We investigate the neutrino sector in the framework of flavor deconstruction with an inverse-seesaw realization. This setup naturally links the hierarchical charged-fermion masses to the anarchic pattern of light-neutrino mixing. We determine the viable parameter space consistent with oscillation data and study the phenomenology of heavy neutral leptons (HNL) and lepton-flavor-violating (LFV) processes. Current bounds from direct HNL searches and LFV decays constrain the right-handed neutrino scale to a few TeV, while future μ → e experiments will probe most of the region with Λ ≲ 10 TeV. Among possible realizations, models deconstructing SU(2)L × U(1)B−L or SU(2)L × U(1)R × U(1)B−L are those allowing the lowest deconstruction scale.

This study examines the impact of an unintended fire at the Drava International plastic processing facility near Osijek, Croatia, on soil quality and the potential human health risks associated with agricultural soils within a 10 and 20 km radius. Surface soil samples (0–5 cm) were collected from ten locations within 10 km three days after the incident, and eight composite samples were taken from sites 10–20 km away 17 days’ post-event. Additionally, 18 control samples previously collected for soil fertility or quality monitoring were included for comparative analysis. In total, 36 agricultural soil samples were analyzed for pH, organic matter, total phosphorus, potassium, calcium, magnesium, and trace elements (Cr, Co, Ni, Cu, Zn, As, Pb). Eighteen post-fire samples were also analyzed for polycyclic aromatic hydrocarbons (PAHs), dioxins, and perfluoroalkyl substances (PFAS). Ecological risk was assessed using the pollution load index (PLI) and enrichment factor (EF), while human health risk was evaluated through the estimation of incremental lifetime cancer risk (ILCR) and individual carcinogenic risks (CRi) for As, Cr, Ni, and Pb. Results showed that concentrations of dioxins (TEQ LB and UB), dioxin-like PCBs, and non-dioxin-like PCBs in samples within 10 km were either below detection limits or present in trace amounts (4.0 × 10−6 mg/kg). PFAS compounds were not detected (<0.0005 mg/kg). The total concentration of non-dioxin-like PCBs ranged from 0.0023 to 0.0047 mg/kg, well below the maximum permissible levels. Post-fire contamination profiles revealed consistently higher PAH concentrations in the 0–10 km zone (mean 0.100 mg/kg) compared to the 10–20 km zone (mean 0.062 mg/kg). Twenty PLI values exceeded the threshold of 1 (range: 1.00–1.26), indicating moderate pollution, while the remaining values (PLI 0.82–0.99) suggested no pollution. EF values indicated minimal to moderate enrichment (EF < 2), supporting the conclusion that metal presence was predominantly geological with limited anthropogenic influence. All ILCR values for adults and children remained below the acceptable threshold of 1 × 10−4, indicating low carcinogenic risk under both pre- and post-fire conditions. CRi values followed a consistent decreasing trend across exposure pathways: ingestion > dermal absorption > inhalation.

Zeolites are particularly suitable adsorbents due to their pronounced ion-exchange capacity, high efficiency, stability, and the ability to be regenerated and reused multiple times. Their characteristic crystalline structure enables the exchange of sodium, potassium, calcium, and magnesium ions with heavy metal cations present in solution. For the successful application of zeolites under industrial conditions, a detailed understanding of the adsorption mechanisms and kinetics is essential, as it allows for process optimization and identification of key limiting factors. Experimental approaches typically involve varying the adsorbent mass and the initial concentration of the adsorbate in order to determine the optimal conditions for achieving maximum adsorption efficiency. A moisture content of 3.95% and ash content of 91.28% indicate high thermal and structural stability of the zeolite, while the presence of Na⁺ ions (0.2435 mmol g⁻¹) in the material confirms that cation exchange is the dominant mechanism. Adsorption of heavy metals was investigated in a batch reactor at initial concentrations of 10, 50, and 100 mg/L, at a constant temperature of 298 K, with stirring at 200 rpm for 60 minutes. The amount of adsorbed ions was found to increase with rising equilibrium concentrations in the solution. Metal ion concentrations were determined using atomic absorption spectrophotometry. The highest adsorption was observed for Cu(II) ions within 5 minutes, while Cr(III) and Ni(II) ions reached their maximum adsorption within 20 minutes. The experimental data fit best to the Langmuir isotherm model, and the adsorption efficiency followed the order: Cu(II) > Cr(III) > Ni(II).

Perfluorohexyloctane (F6H8) is a semifluorinated alkane recently approved for ophthalmic treatment of dry eye disease. Although considered locally safe for topical use, its structural similarity to persistent per- and polyfluoroalkyl substances (PFAS) raises concerns about systemic accumulation and long-term toxicity. To investigate potential hepatic effects, we examined the metabolic impact of F6H8 exposure in human HepaRG hepatocytes across a broad concentration range representing short- and long-term exposure scenarios. Untargeted metabolic profiling by ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UHPLC-ǪTOFMS) was performed on intracellular extracts and extracellular media. F6H8 induced pronounced, concentration-dependent metabolic alterations, many of which exhibited non-monotonic responses. Low concentrations primarily affected amino acid, fatty acid, and lipid metabolism, while central carbon metabolism was disrupted only at the highest exposures. Notably, a putative biotransformation product, perfluorohexyloctanoic acid, was detected, suggesting metabolic persistence and conversion to a PFAS-like structure. These findings indicate that F6H8 elicits broad metabolic reprogramming and may not be metabolically inert as previously assumed. Given its clinical use and structural similarity to persistent fluorochemicals, the results highlight the need for comprehensive, long-term safety assessment of F6H8 and related semifluorinated alkanes.

Background and Objectives: Resilience and perceived social support are crucial factors influencing psychological well-being among breast cancer survivors. Understanding their levels and interrelations can inform psychosocial interventions aimed at improving survivorship outcomes. This study aimed to examine the relationship between resilience and perceived social support, to evaluate the psychometric properties of the applied scales, and to explore their associations with key sociodemographic factors among breast cancer survivors. Materials and Methods: A total of 193 women in clinical remission, at least six months post-primary treatment, were recruited from the General Hospital Sombor. Participants completed sociodemographic and clinical questionnaires, the Connor–Davidson Resilience Scale (CD-RISC-25), and the Multidimensional Scale of Perceived Social Support (MSPSS). Descriptive statistics, Pearson’s correlations, and group comparisons (t-tests and ANOVA) were conducted to assess the relationships among study variables and sociodemographic factors. Results: Participants demonstrated moderate resilience (57 ± 18), with Coping and Hardiness as the strongest domains and Optimism the lowest. Perceived social support was also moderate (4.65–4.82) across all domains, highest for family and significant others. Resilience and perceived social support were positively correlated (r = 0.616, p < 0.001), with Hardiness most strongly associated with overall resilience (r = 0.899). Support from a significant other was particularly linked to adaptability (r = 0.617). Participants living in urban areas and those with higher income reported significantly higher resilience and social support, though with low effect sizes. No other sociodemographic associations were observed. Conclusions: Breast cancer survivors in this Serbian cohort reported moderate resilience and social support, with a strong interrelationship between the two. These findings underscore the importance of strengthening social support networks as a potential pathway to enhance resilience and psychological well-being in cancer survivorship care.

Background/Objectives: Dupuytren’s contracture is a chronic fibroproliferative disorder of the palmar fascia that leads to progressive flexion deformities and functional impairment. The Southampton Dupuytren’s Scoring Scheme (SDSS) is a disease-specific patient-reported outcome measure designed to quantify disability in this condition. This study aimed to translate, culturally adapt, and evaluate the psychometric properties of the Serbian version of the SDSS. Methods: A cross-sectional study was conducted at the Institute for Orthopedic Surgery “Banjica”, Belgrade, from January 2024 to March 2025. Sixty-eight patients with Dupuytren’s contracture completed the Serbian SDSS, the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire, the 12-Item Short Form Health Survey (SF-12), and a Visual Analogue Scale (VAS) for pain. Translation followed standardized forward–backward procedures. Internal consistency was assessed with Cronbach’s alpha, construct validity with confirmatory factor analysis (CFA), and convergent validity with Pearson’s correlation coefficients. Results: The Serbian SDSS demonstrated excellent internal consistency (Cronbach’s α = 0.914). CFA supported a unidimensional five-item structure with strong factor loadings (0.76–0.93) and acceptable fit indices (χ2 = 10.094, df = 5, p = 0.073; IFI = 0.979; CFI = 0.978; TLI = 0.956). Convergent validity was confirmed by strong correlations with DASH (r = 0.779) and VAS (r = 0.702) and a strong negative correlation with SF-12 PCS (r = −0.802). Conclusions: The Serbian SDSS is a valid and reliable instrument for assessing functional disability in patients with Dupuytren’s contracture and offers a robust, patient-centered measure for clinical and research use.

Background: Artificial intelligence (AI), the overarching field that includes machine learning (ML) and its subfield deep learning (DL), is rapidly transforming clinical research by enabling the analysis of high-dimensional data and automating the output of diagnostic and prognostic tests. As clinical trials become increasingly complex and costly, ML-based approaches (especially DL for image and signal data) offer promising solutions, although they require new approaches in clinical education. Objective: Explore current and emerging AI applications in oncology and cardiology, highlight real-world use cases, and discuss the challenges and future directions for responsible AI adoption. Methods: This narrative review summarizes various aspects of AI technology in clinical research, exploring its promise, use cases, and its limitations. The review was based on a literature search in PubMed covering publications from 2019 to 2025. Search terms included “artificial intelligence”, “machine learning”, “deep learning”, “oncology”, “cardiology”, “digital twin”. and “AI-ECG”. Preference was given to studies presenting validated or clinically applicable AI tools, while non-English articles, conference abstracts, and gray literature were excluded. Results: AI demonstrates significant potential in improving diagnostic accuracy, facilitating biomarker discovery, and detecting disease at an early stage. In clinical trials, AI improves patient stratification, site selection, and virtual simulations via digital twins. However, there are still challenges in harmonizing data, validating models, cross-disciplinary training, ensuring fairness, explainability, as well as the robustness of gold standards to which AI models are built. Conclusions: The integration of AI in clinical research can enhance efficiency, reduce costs, and facilitate clinical research as well as lead the way towards personalized medicine. Realizing this potential requires robust validation frameworks, transparent model interpretability, and collaborative efforts among clinicians, data scientists, and regulators. Interoperable data systems and cross-disciplinary education will be critical to enabling the integration of scalable, ethical, and trustworthy AI into healthcare.

Understanding meat categorization is a fundamental component of veterinary education, especially within the context of food hygiene and public health. Veterinary students must grasp legal classifications of meat, which depend on variables such as species, age, quality, and processing techniques. This knowledge is essential for accurate meat inspection, labeling, and compliance with both national and international food safety standards. Despite prior exposure to muscle anatomy in anatomy course, students often face challenges in applying this knowledge to practical meat classification tasks. This study aimed to assess the effectiveness of three distinct instructional methods in improving veterinary students’ ability to identify meat categories and associated muscle structures: traditional classroom teaching, computer-based instruction using 3D models, and immersive virtual reality (VR). Participants included fourth-year veterinary students during the summer semester of the 2024/2025 academic year. To facilitate digital learning, a dedicated 3D model library “3DMeat” was developed as well as virtual reality environment. Results indicate that technology-enhanced instructional approaches, can significantly enhance student engagement and understanding of complex topics such as meat categorization. Initial test scores were highest in the group using 3D models (16.3 ± 4.1), followed by the traditional lecture-based group (15.6 ± 3.07), and the VR group (11.7 ± 5.1). However, a follow-up assessment conducted 2 weeks later revealed that VR group demonstrated the highest retention of knowledge. These findings suggest that although immediate performance may vary, immersive learning environments such as VR can foster stronger medium-term retention of complex material.

Public concern about environmental issues has led to growing interest in sustainability across various sectors, including entrepreneurship. However, beyond the concern for environmental protection and the presseration of natural resources for future generations, additional conditions are necessary to foster the development of sustainable entrepreneurship. While developed countries provide examples and evidence of the successful implementation of this concept, its application in developing countries presents challenges due to a range of limiting factors. In addition to essential financial support, the literature often highlights the lack and/or complexity of sustainability reporting, the absence of standards and clearly defined sustainability metrics, insufficient regulation, and the lack of support from higher education institutions as barriers to the transition toward sustainable entrepreneurship. This paper aims to examine the feasibility of applying the concept of sustainable entrepreneurship in Western Balkan countries, taking into account the aforementioned constraints. For the purpose of the empirical research, potential limitations were evaluated by managers and business owners in Albania, Bosnia and Herzegovina, North Macedonia, and Serbia. The results of the study answer the question of whether developing countries have the potential to foster sustainable entrepreneurship, given the analyzed constraints, or whether the implementation of this concept is reserved solely for large enterprises and economically advanced countries.

This study presents a machine learning framework for predicting pedestrian accident severity using Amman, Jordan's first complete 10-year traffic dataset (2014–2023). Addressing the critical class imbalance where minor injuries predominate (85%), causing standard models to poorly detect severe cases (< 25% recall), we implement cost-sensitive algorithms and specialized undersampling techniques, such as XGBoost with Balancing the Loss Function (XGBLF) and Random Data Partitioning with Voting Rule (RDPVR), which enhanced learning from underrepresented Major/Fatal cases while maintaining data authenticity. Through mixed-type correlation analysis and statistical testing, vehicle speed, road illumination, vehicle type, driver age, and road conditions emerged as the most significant predictive factors. RDPVR achieved a 63% true positive rate for Major/Fatal injuries, a 2.78-fold improvement over standard classifiers, and XGBLF achieved 95%, but this achievement was on account of the accuracy of the minor cases. Comprehensive interpretability analysis (SHAP, LIME, and Permutation Importance) revealed that heavy vehicles, poor lighting, and high-speed driving strongly predict Major/Fatal outcomes. Notably, the analysis demonstrates Jordan's improved safety trajectory, with 2020–2023 showing reduced severe accidents compared to 2014–2016, indicating measurable policy impact. This study delivers the first interpretable, context-sensitive AI framework for Amman/Jordan pedestrian safety, translating technical insights into actionable recommendations for targeted interventions, urban planning, and data-driven enforcement strategies to reduce pedestrian injury severity in high-risk zones.

Deformable medical image registration is a fundamental task in medical image analysis. While deep learning-based methods have demonstrated superior accuracy and efficiency, they often overlook the critical role of regularization in ensuring robustness and anatomical plausibility. We propose DARE (Deformable Adaptive Regularization Estimator), a novel registration framework that dynamically adjusts elastic regularization based on the gradient norm of the deformation field. The method integrates strain and shear energy terms, adaptively modulated to balance deformation stability and flexibility, and includes a folding-prevention mechanism that penalizes regions with negative deformation Jacobian to reduce folding artifacts and encourage topologically plausible mappings. Extensive experiments on the IXI, OASIS, and MUI-P datasets demonstrate that DARE consistently improves registration accuracy while preserving anatomical plausibility. Compared with classical regularizers and state-of-the-art learning-based methods, DARE achieves higher Dice scores and reduces folding artifacts, while maintaining low strain energy and realistic volume changes in key brain structures. These results confirm that the proposed adaptive regularization mechanism delivers robust, accurate, and physiologically consistent deformation fields across clinical scenarios related to brain registration.

This study examines job performance among judo referees through the lens of personality traits during World Judo Tour events from 2018 to 2022. Sixty-three referees completed an online questionnaire including the Big Five Inventory (BFI) and the Conditions for Work Effectiveness Questionnaire (CWEQ-II). Data were analyzed using descriptive statistics, correlation analysis, and structural equation modeling (SEM). The measurement model showed acceptable validity and reliability, confirming the structural model. Support and resources emerged as the most influential factors affecting job satisfaction (JAS) and organizational role satisfaction (ORS). Incorporating refereeing experience at major events into the model indicated only partial model fit. Findings highlight the role of structural empowerment in mitigating job dissatisfaction among referees. Future research with larger samples should further strengthen the understanding of the relationship between personality traits, empowerment, and job performance.

Background: Breast cancer remains the most common cancer in women worldwide. Treatment has evolved into multimodal approaches, with pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) serving as a key prognostic marker. The aim of this study was to evaluate the value of inflammatory markers in predicting pCR to NAC in breast cancer. Methods: This cross-sectional study of 74 patients with breast cancer who underwent NAC followed by surgery included demographic, tumor, and immune-inflammatory marker data. Receiver operating characteristic curve analysis and the Youden index were used to determine optimal cutoff values. Univariate and multivariate logistic regression assessed associations between markers and pCR, adjusting for tumor stage, human epidermal growth factor receptor 2 (HER2), and estrogen receptor (ER) status. Results: Our multivariate analysis identified the pan-immune-inflammation value (PIV), HER2 status, and ER status as significant independent predictors of pCR. PIV (OR, 4.28; 95% CI, 1.59–16.88) remained significant among inflammatory markers, while the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) did not. HER2-positive (OR, 7.45; 95% CI, 2.30–24.15) and hormone receptor (HR)–negative (OR, 7.02; 95% CI, 2.63–18.70) statuses were also strongly associated with pCR. Conclusion: PIV is a robust predictor of pCR in patients with breast cancer receiving NAC, offering a comprehensive reflection of the immune-inflammatory state. Incorporating PIV with tumor-specific markers (e.g., receptor status, Ki-67, grade) may enhance treatment stratification. Further validation in diverse cohorts is warranted.