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1.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327137

ABSTRACT

Hyperinflammation, coagulopathy and immune dysfunction are prominent in patients with severe infections. Extracellular chromatin clearance by plasma DNases suppresses such pathologies in mice but whether severe infection interferes with these pathways is unclear. Here, we show that patients with severe SARS-CoV-2 infection or microbial sepsis exhibit low extracellular DNA clearance capacity associated with the release of the DNase inhibitor actin. Unlike naked DNA degradation (DNase), neutrophil extracellular trap degradation (NETase) was insensitive to G-actin, indicating distinct underlying mechanisms. Functional proteomic profiling of severely ill SARS-CoV-2 patient plasma revealed that patients with high NETase and DNase activities exhibited 18-fold higher survival compared to patients with low activity proteomic profiles. Remarkably, low DNA clearance capacity was also prominent in healthy individuals with chronic inflammation, suggesting that pre-existing inflammatory conditions may increase the risk for mortality upon infection. Hence, functional proteomic profiling illustrates that non-redundant DNA clearance activities protect critically ill patients from mortality, uncovering protein combinations that can accurately predict mortality in critically ill patients.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-293746

ABSTRACT

To predict the effects of the 2020 U.S. ‘CARES’ act on consumption, we extend a model that matches responses of households to past consumption stimulus packages. The extension allows us to account for two novel features of the coronavirus crisis. First, during the lockdown, many types of spending are undesirable or impossible. Second, some of the jobs that disappear during the lockdown will not reappear when it is lifted. We estimate that, if the lockdown is short-lived, the combination of expanded unemployment insurance benefits and stimulus payments should be sufficient to allow a swift recovery in consumer spending to its pre-crisis levels. If the lockdown lasts longer, an extension of enhanced unemployment benefits will likely be necessary if consumption spending is to recover.

3.
National Bureau of Economic Research Working Paper Series ; No. 27876, 2020.
Article in English | NBER, Grey literature | ID: grc-748485

ABSTRACT

To predict the effects of the 2020 U.S. CARES Act on consumption, we extend a model that matches responses to past consumption stimulus packages. The extension allows us to account for two novel features of the coronavirus crisis. First, during lockdowns, many types of spending are undesirable or impossible. Second, some of the jobs that disappear during the lockdown will not reappear. We estimate that, if the lockdown is short-lived (the median point of view as we are writing in April 2020), the combination of expanded unemployment insurance benefits and stimulus payments should be sufficient to allow a swift recovery in consumer spending to pre-crisis levels. If the lockdown lasts longer (or there is a ‘second wave’), an extension of enhanced unemployment benefits will likely be necessary for consumption spending to recover quickly.

4.
Cell Syst ; 12(8): 780-794.e7, 2021 08 18.
Article in English | MEDLINE | ID: covidwho-1267622

ABSTRACT

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.


Subject(s)
Biomarkers/analysis , COVID-19/pathology , Disease Progression , Proteome/physiology , Age Factors , Blood Cell Count , Blood Gas Analysis , Enzyme Activation , Humans , Inflammation/pathology , Machine Learning , Prognosis , Proteomics , SARS-CoV-2/immunology
5.
Nat Biotechnol ; 39(7): 846-854, 2021 07.
Article in English | MEDLINE | ID: covidwho-1152861

ABSTRACT

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.


Subject(s)
Proteomics/methods , Tandem Mass Spectrometry , Arabidopsis/metabolism , Biomarkers/metabolism , COVID-19/blood , COVID-19/diagnosis , Cell Line , Humans , Peptides/analysis , Proteome/analysis , Saccharomyces cerevisiae/metabolism , Saccharomyces cerevisiae Proteins/metabolism , Severity of Illness Index
6.
BMJ Open ; 11(3): e045120, 2021 03 05.
Article in English | MEDLINE | ID: covidwho-1119316

ABSTRACT

OBJECTIVES: Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images. DESIGN: A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians. SETTING: Two tertiary Canadian hospitals. PARTICIPANTS: 612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE). RESULTS: The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01. CONCLUSIONS: A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Lung/diagnostic imaging , Neural Networks, Computer , Pulmonary Edema/diagnostic imaging , Respiratory Distress Syndrome/diagnostic imaging , Canada , Diagnosis, Differential , Humans
7.
Cell Syst ; 11(1): 11-24.e4, 2020 07 22.
Article in English | MEDLINE | ID: covidwho-459007

ABSTRACT

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.


Subject(s)
Blood Proteins/metabolism , Coronavirus Infections/blood , Pneumonia, Viral/blood , Proteomics/methods , Adult , Aged , Aged, 80 and over , Betacoronavirus/isolation & purification , Biomarkers/blood , Blood Proteins/analysis , COVID-19 , Coronavirus Infections/classification , Coronavirus Infections/pathology , Coronavirus Infections/virology , Female , Humans , Male , Middle Aged , Pandemics/classification , Pneumonia, Viral/classification , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , SARS-CoV-2 , Young Adult
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