Quantitative Assessment of an Artificial Neural Network for the Variation in Immunity to Salmonella Infection Among Sudanese and Chinese Populations and the Relationship Between HLA-DQB1 and Antibody: A Preliminary Study

Abstract

Background: Salmonella enterica serovar typhi infection is a worldwide bacterial disease still remains a public health problem and the resistance to the infection and clearance of the bacteria may differ between Sudanese and Chinese populations. Objectives: We aimed to evaluate the difference in immunity to Salmonella infection and the relationship of HLA-DQB1 and antibody in the two populations using an artificial neural network (ANN). Methods: An ANN was constructed by data collected from resolved Salmonella-infected patients. The variation in immunity to Salmonella in the two groups was compared using the Mann-Whitney U test. The relationship between human leukocyte antigen (HLA) gene and the antibody was assessed by Spearman correlation. Binary Logistic regression assessed whether variables were independent in the two groups. The quantitative variations in immunity to S. typhi in the two groups were assessed by ANN using SPSS software version 21.0. Results: A high variation in immunity to S. typhi between Sudanese and Chinese in affinity (P 0.05). There was a high variation in HLA-DQB1*03 (P 0.05). A clear correlation was observed between HLA-DQB1*05 and the antibody (P < 0.05). The standardized percentage of the effect of HLA-DQB1 gene on the variation of immunity in the two groups for HLA-DQB1*06 (100%) was much higher than the other HLA-DQB1 alleles. Conclusions: A high variation in the HLA-DQB1 gene between Sudanese and Chinese populations may be associated with the difference in immunity against S. typhi infection.