Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.

Background Recent research has closely linked adipocytokines to liver inflammation and fibrosis progression in patients with non-alcoholic liver disease. This study aimed to determine the relationship of serum adiponectin and resistin levels with the severity of liver fibrosis in patients with chronic hepatitis B (CHB), depending on the duration of antiviral therapy. Methods The cross-sectional study included 75 patients with CHB divided into two groups: the T1 group (undergoing antiviral therapy for up to 2 years) and the T2 group (undergoing antiviral therapy over 2 years). The control group consisted of 40 healthy people. Serum concentrations of adiponectin and resistin were estimated with the ELISA method, while the degree of liver fibrosis was determined using FIB-4 and APRI score. Results There were no statistically significant differences in the mean serum adiponectin levels in relation to the duration of antiviral therapy. Higher values of serum resistin concentration were confirmed in patients of the T1 group compared to healthy controls (p=0.001) and to the T2 group (p=0.031). The mean level of serum resistin concentration was significantly higher in the group of patients with a higher FIB-4 score (9.12±3.39 vs 5.58±3.36 ng/mL, p=0.001) and higher APRI score (17.45±3.96 ng/mL vs 4.82±1.11 ng/mL, p=0.001). A positive correlation was found between serum resistin levels and the degree of liver fibrosis (p<0.001). There was no significant difference between mean serum adiponectin levels according to the values of FIB-4 and APRI scores. Conclusions Progression of liver fibrosis estimated by FIB4 and APRI scores as well as the length of antiviral treatment had a significant effect on serum resistin values in CHB patients on antiviral therapy.

Angular rough shark, Oxynotus centrina (Linnaeus, 1758), is a poorly known and rare bathydemersal shark inhabiting continental shelves and upper slopes with a significant lack of data and almost no published records in the Adriatic Sea in this century. In this paper, we present 20 new occurrences recorded from May 2015 to September 2021, of which 19 are in Croatian and 1 in Montenegrin territorial waters. Records of juveniles, subadults and adults are reported. The number of described records and available data on HSI/BMI calculations points out that the living conditions are probably most favoured in the area off the Kornati archipelago (central Adriatic Sea), compared to the habitats in the Southern Adriatic where the populations might have significantly lower density. Due to the non-systematic research and non-probabilistic data collection, it is difficult to establish with certainty whether greater number of records in the continental shelf is just an ostensible phenomenon. This article is protected by copyright. All rights reserved.

The paper presents a calculation of a system supported on piles according to the second order theory. The influence of piles as supports on the structure is replaced by elastic supports. In the numerical model, the supports are modeled as elastic springs. To compare the calculation results, a system based on rigid and deformable supports was analyzed. The analysis of the system was performed according to the first order theory and the second order theory, which introduces geometric nonlinearity into the calculation. The process of soil modeling around a pile with replacement springs is presented. The applicability of the described procedure is shown in a numerical example. The comparison of the calculation results was done on numerical models of systems with rigid and elastic supports.

The Quality of Service (QoS) and the Quality of user Experience (QoE) are measures of the quality of satisfaction with the use of the service. The paper provides a detailed overview of QoS metrics applied by telecom operators in Bosnia and Hercegovina (BiH). Given that currently the focus of research is algorithms based on machine learning, which determine QoE, an overview of previous work in this field is given. The focus of the paper is an overview of activities on measuring QoS and QoE parameters performed by dominant telecom operators from Bosnia and Herzegovina.

Machine learning algorithms have been drawing attention in lung disease research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. This study reviews the input parameters and the performance of machine learning applied to diagnosis of chronic obstructive pulmonary disease (COPD). One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 179, 1032, and 36,500 titles were identified from the PubMed, Scopus, and Google Scholar databases respectively. Studies that used machine learning to detect COPD and provided performance measures were included in our analysis. In the final analysis, 24 studies were included. The analysis of machine learning methods to detect COPD reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. The performance of machine learning for diagnosis of COPD was considered satisfactory for several studies; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings.

Diagnostic medical imaging and the interpretation of the imaging results pose a great challenge for the medical profession as the final conclusions are highly susceptible to human error and subjectivity. The necessity for standardization of interpretation of medical images is very necessary to bypass these problems. The only way of achieving this is using a methodology which excludes the human eye and employs artificial intelligence. However, another challenge is selecting the most suitable AI algorithm fit for the challenging task of imaging results interpretation. This study was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines published in 2020. Research was done using PubMed, ScienceDirect and Google Scholar databases where the key inclusion criteria were language, journal credibility, open access to full-text publications and the most recent papers. In order to focus on only the most recent research, only the papers published in the last 5 years were evaluated. The search through PubMed, ScienceDirect and Google Scholar has yielded 81, 205, and 520 papers respectively. Out of this number of papers, 26 of them have met all of the inclusion criteria and were included in the research. The observed accuracies of the models and the overall rising interest in the topic denote that this field is rapidly growing and has a great potential to be applied in daily medical practice in the future.

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.

Over the past years, peptides have attracted increasing interest for G protein-coupled receptor (GPCR) drug discovery and development. Peptides occupy a unique chemical space that is not easily accessible for small molecules and antibodies and provide advantages over these ligand classes such as lower toxicity and higher selectivity. The κ-opioid receptor (KOR) is a prototypic GPCR and an appealing therapeutic target for the development of safer and more effective analgesics. Recently, peptides have emerged as analgesic drug candidates with improved side effect profiles. We have previously identified plant-derived peptides, which activate KOR. Based on this precedent, here we relied on publicly available databases to discover novel KOR peptide ligands by genome mining. Using human preprodynorphin as a query, we identified blenny fish-derived peptides, referred to as blenniorphins, capable of binding to and activating KOR with nanomolar affinity and potency, respectively. Additionally, the blenniorphins altered β-arrestin-2 recruitment at the KOR. Our study demonstrates the utility of genome mining to identify peptide GPCR ligands with intriguing pharmacological properties and unveils the potential of blenny fishes as a source for novel KOR ligands.

ABSTRACT Lifestyle-related phenotypes have been shown to be heritable and associated with DNA methylation. We aimed to investigate whether genetic predisposition to tobacco smoking, alcohol consumption, and higher body mass index (BMI) moderates the effect of these phenotypes on blood DNA methylation. We calculated polygenic scores (PGS) to quantify genetic predisposition to these phenotypes using training (N = 7,431) and validation (N = 4,307) samples. Using paired genetic-methylation data (N = 4,307), gene–environment interactions (i.e., PGS × lifestyle) were assessed using linear mixed-effects models with outcomes: 1) methylation at sites found to be strongly associated with smoking (1,061 CpGs), alcohol consumption (459 CpGs), and BMI (85 CpGs) and 2) two epigenetic ageing measures, PhenoAge and GrimAge. In the validation sample, PGS explained ~1.4% (P = 1 × 10−14), ~0.6% (P = 2 × 10−7), and ~8.7% (P = 7 × 10−87) of variance in smoking initiation, alcohol consumption, and BMI, respectively. Nominally significant interaction effects (P < 0.05) were found at 61, 14, and 7 CpGs for smoking, alcohol consumption, and BMI, respectively. There was strong evidence that all lifestyle-related phenotypes were positively associated with PhenoAge and GrimAge, except for alcohol consumption with PhenoAge. There was weak evidence that the association of smoking with GrimAge was attenuated in participants genetically predisposed to smoking (interaction term: −0.022, standard error [SE] = 0.012, P = 0.058) and that the association of alcohol consumption with PhenoAge was attenuated in those genetically predisposed to drink alcohol (interaction term: −0.030, SE = 0.015, P = 0.041). In conclusion, genetic susceptibility to unhealthy lifestyles did not strongly modify the association between observed lifestyle behaviour and blood DNA methylation. Potential associations were observed for epigenetic ageing measures, which should be replicated in additional studies.

ABSTRACT Teachers’ attitudes towards the education of children with autism spectrum disorder (ASD) play a significant part in successfulness of the inclusive education. The goal of the present study was to examine the attitudes of teachers from Bosnia and Herzegovina (BIH) towards the inclusion of students with ASD in general education classes. The sample for this study comprised 105 elementary school teachers (75 females, 30 males) from different parts of BIH. We examined the attitudes in relation to teachers’ age, gender, education level, experience in work with children with ASD, self-efficacy, and additional training on ASD. The findings of this study indicate mainly positive attitudes of teachers towards the inclusion of students with ASD. However, at the same time, results reveal a sense of non-competence of teachers in providing educational and behavioural support to students with ASD. Teachers who had ASD training and who felt competent to work with children with ASD had significantly more favourable attitudes than teachers without such training and without a sense of teaching competence. Gender, age, and education level had no significant effect on the teachers’ attitudes. Results of this study clearly indicate the need for additional teacher training regarding the education of children with ASD.

Marine heterotrophic flagellates (HFs) form a diverse and ecologically relevant functional group of bacterial grazers and nutrient remineralizers in oceanic waters. Despite playing a crucial role in marine biogeochemical cycles, there is still a lack of information on which specific taxa dominate HF assemblages and what are their patterns of distribution in a global context. In the present work, we addressed this issue by analyzing amplicon sequencing data sets retrieved from samples taken in tropical and subtropical oceanic regions at depths from surface to 4000 m. Only a few dozens of widespread taxa, mostly affiliating to MAST clades, Picozoa, Bicosoecida and Chrysophyceae, seemed to dominate surface HF assemblages. The majority of these dominant HFs were present at relatively constant abundances, while others were influenced by temperature or displayed a patchy distribution. In the deep ocean, only a handful of taxa belonging to Bicosoecida and Chrysophyceae, together with Diplonemea and Kinetoplastida, explained most of the HF signal. Co‐occurrence networks between HF and prokaryotic taxa at the surface ocean revealed two main clusters influenced by temperature that did not seem to show specific patterns of interaction. However, some correlations emerged outside these thermal groups that could represent new prey–predator interactions. Overall, we identified the putatively most ecologically relevant HF taxa in the ocean, which become promising targets for further experimental and genomic studies.