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Accurately capturing muscle behavior remains a challenging task in computational biomechanics, primarily due to the nonlinear response, anisotropy, and time-dependent characteristics of muscle tissue. In this context, finite element methods have proven to be a suitable framework for representing such complex mechanical behavior. Among the available constitutive approaches, Hill’s three-element model continues to be widely adopted, largely because it offers a reasonable balance between physiological interpretability and computational efficiency. In this work, a parametric and sensitivity-oriented analysis of the Hill three-element muscle model is performed within a finite element formulation originally proposed by Kojić, Mijailović, and Zdravković (1998) and implemented in the PAK software environment. The analysis considers five key parameters, which are varied independently: the stiffness parameter of the series elastic element (α), the corresponding stress scaling parameter (β), the modulus of the parallel elastic element (E), the activation level (a), and the length ratio constant (k). To enable comparison between parameters of different physical nature, normalized sensitivity indices are used. The results show that the activation parameter a has the strongest influence on active force generation, with an increase of 36.4% at the highest considered activation level. In contrast, parameters α and β primarily affect the behavior of the series elastic component, with variations on the order of ±15–18%. It can also be observed that the influence of individual parameters depends on the deformation regime. At lower deformation levels, the response is mainly governed by the parameter E, while α and β become more relevant in the intermediate nonlinear range. At higher deformation levels, the activation parameter a becomes dominant. From a modeling perspective, these findings suggest a structured approach to parameter calibration in Hill-type finite element models. In addition, they provide further insight into the sensitivity characteristics of such formulations within computational biomechanics.

The treatment of moderate-to-severe ulcerative colitis often requires escalation from 5-ASA therapy to immunosuppressants or biologic agents. However, the effects of azathioprine and anti-TNF therapies on cardiac status and associated biomolecules remain insufficiently studied. In this prospective observational study, we included 84 patients with moderate-to-severe ulcerative colitis, divided into three treatment groups based on the therapy received (azathioprine, infliximab, or adalimumab), along with 25 healthy controls. Levels of proBNP, NT-proBNP, creatine kinase (CK), CK-MB, and high-sensitivity troponin I (hsTnI) were measured at baseline, 6 h after treatment initiation, and after 3 months of therapy. Troponin levels did not significantly change across the three time points in any treatment group. In contrast, levels of proBNP, NT-proBNP, CK, and CK-MB significantly decreased after 3 months in patients treated with biologics, whereas a significant increase was observed in the azathioprine group. Further prospective trials are needed to adequately assess the cardiac safety of these therapies, particularly in patients with preexisting cardiac impairment.

Background and Objectives: Diabetes is one of the most common chronic non-communicable diseases and represents a major public health problem. At the global level, the epidemic character of diabetes mellitus can be attributed to an extended life expectancy but also to lifestyle. The aim of this study was to examine the sociodemographic, health, and lifestyle characteristics of adults with type 2 diabetes mellitus in Serbia. Materials and Methods: The research is part of the Serbian Population Health Survey conducted in the period from October to December 2019 by the Republic Statistical Office, in cooperation with the Institute of Public Health of Serbia “Dr Milan Jovanović Batut” and the Ministry of Health of the Republic of Serbia. The research instrument was standardized questionnaires constructed in accordance with the European Health Interview Survey (EHIS—European Health Interview Survey, wave 3) questionnaire, which were adapted to the specifics of our area. The research was conducted as a cross-sectional study on a representative sample of the adult population of Serbia. Results: Among 1138 adults with type 2 diabetes in Serbia (52.8% female; mean age 66.0 ± 11.9 years), overweight and obesity were highly prevalent (40.1% and 34.4%, respectively), with Obesity I predominating. Significant gender differences were observed: female more often reported obesity, multimorbidity, and depressive symptoms, whereas men were more physically active and more frequently overweight. Most participants were physically inactive, consumed breakfast and bread daily, and had low engagement in cycling and sports. Alcohol consumption was significantly higher in men, while dietary habits differed by gender for bread intake. These findings highlight substantial gender- and lifestyle-related disparities among adults with type 2 diabetes in Serbia. Conclusions: Targeted interventions promoting healthy lifestyle, physical activity, psychosocial support, and chronic disease management are urgently needed to address gender- and lifestyle-related disparities in adults with type 2 diabetes in Serbia.

Background and Objectives: Urban–rural health disparities reflect differences in health outcomes, healthcare access, and socio-economic conditions between populations. In Serbia, limited research has quantified how socio-demographic and socio-economic characteristics influence settlement type and healthcare utilization. The aim of this study was to examine the relationship between settlement type and socio-demographic/socio-economic factors, and to assess whether these differences are reflected in patterns of healthcare utilization. Materials and Methods: Data were drawn from the 2019 Serbian National Health Survey, a nationally representative, stratified, two-stage random sample including 12,439 adults aged ≥20 years. Settlement type (urban vs. rural) was the primary dependent variable. Descriptive statistics, Chi-square and t-tests, and bivariate and multivariate logistic regression models were used to assess associations. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated, with significance set at p < 0.05. Results: Urban residence was more likely among unmarried individuals, those living in Šumadija/Central Serbia, and those with higher education. Primary or lower education reduced the odds of urban residence, and middle-income groups were less likely to live in urban areas compared to the richest. Settlement type was not significantly associated with hospital or day hospital use. However, rural residents had lower use of prescribed medicines, higher use of non-prescribed medicines, and more frequent physiotherapy visits. Private practice use was over twice as likely in urban settlements. Conclusions: To address urban–rural healthcare disparities in Serbia, targeted strategies could include enhancing health literacy in rural areas, incentivizing physicians to work in underserved regions, expanding telemedicine and mobile health services, improving access to prescribed medications, and strengthening public–private healthcare integration to ensure equitable access across all settlement types.

Three-dimensional (3D) bioprinting has reached a complexity limit where empirical, parameter-by-parameter optimization no longer scales. The dominant mode of artificial intelligence (AI) integration remains AI-augmented, where AI is treated as an analytical addition to a conventional pipeline. We argue that the field is approaching a discontinuous transition towards AI-native bioprinting, in which AI represents the operational layer of system intelligence, not an ancillary tool. A systematic analysis of 365 publications on the intersection of bioprinting and AI (2015–2026), performed through 18 queries organized by the four search axes of the PubMed database, shows that the intersection grew 136 times during the decade, with an acceleration of 3.16 times only between 2024 and 2025. Mapping the publications to the six functional domains reveals a marked asymmetry: clinical translation counts 154 papers, while cell viability prediction—the biological foundation that every closed-loop system requires—counts only three. We define AI-native bioprinting as a system architecture that combines continuous learning, multi-modal sensing fused through visual, mechanical and biological signals, and biologically closed control loops. We present a conceptual shift from printing accuracy to biological intelligence as a success criterion. The transition requires open datasets, consensus biological metrics, inter-laboratory validation, and early regulatory engagement.