Current perspectives on opisthorchiasis control and cholangiocarcinoma detection in southeast asia (Review)

Similar to bile duct cancer or cholangiocarcinoma (CCA) in the western world, opisthorchiasis-associated CCA in Southeast Asia is an aggressive cancer with high mortality rates. It is known to cause a significant health burden in the opisthorchiasis region in Thailand and possibly throughout mainland Southeast. To reduce this health burden, a comprehensive prevention and control program for opisthorchiasis, as well as CCA, is required. In this review, our aim is to provide a brief update of the current situation regarding the natural history of opisthorchiasis and health burden of CCA in Southeast Asia. A comprehensive approach to tackling these issues being implemented in Thailand under the "Cholangiocarcinoma Screening and Care Program" is described. This comprehensive program consists of a three stage prevention and patient care program. The primary prevention component involves opisthorchiasis screening using a new and sensitive urine assay. The secondary prevention component involves screening for CCA and periductal fibrosis, with suspected CCA patients following the protocol for confirmation and appropriate treatment. Due to the eco-epidemiology of opisthorchiasis-induced CCA, the anticipated impacts and outcomes of the program include short-, medium-, and the long-term goals for the reduction of CCA incidence. To achieve long-term sustainable impacts, concerted efforts to raise social awareness and participating action by general public, non-government organizations, and government agencies are necessary. The strategic plans developed for this program can be expanded for use in other endemic areas as well as being a model for use in other chronic diseases

Trichinella inflammatory myopathy: host or parasite strategy?

The parasitic nematode Trichinella has a special relation with muscle, because of its unique intracellular localization in the skeletal muscle cell, completely devoted in morphology and biochemistry to become the parasite protective niche, otherwise called the nurse cell. The long-lasting muscle infection of Trichinella exhibits a strong interplay with the host immune response, mainly characterized by a Th2 phenotype

Machine-Learning-Based Forest Classification and Regression (FCR) for Spatial Prediction of Liver Fluke Opisthorchis viverrini (OV) Infection in Small Sub-Watersheds

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. © 2023 by the authors.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]