This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s 2 Key Points 1. The positive rates of RT-PCR assay and chest CT imaging in our cohort were 59% (601/1014), and 88% (888/1014) for the diagnosis of suspected patients with COVID-19, respectively. 2.With RT-PCR as a reference, the sensitivity of chest CT imaging for COVID-19 was 97% (580/601). In patients with negative RT-PCR results but positive chest CT scans (n=308 patients), 48% (147/308) of patients were re-considered as highly likely cases, with 33% (103/308) as probable cases by a comprehensive evaluation. 3.With analysis of serial RT-PCR assays and CT scans, 60% to 93% of patients had initial positive chest CT consistent with COVID-19 before the initial positive RT-PCR results. 42% of patients showed improvement of follow-up chest CT scans before the RT-PCR results turning negative. Summary StatementChest CT had higher sensitivity for diagnosis of COVID-19 as compared with initial reverse-transcription polymerase chain reaction (RT-PCR) from swab samples in the epidemic area of China. Abbreviations RT-PCR = reverse transcription polymerase chain reaction NCP = novel coronavirus pneumonia PPV = positive predictive value NPV = negative predictive value I n P r e s s 3 Abstract Background: Chest CT is used for diagnosis of 2019 novel coronavirus disease (COVID-19), as an important complement to the reverse-transcription polymerase chain reaction (RT-PCR) tests. Purpose: To investigate the diagnostic value and consistency of chest CT as compared with comparison to RT-PCR assay in COVID-19. underwent both chest CT and RT-PCR tests were included. With RT-PCR as reference standard, the performance of chest CT in diagnosing COVID-19 was assessed. Besides, for patients with multiple RT-PCR assays, the dynamic conversion of RT-PCR results (negative to positive, positive to negative, respectively) was analyzed as compared with serial chest CT scans for those with time-interval of 4 days or more. Results: Of 1014 patients, 59% (601/1014) had positive RT-PCR results, and 88% (888/1014) had positive chest CT scans. The sensitivity of chest CT in suggesting COVID-19 was 97% (95%CI, 95-98%, 580/601 patients) based on positive RT-PCRresults. In patients with negative RT-PCR results, 75% (308/413) had positive chest CT findings; of 308, 48% were considered as highly likely cases, with 33% as probable cases. By analysis of serial RT-PCR assays and CT scans, the mean interval time between the initial negative to positive RT-PCR results was 5.1 ± 1.5 days; the initial positive to subsequent negative RT-PCR result was 6.9 ± 2.3 days). 60% to 93% of cases had initial positive CT consistent with COVID-19 prior (or parallel) to the initial positive RT-PCR results. 42% (24/57) cases showed improvement in follow-up chest CT scans before the RT-PCR results turning negative. Conclusion:Chest CT has a high sensitivity for diagnosis of COVID-19. Chest CT may be considered as a primary tool for the current COVID-19 detection in epidemic ar...

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Objectives. This study aimed to determine the IgM and IgG responses against severe acute respiratory syndrome coronavirus (SARS-CoV)-2 in coronavirus disease 2019 (COVID-19) patients with varying illness severities. Methods. IgM and IgG antibody levels were assessed via chemiluminescence immunoassay in 338 COVID-19 patients. Results. IgM levels increased during the first week after SARS-CoV-2 infection, peaked 2 weeks and then reduced to near-background levels in most patients. IgG was detectable after 1 week and was maintained at a high level for a long period. The positive rates of IgM and/or IgG antibody detections were not significantly different among the mild, severe and critical disease groups. Severe and critical cases had higher IgM levels than mild cases, whereas the IgG level in critical cases was lower than those in both mild and severe cases. This might be because of the high disease activity and/or a compromised immune response in critical cases. The IgM antibody levels were slightly higher in deceased patients than recovered patients, but IgG levels in these groups did not significantly differ. A longitudinal detection of antibodies revealed that IgM levels decreased rapidly in recovered patients, whereas in deceased cases, either IgM levels remained high or both IgM and IgG were undetectable during the disease course. Conclusion. Quantitative detection of IgM and IgG antibodies against SARS-CoV-2 quantitatively has potential significance for evaluating the severity and prognosis of COVID-19.

Research question Whether SARS-CoV-2 infection has effects on ovarian reserve, sex hormone and menstruation of women of child-bearing age. Design This is a retrospective, cross-sectional study. Clinical and laboratory data from 237 women of child-bearing age diagnosed with COVID-19 were retrospectively reviewed. Menstrual data from 177 patients were analyzed. Blood samples from the early follicular phase were tested for sex hormones and Anti-mullerian hormone (AMH). Results Among 237 patients confirmed with COVID-19, severely ill patients had more comorbidities than mildly ill patients (34% vs 8%), especially for patients with diabetes, hepatic disease and malignant tumors. Among 177 patients with menstrual records, 45 (25%) patients presented with menstrual volume changes, and 50 (28%) patients had menstrual cycle changes, mainly a decreased volume (21%) and a prolonged cycle (19%). The average sex hormone and AMH levels of women of child-bearing age with COVID-19 were not different from those of age-matched controls. Conclusions Average sex hormone levels and ovarian reserve did not change significantly in COVID-19 women of child-bearing age. Nearly one-fifth of patients exhibited a menstrual volume decrease or cycle prolongation. The menstruation changes of these patients might be the consequence of transient sex hormone change cause by suppression of ovarian function that soon resumed after recovery.

Effective laboratory markers for the estimation of disease severity and predicting the clinical progression of coronavirus disease-2019 (COVID-19) is urgently needed. Laboratory tests, including blood routine, cytokine profiles and infection markers, were collected from 389 confirmed COV-ID-19 patients. The included patients were classified into mild (n = 168), severe (n = 169) and critical groups (n = 52). The leukocytes, neutrophils, infection biomarkers [such as C-reactive protein (CRP), procalcitonin (PCT) and ferritin] and the concentrations of cytokines [interleukin (IL)-2R, IL-6, IL-8, IL-10 and tumor necrosis factor (TNF)-α] were significantly increased, while lymphocytes were significantly decreased with increased severity of illness. The amount of IL-2R was positively correlated with the other cytokines and negatively correlated with lymphocyte number. The ratio of IL-2R to lymphocytes was found to be remarkably increased in severe and critical patients. IL-2R/lymphocytes were superior compared with other markers for the identification of COVID-19 with critical illness, not only from mild but also from severe illness. Moreover, the cytokine profiles and IL-2R/lymphocytes were significantly decreased in recovered patients, but further increased in disease-deteriorated patients, which might be correlated with the outcome of COVID-19. Lymphopenia and increased levels of cytokines were closely associated with disease severity. The IL-2R/lymphocyte was a prominent biomarker for early identification of severe COVID-19 and predicting the clinical progression of the disease.

Background The impacts of chronic airway diseases on coronavirus disease 2019 (COVID‐19) are far from understood. Objective To explore the influence of asthma and chronic obstructive pulmonary disease (COPD) comorbidity on disease expression and outcomes, and the potential underlying mechanisms in COVID‐19 patients. Methods A total of 961 hospitalized COVID‐19 patients with a definite clinical outcome (death or discharge) were retrospectively enrolled. Demographic and clinical information were extracted from the medical records. Lung tissue sections from patients suffering from lung cancer were used for immunohistochemistry study of angiotensin‐converting enzyme II (ACE2) expression. BEAS‐2B cell line was stimulated with various cytokines. Results In this cohort, 21 subjects (2.2%) had COPD and 22 (2.3%) had asthma. After adjusting for confounding factors, COPD patients had higher risk of developing severe illness (OR: 23.433; 95% CI 1.525‐360.135; P < .01) and acute respiratory distress syndrome (OR: 19.762; 95% CI 1.461‐267.369; P = .025) than asthmatics. COPD patients, particularly those with severe COVID‐19, had lower counts of CD4+ T and CD8+ T cells and B cells and higher levels of TNF‐α, IL‐2 receptor, IL‐10, IL‐8, and IL‐6 than asthmatics. COPD patients had increased, whereas asthmatics had decreased ACE2 protein expression in lower airways, compared with that in control subjects without asthma and COPD. IL‐4 and IL‐13 downregulated, but TNF‐α, IL‐12, and IL‐17A upregulated ACE2 expression in BEAS‐2B cells. Conclusion Patients with asthma and COPD likely have different risk of severe COVID‐19, which may be associated with different ACE2 expression.

BackgroundDiarrhea is the leading infectious cause of childhood morbidity and mortality. Among bacterial agents, diarrheagenic Escherichia coli (DEC) is the major causal agent of childhood diarrhea in developing countries, particularly in children under the age of 5 years. Here, we performed a hospital-based prospective study to explore the pathotype distribution, epidemiological characteristics and antibiotic resistance patterns of DEC from < 5-year-old diarrheal children.MethodsBetween August 2015 and September 2016, 684 stool samples were collected from children (< 5 years old) with acute diarrhea. All samples were cultured and identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and biochemical tests. PCR was used for subtyping, and enteropathogenic E. coli (EPEC) isolates were identified simultaneously with serology. Furthermore, antimicrobial sensitivity tests and sequencing of antibiotic resistance-related genes were conducted.ResultsDEC strains were identified in 7.9% of the 684 stool samples. Among them, the most commonly detected pathotype was EPEC (50.0% of DEC), of which 77.8% were classified as atypical EPEC (aEPEC). Age and seasonal distribution revealed that DEC tended to infect younger children and to occur in summer/autumn periods. Multidrug-resistant DEC isolates were 66.7%; resistance rates to ampicillin, co-trimoxazole, cefazolin, cefuroxime, cefotaxime, and ciprofloxacin were ≥ 50%. Among 5 carbapenem-resistant DEC, 60.0% were positive for carbapenemase genes (2 blaNDM-1 and 1 blaKPC-2). Among 30 cephalosporin-resistant DEC, 93.3% were positive for extended-spectrum β-lactamase (ESBL) genes, with blaTEM-1 and blaCTX-M-55 being the most common types. However, no gyrA or gyrB genes were detected in 16 quinolone-resistant isolates. Notably, aEPEC, which has not received much attention before, also exhibited high rates of drug resistance (81.0%, 66.7%, and 14.3% for ampicillin, co-trimoxazole, and carbapenem resistance, respectively).ConclusionsEPEC was the most frequent DEC pathotype in acute diarrheal children, with aEPEC emerging as a dominant diarrheal agent in central China. Most DEC strains were multidrug-resistant, making even ciprofloxacin unsuitable for empiric treatment against DEC infection. Among carbapenem-resistant DEC strains, those harboring blaNDM-1 and blaKPC-2 were the main causal agents. blaTEM-1 and blaCTX-M-55 were the major genetic determinants associated with high levels of cephalosporin resistance.Electronic supplementary materialThe online version of this article (10.1186/s12879-017-2936-1) contains supplementary material, which is available to authorized users.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.