Differentiation of Adipose Tissue-Derived Stem Cells into Cardiomyocytes: An Overview

Cardiovascular diseases, including congenital and acquired cardiovascular diseases, impose a severe burden on healthcare systems worldwide. Although bone marrow-derived stem cells (BMSCs) therapy can be an effective therapeutic strategy for the heart disease, relatively low abundance, difficult accessibility, and small tissue volume hinder the clinical usefulness. Adipose tissue-derived stem cells (ADSCs) show similar potential with BMSCs to differentiate into lineages and tissues, such as smooth muscle cells, endothelial cells, and adipocytes, with attractiveness of obtaining adipose tissue easily and repeatedly, and a simple separation procedure. We briefly summarize the current understanding of the cardiomyocytes differentiated from ADSCs

Exploration of Estimated Emigration Trends of Polish Health Professionals

Background: Due to the significant staff shortages, emigration of health professionals is one of the key challenges for many healthcare systems. Objective: The aim of this article is to explore the estimated trends and directions of emigration among Polish health professionals. Methods: The emigration phenomenon of Polish health professionals is still under-researched and the number of studies in this field is limited. Thus, the authors have triangulated data using two methods: a data analysis of five national registers maintained by chambers of professionals (doctors, nurses, midwives, physiotherapists, pharmacists, and laboratory diagnosticians), and data analysis from the Regulated Profession Database in The EU Single Market. Results: According to the data from national registers, between 7–9% of practicing doctors and nurses have applied for certificates, which confirm their right to practice their profession in other European countries (most often the United Kingdom, Germany, Sweden, Spain, and Ireland). The relatively high number of such certificates applied for by physiotherapists is also worrying. Emigration among pharmacists and laboratory diagnosticians is rather marginal. Conclusions: Urgent implementation of an effective mechanism for monitoring emigration trends is necessary. Furthermore, it is not possible to retain qualified professionals without systemic improvement of working conditions within the Polish healthcare system.

Socioeconomic analysis of infectious diseases based on different scenarios using uncertain SEIAR system dynamics with effective subsystems and ANFIS

PurposeThis paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries.Design/methodology/approachThe proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers.FindingsThe proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic.Practical implicationsThe proposed methods can be applied to conduct infectious diseases impacts analysis.Originality/valueIn this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities.Highlights:A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;A real case study is considered to evaluate the performances of the proposed models.

IMPACT OF AI AND DEEP LEARNING ON THE HEALTHCARE INDUSTRY

Many healthcare organizations and facilities are currently attempting to improve either managerial or systematic operations. As a result, those businesses' performance has improved, as has their financial growth and reputation in local and global marketplaces. Deep learning and AI are utilized to control healthcare systems in this case. It aids in the provision of better service, the diagnosis of different diseases, and a variety of other tasks. Based on this, this paper will expound on the definitions of deep learning and AI, as well as the importance and change management applications of these tools.

A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning

The unbounded increase in network traffic and user data has made it difficult for network intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e-healthcare since the patients' medical records should be kept highly secure, confidential, and accurate. Any change in the actual patient data can lead to errors in the diagnosis and treatment. Most of the existing artificial intelligence-based systems are trained on outdated intrusion detection repositories, which can produce more false positives and require retraining the algorithm from scratch to support new attacks. These processes also make it challenging to secure patient records in medical systems as the intrusion detection mechanisms can become frequently obsolete. This paper proposes a hybrid framework using Deep Learning named “ImmuneNet” to recognize the latest intrusion attacks and defend healthcare data. The proposed framework uses multiple feature engineering processes, oversampling methods to improve class balance, and hyper-parameter optimization techniques to achieve high accuracy and performance. The architecture contains <1 million parameters, making it lightweight, fast, and IoT-friendly, suitable for deploying the IDS on medical devices and healthcare systems. The performance of ImmuneNet was benchmarked against several other machine learning algorithms on the Canadian Institute for Cybersecurity's Intrusion Detection System 2017, 2018, and Bell DNS 2021 datasets which contain extensive real-time and latest cyber attack data. Out of all the experiments, ImmuneNet performed the best on the CIC Bell DNS 2021 dataset with about 99.19% accuracy, 99.22% precision, 99.19% recall, and 99.2% ROC-AUC scores, which are comparatively better and up-to-date than other existing approaches in classifying between requests that are normal, intrusion, and other cyber attacks.

An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.

Growing Concern on Healthcare Cyberattacks & Need for Cybersecurity

Modern healthcare systems have been dominated by virtual approaches and digital technologies. This has increased the concern for the security of healthcare devices and data due to the lack of information confidentiality and data integrity in this sector. Information category at risk and the importance of patient safety make cybersecurity unique in the field of health. Regarding the context of this problem construction of cyber resilience in healthcare organizations has become a vital task. A comprehensive solution to this problem can be obtained by the combination of human behavioral changes, technological enhancements, process modifications, and new legislations and regulations.

Validity of Apgar Score as an Indicator of Neonatal SARS-CoV-2 Infection: A Scoping Review

The coronavirus disease 2019 (COVID-19) pandemic has had profound impacts on healthcare systems worldwide, particularly regarding the care of pregnant women and their neonates. The use of the Apgar score—a discrete numerical index used to evaluate neonatal condition immediately following delivery that has been used ubiquitously as a clinical indicator of neonatal condition and widely reported in the literature for decades—has continued during the pandemic. Although health systems adopted protocols that addressed pregnant women and their neonates during the pandemic, limited research has assessed the validity of Apgar scores for determining neonatal conditions in the context of COVID-19. Therefore, this scoping review was conducted on the first 2 years of the pandemic and included mothers with reverse transcription-polymerase chain reaction confirmed COVID-19 and their resulting positive or negative neonates. In total, 1,966 articles were assessed for eligibility, yielding 246 articles describing 663 neonates. Neonates who tested negative had median Apgar scores of 9 and 9 at 1 and 5 mins, respectively, while test-positive neonates had median Apgar scores of 8 and 9 at the same time points. The proportions of test-negative neonates with Apgar scores below 7 were 29 (4%) and 11 (2%) at 1 and 5 mins, which was not statistically significant (p = 0.327, χ2 = 0.961). These proportions were even lower for positive neonates: 22 (3%) and 11 (2%) at 1 and 5 mins, respectively, which was not statistically significant (p = 1, χ2 = 0). The low proportion of Apgar scores below 7 suggests that low Apgar scores are likely to be associated with severe maternal COVID-19 symptoms during delivery rather than neonatal COVID-19. Therefore, this study indicated that Apgar scores are poor indicators of neonatal COVID-19 status.