Spss 24.0 statistics package

The data were analysed with the IBM SPSS 24.0 Statistics package (IBM, Chicago, IL, USA). Percentage data were compared using Fisher’s exact test. Scored data (i.e., embryonic developmental stage) were analysed using the Kruskal–Wallis test and, if necessary, two by two comparisons for two independent samples with the Mann–Whitney U-test. The total cell number per blastocyst was analysed to evaluate normality and homogeneity of variances by the Kolmogorov–Smirnov and Levene tests, respectively, and groups were compared by a mixed-model ANOVA. The ANOVA model included the main effects of temperature and supplementation and their interactions and the random effect of the replicate. When the ANOVA indicated a significant effect, the means were compared by Bonferroni’s test. Differences were considered significant at P < 0.05. The results are presented as mean ± SD.

Differences between groups were examined with the IBM SPSS 24.0 Statistics package (SPSS, Chicago, IL, USA). The continuous variables are presented as the means ± standard errors of the means (SEM). The mean percentage ± SEM of the binary variables was obtained by calculating the percentage in every well of each group and each of the replicates. The normality of the variables was examined using the Shapiro–Wilk test. Groups were compared using an unpaired Student’s t-test (Experiments 1 and 6), ANOVA followed by the Bonferroni post-hoc test (Experiments 1, 2, 3, 4, 5, and 7), or a Fisher’s exact test (Experiment 6), as applicable. A p value < 0.05 was considered significant.

Statistical analysis was performed using the IBM SPSS 24.0 Statistics package (SPSS, Chicago, IL, USA). The mean ± SEM of binary variables (maturation, IVF efficiency, embryos with 2–8 cells, and blastocyst rates) was obtained by calculating the variable percentage in each replicate. All variables (maturation rates, fertilization efficiency, blastocyst rate, number of cells/blastocysts, and relative gene expression) were analyzed to evaluate normality by the Kolmogorov–Smirnov test and using a mixed-model ANOVA followed by the Bonferroni post hoc test. In vitro mature oocytes were clustered by total oocyte diameter, oocyte diameter, and thickness of ZP using iterative k-means cluster analysis to classify the oocytes of the dataset into a reduced number of subpopulations according to their oocyte dimensions. The effect of clusters, as fixed variable, on developmental stage (undeveloped, total embryos at the 2–8-cell stage, embryos arrested at the 2–8-cell stage, and blastocyst rates) were calculated and compared using a mixed-model ANOVA followed by a Bonferroni post hoc test. A logistic regression was performed to analyze the relationship between morphometric parameters (total oocyte diameter, oocyte diameter, and thickness of ZP) and blastocyst formation. Differences were considered significant when p < 0.05.