strains using in this research, new various ZnO nanofluids showed noticeable results for dermatophyte fungi like Trichophyton mentagrophytes, Microsporum gypseum, Microsporum canis, Candida albicans and Candid tropicalis which had un growth zones in order 70, 40, 35, 30 and 30 mm in comparing with Clotrimazole reference reagent: 30, 25, 25, 18 and 20 mm by well method. The performance of MIC for ZnO nanofluids on fungi was determined to be equal to 0.35, 3.12, 6.25, 6.25 and 6.25 μgr/ml and MFC of nanoproducts showed the 1.5, 12.5, 25, 25 and 25 μgr/ml. Therefore, the designed ZnO nanofluids could reveal the most effect on fungi which cause dermal (ringworm), mucosal (thrush) and vaginal infections, so we are able to apply these surface high energetic ZnO water-based nanofluid formulations as in vitro nanomedicine and nanohygiene for the first time.

Propulsion Modeling and Analysis of a Biomimetic Swimmer

We have studied a biomimetic swimmer based on the motion of bacteria such as Escherichia coli (E. coli) theoretically andexperimentally. The swimmer has an ellipsoidal cell body propelled by a helical filament. The performance of this swimmer wasestimated by modeling the dynamics of a swimmer in viscous fluid. We applied the Resistive Force Theory (RFT) on this modelto calculate the linear swimming speed and the efficiency of the model. A parametric study on linear velocity and efficiency tooptimize the design of this swimmer was demonstrated. In order to validate the theoretical results, a biomimetic swimmer wasfabricated and an experiment setup was prepared to measure the swimming speed and thrust force in silicone oil. The experimentalresults agree well with the theoretical values predicted by RFT. In addition, we studied the flow patterns surrounding thefilament with a finite element simulation with different Reynolds number (Re) to understand the mechanism of propulsion. Thesimulation results provide information on the nature of flow patterns generated by swimming filament. Furthermore, the thrustforces from the simulation were compared with the thrust forces from theory. The simulation results are in good agreement withthe theoretical results.