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A Medical Cyber-Physical System (MCPS) represents a sophisticated healthcare framework seamlessly integrating cyber and physical elements to enhance medical processes, diagnostics, and patients. The integration of Artificial Intelligence (AI) into the healthcare system has been pivotal in advancing intelligent MCPS and ushering in an era of advanced healthcare solutions. The paradigm of smart hospitals aspires to implement intelligent solutions seamlessly integrating hardware and software to control, supervise, and monitor patients while assisting healthcare professionals. Such solution is essential for smart decision-making and enhancing healthcare services. However, complete utilization of this intelligent MCPS relies on an effective framework that should facilitate the interaction among patients, medical devices, AI services and hospital staff. This paper introduces a Digital Twin (DT)-based Smart Medical Cyber-Physical System (DT-MCPS) designed to enhance smart hospitals. Leveraging DT technology, DT-MCPS constructs a virtual replica of the hospital, facilitating precise control and supervision of patient care, coupled with service optimization through comprehensive data integration. DT-MCPS promotes personalized decision-making by seamlessly integrating medical records and real-time monitoring of physiological data, enabling predictive insights into disease progression. Moreover, DT-MCPS employs a model-based platform founded on web services to monitor the patient’s state in real-time while accurately simulating the hospital medical systems workflows and contributing to long-term health management. Experimental results showcase the efficacy of DT-MCPS in enhancing hospitalization services, streamlining real-time control, and achieving highly precise personalized patient diagnostics.

Traditional Chinese medicine (TCM) has protected the health of Chinese people for thousands of years. With the rapid development of artificial intelligence (AI), various fields of TCM are facing both opportunities and challenges. This review discusses the development prospects and challenges of Chinese medicine in the AI era, emphasizing that AI, as an important tool in the process of Chinese medicine healthcare services, can assist doctors in making objective, rational and professional treatment decisions, and that AI has a strong potential for development in the field of Chinese medicine. However, the emotions, complex thoughts, and humanistic values of doctors are qualities that AI is currently unable to realize, so as the dominant player, the doctor is indispensable to the medical process. By summarizing and analyzing the current development status of AI in diagnosis, drug research, health management and education in TCM, this paper reveals the development prospects and potential risks of combining TCM with AI, and suggests that AI is an important aid for modernizing and improving the quality of TCM medical care in a coordinated manner.

Wireless sensor networks (WSNs) are a powerful support system for the fundamental infrastructure that is required to monitor physiological and activity parameters (WSN). Wearable devices, which are also referred to as wireless nodes in the scientific world, are what are used in order to measure one or more of the user’s vital signs. Each and every wireless node is a teeny-tiny device that is meant to be supplied with enough amounts of storage space, power, and transmission capability. The loss of data packets may occur during the transmission of data via a wireless medium for a number of reasons. These reasons include interferences, improper deployment circumstances, distance, and inadequate signal strength. The monitoring of a user’s physiological information and postural activity information in various applications, such as home care and hospital care, is the primary emphasis of this study. In this work, the WSN was shown thanks to the introduction of wireless sensor nodes that were created locally. These wireless sensor nodes are used in the process of analyzing many aspects of a network, such as the received signal strength, transmission offset, packet delivery ratio (PDR), and signal-to-noise interference. The work significantly improves the capabilities of conventional WSN by implementing a variety of alternative communication approaches, such as network-coded cooperative communication (NC-CC) and cooperative communication (CC). The system that is being shown makes it feasible to localize the user’s approximate position inside an indoor setting without making use of any camera network connections. This is made possible by the system’s ability to determine the user’s location via triangulation. This is one of the benefits that the system provides. A hospital sensor network, an example of which is being shown here, is capable of doing real-time monitoring of a patient’s postural activity as well as their general health. The method is being promoted in order to ensure that the patient will get assistance in a timely manner that is adequate to his/her needs. Involving NC-CC enables the effective sharing of real-time data among the group of privileged duty nurses while simultaneously minimizing the amount of network traffic, latency, and throughput. This is possible because of NC-CC protocol. The findings of the experiments showed that the proposed method of communication, which is known as dynamic retransmit/rebroadcast decision control, is a significant advancement in the network coding approach that is presently being utilized. This was demonstrated by the fact that the method was shown to be significantly more effective.

Robotics is a disruptive technology that has already revolutionized patient healthcare globally. This technology is presently helping to perform various essential tasks such as conducting operations via numerous specializations and managing the entire operating room. Robot surgery is, in reality, available worldwide for knee substitution, correction of the hernia, and colon resection. Surgical robots entered the operating theatres far before entering other medicine-related robotics applications and now facilitate better outcomes for a whole range of healthcare products. In the COVID-19 pandemic, some robots were used in hospitals to deliver medicines, screen, perform odd jobs, and maintain hygienic conditions. This paper provides an overview about robotics and its various applications useful for healthcare. Significant enhancement, quality services, and advancements in healthcare services are also discussed. Here, we have identified the role of robotics in healthcare as a technology that dramatically changes the healthcare field. An artificial intelligence robot can duplicate creativity via algorithms, and its programming too plays a crucial role. Hospitals can now save time and money by removing the need for physical chores for different jobs. It is helpful for surgical training, exoskeletons, intelligent prostheses and bionics, robotic nurses, treatment, medicines, logistics, telepresence, and cleaning services. Robotics technologies such as gesture control, machine view, voice recognition, and touch sensor technology are also available. The future is bright with lower installation and maintenance costs.

The revolutionary digital technology of drones, also known as unmanned aerial vehicles (UAVs), has altered healthcare. This technology proved highly effective for healthcare, research, start-ups, and large corporations. Various drones are used across the industries such as infrastructure, transportation, insurance, telecommunications, agriculture, media and entertainment, security, and mining. Drones have been utilized to help the medical industry for several years, with numerous start-up firms with considerable investment-testing innovative methods. This new drone delivery network will give healthcare practitioners and the communities they serve better access to critically needed therapeutic goods. Compared to traditional approaches, drones to map disaster zones give better cost savings and faster reaction times. Drones can immediately deploy, provide high-resolution and three-dimensional (3D) mapping, identify hotspot locations with the most damage, and upload data in real-time to coordinate rescue operations. This study is mainly about drones and their primary functions. Devoted features and various aspects associated with drone technology for healthcare are briefly discussed and, finally, significant applications of drones for healthcare are identified and discussed. The COVID-19 pandemic has brought to light several long-hidden health disparities worldwide. Drones have resulted in innovation, such as the unprecedented success of developing new vaccinations at record speed. Drones can distribute vaccinations in low-income countries, lowering transportation costs and increasing immunization rates. People are now receiving good care, and the medical infrastructure is also improving, which is made possible by drone technology.

Audio-based healthcare technologies are among the most significant applications of pattern recognition and Artificial Intelligence. Lately, a major chunk of the World population has been infected with serious respiratory diseases such as COVID-19. Early recognition of lung health abnormalities can facilitate early intervention, and decrease the mortality rate of the infected population. Research has shown that it is possible to automatically monitor lung health abnormalities through respiratory sounds. In this paper, we propose an approach that employs filter bank energy-based features and Random Forests to classify lung problem types from respiratory sounds. The adventitious sounds, crackles and wheezes appear distinct to the human ear. Moreover, different sounds are characterized by different frequency ranges that are dominant. The proposed approach attempts to distinguish the adventitious sounds (crackles and wheezes) by modeling the human auditory perception of these sounds. Specifically, we propose a respiratory sounds representation technique capable of modeling the dominant frequency range present in such sounds. On a publicly available dataset (ICBHI) of size 6898 cycles spanning over 5h, our results can be compared with the state-of-the-art results, in distinguishing two different types of adventitious sounds: crackles and wheezes.

Background: The world is transitioning to Industry 4.0, representing the transition to digital, fully machine-driven environments and cyberphysical systems. Industry 4.0 comprises various technologies and innovations that enable development in multiple perspectives, which are implemented in many different sectors. Problem: The major challenges are the high cost, high rate of failure, security and privacy issues, and there is a need for highly skilled labor for applying healthcare data analysis. Aim: To resolve these issues, we employ the proposed system of Industry 4.0 smart manufacturing for IoT-enabled healthcare data analysis in virtual hospital systems with machine learning (ML) techniques. Methods: The proposed system contains five alternative solutions under smart manufacturing. First, the healthcare data analysis is applied for Weber’s syndrome. That is, this will be used to analyze Weber’s syndrome during its consistent treatment. Second, the IoT-enabled healthcare data handling system works based on edge-assisted edge computing that is used to apply IoT to the healthcare data handling system. The healthcare data analysis in virtual hospital systems uses machine learning for driving data synthesis. Finally, the Industry 4.0 smart manufacturing is applied to the IoT-enabled healthcare data analysis to realize efficient data digitization, especially in smart hospitals with smart sensors for virtual IoT-enabled devices surveillance of Weber’s syndrome. Result: The data digitization based on Industry 4.0 smart manufacturing analysis is considered for data processing, storage and transmission. The proposed system is 62% more efficient than the other analyzed methods. The identification of Weber’s syndrome is 69.8% more efficient than the existing midbrain stroke syndrome identification. The processing and storage of data results are 45.78% more efficient than the current encryption method. Finally, the priority-aware healthcare data analysis based on ML provides 63.4% efficient, faster and more accurate diagnoses in the personalized treatment.

Implantable biomedical devices (IBDs) play a vital role in today’s healthcare industry. Such applications demand high data rate, low power and small-sized demodulators. This work presents a simple small-sized low-power architecture for differential quadrature phase shift keying (DQPSK) demodulator for these devices. The proposed circuitry is designed in UMC 90-nm CMOS technology and occupies a layout area of 0.015mm². It is operated at 1-V supply voltage with a power consumption of 405$\mu$W. The carrier frequency is 10MHz and the obtained data rate is 20Mbps. Hence it exhibits a high data-rate-to-carrier-frequency (DRCF) ratio of 200% making it ideal for IBDs.

The Internet of Things (IoT) is gaining a tons of attention in numerous industries due to its low-cost autonomous sensor operations. IoT devices in healthcare and medical activities establish an environment that recognizes the patients’ health status such as stress levels, oxygen supply, pulse and warmth, and responds quickly in the event of an emergency. Moreover, various systems founded on low-powered biosensor nodules have been proposed to monitor patients’ medical conditions utilizing Wireless Body Area Network (WBAN); despite the fact that controlling increasing power usage and communication expenses is time-consuming and attention-demanding. Another difficult research problem is data privacy and integrity in the presence of malicious traffic. Therefore, to overcome the above-stated limitations, this research introduces a Safe and the Energy-Efficient Framework for e-Healthcare using Internet of Medical Things (IoMT), whose main goal is to reduce transmission cost but also power usage among biomaterials while sending health records conveniently and, on the other hand, to protect patients’ medical data from unverified and malevolent base stations to increase internet confidentiality and protection.

Recently, Internet of Things (IoT) technology has become popular and applicable in different fields. The exchangeable data through IoT systems are extremely huge. This poses a great challenge in constructing a scalable and reliable computing scheme for IoT environments. Therefore, an efficient computing scheme for an IoT-enabled healthcare environment was proposed in this paper. The computing scheme idea was based on the use of cloud, fog, mist and edge computing strategies after dividing their resources into N-tiers. For easy management, these tiers were clustered depending on their geographical distribution. The number of tiers for each computing strategy dynamically changed depending on the computing load. Each tier had its own capabilities. Therefore, selecting a computing strategy and its tier was achieved regarding the data requirements and the specs of each computing resource. To evaluate the proposed computing scheme, a network simulator package (NS-3) was used to construct a simulation for the IoT-enabled healthcare environment and the computing scheme. Most simulation results of our computing scheme were compared with the cloud/fog/mist, cloud/fog and cloud computing schemes. Finally, the simulation results proved that the proposed computing scheme outperformed the performance of other traditional computing schemes regarding the following performance metrics: Delay, utilization, energy consumption, beneficial users and different healthcare organization complexities.

Delay or queue length information has the potential to influence the decision of a customer to join a queue. Thus, it is imperative for managers of queueing systems to understand how the information that they provide will affect the performance of the system. To this end, we construct and analyze two two-dimensional deterministic fluid models that incorporate customer choice behavior based on delayed queue length information. In the first fluid model, customers join each queue according to a Multinomial Logit Model, however, the queue length information the customer receives is delayed by a constant $\mathrm{\Delta }$. We show that the delay can cause oscillations or asynchronous behavior in the model based on the value of $\mathrm{\Delta }$. In the second model, customers receive information about the queue length through a moving average of the queue length. Although it has been shown empirically that giving patients moving average information causes oscillations and asynchronous behavior to occur in U.S. hospitals, we analytically and mathematically show for the first time that the moving average fluid model can exhibit oscillations and determine their dependence on the moving average window. Thus, our analysis provides new insight on how operators of service systems should report queue length information to customers and how delayed information can produce unwanted system dynamics.

Considerable research has recently focused on integrating cyber-physical systems in a social context. However, several challenges remain concerning appropriate methodologies, frameworks and techniques for supporting socio-cyber-physical collaboration. Existing systems do not recognize how cyber-physical resources can be socially connected so that they interact in collaborative decision-making like humans. Furthermore, the lack of semantic representations for heterogeneous cyber-social-collaborative networks limits integration, interoperability and knowledge discovery from their underlying data sources. Semantic Web ontology models can help to overcome this limitation by semantically describing and interconnecting cyber-physical objects and human participants in a social space. This research addresses the establishment of both cyber-physical and human relationships and their interactions within a social-collaborative network. We discuss how nonhuman resources can be represented as socially connected nodes and utilized by software agents. A software agent-centric Semantic Social-Collaborative Network (SSCN) is then presented that provides functionality to represent and manage cyber-physical resources in a social network. It is supported by an extended ontology model for semantically describing human and nonhuman resources and their social interactions. A software agent has been implemented to perform some actions on behalf of the nonhuman resources to achieve cyber-physical collaboration. It is demonstrated within a real-world decision support system, GRiST (www.egrist.org), used by mental-health services in the UK.

Mental illnesses are becoming increasingly prevalent, in turn leading to an increased interest in exploring artificial intelligence (AI) solutions to facilitate and enhance healthcare processes ranging from diagnosis to monitoring and treatment. In contrast to application areas where black box systems may be acceptable, explainability in healthcare applications is essential, especially in the case of diagnosing complex and sensitive mental health issues. In this paper, we first summarize recent developments in AI research for mental health, followed by an overview of approaches to explainable AI and their potential benefits in healthcare settings. We then present a recent case study of applying explainable AI for ADHD diagnosis which is used as a basis to identify challenges in realizing explainable AI solutions for mental health diagnosis and potential future research directions to address these challenges.

Sepsis is a common and deadly condition that must be treated eloquently within 19 hours. Numerous deep learning techniques, including Recurrent Neural Networks, Convolution Neural Networks, Long Short-Term Memory, and Gated Recurrent Units, have been suggested for diagnosing long-term sepsis. Regardless, a sizable portion of them are computationally risky and have precision problems. The primary issue described is that output will degrade, and resource utilization will expand proportionately as the volume of dependencies grows. To overcome these issues, we propose a G-Sep technique utilizing Bidirectional Gated Recurrent Unit Algorithm, which consumes much less resource to detect the disease and in a short time with better accuracy than the existing methods to diagnose the sepsis. AI models could assist with distinguishing potential clinical factors and give better than existing conventional low-execution models. The proposed model is implemented utilizing Conda and Tensorflow Framework using the California Inpatient Severe Sepsis (CISS) Patient Dataset. The comparative simulation of the various existing models and the proposed G-Sep model is done using Conda and Tensor frameworks. The simulation results revealed that the proposed model had outperformed other frameworks in terms of mean average precision (mAP), receiver operating characteristic curve (ROC), and Area under the ROC Curve (AUROC) metrics linearly.

Mental Health Survey of Junior Medical Officers.

Gene Silencing Tools Released by CSIRO.

China Participated in Global Sleeping Disorder Survey.

Beijing Opens Liver Cancer Prevention Center.

Hong Kong Bird Flu Virus Strikes Again.

Emergency BP Drug Could be Potentially Fatal.

Vitamin Prices Spirals in India Amidst Fears of Supply Cuts.

India's Healthcare Industry Likely to Get Tax Concessions.

Egg Bank Opens in Japan.

AstraZeneca Under Investigation for Research Suppression on Lung Cancer Drug Irresa.

Paper Review for Genetically Modified Crops.

Harmful Corticosteroid Dosage in Asthma Inhalers.

This article is about the relationship of law firms and the life sciences sector in Australia. It discusses about an example of a law firm, Lander & Rogers Lawyers and the life sciences sector in Australia.

Applications of Nanomedicine.

A Comprehensive Overview of the Development and Prospects of Nanobiotechnology in China.

Nanobiotechnology in the Republic of Korea.

Nanomedicine: Prospective Diagnostic and Therapeutic Potentials.

Nanotechnology: What it is and how it can be Applied in Healthcare.

AUSTRALIA — Lung Cancer Cases on the Rise.

AUSTRALIA — Cantox Announces the Launch of Ashuren Health Sciences Pharmaceutical Division.

CHINA — Biotech Crops Exhibit Impressive Double-digit Growth.

CHINA — World Genomes Project to Promote Research on Human Diseases.

HONG KONG — New Anti-Hepatitis Drug Found.

INDIA — Pneumonia Kills More Than 1000 Children Daily in India.

INDIA — PepsiCo to Open Carrageenan Biopolymer Plant in India.

INDIA — Medical Tourism Insurance Covers Will Soon be Available in the West.

JAPAN — Green Tea May Reduce Prostate Cancer Risk.

SINGAPORE — QIAGEN to Supply Avian Flu Surveillance Solutions to Singapore.

TAIWAN — NTU Discovery May Help Cardiovascular and Cancer Experiments.

VIETNAM — Bird Flu Hits Poultry Farm in Northern Vietnam.

Greater Global Awareness of Chinese Medicine.

Pharma Marketing, Philippines.

Asia Pacific's Finest Healthcare Players Recognized.

AUSTRALIA – PAST Protocol to Fast-track Stroke Treatment

AUSTRALIA – Great Potential of Regenerative Heart Tissue in Embryonic Mice

CHINA – Babies Killed by Tainted Milk Formula Increased to Six

CHINA – Chinese Society of Hematology and Bayer Partner to Develop Comprehensive Care Hemophilia Treatment Centers throughout China

CHINA – Drug Information Association (DIA) Opens New Office in China

CHINA – China Leads Way to Develop Bird Flu Pandemic Forewarning System

CHINA – Herbal Drug Recalled after Infant's Death

CHINA – Toddler Virus Flares Up in China Again

CHINA – Functional Gene Discovered for Rice's Grain-Filling

CHINA – China Tops the Health List among Developing Countries

CHINA – U.S. Food, Drug Regulator to Set Up Offices in China

INDIA – Maharashtra Plans Two More Biotech Parks at Khalapur, Alibag

INDIA – Institute of Clinical Research India Partners with SingHealth

INDONESIA – 113th Bird Flu Death Recorded in Indonesia

JAPAN – World's First Made-to-order Bones on Clinical Trial

JAPAN – Dioxin Tied to Metabolic Syndrome in Japan

MALAYSIA – Medicine Study at Newcastle University in Malaysia

NEW ZEALAND – NZ Research Implants Pig Cells in Human Diabetics

SINGAPORE – Singapore Develops Cell Therapy Treatment for Cancer

SINGAPORE – Singapore Sets Up Second Heart Center to Meet Demand

SINGAPORE – Singapore is First in Southeast Asia to Offer Robotic Surgery for Gynaecologic Cancers

SINGAPORE – SNEC Takes the Lead at the Forefront of Lasik and Corneal Transplant Technology

SINGAPORE – Singapore Pumps Funding to Boost Dengue, Diabetes Research

SINGAPORE – Second Case of Rare Genetic Disorder that Afflict Toddlers Detected

SINGAPORE – Singapore Landfill Gets New Lease of Life as an Eco Park

SINGAPORE – Fusionopolis – World Within a City – Singapore's 2nd R&D Hub

SINGAPORE – Singapore's Stem Cell Research Signifies A Global Breakthrough

THAILAND – Thai Scientists' First Genetic Decode Advances Thailand into “Genomic” Era

THAILAND – Bird Flu Found in Northern Thailand, First Outbreak in 10 months

TAIWAN – A New Food Regulatory Agency to be Set Up in Taiwan

VIETNAM – Vietnam to Host Global Rice Meet in 2010

VIETNAM – Liver Transplant on Vietnam's Youngest Receiver Successful

VIETNAM – Ministries Unite For Greater Synergy to Develop Pharmaceutical Industry

VIETNAM – Vietnam Launches Diabetes Awareness Project