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1.
Environ Int ; 141: 105776, 2020 May 08.
Artigo em Inglês | MEDLINE | ID: mdl-32402983

RESUMO

To improve air quality, China has been implementing strict clean air policies since 2013. These policies not only substantially improved air quality but may also modify the spatial distribution of air pollution, since urban emission sources were under stricter control and some were moved to rural regions with lower air quality improvement targets and lacking of monitoring. Here, we predicted satellite-based monthly PM2.5 concentrations during 2000-2018 at a 1-km resolution with complete spatial-temporal coverage to analyze changes in the spatial pattern of PM2.5 pollution in China. We found that the PM2.5 concentration in urban regions was higher than that in rural regions of the same city by an average of 3.3 µg/m3 during 2000-2018. This urban-rural disparity in PM2.5 concentration significantly increased from 2.5 µg/m3 in 2000 and peaked in 2007 of 3.8 µg/m3, then it sharply declined by 49% during 2013-2018 with the implementation of clean air policies. This shrinkage in the urban-rural PM2.5 gap was partly due to the 1.3 µg/m3 greater average decrease in the PM2.5 level in the urban region than in the rural region of the same town during 2013-2018 on average. We also observed that cities that started monitoring earlier experienced greater decreases in the urban-rural PM2.5 difference, and regions surrounding monitor showed significantly greater PM2.5 decrease than regions far away from monitor during 2013-2018. Additionally, clean air policies modified the relationship between PM2.5 concentrations and per capita gross domestic product (GDP), leading to a lower PM2.5 level with the same per capita GDP after 2013. Emissions in rural and suburban regions should be considered to further improve air quality in China.

2.
Biochem Biophys Res Commun ; 522(2): 471-478, 2020 Feb 05.
Artigo em Inglês | MEDLINE | ID: mdl-31780258

RESUMO

The inhibition of high glucose on the proliferation and differentiation of osteoblast in alveolar bone are well documented. However, a comprehensive study focused on the molecular mechanisms is still unknown. Recent studies have revealed that caspase-1 participates in the pathological processes of hepatic injury, cancers and diabetes related complications. However, the relationship between pyroptosis and proliferation and differentiation of osteoblasts has not been investigated. This study aimed to explore the possible pyroptosis participating in the inhibition of high glucose on the proliferation and differentiation of osteoblast in alveolar bone. The diabetes model was constructed both in vitro and in vivo to detect the expression of pyroptosis related factors. These results show that high glucose inhibits proliferation and differentiation of osteoblast in alveolar bone through pyroptosis pathway. Furthermore, caspase-1 inhibitor was co-administered with high glucose in ME3T3-E1 cells, which shows that caspase-1 inhibitor could repress effect of high glucose on the proliferation and differentiation of osteoblast. In conclusion, High glucose could activate the pyroptosis through the caspase-1/GSDMD/IL-1ß pathway to inhibit the proliferation and differentiation of osteoblast in alveolar bone, which provides a theoretical basis for clinical treatment of alveolar bone disease in diabetic patients.

3.
Chemosphere ; 239: 124678, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31494323

RESUMO

In the developing countries such as China, most well-developed areas have suffered severe haze pollution, which was associated with increased premature morbidity and mortality and attracted widespread public concerns. Since ground-based PM2.5 monitoring has limited temporal and spatial coverage, satellite aerosol remote sensing data has been increasingly applied to map large-scale PM2.5 characteristics through advanced spatial statistical models. Although most existing research has taken advantage of the polar orbiting satellite instruments, a major limitation of the polar orbiting platform is its limited sampling frequency (e.g., 1-2 times/day), which is insufficient for capturing the PM2.5 variability during short but intense heavy haze episodes. As the first attempt, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding monitoring PM2.5 concentrations, meteorological and land use data on a 6-km modeling grid with complete coverage in time and space. The 10-fold cross-validation R2 was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM2.5 concentrations. After gap filling, the R2 value for the three-stage model was 0.68. We further analyzed two representative large regional episodes, i.e., a "multi-process diffusion episode" during December 21-26, 2015 and a "Chinese New Year episode" during February 7-8, 2016. We concluded that AOD retrieved by geostationary satellites could serve as a new valuable data source for analyzing the heavy air pollution episodes.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental/métodos , Material Particulado/análise , Tecnologia de Sensoriamento Remoto/métodos , Aerossóis/análise , China , Meteorologia , Modelos Estatísticos , Rios , Estações do Ano , Astronave
4.
Proc Natl Acad Sci U S A ; 116(49): 24463-24469, 2019 Dec 03.
Artigo em Inglês | MEDLINE | ID: mdl-31740599

RESUMO

From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population-weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3-70.0) to 42.0 µg/m3 (95% CI: 35.7-48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9-7.1), 4.4- (95% CI: 3.8-4.9), 2.8- (95% CI: 2.5-3.0), and 2.2- (95% CI: 2.0-2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35-0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China's recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

5.
Nat Commun ; 10(1): 4337, 2019 09 25.
Artigo em Inglês | MEDLINE | ID: mdl-31554811

RESUMO

Substantial quantities of air pollution and related health impacts are ultimately attributable to household consumption. However, how consumption pattern affects air pollution impacts remains unclear. Here we show, of the 1.08 (0.74-1.42) million premature deaths due to anthropogenic PM2.5 exposure in China in 2012, 20% are related to household direct emissions through fuel use and 24% are related to household indirect emissions embodied in consumption of goods and services. Income is strongly associated with air pollution-related deaths for urban residents in which health impacts are dominated by indirect emissions. Despite a larger and wealthier urban population, the number of deaths related to rural consumption is higher than that related to urban consumption, largely due to direct emissions from solid fuel combustion in rural China. Our results provide quantitative insight to consumption-based accounting of air pollution and related deaths and may inform more effective and equitable clean air policies in China.


Assuntos
Poluição do Ar/análise , Exposição Ambiental/estatística & dados numéricos , Mortalidade Prematura/tendências , Saúde da População Rural/estatística & dados numéricos , Fatores Socioeconômicos , Saúde da População Urbana/estatística & dados numéricos , Poluição do Ar/efeitos adversos , Grupo com Ancestrais do Continente Asiático/estatística & dados numéricos , China , Exposição Ambiental/efeitos adversos , Exposição Ambiental/análise , Política Ambiental/legislação & jurisprudência , Política Ambiental/tendências , Características da Família , Humanos , Mortalidade Prematura/etnologia , Material Particulado/análise
6.
Environ Int ; 133(Pt A): 105151, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31520956

RESUMO

BACKGROUND: Substantial increases in wildfire activity have been recorded in recent decades. Wildfires influence the chemical composition and concentration of particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5). However, relatively few epidemiologic studies focus on the health impacts of wildfire smoke PM2.5 compared with the number of studies focusing on total PM2.5 exposure. OBJECTIVES: We estimated the associations between cardiorespiratory acute events and exposure to smoke PM2.5 in Colorado using a novel exposure model to separate smoke PM2.5 from background ambient PM2.5 levels. METHODS: We obtained emergency department visits and hospitalizations for acute cardiorespiratory outcomes from Colorado for May-August 2011-2014, geocoded to a 4 km geographic grid. Combining ground measurements, chemical transport models, and remote sensing data, we estimated smoke PM2.5 and non-smoke PM2.5 on a 1 km spatial grid and aggregated to match the resolution of the health data. Time-stratified, case-crossover models were fit using conditional logistic regression to estimate associations between fire smoke PM2.5 and non-smoke PM2.5 for overall and age-stratified outcomes using 2-day averaging windows for cardiovascular disease and 3-day windows for respiratory disease. RESULTS: Per 1 µg/m3 increase in fire smoke PM2.5, statistically significant associations were observed for asthma (OR = 1.081 (1.058, 1.105)) and combined respiratory disease (OR = 1.021 (1.012, 1.031)). No significant relationships were evident for cardiovascular diseases and smoke PM2.5. Associations with non-smoke PM2.5 were null for all outcomes. Positive age-specific associations related to smoke PM2.5 were observed for asthma and combined respiratory disease in children, and for asthma, bronchitis, COPD, and combined respiratory disease in adults. No significant associations were found in older adults. DISCUSSION: This is the first multi-year, high-resolution epidemiologic study to incorporate statistical and chemical transport modeling methods to estimate PM2.5 exposure due to wildfires. Our results allow for a more precise assessment of the population health impact of wildfire-related PM2.5 exposure in a changing climate.

7.
Sci Total Environ ; 692: 361-370, 2019 Nov 20.
Artigo em Inglês | MEDLINE | ID: mdl-31351280

RESUMO

In 2013, the Chinese government announced its first air quality standard for PM2.5 (particulate matter with a diameter < 2.5 µm) which requires annual mean PM2.5 concentration to achieve the World Health Organization (WHO) interim target 1 of 35 µg/m3 nationwide including the most polluted region of Beijing-Tianjin-Hebei (BTH). Here, we explore the future mitigation pathways for the BTH region to investigate the possibility of air quality attainment by 2030 in that region, by developing two energy scenarios (i.e., baseline energy scenario and enhanced energy scenario) and two end-of-pipe scenarios (i.e., business as usual scenario and best available technology scenario) and simulating future air quality for different scenarios using the WRF/CMAQ model. Results showed that without stringent energy and industrial structure adjustment, even the most advanced end-of-pipe technologies did not allow the BTH region to attain the 35 µg/m3 target. Under the most stringent scenario that coupled the enhanced structure adjustment measures and the best available end-of-pipe measures, the emissions of SO2, NOx, PM2.5 and NMVOCs (nonmethane volatile organic compounds) were estimated to be reduced by 85%, 74%, 82% and 72%, respectively, in 2030 over the BTH region. As a result, the simulated annual mean PM2.5 concentrations in Beijing, Tianjin and Hebei could decline to 23, 28 and 28 µg/m3, respectively, all of which achieved the 35 µg/m3 target by 2030. Our study identified a feasible pathway to achieve the 2030 target and highlighted the importance of reshaping the energy and industrial structure of the BTH region for future air pollution mitigation.

8.
J Environ Sci (China) ; 83: 8-20, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31221390

RESUMO

With rapid economic growth and urbanization, the Yangtze River Delta (YRD) region in China has experienced serious air pollution challenges. In this study, we analyzed the air pollution characteristics and their relationship with emissions and meteorology in the YRD region during 2014-2016. In recent years, the concentrations of all air pollutants, except O3, decreased. Spatially, the PM2.5, PM10, SO2, and CO concentrations were higher in the northern YRD region, and NO2 and O3 were higher in the central YRD region. Based on the number of non-attainment days (i.e., days with air quality index greater than 100), PM2.5 was the largest contributor to air pollution in the YRD region, followed by O3, PM10, and NO2. However, particulate matter pollution has declined gradually, while O3 pollution worsened. Meteorological conditions mainly influenced day-to-day variations in pollutant concentrations. PM2.5 concentration was inversely related to wind speed, while O3 concentration was positively correlated with temperature and negatively correlated with relative humidity. The air quality improvement in recent years was mainly attributed to emission reductions. During 2014-2016, PM2.5, PM10, SO2, NOx, CO, NH3, and volatile organic compound (VOC) emissions in the YRD region were reduced by 26.3%, 29.2%, 32.4%, 8.1%, 15.9%, 4.5%, and 0.3%, respectively. Regional transport also contributed to the air pollution. During regional haze periods, pollutants from North China and East China aggravated the pollution in the YRD region. Our findings suggest that emission reduction and regional joint prevention and control helped to improve the air quality in the YRD region.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/estatística & dados numéricos , Monitoramento Ambiental , Conceitos Meteorológicos , China , Meteorologia , Ozônio , Material Particulado/análise , Rios , Estações do Ano , Temperatura , Urbanização
9.
Lancet Planet Health ; 3(5): e219-ee225, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-31128767

RESUMO

BACKGROUND: Increasing evidence from epidemiological studies suggests that maternal exposure to ambient PM2·5 can increase the risk of pregnancy loss. However, no studies have been done in low-income countries such as those in Africa, which have the highest incidences of pregnancy loss. In this study, we aimed to analyse the association between PM2·5 and pregnancy loss, including miscarriage and stillbirth. METHODS: This self-compared case control study collected data on mothers who reported at least two births (at least one case of pregnancy loss plus at least one normal delivery) in the African Demographic and Health Surveys. Gestational exposure to PM2·5 was assessed using a state-of-the-art estimator based on satellite measurements and chemical transport model outputs. Each case of pregnancy loss was compared with the successful control deliveries in the same mother. We estimated the association between gestational exposure to PM2·5 and pregnancy loss using a conditional logistical model with multiple adjustments, and assessed it using the odds ratio (OR) derived from the regression. FINDINGS: Between Jan 1, 1998, and Dec 31, 2016, 67 566 cases of pregnancy loss were reported across Africa in the DHS. After removal of mothers who did not report at least one successful delivery and those with missing spatial information, 42 952 were included in the study. Of these, 30 418 were categorised as miscarriages and 12 534 were stillbirths. Each increment of 10 µg/m3 PM2·5 was associated with an adjusted OR of 1·122 (95% CI 1·107-1·137) for pregnancy loss, including miscarriage (1·125; 1·109-1·142) and stillbirth (1·094; 1·051-1·138). INTERPRETATION: Our findings from African DHS data showing that PM2·5 exposure is significantly associated with increased risk of pregnancy loss add to the existing epidemiological evidence from middle-income and high-income countries on the health impacts of poor air quality. Our results support public health interventions for reducing ambient particulate matter to improve maternal health in Africa. FUNDING: China National Natural Science Foundation and China Ministry of Science and Technology.

10.
Sci Total Environ ; 697: 134094, 2019 Dec 20.
Artigo em Inglês | MEDLINE | ID: mdl-32380602

RESUMO

Previous PM2.5 related epidemiological studies mainly relied on data from sparse regulatory monitors to assess exposure. The introduction of non-regulatory PM2.5 monitors presents both opportunities and challenges to researchers and air quality managers. In this study, we evaluated the advantages and limitations of integrating non-regulatory PM2.5 measurements into a satellite-based daily PM2.5 model at 100 m resolution in New York City in 2015. Two separate machine learning models were developed, one using only PM2.5 data from the US Environmental Protection Agency (EPA), and the other with measurements from both EPA and the New York City Community Air Survey (NYCCAS). The EPA-only model obtained a cross-validation (CV) R2 of 0.85 while the EPA + NYCCAS model obtained a CV R2 of 0.73. With the help of the NYCCAS measurements, the EPA + NYCCAS model predicted distinctly different PM2.5 spatial patterns and more pollution hotspots compared with the EPA model, and its predictions were >15% higher than the EPA model along major roads and in densely populated areas. Our results indicated that satellite AOD and non-regulatory PM2.5 measurements can be fused together to capture neighborhood-scale PM2.5 levels and previous studies may have underestimated the disease burden due to PM2.5 in densely populated areas.

11.
Environ Sci Technol ; 52(22): 13260-13269, 2018 11 20.
Artigo em Inglês | MEDLINE | ID: mdl-30354085

RESUMO

The long satellite aerosol data record enables assessments of historical PM2.5 level in regions where routine PM2.5 monitoring began only recently. However, most previous models reported decreased prediction accuracy when predicting PM2.5 levels outside the model-training period. In this study, we proposed an ensemble machine learning approach that provided reliable PM2.5 hindcast capabilities. The missing satellite data were first filled by multiple imputation. Then the modeling domain, China, was divided into seven regions using a spatial clustering method to control for unobserved spatial heterogeneity. A set of machine learning models including random forest, generalized additive model, and extreme gradient boosting were trained in each region separately. Finally, a generalized additive ensemble model was developed to combine predictions from different algorithms. The ensemble prediction characterized the spatiotemporal distribution of daily PM2.5 well with the cross-validation (CV) R2 (RMSE) of 0.79 (21 µg/m3). The cluster-based subregion models outperformed national models and improved the CV R2 by ∼0.05. Compared with previous studies, our model provided more accurate out-of-range predictions at the daily level ( R2 = 0.58, RMSE = 29 µg/m3) and monthly level ( R2 = 0.76, RMSE = 16 µg/m3). Our hindcast modeling system allows for the construction of unbiased historical PM2.5 levels.


Assuntos
Poluentes Atmosféricos , Material Particulado , China , Monitoramento Ambiental , Aprendizado de Máquina
12.
Environ Int ; 121(Pt 1): 550-560, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30300813

RESUMO

Exposure to fine particulate matter (PM2.5) has been associated with a wide range of negative health outcomes. The overwhelming majority of the epidemiological studies that helped establish such associations was conducted in regions with sufficient ground observations and other supporting data, i.e., the data-rich regions. However, air pollution health effects research in the data-poor regions, where pollution levels are often the highest, is still very limited due to the lack of high-quality exposure estimates. To improve our understanding of the desired input datasets for the application of satellite-based PM2.5 exposure models in data-poor areas, we applied a Bayesian ensemble model in the southeast U.S. that was selected as a representative data-rich region. We designed four groups of sensitivity tests to simulate various data-poor scenarios. The factors considered that would influence the model performance included the temporal sampling frequency of the monitors, the number of ground monitors, the accuracy of the chemical transport model simulation of PM2.5 concentrations, and different combinations of the additional predictors. While our full model achieved a 10-fold cross-validated (CV) R2 of 0.82, we found that when reducing the sampling frequency from the current 1-in-3 day to 1-in-9 day, the CV R2 decreased to 0.58, and the predictions could not capture the daily variations of PM2.5. Half of the current stations (i.e., 30 monitors) could still support a robust model with a CV R2 of 0.79. With 20 monitors, the CV R2 decreased from 0.71 to 0.55 when 100% additional random errors were added to the original CMAQ simulations. However, with a sufficient number of ground monitors (e.g., 30 monitors), our Bayesian ensemble model had the ability to tolerate CMAQ errors with only a slight decrease in CV R2 (from 0.79 to 0.75). With fewer than 15 monitors, our full model collapsed and failed to fit any covariates, while the models with only time-varying variables could still converge even with only five monitors left. A model without the land use parameters lacked fine spatial details in the prediction maps, but could still capture the daily variability of PM2.5 (CV R2 ≥ 0.67) and might support a study of the acute health effects of PM2.5 exposure.


Assuntos
Monitoramento Ambiental/métodos , Material Particulado/análise , Astronave , Poluentes Atmosféricos/análise , Teorema de Bayes , Humanos , Estados Unidos
13.
Environ Sci Technol ; 52(21): 12905-12914, 2018 11 06.
Artigo em Inglês | MEDLINE | ID: mdl-30249091

RESUMO

As the largest energy infrastructure in China, the power sector consumed approximately half of China's coal over the past decade and threatened air quality and greenhouse gas (GHG) abatement targets. In this work, we assessed the evolution of coal-fired power plants and associated emissions in China during 2010-2030 by using a unit-based emission projection model, which integrated the historical power plant information, turnover of the future power plant fleet, and evolution of end-of-pipe control technologies. We found that, driven by stringent environmental legislation, SO2, NO x, and PM2.5 (particulate matter less than 2.5 µm in diameter) emissions from coal-fired power plants decreased by 49%, 45%, and 24%, respectively, during 2010-2015, compared to 15% increase in CO2 emissions. In contrast to ever-increasing CO2 emissions until 2030 under current energy development planning, we found that aggressive energy development planning could curb CO2 emissions from the peak before 2030. Owing to the implementation of a "near zero" emission control policy, we projected emissions of air pollutants will significantly decrease during 2016-2030. Early retirement of small and low-efficiency power plants would further reduce air pollutants and CO2 emissions. Our study explored various mitigation pathways for China's coal-fired power plants, which could reduce coal consumption, air pollutants, and CO2 emissions and improve energy efficiency.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , China , Carvão Mineral , Centrais Elétricas
14.
Environ Pollut ; 242(Pt A): 675-683, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30025341

RESUMO

Exposure to fine particulate matter (PM2.5) remains a worldwide public health issue. However, epidemiological studies on the chronic health impacts of PM2.5 in the developing countries are hindered by the lack of monitoring data. Despite the recent development of using satellite remote sensing to predict ground-level PM2.5 concentrations in China, methods for generating reliable historical PM2.5 exposure, especially prior to the construction of PM2.5 monitoring network in 2013, are still very rare. In this study, a high-performance machine-learning model was developed directly at monthly level to estimate PM2.5 levels in North China Plain. We developed a random forest model using the latest Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD), meteorological parameters, land cover and ground PM2.5 measurements from 2013 to 2015. A multiple imputation method was applied to fill the missing values of AOD. We used 10-fold cross-validation (CV) to evaluate model performance and a separate time period, January 2016 to December 2016, was used to validate our model's capability of predicting historical PM2.5 concentrations. The overall model CV R2 and relative prediction error (RPE) were 0.88 and 18.7%, respectively. Validation results beyond the modeling period (2013-2015) shown that this model can accurately predict historical PM2.5 concentrations at the monthly (R2 = 0.74, RPE = 27.6%), seasonal (R2 = 0.78, RPE = 21.2%) and annual (R2 = 0.76, RPE = 16.9%) level. The annual mean predicted PM2.5 concentration from 2013 to 2016 in our study domain was 67.7 µg/m3 and Southern Hebei, Western Shandong and Northern Henan were the most polluted areas. Using this computationally efficient, monthly and high-resolution model, we can provide reliable historical PM2.5 concentrations for epidemiological studies on PM2.5 health effects in China.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/estatística & dados numéricos , Monitoramento Ambiental/métodos , Material Particulado/análise , Aerossóis/análise , Poluição do Ar/análise , China , Poluição Ambiental , Humanos , Aprendizado de Máquina , Meteorologia
15.
Front Immunol ; 9: 187, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29497417

RESUMO

This study was aimed to evaluate the role of B-cell epitopes of Epstein-Barr virus (EBV) Early antigen protein D (EA), envelope glycoprotein GP340/membrane antigen (MA), latent membrane protein (LMP)-1, and LMP-2A in systemic lupus erythematosus (SLE). B-cell epitopes were predicted by analyzing secondary structure, transmembrane domains, surface properties, and homological comparison. 60 female mice were randomized equally into 12 groups: 1-10 groups were immunized by epitope peptides (EPs) 1-10, respectively, while 11 and 12 groups were PBS and Keyhole limpet hemocyanin (KLH) control groups. Immunoglobulin G (IgG) and autoantibody to nuclear antigen (ANA) concentrations in mice serum were determined at week 8. Indirect levels of EP1-10 were further detected by enzyme-linked immuno sorbent assay (ELISA) in 119 SLE patients and 64 age- and gender-matched health controls (HCs). 10 probable EBV EA, MA, LMP-1, and LMP-2A B-cell epitopes related to SLE self-antigens were predicted and corresponding EP1-10 were synthesized. IgG concentrations at week 8 were increased in EP1-10 and KLH groups compared with PBS group in mice; while ANA levels were elevated in only EP1-4, EP6-7, and EP10 groups compared to KLH group by ELISA, and ANA-positive rates were increased in only EP1, EP2, EP4, EP6, and EP10 groups by indirect immunofluorescence assay. EP1-4, EP6, and EP10 indirect levels were increased in SLE patients than HCs, while EP1, EP3, EP6, and EP9 were correlated with SLE disease activity index score. In conclusion, EBV EA, MA, LMP-1, and LMP-2A B-cell EPs increased SLE-related autoantibodies in mice, and their indirect levels might be served as potential biomarkers for SLE diagnosis and disease severity.


Assuntos
Anticorpos Antinucleares/sangue , Epitopos de Linfócito B/imunologia , Antígenos Nucleares do Vírus Epstein-Barr/imunologia , Lúpus Eritematoso Sistêmico/sangue , Adulto , Animais , Anticorpos Antivirais/sangue , Antígenos Virais/imunologia , Ensaio de Imunoadsorção Enzimática , Feminino , Humanos , Imunoglobulina G/sangue , Lúpus Eritematoso Sistêmico/imunologia , Masculino , Camundongos , Pessoa de Meia-Idade , Peptídeos/imunologia , Proteínas da Matriz Viral/imunologia
16.
Front Immunol ; 9: 3099, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30687316

RESUMO

Adult-onset Still's disease (AOSD) is a systemic inflammatory disease characterized by cytokine storm. However, a diagnostic test for AOSD in clinical use is yet to be validated. The aim of our study was to identify non-invasive biomarkers with high specificity and sensitivity to diagnosis of AOSD. MicroRNA (miRNA) profiles in PBMC from new-onset AOSD patients without any treatment and healthy controls (HCs) were analyzed by miRNA deep sequencing. Plasma samples from 100 AOSD patients and 60 HCs were used to validated the expression levels of miRNA by qRT-PCR. The correlations between expression levels of miRNAs and clinical manifestations were analyzed using advanced statistical models. We found that plasma samples from AOSD patients showed a distinct miRNA expression profile. Five miRNAs (miR-142-5p, miR-101-3p, miR-29a-3p, miR-29c-3p, and miR-141-3p) were significantly upregulated in plasma of AOSD patients compared with HCs both in training and validation sets. We discovered a panel including 3 miRNAs (miR-142-5p, miR-101-3p, and miR-29a-3p) that can predict the probability of AOSD with an area under the receiver operating characteristic (ROC) curve of 0.8250 in training and validation sets. Moreover, the expression levels of 5 miRNAs were significantly higher in active AOSD patients compared with those in inactive patients. In addition, elevated level of miR-101-3p was found in AOSD patients with fever, sore throat and arthralgia symptoms; the miR-101-3p was also positively correlated with the levels of IL-6 and TNF-α in serum. Furthermore, five miRNAs (miR-142-5p, miR-101-3p, miR-29c-3p, miR-29a-3p, and miR-141-3p) expressed in plasma were significantly higher in AOSD patients than in sepsis patients (P < 0.05). The AUC value of 4-miRNA panel (miR-142-5p, miR-101-3p, miR-29c-3p, and miR-141-3p) for AOSD diagnosis from sepsis was 0.8448, revealing the potentially diagnostic value to distinguish AOSD patients from sepsis patients. Our results have identified a specific plasma miRNA signature that may serve as a potential non-invasive biomarker for diagnosis of AOSD and monitoring disease activity.


Assuntos
MicroRNA Circulante/sangue , Sepse/diagnóstico , Doença de Still de Início Tardio/diagnóstico , Adulto , Biomarcadores/sangue , Biomarcadores/metabolismo , MicroRNA Circulante/metabolismo , Diagnóstico Diferencial , Feminino , Perfilação da Expressão Gênica , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Leucócitos Mononucleares , Masculino , Pessoa de Meia-Idade , Curva ROC , Reação em Cadeia da Polimerase em Tempo Real , Sepse/sangue , Sepse/genética , Sepse/imunologia , Doença de Still de Início Tardio/sangue , Doença de Still de Início Tardio/genética , Doença de Still de Início Tardio/imunologia , Adulto Jovem
17.
J Geophys Res Atmos ; 123(15): 8159-8171, 2018 Aug 16.
Artigo em Inglês | MEDLINE | ID: mdl-31289705

RESUMO

The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM2.5) from wildfires are limited by the lack of accurate high-resolution PM2.5 exposure data over fire days. Satellite-based aerosol optical depth (AOD) data can provide additional information in ground PM2.5 concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM2.5 concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM2.5 concentrations over fire seasons (April to September) in Colorado for 2011-2014. Our model had a 10-fold cross-validated R 2 of 0.66 and root-mean-squared error of 2.00 µg/m3, outperformed the multistage model, especially on the fire days. Elevated PM2.5 concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short-term and long-term epidemiological studies of wildfire PM2.5.

18.
Huan Jing Ke Xue ; 39(12): 5289-5295, 2018 Dec 08.
Artigo em Chinês | MEDLINE | ID: mdl-30628371

RESUMO

Based on the high-resolution coal-fired power plant emission database, GEOS-Chem Adjoint, a global-regional nested atmospheric chemistry model and its adjoint were applied to analyze PM2.5-related premature deaths caused by the power sector in six grid regions of China due to air pollutant emissions and subsequent pollution. The results show that power sector-related PM2.5 pollution caused 106000 (95% CI:68000-132000) premature deaths in 2010, accounting for 9.8% of China's anthropogenic PM2.5-related premature deaths. The health loss intensity (defined as number of premature deaths caused by a unit of power generation) of small and old units is significantly higher than that of large and new units:units with a capacity below 100 MW reach 62 people·(TW·h)-1, 2.8 times that of units with a capacity above 600 MW. Similarly, the health loss intensity of units older than thirty years is 58 people·(TW·h)-1, 2.1 times that of new units. From the perspective of regional grids, the health impact index of Central China is relatively large, reaching 77 people·(TW·h)-1. Further analysis reveals that transregional power transmission led to a net increase of 680 premature deaths compared with the scenario without transmission in 2010. Our study implies that China should accelerate the pace of phasing out small and old units and optimize the power transmission distribution between grid regions to reduce the overall level of pollution and health losses.


Assuntos
Poluição do Ar/efeitos adversos , Carvão Mineral , Mortalidade , Centrais Elétricas , Poluentes Atmosféricos , China , Humanos , Material Particulado/efeitos adversos
19.
Environ Pollut ; 226: 143-153, 2017 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-28419921

RESUMO

High resolution pollution maps are critical to understand the exposure and health effect of local residents to air pollution. Currently, none of the single technologies used to measure or estimate concentrations of pollutants can provide sufficient resolved exposure data. Land use regression (LUR) models were developed to combine ground-based measurements, satellite remote sensing (SRS) and air quality model (AQM), together with geographic and local source related spatial inputs, to generate high resolution pollution maps for both PM2.5 and NO2 in Pearl River Delta (PRD), China. Four sets of LUR models (LUR without SRS or AQM, with SRS only, with AQM only, and with both SRS and AQM), all including local traffic emissions and land use variables, were compared to evaluate the contribution of SRS and AQM data to the performance of LUR models in PRD region. For NO2, the annual model with SRS estimate performed best, explaining 60.5% of the spatial variation. For PM2.5, the annual model with traditional predictor variables without SRS or AQM estimates showed the best performance, explaining 88.4% of the spatial variation. Pollution surfaces at 200 m*200 m resolution were generated according to the best performed models.


Assuntos
Poluentes Atmosféricos/análise , Monitoramento Ambiental/métodos , Modelos Químicos , Dióxido de Nitrogênio/análise , Material Particulado/análise , Poluição do Ar/análise , China , Humanos , Tecnologia de Sensoriamento Remoto , Rios , Imagens de Satélites
20.
Nature ; 543(7647): 705-709, 2017 03 29.
Artigo em Inglês | MEDLINE | ID: mdl-28358094

RESUMO

Millions of people die every year from diseases caused by exposure to outdoor air pollution. Some studies have estimated premature mortality related to local sources of air pollution, but local air quality can also be affected by atmospheric transport of pollution from distant sources. International trade is contributing to the globalization of emission and pollution as a result of the production of goods (and their associated emissions) in one region for consumption in another region. The effects of international trade on air pollutant emissions, air quality and health have been investigated regionally, but a combined, global assessment of the health impacts related to international trade and the transport of atmospheric air pollution is lacking. Here we combine four global models to estimate premature mortality caused by fine particulate matter (PM2.5) pollution as a result of atmospheric transport and the production and consumption of goods and services in different world regions. We find that, of the 3.45 million premature deaths related to PM2.5 pollution in 2007 worldwide, about 12 per cent (411,100 deaths) were related to air pollutants emitted in a region of the world other than that in which the death occurred, and about 22 per cent (762,400 deaths) were associated with goods and services produced in one region for consumption in another. For example, PM2.5 pollution produced in China in 2007 is linked to more than 64,800 premature deaths in regions other than China, including more than 3,100 premature deaths in western Europe and the USA; on the other hand, consumption in western Europe and the USA is linked to more than 108,600 premature deaths in China. Our results reveal that the transboundary health impacts of PM2.5 pollution associated with international trade are greater than those associated with long-distance atmospheric pollutant transport.


Assuntos
Poluentes Atmosféricos/efeitos adversos , Poluição do Ar/efeitos adversos , Poluição do Ar/estatística & dados numéricos , Comércio/estatística & dados numéricos , Internacionalidade , Mortalidade Prematura , Material Particulado/efeitos adversos , Poluentes Atmosféricos/análise , Atmosfera/química , China/epidemiologia , Exposição Ambiental/efeitos adversos , Exposição Ambiental/estatística & dados numéricos , Europa (Continente)/epidemiologia , Saúde Global , Humanos , Material Particulado/análise , Saúde Pública , Estados Unidos/epidemiologia , Vento
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