Baseline characteristics of patients with and without beta-blocker therapy (n = 625).

<p>SD: Standard deviation, IQR: Interquartile range,</p><p>*t-tests, Wilcoxon rank-sum tests and chi-square tests as appropriate</p><p>Baseline characteristics of patients with and without beta-blocker therapy (n = 625).</p

Effect of beta-blocker therapy on post-stroke infections stratified by statin therapy.

<p>*p-value for interaction based on a Likelihood Ratio Test comparing a model with interaction term with the corresponding model without interaction term</p><p>Effect of beta-blocker therapy on post-stroke infections stratified by statin therapy.</p

Post-stroke pneumonia, urinary tract infection and mortality in patients with and without beta-blocker therapy (n = 625).

<p>HR/RR: Hazard-Ratio and Rate Ratio (adjusted for age, sex and baseline NIHSS) obtained using Poisson (pneumonia, urinary tract infection) and Cox (death) regression models, CI: Confidence interval,</p><p>*Likelihood Ratio Test,</p><p>‡ competing risk situation</p><p>Post-stroke pneumonia, urinary tract infection and mortality in patients with and without beta-blocker therapy (n = 625).</p

C-reactive protein, leukocyte count, NIHSS, modified ranking scale and Barthel-index in patients with and without beta-blocker therapy (n = 625).

<p>NIHSS: National Institute of Health Stroke Scale; CRP: C-reactive protein; mRS: modified Ranking Scale; SE: Standard error; ANCOVAs adjusted for age, sex, baseline NIHSS and individual follow-up; Change in outcome parameters were calculated as follows: baseline value—follow-up value (for NIHSS) or highest value—baseline value (for CRP and leukocyte count)</p><p>C-reactive protein, leukocyte count, NIHSS, modified ranking scale and Barthel-index in patients with and without beta-blocker therapy (n = 625).</p

Kaplan-Meier plot displaying survival after stroke in patients with and without beta-blocker therapy.

<p>Patients with beta blocker therapy showed a higher 30 days mortality than those without beta blocker therapy in the univariable (log-rank test, p = 0.003) and multivariable analyses (Cox regression model, p = 0.006).</p

Additional file 3 of Small and long RNA transcriptome of whole human cerebrospinal fluid and serum as compared to their extracellular vesicle fractions reveal profound differences in expression patterns and impacts on biological processes

Additional file 3: Target sets of miR expressed in serum and CSF fraction

Additional file 4 of Small and long RNA transcriptome of whole human cerebrospinal fluid and serum as compared to their extracellular vesicle fractions reveal profound differences in expression patterns and impacts on biological processes

Additional file 4: Read counts of differentially expressed long RNA transcripts listed in table 2 

Additional file 2 of Small and long RNA transcriptome of whole human cerebrospinal fluid and serum as compared to their extracellular vesicle fractions reveal profound differences in expression patterns and impacts on biological processes

Additional file 2: Read counts of differentially expressed small RNA transcripts listed in table1

Additional file 5 of Small and long RNA transcriptome of whole human cerebrospinal fluid and serum as compared to their extracellular vesicle fractions reveal profound differences in expression patterns and impacts on biological processes

Additional file 5: Transcript sets of mRNA expressed in serum and CSF fractions

Additional file 1 of Small and long RNA transcriptome of whole human cerebrospinal fluid and serum as compared to their extracellular vesicle fractions reveal profound differences in expression patterns and impacts on biological processes

Additional file 1: Age distribution of patients, volume size, and erythrocyte and leucocyte numbers of CSF and serum samples. Fig. S2 RNA content of 100 KDa column concentrates and filtrates. Fig. S3 Age- and sex-distribution of serum samples. Fig. S4 RNA content of CSF samples in dependence of the storage temperature and RNAse treatment. Fig. S5 RNA content of CSF and serum samples. Fig. S6 Gel analysis of all RNA samples. Fig. S7 Electropherogramms of the RNA samples from serum. Fig. S8 Electropherogramms of the RNA samples from CSF. Fig. S9 Comparison of RNA content and electropherogramms of the RNA samples from CSF and blood-contaminated CSF. Fig. S10 Venn diagrams and UpSet plots form small and long RNA. Fig. S11 Heat maps of small RNA read counts not shown in figure 4. Fig. S12 GO Slim summaries of miR targets from whole CSF, CSF-EV, whole serum, and serum EV. Fig. S13 Heat maps of read counts encompassing all possible comparisons of down- and up-regulated long RNA transcripts. Fig. S14 Venn diagrams of significantly expressed small RNA transcripts, expressed miR, and target transcripts of expressed miR. Fig. S15 Distribution of small RNAs in body fluids and their respective EV