Activation of the RIG-I/MAVS Signaling Pathway during Human Adenovirus Type 3 Infection Impairs the Pro-Inflammatory Response Induced by Secondary Infection with Staphylococcus aureus.

The exacerbation of pneumonia in children with human adenovirus type 3 (HAdV-3E) is secondary to a Staphylococcus aureus (S. aureus) infection. The influence of host-pathogen interactions on disease progression remains unclear. It is important to note that S. aureus infections following an HAdV-3E infection are frequently observed in clinical settings, yet the underlying susceptibility mechanisms are not fully understood. This study utilized an A549 cell model to investigate secondary infection with S. aureus following an HAdV-3E infection. The findings suggest that HAdV-3E exacerbates the S. aureus infection by intensifying lung epithelial cell damage. The results highlight the role of HAdV-3E in enhancing the interferon signaling pathway through RIG-I (DDX58), resulting in the increased expression of interferon-stimulating factors like MX1, RSAD2, and USP18. The increase in interferon-stimulating factors inhibits the NF-κB and MAPK/P38 pro-inflammatory signaling pathways. These findings reveal new mechanisms of action for HAdV-3E and S. aureus in secondary infections, enhancing our comprehension of pathogenesis.

A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856-0.988) and 0.959 (95% CI 0.910-1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

COVID-19 with acute cholecystitis: a case report.

BACKGROUND: The 2019 novel coronavirus (COVID-19) presents a major threat to public health and has rapidly spread worldwide since the outbreak in Wuhan, Hubei Province, China in 2019. To date, there have been few reports of the varying degrees of illness caused by the COVID-19. CASE PRESENTATION: A case of 68-year-old female with COVID-19 pneumonia who had constant pain in the right upper quadrant of her abdomen during her hospitalization that was finally diagnosed as acute cholecystitis. Ultrasound-guided percutaneous transhepatic gallbladder drainage (PTGD) was performed, and the real-time fluorescence polymerase chain reaction (RT-PCR) COVID-19 nucleic acid assay of the bile was found to be negative. PTGD, antibacterial and anti-virus combined with interferon inhalation treatment were successful. CONCLUSION: The time course of chest CT findings is typical for COVID-19 pneumonia. PTGD is useful for acute cholecystitis in COVID-19 patients. Acute cholecystitis is likely to be caused by COVID-19 .

Author Correction: Influences of scan-position on clinical ultra-high-resolution CT scanning: a preliminary study.

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Influences of scan-position on clinical ultra-high-resolution CT scanning: a preliminary study.

The aim of this study is to access influences of scan-position on clinical ultra-high-resolution CT scanning. We proposed a breath-hold assisted ultra-high-resolution scanning technology (scan scheme G) and compared with scan scheme A (regular CT plain scan) and scheme B (1024 ultra-high-resolution scan with patients stay in supine position). A total of 30 patients with fGGO were included in this study. Three highly experienced chest imaging doctors were employed to score the image and to select regions of interest (ROIs) for CT value and signal-to-noise ratio (SNR) calculation. In comparison with scan A and B, this new scan scheme G shows more clear CT images and higher SNRs at overall lung field (the p-values of A versus G and B versus G are 0.041 and 0.065, respectively). These findings suggest that scan-G provides a better image quality and contributes significantly to clinical detection accuracy of fGGO.