Autonomic nervous system (ANS) dysfunction has emerged as a central feature of post-infectious syndromes, including post-COVID syndrome (PCS), chronic fatigue syndrome (CFS), and late-stage Lyme disease. This cross-sectional study included 1036 patients evaluated in the Neurocardiological Laboratory of the Institute for Cardiovascular Diseases “Dedinje,” divided into four groups: PCS, CFS after COVID-19, CFS of insidious onset, and Lyme disease. All patients underwent head-up tilt testing (HUTT), and serological testing was performed in accredited institutions. The Lyme disease group demonstrated the highest prevalence of positive HUTT responses and a significantly greater frequency of orthostatic hypotension and syncope. Approximately 50–65% of patients in the PCS and Lyme groups were positive for IgM antibodies against at least one microorganism, with more than 10% showing positivity for three or more pathogens. Logistic regression analysis revealed that, beyond classical hemodynamic parameters, antibody status served as a significant predictor of HUTT outcomes, with specific associations identified for HSV-1, HHV-6, Coxiella burnetii, Toxoplasma gondii, and Borrelia spp. Multinomial regression further indicated that negative IgG antibodies, particularly to HSV-1 and VZV, predicted Lyme disease group membership. These findings support the hypothesis that ANS dysfunction in post-infectious syndromes may be driven by persistent or prior infections, highlighting the need for integrative diagnostic approaches.

Background: Testicular torsion scoring systems, based on a combination of clinical and imaging factors, have been developed to improve the diagnostic accuracy of testicular torsion in patients presenting with acute scrotum. This study aimed to validate and compare two current testicular torsion scores the Boettcher Alert Score (BAL) and the Testicular Workup for Ischemia and Suspected Torsion (TWIST)-in a retrospective cohort of pediatric patients with acute scrotum. Methods: We conducted a retrospective study of all pediatric patients admitted to our institution for acute scrotum between January 2010 and December 2022. Patients were categorized into the testicular torsion (TT) group and the non-testicular torsion (NTT) group. Collected data were used to calculate the scoring systems and perform statistical analyses. Results: A total of 241 patients were included, of whom 80 (33.2%) had testicular torsion. The mean age in the TT group was 13 years. The optimal individual cut-off value for the BAL score was >1 (sensitivity 90%, specificity 80.75%), and for the TWIST score >4 (sensitivity 82.5%, specificity 80.75%). A high-risk TWIST score >5 had a specificity of 80.75% and a negative predictive value (NPV) of 90.28%, while a BAL score of 4 showed a specificity of 98.48% and NPV of 94.2%. The area under the ROC curve was slightly higher for the BAL score (0.917; 95% CI, 0.875–0.949) than for the TWIST score (0.897; 95% CI, 0.851–0.932). The difference between the two scores was not statistically significant. Conclusion: The TWIST and BAL clinical scores have significant diagnostic value and may assist in the evaluation of testicular torsion in children. Both scores could be incorporated into a standardized approach for assessing pediatric acute scrotum, potentially reducing time to definitive diagnosis, and minimizing ischemia duration.

The energy transition intensifies spatial conflicts between energy infrastructure (EI) and migratory birds, fueling public resistance, litigation and permitting delays. These conflicts are often addressed only at the project level, after EI sites are chosen, leaving little room for proactive impact avoidance. We present a globally scalable method using existing weather radar infrastructure and open data to map nocturnal bird migration at high spatial resolution across large areas, demonstrating its application in the Netherlands. We find that around 50% of migration occurs at altitudes of EI. Contrary to assumed homogeneous distributions or discrete routes, our maps reveal that migration intensity varies over distances relevant for spatial planning. We demonstrate how such maps can inform selection of wind energy sites with considerable variation in migration and wind potential. Our method facilitates early-stage impact avoidance through strategic EI siting, targeted mitigation and faster permitting, offering a low-cost, scalable path toward a more nature-inclusive energy transition.

Future wireless communications will rely on multiple-input multiple-output (MIMO) beamforming operating at millimeter wave (mmWave) frequency bands to deliver high data rates. To support flexible spatial processing and meet the demands of latency-critical applications, it is essential to use fully digital mmWave MIMO beamforming, which relies on accurate channel estimation. However, ensuring power efficiency in fully digital mmWave MIMO systems requires the use of low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). The reduced resolution of these quantizers introduces distortion in both transmitted and received signals, ultimately degrading system performance. In this paper, we investigate the channel estimation performance of mmWave MIMO systems employing fully digital beamforming with low-resolution quantization, under practical system constraints. We evaluate the system performance in terms of spectral efficiency (SE) and energy efficiency (EE). Simulation results demonstrate that a moderate quantization resolutions of 4-bit per DAC/ADC offers a favorable trade-off between energy consumption and achievable data rate.

Within the digital pulse of modern industry, digital strategic orientation acts as an architect of the future, laying the foundation for technological adaptability and data mastery while constructing a pathway to sustainability that transcends conventional business norms. This study aimed to uncover the extent to which digital strategic orientation shapes sustainability performance. Anchored in the resource-based view theory, this study employs a thoroughly structured methodology, utilizing multiple regression analysis to derive insights from data gathered across 115 enterprises operating within the business landscape of Bosnia and Herzegovina. The research findings underscore a strong, positive impact of digital strategic orientation on sustainable performance, manifesting across its three critical dimensions—financial outcomes, social responsibility, and environmental performance. This study provides a novel contribution to the literature and offers tangible managerial insights by empirically showcasing how digital strategic orientation serves as a catalyst for enhancing sustainable performance. More specifically, the practical implications indicate that firms actively integrating digital strategic orientation into their operations can enhance efficiency, minimize resource waste, and improve financial stability, while simultaneously strengthening stakeholder trust and ensuring regulatory compliance, ultimately positioning themselves for long-term sustainable growth in an increasingly digital and environmentally conscious market.

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).

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 $\mu \to e$ experiments will probe most of the region with $\Lambda \lesssim 10~\text{TeV}$. Among possible realizations, models deconstructing $\mathrm{SU}(2)_\mathrm{L} \times \mathrm{U}(1)_\mathrm{B-L}$ or $\mathrm{SU}(2)_\mathrm{L} \times \mathrm{U}(1)_\mathrm{R} \times \mathrm{U}(1)_\mathrm{B-L}$ 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.

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.