The rapid advancement of high-throughput technologies has led to the generation of vast amounts of omics data, including genomics, epigenomics, and metabolomics. Integrating these diverse datasets has become essential for gaining comprehensive insights into complex biological systems and enhancing personalized healthcare solutions. This critical review examines the current state of multi-omics data integration platforms, highlighting both the strengths and limitations of existing tools. By evaluating the latest digital platforms, such as GraphOmics, OmicsAnalyst, and others, the paper explores how they support seamless integration and analysis of omics data in healthcare applications. Special attention is given to their role in clinical decision-making, disease prediction, and personalized medicine, with a focus on their interoperability, scalability, and usability. The review also discusses the challenges these platforms face, such as data complexity, standardization issues, and the need for improved machine learning and AI-based analytics. Finally, the paper proposes directions for future research and development, emphasizing the importance of more advanced, user-friendly, and secure platforms that can better serve comprehensive healthcare needs.

During the last few years, there has been a growing interest in students getting engaged in digital game-making activities so as to enhance their thinking skills. The findings of studies that have examined the impact of such initiatives are quite positive, especially concerning the promotion of 21st century skills; however, many students seem to face difficulties in getting a deeper understanding of the game development life cycle. Additionally, students often have difficulties in meaningfully reusing and applying the concepts from various subjects, mainly mathematics and physics, into their game-making tasks or in understanding advanced programming commands while creating their games. The present study presents an innovative game-making teaching approach that suggests a series of steps for the systematic design and rapid development of motion-based touchless games, i.e., games that are based on natural user interaction technologies, like the Microsoft Kinect camera. Findings from evaluation studies in two (2) secondary schools indicate that this approach can increase student motivation, strengthen their computational thinking, enhance their understanding of geometric principles and improve their social skills.

Abdominal aortic aneurysm (AAA) is a complex vascular condition associated with high mortality rates. Accurate abdominal aorta segmentation is essential in medical imaging, facilitating diagnosis and treatment for a range of cardiovascular diseases. In this regard, deep learning-based automated segmentation has shown significant promise in the precise delineation of the aorta. However, comparisons across different models remain limited, with most studies performing algorithmic training and testing on the same dataset. Furthermore, due to the variability in AAA presentation, using healthy controls for deep learning AAA segmentation poses a significant challenge. This study provides a detailed comparative analysis of four deep learning architectures—UNet, SegResNet, UNet Transformers (UNETR), and Shifted-Windows UNet Transformers (SwinUNETR)—for full abdominal aorta segmentation. The models were evaluated both qualitatively and quantitatively using private and public 3D (Computed Tomography) CT datasets. Moreover, they were successful in attaining high performance in delineating AAA aorta, while being trained on healthy aortic imaging data. Our findings indicate that the UNet architecture achieved the highest segmentation accuracy among the models tested.

Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by continuous inflammation of the colon and rectum. Accurate disease assessment is essential for effective treatment, with endoscopic evaluation, particularly the Mayo Endoscopic Score (MES), serving as a key diagnostic tool. However, MES measurement can be subjective and inconsistent, leading to variability in treatment decisions. Deep learning approaches have shown promise in providing more objective and standardized assessments of UC severity. Methods: This study utilized publicly available endoscopic images of UC patients to analyze and compare the performance of state-of-the-art deep neural networks for automated MES classification. Several state-of-the-art architectures were tested to determine the most effective model for grading disease severity. The F1 score, accuracy, recall, and precision were calculated for all models, and statistical analysis was conducted to verify statistically significant differences between the networks. Results: VGG19 was found to be the best-performing network, achieving a QWK score of 0.876 and a macro-averaged F1 score of 0.7528 across all classes. However, the performance differences among the top-performing models were very small suggesting that selection should depend on specific deployment requirements. Conclusions: This study demonstrates that multiple state-of-the-art deep neural network architectures could automate UC severity classification. Simpler architectures were found to achieve competitive results with larger models, challenging the assumption that larger networks necessarily provide better clinical outcomes.

Background: Healthcare robotics needs context-aware policy-compliant reasoning to achieve safe human–agent collaboration. The current ontologies fail to provide healthcare-relevant information and flexible semantic enforcement systems. Methods: HERON represents a modular upper ontology which enables healthcare robotic systems to communicate and collaborate while ensuring safety during operations. The system enables domain-specific instantiations through SPARQL queries and SHACL-based constraint validation to perform context-driven logic. The system models robotic task interactions through simulated eldercare and diagnostic and surgical support scenarios which follow ethical and regulatory standards. Results: The validation tests demonstrated HERON’s capacity to enable safe and explainable autonomous operations in changing environments. The semantic constraints enforced proper eligibility for roles and privacy conditions and policy override functionality during agent task execution. The HERON system demonstrated compatibility with healthcare IT systems and demonstrated adaptability to the GDPR and other policy frameworks. Conclusions: The semantically rich framework of HERON establishes an interoperable foundation for healthcare robotics. The system architecture maintains an open design which enables HL7/FHIR standard integration and robotic middleware compatibility. HERON demonstrates superior healthcare-specific capabilities through its evaluation against SUMO HL7 and MIMO. The future research will focus on optimizing HERON for low-resource clinical environments while extending its applications to remote care emergency triage and adaptive human–robot collaboration.

In the era of networked learning, the “network” component plays an important role in the formation of learning communities – the networked learning communities (NLC) – since it promotes and facilitates collaborative and co-operative connections: between one learner and other learners; between learners and tutors; between learners and learning resources, so that learners and tutors can extend and develop their understanding and capabilities in ways that are important to them, and over which they have significant control. In a NLC, where individual and collective actions take place, educators face great difficulties in evaluating the broad spectrum of interactions among the interacted participants (student-student, student-educator, student-learning resources) during learning processes. It becomes difficult and time consuming for educators to thoroughly capture, track and assess the various interactive learning activities performed by all learners. The need for designing specific tools, which will be based on well-grounded conceptual frameworks, for analysing the grid of all these interactions is emerged as the issue of evaluation tends to be multifaceted and complex for educators. Our paper proposes such a framework and identifies specific indicators that can help educators’ analyse and evaluate multiple dimensions of interactivity that is developed in a NLC. In this paper, we also present the Analyticstool, a system for the automatic analysis and visualization of data collected during the networked collaborative learning process. This tool is expected to be useful for educators who need to assess the individuals’ contributions in a NLC. AnalyticsT??l is an interaction analysis tool that automatically gathers, analyzes and visualizes data related to participants’ asynchronous interaction in a NLC. The innovative feature of AnalyticsTool is that it can be used to analyse data of asynchronous networked learning interactions that occurred within the Moodle learning management system (LMS) which is the most popular open source LMS. A teacher, using Analyticstool, can import data from the various fora that had been used for supporting the learners’ tasks within a course into the tool, which will, then, be organized and structured in such manner that can be analyzed at various levels. The analyzed data can also be exported in appropriate formats so that it can be used as input to the SPSS statistical package or other tools such as the NetDraw software for further analysis. Another innovative feature of Analyticstool is that it guides the teacher in performing specific analysis of the data collected according to the teaching strategy followed in his/her course. For example, if a teacher uses the Think Pair Share (TPS) learning strategy in a course, AnalyticsTool will automatically create the most appropriate statistical tables and diagrams indicators for this strategy, which will be related to the quantity and quality of participation and collaboration among learners.