Objective:The aim of this study was to show the reliability and validity of a Turkish version of Diabetes Eating Problem Survey-Revised (DEPS-R) in children and adolescents with type 1 diabetes mellitus.Methods:A total of 200 children and adolescents with type 1 diabetes, ages 9-18 years, completed the DEPS-R Turkish version. In addition to tests of validity, confirmatory factor analysis was conducted to investigate the factor structure of the 16-item Turkish version of DEPS-R.Results:The Turkish version of DEPS-R demonstrated satisfactory Cronbach’s ∝ (0.847) and was significantly correlated with age (r=0.194; p<0.01), hemoglobin A1c levels (r=0.303; p<0.01), and body mass index-standard deviation score (r=0.412; p<0.01) indicating criterion validity. Median DEPS-R scores of Turkish version for the total samples, females, and males were 11.0, 11.5, and 10.5, respectively.Conclusion:Disturbed eating behaviors and insulin restriction were associated with poor metabolic control. A short, self-administered diabetes-specific screening tool for disordered eating behavior can be used routinely in the clinical care of adolescents with type 1 diabetes. The Turkish version of DEPS-R is a valid screening tool for disordered eating behaviors in type 1 diabetes and it is potentially important to early detect disordered eating behaviors.

Missing observations are always a challenging problem that we have to deal with in diseases that require follow-up. In hospital records for vesicoureteral reflux (VUR) and recurrent urinary tract infection (rUTI), the number of complete cases is very low on demographic and clinical characteristics, laboratory findings, and imaging data. On the other hand, deep learning (DL) approaches can be used for highly missing observation scenarios with its own missing ratio algorithm. In this study, the effects of multiple imputation techniques MICE and FAMD on the performance of DL in the differential diagnosis were compared. The data of a retrospective cross-sectional study including 611 pediatric patients were evaluated (425 with VUR, 186 with rUTI, 26.65% missing ratio) in this research. CNTK and R 3.6.3 have been used for evaluating different models for 34 features (physical, laboratory, and imaging findings). In the differential diagnosis of VUR and rUTI, the best performance was obtained by deep learning with MICE algorithm with its values, respectively, 64.05% accuracy, 64.59% sensitivity, and 62.62% specificity. FAMD algorithm performed with accuracy=61.52, sensitivity=60.20, and specificity was found out to be 61.00 with 3 principal components on missing imputation phase. DL-based approaches can evaluate datasets without doing preomit/impute missing values from datasets. Once DL method is used together with appropriate missing imputation techniques, it shows higher predictive performance.

In this study, we investigated whether the CHA2DS2-VASc score could be used to estimate the need for hospitalization in the intensive care unit (ICU), the length of stay in the ICU, and mortality in patients with COVID-19. Patients admitted to Merkezefendi State Hospital because of COVID-19 diagnosis confirmed by RNA detection of virus by using polymerase chain reaction between March 24, 2020 and July 6, 2020, were screened retrospectively. The CHA2DS2-VASc and modified CHA2DS2-VASc score of all patients was calculated. Also, we received all patients' complete biochemical markers including D-dimer, Troponin I, and c-reactive protein on admission. We enrolled 1000 patients; 791 were admitted to the general medical service and 209 to the ICU; 82 of these 209 patients died. The ROC curves of the CHA2DS2-VASc and M-CHA2DS2-VASc scores were analyzed. The cutoff values of these scores for predicting mortality were ≥ 3 (2 or under and 3). The CHA2DS2-VASc and M-CHA2DS2-VASc scores had an area under the curve value of 0.89 on the ROC. The sensitivity and specificity of the CHA2DS2-VASc scores were 81.7% and 83.8%, respectively; the sensitivity and specificity of the M-CHA2DS2-VASc scores were 85.3% and 84.1%, respectively. Multivariate logistic regression analysis showed that CHA2DS2-VASc, Troponin I, D-Dimer, and CRP were independent predictors of mortality in COVID-19 patients. Using a simple and easily available scoring system, CHA2DS2-VASc and M-CHA2DS2-VASc scores can be assessed in patients diagnosed with COVID-19. These scores can predict mortality and the need for ICU hospitalization in these patients.

The recent pandemic of COVID-19 has not only shaken the healthcare but also economic structure around the world. In addition to these direct effects, it has also brought in some indirect difficulties owing to the information epidemic (hereafter termed as infodemic) on social media. We aimed to understand the nature of panic social media users in India are experiencing due to the flow of (mis)information. We further extend this investigation to other countries. We performed a cross-sectional study on 1075 social media users from India and 29 other countries. This revealed a significant increase in social media usage and the rise of panic (symbolizing a sense of alarm and/or fear) over time in India. Several of these behaviors are unique to social media users in India possibly because of later outbreak of COVID-19 and a prolonged uninterrupted lockdown. The amount of social media usage might not be causal but has a significant role in generating panic among the people in India. As multiple countries are entering into the second phase of lockdown, this study focused on India might provide a unique perspective of how various factors, including infodemic, affect the mental state of individuals around the globe. Supplementary Information The online version supplementary material available at 10.1007/s13278-021-00750-2.

AimsThe recent pandemic of COVID-19 has not only shaken the healthcare but also economic structure around the world. In addition to these direct effects, it has also brought in some indirect difficulties owing to the information epidemic on social media. As India experienced a later outbreak of COVID-19 and a prolonged uninterrupted lockdown, we aimed to understand the nature of panic social media users in India are experiencing due to the flow of (mis)information. We further extend this investigation to other countries.MethodsWe performed a cross-sectional study by conducting survey on multiple social media platforms. We received 1075 responses (sex ratio 2:1) through opportunity sampling from social media users of 30 different countries (between April 11, 2020 and May 15, 2020). We performed both quantitative and qualitative analyses on the 935 respondents from India. Several hypotheses are statistically tested on them and are further examined on rest of the 140 social media users from 29 other countries. We also performed a separate Twitter hashtag analysis and sentiment analysis on the responses. We applied a citizen science approach to involve the respondents in the analysis pipeline after the survey.ResultsThis cross-sectional study on 1075 social media users from India and 29 other countries revealed a significant increase of social media usage and rise of panic over time in India. Middle-aged people and female exhibit a higher panic in India. The amount of panic was independent of the nature of association with COVID-19. The change of mental health was associated with panic level and productivity. Further qualitative analysis highlights the occurrences of information panic, economic panic, moral panic and spiritual panic, among other causes.ConclusionsSeveral panic behaviors are unique to social media users in India possibly because COVID-19 broke out relatively later in comparison with the other countries and the uninterrupted lockdown prolonged for a long time. The amount of social media usage might not be causal but has a significant role in generating panic among the people in India. A significantly higher level of panic among the middle-aged people can be attributed to their higher amount of responsibility. The popularity of different hashtags, including the names of drugs under trial for COVID-19, in limited countries highlight that the causes of panic are not the same everywhere. As some of the respondents took part as citizen scientists a robust perspective to the outcome is obtained.