Production and Immunological Characterization of scFv Specific to Epitope of Opisthorchis viverrini Rhophilin-Associated Tail Protein 1-like (OvROPN1L)

Geadkaew-Krenc, Amornrat; Krenc, Dawid; Thanongsaksrikul, Jeeraphong; Grams, Rudi; Phadungsil, Wansika; Glab-ampai, Kittirat; Chantree, Pathanin; Martviset, Pongsakorn

doi:10.3390/tropicalmed8030160

Cited by 4 publications

(4 citation statements)

References 53 publications

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“…Severe liver fluke infections have been detected in Ponna Kaeo district, Sakon Nakhon province, Thailand [1]. The liver fluke, scientifically named Opisthorchis viverrini, causes cholangiocarcinoma (CCA) [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…The physical nature of the swamp is a large natural water source, full of water throughout the year, as it is a waterfront source from several streams, making it an important food source for the community. The livelihood of people living in the watershed derives from finding fish, which is an important source of protein, and there is a consumption culture that is familiar with the taste of raw fish [1,4], fascinated by the sweet taste of fresh, sour, spicy, hot fish with herbs cooked in meals. Fish is therefore a regular food for every meal of villagers who live near the river basin.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Analysis of Influence Factors Associated With Liver Fluke (Opisthorchis viverrini) Infection in Small Sub-Watershed Using GWR Modeling

Pumhirunroj,

Littidej,

Boonmars

et al. 2023

Preprint

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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. Spatial monitoring of fluke infection at the small -basin analysis scale is important because this can enable analysis at the level of the spatial factors involved and influencing infections. A geographic weighted regression model was developed to analyze the spatial characteristics of liver fluke infection, aiming to 1. analyze the spatial factors associated with human liver fluke infection according to sub-basin boundaries and 2. generate an alternative model for enhancing the effectiveness of preventive public health management to reduce the risk of liver fluke infection in humans. The number of infected persons was obtained from local authorities and converted into a percentage of infected people and generated as raster data with a heat map so that the data were continuous and defined as dependent variables. The independent set consisted of nine variables, both vector and raster data, that correlated the location with the village location of an infected person. The results showed that the variables X5stream, X7ndmi, and X9savi were statistically significantly correlated to the percentage of infected people, with the t-stat and p-value being (-2.068, 1.875, and -2.661) and (0.048, 0.034, and 0.021), respectively. The GWR model was able to increase accuracy more than the comparable models such as OLS, in all tests of the four alternative models, with an accuracy increase in R2 of 7.69% (0.576 to 0.624). This study confirms that the development of spatial models with GWR models can screen for factors associated with liver fluke infection at the level of small spatial units such as sub-basins.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Analysis of Influence Factors Associated With Liver Fluke (Opisthorchis viverrini) Infection in Small Sub-Watershed Using GWR Modeling

Pumhirunroj,

Littidej,

Boonmars

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Severe liver fluke infections have been detected in Ponna Kaeo district, Sakon Nakhon Province, Thailand [1]. The liver fluke, scientifically named Opisthorchis viverrini (OV), causes cholangiocarcinoma (CCA) [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…The physical nature of the swamp is a large natural water source, full of water throughout the year, as it is a waterfront source from several streams, making it an important food source for the community. The livelihood of people living in the watershed derives from finding fish, which is an important source of protein, and there is a consumption culture that is familiar with the taste of raw fish [1,4]. According to preliminary screening results from 2019 to 2021 [2], a small number of people contracted liver flukes.…”

Section: Introductionmentioning

confidence: 99%

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

Pumhirunroj,

Littidej,

Boonmars

et al. 2023

IJGI

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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.

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Production and immunological characterization of the novel single-chain variable fragment (scFv) antibodies against the epitopes on Opisthorchis viverrini cathepsin F (OvCatF)

Martviset,

Thanongsaksrikul,

Geadkaew-Krenc

et al. 2024

Acta Tropica

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Production and Immunological Characterization of scFv Specific to Epitope of Opisthorchis viverrini Rhophilin-Associated Tail Protein 1-like (OvROPN1L)

Cited by 4 publications

References 53 publications

Spatial Analysis of Influence Factors Associated With Liver Fluke (Opisthorchis viverrini) Infection in Small Sub-Watershed Using GWR Modeling

Spatial Analysis of Influence Factors Associated With Liver Fluke (Opisthorchis viverrini) Infection in Small Sub-Watershed Using GWR Modeling

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

Production and immunological characterization of the novel single-chain variable fragment (scFv) antibodies against the epitopes on Opisthorchis viverrini cathepsin F (OvCatF)

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