During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely ...utilised because of its usability and safety in providing healthcare services during the COVID-19 pandemic. However, a systematic literature review which provides extensive evidence on the impact of COVID-19 through telehealth and which covers multiple directions in a large-scale research remains lacking. This study aims to review telehealth literature comprehensively since the pandemic started. It also aims to map the research landscape into a coherent taxonomy and characterise this emerging field in terms of motivations, open challenges and recommendations. Articles related to telehealth during the COVID-19 pandemic were systematically searched in the WOS, IEEE, Science Direct, Springer and Scopus databases. The final set included (n = 86) articles discussing telehealth applications with respect to (i) control (n = 25), (ii) technology (n = 14) and (iii) medical procedure (n = 47). Since the beginning of the pandemic, telehealth has been presented in diverse cases. However, it still warrants further attention. Regardless of category, the articles focused on the challenges which hinder the maximisation of telehealth in such times and how to address them. With the rapid increase in the utilization of telehealth in different specialised hospitals and clinics, a potential framework which reflects the authors’ implications of the future application and opportunities of telehealth has been established. This article improves our understanding and reveals the full potential of telehealth during these difficult times and beyond.
•State-of-the-art Literature Categorization for Telehealth utilization during COVID-19.•Challenges, motivations and recommended solutions are identified for Telehealth during COVID-19.•Different Applications of Telehealth during the COVID-19 pandemic.
The Internet of Things (IoT) has been identified in various applications across different domains, such as in the healthcare sector. IoT has also been recognised for its revolution in reshaping ...modern healthcare with aspiring wide range prospects, including economical, technological and social. This study aims to establish IoT-based smart home security solutions for real-time health monitoring technologies in telemedicine architecture. A multilayer taxonomy is driven and conducted in this study. In the first layer, a comprehensive analysis on telemedicine, which focuses on the client and server sides, shows that other studies associated with IoT-based smart home applications have several limitations that remain unaddressed. Particularly, remote patient monitoring in healthcare applications presents various facilities and benefits by adopting IoT-based smart home technologies without compromising the security requirements and potentially large number of risks. An extensive search is conducted to identify articles that handle these issues, related applications are comprehensively reviewed and a coherent taxonomy for these articles is established. A total number of (
n
= 3064) are gathered between 2007 and 2017 for most reliable databases, such as ScienceDirect, Web of Science and Institute of Electrical and Electronic Engineer Xplore databases. Then, the articles based on IoT studies that are associated with telemedicine applications are filtered. Nine articles are selected and classified into two categories. The first category, which accounts for 22.22% (
n
= 2/9), includes surveys on telemedicine articles and their applications. The second category, which accounts for 77.78% (
n
= 7/9), includes articles on the client and server sides of telemedicine architecture. The collected studies reveal the essential requirement in constructing another taxonomy layer and review IoT-based smart home security studies. Therefore, IoT-based smart home security features are introduced and analysed in the second layer. The security of smart home design based on IoT applications is an aspect that represents a crucial matter for general occupants of smart homes, in which studies are required to provide a better solution with patient security, privacy protection and security of users’ entities from being stolen or compromised. Innovative technologies have dispersed limitations related to this matter. The existing gaps and trends in this area should be investigated to provide valuable visions for technical environments and researchers. Thus, 67 articles are obtained in the second layer of our taxonomy and are classified into six categories. In the first category, 25.37% (
n
= 17/67) of the articles focus on architecture design. In the second category, 17.91% (
n
= 12/67) includes security analysis articles that investigate the research status in the security area of IoT-based smart home applications. In the third category, 10.44% (
n
= 7/67) includes articles about security schemes. In the fourth category, 17.91% (n = 12/67) comprises security examination. In the fifth category, 13.43% (
n
= 9/67) analyses security protocols. In the final category, 14.92% (
n
= 10/67) analyses the security framework. Then, the identified basic characteristics of this emerging field are presented and provided in the following aspects. Open challenges experienced on the development of IoT-based smart home security are addressed to be adopted fully in telemedicine applications. Then, the requirements are provided to increase researcher’s interest in this study area. On this basis, a number of recommendations for different parties are described to provide insights on the next steps that should be considered to enhance the security of smart homes based on IoT. A map matching for both taxonomies is developed in this study to determine the novel risks and benefits of IoT-based smart home security for real-time remote health monitoring within client and server sides in telemedicine applications.
Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe ...acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.
•Understanding sentiment analysis role and opinion mining in Covid-19 and other infectious diseases.•Literature’s categorization for sentiment analysis and infectious disease.•Academic challenges and ...motivations of sentiment analysis with infectious diseases.•Different applications for mitigating infectious diseases by sentiment analysis.
The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 occurred unexpectedly in China in December 2019. Tens of millions of confirmed cases and more than hundreds of thousands of confirmed deaths are reported worldwide according to the World Health Organisation. News about the virus is spreading all over social media websites. Consequently, these social media outlets are experiencing and presenting different views, opinions and emotions during various outbreak-related incidents. For computer scientists and researchers, big data are valuable assets for understanding people’s sentiments regarding current events, especially those related to the pandemic. Therefore, analysing these sentiments will yield remarkable findings. To the best of our knowledge, previous related studies have focused on one kind of infectious disease. No previous study has examined multiple diseases via sentiment analysis. Accordingly, this research aimed to review and analyse articles about the occurrence of different types of infectious diseases, such as epidemics, pandemics, viruses or outbreaks, during the last 10 years, understand the application of sentiment analysis and obtain the most important literature findings. Articles on related topics were systematically searched in five major databases, namely, ScienceDirect, PubMed, Web of Science, IEEE Xplore and Scopus, from 1 January 2010 to 30 June 2020. These indices were considered sufficiently extensive and reliable to cover our scope of the literature. Articles were selected based on our inclusion and exclusion criteria for the systematic review, with a total of n = 28 articles selected. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature in accordance with four main categories: lexicon-based models, machine learning-based models, hybrid-based models and individuals. The obtained articles were categorised into motivations related to disease mitigation, data analysis and challenges faced by researchers with respect to data, social media platforms and community. Other aspects, such as the protocol being followed by the systematic review and demographic statistics of the literature distribution, were included in the review. Interesting patterns were observed in the literature, and the identified articles were grouped accordingly. This study emphasised the current standpoint and opportunities for research in this area and promoted additional efforts towards the understanding of this research field.
Ranking the strengths and weaknesses of software engineering students in software development life cycle (SDLC) process level is a challenging task owing to (1) data variation, (2) multievaluation ...criteria, (3) criterion importance and (4) alternative member importance. According to the existing literature, no specified procedure can rank the ability of software engineering students based on SDLC process levels to figure out the strengths and weaknesses of each student. This study aims to present a novel triplex procedure for ranking the ability of software engineering students to address the literature gap. The methodology of the proposed work is presented on the basis of three phases. In the identification phase, four steps are implemented, namely, processing dataset, identifying the criteria, distributing the courses to the software engineering body of knowledge and proposing the pre-decision matrix (DM). The data comprise the GPA and soft skills from 60 software engineering students who graduated from Universiti Pendidikan Sultan Idris in 2016. In the pre-processing phase, three steps are involved as follows. Analytic hierarchy process (AHP) is first used to assign weights to the courses and then multiply the assigned weight by courses, which is the first procedure in the proposed work. In this phase, the construction of DM is presented based on multimeasurement criteria (GPA and soft skills), with SDLC process levels as alternatives. In the development phase, AHP is used again to weight the multimeasurement criteria, and this is the second procedure. In such case, the coordinator and head of the software engineering department are consulted to obtain subjective judgments for each criterion. Technique for order performance by similarity to ideal solution (TOPSIS) is then used to rank the students, which is the third procedure. In the validation, statistical analysis is performed to validate the results by checking the accuracy of the systematic ranking. Results show that (1) integrating AHP and group TOPSIS is suitable for ranking the ability of students. (2) The 60 students are categorized into five ranking groups based on their strength level: 14 collector requirements, 13 designers, 5 programmers, 13 testers and 15 maintenances. (3) Significant differences are observed between the groups’ scores for each level of SDLC, indicating that the ranking results are identical for all levels.
•A novel technique for reorganisation of opinion order to interval levels (TROOIL) is presented.•A smart real-time remote health-monitoring framework is developed on the basis of TROOIL technique to ...prioritise patients with multiple chronic diseases (MCDs).•The prioritisation of patients with MCDs is validated objectively.•Different scenarios are provided to evaluate the proposed framework.
Telemedicine has been increasingly used in healthcare to provide services to patients remotely. However, prioritising patients with multiple chronic diseases (MCDs) in telemedicine environment is challenging because it includes decision-making (DM) with regard to the emergency degree of each chronic disease for every patient.
This paper proposes a novel technique for reorganisation of opinion order to interval levels (TROOIL) to prioritise the patients with MCDs in real-time remote health-monitoring system.
The proposed TROOIL technique comprises six steps for prioritisation of patients with MCDs: (1) conversion of actual data into intervals; (2) rule generation; (3) rule ordering; (4) expert rule validation; (5) data reorganisation; and (6) criteria weighting and ranking alternatives within each rule. The secondary dataset of 500 patients from the most relevant study in a remote prioritisation area was adopted. The dataset contains three diseases, namely, chronic heart disease, high blood pressure (BP) and low BP.
The proposed TROOIL is an effective technique for prioritising patients with MCDs. In the objective validation, remarkable differences were recognised among the groups’ scores, indicating identical ranking results. In the evaluation of issues within all scenarios, the proposed framework has an advantage of 22.95% over the benchmark framework.
Patients with the most severe MCD were treated first on the basis of their highest priority levels. The treatment for patients with less severe cases was delayed more than that for other patients.
The proposed TROOIL technique can deal with multiple DM problems in prioritisation of patients with MCDs.
Evaluating immune responses following COVID-19 vaccination is paramount to understanding vaccine effectiveness and optimizing public health interventions. This study seeks to elucidate individuals' ...immune status after administering a second dose of diverse COVID-19 vaccines. By analyzing immune responses through serological markers, we aim to contribute valuable insights into the uniformity of vaccine performance.
A total of 80 participants were enrolled in this study, with demographic and COVID-19 infection-related data collected for categorization. Serum samples were acquired within a specified timeframe, and SARS-CoV-2 IgM/IgG rapid tests were conducted. Moreover, CTLA-4 levels were measured through ELISA assays, allowing us to assess the immune responses comprehensively. The participants were divided into eight groups based on various factors, facilitating a multifaceted analysis.
The outcomes of our investigation demonstrated consistent immune responses across the diverse types of COVID-19 vaccines administered in Iraq. Statistical analysis revealed no significant distinctions among the vaccine categories. In contrast, significant differences were observed in CTLA-4 among the control group (non-infected/non-vaccinated, infected/non-vaccinated) and infected/Pfizer, non-infected/Pfizer, and infected/Sinopharm, non-infected/sinopharm (P = 0.001, < 0.001, 0.023, respectively). This suggests that these vaccines exhibit comparable effectiveness in eliciting an immune response among the study participants.
In conclusion, our study's results underscore the lack of discriminatory variations between different COVID-19 vaccine types utilized in Iraq. The uniform immune responses observed signify the equitable efficacy and performance of these vaccines. Despite minor quantitative discrepancies, these variations do not hold statistical significance, reaffirming the notion that the various vaccines serve a similar purpose in conferring protection against COVID-19.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The emerging technology breakthrough of the Internet of Things (IoT) is expected to offer promising solutions for indoor/outdoor healthcare, which may contribute significantly to human health and ...well-being. In this paper, we investigated the technologies of healthcare service applications in telemedicine architecture. We aimed to resolve a series of healthcare problems on the frequent failures in telemedicine architecture through IoT solutions, particularly the failures of wearable body sensors (Tier 1) and a medical center server (Tier 3). For improved generalisability, we demonstrated an effective research approach, the fault-tolerant framework on mHealth or the so-called FTF-mHealth-IoT in the context of IoT, to resolve essential problems in current investigations on healthcare services. First, we propose a risk local triage algorithm known as the risk-level localization triage (RLLT), which can exclude the control process of patient triage from the medical center by using mHealth and can warn about failures related to wearable sensors. RLLT performs this initial step towards detecting a patient's emergency case and then identifying the healthcare service package of the risk-level. Second, according to the risk-level package, our framework can aid decision makers in hospital selection through multi-criteria decision making (MCDM). Accordingly, mHealth can connect directly with the servers of distributed hospitals to ascertain available healthcare services for the risk-level package in those hospitals. The time of arrival of the patient at the hospital (TAH) is considered for each hospital to reach a final decision and select the appropriate institution in case of medical center failure. This paper used two datasets. The first dataset involved 572 patients with chronic heart disease. Their triage levels were evaluated using our RLLT algorithm. The second dataset included hospital healthcare services with two levels of availability within distributed hospitals to show variety when testing the proposed framework. The former dataset is an actual dataset of services collected from 12 hospitals located in the capital Baghdad, which represents the maximum level of availability. The latter is an assumption dataset of the services within the 12 hospitals located in the capital Kuala Lumpur, which represents the minimum level of availability. Subsequently, the hospitals were prioritized using a unique MCDM method for estimating small power consumption, namely, the analytic hierarchy process (AHP), based on a crossover between the "healthcare services package/TAH" of each hospital and the "hospital list". The results showed that the AHP is effective for solving hospital selection problems within mHealth. The implications of this study support the patients, organizations, and medical staff in a modern lifestyle.
Secure updating and sharing for large amounts of healthcare information (such as medical data on coronavirus disease 2019 COVID-19) in efficient and secure transmission are important but challenging ...in communication channels amongst hospitals. In particular, in addressing the above challenges, two issues are faced, namely, those related to confidentiality and integrity of their health data and to network failure that may cause concerns about data availability. To the authors’ knowledge, no study provides secure updating and sharing solution for large amounts of healthcare information in communication channels amongst hospitals. Therefore, this study proposes and discusses a novel steganography-based blockchain method in the spatial domain as a solution. The novelty of the proposed method is the removal and addition of new particles in the particle swarm optimisation (PSO) algorithm. In addition, hash function can hide secret medical COVID-19 data in hospital databases whilst providing confidentiality with high embedding capacity and high image quality. Moreover, stego images with hash data and blockchain technology are used in updating and sharing medical COVID-19 data between hospitals in the network to improve the level of confidentiality and protect the integrity of medical COVID-19 data in grey-scale images, achieve data availability if any connection failure occurs in a single point of the network and eliminate the central point (third party) in the network during transmission. The proposed method is discussed in three stages. Firstly, the pre-hiding stage estimates the embedding capacity of each host image. Secondly, the secret COVID-19 data hiding stage uses PSO algorithm and hash function. Thirdly, the transmission stage transfers the stego images based on blockchain technology and updates all nodes (hospitals) in the network. As proof of concept for the case study, the authors adopted the latest COVID-19 research published in the Computer Methods and Programs in Biomedicine journal, which presents a rescue framework within hospitals for the storage and transfusion of the best convalescent plasma to the most critical patients with COVID-19 on the basis of biological requirements. The validation and evaluation of the proposed method are discussed.
ABSTRACT The function of medial entorhinal cortex layer II (MECII) excitatory neurons has been recently explored. MECII dysfunction underlies deficits in spatial navigation and working memory. MECII ...neurons comprise two major excitatory neuronal populations, pyramidal island and stellate ocean cells, in addition to the inhibitory interneurons. Ocean cells express reelin and surround clusters of island cells that lack reelin expression. The influence of reelin expression by ocean cells and interneurons on their own morphological differentiation and that of MECII island cells has remained unknown. To address this, we used a conditional reelin knockout (RelncKO) mouse to induce reelin deficiency postnatally in vitro and in vivo. Reelin deficiency caused dendritic hypertrophy of ocean cells, interneurons and only proximal dendritic compartments of island cells. Ca2+ recording showed that both cell types exhibited an elevation of calcium frequencies in RelncKO, indicating that the hypertrophic effect is related to excessive Ca2+ signalling. Moreover, pharmacological receptor blockade in RelncKO mouse revealed malfunctioning of GABAB, NMDA and AMPA receptors. Collectively, this study emphasizes the significance of reelin in neuronal growth, and its absence results in dendrite hypertrophy of MECII neurons.
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