A daily and complete PM2.5 dataset derived from space observations for Vietnam from 2012 to 2020.

PM2.5 pollution is a serious problem in Vietnam and around the world, having bad impacts on human health, animals and environment. Regular monitoring at a large scale is important to assess the status of air pollution, develop solutions and evaluate the effectiveness of policy implementation. However, air quality monitoring stations in Vietnam are limited. In this article, we propose an approach to estimate daily PM2.5 concentration from 2012 to 2020 over the Vietnamese territory, which is strongly affected by cloudy conditions, using a modern statistical model named Mixed Effect Model (MEM) on a dataset consisting of ground PM2.5 measurements, integrated satellite Aerosol Optical Depth (AOD), meteorological and land use maps. The result of this approach is the first long-term, full coverage and high quality PM2.5 dataset of Vietnam. The daily mean PM2.5 maps have high validation results in comparison with ground PM2.5 measurement (Pearson r of 0.87, R2 of 0.75, RMSE of 11.76 µg/m3, and MRE of 36.57 % on a total of 13,886 data samples). The aggregated monthly and annual average maps from 2012 to 2020 in Vietnam have outstanding quality when compared with another global PM2.5 product. The PM2.5 concentration maps has shown spatial distribution and seasonal variations of PM2.5 concentration in Vietnam in a long period from 2012 to 2020 and has been used in other studies and applications in the environment and public health at the national scale, which has not been possible before because of the lack of monitoring stations and an appropriate PM2.5 modeling approach.

Panels of circulating microRNAs as potential diagnostic biomarkers for breast cancer: a systematic review and meta-analysis.

PURPOSE: Circulating microRNAs (miRNAs) are potential diagnostic biomarkers for breast cancer (BC). The application of miRNA panels could improve the performance of screening tests. Here, we integrated bioinformatic tools and meta-analyses to select circulating miRNAs with high diagnostic accuracy and combined these markers to develop diagnostic panels for BC. METHODS: Analyses across databases were performed to identify potential BC-related circulating miRNAs. Next, a comprehensive meta-analysis was conducted for each miRNA following the PRISMA guidelines. An electronic and manual search for relevant literature was carried out by two reviewers through PubMed, ScienceDirect, Biomed Central, and Google Scholar. The quality of the included studies was assessed using the QUADAS-2, and the statistical analyses were performed using R software 4.1.1. Finally, the accurate biomarkers confirmed through meta-analyses were combined into diagnostic models for BC. RESULTS: Twenty-seven circulating miRNAs were identified as BC-related by bioinformatic tools. After screening, only 10 miRNAs presented in 45 studies were eligible for meta-analyses. By assessing pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio, 8 miRNAs (miR-21, miR-30b, miR-125b, miR-145, miR221 miR-222, and miR-335) were revealed as promising BC diagnostic biomarkers. Two panels constructed from these miRNAs showed excellent diagnostic accuracy for BC, with areas under the SROC curve of 0.917 and 0.944. CONCLUSION: We identified 8 potential circulating miRNAs and 2 diagnostic models that are useful for diagnosing BC. However, the established miRNA panels have not been tested in any experimental studies and thus should be validated in large case-control studies for clinical use.

Genetic Association Analysis Implicates Six MicroRNA-Related SNPs With Increased Risk of Breast Cancer in Australian Caucasian Women.

INTRODUCTION: Breast cancer (BC), a heterogeneous disease, features microRNA-related single nucleotide polymorphisms (miRSNPs) as underlying factors of BC development, thus providing targets for novel diagnostic and therapeutic strategies. This study investigated miRSNPs in BC susceptibility in Australian Caucasian women. PATIENTS AND METHODS: The study population included patients 33 to 80 years of age stratified by molecular subtypes of breast tumors (luminal A, 47.59%), stage (stage I, 36.96%), tumor-type (ductal, 44.95%), grading (intermediate, 35.52%), size (10.1-25 mm, 31.14%), and lymph node (sentinel negative, 38.93%). Sixty-five miRSNPs underwent allelic analysis in two independent case-control cohorts (GU-CCQ-BB, 377 cases and 521 controls; GRC-BC, 267 cases and 201 controls) using a MassARRAY platform. GU-CCQ-BB, GRC-BC, and the combined populations (BC-CA) (644 cases and 722 controls) underwent independent statistical analysis. RESULTS: In the GU-CCQ-BB population, miRSNPs TET2-rs7670522, ESR1-rs2046210, FGFR2-rs1219648, MIR210-rs1062099, HIF1A-rs2057482, and CASC16-rs4784227 were found to be associated with increased BC risk (P ≤ .05). Only ESR1-rs2046210 was also significantly associated (P ≤ .05) when replicated in the GRC-BC and BC-CA populations. No significant association was correlated with BC-clinical features (pathological types and ER/PR/HER2 status); however, MIR210-rs1062099 was found to be significantly associated (P ≤ .05) with age (>50 years) in the GU-CCQ-BB cohort. CONCLUSION: This is the first study to demonstrate the association of MIR210-rs1062099 and TET2-rs7670522 with increased BC risk. Additionally, four previously reported SNPs (ESR1-rs2046210, FGFR2-rs1219648, HIF1A-rs2057482, and CASC16-rs4784227) were confirmed as BC risk variants. Replication and functional studies in larger Caucasian cohorts are necessary to elucidate the role of these miRSNPS in the development of BC.

Spatiotemporal analysis of ground and satellite-based aerosol for air quality assessment in the Southeast Asia region.

Satellite observations for regional air quality assessment rely on comprehensive spatial coverage, and daily monitoring with reliable, cloud-free data quality. We investigated spatiotemporal variation and data quality of two global satellite Aerosol Optical Depth (AOD) products derived from MODIS and VIIRS imagery. AOD is considered an essential atmospheric parameter strongly related to ground Particulate Matter (PM) in Southeast Asia (SEA). We analyze seasonal variation, urban/rural area influence, and biomass burning effects on atmospheric pollution. Validation indicated a strong relationship between AERONET ground AOD and both MODIS AOD (R2 = 0.81) and VIIRS AOD (R2 = 0.68). The monthly variation of satellite AOD and AERONET AOD reflects two seasonal trends of air quality separately for mainland countries including Myanmar, Laos, Cambodia, Thailand, Vietnam, and Taiwan, Hong Kong, and for maritime countries consisting of Indonesia, Philippines, Malaysia, Brunei, Singapore, and Timor Leste. The mainland SEA has a pattern of monthly AOD variation in which AODs peak in March/April, decreasing during wet season from May-September, and increasing to the second peak in October. However, in maritime SEA, AOD concentration peaks in October. The three countries with the highest annual satellite AODs are Singapore, Hong Kong, and Vietnam. High urban population proportions in Singapore (40.7%) and Hong Kong (21.6%) were associated with high AOD concentrations as expected. AOD values in SEA urban areas were a factor of 1.4 higher than in rural areas, with respective averages of 0.477 and 0.336. The AOD values varied proportionately to the frequency of biomass burning in which both active fires and AOD peak in March/April and September/October. Peak AOD in September/October in some countries could be related to pollutant transport of Indonesia forest fires. This study analyzed satellite aerosol product quality in relation to AERONET in SEA countries and highlighted framework of air quality assessment over a large, complicated region.

Satellites may underestimate rice residue and associated burning emissions in Vietnam.

In this study, we estimate rice residue, associated burning emissions, and compare results with existing emissions inventories employing a bottom-up approach. We first estimated field-level post-harvest rice residues, including separate fuel-loading factors for rice straw and rice stubble. Results suggested fuel-loading factors of 0.27 kg m-2 (±0.033), 0.61 kg m-2 (±0.076), and 0.88 kg m-2 (±0.083) for rice straw, stubble, and total post-harvest biomass, respectively. Using these factors, we quantified potential emissions from rice residue burning and compared our estimates with other studies. Our results suggest total rice residue burning emissions as 2.24 Gg PM2.5, 36.54 Gg CO and 567.79 Gg CO2 for Hanoi Province, which are significantly higher than earlier studies. We attribute our higher emission estimates to improved fuel-loading factors; moreover, we infer that some earlier studies relying on residue-to-product ratios could be underestimating rice residue emissions by more than a factor of 2.3 for Hanoi, Vietnam. Using the rice planted area data from the Vietnamese government, and combining our fuel-loading factors, we also estimated rice residue PM2.5 emissions for the entirety of Vietnam and compared these estimates with an existing all-sources emissions inventory, and the Global Fire Emissions Database (GFED). Results suggest 75.98 Gg of PM2.5 released from rice residue burning accounting for 12.8% of total emissions for Vietnam. The GFED database suggests 42.56 Gg PM2.5 from biomass burning with 5.62 Gg attributed to agricultural waste burning indicating satellite-based methods may be significantly underestimating emissions. Our results not only provide improved residue and emission estimates, but also highlight the need for emissions mitigation from rice residue burning.