In chronic heart failure (HF), some parameters of oxidative stress are correlated with disease severity. The aim of this study was to evaluate the importance of oxidative stress biomarkers in prognostic risk stratification (death and combined endpoint: heart transplantation or death). In 774 patients, aged 48-59 years, with chronic HF with reduced ejection fraction (median: 24.0 (20-29)%), parameters such as total antioxidant capacity, total oxidant status, oxidative stress index, and concentration of uric acid (UA), bilirubin, protein sulfhydryl groups (PSH), and malondialdehyde (MDA) were measured. The parameters were assessed as predictive biomarkers of mortality and combined endpoint in a 1-year follow-up. The multivariate Cox regression analysis was adjusted for other important clinical and laboratory prognostic markers. Among all the oxidative stress markers examined in multivariate analysis, only MDA and UA were found to be independent predictors of death and combined endpoint. Higher serum MDA concentration increased the risk of death by 103.0% (HR = 2.103; 95% CI (1.330-3.325)) and of combined endpoint occurrence by 100% (HR = 2.000; 95% CI (1.366-2.928)) per μmol/L. Baseline levels of MDA in the 4th quartile were associated with an increased risk of death with a relative risk (RR) of 3.64 (95% CI (1.917 to 6.926), p < 0.001) and RR of 2.71 (95% CI (1.551 to 4.739), p < 0.001) for the occurrence of combined endpoint as compared to levels of MDA in the 1st quartile. Higher serum UA concentration increased the risk of death by 2.1% (HR = 1.021; 95% CI (1.005-1.038), p < 0.001) and increased combined endpoint occurrence by 1.4% (HR = 1.014; 95% CI (1.005-1.028), p < 0.001), for every 10 μmol/L. Baseline levels of UA in the 4th quartile were associated with an increased risk for death with a RR of 3.21 (95% CI (1.734 to 5.931)) and RR of 2.73 (95% CI (1.560 to 4.766)) for the occurrence of combined endpoint as compared to the levels of UA in the 1st quartile. In patients with chronic HF, increased MDA and UA concentrations were independently related to poor prognosis in a 1-year follow-up.
IoT enabled predictive maintenance allows companies in the energy sector to identify potential problems in the production devices far before the failure occurs. In this paper, we propose a method for early detection of faults in boiler feed pumps using existing measurements currently captured by control devices. In the experimental part, we work on real measurement data and events from a coal fired power plant. The main research objective is to implement a model that detects deviations from the normal operation state based on regression and to check which events or failures can be detected by it. The presented technique allows the creation of a predictive system working on the basis of the available data with a minimal requirement of expert knowledge, in particular the knowledge related to the categorization of failures and the exact time of their occurrence, which is sometimes difficult to identify. The paper shows that with modern technologies, such as the Internet of Things, big data, and cloud computing, it is possible to integrate automation systems, designed in the past only to control the production process, with IT systems that make all processes more efficient through the use of advanced analytic tools.
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