Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin

Modeling and Remodeling the Cell: How Digital Twins and HCMV Can Elucidate the Complex Interactions of Viral Latency, Epigenetic Regulation, and Immune Responses

Purpose of Review

Human cytomegalovirus (HCMV), while asymptomatic in most, causes significant complications during fetal development, following transplant or in immunosuppressed individuals. The host-virus interactions regulating viral latency and reactivation and viral control of the cellular environment (immune regulation, differentiation, epigenetics) are highly complex. Understanding these processes is essential to controlling infection and can be leveraged as a novel approach for understanding basic cell biology.

Recent Findings

Immune digital twins (IDTs) are digital simulations integrating knowledge of human immunology, physiology, and patient-specific clinical data to predict individualized immune responses and targeted treatments. Recent studies used IDTs to elucidate mechanisms of T cells, dendritic cells, and epigenetic control-all key to HCMV biology.

Summary

Here, we discuss how leveraging the unique biology of HCMV and IDTs will clarify immune response dynamics, host-virus interactions, and viral latency and reactivation and serve as a powerful IDT-validation platform for individualized and holistic health management.

Literature review of digital twin in healthcare

This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified. The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.

Understanding chronic pain in the ubiquitous community: the role of open data

The combined use of social media, open data, and Artificial Intelligence has the potential to support practitioners and empower patients/citizens living with persistent pain, both as local and online communities. Given the wide availability of digital technology today, both practitioners and interested individuals can be connected with virtual communities and can support each other from the comfort of their homes. Digital means may represent new avenues for exploring the complexity of the pain experience. Online interactions of patients, data on effective treatments, and data collected by wearable devices may represent an incredible source of psychological, sociological, and physiological pain-related information. Digital means might provide several solutions that enhance inclusiveness and motivate patients to share personal experiences, limiting the sense of isolation in both rural and metropolitan areas. Building on the consensus of the usefulness of social media in enhancing the understanding of persistent pain and related subjective experiences via online communities and networks, we provide relevant scenarios where the effectiveness and efficiency of healthcare delivery might be improved by the adoption of the digital technologies mentioned above and repeated subsequently. The aim of this perspective paper is to explore the potential of open data, social media, and Artificial Intelligence in improving the prevention and management of persistent pain by adopting innovative non-biomedical approaches.

Digital Twin: A Future Health Challenge in Prevention, Early Diagnosis and Personalisation of Medical Care in Paediatrics

Modern medicine must move from a wait-and-see and remedial system to a preventive and interdisciplinary science that aims to provide patients with personalised and precise treatment planning [...].

What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health

The idea of a digital twin has recently gained widespread attention. While, so far, it has been used predominantly for problems in engineering and manufacturing, it is believed that a digital twin also holds great promise for applications in medicine and health. However, a problem that severely hampers progress in these fields is the lack of a solid definition of the concept behind a digital twin that would be directly amenable for such big data-driven fields requiring a statistical data analysis. In this paper, we address this problem. We will see that the term 'digital twin', as used in the literature, is like a Matryoshka doll. For this reason, we unstack the concept via a data-centric machine learning perspective, allowing us to define its main components. As a consequence, we suggest to use the term Digital Twin System instead of digital twin because this highlights its complex interconnected substructure. In addition, we address ethical concerns that result from treatment suggestions for patients based on simulated data and a possible lack of explainability of the underling models.