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Twee jaar geleden dook in Wuhan het Sars-Cov-2 virus op. Het overspoelde in een mum van tijd de wereld. Half maart 2020 besliste de Belgische regering om een lockdown af te kondigen. Een begrip dat tot kort daarvoor letterlijk onvoorstelbaar was. In landen over de hele wereld werd een noodtoestand afgekondigd die overheden ongeziene slagkracht gaf om ingrijpende beslissingen te nemen. Zonder daarbij de geijkte democratische procedures te moeten volgen. Het onbekende virus en de angstaanjagende beelden die de wereld rondgingen, hielden hele bevolkingen aan het scherm gekluisterd, terwijl nieuwsuitzendingen zich beperkten tot één item: het nieuwe coronavirus en de daaraan gerelateerde cijfers, statistieken en beslissingen. Angst regeerde het land en beslissingen moesten worden genomen onder grote druk en onzekerheid. Dat het gevoerd beleid er een was van vallen en opstaan is in die omstandigheden volledig te begrijpen. Maar de onbekendheid van het virus maakte plaats voor een nooit geziene hoeveelheid wetenschappelijke publicaties – we naderen de 1 miljoen peer reviewed papers . Merkwaardig genoeg ontbreekt nu een grondige reflectie van het gevoerde beleid van de afgelopen twee jaar Two years ago, the Sars-Cov-2 virus surfaced in Wuhan. It engulfed the world in no time. In mid-March 2020, the Belgian government decided to declare a lockdown. A concept that was literally unimaginable until recently. In countries all over the world a state of emergency was declared that gave governments unprecedented power to take far-reaching decisions. Without having to follow the usual democratic procedures. The unknown virus and the terrifying images that circulated around the world kept entire populations glued to the screen, while news broadcasts were limited to one item: the new coronavirus and the related figures, statistics and decisions. Fear ruled the country and decisions had to be made under great pressure and uncertainty. That the policy pursued was one of trial and error is entirely understandable in these circumstances. But the obscurity of the virus gave way to an unprecedented number of scientific publications - we are approaching 1 million peer reviewed papers. Strangely enough, a thorough reflection of the policies pursued over the past two years is now missing.

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.

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.

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

Flyer series #01-#06 (1 HEX): dataset/-container. The flyer-series seeks to expand the Klein's group—as referred to by Rosalind Krauss (1979)—in the area of asymmetric interaction in various domains: communication, transaction, strategy and embodiment as an aspect of spatial competence becoming potentially more accentuated in the new digital strategies emerging under the conditions of quarantine (covid 19).

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.

SARS-CoV-2 entry into host cells is orchestrated by the spike (S) glycoprotein that contains an immunodominant receptor-binding domain (RBD) targeted by the largest fraction of neutralizing antibodies (Abs) in COVID-19 patient plasma. Little is known about neutralizing Abs binding to epitopes outside the RBD and their contribution to protection. Here, we describe 41 human monoclonal Abs (mAbs) derived from memory B cells, which recognize the SARS-CoV-2 S N-terminal domain (NTD) and show that a subset of them neutralize SARS-CoV-2 ultrapotently. We define an antigenic map of the SARS-CoV-2 NTD and identify a supersite recognized by all known NTD-specific neutralizing mAbs. These mAbs inhibit cell-to-cell fusion, activate effector functions, and protect Syrian hamsters from SARS-CoV-2 challenge. SARS-CoV-2 variants, including the 501Y.V2 and B.1.1.7 lineages, harbor frequent mutations localized in the NTD supersite suggesting ongoing selective pressure and the importance of NTD-specific neutralizing mAbs to protective immunity. ispartof: bioRxiv ispartof: bioRxiv ispartof: location:United States status: Published online

The SARS-CoV-2 Gamma variant spread rapidly across Brazil, causing substantial infection and death waves. We use individual-level patient records following hospitalisation with suspected or confirmed COVID-19 to document the extensive shocks in hospital fatality rates that followed Gamma's spread across 14 state capitals, and in which more than half of hospitalised patients died over sustained time periods. We show that extensive fluctuations in COVID-19 in-hospital fatality rates also existed prior to Gamma's detection, and were largely transient after Gamma's detection, subsiding with hospital demand. Using a Bayesian fatality rate model, we find that the geographic and temporal fluctuations in Brazil's COVID-19 in-hospital fatality rates are primarily associated with geographic inequities and shortages in healthcare capacity. We project that approximately half of Brazil's COVID-19 deaths in hospitals could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization, and pandemic preparedness are critical to minimize population wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries. NOTE: The following manuscript has appeared as 'Report 46 - Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals' at https://spiral.imperial.ac.uk:8443/handle/10044/1/91875 . ONE SENTENCE SUMMARY: COVID-19 in-hospital fatality rates fluctuate dramatically in Brazil, and these fluctuations are primarily associated with geographic inequities and shortages in healthcare capacity. ispartof: medRxiv ispartof: medRxiv ispartof: location:United States status: Published online