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DNA damage could lead to the accumulation of cytosolic DNA, and the cytosolic DNA-sensing pathway has been implicated in multiple inflammatory diseases. However, the role of cytosolic DNA-sensing pathway in asthma pathogenesis is still unclear. This article explored the role of airway epithelial cyclic GMP-AMP synthase (cGAS), the major sensor of cytosolic dsDNA, in asthma pathogenesis. Cytosolic dsDNA accumulation in airway epithelial cells (ECs) was detected in the setting of allergic inflammation both in vitro and in vivo. Mice with cGAS deletion in airway ECs were used for OVA-or house dust mite (HDM)-induced allergic airway inflammation. Additionally, the effects of cGAS knockdown on IL-33-induced GM-CSF production and the mechanisms by which IL-33 induced cytosolic dsDNA accumulation in human bronchial epithelial (HBE) cells were explored. Increased accumulation of cytosolic dsDNA was observed in airway epithelium of OVA-or HDM-challenged mice and in HBE cells treated with IL-33. Deletion of cGAS in the airway ECs of mice significantly attenuated the allergic airway inflammation induced by OVA or HDM. Mechanistically, cGAS participates in promoting T H 2 immunity likely via regulating the production of airway epithelial GM-CSF. Furthermore, Mito-TEMPO could reduce IL-33-induced cytoplasmic dsDNA accumulation in HBE cells possibly through suppressing the release of mitochondrial DNA into the cytosol. In conclusion, airway epithelial cGAS plays an important role via sensing the cytosolic dsDNA in asthma pathogenesis and could serve as a promising therapeutic target against allergic airway inflammation.

BackgroundIt is now recognized that asthma can present in different forms. Typically, asthma present with symptoms of wheeze, breathlessness and cough. Atypical forms of asthma such as cough variant asthma (CVA) or chest tightness variant asthma (CTVA) do not wheeze. We hypothesize that these different forms of asthma may have distinctive cellular and molecular features.Methods30 patients with typical or classical asthma (CA), 27 patients with CVA, 30 patients with CTVA, and 30 healthy control adults were enrolled in this prospective study. We measured serum IgE, lung function, sputum eosinophils, nitric oxide in exhaled breath (FeNO). We performed proteomic analysis of induced-sputum supernatants by mass spectrometry.ResultsThere were no significant differences in atopy and FEV1 among patients with CA, CVA, and CTVA. Serum IgE, sputum eosinophil percentages, FeNO, anxiety and depression scores were significantly increased in the three presentations of asthmatic patients as compared with healthy controls but there was no difference between the asthmatic groups. Comprehensive mass spectrometric analysis revealed more than a thousand proteins in the sputum from patients with CA, CVA, and CTVA, among which 23 secreted proteins were higher in patients than that in controls.ConclusionsPatients with CA, CVA, or CTVA share common clinical characteristics of eosinophilic airway inflammation. And more importantly, their sputum samples were composed with common factors with minor distinctions. These findings support the concept that these three different presentations of asthma have similar pathogenetic mechanism in terms of an enhanced Th2 associated with eosinophilia. In addition, this study identified a pool of novel biomarkers for diagnosis of asthma and to label its subtypes. Trial registration http://www.chictr.org.cn (ChiCTR-OOC-15006221)Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-017-1264-y) contains supplementary material, which is available to authorized users.

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.

Precise and high-resolution carbon dioxide (CO 2 ) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO 2 Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO 2 emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO 2 ) retrievals. Our study on the global CO 2 emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO 2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO 2 emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.