Harokopio University of Athens

Background: Precision nutrition (PN) aims to personalize dietary guidance by accounting for inter-individual variability across biological, metabolic, lifestyle, and environmental factors influencing nutritional needs and health outcomes. While traditional Artificial Intelligence (AI) has advanced nutritional research through systems like automated dietary assessment, these models often operate rigidly. Generative AI (GenAI) introduces the capacity for adaptive interventions for enhanced PN. However, the scope and maturity of its applications remain insufficiently characterized. Objective: This review examined original works applying GenAI in PN, focusing on application, methodology, and limitations. Methods: A systematic search was conducted in PubMed, ACM Digital Library, and Scopus. Inclusion criteria focused on original works deploying GenAI models in PN contexts. Included works were further formally assessed based on data used, validation, transparency, bias, and security and privacy. Results: 21 eligible studies were identified, all published after 2024. The literature indicated a surge in large language model-based systems for personalized dietary recommendations, followed by applications in data foundation building and food effect understanding. A recurrent limitation was questionable evaluation on synthetic data and hallucinations, necessitating a human-expert-in-the-loop, especially in high-stakes clinical settings. Additionally, only 4 of 21 reviewed studies incorporated biological content or biological inputs, and fewer approached biologically grounded PN within implemented personalization workflows using metabolic and/or genomic variables. Conclusions: Although GenAI research in PN is expanding rapidly, most applications remain personalized at a user-preference level rather than including biological determinants. The need for standardized reporting, stronger genome-informed modeling, and consistent human-in-the-loop validation protocols is further highlighted to advance towards holistic PN.

Venture capital is a significant component of funding and growing new businesses, offering them capital, knowledge, and managerial know-how that supports thriving in a globalised economy and address technological challenges. However, the connexion to larger innovation ecosystems in Europe is still not well understood. This study fills this gap by looking at how research and development funding, patent activity, education, and government effectiveness affect venture capital dynamics in 28 European countries from 2007 to 2024. We evaluate causal interactions between innovation inputs and VC flows utilising cross-sectional dependence tests and the JKS Granger non-causality framework. The findings indicate that patents, GDP, R&D investments, and human capital are significant predictors of venture capital activity, with multiple instances of bidirectional causality. Early-stage VC is particularly sensitive to innovation and macroeconomic performance, while late-stage VC is closely tied to innovation, government effectiveness, and human capital. These results show that an innovation ecosystem that is proactive and sensitive to different stages is not only a contextual factor but also a major driver of venture capital flows in Europe. Policymakers should therefore design policies that are specific to each situation and that improve the quality of institutions and the ability to innovate. This will lead to more active VC markets and faster commercialisation of new technologies.

This qualitative study explores how primary and secondary educators perceive the integration of Artificial Intelligence (AI) in gifted STEAM education within mixed-ability classrooms. This study examines how AI is framed as a pedagogical tool for creativity, differentiation, and inclusion. Educators recognized AI's potential to inspire imaginative engagement and support individualized learning but voiced concerns about student dependency, weakened collaboration, and reduced critical inquiry. Findings reveal ambivalence: AI is seen both as a creative ally and a pedagogical risk. Meaningful integration depends less on technology itself and more on socio-pedagogical frameworks, teacher expertise, curricular fit, and ethical awareness. The study calls for reflective, critically literate approaches to AI in gifted education, challenging techno-optimistic narratives and emphasizing the relational dimensions of teaching and learning.

The recent technological advancements in the field of Unmanned Aerial Vehicles (UAVs) and Artificial Ιintelligence (ΑΙ) have led to their widespread adoption across different sectors of agriculture. In particular, there has been a growing interest in the application of these technologies in viticulture, but their operational implementation and validation under diverse environmental and management conditions remain limited. To this end, the evaluation of different AI methodological frameworks for vine detection across different settings constitutes an important step for the sufficient and cost-effective deployment in real-world vineyards.Our study aims at contributing towards this direction by evaluating different segmentation approaches for vines detection tested in a real-world vineyard of Ktima Lazaridi vinery located in the prefecture of Drama, Macedonia, Northern Greece. The vineyard acting as the study’s experimental site spanned across approximately 4.94 hectares and consisted of Sauvignon Blanc vines. In this site, multispectral imagery was acquired at 40 meters Above Ground Level (AGL) on 30 July 2025 from a UAV equipped with a high-definition RGB camera, a red-edge and Near infrared bands. Experiments were performed using state-of-the-art segmentation methods such as Segment Anything Model (SAM) and object-based image analysis frameworks using multimodal UAV imagery (RGB, NIR, Red Edge bands, Vegetation Indices). Standard statistical metrics were employed to quantitatively assess the modelling results using as reference ground truth masks generated with direct photointerpretation. ArcGIS Pro was used for the implementation of the AI algorithms as well as for the evaluation of the experimental analysis.Our study findings suggest that UAV-based multimodal imagery combined with advanced AI algorithms, can serve as a cost-effective and scalable solution for vineyard monitoring, management, and decision-making. Future work will focus on evaluating these methods under different grape varieties, phenological stages, and environmental conditions to further generalize their applicability and optimize vineyard management strategies.Keywords: UAVs, Artificial Intelligence, SAM, Vineyards, ACCELERATEAcknowledgementThis study is financially supported by the ACCELERATE MSCA SE program of the European Union’s Horizon research and innovation program under grant agreement No. 10118293

Background/Objectives: Sensory processing constitutes a fundamental neurobiological mechanism that allows individuals to perceive, interpret, and respond adaptively to sensory input. Atypical patterns of sensory processing are frequently observed in neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). The purpose of this study was to evaluate the psychometric properties of the Greek Sensory Profile 2 Short Form following its linguistic and cross-cultural adaptation. Methods: An on-site parent-report survey was conducted among children and adolescents aged 3.0 to 14.11 years across Greece. The study included the Sensory Profile 2 Short Form (SSP2), the Child Behavior Checklist (CBCL), and the Social Communication Questionnaire (SCQ). A total of 350 parents participated in the study. Results: The present study demonstrated that the Greek version of the SSP2 possesses good psychometric properties. The results of the Exploratory Factor Analysis (EFA) of the 34 items explained 63.6% of the total variance. Internal consistency for both primary scales-Sensory (α = 0.93) and Behavioral (α = 0.96)-was satisfactory, as were the subscales Seeking (α = 0.88), Avoiding (α = 0.94), Sensitivity (α = 0.94), and Registration (α = 0.91). Significant correlations with the CBCL and SCQ supported convergent validity. The findings also highlighted associations with sociodemographic variables, providing insights into population-specific variations in sensory processing. Conclusions: The Greek version of the SSP2 demonstrates strong psychometric properties and is suitable for assessing sensory processing patterns in children and adolescents. Its use enables culturally appropriate screening for atypical sensory processing, informing both research and clinical practice and supporting early identification and intervention strategies.

The BETTER4U project is an EU-funded project (GAP: 101080117) producing a coordinated sequence of publications that together aim to advance the scientific underpinnings of personalized obesity prevention and management. This collection seeks to elucidate how biological and behavioral tailored products, supported by large scale data analyses, innovative assessment tools, and newly emerging digital and artificial intelligence (AI)-enabled technologies, could transform current paradigms in obesity research and care. The series will spell out the objective, the scope, and the scientific rationale of this collective effort to showcase the transformative potential of personalization and its critical importance in achieving meaningful and equitable, as well as scalable, impact. Across the series, BETTER4U aims to define the parameters of personalization in obesity research. This series makes the case for joining forces between diverse scientific sectors, namely, genomics, metabolomics, microbiome, behavior, digital health, health economics and population level analytics, to accelerate the delivery of personalized obesity prevention and management. The contributions identify gaps and highlight findings in the effective communication of personalized risk, understanding heterogeneity and vulnerability, real-time monitoring tools and the need for stronger economic evaluations to guide policy and practice. Through identification and mapping of these gaps, the series sets out a forward-looking research agenda that could enable the development of the next generation of personalized interventions. Together, the papers outline a roadmap for future research and implementation that can support the emergence of a more precise and context-sensitive standard of care. We are pleased to partner with Obesity Reviews in this endeavor. In addition to the core BETTER4U manuscripts, the Special Issue will also feature a number of papers submitted in response to a dedicated Call for Papers under the banner of “Personalized obesity prevention and management.” These contributions, appearing in upcoming issues of Obesity Reviews, will further enrich the scientific dialogue and broaden the evidence base supporting personalization in obesity research and care. The series begins with the strategic framework paper, Advancing Personalized Strategies for Obesity Prevention Through Biologically and Behaviorally Tailored Interventions, which explains the conceptual basis of BETTER4U and the scientific pillars of the initiative. Following pieces each take on interrelated aspects of the various personalization elements. One examines the effectiveness of behavior change strategies for communicating genetic risk, offering insights into how individuals interpret and act upon personalized genetic information. Another meta-review identifies the behavior change techniques consistently used in effective interventions for vulnerable populations, showing how personalization can support equity rather than amplify disparity. Additionally, a comprehensive review of microbiome and metabolomics research synthesizes emerging evidence across the lifespan and explores its implications for tailored interventions. Economic considerations are addressed through a systematic review of the cost-effectiveness of personalized lifestyle interventions, clarifying when personalization may offer added value. Furthermore, methodological innovation is represented by the introduction of the BETTER4U indicators, novel tools for unobtrusive, scalable assessment of behavioral and environmental determinants relevant to obesity risk. Moreover, the BETTER4U Database manuscript demonstrates the power of integrating harmonized data from 14 countries and over 1.1 million participants, enabling unprecedented cross-national analyses of obesity-related determinants. Further empirical depth is provided by an individual-level meta-analysis across 21 BETTER4U cohorts, examining associations between BMI, lifestyle factors, and mental health in vulnerable contexts. Finally, the clinical trial protocol for the BETTER4All personalized intervention illustrates how AI-driven recommendations and real-time monitoring can be operationalized in a multicenter trial, translating the project's conceptual and methodological innovations into practice. Collectively, these results represent a well-integrated scientific plan with predictive capacity. Through its examination of the biological, behavioral, environmental, technological, and economic dimensions of obesity prevention, the BETTER4U series demonstrates how complementary lines of inquiry can work collectively toward a new standard of care, one that is data-driven, equitable and responsive across the life course. The series papers are united by a common view; to help the world move away from one size fits all approaches to tailored approaches that can achieve meaningful and scalable impact. The BETTER4U project has received funding from the European Union's Horizon Europe Research and Innovation program under Grant Agreement no. 101080117. Additionally, BETTER4U is funded by UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee (grant number 10093560 for QMUL and 10106435 for BiB) and by the Swiss State Secretariat for Education, Research and Innovation (SERI). The authors declare no conflicts of interest.

Cocoa is a significant source of theobromine, a bioactive compound proposed to provide cardiovascular protection. However, relatively few studies have provided conclusive evidence of the potential beneficial health effects. The aim of this study was to determine the relationship between the consumption of cocoa products containing theobromine and cardiovascular risk factors in the U.S. population sample using 2015-2020 National Health and Nutrition Examination Survey (NHANES) data. This cross-sectional study included 8531 individuals aged 20-70years and 3808 people in a fasting sub-sample. Theobromine and cocoa product intake levels were obtained from 24-h dietary recalls and linked to the Food and Nutrient Database for Dietary Studies database. Primary outcomes included systolic and diastolic blood pressure, hypertension, serum total, HDL and LDL cholesterol, triglyceride, glycohemoglobin, insulin, plasma glucose, and high-sensitivity C-reactive protein (hs-CRP). Linear regression models were applied to analyze the associations between theobromine or cocoa product consumption and cardiovascular risk factors. The consumption of cocoa was not associated with cardiovascular disease risk factors compared to non-consumption. The amounts of cocoa consumed were inversely associated with glycohemoglobin levels (mean ± SE, - 0.04 ± 0.02% per 100g, P = 0.04). Theobromine intake (vs. no intake) was positively associated with serum insulin (1.85 ± 0.85 μU/mL; P = 0.03). Theobromine levels consumed were inversely associated with glycohemoglobin (- 0.05 ± 0.03% per 100mg, P = 0.04), serum triglycerides (- 5.4 ± 2.73mg/dL, P = 0.05), and hs-CRP levels (- 0.26 ± 0.12mg/L, P = 0.04). These associations were also mostly observed in people with healthier profiles. Increased cocoa consumption is associated with reduced glycohemoglobin levels, while higher theobromine intake is associated with reduced triglyceride, glycohemoglobin, and hs-CRP levels compared to a lower intake. The positive link between theobromine intake, as opposed to no intake, and serum insulin required further investigation. Overall findings support the inclusion of cocoa-derived compounds in healthy diets.

Background/objectivesThe purpose of this systematic review was to synthesize available human studies, present and weigh the evidence regarding the efficacy of ketogenic dietary interventions (KDIs) for ADPKD, and provide a direction for future research and relevant recommendations.MethodsThree databases were searched and risk of bias (RoB) of the studies was assessed using Cochrane's RoB 2.0, the Newcastle-Ottawa scale and the ROBINS-IΙ tool. The Synthesis Without Meta-analysis (SWiM) extension was used to present the results.ResultsEight studies were identified delivering interventions with ketogenic diets, intermittent fasting, time-restricted feeding, etc. KDIs were generally associated with weight loss and a more favorable anthropometric profile in most interventions (nine). Blood pressure remained unchanged in most interventions (five), similar to blood cholesterol, HDL-cholesterol, triglycerides and LDL concentrations. Regarding renal outcomes, eGFR was either higher (4 interventions) or remained stable post-intervention. htTKV remained unchanged in most of the studies. Evidence for renal structural change was inconsistent and limited by short intervention duration and small sample sizes.ConclusionsThe evidence on KDIs for ADPKD is still limited. However, KDIs, particularly caloric-restriction diets, appear promising tools for managing ADPKD. Current human data support metabolic feasibility more consistently than renal disease modification. The effects of KDIs on renal structural outcomes remain uncertain and longer trials with appropriate comparators, namely the standard of care diet for ADPKD, are required before KDIs can be recommended for ADPKD.

BackgroundHealth apps designed to monitor, motivate, and educate people towards their health goals are getting more users and features each time. These apps offer valuable support for self-managing health behaviors and achieving long-term objectives. However, there is limited understanding of user preferences regarding essential app features. The aim of the study is to get insights about potential users’ preferences, in order to tailor better apps for lifestyle management.MethodsWe conducted a three-part web survey with 389 respondents from four countries as part of the DigiCare4You European Union (EU) project. In the first part, we collected the socioeconomic characteristics and health status of each respondent. In the following stage, we asked five questions on a Likert scale to ascertain the individual level of usage and general attitude towards technology. Finally, we performed a discrete choice experiment (DCE) using an unlabeled design and estimated the odds ratio for each feature using conditional logit analysis. We also ran alternative estimations stratifying by non-communicable disease (NCD) patients and non-NCD patients, and explored latent profile analysis (LPA) to understand whether the general attitude towards technology impacts the preference pattern between users.ResultsThe DCE revealed that respondents showed a clear preference for monitoring physical health over emotional status. They favored receiving lifestyle achievement notifications weekly rather than daily, and daily rather than more frequently. Similarly, respondents preferred uploading body weight measurements on a weekly or monthly basis rather than daily. Users expressed a preference for collaborating with their doctors to set exercise and diet goals, rather than either deciding independently or delegating entirely to their doctors. End-users also show a pattern of preferring notifications for goals instead of challenging other users. Preferences regarding the subjects of health content between workout routines, food recipes, and new scientific evidence were not significant; also, no statistical significance was found for the decision between follow-up visits with their doctor in person or remotely. LPA returned two groups regarding their general attitude towards technology: a lower, an intermediate, and a higher usage in their private life based on their responses to the questionnaire. Stratified DCEs have shown heterogeneity of users’ preferences according to their specific attitude towards technology.ConclusionsOur study indicates that potential mobile health (mHealth) app users managing chronic conditions prefer platforms that enable shared responsibility with their doctors in defining health goals while having an intermediate level of interaction frequency with the app. These findings are key to tailoring mHealth apps that can optimize motivation triggers, support healthier lifestyles, and empower patients with chronic conditions.

Zero-Touch Network (ZTN) represents a cornerstone approach of Next Generation Networks (NGNs), enabling fully automated and AI-driven network and service management. However, their distributed and multi-domain nature introduces critical security challenges, particularly regarding service identity and data integrity. This paper proposes a novel blockchain-based framework to enhance the security of ZTN through two complementary mechanisms: decentralized digital identity management and oracle-assisted network monitoring. First, a Decentralized Identity Management framework aligned with Zero-Trust Architecture principles is introduced to ensure tamper-proof authentication and authorization in a trustless environment among network components. By leveraging decentralized identifiers, verifiable credentials, and zero-knowledge proofs, the proposed Decentralized Authentication and Authorization component eliminates reliance on centralized authorities, while preserving privacy and interoperability across domains. Second, the paper investigates blockchain oracle mechanisms as a means to extend data integrity guarantees beyond the blockchain, enabling secure monitoring of Network Services and validation of Service-Level Agreements. We propose a four-dimensional framework for oracle design, based on qualitative comparison of oracle types—decentralized, compute-enabled, and consensus-based—to identify their suitability for NGN scenarios. This work proposes an architectural and design framework for Zero-Touch Networks, focusing on system integration and security-aware orchestration rather than large-scale experimental evaluation. The outcome of our study highlights the potential of integrating blockchain-based identity and oracle solutions to achieve resilient, transparent, and self-managed network ecosystems. This research bridges the gap between theory and implementation by offering a holistic approach that unifies identity security and data integrity in ZTNs, paving the way towards trustworthy and autonomous 6G infrastructures.