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Biofilm infections are one of the modern medical world's greatest challenges. Probably, all non-obligate intracellular bacteria and fungi can establish biofilms. In addition, there are numerous biofilm-related infections, both foreign body-related and non-foreign body-related. Although biofilm infections can present in numerous ways, one common feature is involvement of the host response with significant impact on the course. A special characteristic is the synergy of the innate and the acquired immune responses for the induced pathology. Here, we review the impact of the host response for the course of biofilm infections, with special focus on cystic fibrosis, chronic wounds and infective endocarditis., (© 2017 APMIS. Published by John Wiley & Sons Ltd.)

Biomarkers for early detection of toxicity hold the promise of improving the failure rates in drug development. In the present study, gene expression levels were measured using full-genome RAE230 version 2 Affymetrix GeneChips on rat liver tissue 48 h after administration of six different compounds, three toxins (ANIT, DMN and NMF) and three non-toxins (Caeruelein, Dinitrophenol and Rosiglitazone). We identified three gene transcripts with exceptional predictive performance towards liver toxicity and/or changes in histopathology. The three genes were: glucokinase regulatory protein (GCKR), ornithine aminotransferase (OAT) and Cytochrome P450, subfamily IIC (mephenytoin 4-hydroxylase) (Cyp2C29). RT-PCR for these three genes was performed and four additional compounds were included for validation. The quantitative RT-PCR analysis confirmed the findings based on the microarray data and using the three genes a classification rate of 55 of 57 samples was achieved for the classification of not toxic versus toxic. The single most promising biomarker (OAT) alone resulted in a surprisingly 100% correctly classified samples. OAT has not previously been linked to toxicity and cell death in the literature and the novel finding represents a putative hepatotoxicity biomarker.

Background: Cardiovascular disease risk can be estimated in part on the basis of the plasma lipoprotein profile. Analysis of lipoprotein subclasses improves the risk evaluation, but the traditional methods are very time-consuming. Novel, rapid, and productive methods are therefore needed., Methods: We obtained plasma samples from 103 fasting people and determined the plasma lipoprotein subclass profiles by an established ultracentrifugation-based method. Proton nuclear magnetic resonance (NMR) spectra were obtained from replicate samples on a 600 MHz NMR spectrometer. From the ultracentrifugation-based reference data and the NMR spectra, we developed partial least-squares (PLS) regression models to predict cholesterol and triglyceride (TG) concentrations in plasma as well as in VLDL, intermediate-density lipoprotein (IDL), LDL, 3 LDL fractions, HDL, and 3 HDL subclasses., Results: The correlation coefficients (r) between the plasma TG and cholesterol concentrations measured by the 2 methods were 0.98 and 0.91, respectively. For LDL- and HDL-cholesterol concentrations, r = 0.90 and 0.94, respectively. For cholesterol concentrations in the LDL-1, LDL-2, and LDL-3 fractions, r = 0.74, 0.78, and 0.69, respectively, and for HDL subclasses HDL(2b), HDL(2a), and HDL(3), cholesterol concentrations were predicted with r = 0.92, 0.94, and 0.75, respectively. TG concentrations in VLDL, IDL, LDL, and HDL were predicted with correlations of 0.98, 0.85, 0.77, and 0.74, respectively. The cholesterol and TG concentrations in the main lipoprotein fractions and in LDL fractions and HDL subclasses predicted by the PLS models were 94%-100% of the concentrations obtained by ultracentrifugation., Conclusion: NMR-based PLS regression models are appropriate for use in research in which analyses of the plasma lipoprotein profile, including LDL and HDL subclasses, are required in large numbers of samples.

The application of the NMR-MObile Universal Surface Explorer (NMR-MOUSE) to study food systems is evaluated using oil-in-water emulsions, and the results are compared to those obtained using a conventional low-field NMR (LF-NMR) instrument. The NMR-MOUSE is a small and portable LF-NMR system with a one-sided magnet layout that is used to replace the conventional magnet and probe on a LF-NMR instrument. The high magnetic field gradients associated with the one-sided MOUSE magnet result in NMR signal decays being dominated by molecular diffusion effects, which makes it possible to discriminate between the NMR signals from oil and water. Different data acquisition parameters as well as different approaches to the analysis of the NMR data from a range of oil-in-water emulsions are evaluated, and it is demonstrated how the concentration of oil and water can be determined from the NMR-MOUSE signals. From these model systems it is concluded that the NMR-MOUSE has good potential for the quantitative analysis of intact food products.

In this work an alternative method, named SLICING, for two-dimensional and noniterative T(2) decomposition of low-field pulsed NMR data (LF-NMR) is proposed and examined. The method is based on the Direct Exponential Curve Resolution Algorithm (DECRA) proposed by W. Windig and A. Antalek (1997, Chemom. Intell. Lab. Syst.37, 241-254) and takes advantage of the fact that exponential decay functions, when translated in time, retain their characteristic relaxation times while only their relative amounts or concentrations change. By such simple translations (slicing) it is possible to create a new "pseudo" direction in the relaxation data and thus facilitate application of trilinear (multiway) data-analytical methods. For the application on LF-NMR relaxation data, the method has two basic requirements in practice: (1) two or more samples must be analyzed simultaneously and (2) all samples must contain the same qualities (i.e., identical sets of distinct T(2) values). In return, if these requirements are fulfilled, the SLICING (trilinear decomposition) method provides very fast and unique curve-resolution of multiexponential LF-NMR relaxation curves and, as a spin-off, calibrations to reference data referring to individual proton components require only scaling of the resulting unique concentrations. In this work the performance of the SLICING method (including multiple slicing schemes) is compared to a traditional two-dimensional curve fitting algorithm named MATRIXFIT through application to simulated data in a large-scale exhaustive experimental design and the results validated by application to two small real data sets. Finally a new algorithm, Principal Phase Correction (PPC) based on principal component analysis, is proposed for phase rotation of CPMG quadrature data, an important prerequisite to optimal SLICING analysis.