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Loneliness and lack of social well-being are associated with adverse health outcomes and have increased during the COVID-19 pandemic. Smartphone communication data have been suggested to help monitor loneliness, but this requires further evidence. We investigated the informative value of smartphone communication app data for predicting subjective loneliness and social well-being in a sample of 364 participants ranging from 18 to 78 years of age (52.2% female; mean age = 42.54, SD = 13.22) derived from the CORONA HEALTH APP study from July to December 2020 in Germany. The participants experienced relatively high levels of loneliness and low social well-being during the time period characterized by the COVID-19 pandemic. Apart from positive associations with phone call use times, smartphone communication app use was associated with social well-being and loneliness only when considering the age of participants. Younger participants with higher use times tended to report less social well-being and higher loneliness, while the opposite association was found for older adults. Thus, the informative value of smartphone communication use time was rather small and became evident only in consideration of age. The results highlight the need for further investigations and the need to address several limitations in order to draw conclusions at the population level.
Objective: In light of the ongoing COVID-19 pandemic and the associated hospitalization of an overwhelming number of ventilator-dependent patients, medical and/or ethical patient triage paradigms have become essential. While guidelines on the allocation of scarce resources do exist, such work within the subdisciplines of intensive care (e.g., neurocritical care) remains limited.
Methods: A 16-item questionnaire was developed that sought to explore/quantify the expert opinions of German neurointensivists with regard to triage decisions. The anonymous survey was conducted via a web-based platform and in total, 96 members of the Initiative of German Neurointensive Trial Engagement (IGNITE)-study group were contacted via e-mail. The IGNITE consortium consists of an interdisciplinary panel of specialists with expertise in neuro-critical care (i.e., anesthetists, neurologists and neurosurgeons).
Results: Fifty members of the IGNITE consortium responded to the questionnaire; in total the respondents were in charge of more than 500 Neuro ICU beds throughout Germany. Common determinants reported which affected triage decisions included known patient wishes (98%), the state of health before admission (96%), SOFA-score (85%) and patient age (69%). Interestingly, other principles of allocation, such as a treatment of “youngest first” (61%) and members of the healthcare sector (50%) were also noted. While these were the most accepted parameters affecting the triage of patients, a “first-come, first-served” principle appeared to be more accepted than a lottery for the allocation of ICU beds which contradicts much of what has been reported within the literature. The respondents also felt that at least one neurointensivist should serve on any interdisciplinary triage team.
Conclusions: The data gathered in the context of this survey reveal the estimation/perception of triage algorithms among neurointensive care specialists facing COVID-19. Further, it is apparent that German neurointensivists strongly feel that they should be involved in any triage decisions at an institutional level given the unique resources needed to treat patients within the Neuro ICU.
Viruses play a key role in explaining the pathogenesis of various autoimmune disorders, whose underlying principle is defined by the activation of autoreactive T-cells. In many cases, T-cells escape self-tolerance due to the failure in encountering certain MHC-I self-peptide complexes at substantial levels, whose peptides remain invisible from the immune system. Over the years, contribution of unstable defective ribosomal products (DRiPs) in immunosurveillance has gained prominence. A class of unstable products emerge from non-canonical translation and processing of unannotated mammalian and viral ORFs and their peptides are cryptic in nature. Indeed, high throughput sequencing and proteomics have revealed that a substantial portion of our genomes comprise of non-canonical ORFs, whose generation is significantly modulated during disease. Many of these ORFs comprise short ORFs (sORFs) and upstream ORFs (uORFs) that resemble DRiPs and may hence be preferentially presented. Here, we discuss how such products, normally “hidden” from the immune system, become abundant in viral infections activating autoimmune T-cells, by discussing their emerging role in infection and disease. Finally, we provide a perspective on how these mechanisms can explain several autoimmune disorders in the wake of the COVID-19 pandemic.
Background: The majority of breast cancer patients are severely psychologically affected by breast cancer diagnosis and subsequent therapeutic procedures. The COVID-19 pandemic and associated restrictions on public life have additionally caused significant psychological distress for much of the population. It is therefore plausible that breast cancer patients might be particularly susceptible to the additional psychological stress caused by the pandemic, increasing suffering. In this study we therefore aimed to assess the level of psychological distress currently experienced by a defined group of breast cancer patients in our breast cancer centre, compared to distress levels preCOVID-19 pandemic.
Methods: Female breast cancer patients of all ages receiving either adjuvant, neoadjuvant, or palliative therapies were recruited for the study. All patients were screened for current or previous COVID-19 infection. The participants completed a self-designed COVID-19 pandemic questionnaire, the Stress and Coping Inventory (SCI), the National Comprehensive Cancer Network (R) (NCCN (R)) Distress Thermometer (DT), the European Organization for Research and Treatment of Cancer (EORTC) QLQ C30, and the BR23.
Results: Eighty-two breast cancer patients were included. Therapy status and social demographic factors did not have a significant effect on the distress caused by the COVID-19 pandemic. The results of the DT pre and during COVID-19 pandemic did not differ significantly. Using the self-designed COVID-19 pandemic questionnaire, we detected three distinct subgroups demonstrating different levels of concerns in relation to SARS-CoV-2. The subgroup with the highest levels of concern reported significantly decreased life quality, related parameters and symptoms.
Conclusions: This monocentric study demonstrated that the COVID-19 pandemic significantly affected psychological health in a subpopulation of breast cancer patients. The application of a self-created "COVID-19 pandemic questionnaire"could potentially be used to help identify breast cancer patients who are susceptible to increased psychological distress due to the COVID-19 pandemic, and therefore may need additional intensive psychological support.
Background
The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2), has escalated rapidly to a global pandemic stretching healthcare systems worldwide to their limits. Surgeonshave had to immediately react to this unprecedented clinical challenge by systematically repurposing surgical wards.
Purpose
To provide a detailed set of guidelines developed in a surgical ward at University Hospital Wuerzburg to safelyaccommodate the exponentially rising cases of SARS-CoV-2 infected patients without compromising the care of emergencysurgery and oncological patients or jeopardizing the well-being of hospital staff.
Conclusions
The dynamic prioritization of SARS-CoV-2 infected and surgical patient groups is key to preserving life whilemaintaining high surgical standards. Strictly segregating patient groups in emergency rooms, non-intensive care wards andoperating areas prevents viral spread while adequately training and carefully selecting hospital staff allow them to confidentlyand successfully undertake their respective clinical duties.
Strategies in Times of Pandemic Crisis — Retailers and Regional Resilience in Würzburg, Germany
(2021)
Research on the COVID-19 crisis and its implications on regional resilience is still in its infancy. To understand resilience on its aggregate level it is important to identify (non)resilient actions of individual actors who comprise regions. As the retail sector among others represents an important factor in an urban regions recovery, we focus on the resilience of (textile) retailers within the city of Würzburg in Germany to the COVID-19 pandemic. To address the identified research gap, this paper applies the concept of resilience. Firstly, conducting expert interviews, the individual (textile) retailers’ level and their strategies in coping with the crisis is considered. Secondly, conducting a contextual analysis of the German city of Würzburg, we wish to contribute to the discussion of how the resilience of a region is influenced inter alia by actors. Our study finds three main strategies on the individual level, with retailers: (1) intending to “bounce back” to a pre-crisis state, (2) reorganising existing practices, as well as (3) closing stores and winding up business. As at the time of research, no conclusions regarding long-term impacts and resilience are possible, the results are limited. Nevertheless, detailed analysis of retailers’ strategies contributes to a better understanding of regional resilience.
(1) Background: The aim of our study was to identify specific risk factors for fatal outcome in critically ill COVID-19 patients. (2) Methods: Our data set consisted of 840 patients enclosed in the LEOSS registry. Using lasso regression for variable selection, a multifactorial logistic regression model was fitted to the response variable survival. Specific risk factors and their odds ratios were derived. A nomogram was developed as a graphical representation of the model. (3) Results: 14 variables were identified as independent factors contributing to the risk of death for critically ill COVID-19 patients: age (OR 1.08, CI 1.06–1.10), cardiovascular disease (OR 1.64, CI 1.06–2.55), pulmonary disease (OR 1.87, CI 1.16–3.03), baseline Statin treatment (0.54, CI 0.33–0.87), oxygen saturation (unit = 1%, OR 0.94, CI 0.92–0.96), leukocytes (unit 1000/μL, OR 1.04, CI 1.01–1.07), lymphocytes (unit 100/μL, OR 0.96, CI 0.94–0.99), platelets (unit 100,000/μL, OR 0.70, CI 0.62–0.80), procalcitonin (unit ng/mL, OR 1.11, CI 1.05–1.18), kidney failure (OR 1.68, CI 1.05–2.70), congestive heart failure (OR 2.62, CI 1.11–6.21), severe liver failure (OR 4.93, CI 1.94–12.52), and a quick SOFA score of 3 (OR 1.78, CI 1.14–2.78). The nomogram graphically displays the importance of these 14 factors for mortality. (4) Conclusions: There are risk factors that are specific to the subpopulation of critically ill COVID-19 patients.
Emotion-motivation models propose that behaviors, including health behaviors, should be predicted by the same variables that also predict negative affect since emotional reactions should induce a motivation to avoid threatening situations. In contrast, social cognitive models propose that safety behaviors are predicted by a different set of variables that mainly reflect cognitive and socio-structural aspects. Here, we directly tested these opposing hypotheses in young adults (N = 4134) in the context of COVID-19-related safety behaviors to prevent infections. In each participant, we collected measures of negative affect as well as cognitive and socio-structural variables during the lockdown in the first infection wave in Germany. We found a negative effect of the pandemic on emotional responses. However, this was not the main predictor for young adults’ willingness to comply with COVID-19-related safety measures. Instead, individual differences in compliance were mainly predicted by cognitive and socio-structural variables. These results were confirmed in an independent data set. This study shows that individuals scoring high on negative affect during the pandemic are not necessarily more likely to comply with safety regulations. Instead, political measures should focus on cognitive interventions and the societal relevance of the health issue. These findings provide important insights into the basis of health-related concerns and feelings as well as behavioral adaptations.
During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags #MentalHealth and #Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags #Depression and especially #MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with #MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81% for #MentalHealth and 79% for #Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.