Vancomycin resistant enterococci (VRE) constitute a challenging problem in health care institutions worldwide. Novel methods to rapidly identify resistances are highly required to ensure an early start of tailored therapy and to prevent further spread of the bacteria. Here, a spectroscopy-based rapid test is presented that reveals resistances of enterococci towards vancomycin within 3.5 hours. Without any specific knowledge on the strain, VRE can be recognized with high accuracy in two different enterococci species. By means of dielectrophoresis, bacteria are directly captured from dilute suspensions, making sample preparation very easy. Raman spectroscopic analysis of the trapped bacteria over a time span of two hours in absence and presence of antibiotics reveals characteristic differences in the molecular response of sensitive as well as resistant Enterococcus faecalis and Enterococcus faecium. Furthermore, the spectroscopic fingerprints provide an indication on the mechanisms of induced resistance in VRE.

Vancomycin is an important glycopeptide antibiotic which is used to treat serious infections caused by Gram-positive bacteria. However, during the last years, a tremendous rise in vancomycin resistances, especially among Enterococci, was reported, making fast diagnostic methods inevitable. In this contribution, we apply Raman spectroscopy to systematically characterize vancomycin-enterococci interactions over a time span of 90 min using a sensitive Enterococcus faecalis strain and two different vancomycin concentrations above the minimal inhibitory concentration (MIC). Successful action of the drug on the pathogen could be observed already after 30 min of interaction time. Characteristic spectral changes are visualized with the help of multivariate statistical analysis (linear discriminant analysis and partial least squares regressions). Those changes were employed to train a statistical model to predict vancomycin treatment based on the Raman spectra. The robustness of the model was tested using data recorded by an independent operator. Classification accuracies of >90 % were obtained for vancomycin concentrations in the lower range of a typical trough serum concentration recommended for most patients during appropriate vancomycin therapy. Characterization of drug-pathogen interactions by means of label-free spectroscopic methods, such as Raman spectroscopy, can provide the knowledge base for innovative and fast susceptibility tests which could speed up microbiological analysis as well as finding applications in novel antibiotic screenings assays. Graphical Abstract E. faecalis is incubated with vancomycin and characterized by means of Raman spectroscopy after different time points. Characteristic spectral changes reveal efficient vancomycin-enterococci-interaction.

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Background: Most clinical trials of sepsis treatment modalities fail at their primary objective of establishing superiority over placebo when added to background standard of care. While there is no definitive explanation for the high failure rate, it might be stated that our attempts to insert a new therapeutic agent into standard of care encounters severe problems with definition of exactly what stage is ongoing, and what are the criteria for progression or resolution from that time point onwards. Clearly there is need for a means of defining steps in the septic process that would apply to individuals, and to better define the course of sepsis in each patient after they are enrolled in a trial. Methods: For core model development, 30 septic patients were studied for time-related progression in relation to biomarkers, employing a Load Model in a neural net algorithm in MatLab. Causative bacterial infections were linked to primary infection sites. In order to minimize overparameterization, the model was allowed to estimate outputs using the best three input parameters. Bacterial load was tracked from origin using clinical and microbiologic data to provide an estimate at the start of sepsis. The bacterial load as well as clinical and laboratory parameters were model inputs with the output parameter being organ failures and/ or mortality. Results: At onset of sepsis, human bacterial load estimates ranged from between 10 8 and 10 11 CFU, which is consistent with inocula in animal models of sepsis. Sepsis proceeds to organ failures and mortality in a series of steps that are initially linked to bacterial load and inflammatory response, followed by coagulopathy, ischemia, oxygen deprivation in organs and tissues, and culminating in organ failures. The later stages of sepsis are all driven by metabolic parameters, and there seems to be little benefit to blocking inflammation at later stages. Substrate and oxygen deficiencies must be addressed first. Conclusion: Neural net progression models based on biomarkers and physiological markers are able to describe the evolution of sepsis to septic shock, organ failures, and provide some evidence that mortality may be a consequence of the stages of sepsis. Overall, these models appear useful to the task of sorting out organ failure endpoints and mechanisms in individual patients with sepsis progression across sepsis to septic shock. P2 Extracellular matrix turnover, angiogenesis and endothelial function in acute lung injury: relationship to pulmonary dysfunction and outcome

BackgroundThe human immune system is responsible for protecting the host from infection. However, in immunocompromised individuals the risk of infection increases substantially with possible drastic consequences. In extreme, systemic infection can lead to sepsis which is responsible for innumerous deaths worldwide. Amongst its causes are infections by bacteria and fungi. To increase survival, it is mandatory to identify the type of infection rapidly. Discriminating between fungal and bacterial pathogens is key to determine if antifungals or antibiotics should be administered, respectively. For this, in situ experiments have been performed to determine regulation mechanisms of the human immune system to identify biomarkers. However, these studies led to heterogeneous results either due different laboratory settings, pathogen strains, cell types and tissues, as well as the time of sample extraction, to name a few.MethodsTo generate a gene signature capable of discriminating between fungal and bacterial infected samples, we employed Mixed Integer Linear Programming (MILP) based classifiers on several datasets comprised of the above mentioned pathogens.ResultsWhen combining the classifiers by a joint optimization we could increase the consistency of the biomarker gene list independently of the experimental setup. An increase in pairwise overlap (the number of genes that overlap in each cross-validation) of 43% was obtained by this approach when compared to that of single classifiers. The refined gene list was composed of 19 genes and ranked according to consistency in expression (up- or down-regulated) and most of them were linked either directly or indirectly to the ERK-MAPK signalling pathway, which has been shown to play a key role in the immune response to infection. Testing of the identified 12 genes on an unseen dataset yielded an average accuracy of 83%.ConclusionsIn conclusion, our method allowed the combination of independent classifiers and increased consistency and reliability of the generated gene signatures.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-4006-x) contains supplementary material, which is available to authorized users.