Action of HMGB1 on miR221/222 cluster in neuroblastoma cell lines

microRNA (miR/miRNA) are small non-coding RNAs that control gene expression at the post-transcriptional level by targeting mRNAs. Aberrant expression of miRNAs is often observed in different types of cancer. Specific miRNAs function as tumor suppressors or oncogenes and interfere with various aspects of carcinogenesis, including differentiation, proliferation and invasion. Upregulation of miRNAs 221 and 222 has been shown to induce a malignant phenotype in numerous human cancers via inhibition of phosphatase and tensin homolog (PTEN) expression. Neuroblastoma is the most common extracranial solid malignancy in children, which is characterized by cellular heterogeneity that corresponds to different clinical outcomes. The different cellular phenotypes are associated with different gene mutations and miRs that control genetic and epigenetic factors. For this reason miRs are considered a potential therapeutic target in neuroblastoma. The aim of the present study was to investigate the mechanisms by which extracellular high mobility group box 1 (HMGB1) promotes cell growth in neuroblastoma. SK-N-BE(2) and SH-SY5Y neuroblastoma derived cell lines were transfected with the antisense oligonucleotides, anti-miR-221 and -222, followed by treatment with HMGB1 to investigate the expression of the oncosuppressor PTEN. In this study, it was demonstrated that HMGB1, which is released by damaged cells and tumor cells, upregulates miR-221/222 oncogenic clusters in the two human neuroblastoma derived cell lines. The results revealed that the oncogenic cluster miRs 221/222 were more highly expressed by the most undifferentiated cell line [SK-N-BE(2)] compared with the the less tumorigenic cell line (SH-SY5Y) and that exogenous HMGB1 increases this expression. In addition, HMGB1 modulates PTEN expression via miR-221/222, as demonstrated by transiently blocking miR-221/222 with anti-sense oligonucleotides. These results may lead to the development of novel therapeutic strategies for neuroblastoma

ARCHAEOLOGICAL MATERIALS FROM GABII (CENTRAL ITALY): KNOWLEDGE OF OFFERINGS AND RITUALS AT THE INFANT BURIALS THROUGH AN INTEGRATED APPROACH

The ancient Latin city of Gabii is situated 18 km (11.2 miles) to the east of Rome (Central Italy) along the modern Via Prenestina. Gabii was a renowned city in Roman times, particularly during the Republican period and there are various influences in the site that can be identified in Roman culture itself. Gabii is also one of the most significant and important archaeological sites in the territory of the Municipality of Rome and due to its characteristics, it represents today an extraordinary research context. From the excavations carried out in the past it is possible to see how, under the soil, the main structures and buildings of the ancient city are still largely preserved. Among the various testimonies of the past, the tombs, and the micro and macro remains that these contain, represent an opportunity to investigate such practices in the context of Early Iron Age and Orientalizing Latium. In particular, the finds from the Area D baby burials of Gabii enriched the existing dataset so far significantly, allowing us to explore funerary ritual behavior in a more systematic way. This work reports the results of the detailed examination of four tombs (Tombs 30, 50, 51 and 52) of archaeological site. The field strategy for the excavation of the tombs was geared from the start towards both the systematic retrieval of archaeobotanical and zooarchaeological remains and the sampling for organic residue analysis. Aiming for total recovery, the sediments from the tomb fills were sifted in their entirety as their stratigraphic excavation progressed, and samples were taken for flotation. This careful screening allowed for the detection of concentrations of organic material that represent plant and/or animal depositions. The excavation and removal of the grave goods was carried out following strict protocols for residue sampling, minimizing the risk of organic contamination. Samples were analysed by High Temperature Gas Chromatography/Mass Spectrometry (HTGC/MS) and Gas chromatography/Combustion/Isotope ratio mass spectrometry (GC-C-IRMS). For each burial, a subset of vessels including both closed and open shapes was selected, such as cups, open bowl without foot, amphoretta, amphora with dots, Kantharos, plate on a foot, olla, and olpe in bucchero. The results demonstrate the still largely unexploited potential of this sort of integrated studies, encouraging us to expand the application of chemical methods to contexts from other well–controlled excavations

Glucose-6-phosphate dehydrogenase plays a crucial role in the protection from redox-stress induced apoptosis.

Glucose-6-phosphate dehydrogenase-deleted embryonic stem (ES) cells (G6pdD) proliferate in vitro without special requirements, but when challenged with oxidants fail to sustain glutathione disulphide reconversion to reduced glutathione (GSH), entering a condition of oxidative stress. Here, we investigate the signalling events downstream of GSH oxidation in G6pdD and wild-type (wt) ES cells. We found that G6pdD ES cells are very sensitive to oxidants, activating an apoptotic pathway at oxidant concentrations otherwise sublethal for wt ES cells. We show that the apoptotic pathway activated by low oxidant concentrations is accompanied by mitochondria dysfunction, and it is therefore blocked by the overexpression of Bcl-XL. Bcl-XL does not inhibit the decrease in cellular GSH and reactive oxygen species formation following oxidant treatment. We also found that oxidant treatment in ES cells is followed by the activation of the MEK/ extracellular signal-regulated kinase (ERK) pathway. Interest¬ingly, ERK activation has opposite outcomes in G6pdD ES cells compared to wt, which has a proapoptotic function in the first and a prosurvival function in the latter. We show that this phenomenon can be regulated by the cellular GSH level

Scaling up health knowledge at European level requires sharing integrated data: An approach for collection of database specification

Computerized health care databases have been widely described as an excellent opportunity for research. The availability of “big data” has brought about a wave of innovation in projects when conducting health services research. Most of the available secondary data sources are restricted to the geographical scope of a given country and present heterogeneous structure and content. Under the umbrella of the European Innovation Partnership on Active and Healthy Ageing, collaborative work conducted by the partners of the group on “adherence to prescription and medical plans” identified the use of observational and large-population databases to monitor medication-taking behavior in the elderly. This article describes the methodology used to gather the information from available databases among the Adherence Action Group partners with the aim of improving data sharing on a European level. A total of six databases belonging to three different European countries (Spain, Republic of Ireland, and Italy) were included in the analysis. Preliminary results suggest that there are some similarities. However, these results should be applied in different contexts and European countries, supporting the idea that large European studies should be designed in order to get the most of already available databases

1053pdgenomics features gf and integration with mri radiomics features rf to develop a prognostic model in oral cavity squamous cell carcinoma oscc

n/

A dashboard-based system for supporting diabetes care

[EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿<¿.01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system¿s capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.This work was supported by the European Union in the Seventh Framework Programme, grant number 600914.Dagliati, A.; Sacchi, L.; Tibollo, V.; Cogni, G.; Teliti, M.; Martinez-Millana, A.; Traver Salcedo, V.... (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association. 25(5):538-547. https://doi.org/10.1093/jamia/ocx159S538547255Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. Journal of the American Medical Informatics Association, 8(6), 527-534. doi:10.1136/jamia.2001.0080527Palmer, A. J., Roze, S., Valentine, W. J., Minshall, M. E., Foos, V., Lurati, F. M., … Spinas, G. A. (2004). The CORE Diabetes Model: Projecting Long-term Clinical Outcomes, Costs and Costeffectiveness of Interventions in Diabetes Mellitus (Types 1 and 2) to Support Clinical and Reimbursement Decision-making. Current Medical Research and Opinion, 20(sup1), S5-S26. doi:10.1185/030079904x1980O’Connor, P. J., Bodkin, N. L., Fradkin, J., Glasgow, R. E., Greenfield, S., Gregg, E., … Wysham, C. H. (2011). Diabetes Performance Measures: Current Status and Future Directions. Diabetes Care, 34(7), 1651-1659. doi:10.2337/dc11-0735Donsa, K., Beck, P., Höll, B., Mader, J. K., Schaupp, L., Plank, J., … Pieber, T. R. (2016). Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study. International Journal of Medical Informatics, 90, 58-67. doi:10.1016/j.ijmedinf.2016.03.007Sáenz, A., Brito, M., Morón, I., Torralba, A., García-Sanz, E., & Redondo, J. (2012). Development and Validation of a Computer Application to Aid the Physician’s Decision-Making Process at the Start of and during Treatment with Insulin in Type 2 Diabetes: A Randomized and Controlled Trial. Journal of Diabetes Science and Technology, 6(3), 581-588. doi:10.1177/193229681200600313Ampudia-Blasco, F. J., Benhamou, P. Y., Charpentier, G., Consoli, A., Diamant, M., Gallwitz, B., … Stoevelaar, H. (2015). A Decision Support Tool for Appropriate Glucose-Lowering Therapy in Patients with Type 2 Diabetes. Diabetes Technology & Therapeutics, 17(3), 194-202. doi:10.1089/dia.2014.0260Lim, S., Kang, S. M., Shin, H., Lee, H. J., Won Yoon, J., Yu, S. H., … Jang, H. C. (2011). Improved Glycemic Control Without Hypoglycemia in Elderly Diabetic Patients Using the Ubiquitous Healthcare Service, a New Medical Information System. Diabetes Care, 34(2), 308-313. doi:10.2337/dc10-1447Lipton, J. A., Barendse, R. J., Akkerhuis, K. M., Schinkel, A. F. L., & Simoons, M. L. (2010). Evaluation of a Clinical Decision Support System for Glucose Control. Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine, 9(3), 140-147. doi:10.1097/hpc.0b013e3181e7d7caNeubauer, K. M., Mader, J. K., Höll, B., Aberer, F., Donsa, K., Augustin, T., … Pieber, T. R. (2015). Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards. Diabetes Technology & Therapeutics, 17(10), 685-692. doi:10.1089/dia.2015.0027Rodbard, D., & Vigersky, R. A. (2011). Design of a Decision Support System to Help Clinicians Manage Glycemia in Patients with Type 2 Diabetes Mellitus. Journal of Diabetes Science and Technology, 5(2), 402-411. doi:10.1177/193229681100500230Augstein, P., Vogt, L., Kohnert, K.-D., Heinke, P., & Salzsieder, E. (2010). Translation of Personalized Decision Support into Routine Diabetes Care. Journal of Diabetes Science and Technology, 4(6), 1532-1539. doi:10.1177/193229681000400631Reza, A. W., & Eswaran, C. (2009). A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy. Journal of Medical Systems, 35(1), 17-24. doi:10.1007/s10916-009-9337-yKumar, S. J. J., & Madheswaran, M. (2012). An Improved Medical Decision Support System to Identify the Diabetic Retinopathy Using Fundus Images. Journal of Medical Systems, 36(6), 3573-3581. doi:10.1007/s10916-012-9833-3Cho, B. H., Yu, H., Kim, K.-W., Kim, T. H., Kim, I. Y., & Kim, S. I. (2008). Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artificial Intelligence in Medicine, 42(1), 37-53. doi:10.1016/j.artmed.2007.09.005Cleveringa, F. G. W., Gorter, K. J., van den Donk, M., & Rutten, G. E. H. M. (2008). Combined Task Delegation, Computerized Decision Support, and Feedback Improve Cardiovascular Risk for Type 2 Diabetic Patients: A cluster randomized trial in primary care. Diabetes Care, 31(12), 2273-2275. doi:10.2337/dc08-0312Haussler, B., Fischer, G. C., Meyer, S., & Sturm, D. (2007). Risk assessment in diabetes management: how do general practitioners estimate risks due to diabetes? Quality and Safety in Health Care, 16(3), 208-212. doi:10.1136/qshc.2006.019539Heselmans, A., Van de Velde, S., Ramaekers, D., Vander Stichele, R., & Aertgeerts, B. (2013). Feasibility and impact of an evidence-based electronic decision support system for diabetes care in family medicine: protocol for a cluster randomized controlled trial. Implementation Science, 8(1). doi:10.1186/1748-5908-8-83Koopman, R. J., Kochendorfer, K. M., Moore, J. L., Mehr, D. R., Wakefield, D. S., Yadamsuren, B., … Belden, J. L. (2011). A Diabetes Dashboard and Physician Efficiency and Accuracy in Accessing Data Needed for High-Quality Diabetes Care. The Annals of Family Medicine, 9(5), 398-405. doi:10.1370/afm.1286Den Ouden, H., Vos, R. C., Reidsma, C., & Rutten, G. E. (2015). Shared decision making in type 2 diabetes with a support decision tool that takes into account clinical factors, the intensity of treatment and patient preferences: design of a cluster randomised (OPTIMAL) trial. BMC Family Practice, 16(1). doi:10.1186/s12875-015-0230-0Holbrook, A., Thabane, L., Keshavjee, K., Dolovich, L., Bernstein, B., … Chan, D. (2009). Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. Canadian Medical Association Journal, 181(1-2), 37-44. doi:10.1503/cmaj.081272O’Reilly, D., Holbrook, A., Blackhouse, G., Troyan, S., & Goeree, R. (2012). Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records. Journal of the American Medical Informatics Association, 19(3), 341-345. doi:10.1136/amiajnl-2011-000371Parker, R. F., Mohamed, A. Z., Hassoun, S. A., Miles, S., & Fernando, D. J. S. (2014). The Effect of Using a Shared Electronic Health Record on Quality of Care in People With Type 2 Diabetes. Journal of Diabetes Science and Technology, 8(5), 1064-1065. doi:10.1177/1932296814536880Caban, J. J., & Gotz, D. (2015). Visual analytics in healthcare - opportunities and research challenges. Journal of the American Medical Informatics Association, 22(2), 260-262. doi:10.1093/jamia/ocv006Mick, J. (2011). Data-Driven Decision Making. JONA: The Journal of Nursing Administration, 41(10), 391-393. doi:10.1097/nna.0b013e31822edb8cBatley, N. J., Osman, H. O., Kazzi, A. A., & Musallam, K. M. (2011). Implementation of an Emergency Department Computer System: Design Features That Users Value. The Journal of Emergency Medicine, 41(6), 693-700. doi:10.1016/j.jemermed.2010.05.014Sprague, A. E., Dunn, S. I., Fell, D. B., Harrold, J., Walker, M. C., Kelly, S., & Smith, G. N. (2013). Measuring Quality in Maternal-Newborn Care: Developing a Clinical Dashboard. Journal of Obstetrics and Gynaecology Canada, 35(1), 29-38. doi:10.1016/s1701-2163(15)31045-8WILBANKS, B. A., & LANGFORD, P. A. (2014). A Review of Dashboards for Data Analytics in Nursing. CIN: Computers, Informatics, Nursing, 32(11), 545-549. doi:10.1097/cin.0000000000000106Hartzler, A. L., Izard, J. P., Dalkin, B. L., Mikles, S. P., & Gore, J. L. (2015). Design and feasibility of integrating personalized PRO dashboards into prostate cancer care. Journal of the American Medical Informatics Association, 23(1), 38-47. doi:10.1093/jamia/ocv101Dixon, B. E., Jabour, A. M., Phillips, E. O., & Marrero, D. G. (2014). An informatics approach to medication adherence assessment and improvement using clinical, billing, and patient-entered data. Journal of the American Medical Informatics Association, 21(3), 517-521. doi:10.1136/amiajnl-2013-001959Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130. doi:10.1136/jamia.2009.000893Shahar, Y., & Musen, M. A. (1996). Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine, 8(3), 267-298. doi:10.1016/0933-3657(95)00036-4Sacchi, L., Capozzi, D., Bellazzi, R., & Larizza, C. (2015). JTSA: An open source framework for time series abstractions. Computer Methods and Programs in Biomedicine, 121(3), 175-188. doi:10.1016/j.cmpb.2015.05.006Dagliati, A., Sacchi, L., Zambelli, A., Tibollo, V., Pavesi, L., Holmes, J. H., & Bellazzi, R. (2017). Temporal electronic phenotyping by mining careflows of breast cancer patients. Journal of Biomedical Informatics, 66, 136-147. doi:10.1016/j.jbi.2016.12.012Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. doi:10.1136/amiajnl-2012-001145Bijlsma, M. J., Janssen, F., & Hak, E. (2015). Estimating time-varying drug adherence using electronic records: extending the proportion of days covered (PDC) method. Pharmacoepidemiology and Drug Safety, 25(3), 325-332. doi:10.1002/pds.3935Robusto, F., Lepore, V., D’Ettorre, A., Lucisano, G., De Berardis, G., Bisceglia, L., … Nicolucci, A. (2016). The Drug Derived Complexity Index (DDCI) Predicts Mortality, Unplanned Hospitalization and Hospital Readmissions at the Population Level. PLOS ONE, 11(2), e0149203. doi:10.1371/journal.pone.0149203De Berardis, G., D’Ettorre, A., Graziano, G., Lucisano, G., Pellegrini, F., Cammarota, S., … Nicolucci, A. (2012). The burden of hospitalization related to diabetes mellitus: A population-based study. Nutrition, Metabolism and Cardiovascular Diseases, 22(7), 605-612. doi:10.1016/j.numecd.2010.10.016Van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. R. (2011). A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies. Journal of Medical Internet Research, 13(4), e111. doi:10.2196/jmir.1672Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1-2), 79-133. doi:10.1016/s0004-3702(96)00025-2Tenenbaum, J. D., Avillach, P., Benham-Hutchins, M., Breitenstein, M. K., Crowgey, E. L., Hoffman, M. A., … Freimuth, R. R. (2016). An informatics research agenda to support precision medicine: seven key areas. Journal of the American Medical Informatics Association, 23(4), 791-795. doi:10.1093/jamia/ocv213Bottomly, D., McWeeney, S. K., & Wilmot, B. (2015). HitWalker2: visual analytics for precision medicine and beyond. Bioinformatics, 32(8), 1253-1255. doi:10.1093/bioinformatics/btv739Fabris, C., Facchinetti, A., Fico, G., Sambo, F., Arredondo, M. T., & Cobelli, C. (2015). Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis. Journal of Diabetes Science and Technology, 10(1), 119-124. doi:10.1177/1932296815596173Hassenzahl, M., Wiklund-Engblom, A., Bengs, A., Hägglund, S., & Diefenbach, S. (2015). Experience-Oriented and Product-Oriented Evaluation: Psychological Need Fulfillment, Positive Affect, and Product Perception. International Journal of Human-Computer Interaction, 31(8), 530-544. doi:10.1080/10447318.2015.106466

A bio-guided assessment of the anti-inflammatory activity of hop extracts (Humulus lupulus L. cv. Cascade) in human gastric epithelial cells

The present work aims to characterize and investigate the anti-inflammatory activity of hop extracts (cv. Cascade) in an in vitro model of gastric inflammation. The biological activities of hydroalcoholic and aqueous extracts from cones were evaluated by comparing IL-8 inhibition induced by TNF\u3b1. The hydroalcoholic extract demonstrated a higher inhibitory effect, which was just slightly affected by an in vitro simulated gastric digestion. The identification of active compounds was performed by a bio-guided fractionation which afforded 11 fractions, one of which inhibited IL-8 release in a concentration-dependent fashion in human gastric epithelial AGS cells. Phytochemical analysis revealed xanthohumol A and xanthohumol D as the main active components. The present study provides some experimental evidences that Humulus lupulus L. may exert an anti-inflammatory activity on the gastric district by the inhibition of the IL-8 secretion, partially due to its prenylated chalcones content

Nutritional Characterization and Phenolic Profiling of Moringa oleifera Leaves Grown in Chad, Sahrawi Refugee Camps, and Haiti

Moringa oleifera is a plant that grows in tropical and subtropical areas of the world. Its leaves are rich of nutrients and bioactive compounds. However, several differences are reported in the literature. In this article we performed a nutritional characterization and a phenolic profiling of M. oleifera leaves grown in Chad, Sahrawi refugee camps, and Haiti. In addition, we investigated the presence of salicylic and ferulic acids, two phenolic acids with pharmacological activity, whose presence in M. oleifera leaves has been scarcely investigated so far. Several differences were observed among the samples. Nevertheless, the leaves were rich in protein, minerals, and \u3b2-carotene. Quercetin and kaempferol glycosides were the main phenolic compounds identified in the methanolic extracts. Finally, salicylic and ferulic acids were found in a concentration range of 0.14-0.33 and 6.61-9.69 mg/100 g, respectively. In conclusion, we observed some differences in terms of nutrients and phenolic compounds in M. oleifera leaves grown in different countries. Nevertheless, these leaves are a good and economical source of nutrients for tropical and sub-tropical countries. Furthermore, M. oleifera leaves are a source of flavonoids and phenolic acids, among which salicylic and ferulic acids, and therefore they could be used as nutraceutical and functional ingredients

GATEKEEPER’s Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases

Background: The World Health Organization’s strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs. Objective: We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform. Methods: The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities. Results: Seven European countries were selected, covering Europe’s geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence–based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors. Conclusions: This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space