(1) Background: Automated blood culture headspace analysis for the detection of volatile organic compounds of microbial origin (mVOC) could be a non-invasive method for bedside rapid pathogen identification. We investigated whether analyzing the gaseous headspace of blood culture (BC) bottles through gas chromatography-ion mobility spectrometry (GC-IMS) enables differentiation of infected and non-infected; (2) Methods: BC were gained out of a rabbit model, with sepsis induced by intravenous administration of E. coli (EC group; n = 6) and control group (n = 6) receiving sterile LB medium intravenously. After 10 h, a pair of blood cultures was obtained and incubated for 36 h. The headspace from aerobic and anaerobic BC was sampled every two hours using an autosampler and analyzed using a GC-IMS device. MALDI-TOF MS was performed to confirm or exclude microbial growth in BCs; (3) Results: Signal intensities (SI) of 113 mVOC peak regions were statistically analyzed. In 24 regions, the SI trends differed between the groups and were considered to be useful for differentiation. The principal component analysis showed differentiation between EC and control group after 6 h, with 62.2% of the data variance described by the principal components 1 and 2. Single peak regions, for example peak region P_15, show significant SI differences after 6 h in the anaerobic environment (p < 0.001) and after 8 h in the aerobic environment (p < 0.001); (4) Conclusions: The results are promising and warrant further evaluation in studies with an extended microbial panel and indications concerning its transferability to human samples.

We aimed to identify biomarkers in breath analysis with multicapillary column-ion mobility spectrometry (MCC-IMS) to monitor the haemodialysis for chronic kidney disease (CKD) patients fast and non-invasive. Six patients’ breath was analyzed via MCC-IMS before and after dialysis and compared to blood plasma samples analyzed via ultra performance liquid chromatography-fluorescence detector for potential renal failure biomarkers. Additionally, breath from six healthy control persons was analyzed. Phenol was found as a breath marker for CKD. For three patients the phenol concentration in breath and plasma was elevated before and decreased during dialysis and reached values in the range of healthy control persons. The peak-intensity of phenol-monomer peaks of P01–P04 was reduced from an average of 16.58 (5.42–27.28) a.U. to 7.03 (0.00–13.65) a.U., which is a reduction by 42.51 (−10.55–100.00) %. The control group has an average peak-intensity of 8.50 (5.00–12.00) a.U. This study shows that the measurement of phenol via breath analysis could be used to monitor the haemodialysis for CKD-patients and might also be usable for the calculation of haemodialysis dose in the future. The study is registered in the German Clinical Trials Register under number DRKS00029679.

Background We aimed to identify biomarkers in breath analysis with MCC-IMS to monitor the haemodialysis for CKD patients fast and non-invasive. Material & methods Six patients’ breath was analysed via MCC-IMS before and after dialysis and compared to blood plasma samples analysed via UPLC-FLD for potential renal failure biomarkers. Additionally, breath from six healthy control persons was analysed. Results Phenol was found as a breath marker for CKD. For three patients the phenol concentration in breath and plasma was elevated before and decreased during dialysis and reached values in the range of healthy control persons. Conclusion This study shows that the measurement of phenol via breath analysis could be used to monitor the haemodialysis for CKD-patients and might also be usable for the calculation of haemodialysis dose in the future.