Statistica 13.3 pl

A one-way ANOVA with post hoc Tukey’s multiple comparison tests (GraphPad Prism, San Diego, CA, USA) was used to determine the statistical significance of differences between phytochemical profiles and, in parallel, between means of expression data sets. Differences were regarded as significant at p < 0.05. (Supplementary Table S4).
Pearson coefficient analysis (p < 0.05) was performed using Statistica 13.3PL (StatSoft, Krakow, Poland). Species-adjusted partial correlation analyses were performed to determine the association between metabolite levels with gene expression levels in leaves and roots of S. yangii and S. abrotanoides at different time intervals.

Statistical analysis was performed using Statistica 13.3 PL (StatSoft Polska, Krakow, Poland). The results were expressed as means and standard deviations (SD) or medians and interquartile range (Q1, Q3). The Shapiro-Wilk test confirmed that HA, ESR, CRP, PLT (except in SSc), Hb (except in RA and SSc) and RF were not normally distributed. Since the majority of data were not normally distributed the differences between the study group and healthy subjects for all parameters were evaluated by non-parametric Mann-Whitney U test. To test the effect of rheumatic diseases on the concentration of HA, CRP, RF, ESR value and PLT count, the analysis of variance (ANOVA) rank Kruskal-Wallis test was performed. If P-value was statistically significant, further, the post hoc test for multiple comparisons was done and the P given. The Chi² test was used to compare differences between genders. The correlation between variables was assessed by Spearman’s rank correlation coefficient. The results were considered to be statistically significant when P values were less than 0.05.

Statistical analysis was performed using Statistica 13.3 PL software (StatSoft, Tulsa, OK, USA). The results were presented as the mean ± standard deviation or the median, interquartile ranges based on the Shapiro–Wilk and the Lilliefors normality test results. The Student′s t-test or the Mann–Whitney U-test was used to compare two groups of variables. ANOVA test or Kruskal–Wallis test and post hoc test were performed to analyze differences between three subgroups. The relationship between variables was evaluated using the Pearson or the Spearman′s rank correlation. Odds ratio (OR), including 95% confidence interval (CI), were calculated by univariate and multivariate logistic regression analysis to identify variables associated with the presence of renal scars. Variables associated with renal scars in the univariate analysis were included in the multivariate model. Variables that correlated with each other with r > 0.600 were excluded from the regression model to avoid collinearity. Receiver operating curve (ROC) analysis was used to calculate the area under the curves (AUC) for laboratory variables and to find the best cut-off values (including 95% CI), sensitivity, and specificity in the detection of renal scarring for each variable. p values less than 0.05 were considered statistically significant for all tests.

The current study presents detailed characteristics of the subjects with regard to gender, duration of stay at an institution, degree of dementia and risk of depression. The Shapiro–Wilk test was used to assess the normality of data distributions. The χ² test, Kruskal-Wallis test and Wilcoxon test were used to test for significance of differences. Spearman’s non-parametric correlation test was used to analyse the relationships between the variables.
The results for which p < 0.05 were considered statistically significant. Statistical analysis was performed using Statistica 13.3 PL (StatSoft Polska, Kraków, Poland).

Acylcarnitine identification was performed using LipidSearch 4.1.30 (Thermo Fisher Scientific, San Jose, CA, USA) software, which is capable of identifying simple-chain carnitine esters with eight or more carbons in their structure (AC C8:0) (Table S5 in the Supplementary Materials). As such, carnitine and acylcarnitines with shorter chains were searched manually using mzCloud and the Human Metabolome Database (HMDB).
Statistical analysis was conducted using Statistica 13.3 PL (StatSoft, Inc., Tulsa, OK, USA) software. The average peak area for all analytes was calculated, and statistical tests were applied. In particular, Levene’s test was used to assess variation, and the Shapiro–Wilk test was applied to assess normality. A T-test was subsequently applied when variation was homogenous and the variables were normal, while the Mann–Whitney U Test was used in all other cases.
For in-depth analysis of particular acylcarnitines, normalization on acylcarnitine groups was performed (SCAC, MCAC, LCAC).

Detected compounds were identified first by matching the acquired spectrum with the NIST 2017 (Gaithersburg, MD, USA) Mass Spectra Library and additionally confirmed by the retention time of a reference substance. All substances used for identification of detected metabolites were purchased from Alchem (Alchem, Toruń, Poland). Chromatographic peaks were initially integrated using a customized method in GCMS Postrun Analysis software (Shimadzu, Kyoto, Japan) and, if necessary, manually corrected by an experienced analyst. A received compound table was produced in Metaboanalyst 5.0 online software [27 ], where peak areas were log-transformed. The statistical significance between VOC levels in A549 cells and reference the RPMI1640 medium was calculated in Statistica 13.3 PL software (StatSoft, Inc., Tulsa, OK, USA) using the U Mann-Whitney test, which is a nonparametric test to compare samples from two groups of independent observations, where p-values < 0.05 were considered to be significant. This test was chosen due to its stability to outliers and no requirement for the groups to be normally distributed. To determine LOD/LOQ and summarize the data, results of calibrations and optimization measurements were plotted using Microsoft Excel, while the results of in vitro experiments with A549 cells were plotted using Metaboanalyst 5.0 online software [27 ].

The microbiological and physicochemical analyses, as well as the fermentation tests, were carried out in triplicate. Data from the microbiological, physicochemical, and instrumental analyses were subjected to two-way ANOVA. A Bonferroni correction was applied to all ANOVA results. Analysis of the sensory test results was performed using the Mann–Whitney U test. The results of the QDP were assessed based on the principal component analysis (PCA). Statistical significance was recognized when p < 0.05. All tests were performed using STATISTICA 13.3 PL software (StatSoft, Kraków, Poland).