Search Results (2,909)

The immunoglobulins (IG) or antibodies and the T cell receptors (TR) are the antigen receptors of the adaptive immune responses (AIR) of jawed vertebrates (Gnathostomata). IMGT^®, the international ImMunoGeneTics information system^®, was created in 1989 by Marie-Paule Lefranc (Laboratoire d’ImmunoGénétique Moléculaire (LIGM), Université de Montpellier and CNRS) to deal with and to manage the huge diversity of IG or antibodies and TR. The founding of IMGT^® marked the advent of immunoinformatics, a new science which emerged at the interface between immunogenetics and bioinformatics. For the first time, the IG and TR variable (V), diversity (D), joining (J) and constant (C) genes were officially recognized as ‘genes’, as were the conventional genes. The IMGT-ONTOLOGY CLASSIFICATION axiom and the concepts of classification have generated the IMGT nomenclature and the IMGT Scientific chart rules for assigning IMGT names to IG and TR genes and alleles of Homo sapiens and of any other jawed vertebrate species. The IMGT nomenclature is used for genes in locus, in sequences (genomic or rearranged, expressed or not) and in structures enabling comparative immunology, evolutionary immunogenetics, standardized analysis and comparison of IG and TR repertoires analysis in normal or pathologic situations. IMGT nomenclature is used in basic, veterinary, and medical research, in clinical applications (mutation analysis in leukemia and lymphoma), and in therapeutic antibody design, engineering and humanization. By providing consistent and high standard biocuration for the description of the IG and TR loci, genes and alleles, and for the analysis of the IG or antibody and TR-expressed rearranged sequences and proteins and structures, the IMGT nomenclature is the common language for immunoinformatics and artificial intelligence (AI). Full article

Background: Generative artificial intelligence (AI) can produce realistic human faces that are shared on social media, from where younger generations often derive body image norms. Aesthetic bias in these systems may promote unrealistic standards of beauty. This study examines whether generative AI produces facial images that are perceived by humans as more attractive than real human faces. Thus, we examine AI-generated facial imagery as a contemporary site of consumer culture, where beauty may become biased, unrealistic, and commodified: generating an algorithmically optimized product circulating through social media and digital platforms without proper regulation. Methods: Fifty AI-generated female faces were prospectively compared with 50 photographs of female models from a model agency. Facial attractiveness was rated by plastic surgeons, using a Likert scale and Mann–Whitney U for analysis. Results: AI-generated images received higher mean aesthetic scores than real photographs (7.79 vs. 6.88, p < 0.05), despite prompts requesting unattractive features. Conclusions: The AI model showed a small but consistent bias toward enhanced facial attractiveness. As AI-generated imagery increasingly shapes visual culture, this bias may contribute to unrealistic beauty standards, highlighting the need for AI literacy, responsible use of AI, and ethical oversight, especially when shared on social media. Full article

Artificial intelligence (AI) is increasingly embedded in digital marketing, enabling organisations to personalise communication, analyse consumer data, and optimise decision-making processes. Despite its widespread adoption, limited empirical research has examined whether AI-driven digital marketing contributes to responsible consumption and production, as articulated in Sustainable Development Goal 12 (SDG 12). Grounded in a capability-based and marketing intelligence framework, this study investigates the mechanisms through which AI-driven digital marketing influences responsible marketing outcomes. Using survey data from 120 professionals in multinational corporations (MNCs) operating in South Africa, the study examines how AI-driven digital marketing influences responsible marketing outcomes aligned with Sustainable Development Goal 12 (SDG 12), with particular emphasis on the mediating roles of predictive consumer analytics and sentiment-based consumer understanding as distinct dimensions of AI-enabled marketing intelligence. Instead, its influence operates indirectly through sentiment-based consumer understanding, while predictive consumer analytics show no significant effect. These results suggest that AI contributes to responsible consumption primarily when it enhances firms’ capacity to interpret consumer values, emotions, and ethical concerns. The study advances the digital marketing and sustainability literature by reframing AI as a relational and sense-making capability while offering practical guidance for aligning AI-driven marketing strategies with SDG 12 in emerging markets. Full article

Personalized transdermal drug delivery systems (TDDS) represent a transformative approach in precision medicine by enabling patient-specific, non-invasive, and controlled therapeutic administration. Conventional transdermal patches are limited by fixed dosing, passive diffusion, and interindividual variability in skin permeability and metabolism, often leading to suboptimal therapeutic outcomes. Recent advances in materials science, nanotechnology, microneedle engineering, and digital health have enabled the development of next-generation personalized TDDS capable of programmable, adaptive, and feedback-controlled drug release. Smart wearable patches integrating biosensors, microfluidics, microneedles, and wireless connectivity allow real-time monitoring of physiological and biochemical parameters, enabling closed-loop drug delivery tailored to individual metabolic profiles. Nanocarriers such as lipid nanoparticles, polymeric nanoparticles, and stimuli-responsive hydrogels further enhance drug stability, penetration, and controlled release, while 3D-printing technologies facilitate patient-specific customization of patch geometry, drug loading, and release kinetics. Artificial intelligence (AI) and machine learning tools are increasingly being employed to predict drug permeation behavior, optimize enhancer combinations, and personalize dosing regimens based on pharmacogenomic and pharmacokinetic data. Despite these advances, regulatory complexity, manufacturing standardization, long-term biocompatibility, and cybersecurity considerations remain critical challenges for clinical translation. This review highlights recent innovations in personalized TDDS, discusses their clinical potential, and examines regulatory and technological barriers. Collectively, these emerging smart transdermal platforms offer a promising pathway toward adaptive, patient-centered therapeutics that can significantly improve treatment efficacy, safety, and compliance. Future research should focus on integrating multimodal biosensing, advanced biomaterials, scalable manufacturing strategies, and robust regulatory frameworks to enable clinically validated, fully autonomous transdermal systems that can dynamically adapt to real-time patient needs in diverse therapeutic settings. Full article

While Artificial Intelligence (AI) approaches date back more than 60 years, there is no doubt that in the last 4 years, we have entered the era of AI. The advanced capabilities of Generative AI (GenAI) and Large Language Models (LLMs) have noticeably reshaped multiple sectors, becoming a driving force in participatory environments. Recent developments in Machine/Deep Learning (ML/DL) and Natural Language Processing (NLP) have enabled the introduction of tools and applications integrated into various professional fields. Areas ranging from education and media to art, tourism, and food science incorporate AI technologies to optimize established workflows, facilitate change, enhance creativity, and foster interaction. The current Special Issue includes nineteen multidisciplinary research works exploring AI in participatory environments, primarily focusing on technologies, ethics, and literacy aspects. Employing diverse methodologies, the research identifies various uses of AI along with the critical ethical and legal risks and challenges they entail. Concerns about inaccuracy, algorithmic bias, data infringements, and the potential erosion of transparency and interpretability need to be addressed in every phase of the design and implementation of AI technologies. Co-creative human-in-the-loop processes and human judgment need to be further strengthened and supported through digital/AI literacy initiatives. In this regard, effective regulatory frameworks, inclusive institutional strategies, and targeted training programs can ensure responsible and trustworthy AI use with a balance between technological evolution and human oversight. Full article

Autonomous supply chains (ASC) are the next generation of digitally empowered logistics and operations systems that can make adaptive, data-driven, and intelligent decisions. Innovations in artificial intelligence (AI), digital twins (DT), and predictive analytics (PA) are transforming traditional supply chains into integrated and interactive networks to detect disruptions, simulate the future, and automatically modify operational decisions. This paper reviews the ASC mechanism and summarizes the increasing literature on the technologies and analytical capabilities available to support intelligent supply chain decision systems. A structured literature review was conducted using Scopus, Web of Science, and Google Scholar, resulting in 52 relevant studies after screening and eligibility assessment. The paper discusses the recent advances in AI-based forecasting, simulation environments using digital twins, data integration using the Internet of Things (IoT), and predictive analytics. These technologies can help an organization gain real-time visibility of the supply chain networks. They improve the precision of demand forecasting, optimize inventory and production planning, and dynamically coordinate logistics operations. Digital twins allow the development of virtual models of supply chain ecosystems, which could be used to test scenarios, analyze risks, and plan strategies. These capabilities combined can be used to create predictive and self-adaptive supply networks capable of being responsive to uncertainty and market volatility. Besides examining the technological foundations, the paper also tracks key challenges related to the move towards autonomous supply chains, such as data governance, system interoperability, cybersecurity risks, algorithm transparency, and the necessity of successful human-AI collaboration in decision-making. The synthesis leads to a multi-layered framework that integrates data acquisition, analytics, simulation, and execution for autonomous decision-making in supply chains. Future research directions in relation to resilient supply networks, intelligent automation, and adaptive supply chain ecosystems are also provided in the study. Through integrating existing information on the new forms of intelligent technology and how it can be incorporated into the supply chain systems, this review contributes to the literature on next-generation supply chains. It will also offer information to both researchers and practitioners aiming at designing autonomous as well as data-driven supply networks. Full article

Background: In Spain, the number of patients anticoagulated with vitamin K antagonists (VKAs) is high. Among them, poor adherence is common, which may be justified by a low level of knowledge, and could affect their quality of life. We analyzed treatment adherence, health-related quality of life, and knowledge level about treatment, and evaluated the possible influence of these factors on patients’ time in the therapeutic range while also studying potential differences between patients under routine monitoring or self-monitoring. Methodology: A cross-sectional descriptive study was conducted using three validated and cross-culturally adapted questionnaires to study therapeutic adherence, health-related quality of life, and knowledge level about VKA treatment in a sample of anticoagulated patients. Additionally, it was assessed whether they were self-monitoring or not; the Rosendaal Time in Therapeutic Range (TTRr) was also administered for each patient at the time of recruitment. Descriptive analysis of all variables was performed, and a logistic regression model was constructed to evaluate the possible interaction of variables. Results: Ninety-eight patients participated and were selected sequentially from those attending the oral anticoagulation clinic at Hospital Universitario Virgen de la Victoria in Malaga. Of these, 39 were men and 59 were women. The mean age of these participants was 60.62 years (SD 11.67). Sixty-six were under conventional monitoring and thirty-two followed the self-monitoring program. The DecaMIRT had a mean score of 39.22 (SD 8.57), the SF-12 mean score was 31.73 (SD 6.21), and the knowledge questionnaire’s was 14.2 (SD 2.6). The mean TTRr value was 63.88 (SD 22.99). Self-monitored patients showed better results in DECAMirt and knowledge. Discussion: Overall, patients included in the sample presented satisfactory values in these three questionnaires, which seems to indicate that this was a treatment-compliant group with a correct quality of life, and adequately informed about their treatment. Conclusions: The work of nurses responsible for these aspects appears crucial in achieving these results. We aim to extend this study by focusing on groups with poorer results to design specific activities that allow for improvement in care and, as much as possible, homogenize outcomes. For this purpose, we intend to use all available tools, including those derived from the use of health-oriented artificial intelligence. Full article

The integration of artificial intelligence in healthcare has revolutionized research, diagnostics, and patient care. In particular, the emergence of ChatGPT and the recent rise of DeepSeek have drawn significant attention to this landscape. This empirical work provides a comparative performance evaluation of these models and their role in medical education from a privacy perspective. Our study shows that, at present, DeepSeek-R1 is based on enhanced reasoning capabilities, while the ChatGPT-4o counterpart, on the other hand, is designed with support for text, audio, and vision which is lacking in standard DeepSeek models. However, due to the distillation techniques used, DeepSeek has an advantage over ChatGPT in terms of resource requirements. We evaluate both models on the MedQA dataset from privacy perspectives where ChatGPT returns 94% correct answers compared to 91% with DeepSeek, though DeepSeek demonstrates consistency in explanatory responses. Full article

Constant composition expansion (CCE) experiments provide critical relative-volume and density information describing the thermodynamic behavior of reservoir oils and gases under varying pressure. These properties are vital inputs for hydrocarbon reservoir engineering, as they impact how oil and gas move through the reservoir during production. However, the need for specialized personnel, high-end equipment and measures taken to ensure safety in handling high pressure fluids often render the CCE experiments expensive and slow. This work introduces a Local Interpolation Method (LIM), a proximity-informed, end-to-end CCE fluid properties prediction Artificial Intelligence (AI) model that leverages domain expertise and synthetic Pressure–Volume–Temperature (PVT) data archives that mimics the actual data. The AI model generates surrogate CCE behavior for new fluids, thereby reducing the need for completing laboratory CCE measurements when sufficiently similar fluids exist in the available archive and neighborhood support is strong. Each new fluid is embedded in a compositional–thermodynamic descriptor space, and its response is inferred from a small neighborhood of thermodynamically similar fluids. Within this locality, the LIM combines hybrid local interpolation for key scalar properties (such as saturation-point quantities and expansion endpoints) with shape-preserving reconstruction of monophasic and diphasic relative-volume curves, enforcing continuity at saturation and consistency between relative volume, density and compressibility. The workflow operates purely at inference time and does not require case-specific retraining. Application to a curated archive of CCE tests shows that LIM reproduces key CCE features with very good agreement to existing data across a range of fluid types, indicating that proximity-based AI modeling can substantially reduce reliance on new CCE experiments while maintaining engineering-useful agreement for compositional simulation workflows. Under leave-one-out evaluation on 488 CCE tests, mean curve-level Mean Absolute Percentage Error (MAPE) is 0.07% for monophasic relative volume and 0.07% for monophasic density. For well-supported neighborhoods (Tiers 1–3, n = 376), mean MAPE is 0.04% for both, with 2.65% for derived compressibility and 1.78% for diphasic relative volume. The workflow is automated in software to facilitate reproducible inference on operator-owned archives and can reduce turnaround time and laboratory burden in well-supported neighborhoods. The proposed AI model uses available experimental data owned by each operator and does not use others’ data while respecting the data privacy and data ownership. Full article

This bibliometric study examines the role of ubiquitous computing and intelligent systems in the treatment of mental and neurological disorders. Ubiquitous computing integrates computational intelligence into everyday environments, enabling seamless monitoring and support of patients. Intelligent systems, including wearable devices, environmental sensors, and mobile health applications, collect real-time data on behavior, physiology, and environmental factors. These systems support early detection of symptom changes, adherence to treatment, and crisis prediction through context-aware analysis. Artificial intelligence (AI) processes the collected data to generate personalized therapeutic feedback and notify healthcare providers when intervention is needed. In mental health care, intelligent environments can monitor mood, sleep, and social interaction patterns, providing valuable objective information about mental health status. In the case of neurological conditions such as Parkinson’s disease or epilepsy, intelligent systems facilitate movement tracking, seizure detection, and cognitive assessment outside of the clinical setting. Integration with electronic health records and telemedicine platforms ensures coordinated and responsive care. Ethical design, privacy protection, and patient consent remain key to successful implementation. In this way, ubiquitous computing is transforming care models by increasing autonomy, precision, and continuity in the treatment of complex neurodegenerative diseases, including those related to neurodegeneration in aging. Full article

Endovascular therapies have transformed cardiovascular medicine, yet restenosis, thrombosis, and device failure remain common and poorly predictable complications. Increasing evidence suggests that immunothrombotic processes critically shape vascular recovery after device implantation. This includes neutrophil extracellular trap (NET) formation, innate immune polarization, and endothelial damage responses. Concurrently, advances in artificial intelligence (AI) are increasingly enabling continuous multimodal monitoring and adaptive clinical decision-making throughout the medical device life cycle. Here, we propose the concept of immunologically adaptive endovascular devices: a closed-loop paradigm in which patient immune status informs device selection, device–tissue interactions are interpreted via mechanistic biomarkers, and real-world monitoring dynamically updates risk and management. The study introduces (i) an immune–device interaction phenotype taxonomy linking device design features to measurable thrombo-inflammatory trajectories, (ii) a mechanistic framework defining interface signaling processes that enhance or resolve NET-driven responses, (iii) a minimum evidence model encompassing preclinical testing, clinical validation, and post-market surveillance, and (iv) a reference AI architecture for risk prediction, drift detection, and safety monitoring. This study also outlined testable predictions and a translational roadmap toward precision endovascular intervention and next-generation adaptive cardiovascular devices. Full article

Background/Objectives: Integrating heterogeneous One Health time series into transparent and usable surveillance workflows remains difficult because data preparation, modeling, and interpretation are often separated across tools. In this paper, we introduce OH-MEMA (One Health Mixed-Effects Modeling and Analytics), an interactive visual analytics framework that integrates heterogeneous One Health data streams, including human clinical outcomes, environmental factors, and wastewater surveillance data, to support syndromic surveillance and pandemic preparedness. Methods: The system enables users to upload and analyze multi-source datasets through an interactive web-based interface. The modeling component supports fixed effects for multi-source predictors, random effects for spatial, temporal, and demographic grouping variables, optional random slopes, and rolling time-series validation. Model results are visualized as time series comparing observed and predicted outcomes, with evaluation metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation. To support iterative exploration, the system incorporates analytic provenance through a visual model tree that records prior configurations. Results: OH-MEMA was validated through both quantitative and qualitative evaluations. Quantitatively, mixed-effects models were assessed across multiple counties and outcomes using RMSE, MAE, and correlation, demonstrating robust predictive performance. Qualitatively, expert users, including epidemiologists and disease surveillance analysts, evaluated the system using the NASA Task Load Index and open-ended interviews, indicating improved interpretability, manageable cognitive workload, and effective workflow integration. Conclusions: OH-MEMA provides an interpretable, human-in-the-loop platform for exploratory forecasting and comparative model analysis in syndromic surveillance. The framework effectively bridges data integration, modeling, and interpretation, supporting user-centered analytical reasoning and decision-making in One Health applications. Full article

Advances in medical imaging modalities such as multiparametric and functional MRI, PET, and CT/CBCT, together with complementary innovations such as radiomics, artificial intelligence (AI), adaptive radiotherapy, and theranostics, have expanded the role of imaging from anatomical guidance to biologically informed treatment planning, adaptation, and response assessment. In this review, we provide a comprehensive overview of the technical foundations of imaging biomarkers in radiotherapy (RT), spanning functional and molecular imaging techniques and data-driven analytic approaches. We synthesize current clinical evidence across major disease sites, highlighting how imaging biomarkers are being used to refine target delineation, guide dose painting and functional avoidance, predict and monitor treatment response, and support adaptive and personalized RT strategies. We also critically examine key challenges to clinical translation and implementation, including standardization and reproducibility, validation and generalizability, interpretability of AI-driven models, regulatory and ethical considerations, issues of data sharing, reimbursement, and equity. Finally, we propose a multi-stage translational roadmap to guide the development, validation, and clinical deployment of imaging biomarkers in radiotherapy. Collectively, this review underscores the central role of imaging biomarkers in advancing biologically adaptive and precision radiotherapy and outlines priorities for their responsible and equitable integration into routine clinical practice. Full article

Background: Generative artificial intelligence (GenAI) models are increasingly used for medical information retrieval, due to their accessibility and efficiency. However, the accuracy and readability of their responses, specifically for upper gastrointestinal cancers, remain inadequately evaluated. This gap highlights the need for rigorous assessment to ensure reliable patient education and clinical integration. Objective: This study aimed to assess the accuracy and readability of responses generated by four prominent GenAI models (Kimi, DeepSeek, ChatGPT, and Gemini) when addressing patient-focused questions related to esophageal and gastric cancers. Methods: Twenty-five standardized medical questions about esophageal and gastric cancer covering domains of disease definition, treatment and management were posed to each model. Responses were assessed by four oncologists for accuracy by a 5-point Likert scale and analyzed for readability using Flesch–Kincaid Reading Ease, Flesch–Kincaid Grade Level, and SMOG metrics. High-interest questions for patients were identified via questionnaires. Results: Comparing the accuracy of GenAI-generated responses, DeepSeek achieved the highest overall accuracy score and outperformed other models in questions about definitions and treatments, while ChatGPT excelled in management-related inquiries. In subgroup analysis, GenAI models exhibited higher accuracy in answering definition and management questions, which patients preferred to inquire, compared with questions about cancer therapies. The responses produced by all models required a reading capacity from 11th-grade to college level. Conclusions: This study revealed that in this comparative evaluation application of GenAI models, DeepSeek provides the most accurate responses for upper GI cancer inquiries about definition and treatment, while ChatGPT showed superiority in management-related questions. However, all models generate texts requiring advanced reading levels, highlighting a need for readability optimization without compromising accuracy. GenAI shows promise for patient education but requires rigorous validation for clinical integration. Full article

The study explores the relatively underexamined role of artificial intelligence policies in sustainable agricultural development by investigating how governments, enterprises, and farmers interact under different policy incentives. A combination of tripartite evolutionary and Stackelberg game models is employed to examine how artificial intelligence can support more effective policy design, improve the speed of response, and foster greater collaboration among stakeholders. The analysis primarily draws on simulated data, reflecting the impact of policy incentives across various contexts. Findings suggest that artificial intelligence policies can meaningfully enhance cooperation, thereby promoting sustainable agricultural development. Higher levels of government incentives appear to encourage participation from both enterprises and farmers, while artificial intelligence contributes to faster and more precise policy adjustments. Theoretically, the study offers a framework for understanding artificial intelligence policy in agriculture and elucidates the mechanisms governing stakeholder interactions. From a practical perspective, the results provide cautious guidance for the design of artificial intelligence policies aimed at fostering sustainability. Full article