Intestinal parasitic infections are still a crucial problem among communities in Northeast Thailand. Misuse of antiparasitic drugs and unhealthy food behaviors are known. This study aimed to explore the prevalence, behavioral health factors, and motivation for self-treatment of anti-parasitic drugs in this area. A community-based cross-sectional study was conducted in Phra Lap sub-district, Mu Khon Kaen district, Khon Kaen province, Northeast Thailand, in 2016. A total of 419 participants were recruited to complete a self-administered questionnaire and stool examination. Binary logistic regression was used to assess the association between the risk factor and parasitic infection. Forty-two participants (10%; 95%CI 7.5–13.3) were positive for at least one parasite species. In this community, the most detected intestinal parasite was Opisthorchis viverrini (5.3%), followed by Strongyloides stercoralis (3.1%). A total of 67.5% of the participants had the experience of anti-parasitic drug treatment within previous 1 year, and “Often eat raw food” was the most common reason for the use of anti-parasitic drugs. On multivariate analysis, parasitic infections were significantly associated with male gender (ORadj. 2.42; 95%CI 1.00–5.85), age ≥ 60 years (ORadj. 7.55; 95%CI 1.60–35.76), and often consuming raw food of at least one type (ORadj. 2.37; 95%CI 1.03–5.44). Given these findings, correction of the dietary habit of eating raw fish/meat, which is the most important measure, and limitation of the use of anthelmintic treatment for individuals with stools positive for ova as well as emphasis on sanitary toilets will be implemented for the prevention and control of parasitic infection in endemic communities.

The main purpose of the study is to apply symmetry principles to general mathematical modelling based on multi-criteria decision making (MCDM) approach for use in development in conjunction with geographic weighted regression (GWR) model and optimize the artificial neural network-cellular automaton (ANN-CA) model for forecasting the sugarcane plot burning area of Northeast Thailand. First, to calculate the service area boundaries of sugarcane transport that caused the burning of sugarcane with a fire radiative power (FRP) values using spatial correlation analysis approach. Second, the analysis of the spatial factors influencing sugarcane burning. The study uses the approach of symmetry in the design of algorithm for finding the optimal service boundary distance (called as cut-off) in the analysis of hot-spot clustering and uses calculations with the geographic information system (GIS) approach, and the final stage is the use of screened independent variable factors to predict the plots of burned sugarcane in 2031. The results showed that the positively related factors for the percentage of cane plot sintering in the sub-area units of each sugar plant’s service were the distance to transport sugarcane plots index and percentage of sugarcane plantations in service areas, while the negative coefficients were FRP differences and density of sugarcane yield factors, according to the analysis with a total of seven spatial variables. The best GWR models display local R² values at levels of 0.902 to 0.961 in the service zones of Khonburi and Saikaw. An influential set of independent variables can increase the accuracy of the ANN-CA model in forecasting with kappa statistical estimates in the range of 0.81 to 0.85 The results of the study can be applied to other regions of Thailand, including countries with similar sugarcane harvesting industries, to formulate policies to reduce the exposure of sugarcane harvested by burning methods and to support the transportation of sugarcane within the appropriate scope of service so that particulate matter less than 2.5 microns (${\mathrm{PM}}_{2.5}$) can be reduced.

We investigated the anti-cholangiocarcinoma effect of α-mangostin from Garcinia mangostana pericarp extract (GM) in a human cholangiocarcinoma (CCA) cell line and a hamster CCA allograft model. In vitro, human CCA cells were treated with GM at various concentrations and for different time periods; then cell-cycle arrest and apoptosis were evaluated using flow cytometry, and metastatic potential with wound healing assays. In vivo, hamster allografts were treated with GM, gemcitabine (positive control) and a placebo (negative control) for 1 month; tumor weight and volume were then determined. Histopathological features and immunostaining (CK19 and PCNA) characteristics were examined by microscopy. The present study found that α-mangostin could: inhibit CCA cell proliferation by inducing apoptosis through the mitochondrial pathway; induce G1 cell-cycle arrest; and inhibit metastasis. Moreover, α-mangostin could inhibit CCA growth, i.e. reduce tumor mass (weight and size) and alter CCA pathology, as evidenced by reduced positive staining for CK19 and PCNA. The present study thus suggested that α-mangostin is a promising anti-CCA compound whose ready availability in tropical countries might indicate use for prevention and treatment of CCA.

Rubber is a perennial plant grown for natural rubber production, which is used in various global products. Ensuring the sustainability of rubber cultivation is crucial for smallholder farmers and economic development. Accurately predicting rubber yields is necessary to maintain price stability. Remote sensing technology is a valuable tool for collecting spatial data on a large scale. However, for smaller plots of land owned by smallholder farmers, it is necessary to process productivity estimates from high-resolution satellite data that are accurate and reliable. This study examines the impact of spatial factors on rubber yield and evaluates the technical suitability of using grouping analysis with the forest classification and regression (FCR) method. We developed a high-density variable using spatial data from rubber plots in close proximity to each other. Our approach incorporates eight environmental variables (proximity to streamlines, proximity to main river, soil drainage, slope, aspect, NDWI, NDVI, and precipitation) using an FCR model and GIS. We obtained a dataset of 1951 rubber yield locations, which we split into a training set (60%) for model development and a validation set (40%) for assessment using area under the curve (AUC) analysis. The results of the alternative FCR models indicate that Model 1 performs the best. It achieved the lowest root mean square error (RMSE) value of 19.15 kg/ha, the highest R-squared (R²) value (FCR) of 0.787, and also the highest R² (OLS) value of 0.642. The AUC scores for Model 1, Model 2, and Model 3 were 0.792, 0.764, and 0.732, respectively. Overall, Model 4 exhibited the highest performance according to the AUC scores, while Model 3 performed the poorest with the lowest AUC score. Based on these findings, it can be concluded that Model 1 is the most effective in predicting FCR compared to the other alternative models.

Infection of liver flukes (Opisthorchis viverrini) is partly due to their suitability for habitats in sub-basin areas, which causes the intermediate host to remain in the watershed system in all seasons. The spatial monitoring of fluke at the small basin scale is important because this can enable analysis at the level of the factors involved that influence infections. A spatial mathematical model was weighted by the nine spatial factors X₁ (index of land-use types), X₂ (index of soil drainage properties), X₃ (distance index from the road network, X₄ (distance index from surface water resources), X₅ (distance index from the flow accumulation lines), X₆ (index of average surface temperature), X₇ (average surface moisture index), X₈ (average normalized difference vegetation index), and X₉ (average soil-adjusted vegetation index) by dividing the analysis into two steps: (1) the sub-basin boundary level was analyzed with an ordinary least square (OLS) model used to select the spatial criteria of liver flukes aimed at analyzing the factors related to human liver fluke infection according to sub-watersheds, and (2) we used the infection risk positional analysis level through machine-learning-based forest classification and regression (FCR) to display the predictive results of infection risk locations along stream lines. The analysis results show four prototype models that import different independent variable factors. The results show that Model 1 and Model 2 gave the most AUC (0.964), and the variables that influenced infection risk the most were the distance to stream lines and the distance to water bodies; the NDMI and NDVI factors rarely affected the accuracy. This FCR machine-learning application approach can be applied to the analysis of infection risk areas at the sub-basin level, but independent variables must be screened with a preliminary mathematical model weighted to the spatial units in order to obtain the most accurate predictions.

Infection with liver flukes (Opisthorchis viverrini) is partly due to their ability to thrive in habitats in sub-basin areas, causing the intermediate host to remain in the watershed system throughout the year. Spatial modeling is used to predict water source infections, which involves designing appropriate area units with hexagonal grids. This allows for the creation of a set of independent variables, which are then covered using machine learning techniques such as forest-based classification regression methods. The independent variable set was obtained from the local public health agency and used to establish a relationship with a mathematical model. The ordinary least (OLS) model approach was used to screen the variables, and the most consistent set was selected to create a new set of variables using the principal of component analysis (PCA) method. The results showed that the forest classification and regression (FCR) model was able to accurately predict the infection rates, with the PCA factor yielding a reliability value of 0.915. This was followed by values of 0.794, 0.741, and 0.632, respectively. This article provides detailed information on the factors related to water body infection, including the length and density of water flow lines in hexagonal form, and traces the depth of each process.