BackgroundThree decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data.ObjectivesWe estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations.MethodsWe developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China’s recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations.ResultsThe overall model cross-validation R2 and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R2 = 0.73, regression slope = 0.91) and seasonal (R2 = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 μg/m3 between 2004 and 2007 and a decrease of 0.46 μg/m3 between 2008 and 2013.ConclusionsOur satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.CitationMa Z, Hu X, Sayer AM, Levy R, Zhang Q, Xue Y, Tong S, Bi J, Huang L, Liu Y. 2016. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013. Environ Health Perspect 124:184–192; http://dx.doi.org/10.1289/ehp.1409481

No abstract

Triple negative breast cancer (TNBC), which constitutes 10%-20% of all breast cancers, is associated with aggressive progression, a high rate of metastasis, and poor prognosis. The treatment of patients with TNBC remains a great clinical challenge. Preclinical reports support the combination immunotherapy of cancer vaccines and immune checkpoint blockades in non-immunogenic tumors. In this study, we constructed nanoparticles (NPs) to deliver an mRNA vaccine encoding tumor antigen MUC1 to dendritic cells (DCs) in lymph nodes to activate and expand tumor-specific T cells. An anti-CTLA-4 (cytotoxic T-lymphocyte-associated protein 4) monoclonal antibody was combined with the mRNA vaccine to enhance the anti-tumor benefits. In vivo studies demonstrated that the NP-based mRNA vaccine, targeted to mannose receptors on DCs, could successfully express tumor antigen in the DCs of the lymph node; that the NP vaccine could induce a strong, antigen-specific, in vivo cytotoxic T lymphocyte response against TNBC 4T1 cells; and that combination immunotherapy of the vaccine and anti-CTLA-4 monoclonal antibody could significantly enhance anti-tumor immune response compared to the vaccine or monoclonal antibody alone. These data support both the NP as a carrier for delivery of mRNA vaccine and a potential combination immunotherapy of the NP-based mRNA vaccine and the CTLA-4 inhibitor for TNBC.

No abstract