• COVID-19
• Research softwareclose
• Other research productsclose
• Other ORP type close
• BE close
• COVID-19 close

no abstract available

Abstract: Dieses Papier ist im Kontext der Vorarbeiten für das neue Portal www.digitale-lehre-germanistik.de entstanden. Neben pragmatischen Überlegungen, wie sich die Mitglieder der germanistischen Fachgemeinschaft in der aktuellen Situation bei der Aufgabe der Digitalisierung der Lehre gegenseitig solidarisch unterstützen können, gab es in der Diskussion zwischen gut zwei Dutzend in- und ausländischen Germanist*innen einen breiten Konsens, dass der Prozess selbst einige Fragen aufwirft, die bei aller gebotenen Eile nicht übersehen werden sollten. Diese Fragen stellen sich nicht nur im Kontext der Germanistik, sondern sind naturgemäß auch für ein breiteres Spektrum anderer Fächer relevant.

On 11 December 2019, the European Commission published the Communication on the European Green Deal, preparing the ground for a new growth policy based on the ambitious climate and environmental objective of achieving a climate-neutral Europe by 2050. To that end, the European Green Deal has proposed the establishment of the Just Transition Mechanism, a robust funding scheme aiming to complement actions closely intertwined with the transition towards a low-carbon and climate-resilient future. Although of European provenance, the new growth strategy builds closely on the principles of the Paris Agreement – a landmark international legal act to combat climate change in the context of sustainable development and efforts to eradicate poverty. This paper examines the legal specificities of the green and just transition in the EU through the prism of the European Green Deal. The emphasis is on the role of the Just Transition Fund (JTF), the principal pillar of the Just Transition Mechanism, established and implemented under Cohesion policy rules to help the European economy and society become environmentally sustainable and conscious. In particular, the focus is on the possibilities offered by the new instrument in financing the diversification of local economy and mitigating negative impacts on employment, both alone and in conjunction with other instruments, mechanisms and facilities of the forthcoming financial period 2021 – 2027. In order to illustrate the manifold challenges of reshaping Cohesion policy in the course of the green transition, the paper also summarises the critical elements of the EU green reform at the time of the unprecedented COVID-19 crisis.

Une opinion rédigée et transmise à la Première ministre et aux Ministresprésidents régionaux par un collectif de signataires.

Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present a explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding. We have optimized the architecture of two different networks for CT images: (i) a novel conditional variational autoencoder (CVAE) with a specific architecture that integrates the class labels inside the encoder layers and uses side information with shared attention layers for the encoder, which make the most of the contextual clues for representation learning, and (ii) a downstream convolutional neural network for supervised classification using the encoder structure of the CVAE. With the explainable classification results, the proposed diagnosis system is very effective for COVID-19 classification. Based on the promising results obtained qualitatively and quantitatively, we envisage a wide deployment of our developed technique in large-scale clinical studies.

Background SARS-CoV-2 is associated with significant mortality and morbidity in a subgroup of patients who develop cytokine releasing syndrome (CRS) and the related acute respiratory distress syndrome. Precedent evidence suggests that deficiency of the element zinc can be associated with similar complications as well as impaired antiviral response. Herein, beyond determining the zinc status, we explore the association between the plasma zinc concentration, the development of CRS, and the clinical outcomes in hospitalized COVID-19 patients. Methods We conducted a prospective, single-center, observational study in a tertiary university hospital (CUB-Hôpital Erasme, Brussels). Hospitalized adult patients with PCR-confirmed SARS-CoV-2 infection were enrolled within 72 hours of hospital admission. As a surrogate endpoint for CRS, we assessed the presence and severity of COVID-19-associated hyperinflammatory syndrome, using an additive six-point clinical scale (cHIS) that we independently validated in the current study. We defined the clinical outcomes as the length of hospitalization, the incidence of mechanical ventilation, and mortality. We recorded the outcomes with a follow-up of 90 days from hospital admission. Results One hundred and thirty-nine eligible patients were included between May 2020 and November 2020 (median age of 65 years [IQR, 54 to 77]). Our cohort’s mean plasma zinc concentration was 56.2 mcg/dL (standard deviation [SD], 14.8). The absolute majority of patients (96%) were zinc deficient (<80mcg/dL). The mean plasma zinc concentration was lower in patients with CRS (cHIS ≧ 2) compared to those without CRS (−5 mcg/dL; 95% CI, -10.5 to 0.051; p = 0.048). We observed that the plasma zinc concentration is weakly but significantly correlated with the length of hospital stay (rho = -0.19; p = 0.022). However, the plasma zinc concentration was not significantly associated with mortality or morbidity. Conclusions Markedly, an absolute majority of hospitalized COVID-19 patients are zinc deficient. We found no significant association between zinc plasma concentration and cHIS. We find a weak (reverse) correlation between plasma zinc concentration and the length of hospital stay, but not with mortality or morbidity. As such, our findings do not support the role of zinc as a robust prognostic factor among hospitalized COVID-19 patients. We encourage further studies to explore the role of zinc as a biomarker for assessing the risk of developing a tissue-damaging CRS and predicting outcomes in patients diagnosed with COVID-19.

Before the COVID-19 pandemic, the Teaching and Learning Centre of the University of Liège (Belgium) administered a questionnaire to disclose the main predictors of faculty’s intention to resort, within a timespan of two years, to blended learning in at least one of their courses. The instrument was constructed according to the Theory of Planned Behaviour, a sound conceptual framework for probing intentions and their antecedents. In the responses of 114 faculty members, multiple regression analyses detected that attitude towards blended learning, subjective norm and perceived control explained 73% of the intention to use blended designs for teaching purposes in a not too distant future. Data treatment also singled out beliefs working indirectly in favour of the intention to use hybrid approaches: the inclination to think of blended learning altogether in a student-centred, conformist and pragmatic way. Moreover, respondents who declared having already used blended learning in the past expressed stronger opinions congruent with the constructs of attitude, norm and perceived control. The results also establish that pre-lockdown e-learning practices remained rather basic. A better knowledge of what can facilitate or impair the diffusion of blended learning is of importance for higher education institutions and their staff training efforts.

Objective To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population. Design Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sources PubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24 th March 2020. Study selection Studies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text. Data extraction Data from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST. Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance. Results 2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China. Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in infected patients included age, sex, features derived from CT, C-reactive protein, lactic dehydrogenase, and lymphocyte count. Estimated C-index estimates for the prediction models ranged from 0.73 to 0.81 in those for the general population (reported for all 3 general population models), from 0.81 to > 0.99 in those for diagnosis (reported for 13 of the 18 diagnostic models), and from 0.85 to 0.98 in those for prognosis (reported for 6 of the 10 prognostic models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and poor statistical analysis, including high risk of model overfitting. Reporting quality varied substantially between studies. A description of the study population and intended use of the models was absent in almost all reports, and calibration of predictions was rarely assessed. Conclusion COVID-19 related prediction models are quickly entering the academic literature, to support medical decision making at a time where this is urgently needed. Our review indicates proposed models are poorly reported and at high risk of bias. Thus, their reported performance is likely optimistic and using them to support medical decision making is not advised. We call for immediate sharing of the individual participant data from COVID-19 studies to support collaborative efforts in building more rigorously developed prediction models and validating (evaluating) existing models. The aforementioned predictors identified in multiple included studies could be considered as candidate predictors for new models. We also stress the need to follow methodological guidance when developing and validating prediction models, as unreliable predictions may cause more harm than benefit when used to guide clinical decisions. Finally, studies should adhere to the TRIPOD statement to facilitate validating, appraising, advocating and clinically using the reported models. Systematic review registration protocol osf.io/ehc47/ , registration: osf.io/wy245 Summary boxes What is already known on this topic - The sharp recent increase in COVID-19 infections has put a strain on healthcare systems worldwide, necessitating efficient early detection, diagnosis of patients suspected of the infection and prognostication of COVID-19 confirmed cases. - Viral nucleic acid testing and chest CT are standard methods for diagnosing COVID-19, but are time-consuming. - Earlier reports suggest that the elderly, patients with comorbidity (COPD, cardiovascular disease, hypertension), and patients presenting with dyspnoea are vulnerable to more severe morbidity and mortality after COVID-19 infection. What this study adds - We identified three models to predict hospital admission from pneumonia and other events (as a proxy for COVID-19 pneumonia) in the general population. - We identified 18 diagnostic models for COVID-19 detection in symptomatic patients. - 13 of these were machine learning models based on CT images. - We identified ten prognostic models for COVID-19 infected patients, of which six aimed to predict mortality risk in confirmed or suspected COVID-19 patients, two aimed to predict progression to a severe or critical state, and two aimed to predict a hospital stay of more than 10 days from admission. - Included studies were poorly reported compromising their subsequent appraisal, and recommendation for use in daily practice. All studies were appraised at high risk of bias, raising concern that the models may be flawed and perform poorly when applied in practice, such that their predictions may be unreliable. ispartof: medRxiv ispartof: medRxiv status: published