| 1. |
- Tran, Ngoc Hieu, et al.
(författare)
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Genetic profiling of Vietnamese population from large-scale genomic analysis of non-invasive prenatal testing data
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The under-representation of several ethnic groups in existing genetic databases and studies have undermined our understanding of the genetic variations and associated traits or diseases in many populations. Cost and technology limitations remain the challenges in performing large-scale genome sequencing projects in many developing countries, including Vietnam. As one of the most rapidly adopted genetic tests, non-invasive prenatal testing (NIPT) data offers an alternative untapped resource for genetic studies. Here we performed a large-scale genomic analysis of 2683 pregnant Vietnamese women using their NIPT data and identified a comprehensive set of 8,054,515 single-nucleotide polymorphisms, among which 8.2% were new to the Vietnamese population. Our study also revealed 24,487 disease-associated genetic variants and their allele frequency distribution, especially 5 pathogenic variants for prevalent genetic disorders in Vietnam. We also observed major discrepancies in the allele frequency distribution of disease-associated genetic variants between the Vietnamese and other populations, thus highlighting a need for genome-wide association studies dedicated to the Vietnamese population. The resulted database of Vietnamese genetic variants, their allele frequency distribution, and their associated diseases presents a valuable resource for future genetic studies.
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| 2. |
- Dao, Trong Tuan, et al.
(författare)
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Resveratrol suppressed lps-induced cox-2 VIA miR-146a-5p inhibition in raw246.7 cells
- 2017
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Ingår i: Farmacia. - 0014-8237. ; 65:2, s. 214-218
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Tidskriftsartikel (refereegranskat)abstract
- Trans-resveratrol (Res) is a well-known natural stilbene frequently found in grapes which have been reported to possess antioxidant, anti-cancer activities and inhibited COX-2 expression. MicroRNAs (miRNAs) are short endogenous non-coding RNAs involved in the regulation of mRNA stability and protein synthesis. In our research, resveratrol isolated from Vitis heyneana Roem. & Schult Vitis heyneana was observed to suppress lipopolysaccharides (LPS)-induced COX-2 expression in Raw264.7 cells in a dose dependent manner. Using qPCR it was revealed that LPS induced the expression of miR-25, miR- 125a, miR-125b, miR-146a-5p, miR-146a-3p and miR-455. However, we only observed miR-146a-5p expression significantly decreased in resveratrol compared to untreated-control group. In addition, resveratrol abrogated the effect of miR-146a-5p mimic induced-COX-2 expression in Raw264.7 cells. Taken together, this study demonstrated for the first time the involvement of miR-146a-5p in resveratrol inhibited LPS-induced COX-2 expression in Raw264.7 cells.
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| 3. |
- Hien Tran, Thi, et al.
(författare)
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Triolein from Coix lacryma-jobi induces cell cycle arrest through p53/p21 signaling pathway
- 2016
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Ingår i: Biomedical and Pharmacology Journal. - : Oriental Scientific Publishing Company. - 0974-6242. ; 9:2, s. 519-524
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Tidskriftsartikel (refereegranskat)abstract
- p53, a tumor suppressor protein, has important roles in DNA repair, cell cycle and apoptosis, is a one of the key events in cancer development. Coix lacryma-jobi seed has been used as a food and traditional medicine plant with anti-oxidant, anti-cancer and anti-diabetic effects. In currently research, we identified the most potent p53-increasing compound among 4 compounds (1-4) found in Coix lacryma-jobi and demonstrated its molecular mechanism in MCF-7 cells. Among the four isolated compounds (1-4), triolein most increased p53. Triolein treatment induced p53, p21, p27 and Bax in MCF-7 cells. Moreover, triolein caused S phase arrest through suppression of CDK1, phopho-Rb and E2F1 in dose-dependent manner. We also observed the decreasing of DNA synthesis by triolein. These data suggest that triolein may induced cell cycle restart involve DNA synthesis and apoptosis pathway in MCF-7 cells.
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| 4. |
- Thi Thanh Ngo, Huong, et al.
(författare)
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Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network: A Case Study of Vietnam
- 2023
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Ingår i: CMES - Computer Modeling in Engineering & Sciences. - : Tech Science Press. - 1526-1492 .- 1526-1506. ; 135:3, s. 2219-2241
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Tidskriftsartikel (refereegranskat)abstract
- Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were used for the development of flash flood susceptibility maps for hilly road section (115 km length) of National Highway (NH)-6 in Hoa Binh province, Vietnam. In the proposed models, 88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors. The performance of the models was evaluated using standard statistical measures including Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC) and Root Mean Square Error (RMSE). The results revealed that all the models performed well (AUC > 0.80) in predicting flash flood susceptibility zones, but the performance of the DL model is the best (AUC: 0.972, RMSE: 0.352). Therefore, the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.
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| 5. |
- Bui, Quynh-Anh Thi, et al.
(författare)
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Hybrid Model: Teaching Learning-Based Optimization of Artificial Neural Network (TLBO-ANN) for the Prediction of Soil Permeability Coefficient
- 2022
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Ingår i: Mathematical problems in engineering (Print). - : Hindawi Publishing Corporation. - 1024-123X .- 1563-5147. ; 2022
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Tidskriftsartikel (refereegranskat)abstract
- The permeability coefficient (k-value) of the soil is an important parameter used in the civil engineering design of roads, tunnels, dams, and other structures. However, the determination of k-value by experimental methods in the laboratory or the field is still costly and time-consuming. Moreover, it requires special equipment and special care in the collection of soil samples for laboratory study. Therefore, in this study, we have proposed machine learning (ML) hybrid model: teaching learning-based optimization of artificial neural network (TLBO-ANN) to predict the k-value of soil based on limited parameters (natural water content, void ratio, specific gravity, liquid limit, plastic limit, and clay content) which can be determined easily in the laboratory. Test results of 84 soil samples obtained from the Da Nang-Quang Ngai expressway project in Vietnam are used in the model development. Statistical indicators such as correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are used to validate and evaluate the accuracy of the model. The results show that the TLBO-ANN model is an effective tool in predicting correctly the k-value (R = 0.905) of soil for the consideration in the design of structures founded on the soil.
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| 6. |
- Pham, Binh Thai, et al.
(författare)
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A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping
- 2020
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Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 12:1, s. 1-21
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Tidskriftsartikel (refereegranskat)abstract
- Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.
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