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INTRODUCTION The pandemic readiness toolbox needs to be extended, providing diagnostic tools that target different biomolecules, using orthogonal experimental setups and fit-for-purpose specification of detection. Here we build on a previous Cov-MS effort that used liquid chromatography-mass spectrometry (LC-MS) and describe a method that allows accurate, high throughput measurement of SARS-CoV-2 nucleocapsid (N) protein. MATERIALS and METHODS We used Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA) technology to enrich and quantify proteotypic peptides of the N protein from trypsin-digested samples from COVID-19 patients. RESULTS The Cov2MS assay was shown to be compatible with a variety of sample matrices including nasopharyngeal swabs, saliva and blood plasma and increased the sensitivity into the attomole range, up to a 1000-fold increase compared to direct detection in matrix. In addition, a strong positive correlation was observed between the SISCAPA antigen assay and qPCR detection beyond a quantification cycle (Cq) of 30-31, the level where no live virus can be cultured from patients. The automatable “addition only” sample preparation, digestion protocol, peptide enrichment and subsequent reduced dependency upon LC allow analysis of up to 500 samples per day per MS instrument. Importantly, peptide enrichment allowed detection of N protein in a pooled sample containing a single PCR positive sample mixed with 31 PCR negative samples, without loss in sensitivity. MS can easily be multiplexed and we also propose target peptides for Influenza A and B virus detection. CONCLUSIONS The Cov2MS assay described is agnostic with respect to the sample matrix or pooling strategy used for increasing throughput and can be easily multiplexed. Additionally, the assay eliminates interferences due to protein-protein interactions including those caused by anti-virus antibodies. The assay can be adapted to test for many different pathogens and could provide a tool enabling longitudinal epidemiological monitoring of large numbers of pathogens within a population, applied as an early warning system.

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

Na weken lockdown is niets noemenswaardigs gebeurd om social distancing in de openbare ruimte te faciliteren. In dit artikel bespreken we hoe de veiligheidsmaatregelen in de strijd tegen covid-19 aanleiding geven tot een herverdeling van de publieke ruimte. Het artikel situeert enkele weerstanden tegen ruimtelijke maatregelen en hoe deze te overwinnen. ispartof: De Standaard issue:15 April 2020 pages:26-27 status: published

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.

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The host immune response in symptomatic COVID-19 patients seems ineffective in clearing SARS-CoV-2, yet excessive in causing local tissue damage and hypercytokinemia. To elucidate the immunopathology underlying COVID-19 severity, cytokine profiling was performed in mild-moderate and critically-ill COVID-19 patients. Hypercytokinemia in COVID-19 differed from the IFN-γ-driven cytokine storm in macrophage activation syndrome, but was increased in critical versus mild-moderate COVID-19. Systems modelling of cytokine levels followed by deep-immune profiling showed that classical monocytes drive this hyper-inflammatory phenotype and that both a quantitative and qualitative reduction in T-lymphocytes correlate with disease severity, with CD8+ cells being disproportionately affected. Expression of antigen presentation and co-stimulatory molecules was reduced in critical disease, while also neutrophils contributed to disease severity and local tissue damage by amplifying hypercytokinemia and neutrophil extracellular trap formation. We suggest a monocyte-driven immunopathology, in which hyperactivated neutrophils and an ineffective adaptive immune system act as mediators of COVID-19 disease severity.Funding: This work was funded by KU Leuven (internal fund and grant C14/17/084 and C16/17/010), VIB (Grand Challenge project), UZ Leuven (KOOR project), FWO grant I007418N, the Rega Foundation (research expert fellowship to G.M.) and ‘het Leuvens Kankerinstituut’. E.D. is a postdoctoral research fellow of the Research Foundation – Flanders (FWO), Belgium (grant number 12X9420N). J.G. holds a postdoctoral research fellowship granted by the clinical research and education council of the University Hospitals Leuven. Y.S. received funding from the Flemish Government (AI Research Program). L.V. is supported by an FWO PhD fellowship (grant number 11E9819N). P.V.M. is supported by an FWO PhD fellowship (grant number 1S66020N). S.V.G. is an ISAC Marylou Ingram Scholar and supported by an FWO postdoctoral research grant (Research Foundation – Flanders). E.W. is supported by Stichting tegen Kanker (Mandate for basic & clinical oncology research). J.W. is supported by an FWO Fundamental Clinical Mandate (1833317N). Conflict of Interest: The authors declare no competing interests.Ethical Approval: All study procedures were approved by the Ethics Committee of the University Hospitals Leuven. Informed consent was obtained from all individuals or their legal guardians. ispartof: SSRN ispartof: SSRN status: published