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Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
Previous EEG research only investigated one stage ultimatum games (UGs). We investigated the influence of a second bargaining stage in an UG concerning behavioral responses, electro-cortical correlates and their moderations by the traits altruism, anger, anxiety, and greed in 92 participants. We found that an additional stage led to more rejection in the 2-stage UG (2SUG) and that increasing offers in the second stage compared to the first stage led to more acceptance. The FRN during a trial was linked to expectance evaluation concerning the fairness of the offers, while midfrontal theta was a marker for the needed cognitive control to overcome the respective default behavioral pattern. The FRN responses to unfair offers were more negative for either low or high altruism in the UG, while high trait anxiety led to more negative FRN responses in the first stage of 2SUG, indicating higher sensitivity to unfairness. Accordingly, the mean FRN response, representing the trait-like general electrocortical reactivity to unfairness, predicted rejection in the first stage of 2SUG. Additionally, we found that high trait anger led to more rejections for unfair offer in 2SUG in general, while trait altruism led to more rejection of unimproving unfair offers in the second stage of 2SUG. In contrast, trait anxiety led to more acceptance in the second stage of 2SUG, while trait greed even led to more acceptance if the offer was worse than in the stage before. These findings suggest, that 2SUG creates a trait activation situation compared to the UG.
To slow down the spread of the SARS-Cov-2 virus, countries worldwide severely restricted public and social life. In addition to the physical threat posed by the viral disease (COVID-19), the pandemic also has implications for psychological well-being. Using a small sample (N = 51), we examined how Big Five personality traits relate to coping with contact restrictions during three consecutive weeks in the first wave of the COVID-19 pandemic in Germany. We showed that extraversion was associated with suffering from severe contact restrictions and with benefiting from their relaxation. Individuals with high neuroticism did not show a change in their relatively poor coping with the restrictions over time, whereas conscientious individuals seemed to experience no discomfort and even positive feelings during the period of contact restrictions. Our results support the assumption that neuroticism is a vulnerability factor in relation to psychological wellbeing but also show an influence of contact restrictions on extraverted individuals.
We investigated the influence of social status on behavior in a modified dictator game (DG). Since the DG contains an inherent dominance gradient, we examined the relationship between dictator decisions and recipient status, which was operationalized by three social identities and an artificial intelligence (AI). Additionally, we examined the predictive value of social dominance orientation (SDO) on the behavior of dictators toward the different social and non-social hierarchical recipients. A multilevel model analysis showed that recipients with the same status as the dictator benefited the most and the artificial intelligence the least. Furthermore, SDO, regardless of social status, predicted behavior toward recipients in such a way that higher dominance was associated with lower dictator offers. In summary, participants treated other persons of higher and lower status equally, those of equal status better and, above all, an algorithm worst. The large proportion of female participants and the limited variance of SDO should be taken into account with regard to the results of individual differences in SDO.
Background: Since the replication crisis, standardization has become even more important in psychological science and neuroscience. As a result, many methods are being reconsidered, and researchers’ degrees of freedom in these methods are being discussed as a potential source of inconsistencies across studies.
New Method: With the aim of addressing these subjectivity issues, we have been working on a tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig.
Results: Two scripts are presented and explained step-by-step to perform basic, informed ERP and frequency-domain analyses, including data export to statistical programs and visual representations of the data. The open-source software EEGlab in MATLAB is used as the data handling platform, but scripts based on code provided by Mike Cohen (2014) are also included.
Comparison with existing methods: This accompanying tutorial-like article explains and shows how the processing of our automated pipeline affects the data and addresses, especially beginners in EEG-analysis, as other (pre)-processing chains are mostly targeting rather informed users in specialized areas or only parts of a complete procedure. In this context, we compared our pipeline with a selection of existing approaches.
Conclusion: The need for standardization and replication is evident, yet it is equally important to control the plausibility of the suggested solution by data exploration. Here, we provide the community with a tool to enhance the understanding and capability of EEG-analysis. We aim to contribute to comprehensive and reliable analyses for neuro-scientific research.
Introduction: The German PID-NET registry was founded in 2009, serving as the first national registry of patients with primary immunodeficiencies (PID) in Germany. It is part of the European Society for Immunodeficiencies (ESID) registry. The primary purpose of the registry is to gather data on the epidemiology, diagnostic delay, diagnosis, and treatment of PIDs.
Methods: Clinical and laboratory data was collected from 2,453 patients from 36 German PID centres in an online registry. Data was analysed with the software Stata® and Excel.
Results: The minimum prevalence of PID in Germany is 2.72 per 100,000 inhabitants. Among patients aged 1-25, there was a clear predominance of males. The median age of living patients ranged between 7 and 40 years, depending on the respective PID. Predominantly antibody disorders were the most prevalent group with 57% of all 2,453 PID patients (including 728 CVID patients). A gene defect was identified in 36% of patients. Familial cases were observed in 21% of patients. The age of onset for presenting symptoms ranged from birth to late adulthood (range 0-88 years). Presenting symptoms comprised infections (74%) and immune dysregulation (22%). Ninety-three patients were diagnosed without prior clinical symptoms. Regarding the general and clinical diagnostic delay, no PID had undergone a slight decrease within the last decade. However, both, SCID and hyper IgE-syndrome showed a substantial improvement in shortening the time between onset of symptoms and genetic diagnosis. Regarding treatment, 49% of all patients received immunoglobulin G (IgG) substitution (70%-subcutaneous; 29%-intravenous; 1%-unknown). Three-hundred patients underwent at least one hematopoietic stem cell transplantation (HSCT). Five patients had gene therapy.
Conclusion: The German PID-NET registry is a precious tool for physicians, researchers, the pharmaceutical industry, politicians, and ultimately the patients, for whom the outcomes will eventually lead to a more timely diagnosis and better treatment.
Background
Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.
Methods
A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported.
Results
1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy.
Conclusions
Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models.
Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Background:
We determined antibodies to the pandemic influenza A (H1N1) 2009 virus in children to assess: the incidence of (H1N1) 2009 infections in the 2009/2010 season in Germany, the proportion of subclinical infections and to compare titers in vaccinated and infected children.
Methodology/Principal Findings:
Eight pediatric hospitals distributed over Germany prospectively provided sera from in-or outpatients aged 1 to 17 years from April 1(st) to July 31(st) 2010. Vaccination history, recall of infections and sociodemographic factors were ascertained. Antibody titers were measured with a sensitive and specific in-house hemagglutination inhibition test (HIT) and compared to age-matched sera collected during 6 months before the onset of the pandemic in Germany. We analyzed 1420 post-pandemic and 300 pre-pandemic sera. Among unvaccinated children aged 1-4 and 5-17 years the prevalence of HI titers (>= 1:10) was 27.1% (95% CI: 23.5-31.3) and 53.5% (95% CI: 50.9-56.2) compared to 1.7% and 5.5%, respectively, for pre-pandemic sera, accounting for a serologically determined incidence of influenza A (H1N1) 2009 during the season 2009/2010 of 25,4% (95% CI : 19.3-30.5) in children aged 1-4 years and 48.0% (95% CI: 42.6-52.0) in 5-17 year old children. Of children with HI titers >= 1: 10, 25.5% (95% CI: 22.5-28.8) reported no history of any infectious disease since June 2009. Among vaccinated children, 92% (95%-CI: 87.0-96.6) of the 5-17 year old but only 47.8% (95%-CI: 33.5-66.5) of the 1-4 year old children exhibited HI titers against influenza A virus (H1N1) 2009.
Conclusion:
Serologically determined incidence of influenza A (H1N1) 2009 infections in children indicates high infection rates with older children (5-17 years) infected twice as often as younger children. In about a quarter of the children with HI titers after the season 2009/2010 subclinical infections must be assumed. Low HI titers in young children after vaccination with the AS03(B)-adjuvanted split virion vaccine need further scrutiny.
Non-Alcoholic Fatty Liver Disease Epidemiology, Clinical Course, Investigation, and Treatment
(2014)
Background: The global obesity epidemic has increased the prevalence of fatty liver disease. At present, 14% to 27% of the general population in the industrialized world has non-alcoholic fatty liver disease (NAFLD).
Methods: We review pertinent publications retrieved by a selective search of the PubMed database for the years 1995 to 2013.
Results: The term “non-alcoholic fatty liver disease” covers cases of a wide spectrum of severity, ranging from bland fatty liver without any inflammation and with little or no tendency to progress all the way to non-alcoholic steatohepatitis (NASH) with inflammatory reactions and hepatocyte damage, with or without fibrosis. Some 5% to 20% of patients with NAFLD develop NASH, which undergoes a further transition to higher-grade fibrosis in 10% to 20% of cases. In fewer than 5% of cases, fibrosis progresses to cirrhosis. These approximate figures lead to an estimate of 0.05% to 0.3% for the prevalence of cirrhosis in the general population. About 2% of all cirrhosis patients per year develop hepatocellular carcinoma. The diagnosis of fatty liver disease can be suspected initially on the basis of abnormally high aspartate aminotransferase (ASAT) and/or alanine aminotransferase (ALAT) levels and abnormal ultrasonographic findings. The positive predictive value of an ultrasonographic study for mild steatosis is 67% at most. The NAFLD fibrosis score, which is computed on the basis of multiple parameters (age, body-mass index, diabetes status, ASAT, ALAT, platelet count, and albumin level), has a positive predictive value of 82% to 90% and a negative predictive value of 88% to 93%. Liver biopsy is the gold standard for diagnosis but should be performed sparingly in view of its rare but sometimes life-threatening complications, such as hemorrhage. The treatment of NAFLD and NASH consists mainly of changes in lifestyle and nutrition.
Conclusion: NAFLD can, in principle, be reversed. This is only possible with weight reduction by at least 3% to 5%.
In human interactions, the facial expression of a bargaining partner may contain relevant information that affects prosocial decisions. We were interested in whether facial expressions of the recipient in the dictator game influence dictators´ ehavior. To test this, we conducted an online study (n = 106) based on a modified version of a dictator game. The dictators allocated money between themselves and another person (recipient), who had no possibility to respond to the dictator.
Importantly, before the allocation decision, the dictator was presented with the facial expression of the recipient (angry, disgusted, sad, smiling, or neutral). The results showed that dictators sent more money to recipients with sad or smiling facial expressions and less to recipients with angry or disgusted facial expressions compared with a neutral facial expression. Moreover, based on the sequential analysis of the decision and the interaction partner in the preceding trial, we found that decision-making depends upon previous interactions.