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Acute myocardial infarction (AMI) usually represents the clinical manifestation of atherothrombotic coronary artery disease (CAD) resulting from atherosclerotic plaque rupture. However, there are cases in which coronary angiography or coronary computed tomography angiography reveals patients with acute coronary syndrome with non-obstructive CAD. This clinical entity is defined as myocardial infarction with non-obstructive coronary arteries (MINOCA) and often considered as a clinical dynamic working diagnosis that needs further investigations for the establishment of a final etiologic diagnosis. The main causes of a MINOCA working diagnosis include atherosclerotic, non-atherosclerotic (vessel-related and non-vessel-related), and thromboembolic causes This literature review aimed to investigate the major thromboembolic causes in patients presenting with MINOCA regarding their etiology and pathophysiologic mechanisms, as well as diagnostic and treatment methods., (Copyright © 2024 Hellenic Society of Cardiology. Published by Elsevier Inc. All rights reserved.)

When forecasting financial time series, incorporating relevant sentiment analysis data into the feature space is a common assumption to increase the capacities of the model. In addition, deep learning architectures and state-of-the-art schemes are increasingly used due to their efficiency. This work compares state-of-the-art methods in financial time series forecasting incorporating sentiment analysis. Through an extensive experimental process, 67 different feature setups consisting of stock closing prices and sentiment scores were tested on a variety of different datasets and metrics. In total, 30 state-of-the-art algorithmic schemes were used over two case studies: one comparing methods and one comparing input feature setups. The aggregated results indicate, on the one hand, the prevalence of a proposed method and, on the other, a conditional improvement in model efficiency after the incorporation of sentiment setups in certain forecast time frames.

In practice, time series forecasting involves the creation of models that generalize data from past values and produce future predictions. Moreover, regarding financial time series forecasting, it can be assumed that the procedure involves phenomena partly shaped by the social environment. Thus, the present work is concerned with the study of the use of sentiment analysis methods in data extracted from social networks and their utilization in multivariate prediction architectures that involve financial data. Through an extensive experimental process, 22 different input setups using such extracted information were tested, over a total of 16 different datasets, under the schemes of 27 different algorithms. The comparisons were structured under two case studies. The first concerns possible improvements in the performance of the forecasts in light of the use of sentiment analysis systems in time series forecasting. The second, having as a framework all the possible versions of the above configuration, concerns the selection of the methods that perform best. The results, as presented by various illustrations, indicate, on the one hand, the conditional improvement of predictability after the use of specific sentiment setups in long-term forecasts and, on the other, a universal predominance of long short-term memory architectures.

The first year of the coronavirus disease 2019 (COVID-19) pandemic has been a year of unprecedented changes, scientific breakthroughs, and controversies. The radiology community has not been spared from the challenges imposed on global healthcare systems. Radiology has played a crucial part in tackling this pandemic, either by demonstrating the manifestations of the virus and guiding patient management, or by safely handling the patients and mitigating transmission within the hospital. Major modifications involving all aspects of daily radiology practice have occurred as a result of the pandemic, including workflow alterations, volume reductions, and strict infection control strategies. Despite the ongoing challenges, considerable knowledge has been gained that will guide future innovations. The aim of this review is to provide the latest evidence on the role of imaging in the diagnosis of the multifaceted manifestations of COVID-19, and to discuss the implications of the pandemic on radiology departments globally, including infection control strategies and delays in cancer screening. Lastly, the promising contribution of artificial intelligence in the COVID-19 pandemic is explored., Competing Interests: Conflict-of-interest statement: The authors have no conflicts of interest to declare., (©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.)