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This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-km resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011–2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R2) and root mean square error (RMSE), and the results indicate high accuracy for training (R2: 0.95, RMSE: 5.58 μg/m3), validation (R2: 0.78, RMSE: 11.18 μg/m3), and testing (R2: 0.71, RMSE: 8.79 μg/m3) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.

This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-kilometer resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011-2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R2) and root mean square error (RMSE), and the results indicate high accuracy for training (R2: 0.95, RMSE: 5.58 μg/m3), validation (R2: 0.78, RMSE: 11.18 μg/m3), and testing (R2: 0.71, RMSE: 8.79 μg/m3) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.

The Moderate Resolution Imaging Spectroradiometer (MODIS) of the National Aeronautics and Space Administration (NASA) offers numerous land products of the Earth's datasets. On the other hand, researchers find it difficult to retrieve this data for specific places. The methods for extracting and analyzing land surface temperature (LST), land use and land cover (LULC), and elevation are presented in this study. The R commands provided make the time-consuming process of extracting data for specific places much more accessible. As a result, a statistical study of LST over Bali is shown as an example. Over the 15 regions of Bali, a quadratic polynomial identified five possible warming patterns, while a logistic regression model assessed the probability of warming. The findings suggest that 25.2% of Bali has warmed during the last two decades, with temperatures being highest in urban and built-up areas and deciduous forests and inversely associated with elevation. Global warming has sparked a lot of academic interest and has become a serious climate problem. The techniques proposed in this work simplify the extraction of LST, LULC, and elevation data from MODIS satellites. These approaches can also be used on other datasets with identical topologies, such as the normalized difference vegetation index (NDVI), aerosol optical depth (AOD), and night light data.

Objective: MicroRNA (miRNA), a short noncoding RNA, is claimed to be a potential blood-based biomarker. We aimed to identify and evaluate miRNAs as diagnostic biomarkers for non-small cell lung cancer (NSCLC). Methods: Profiles of 745 miRNAs were screened in the serum of 8 patients with NSCLC and 8 age- and sex-matched controls using TaqMan low-density arrays (TLDAs) and validated in 25 patients with NSCLC and 30 with other lung diseases (OLs) as well as in 19 healthy persons (HPs). The diagnostic performance of the candidate miRNAs was assessed in 117 cases of NSCLC and 113 OLs using quantitative real-time polymerase chain reaction (qRT-PCR). Differences in miRNA expression between patients with NSCLC and controls were assessed using the Mann-Whitney U test. The area under receiver operating characteristic (ROC) curve (AUC) was obtained based on the logistic regression model. Results: Ten miRNAs were found to be differentially expressed between patients with NSCLC and controls, including miR-769, miR-339-3p, miR-339-5p, miR-519a, miR-1238, miR-99a#, miR-134, miR-604, miR-539, and miR-342. The expression of miR-339-3p was significantly higher in patients with NSCLC than in those with OLs (P < 0.001) and HPs (P = 0.020). ROC analysis revealed an miR-339-3p expression AUC of 0.616 [95% confidence interval (CI): 0.561-0.702]. The diagnostic prediction was increased (AUC = 0.706, 95% CI: 0.649-0.779) in the model combining miR-339-3p expression and other known risk factors (i.e., age, smoking status, and drinking status). Conclusions: MiR-339-3p was significantly upregulated in patients with NSCLC compared with participants without cancer, suggesting a diagnostic prediction value for high-risk individuals. Therefore, miR-339-3p expression could be a potential blood-based biomarker for NSCLC.

Previous studies have reported the diagnostic and prognostic value of serum microRNA (miR)-145 and vascular endothelial growth factor (VEGF) levels in various types of cancer; however, their clinical use in non-small cell lung cancer (NSCLC) remains unclear. The present study included 215 patients, 106 with NSCLC and 109 with other lung diseases (OLDs). miR-145 expression levels were determined using reverse transcription-quantitative PCR (RT-qPCR) and VEGF levels were measured using an ELISA. The diagnostic performance was assessed using a receiver operating characteristic curve and area under the curve (AUC) analysis. A Kaplan-Meier survival curve and Cox regression analysis were employed to evaluate the prognostic significance of the markers. The biological function of miR-145 was examined in A549 and H1792 cell lines. The effects of miR-145 on cell proliferation of NSCLC cells were evaluated by flow cytometry, and the expression levels of miR-145 and cell cycle-related genes were determined by RT-qPCR. The results revealed that miR-145 and VEGF exhibited fair diagnostic performance [AUC, 0.61 (95% CI, 0.55-0.68) and AUC, 0.64 (95% CI, 0.57-0.71), respectively]. Combining age and smoking status with miR-145 and VEGF provided the best model for differentiating patients with NSCLC from those with OLDs (AUC, 0.76; 95% CI, 0.69-0.83). Furthermore, low serum miR-145 levels were associated with poor overall survival [hazard ratio (HR), 0.48; 95% CI, 0.27-0.85], whereas high VEGF levels were not associated with poor overall survival (HR, 1.47; 95% CI, 0.81-2.68). In addition, the results of the in vitro experiments indicated that miR-145 decreased cell proliferation via the induction of cell cycle arrest. In conclusion, the findings of the present study suggested that combining miR-145 and VEGF levels with clinical risk factors may be a potential diagnostic scheme for NSCLC. In addition, serum miR-145 may be used as a prognostic marker. These results indicated that miR-145 may function as a tumor suppressor in NSCLC.

Indonesia, as the largest archipelago in the world, still depends on agriculture and fisheries activities. Climate change will affect agricultural yields and fish catches, so research on climate change is essential for Indonesia and neighboring countries. Land Surface Temperature (LST) is a critical indicator in climate science to measure the temperature of an area. This study examines the LST variation and seasonal pattern with 189 sub-regions covering 5 main islands of the Indonesia archipelago with 105 pixels (95 km) distance apart. The data was downloaded from NASA MODIS and covered a 20-year from 2001 to 2020. Overall, Indonesia has had a stable LST with a total average increase of 0.009oC (95% confidence interval: -0.041,0.059 oC).

Background Kidney dysfunction is considered a cardiovascular risk factor. However, few longitudinal studies have examined the effects of air pollution on kidney function. We evaluated associations between long-term air pollution exposure and estimated glomerular filtration rate (eGFR) using data from a cohort of the Electricity Generating Authority of Thailand (EGAT) study in Bangkok Metropolitan Region, Thailand. Methods This longitudinal study included 1839 subjects (aged 52–71 years in 2002) from the EGAT1 cohort study during 2002–2012. eGFR, based on creatinine, was measured in 2002, 2007, and 2012. Annual mean concentrations of air pollutants (i.e., particulate matter with an aerodynamic diameter ≤10 μm (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)) prior to a measurement of creatinine were assessed with the ordinary kriging method. Mixed-effect linear regression models were used to assess associations between air pollutants and eGFR, while controlling for potential covariates. eGFR values are expressed as percent change per interquartile range (IQR) increments of each pollutant. Results Lower eGFR was associated with higher concentrations of PM10 (−1.99%, 95% confidence interval (CI): −3.33, −0.63), SO2 (−4.89%, 95%CI: −6.69, −3.07), and CO (−0.97%, 95%CI: −1.96, 0.03). However, after adjusting for temperature, relative humidity, PM10, and SO2, no significant association was observed between CO and eGFR. Conclusions Our findings support the hypothesis that long-term exposure to high concentrations of PM10 and SO2 is associated with the progression of kidney dysfunction in subjects of the EGAT cohort study.

Background Several studies have shown the health effects of air pollutants, especially in China, North American and Western European countries. But longitudinal cohort studies focused on health effects of long-term air pollution exposure are still limited in Southeast Asian countries where sources of air pollution, weather conditions, and demographic characteristics are different. The present study examined the association between long-term exposure to air pollution and self-reported morbidities in participants of the Thai cohort study (TCS) in Bangkok metropolitan region (BMR), Thailand. Methods This longitudinal cohort study was conducted for 9 years from 2005 to 2013. Self-reported morbidities in this study included high blood pressure, high blood cholesterol, and diabetes. Air pollution data were obtained from the Thai government Pollution Control Department (PCD). Particles with diameters ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) exposures were estimated with ordinary kriging method using 22 background and 7 traffic monitoring stations in BMR during 2005–2013. Long-term exposure periods to air pollution for each subject was averaged as the same period of person-time. Cox proportional hazards models were used to examine the association between long-term air pollution exposure with self-reported high blood pressure, high blood cholesterol, diabetes. Results of self-reported morbidity were presented as hazard ratios (HRs) per interquartile range (IQR) increase in PM10, O3, NO2, SO2, and CO. Results After controlling for potential confounders, we found that an IQR increase in PM10 was significantly associated with self-reported high blood pressure (HR = 1.13, 95% CI: 1.04, 1.23) and high blood cholesterol (HR = 1.07, 95%CI: 1.02, 1.12), but not with diabetes (HR = 1.05, 95%CI: 0.91, 1.21). SO2 was also positively associated with self-reported high blood pressure (HR = 1.22, 95%CI: 1.08, 1.38), high blood cholesterol (HR = 1.20, 95%CI: 1.11, 1.30), and diabetes (HR = 1.21, 95%CI: 0.92, 1.60). Moreover, we observed a positive association between CO and self-reported high blood pressure (HR = 1.07, 95%CI: 1.00, 1.15), but not for other diseases. However, self-reported morbidities were not associated with O3 and NO2. Conclusions Long-term exposure to air pollution, especially for PM10 and SO2 was associated with self-reported high blood pressure, high blood cholesterol, and diabetes in subjects of TCS. Our study supports that exposure to air pollution increases cardiovascular disease risk factors for younger population., Highlights • Evidence on health effects of long-term air pollution exposure are still limited in Southeast Asia. • SO2 was associated with an increased risks of self-reported high blood pressure, high blood cholesterol, and diabetes. • Increase of PM10 was also associated with the incidences of high blood pressure and high blood cholesterol. • The effects were observed even at the level below the National Standard Limit. • This study suggests air pollution increases cardiovascular disease risk factors for younger population.

Modelling of land-use change over time gives useful information relating to land development. Logistic regression is arguably one of the most used in the land-use study when a binary outcome variable. Multinomial logistic regression used when multinomial outcome variable. This study aims to model of land-use change using logistic regression and multinomial logistic regression. These methods were applied to data from a survey on the land-use of Krabi Province, Thailand, in 2000, 2009 and 2018. The data is based on an analogue-to-digital conversion which replaces polygonal shapes by coded grid points. The land-use observations were categorized into four, namely developed, rubber plantation, agricultural and undeveloped lands. Our findings show that developed and agricultural lands increased by two periods of study 2000–2009 and 2009–2018. Whereas rubber plantation was decreased on two periods observes. The logistic regression models for developed land, agricultural land and rubber plantation were used for modelling land-use change, consequently as the multinomial logistic regression model with the undeveloped land being the reference category. The results showed that the modelling of land-use in 2009 and 2018 with logistic regression and multinomial logistic regression does not differ by the area under the curves (AUC) and prediction accuracy. Therefore, logistic regression can be used for the modelling of the land-use change using land-use in the previous year as predictors instead of the multinomial logistic regression, since it is not a complicated formula for adjusting spatial correlations using the variance inflation factors (VIF) and displaying the modelling result on 95% confidence intervals (CI) based weighted sum contrasts.

Only a few studies have investigated the association between long-term exposure to air pollution and alterations of serum lipids and blood sugar level in developing countries. The present longitudinal study examined associations between long-term air pollution exposure and serum lipids [total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)] and fasting glucose (FG) in workers of the Electricity Generating Authority of Thailand (EGAT) in the Bangkok metropolitan region (BMR) of Thailand. We performed secondary analyses using the data obtained from 1,839 participants (mean age, 58.3 years as of 2002) of the EGAT1 cohort study (2002–2012). The average concentration of each air pollutants (PM10, O3, NO2, SO2, and CO) at the sub-district level in BMR from 2002 to 2012 were estimated using the ordinary kriging method. Exposure periods were averaged to 3 months prior to laboratory testing. Linear mixed effects models were used to estimate associations between air pollution and serum lipids and blood sugar. After controlling for potential confounders, an interquartile range increment of PM10, SO2, and CO was associated with elevated LDL-C [6.6% (95%CI: 4.3, 9.0), 11.1% (7.2, 15.2), and 1.9% (1.1, 2.7), respectively] and FG [2.8% (1.5, 4.2), 6.8% (4.5, 9.1), and 1.1% (0.6, 1.5), respectively]. In addition, PM10, SO2, and CO were inversely associated with HDL-C [-1.8% (−3.7, 0.1), −3.3% (−6.2, −0.3), and −1.1 (−1.7, −0.5), respectively]. O3 was negatively associated with TC, LDL-C, TG, and FG. These findings suggest inhalation of air pollutants may increase the risk of impaired metabolism of glucose and lipids.

Background Ankle-brachial index (ABI) and cardio-ankle vascular index (CAVI) are surrogate measures of atherosclerosis based on the functional performance of vessels, and are highly related to cardiovascular events. However, only a few longitudinal studies have been conducted on their associations with long-term air pollution exposure. Objective This study aimed to examine whether long-term air pollution exposure is associated with ABI and CAVI in workers of the Electricity Generating Authority of Thailand (EGAT) in the Bangkok Metropolitan Region (BMR). Methods This longitudinal study included 1261 participants (age range, 57–76 years as of 2007) of the EGAT study (2007–2017). ABI and CAVI were measured in 2007, 2012, and 2017. Annual mean concentrations of particulate matter ≤10 μm in diameter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) were estimated by ordinary kriging using data from 22 background and 7 traffic monitoring stations in BMR between 2002 and 2017. Linear mixed-effects models were used to assess associations between air pollution (expressed as 1-year, 3-year, and 5-year average concentration) and ABI and CAVI (expressed as percent changes per interquartile range (IQR) increase in PM10, O3, NO2, SO2, and CO). We also applied the mixed-effect ordinal logistic models to calculate odds ratios (ORs) of having high or moderate CAVI per an IQR increase in air pollution. Results After controlling for potential confounders, 1-year average CO was negatively associated with ABI, but not significantly (−0.48%, 95% CI: −1.03, 0.07). Three-year average NO2 was positively associated with CAVI (6.67%, 95% CI: 0.21, 13.1). In contrast, 1-year average PM10 was inversely associated with CAVI although the association was not significant. Although not significantly, 1-year average NO2 and CO were positively associated with prevalence of high or moderate CAVI. Conclusions Although not statistically significant, long-term NO2 and CO exposure was associated with ABI and CAVI in the participants of the EGAT study.