Amplex® Red (10-acetyl-3,7-dihydroxyphenoxazine) is a fluorogenic probe widely used to detect and quantify hydrogen peroxide in biological systems. Detection of hydrogen peroxide is based on peroxidase-catalyzed oxidation of Amplex® Red to resorufin. In this study we investigated the mechanism of one-electron oxidation of Amplex® Red and we present the spectroscopic characterization of transient species formed upon the oxidation. Oxidation process has been studied by a pulse radiolysis technique with one-electron oxidants (N3•, CO3•−, •NO2 and GS•). The rate constants for the Amplex® Red oxidation by N3• (2k = 2.1·109 M−1s−1, at pH = 7.2) and CO3•− (2k = 7.6·108 M−1s−1, at pH = 10.3) were determined. Two intermediates formed during the conversion of Amplex® Red into resorufin have been characterized. Based on the results obtained, the mechanism of transformation of Amplex® Red into resorufin, involving disproportionation of the Amplex® Red-derived radical species, has been proposed. The results indicate that peroxynitrite-derived radicals, but not peroxynitrite itself, are capable to oxidize Amplex® Red to resorufin. We also demonstrate that horseradish peroxidase can catalyze oxidation of Amplex® Red not only by hydrogen peroxide, but also by peroxynitrite, which needs to be considered when employing the probe for hydrogen peroxide detection.

Over the last forty years, there has been tremendous progress in understanding the biological reactions of reactive oxygen species (ROS) and reactive nitrogen species (RNS). It is widely accepted that the generation of ROS and RNS is involved in physiological and pathophysiological processes. To understand the role of ROS and RNS in a variety of pathologies, the specific detection of ROS and RNS is fundamental. Unfortunately, the intracellular detection and quantitation of ROS and RNS remains a challenge. In this short review, we have focused on the mechanistic and quantitative aspects of their detection with the use of selected fluorogenic probes. The challenges, limitations and perspectives of these methods are discussed.

This review presents and promotes preprocessing methods applied in electrochemistry prior to data exploration, calibration and classification. Smoothing by digital filtering, baseline correction, separation of overlapping signals, and also transformations to extract the relevant features of the curve are the algorithms of which the application, operation, and optimization of parameters are described. The presentation completes the description of the procedure enabling orthogonal correction of the signals. The theoretical consideration is demonstrated by examples of the practical application of the described algorithms in electrochemistry in recent years. Software packages useful in signal processing in electrochemistry are also listed. The paper underlines the point that particular methods show the different effects of signal processing and in the case of the improper selection of the algorithm or its parameters, undesirable distortions of the curves may be observed. Therefore a knowledge of signal processing methodology is necessary before its application.

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Recent advances in metagenomics provided a valuable alternative to culture-based approaches for better sampling viral diversity. However, some of newly identified viruses lack sequence similarity to any of previously sequenced ones, and cannot be easily assigned to their hosts. Here we present a bioinformatic approach to this problem. We developed classifiers capable of distinguishing eukaryotic viruses from the phages achieving almost 95% prediction accuracy. The classifiers are wrapped in Host Taxon Predictor (HTP) software written in Python which is freely available at https://github.com/wojciech-galan/viruses_classifier . HTP’s performance was later demonstrated on a collection of newly identified viral genomes and genome fragments. In summary, HTP is a culture- and alignment-free approach for distinction between phages and eukaryotic viruses. We have also shown that it is possible to further extend our method to go up the evolutionary tree and predict whether a virus can infect narrower taxa.