COVID-19: booster(s) vs. hospitalization and Intensive Care Unit admission.

OBJECTIVE: As the pandemic continues, different vaccine protocols have been implemented to maintain the protection of vaccines and to provide protection against new variants. The aim of this study was to assess hospitalized patients' vaccination status and document the efficacy of boosters. PATIENTS AND METHODS: The patients that were hospitalized due to COVID-19 were enrolled from 28 hospitals in Turkey for five months from September 2021. 5,331 confirmed COVID-19 patients from collaborating centers were randomly enrolled to understand/estimate the distribution of vaccination status in hospitalized patients and to compare the efficacy of vaccination/booster protocols. RESULTS: 2,779 men and 2,552 women of which 2,408 (45.2%) were admitted to Intensive Care Units participated in this study. It was found that the highest risk reduction for all age groups was found in groups that received 4 doses. Four doses of vaccination for every 3.7 people under 50 years of age, for every 5.7 people in the 50-64 age group, and for every 4.3 people over 65 years of age will prevent 1 patient from being admitted to intensive care. Regardless of the type of vaccine, it was found that the risk of ICU hospitalization decreased in those who were vaccinated compared to those who were not vaccinated. Regardless of the type of vaccine, the ICU risk was found to decrease 1.25-fold in those who received 1 or 2 doses of vaccine, 1.18-fold in those who received 3 doses, and 3.26-fold in those who received 4 doses. CONCLUSIONS: The results suggested that the addition of a fourth dose is more effective in preventing intensive unit care even in disadvantaged groups.

COVID-19 detection by X-Ray images and Deep Learning

The new Coronavirus, also called COVID-19, appeared in Wuhan, China in December 2019 Spread out big. The virus has now made the COVID-19 disease a worldwide epidemic. This virus has affected the global health, economy, and the daily lives of individuals. The timely diagnosis of COVID-19 is a crucial task as it reduces the risk of pandemic spread. Chest x-rays play an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. Advanced Artificial Intelligence techniques such as Deep Learning have shown high efficiency in detecting patterns such as those that can be found in diseased tissue, so convolution neural networks were evaluated for their ability to detect infected patients from chest X-ray images. The key component of deep learning research is the availability of training data sets. In this study, a parallel Convolutional Neural Networks (CNNs) model has been proposed for the detection of covid-19 infected patients using chest X-ray radiographs. This paper also presents how to evaluate the effectiveness of the state-of-the-art CNN proposed by the scientific community about their expertise in the automatic diagnosis of Covid-19 from thoracic x-rays. To validate results, we trained the CNN model network by using 500 Covid-19 Positive images and 1600 Covid-19 Negative images. It was given a classification accuracy of 90% and validation accuracy of 88%. © 2021 ACM.

Covid-19 in Turkey: Leisure boredom, psychological resilience, physical activity and emotional state

This research was conducted in the quarantine process implemented in the CoViD-19 outbreak to examine the relationship between the perception of the boredom of individuals in leisure times and psychological resilience levels, and to describe the leisure activities of individuals, participation in physical activity and emotional state. Accordingly, a total of 2214 voluntary individuals living in Turkey participated with 909 men (41.1%) ((X) over bar age=33.83 +/- 10.73), and 1305 women (58.9%) ((X) over bar age=32,41 +/- 10.02). Within the scope of the study, information about demographic variables, physical activity and emotional state were collected with the form created by the researchers. In the study, "Leisure Boredom Scale" and "Psychological Resilience Scale" were used as the measurement tools. In the study, individuals were asked to write a word expressing their thoughts on the CoViD-19 process for descriptive analyses, and the collected data were visualized with the "MAXQDA" qualitative data analysis program. In addition, the data are presented in charts in the analysis of other variables. In the statistical analysis of the study, descriptive statistics were used, t-test was used to determine the difference between independent groups, Pearson correlation analysis was used to determine the relationships between variables, and simple linear regression analysis was used to determine the strength of the independent variable in predicting the dependent variable. As a result, predominantly negative emotional states were observed in individuals during the quarantine period. However, as the participation in physical activity increases, the level of psychological resilience will increase and the perception of boredom in leisure time will decrease. Besides, it was found that the perception of boredom in leisure time was an important determinant of the level of psychological resilience, and that it explains about 15% of the variance.