Rstudio version 1

Comparison between continuous, non-normally distributed variables was estimated by Wilcoxon rank-sum test. Differences between two nominal variables were evaluated using Pearson’s chi-square or Fisher’s exact test (for expected groups sizes ≤ 5). For exploratory survival analysis, the primary endpoints were overall survival (OS), progression-free survival (PFS) and disease-specific survival (DSS). OS was defined as the time from diagnosis until death (from any cause). PFS was defined as the time from diagnosis until death or relapse or progression [12 (link)]. DSS was defined as the time from diagnosis until death from DLBCL. Surviving patients were censored at the last date of follow-up. Survival curves were estimated according to the Kaplan-Meier method. Cox regression was used for univariate and multivariate survival analyses and results were reported as hazard ratio (HR), 95% confidence interval (CI) and p value based on statistical Wald test. A two-tailed p value of less than 0.05 indicated statistical significance. All analyses were performed using R version 3.4.1 and R-studio version 1.0.153 software.

EPX activity, EDN release, and in vitro chemotaxis data were analyzed using Kruskal-Wallis test or one-way ANOVA test, based on normality of the data. Following this the Tukey test was used for pair-wise comparisons (Sigmaplot 13.0, Systat Software). Differences were considered significant when p < 0.05.
For flow cytometry studies, MFI values were obtained from flow cytometry analysis software (FlowJo 10.2). MFI fold change values were normalized by performing log transformation of the ratio between cytokine stimulated MFI values and unstimulated MFI values. Ratio-t-tests were performed on the log-transformed data (RStudio Version 1.0.153). Differences were considered significant according to the stated Bonferroni corrected p-value.

We evaluated the difference in the number of strains belonging to pathogenic species isolated from different sampling sites and different bat species using Fisher’s exact test, using a significance level of α = 0.05. The data were analysed using RStudio Version 1.0.153 for macOS (https://github.com/rstudio/rstudio, accessed on 21 September 2022). The built-in function fisher.test was used to calculate the p-values.

Statistical analysis of the R261H NEK1 variant in patients and healthy individuals was carried out according to the guidelines of case–control allelic association study design (Lewis, 2002 (link)). All statistical analyses were performed using RStudio version 1.0.153 (RStudio Team, 2015). Frequencies were compared using X² statistics (p < 0.05).

Comparisons of the relative abundances of the seven dominant bacterial genera and the abundance of the dominant ARG drug classes were assessed using Kruskal–Wallis with inference based on permutation tests and multiple-comparison [46 ] correction via the Holm method [47 ]. To identify specific bacterial species that had a relative abundance significantly associated with antibiotic use at each time sampled, we used multivariate association with the linear mixed-effect model (MaAsLin2) controlled for multiple comparison via FDR [48 (link)]; Fixed effects: antibiotic treatment, age, race, and sex; random effects: subject [49 (link)]. Permutation-based inference was used to improve inference validity. To test for differences among the subject demographics by treatment group we used an ANOVA and Fisher exact test of independence for numerical and categorical data, respectively. All tests were run in RStudio Version 1.0.153.

Prehospital stroke scales were reconstructed with the NIHSS items assessed at baseline in the intervention centre. The scales were assessed as positive or negative, using the cut point proposed in the original publication. We calculated the sensitivity for the detection of LVO for each prehospital stroke scale, both stratified by occlusion location and for all occlusion locations combined. For each prehospital stroke scale, the sensitivities for different occlusion locations were compared using Chi-square tests. Additionally, we plotted the sensitivity for all possible cut points of the prehospital stroke scales, stratified by occlusion location. Potential differences in sensitivity across prehospital stroke scales may be caused by variation in the included NIHSS items. Therefore, we calculated the percentage of patients in our cohort who had an abnormal score on each NIHSS item. All analyses were performed using R software version 3.6.1 and Rstudio version 1.0.153.

We used IBM SPSS Statistics version 23 (SPSS, Inc.) and RStudio version 1.0.153 (RStudio, Inc.) software for all statistical analyses and data processing. We considered P < 0.05 (two-tailed) as statistically significant. We used the χ² test and the Fisher’s exact test, as appropriate, to investigate the relationships between categorical parameters. The Mann–Whitney U test was used for continuous variables.