The destructive consequences of the COVID-19 pandemic have negatively affected socioeconomic indicators and disrupted macroeconomic stability. The aim of the study is to determine the optimal combination of financial, socioeconomic, and public health determinants based on their relevance for the post-pandemic recovery of macroeconomic stability. For this purpose, principal component analysis was used to form an initial macroeconomic stability index by integrating such indicators as GDP growth, unemployment rate, consumer price index, current account balance, and trade volume. Next, the Granger test and panel data regression modeling was employed to identify the causality between the level of macroeconomic stability and a set of financial, socioeconomic and public health determinants. Finally, the financial, socioeconomic, and public health determinants were ranked according to their impact on macroeconomic stability. The obtained empirical results can be used to improve the financial, economic, and health care state policies in terms of strengthening country resistance to risks caused by a pandemic or other similar threats in the future.

Background/Objectives: Virtual reality (VR) is increasingly used to reduce pain during pediatric needle procedures, but its effectiveness may vary by developmental stage and gender. This study evaluated whether immersive VR reduces venipuncture pain in children and adolescents and examined parent–patient agreement and gender-specific response patterns. Methods: A prospective nonrandomized clinical study was conducted within a hospital-based pediatric venipuncture service using an alternating 1:1 allocation sequence. Participants aged 4–18 years underwent venipuncture with either VR (n = 49) or standard care (n = 29). Procedural pain was measured using the Faces Pain Scale–Revised (FPS-R) with independent parent ratings. Analysis of covariance (ANCOVA) compared post-procedural FPS-R scores while adjusting for baseline pain. Exploratory age and gender-specific analyses were also performed. Results: VR led to a clear reduction in pain for children, even after adjusting for baseline scores (3.55 vs. 4.73; p = 0.003). Adolescents, however, reported similarly low pain in both groups (2.81 vs. 2.79; p = 0.60), and several mentioned that the PEGI 3 content felt too young for them, which likely limited how engaged they were. Among children, girls showed the most noticeable drop in pain, which matches the subgroup’s adjusted significance (p = 0.011). Parent–patient agreement was stronger in children (r ≈ 0.7–0.8) than in adolescents (r ≈ 0.4–0.5), and VR did not change this pattern. Most participants said they would choose VR again for future procedures. Conclusions: Immersive VR helped reduce venipuncture pain in children but had little effect in adolescents, underscoring the need for age-appropriate or more interactive VR content for older patients. Overall, these findings support using VR selectively as a distraction tool that fits the developmental needs of pediatric groups.

Lung cancer, a leading cause of cancer-related mortality worldwide, often escapes early detection due to the absence of distinct symptoms in its initial stages. This work investigates how Machine Learning (ML) might improve early diagnosis by analyzing Electronic Health Records (EHR) data. Multiple ML models were developed and evaluated on a synthetic dataset created to replicate real-world patient characteristics, allowing controlled experimentation while safeguarding privacy. Model performance was tuned using both conventional optimization methods and nature-inspired approaches, with the aim of balancing predictive accuracy and computational efficiency. In our synthetic dataset experiments, ensemble learners optimized with metaheuristic techniques reached accuracy levels approaching 99 percent while maintaining computational efficiency and generally outperformed simpler baselines. The contribution of this work lies in exploring the integration of GFO and WOA for feature selection and hyperparameter tuning of XGBoost, together with a soft-voting ensemble. This approach provides an experimental pathway for enhancing predictive performance under computational constraints. However, as the dataset is synthetic, the conclusion remains experimental; validation against clinical records will be essential before translation into practice.

In a previous study, it was concluded that the da Vinci robotic systems installed in the Hungarian healthcare system were, with one privately funded exception, fully financed by EU funds. The present article drives a similar conclusion on a global scale. Massive public investments, or reimbursements, are needed to ensure the prevalence and financial sustainability of robotic surgical systems. The financial sustainability of RAS depends on the economic development of a country and its healthcare system, the number of medical schools, the history of robotic surgery and the funding mechanisms of the healthcare system. Even in the most advanced economies of the world, access to the benefits of RAS may be burdened by limitations in terms of financial (funding) and geographical access.