Background In the case of people who carry an increased number of anxiety traits and maladaptive coping strategies, psychosocial stressors may further increase the level of perceived stress they experience. In our research study, we aimed to examine the levels of perceived stress and health anxiety as well as coping styles among university students amid the COVID-19 pandemic. Methods A cross-sectional study was conducted using an online-based survey at the University of Debrecen during the official lockdown in Hungary when dormitories were closed, and teaching was conducted remotely. Our questionnaire solicited data using three assessment tools, namely, the Perceived Stress Scale (PSS), the Ways of Coping Questionnaire (WCQ), and the Short Health Anxiety Inventory (SHAI). Results A total of 1320 students have participated in our study and 31 non-eligible responses were excluded. Among the remaining 1289 participants, 948 (73.5%) and 341 (26.5%) were Hungarian and international students, respectively. Female students predominated the overall sample with 920 participants (71.4%). In general, there was a statistically significant positive relationship between perceived stress and health anxiety. Health anxiety and perceived stress levels were significantly higher among international students compared to domestic ones. Regarding coping, wishful thinking was associated with higher levels of stress and anxiety among international students, while being a goal-oriented person acted the opposite way. Among the domestic students, cognitive restructuring as a coping strategy was associated with lower levels of stress and anxiety. Concerning health anxiety, female students (domestic and international) had significantly higher levels of health anxiety compared to males. Moreover, female students had significantly higher levels of perceived stress compared to males in the international group, however, there was no significant difference in perceived stress between males and females in the domestic group. Conclusion The elevated perceived stress levels during major life events can be further deepened by disengagement from home (being away/abroad from country or family) and by using inadequate coping strategies. By following and adhering to the international recommendations, adopting proper coping methods, and equipping oneself with the required coping and stress management skills, the associated high levels of perceived stress and anxiety could be mitigated.
25Objectives 26 The current form of severe acute respiratory syndrome called coronavirus disease 2019 27 (COVID-19) caused by a coronavirus (SARS-CoV-2) is a major global health problem. The 28 aim of our study was to use the official epidemiological data and predict the possible outcomes 29 of the COVID-19 pandemic using artificial intelligence (AI)-based RNNs (Recurrent Neural 30 Networks), then compare and validate the predicted and observed data. 31 Materials and Methods 32We used the publicly available datasets of World Health Organization and Johns Hopkins 33 University to create the training dataset, then have used recurrent neural networks (RNNs) with 34 gated recurring units (Long Short-Term Memory -LSTM units) to create 2 Prediction Models. 35Information collected in the first t time-steps were aggregated with a fully connected (dense) 36 neural network layer and a consequent regression output layer to determine the next predicted 37 value. We used root mean squared logarithmic errors (RMSLE) to compare the predicted and 38 observed data, then recalculated the predictions again. 39 Results 40The result of our study underscores that the COVID-19 pandemic is probably a propagated 41 source epidemic, therefore repeated peaks on the epidemic curve (rise of the daily number of 42 the newly diagnosed infections) are to be anticipated. The errors between the predicted and 43 validated data and trends seems to be low. 44 Conclusions 45 3The influence of this pandemic is great worldwide, impact our everyday lifes. Especially 46 decision makers must be aware, that even if strict public health measures are executed and 47 sustained, future peaks of infections are possible. The AI-based predictions might be useful 48 tools for predictions and the models can be recalculated according to the new observed data, 49 to get more precise forecast of the pandemic. 50 51 52 53 54 55 56 57 58 59 60 61 4 62
Objectives The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. Methods We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data. Results We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low. Conclusion Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.
Background Mild cognitive impairment (MCI) often presages the development of Alzheimer’s disease (AD). Accurate and early identification of cognitive impairment will substantially reduce the burden on the family and alleviate the costs for the whole society. There is a need for testing methods that are easy to perform even in a general practitioner’s office, inexpensive and non-invasive, which could help the early recognition of mental decline. We have selected the Test Your Memory (TYM), which has proven to be reliable for detecting AD and MCI in several countries. Our study was designed to test the usability of the Hungarian version of the TYM (TYM-HUN) comparing with the Mini-Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) in MCI recognition in the Hungarian population. Methods TYM test was translated and validated into Hungarian (TYM-HUN) in a previous study. The TYM-HUN test was used in conjunction with and compared with the MMSE and the ADAS-Cog. For our study, 50 subjects were selected: 25 MCI patients and 25 healthy controls (HC). Spearman’s rank correlation was used to analyse the correlation between the scores of MMSE and ADAS-Cog with TYM-HUN and the receiver operating characteristic (ROC) curve was established. Results MCI can be distinguished from normal aging using TYM-HUN. We established a ‘cut-off’ point of TYM-HUN (44/45points) where optimal sensitivity (80%) and specificity (96%) values were obtained to screen MCI. The total TYM-HUN scores significantly correlated with the MMSE scores (ρ = 0.626; p < 0.001) and ADAS-Cog scores (ρ = − 0.723; p < 0.001). Conclusions Our results showed that the TYM-HUN is a reliable, fast, self-administered questionnaire with the right low threshold regarding MCI and can be used for the early diagnosis of cognitive impairment.
Background: Our study aimed to assess the differences between domestic and international students in terms of social support, vital exhaustion, and depression during the period of COVID-19 and to examine the relationships and potential effects of these factors on each other. Methods: The online cross-sectional survey was conducted via Google Forms® at three time intervals during the pandemic. Results: Here, 1320, 246, and 139 students completed our questionnaires in the different time intervals. The international students reported significantly lower values in terms of perceived social support. Concerning depression, the international female students reported higher values than the domestic female students. Significant correlations were found in both samples between vital exhaustion and depression, as well as between perceived social support and depression. Conclusion: In this study, the international students reported lower levels of perceived social support and higher levels of depression, particularly among females. The correlations between depression, social support, and vital exhaustion might highlight protective and risk factors. These findings emphasize the importance of addressing social support and mental health among university students, especially among international students who have a difficult time finding social support during times of stress, such as during the COVID-19 pandemic.
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