Evaluation of the performance of teleoperation systems plays an important role in assessing the efficacy and reliability of such systems. The evaluation is usually performed based on factors such as stability, transparency, and user satisfaction. However, very few studies have addressed the numerical evaluation of transparency in teleoperation systems so far. This letter presents a novel method to numerically assess the transparency of teleoperation systems based on representing recorded experimental data algebraically by fitting parametric curves using Elliptic Fourier Descriptors (EFD). The EFD coefficients are used to compute the Hybrid Matrix of the teleoperation system, which provides a metric for judging how transparent a teleoperation system is. This letter validates the proposed method using real experimental position and force data for teleoperation systems with and without time delay, as well as providing an analysis of the effect of the number of harmonics on the calculation of the Hybrid Matrix.

This study investigated the potential of high-voltage electrical discharge (HVED), as a green, non-thermal extraction technology, for recovering polyphenols from winter savory (Satureja montana L.). Key process parameters, including frequency (40, 70, 100 Hz) and extraction time (1, 5, 15, 30, 45 min), were optimized, using water as a solvent and maintaining a constant solid-to-liquid ratio of 1:100 g/mL. The extracts were characterized for total polyphenol content (TPC), total flavonoid content (TFC), and antioxidant activity (DPPH, ABTS, FRAP), while individual phenolics were quantified via HPLC-DAD. Multivariate chemometric analyses, including Pearson correlation, heatmap clustering, and principal component analysis (PCA), were employed to reveal relationships between extraction conditions, polyphenolic profiles, and antioxidant activities. The results showed strong correlations between TPC, TFC, and antioxidant activity, with compounds such as quercetin-3-D-galactoside, procyanidin A2, and rutin identified as key contributors. Among the tested conditions, extraction at 70 Hz for 45 min provided the highest polyphenol yield and bioactivity. The application of HVED demonstrated its potential as an efficient and environmentally friendly technique for obtaining phenolic-rich extracts. In addition, the use of chemometric tools provided useful insights for optimizing extraction conditions and understanding the contributions of specific compounds to bioactivity. These results support future applications in clean-label product development and contribute to broader efforts in sustainable ingredient production for the food, cosmetic, and nutraceutical sectors.

Narrative review synthesizes the most current literature on the SARS-CoV-2 XEC variant, focusing on its genomic evolution, immune evasion characteristics, epidemiological dynamics, and public health implications. To achieve this, we conducted a structured search of the literature of peer-reviewed articles, preprints, and official surveillance data from 2023 to early 2025, prioritizing virological, clinical, and immunological reports related to XEC and its parent lineages. Defined by the distinctive spike protein mutations, T22N and Q493E, XEC exhibits modest reductions in neutralization in vitro, although current evidence suggests that mRNA booster vaccines, including those targeting JN.1 and KP.2, retain cross-protective efficacy against symptomatic and severe disease. The XEC strain of SARS-CoV-2 has drawn particular attention due to its increasing prevalence in multiple regions and its potential to displace other Omicron subvariants, although direct evidence of enhanced replicative fitness is currently lacking. Preliminary analyses also indicated that glycosylation changes at the N-terminal domain enhance infectivity and immunological evasion, which is expected to underpin the increasing prevalence of XEC. The XEC variant, while still emerging, is marked by a unique recombination pattern and a set of spike protein mutations (T22N and Q493E) that collectively demonstrate increased immune evasion potential and epidemiological expansion across Europe and North America. Current evidence does not conclusively associate XEC with greater disease severity, although additional research is required to determine its clinical relevance. Key knowledge gaps include the precise role of recombination events in XEC evolution and the duration of cross-protective T-cell responses. New research priorities include genomic surveillance in undersampled regions, updated vaccine formulations against novel spike epitopes, and long-term longitudinal studies to monitor post-acute sequelae. These efforts can be augmented by computational modeling and the One Health approach, which combines human and veterinary sciences. Recent computational findings (GISAID, 2024) point to the potential of XEC for further mutations in under-surveilled reservoirs, enhancing containment challenges and risks. Addressing the potential risks associated with the XEC variant is expected to benefit from interdisciplinary coordination, particularly in regions where genomic surveillance indicates a measurable increase in prevalence.

Introduction. Sentinel lymph node biopsy (SLNB) has significantly advanced axillary staging in clinically node-negative breast cancer, offering lower morbidity compared to traditional axillary lymph node dissection (ALND). Nonetheless, precise prediction of non-sentinel lymph node (non-SLN) involvement remains essential for optimizing surgical decisions and preventing unnecessary ALND. Methods. A retrospective cohort analysis was performed on 176 patients with clinically node-negative breast cancer who underwent SLNB. Clinicopathological data were reviewed to evaluate associations between various predictive factors and non-SLN involvement. Variables analyzed included tumor size, histological grade, lymphovascular invasion (LVI), Ki-67 proliferation index, and sentinel lymph node characteristics. Results. Multivariable logistic regression identified the type of SLN metastasis (OR=21.4; 95% CI 1.7–43.6; p=0.01), the number of positive SLNs (OR=5.66; 95% CI 1.18–36.6; p=0.03), and the number of negative SLNs (OR=0.04; 95% CI 0.006–0.27; p=0.001) as independent predictors of non-SLN metastases. The predictive model demonstrated excellent discriminatory power, with an area under the receiver operating characteristic curve (AUC) of 0.91. Conclusion. Specific clinical and histopathological variables reliably predict non-SLN involvement in SLN-positive breast cancer patients. Incorporation of these predictors into clinical practice may enhance individualized axillary management and reduce unnecessary ALND procedures. Further validation through larger prospective studies is warranted. Key words: Breast Neoplasms, Sentinel Lymph Node Biopsy, Axillary Lymph Nodes, Lymph Node Dissection, Neoplasm Staging.

Background Non-ST-elevation myocardial infarction (NSTEMI) is frequently associated with systemic inflammation and metabolic dysregulation. Indices derived from routine laboratory tests that reflect systemic inflammatory and lipid-inflammatory status may offer better prognostic insight. This study aimed to evaluate the association between selected indices and short-term major adverse cardiovascular events (MACE) and all-cause mortality in patients with NSTEMI treated with dual antiplatelet therapy (DAPT) and statin. The selected indices reflect key mechanisms involved in NSTEMI pathophysiology, including insulin resistance, atherogenic dyslipidemia, and inflammation. Materials and methods This prospective observational study included 171 patients with NSTEMI admitted to the Intensive Care Unit of the Clinic for Internal Medicine at the University Clinical Centre Tuzla between February 1, 2022, and January 31, 2023. Blood samples were collected upon admission and 24 hours subsequently. The following indices were calculated: triglyceride-glucose index (TyG), triglyceride-to-high-density lipoprotein ratio (TG/HDL), atherogenic index of plasma (AIP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and pan-immune-inflammation value (PIV). Outcomes were tracked during hospitalization and up to three months post-discharge. MACE was defined as cardiovascular death, reinfarction, stroke, or unplanned revascularization. All patients underwent coronary angiography; revascularization was performed when clinically indicated. Exclusion criteria included active malignancy, infection, or inflammatory disease. Logistic regression was adjusted for age, diabetes, and other clinical variables. Missing data were handled using the pairwise deletion method. Results High levels of TyG at admission were independently associated with MACE (odds ratio (OR) 1.7; 95% confidence interval (CI) 1.0-2.8; p = 0.037). All-cause mortality occurred in 14.6% of patients (n = 25), while MACE occurred in 60 patients. Independent predictors of mortality included elevated TyG at admission (OR 2.2; 95% CI 1.1-4.4; p = 0.034), TG/HDL at 24 hours (OR 1.4; 95% CI 1.1-1.7; p = 0.007), AIP at 24 hours (OR 5.7; 95% CI 1.1-28.9; p = 0.035), and NLR at 24 hours (OR 1.1; 95% CI 1.0-1.2; p = 0.002). PLR and PIV at 24 hours were also significantly associated with mortality. Optimal cut-off values were TyG ≥ 8.9, AIP ≥ 0.35, and NLR ≥ 4.5. NLR had the highest estimated area under the curve (AUC ≈ 0.78). Conclusion In NSTEMI patients treated with DAPT and statin, several inflammatory and lipid-inflammatory indices were independently associated with short-term mortality. Indices measured at 24 hours had a stronger prognostic value than baseline values. Serial monitoring may aid early risk stratification. Outcomes were assessed during hospitalization and via structured follow-up up to three months post-discharge.

This work proposes a motion planning algorithm for robotic manipulators that combines sampling-based and search-based planning methods. The core contribution of the proposed approach is the usage of burs of free configuration space ($\mathcal{C}$-space) as adaptive motion primitives within the graph search algorithm. Due to their feature to adaptively expand in free $\mathcal{C}$-space, burs enable more efficient exploration of the configuration space compared to fixed-sized motion primitives, significantly reducing the time to find a valid path and the number of required expansions. The algorithm is implemented within the existing SMPL (Search-Based Motion Planning Library) library and evaluated through a series of different scenarios involving manipulators with varying number of degrees-of-freedom (DoF) and environment complexity. Results demonstrate that the bur-based approach outperforms fixed-primitive planning in complex scenarios, particularly for high DoF manipulators, while achieving comparable performance in simpler scenarios.

Summary The rapid rise of 3D printing, both in industrial and home settings, presents emerging health and environmental risks. While 3D printing enhances sustainability by reducing waste and optimizing resource use, its impact on human health remains poorly understood. The use of metals and polymers linked to health risks, coupled with the release of inhalable particles and volatile organic compounds, raises concerns about respiratory and systemic effects. The absence of clear guidelines creates high public demand for information and limits safe implementation, particularly in schools and homes where millions of 3D printers are expected by 2030. Additionally, improper disposal of 3D printing polymer materials may exacerbate plastic pollution. This article proposes the perspective of a structured risk assessment framework set on particle emissions from industrial 3D printing. It will offer a practical tool to bridge current knowledge gaps and to inform safe practice and policy development, because immediate action is necessary to balance innovation with safety.

Procedural modeling methods are used to automatically generate virtual scenes. There is a large number of available top‐down methods for generating partial content for specific purposes. However, little research was done on enabling the generation of content in the presence of manually modeled elements, from the bottom‐up direction, or without significant assistance from the user. No existing approach provides a platform that can combine the results of different methods, which leaves them isolated. This paper presents an integration approach that generates complete virtual space organizations by combining the usage of top‐down and bottom‐up procedural generation of content, with support for the placement of manually modeled content. The integration is made possible by using shape conversion to match the input and output shape types of different methods. The evaluation of the proposed approach was performed on a 2D polygon dataset by using four different scenarios, validating that it works as intended. Additional testing was performed by using a case study of organizing 3D virtual space around the manually modeled element of virtual heritage Tašlihan to demonstrate all capabilities of the integration approach and the different outputs depending on the level of user interaction and the desired results.

The ideological underpinnings of the Great Replacement Theory, which frames Muslims as a threat to Europe, originated in Serbia and emboldened a wider narrative of anti-Muslim hate across Western milieus. The othering of Bosnian Muslims (Bosniaks), an autochthonous ethnic group in Southeastern Europe, has contributed to the normalization of the alienation of Muslims throughout Europe, engendering Educational Displacement—an internalized sense of invisibility and devaluation within targeted individuals, diminishing their participation and trust in the societal institutions. In this complex socio-political and historical context, Bosniaks have nonetheless chosen to principally champion interfaith coexistence, offering an instructive and community-based model of resilience to hate and violence. The study investigates the Bosniaks’ affinity for coexistence by examining the underexplored case of interfaith solidarity and entente between Muslims and Jews in Bosnia and Herzegovina from 1540 to the present.

Abstract Polygenic risk scores (PRS) combine the effects of multiple genetic variants to predict an individual’s genetic predisposition to a disease. PRS typically rely on linear models, which assume that all genetic variants act independently. They often fall short in predictive accuracy and are not able to explain the genetic variability of a trait to the full extent. There is growing interest in applying deep learning neural networks to model PRS given their ability to model non-linear relationships and strong performance in other domains. We conducted a survey of the literature to investigate how neural networks model PRS. We categorize deep learning-based approaches by their underlying architecture, highlighting their modeling assumptions, likely strengths and potential weaknesses of the architectures. Several categories of neural network architectures exhibited promising signs for the improvement of PRS’ predictive power, namely sequence-based architectures, graph neural networks and those that incorporated biological knowledge. Additionally, the use of latent representations in autoencoders has improved predictive performance across diverse ancestries. However, a lack of existing model benchmarks on consistent datasets and phenotypes makes it challenging to understand the extent to which different architectures improve performance. Interpretability of deep learning-based PRS is also challenging with great care required when inferring causation. To address these challenges, we suggest the establishment and adherence to reporting standards and benchmarks to aid the development of deep learning-based PRS to find quantifiable trends in neural network architectures.

Psychological studies on close relationships have often overlooked cultural diversity, dynamic processes, and potentially universal principles that shape intimate partnerships. To address the limited generalizability of previous research and advance our understanding of romantic love experiences, mate preferences, and physical attractiveness, we conducted a large-scale cross-cultural survey study on these topics. A total of 404 researchers collected data in 45 languages from April to August 2021, involving 117,293 participants from 175 countries. Aside from standard demographic questions, the survey included valuable information on variables relevant to romantic relationships: intimate, passionate, and committed love within romantic relationships, physical-attractiveness enhancing behaviors, gender equality endorsement, collectivistic attitudes, personal history of pathogenic diseases, relationship quality, jealousy, personal involvement in sexual and/or emotional infidelity, relational mobility, mate preferences, and acceptance of sugar relationships. The resulting dataset provides a rich resource for investigating patterns within, and associations across, a broad range of variables relevant to romantic relationships, with extensive opportunities to analyze individual experiences worldwide.

Background Acute cholecystitis (AC) is a frequent surgical emergency associated with significant variability in clinical outcomes and hospital length of stay (LOS). Early identification of patients at risk for prolonged hospitalization can improve triage and resource planning. Inflammatory markers such as C-reactive protein (CRP), white blood cell count (WBC), and total bilirubin (TBil), along with biliary complications like choledocholithiasis and Mirizzi syndrome, may have prognostic value. Materials and methods This retrospective study included 150 patients who underwent cholecystectomy for AC at the Department of General and Abdominal Surgery, University Clinical Centre Tuzla, Tuzla, Bosnia and Herzegovina, between January 1, 2024, and January 31, 2025. Demographic, laboratory, and intraoperative data were collected. Receiver operating characteristic (ROC) analysis identified optimal cut-offs for inflammatory markers predicting prolonged LOS (≥7 days). Multivariate linear regression was used to assess independent predictors, including CRP, WBC, TBil, and intraoperative findings. Results We found that CRP was significantly higher in patients with prolonged LOS and demonstrated the highest predictive accuracy, with an area under the curve (AUC) of 0.733 (95% CI: 0.630-0.835), followed by TBil and WBC. In multivariate analysis, only CRP ≥110.5 mg/L (p<0.001), the presence of choledocholithiasis in 26 patients (17.3%; p=0.010), and Mirizzi syndrome in seven patients (4.7%; p=0.017) remained significant predictors. WBC and TBil lost significance after adjustment. Conclusion CRP is the most reliable independent laboratory predictor of prolonged LOS in AC. The presence of choledocholithiasis and Mirizzi syndrome further contributes to extended hospitalization. These factors should be considered in early clinical risk assessment.

Background/Objectives: This research reports the synthesis and evaluation of novel charged thienobenzo-triazoles as non-selective cholinesterase inhibitors (AChEs and BChEs), their anti-inflammatory properties, and a computational study. Methods: Fifteen derivatives were created through photochemical cyclization and quaternization of the triazole core. The compounds were tested for AChE and BChE inhibition. They showed greater potency and selectivity toward BChE. Results: The most potent compound, derivative 14, inhibited BChE with an IC50 of 98 nM, while derivative 9 also displayed significant anti-inflammatory activity by inhibiting LPS-induced TNF-α production (IC50 = 0.66 µM). Molecular docking revealed that triazolinium salts form key π-π and electrostatic interactions within enzyme active sites. In silico predictions indicated favorable ADME-Tox properties for compounds 9 and 11, including low mutagenicity and moderate CNS permeability. Conclusions: These findings highlight the potential of new charged triazolinium salts as peripherally selective cholinesterase inhibitors with additional anti-inflammatory potential.

The conducted research aimed to determine the dietary habits of elderly people. The research was conducted on a sample of 237 respondents differentiated by gender, 133 males and 104 females and by age 65 -89 years. Data processing was done in the SPSS program and descriptive and comparative statistics were used to obtain data. Descriptive parameters were created for the analysis of the factual state of the respondents. The collected data were analyzed using the Microsoft Excel 2007 tool, and are presented in tables. It is evident that the health status of the respondents worsens with age, and dietary habits change in a positive sense with age. Given that the world's population is aging, it is necessary to emphasize attention to certain needs and challenges faced by many older people. Nutrition is an important element of health in the elderly population and affects the aging process. Although it is a study on a relatively small number of subjects, in comparison with a larger European study, it becomes relevant for proving the present problem of malnutrition among elderly patients. It is necessary to take all measures to increase the awareness of health workers about this problem in order to successfully prevent it or stop it in time.Looking at the entire sample of respondents globally, we can conclude that overall, a significant number of respondents do not pay enough attention to the structure of foods in their diet and water consumption.In line with previous research, the results of this study highlight the importance of dietary habits in creating a healthy lifestyle and preventing chronic non-communicable diseases. Key words:eating habits, age, respondents, differences, lifestyle