Abstract

Employing the Internet of Things (IoT) in healthcare provides many advantages for patient monitoring and analysis of the patient’s health with the help of generated data. The basic role of IoT in healthcare is to ease the patient’s life by giving them a monitor over their medical condition. The use of IoT in medical devices requires a focus on the end-user. Medical devices such as glucose meters are designed to record the data of the patient and their vital signs. The generated information can be used to enhance decision-making for the physician. The collected information about the patient can be at risk due to certain security issues during the transferring of information can compromise the identity and social life of the patient. This review explores the IoT regarding its structural requirement and its role in various fields with special emphasis on healthcare. The security and privacy issues than can hinder the utilization of IoT at its potential and ways to overcome these issues are being addressed.

Role of IoT in medical devices

Medical Devices (MDs) are any instrument, reagent, apparatus, implement, machine or any other in-vitro reagent or calibrator, software which is intended to be used alone or in combination for the diagnosis, mitigation, prevention, treatment or alleviation of diseases in human beings.1 Generally, MDs are classified based on the risk associated with them. As per USFDA MDs are classified into three categories I, II, III where category I has lowest risk and category III has highest risk as shown in Table 12 (see Fig. 4)A report from Fortune Business Insights states that from 2018 to 2025 the Medical Device Industry will experience major investment in Research & Development of new technology from pharmaceutical players, approvals from regulatory authorities will also contribute to increase this number. The medical device global market was USD 42.5 Billion in 2018 and is estimated to reach USD 612.7 Billion by 2025. CAGR is expected at 5.4 % from 2018 to 2025.3

Further Medical Devices have been showing an increasing growth in the previous decade and with promising technology and innovations being made, it will keep growing more. Although there is conventional research and development being done by existing companies. With the entry of giants like Apple and Google, new technological advancements in the form of Artificial Intelligence, Data Analytics and Internet of Things (IoT) are being implemented in this sector.4

Conclusion

The amalgamation of a variety of technology is required to build an IoT. Due to this complexity, the system becomes more vulnerable to security and privacy threats. The generic engineering framework involved in patient monitoring remotely should meet the security requirements with the help of cryptographic algorithms. These algorithms should be device-specific to nurture the design & environment of patient-specific needs. The security and safety issues of a patient which arise from the application of IoT cannot be solved with the help of a single solution. To practice, the best security controls an informed user is also necessary. The management ability of the network and device automation can be of great help to deliver patient well-being and care. Delivering healthcare to patients efficiently and effectively, independent of time and location requires a new paradigm that is opened by IoT. The existing foundations of telemedicine have now been extended and better assessment of normal public health in a cohort sense has shifted the passive involvement of patient data gathering. Improving the outcomes for the patient and reducing the care cost are being balanced against the privacy and confidentiality of data and threats of cyber security. There is a vast source of data collection and potential to transfer clinical data, but there is a challenge to triage this data for clinical use. The cyber security and safety of patient data need to move faster than regulation and implementation of policy. The way forward for IoT needs superior standards every day to use the connected devices.

Abstract

Keywords

1. Introduction

2. Sensors

3. Wireless Communication Technologies

4. Cloud Computing

5. Machine Learning Techniques

6. Fog/Edge Computing

7. Applications

8. Conclusion

Declaration of Competing Interest

References

Vitae

Abstract

The goal of Internet of Medical Things (IoMT) and digital healthcare systems is to provide people with the ease of receiving quality healthcare at the comfort of their homes. Hence, the aim of IoMT is the ubiquitous deployment of home-based healthcare systems. Making such systems intelligent and efficient for timely prediction of critical diseases can save millions of lives while simultaneously reducing the burden on the traditional healthcare systems e.g., hospitals. The advancement in IoT has enabled both patients and doctors to access real time data. This advancement has reduced the cost and energy consumption of digital healthcare systems by using efficient sensors and communication technologies. This paper provides a comprehensive review of various studies conducted for the development and improvement of IoMT. It analyses different sensors used for measurement of various parameters ranging from physiological to emotional signals. It also provides a detailed investigation of different communication technologies being used, their advantages, and limitations. Moreover, digital healthcare systems are now deploying machine learning technology for the prediction of health status of patients. These techniques and algorithms are also discussed. Data security and prediction accuracy are the main concerns in the development of this area. In conclusion, this paper reviews the various digital system designs in the context of healthcare, their methodology, limitations, and the present challenges faced by the e-health sector.

1. Introduction

Quality healthcare is a basic human right, but one which fails to be provided adequately worldwide. The economic, environmental, and social development of this world and subsequent lifestyle changes have led to a drastic increase in chronic diseases such as heart disease, cancer, and diabetes. These chronic illnesses symbolize the greatest threat to human health. Moreover, each time an infectious disease breaks out, the hospitals are flooded with people which takes a huge toll on healthcare services. For example, currently there is a continuous stress on the worlds healthcare resources with the rampant spread of COVID-19. This kind of situation leads to inefficiency in managing patients and their data.

Experts believe that digital healthcare systems in Internet of Things (IoT) environments seem to be a compelling solution to this major healthcare problem. The building blocks and general architecture of a system in the IoT environment is shown in Fig. 1. In the context of medical services, the traditionally proposed remote health monitoring system architectures are divided into three layers: the vitals data collection layer from sensors; the transmission layer; and the analysis layer. The collection layer consists of sensors in the body area network (BAN). BAN collects the sensor data and transmits it to a gateway node. The transmission layer stores that data and analyzes it using conventional threshold values to report any abnormality. Additionally, the data may also be sent to the cloud for storage and heavy computations.

Abstract

     Magnetic nanoparticles (MNPs) represent an advanced tool in the medical field because they can be modified according to biomedical approaches and guided by an external magnetic field in the human body. The first objective of this review is to exemplify some promising applications in the medical field, including smart drug-delivery systems, therapies against cancer cells, radiotherapy, improvements in diagnostics using magnetic resonance imaging (MRI), and tissue engineering. Complementarily, the second objective is to illustrate the mechanisms of action and theoretical foundations related to magnetoresponsive materials.

Introduction

     Magnetic nanoparticles (MNPs) are nanoscale particles (1–100 nm) that can be guided through an external magnetic field due to their superparamagnetic, ferrimagnetic, and ferromagnetic properties, which may provide features for biomedical applications. MNPs with superparamagnetic properties are of special interest because they exhibit strong magnetic interactions under an external magnetic field, which disappear once the external magnetic field is removed. This property allows for the design of ferrofluids, since MNPs can be stabilized in solutions because they do not present magnetic interactions when the external magnetic field is switched off; and allows for in vivo performance, as in (i) cell marking [1], (ii) drug systems guided by a magnetic field [2], (iii) image contrast agents [3], and (iv) as heat generators in hyperthermia treatments [4].

     MNPs generally contains two main parts: the core and the coating. The core presents a predominant quantum effect, which commonly incorporates magnetic elements such as Fe, Ni, or Co as well as their corresponding oxides, while the coating is responsible for stabilizing and protecting the core from the chemical effects of the medium [5]. The coating also plays an essential role since it provides specific properties and functions to the nucleus; for example, biocompatible natural polymers, such as chitosan or cellulose, work as cargo vectors of therapeutic agents to release in a controlled manner (Figure 1). Due to these characteristics, biomedical device synthesis has shown great interest in low toxic superparamagnetic MNPs, to avoid embolization or other secondary effects for the patient at the molecular or cellular level [6].

Conclusions

     MNPs, in combination with superconducting magnets, have begun to play an essential role in advances in areas such as MDDS, MRI, and hyperthermia, among other fields. Therefore, it is crucial to continue the synthesis of MNPs and de

Abstract

     The global occurrence of oral cancer has increased in recent years. Oral cancer diagnosed in the advanced stages results in morbidity and mortality. The use of technology may be beneficial for early detection and diagnosis, and thus help the clinician with better patient management. The advent of artificial intelligence (AI) has the potential to improve oral cancer screening. AI can precisely analyze an enormous dataset from various imaging modalities and provide assistance in the field of oncology. This review focused on the applications of artificial intelligence in the early diagnosis and prevention of oral cancer. A literature search was conducted in the PubMed and Scopus databases using the search terminology “oral cancer” and “artificial intelligence”. Further information regarding the topic was collected by scrutinizing the reference lists of selected articles. Based on the information obtained, this article reviews and discusses the applications and advantages of AI in oral cancer screening, early diagnosis, disease prediction, treatment planning, and prognosis. Limitations and the future scope of AI in oral cancer research are also highlighted.

Introduction

     The global occurrence of Oral cancer (OC) has increased in recent years, with Oral squamous cell carcinomas (OSCCs) counting for more than 90% of these cancers.[1] OSCCs are also the sixth most common malignancy in the world. In 2012, The World Health Organization reported 529 000 new cases of OC and 300 000 deaths due to OC each year.[2] Oral cancer diagnosed in the advanced stage results in morbidity and mortality. A crucial factor in providing successful treatment is the early detection of cancerous lesions. Inaccessible lesions and the late detection of cancers are associated with low survival, increased symptoms, and a higher treatment cost.[3] Early diagnosis can increase the survival rate to 75–90%.1,4

Early detection includes the diagnosis of oral potentially malignant disorders and regular follow-ups. Oral potentially malignant disorders (OPMDs) have been defined as “any oral mucosal abnormality that is associated with a statistically increased risk of developing oral cancer.”[5] OPMDs include oral leukoplakia, proliferative verrucous leukoplakia, erythroplakia, oral lichen planus, oral submucous fibrosis, palatal lesions in reverse smokers, oral lupus erythematosus, actinic keratosis, and dyskeratosis congenita. Newly included lesions in the recent classification are oral lichenoid lesion and oral chronic graft-versus-host disease.[5]

Conclusion

     In recent years, the use of AI for the diagnosis and prognosis of diseases has evolved. Previous studies have proved that ML produces accurate results for OC detection. It assists clinicians in diagnostic processes and minimizes inadvertent errors. However, previous studies based on deep learning (neural network) provided more accuracy with the early detection of OC as compared to machine learning. AI presents the opportunity to develop new techniques combined with traditional approaches to improve the accuracy of detection of OC and OPMDs, as well as to predict the course of the precancerous/cancerous lesions from retrospective data. Future research could consider the innovation of data fusion algorithms combining various modalities, such as clinical, radiological, histological, and molecular assessments, to support the early diagnosis and outcome estimation of the disease.

Abstract

Introduction

Background

Systematic literature review methodologies

Related works

Methods

Search strategy

Eligibility criteria

Data extraction

Risk of bias analyses

Results

Quality assessment

Risk of bias analyses

Discussion

Conclusions

References

Abstract

     Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model’s efficiency. Models trained on tidy datasets achieved almost perfect models.

Introduction

     Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both [1]. In particular, type 2 diabetes is associated with insulin resistance (insulin action defect), i.e., where cells respond poorly to insulin, affecting their glucose intake [2]. The diagnostic criteria established by the American Diabetes Association are: (1) a level of glycated hemoglobin (HbA1c) greater or equal to 6.5%; (2) basal fasting blood glucose level greater than 126 mg/dL, and; (3) blood glucose level greater or equal to 200 mg/dL 2 h after an oral glucose tolerance test with 75 g of glucose [1].

     Diabetes mellitus is a global public health issue. In 2019, the International Diabetes Federation estimated the number of people living with diabetes worldwide at 463 million and the expected growth at 51% by the year 2045. Moreover, it is estimated that there is one undiagnosed person for each diagnosed person with a diabetes diagnosis [2].

     The early diagnosis and treatment of type 2 diabetes are among the most relevant actions to prevent further development and complications like diabetic retinopathy [3]. According to the ADDITION-Europe Simulation Model Study, an early diagnosis reduces the absolute and relative risk of suffering cardiovascular events and mortality [4]. A sensitivity analysis on USA data proved a 25% relative reduction in diabetes-related complication rates for a 2-year earlier diagnosis.

Results

Search results and reduction
     The initial search generated 1327 records, 925 from PubMed and 402 from Web of Science. Only 130 records were excluded when filtering by publication year (2017–2021). Therefore, further searches were conducted using fine-tuned search strings and options for both databases to narrow down the results. The new search was carried out using the original keywords but restricting the word ‘diabetes’ to be in the title, which generated 517 records from both databases. Fifty-one duplicates were discarded. Therefore, 336 records were selected for further screening.

     Further selection was conducted by applying the exclusion criteria to the 336 records above. Thirty-seven records were excluded since the study reported used non-omittable genetic attributes as model inputs, something out of this review’s scope. Thirty-eight records were excluded as they were review papers. All in all, 261 articles that fulfill the criteria were included in the quality assessment.

Abstract

Keywords

1. Introduction

2. MFNPs-based magnetoresponsive effects for biomedical applications

3. Conclusion and perspectives

Declaration of competing interest

Acknowledgments

References

Abstract

One of the most attractive features of magnetic ferrite nanoparticles (MFNPs) in biomedical application is that they can mediate external magnetic field to produce local magnetic field, magnetic thermal and magnetic force effects. These generated effects can later be utilized in the diagnosis and treatment of various diseases. The application performance is mainly determined by the nano-magnetism of MFNPs. Therefore, by modulating the magnetic properties, the improved magnetic resonance (MR) signals, magnetothermal, and magnetomechanical effects of the MFNPs can be achieved. In this review, we summarize the strategies used in the engineering of MFNPs to enhance MR imaging sensitivity and magnetic thermal conversion efficiencies. We will also discuss the detailed magnetoresponsive mechanism arising from the critical magnetic properties of MFNPs. Furthermore, we will highlight the recent progresses of the engineered MFNPs in biomedical applications, with emphasis in MR signal amplification, magnetothermal, and magnetomechanical response in biomedical applications.

1. Introduction

Since ancient Greek time, lodestone, i.e., bulk magnetite (Fe3O4), has been used in the treatment of common ailments. With the advancement in nano-biotechnology, the applications of such magnetic ferrite nanoparticles (MFNPs) have evolved, which extends to the modern biomedicine and clinical study [[1], [2], [3], [4], [5], [6], [7]]. As biological tissues are transparent to magnetic fields, the most attractive advantage of MFNPs in biomedical applications is that they can complete various functions triggered by invisible, tissue-penetrating magnetic field [8,9]. For instance, natural magnetotactic bacteria build iron oxide nanoparticles (IONPs) in a chain configuration to generate a permanent dipole in their cells so as to navigate toward their favorable habitats by sensing the Earth’s magnetic field [10]. These MFNPs possess good biocompatibility, which have already been used as imaging and therapeutic agents in preclinical and clinical settings [2,3,11]. As a result of the excellent magnetoresponsive properties of MFNPs, numerous exciting in vivo biomedical applications are created. These include imaging for non-invasive diagnosis of diseases [12], treatment of diseases with hyperthermia [13], magnetically driven nanorobots [14], magnetic activation of ion channel for cell signaling [15], manipulation of cell’s function and fate [16,17], and so on. Despite the good establishment of these concepts, there is a significant challenge in obtaining effective MFNPs that can achieve full functionality via the efficient conversion of the external energy of magnetic field. As such, due to their poor conversion ability, this greatly hinders their transition from bench to bed.

1. Introduction

2. Methods

3. Results

4. Discussion

5. Conclusions

Acknowledgements

Annexes

References

Abstract

Motivation: Emergency Departments’ (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage.

Objectives: The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation.

Methods: We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers’ title, abstract and key words for the topics “triage”, “emergency department”/“emergency room” and concepts within the field of intelligent systems.

Results: From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients’ age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patient’s prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage.

Conclusions: In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals’ decision-making thereby leading to better clinical management and patients’ outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.

Introduction

The growing demand for emergency services, combined with the priority sorting due to patient’s acuity, results in long waiting times for patients. Waiting times have a significant impact on patient mortality, morbidity with readmission in less than 30 days, number of preIntensive Care Units (ICU) resuscitation, length of stay (LOS), patient satisfaction and costs [1–7]. The outcome of patients’ medical treatment is time-sensitive, therefore the sooner the treatment is rendered, the better the outcome [3–7].

1- Introduction

2- Feature extraction

3- Epileptic seizure detection

4- Methods for feature evaluation

5- Experimental results

6- Conclusions

References

Abstract

Since the manual detection of electrographic seizures in continuous electroencephalogram (EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop automatic seizure detection are diverse and ongoing. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre-screened results for neurologists. Several features, data transformations, and classifiers have been explored to analyze and classify seizures via EEG signals. In the literature, some jointly-applied features used in the classification may have shared similar contributions, making them redundant in the learning process. Therefore, this paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics. To provide meaningful information of feature selection, we conducted an experiment to examine the quality of each feature independently. The Bayesian error and non-parametric probability distribution estimation were employed to determine the significance of the individual features. Moreover, a redundancy analysis using a correlation-based feature selection was applied. The results showed that the following features – variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEG signal, as well as variance, energy, kurtosis, and line length calculated on wavelet coefficients – were able to significantly capture the seizures. When compared with a baseline method of classifying all epochs as normal, an improvement of 4.77–13.51% in the Bayesian error was obtained.

Introduction

An epileptic seizure, as defined by the International League Against Epilepsy [1], is a temporary event of symptoms due to synchronization of abnormally excessive activities of neurons in the brain. It has been estimated that approximately 65 million people around the world are affected by epilepsy [2]. Nevertheless, it is still a time-consuming process for neurologists to review continuous electroencephalograms (EEGs) to monitor epileptic patients. Therefore, several researchers have developed differenttechniques that help neurologists to identify an epilepsy occurrence [3–5]. The whole process of automated epileptic seizure analysis primarily consists of data acquisition, signal pre-processing, feature extraction, feature or channel selection, and classification. This paper focuses on a selection of features commonly used in the literature, including statistical parameters (mean, variance, skewness, and kurtosis), amplitude-related parameters (energy, nonlinear energy, line length, maximum and minimum values) and entropyrelated measures. These features can be categorized based on their interpretation or the domain from which the features are calculated. While some studies have considered a particular group of features applicable to their proposed classification method [6–8], others have applied various groups of features extracted from the time, frequency, and time-frequency domains. For example, 55 features were used with a support vector machine (SVM) and postprocessing for neonatal seizure detection, which provided a good detectionrate of 89.2% withone false detectionperhour [9]. Feature redundancy and relevance analysis were applied to 132 features to reduce the vector dimension [10]. Fast correlation based-filter proposed in [11], correlation-based selection (CFS) [12], and ReliefF established in [13], were utilized to select non-redundant features and the filtered features were classified by an artificial neural network (ANN). It turned out that 30-optimal selected features by ReliefF achieved the best result with 91% sensitivity and 95% specificity. These studies showed thatthere is the need to review feature selection as relevant features are directly related to the seizure classification performance.

Since the manual detection of electrographic seizures in continuous electroencephalogram (EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop automatic seizure detection are diverse and ongoing. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre-screened results for neurologists. Several features, data transformations, and classifiers have been explored to analyze and classify seizures via EEG signals. In the literature, some jointly-applied features used in the classification may have shared similar contributions, making them redundant in the learning process. Therefore, this paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics. To provide meaningful information of feature selection, we conducted an experiment to examine the quality of each feature independently. The Bayesian error and non-parametric probability distribution estimation were employed to determine the significance of the individual features. Moreover, a redundancy analysis using a correlation-based feature selection was applied. The results showed that the following features – variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEG signal, as well as variance, energy, kurtosis, and line length calculated on wavelet coefficients – were able to significantly capture the seizures. When compared with a baseline method of classifying all epochs as normal, an improvement of 4.77–13.51% in the Bayesian error was obtained.

Introduction

An epileptic seizure, as defined by the International League Against Epilepsy [1], is a temporary event of symptoms due to synchronization of abnormally excessive activities of neurons in the brain. It has been estimated that approximately 65 million people around the world are affected by epilepsy [2]. Nevertheless, it is still a time-consuming process for neurologists to review continuous electroencephalograms (EEGs) to monitor epileptic patients. Therefore, several researchers have developed differenttechniques that help neurologists to identify an epilepsy occurrence [3–5]. The whole process of automated epileptic seizure analysis primarily consists of data acquisition, signal pre-processing, feature extraction, feature or channel selection, and classification. This paper focuses on a selection of features commonly used in the literature, including statistical parameters (mean, variance, skewness, and kurtosis), amplitude-related parameters (energy, nonlinear energy, line length, maximum and minimum values) and entropyrelated measures. These features can be categorized based on their interpretation or the domain from which the features are calculated. While some studies have considered a particular group of features applicable to their proposed classification method [6–8], others have applied various groups of features extracted from the time, frequency, and time-frequency domains.

1. Introduction

2. The hypothesis

3. Proposed protocol

4. Implications for practice

References

Abstract

Cyberbullying is a prevalent concern around the world. Research shows that interactions online are associated with similar structural correlates and patterns of brain activity to real-world (offline) relationships, and that the brain experiences peer victimisation (e.g., cyberbullying) in the same way that it experiences physical pain. Furthermore, these experiences can become biologically embedded in the physiology of the developing person, thereby increasing their risk of developing mental health problems. With the increasing prevalence of cyberbullying and youth internet usage, there is a pressing need to further understand the brain’s response to cyberbullying.

We hypothesise that a unique pattern of brain activation is associated with cyberbullying and can be identified using task-based functional magnetic resonance imaging (tbfMRI). However, there is a dearth of research regarding cyberbullying and no fMRI paradigm exists in a real-time situation such as observing a cyberbullying scenario. Here, we propose a tbfMRI protocol we have developed specifically for this purpose

his paper will describe a tbfMRI protocol that can be used to investigate the hypothesis. The overall aim of such a protocol is to elucidate the neurobiological underpinnings of cyberbullying by exploring the brain responses in passive cyber-bystanders (those who witness cyberbullying). This would be the first research to use fMRI to examine brain activation in cyberbystanders, and will bring us closer to understanding the various neurobiological underpinnings that may be associated with cybervictim/bully status and outcomes.

Introduction

Cyberbullying can have serious impacts on mental health (Fahy et al., 2016; Le et al., 2017; McLoughlin, Spears, & Taddeo, 2018; McLoughlin, Spears, Taddeo, & Hermens, 2019), and is commonly defined as an aggressive, repeated, intentional act carried out on an individual using electronic forms (Smith et al., 2008). Cybervictims report significantly more social difficulties, higher levels of anxiety and depression, and are more likely to suffer suicidal ideation (Kowalski, Giumetti, Schroeder, & Lattanner, 2014; van Geel, Vedder, & Tanilon, 2014) than victims of traditional bullying. Prevalence estimates vary, however, The AU Kids Online study (Green, Brady, Ólafsson, Hartley, & Lumby, 2012) found that 29% of Australian children (19% across Europe) said they had been bullied, and 13% of those bullied said this occurred on the internet.

1. Introduction and rationale

2. Background

3. Method

4. Results

5. Discussion

6. Conclusion

Appendix A. Supplementary data

References

Abstract

Background: Blockchain can be described as an immutable ledger, logging data entries in a decentralized manner. This new technology has been suggested to disrupt a wide range of data-driven domains, including the health domain.

Objective: The purpose of this study was to systematically review, assess and synthesize peer-reviewed publications utilizing/proposing to utilize blockchain to improve processes and services in healthcare, health sciences and health education.

Method: A structured literature search on the topic was conducted in October 2018 relevant bibliographic databases. Result: 39 publications fulfilled the inclusion criteria. The result indicates that Electronic Health Records and Personal Health Records are the most targeted areas using blockchain technology. Access control, interoperability, provenance and data integrity are all issues that are meant to be improved by blockchain technology in this field. Ethereum and Hyperledger fabric seem to be the most used platforms/frameworks in this domain.

Conclusion: This study shows that the endeavors of using blockchain technology in the health domain are increasing exponentially. There are areas within the health domain that potentially could be highly impacted by blockchain technology.

Introduction and rationale

The technology of blockchain, with inherited characteristics such as decentralization, transparency and anonymization, was introduced in the cryptocurrency Bitcoin in 2008 [1]. Bitcoin, with close to 400 million completed transactions (March 19, 2019) [2], represents a solid use-case that blockchain technology works. This has led to discussions and proposals that blockchain technology could be useful in a range of other data-driven domains, including healthcare [3].

According to IBM, 70 % of healthcare leaders predict that the greatest impact of blockchain within the health domain will be improvement of clinical trial management, regulatory compliance and providing a decentralized framework for sharing electronic health records (EHR) [4]. Moreover, the global blockchain technology market in the healthcare industry is expected to cross $500 million by 2022 [61]. Although blockchain technology is considered to have potential for real improvement of health information systems [3], the recent hype surrounding this technology similarly entails unrealistic proposals and ideas and current literature provides little overview of applications that have been developed, tested and/or deployed.

It is valuable to investigate if the current research meets the expectations to blockchain technology within healthcare, health sciences and health education (from hereinafter, referred to as “the health domain”). This study aims to systematically review, assess and synthesize published peer-reviewed studies where blockchain has been utilized (or proposed to be utilized) to improve processes and services within the health domain. In addition to examining the evidence, we also aim to provide an overview of what has been done, what is known, and the potential directions forward on this topic.

Abstract

1- Introduction

1-1- Ag as antimicrobial particles

1-2- Surface Ag-NPs characteristics

1-3- Ag-NPs leading to bacterial inactivation in the dark

1-4- Ag-NPs surfaces leading to bacterial inactivation

1-5- Microstructure of nanoparticles on surfaces sputtered by DCMS, DCPMS and HIPIMS: Critical issues

1-6- Nitride selection when using antimicrobial metal nitrides under light irradiation: TEM-microstructure of Ag-nitride films

1-7- Evidence for the interfacial charge transfer (IFCT) mechanism in semiconductors under light leading to bacterial inactivation

1-8- Dependence of the Ag-nitride mediated bacterial inactivation kinetics on the applied light dose

1-9- Effect of the sputtering energies on the film microstructure and the bacterial inactivation kinetics

2- Introduction

2-1- Cu as antimicrobial particles (Cu-NPs) and Cu-films: Critical issues

2-2- Cu-surfaces and catheter devices obtained by sol-gel methods

2-3- Sputtered films leading to bacterial inactivation in the dark and under light

2-4- Cu bacterial inactivation under light and in the dark: Scientific bases

2-5- Features of antimicrobial Cu-decorated surfaces under light and in the dark

2-6- Effects of the Cu/TiO2 films microstructure on bacterial inactivation kinetics

2-7- Protection of the Cu-ion release in sputtered films increases the sample stability

2-8- Inactivation of Gram-positive methicillin resistant staphylococcus aureus (MRSA) and other pathogens on Cu-surfaces

2-9- Cu-sputtered surfaces: Implications of the applied deposition energy

3- Introduction

3-1- Ag-Cu as antibacterial bimetal materials

3-2- Colloidal loaded Ag-Cu NPs and films leading to bacterial inactivation

3-3- Ag-Cu films leading to bacterial inactivation with a quasi-instantaneous kinetics

3-4- Mechanism suggested for the bacterial inactivation of Ag-Cu films

3-5- Surface potential and pH-changes during bacterial inactivation

3-6- TEM imaging and XPS analysis of Ag-Cu films

3-7- E. coli and MRSA inactivation on Ag-Cu films: corrosion of the film during the bacteria/fungi inactivation

3-8- Effect of the Ag, Cu and Ag-Cu films on normal and porinless E. coli. Differentiation of ionic and surface-contact effects

3-9- Infrared spectroscopy of the functional groups abatement leading to bacterial lysis

3-10- Mechanism of the interfacial charge transfer (IFCT) during bacterial inactivation

3-11- Conclusions

3-12- Outlook

References

Abstract

The design, synthesis, fundamentals and evaluation of 2D/3D antimicrobial surfaces are addressed in detail in the current review. Recent advances in the antimicrobial mechanism, kinetics and properties of Ag, Cu and AgCu surfaces in the dark and under light irradiation are described and discussed. The structure-reactivity relations in the catalyst/photocatalyst layers were described by way of the surface characterization and the observed antibacterial kinetics. Escherichia coli (E. coli) and Methicillin resistant Staphylococcus aureus MRSA bacteria are selected as model pathogens to evaluate the antimicrobial inactivation kinetics. The separate antimicrobial properties of ions and the antimicrobial surface-contact effects are presented in a detailed way. The interfacial charge transfer (IFCT) mechanism and the identification of the most relevant reactive oxygen species (ROS) leading to bacterial disinfection are considered. The recently developed monitoring of the changes of the film surface potential (Eigenvalues) during bacterial inactivation and the redox reactions associated with catalyst/ photocatalyst surfaces are also presented. The potential for practical applications of these innovative 2D films and 3D sputtered medical devices in health-care facilities are accounted for in the present review.

Introduction

Since decades, nano-silver has been used as an antibacterial material. Ag-nanoparticles cannot be compared to conventional chemicals or bulk materials when trying to elucidate its bactericide action. Products containing nano-silver particles have been commercially available for over 100 years and have been used in different applications [1]. The medical uses of Ag-suspensions, Ag-healing pads and Ag-ions have been reported for the last few decades. Lately, there has been a renewed interest in silver coatings for antimicrobial purposes due to the increased resistance of bacteria to antibiotics [2]. The bacterial inactivation by Ag proceeds by the release of silver-ions penetrating the bacteria cytoplasm through the bacterial porins and by the silver nanoparticles in contact with the bacterial outer-wall [2]. Also, silver nanoparticles (Ag-NPs) have been reported to inactivate bacteria and other pathogens through the oligodynamic effect involving the diffusion of 10−6 -10-9 mol/l (ppm-ppb) amounts in contact with the bacterial outer cell envelope [2–6]. Ag-NPs disinfection proceeds in suspension or deposited on films through the destruction of cell wall by reactive oxygen species (ROS) [1–9]. The Ag-toxicity towards bacteria, viruses and fungi is significantly higher compared to the toxicity reported for mammalian cells [4,5]. Silver ions have an affinity towards sulfhydryl groups located at the cell wall, interfering with the electron transport chain through the cell wall porins diffusing to the interior of the bacteria. Ag-ions have also been reported to block the respiratory chain of microorganisms without inducing resistance to silver ions with a few exceptions [7]. The concentrations required for bactericidal activity are in the ppb range (10−9 mol/l). Metallic silver particles react with moisture, releasing highly oxidative Ag-ions. These Ag-ions complex with DNA and RNA, which inhibit the microorganism replication [8]. Recent reviews report that the size, shape and concentration of Ag-NPs control their antimicrobial kinetics and efficiency [9]. Treatment of thermal burns by Ag-NPs to preclude infections during the healing process has been known for over The ability of Ag-NPs and Cu-NPs associated ions to destroy biofilms is based on their ability to penetrate the biofilm due to their small size. The hindrance to the biofilm formation due to the fast bacterial inactivation kinetics of metal Ag-NPs is an important focus of current research. In contrast to traditional antibiotics, M-NPs have small dimensions < 100 nm and present a larger surface area-to-mass ratio [1]. Toxic biofilms spread highly toxic pathogens in healthcare facilities and public places. Biofilms provide protection to bacterial strains by embedding them in a polymeric network structure. This hinders the penetration of most antibiotics. Bacteria biofilms produce extracellular polymeric substances (ESP) when sticking to surfaces that subsequently protect the biofilm. This allows the film matrix to remain stable for a long-time [11]. The hydrophilic–hydrophobic balance between the surface of polymer films and the bacteria envelope has a great influence on the release of Ag-NPs-ions deposited on a polymer network [12].

Brief Overview of Normal RH Anatomy

Echocardiographic Imaging of the TV and RH

CMR Imaging of the TV and RH

MDCT Imaging of the TV and RH

Fluoroscopic RHAnatomy

Conclusions

References

BRIEF OVERVIEW OF NORMAL RH ANATOMY

The TV apparatus includes the 3 leaflets, the annulus, and the subvalvular apparatus (chordae tendineae, papillary muscles), and is closely linked with the right atrium (RA) and the right ventricle (RV) in the socalled “valve-ventricular complex” (6). The TV is the largest among the 4 cardiac valves. In relation to the mitral valve (MV), the TV is located more apically (#10 mm), anteriorly, and to the right (Figure 1). The tricuspid leaflets are thinner than the mitral leaflets and therefore are more difficult to image. The TV annular plane is oriented nearly vertically and is rotated w45
from the sagittal plane (7). The TV usually has 3 leaflets assigned anatomically as the septal, anterior, and posterior which close in a trifoliate fashion (Figures 1A and 1B). The tricuspid annulus (TA) is a complex, saddle-shaped, dynamic structure (8). The nonplanar TA has higher points in the anteroseptal and posterolateral portions and lower points in anterolateral and posteroseptal portions (Figures 1 and 2). TA size and shape change significantly during the cardiac cycle. RIGHT VENTRICLE. The RV is triangular in shape and crescent-shaped when viewed in cross-section. The RV chamber is divided into 3 components: inlet, apical trabecular, and outlet. The TV separates the RV inlet from the RA. The apical component of the RV has coarser trabeculations than the left ventricle (LV) apex. The RV outlet presents a “crossover” relationship with the LV outflow tract (Figure 1B). The RV roof is formed by the crista supraventricularis (or supraventricular crest) consisting of the ventriculoinfundibular fold and the trabecula septomarginalis, which contains the right bundle branch and continues as the moderator band (Figures 1B and 1E). Because of the complex shape of the RV, the 3 components are difficult to simultaneously image in a single cross-sectional view by using 2-dimensional (2D) echocardiography (2DE). A complete anatomical overview of the RH can be found in an accompanying publication (9). INTERVENTIONAL CONSIDERATIONS.