One of the many unresolved obstacles in the field of cardiovascular research is an uncompromising in vitro cardiac model. While primary cell sources from animal models offer both advantages and disadvantages, efforts over the past half-century have aimed to reduce their use. Additionally, obtaining a sufficient quantity of human primary cardiomyocytes faces ethical and legal challenges. As the practically unlimited source of human cardiomyocytes from induced pluripotent stem cells (hiPSC-CM) is now mostly resolved, there are great efforts to improve their quality and applicability by overcoming their intrinsic limitations. The greatest bottleneck in the field is the in vitro ageing of hiPSC-CMs to reach a maturity status that closely resembles that of the adult heart, thereby allowing for more appropriate drug developmental procedures as there is a clear correlation between ageing and developing cardiovascular diseases. Here, we review the current state-of-the-art techniques in the most realistic heart models used in disease modelling and toxicity evaluations from hiPSC-CM maturation through heart-on-a-chip platforms and in silico models to the in vitro models of certain cardiovascular diseases.

This article describes the coaching strategies of the NESTORE e-coach, a virtual coach for promoting healthier lifestyles in older age. The novelty of the NESTORE project is the definition of a multi-domain personalized pathway where the e-coach accompanies the user throughout different structured and non-structured coaching activities and recommendations. The article also presents the design process of the coaching strategies, carried out including older adults from four European countries and experts from the different health domains, and the results of the tests carried out with 60 older adults in Italy, Spain and The Netherlands.

This paper presents the decision support system that has been defined and developed under the umbrella of the NESTORE project. The main goal of the proposed system is to help users in selecting coaching plans by proposing personalised recommendations based on their behaviours and preferences. Recognising such behaviours and their evolution over time is therefore a crucial element for tailoring the interaction of the system with the user. A three-layer system composed of pathways, coaching activity plans, and coaching events, constitutes the so-called coaching timeline on which the analysis is grounded. Various techniques are used to model and personalise the recommendations and feedback. Firstly, the indicators are extracted from disparate data sources, then these are modelled through a profiling system and, finally, recommendations on the pathways and coaching plans are performed through a scoring and a tagging system.

Currently, there is no therapy targeting septic cardiomyopathy (SC), a key contributor to organ dysfunction in sepsis. In this study, we used a machine learning (ML) pipeline to explore transcriptomic, proteomic, and metabolomic data from patients with septic shock, and prospectively collected measurements of high-sensitive cardiac troponin and echocardiography. The purposes of the study were to suggest an exploratory methodology to identify and characterise the multiOMICs profile of (i) myocardial injury in patients with septic shock, and of (ii) cardiac dysfunction in patients with myocardial injury. The study included 27 adult patients admitted for septic shock. Peripheral blood samples for OMICS analysis and measurements of high-sensitive cardiac troponin T (hscTnT) were collected at two time points during the ICU stay. A ML-based study was designed and implemented to untangle the relations among the OMICS domains and the aforesaid biomarkers. The resulting ML pipeline consisted of two main experimental phases: recursive feature selection (FS) assessing the stability of biomarkers, and classification to characterise the multiOMICS profile of the target biomarkers. The application of a ML pipeline to circulate OMICS data in patients with septic shock has the potential to predict the risk of myocardial injury and the risk of cardiac dysfunction.

This paper explores the impacts of artificial intelligence (AI) systems on human autonomy and identifies the responsibilities of different stakeholders in addressing them. As AI systems increasingly shape not only the outcomes but also the conditions of everyday decision making, they directly influence individuals’ ability to exercise free and informed choice, a capacity central to democratic participation. Hence, it becomes crucial to examine how the definition, design, and implementation of AI systems may effectively support or constrain autonomy, through a well-defined organizational governance framework. Yet, most existing governance approaches overlook or marginalize the role of autonomy, leaving a critical gap. To address this, we introduce a governance framework structured around a set of diagnostic questions that link design choices to concrete dimensions of human autonomy at both system and user levels, and that map these impacts across the different stages of the decision-making process. The framework clarifies how responsibility for answering these questions can be distributed across different organizational roles, promoting internal reflection and collaboration. A dedicated section illustrates how the framework can be applied in practice to recognize and address autonomy impacts in AI systems within different organizational settings and development processes. This paper provides a practical tool to support autonomy-centred AI governance, grounded in the view that upholding human autonomy today is essential to secure sustainable and democratic futures tomorrow.

(1) Background: Duchenne (DMD) is a rare neuromuscular disease that progressively weakens muscles, which severely impairs gait capacity. The Six Minute-Walk Test (6MWT), which is commonly used to evaluate and monitor the disease’s evolution, presents significant variability due to extrinsic factors such as patient motivation, fatigue, and learning effects. Therefore, there is a clear need for the establishment of precise clinical endpoints to measure patient mobility. (2) Methods: A novel score (6M+ and 2M+) is proposed, which is derived from the use of a new portable monitoring system capable of carrying out a complete gait analysis. The system includes several biomechanical sensors: a heart rate band, inertial measurement units, electromyography shorts, and plantar pressure insoles. The scores were obtained by processing the sensor signals and via gaussian-mixture clustering. (3) Results: The 6M+ and 2M+ scores were evaluated against the North Star Ambulatory Assessment (NSAA), the gold-standard for measuring DMD, and six- and two-minute distances. The 6M+ and 2M+ tests led to superior distances when tested against the NSAA. The 6M+ test and the 2M+ test in particular were the most correlated with age, suggesting that these scores better characterize the gait regressions in DMD. Additionally, the 2M+ test demonstrated an accuracy and stability similar to the 6M+ test. (4) Conclusions: The novel monitoring system described herein exhibited good usability with respect to functional testing in a clinical environment and demonstrated an improvement in the objectivity and reliability of monitoring the evolution of neuromuscular diseases.