Statistica 10

Data distribution was evaluated by the Kolmogorov–Smirnov test. The results were presented as a standard deviation and mean value or median and percentile distribution. Student’s t-test and paired Student’s t-test were used, respectively, for independent and dependent variables with the normal distribution. Yates’s c2 test and Fisher’s exact test were used for assessing differences among categorical parameters. The Mann–Whitney U test was used for independent nonparametric variables. The Wilcoxon test was used to compare dependent nonparametric variables. A value of p < 0.05 was considered statistically significant. Heart failure (HF) decompensation-free survival, based on hospitalization due to exacerbation of HF or necessity of intensification of HF treatment, and death-free survival were analyzed using the Kapplan–Meier estimator and log-rank tests. Analysis was conducted by using Statistica 10 software (TIBCO Software Inc., Palo Alto, CA, USA).

Statistical analysis was performed using Statistica 10 (StatSoft 13.3, TIBCO Software Inc., Palo Alto, CA, USA). Normal distribution of data was checked by normal probability plots and the homogeneity of variance by the Brown–Forsythe test. Differences within and between groups were assessed using one-way ANOVA followed by a multicomparison Duncan’s test. Results are presented as means ± SD. Significance was considered at p ≤ 0.05. Dixon’s Q-test was used to eliminate uncertain data.

Student’s t-criterion was used to determine the statistical validity of the indicator. Due to a relatively small number of groups and deviations of variables from normal distributions, a Kruskal–Wallis non-parametric analysis of variance test was performed using licensed software package Statistica 10 (Tibco Software Inc., CA, USA.). The level of significance was set at p<0.05.

Parametric independent t-tests were performed to examine the significance of fibre diameters and specific loading capacity values among different samples. Pearson test was also performed for the fibre diameter-specific loading capacity correlation. For the analysis, STATISTICA 10 software (TIBCO Software Inc., Palo Alto, CA, USA) was used.

Statistical analysis was carried out using STATISTICA 10 software (TIBCO Software, Palo Alto, CA, USA). Nominal data were described with absolute values. Quantitative data with a normal distribution were combined into a variation series, in which the arithmetic mean (M) and standard deviations (SD) were calculated. In the absence of a normal distribution, quantitative data were presented as median and quartiles (25–75% margins of the interquartile range). The Kolmogorov–Smirnov test was used to verify the nature of the distribution. When comparing the mean values in normally distributed populations of quantitative data, Student’s t-test was calculated. Mann–Whitney U-test was used to compare two independent groups when there was no evidence of normal distribution. The Kruskal–Wallis test was used when comparing several samples of quantitative data with a distribution other than normal. Wilcoxon’s W test was used to analyze the statistical significance of differences in quantitative characteristics for two dependent samples. Differences were considered statistically significant at p < 0.05.

Statistical analysis was carried out using Statistica 10 (TIBCO Software, Palo Alto, CA, USA) software. All data were expressed as means with standard errors. Mann–Whitney or Kruskal–Wallis tests were used to evaluate statistical differences between groups, and p-values < 0.05 were considered statistically significant. Correlation analysis of the data was carried out with the Spearman Rank Correlation test. p-Values < 0.05 were considered statistically significant. Differences in the HSP and MMP composition of sEVs for RCPs with partial and complete tumor response were assessed using the repeated measures ANOVA test.