The impact of temperature changes on patients with acute coronary syndrome

Abstract Funding Acknowledgements Type of funding sources: None. Background There are few studies about changes in temperature as a risk factor instability of atheromatous plaque and Acute Coronary Syndrome (ACS). It is the first study in our country. Purpose To evaluate the impact of temperature on patients with Acute Coronary Syndrome. Methods This is a retrospective, observational, analytical study conducted in a single-center, in the period January-December 2018. We included patients with the diagnosis of ACS, hospitalized in CCU, who performed emergency coronary angiography. Data were collected retrospectively using patient records from archived files at the Statistics Center. Data on atmospheric parameters, measured at the weather monitoring station, were obtained from the National Meteorological Service database (average daily temperature in each district of the country). The number of inhabitants for the respective districts is taken from the National Institute of Statistics. Results The study included 1165 patients. A significant association was found between the number of ACS per day with temperature changes (r=-0.13, p&amp;lt;0.01). The highest number of ACS was in October 10.4%, whereas the lowest number was in January 10.6%, with a significant decreasing trend during May-June and the peak in October (p=0.04). Significant changes in the average monthly values of temperature were accompanied by a statistically significant increase in the number of cases as occurred in March-April and October-November. (p≤0.05) A statistically significant relationship was observed between seasonal changes in temperature with the number of cases with ACS. The autumn season prevails with 27.9% of the total cases, followed by the spring season with 25.6%, the summer season with 24.2%, and the winter season with 22.3%, (p = 0.04). Most cases in the cold period (November-March) occurred on days with statistically significant changes in Temperature. Conclusion The study notes an important relationship between seasonal, monthly, and daily changes of temperatures, in relation to the frequency of cases with Acute Coronary Syndrome. </jats:sec

Emotion Recognition Based on Facial Expressions Using Convolutional Neural Network (CNN)

Over the last few years, there has been an increasing number of studies about facial emotion recognition because of the importance and the impact that it has in the interaction of humans with computers. With the growing number of challenging datasets, the application of deep learning techniques have all become necessary. In this paper, we study the challenges of Emotion Recognition Datasets and we also try different parameters and architectures of the Conventional Neural Networks (CNNs) in order to detect the seven emotions in human faces, such as: anger, fear, disgust, contempt, happiness, sadness and surprise. We have chosen iCV MEFED (Multi-Emotion Facial Expression Dataset) as the main dataset for our study, which is relatively new, interesting and very challenging. © 2020 IEEE