Spss software version 25.0 for windows

Statistical analysis was carried out using SPSS software version 25.0 for Windows (SPSS, Inc., Chicago, IL, USA). Before statistical analysis, individual Campylobacter counts were transformed to log₁₀ counts and then used as the experimental unit. The Shapiro–Wilk test was used to test the normal distribution of the data. Since our data did not meet criteria of normal distribution, we applied pairwise comparisons using the non-parametric Mann–Whitney U-test. To ensure alpha error of 0.05, β-error of 0.18 and power of 0.80, 90 animals per group were included in the present study. In order to determine statistically significant differences, 36 animals were sampled during the animal trial. Probability (p)-values below 0.05 were considered statistically significant.

Normality of distribution for continuous variables was tested by the Kolmogorov–Smirnov test. Then the data conforming to the normal distribution was described by mean ± standard deviation (SD), and the unpaired Student’s t-test or analysis of variance (ANOVA) was used to compare the differences among groups. For qualitative data, the chi-square test or Fisher’s exact test was performed. In addition, the Hardy–Weinberg equilibrium (HWE) was also tested by the chi-square test. The HWE was a principle stating that the participants were representative of the population. The relationship between SNP genotypes and GDM risks was analyzed by multiple logistic regression. All tests were two-sided, and p < 0.05 was considered statistically significant. Analyses were performed by using SPSS Software, Version 25.0 for Windows (SPSS Inc., Chicago, IL, USA).

All the statistical analyses were performed with SPSS software version 25.0 for Windows (SPSS Inc., Chicago, IL, USA). P < 0.05 was considered indicative of a statistically significant difference. Continuous variables were compared between the two groups using the t test, whereas categorical variables were compared using the Chi-square test or Fisher’s exact test. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to assess the ability of each inflammatory marker to predict the Neo-CRT response. The Youden index was used to determine the optimal cutoff thresholds for subsequent analysis. Overall survival (OS) was defined as the time from the date of diagnosis to the date of death from any cause, and disease-free survival (DFS) was defined as the time from the date of diagnosis to the date on which recurrence, either local or distant, was first detected or to the date of death from any cause. Univariate and multivariate Cox regression analyses were performed to assess predictors of prognosis. Variables with P < 0.05 in the univariate analysis were examined by multivariate analysis. Survival curves were constructed using the Kaplan–Meier method, with the log-rank test applied to compare survival curves.