People living with HIV (PLWH) are at higher risk of poorer COVID‐19 outcomes. Vaccination is a safe and effective method of prevention against many infectious diseases, including COVID‐19. Here we investigate the strategies for national COVID‐19 vaccination programmes across central and eastern Europe and the inclusion of PLWH in vaccination programmes.

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.

We investigate the scattering attenuation characteristics of the Martian crust and uppermost mantle to understand the structure of the Martian interior. We examine the energy decay of the spectral envelopes for 21 high-quality Martian seismic events from Sol 128 to Sol 500 of InSight operations. We use the model of Dainty et al. (1974b) to approximate the behavior of energy envelopes resulting from scattered wave propagation through a single diffusive layer over an elastic half-space. Using a grid search, we mapped the layer parameters that fit the observed InSight data envelopes. The single diffusive layer model provided better fits to the observed energy envelopes for High Frequency (HF) and Very High Frequency (VF) than for the Low Frequency (LF) and Broadband (BB) events. This result is consistent with the suggested source depths (Giardini et al., 2020) for these families of events and their expected interaction with a shallow scattering layer. The shapes of the observed data envelopes do not show a consistent pattern with event distance, suggesting that the diffusivity and scattering layer thickness is non-uniform in the vicinity of InSight at Mars. Given the consistency in the envelope shapes between HF and VF events across epicentral distances and the tradeoffs between the parameters that control scattering, the dimensions of the scattering layer remain unconstrained but require that scattering strength decreases with depth and that the rate of decay in scattering strength is fastest near the surface. This is generally consistent with the processes that would form scattering structures in planetary lithospheres.

Selecting the optimal binder and the sorbent affinity for selected compounds can cause the composite to behave either as an efficient extraction coating, as a permeable membrane, or as an impermeable barrier. If the compound partitions onto the sorbent with high preference, it becomes stationary and the composite behaves as an impermeable barrier, while appropriately optimized affinity will result in effective permeation. To understand this phenomenon, we utilize solid-phase microextraction to characterize the mass transfer attributes of different separation composites. Our results indicate that for strong sorbents, the extraction rate is primarily controlled by the diffusion in the extraction phase rather than the sample matrix, even if it is relatively thin. Low analyte diffusion is caused by the retarding force generated by the partitioning of analytes into the sorbent, as migration through the composite is driven by the unbound form of the compound in the binder. One of the main contributions of this work is that an understanding of the extraction composite parameters that control mass transfer during extraction enables better optimization of binder/sorbent extraction phase composition for a given application. Another contribution of this work shows how a heterogeneous coating model can be simplified into a homogeneous coating model. The developed models enable an enhanced understanding of mass transfer kinetics, and they provide insight into how to optimize the extraction phase parameters for a given method involving sorbent particles in polymeric media, including membranes and paints, in addition to extraction coatings.

Introduction: There is limited knowledge on the sensitization patterns to peanut proteins and food allergy in the Middle East. The objective of this study is to analyze the relationship between sensitization patterns to peanut proteins and clinical symptoms in a group of patients with physician-diagnosed peanut allergy (PA) in Kuwait. Methods: PA patients were evaluated by the skin prick test (SPT), serum total IgE, peanut-specific IgE (sIgE), and sIgE against Ara h 1–3, 8, and 9, and clinical data were collected. Results: Sixty-nine patients were included. A positive correlation between peanut SPT and sIgE was detected for all 3 storage proteins (Ara h 1–3) in patients <6 years old and for Ara h 1 and 2 in older patients. ROC analysis of positive correlations showed that oral food challenge should be considered for definite diagnosis of PA only if the level of Ara h 2 is <22.25 KUA/L, with level of Ara h 2 ≥15.4 allowing the detection of systemic reactions with a sensitivity of 55.56%. Patients presenting with systemic reactions more frequently had positive Ara h 1 (88.9%) and Ara h 2 (83.3%), compared with 44.1% and 52.9% in those with local reaction (p = 0.0046 and p = 0.0378). The levels of Ara h 1 and 2 were also significantly higher in patients with systemic reactions compared to those with a local reaction, with those differences being especially relevant for Ara h 2 (15.9 vs. 0.4) (p = 0.0005). Conclusions: The pattern of sensitization to peanut proteins in the Middle East is similar to that of the Western world. Measurement of sIgE antibodies to Ara h 1, 2, and 3 is useful in the diagnosis of PA and in the investigation of reactions to raw and roasted peanuts.

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.

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.

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.

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.

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.

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.

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.