Το Σλαβικό Έπος του Αλφόνσο Μούχα στο πλαίσιο της διάδοσης των πανσλαβιστικών ιδεών

The Slav Epic of Alphons Mucha in the context of dissemination of the pan-Slavistic idea

Applicability of river bass otoliths (Perca fluviatilis) to determine chane in trophic web using C and N stable isotope analysis

The use of fish otoliths of river bass in studies focusing on its trophic position in the fish community using SIA is possible. For analyzes relying on carbon, their use is even possible without treatment of otolith samples, as has been confirmed in the literature in the past. However, when it comes to nitrogen analysis, it is a slightly more complicated process. The amount of nitrogen in otoliths is not very high so it is completely undetectable with a mass spectrometer without a preliminary treatment of the samples. We managed to extract nitrogen from the otoliths, which is locked in the structure thanks to the proteins conducting its crystallization. After this extraction its content was sufficient for analysis, but only for the fraction that is made up of osoluble proteins. It was possible to get results for the insoluble fraction only for carbon, that is the reason why results of this fraction were not used. The process of nitrogen extraction is not very complicated, and even when using perch otoliths, which are relatively small, it is possible to get results. However, the small size and weight of perch otoliths will preclude the use of individual otoliths from individual fish. For that reason, summary samples were used for analysis. For this procedure, it is necessary to have a sample of 100...Využití rybích otolitů okouna říčního ve studiích zaměřujících se na jeho trofickou pozici ve společenstvu pomocí SIA je možné. Pro analýzy spoléhající se na uhlík je jejich užití dokonce možné i bez úpravy vzorků otolitu, jak bylo potvrzeno v literatuře už v minulosti. Ovšem pokud se jedná o použití analýzy dusíku, jde o trochu složitější proces. Dusíku je v otolitech tak malé množství, že bez předúpravy vzorku je hmotnostním spektrometrem naprosto nedetekovatelný. Nám se podařilo z otolitů extrahovat dusík, který je ve struktuře uzamčen díky proteinům zajišťujícímu jeho krystalizaci. Zjistili jsme, že jeho obsah je dostatečný k analýze, ale pouze ve frakci tvořené rozpustnými proteiny. Z nerozpustné frakce bylo možné získat výsledky jen pro uhlík, proto frakce nebyla k vyhodnocení použita. Proces extrakce dusíku není příliš složitý, a i při využití otolitů okounů, které jsou poměrně malé, je možné získat výsledky. Malá velikost a váha otolitů okouna však znemožní použití jednotlivých otolitů od jednotlivých ryb. Z toho důvodu byly v práci použité souhrnné vzorky. Pro tento postup je nutné mít k analýze navážku 100mg rozemletého otolitu. To byl další důvod, proč byly v práci použité souhrnné vzorky. Po vyhodnocení SIA pro 15N bylo zjištěno, že v rozmezí let 2004 a 2021 dochází k relativnímu...Institute for Environmental StudiesÚstav pro životní prostředíPřírodovědecká fakultaFaculty of Scienc

Changes in Lipids and Lipoproteins after Selective LDL Apheresis (7-Year Experience)

Background. The aim of the study was to investigate the changes in plasma lipids and lipoproteins and the cardiovascular events after selective LDL apheresis. Methods and Results. Two pediatric patients with familial hypercholesterolemia aged 11 and 13 years and 19 dyslipidemic adults aged 41 ± 14 years underwent direct adsorption of lipoproteins (DALI) sessions. The mean follow-up period was 47 ± 23 months. The total cholesterol (TC) values before and after treatment were 8.2 ± 2.2 and 3.1 ± 1.6 mmol/l (318 ± 86 and 122 ± 62 mg/dL), respectively. The interval mean of TC was 6.9 ± 1.9 mmol/l (268 ± 75 mg/dL). The LDL cholesterol concentrations before and after treatment were 6.6 ± 2.1 and 1.7 ± 1.1 mmol/l, (256 ± 82 mg/dL and 65 ± 41 mg/dL), respectively. The percentage of acute LDL cholesterol reduction was 75 ± 11%. Cardiovascular events were observed in seven patients. The average annual event rate was 5.51%. Conclusion. LDL apheresis is a very important therapeutic tool in managing patients at high risk for premature CAD or with aggressive CAD, despite adequate medical treatment

Left ventricular T2 distribution in Duchenne Muscular Dystrophy

<p>Abstract</p> <p>Background</p> <p>Although previous studies have helped define the natural history of Duchenne Muscular Dystrophy (DMD)-associated cardiomyopathy, the myocardial pathobiology associated with functional impairment in DMD is not yet known.</p> <p>The objective of this study was to assess the distribution of transverse relaxation time (T2) in the left ventricle (LV) of DMD patients, and to determine the association of myocardial T2 heterogeneity to the severity of cardiac dysfunction. DMD patients (n = 26) and normal control subjects (n = 13) were studied by Cardiovascular Magnetic Resonance (CMR). DMD subject data was stratified based on subject age and LV Ejection Fraction (EF) into the following groups: A (<12 years old, n = 12); B (≥12 years old, EF ≤ 55%, n = 8) and C (≥12 years old, EF = 55%, n = 6). Controls were also stratified by age into Groups N1 (<12 years, n = 6) and N2 (>12 years, n = 5). LV mid-slice circumferential myocardial strain (ε_cc) was calculated using tagged CMR imaging. T2 maps of the LV were generated for all subjects using a black blood dual spin echo method at two echo times. The Full Width at Half Maximum (<it>FWHM</it>) was calculated from a histogram of LV T2 distribution constructed for each subject.</p> <p>Results</p> <p>In DMD subject groups, <it>FWHM </it>of the T2 histogram rose progressively with age and decreasing EF (Group A <it>FWHM</it>= 25.3 ± 3.8 ms; Group B <it>FWHM</it>= 30.9 ± 5.3 ms; Group C <it>FWHM</it>= 33.0 ± 6.4 ms). Further, <it>FWHM </it>was significantly higher in those with reduced circumferential strain (|ε_cc| ≤ 12%) (Group B, and C) than those with |ε_cc| > 12% (Group A). Group A <it>FWHM </it>was not different from the two normal groups (N1 <it>FWHM </it>= 25.3 ± 3.5 ms; N2 <it>FWHM</it>= 24.0 ± 7.3 ms).</p> <p>Conclusion</p> <p>Reduced EF and ε_cccorrelates well with increased T2 heterogeneity quantified by <it>FWHM</it>, indicating that subclinical functional impairments could be associated with pre-existing abnormalities in tissue structure in young DMD patients.</p

Direct Action of Non-Digestible Oligosaccharides against a Leaky Gut

The epithelial monolayer is the primary determinant of mucosal barrier function, and tight junction (TJ) complexes seal the paracellular space between the adjacent epithelial cells and represent the main "gate-keepers" of the paracellular route. Impaired TJ functionality results in increased permeation of the "pro-inflammatory" luminal contents to the circulation that induces local and systemic inflammatory and immune responses, ultimately triggering and/or perpetuating (chronic) systemic inflammatory disorders. Increased gut leakiness is associated with intestinal and systemic disease states such as inflammatory bowel disease and neurodegenerative diseases such as Parkinson's disease. Modulation of TJ dynamics is an appealing strategy aiming at inflammatory conditions associated with compromised intestinal epithelial function. Recently there has been a growing interest in nutraceuticals, particularly in non-digestible oligosaccharides (NDOs). NDOs confer innumerable health benefits via microbiome-shaping and gut microbiota-related immune responses, including enhancement of epithelial barrier integrity. Emerging evidence supports that NDOs also exert health-beneficial effects on microbiota independently via direct interactions with intestinal epithelial and immune cells. Among these valuable features, NDOs promote barrier function by directly regulating TJs via AMPK-, PKC-, MAPK-, and TLR-associated pathways. This review provides a comprehensive overview of the epithelial barrier-protective effects of different NDOs with a special focus on their microbiota-independent modulation of TJs

Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models

Background: Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRCML) and compares against statistically derived CVRC (CVRCStat) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors). Methods: The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRCStat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRCStat, the patients were again risk-stratified using CVRCML based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events. Results: An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRCStat calculators were: AECRS2.0 (AUC 0.83, P&lt;0.001), QRISK3 (AUC 0.72, P&lt;0.001), WHO (AUC 0.70, P&lt;0.001), ASCVD (AUC 0.67, P&lt;0.001), FRScardio (AUC 0.67, P&lt;0.01), FRSstroke (AUC 0.64, P&lt;0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P&lt;0.001), NIPPON (AUC 0.63, P&lt;0.001), PROCAM (AUC 0.59, P&lt;0.001), RRS (AUC 0.57, P&lt;0.001), UKPDS60 (AUC 0.53, P&lt;0.001), and SCORE (AUC 0.45, P&lt;0.001), while the AUC for the CVRCML with integrated risk factors (AUC 0.88, P&lt;0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRCML with integrated risk factors was 92.52% which was higher compared all the other CVRCStat. Conclusions: ML-based CVD/stroke risk calculator provided a higher predictive ability of 10-year CVD/ stroke compared to the 13 different types of statistically derived risk calculators including integrated model AECRS 2.0

Calibration of myocardial T2 and T1 against iron concentration.

BACKGROUND: The assessment of myocardial iron using T2* cardiovascular magnetic resonance (CMR) has been validated and calibrated, and is in clinical use. However, there is very limited data assessing the relaxation parameters T1 and T2 for measurement of human myocardial iron. METHODS: Twelve hearts were examined from transfusion-dependent patients: 11 with end-stage heart failure, either following death (n=7) or cardiac transplantation (n=4), and 1 heart from a patient who died from a stroke with no cardiac iron loading. Ex-vivo R1 and R2 measurements (R1=1/T1 and R2=1/T2) at 1.5 Tesla were compared with myocardial iron concentration measured using inductively coupled plasma atomic emission spectroscopy. RESULTS: From a single myocardial slice in formalin which was repeatedly examined, a modest decrease in T2 was observed with time, from mean (± SD) 23.7 ± 0.93 ms at baseline (13 days after death and formalin fixation) to 18.5 ± 1.41 ms at day 566 (p<0.001). Raw T2 values were therefore adjusted to correct for this fall over time. Myocardial R2 was correlated with iron concentration [Fe] (R2 0.566, p<0.001), but the correlation was stronger between LnR2 and Ln[Fe] (R2 0.790, p<0.001). The relation was [Fe] = 5081•(T2)-2.22 between T2 (ms) and myocardial iron (mg/g dry weight). Analysis of T1 proved challenging with a dichotomous distribution of T1, with very short T1 (mean 72.3 ± 25.8 ms) that was independent of iron concentration in all hearts stored in formalin for greater than 12 months. In the remaining hearts stored for <10 weeks prior to scanning, LnR1 and iron concentration were correlated but with marked scatter (R2 0.517, p<0.001). A linear relationship was present between T1 and T2 in the hearts stored for a short period (R2 0.657, p<0.001). CONCLUSION: Myocardial T2 correlates well with myocardial iron concentration, which raises the possibility that T2 may provide additive information to T2* for patients with myocardial siderosis. However, ex-vivo T1 measurements are less reliable due to the severe chemical effects of formalin on T1 shortening, and therefore T1 calibration may only be practical from in-vivo human studies

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients