METHODS: The genotypes were assessed on 144 histologically confirmed NAFLD patients and 198 controls using a Sequenom MassARRAY platform.
RESULTS: The GCKR rs1260326 and rs780094 allele T were associated with susceptibility to NAFLD (OR 1.49, 95 % CI 1.09-2.05, p = 0.012; and OR 1.51, 95 % CI 1.09-2.09, p = 0.013, respectively), non-alcoholic steatohepatitis (NASH) (OR 1.55, 95 % CI 1.10-2.17, p = 0.013; and OR 1.56, 95 % CI 1.10-2.20, p = 0.012, respectively) and NASH with significant fibrosis (OR 1.50, 95 % CI 1.01-2.21, p = 0.044; and OR 1.52, 95 % CI 1.03-2.26, p = 0.038, respectively). Following stratification by ethnicity, significant association was seen in Indian patients between the two SNPs and susceptibility to NAFLD (OR 2.64, 95 % CI 1.28-5.43, p = 0.009; and OR 4.35, 95 % CI 1.93-9.81, p < 0.0001, respectively). The joint effect of GCKR with adiponutrin rs738409 indicated greatly increased the risk of NAFLD (OR 4.14, 95 % CI 1.41-12.18, p = 0.010). Histological data showed significant association of GCKR rs1260326 with high steatosis grade (OR 1.76, 95 % CI 1.08-2.85, p = 0.04).
CONCLUSION: This study suggests that risk allele T of the GCKR rs780094 and rs1260326 is associated with predisposition to NAFLD and NASH with significant fibrosis. The GCKR and PNPLA3 genes interact to result in increased susceptibility to NAFLD.
METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.
RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.
CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.