Meiyi Fan's Quick Files

The Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity

Changes of Emission Sources to Nitrate Aerosols in Beijing after the Clean Air Actions

Air pollutio

Protective immune mechanisms of Yifei Tongluo, a Chinese herb formulation, in the treatment of mycobacterial infection.

Yifei Tongluo (YFTL) is a traditional Chinese medicine (TCM) formulation which has been shown clinical efficacy in treatment of patients with multidrug-resistant tuberculosis in China. However, the underlying mechanisms of the effects of YFTL are lacking. This study investigated the effects of YFTL on immune regulation with a mouse lung infection model with Bacille Calmette-Guérin (BCG). We found that compared with untreated mice, the lung mycobacterial load in YFTL-treated mice was significantly reduced, accompanied by alleviated pulmonary inflammation with reduction of pro-inflammatory cytokines and increase of prostaglandin E2 (PGE2). Flow cytometry analyses showed that Th1 cells were significantly higher in the lungs of YFTL-treated mice at early infection time. The results suggest that YFTL-treatment down-regulates pulmonary inflammation, which facilitates a rapid infiltration of Th1 cells into the lungs. Moreover, the Th1 cells in the lungs were resolved faster at later time concomitant with increased the regulatory T cells (Tregs). The reduction of mycobacterial burden associated with improved tissue pathology, faster Th1 cell trafficking, and accelerated resolution of Th1 cells in the lungs of YFTL-treated mice indicates that YFTL improves mycobacterial clearance by maintaining lung homeostasis and dynamically regulating T cells in the lung parenchyma, and suggests that YFTL can be used as host-directed therapies that target immune responses to mycobacterial infection

Chemical characteristics of dicarboxylic acids and related organic compounds in PM2.5 during biomass-burning and non-biomass-burning seasons at a rural site of Northeast China

Fine particulate matter (PM2.5) samples were collected using a high-volume air sampler and pre-combusted quartz filters during May 2013 to January 2014 at a background rural site (47 degrees 35 N, 133 degrees 31 E) in Sanjiang Plain, Northeast China. A homologous series of dicarboxylic acids (C-2-C-11) and related compounds (oxoacids, alpha-dicarbonyls and fatty acids) were analyzed by using a gas chromatography (GC) and GC-MS method employing a dibutyl ester derivatization technique. Intensively open biomass-burning (BB) episodes during the harvest season in fall were characterized by high mass concentrations of PM2.5, dicarboxylic acids and levoglucosan. During the BB period, mass concentrations of dicarboxylic acids and related compounds were increased by up to >20 times with different factors for different organic compounds (i.e., succinic (C-4) acid > oxalic (C-2) acid > malonic (C-3) acid). High concentrations were also found for their possible precursors such as glyoxylic acid (omega C-2), 4-oxobutanoic acid, pyruvic acid, glyoxal, and methylglyoxal as well as fatty acids. Levoglucosan showed strong correlations with carbonaceous aerosols (OC, EC, WSOC) and dicarboxylic acids although such good correlations were not observed during non-biomass-burning seasons. Our results clearly demonstrate biomass burning emissions are very important contributors to dicarboxylic acids and related compounds. The selected ratios (e.g., C-3/C-4, maleic acid/fumaric acid, C-2/omega C-2, and C-2/levoglucosan) were used as tracers for secondary formation of organic aerosols and their aging process. Our results indicate that organic aerosols from biomass burning in this study are fresh without substantial aging or secondary production. The present chemical characteristics of organic compounds in biomass-burning emissions are very important for better understanding the impacts of biomass burning on the atmosphere aerosols. (C) 2017 Elsevier Ltd. All rights reserved

DataSheet1_A necroptosis-related prognostic model for predicting prognosis, immune landscape, and drug sensitivity in hepatocellular carcinoma based on single-cell sequencing analysis and weighted co-expression network.ZIP

Background: Hepatocellular carcinoma (HCC) is a highly lethal cancer and is the second leading cause of cancer-related deaths worldwide. Unlike apoptosis, necroptosis (NCPS) triggers an immune response by releasing damage-related molecular factors. However, the clinical prognostic features of necroptosis-associated genes in HCC are still not fully explored.Methods: We analyzed the single-cell datasets GSE125449 and GSE151530 from the GEO database and performed weighted co-expression network analysis on the TCGA data to identify the necroptosis genes. A prognostic model was built using COX and Lasso regression. In addition, we performed an analysis of survival, immunity microenvironment, and mutation. Furthermore, the hub genes and pathways associated with HCC were localized within the single-cell atlas.Results: Patients with HCC in the TCGA and ICGC cohorts were classified using a necroptosis-related model with significant differences in survival times between high- and low-NCPS groups (p Conclusion: Through the analysis of single-cell and bulk multi-omics sequencing data, we constructed a prognostic model related to necroptosis and explored the relationship between high- and low-NCPS groups and immune cell infiltration in HCC. This provides a new reference for further understanding the role of necroptosis in HCC.</p