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.

[Infectious pulmonary diseases]. / Infektiöse Lungenerkrankungen.

Infectious pulmonary diseases and pneumonias are important causes of death within the group of infectious diseases in Germany. Most cases are triggered by bacteria. The morphology of the inflammation is often determined by the agent involved but several histopathological types of reaction are possible. Histology alone is only rarely able to identify the causal agent; therefore additional microbiological diagnostics are necessary in most cases. Clinically cases are classified as community acquired and nosocomial pneumonia, pneumonia under immunosuppression and mycobacterial infections. Histologically, alveolar and interstitial as well as lobar and focal pneumonia can be differentiated.

The role of atypical pathogens in community-acquired pneumonia.

The term atypical pneumonia was first used in 1938, and by the 1970s it was widely used to refer to pneumonia due to Mycoplasma pneumoniae, Legionella pneumophila (or other Legionella species), and Chlamydophila pneumoniae. However, in the purest sense all pneumonias other than the classic bacterial pneumonias are atypical. Currently many favor abolition of the term atypical pneumonia.This review categorizes atypical pneumonia pathogens as conventional ones; viral agents and emerging atypical pneumonia pathogens. We emphasize viral pneumonia because with the increasing availability of multiplex polymerase chain reaction we can identify the agent(s) responsible for viral pneumonia. By using a sensitive assay for procalcitonin one can distinguish between viral and bacterial pneumonia. This allows pneumonia to be categorized as bacterial or viral at the time of admission to hospital or at discharge from the emergency department and soon thereafter further classified as to the etiology, which should be stated as definite or probable.

Differential diagnoses of acute ground-glass opacity in chest computed tomography: pictorial essay.

Ground-glass opacity is a very frequent and unspecified finding in chest computed tomography. Therefore, it admits a wide range of differential diagnoses in the acute context, from viral pneumonias such as influenza virus, coronavirus disease 2019 and cytomegalovirus and even non-infectious lesions, such as vaping, pulmonary infarction, alveolar hemorrhage and pulmonary edema. For this diagnostic differentiation, ground glass must be correlated with other findings in imaging tests, with laboratory tests and with the patients' clinical condition. In the context of a pandemic, it is extremely important to remember the other pathologies with similar findings to coronavirus disease 2019 in the imaging exams.

Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan.

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.

Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images.

Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.

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.

[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.

Bedside clinical assessment predicts recurrence after hospitalization due to viral lower respiratory tract infection in young children.

Infants requiring hospitalization due to a viral lower respiratory tract infection (LRTI) have a high risk of developing recurrent respiratory illnesses in early life and asthma beyond childhood. Notably, all validated clinical scales for viral LRTI have focused on predicting acute severity instead of recurrence. We present a novel clinical approach combining individual risk factors with bedside clinical parameters to predict recurrence after viral LRTI hospitalization in young children. A retrospective longitudinal cohort of young children (≤3 years) designed to define clinical predictive factors of recurrent respiratory illnesses within 12 months after hospitalization due to PCR-confirmed viral LRTI. Data collection was through electronic medical record. We included 138 children hospitalized with viral LRTI. Using automatic stepwise logistic model selection, we found that the strongest predictors of recurrence in infants hospitalized for the first time were severe prematurity (≤32 weeks' gestational age, OR=5.19; 95% CI 1.76 to 15.32; p=0.002) and a clinical score that weighted hypoxemia, subcostal retractions and wheezing (OR=3.33; 95% CI 1.59 to 6.98; p<0.001). After the first hospitalization, the strongest predictors of subsequent episodes were wheezing (OR=5.62; 95% CI 1.03 to 30.62; p=0.04) and family history of asthma (OR=5.39; 95% CI 1.04 to 27.96; p=0.04). We found that integrating individual risk factors (eg, prematurity or family history of asthma) with bedside clinical assessment (eg, wheezing, subcostal retractions or hypoxemia) can predict the risk of recurrence after viral LRTI hospitalization in infants. This strategy may enable clinically oriented subsetting of infants with viral LRTI based on individual predictors for recurrent respiratory illnesses during early life.

[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.