Blockchain in Healthcare: Insights on COVID-19

An architecture for COVID-19 analysis and detection using big data, AI, and data architectures

The COVID-19 epidemic is affecting individuals in many ways and continues to spread all over the world. Vaccines and traditional medical techniques are still being researched. In diagnosis and therapy, biological and digital technology is used to overcome the fear of this disease. Despite recovery in many patients, COVID-19 does not have a definite cure or a vaccine that provides permanent protection for a large number of people. Current methods focus on prevention, monitoring, and management of the spread of the disease. As a result, new technologies for combating COVID-19 are being developed. Though unreliable due to a lack of sufficient COVID-19 datasets, inconsistencies in the datasets availability, non-aggregation of the database because of conflicting data formats, incomplete information, and distortion, they are a step in the right direction. Furthermore, the privacy and confidentiality of people's medical data are only partially ensured. As a result, this research study proposes a novel, cooperative approach that combines big data analytics with relevant Artificial Intelligence (AI) techniques and blockchain to create a system for analyzing and detecting COVID-19 instances. Based on these technologies, the reliability, affordability, and prominence of dealing with the above problems required time. The architecture of the proposed model will analyze different data sources for preliminary diagnosis, detect the affected area, and localize the abnormalities. Furthermore, the blockchain approach supports the decentralization of the central repository so that it is accessible to every stakeholder. The model proposed in this study describes the four-layered architecture. The purpose of the proposed architecture is to utilize the latest technologies to provide a reliable solution during the pandemic; the proposed architecture was sufficient to cover all the current issues, including data security. The layers are unique and individually responsible for handling steps required for data acquisition, storage, analysis, and reporting using blockchain principles in a decentralized P2P network. A systematic review of the technologies to use in the pandemic covers all possible solutions that can cover the issue best and provide a secure solution to the pandemic.

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

Clinical Impact of "Real World Data" and Blockchain on Public Health: A Scoping Review

The digitisation of healthcare has allowed a significant rethinking of the previous clinical protocols, improving their interoperability through substantial standardisation. These technological advances have ensured that data are comparable, as they are obtained from 'reliable' and certified processes; however, there are billions of data that are neither structured nor quality-controlled. They are collectively referred to as 'Real World Data' (RWD). Blockchain (BC) is a procedure with specific characteristics and algorithms that ensure that the stored data cannot be tampered with. Nowadays, there is an increasing need to rethink blockchain in a one-health vision, making it more than just a 'repository' of data and information, but rather an active player in the process of data sharing. In this landscape, several scholars have analysed the potential benefits of BC in healthcare, focusing on the sharing and safety of clinical data and its contact tracing applications. There is limited research on this matter; moreover, there is a strategic interest in managing RWD in a reliable and comparable way, despite the lack of knowledge on this topic. Our work aims to analyse systematically the most impacting literature, highlighting the main aspects of BC within the context of the new digital healthcare, and speculating on the unexpressed potential of RWD.

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

Challenges and future opportunities to unlock the critical supply chain of personal and protective equipment (PPE) encompassing decontamination and reuse under emergency use authorization (EUA) conditions during the COVID-19 pandemic: Through a reflective circularity and sustainability lens

Severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), and the resulting coronavirus disease (COVID-19), was declared a public health emergency of global concern by the World Health Organization (WHO) in the early months of 2020. There was a marked lack of knowledge to inform national pandemic response plans encompassing appropriate disease mitigation and preparation strategies to constrain and manage COVID-19. For example, the top 16 "most cited" papers published at the start of the pandemic on core knowledge gaps collectively constitute a staggering 29,393 citations. Albeit complex, appropriate decontamination modalities have been reported and developed for safe reuse of personal and protective equipment (PPE) under emergency use authorization (EUA) where critical supply chain shortages occur for healthcare workers (HCWs) caused by the COVID-19 pandemic. Commensurately, these similar methods may provide solutions for the safe decontamination of enormous volumes of PPE waste promoting opportunities in the circular bioeconomy that will also protect our environment, habitats and natural capital. The co-circulation of the highly transmissive mix of COVID-19 variants of concern (VoC) will continue to challenge our embattled healthcare systems globally for many years to come with an emphasis placed on maintaining effective disease mitigation strategies. This viewpoint article addresses the rationale and key developments in this important area since the onset of the COVID-19 pandemic and provides an insight into a variety of potential opportunities to unlock the long-term sustainability of single-use medical devices, including waste management.

Analysis of Costs Associated with the Use of Personalized Automated Dosing Systems versus Manual Preparation in a Residential Center for the Elderly in Extremadura

INTRODUCTION

During the SARS-CoV-2 pandemic, there has been a decrease in the supervision of the medication of subjects with chronic diseases. Customized automated dosing systems (SPDA) are devices that allow medication to be dispensed and administered, which have proven to be safe and effective for the patient and cost-effective for the healthcare system.

METHODS

an intervention study was carried out on patients from January to December 2019 in a residential centre for the elderly with more than 100 beds. The economic costs derived from manual dosing were compared with those of an automated preparation (Robotik Technology^®).

RESULTS

Of the 198 patients included, 195 (97.47%) of them were polymedicated. Of the total of 276 active substances of registered medicinal products, it was possible to include them in the process of automating the preparation of the SPDA 105 active pharmaceutical ingredients. A cost reduction of EUR 5062.39 per year was found using SPDA. Taking into account the active ingredients of emblistable and non-emblistable medicines, the use of SPDA resulted in savings of EUR 6120.40 per year. The system contributed to the detection of cases of therapeutic duplication and reduced the time to prepare the medication.

CONCLUSIONS

the use of SPDA is a useful and economically profitable strategy for its use in residential centres for the elderly.

A critical analysis of the integration of blockchain and artificial intelligence for supply chain

The integration between blockchain and artificial intelligence (AI) has gained a lot of attention in recent years, especially since such integration can improve security, efficiency, and productivity of applications in business environments characterised by volatility, uncertainty, complexity, and ambiguity. In particular, supply chain is one of the areas that have been shown to benefit tremendously from blockchain and AI, by enhancing information and process resilience, enabling faster and more cost-efficient delivery of products, and augmenting products' traceability, among others. This paper performs a state-of-the-art review of blockchain and AI in the field of supply chains. More specifically, we sought to answer the following three principal questions: Q1-What are the current studies on the integration of blockchain and AI in supply chain?, Q2-What are the current blockchain and AI use cases in supply chain?, and Q3-What are the potential research directions for future studies involving the integration of blockchain and AI? The analysis performed in this paper has identified relevant research studies that have contributed both conceptually and empirically to the expansion and accumulation of intellectual wealth in the supply chain discipline through the integration of blockchain and AI.

The COVID-19 Impact on Oral Healthcare Demand and Performance: The Experience of a Clinical and Academic Centre in Portugal (EU)

INTRODUCTION

The SARS-CoV-2 pandemic has reshaped the global landscape as we know it and had a tremendous effect on healthcare systems around the world. However, its impact on oral healthcare is still to be fully assessed. The aim of this study was to understand if and how COVID-19 affected the demand and performance of oral healthcare, taking the Clinical and Academic Centre of Coimbra as an example, more specifically, the Department of Stomatology of the Coimbra Hospital and University Centre and the Dentistry Department of the Faculty of Medicine of the University of Coimbra.

MATERIAL AND METHODS

An observational study was designed for collecting the data of a series of key oral healthcare indicators: number of appointments; referrals from primary healthcare; missed appointments; number of surgeries performed in the operating room; number of biopsies; number of patients admitted through the emergency department and epidemiologic parameters over two 18-month periods between September 2018 and August 2021: pre-COVID-19 and during the COVID-19 pandemic, with the latter divided in four stages. A statistical analysis which included descriptive and inferential procedures was then performed, with an established significance level of 5% and the application of parametric tests, t-Student test for a sample and for independent samples and One-Way ANOVA for the variance analysis.

RESULTS

There was a general decline in all indicators comparing the pre-COVID-19 with the COVID-19 period, with a reduction of 50.61% in the number of appointments, 44.06% in referrals, 24.41% in surgeries, 26.30% in biopsies and 32.33% in patients seen in the Emergency Room. The number of missed appointments also increased by 181.82%. All variations revealed statistically significant differences (p < 0.05). The individual COVID-19 stage analysis, when compared with the pre-COVID-19 reference, and variance analysis of these different stages also showed statistically significant differences (p < 0.05 and p < 0.001), except for the number of biopsies during the third and fourth stages.

CONCLUSION

The results of this study suggest that the SARS-CoV-2 pandemic has had a considerable impact on oral healthcare demand and performance. However, results also show a remarkable adjustment and improvement in the provided care, with a positive evolution throughout the COVID-19 period.

SanJeeVni: Secure UAV-Envisioned Massive Vaccine Distribution for COVID-19 Underlying 6G Network

Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject's intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.

AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions

Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.

Tamper-proof multitenant data storage using blockchain

Technologies like Internet of Things (IoT), cloud, artificial intelligence, blockchain etc. have become a perceptible part of our lives resulting in the generation of enormous amounts of data. Consequently, the systems used for storage and processing of this data are required to be scalable for handling the huge volumes of data. A shared, multitenant system such as a cloud-based storage-as-a-service provides scalability of storage as well as economics of sharing. However, there is a risk of data tampering when multiple tenants work in a shared environment. The benefits of a multitenant solution can be leveraged only if tenants' data is isolated from each other. Further, prevention of data tampering from malicious tenant nodes is also required. Therefore, the paper proposes the use of a private blockchain for an implementation of a multi-tenant-based storage system. The objective is to develop a scalable system where tenants' data is not at a risk of tampering. The efficacy of the proposed system has been demonstrated with synthetic data of multiple tenants using a Software as a Service (SaaS) healthcare application.

Efficacy of Adjunctive Photobiomodulation in the Management of Medication-Related Osteonecrosis of the Jaw: A Systematic Review

Background: Medication-related osteonecrosis of the jaw (MRONJ) is a severe adverse medication response that manifests as progressive bone necrosis in the craniofacial area. There is still no clear treatment protocol for the management of MRONJ. The purpose of this study was to conduct a systematic review to assess the efficacy of photobiomodulation (PBM) as an adjunct to MRONJ therapy. Methods: In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, a literature search was performed on PubMed/MEDLINE, Scopus, Web of Science, and Cochrane databases. Two examiners examined eligibility and risk of bias separately before extracting data. Results: Two hundred sixty-nine articles were found through electronic search, out of which only 11 met the inclusion criteria and were included in qualitative synthesis (9 retrospectives, 1 prospective, and 1 case series). A total number of 759 patients and a mean age ranging from 54 to 74 years were reviewed. Females were the most frequent gender in all of the selected studies (72% females to 28% males), and the most frequent stage in the studies mentioned above was stage II (66%). Most of the studies had shown a significant improvement when PBM was used as an adjunctive treatment. Conclusions: Based on the results of this study, PBM as an adjuvant therapy can significantly improve the outcomes of each treatment plan. However, surgical intervention for the complete healing of the lesions is suggested.

COVID-19 and future pandemics: A blockchain-based privacy-aware secure borderless travel solution from electronic health records

COVID-19 pandemic undoubtedly lingers on and has brought unprecedented changes globally including travel arrangements. Blockchain-based solutions have been proposed to aid travel amid the pandemic hap. Presently, extant solutions are country or regional-based, downplay privacy, non-responsive, often impractical, and come with blockchain-related complexities presenting technological hurdle for travelers. We therefore propose a solution namely, Borderless to foster global travel allowing travelers and countries collaboratively engage in a secure adaptive proof protocol dubbed Proof-of-COVID-19 status a number of arbitrary statements to ascertain the fact that the traveler poses no danger irrespective of the country located. As far as we know, this is first of its kind. Borderless is implemented as a decentralized application leveraging blockchain as a trust anchor and decentralized storage technology. Security analysis and evaluation are performed proving security, privacy-preservation, and cost-effectiveness along with implementation envisioning it as a blueprint to facilitate cross-border travel during the present and future pandemics. Our experimental results show it takes less than 60 and 3 s to onboard users and perform proof verification respectively attesting to real usability scenarios along with the traits of arbitrary proofs to aid responsiveness to the dynamics of pandemics and blockchain abstraction from travelers.

Trust based COVID-19 vaccine distribution using blockchain technology

The COVID-19 outbreak has impacted huge number of individuals all around the world and has caused a great economic loss all over the world. Vaccination is most effective solution to prevent this disease. It helps in protecting the whole community. It improves the human immune system and fights against corona virus reducing the death rate. This paper deals with the different types of COVID-19 vaccine and their related distribution, it includes measures to ensure safe and secured distribution of the vaccine through block chain technology with the help of supply chain. Any malfunction in the chain is identified by the trust value of the function point method and the value of the Markov Chain.

HOX genes in stem cells: Maintaining cellular identity and regulation of differentiation

Stem cells display a unique cell type within the body that has the capacity to self-renew and differentiate into specialized cell types. Compared to pluripotent stem cells, adult stem cells (ASC) such as mesenchymal stem cells (MSCs) and hematopoietic stem cells (HSCs) exhibit restricted differentiation capabilities that are limited to cell types typically found in the tissue of origin, which implicates that there must be a certain code or priming determined by the tissue of origin. HOX genes, a subset of homeobox genes encoding transcription factors that are generally repressed in undifferentiated pluripotent stem cells, emerged here as master regulators of cell identity and cell fate during embryogenesis, and in maintaining this positional identity throughout life as well as specifying various regional properties of respective tissues. Concurrently, intricate molecular circuits regulated by diverse stem cell-typical signaling pathways, balance stem cell maintenance, proliferation and differentiation. However, it still needs to be unraveled how stem cell-related signaling pathways establish and regulate ASC-specific HOX expression pattern with different temporal-spatial topography, known as the HOX code. This comprehensive review therefore summarizes the current knowledge of specific ASC-related HOX expression patterns and how these were integrated into stem cell-related signaling pathways. Understanding the mechanism of HOX gene regulation in stem cells may provide new ways to manipulate stem cell fate and function leading to improved and new approaches in the field of regenerative medicine.

Antibiotic Abuse and Antimicrobial Resistance in Hospital Environment: A Retrospective Observational Comparative Study

Background and Objectives: Antimicrobial resistance represents a serious problem, and it may be life-threatening in the case of severe hospital-acquired infections (HAI). Antibiotic abuse and multidrug resistance (MDR) have significantly increased this burden in the last decades. The aim of this study was to investigate the distribution and susceptibility rates of five selected bacterial species (E. coli, K. pneumoniae, P. aeruginosa, S. aureus and E. faecium) in two healthcare settings located in the Apulia region (Italy). Materials and Methods: Setting n.1 was a university hospital and setting n.2 was a research institute working on oncological patients. All the enrolled patients were diagnosed for bacterial HAI. The observation period was between August and September 2021. Clinical samples were obtained from several biological sources, in different hospital wards. Bacterial identification and susceptibility were tested by using the software VITEC 2 Single system. Results: In this study, a higher incidence of multi-drug-resistant K. pneumoniae was reported (42,2% in setting n.1 and 50% in setting n.2), with respect to the Italian 2019 statistics report (30.3%). All the isolates of E. faecium and S. aureus were susceptible to linezolid. All the bacterial isolates of P. aeruginosa and most of K. pneumoniae were susceptible to ceftazidime–avibactam. Amikacin and nitrofurantoin represented a good option for treating E. coli infections. Multidrug-resistant (MDR) P. aeruginosa, methicillin-resistant S. aureus (MRSA) and vancomycin-resistantE. faecium (VRE) had a lower incidence in the clinical setting, with respect to E. coli and K. pneumoniae. Conclusions: The data obtained in this study can support clinicians towards a rational and safe use of antibiotics for treating the infections caused by these resistant strains, to enhance the overall efficacy of the current antibiotic protocols used in the main healthcare environments.

Blockchain Powered Vaccine Efficacy for Pharma Sector

Infectious and contagious diseases exist in humanity for many centuries which causes a curb in the growth of the population. Immunization plays a vital role to prevent mortality and morbidity against infectious diseases. COVID-19 pandemic continues to rage the urgency of developing a vaccine that should ensure the safety, efficacy, swift and fair deployment, implementation, and monitoring of vaccines across the globe. In the present context, the vaccine production to immunization campaign is a critical challenge. Therefore, an effective vaccine supply chain mechanism is required to address issues such as counterfeit vaccines, reduce vaccine wastages, and vaccine record fraud. In this paper, a blockchain-enabled vaccine supply chain is proposed to ensure the correctness, transparency, trust, and immutable log and improve the efficiency of vaccine distribution in the cold chain. The uniqueness of the proposed system is to provide distributed system to verify the reliability and efficacy of the vaccine from production to end beneficiaries' feedback about the vaccine. Our proposed system gives a clear view to the users as well as to the healthcare provider about the vaccination and ensures the anticounterfeit vaccine. The proposed system minimizes counterfeit vaccines and records, provides transparent communication between stakeholders in the supply chain, and improves the security of the vaccine supply chain and immutable feedback system about the vaccine.

Towards the Use of Big Data in Healthcare: A Literature Review

The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

PURPOSE OF REVIEW

The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each.

RECENT FINDINGS

Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks.

SUMMARY

AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

The Dental-BIOfilm Detection TECHnique (D-BioTECH): A Proof of Concept of a Patient-Based Oral Hygiene

To date, no strong long-term data have been reported about new innovative clinical protocols to manage oral hygiene. An improper management of oral hygiene may lead to an increase in dental implant failure, and to an increase in infective complications in prosthetic rehabilitation. Personalized techniques are strongly required in dentistry and dental hygiene. A customized and personalized approach to oral hygiene is crucial in ensuring not only effective treatment, but also a careful analysis of the general health status of the patient involved in the therapeutic process. D-BioTECH is an acronym for Dental BIOfilm Detection Technique: it is based on a tailored approach to patients, ensuring that the operator actively interacts with the patient and their specific needs, especially during the domiciliary therapy. D-BioTECH is an approach to preventive care: in D-BioTECH, both dental hygienists and dentists play a central role. The use of a personalized approach to oral hygiene is the first step towards increasing implant and prosthesis survival rate; moreover, personalized medicine is strategic for managing and preventing the biological complications associated with several dental risk factors.

Blockchain technology in healthcare: A systematic review

Blockchain technology (BCT) has emerged in the last decade and added a lot of interest in the healthcare sector. The purpose of this systematic literature review (SLR) is to explore the potential paradigm shift in healthcare utilizing BCT. The study is compiled by reviewing research articles published in nine well-reputed venues such as IEEE Xplore, ACM Digital Library, Springs Link, Scopus, Taylor & Francis, Science Direct, PsycINFO, Ovid Medline, and MDPI between January 2016 to August 2021. A total of 1,192 research studies were identified out of which 51 articles were selected based on inclusion criteria for this SLR that presents the modern information on the recent implications and gaps in the use of BCT for enhancing the healthcare procedures. According to the outcomes, BCT is being applied to design the novel and advanced interventions to enrich the current protocol of managing, distributing, and processing clinical records and personal medical information. BCT is enduring the conceptual development in the healthcare domain, where it has summed up the substantial elements through better and enhanced efficiency, technological innovation, access control, data privacy, and security. A framework is developed to address the probable field where future researchers can add considerable value, such as data protection, system architecture, and regulatory compliance. Finally, this SLR concludes that the upcoming research can support the pervasive implementation of BCT to address the critical dilemmas related to health diagnostics, enhancing the patient healthcare process in remote monitoring or emergencies, data integrity, and avoiding fraud.

The Impact of the COVID-19 Pandemic on Romanian Postgraduate Periodontal Residency Teaching: Past Experience, Present Imperatives and Future Considerations in a Multicentric Evaluation

The aims of this study were to identify the challenges in periodontology postgraduate residency programs during the COVID-19 pandemic by identifying the modifications of educational instruments, to evaluate the impact of hybrid education on periodontology postgraduate programs in terms of resident-centred outcomes, and to evaluate the education efficiency of an innovative teaching approach. Resident doctors from three Romanian dental faculties were included in study groups based on the intensity of clinical training. A web-based questionnaire was used to collect information on residents’ perception about teaching activity. Important educational changes were identified. Moreover, residents learned a periodontal procedure through online training and then performed it on preclinical models three times. The working times were recorded. Statistical analysis was performed. Resident doctors were unsatisfied with clinical practice during the pandemic year, but they positively valued the development of online courses. Learning efficiency improved by repeating the same procedure on preclinical models, as proved by the significant decrease of the working times. E-learning was appreciated as an important component of the new hybrid teaching approach. Reorganization and further emphasis on both preclinical and medical practice, targeted to aid residents perform more accurate and efficient procedures, are recommended.

Designing an E-voting Framework using Blockchain Technology

Blockchain technology provides a stable spot in electronic voting systems by providing a transparent platform for an election which prevents the failures faced by the traditional electronic voting systems and makes it a more accurate and secure platform for voting. However, little attention has been paid to the possible impacts of new technological disruptions on politics and the electoral systems. The main goal of this paper is to design a framework for electronic voting systems using Blockchain technology. To achieve this goal, the paper outlines these three objectives: (1) to explore and identify the challenges in existing voting systems, (2) to identify the methods that could enhance the security and privacy of the electoral voting systems using Blockchain technology, and (3) to design a framework for electronic voting systems that use Blockchain technology in order to address the flaws in current voting systems. A mixed approach is applied in this study to explore the opportunities and challenges of the Blockchain voting system, and design the framework accordingly.

Vaccine Efficacy Denial: A Growing Concern Affecting Modern Science, and Impacting Public Health

BACKGROUND

The discovery of the vaccination technique has been revealed by Edward Jenner in 1796: undoubtedly, it represents the first scientific attempt to control an infectious disease by vaccines, followed by other important studies carried out by Pasteur and Koch, and Sabin, who developed the first technique to attenuate the virus. In recent decades, numerous scholars have begun to create dangerous theories against the effectiveness of vaccines through scientifically invalid or fraudulent studies.

AIM

This critical review of the literature aims to analyse the main factors that have undermined the credibility of vaccines in the general population, to disprove false information and, on the other hand, emphasize the benefits of vaccines over the last 200 years.

DISCUSSIONS

Unfortunately, several studies have been carried out without the proper scientific rigour. The most impacting example is the study published by Andrew Wakefield in the Lancet journal that tried to correlate vaccines with the development of autism: this publication was withdrawn from the journal a few years after its publication, but the impact of incorrect scientific studies, fake news, and ambiguous healthcare policies has led to an adverse general opinion about the effectiveness of vaccines.

CONCLUSION

The excess of uncontrolled information is a serious concerning in the Coronavirus pandemic. The modern science must tackle this problem with a better willingness to communicate even the clinical studies to those people not able to understand the medical information autonomously. Nevertheless, a reliable science must also limit the dissemination of studies that do not meet the basic criteria of a methodological rigor and certainty of results, in order not to feed confusion in the scientific community.

Potential impact of functional biomolecules-enriched foods on human health: A randomized controlled clinical trial

Naturally occurring milk compounds have recently been investigated for their health-promoting properties; in fact, their anti-microbial, immuno-modulatory, antioxidant and anti-thrombotic activities, have increasingly gained interest within the scientific community. We have reported a translational, randomized, controlled clinical trial (RCT) on human subjects with a moderate to high cardiovascular risk, and a body mass index (BMI) >25.1 kg/m2, to evaluate the clinical impact of biomolecules-enriched Mediterranean Buffalo (Bubalus bubalis) milk and its derived dairy foods, produced with innovative breeding techniques. The experimental arm involved patients that followed a diet including the above-described products (treated group; n= 11); the control arm was based on a diet including cow milk and its dairy products (control group; n= 9). The results of this study have been statistically evaluated, pointing out a specific significance related to the comparative analysis of the blood pressure among the 2 arms; in fact, this value showed a significant improvement in an extremely short experimental time. Nevertheless, this study also reported not-significant results that were indicative of an interesting and promising tendency in modulating specific diet-depending haematological and biomedical values. In conclusion, this RCT has assessed that the foods derived from buffalo milk naturally enriched with biomolecules, was able to improve the overall blood glucose levels, the BMI and the body weight. These preliminary results are suitable for the design of future strategies in the prevention of cardiometabolic diseases, thus improving the overall quality of life and the policies of healthcare management.

Rehabilitation of the Completely Edentulous Mandible by All-on-Four Treatment Concept: A Retrospective Cohort Study with Up to 10 Years Follow-Up

(1) Background and Objectives. Currently, there are no definitive long-term data about clinically significant difference in the failure of prosthesis and implant or marginal bone loss related to the rehabilitation of the completely edentulous mandible by all-on-four treatment concept. The main aim of present investigation was to report the long-term outcomes (10-years follow-up) of complete-arch mandibular rehabilitations based on the all-on-four concept. (2) Materials and Methods. Patients in need of extractions of teeth due to the occurrence of caries and/or severe periodontal disease and patients presented with edentulous mandibles were enrolled to the study. A total of 96 participants (mean follow-up period after intervention of 3185.2 days) were enrolled in the study. Participants were evaluated at the first visit, 10 days after intervention and every year after the intervention. Implant and prosthesis survival, bone loss and both local biological and mechanical complications were evaluated during the follow-up period. (3) Results. An implants’ survival rate of 97.9% was observed at the end of the follow-up period. Biological complications were reported in 19.8% of patients, whereas mechanical complications were reported in 27.1% of cases. The average marginal bone level at baseline was −0.03 mm. A significant marginal bone loss was observed after 10-years follow-up (2.5 mm). Binary logistic regression analysis showed significant association between smoke and both marginal bone loss and local biological complications. Lastly, a significant association was observed between bruxism and mechanical complications. (4) Conclusions. The high implant and prosthesis survival rate and the moderate incidence of biological and mechanical complications observed in present investigation can be associated to several factors such as high implant primary stability, prosthetic design, and control of the occlusal forces.

A SWOT Analysis of the Guidelines on Prevention of HIV/AIDS in Japan in the Context of COVID-19

In January 2018, the Minister of Health, Labour and Welfare, Japan, released an amended Guideline on the Prevention of Specified Infectious Diseases on Acquired Immunodeficiency Syndrome (AIDS) to propose measures to control the human immunodeficiency virus (HIV)/AIDS. Content analysis was performed to examine the strengths, weaknesses, opportunities, and threats of the guidelines in the context of the ongoing COVID-19 pandemic, thus aiming to promote discussions on the guideline itself and the national HIV/AIDS strategy in Japan in the years ahead. The strengths included the incorporation of the latest scientific advancements, clarification of high-risk populations, an alignment with measures against sexually transmitted diseases (STDs), and willingness towards international cooperation in the Asia-Pacific region. The weaknesses that were exposed included a lack of explicit targets for controlling and containing HIV/AIDS, insufficient descriptions about pre-exposure prophylaxis (PrEP), and aggregated discussions on HIV/AIDS among foreign residents. Although several opportunities for re-energizing the discussions around HIV/AIDS were recognized, insufficient political will and funding, along with the emergence of the ongoing COVID-19 pandemic, could operate as threats. Addressing barriers that were recognized before 2019 and exposed due to the COVID-19 pandemic, and tackling underlying health inequalities through the concept of social determinants of health will be critical.

Diagnosis of COVID-19, vitality of emerging technologies and preventive measures

Coronavirus diseases-2019 (COVID-19) is becoming increasing serious and major threat to public health concerns. As a matter of fact, timely testing enhances the life-saving judgments on treatment and isolation of COVID-19 infected individuals at possible earliest stage which ultimately suppresses spread of infectious diseases. Many government and private research institutes and manufacturing companies are striving to develop reliable tests for prompt quantification of SARS-CoV-2. In this review, we summarize existing diagnostic methods as manual laboratory-based nucleic acid assays for COVID-19 and their limitations. Moreover, vitality of rapid and point of care serological tests together with emerging biosensing technologies has been discussed in details. Point of care tests with characteristics of rapidity, accurateness, portability, low cost and requiring non-specific devices possess great suitability in COVID-19 diagnosis and detection. Besides, this review also sheds light on several preventive measures to track and manage disease spread in current and future outbreaks of diseases.

Blockchain: Future of e-Governance in Smart Cities

In recent times, Blockchain has emerged as a transformational technology with the ability to disrupt and evolve multiple domains. As a decentralized, immutable distributed ledger, Blockchain technology is one of the most recent entrants to the comprehensive ideology of Smart Cities. The rise of urbanization and increased citizen participation have led to various technology integrations in our present-day cities. For cities to become smart, we need standard frameworks and procedures for integrating technology, citizens and governments. In this paper, we explore the potential of Blockchain technology as an enabler for e-governance in smart cities. We examine the daily challenges of citizens and compare them with the benefits being offered by Blockchain integration. On the basis of a comprehensive literature review, we identified four key areas of e-governance wherein Blockchain can provide monumental advantages. In the context of Blockchain integration for e-governance, the paper presents a survey of prominent published works discussing various urban applications.

Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials

The COVID-19 pandemic has profoundly affected almost all facets of peoples’ lives, various economic areas and regions of the world. In such a situation implementation of a vaccination can be viewed as essential but its success will be dependent on availability and transparency in the distribution process that will be shared among the stakeholders. Various distributed ledgers (DLTs) such as blockchain provide an open, public, immutable system that has numerous applications due the mentioned abilities. In this paper the authors have proposed a solution based on blockchain to increase the security and transparency in the tracing of COVID-19 vaccination vials. Smart contracts have been developed to monitor the supply, distribution of vaccination vials. The proposed solution will help to generate a tamper-proof and secure environment for the distribution of COVID-19 vaccination vials. Proof of delivery is used as a consensus mechanism for the proposed solution. A feedback feature is also implemented in order to track the vials lot in case of any side effect cause to the patient. The authors have implemented and tested the proposed solution using Ethereum test network, RinkeyBy, MetaMask, one clicks DApp. The proposed solution shows promising results in terms of throughput and scalability.

Blockchain-Based Decentralized Digital Manufacturing and Supply for COVID-19 Medical Devices and Supplies

Coronavirus 2019 (COVID-19) has disclosed the deficiencies and limitations of the existing manufacturing and supply chain systems used for medical devices and supplies. It enforces the necessity to accelerate the shift towards decentralized digital manufacturing and supply chain networks. This paper proposes a blockchain-based solution for decentralized digital manufacturing of medical devices and their supply. We develop Ethereum smart contracts to govern and track transactions in a decentralized, transparent, traceable, auditable, trustworthy, and secure manner. This allows overcoming certain issues hindering the transition towards decentralized digital manufacturing and supply, including trusted traceability, attestations, certifications, and secured intellectual property (IP) rights. We incorporate the decentralized storage of the InterPlanetary file system (IPFS) into the Ethereum blockchain to store and fetch Internet of things (IoT)-based devices records and additional manufacturing and supply details. We present the system architecture and algorithms along with their full implementation and testing details. Furthermore, we present cost and security analyses to show that the proposed solution is cost-efficient and resilient against well-known vulnerabilities and security attacks. We make our smart contracts code publicly available on GitHub.

On Deploying Blockchain Technologies in Supply Chain Strategies and the COVID-19 Pandemic: A Systematic Literature Review and Research Outlook

The emergence of a new pandemic, known as COVID-19, has touched various sections of the supply chain (SC). Since then, numerous studies have been conducted on the issue, but the need for a holistic review study that highlights the gaps and limits of previous research, as well as opportunities and agendas for future studies, is palpable. Through a systematic literature review on blockchain technology (BCT) deployment in supply-chain management (SCM) concerning the COVID-19 pandemic, this research seeks to add to the content of previous studies and to enlighten the path for future studies. Relevant papers were found using a variety of resources (Scopus, Google Scholar, Web of Science, and ProQuest). Seventy-two articles were systematically selected, considering the PRISMA procedure, and were thoroughly analyzed based on BCT, methodologies, industrial sectors, geographical, and sustainability context. According to our findings, there is a significant lack of empirical and quantitative methodologies in the literature. The majority of studies did not take specific industries into account. Furthermore, the articles focusing on the sustainability context are few, particularly regarding social and environmental issues. In addition, most of the reviewed papers did not consider the geographical context. The results indicate that the deployment of BCT in several sectors is not uniform, and this utilization is reliant on their services during the COVID-19 pandemic. Furthermore, the concentration of research on the impacts of the BCT on SCM differs according to the conditions of various countries in terms of the consequences of the COVID-19 pandemic. The findings also show that there is a direct relationship between the deployment of BCT and sustainability factors, such as economic and waste issues, under the circumstances surrounding COVID-19. Finally, this study offers research opportunities and agendas to help academics and other stakeholders to gain a better knowledge of the present literature, recognize aspects that necessitate more exploration, and drive prospective studies.

Blockchain as an enabling technology in the COVID-19 pandemic: a systematic review

The impacts caused by the unprecedented transmission of COVID-19 have given rise to new challenges that are shaking the structures of humanity. Several enabling technologies are currently being used as key strategies in creating improvements and responses to the difficulties created by the pandemic and blockchain is one of these solution proposals. Within this scenario, this work aims to study and analyze how the blockchain technology can help in the struggle against the COVID-19 pandemic through a systematic review of the literature. Although the study is limited by the moment when the crisis is still in progress, the results show that it is clear that the adoption of the blockchain can effectively help in the fight against the coronavirus, considering that the main features of the blockchain can support the successful implementation of many use cases. This paper has the role of assisting academics and professionals in identifying the application focus of the blockchain, as well as showing the main opportunities and challenges and the relevance of the subject to the current context of the pandemic.

A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health

Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

VaCoChain: Blockchain-based 5G-assisted UAV Vaccine distribution scheme for future pandemics

This paper proposes a generic scheme VaCoChain, that fuses blockchain (BC) and unmanned aerial vehicles (UAVs) underlying fifth-generation (5G) communication services for timely vaccine distribution during novel coronavirus (COVID-19) and future pandemics. The scheme offers 5G-tactile internet (5G-TI)-based services for UAV communication networks (UAVCN) monitored through ground controller stations (GCs). 5G-TI enabled UAVCN supports real-time dense connectivity at ultra-low round-trip time (RTT) latency of < 1 and high availability of 99.9999%. Thus, it can support resilient vaccine distributions in a phased manner at government-designated nodal centers (NCs) with reduced round trip delays from vaccine production warehouses (VPW). Further, UAVCNs ensure minimizes human intervention and controls vaccine health conditions due to shorter trip times. Once vaccines are supplied at NCs warehouses, then the BC ensures timestamped documentation of vaccinated persons with chronology, auditability, and transparency of supply-chain checkpoints from VPW to NCs. Through smart contracts (SCs), priority groups can be formed for vaccination based on age, healthcare workers, and general commodities. In the simulation, for UAV efficacy, we have compared the scheme against fourth-generation (4G)-assisted long term evolution-advanced (LTE-A), orthogonal frequency division multiplexing (OFDM) channels, and traditional logistics for round-trip time (RTT) latency, logistics, and communication costs. In the BC setup, we have compared the scheme against the existing 5G-TI delivery scheme (Gupta et al.) for processing latency, packet losses, and transaction time. For example, in communication costs, the proposed scheme achieves an average improvement of 9.13 for block meta-information. For 4000 transactions, the proposed scheme has a communication latency of 16s compared to 36s. The packet loss is significantly reduced to 2.5% using 5G-TI compared to 16% in 4G-LTE-A. The proposed scheme has a computation cost of 1.6 ms and a communication cost of 157 bytes, which indicates the proposed scheme efficacy against conventional approaches.

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Impact and Challenges of Integrating Artificial Intelligence and Telemedicine into Clinical Ophthalmology

ABSTRACT

Aging populations and worsening burden of chronic, treatable disease is increasingly creating a global shortfall in ophthalmic care provision. Remote and automated systems carry the promise to expand the scale and potential of health care interventions, and reduce strain on health care services through safe, personalized, efficient, and cost-effective services. However, significant challenges remain. Forward planning in service design is paramount to safeguard patient safety, trust in digital services, data privacy, medico-legal implications, and digital exclusion. We explore the impact and challenges facing patients and clinicians in integrating AI and telemedicine into ophthalmic care-and how these may influence its direction.

Security Aspects of Blockchain Technology Intended for Industrial Applications

Blockchain technology plays a significant role in the industrial development. Many industries can potentially benefit from the innovations blockchain decentralization technology and privacy protocols offer with regard to securing, data access, auditing and managing transactions within digital platforms. Blockchain is based on distributed and secure decentralized protocols in which there is no single authority, and no single point of control; the data blocks are generated, added, and validated by the nodes of the network themselves. This article provides insights into the current developments within blockchain technology and explores its ability to revolutionize the multiple industrial application areas such as supply chain industry, Internet of Things (IoT), healthcare, governance, finance and manufacturing. It investigates and provides insights into the security issues and threats related to the blockchain implementations by assessing the research through a systematic literature review. This article proposes possible solutions in detail for enhancing the security of the blockchain for industrial applications along with significant directions for future explorations. The study further suggests how in recent years the adoption of blockchain technology by multiple industrial sectors has gained momentum while in the finance sector it is touching new heights day by day.

Blockchain Platform For COVID-19 Vaccine Supply Management

In the context of COVID-19 pandemic, the rapid roll-out of a vaccine and the implementation of a worldwide immunization campaign is critical, but its success will depend on the availability of an operational and transparent distribution chain that can be audited by all relevant stakeholders. In this paper, we discuss how blockchain technology can help in several aspects of COVID-19 vaccination scheme. We present a system in which blockchain technology is used to guaranty data integrity and immutability of beneficiary registration for vaccination, avoiding identity thefts and impersonations. Smart contracts are defined to monitor and track the proper vaccine distribution conditions against the safe handling rules defined by vaccine producers enabling the awareness of all network peers. For vaccine administration, a transparent and tamper-proof solution for side effects self-reporting is provided considering beneficiary and administrated vaccine association. A prototype was implemented using the Ethereum test network, Ropsten, considering the COVID-19 vaccine distribution conditions. The results obtained for each on-chain operation can be checked and validated on the Etherscan. In terms of throughput and scalability, the proposed blockchain system shows promising results while the estimated cost in terms of gas for vaccination scenario based on real data remains within reasonable limits.

Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

More Is Not Enough: A Deeper Understanding of the COVID-19 Impacts on Healthcare, Energy and Environment Is Crucial

The coronavirus disease 2019 (COVID-19) pandemic has magnified the insufficient readiness of humans in dealing with such an unexpected occurrence. During the pandemic, sustainable development goals have been hindered severely. Various observations and lessons have been highlighted to emphasise local impacts on a single region or single sector, whilst the holistic and coupling impacts are rarely investigated. This study overviews the structural changes and spatial heterogeneities of changes in healthcare, energy and environment, and offers perspectives for the in-depth understanding of the COVID-19 impacts on the three sectors, in particular the cross-sections of them. Practical observations are summarised through the broad overview. A novel concept of the healthcare-energy-environment nexus under climate change constraints is proposed and discussed, to illustrate the relationships amongst the three sectors and further analyse the dynamics of the attention to healthcare, energy and environment in view of decision-makers. The society is still on the way to understanding the impacts of the whole episode of COVID-19 on healthcare, energy, environment and beyond. The raised nexus thinking could contribute to understanding the complicated COVID-19 impacts and guiding sustainable future planning.

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.