Publisher Correction: SARS-CoV-2 infection of human ACE2-transgenic mice causes severe lung inflammation and impaired function.

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SARS-CoV-2 infection of human ACE2-transgenic mice causes severe lung inflammation and impaired function.

Although animal models have been evaluated for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, none have fully recapitulated the lung disease phenotypes seen in humans who have been hospitalized. Here, we evaluate transgenic mice expressing the human angiotensin I-converting enzyme 2 (ACE2) receptor driven by the cytokeratin-18 (K18) gene promoter (K18-hACE2) as a model of SARS-CoV-2 infection. Intranasal inoculation of SARS-CoV-2 in K18-hACE2 mice results in high levels of viral infection in lungs, with spread to other organs. A decline in pulmonary function occurs 4 days after peak viral titer and correlates with infiltration of monocytes, neutrophils and activated T cells. SARS-CoV-2-infected lung tissues show a massively upregulated innate immune response with signatures of nuclear factor-κB-dependent, type I and II interferon signaling, and leukocyte activation pathways. Thus, the K18-hACE2 model of SARS-CoV-2 infection shares many features of severe COVID-19 infection and can be used to define the basis of lung disease and test immune and antiviral-based countermeasures.

SARS-CoV-2 infection in the lungs of human ACE2 transgenic mice causes severe inflammation, immune cell infiltration, and compromised respiratory function.

Severe Acute Respiratory Syndrome Coronavirus -2 (SARS-CoV-2) emerged in late 2019 and has spread worldwide resulting in the Coronavirus Disease 2019 (COVID-19) pandemic. Although animal models have been evaluated for SARS-CoV-2 infection, none have recapitulated the severe lung disease phenotypes seen in hospitalized human cases. Here, we evaluate heterozygous transgenic mice expressing the human ACE2 receptor driven by the epithelial cell cytokeratin-18 gene promoter (K18-hACE2) as a model of SARS-CoV-2 infection. Intranasal inoculation of SARS-CoV-2 in K18-hACE2 mice results in high levels of viral infection in lung tissues with additional spread to other organs. Remarkably, a decline in pulmonary function, as measured by static and dynamic tests of respiratory capacity, occurs 4 days after peak viral titer and correlates with an inflammatory response marked by infiltration into the lung of monocytes, neutrophils, and activated T cells resulting in pneumonia. Cytokine profiling and RNA sequencing analysis of SARS-CoV-2-infected lung tissues show a massively upregulated innate immune response with prominent signatures of NF-kB-dependent, type I and II interferon signaling, and leukocyte activation pathways. Thus, the K18-hACE2 model of SARS-CoV-2 infection recapitulates many features of severe COVID-19 infection in humans and can be used to define the mechanistic basis of lung disease and test immune and antiviral-based countermeasures.

Unsupervised dealiasing and denoising of color-Doppler data.

Color Doppler imaging (CDI) is the premiere modality to analyze blood flow in clinical practice. In the prospect of producing new CDI-based tools, we developed a fast unsupervised denoiser and dealiaser (DeAN) algorithm for color Doppler raw data. The proposed technique uses robust and automated image post-processing techniques that make the DeAN clinically compliant. The DeAN includes three consecutive advanced and hands-off numerical tools: (1) statistical region merging segmentation, (2) recursive dealiasing process, and (3) regularized robust smoothing. The performance of the DeAN was evaluated using Monte-Carlo simulations on mock Doppler data corrupted by aliasing and inhomogeneous noise. Fifty aliased Doppler images of the left ventricle acquired with a clinical ultrasound scanner were also analyzed. The analytical study demonstrated that color Doppler data can be reconstructed with high accuracy despite the presence of strong corruption. The normalized RMS error on the numerical data was less than 8% even with signal-to-noise ratio as low as 10dB. The algorithm also allowed us to recover highly reliable Doppler flows in clinical data. The DeAN is fast, accurate and not observer-dependent. Preliminary results showed that it is also directly applicable to 3-D data. This will offer the possibility of developing new tools to better decipher the blood flow dynamics in cardiovascular diseases.