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Objectives: To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease (ILD) patients compared with HRCT reconstructed with hybrid iterative reconstruction (hybrid-IR). Methods: Seventy ILD patients were prospectively enrolled and underwent HRCT (120 kVp, automatic tube current) and LDCT (120 kVp, 30 mAs) scans. HRCT images were reconstructed with hybrid-IR (Adaptive Iterative Dose Reduction 3-Dimensional [AIDR3D], standard-setting); LDCT images were reconstructed with DLR (Advanced Intelligence Clear-IQ Engine [AiCE], lung/bone, mild/standard/strong setting). Image noise, streak artifact, overall image quality, and visualization of normal and abnormal features of ILD were evaluated. Results: The mean radiation dose of LDCT was 38% of HRCT. Objective image noise of reconstructed LDCT images was 33.6 to 111.3% of HRCT, and signal-to-noise ratio (SNR) was 0.9 to 3.1 times of the latter (p < 0.001). LDCT-AiCE was not significantly different from or even better than HRCT in overall image quality and visualization of normal lung structures. LDCT-AiCE (lung, mild/standard/strong) showed progressively better recognition of ground glass opacity than HRCT-AIDR3D (p < 0.05, p < 0.01, p < 0.001), and LDCT-AiCE (lung, mild/standard/strong; bone, mild) was superior to HRCT-AIDR3D in visualization of architectural distortion (p < 0.01, p < 0.01, p < 0.01; p < 0.05). LDCT-AiCE (bone, strong) was better than HRCT-AIDR3D in the assessment of bronchiectasis and/or bronchiolectasis (p < 0.05). LDCT-AiCE (bone, mild/standard/strong) was significantly better at the visualization of honeycombing than HRCT-AIDR3D (p < 0.05, p < 0.05, p < 0.01). Conclusion: Deep learning reconstruction could effectively reduce radiation dose and keep image quality in ILD patients compared to HRCT with hybrid-IR. Key Points: • Deep learning reconstruction was a novel image reconstruction algorithm based on deep convolutional neural networks. It was applied in chest CT studies and received auspicious results. • HRCT plays an essential role in the whole process of diagnosis, treatment efficacy evaluation, and follow-ups for interstitial lung disease patients. However, cumulative radiation exposure could increase the risks of cancer. • Deep learning reconstruction method could effectively reduce the radiation dose and keep the image quality compared with HRCT reconstructed with hybrid iterative reconstruction in patients with interstitial lung disease. [ABSTRACT FROM AUTHOR]

To describe the epidemiologic, clinical, laboratory, and radiological characteristics and prognoses of COVID-19 confirmed patients in a single center in Beijing, China. The study retrospectively included 19 patients with nucleic acid-confirmed SARS-CoV-2 infection at our hospital from January 20 to March 5, 2020. The final follow-up date was March 14, 2020. The epidemiologic and clinical information was obtained through direct communication with the patients or their family members. Laboratory results retrieved from medical records and radiological images were analyzed both qualitatively by two senior chest radiologists as well as quantitatively via an artificial intelligence software. We identified 5 family clusters (13/19, 68.4%) from the study cohort. All cases had good clinical prognoses and were either mild (3/19) or moderate (16/19) clinical types. Fever (15/19, 78.9%) and dry cough (11/19, 57.9%) were common symptoms. Two patients received negative results for more than three consecutive viral nucleic acid tests. The longest interval between an initial CT abnormal finding and a confirmed diagnosis was 30 days. One patient's nucleic acid test turned positive on the follow-up examination after discharge. The presence of radiological abnormalities was non-specific for the diagnosis of COVID-19. COVID-19 patients with mild or no clinical symptoms are common in Beijing, China. Radiological abnormalities are mostly non-specific and massive CT examinations for COVID-19 screening should be avoided. Analyses of the contact histories of diagnosed cases in combination with clinical, radiological and laboratory findings are crucial for the early detection of COVID-19. Close monitoring after discharge is also recommended. [ABSTRACT FROM AUTHOR]

Highlights • LX,called as the braking signal in inflammation,has a significant impact on preventing and resolving inflammation. • LXs play critical roles in a lot of inflammatory processes, whereas the anti-inflammatory effects on COPD remain unknown. • We investigate the role of the lipoxin receptor agonist (BML-111) in inflammasome activation and ROS production in COPD. • This study provides a new theoretical foundation for the development of related drugs and therapeutic schedules for COPD. Abstract The aim of this study is to investigate whether the lipoxin receptor agonist BML-111 exerts a protective effect against inflammation in a mouse model of chronic obstructive pulmonary disease (COPD) by regulating NLRP3 inflammasome activation and reactive oxygen species (ROS) production. In this study, mice were randomly divided into the following five groups: control group (Control), COPD model group (Model), BML-111 low-dose group (Low-BML), BML-111 high-dose group (High-BML) and Dexamethasone group (Dex). NLRP3 involvement and oxidative stress were evaluated. Differential cell counts in the BALF were calculated to obtain a reliable enumeration of each cell type, and the levels of TGF-β, TNF-α, IL-1β, and IL-10 in BALF were evaluated using ELISA. We found that the white blood cell and lymphocyte numbers in the BALF were significantly lower in the High-BML group than in the Model group. ELISA of the BALF showed that BML-111 reduced TGF-β and IL-1β levels to some extent. HE staining showed various degrees of reduction in inflammatory cell infiltration in the bronchopulmonary tissue and blood vessels of the Low-BML, High-BML and Dex groups. Measurement of oxidative stress showed that SOD activity was significantly upregulated and that the increase in MDA content was prevented in the High-BML and Dex groups. According to the Western blotting analysis, the levels of NLRP3, Cleaved-IL-1β and Cleaved-caspase-1 were decreased and Nrf-2 was increased to various extents in the Low-BML, High-BML and Dex groups. Based on these findings, BML-111 may prevent NLRP3 inflammasome activation and inhibit ROS production via upregulation of Nrf-2, thereby exerting an anti-inflammatory effect on COPD model mice. [ABSTRACT FROM AUTHOR]