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.

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.

Unmanned aircraft are increasingly recognized for their potential to enhance healthcare logistics, offering rapid and reliable transport solutions. Among the many envisioned use cases, emergency medical deliveries stand out as particularly promising due to their immediate societal value. This study investigates the potential of drones operating under U-space to support hospital-to-hospital emergency deliveries in Madrid. Using the GEMMA tool, we modeled and simulated operations with two drone types along direct routes between four hospitals, resulting in six hospital pairs. Drone travel times were estimated and compared against road transport times obtained from the Google Routes API, incorporating one week of traffic data to capture daily and weekend variability. The results show substantial advantages of aerial transport, with time savings ranging from 2 to 26 min, equivalent to 35–58% compared to road transport. Drones consistently ensured deliveries within 15 min, outperforming regular cars (39%) and ambulances or motorcycles in highly congested periods. Sensitivity analysis confirms their reliability in scenarios with strict time constraints, especially under 15 min. These findings demonstrate that drones reduce travel times and improve predictability, providing a robust evidence base for policymakers and regulators to advance U-space integration in healthcare logistics.

Deformable medical image registration is a fundamental task in medical image analysis. While deep learning-based methods have demonstrated superior accuracy and computational efficiency compared to traditional techniques, 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. Our approach integrates strain and shear energy terms, which are adaptively modulated to balance stability and flexibility. To ensure physically realistic transformations, DARE includes a folding-prevention mechanism that penalizes regions with negative deformation Jacobian. This strategy mitigates non-physical artifacts such as folding, avoids over-smoothing, and improves both registration accuracy and anatomical plausibility

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.

Functional Safety system (software & hardware) development is typically a V-Model process, which is governed by strenuous regulations & norms. This, along with use case specificity, and the scrupulous nature of functional safety creates various bottlenecks across the V-Model, i.e., redundant aspects of functional safety system development. To alleviate these bottlenecks, we introduce two LLM assistants designed to support key V-Model phases. The first assistant, the Digital Safety Assistant (DSA), provides safety engineers with general knowledge of functional safety norms through Retrieval Augmented Generation, thus decreasing norm and application domain adaptation overhead. We benchmark various models and assess the DSA using an official functional safety Certification exam, where the DSA achieves up to 70%, surpassing typical performance levels. A second assistant, the Automated Testing Assistant, developed through Parameter Efficient Fine-tuning to support the V-Model verification phase, is capable of correctly generating and debugging PLC test code with 93% correctness.

The β-catenin destruction complex (BDC) is a central node in WNT/β-catenin signaling, governing embryonic development and adult tissue homeostasis. Although recognized as a prime therapeutic target in colorectal cancer (CRC) for three decades, its dynamic architecture and biochemical complexity have hindered mechanistic understanding. Here, we systematically mapped the sequence-function landscape of the BDC using tiled base editor screens across four endogenous components—CTNNB1, AXIN1, APC, and GSK3B. Validation studies identified ∼150 previously unreported mutations across these genes that affected WNT/β-catenin signaling. In addition to known cancer-associated mutations, we discovered rare gain-of-function and separation-of-function alleles of AXIN1 and CTNNB1 that provide mechanistic insights into complex assembly and regulation. We describe a region in β-catenin that regulates its binding to TCF/LEF transcription factors and demonstrate that the AXIN1–β-catenin interface is critical for controlling signaling flux through the oncogenic BDC. Mechanistic studies revealed that assembly of the oncogenic BDC is scaffolded by its own substrate β-catenin, establishing an autoregulatory mechanism that represents an unexploited vulnerability in cancers harboring common APC truncations. Our comprehensive mutational resource provides a foundation for understanding WNT/β-catenin signaling mechanisms in health and disease, while revealing strategies for therapeutic intervention in WNT-driven cancers.

We consider a large-scale data center where a fleet of heterogeneous mobile robots and human workers collaborate to handle various installation and maintenance tasks. We focus on the underlying multi-agent task assignment problem which is crucial to optimize the overall system. We formalize the problem as a Markov Decision Process and propose an end-to-end learning approach to solve it. We demonstrate the effectiveness of our approach in simulation with realistic data and in the presence of uncertainty.

ObjectiveTo evaluate the predictive value of LA strain parameters and LASI for AF recurrence following electrical CV, and to compare them to conventional echocardiographic, biochemical, and clinical markers.MethodsIn this prospective, observational pilot study, 31 patients with persistent AF underwent electrical CV and were followed for six months. Echocardiographic evaluation included LA reservoir, conduit, and contractile strain, left atrial stiffness index, left atrial volume index (LAVI), left atrial appendage (LAA) morphology, left ventricular ejection fraction (LVEF), right atrial (RA) area, and right ventricular systolic pressure (RVSP). AF recurrence was assessed at three and six months.ResultsAt three months post-CV, LA reservoir, conduit, and contractile strain values were significantly negatively associated with AF recurrence (p < 0.001), while LASI and E/E' ratios were positively associated (p < 0.001). At six months, only contractile strain retained prognostic significance (p = 0.008). LVEF showed a positive correlation with recurrence at six months (p = 0.003), potentially reflecting the role of diastolic dysfunction.ConclusionLA strain parameters and LASI are valuable tools for predicting AF recurrence after CV, particularly in the early post-procedural period. Contractile strain may serve as a more reliable long-term predictor, emphasizing the importance of longitudinal atrial function assessment in rhythm outcome prediction. However, given the small sample size and single-center design, these results should be considered hypothesis-generating, requiring validation in larger studies.

Aim: This manuscript summarizes the key scientific and practical outcomes of the #DHPSP2024 digital networking event, focusing on emerging trends in digital health technologies, innovations in patient safety, and their implications for improving healthcare delivery. Methods: The #DHPSP2024 event was held from June 18 to 20, 2024, on X (formerly Twitter) and LinkedIn, connecting professionals and stakeholders in digital health and patient safety from different sectors. Data from posts on X and LinkedIn were analyzed for geographical distribution, engagement metrics (impressions, likes, shares), top hashtags, and frequently used terms. A qualitative analysis of the central themes and key online messaging discussions of the network event was also conducted. Results: On X, 2,329 posts by 179 participants from 38 countries generated over 231,000 impressions, with the most activity in Austria, China, and India. LinkedIn engagement included 3,475 likes, 217 comments, and 2,030 shares. Both platforms highlighted core themes such as digital health, patient safety, treatment quality, research on natural compounds, and interdisciplinary collaboration. Online messaging discussions emphasized technologies like telemedicine and artificial intelligence as critical tools for enhancing care delivery and patient safety. Participants also promoted special issues of scientific journals and explored collaborative research opportunities. Conclusions: The #DHPSP2024 event underscored the pivotal role of digital technologies in transforming healthcare, particularly in improving the quality and safety of interventions. The findings demonstrate how digital networking events, grounded in open innovation, foster global research communities, accelerate knowledge exchange, and support the integration of clinically relevant digital solutions. The strong engagement reflects growing interest in leveraging digital platforms to advance health outcomes and professional development. Overall, the event contributed to greater visibility of ongoing research, encouraged interdisciplinary cooperation, and may positively influence both the adoption of innovations in healthcare practice and the dissemination of scientific knowledge.