Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Consumer Informatics and One Health: Shifting the Focus from the Individual to the Globe. Findings from the Yearbook 2023 Section on Education and Consumer Health Informatics

OBJECTIVE

To summarise the state of the art during the year 2022 in consumer health informatics and education, with a special emphasis on "One Health".

METHODS

We conducted a systematic search of articles published in PubMed. We build queries to merge terms related to "consumer health informatics", "one health", and "digital". We retrieved 94 potential articles for review. These articles were screened according to topic relevance and 12 were selected for consideration of best paper candidates, which were then presented to a panel of international experts for full paper review and scoring. The top five papers were discussed in a consensus meeting. Three papers received the highest score from the expert panel, and these papers were selected to be representative papers on consumer informatics for exploring one health from consumer perspective in the year 2022.

RESULTS

Bibliometrics analysis conducted on words found in abstracts of the 12 candidate papers revealed four clusters of articles, where clustering outcomes explained 96.91% of the dispersion. The first cluster composes three papers related to patient engagement in primary care practices, using digital-delivered diabetes prevention programmes, or exploring citizen involvement in co-designing environmental projects (such as air pollution exposure and health). The second cluster represents four papers related to digital health literacy and consumer behavior, such as digital vaccine literacy, and food labelling influences and whether displaying Nutri- and Eco-Score at food product level led to improved consumer choices. The third cluster consists of two papers exploring strategies to involve citizens in various science projects while analyzing the quality of citizen-collected data (e.g., mosquito bites or gastropod community dataset). The last cluster contains three papers related to the relationships between human behavior with their environment and their contribution to citizen science projects (e.g., biological water quality in the Netherlands distribution, composition, abundance of debris across sandy beaches in Australia and its regions, urbanization and reptile biodiversity across Florida).

CONCLUSION

Traditionally, consumer health informatics focuses on providing individuals with tools and resources to actively manage their own health. By incorporating a global health (or one health) perspective, our field is now at a crossroad, demanding us to think beyond the individual and challenging us to instill the thinking that our actions not only have consequences on the individual but also on the population and the environment. Perhaps this is also a reflective time for the consumer informatics field, to consider shifting the focus from the individual to one that is more aligned with one health, helping consumers gain awareness of how their actions impact on the individual, the population and the environment, and providing them with tools to work collectively to help decide how their actions may bring benefits (as well as harms) across these levels.

Leveraging Data and Technology to Enhance Interdisciplinary Collaboration and Health Outcomes

OBJECTIVE

To give an overview of recent research and propose a selection of best papers published in 2022 in Informatics for One Health.

METHODS

An extensive search using PubMed and Web of Science was conducted to identify peer-reviewed articles published between December 2021 and December 2022, in order to find relevant publications in the 'Informatics for One Health' field. The selection process comprised three steps: (i) eight candidate best papers were first selected by the two section editors; (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper; and (iii) the editorial committee of the Yearbook conducted the final best paper selection.

RESULTS

The candidate best papers represent studies that characterized significant challenges facing Informatics for One Health. Other trends of interest related to the deployment of medical artificial intelligence tools and the implementation of the FAIR principles within the One Health broad scenario. In general, papers identified in the search fell into one of the following categories: 1) Health improvement via digital technology; 2) Climate change/Environment/Biodiversity; and 3) Maturity of healthcare services.

CONCLUSION

The topic turns extremely important in the next future for what concerns the need to understand complex interactions in order to safeguard the health of populations and ecosystems.

Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.

Disease-Gene Networks of Skin Pigmentation Disorders and Reconstruction of Protein-Protein Interaction Networks

Melanin, a light and free radical absorbing pigment, is produced in melanocyte cells that are found in skin, but also in hair follicles, eyes, the inner ear, heart, brain and other organs. Melanin synthesis is the result of a complex network of signaling and metabolic reactions. It therefore comes as no surprise that mutations in many of the genes involved are associated with various types of pigmentation diseases and phenotypes ('pigmentation genes'). Here, we used bioinformatics tools to first reconstruct gene-disease/phenotype associations for all pigmentation genes. Next, we reconstructed protein-protein interaction (PPI) networks centered around pigmentation gene products ('pigmentation proteins') and supplemented the PPI networks with protein expression information obtained by mass spectrometry in a panel of melanoma cell lines (both pigment producing and non-pigment producing cells). The analysis provides a systems network representation of all genes/ proteins centered around pigmentation and melanin biosynthesis pathways ('pigmentation network map'). Our work will enable the pigmentation research community to experimentally test new hypothesis arising from the pigmentation network map and to identify new targets for drug discovery.

Hepatocyte growth factor, colony-stimulating factor 1, CD40, and 11 other inflammation-related proteins are associated with pain in diabetic neuropathy: exploration and replication serum data from the Pain in Neuropathy Study

ABSTRACT

One in 5 patients with diabetes suffers from chronic pain with neuropathic characteristics, but the pathophysiological mechanisms underlying the development of neuropathic pain in patients with diabetic distal symmetrical polyneuropathy (DSP) are poorly understood. Systemic low-grade inflammation has been implicated, but there is still a considerable knowledge gap concerning its scope and meaning in this context. The aim of the study was to establish the broad inflammatory signature of painful diabetic DSP in serum samples from the Pain in Neuropathy Study, an observational cross-sectional multicentre study in which participants underwent deep phenotyping. In the present two cohorts exploration-replication study (180 participants in each cohort), serum samples from Pain in Neuropathy Study participants were analyzed with the Olink INFLAMMATION panel (Olink Bioscience, Uppsala, Sweden) that enables the simultaneous measurement of 92 inflammation-related proteins (mainly cytokines, chemokines, and growth factors). In both the exploration and the replication cohort, we identified a high-inflammation subgroup where 14 inflammation-related proteins in particular were associated with more neuropathy and higher pain intensity. The top 3 proteins were hepatocyte growth factor, colony-stimulating factor 1, and CD40 in both cohorts. In the exploratory cohort, additional clinical data were available, showing an association of inflammation with insomnia and self-reported psychological distress. Hence, this cross-sectional exploration-replication study seems to confirm that low-grade systemic inflammation is related to the severity of neuropathy and neuropathic pain in a subgroup of patients with diabetic DSP. The pathophysiological relevance of these proteins for the development of neuropathic pain in patients with diabetic DSP must be explored in more depth in future studies.

Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

BACKGROUND

The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data 'silos' that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR.

RESULTS

In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors' research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital.

CONCLUSIONS

Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

Reasons for Utilizing Telemedicine during and after the COVID-19 Pandemic: An Internet-Based International Study

The COVID-19 pandemic challenges healthcare services. Concomitantly, this pandemic had a stimulating effect on technological expansions related to telehealth and telemedicine. We sought to elucidate the principal patients' reasons for using telemedicine during the COVID-19 pandemic and the propensity to use it thereafter. Our primary objective was to identify the reasons of the survey participants' disparate attitudes toward the use of telemedicine. We performed an online, multilingual 30-question survey for 14 days during March-April 2021, focusing on the perception and usage of telemedicine and their intent to use it after the pandemic. We analyzed the data to identify the attributes influencing the intent to use telemedicine and built decision trees to highlight the most important related variables. We examined 473 answers: 272 from Israel, 87 from Uruguay, and 114 worldwide. Most participants were women (64.6%), married (63.8%) with 1-2 children (52.9%), and living in urban areas (84.6%). Only a third of the participants intended to continue using telemedicine after the COVID-19 pandemic. Our main findings are that an expected substitution effect, technical proficiency, reduced queueing times, and peer experience are the four major factors in the overall adoption of telemedicine. Specifically, (1) for most participants, the major factor influencing their telemedicine usage is the implicit expectation that such a visit will be a full substitute for an in-person appointment; (2) another factor affecting telemedicine usage by patients is their overall technical proficiency and comfort level in the use of common web-based tools, such as social media, while seeking relevant medical information; (3) time saving as telemedicine can allow for asynchronous communications, thereby reducing physical travel and queuing times at the clinic; and finally (4) some participants have also indicated that telemedicine seems more attractive to them after watching family and friends (peer experience) use it successfully.

The Gene Regulation Knowledge Commons: The action area of GREEKC

The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.org) organized nine workshops in a four-year period, starting September 2016. The workshops brought together a wide range of experts from all over the world working on various parts of the knowledge cycle that is central to understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to update and standardise existing knowledge management workflows, encourage new experimental approaches and thoroughly involve end-users in the process to design the Gene Regulation Knowledge Commons (GRKC). The GREEKC consortium describes its main achievements, contextualised in a state-of-the-art of current tools and resources that today represent the GRKC.

Soluble GC stimulators and activators: Past, present and future

The discovery of soluble GC (sGC) stimulators and sGC activators provided valuable tools to elucidate NO-sGC signalling and opened novel pharmacological opportunities for cardiovascular indications and beyond. The first-in-class sGC stimulator riociguat was approved for pulmonary hypertension in 2013 and vericiguat very recently for heart failure. sGC stimulators enhance sGC activity independent of NO and also act synergistically with endogenous NO. The sGC activators specifically bind to, and activate, the oxidised haem-free form of sGC. Substantial research efforts improved on the first-generation sGC activators such as cinaciguat, culminating in the discovery of runcaciguat, currently in clinical Phase II trials for chronic kidney disease and diabetic retinopathy. Here, we highlight the discovery and development of sGC stimulators and sGC activators, their unique modes of action, their preclinical characteristics and the clinical studies. In the future, we expect to see more sGC agonists in new indications, reflecting their unique therapeutic potential.

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective-Results of a Qualitative Study

Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.

Using Stock-Flow Diagrams to Visualize Theranostic Approaches to Solid Tumors in Personalized Nanomedicine

Personalized nanomedicine has rapidly evolved over the past decade to tailor the diagnosis and treatment of several diseases to the individual characteristics of each patient. In oncology, iron oxide nano-biomaterials (NBMs) have become a promising biomedical product in targeted drug delivery as well as in magnetic resonance imaging (MRI) as a contrast agent and magnetic hyperthermia. The combination of diagnosis and therapy in a single nano-enabled product (so-called theranostic agent) in the personalized nanomedicine has been investigated so far mostly in terms of local events, causes-effects, and mutual relationships. However, this approach could fail in capturing the overall complexity of a system, whereas systemic approaches can be used to study the organization of phenomena in terms of dynamic configurations, independent of the nature, type, or spatial and temporal scale of the elements of the system. In medicine, complex descriptions of diseases and their evolution are daily assessed in clinical settings, which can be thus considered as complex systems exhibiting self-organizing and non-linear features, to be investigated through the identification of dynamic feedback-driven behaviors. In this study, a Systems Thinking (ST) approach is proposed to represent the complexity of the theranostic modalities in the context of the personalized nanomedicine through the setting up of a stock-flow diagram. Specifically, the interconnections between the administration of magnetite NBMs for diagnosis and therapy of tumors are fully identified, emphasizing the role of the feedback loops. The presented approach has revealed its suitability for further application in the medical field. In particular, the obtained stock-flow diagram can be adapted for improving the future knowledge of complex systems in personalized nanomedicine as well as in other nanosafety areas.

In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer

The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.

Reasons for Taking the COVID-19 Vaccine by US Social Media Users

Political and public health leaders promoting COVID-19 vaccination should identify the most relevant criteria driving the vaccination decision. Social media is increasingly used as a source of vaccination data and as a powerful communication tool to increase vaccination. In December 2020, we performed a cross-sectional social media-based survey addressing personal sentiments toward COVID-19 vaccination in the USA. Our primary research objective is to identify socio-demographic characteristics and the reasons for the 1644 survey participants’ attitudes regarding vaccination. We present clear evidence that, contrary to the prevailing public perceptions, young audiences using social media have mostly a positive attitude towards COVID-19 vaccination (81.5%). These younger individuals want to protect their families and their relatives (96.7%); they see vaccination as an act of civic responsibility (91.9%) and express strong confidence in their healthcare providers (87.7%). Another critical factor is the younger population’s fear of personal COVID-19 infection (88.2%); moreover, the greater the number of children the participants have, the greater is their intent to get the COVID-19 vaccine. These results enable a practical public-messaging pathway to reinforce vaccination campaigns addressing the younger population.

One Digital Health: A Unified Framework for Future Health Ecosystems

One Digital Health is a proposed unified structure. The conceptual framework of the One Digital Health Steering Wheel is built around two keys (ie, One Health and digital health), three perspectives (ie, individual health and well-being, population and society, and ecosystem), and five dimensions (ie, citizens’ engagement, education, environment, human and veterinary health care, and Healthcare Industry 4.0). One Digital Health aims to digitally transform future health ecosystems, by implementing a systemic health and life sciences approach that takes into account broad digital technology perspectives on human health, animal health, and the management of the surrounding environment. This approach allows for the examination of how future generations of health informaticians can address the intrinsic complexity of novel health and care scenarios in digitally transformed health ecosystems. In the emerging hybrid landscape, citizens and their health data have been called to play a central role in the management of individual-level and population-level perspective data. The main challenges of One Digital Health include facilitating and improving interactions between One Health and digital health communities, to allow for efficient interactions and the delivery of near–real-time, data-driven contributions in systems medicine and systems ecology. However, digital health literacy; the capacity to understand and engage in health prevention activities; self-management; and collaboration in the prevention, control, and alleviation of potential problems are necessary in systemic, ecosystem-driven public health and data science research. Therefore, people in a healthy One Digital Health ecosystem must use an active and forceful approach to prevent and manage health crises and disasters, such as the COVID-19 pandemic.