Search Results (78)

Cardiovascular disease (CVD) remains Europe’s leading cause of mortality, responsible for >45% of deaths. Beyond established risk factors (hypertension, diabetes, dyslipidaemia, smoking, obesity), psychosocial elements—depression, anxiety, financial stress, personality traits, and trauma—significantly influence CVD development and progression. Behavioural Cardiology addresses this connection by systematically incorporating psychosocial factors into prevention and rehabilitation protocols. This review examines the HEARTBEAT model, developed by Greece’s first Behavioural Cardiology Unit, which aligns with current European guidelines. The model serves dual purposes: primary prevention (targeting at-risk individuals) and secondary prevention (treating established CVD patients). It is a personalised medicine approach that integrates psychosocial profiling with traditional risk assessment, utilising tailored evaluation tools, caregiver input, and multidisciplinary collaboration to address personality traits, emotional states, socioeconomic circumstances, and cultural contexts. The model emphasises three critical implementation aspects: (1) digital health integration, (2) cost-effectiveness analysis, and (3) healthcare system adaptability. Compared to international approaches, it highlights research gaps in psychosocial interventions and advocates for culturally sensitive adaptations, particularly in resource-limited settings. Special consideration is given to older populations requiring tailored care strategies. Ultimately, Behavioural Cardiology represents a transformative systems-based approach bridging psychology, lifestyle medicine, and cardiovascular treatment. This integration may prove pivotal for optimising chronic disease management through personalised interventions that address both biological and psychosocial determinants of cardiovascular health. Full article

The present study explores the use of Instagram as an innovative strategy in the teaching–learning process in the context of animal reproduction topics. In the current era, with digital technology and social media transforming how information is accessed and consumed, it is essential for teachers to adapt and harness the potential of these tools for educational purposes. This article delves into the need for teachers to stay updated with current trends and the importance of promoting digital competences among teachers. This research aims to provide insights into the benefits of integrating social media into the educational landscape. Students of Veterinary Science degrees, Master’s degrees in Equine Sport Medicine as well as vocational education and training (VET) were involved in this study. An Instagram account named “UCOREPRO” was created for educational use, and it was openly available to all users. Instagram usage metrics were consistently tracked. A voluntary survey comprising 35 questions was conducted to collect feedback regarding the educational use of smartphone technology, social media habits and the UCOREPRO Instagram account. The integration of Instagram as an educational tool was positively received by veterinary students. Survey data revealed that 92.3% of respondents found the content engaging, with 79.5% reporting improved understanding of the subject and 71.8% acquiring new knowledge. Students suggested improvements such as more frequent posting and inclusion of academic incentives. Concerns about privacy and digital distraction were present but did not outweigh the perceived benefits. The use of short videos and microlearning strategies proved particularly effective in capturing students’ attention. Overall, Instagram was found to be a promising platform to enhance motivation, engagement, and informal learning in veterinary education, provided that thoughtful integration and clear educational objectives are maintained. In general, students expressed positive opinions about the initiative, and suggested some ways in which it could be improved as an educational tool. Full article

Opioid use disorder (OUD) is a chronic disease that remains difficult to treat, even with significant improvements in available medications. While current treatments work well for some, they often do not account for the unique needs of individual patients, leading to less-than-ideal results. Precision medicine offers a new path forward by tailoring treatments to fit each person’s genetic, psychological, and social needs. This review takes a close look at medications for OUD, including methadone, buprenorphine, and naltrexone, as well as long-acting options that may improve adherence and convenience. Beyond medications, the review highlights the importance of addressing mental health co-morbidities, trauma histories, and social factors like housing or support systems to create personalized care plans. The review also explores how emerging technologies, including artificial intelligence and digital health tools, can enhance how care is delivered. By identifying research gaps and challenges in implementing precision medicine into practice, this review emphasizes the potential to transform OUD treatment. A more individualized approach could improve outcomes, reduce relapse, and establish a new standard of care focused on recovery and patient well-being. Full article

Barrett’s esophagus (BE), a metaplastic transformation of an esophageal squamous epithelium into an intestinal-type columnar epithelium, is the primary precursor to esophageal adenocarcinoma (EAC). Traditional management strategies have relied heavily on selective screening, tailored surveillance intervals, and early dysplasia detection and treatment algorithms. However, the heterogeneity in progression risk among BE patients necessitates a more nuanced, personalized approach involving precision care, tailoring decisions to individual patient characteristics, promises to enhance outcomes in BE through more targeted screening, personalized surveillance intervals, and risk-based therapeutic strategies. This review explores the current landscape and emerging trends in precision medicine for Barrett’s esophagus, highlighting genomic markers, digital pathology, and AI-driven models as tools to transform how we approach this complex disease and prevent progression to EAC. Full article

Precision medicine transforms healthcare by tailoring prevention, diagnosis, and treatment strategies to individual characteristics such as genetics, molecular profiles, environmental factors, and lifestyle. This approach has shown promise in improving treatment efficacy, minimizing adverse effects, and enhancing disease prevention across various conditions, including age-related illnesses, cancer, type 2 diabetes, cardiovascular disease, and rare genetic disorders. However, major challenges remain that limit the potential of precision medicine. A key limitation is the underrepresentation of diverse populations in genetic research, leading to disparities in treatment outcomes and the potential misinterpretation of genetic risks. Current clinical reference intervals often fail to reflect the biological changes associated with aging, increasing the risk of misdiagnosis or inappropriate treatment in older adults. Our model calls for a broader, more inclusive framework, one that incorporates not only individual variability but also population-level factors such as aging and genetic diversity. Emerging technologies in artificial intelligence (AI), digital health, and multi-omics can help support this expanded approach. Precision medicine must include underrepresented populations in research, develop age-specific clinical guidelines, and address socioeconomic barriers. Here, we provide a brief introduction to our model. By integrating aging and genetics, precision medicine can evolve into a truly global approach—one that promotes health equity, respects biological diversity, and improves outcomes for all populations. Full article

Anatomy education has long served as a cornerstone of medical training, equipping healthcare professionals with the foundational knowledge necessary for clinical practice. However, the discipline has undergone significant transformations in response to evolving curricula, ethical considerations, and technological advancements. This paper explores the historical development, current state, and future trajectory of anatomy education, focusing on challenges such as ethical concerns regarding cadaveric dissection, and the need for cost-effective alternatives. The study examines innovative teaching methods, including virtual reality, augmented reality and artificial intelligence, which enhance anatomical learning by providing interactive, scalable educational experiences. Additionally, it discusses the integration of anatomy with clinical practice through imaging technologies, competency-based education, and evidence-based approaches. While modern innovations offer valuable learning tools, they cannot entirely replace the hands-on experience and professional identity formation fostered by cadaveric dissection. A balanced approach that combines traditional methodologies with digital advancements is essential for optimizing anatomy education. By leveraging both physical and virtual resources, educators can enhance anatomical comprehension, improve clinical preparedness, and ensure that future healthcare professionals develop both technical expertise and ethical awareness. This paper underscores the need for continued adaptation in anatomy education to align with the demands of modern medicine while preserving its core educational values. Full article

Background/Objectives: Integrating Artificial Intelligence Clinical Decision Support Systems (AI-CDSSs) into healthcare can improve diagnostic accuracy, optimize clinical workflows, and support evidence-based medical decision-making. However, the adoption of AI-CDSSs remains uneven, influenced by technological, organizational, and perceptual factors. This study, conducted between November 2024 and February 2025, analyzes the determinants of AI-CDSS adoption among healthcare professionals through investigating the impacts of perceived benefits, technological costs, and social and institutional influence, as well as the transparency and control of algorithms, using an adapted Path Dependence-Augmented–Unified Theory of Acceptance and Use of Technology model. Methods: This research was conducted through a cross-sectional study, employing a structured questionnaire administered to a sample of 440 healthcare professionals selected using a stratified sampling methodology. Data were collected via specialized platforms and analyzed using structural equation modeling (PLS-SEM) to examine the relationships between variables and the impacts of key factors on the intention to adopt AI-CDSSs. Results: The findings highlight that the perceived benefits of AI-CDSSs are the strongest predictor of intention to adopt AI-CDSSs, while technology effort cost negatively impacts attitudes toward AI-CDSSs. Additionally, social and institutional influence fosters acceptance, whereas perceived control and transparency over AI enhance trust, reinforcing the necessity for explainable and clinician-supervised AI systems. Conclusions: This study confirms that the intention to adopt AI-CDSSs in healthcare depends on the perception of utility, technological accessibility, and system transparency. The creation of interpretable and adaptive AI architectures, along with training programs dedicated to healthcare professionals, represents measures enhancing the degree of acceptance. Full article

Background: Digital twin (DT) technology, integrated with artificial intelligence (AI) and machine learning (ML), holds significant potential to transform oncology care. By creating dynamic virtual replicas of patients, DTs allow clinicians to simulate disease progression and treatment responses, offering a personalized approach to cancer treatment. Aim: This narrative review aimed to synthesize existing review studies on the application of digital twins in oncology, focusing on their potential benefits, challenges, and ethical considerations. Methods: The narrative review of reviews (NRR) followed a structured selection process using a standardized checklist. Searches were conducted in PubMed and Scopus with a predefined query on digital twins in oncology. Reviews were prioritized based on their synthesis of prior studies, with a focus on digital twins in oncology. Studies were evaluated using quality parameters (clear rationale, research design, methodology, results, conclusions, and conflict disclosure). Only studies with scores above a prefixed threshold and disclosed conflicts of interest were included in the final synthesis; seventeen studies were selected. Results and Discussion: DTs in oncology offer advantages such as enhanced decision-making, optimized treatment regimens, and improved clinical trial design. Moreover, economic forecasts suggest that the integration of digital twins into healthcare systems may significantly reduce treatment costs and drive growth in the precision medicine market. However, challenges include data integration issues, the complexity of biological modeling, and the need for robust computational resources. A comparison to cutting-edge research studies contributes to this direction and confirms also that ethical and legal considerations, particularly concerning AI, data privacy, and accountability, remain significant barriers. Conclusions: The integration of digital twins in oncology holds great promise, but requires careful attention to ethical, legal, and operational challenges. Multidisciplinary efforts, supported by evolving regulatory frameworks like those in the EU, are essential for ensuring responsible and effective implementation to improve patient outcomes. Full article

In recent years, emerging digital microfluidic technology has shown great application potential in fields such as biology and medicine due to its simple structure, sample-saving properties, ease of integration, and wide range of manipulation. Currently, due to potential faults in chips during production and usage, as well as high safety requirements in their application domains, thorough testing of chips is essential. This study records data using a machine vision-based digital microfluidic driving control system. As chip usage frequency rises, device degradation introduces seasonal and trend patterns in droplet motion time data, complicating predictive modeling. This paper first employs the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to analyze the droplet motion time data in digital microfluidic systems. Subsequently, principal component analysis (PCA) is applied for dimensionality reduction on the clustered data. Using the INFORMER model, we predict changes in droplet motion time and conduct correlation analysis, comparing results with traditional long short-term memory (LSTM), frequency-enhanced decomposed transformer (FEDformer), inverted transformer (iTransformer), INFORMER, and DBSCAN-INFORMER prediction models. Experimental results show that the DBSCAN-PCA-INFORMER model substantially outperforms LSTM and other benchmark models in prediction accuracy. It achieves an R² of 0.9864, an MSE of 3.1925, and an MAE of 1.3661, indicating an excellent fit between predicted and observed values.The results demonstrate that the DBSCAN-PCA-INFORMER model achieves higher prediction accuracy than traditional LSTM and other approaches, effectively identifying the health status of experimental devices and accurately predicting failure times, underscoring its efficacy and superiority. Full article

Sarcomas, a rare and complex group of cancers, require multidisciplinary care across multiple healthcare settings, often leading to delays, redundant testing, and fragmented data. This fragmented care landscape obstructs the implementation of Value-Based Healthcare (VBHC), where care efficiency is tied to measurable patient outcomes.ShapeHub, an interoperable digital platform, aims to streamline sarcoma care by centralizing patient data across providers, akin to a logistics system tracking an item through each stage of delivery. ShapeHub integrates diagnostics, treatment records, and specialist consultations into a unified dataset accessible to all care providers, enabling timely decision-making and reducing diagnostic delays. In a case study within the Swiss Sarcoma Network, ShapeHub has shown substantial impact, improving diagnostic pathways, reducing unplanned surgeries, and optimizing radiotherapy protocols. Through AI-driven natural language processing, Fast Healthcare Interoperability Resources, and Health Information Exchanges, HIEs, the platform transforms unstructured records into real-time, actionable insights, enhancing multidisciplinary collaboration and clinical outcomes. By identifying redundancies, ShapeHub also contributes to cost efficiency, benchmarking treatment costs across institutions and optimizing care pathways. This data-driven approach creates a foundation for precision medicine applications, including digital twin technology, to predict treatment responses and personalize care plans. ShapeHub offers a scalable model for managing rare cancers and complex diseases, harmonizing care pathways, improving precision oncology, and transforming VBHC into a reality. This article outlines the potential of ShapeHub to overcome fragmented data barriers and improve patient-centered care. Full article

Background: In recent years, there has been an exponential growth in global anti-cancer drug research, prompting the necessity for comprehensive analyses of publication output and thematic shifts. Methods: This study utilized a comprehensive set of PubMed records from 1962 to 2024 and examined growth patterns, content classification, and co-occurrence of key pharmacological and molecular terms. Results: Our results highlight an exponential rise in publications, with an annual compound growth rate of over 14%, influenced by advancements in digital knowledge sharing and novel therapeutic breakthroughs. A pronounced surge occurred during the COVID-19 pandemic, suggesting a sustained shift in research dynamics. The content analyses revealed a strong emphasis on classical chemotherapeutic agents—often studied in combination with targeted therapies or immunotherapies—and a growing focus on immune checkpoint inhibitors and vaccine platforms. Furthermore, co-occurrence networks indicated robust links between chemotherapy and supportive care, as well as emerging synergies between immuno-oncology, precision medicine approaches. Conclusions: Our study suggests that while novel modalities are reshaping treatment paradigms, chemotherapy remains central, underscoring the value of integrative regimens. This trend toward personalized, combination-based strategies indicates a transformative era in oncology research, where multidimensional data assessment is instrumental in guiding future therapeutic innovations. Full article

Background: Type 1 diabetes (T1D) is a chronic autoimmune disorder characterized by the destruction of pancreatic β-cells, leading to absolute insulin deficiency. Despite advancements in insulin therapy and glucose monitoring, achieving optimal glycemic control remains a challenge. Emerging technologies and novel therapeutic strategies are transforming the landscape of T1D management, offering new opportunities for improved outcomes. Methods: This review synthesizes recent advancements in T1D treatment, focusing on innovations in continuous glucose monitoring (CGM), automated insulin delivery systems, smart insulin formulations, telemedicine, and artificial intelligence (AI). Additionally, we explore biomedical approaches such as stem cell therapy, gene editing, immunotherapy, gut microbiota modulation, nanomedicine-based interventions, and trace element-based therapies. Results: Advances in digital health, including CGM integration with hybrid closed-loop insulin pumps and AI-driven predictive analytics, have significantly improved real-time glucose management. AI and telemedicine have enhanced personalized diabetes care and patient engagement. Furthermore, regenerative medicine strategies, including β-cell replacement, CRISPR-based gene editing, and immunomodulatory therapies, hold potential for disease modification. Probiotics and microbiome-targeted therapies have demonstrated promising effects in maintaining metabolic homeostasis, while nanomedicine-based trace elements provide additional strategies to regulate insulin sensitivity and oxidative stress. Conclusions: The future of T1D management is shifting toward precision medicine and integrated technological solutions. While these advancements present promising therapeutic avenues, challenges such as long-term efficacy, safety, accessibility, and clinical validation must be addressed. A multidisciplinary approach, combining biomedical research, artificial intelligence, and nanotechnology, will be essential to translate these innovations into clinical practice, ultimately improving the quality of life for individuals with T1D. Full article

Background: In recent years, more and more numerical tools have been utilized in medicine in or-der to assist the evaluation and decision-making processes for complex clinical cases. Towards this direction, Finite Element Models (FEMs) have emerged as a pivotal tool in medical research, particularly in simulating and understanding the complex fluid and structural behaviors of the circulatory system. Furthermore, this tool can be used for the calculation of certain risks regarding the function of the blood vessels. Methods: The current study developed a computational tool utilizing the finite element method in order to numerically evaluate stresses in aortas with abdominal aneurysms and provide the necessary data for the creation of a patient-specific digital twin of an aorta. More specifically, 12 different cases of aortas with abdominal aneurysms were examined and evaluated. Results: The first step was the 3D reconstruction of the aortas trans-forming the DICOM file into 3D surface models. Then, a finite element material model was developed simulating accurately the mechanical behavior of aortic walls. Conclusions: Through the results of these finite element analyses the values of tension, strain, and displacement were quantified and a rapid risk assessment was provided revealing that larger aneurysmatic regions elevate the risk of aortic rupture with some cases reaching an above 90% risk. Full article

Polymerase chain reaction (PCR) chips are advanced, microfluidic platforms that have revolutionized biomarker discovery and validation because of their high sensitivity, specificity, and throughput levels. These chips miniaturize traditional PCR processes for the speed and precision of nucleic acid biomarker detection relevant to advancing drug development. Biomarkers, which are useful in helping to explain disease mechanisms, patient stratification, and therapeutic monitoring, are hard to identify and validate due to the complexity of biological systems and the limitations of traditional techniques. The challenges to which PCR chips respond include high-throughput capabilities coupled with real-time quantitative analysis, enabling researchers to identify novel biomarkers with greater accuracy and reproducibility. More recent design improvements of PCR chips have further expanded their functionality to also include digital and multiplex PCR technologies. Digital PCR chips are ideal for quantifying rare biomarkers, which is essential in oncology and infectious disease research. In contrast, multiplex PCR chips enable simultaneous analysis of multiple targets, therefore simplifying biomarker validation. Furthermore, single-cell PCR chips have made it possible to detect biomarkers at unprecedented resolution, hence revealing heterogeneity within cell populations. PCR chips are transforming drug development, enabling target identification, patient stratification, and therapeutic efficacy assessment. They play a major role in the development of companion diagnostics and, therefore, pave the way for personalized medicine, ensuring that the right patient receives the right treatment. While this tremendously promising technology has exhibited many challenges regarding its scalability, integration with other omics technologies, and conformity with regulatory requirements, many still prevail. Future breakthroughs in chip manufacturing, the integration of artificial intelligence, and multi-omics applications will further expand PCR chip capabilities. PCR chips will not only be important for the acceleration of drug discovery and development but also in raising the bar in improving patient outcomes and, hence, global health care as these technologies continue to mature. Full article

Healthcare 4.0 addresses modernization and digital transformation challenges, such as home-based care and precision treatments, by leveraging advanced technologies to enhance accessibility and efficiency. Semantic technologies, particularly knowledge graphs (KGs), have proven instrumental in representing interconnected medical data and improving clinical decision-support systems. We previously introduced a semantic framework to assist medical experts during patient interactions. Operating iteratively, the framework prompts medical experts with relevant questions based on patient input, progressing toward accurate diagnoses in time-constrained settings. It comprises two components: (a) a KG representing symptoms, diseases, and their relationships, and (b) algorithms that generate questions and prioritize hypotheses—a ranked list of symptom–disease pairs. An earlier extension enriched the KG with a symptom ontology, incorporating hierarchical structures and inheritance relationships to improve accuracy and question-generation capabilities. This paper further extends the framework by introducing strategies tailored to specific medical domains. Strategies integrate domain-specific knowledge and algorithms, refining decision making while maintaining the iterative nature of expert–patient interactions. We demonstrate this approach using an emergency medicine case study, focusing on life-threatening conditions. The KG is enriched with attributes tailored to emergency contexts and supported by dedicated algorithms. Boolean rules attached to graph edges evaluate to TRUE or FALSE at runtime based on patient-specific data. These enhancements optimize decision making by embedding domain-specific goal-oriented knowledge and inference processes, providing a scalable and adaptable solution for diverse medical contexts. Full article