Spss statistics software version 26

All statistical analyses were performed using SPSS Statistics software, Version 26 (IBM), and results were expressed as group means ± standard deviations. Tests for normality (Shapiro—Wilk's) were performed on each treatment group (SPS/Control) within each time point (Day 1/Day 3/Day 7/Day 17) to ensure that all data sets were normally distributed and that subsequent parametric analyses could be used. To determine differences in fear expression during CFC, two‐way repeated measures ANOVA was performed with unconditioned stimulus (US) as a within‐subjects factor and SPS‐exposure status as a between‐subjects factor. To determine differences in fear expression during Fear Extinction, two‐way repeated measure ANOVA was performed with “Day” as a within‐subjects factor and SPS‐exposure status as a between‐subjects factor. Differences between the means of the changes in gene expression (2^−ΔCt values and 2^–ΔCt ratios), GR protein expression, and %Freezing Time were evaluated using one‐way ANOVA (significance threshold was p < .05). Correlation between gene expression and fear behavior was evaluated using the Pearson Correlation.

The statistical analysis of the results was performed using the IBM SPSS Statistics software version 26 (IBM, Armonk, NY, USA). The non-parametric test Kruskal-Wallis for independent samples was used, considering p = 0.050 as the level of statistical significance. Statistically significant differences are given by p values < 0.050. All quantitative values are expressed as mean ± standard error of the mean (S.E.M.) for cell culture studies, and mean ± standard deviation (SD) for nanoparticle characterization studies of three replicates.

Descriptive statistics were used to calculate typical measures in patients’ demographic and clinical characteristics. Data were expressed as median with a range, and categorical data were expressed as counts and frequencies. Statistical analyses were carried out using IBM SPSS Statistics software version 26 (Statistical package for the Social Sciences Statistical Software; SPSS Inc, IBM Corporation, Armonk, NY, USA).
We analyzed the FET outcome data using R statistical software (R version 3.1.1 (2014-07-10), R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (accessed on 16 June 2022). To statistically assess significant differences in ROC curves we performed a non-parametric ROC analysis [20 (link)] We used the optimal operating point on the ROC curve using the Youden index, and uncertainty in cut-off values was modelled using a large number approach [21 (link)]. We compared patient groups using the Mann-Whitney-Wilcoxon rank-sum U test [22 (link)]. We combined TAC score, IDH mutation status, and TBR_mean predictions using the logistic regression (LR) model. When used on patients grouped by IDH mutation status, we only used TAC score and TBR_mean variables in the LR model. Results with a p-value below 0.05 were deemed statistically significant, and 95% confidence intervals (CI_95%) were used to quantify uncertainty in statistically derived values.