PROJECT PUBLICATIONS
Contributors
Karamintziou, S., Mavropoulos, T., Ntioudis, D., Meditskos, G., Vrochidis, S., & Kompatsiaris, I. Y.
Abstract
We face up to the challenge of explainability in Multimodal Artificial Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum computing, interpretable and transparent spin-geometrical neural architectures for early fusion of large-scale, heterogeneous, graph-structured data are envisioned, harnessing recent evidence for relativistic quantum neural coding of (co-)behavioral states in the self-organizing brain, under competitive, multidimensional dynamics. The designs draw on a self-dual classical description – via special Clifford-Lipschitz operations – of spinorial quantum states within registers of at most 16 qubits for efficient encoding of exponentially large neural structures. Formally ‘trained’, Lorentz neural architectures with precisely one lateral layer of exclusively inhibitory interneurons accounting for anti-modalities, as well as their co-architectures with intra-layer connections are highlighted. The approach accommodates the fusion of up to 16 timeinvariant interconnected (anti-)modalities and the crystallization of latent multidimensional patterns. Comprehensive insights are expected to be gained through applications to Multimodal Big Data, under diverse real-world scenarios.
Contributors
D Corrales, A Santos-Lozano, S López-Ortiz, A Lucia, D Rios-Insua
Abstract
Background and Objective: Only about 14% of eligible EU citizens finally participate in colorectal cancer (CRC) screening programs despite it being the third most common type of cancer worldwide. The development of CRC risk models can enable predictions to be embedded in decision-support tools facilitating CRC screening and treatment recommendations. This paper develops a predictive model that aids in characterizing CRC risk groups and assessing the influence of a variety of risk factors on the population.
Methods: A CRC Bayesian Network is learnt by aggregating extensive expert knowledge and data from a observational study and making use of structure learning algorithms to model the relations between variables. The network is then parametrised to characterize these relations in terms of local probability distributions at each of the nodes. It is finally used to predict the risks of developing CRC together with the uncertainty around such predictions.
Results: A graphical CRC risk mapping tool is developed from the model and used to segment the population into risk subgroups according to variables of interest. Furthermore, the network provides insights on the predictive influence of modifiable risk factors such as alcohol consumption and smoking, and medical conditions such as diabetes or hypertension linked to lifestyles that potentially have an impact on an increased risk of developing CRC.
Conclusion: CRC is most commonly developed in older individuals. However, some modifiable behavioral factors seem to have a strong predictive influence on its potential risk of development. Modeling these effects facilitates identifying risk groups and targeting influential variables which are subsequently helpful in the design of screening and treatment programs.
Contributors
Hans Graux, Magdalena Gad-Nowak, Liesa Boghaert, Pieter Gryffroy
Abstract
The general impact of artificial intelligence (AI) systems on businesses, governments and the global economy is currently a hot topic. This isn’t surprising, considering that AI is believed to have the potential to bring about radical, unprecedented changes in the way people live and work.
The transformative potential of AI originates to a large extent from its ability to analyse data at scale, and to notice and internalise patterns and correlations in that data that humans (or fully deterministic algorithms) would struggle to identify. In simpler terms: modern AIs flourish especially if they can be trained on large volumes of data, and when they are used in relation to large volumes of data.
Contributors
Arnab Maity, Yael Hershkovitz-Pollak, Ritu Gupta, Weiwei Wu, and Hossam Haick
Abstract
This article reports on a molecular-spin-sensitive-antenna (MSSA) that is based on stacked layers of organically functionalized graphene on a fibrous helical cellulose network for carrying out spatiotemporal identification of chiral enantiomers. The MSSA structures combine three complementary features: (i) chiral separation via a helical quantum sieve for chiral trapping, (ii) chiral recognition by a synthetically implanted spin-sensitive center in a graphitic lattice; and (iii) chiral selectivity by a chirality-induced-spin mechanism that polarizes the local electronic band-structure in graphene through chiral-activated Rashba spin–orbit interaction field. Combining the MSSA structures with decision-making principles based on neuromorphic artificial intelligence shows fast, portable, and wearable spectrometry for the detection and classification of pure and a mixture of chiral molecules, such as butanol (S and R), limonene (S and R), and xylene isomers, with 95–98% accuracy. These results can have a broad impact where the MSSA approach is central as a precautionary risk assessment against potential hazards impacting human health and the environment due to chiral molecules; furthermore, it acts as a dynamic monitoring tool of all parts of the chiral molecule life cycles.
Contributors
Hossam Haick
Abstract
In recent years, wearable technology has transcended its initial emphasis on fitness and lifestyle applications, expanding its horizons to encompass a critical role in healthcare and environmental monitoring. This remarkable evolution has been propelled by the advancement of wearable chemical sensors, a burgeoning field that has piqued the interest of both the scientific community and the general public. Wearable chemical sensors are distinct in their unparalleled ability to offer direct and precise insights into our health and surroundings. This trait is crucial in providing real-time insights into various personalised healthcare, environmental safety, and ubiquity of Internet of Things (IoT) that cannot bematched by other sensor types. For instance, these sensors can identify biomarkers in sweat or monitor air quality, yielding critical information that can lead to early disease detection or the identification of environmental risks.
Contributors
César R. Casanova, Marta R. Casanova, Rui L. Reis & Joaquim M. Oliveira
Abstract
Soft microfluidic systems play a pivotal role in personalized medicine, particularly in in vitro diagnostics tools and disease modeling. These systems offer unprecedented precision and versatility, enabling the creation of intricate three dimensional (3D) tissue models that can closely emulate both physiological and pathophysiological conditions. By leveraging innovative biomaterials and bioinks, soft microfluidic systems can circumvent the current limitations involving the use of polydimethylsiloxane (PDMS), thus facilitating the development of customizable systems capable of sustaining the functions of encapsulated cells and mimicking complex biological microenvironments. The integration of lab-on-a-chip technologies with soft nanodevices further enhances disease models, paving the way for tailored therapeutic strategies. The current research concepts underscore the transformative potential of soft microfluidic systems, exemplified by recent breakthroughs in soft lithography and 3D (bio)printing. Novel applications, such as multi-layered tissues-on-chips and skin-on-a-chip devices, demonstrate significant advancements in disease modeling and personalized medicine. However, further exploration is warranted to address challenges in replicating intricate tissue structures while ensuring scalability and reproducibility. This exploration promises to drive innovation in biomedical research and healthcare, thus offering new insights and solutions to complex medical challenges and unmet needs.
Contributors
Baron R. and Haick H.
Abstract
This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of “Mobile Diagnostic Clinics” is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI’s role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.
Contributors
D De Pascale, G Cascavilla, DA Tamburri, WJVD Heuvel
Abstract
Dark web crawling is a complex process that involves specific methodologies and techniques to navigate the Tor network and extract data from hidden services. This study proposes a general dark web crawler designed to extract pages handling security protocols, such as captchas, efficiently. Our approach uses a combination of seed URL lists, link analysis, and scanning to discover new content. We also incorporate methods for user-agent rotation and proxy usage to maintain anonymity and avoid detection. We evaluate the effectiveness of our crawler using metrics such as coverage, performance and robustness. Our results demonstrate that our crawler effectively extracts pages handling security protocols while maintaining anonymity and avoiding detection. Our proposed dark web crawler can be used for various applications, including threat intelligence, cybersecurity, and online investigations. Index Terms—LEA, TOR, Dark Web, crawler, Open Source Intelligence
Contributors
Ioannis K. Gallos, Dimitrios Tryfonopoulos, Gidi Shani, Angelos Amditis, Hossam Haick and Dimitra D. Dionysiou
Abstract
Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.
Contributors
Guy Decante, Ibrahim Fatih Cengiz, João B. Costa, Maurice N. Collins, Rui L. Reis, Joana Silva-Correia, J. Miguel Oliveira
Abstract
Hydrogels and bioinks obtained from gelatin (Gel) generally present poor mechanical properties and require a series of time-consuming and stepwise chemical processes to exhibit improved elasticity and resistance to fatigue. Alkali lignin (AL) is an underutilized by-product of the paper and pulp industry. It is a widely available and inexpensive biomaterial that presents enormous potential for high-value applications owing to its ease of chemical modification and unique naturally occurring polyaromatic structure. This work aims to develop different GelAL hydrogel formulations with a single-step method that are innovative and sustainable. The results obtained from the mechanical, rheological, and degradation studies of the developed GelAL hydrogels demonstrated that their properties can be easily modified and tuned using straightforward processing techniques, allowing these stretchable and tough hydrogels to be used as bioinks in 3D printing. The modulation of mechanical properties through hydrogel formulations is a result of interactions between the Gel and AL which can be associated with the interplay of anionic sulfonates in AL and the arginine and lysine residues from Gel. The tensile stress at the break for the Gel20AL10 formulation was 32% higher than the value for Gel20AL5 and 157% higher than that of Gel10AL10. The elongation at break also decreased as it averaged 659 ± 149% for the Gel20AL10 formulation, which is 20% more than that of Gel20AL5 and 55% more than the average elongation at break of Gel10AL10. Further zeta potential measurements and quartz crystal microbalance with energy dissipation studies demonstrated that Gel and AL seem to form neutral complexes when mixed. These assays support the idea that AL and Gel are readily bound through weak interactions, and chemical crosslinking strategies need to be considered when degradability and mechanical properties tuning are envisioned. Altogether, these highperformance GelAL hydrogels display mechanical properties similar to soft tissues with high elasticity beyond that of natural hydrogels and fulfill the requirements of a broad range of biomedical and tissue engineering scaffolding applications.
Contributors
Simon Van den Bergh , Lidia Casas, Gökhan Ertaylan, Guido Van Hal, and Jos Bessems
Abstract
Despite its potential with regard to the prevention and early detection of colorectal cancer (CRC), participation in the organized CRC screening programme of the Belgian region of Flanders is suboptimal. The role of language discordance as a determinant of screening participation in Europe is poorly understood, despite being identified as a potential barrier in qualitative and non-European studies.
Contributors
Daniel Corrales a,b , David Ríos Insua a , Marino J. González
Abstract
With minor differences, most national colorectal cancer (CRC) screening programs in Europe consist of one-size-fits-all aged-based strategies. This paper provides a decision analysis-basedapproach to personalized CRC screening in a general population setting, supporting decisions concerning whether and which screening method to consider and/or whether a colonoscopy should be administered.
Contributors
Carvalho, M. R., Caballero, D., Subhas, K. C., and Reis, R. L., Oliveira, J. M.
Abstract
Integrating biological material within soft microfluidic systems made of hydrogels offers countless possibilities in biomedical research to overcome the intrinsic limitations of traditional microfluidics based on solid, non-biodegradable, and non-biocompatible materials. Hydrogel-based microfluidic technologies have the potential to transform in vitro cell/tissue culture and modeling. However, most hydrogel-based microfluidic platforms are associated with device deformation, poor structural definition, reduced stability/reproducibility due to swelling, and a limited range in rigidity, which threatens their applicability. Herein, we describe a new methodological approach for developing a soft cell-laden microfluidic device based on enzymatically-crosslinked silk fibroin (SF) hydrogels. Its unique mechano-chemical properties and high structural fidelity, make this platform especially suited for in vitro disease modelling, as demonstrated by reproducing the native dynamic 3D microenvironment of colorectal cancer and its response to chemotherapeutics in a simplistic way. Results show that from all the tested concentrations, 14 wt% enzymatically-crosslinked SF microfluidic platform has outstanding structural stability and the ability to perfuse fluid while displaying in vivo-like biological responses. Overall, this work shows a novel technique to obtain an enzymatically-crosslinked SF microfluidic platform that can be employed for developing soft lab-on-a-chip in vitro models.
Contributors
Aleksandra Serafin, César R. Casanova, Arvind K. Singh Chandal, Rui L. Reis, Joaquim Miguel Oliveira, and Maurice N. Collins
Abstract
Integrating biological material within soft microfluidic systems made of hydrogels offers countless possibilities in biomedical research to overcome the intrinsic limitations of traditional microfluidics based on solid, non-biodegradable, and non-biocompatible materials. Hydrogel-based microfluidic technologies have the potential to transform in vitro cell/tissue culture and modeling. However, most hydrogel-based microfluidic platforms are associated with device deformation, poor structural definition, reduced stability/reproducibility due to swelling, and a limited range in rigidity, which threatens their applicability. Herein, we describe a new methodological approach for developing a soft cell-laden microfluidic device based on enzymatically-crosslinked silk fibroin (SF) hydrogels. Its unique mechano-chemical properties and high structural fidelity, make this platform especially suited for in vitro disease modelling, as demonstrated by reproducing the native dynamic 3D microenvironment of colorectal cancer and its response to chemotherapeutics in a simplistic way. Results show that from all the tested concentrations, 14 wt% enzymatically-crosslinked SF microfluidic platform has outstanding structural stability and the ability to perfuse fluid while displaying in vivo-like biological responses. Overall, this work shows a novel technique to obtain an enzymatically-crosslinked SF microfluidic platform that can be employed for developing soft lab-on-a-chip in vitro models.
Contributors
Lorena Rudolph, Renia Krellmann, Darko Castven, Lina Jegodzinski, Helena Deriš, Jerko Štambuk, Jarne Mölbitz, Luna Dechent, Kai Sperling, Melissa Lindloge, Nele Friedrich, Franziska Schmelter, Bandik Föh, Irena Trbojević-Akmačić, Christian Sina, Matthias Nauck,, Astrid Petersmann, Jens U. Marquardt, Ulrich L. Günther*, Alvaro Mallagaray*
Abstract
Nuclear magnetic resonance (NMR) spectra of blood serum and plasma show signals arising from metabolites, lipoproteins, and N-acetyl methyl groups of N-glycans covalently linked to acute-phase proteins. These glycan signals often called glycoprotein A (GlycA) and glycoprotein B (GlycB) arise from N-acetyl methyl groups and have been proposed as biomarkers, initially for cardiovascular diseases, but also for other inflammatory conditions. For the detection of glycan resonances, J-edited, diffusion, and relaxation filtered NMR spectroscopy (JEDI) has been proposed to suppress the lipoprotein signals. JEDI is however limited to measure those acetyl signals, whereas all other glycan resonance cannot be observed. For improved glycoprotein profiling, the signals arising from the pyranose ring protons are essential. Here, we show how selective frequency excitation combined with scalar coupling filtering can be used to dramatically increase the number of N-glycan signals observable in NMR spectra of serum and plasma samples, facilitating glycosylation profiling in less than 30 min. This approach grants selective detection of sialylation, galactosylation, N-acetylglucosaminylation, and fucosylation of dominant N-glycans and, to some extent, N-glycan branching complexity. Notably, sialylated and nonsialylated Lewisx and Lewisa antigens can also be observed. Lewisa antigen is well established as a cancer biomarker, known as CA19-9. NMR glycosylation profiles from nine isolated serum glycoproteins show excellent agreement with well-established UHPLC-MS analysis. The proposed NMR method facilitates the detection of glycoprotein biomarkers without the need for enzymatic treatment of serum or plasma and provides a robust read-out as exemplified by samples from 33 patients with hepatocellular carcinoma.
Contributors
Natália Akemi Kohori, Ibrahim Fatih Cengiz, Mariana Carvalho, Joana Silva-Correia, Ricardo A. Pires, Carla dos Santos Riccardi, Lilian Cristina Pereira, Rui L. Reis & Joaquim Miguel Oliveira
Abstract
The liver is vital for the human body’s metabolism, making the development of more predictable and reproducible pathophysiological-relevant three-dimensional (3D) liver models crucial for studying the accumulation of xenobiotics and associated liver diseases. In this work, we describe the development of a bioink comprising 0.2% w/v TayaGel® HA (high acyl gellan gum) and 1.2% w/v Gelzan™ CM (low acyl gellan gum) for the bioprinting of 3D liver models. The bioink underwent physicochemical characterization, including rheological and dynamic mechanical analyses, and cytotoxicity testing using HepG2 cell line in vitro. The shape fidelity assay suggested an ink fluid-like behavior and good filament formation with Young’s modulus resembling liver tissue. Moreover, the dynamic mechanical analysis showed the tan δ aligned with liver tissue, while the hydrogel degraded over 60% within 24 h. No increase in cell number and low viability were observed in loaded cells during 7 days of cultivation. Furthermore, the hydrogel presented a non-angiogenic nature on the chicken chorioallantoic membrane model. Collectively, these findings highlight the potential of gellan gum (GG), particularly high acyl gellan gum, as a bioink due to its mechanical mimicry and suitability for bioprinted 3D in vitro as sacrificial bioink, combining soft-elastic properties with controlled degradation.
Contributors
Casanova C R., Casanova M R., Reis R L., Oliveira J M.
Abstract
The field of tissue engineering, regenerative medicine, and in vitro models is undergoing transformative advancements, driven by the urgent need for structures that mimic the complex architecture and functionality of native tissues. Hierarchical scaffolds, characterized by macro-, micro-, and nanoscale features, have emerged as pivotal tools for efficiently regenerate different tissues/organs. These scaffolds can replicate the extracellular matrix with remarkable precision, offering structural support, enhanced cell alignment, and biocompatibility essential for effective tissue regeneration and in vitro models. Herein, the current advances in the biofabrication of hierarchical scaffolds, emphasizing the integration of advanced techniques such as additive manufacturing, electrospinning, freeze-drying, sol-gel, and self-assembly, are discussed. These methods enable the precise design of scaffolds tailored for diverse applications, from bone regeneration to disease modeling. The discussion extends to material innovation and selection criteria, balancing natural and synthetic polymers for optimized biocompatibility, mechanical stability, and controlled degradation. Additionally, sustainability is highlighted, showcasing renewable materials, energy-efficient processes, and circular economy principles that align with environmental responsibility. Hierarchical scaffolds offer transformative potential in regenerative medicine and in vitro three-dimensional (3D) models, overcoming challenges like vascularization and scalability. By harmonizing biofabrication, functionality, and sustainability, they pave the way for next-generation diagnostics and advanced in vitro 3D testing platforms to revolutionize healthcare and biomedical research.
Contributors
Chinmoy Saha, Dior Beerens, Peter van Baarlen, and Rogier Louwen
Abstract
The field of tissue engineering, regenerative medicine, and in vitro models is undergoing transformative advancements, driven by the urgent need for structures that mimic the complex architecture and functionality of native tissues. Hierarchical scaffolds, characterized by macro-, micro-, and nanoscale features, have emerged as pivotal tools for efficiently regenerate different tissues/organs. These scaffolds can replicate the extracellular matrix with remarkable precision, offering structural support, enhanced cell alignment, and biocompatibility essential for effective tissue regeneration and in vitro models. Herein, the current advances in the biofabrication of hierarchical scaffolds, emphasizing the integration of advanced techniques such as additive manufacturing, electrospinning, freeze-drying, sol-gel, and self-assembly, are discussed. These methods enable the precise design of scaffolds tailored for diverse applications, from bone regeneration to disease modeling. The discussion extends to material innovation and selection criteria, balancing natural and synthetic polymers for optimized biocompatibility, mechanical stability, and controlled degradation. Additionally, sustainability is highlighted, showcasing renewable materials, energy-efficient processes, and circular economy principles that align with environmental responsibility. Hierarchical scaffolds offer transformative potential in regenerative medicine and in vitro three-dimensional (3D) models, overcoming challenges like vascularization and scalability. By harmonizing biofabrication, functionality, and sustainability, they pave the way for next-generation diagnostics and advanced in vitro 3D testing platforms to revolutionize healthcare and biomedical research.
Contributors
Daniela A. R. Santos, Mariana Eiras, Miguel Gonzalez-Santos, Marlene Santos, Carina Pereira, Lúcio Lara Santos, Mário Dinis-Ribeiro & Luís Lima
Abstract
Colorectal cancer screening methods are well established worldwide as a fundamental pilar in CRC management, namely through non-invasive faecal occult blood testing. However, the limited sensitivity of faecal occult blood test for detecting precancerous lesions highlights the need to search for alternative tools, such as microRNAs (miRs). The main aim of this study was to identify stool-based miR profiles for early colorectal cancer detection. A panel with miR-21-5p, miR-199a-5p, and age showed a moderate performance for colorectal cancer detection (sensitivity: 88%). Additionally, miR-451a, miR-21-5p, miR-199a-5p, age, and gender showed high performance for discriminating high-grade dysplasia lesions (sensitivity: 91%). Moreover, when we obtained a positive result in either panel, we achieved a sensitivity of 96% for high-grade dysplasia lesions identification. Finally, when a negative result was obtained in these panels after a positive faecal occult blood test result, we accurately identified individuals without lesions. These findings demonstrate the potential of miR panels as non-invasive biomarkers for colorectal cancer and high-grade dysplasia lesions detection and could constitute a secondary screening method following a positive faecal occult blood test.
Contributors
Tomas Kulhanek, Filip Jezek, Jiri Kofranek, Marek Matejak, Stef Rommes
Abstract
Mechanistic modeling of drug behavior and response isessential for rational drug development and personalizedtherapy, yet constructing, maintaining, reusing andcustomizing complex pharmacokinetic–pharmacodynamic andphysiologically based pharmacokinetic models can beerror-prone when implemented solely via equations or code.We introduce Pharmacolibrary, a free Modelica libraryoffering standardized acausal components forpharmacokinetics, pharmacodynamics,toxicokinetics/toxicodynamics and pharmacogenomics fromcompartmental and physiologically based templates to effectmodels and genotype–phenotype records—to simplify modelreuse, customization, and interoperability. Its utility isshowcased with gentamicin, midazolam, and fentanyl casestudies, including pharmacogenomics-driven clearanceadjustments and pharmacodynamics simulations.
Contributors
Corrales, D., & Insua, D. R.
Abstract
This paper presents a framework for incentivising participation in colorectal cancer (CRC) screening programs from the perspective of a policymaker, assuming that citizens participating in the program have misaligned objectives. For this, it presents novel adversarial risk analysis tools to propose an optimal incentive scheme under uncertainty. The work relies on previous approaches to modelling CRC risk and optimal personalised screening strategies and provides use cases regarding individual and group-based optimal incentives based on a simple financial scheme.
Contributors
Casanova MR., Casanova C R., Reis R L., Oliveira J M.
Abstract
Osteochondral metastasis, involving both bone and adjacent cartilage, poses significant diagnostic challenges due to its anatomical complexity and the subtle onset of molecular changes. Conventional imaging modalities often fail to detect early-stage lesions, underscoring the need for more sensitive and specific diagnostic tools. In response, biomarker-driven strategies, including protein markers, circulating tumor cells (CTCs), extracellular vesicles (EVs), and noncoding RNAs, have emerged as promising alternatives, yet they remain limited by low abundance, specificity, and clinical validation. This chapter explores the transformative potential of biomimetic systems, engineered platforms that mimic the structural and biochemical characteristics of native osteochondral tissue, to overcome these limitations. We highlight advanced fabrication techniques such as electrospinning and 3D bioprinting, which enable the development of high-surface-area, functionalized scaffolds capable of enhanced biomarker capture and real-time biosensing. Emerging applications include nanorobotic sensors, aptamer-based probes, and integrated platforms that combine diagnostics with therapeutic delivery. By bridging biological fidelity with engineering precision, biomimetic systems are redefining the landscape of metastatic cancer diagnostics. This chapter offers a comprehensive overview of these innovations, their clinical relevance, and the future direction toward personalized, minimally invasive, and real-time monitoring of osteotropic cancer spread.
Contributors
Gernot Fiala, Markus Plass, Robert Harb, Peter Regitnig, Kristijan Skok, Wael Al Zoughbi, Carmen Zerner, Paul Torke, Michaela Kargl, Heimo Müller, Tomas Brazdil, Matej Gallo, Jaroslav Kubín, Roman Stoklasa, Rudolf Nenutil, Norman Zerbe, Andreas Holzinger, Petr Holub
Abstract
Contributors
Gernot Fiala, Markus Plass, Robert Harb, Peter Regitnig, Kristijan Skok, Wael Al Zoughbi, Carmen Zerner, Paul Torke, Michaela Kargl, Heimo Müller, Tomas Brazdil, Matej Gallo, Jaroslav Kubín, Roman Stoklasa, Rudolf Nenutil, Norman Zerbe, Andreas Holzinger, Petr Holub
Abstract
A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images are digitally viewable, analyzable, and shareable, and are widely used for Artificial Intelligence (AI) algorithm development. WSIs play an important role in pathology for disease diagnosis and oncology for cancer research, but are also applied in neurology, veterinary medicine, hematology, microbiology, dermatology, pharmacology, toxicology, immunology, and forensic science. When assembling cohorts for AI training or validation, it is essential to know the content of a WSI. However, no standard currently exists for this metadata, and such a selection has largely relied on manual inspection, which is not suitable for large collections with millions of objects. We propose a general framework to generate 2D index maps (tissue maps) that describe the morphological content of WSIs using common syntax and semantics to achieve interoperability between catalogs. The tissue maps are structured in three layers: source, tissue type, and pathological alterations. Each layer assigns WSI segments to specific classes, providing AI-ready metadata. We demonstrate the advantages of this standard by applying AI-based metadata extraction from WSIs to generate tissue maps and integrating them into a WSI archive. This integration enhances search capabilities within WSI archives, thereby facilitating the accelerated assembly of high-quality, balanced, and more targeted datasets for AI training, validation, and cancer research.
Contributors
George Suciu; Cosmina Stalidi; Mustață Carmen; Ecaterina Grecu; Eduard-Cristian Popovici; Ioana Suciu
Abstract
This article explores the current state of wearable health technology, the role of IoT in aiding these innovations, and the potential influence on the future of wellness management. By assessing latest advancements and the challenges associated with their implementation, our objective is to provide an in-depth analysis of the opportunities and restrictions of wearable technology in health maintenance.
Contributors
Mira Raheem, Michael Papazoglou, Bernd Krämer, Neamat El-Tazi, Amal Elgammal
Abstract
Connected health is a multidisciplinary approach focused on health management, prioritizing pa-tient needs in the creation of tools, services, and treatments. This paradigm ensures proactive and efficient care by facilitating the timely exchange of accurate patient information among all stake-holders in the care continuum. The rise of digital technologies and process innovations promises to enhance connected health by integrating various healthcare data sources. This integration aims to personalize care, predict health outcomes, and streamline patient management, though challeng-es remain, particularly in data architecture, application interoperability, and security. Data analytics can provide critical insights for informed decision-making and health co-creation, but solutions must prioritize end-users, including patients and healthcare professionals. This perspective was explored through an agile System Development Lifecycle in an EU-funded project aimed at developing an integrated AI-generated solution for managing cancer patients undergoing immunotherapy. This paper contributes with a collaborative digital framework integrating stakeholders across the care continuum, leveraging federated big data analytics and artificial intelligence for improved decision-making while ensuring privacy. Analytical capabilities, such as treatment recommendations and adverse event predictions, were validated using real-life data, achieving 70%-90% accuracy in a pilot study with the medical partners, demonstrating the framework’s effectiveness.
Contributors
Mira Raheem, Michael Papazoglou, Bernd Krämer, Neamat El-Tazi, Amal Elgammal
Abstract
Personalized chronic care requires the integration of multimodal health data to enable precise, adaptive, and preventive decision-making. Yet most current digital twin (DT) applications remain organ-specific or tied to isolated data types, lacking a unified and privacy-preserving foundation. This paper introduces the Patient Medical Digital Twin (PMDT), an ontology-driven in silico patient framework that integrates physiological, psychosocial, behavioral, and genomic information into a coherent, extensible model. Implemented in OWL 2.0, the PMDT ensures semantic interoperability, supports automated reasoning, and enables reuse across diverse clinical contexts. Its ontology is structured around modular Blueprints (patient, disease and diagnosis, treatment and follow-up, trajectories, safety, pathways, and adverse events), formalized through dedicated conceptual views. These were iteratively refined and validated through expert workshops, questionnaires, and a pilot study in the EU H2020 QUALITOP project with real-world immunotherapy patients. Evaluation confirmed ontology coverage, reasoning correctness, usability, and GDPR compliance. Results demonstrate the PMDT’s ability to unify heterogeneous data, operationalize competency questions, and support descriptive, predictive, and prescriptive analytics in a federated, privacy-preserving manner. By bridging gaps in data fragmentation and semantic standardization, the PMDT provides a validated foundation for next-generation digital health ecosystems, transforming chronic care toward proactive, continuously optimized, and equitable management.
Contributors
Mira Raheem, Amal Elgammal, Michael Papazoglou, Bernd Krämer, Neamat El-Tazi
Abstract
Artificial intelligence (AI) has the potential to transform healthcare by supporting more accurate diagnoses and personalized treatments. However, its adoption in practice remains constrained by fragmented data sources, strict privacy rules, and the technical complexity of building reliable clinical systems. To address these challenges, we introduce a model-driven engineering (MDE) framework designed specifically for healthcare AI. The framework relies on formal metamodels, domain-specific languages (DSLs), and automated transformations to move from high-level specifications to running software. At its core is the Medical Interoperability Language (MILA), a graphical DSL that enables clinicians and data scientists to define queries and machine learning pipelines using shared ontologies. When combined with a federated learning architecture, MILA allows institutions to collaborate without exchanging raw patient data, ensuring semantic consistency across sites while preserving privacy. We evaluate this approach in a multi-center cancer immunotherapy study. The generated pipelines delivered strong predictive performance, with best-performing models achieving up to 98.5% accuracy on selected prediction tasks, while substantially reducing manual coding effort. These findings suggest that MDE principles—metamodeling, semantic integration, and automated code generation—can provide a practical path toward interoperable, reproducible, and reliable digital health platforms.
Contributors
Michael P. Papazoglou, Bernd J. Krämer, Mira Raheem, Amal Elgammal
Abstract
Chronic diseases constitute the principal burden of morbidity, mortality, and healthcare costs worldwide, yet current health systems remain fragmented and predominantly reactive. Patient Medical Digital Twins (PMDTs) offer a paradigm shift: holistic, continuously updated digital counterparts of patients that integrate clinical, genomic, lifestyle, and quality-of-life data. We report early implementations of PMDTs via ontology-driven modeling and federated analytics pilots. Insights from the QUALITOP oncology study and a distributed AI platform confirm both feasibility and challenges: aligning with HL7 FHIR and OMOP standards, embedding privacy governance, scaling federated queries, and designing intuitive clinician interfaces. We also highlight technical gains, such as automated reasoning over multimodal blueprints and predictive analytics for patient outcomes. By reflecting on these experiences, we outline actionable insights for software engineers and identify opportunities, such as DSLs and model-driven engineering, to advance PMDTs toward trustworthy, adaptive chronic care ecosystems.
Contributors
Michael P. Papazoglou, Bernd J. Krämer, Mira Raheem, Amal Elgammal
Abstract
This work introduces the concept of Patient Medical Digital Twins (PMDTs) to simulate treatment outcomes, optimize drug dosages, and deliver personalized chronic care. The PMDT model, supported by an interconnected ecosystem, is validated iteratively by medical institutions to ensure its efficacy and applicability.
At its core, the PMDT leverages expressive knowledge structures to capture a patient’s psychosomatic, cognitive, biometric, and genetic data, creating a comprehensive personal digital footprint. This enables medical professionals to run simulations predicting health issues over time and to proactively implement personalized preventive interventions.
The PMDT ecosystem integrates big data analytics, continuous monitoring, cognitive simulation, and AI technologies. By connecting stakeholders across the care continuum, it provides deeper insights into a patient’s medical history and supports informed, shared decision-making. Validated in a pilot study through an EU-funded healthcare initiative, the PMDT demonstrates its transformative potential at the intersection of Big Data and AI, positioning itself as a critical tool for advancing personalized preventive care.
Contributors
Lina Jegodzinski, Lorena Rudolph, Darko Castven, Friedhelm Sayk, Ashok Kumar Rout, Bandik Föh, Laura Hölzen, Svenja Meyhöfer, Andrea Schenk, Susanne N. Weber, Monika Rau, Sebastian M. Meyhöfer, Jörn M. Schattenberg, Marcin Krawczyk, Andreas Geier, Alvaro Mallagaray, Ulrich L. Günther, Jens U. Marquardt
Abstract
Background & Aims
Methods
Results
PNPLA3 GG displayed a distinct metabolic profile, with notable alterations between fasting and non-fasting states. During the latter, GG carriers showed lower levels of VLDL-1, reflecting impaired triglyceride (TG) efflux from hepatocytes. Following an overnight fast, GG carriers exhibited higher tricarboxylic acid cycle metabolites and ketone bodies, overall indicating increased β-oxidation likely attributed to lower PNPLA3 expression, facilitating unrestricted adipose triglyceride lipase activity and consecutive increased hepatic TG secretion. In addition, the ketogenic amino acid lysine, critical for mitochondrial carnitine transport, was significantly reduced (GG 0.14 ± 0.09 mM vs. CC 0.18 ± 0.08 mM, q = 0.015). Consistently, TGs were enriched in LDL and HDL particles, and an increased number of intermediate-density lipoproteins emerged as a distinct marker in fasted GG carriers (GG 202.9 ± 68.2 mg/dl vs. CC 160.8 ± 65.6 mg/dl, q = 0.007). These metabolic changes were enhanced in patients with type 2 diabetes mellitus and/or obesity.
Conclusions
Impact and Implications