Institute of Measurement Science

Phthalic selenoanhydride (R-Se) solved in physiological buffer releases various reactive selenium species including H2Se. It is a potential compound for Se supplementation which exerts several biological effects, but its effect on the cardiovascular system is still unknown. Therefore, herein we aimed to study how R-Se affects rat hemodynamic parameters and vasoactive properties in isolated arteries. The right jugular vein of anesthetized Wistar male rats was cannulated for IV administration of R-Se. The arterial pulse waveform (APW) was detected by cannulation of the left carotid artery, enabling the evaluation of 35 parameters. R-Se (1-2 µmol kg-1), but not phthalic anhydride or phthalic thioanhydride, transiently modulated most of the APW parameters including a decrease in systolic and diastolic blood pressure, heart rate, dP/dtmax relative level, or anacrotic/dicrotic notches, whereas systolic area, dP/dtmin delay, dP/dtd delay, anacrotic notch relative level or its delay increased. R-Se (~10-100 µmol L-1) significantly decreased the tension of precontracted mesenteric, femoral, and renal arteries, whereas it showed a moderate vasorelaxation effect on thoracic aorta isolated from normotensive Wistar rats. The results imply that R-Se acts on vascular smooth muscle cells, which might underlie the effects of R-Se on the rat hemodynamic parameters.

The goal of the study was to simulate body surface potentials (BSP) during ventricular pacing using a realistic homogeneous model of cardiac ventricles and the patient torso. Simulated BSP were compared with BSP measured in a real patient during pacing of the right ventricle. Ventricular activation in the model was started in the known position of the implanted stimulation electrode in the right ventricle near the apex as well as in several other sites near this position. The propagated electrical activation in the ventricular model was modeled using bidomain reaction-diffusion (RD) equations with the ionic transmembrane current density defined by the modified FitzHugh-Nagumo (FHN) equations. The torso was treated as a passive volume conductor region. The whole model was numerically solved in the Comsol Multiphysics environment. The measured BSP were compared with simulated BSP calculated for several positions of the stimulated region. The polarity and shape of selected simulated and measured ECG leads were in the best accordance when the stimulated region was close to the real position of the stimulating electrode. In this case, also the overall distribution of simulated and measured BSP was similar. In the initial phase of ventricular activation, the simulated BSP were strongly dependent on the position of the stimulated region.

We present and discuss a user-friendly wearable brain-computer interface (BCI) system with a head-mounted virtual reality display (HMD) for motor rehabilitation of post-stroke patients. The described design represents the first building block toward developing a low-power wearable and ecologically valid BCI-HMD system for collaborative neurorehabilitation of motor and cognitive impairments. We discuss the system's main hardware and software components and applied methods leading to extracting and classifying the task-relevant electroencephalo-graphic (EEG) brain waves used to control the HMD virtual reality environment. Pilot results using the system are also described.

The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data obtained with a novel graphene-based heartbeat sensor. We produce the sensor by laser-inducing graphene on a flexible polyimide substrate. Both graphene on the polyimide substrate and graphene transferred onto a PDMS substrate show piezoresistive behavior that can be utilized to measure human heartbeat by registering median cubital vein motion during blood pumping. We process electrical resistance data from the graphene sensor using HeartPy and demonstrate extraction of several heartbeat parameters, in agreement with measurements taken with independent reference sensors. We compare the quality of the heartbeat signal from graphene on different substrates, demonstrating that in all cases the device yields results consistent with reference sensors. Our work is a first demonstration of successful application of HeartPy to analysis of data from a sensor in development.

Background and aimCOVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak, several machine learning algorithms were implemented to assess new diagnostic and therapeutic methods for this disease. The aim of this study is to assess gastrointestinal and liver-related predictive factors for SARS-CoV-2 associated risk of hospitalization.MethodsData collection was based on a questionnaire from the COVID-19 outpatient test center and from the emergency department at the University Hospital in combination with the data from internal hospital information system and from a mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls in three different SARS-CoV-2 positive patient groups (divided based on severity of the disease). The data were visualized and analyzed in R version 4.0.5. The Chi-squared or Fisher test was applied to test the null hypothesis of independence between the factors followed, where appropriate, by the multiple comparisons with the Benjamini Hochberg adjustment. The null hypothesis of the equality of the population medians of a continuous variable was tested by the Kruskal Wallis test, followed by the Dunn multiple comparisons test. In order to assess predictive power of the gastrointestinal parameters and other measured variables for predicting an outcome of the patient group the Random Forest machine learning algorithm was trained on the data. The predictive ability was quantified by the ROC curve, constructed from the Out-of-Bag data. Matthews correlation coefficient was used as a one-number summary of the quality of binary classification. The importance of the predictors was measured using the Variable Importance. A 2D representation of the data was obtained by means of Principal Component Analysis for mixed type of data. Findings with the p-value below 0.05 were considered statistically significant.ResultsA total of 710 patients were enrolled in the study. The presence of diarrhea and nausea was significantly higher in the emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on the Random Forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance, although much lower than AST.ConclusionSARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using the machine learning Random Forest algorithm, we have identified the elevated AST as the most important predictor for COVID-19 related hospitalizations.

Investigated metacarbonates are located in the Early Paleozoic rocks of Southern Gemericum unit, Slovakia. Metacarbonates are part of Vlachovo Formation (Tichá Voda, Stará Voda) and Bystrý potok Formation (Betliar, Čučma, Malá Hekerová, Smolník - Mária Terézia, Holec). Detailed studies indicated metamorphic pressure-temperature (P-T) conditions of 3 - 7 kbar at 330 - 370 °C for the Gelnica Group. The calculated P-T conditions were verified using chlorite geothermometer and phengite geobarometer. Mineral association of metacarbonates consists of Si rich (3.10 - 3.35 apfu) phengitic micas, Fe-Mg chlorites (clinochlore and chamosite), quartz, K-feldspars and accessory fluorapatite. Micas and Mg-Fe chlorites originated from metamorphic recrystallization of clay material with incorporations of basic pyroclastic material during the regional metamorphosis of pelitic protolith, which sedimented continuously with carbonates. Metacarbonates belong to the upper parts of Gelnica Group, which were metamorphosed under the greenschist facies conditions.

We determined erythrocyte physiological and biochemical properties after the single and repeated administration of ultra-small superparamagnetic iron-oxide nanoparticles (USPIONs) in normotensive Wistar–Kyoto (WKY) and spontaneously hypertensive (SHR) rats. Polyethylene glycol-coated USPIONs (transmission electron microscope detected a mean size of ~30 nm and hydrodynamic size ~51 nm) were intravenously administered to rats either in one infusion at nominal dose 1 mg Fe/kg or in two infusions (administered with a difference of 24 h) at nominal dose 2 mg Fe/kg. Results showed that USPIONs did not deteriorate erythrocyte deformability, nitric oxide production, and osmotic resistance in both experimental settings. Both the single and repeated USPION administration elevated erythrocyte deformability in WKY. However, this effect was not present in SHR; deformability in USPION-treated SHR was significantly lower than in USPION-treated WKY. Nitric oxide production by erythrocytes was increased after a single USPION treatment in WKY, so it can be associated with improvement in erythrocyte deformability. Using biomagnetometry, we revealed significantly lower amounts of USPION-originated iron in erythrocytes in SHR compared with WKY. We found a much faster elimination of USPIONs from erythrocytes in hypertensive rats compared with the normotensive ones, which might be relevant for clinical practice in hypertensive patients undergoing clinical examination with the use of iron-oxide nanoparticles.

The consistency of the jackknife estimator of the asymptotic covariance matrix of a differentiable function of the symmetrized Tyler matrix is proved for sampling from continuous distribution without any constraint on this sampled continuous distribution. It is shown that under mild regularity conditions the rank of the asymptotic covariance matrix attains maximum possible value. The results are applied to the construction of the asymptotic confidence interval for correlation coefficient. It is illustrated by simulations that for some distributions the proposed interval yields better results than the classical Z-transformation interval.

The cause of cancer diseases is dependent on various internal and external factors including inherited immune conditions, and genetic mutations and unhealthy diet, tobacco-chewing and smoking habits, and infections, respectively. Nanoscale drug delivery platforms have shown promising clinical results in several types of cancer and inflammatory disorders. Nanoparticles have special characteristics of high surface to volume ratio, size controllability, and can be easily functionalized during synthesis. Cytotoxic therapeutics adsorbed to nanoparticles are used during hormone therapy, immunotherapy, chemotherapy, etc. which preferentially kill the rapidly dividing cancer cells while minimizing the damage to the normal tissue. The toxic core of inorganic nanoparticles is protected by providing a physical barrier (functional polymers) between the core and the external environment. After linking to poly (ethylene glycol) molecules, the polymer-coated toxic dots did not change their optical and colloidal properties over a broad range of external environment conditions. This spontaneous encapsulation process removes the problems of particle aggregation in physiological buffers.