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Atrial fibrillation (AF) is the most common arrhythmia in patients with valvular heart disease, and it can be associated with adverse patient outcomes. However, the need for anticoagulation to counterbalance AF-associated stroke risk may further lead to suboptimal outcomes via increasing bleeding events, especially in high-risk individuals. Because the vast majority of thrombi occur in the left atrial appendage, left atrial appendage occlusion (LAAO) is an established procedure for preventing ischemic stroke in patients with AF, while limiting anticoagulation-related bleeding events. Thus, the concept of combining an index procedure for structural heart disease (SHD) with LAAO seems promising for preventing future stroke events. A combined procedure has been described in aortic stenosis (transcatheter aortic valve implantation + LAAO), mitral regurgitation (transcatheter edge-to-edge repair + LAAO), and atrial septal defects (patent foramen ovale/atrial septal defect + LAAO). Evidence shows that a combined procedure can be safely performed in a "1-stop shop" fashion, without increased rates of procedural adverse events, with the potential to limit bleeding risk and provide prophylaxis against stroke events. This review analyses indications and clinical evidence regarding the safety and efficacy of combined SHD+LAAO procedures, while also providing insights into gaps in knowledge and future directions for the evolution of this field., (Copyright © 2024 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)

Lipid lowering, with the use of statins after an acute coronary syndrome (ACS), is a cornerstone, well-established strategy for the secondary prevention of ischemic events in this high-risk cohort. In addition to the positive effect on lipid levels, statins have also been linked to improved atherosclerotic plaque characteristics, such as plaque regression and inflammation reduction, associated with the extent of reduction in LDL-C. The recent emergence of PCSK9 inhibitors for the management of dyslipidemia and the more extensive lipid lowering provided by these agents may provide better prevention for ACS patients when initiated after the ACS event. Several trials have evaluated the immediate post-ACS initiation of PCSK9 inhibitors, which has shown, to date, beneficial results. Furthermore, PCSK9 inhibitors have been linked with positive plaque remodeling and associated mortality benefits, which makes their use in the initial management strategy of such patients appealing. Therefore, in this review, we will analyze the rationale behind immediate lipid lowering after an ACS, report the evidence of PCSK9 inhibition immediately after the ACS event and the available data on plaque stabilization, and discuss treatment algorithms and clinical perspectives for the use of these agents in this clinical setting.

The children's fairytale is a playful educational tool that can be modified in such a way to enhance motor proficiency. This study investigated the effect of an online exercise program with modified fairytales on children's motor proficiency during the kindergarten curriculum. Forty preschool children (20 girls and 20 boys; 5.13 ± 0.24 years old) were divided into two equal groups: an intervention (IG) group and a control group (CG). The IG followed a 3-month (3 times/week) online exercise intervention program (supervised by the class's kindergarten teacher) with modified children's fairytales, during the COVID-19 pandemic, with an aim to improve their motor proficiency. The CG did not attend any exercise intervention program. The Democritos Movement Screening Tool for Preschool Children (DEMOST-PRE), consisting of ten tests, was used to assess the children's motor proficiency. Τhe IG, after the end of the intervention program, significantly improved in the DEMOST-PRE total score ( p < 0.001; mean change: 40.7%), while the CG did not significantly improve in the total score ( p > 0.05). Furthermore, in the IG, a significant negative correlation between the pre-training DEMOST-PRE total score and the percentage change following the intervention (r = -0.64; p = 0.002) was observed. A modified exercise program using children's fairytales may be incorporated into the kindergarten curriculum as an effective educational tool for the improvement of motor proficiency.

Low-density lipoprotein (LDL) and oxidized LDL (oxLDL) are major contributors to atherogenesis, as endogenous antigens, via several receptors such as LOX 1. A PubMed search was conducted in order to identify relevant articles regarding LOX-1's role in the atherosclerosis, diagnosis, prognostic use and molecules that could be used for therapy. The references of the manuscripts obtained were also reviewed, in order to find additional relevant bibliography. LOX-1 is a lectin-like pattern recognition receptor, mostly expressed in endothelial cells (ECs) which can bind a variety of molecules, including oxLDL and C-reactive protein (CRP). LOX-1 plays a key role in oxLDL's role as a causative agent of atherosclerosis through several pathologic mechanisms, such as oxLDL deposition in the subintima, foam cell formation and endothelial dysfunction. Additionally, LOX-1 acts a scavenger receptor for oxLDL in macrophages and can be responsible for oxLDL uptake, when stimulated. Serum LOX-1 (sLOX-1) has emerged as a new, potential biomarker for diagnosis of acute coronary syndromes, and it seems promising for use along with other common biomarkers in everyday clinical practice. In a therapeutic perspective, natural as well as synthetic molecules exert anti-LOX-1 properties and attain the receptor's pathophysiological effects, thus extensive research is ongoing to further evaluate molecules with therapeutic potential. However, most of these molecules need further trials in order to properly assess their safety and efficacy for clinical use. The aim of this review is to investigate LOX-1 role in atherogenesis and explore its potential as diagnostic tool and therapeutic target., Competing Interests: Declaration of Competing Interest All authors have no conflict of interest to declare., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Sleep disordered breathing (SDB), mostly constituting of obstructive and central sleep apnea (OSA and CSA, respectively), is highly prevalent in the general population, and even more among patients with cardiovascular disease, heart failure (HF) and valvular heart disease, such as mitral regurgitation (MR). The coexistence of HF, MR and SDB is associated with worse cardiovascular outcomes and increased morbidity and mortality. Pulmonary congestion, as a result of MR, can exaggerate and worsen the clinical status and symptoms of SDB, while OSA and CSA, through various mechanisms that impair left ventricular dynamics, can promote left ventricular remodelling, mitral annulus dilatation and consequently MR. Regarding treatment, positive airway pressure devices used to ameliorate symptoms in SDB also seem to result in a reduction of MR severity, MR jet fraction and an improvement of left ventricular ejection fraction. However, surgical and transcatheter interventions for MR, and especially transcatheter edge to edge mitral valve repair (TEER), seem to also have a positive effect on SDB, by reducing OSA and CSA-related severity indexes and improving symptom control. The purpose of this review is to provide a comprehensive analysis of the common pathophysiology between SDB and MR, as well as to discuss the available evidence regarding the effect of SDB treatment on MR and the effect of mitral valve surgery or transcatheter repair on both OSA and CSA., Competing Interests: Declaration of competing interest All authors have nothing to declare., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Aims: The beneficial cardiovascular effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors irrespective of the presence of diabetes mellitus are nowadays well established and they already constitute a significant pillar for the management of heart failure, irrespective of the ejection fraction. The exact underlying mechanisms accountable for these effects, however, remain largely unknown. The direct effect on endothelial function and microcirculation is one of the most well studied. The broad range of studies presented in this review aims to link all available data from the bench to bedside and highlight the existing gaps as well as the future directions in the investigations concerning the effects of SGLT2 inhibitors on the endothelium and the microcirculation., Methods and Results: An extensive search has been conducted using the MEDLINE/PubMed database in order to identify the relevant studies. Preclinical data suggest that SGLT2 inhibitors directly affect endothelial function independently of glucose and specifically via several interplaying molecular pathways, resulting in improved vasodilation, increased NO production, enhanced mitochondrial homeostasis, endothelial cell viability, and angiogenesis as well as attenuation of oxidative stress and inflammation. Clinical data systematically confirm this beneficial effect on the endothelium, whereas the evidence concerning the effect on the microcirculation is conflicting., Conclusion: Preclinical and clinical studies indicate that SGLT2 inhibitors attenuate endothelial and microvascular dysfunction via a combination of mechanisms, which play a role in their beneficial cardiovascular effect., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson's Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier's performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter., Competing Interests: The authors declare no conflicts of interest.

Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this context, this study proposes and describes a holistic methodology starting from the generation of zero-day-type, yet realistic, data in tabular format and concluding to the evaluation of a Neural Network zero-day attacks' detector which is trained with and without synthetic data. This methodology involves the design and employment of Generative Adversarial Networks (GANs) for synthetically generating a new and larger dataset of zero-day attacks data. The newly generated, by the Zero-Day GAN (ZDGAN), dataset is then used to train and evaluate a Neural Network classifier for zero-day attacks. The results show that the generation of zero-day attacks data in tabular format reaches an equilibrium after about 5000 iterations and produces data that are almost identical to the original data samples. Last but not least, it should be mentioned that the Neural Network model that was trained with the dataset containing the ZDGAN generated samples outperformed the same model when the later was trained with only the original dataset and achieved results of high validation accuracy and minimal validation loss.

The unceasingly increasing needs for data acquisition, storage and analysis in transportation systems have led to the adoption of new technologies and methods in order to provide efficient and reliable solutions. Both highways and vehicles, nowadays, host a vast variety of sensors collecting different types of highly fluctuating data such as speed, acceleration, direction, and so on. From the vast volume and variety of these data emerges the need for the employment of big data techniques and analytics in the context of state-of-the-art intelligent transportation systems (ITS). Moreover, the scalability needs of fleet and traffic management systems point to the direction of designing and deploying distributed architecture solutions that can be expanded in order to avoid technological and/or technical entrapments. Based on the needs and gaps detected in the literature as well as the available technologies for data gathering, storage and analysis for ITS, the aim of this study is to provide a distributed architecture platform to address these deficiencies. The architectural design of the system proposed, engages big data frameworks and tools (e.g., NoSQL Mongo DB, Apache Hadoop, etc.) as well as analytics tools (e.g., Apache Spark). The main contribution of this study is the introduction of a holistic platform that can be used for the needs of the ITS domain offering continuous collection, storage and data analysis capabilities. To achieve that, different modules of state-of-the-art methods and tools were utilized and combined in a unified platform that supports the entire cycle of data acquisition, storage and analysis in a single point. This leads to a complete solution for ITS applications which lifts the limitations imposed in legacy and current systems by the vast amounts of rapidly changing data, while offering a reliable system for acquisition, storage as well as timely analysis and reporting capabilities of these data.

Coronavirus disease 2019 (COVID-19) is a systemic illness with an increased host inflammatory response that affects multiple extra-pulmonary organs, including the gastrointestinal tract. Abnormalities in liver biochemistry have been observed in a significant proportion of patients with COVID-19 upon admission, and this proportion increases with hospitalization. These abnormalities are typically manifested as elevations in aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels, with less frequently detected elevations in the levels of cholestatic enzymes. Elevated aminotransaminase levels have been linked to an increased risk of mortality and complications, indicating the severity of COVID-19 infection. The present study evaluated the prevalence and the baseline factors associated with the development of acute hepatitis (ΑΗ), liver injury (LI) and associated patterns, as well as the presence of abnormalities in the levels of aminotransferases at discharge in the same cohort. For this purpose, 1,304 patients with confirmed COVID-19 infection were enrolled in the study. According to the results obtained, AST levels at baseline were the only independent factor for AH during hospital stay, while AST, alkaline phosphatase and ferritin levels were independent baseline factors for the development of LI. The patients with hepatocellular, compared to those with cholestatic LI, exhibited similar survival rates, as well as similarities in the development of acute kidney injury and the need for oxygen via high-flow nasal cannula and/or mechanical ventilation. In addition, age and ALT were independent risk factors for persistent abnormal values of AST and ALT at discharge., Competing Interests: DAS is the Editor-in-Chief for the journal, but had no personal involvement in the reviewing process, or any influence in terms of adjudicating on the final decision, for this article. The other authors declare that they have no competing interests., (Copyright: © Georgakopoulou et al.)

Background: We investigated the impact of the indoor mass gathering of young people during the Patras Carnival in Greece on the course of the COVID-19 pandemic and the influenza A epidemic. Materials and Methods: For influenza A, we tested 331 subjects with high fever (>38 °C), who arrived at five separate private laboratories over a two-week period after the carnival, via rapid test. One hundred and eighty-eight of them were young adults (17−35 years old), all unvaccinated against influenza A but all immunized against SARS-CoV-2, either through vaccination or previous infection. For the SARS-CoV-2 pandemic, we tested 2062 subjects at two time periods, two weeks before and two weeks after the carnival, also via rapid test. Additionally, we examined 42 samples positive for influenza A and 51 samples positive for SARS-CoV-2 for the possibility of co-infection via molecular testing (i.e., RT-PCR). Results: 177/331 (53.5%) subjects tested positive for influenza A, and 109/177 (61.6%) of the positive subjects were young adults, and 93/109 (85.3%) of these subjects were tested in the first week after the carnival. Additionally, 42 samples of those subjects were molecularly tested, and 5 were found negative for influenza A but positive for SARS-CoV-2. Regarding the SARS-CoV-2 pandemic, the increase in the positivity index for young adults between the pre-carnival and post-carnival periods was moderate. Conclusions: Our study indicates that the indoor mass gathering of young people during the carnival contributed to the outbreak of an influenza A epidemic and had a moderate but not statistically significant impact on the course of the SARS-CoV-2 pandemic, corroborating the crucial role of vaccination against the epidemic’s waves. It also showed the need for the use of high-quality rapid tests for their management.

The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper's goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

The rapid evolution of sensors and communication technologies has led to the production and transfer of mass data streams from vehicles either inside their electronic units or to the outside world using the internet infrastructure. The "outside world", in most cases, consists of third-party applications, such as fleet or traffic management control centers, which utilize vehicular data for reporting and monitoring functionalities. Such applications, in most cases, in order to facilitate their needs, require the exchange and processing of vast amounts of data which can be handled by the so-called Big Data technologies. The purpose of this study is to present a hybrid platform suitable for data collection, storing and analysis enhanced with quality control actions. In particular, the collected data contain various formats originating from different vehicle sensors and are stored in the aforementioned platform in a continuous way. The stored data in this platform must be checked in order to determine and validate them in terms of quality. To do so, certain actions, such as missing values checks, format checks, range checks, etc., must be carried out. The results of the quality control functions are presented herein, and useful conclusions are drawn in order to avoid possible data quality problems which may occur in further analysis and use of the data, e.g., for training of artificial intelligence models.