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With the maturity of information and communication technology (ICT), numerous innovative applications are proposed in different arenas including smart living environments. Technology-enabled smart living has transformed the traditional living system to an enhanced user satisfaction model by providing a balanced environment, thus, securing the residents from disruptions and risks. Besides these magnified advantages, it is found almost full of faints in emergency situations. The researchers and architects put their full potential towards the development of new applications, but no significant attention is paid to analyze the existing designs to identify flaws and suggest enhanced solutions accordingly. To bridge this gap in the literature, this paper presents a comprehensive review to evaluate the capabilities of available smart home designs to counter any emergency situations. Along with highlighting safety, healthcare, and many other unwanted challenges, we also discussed the key problems that obfuscate the trustworthiness of smart homes for its residents. Moreover, the design limitations to present an early alarming and automatic evacuation mechanism especially for deaf, blind, and other visually impaired people is another big challenge to tackle. Finally, we elaborate on the limitations of available smart home solutions and suggest various open research problems that require further development.

In today’s digital era, the escalating phenomenon of cyberbullying is a pervasive and growing concern. With the increasing prevalence of social media platforms, such as Twitter, online abusive behavior has become a significant issue that often leads to unpleasant experiences for users. Manual detection of abnormal and bullying behavior within the realm of social media is inherently not scalable. Moreover, most existing studies on cyberbullying detection have been predominantly conducted in English and very limited work has been done on Urdu (a widely used language in Asia). This paper presents an approach for detecting cyberbullying in Roman Urdu tweets and identifying abuser profiles on Twitter. Firstly, we develop a text corpus of Roman Urdu tweets with user profile data. Subsequently, we employ Gated Recurrent Unit (GRU) model coupled with the application of word2vec technique for word embedding to develop a cyberbullying detection model. Furthermore, we present temporal abusive tweet probability analysis method to provide a nuanced analysis of the number of bullying and non-bullying tweets sent by individuals within a specific time interval. To evaluate the performance, we compare the GRU-based approach with other machine learning models. The results show that the GRU model with lexical normalization gives the best results with an accuracy of 97% and F1-measure of 97%.

Despite the advancement in the left ventricular assist devices (LVADs), right heart failure (RHF) remains a challenging adverse event after LVAD implantation and is associated with increased morbidity and mortality. In this study, we sought to assess the role of cardiac magnetic resonance-derived right ventricular ejection fraction (CMR-RVEF) in predicting the risk of post-LVAD RHF. Overall baseline characteristics and clinical outcomes were compared between the patients who developed post-LVAD RHF and those who did not. A total of 42 patients who underwent CMR before LVAD implantation were included in this study. The mean CMR-RVEF was 25 ± 13%, with no statistically significant difference between the 2 groups (27.7 ± 13.9% vs 24.5 ± 12.3, p = 0.5). The mean of the CMR-derived right ventricular volume index trend was higher in those with post-LVAD RHF (76 ± 28 ml/m2 vs 65 ± 25 ml/m2; p = 0.31). In conclusion, in patients who underwent CMR before LVAD implantation, CMR-RVEF may not predict post-LVAD RHF. Large multicenter studies are needed to confirm this finding.

Abstract The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.

Abstract Aim To study effect of change in position (supine and standing) on pulmonary artery pressure (PAP) in ambulatory heart failure (HF) patients. Methods Seventeen patients with CardioMEMS® sensor and stable heart failure were consented and included in this single center study. Supine and standing measurements were obtained with at least 5 min interval between the two positions. These measurements included PAP readings utilizing the manufacturer handheld interrogator obtaining 10 s data in addition to the systemic blood pressure and heart rate recordings. Results Mean supine and standing readings and their difference (Δ) were as follows respectively: Systolic PAP were 33.4 (± 11.19), 23.6 (± 10) and Δ was 9.9 mmHg (p = 0.0001), diastolic PAP were 14.2 (± 5.6), 7.9 (± 5.7) and Δ was 6.3 mmHg (p = 0.0001) and mean PAP were 21.8 (± 7.8), 14 (± 7.2) and Δ was 7.4 mmHg (p = 0.0001) while the systemic blood pressure did not vary significantly. Conclusion There is orthostatic variation of PAP in ambulatory HF patients demonstrating a mean decline with standing in diastolic PAP by 6.3 mmHg, systolic PAP by 9.9 mmHg and mean PAP by 7.4 mmHg in absence of significant orthostatic variation in systemic blood pressure or heart rate. These findings have significant clinical implications and inform that PAP in each patient should always be measured in the same position. Since initial readings at the time of implant were taken in supine position, it may be best to use supine position or to obtain a baseline standing PAP reading if standing PAP is planned on being used.

Abstract Background Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis. Objective This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field. Methods In this review, we searched Pubmed, Scopus, IEEEXplore, and Google Scholar online databases. The search terms included intervention terms (vision transformers) and the task (i.e., lung cancer, adenocarcinoma, etc.). Two reviewers independently screened the title and abstract to select relevant studies and performed the data extraction. A third reviewer was consulted to validate the inclusion and exclusion. Finally, the narrative approach was used to synthesize the data. Results Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. Researchers have used the publicly available lung cancer datasets of the lung imaging database consortium and the cancer genome atlas. One study used a cluster of 48 GPUs, while other studies used one, two, or four GPUs. Conclusion It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/ .

BackgroundResearch gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. ObjectiveIn this paper, we propose a machine learning–based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. MethodsWe conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). ResultsAfter applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: “virus of COVID-19,” “risk factors of COVID-19,” “prevention of COVID-19,” “treatment of COVID-19,” “health care delivery during COVID-19,” “and impact of COVID-19.” The most prominent topic, observed in over half of the analyzed studies, was “the impact of COVID-19.” ConclusionsThe proposed machine learning–based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

Purpose: The emergence of green financing has come to be a important tool in advancing sustainable improvement and tackling environmental troubles. This research delves into the fame of green financing within Pakistan, elucidating the barriers it encounters and presenting techniques for its enhancement in the country. The research exhibits that green financing holds the ability to channel funds into green ventures, thereby curtailing the state's carbon emissions. Approach: Pakistan faces various impediments in the realm of green financing, encompassing inadequate awareness, restrained engagement from the non-public sector, insufficient financial backing, and the absence of standardized practices. Analysis of various encompassed theoretical journals, administration reviews, and coverage papers have been used. Findings: To surmount these hurdles, the research advocates for heightened consciousness, incentivizing private section involvement, providing financial help, and setting up uniform descriptions and processes. Implications: In end, the studies asserts that with continual energies and cooperative endeavors, the demanding situations obstructing green financing in Pakistan can be conquered, ushering in a greater sustainable future with increased capital mobilization.

ABSTRACTSocial media platform, particularly Twitter, is a rich data source that allows monitoring of public opinions and attitudes toward vaccines.Established behavioral models like the 5C psychological antecedents model and the Health Belief Model (HBM) provide a well-structured framework for analyzing shifts in vaccine-related behavior. This study examines if the extracted data from Twitter contains valuable insights regarding public attitudes toward vaccines and can be mapped to two behavioral models. This study focuses on the Arab population, and a search was carried out on Twitter using: ’ تلقيحي OR تطعيم OR تطعيمات OR لقاح OR لقاحات’ for two years from January 2020 to January 2022. Then, BERTopicmodeling was applied, and several topics were extracted. Finally, the topics were manually mapped to the factors of the 5C model and HBM. 1,068,466 unique users posted 3,368,258 vaccine-related tweets in Arabic. Topic modeling generated 25 topics, which were mapped to the 15 factors of the 5C model and HBM. Among the users, 32.87%were male, and 18.06% were female. A significant 55.77% of the users were from the MENA (Middle East and North Africa) region. Twitter users were more inclined to accept vaccines when they trusted vaccine safety and effectiveness, but vaccine hesitancy increased due to conspiracy theories and misinformation. The association of topics with these theoretical frameworks reveals the availability and diversity of Twitter data that can predict behavioral change toward vaccines. It allows the preparation of timely and effective interventions for vaccination programs compared to traditional methods.

ABSTRACTWhile vaccines have played a pivotal role in the fight against infectious diseases, individuals engage in online resources to find vaccine-related support and information. The benefits and consequences of these online peers are unclear and mainly cause a behavioral shift in user sentiment toward vaccination. This scoping review aims to identify the community and individual factors that longitudinally influence public behavior toward vaccination. The secondary aim is to gain insight into techniques and methodologies used to extract these factors from Twitter data. We followed PRISMA-ScR guidelines to search various online repositories. From this search process, a total of 28 most relevant articles out of 705 relevant studies. Three main themes emerged including individual and community factors influencing public attitude toward vaccination, and techniques employed to identify these factors. Anti-vax, Pro-vax, and neutral are the major communities, while misinformation, vaccine campaign, and user demographics are the common individual factors assessed during this reviewing process. Twitter user sentiment (positive, negative, and neutral) and emotions (fear, trust, sadness) were also discussed to identify the intentions to accept or refuse vaccines. SVM, LDA, BERT are the techniques used for topic modeling, while Louvain, NodeXL, and Infomap algorithms are used for community detection. This research is notable for being the first systematic review that emphasizes the dearth of longitudinal studies and the methodological and underlying practical constraints underpinning the lucrative implementation of an explainable and longitudinal behavior analysis system. Moreover, new possible research directions are suggested for the researchers to perform accurate human behavior analysis.

BackgroundTransformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation. ObjectiveThis study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging. MethodsThis review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach. ResultsOf the 736 retrieved studies, 22 (3%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer–based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data. ConclusionsIt can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer.

BackgroundHepatocellular carcinoma is a malignant neoplasm of the liver and a leading cause of cancer-related deaths worldwide. The multimodal data combines several modalities, such as medical images, clinical parameters, and electronic health record (EHR) reports, from diverse sources to accomplish the diagnosis of liver cancer. The introduction of deep learning models with multimodal data can enhance the diagnosis and improve physicians' decision-making for cancer patients.ObjectiveThis scoping review explores the use of multimodal deep learning techniques (i.e., combining medical images and EHR data) in diagnosing and prognosis of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA).MethodologyA comprehensive literature search was conducted in six databases along with forward and backward references list checking of the included studies. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review guidelines were followed for the study selection process. The data was extracted and synthesized from the included studies through thematic analysis.ResultsTen studies were included in this review. These studies utilized multimodal deep learning to predict and diagnose hepatocellular carcinoma (HCC), but no studies examined cholangiocarcinoma (CCA). Four imaging modalities (CT, MRI, WSI, and DSA) and 51 unique EHR records (clinical parameters and biomarkers) were used in these studies. The most frequently used medical imaging modalities were CT scans followed by MRI, whereas the most common EHR parameters used were age, gender, alpha-fetoprotein AFP, albumin, coagulation factors, and bilirubin. Ten unique deep-learning techniques were applied to both EHR modalities and imaging modalities for two main purposes, prediction and diagnosis.ConclusionThe use of multimodal data and deep learning techniques can help in the diagnosis and prediction of HCC. However, there is a limited number of works and available datasets for liver cancer, thus limiting the overall advancements of AI for liver cancer applications. Hence, more research should be undertaken to explore further the potential of multimodal deep learning in liver cancer applications.

Data generated from sources such as wearable sensors, medical imaging, personal health records, and public health organizations have resulted in a massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, graphical processing units (GPUs), Field-programmable gate arrays (FPGAs) and tensor processing units (TPUs), provide the means to utilize these data. Consequently, an array of sophisticated Artificial Intelligence (AI) techniques have been devised to extract valuable insights from the extensive datasets in the healthcare industry. Here, we present an overview of recent progress in AI and biosensors in medical and life sciences. We discuss the role of machine learning in medical imaging, precision medicine, and biosensors for the Internet of Things (IoT). We review the latest advancements in wearable biosensing technologies. These innovative solutions employ AI to assist in monitoring of bodily electro-physiological and electro-chemical signals, as well as in disease diagnosis. These advancements exemplify the trend towards personalized medicine, delivering highly effective, cost-efficient, and precise point-of-care treatment.Furthermore, an overview of the advances in computing technologies, such as accelerated AI, edge computing, and federated learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential issues generated by biosensors and IoT-based healthcare, and the distribution shifts that occur among different data modalities, concluding with an overview of future prospects.

Abstract Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. The most important question when using multimodal data is how to fuse them—a field of growing interest among researchers. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. More specifically, we focus on studies that only fused EHR with medical imaging data to develop various AI methods for clinical applications. We present a comprehensive analysis of the various fusion strategies, the diseases and clinical outcomes for which multimodal fusion was used, the ML algorithms used to perform multimodal fusion for each clinical application, and the available multimodal medical datasets. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched Embase, PubMed, Scopus, and Google Scholar to retrieve relevant studies. After pre-processing and screening, we extracted data from 34 studies that fulfilled the inclusion criteria. We found that studies fusing imaging data with EHR are increasing and doubling from 2020 to 2021. In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies). We found that multimodality fusion models outperformed traditional single-modality models for the same task. Disease diagnosis and prediction were the most common clinical outcomes (reported in 20 and 10 studies, respectively) from a clinical outcome perspective. Neurological disorders were the dominant category (16 studies). From an AI perspective, conventional ML models were the most used (19 studies), followed by DL models (16 studies). Multimodal data used in the included studies were mostly from private repositories (21 studies). Through this scoping review, we offer new insights for researchers interested in knowing the current state of knowledge within this research field.

IntroductionDetecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance.ObjectiveThis scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection.MethodsThe review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included.Results and discussionsThe review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.

Abstract The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.

Abstract For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures.

BackgroundDoxorubicin, an anthracycline chemotherapeutic known to incur heart damage, decreases heart function in up to 11% of patients. Recent investigations have implicated the Wnt signaling cascade as a key modulator of cardiac tissue repair after myocardial infarction. Wnt upregulation in murine models resulted in stimulation of angiogenesis and suppression of fibrosis after ischemic insult. However, the molecular mechanisms of Wnt in mitigating doxorubicin-induced cardiac insult require further investigation. Identifying cardioprotective mechanisms of Wnt is imperative to reducing debilitating cardiovascular adverse events in oncologic patients undergoing treatment.MethodsExposing human cardiomyocyte AC16 cells to varying concentrations of Wnt10b and DOX, we observed key metrics of cell viability. To assess the viability and apoptotic rates, we utilized MTT and TUNEL assays. We quantified cell and mitochondrial membrane stability via LDH release and JC-1 staining. To investigate how Wnt10b mitigates doxorubicin-induced apoptosis, we introduced pharmacologic inhibitors of key enzymes involved in apoptosis: FR180204 and SB203580, ERK1/2 and p38 inhibitors. Further, we quantified apoptotic executor enzymes, caspase 3/7, via immunofluorescence.ResultsAC16 cells exposed solely to doxorubicin were shrunken with distorted morphology. Cardioprotective effects of Wnt10b were demonstrated via a reduction in apoptosis, from 70.1% to 50.1%. LDH release was also reduced between doxorubicin and combination groups from 2.27-fold to 1.56-fold relative to the healthy AC16 control group. Mitochondrial membrane stability was increased from 0.67-fold in the doxorubicin group to 5.73 in co-treated groups relative to control. Apoptotic protein expression was stifled by Wnt10b, with caspase3/7 expression reduced from 2.4- to 1.3-fold, and both a 20% decrease in p38 and 40% increase in ERK1/2 activity.ConclusionOur data with the AC16 cell model demonstrates that Wnt10b provides defense mechanisms against doxorubicin-induced cardiotoxicity and apoptosis. Further, we explain a mechanism of this beneficial effect involving the mitochondria through simultaneous suppression of pro-apoptotic p38 and anti-apoptotic ERK1/2 activities.

Background Vaccination programs are effective only when a significant percentage of people are vaccinated. Social media usage is arguably one of the factors affecting public attitudes toward vaccines. Objective This study aims to identify if the social media usage factors can predict Arab people’s attitudes and behavior toward the COVID-19 vaccines. Methods An online survey was conducted in the Arab countries, and 217 Arab nationals participated in this study. Logistic regression was applied to identify what demographics and social media usage factors predict public attitudes and behavior toward the COVID-19 vaccines. Results Of the 217 participants, 56.2% (n = 122) were willing to get the vaccines, and 41.5% (n = 90) were hesitant. This study shows that none of the social media usage factors were significant enough to predict the actual vaccine acceptance behavior. However, some social media usage factors could predict public attitudes toward the COVID-19 vaccines. For example, compared to infrequent social media users, frequent social media users were 2.85 times more likely to agree that the risk of COVID-19 was being exaggerated (OR = 2.85, 95% CI = 0.86–9.45, p = .046). On the other hand, participants with more trust in vaccine information shared by their contacts were less likely to agree that decision-makers had ensured the safety of vaccines (OR = 0.528, 95% CI = 0.276–1.012, p = .05). Conclusion Information shared on social media may affect public attitudes toward COVID-19 vaccines. Therefore, disseminating correct and validated information about the COVID-19 vaccines on social media is important to increase public trust and counter the impact of incorrect misinformation.

With the success of COVID-19 vaccines in clinical trials, vaccination programs are being administered for the population with the hopes of herd immunity. However, the success of any vaccination program depends on the percentage of people willing to get vaccination which is influenced by social, economic, demographic, and vaccine-specific factors. Thus, it is important to understand public attitudes and perceptions toward vaccination. This study aims to measure public attitude toward vaccines and vaccinations before and during the COVID-19 pandemic, using public data from Twitter. A total of 880,586 tweets for 57,529 unique users were included in the study. Most of the tweets were posted in five languages: French, English, Swedish, Dutch, and Italian. These tweets were divided into two time periods: before COVID-19 (T1) and during COVID-19 (T2). This study observed the shift in the sentiments of the public attitude toward vaccines before and during COVID-19 pandemic. Both positive and negative shifts in sentiments were observed for the users of various languages but shifts toward positive sentiments were more prominent during the COVID-19 pandemic.

Although seen in ∼5% of sarcoidosis patients, cardiac sarcoidosis (CS) accounts for nearly 25% of disease-related deaths. This study aimed to describe characteristics and outcomes among CS patients. Patients diagnosed with CS in 2016–2017 in the US National Inpatient Sample Database were evaluated to study patient characteristics, reasons ascribed to admission, in-hospital outcomes, and complications. A total of 2420 patients (median age 56 years) were included in the analysis. Most admissions occurred due to ventricular tachycardia (12.8%), followed by myocarditis (9.9%) with a mean length of stay of 7 ± 7 days. The overall incidence of in-hospital mortality was 2.5%.

Abstract Background Left ventricular thrombus (LVT) is not uncommon and pose a risk of systemic embolism, which can be mitigated by adequate anticoagulation. Direct oral anticoagulants (DOACs) are increasingly being used as alternatives to warfarin for anticoagulation, but their efficacy and safety profile has been debated. We aim to compare the therapeutic efficacy and safety of DOACs versus warfarin for the treatment of LVT. Methodology We systematically searched PubMed/Medline, Google Scholar, Cochrane library, and LILCAS databases from inception to 14th August 2020 to identify relevant studies comparing warfarin and DOACs for LVT treatment and used the pooled data extracted from retrieved studies to perform a meta-analysis. Results We report pooled data on 1955 patients from 8 studies, with a mean age of 61 years and 59.7 years in warfarin and DOACs group, respectively. The pooled odds ratio for thrombus resolution was 1.11 (95% CI 0.51–2.39) on comparing warfarin to DOAC, but it did not reach a statistical significance (p = 0.76). The pooled risk ratio (RR) of stroke or systemic embolization and bleeding in patients treated with warfarin vs DOACs was 1.04 (95% CI 0.64–1.68; p = 0.85), and 1.15 (95% CI 0.62–2.13; p = 0.57), respectively; with an overall RR of 1.09 (95% CI 0.70–1.70; p = 0.48) for mortality. Conclusions DOACs appears to be non-inferior or at least as effective as warfarin in the treatment of left ventricular thrombus without any statistical difference in stroke or bleeding complications.

Background: Coronavirus disease 2019 (COVID-19) has been reported to cause worse outcomes in patients with underlying cardiovascular disease, especially in patients with acute cardiac injury, which is determined by elevated levels of high-sensitivity troponin. There is a paucity of data on the impact of congestive heart failure (CHF) on outcomes in COVID-19 patients. Methods: We conducted a literature search of PubMed/Medline, EMBASE, and Google Scholar databases from 11/1/2019 till 06/07/2020, and identified all relevant studies reporting cardiovascular comorbidities, cardiac biomarkers, disease severity, and survival. Pooled data from the selected studies was used for metanalysis to identify the impact of risk factors and cardiac biomarker elevation on disease severity and/or mortality. Results: We collected pooled data on 5967 COVID-19 patients from 20 individual studies. We found that both non-survivors and those with severe disease had an increased risk of acute cardiac injury and cardiac arrhythmias, our pooled relative risk (RR) was — 8.52 (95% CI 3.63–19.98) (p

BackgroundResearch on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. ObjectiveThis review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. MethodsA search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as “generative adversarial networks” and “GANs,” and application keywords, such as “COVID-19” and “coronavirus.” The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. ResultsThis review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies. ConclusionsStudies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs’ performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications.

Abstract Objective To study the demographics, clinical presentations, and outcomes of emergency department (ED) visits of patients with heart transplantation (HT) in the United States. Methods We performed a secondary analysis of the National Emergency Department Sample database from 2016 to 2018. All ED visits of patients with HT aged ≥ 18 years were identified using International Classification of Diseases, Tenth Revision codes. Results Out of a total 308,182,495 national ED visits, 55,583 were HT‐related visits. The median age was 61.07 years (interquartile range [IQR]: 46.91–69.38) and 69.44% were males. The hospital admission rate was 54.3% and median inpatient length of stay was 3.19 days (IQR: 1.63–5.92). The mortality rate during inpatient stay was 1.16%. Median inpatient and ED charges among admitted patients were $37,911 (IQR: $21,487–$71,262). The most common primary diagnosis of HT‐related ED visits was sepsis (4.3%) followed by acute kidney injury (3.57%) and chest pain (3%). Conclusion More than half of total ED visits among HT patients resulted in hospital admission. The most common cause for ED visit in these patients was sepsis followed by acute kidney injury and chest pain.

Abstract Background Left ventricular assist devices (LVAD) have been increasingly used in the treatment of end-stage heart failure. While warfarin has been uniformly recommended in the long-term as anticoagulation strategy, no clear recommendation exists for the post-operative period. We sought to evaluate the feasibility of enoxaparin in the immediate and early postoperative period after LVAD implantation. Methods This is a two-center, retrospective analysis of 250 consecutive patients undergoing LVAD implantation between January 2017 and December 2018. Patients were bridged postoperatively to therapeutic INR by either receiving unfractionated heparin (UFH) or low molecular weight heparin (LMWH). Patients were followed while inpatient and for 3 months after LVAD implantation. The efficacy outcome was occurrence of first and subsequent cerebrovascular accident while safety outcome was the occurrence of bleeding events. Length of stay (LOS) was also assessed. Results Two hundred fifty and 246 patients were analyzed for index admission and 3-month follow up respectively. No statistically significant differences were found between the two groups in CVA (OR = 0.67; CI = 0.07–6.39, P = 0.73) or bleeding events (OR = 0.91; CI = 0.27–3.04, P = 0.88) during index admission. Similarly, there were no differences at 3 months in either CVAs or bleeding events (OR = 0.85; 0.31–2.34; p = 0.76). No fatal events occurred during the study follow-up period. Median LOS was significantly lower (4 days; p = 0.03) in the LMWH group. Conclusions LMWH in the immediate and early postoperative period after LVAD implantation appears to be a concurrently safe and efficacious option allowing earlier postoperative discharge and avoidance of recurrent hospitalizations due to sub-therapeutic INR.

Abstract Left ventricular thrombus (LVT) is associated with a significant risk of ischemic stroke (IS) and peripheral embolization. Societal guidelines recommend the use of warfarin, with direct oral anticoagulants (DOACs) only for patients unable to tolerate warfarin. We studied the natural history of LVT with anticoagulation (AC) with emphasis on comparing warfarin and DOAC use. In this single center study, we identified patients with a confirmed LVT. Type and duration of anticoagulation, INR levels and clinical outcomes (bleeding, ischemic stroke or peripheral embolization, and thrombus resolution) were recorded. LVT was confirmed in a total of 110 patients. Mean age was 59 + 14 years. 79% were men. Underlying etiology was chronic ischemic cardiomyopathy in 58%, non-ischemic cardiomyopathy in 23%. AC was started in 96 (87%) patients. At 1 year follow up, 11 patients (10%) had a stroke while on any AC (2 had hemorrhagic stroke and 9 had IS). Of those with IS, 7 were on warfarin (71% of those had subtherapeutic INR) and 2 patients on DOACs had IS. The 1-year risk of any stroke was 15% in warfarin group (12% risk of ischemic stroke) compared to 6% in the DOACs group (p = 0.33). 37 (63%) patients on warfarin and 18 (53%) on DOACs had resolution of thrombus (p = 0.85). One-year risk of stroke with LVT is high (10%) even with AC. Most patients IS on warfarin had subtherapeutic INR. There was no statistical difference in stroke risk or rate of thrombus resolution between warfarin and DOACs treated patients.

Introduction: Tricuspid valve (TV) regurgitation is the most common valvular pathology after orthotopic heart transplant (OHT). The number of transplants being performed is increasing with patients living longer after heart transplant. Data on TV surgery in OHT recipients is limited. Methods: We sought to analyze the outcomes of patients undergoing TV surgery from a large diverse, multicentric, nationwide cohort using the Nationwide Inpatient Sample (NIS) database. Results:A total of 42,766 TV repair or replacement (bioprosthetic and mechanical) involving adult patients (age ≥18 years) between 2007 and September 2015 were identified. Of these, 366 were performed in patients with OHT. TV repair was the most common surgery performed in both groups (OHT group and native heart group). Compared to the native group, patients with OHT had a significantly higher incidence of cardiogenic shock (20 vs. 11%, p = 0.024), acute kidney injury (AKI) (59 vs. 30%, p < 0.001), and AKI requiring hemodialysis (13 vs. 4%, p < 0.001). Also, the mean length of stay for the index admission was significantly longer in the OHT group (27 vs. 17 days, p = 0.008). The mortality rate was similar between the two groups (7 vs. 8%, p = 0.753). The number of TV surgeries performed in OHT patients from 2007 to 2014 have remained stable (p = 0.803) compared to those in native heart patients which showed a significantly increasing trend (p = 0.019) during the same time period. Conclusions: TV surgery remains an important treatment modality among the OHT population and carries a similar mortality during index hospitalization as that in native heart patients undergoing TV surgery.

Background: There is a paucity of data on readmission rates and predictors of readmissions in cardiogenic shock patients after contemporary Extracorporeal Membrane Oxygenation (ECMO) use. Methods: Using the Nationwide Readmission Database, we included adult patients (≥18 years old) hospitalized between January to November 2016–2018 for cardiogenic shock requiring ECMO support. Thirty-day readmission rates, associated variables, and predictors of readmission were assessed. Results: A total of 10,723 patients underwent ECMO for cardiogenic shock. After excluding patients who died (n = 5602; 52%) and who underwent LVAD or OHT during index admission (n = 892; 8%), 4229 patients discharged alive were included. Of those, 694 (16.4%) were readmitted within 30 days. The median time to readmission was 10 days. Diabetes mellitus (OR = 1.77; 95% CI 1.32–2.37), chronic liver disease (OR = 1.35; 95% CI 1.03–1.77), and prolonged LOS (≥30 days; OR = 1.38; 95% CI 1.05–1.81) were associated with increased risk of 30-day readmissions while heart failure diagnosis (OR = 0.69; 95% CI 0.50–0.95) and short-term hospital post-discharge care (OR = 0.53; 95% CI 0.28–0.99) conferred a lower risk. Sepsis, followed by congestive heart failure, was the most common readmission diagnoses. Conclusions: Patients with CS requiring ECMO support have high mortality and high 30-day readmission rates, with sepsis being the leading cause of readmissions followed by heart failure.

BackgroundCardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. ObjectiveThis study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. MethodsA scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. ResultsOut of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of

Background The usefulness of right heart catherization (RHC) has long been debated, and thus, we aimed to study the real‐world impact of the use of RHC in cardiogenic shock. Methods and Results In the Nationwide Readmissions Database using International Classification of Diseases, Tenth Revision (ICD‐10), we identified 236 156 patient hospitalizations with cardiogenic shock between 2016 and 2017. We sought to evaluate the impact of RHC during index hospitalization on management strategies, complications, and outcomes as well as on 30‐day readmission rate. A total 25 840 patients (9.6%) received RHC on index admission. The RHC group had significantly more comorbidities compared with the non‐RHC group. During the index admission, the RHC group had lower death (25.8% versus 39.5%, P

Objective: To assess the influence of body-mass index (BMI) on the association of ankle-brachial index (ABI) with mortality. Patients and Methods: We conducted a prospective study of National Health and Nutrition Examination Survey participants enrolled from January 1, 1999 to December 31, 2002 with BMI and ABI data available. ABI categories were 1.3 (high). BMI categories were

Introduction: Human papillomavirus (HPV) vaccine coverage in Australia is 80% for females and 76% for males. Attitudes may influence coverage but surveys measuring attitudes are resource-intensive. The aim of this study was to determine whether Twitter-derived estimates of HPV vaccine information exposure were associated with differences in coverage across regions in Australia. Methods: Regional differences in information exposure were estimated from 1,103,448 Australian Twitter users and 655,690 HPV vaccine related tweets posted between 6 September 2013 and 1 September 2017. Tweets about HPV vaccines were grouped using topic modelling; an algorithm for clustering text-based data. Proportional exposure to topics across 25 regions in Australia were used as factors to model HPV vaccine coverage in females and males, and compared to models using employment and education as factors. Results: Models using topic exposure measures were more closely correlated with HPV vaccine coverage (female: Pearson’s R = 0.75 [0.49 to 0.88]; male: R = 0.76 [0.51 to 0.89]) than models using employment and education as factors (female: 0.39 [−0.02 to 0.68]; male: 0.36 [−0.04 to 0.66]). In Australia, positively-framed news tended to reach more Twitter users overall, but vaccine-critical information made up higher proportions of exposures among Twitter users in low coverage regions, where distorted characterisations of safety research and vaccine-critical blogs were popular. Conclusions: Twitter-derived models of information exposure were correlated with HPV vaccine coverage in Australia. Topic exposure measures may be useful for providing timely and localised reports of the information people access and share to inform the design of targeted vaccine promotion interventions.

The toxicity of doxorubicin to the cardiovascular system often limits its benefits and widespread use as chemotherapy. The mechanisms involved in doxorubicin-induced cardiovascular damage and possible protective interventions are not well-explored. Using human aortic endothelial cells, we show vitamin D3 strongly attenuates doxorubicin-induced senescence and cell cycle arrest. We further show the protective effects of vitamin D3 are mediated by the upregulation of IL-10 and FOXO3a expression through fine modulation of pAMPKα/SIRT1/FOXO3a complex activity. These results have great significance in finding a target for mitigating doxorubicin-induced cardiovascular toxicity.

Background: As public health strategists and policymakers explore different approaches to lessen the devastating effects of novel coronavirus disease (COVID-19), blockchain technology has emerged as a resource that can be utilized in numerous ways. Many blockchain technologies have been proposed or implemented during the COVID-19 pandemic; however, to the best of our knowledge, no comprehensive reviews have been conducted to uncover and summarise the main feature of these technologies.Objective: This study aims to explore proposed or implemented blockchain technologies used to mitigate the COVID-19 challenges as reported in the literature.Methods: We conducted a scoping review in line with guidelines of PRISMA Extension for Scoping Reviews (PRISMA-ScR). To identify relevant studies, we searched 11 bibliographic databases (e.g., EMBASE and MEDLINE) and conducted backward and forward reference list checking of the included studies and relevant reviews. The study selection and data extraction were conducted by 2 reviewers independently. Data extracted from the included studies was narratively summarised and described.Results: 19 of 225 retrieved studies met eligibility criteria in this review. The included studies reported 10 used cases of blockchain to mitigate COVID-19 challenges; the most prominent use cases were contact tracing and immunity passports. While the blockchain technology was developed in 10 studies, its use was proposed in the remaining 9 studies. The public blockchain technology was the most commonly utilized type in the included studies. All together, 8 different consensus mechanisms were used in the included studies. Out of 10 studies that identified the used platform, 9 studies used Ethereum to run the blockchain. Solidity was the most prominent programming language used in developing blockchain technology in the included studies. The transaction cost was reported in only 4 of the included studies and varied between USD 10−10 and USD 5. The expected latency and expected scalability were not identified in the included studies.Conclusion: Blockchain technologies are expected to play an integral role in the fight against the COVID-19 pandemic. Many possible applications of blockchain were found in this review; however, most of them are not mature enough to reveal their expected impact in the fight against COVID-19.We encourage governments, health authorities, and policymakers to consider all blockchain applications suggested in the current review to combat COVID-19 challenges. There is a pressing need to empirically examine how effective blockchain technologies are in mitigating COVID-19 challenges. Further studies are required to assess the performance of blockchain technologies’ fight against COVID-19 in terms of transaction cost, scalability, and/or latency when using different consensus algorithms, platforms, and access types.

Coronavirus disease 2019 (COVID-19) predominantly presents with symptoms of fever, fatigue, cough and respiratory failure. However, it appears to have a unique interplay with cardiovascular disease (CVD); patients with pre-existing CVD are at highest risk for mortality from COVID-19, along with the elderly. COVID-19 contributes to cardiovascular complications including arrhythmias, myocardial dysfunction and myocardial inflammation. Although the exact mechanism of myocardial inflammation in patients with COVID-19 is not known, several plausible mechanisms have been proposed based on early observational reports. In this article, the authors summarise the available literature on mechanisms of myocardial injury in COVID-19.

Endomyocardial biopsy (EMB) continues to remain the gold standard for surveillance of rejection post orthotropic heart transplantation (OHT). It can be performed under fluoroscopic or echocardiographic guidance. In the hands of an experienced operator, the complications of EMB are uncommon with

Background: The current crisis created by the coronavirus pandemic is impacting all facets of life. Coronavirus vaccines have been developed to prevent coronavirus infection and fight the pandemic. Since vaccines might be the only way to prevent and stop the spread of coronavirus. The World Health Organization (WHO) has already approved several vaccines, and many countries have started vaccinating people. Misperceptions about vaccines persist despite the evidence of vaccine safety and efficacy. Objectives: To explore the scientific literature and find the determinants for worldwide COVID-19 vaccine hesitancy as reported in the literature. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines were followed to conduct a scoping review of literature on COVID-19 vaccine hesitancy and willingness to vaccinate. Several databases (e.g., MEDLINE, EMBASE, and Google Scholar) were searched to find relevant articles. Intervention- (i.e., COVID-19 vaccine) and outcome- (i.e., hesitancy) related terms were used to search in these databases. The search was conducted on 22 February 2021. Both forward and backward reference lists were checked to find further studies. Three reviewers worked independently to select articles and extract data from selected literature. Studies that used a quantitative survey to measure COVID-19 vaccine hesitancy and acceptance were included in this review. The extracted data were synthesized following the narrative approach and results were represented graphically with appropriate figures and tables. Results: 82 studies were included in this scoping review of 882 identified from our search. Sometimes, several studies had been performed in the same country, and it was observed that vaccine hesitancy was high earlier and decreased over time with the hope of vaccine efficacy. People in different countries had varying percentages of vaccine uptake (28–86.1%), vaccine hesitancy (10–57.8%), vaccine refusal (0–24%). The most common determinants affecting vaccination intention include vaccine efficacy, vaccine side effects, mistrust in healthcare, religious beliefs, and trust in information sources. Additionally, vaccination intentions are influenced by demographic factors such as age, gender, education, and region. Conclusions: The underlying factors of vaccine hesitancy are complex and context-specific, varying across time and socio-demographic variables. Vaccine hesitancy can also be influenced by other factors such as health inequalities, socioeconomic disadvantages, systemic racism, and level of exposure to misinformation online, with some factors being more dominant in certain countries than others. Therefore, strategies tailored to cultures and socio-psychological factors need to be developed to reduce vaccine hesitancy and aid informed decision-making.

Over the past five decades, the incidence of intravenous drug use- (IVDU-) associated infective endocarditis (IE) has been on the rise in North America. Classically, IVDU has been thought to affect right-sided valves. However, in recent times a more variable presentation of IVDU-associated IE has been reported. Here, we report a case of a patient with a known history of IVDU who presented with clinical symptoms concerning for right- as well as left-sided endocarditis. In addition, we also discuss what should be considered adequate evaluation for patients with suspected endocarditis, and more specifically, what should be the role of transesophageal echocardiography in patients with IE noted on transthoracic echocardiography.

An amendment to this paper has been published and can be accessed via the original article.

Massive Online Open Courses (MOOCs) are a form of online learning that has become increasingly popular across the globe, yet they often suffer from low completion rates. This study aims to advance understanding of the Dropout Phenomenon in MOOCs through a qualitative case study conducted in Pakistan. The purpose of this research is to understand why learners drop out of MOOCs by identifying the factors that influence the decision of learners. Semi-structured interviews with MOOC participants were conducted and analysed who had enrolled in two MOOCs that had a high dropout rate. The responses from the interviews were categorised under three overarching themes: lack of motivation, perception of course content, and inability to manage time. Within those themes, several factors that affect course dropout rates were identified, including perceived rewards, difficulties in comprehension, academic and family commitments, and duration of the course.

Anomaly detection is an important aspect of data mining, where the main objective is to identify anomalous or unusual data from a given dataset. However, there is no formal categorization of application-specific anomaly detection techniques for big data and this ignites a confusion for the data miners. In this paper, we categorise anomaly detection techniques based on nearest neighbours, clustering and statistical approaches and investigate the performance analysis of these techniques in critical infrastructure applications such as SCADA systems. Extensive experimental analysis is conducted to compare representative algorithms from each of the categories using seven benchmark datasets (both real and simulated) in SCADA systems. The effectiveness of the representative algorithms is measured through a number of metrics. We highlighted the set of algorithms that are the best performing for SCADA systems.

The development of insulin resistance (IR) in mouse models of obesity and type 2 diabetes mellitus (DM) is characterized by progressive accumulation of inflammatory macrophages and subpopulations of T cells in the visceral adipose. Regulatory T cells (Tregs) may play a critical role in modulating tissue inflammation via their interactions with both adaptive and innate immune mechanisms. We hypothesized that an imbalance in Tregs is a critical determinant of adipose inflammation and investigated the role of Tregs in IR/obesity through coordinated studies in mice and humans.Foxp3-green fluorescent protein (GFP) "knock-in" mice were randomized to a high-fat diet intervention for a duration of 12 weeks to induce DIO/IR. Morbidly obese humans without overt type 2 DM (n = 13) and lean controls (n = 7) were recruited prospectively for assessment of visceral adipose inflammation. DIO resulted in increased CD3(+)CD4(+), and CD3(+)CD8(+) cells in visceral adipose with a striking decrease in visceral adipose Tregs. Treg numbers in visceral adipose inversely correlated with CD11b(+)CD11c(+) adipose tissue macrophages (ATMs). Splenic Treg numbers were increased with up-regulation of homing receptors CXCR3 and CCR7 and marker of activation CD44. In-vitro differentiation assays showed an inhibition of Treg differentiation in response to conditioned media from inflammatory macrophages. Human visceral adipose in morbid obesity was characterized by an increase in CD11c(+) ATMs and a decrease in foxp3 expression.Our experiments indicate that obesity in mice and humans results in adipose Treg depletion. These changes appear to occur via reduced local differentiation rather than impaired homing. Our findings implicate a role for Tregs as determinants of adipose inflammation.