A copper-hydrogen peroxide redox system induces dityrosine cross-links and chemokine oligomerisation

The activity of the chemoattractant cytokines, the chemokines, in vivo is enhanced by oligomerisation and aggregation on glycosaminoglycan (GAG), particularly heparan sulphate, side chains of proteoglycans. The chemokine RANTES (CCL5) is a T-lymphocyte and monocyte chemoattractant, which has a minimum tetrameric structure for in vivo activity and a propensity to form higher order oligomers. RANTES is unusual among the chemokines in having five tyrosine residues, an amino acid susceptible to oxidative cross-linking. Using fluorescence emission spectroscopy, Western blot analysis and LCMS-MS, we show that a copper/H2O2 redox system induces the formation of covalent dityrosine cross-links and RANTES oligomerisation with the formation of tetramers, as well as higher order oligomers. Amongst the transition metals tested, namely copper, nickel, mercury, iron and zinc, copper appeared unique in this respect. At high (400 µM) concentrations of H2O2, RANTES monomers, dimers and oligomers are destroyed, but heparan sulphate protects the chemokine from oxidative damage, promoting dityrosine cross-links and multimer formation under oxidative conditions. Low levels of dityrosine cross-links were detected in copper/H2O2-treated IL-8 (CXCL8), which has one tyrosine residue, and none were detected in ENA-78 (CXCL5), which has none. Redox-treated RANTES was fully functional in Boyden chamber assays of T-cell migration and receptor usage on activated T-cells following RANTES oligomerisation was not altered. Our results point to a protective, anti-oxidant, role for heparan sulphate and a previously unrecognised role for copper in chemokine oligomerisation that may offer an explanation for the known anti-inflammatory effect of copper-chelators such as penicillamine and tobramycin.

Individual variability analysis of fluorescence parameters measured in skin with different levels of nutritive blood flow

Fluorescence spectroscopy has recently become more common in clinical medicine. However, there are still many unresolved issues related to the methodology and implementation of instruments with this technology. In this study, we aimed to assess individual variability of fluorescence parameters of endogenous markers (NADH, FAD, etc.) measured by fluorescent spectroscopy (FS) in situ and to analyse the factors that lead to a significant scatter of results. Most studied fluorophores have an acceptable scatter of values (mostly up to 30%) for diagnostic purposes. Here we provide evidence that the level of blood volume in tissue impacts FS data with a significant inverse correlation. The distribution function of the fluorescence intensity and the fluorescent contrast coefficient values are a function of the normal distribution for most of the studied fluorophores and the redox ratio. The effects of various physiological (different content of skin melanin) and technical (characteristics of optical filters) factors on the measurement results were additionally studied.The data on the variability of the measurement results in FS should be considered when interpreting the diagnostic parameters, as well as when developing new algorithms for data processing and FS devices.

The development of attenuation compensation models of fluorescence spectroscopy signals

This study examines the effect of blood absorption on the endogenous fluorescence signal intensity of biological tissues. Experimental studies were conducted to identify these effects. To register the fluorescence intensity, the fluorescence spectroscopy method was employed. The intensity of the blood flow was measured by laser Doppler flowmetry. We proposed one possible implementation of the Monte Carlo method for the theoretical analysis of the effect of blood on the fluorescence signals. The simulation is constructed as a four-layer skin optical model based on the known optical parameters of the skin with different levels of blood supply. With the help of the simulation, we demonstrate how the level of blood supply can affect the appearance of the fluorescence spectra. In addition, to describe the properties of biological tissue, which may affect the fluorescence spectra, we turned to the method of diffuse reflectance spectroscopy (DRS). Using the spectral data provided by the DRS, the tissue attenuation effect can be extracted and used to correct the fluorescence spectra.

Development of a non-destructive fluorescence technique for analysis of contact lens deposition levels

The primary objective of this research has been to determine the potential of fluorescence spectroscopy as a method for analysis of surface deposition on contact lenses. In order to achieve this it was first necessary to ascertain whether fluorescence analysis would be able to detect and distinguish between protein and lipid deposited on a lens surface. In conjunction with this it was important to determine the specific excitation wavelengths at which these deposited species were detected with the greatest sensitivity. Experimental observations showed that an excitation wavelength of 360nm would detect lipid deposited on a lens surface, and an excitation wavelength of 280nm would detect and distinguish between protein and lipid deposited on a contact lens. It was also very important to determine whether clean unspoilt lenses showed significant levels of fluorescence themselves. Fluorescence spectra recorded from a variety of unworn contact lenses at excitation wavelengths of 360nm and 280nm indicated that most contact lens materials do not fluoresce themselves to any great extent. Following these initial experiments various clinically and laboratory based studies were performed using fluorescence spectroscopy as a method of analysing contact lens deposition levels. The clinically based studies enabled analysis of contact lenses with known wear backgrounds to be rapidly and individually analysed following discontinuation of wear. Deposition levels in the early stages of lens wear were determined for various lens materials. The effect of surfactant cleaning on deposition levels was also investigated. The laboratory based studies involved comparing some of the in vivo results with those of identical lenses that had been spoilt using an in vitro method. Finally, an examination of lysosyme migration into and out of stored ionic high water contact lenses was made.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.