StatementChest x-ray abnormalities in COVID-19 mirror those of CT, demonstrating bilateral peripheral consolidation. Chest x-ray findings have a lower sensitivity than initial RT-PCR testing (69% versus 91%, respectively). Key Results In a cohort of patients with COVID-19 infection and imaging follow-up, baseline chestx-ray had a sensitivity of 69%, compared to 91% for initial RT-PCR. Chest x-ray abnormalities preceded positive RT-PCR in 6/64 (9%) patients. Common chest x-ray findings mirror those previously described for CT: bilateral, peripheral, consolidation and/or ground glass opacities. I n P r e s sBackground Current COVID-19 radiological literature is dominated by CT and a detailed description of chest x-ray (CXR) appearances in relation to the disease time course is lacking. PurposeTo describe the time course and severity of the CXR findings of COVID-19 and correlate these with real time reverse transcription polymerase chain reaction (RT-PCR) testing for SARS-Cov-2 nucleic acid. Materials and MethodsRetrospective study of COVID-19 patients with RT-PCR confirmation and CXRs admitted across 4 hospitals evaluated between January and March 2020. Baseline and serial CXRs (total 255 CXRs) were reviewed along with RT-PCRs. Correlation with concurrent CTs (total 28 CTs) was made when available. Two radiologists scored each CXR in consensus for: consolidation, ground glass opacity (GGO), location and pleural fluid. A severity index was determined for each lung. The lung scores were summed to produce the final severity score. ResultsThere were 64 patients (26 men, mean age 5619 years). Of these, 58, 44 and 38 patients had positive initial RT-PCR (91%, [CI: 81-96%]), abnormal baseline CXR (69%, [CI: 56-80%]) and positive initial RT-PCR with abnormal baseline CXR (59 [CI:46-71%]) respectively. Six patients (9%) showed CXR abnormalities before eventually testing positive on RT-PCR. Sensitivity of initial RT-PCR (91% [95% CI: 83-97%]) was higher than baseline CXR (69% [95% CI: 56-80%]) (p = 0.009). Radiographic (mean 6  5 days) and virologic recovery (mean 8  6 days) were not significantly different (p= 0.33). Consolidation was the most common finding (30/64, 47%), followed by GGO (21/64, 33%). CXR abnormalities had a peripheral (26/64, 41%) and lower zone distribution (32/64, 50%) with bilateral involvement (32/64, 50%). Pleural effusion was uncommon (2/64, 3%). The severity of CXR findings peaked at 10-12 days from the date of symptom onset. ConclusionChest x-ray findings in COVID-19 patients frequently showed bilateral lower zone consolidation which peaked at 10-12 days from symptom onset. Abbreviations:RT-PCR -reverse transcriptase polymerase chain reaction, GGO-ground glass opacity

The miR-17-92 locus encodes a cluster of 7 microRNAs transcribed as a single primary transcript. It can accelerate c-Myc induced B cell lymphoma development and is highly expressed in many tumors, including lung tumors. However, the role of miR-17-92 in development has not been well studied. From analysis of microRNAs during lung development, expression of the miR-17-92 cluster is high at early stages, but declines as development proceeds. We used the mouse surfactant protein C (Sftpc) promoter to over-express the cluster in embryonic lung epithelium. Transgenic lungs have a very abnormal lethal phenotype. They contain numerous proliferative epithelial cells that retain high levels of Sox9, a marker of distal progenitors. The differentiation of proximal epithelial cells was also inhibited. Furthermore, a significant increase in the number of neuroendocrine cell clusters was observed in the lungs of dead transgenic pups. We identify a tumor suppressor, Rbl2 which belongs to the Rb family, as a new target for miR-17-5p. Together, these studies suggest that mir-17-92 normally promotes the high proliferation and undifferentiated phenotype of lung epithelial progenitor cells.

During the first two decades of the 21st century, there have been three coronavirus infection outbreaks raising global health concerns by severe acute respiratory syndrome coronavirus (SARS-CoV), the Middle East respiratory syndrome coronavirus (MERS-CoV), and the SARS-CoV-2. Although the reported imaging findings of coronavirus infection are variable and non-specific, the most common initial chest radiograph (CXR) and CT findings are ground-glass opacities and consolidation with peripheral predominance and eventually spread to involve both lungs as the disease progresses. These findings can be explained by the immune pathogenesis of coronavirus infection causing diffuse alveolar damage. Although it is insensitive in mild or early coronavirus infection, the CXR remains as the first-line and the most commonly used imaging modality. That is because it is rapid and easily accessible and helpful for monitoring patient progress during treatment. CT is more sensitive to detect early parenchymal lung abnormalities and disease progression, and can provide an alternative diagnosis. In this pictorial review, various coronavirus infection cases are presented to provide imaging spectrums of coronavirus infection and present differences in imaging among them or from other viral infections, and to discuss the role of imaging in viral infection outbreaks.

Highlights A CT-based radiomics model was developed to differentiate COVID-19 from other causes of GGOs. Classification model for GGO lesions could improve specificity of detecting COVID-19 in a general population. Using radiomics for novel infectious diseases is an advantage when the initial case is limited.

The inferior vena cava (IVC) contrast level sign refers to a contrast-fluid level seen in the IVC on an arterial phase contrast-enhanced computed tomography (CT) scan. This sign has been documented in conditions including cardiac tamponade, myocardial infarction, and cardiac arrest. When this sign is detected, the patient requires immediate attention, with resuscitation initiated as needed, and the referring clinician alerted as soon as possible.

Highlights Developed two simple-to use nomograms for identifying COVID-19 positive patients. Probabilities are provided to allow healthcare leaders to decide suitable cut-offs. Variables are age, white cell count, chest x-ray appearances and contact history. Model variables are easily available in the general hospital setting.

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