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Oxidative stress is a natural phenomenon in the body. Under physiological conditions intracellular reactive oxygen species (ROS) are normal components of signal transduction cascades, and their levels are maintained by a complex antioxidants systems participating in the in-vivo redox homeostasis. Increased oxidative stress is present in several chronic diseases and interferes with phagocytic and nervous cell functions, causing an up-regulation of cytokines and inflammation. Hepatic encephalopathy (HE) occurs in both acute liver failure (ALF) and chronic liver disease. Increased blood and brain ammonium has been considered as an important factor in pathogenesis of HE and has been associated with inflammation, neurotoxicity, and oxidative stress. The relationship between ROS and the pathophysiology of HE is still poorly understood. Therefore, sensing ROS production for a better understanding of the relationship between oxidative stress and functional outcome in HE pathophysiology is critical for determining the disease mechanisms, as well as to improve the management of patients. This review is emphasizing the important role of oxidative stress in HE development and documents the changes occurring as a consequence of oxidative stress augmentation based on cellular and ammonium-based animal models to human data.

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The role and coexistence of oxidative stress (OS) and inflammation in type C hepatic encephalopathy (C HE) is a subject of intense debate. Under normal conditions the physiological levels of intracellular reactive oxygen species are controlled by the counteracting antioxidant response to maintain redox homeostasis. Our previous in-vivo1H-MRS studies revealed the longitudinal impairment of the antioxidant system (ascorbate) in a bile-duct ligation (BDL) rat model of type C HE. Therefore, the aim of this work was to examine the course of central nervous system (CNS) OS and systemic OS, as well as to check for their co-existence with inflammation in the BDL rat model of type C HE. To this end, we implemented a multidisciplinary approach, including ex-vivo and in-vitro electron paramagnetic resonance spectroscopy (EPR) spin-trapping, which was combined with UV-Vis spectroscopy, and histological assessments. We hypothesized that OS and inflammation act synergistically in the pathophysiology of type C HE. Our findings point to an increased CNS- and systemic-OS and inflammation over the course of type C HE progression. In particular, an increase in the CNS OS was observed as early as 2-weeks post-BDL, while the systemic OS became significant at week 6 post-BDL. The CNS EPR measurements were further validated by a substantial accumulation of 8-Oxo-2'-deoxyguanosine (Oxo-8-dG), a marker of oxidative DNA/RNA modifications on immunohistochemistry (IHC). Using IHC, we also detected increased synthesis of antioxidants, glutathione peroxidase 1 (GPX-1) and superoxide dismutases (i.e.Cu/ZnSOD (SOD1) and MnSOD (SOD2)), along with proinflammatory cytokine interleukin-6 (IL-6) in the brains of BDL rats. The presence of systemic inflammation was observed already at 2-weeks post-surgery. Thus, these results suggest that CNS OS is an early event in type C HE rat model, which seems to precede systemic OS. Finally, our results suggest that the increase in CNS OS is due to enhanced formation of intra- and extra-cellular ROS rather than due to reduced antioxidant capacity, and that OS in parallel with inflammation plays a significant role in type C HE.

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Quantification of short-echo time proton magnetic resonance spectroscopy results in >18 metabolite concentrations (neurochemical profile). Their quantification accuracy depends on the assessment of the contribution of macromolecule (MM) resonances, previously experimentally achieved by exploiting the several fold difference in T(1). To minimize effects of heterogeneities in metabolites T(1), the aim of the study was to assess MM signal contributions by combining inversion recovery (IR) and diffusion-weighted proton spectroscopy at high-magnetic field (14.1 T) and short echo time (= 8 msec) in the rat brain. IR combined with diffusion weighting experiments (with δ/Δ = 1.5/200 msec and b-value = 11.8 msec/µm(2)) showed that the metabolite nulled spectrum (inversion time = 740 msec) was affected by residuals attributed to creatine, inositol, taurine, choline, N-acetylaspartate as well as glutamine and glutamate. While the metabolite residuals were significantly attenuated by 50%, the MM signals were almost not affected (< 8%). The combination of metabolite-nulled IR spectra with diffusion weighting allows a specific characterization of MM resonances with minimal metabolite signal contributions and is expected to lead to a more precise quantification of the neurochemical profile.

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Localized proton Magnetic Resonance Spectroscopy brain signals acquired at short echo-time contain contributions from metabolites, water and a ;background' which mainly originates from macromolecules and lipids. The purpose of the present study was to compare the influence of the background-accommodation strategy on the metabolite concentration estimates. Two strategies were investigated to accommodate the background, 1) the measured background signal was incorporated in the metabolite basis-set; and 2) the background signal was estimated and subtracted from the in vivo signal using Subtract-QUEST. The influence of the background-accommodation strategy was addressed with the aid of Monte Carlo and in vivo studies. For the considered signals of this study, the concentration estimates obtained using the first approach were below those obtained with Subtract-QUEST. Indeed, the presence of residual contribution of metabolite signals with short longitudinal relaxation times (T1) in the measured background led to an underestimation of metabolite concentration estimates. Conversely, the observed underestimation of the background contribution using Subtract-QUEST led to an overestimation of the metabolite estimates.

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In vivo1H short echo-time Magnetic Resonance spectra are made up of overlapping spectral components from many metabolites. Typically, they exibit low signal-to-noise ratio. Metabolite concentrations are obtained by quantitating such spectra. Quantitation is difficult due to the superposition of metabolite resonances, macromolecules, lipids and water residue contributions. A fitting algorithm invoking extensive prior knowledge is needed. We quantitated1H in vivo mouse brain spectra obtained at 7 Tesla using the time-domain QUEST method combined with in vitro metabolite basis set signals. Brain metabolite concentrations estimated from eight mouse brain signals are compared to previously reported results.