Children's Specialized Hospital

Intense physical exercise, especially when performed frequently or for prolonged durations, can disturb iron balance through a combination of increased demand, increased loss, and impaired absorption. The strongest evidence comes from endurance sport, military training, and periods of intensified loading, where iron deficiency without anemia and overt iron-deficiency anemia are both common clinical problems. Mechanisms include exercise-induced inflammation and hepcidin upregulation, intravascular hemolysis, gastrointestinal microischemia with occult blood loss, hematuria, sweat losses, menstrual blood loss, low energy availability, and the amplified iron demand of altitude-related erythropoiesis. Importantly, the hematologic response to training also includes plasma volume expansion, which can mimic anemia without true iron deficiency and complicate interpretation. In athletes, clinically meaningful iron deficiency may present before hemoglobin falls, with fatigue, impaired recovery, lower maximal oxygen uptake, diminished training quality, and reduced work efficiency. Diagnosis therefore requires more than hemoglobin alone and should integrate ferritin, transferrin saturation, inflammatory context, and where indicated soluble transferrin receptor or reticulocyte indices. Management begins with confirmation of true deficiency and identification of the mechanism, followed by dietary optimization, oral iron when indicated, and selective use of parenteral iron in carefully chosen cases. This review summarizes the physiology linking exercise to iron homeostasis, identifies athlete groups at highest risk, distinguishes pseudoanemia from true deficiency, and provides a practical framework for diagnosis, prevention, and treatment.

The blending process in raw material yards is essential for maintaining precise material proportions in downstream production, directly influencing product quality and energy efficiency in industries such as steel and coal processing. However, stringent operational constraints, including silo capacity limits, discharge rates, equipment movement delays, and a strict no-empty-silo requirement, result in a strongly coupled, high-dimensional combinatorial scheduling problem. In this paper, we develop a mixed-integer nonlinear programming (MINLP) model to capture the complex dynamics of silo weight and equipment operations. The primary scientific contribution of this work lies in the theoretical discovery of a structural decoupling property within the complex MINLP. We analytically prove that by fixing the replenishment sequence, the intractable global problem can be rigorously decomposed into two subproblems: a linear programming (LP) problem for silo-filling cart scheduling and a shortest-path problem solvable via dynamic programming (DP) for reclaimer scheduling. Leveraging this decomposition, a two-stage metaheuristic algorithm is proposed, combining greedy initialization with multi-round simulated annealing enhanced by local search. Experimental validation using real industrial data demonstrates that the proposed method consistently outperforms the greedy algorithm. Crucially, while the commercial solver Gurobi struggles to converge within a practical 1800 s time limit, our approach yields comparable solution quality in mere seconds. Furthermore, robustness analysis under a 20% demand surge confirms the algorithm’s adaptive capability, maintaining the silo weight stability through re-optimization. This research provides a robust, computationally efficient solution for the blending process in raw material yards.

The digital age has significantly transformed the landscape of entrepreneurship, creating new opportunities and challenges for university students. The concept of an entrepreneurial mindset has gained considerable importance as it equips students with the ability to identify opportunities, take calculated risks, innovate, and adapt to dynamic technological environments. This study examines the development of entrepreneurial mindset among university students in the context of rapid digital transformation. It focuses on factors such as digital literacy, innovation orientation, risk-taking ability, and opportunity recognition that influence students’ entrepreneurial intentions. The research also highlights the role of universities in fostering entrepreneurial skills through curriculum design, incubation centers, digital platforms, and experiential learning. In addition, the study evaluates how exposure to digital tools, online markets, and startup ecosystems encourages students to pursue entrepreneurial ventures. The findings suggest that developing an entrepreneurial mindset among university students is essential for promoting innovation, employment generation, and sustainable economic growth in the digital economy

Pediatric cholecystitis and cholelithiasis management is heterogeneous. We surveyed European centers to map current practices, training exposure, and outcomes of pediatric biliary cholecystectomy. A 24-item cross-sectional international survey was developed by the European Union of Medical Specialists (UEMS) Section of Paediatric Surgery and distributed to centers in 31 UEMS member states. Items covered institutional resources, indications and timing, surgical approach and adjuncts (ERCP, ICG), training exposure, and center-level outcomes; results are reported as n (%), median (IQR). Outcomes were reported at the center level and were self-reported by participating institutions. Thirty-two centers from 23/31 states responded (74.2%). Pediatric surgeons were primary operators in 84% (shared with adult surgeons in 16%); ERCP access was 66%. Trainee operator share was 22.5% (IQR 5-50) and simulator access 56%. ICG cholangiography was routine in 12.5% and selective in 31%. Acute calculous cholecystitis: 6% always index-admission and 59.4% interval (29-41 days) cholecystectomy; post-ERCP choledocholithiasis: 16% always index-admission cholecystectomy. In 2023, 185 cases were reported: 98.9% laparoscopic with 1.6% conversion; median age 14 years (IQR 12.25-15), operative time 90min (IQR 60-110), length of stay 2 days (IQR 1-2); 10 complications (5.4%). Substantial heterogeneity persists in both care pathways and training exposure; most centers lack formal pediatric-specific guidelines, and trainee-led operating remains limited, supporting the need for evidence-based protocols and structured training pathways.

Abstract Purpose To evaluate the reproducibility and consistency of the Radiographic Union Score for Tibial fractures (RUST) in diaphyseal fractures treated with intramedullary nailing among orthopedic residents with different levels of experience, and to compare their performance with artificial intelligence (AI) analysis. Methods Radiographs from 47 patients were assessed by four orthopedic residents (R1–R4) at two independent time points using the RUST scale. The same images were evaluated by AI. Intra- and interobserver agreement were calculated using the Intraclass Correlation Coefficient (ICC). Differences between human and AI evaluations were analyzed using Wilcoxon and Friedman tests with a 5% significance level. Results The sample consisted predominantly of men (71.7%) with a mean age of 32.9 years. AI classified 61.7% of fractures as consolidated (score ≥ 7). Absolute agreement between residents and AI was 17%, with residents overestimating scores in 45.2% and underestimating in 37.8% of cases. Observer R3 showed the best agreement with AI (ICC = 0.58), while R2 demonstrated a significant difference (p = 0.009). Interobserver reproducibility among residents was excellent (ICC = 0.93; 95% CI 0.84–0.96) but decreased to 0.61 when AI was included. Intraobserver consistency was very good (global ICC = 0.72; 95% CI 0.63–0.78), with R4 presenting the highest stability (ICC = 0.78). Conclusion The RUST scale showed high reproducibility among residents but limited agreement with AI, suggesting the need for additional training to improve consistency between human interpretation and algorithmic assessment.

Background and objective: The rapid expansion of artificial intelligence (AI) in healthcare has resulted in substantial advances in diagnostics, prognostics, clinical decision support systems, and patient monitoring. Despite promising performance, many AI-based systems remain insufficiently understood by clinicians and patients due to their “black-box” nature. This lack of transparency may undermine trust, hinder acceptance, and limit safe integration into routine clinical practice. Explainable artificial intelligence (XAI) has emerged as a response to these challenges by enabling human-interpretable explanations of algorithmic decisions. The objective of this narrative review is to synthesize current evidence on XAI in healthcare, with particular emphasis on its technical foundations, clinical applications, influence on trust and decision-making, and broader social and ethical implications. Scope of review: This review synthesizes literature published between 2019 and 2025 addressing explainability in medical AI. The analysis includes methodological studies, clinical evaluations, human–computer interaction research, and social science investigations related to transparency, trust, accountability, and acceptance of AI systems. Relevant publications were identified through structured searches of PubMed, MEDLINE, Scopus, and Google Scholar using keywords related to explainable AI, interpretability, ethics, trust, and clinical decision support. Findings: XAI methods—including feature attribution, model simplification, counterfactual explanations, and visualization techniques—demonstrate potential to enhance clinician understanding of AI outputs and increase confidence in algorithm-assisted decisions. Evidence suggests that explainability may support diagnostic accuracy, reduce automation bias, and facilitate error detection. However, explainability alone does not ensure trust. Clinical context, user expertise, organizational culture, and regulatory frameworks play critical roles in shaping the adoption and appropriate use of explainable systems. Empirical research addressing patient perspectives remains limited. Conclusions: Explainable AI constitutes an important step toward the responsible and socially acceptable integration of intelligent systems in healthcare. While XAI can enhance transparency and trust, its effectiveness depends on thoughtful design, contextual adaptation to clinical workflows, and alignment with user needs. Further interdisciplinary research is required to standardize explainability approaches, evaluate their real-world impact on clinical outcomes, and address the ethical, legal, and societal challenges associated with medical AI.

The WIL-31 study demonstrated efficacy and safety of prophylaxis with the plasma-derived von Willebrand factor/factor VIII concentrate wilate in von Willebrand disease (VWD) of all types and was the only prospective study with an on-demand run-in study as an intra-individual comparator. This subgroup analysis examines the efficacy of wilate prophylaxis in patients with type 3 VWD in WIL-31. Patients received 20-40 IU/kg wilate prophylaxis 2-3 times weekly for 12 months. Twenty-two patients in WIL-31 had type 3 VWD. Mean total annualized bleeding rate (ABRs) during on-demand versus prophylaxis was 37.1 versus 5.2 (86% reduction). During prophylaxis, 115 bleeds occurred, most (95%) of which were minor; the most common bleeding site was the nose (40% of bleeds). Mean overall spontaneous ABRs during on-demand versus prophylaxis were 26.5 versus 2.8 (89% reduction); mean treated spontaneous ABRs were 20.3 versus 1.4, respectively (93% reduction). ABRs were reduced further during the second 6 months of prophylaxis versus the first 6 months. Results were consistent in subgroups by age. No serious adverse events related to study treatment and no thrombotic events were observed. Prophylaxis with wilate was effective and well tolerated in patients with type 3 VWD, in all age groups. NCT04052698; https://clinicaltrials.gov/study/NCT04052698.

Abstract BACKGROUND Accurate preoperative identification of recto-urinary fistulas in male neonates with high anorectal malformations (ARMs) is essential for surgical planning, especially when considering single-stage repair. The ascending cystourethrogram (ACU) provides a simple, minimally invasive approach that eliminates the need for a preliminary colostomy. METHODS This prospective study included 35 male neonates with high ARM who underwent ACU before definitive repair. The presence and level of recto-urinary fistulas are recorded and compared with intraoperative findings. When imaging did not demonstrate the fistula, distal colostrography is performed to confirm the diagnosis. RESULTS ACU identified recto-urinary fistulas in 30 of 35 neonates (85.7%; 95% CI: 70.6–94.1%). Detected fistulas included recto-bladder neck (14.3%), recto-prostatic (42.9%), and recto-bulbar (28.6%) types. In five cases (14.3%), a fistula was not visualised on ACU; however, distal colostogram confirmed the diagnosis in four. Relative to intraoperative findings, ACU demonstrated a sensitivity of 85.7% and a positive predictive value of 100%. CONCLUSION ACU is a reliable, minimally invasive, and readily available technique for early localisation of recto-urinary fistulas in male neonates with high ARM, demonstrating close correlation with intraoperative findings. It can be safely performed within the first 24 hours of life, enabling prompt anatomical assessment and supporting the appropriate selection of candidates for single-stage repair. These findings reinforce ACU’s role as a practical first-line diagnostic tool in the management of high anorectal malformations.