Objective To examine the impact of different numbers of microsatellite markers on the analysis of population genetic diversity of Schistosoma japonicum, so as to provide insights into studies on the population genetic diversity of S. japonicum. Methods Oncomelania hupensis snails were collected from a wasteland in Gong’an County, Hubei Province, and 37 S. japonicum-infected O. hupensis snails were identified using the cercarial shedding method. A single cercaria released from each S. japonicum-infected O. hupensis snail was collected, and 10 cercariae were randomly collected from DNA extraction. Nine previously validated microsatellite loci and 15 additional microsatellite loci screened from literature review and the GenBank database and confirmed with stable amplification efficiency were selected as molecular markers. Genomic DNA from cercariae was subjected to three multiplex PCR amplifications of microsatellite markers with the Type-it Microsatellite PCR kit, and genotyped using capillary electrophoresis. The population genetic diversity of S. japonicum cercariae DNA was analyzed with observed number of alleles (Na), effective number of alleles (Ae), observed heterozygosity (Ho), expected heterozygosity (He), and polymorphism information content (PIC), and tested for Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD). To further investigate the impact of the number of microsatellite loci on the population genetic diversity of S. japonicum, the number of microsatellite markers was sequentially assigned from 1 to 24, and the mean and standard deviation of Na were calculated for S. japonicum populations at different locus numbers. In addition, the coefficient of variation (CV) of allelic number (defined as the ratio of the standard deviation to the mean) was determined, and the variation in Na with increasing microsatellite locus numbers was analyzed. Results Genomic DNA from 345 S. japonicum cercariae was selected for genotyping of 24 microsatellite markers, and all 24 microsatellite loci met linkage equilibrium (standardized linkage disequilibrium coefficient D′ < 0.7, r² < 0.3) and deviated from Hardy-Weinberg equilibrium (P < 0.001). The mean Na, Ae, Ho and He were 27.46 ± 2.18, 12.46 ± 0.95, 0.46 ± 0.03, and 0.91 ± 0.01 for 24 microsatellite loci in S. japonicum cercarial populations, respectively, and PIC ranged from 0.85 to 0.96, indicating high genome-wide representativeness of 24 microsatellite loci. The mean value of Na-Ae was higher in genotyping with 9 previously validated microsatellite loci (19.88 ± 8.43) than with all 24 loci (14.99 ± 8.09). As the number of microsatellite loci increased, the mean Na showed no significant variation; however, the standard deviation gradually decreased. Notably, if the locus number reached 18 or more, the variation in the standard deviation of Na remarkably reduced. In addition, the standard deviation of Na at 18 loci was less than 5% of the mean Na at 24 loci, with a CV of 4.6%. Conclusions The number of microsatellite loci significantly affects the population genetic diversity analysis of S. japonicum. Eighteen or more microsatellite loci are recommended for analysis of the population genetic diversity of S. japonicum under the current conditions of low-prevalence infection and unbalanced genetic distribution of S. japonicum.

Objective To investigate the dietary patterns of rural residents in the high-incidence areas of esophageal cancer (EC), and to explore the clustering and influencing factors of risk factors associated with high-incidence characteristics. Methods A special structured questionnaire was applied to conduct a face-to-face survey on the dietary patterns of rural residents in Yanting county of Sichuan Province from July to August 2021. Univariate and multivariate logistic regression models were used to analyze the influencing factors of risk factor clustering for EC. Results There were 838 valid questionnaires in this study. A total of 90.8% of rural residents used clean water such as tap water. In the past one year, the people who ate fruits and vegetables, soybean products, onions and garlic in high frequency accounted for 69.5%, 32.8% and 74.5%, respectively; the people who ate kimchi, pickled vegetables, sauerkraut, barbecue, hot food and mildew food in low frequency accounted for 59.2%, 79.6%, 68.2%, 90.3%, 80.9% and 90.3%, respectively. The clustering of risk factors for EC was found in 73.3% of residents, and the aggregation of two risk factors was the most common mode (28.2%), among which tumor history and preserved food was the main clustering pattern (4.6%). The logistic regression model revealed that the gender, age, marital status and occupation were independent influencing factors for the risk factors clustering of EC (P<0.05). Conclusion A majority of rural residents in high-incidence areas of EC in Yanting county have good eating habits, but the clustering of some risk factors is still at a high level. Gender, age, marital status, and occupation are influencing factors of the risk factors clustering of EC.