University of Maribor

About: University of Maribor is a education organization based out in Maribor, Slovenia. It is known for research contribution in the topics: Population & KEKB. The organization has 3987 authors who have published 13077 publications receiving 258339 citations. The organization is also known as: Univerza v Mariboru.

Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

Abstract: We introduce a novel method for an automatic classification of subjects to those with or without neuromuscular disorders. This method is based on multiscale entropy of recorded surface electromyograms (sEMGs) and support vector classification. The method was evaluated on a single-channel experimental sEMGs recorded from biceps brachii muscle of nine healthy subjects, nine subjects with muscular and nine subjects with neuronal disorders, at 10%, 30%, 50%, 70% and 100% of maximal voluntary contraction force. Leave-one-out cross-validation was performed, deploying binary (healthy/patient) and three-class classification (healthy/myopathic/neuropathic). In the case of binary classification, subjects were distinguished with 81.5% accuracy (77.8% sensitivity at 83.3% specificity). At three-class classification, the accuracy decreased to 70.4% (myopathies were recognized with a sensitivity of 55.6% at specificity 88.9%, neuropathies with a sensitivity of 66.7% at specificity 83.3%). The proposed method is suitable for fast and non-invasive discrimination of healthy and neuromuscular patient groups, but it fails to recognize the type of pathology.

A comparison of machine learning methods for cutting parameters prediction in high speed turning process

Abstract: Support vector machines are arguably one of the most successful methods for data classification, but when using them in regression problems, literature suggests that their performance is no longer state-of-the-art. This paper compares performances of three machine learning methods for the prediction of independent output cutting parameters in a high speed turning process. Observed parameters were the surface roughness (Ra), cutting force $$(F_{c})$$(Fc), and tool lifetime (T). For the modelling, support vector regression (SVR), polynomial (quadratic) regression, and artificial neural network (ANN) were used. In this research, polynomial regression has outperformed SVR and ANN in the case of $$F_{c}$$Fc and Ra prediction, while ANN had the best performance in the case of T, but also the worst performance in the case of $$F_{c}$$Fc and Ra. The study has also shown that in SVR, the polynomial kernel has outperformed linear kernel and RBF kernel. In addition, there was no significant difference in performance between SVR and polynomial regression for prediction of all three output machining parameters.

Pathophysiology of Myocardial Infarction and Acute Management Strategies.

Abstract: On an annual basis, 13.2% of all deaths are attributable to coronary artery disease (CAD), which makes CAD - with 7.4 million deaths - the leading cause of death in the world. In this review, we discuss current knowledge in the pathophysiology of atherosclerosis with its progression to stable CAD and its destabilization and complication with thrombus formation - myocardial infarction (MI). Next, we describe mechanisms of myocardial cell death in MI, the ischemia-reperfusion injury, leftventricular remodeling and complications of MI. Furthermore, we add acute management strategies concentrating on medical therapy, a decision on the reperfusion strategy, timing and cardiac protection by ischemic preconditioning, post-conditioning and remote ischemic conditioning.

Cytocompatibility and immunomodulatory properties of wood based nanofibrillated cellulose

Abstract: Cellulose nanofibrils (CNFs), unique and promising natural materials have gained significant attention recently for biomedical applications, due to their special biomechanical characteristics, surface chemistry, good biocompatibility and low toxicity. However, their long bio-persistence in the organism may provoke immune reactions and this aspect of CNFs has not been studied to date. Therefore, the aim of this work was to examine and compare the cytocompatibility and immunomodulatory properties of CNFs in vitro. CNFs (diameters of 10–70 nm; lengths of a few microns) were prepared from Norway spruce (Picea abies) by mechanical fibrillation and high pressure homogenisation. L929 cells, rat thymocytes or human peripheral blood mononuclear cells (PBMNCs) were cultivated with CNFs. None of the six concentrations of CNFs (31.25 µg/ml–1 mg/ml) induced cytotoxicity and oxidative stress in the L929 cells, nor induced necrosis and apoptosis of thymocytes and PBMNCs. Higher concentrations (250 µg/ml–1 mg/ml) slightly inhibited the metabolic activities of the L929 cells as a consequence of inhibited proliferation. The same concentrations of CNFs suppressed the proliferation of PBMNCs to phytohemaglutinine, a T-cell mitogen, and the process was followed by down-regulation of interleukin-2 (IL-2) and interferon-γ production. The highest concentration of CNFs inhibited IL-17A, but increased IL-10 and IL-6 production. The secretion of pro-inflammatory cytokines, IL-1β and tumor necrosis factor-α as well as Th2 cytokine (IL-4), remained unaltered. In conclusion, the results suggest that these CNFs are cytocompatible nanomaterial, according to current ISO criteria, with non-inflammatory and non-immunogenic properties. Higher concentrations seem to be tolerogenic to the immune system, a characteristic very desirable for implantable biomaterials.