NETs Promote Inflammatory Injury by Activating cGAS-STING Pathway in Acute Lung Injury.

Acute respiratory distress syndrome (ARDS) threatens the survival of critically ill patients, the mechanisms of which are still unclear. Neutrophil extracellular traps (NETs) released by activated neutrophils play a critical role in inflammatory injury. We investigated the role of NETs and the underlying mechanism involved in acute lung injury (ALI). We found a higher expression of NETs and cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) in the airways, which was reduced by Deoxyribonuclease I (DNase I) in ALI. The administration of the STING inhibitor H-151 also significantly relieved inflammatory lung injury, but failed to affect the high expression of NETs in ALI. We isolated murine neutrophils from bone marrow and acquired human neutrophils by inducing HL-60 to differentiate. After the PMA interventions, exogenous NETs were obtained from such extracted neutrophils. Exogenous NETs intervention in vitro and in vivo resulted in airway injury, and such inflammatory lung injury was reversed upon degrading NETs with or inhibiting cGAS-STING with H-151 as well as siRNA STING. In conclusion, cGAS-STING participates in regulating NETs-mediated inflammatory pulmonary injury, which is expected to be a new therapeutic target for ARDS/ALI.

Early enteral nutrition improves the outcome of critically ill patients with COVID-19: A retrospective study.

BACKGROUND AND OBJECTIVES: To evaluate the nutritional status of critically ill patients with COVID-19 and to determine which route of nutrition support is advantageous. METHODS AND STUDY DESIGN: This retrospective study was conducted in the ICU of a designated COVID-19 hospital. Patients were divided into an enteral nutrition (EN) group and parenteral nutrition (PN) group according to the initial route of nutrition support. NRS-2002 and NUTRIC were used to assess nutritional status. Blood nutritional markers such as albumin, total protein and hemoglobin were compared at baseline and seven days later. The primary endpoint was 28-day mortality. RESULTS: A total of 27 patients were enrolled in the study - 14 in the EN group and 13 in the PN group - and there were no significant demographic differences between groups. Most patients (96.3% NRS2002 score ≥5, 85.2% NUTRIC score ≥5) were at high nutritional risk. There was no significant difference in baseline albumin, total protein and hemoglobin levels between groups. After 7 days, albumin levels were significantly higher in the EN group than in the PN group (p=0.030). There was no significant difference in the other two indicators. The 28-day mortality was 50% in the EN group and 76.9% in the PN group. Kaplan-Meier survival analysis revealed significant differences between the groups (p=0.030). Cox proportional risk regression indicated that route of nutrition support was also an independent prognostic risk factor. CONCLUSIONS: The incidence of nutritional risk in critically ill patients with COVID-19 is very high. Early EN may be beneficial to patient outcomes.

A rapid screening classifier for diagnosing COVID-19.

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction. Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians. Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.