Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy.

Groundwater pollution from nickel (Ni) has been a severe concern in Kanchanaburi Province, Thailand. Recent assessments revealed that the Ni concentration in groundwater, particularly in urban areas, often exceeded the permissible limit. The challenge for groundwater agencies is therefore to delineate regions with high susceptibility to Ni contamination. In this study, a novel modeling approach was applied to a dataset of 117 groundwater samples collected from Kanchanaburi Province between April and July 2021. Twenty site-specific initial variables were considered as influencing factors to Ni contamination. The Random Forest (RF) algorithm with Recursive Feature Elimination (RFE) function was used to select the fourteen most influencing variables. These variables were then used as input features to train a ME model to delineate the Ni contamination susceptibility at a high confidence (Area Under the Curve (AUC) validation value of 0.845). Ten input variables of the altitude, geology, land use, slope, soil type, distance to industrial areas, distance to mining areas, electric conductivity, oxidation-reduction potential, and groundwater depth were discovered in the most explaining the variation of spatial Ni contamination at very high (95.47 km2) and high (86.65 km2) susceptibility. This study devises the novel machine learning approach to identify the conditioning factors and map Ni contamination susceptibility in the groundwater, which provides a baseline dataset and reliable methods for the development of a sustainable groundwater management strategy.

Relationship of Recurrence Rate with some Characteristics in Patients with Thyroid Carcinoma.

Background: Determining the clinical and subclinical characteristics related to the recurrence status in patients with a thyroid carcinoma has great significance for prognosis, prediction of recurrence and monitoring of treatment outcomes. This study aimed to determine the association between recurrence rate and some characteristics in patients with thyroid carcinoma. Patients and Methods: The study was conducted by descriptive method with longitudinal follow-up on 102 thyroid carcinoma patients at 103 Military Hospital, Hanoi, Vietnam, from July 2013 to December 2016. Results: Univariate analysis showed that there was a relationship between the recurrence characteristics in the studied patients and the characteristics of lymph node metastasis (P = 0.026; OR = 15; 95% CI = 1.4-163.2) and BRAF V600E mutation status (P = 0.01; OR = 3.41; 95% CI = 1.31-8.88). When analysing the multivariable Logistic regression model, there was a positive correlation between the occurrence of BRAF V600E gene mutation (P = 0.032; OR = 17.649; 95% CI = 1.290-241.523) and male sex (P = 0.036; OR = 12.788; 95% CI = 1.185-137.961) and the occurrence of recurrence in study patients. The mean time to relapse was earlier in male patients than in female patients (P = 0.02). The mean time to relapse in patients with the BRAF V600E mutation (31.81 ± 1.14 months) was shorter than the mean time to relapse in the group without the mutation (57.82 ± 2.08 months) (P = 0.01). The group of patients with mutations in the BRAF V600E gene increased the risk of recurrence compared with the group without the mutation (HR = 9.14, P = 0.04). Conclusion: There is a positive correlation between recurrence and masculinity, lymph node metastasis and the occurrence of BRAF V600E mutations in thyroid carcinoma patients.