Monitoring changes in thioredoxin and over-oxidised peroxiredoxin in response to exercise in humans

Introduction. Peroxiredoxin (PRDX) and thioredoxin (TRX) are antioxidant proteins that control cellular signalling and redox balance, although their response to exercise is unknown. This study aimed to assess key aspects of the PRDX-TRX redox cycle in response to three different modes of exercise. Methods. Healthy males (n = 10, mean ± SD: 22 ± 3 yrs) undertook three exercise trials on separate days: two steady-state cycling trials at moderate (60% VO2MAX; 27 min, MOD) and high (80% VO2MAX; 20 min, HIGH) intensities, and a low-volume high-intensity interval training trial (10 × 1 min 90% VO2MAX, LV-HIIT). Peripheral blood mononuclear cells were assessed for TRX-1 and over-oxidised PRDX (isoforms I-IV) protein expression before, during, and 30 min following exercise (post + 30). The activities of TRX reductase (TRX-R) and the nuclear factor kappa B (NF-κB) p65 subunit were also assessed. Results. TRX-1 increased during exercise in all trials (MOD, + 84.5%; HIGH, + 64.1%; LV-HIIT, + 205.7%; p < 05), whereas over-oxidised PRDX increased during HIGH only (MOD, - 28.7%; HIGH, + 202.9%; LV-HIIT, - 22.7%; p < .05). TRX-R and NF-κB p65 activity increased during exercise in all trials, with the greatest response in TRX-R activity seen in HIGH (p < 0.05). Discussion. All trials stimulated a transient increase in TRX-1 protein expression during exercise. Only HIGH induced a transient over-oxidation of PRDX, alongside the greatest change in TRX-R activity. Future studies are needed to clarify the significance of heightened peroxide exposure during continuous high-intensity exercise and the mechanisms of PRDX-regulatory control.

Subject identification via ECG fiducial-based systems:influence of the type of QT interval correction

Electrocardiography (ECG) has been recently proposed as biometric trait for identification purposes. Intra-individual variations of ECG might affect identification performance. These variations are mainly due to Heart Rate Variability (HRV). In particular, HRV causes changes in the QT intervals along the ECG waveforms. This work is aimed at analysing the influence of seven QT interval correction methods (based on population models) on the performance of ECG-fiducial-based identification systems. In addition, we have also considered the influence of training set size, classifier, classifier ensemble as well as the number of consecutive heartbeats in a majority voting scheme. The ECG signals used in this study were collected from thirty-nine subjects within the Physionet open access database. Public domain software was used for fiducial points detection. Results suggested that QT correction is indeed required to improve the performance. However, there is no clear choice among the seven explored approaches for QT correction (identification rate between 0.97 and 0.99). MultiLayer Perceptron and Support Vector Machine seemed to have better generalization capabilities, in terms of classification performance, with respect to Decision Tree-based classifiers. No such strong influence of the training-set size and the number of consecutive heartbeats has been observed on the majority voting scheme.