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Additional file 1: Table S1: Borrelia Antigens used in the Borrelia multiplex. Table S2: Assay precision and LOD of the Borrelia multiplex on different Luminex platforms. . Table S3: Cohen’s kappa coefficients for the comparison of the Borrelia multiplex with commercial Borrelia test. Table S4: Serostatus of 1,555 serum samples from a German serological survey (MuSPAD study). Figure S1: Dilution linearity of Borrelia multiplex on different Luminex platforms.. Figure S2: ROC analysis for Borrelia specific IgG/IgM detection. Figure S3: Sample classification algorithms optimization. Figure S4: Sample analyses with commercial Borrelia tests. Figure S5: Comparison of PE detection system on FLEXMAP 3D and INTELLIFLEX platform. Figure S6: Comparison of IgG detection systems on INTELLIFLEX platform. Figure S7: Correlation between single detection and dual detection of IgG/IgM antibodies. Figure S8: Manual and automated processing of Borrelia multiplex.

Additional file 1: Appendix 1. STARD checklist. Appendix 2. Details of changes to published study protocol. Appendix 3. Routine data used from patient data management system for CDSS assessment of all participants. Appendix 4. Strategy for SIRS episode evaluation with examples. Appendix 5. Flow diagram for recruited patients with intensive care days and patient’s PICU stays. Appendix 6. Submitted forms during routine assessment per shift and per day. Appendix 7. False decisions from the CDSS diagnostic approach, classified into error categories.

Additional file 4. Additional data analyses information. This file provides more data analyses details.

Additional file 2. Consideration of additional contacts. This file illustrates how additional contacts were dealt with in the data management process.

Additional file 1. c statistic for all-cause death within 5 years after study visit (sensitivity analysis).

Additional file 1. COVIMOD questionnaire. This additional file includes the questionnaire.

Additional file 3. Additional results. This file provides results additional to the ones provided in the manuscript.

Additional file 1.

Figure S4. Anterior nares’ community diversity indicated by total phylotype number, Simpson index (1-Lambda), and Pielou’s evenness (J’). (PDF 233 kb)

Figure S8. Anterior nares’ community diversity in infected children indicated by total phylotype number, Simpson index (1-Lambda), and Pielou’s evenness (J’). (PDF 229 kb)

The number of genomes of major genera exhibiting individual pathofunctions is displayed, where the percentage of bacteria exhibiting the pathofunction of all members of that genus is given in brackets (Number Genomes (% of genus)). Correlations between pathofunction abundances affiliated with a genus and abundances of all members of respective genera (based on three house-keeping genes) are shown as well (CorrelationTaxonomy). The number of datasets displaying a correlation (p and q 0.35, n = 12) is given and Spearman’s rho (average ± standard deviation) is displayed in brackets. Datasets IV and VI were merged as they derived from one source and have overlapping samples in the healthy control groups. ND: not determined. (PDF 48 kb)

Indications in original studies for differential abundance of the three pathofunctions, namely, the formation of trimethylamine (TMA), secondary bile acids lithocholic/deoxycholic acid (LCA/DCA) and hydrogen sulfide (H2S) between patients and respective controls based on functional and taxonomy-based analyses. Data was queried for functions based on function names and KEGG Orthologies (provided for most studies. CutC/D: K20038/K20038, cntA/B: K22443/ K22444, grdH: K21579, baiCD/E: K15870/K15872, dsrA/B: K11180/ K11180). Taxonomic data was screened for key taxa that correlated with function based on information given in Additional file 4 (TMA (cntAB): Escherichia/Klebsiella, TMA (grdH): Dorea, H2S: Bilophila/Desulfovibrio). Clostridium scindens (DCA/LCA) was included as well. Jie et al. [21] (dataset I) additionally analyzed datasets III, VI and X for TMA associated genes based on selected reference sequences and results are also displayed. Forslund et al. [24] included studies III-V into their analysis and reported a trended increase (p = 0.07) for MF0100 (dissimilatory sulfate reduction) in T2D patients across studies. ?: no information on that function/taxa was retrieved, =: no difference between patients and control group, CD: Crohn’s disease, CRC: colorectal cancer, CVD: cardiovascular disease, HGC: high gene count group, MLG: metagenomic linkage group, T2D: type 2 diabetes, UC: ulcerative colitis. (PDF 56 kb)

Differences in 2014’s risk perception between individuals who responded to both surveys and individuals who responded to the first survey only (PDF 209 kb)

Metagenomic and metatranscriptomic analyses of genes encoding three pathofunctions, namely, the formation of (i) trimethylamine (cutCD, cntAB, grdH), (ii) the secondary bile acids lithocholic/deoxycholic acid (baiA-I) and (iii) hydrogen sulfide (dsrAB) in 78 healthy male adults sampled at four time points [53]. Top panels display pathofunction abundance (A) and expression (B) considering all samples. Results of individual taxa are shown in the panels below (C, D), where relative abundances, i.e., percentage of each taxon from total pathofunction abundance/expression data, are shown. Panels E and F display temporal variability of both levels where $ indicates significant higher variability (p

Table S1: Overview of all sepsis cases with site of infection and relevant corresponding infectiological data. Table S2: Systematic Overview of the Predictors used in the Analysis. Table S3: Overview of all models in the backward selection procedure. Methods S1: Detailed description and explanation of data analysis approach. Code S1: R code for the main analysis. Figure S1: AUCs of the time-split approach with different mtry parameter. Figure S2: ROC analysis without validation procedure (“Apparent Performance”). (DOCX 81 kb)

Comparison of the study population’s composition with the target population’s (inhabitants of Lower Saxony between 15 and 69 years of age) composition. Data about the target population are taken from the Census 2011 by the Federal Statistical Office of Germany [19]. (PDF 322 kb)

R-script k_alpha – syntax, explanation, and analysis of a fictitious data set. (DOCX 25 kb)

Estimators - Estimators of Fleiss’ K (with standard error) and Krippendorff’s alpha. (DOCX 19 kb)

Case study – description, dataset, syntax, and output. (DOCX 21 kb)

Figures A1 and A2 – scatter plot of the point estimates of Fleiss’ K versus Krippendorff’s alpha and empirical coverage probability of the asymptotic confidence interval for Fleiss’ K. (DOCX 92 kb)

Available software – implementation of Fleiss’ K and/or Krippendorff’s alpha in most common statistical software programs used in epidemiology/biometry. (DOCX 14 kb)