Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis. However, little is known about the pathogenesis and biomarkers of COVID-19. Here, we profiled host responses to COVID-19 by performing plasma proteomics of a cohort of COVID-19 patients, including non-survivors and survivors recovered from mild or severe symptoms, and uncovered numerous COVID-19-associated alterations of plasma proteins. We developed a machine-learning-based pipeline to identify 11 proteins as biomarkers and a set of biomarker combinations, which were validated by an independent cohort and accurately distinguished and predicted COVID-19 outcomes. Some of the biomarkers were further validated by enzyme-linked immunosorbent assay (ELISA) using a larger cohort. These markedly altered proteins, including the biomarkers, mediate pathophysiological pathways, such as immune or inflammatory responses, platelet degranulation and coagulation, and metabolism, that likely contribute to the pathogenesis. Our findings provide valuable knowledge about COVID-19 biomarkers and shed light on the pathogenesis and potential therapeutic targets of COVID-19.

Hypercytokinemia in COVID-19: Tear cytokine profile in hospitalized COVID-19 patients.

The aim of this study is to analyze the concentrations of cytokines in tear of hospitalized COVID-19 patients compared to healthy controls. Tear samples were obtained from 41 healthy controls and 62 COVID-19 patients. Twenty-seven cytokines were assessed: interleukin (IL)-1b, IL-1RA, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL9, IL-10, IL-12, IL-13, IL-15, IL-17, eotaxin, fibroblast growth factor basic, granulocyte colony-stimulating factor (G-CSF), granulocyte-monocyte colony-stimulating factor (GM-CSF), interferon (IFN)-γ, interferon gamma-induced protein, monocyte chemo-attractant protein-1, macrophage inflammatory protein (MIP)-1a, MIP-1b, platelet-derived growth factor (PDGF), regulated on activation normal T cell expressed and secreted, tumor necrosis factor-α and vascular endothelial growth factor (VEGF).In tear samples of COVID-19 patients, an increase in IL-9, IL-15, G-CSF, GM-CSF, IFN-γ, PDGF and VEGF was observed, along with a decrease in eotaxin compared to the control group (p < 0.05). A poor correlation between IL-6 levels in tear and blood was found. IL-1RA and GM-CSF were significantly lower in severe patients and those who needed treatment targeting the immune system (p < 0.05). Tear cytokine levels corroborate the inflammatory nature of SARS-CoV-2.

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.

BACKGROUND: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. METHODS: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. RESULTS: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. CONCLUSIONS: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

[CT imaging features of patients with different clinical types of COVID-19].

OBJECTIVE: To investigate the CT findings of patients with different clinical types of coronavirus disease 2019 (COVID-19). METHODS: A total of 67 patients diagnosed as COVID-19 by nucleic acid testing were collected and divided into 4 groups according to the clinical stages based on Diagnosis and treatment of novel coronavirus pneumonia (trial version 6). The CT imaging characteristics were analyzed among patients with different clinical types. RESULTS: Among 67 patients, 3(4.5%) were mild, 35 (52.2%) were moderate, 22 (32.8%) were severe, and 7(10.4%) were critical ill. No significant abnormality in chest CT imaging in mild patients. The 35 cases of moderate type included 3 (8.6%) single lesions, the 22 cases of severe cases included 1 (4.5%) single lesion and the rest cases were with multiple lesions. CT images of moderate patients were mainly manifested by solid plaque shadow and halo sign (18/35, 51.4%); while fibrous strip shadow with ground glass shadow was more frequent in severe cases (7/22, 31.8%). Consolidation shadow as the main lesion was observed in 7 cases, and all of them were severe or critical ill patients. CONCLUSIONS: CT images of patients with different clinical types of COVID-19 have characteristic manifestations, and solid shadow may predict severe and critical illness.

Molecular biology of coronaviruses: an overview of virus-host interactions and pathogenesis.

Coronaviruses (CoV) are enveloped, plus-strand RNA viruses that have the largest known RNA genomes and infect birds and mammals, causing various diseases. Human coronaviruses (HCoVs) were first identified in the mid-1960s and have been known to cause enteric or respiratory infections. In the last two decades, three HCoVs have emerged, including the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initiated the ongoing pandemic. SARS-CoV-2 causes a respiratory illness that presents as a mild upper respiratory disease but may result in acute respiratory distress syndrome, multi-organ failure and can be fatal, especially when underlying comorbidities are present. Children account for a low percentage of coronavirus disease 2019 (COVID-19) cases, with seemingly less severe disease. Most pediatric patients present mild or moderate symptoms or are asymptomatic. However, some cases may be severe. Therefore, SARS-CoV-2 infection and COVID-19 in pediatric patients must be studied in detail. This review describes general features of the molecular biology of CoVs and virus-host interactions that may be implicated in the pathogenesis of SARS-CoV-2.

Los coronavirus son virus envueltos de ARN de polaridad positiva, con los genomas más grandes que se conocen. Infectan aves y mamíferos, y causan una amplia variedad de enfermedades. Los coronavirus humanos se identificaron a mediados de la década de 1960 y se sabe que causan infecciones entéricas y respiratorias. En las últimas dos décadas han emergido tres coronavirus humanos pandémicos, incluido el coronavirus 2 del síndrome agudo respiratorio grave (SARS-CoV-2) que ha causado la pandemia actual. El SARS-CoV-2 produce enfermedad respiratoria que se presenta con padecimientos moderados de las vías respiratorias altas, pero puede resultar en síndrome respiratorio agudo, falla multiorgánica y muerte, en especial en casos con morbilidad subyacente. Los casos de COVID-19 en niños representan un porcentaje bajo y con síntomas menos graves de la enfermedad. La mayoría de los pacientes pediátricos son asintomáticos o presentan enfermedad leve o moderada; sin embargo, también en niños la enfermedad puede ser grave, por lo que la infección con SARS-CoV-2 y la COVID-19 en pacientes pediátricos deben estudiarse con detalle. En esta revisión se describen las características generales de la biología molecular de los coronavirus y de las interacciones virus-hospedero que se conocen para los coronavirus humanos identificados previamente, y que podrían estar implicados en la patogénesis del SARS-CoV-2.

The human coronaviruses (HCoVs) and the molecular mechanisms of SARS-CoV-2 infection.

In humans, coronaviruses can cause infections of the respiratory system, with damage of varying severity depending on the virus examined: ranging from mild-to-moderate upper respiratory tract diseases, such as the common cold, pneumonia, severe acute respiratory syndrome, kidney failure, and even death. Human coronaviruses known to date, common throughout the world, are seven. The most common-and least harmful-ones were discovered in the 1960s and cause a common cold. Others, more dangerous, identified in the early 2000s and cause more severe respiratory tract infections. Among these the SARS-CoV, isolated in 2003 and responsible for the severe acute respiratory syndrome (the so-called SARS), which appeared in China in November 2002, the coronavirus 2012 (2012-nCoV) cause of the Middle Eastern respiratory syndrome (MERS) from coronavirus, which exploded in June 2012 in Saudi Arabia, and actually SARS-CoV-2. On December 31, 2019, a new coronavirus strain was reported in Wuhan, China, identified as a new coronavirus beta strain ß-CoV from group 2B, with a genetic similarity of approximately 70% to SARS-CoV, the virus responsible of SARS. In the first half of February, the International Committee on Taxonomy of Viruses (ICTV), in charge of the designation and naming of the viruses (i.e., species, genus, family, etc.), thus definitively named the new coronavirus as SARS-CoV-2. This article highlights the main knowledge we have about the biomolecular and pathophysiologic mechanisms of SARS-CoV-2.

A Call for Consistency in the Official Naming of the Disease Caused by Severe Acute Respiratory Syndrome Coronavirus 2 in Non-English Languages.

We investigated the adoption of World Health Organization (WHO) naming of COVID-19 into the respective languages among the Group of Twenty (G20) countries, and the variation of COVID-19 naming in the Chinese language across different health authorities. On May 7, 2020, we identified the websites of the national health authorities of the G20 countries to identify naming of COVID-19 in their respective languages, and the websites of the health authorities in mainland China, Hong Kong, Macau, Taiwan and Singapore and identify their Chinese name for COVID-19. Among the G20 nations, Argentina, China, Italy, Japan, Mexico, Saudi Arabia and Turkey do not use the literal translation of COVID-19 in their official language(s) to refer to COVID-19, as they retain "novel" in the naming of this disease. China is the only G20 nation that names COVID-19 a pneumonia. Among Chinese-speaking jurisdictions, Hong Kong and Singapore governments follow the WHO's recommendation and adopt the literal translation of COVID-19 in Chinese. In contrast, mainland China, Macau, and Taiwan refer to COVID-19 as a type of pneumonia in Chinese. We urge health authorities worldwide to adopt naming in their native languages that are consistent with WHO's naming of COVID-19.

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes.

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

[SARS-CoV-2 infection (COVID-19): what can we expect?] / SARS-CoV-2-Infektion (COVID-19): Was kommt auf uns zu?

- Case numbers in China are clearly declining, case numbers in many European regions are no longer increasing exponentially.- Data on mortality from SARS-CoV-2 infection are contradictory; mortality is certainly lower than for SARS and MERS, but probably higher than for most seasonal flu outbreaks in recent years- The main complication of SARS-CoV-2 infection is pneumonia with development of acute respiratory distress syndrome (ARDS)- Asymptomatic and oligosymptomatic courses with virus shedding are not uncommon; they may be more frequent in children than in adults. Virus excretion in asymptomatic people and in the pre-symptomatic phase of an infection is relevant for transmission- An effective antiviral therapy has not yet been established. Steroids for anti-inflammatory therapy are not recommended- It is very important to prepare all actors in the health care system for a longer-term burden of inpatients and complications and to create the necessary capacities. Low-threshold diagnostic testing and rapid detection of infection chains remain essential for better control of the pandemic. An effective vaccine is urgent.

Early use of ICD-10-CM code "U07.1, COVID-19" to identify 2019 novel coronavirus cases in Military Health System administrative data

This report describes early exploratory analysis of ICD-10-CM code U07.1 (2019-nCoV acute respiratory disease [COVID-19]) to assess the use of administrative data for case ascertainment, syndromic surveillance, and future epidemiological studies. Out of the 2,950 possible COVID-19 cases identified between 1 April 2020 and 4 May 2020, 600 (20.3%) were detected in the Defense Medical Surveillance System (DMSS) and not in the Disease Reporting System internet (DRSi) or in Health Level 7 laboratory data from the Composite Health Care System. Among the 150 out of 600 cases identified exclusively in the DMSS and selected for Armed Forces Health Longitudinal Technology Application (AHLTA) review, 16 (10.7%) had a certified positive lab result in AHLTA, 17 (11.3%) met Council of State and Territorial Epidemiologists (CSTE) criteria for a probable case, 46 (30.7%) were not cases based on CSTE criteria, and 71 (47.3%) had evidence of a positive lab result from an outside source. Lack of full capture of lab results may continue to be a challenge as the variety of available tests expands. Administrative data may provide an important stopgap measure for detecting lab positive cases, pending incorporation of new COVID-19 tests and standardization of test and result nomenclature.

From SARS-CoV to SARS-CoV-2: safety and broad-spectrum are important for coronavirus vaccine development.

The global pandemic of COVID-19 caused by SARS-CoV-2 (also known as 2019-nCoV and HCoV-19) has posed serious threats to public health and economic stability worldwide, thus calling for development of vaccines against SARS-CoV-2 and other emerging and reemerging coronaviruses. Since SARS-CoV-2 and SARS-CoV have high similarity of their genomic sequences and share the same cellular receptor (ACE2), it is essential to learn the lessons and experiences from the development of SARS-CoV vaccines for the development of SARS-CoV-2 vaccines. In this review, we summarized the current knowledge on the advantages and disadvantages of the SARS-CoV vaccine candidates and prospected the strategies for the development of safe, effective and broad-spectrum coronavirus vaccines for prevention of infection by currently circulating SARS-CoV-2 and other emerging and reemerging coronaviruses that may cause future epidemics or pandemics.

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.

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.

[Cardiac biomarkers and COVID-19 - Phenotypes and Interpretation]. / Kardiale Biomarker und COVID-19-Phänotypen und Interpretation.

Current pandemic caused by SARS-CoV-2 inducing viral COVID-19 pneumonia, is categorized in 3 stages. Some biomarkers could be assigned to one of these stages, showing a correlation to mortality in COVID-19 patients. Laboratory findings in COVID-19, especially when serially evaluated, may represent individual disease severity and prognosis. These may help planning and controlling therapeutic interventions. Biomarkers for myocardial injury (high sensitive cardiac troponin, hsTn) or hemodynamic stress (NTproBNP) may occur in COVID-19 pneumonia such as in other pneumonias, correlating with severity and prognosis of the underlying disease. In hospitalized COVID-19 patients' mild increases of hsTn or NTproBNP may be explained by cardiovascular comorbidities and direct or indirect cardiac damage or stress caused by or during COVID-19 pneumonia. In case of suspected NSTE-ACS and COVID-19, indications for echocardiography or reperfusion strategy should be carefully considered against the risk of contamination.

Infection Control against COVID-19 in Departments of Radiology.

The COVID-19 epidemic, which is caused by the novel coronavirus SARS-CoV-2, has spread rapidly to become a world-wide pandemic. Chest radiography and chest CT are frequently used to support the diagnosis of COVID-19 infection. However, multiple cases of COVID-19 transmission in radiology department have been reported. Here we summarize the lessons we learned and provide suggestions to improve the infection control and prevention practices of healthcare workers in departments of radiology.