Spss 20

All the data were presented as mean standard error and were estimated using one-way ANOVA at a significance level of 0.05 using SPSS 20.0 (IBM SPSS, Somers, NY, USA) when necessary and the Pearson's correlation also be analyzed by SPSS 20.0. All the graphics in this article were made with SigmaPlot 12.5.

All experiments were conducted in triplicate, and data were expressed in means ± standard deviations. Statistical analysis was performed using SPSS 20.0 software (SPSS 20.0, IBM, Armonk, NY, USA), and the differences among mean values were tested by one-way ANOVA (SPSS 20.0, IBM, Armonk, NY, USA), taking a level of p < 0.05 as significant to Duncan’s multiple range test.

As potential confounders could affect the readmission for obstruction and lactulose was not used in a randomized manner in our study, we established propensity score matching using SPSS 20.0 (IBM, Armonk, NY) or R 3.1.2 (The R Foundation for Statistical Computing) to avoid the potential confounders. A 1:1 Propensity scores matching was performed using the nearest neighbor without replacement matching algorithm and a 0.1 caliper width. The generalized additive model was used to check linear assumption in PS model. After PS matching, statistical comparisons were conducted between the matched lactulose treatment patients and controls using SPSS 20.0 (IBM, Armonk, NY). Continuous data were expressed as means ± SDs or median (interquartile range [IQR]) and were analyzed using Student’s t-test or Mann-Whitney U test respectively. The discrete variables was analyzed by a chi-square test or Fisher’s exact test, and then by estimation of the relative risk between treatment groups. The relative risks for postoperative variables were assessed using cross-tabulation (odds ratio [OR]) or multivariate logistic regression analysis (risk ratio [RR]). The statistical significance was evaluated using a two-tailed 95% confidence interval (CI), and statistical significance was established if P < 0.05.

All data were obtained from three independent replicates, and the data were expressed as the mean ± standard deviation (SD). All statistical analyses were performed with SPSS 20.0 (IBM Corp., Armonk, NY). The data were analyzed by one-way analysis of variance (ANOVA). Mean separations were performed by Duncan’s multiple range tests. Differences with P < 0.05 were considered to be significant. Pearson’s correlation analysis was performed to determine the correlations among heat stress related physiological indexes. The PLSR model was constructed using the Unscrambler software (CAMO AS., Norway), and PA model was constructed using the SPSS 20.0 (IBM Corp., Armonk, NY).