Amplification-Free Detection of SARS-CoV-2 Down to Single Virus Level by Portable Carbon Nanotube Biosensors.

The rapid and sensitive detection of trace-level viruses in a simple and reliable way is of great importance for epidemic prevention and control. Here, a multi-functionalized floating gate carbon nanotube field effect transistor (FG-CNT FET) based biosensor is reported for the single virus level detection of SARS-CoV-2 virus antigen and RNA rapidly with a portable sensing platform. The aptamers functionalized sensors can detect SARS-CoV-2 antigens from unprocessed nasopharyngeal swab samples within 1 min. Meanwhile, enhanced by a multi-probe strategy, the FG-CNT FET-based biosensor can detect the long chain RNA directly without amplification down to single virus level within 1 min. The device, constructed with packaged sensor chips and a portable sensing terminal, can distinguish 10 COVID-19 patients from 10 healthy individuals in clinical tests both by the RNAs and antigens by a combination detection strategy with an combined overall percent agreement (OPA) close to 100%. The results provide a general and simple method to enhance the sensitivity of FET-based biochemical sensors for the detection of nucleic acid molecules and demonstrate that the CNT FG FET biosensor is a versatile and reliable integrated platform for ultrasensitive multibiomarker detection without amplification and has great potential for point-of-care (POC) clinical tests.

Cultural differences in the construal of suffering and the COVID-19 pandemic

The present research examines how suffering is construed across cultures. Study 1 (N1 = 264;N2 = 745) asked participants to provide free associations for suffering. Chinese individuals generated more positive associations than did Euro-Canadians. Study 2 (N = 522) had participants create a hypothetical potion of suffering to represent what people would experience while suffering. Chinese participants added more positive ingredients and fewer negative ingredients than Euro-Canadians did. How would cultural differences in the construal of suffering matter in a real-life negative situation? Study 3 (N = 608) showed that Chinese participants generated a greater proportion of potential positive outcomes for the COVID-19 outbreak and reported more positive affect during the pandemic than did Euro-Canadians. Thus, Chinese construe suffering more positively than Euro-Canadians. These findings are consistent with previous research on cultural differences in dialectical thinking and lay theory of change and have implications for coping and resilience. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Two Binding Sites of SARS-CoV-2 Macrodomain 3 Probed by Oxaprozin and Meclomen.

Severe acute respiratory syndrome-coronavirus-1/2 (SARS-CoV-1/2) macrodomain 3 (Mac3) is critical for replication and transcription of the viral genome and is therefore a potential therapeutic target. Here, we solved the crystal structure of SARS-CoV-2 Mac3, which reveals a small-molecule binding pocket. Two low-molecular-weight drugs, oxaprozin and meclomen, induced different patterns of nuclear magnetic resonance (NMR) chemical shift perturbations (CSPs). Meclomen binds to site I of SARS-CoV-2 Mac3 with binding pose determined by NMR CSP and transferred paramagnetic relaxation enhancement, while oxaprozin binds to site II as revealed by the crystal structure. Interestingly, oxaprozin and meclomen both perturb residues in site I of SARS-CoV Mac3. Fluorescence polarization experiments further demonstrated that oxaprozin and meclomen inhibited the binding of DNA-G4s to SARS-CoV-2 Mac3. Our work identified two adjacent ligand-binding sites of SARS-CoV-2 Mac3 that shall facilitate structure-guided fragment linking of these compounds for more potent inhibitors.

The Temporal Variation of the Microclimate and Human Thermal Comfort in Urban Wetland Parks: A Case Study of Xixi National Wetland Park, China

As an important part of the ecological infrastructure in urban areas, urban wetland parks have the significant ecological function of relieving the discomfort of people during their outdoor activities. In recent years, the specific structures and ecosystem services of urban wetland parks have been investigated from different perspectives. However, the microclimate and human thermal comfort (HTC) of urban wetland parks have rarely been discussed. In particular, the changing trends of HTC in different seasons and times have not been effectively presented. Accordingly, in this research, a monitoring platform was established in Xixi National Wetland Park, China, to continually monitor its microclimate in the long term. Via a comparison with a control site in the downtown area of Hangzhou, China, the temporal variations of the microclimate and HTC in the urban wetland park are quantified, and suggestions for clothing are also provided. The results of this study demonstrate that urban wetland parks can mitigate the heat island effect and dry island effect in summer. In addition, urban wetland parks can provide ecological services at midday during winter to mitigate the cold island effect. More importantly, urban wetland parks are found to exhibit their best performance in improving HTC during the daytime of the hot season and the midday period of the cold season. Finally, the findings of this study suggest that citizens should take protective measures and enjoy their activities in the morning, evening, or at night, not at midday in hot weather. Moreover, extra layers are suggested to be worn before going to urban wetland parks at night in cold weather, and recreational activities involving accommodation are not recommended. These findings provide not only basic scientific data for the assessment of the management and ecological health value of Xixi National Wetland Park and other urban wetland parks with subtropical monsoon climates, but also a reference for visitor timing and clothing suggestions for recreational activities.

Longitudinal trajectories of pneumonia lesions and lymphocyte counts associated with disease severity among convalescent COVID-19 patients: a group-based multi-trajectory analysis.

BACKGROUND: To explore the long-term trajectories considering pneumonia volumes and lymphocyte counts with individual data in COVID-19. METHODS: A cohort of 257 convalescent COVID-19 patients (131 male and 126 females) were included. Group-based multi-trajectory modelling was applied to identify different trajectories in terms of pneumonia lesion percentage and lymphocyte counts covering the time from onset to post-discharge follow-ups. We studied the basic characteristics and disease severity associated with the trajectories. RESULTS: We characterised four distinct trajectory subgroups. (1) Group 1 (13.9%), pneumonia increased until a peak lesion percentage of 1.9% (IQR 0.7-4.4) before absorption. The slightly decreased lymphocyte rapidly recovered to the top half of the normal range. (2) Group 2 (44.7%), the peak lesion percentage was 7.2% (IQR 3.2-12.7). The abnormal lymphocyte count restored to normal soon. (3) Group 3 (26.0%), the peak lesion percentage reached 14.2% (IQR 8.5-19.8). The lymphocytes continuously dropped to 0.75 × 109/L after one day post-onset before slowly recovering. (4) Group 4 (15.4%), the peak lesion percentage reached 41.4% (IQR 34.8-47.9), much higher than other groups. Lymphopenia was aggravated until the lymphocytes declined to 0.80 × 109/L on the fourth day and slowly recovered later. Patients in the higher order groups were older and more likely to have hypertension and diabetes (all P values < 0.05), and have more severe disease. CONCLUSIONS: Our findings provide new insights to understand the heterogeneous natural courses of COVID-19 patients and the associations of distinct trajectories with disease severity, which is essential to improve the early risk assessment, patient monitoring, and follow-up schedule.

Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features.

Objectives: Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia. Methods: In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals. Patients were randomly grouped into a primary cohort and a validation cohort using a seven-to-three ratio, and their clinicoradiologic data on admission were compared. The clinicoradiologic features were optimized by the least absolute shrinkage and selection operator (LASSO) logistic regression analysis to generate a model for differential diagnosis. Receiver operating characteristic (ROC) curves were plotted for assessing the performance of the model in the primary and validation cohorts. Results: The COVID-19 pneumonia mainly presented a peripheral distribution pattern (262/291, 90.0%); in contrast, H1N1 pneumonia most commonly presented a peribronchovascular distribution pattern (52/97, 53.6%). In LASSO logistic regression, peripheral distribution patterns, older age, low-grade fever, and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia, whereas, a peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia, and a higher level of AST were associated with H1N1 pneumonia. For the primary and validation cohorts, the LASSO model containing above eight clinicoradiologic features yielded an area under curve (AUC) of 0.963 and 0.943, with sensitivity of 89.7 and 86.2%, specificity of 89.7 and 89.7%, and accuracy of 89.7 and 87.1%, respectively. Conclusions: Combination of distribution pattern and category of pulmonary opacity on chest CT with clinical features facilitates the differentiation of COVID-19 pneumonia from H1N1 pneumonia.

Rapid and unamplified identification of COVID-19 with morpholino-modified graphene field-effect transistor nanosensor.

SARS-CoV-2 RNA is identified as a pivotal player to bolster energizing zones of COVID-19 detection. Herein, we develop a rapid and unamplified nanosensing platform for detection of SARS-CoV-2 RNA in human throat swab specimens. A gold nanoparticle (AuNP)-decorated graphene field-effect transistor (G-FET) sensor was fabricated, after which complementary phosphorodiamidate morpholino oligos (PMO) probe was immobilized on the AuNP surface. This sensor allowed for highly sensitive testing of SARS-CoV-2 RdRp as PMO does not have charges, leading to low background signal. Not only did the method present a low limit of detection in PBS (0.37 fM), throat swab (2.29 fM), and serum (3.99 fM), but also it achieved a rapid response to COVID-19 patients' samples within 2 min. The developed nanosensor was capable of analyzing RNA extracts from 30 real clinical samples. The results show that the sensor could differentiate the healthy people from infected people, which are in high agreement with RT-PCR results (Kappa index = 0.92). Furthermore, a well-defined distinction between SARS-CoV-2 RdRp and SARS-CoV RdRp was also made. Therefore, we believe that this work provides a satisfactory, attractive option for COVID-19 diagnosis.

A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.

BACKGROUND: The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19. METHODS: One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared. RESULTS: In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOICT) and the percentage of infection (POICT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOICT and clinical features, including age, cluster of differentiation 4 (CD4)+ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOICT, POICT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively). CONCLUSIONS: Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.

Preliminary investigation of relationship between clinical indicators and CT manifestation patterns of COVID-19 pneumonia improvement.

BACKGROUND: To retrospectively evaluate several clinical indicators related to the improvement of COVID-19 pneumonia on CT. METHODS: A total of 62 patients with COVID-19 pneumonia were included. The CT scores based on lesion patterns and distributions in serial CT were investigated. The improvement and deterioration of pneumonia was assessed based on the changes of CT scores. Grouped by using the temperature, serum lymphocytes and high sensitivity CRP (hs-CRP) on admission respectively, the CT scores on admission, at peak time and at discharge were evaluated. Correlation analysis was carried out between the time to onset of pneumonia resolution on CT images and the recovery time of temperature, negative conversion of viral nucleic acid, serum lymphocytes and hs-CRP. RESULTS: The CT scores of the fever group and lymphopenia group were significantly higher than those of normal group on admission, at peak time and at discharge; and the CT scores of normal hs-CRP group were significantly lower than those of the elevated hs-CRP group at peak time and at discharge (P all<0.05). The time to onset of pneumonia resolution on CT image was moderately correlated with negative conversion duration of viral nucleic acid (r =0.501, P<0.05) and the recovery time of hs-CPR (r =0.496, P<0.05). CONCLUSIONS: COVID-19 pneumonia patients with no fever, normal lymphocytes and hs-CRP had mild lesions on admission, and presented with more absorption and fewer pulmonary lesions on discharge. The negative conversion duration of viral nucleic acid and the recovery time of hs-CPR may be the indicator of the pneumonia resolution.

Cultural Differences in the Construal of Suffering and the COVID-19 Pandemic

The present research examines how suffering is construed across cultures. Study 1 (N 1 = 264;N 2 = 745) asked participants to provide free associations for suffering. Chinese individuals generated more positive associations than did Euro-Canadians. Study 2 (N = 522) had participants create a hypothetical potion of suffering to represent what people would experience while suffering. Chinese participants added more positive ingredients and fewer negative ingredients than Euro-Canadians did. How would cultural differences in the construal of suffering matter in a real-life negative situation? Study 3 (N = 608) showed that Chinese participants generated a greater proportion of potential positive outcomes for the COVID-19 outbreak and reported more positive affect during the pandemic than did Euro-Canadians. Thus, Chinese construe suffering more positively than Euro-Canadians. These findings are consistent with previous research on cultural differences in dialectical thinking and lay theory of change and have implications for coping and resilience.

Effect of the Phytochemical Agents against the SARS-CoV and Some of them Selected for Application to COVID-19: A Mini-Review.

BACKGROUND: The aim of the present review is to provide basic knowledge regarding the treatment of Coronavirus via medicinal plants. Coronavirus (COVID-19, SARS-CoV, and MERS-CoV) as a viral pneumonia causative agent, has infected thousands of people in China and worldwide. Currently, there is no specific medicine or vaccine available that can treat or prevent this virus and this has posed a severe threat to human health; therefore, there is an urgent need to develop a novel drug or anticoronavirus vaccine. However, natural compounds to treat coronaviruses are the most effective alternative and complementary therapies due to their diverse range of biological and therapeutic properties. METHODS: We performed an open-ended, English restricted search of Scopus database, Web of Science, and Pubmed for all available literature from Jan-March, 2020, using terms related to phytochemical compounds, medicinal plants and coronavirus. RESULTS: The view on anti-coronavirus (anti-CoV) activity in the plant-derived phytochemicals and medicinal plants gives a strong base to develop a novel treatment employing these compounds for coronavirus. Various phytochemicals and medicinal plant extracts have been revised and are considered as potential anti-CoV agents for effective control of the virus and future drug development. Herein, we discuss some important plants (Scutellaria baicalensis, Psorothamnus arborescens, Glycyrrhiza radix, Glycyrrhiza uralensis, Lycoris radiate, Phyllanthus emblica, Camellia sinensis, Hyptis atrorubens Poit, Fraxinus sieboldiana, Erigeron breviscapus, Citri Reticulatae Pericarpium, Amaranthus tricolor, Phaseolus vulgaris, Rheum palmatum, Curcuma longa and Myrica cerifera) that have emerged to have broad-spectrum antiviral activity. CONCLUSION: Nigella sativa has potent anti-SARS-CoV activity and it might be a useful source for developing novel antiviral therapies for coronavirus.

Clinical Potential of UTE-MRI for Assessing COVID-19: Patient- and Lesion-Based Comparative Analysis.

BACKGROUND: Chest computed tomography (CT) has shown tremendous clinical potential for screening, diagnosis, and surveillance of COVID-19. However, safety concerns are warranted due to repeated exposure of X-rays over a short period of time. Recent advances in MRI suggested that ultrashort echo time MRI (UTE-MRI) was valuable for pulmonary applications. PURPOSE: To evaluate the effectiveness of UTE-MRI for assessing COVID-19. STUDY TYPE: Prospective. POPULATION: In all, 23 patients with COVID-19 and with an average interval of 2.81 days between hospital admission and image examination. FIELD STRENGTH/SEQUENCE: 3T; Respiratory-gated three-dimensional radial UTE pulse sequence. ASSESSMENT: Image quality score. Patient- and lesion-based interobserver and intermethod agreement for identifying the representative image findings of COVID-19. STATISTICAL TESTS: Wilcoxon-rank sum test, Kendall's coefficient of concordance (Kendall's W), intraclass coefficients (ICCs), and weighted kappa statistics. RESULTS: There was no significant difference between the image quality of CT and UTE-MRI (CT vs. UTE-MRI: 4.3 ± 0.4 vs. 4.0 ± 0.5, P = 0.09). Moreover, both patient- and lesion-based interobserver agreement of CT and UTE-MRI for evaluating the image signs of COVID-19 were determined as excellent (ICC: 0.939-1.000, P < 0.05; Kendall's W: 0.894-1.000, P < 0.05.). In addition, the intermethod agreement of two image modalities for assessing the representative findings of COVID-19 including affected lobes, total severity score, ground glass opacities (GGO), consolidation, GGO with consolidation, the number of crazy paving pattern, and linear opacities, as well as pseudocavity were all determined as substantial or excellent (kappa: 0.649-1.000, P < 0.05; ICC: 0.913-1.000, P < 0.05). DATA CONCLUSION: Pulmonary MRI with UTE is valuable for assessing the representative image findings of COVID-19 with a high concordance to CT. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:397-406.

Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study.

BACKGROUND: To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). METHODS: The Ethic Committee of our institution approved the protocol of this study and waived the requirement for patient informed consent. Two hundreds and ninety-five patients were enrolled in this study (healthy person: 149; COVID-19 patients: 146), which were divided into three separate non-overlapping cohorts (training set, n=135, healthy person, n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10; test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained and tested to classify the images as having manifestation of COVID-19 or as healthy. A radiologist also blindly evaluated all the test images and rechecked the misdiagnosed cases by DenseNet. Receiver operating characteristic curves (ROC) and areas under the curve (AUCs) were used to assess the model performance. The sensitivity, specificity and accuracy of DenseNet model and radiologist were also calculated. RESULTS: The DenseNet algorithm model yielded an AUC of 0.99 (95% CI: 0.958-1.0) in the validation set and 0.98 (95% CI: 0.972-0.995) in the test set. The threshold value was selected as 0.8, while for validation and test sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively. The sensitivity of radiologist was 94%, the specificity was 96%, while the accuracy was 95%. CONCLUSIONS: Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists' evaluation) and radiologists' workload.

CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients.

Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients. Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models. Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87~0.99; C-index=0.88, 95% CI 0.81~0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05~1.84, P=0.023) and 1.67 (95% CI 1.17~2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer. Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.

An analysis of spatiotemporal pattern for COIVD-19 in China based on space-time cube.

This study seeks to examine and analyze the spatial and temporal patterns of 2019 novel coronavirus disease (COVID-19) outbreaks and identify the spatiotemporal distribution characteristics and changing trends of cases. Hence, local outlier analysis and emerging spatiotemporal hot spot analysis were performed to analyze the spatiotemporal clustering pattern and cold/hot spot trends of COVID-19 cases based on space-time cube during the period from 23 January 2020 to 24 February 2020. The main findings are as follows: (1) The outbreak had spread rapidly throughout the country within a short time and the current totality incidence rate has decreased. (2) The spatiotemporal distribution of cases was uneven. In terms of the spatiotemporal clustering pattern, Wuhan and Shiyan city were the center as both cities had high-high clustering pattern with a surrounding unstable multiple-type pattern in partial areas of Henan, Anhui, Jiangxi, and Hunan provinces, and Chongqing city. Those regions are continuously in the hot spot on the spatiotemporal tendency. (3) The spatiotemporal analysis technology based on the space-time cube can analyze comprehensively the spatiotemporal pattern of epidemiological data and produce a visual output of the consequences, which can reflect intuitively the distribution and trend of data in space-time. Therefore, the Chinese government should strengthen the prevention and control efforts in a targeted manner to cope with a highly changeable situation.

Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia.

BackgroundThe chest CT findings of patients with 2019 Novel Coronavirus (2019-nCoV) pneumonia have not previously been described in detail.PurposeTo investigate the clinical, laboratory, and imaging findings of emerging 2019-nCoV pneumonia in humans.Materials and MethodsFifty-one patients (25 men and 26 women; age range 16-76 years) with laboratory-confirmed 2019-nCoV infection by using real-time reverse transcription polymerase chain reaction underwent thin-section CT. The imaging findings, clinical data, and laboratory data were evaluated.ResultsFifty of 51 patients (98%) had a history of contact with individuals from the endemic center in Wuhan, China. Fever (49 of 51, 96%) and cough (24 of 51, 47%) were the most common symptoms. Most patients had a normal white blood cell count (37 of 51, 73%), neutrophil count (44 of 51, 86%), and either normal (17 of 51, 35%) or reduced (33 of 51, 65%) lymphocyte count. CT images showed pure ground-glass opacity (GGO) in 39 of 51 (77%) patients and GGO with reticular and/or interlobular septal thickening in 38 of 51 (75%) patients. GGO with consolidation was present in 30 of 51 (59%) patients, and pure consolidation was present in 28 of 51 (55%) patients. Forty-four of 51 (86%) patients had bilateral lung involvement, while 41 of 51 (80%) involved the posterior part of the lungs and 44 of 51 (86%) were peripheral. There were more consolidated lung lesions in patients 5 days or more from disease onset to CT scan versus 4 days or fewer (431 of 712 lesions vs 129 of 612 lesions; P < .001). Patients older than 50 years had more consolidated lung lesions than did those aged 50 years or younger (212 of 470 vs 198 of 854; P < .001). Follow-up CT in 13 patients showed improvement in seven (54%) patients and progression in four (31%) patients.ConclusionPatients with fever and/or cough and with conspicuous ground-glass opacity lesions in the peripheral and posterior lungs on CT images, combined with normal or decreased white blood cells and a history of epidemic exposure, are highly suspected of having 2019 Novel Coronavirus (2019-nCoV) pneumonia.© RSNA, 2020.