A CT-Based Radiomics Model for Prediction of Prognosis in Patients with Novel Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Study.

This study aimed to develop a computed tomography (CT)-based radiomics model to predict the outcome of COVID-19 pneumonia. In total of 44 patients with confirmed diagnosis of COVID-19 were retrospectively enrolled in this study. The radiomics model and subtracted radiomics model were developed to assess the prognosis of COVID-19 and compare differences between the aggravate and relief groups. Each radiomic signature consisted of 10 selected features and showed good performance in differentiating between the aggravate and relief groups. The sensitivity, specificity, and accuracy of the first model were 98.1%, 97.3%, and 97.6%, respectively (AUC = 0.99). The sensitivity, specificity, and accuracy of the second model were 100%, 97.3%, and 98.4%, respectively (AUC = 1.00). There was no significant difference between the models. The radiomics models revealed good performance for predicting the outcome of COVID-19 in the early stage. The CT-based radiomic signature can provide valuable information to identify potential severe COVID-19 patients and aid clinical decisions.

How does risk perception of the COVID-19 pandemic affect the consumption behavior of green food?

Attention to health is on the rise with the global pandemic of COVID-19, especially in food security. Green food is viewed as a healthy, safe, and nutritious food, which plays a significant role in enhancing immunity. This study aimed to investigate how risk perception affects the consumption behavior of green food. Risk perception and health awareness were added to the original model based on the extended theory of planned behavior. And an online survey about the influence of COVID-19 on consumers' green food consumption behavior was conducted with 612 valid respondents recruited. The results indicate that risk perception has a positive effect on both consumption intention and behavior. The mediating effect analysis shows that risk perception influences green food consumption intention by improving people's attitudes, subjective norms, and health awareness. These findings can not only help clarify the relationship between green food consumption behavior and the risk perception of COVID-19 but also provide some valuable implications for policymakers and marketers in promoting green food.

Prognostic Value of Coronary CT Angiography-Derived Fractional Flow Reserve in Non-obstructive Coronary Artery Disease: A Prospective Multicenter Observational Study

Coronary artery disease (CAD) is a major contributor to morbidity and mortality worldwide. Myocardial ischemia may occur in patients with normal or non-obstructive CAD on invasive coronary angiography (ICA). The comprehensive evaluation of coronary CT angiography (CCTA) integrated with fractional flow reserve derived from CCTA (CT-FFR) to CAD may be essential to improve the outcomes of patients with non-obstructive CAD. China CT-FFR Study-2 (ChiCTR2000031410) is a large-scale prospective, observational study in 29 medical centers in China. The primary purpose is to uncover the relationship between the CCTA findings (including CT-FFR) and the outcome of patients with non-obstructive CAD. At least 10,000 patients with non-obstructive CAD but without previous revascularization will be enrolled. A 5-year follow-up will be performed. The primary endpoint is the occurrence of major adverse cardiovascular events (MACE), including all-cause mortality, non-fatal myocardial infarct, unplanned revascularization, and hospitalization for unstable angina. Clinical characteristics, laboratory and imaging examination results will be collected to analyze their prognostic value.

Liquid Chromatography-Multiple Reaction Monitoring-Mass Spectrometry Assay for Quantitative Measurement of Therapeutic Antibody Cocktail REGEN-COV Concentrations in COVID-19 Patient Serum.

REGEN-COV is a cocktail of two human IgG1 monoclonal antibodies (REGN10933 + REGN10987) that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and has shown great promise to reduce the SARS-CoV-2 viral load in COVID-19 patients enrolled in clinical studies. A liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS)-based method, combined with trypsin and rAspN dual enzymatic digestion, was developed for the determination of total REGN10933 and total REGN10987 concentrations in several hundreds of pharmacokinetic (PK) serum samples from COVID-19 patients participating in phase I, II, and III clinical studies. The performance characteristics of this bioanalytical assay were evaluated with respect to linearity, accuracy, precision, selectivity, specificity, and analyte stability before and after enzymatic digestion. The developed LC-MRM-MS assay has a dynamic range from 10 to 2000 µg/mL antibody drug in the human serum matrix, which was able to cover the serum drug concentration from day 0 to day 28 after drug administration in two-dose groups for the clinical PK study of REGEN-COV. The concentrations of REGEN-COV in the two-dose groups measured by the LC-MRM-MS assay were comparable to the concentrations measured by a fully validated electrochemiluminescence (ECL) immunoassay.

Substantial changes of chemical composition and sources of fine particles during the period of COVID-19 pandemic in Taiyuan, Northern China.

To better understand the effects of COVID-19 on air quality in Taiyuan, hourly in situ measurements of PM2.5(particulate matter with an aerodynamic diameter less than 2.5 mm) and chemical components (water-soluble ions, organic carbon (OC), elemental carbon (EC), and trace elements) were conducted before (P1: 1 January-23 January 2020) and during (P2: 24 January-15 February 2020) the coronavirus disease 2019 (COVID-19) outbreak. The average concentrations of PM2.5 dropped from 122.0 µg/m3 during P1 to 83.3 µg/m3 during P2. Compared with P1, except for fireworks burning-related chemical components (K+, Mg2+, K, Cu, Ba), the concentrations of other chemical components of PM2.5 decreased by14.9-69.8%. Although the large decrease of some emission sources, fireworks burning still resulted in the occurrence of pollution events during P2. The analysis results of positive matrix factorization model suggested that six PM2.5 sources changed significantly before and during the outbreak of the epidemic. The contributions of vehicle emission, industrial process, and dust to PM2.5 decreased from 23.1%, 3.5%, and 4.0% during P1 to 7.7%, 3.4%, and 2.3% during P2, respectively, whereas the contributions of secondary inorganic aerosol, fireworks burning, and coal combustion to PM2.5 increased from 62.0%, 1.8%, and 5.5% to 71.5%, 9.0%, and 6.2%, respectively. The source apportionment results were also affected by air mass transport. The largest reductions of vehicle emission, industrial process, and dust source were distinctly seen for the air masses from northwest. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11869-021-01082-y.

Potential health risks of inhaled toxic elements and risk sources during different COVID-19 lockdown stages in Linfen, China.

Levels of toxic elements in ambient PM2.5 were measured from 29 October 2019 to 30 March 2020 in Linfen, China, to assess the health risks they posed and to identify critical risk sources during different periods of the COVID-19 lockdown and haze episodes using positive matrix factorization (PMF) and a health-risk assessment model. The mean PM2.5 concentration during the study period was 145 µg/m3, and the 10 investigated toxic elements accounted for 0.31% of the PM2.5 mass. The total non-cancer risk (HI) and total cancer risk (TCR) of the selected toxic elements exceed the US EPA limits for children and adults. The HI for children was 2.3 times that for adults for all periods, which is likely due to the high inhalation rate per unit body weight for children. While the TCR for adults was 1.7 times that of children, which is mainly attributed to potential longer exposure duration for adults. The HI and TCR of the toxic elements during full lockdown were reduced by 66% and 58%, respectively, compared to their pre-lockdown levels. The HI and TCR were primarily attributable to Mn and As, respectively. Health risks during haze episodes were significantly higher than the average levels during COVID-19 lockdowns, though the HI and TCR of the selected toxic elements during full-lockdown haze episodes were 68% and 17% lower, respectively, than were the levels during pre-lockdown haze episodes. During the study period, fugitive dust and steel-related smelting were the highest contributors to HI and TCR, respectively, and decreased in these emission sources contributed the most to the lower health risks observed during the full lockdown. There, the control of these sources is critical to effectively reduce public health risks.

ResGNet-C: A graph convolutional neural network for detection of COVID-19.

The widely spreading COVID-19 has caused thousands of hundreds of mortalities over the world in the past few months. Early diagnosis of the virus is of great significance for both of infected patients and doctors providing treatments. Chest Computerized tomography (CT) screening is one of the most straightforward techniques to detect pneumonia which was caused by the virus and thus to make the diagnosis. To facilitate the process of diagnosing COVID-19, we therefore developed a graph convolutional neural network ResGNet-C under ResGNet framework to automatically classify lung CT images into normal and confirmed pneumonia caused by COVID-19. In ResGNet-C, two by-products named NNet-C, ResNet101-C that showed high performance on detection of COVID-19 are simultaneously generated as well. Our best model ResGNet-C achieved an averaged accuracy at 0.9662 with an averaged sensitivity at 0.9733 and an averaged specificity at 0.9591 using five cross-validations on the dataset, which is comprised of 296 CT images. To our best knowledge, this is the first attempt at integrating graph knowledge into the COVID-19 classification task. Graphs are constructed according to the Euclidean distance between features extracted by our proposed ResNet101-C and then are encoded with the features to give the prediction results of CT images. Besides the high-performance system, which surpassed all state-of-the-art methods, our proposed graph construction method is simple, transferrable yet quite helpful for improving the performance of classifiers, as can be justified by the experimental results.

Environmental virus detection associated with asymptomatic SARS-CoV-2-infected individuals with positive anal swabs.

In the fight against the outbreak of COVID-19 in China, we treated some asymptomatic infected individuals. This study aimed to detect pathogens in biological and environmental samples of these asymptomatic infected individuals and analyse their association. Using a cross-sectional study design, we collected biological and environmental samples from 19 patients treated in the isolation ward of Nanjing No.2 Hospital. Biological samples included saliva, pharyngeal swabs, blood, anal swabs, and exhaled breath condensate. Swab samples from the ward environment included inside masks, outside masks, palm swabs, bedside handrails, bedside tables, cell phone screens, toilet cell phone shelves, toilet pads and toilet lids. We also obtained some samples from public areas. We used RT-PCR to detect pathogens and colloidal gold to detect antibodies. As results, 19 asymptomatic infected individuals participated in the survey, with 8 positives for pathogens and 11 positives only for antibodies. Three positive samples were detected from among 96 environmental samples, respectively, from a cell phone surface, a cell phone shelf and a bedside handrail. No positive samples were detected in the exhaled breath condensate in this work. All patients identified pathogens in the environment had positive anal swabs. There was a statistical association between positive anal swabs and positive environmental samples. The association of positive samples from the surrounding of asymptomatically infected patients with positive anal swabs suggested that patients might secrete the virus for a more extended period.

A Case Report of Neonatal 2019 Coronavirus Disease in China.

In December 2019, the coronavirus disease (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China and now has spread in many countries. Pregnant women are a population susceptible to COVID-19 and are more likely to have complications and even progress to severe illness. We report a case of neonatal COVID-19 in China with pharyngeal swabs testing positive by real-time reverse-transcription polymerase chain reaction assay 36 hours after birth. However, whether the case is a vertical transmission from mother to child remains to be confirmed.