Laboratori Guglielmo Marconi (Italy)

• Enable patient digital twin deployment via a knowledge-driven, modular framework • Provide clinical data access and simulation through a FHIR-compliant API • Publish MIMO ontology to standardize and increase transparency of AI model interfaces • Automate AI model integration using a manifest-based binding protocol • Benchmark stroke risk models with real-time, time-aware clinical data streams Background: The healthcare sector faces diverse challenges, including poor interoperability and a lack of personalized approaches, which limit patient outcomes. Ineffective data exchange and one-size-fits-all treatments fail to meet individual needs. Emerging technologies like digital twins (DTs), the semantic web, and AI show promise in tackling these obstacles. For this reason, we introduced CONNECTED, a conceptual multi-level framework that combines these techniques to deploy general-purpose patient DTs. Objective: This study assesses CONNECTED’s comprehensiveness, applicability, and utility for developing intelligent, personalized healthcare applications. Specifically, we deliver a preliminary version of the framework to predict future patient states and demonstrate its automation benefits in deploying semantically enriched, AI-powered patient DTs. Methods: We enhanced the CONNECTED architecture by providing a formal definition of DT and modularizing its core functionalities into microservices—Properties, State, Capabilities, and Manifest. The Manifest service facilitates AI model integration through the Model Interface Manifest Ontology (MIMO), enabling automatic data-to-model binding via a reasoner. Using the HeartBeatKG quality assessment tool, we validated MIMO and tested the internal logic by integrating a well-established stroke-risk model. Results: Our implementation comprehends: (1) deploying a FHIR-compliant, patient-centric API for clinical history access, real-time monitoring, and predictive simulation; (2) publishing MIMO; (3) establishing the Manifest protocol for seamless, general-purpose AI model integration tailored to individual patient profiles; and (4) a proof-of-concept benchmarking application comparing multiple stroke risk classifiers. Conclusion: CONNECTED establishes a flexible, scalable foundation for interoperable semantic patient DTs. Automation reduces technical overhead and enables users to focus on delivering personalized, insight-driven care.

To explore the adoption and practical implementation of the three major health data standards (i.e., FHIR, OMOP-CDM, and openEHR), to evaluate their maturity level in terms of how extensively they have been applied and integrated into everyday clinical and research practice. We conducted a systematic review registered in PROSPERO (CRD42024623398) following PRISMA guidelines. Literature searches were performed through PubMed, Cochrane, Scopus, Web of Science, and IEEE Xplore from 2021 to 2024. After de-duplication and screening, 99 studies were included. Data was extracted and classified according to five health application domains and five use cases based on the intended purpose of the standard in the work. Studies were assessed for implementation scale, ETL tools, coverage of the standard (i.e., the number of mapped source variables), and whether standards were adapted or used as-is. Of the 99 included studies, 57% used OMOP-CDM, 39% FHIR, and 8% openEHR. Most applications occurred in research settings (87%) and focused on data reuse (47%) or clinical decision support (23%). OMOP-CDM was preferred for large-scale, longitudinal research, while FHIR was dominant in the public health domain and for real-time data exchange. Only 27% of studies reported the coverage of the standard. FHIR implementations often require customization, complicating interoperability. OMOP-CDM offered strong analytical tooling but posed challenges for mapping and data loss. Few studies using openEHR reported limitations, with its uptake remaining limited. Although FHIR, OMOP-CDM, and openEHR hold significant potential to enhance interoperability, their adoption remains fragmented. Each standard shows specific strengths: FHIR for exchange, OMOP-CDM for analytics, and openEHR for data persistence. A hybrid approach and clearer implementation practices are essential to support scalable, interoperable health data ecosystems.

Abstract Controlling near-wall turbulent structures is essential to achieve significant skin-friction drag reduction. Oscillating walls have been shown to reduce friction drag caused by turbulence when actuation is tuned to turbulent time-scales, but reproducing the same flow field without moving surfaces remains a challenge. Plasma actuators based on Dielectric Barrier Discharge (DBD) provide a promising alternative, enabling wall-parallel oscillating flows without mechanical motion. To assess their performance in realistic conditions, actuator geometry and electrical parameters must be optimized for high values of Reynolds number. The CICLoPE Long Pipe facility offers a unique environment for such studies. Here, we present a characterization of the transient response of DBD plasma actuators designed for Re τ > 10, 000. Using Schlieren imaging with high-speed acquisition up to 10, 000 fps , density and temperature fronts induced by plasma actuators have been tracked. A dedicated image-processing algorithm was developed, enabling the study of wall temperature fields induced by the plasma actuator, as well as providing a reliable tracking of the plasma-induced flow and the phase-actuation symmetry.

Software-Defined Networking traditionally relies on the separation of the control and data planes, centralizing network intelligence within a logically unified controller. However, centralizing control functionalities often introduces limitations that negatively impact the overall Quality of Service, particularly in distributed and heterogeneous network scenarios. In this paper, we explore an adaptive approach to orchestrate a microservice-based SDN controller dynamically at the Edge. Building upon our previously introduced frameworks for Microservice-based SDN Controller and for flexible service-model-aware orchestration, we investigate the benefits of adaptively deploying our microservice-based SDN controller’s functionalities at the Edge to enhance QoS. We leverage our orchestration framework to dynamically decide and execute the optimal placement of latency-critical microservices according to real-time monitoring data and evolving user demands. We evaluate the performance by comparing various deployment strategies, focusing on the tradeoff between control plane latency and placement of controller functionalities. Results demonstrate that our adaptive Edge deployment approach has the potential to reduces control plane latency, demonstrating the practical benefits of integrating SDN controller modularity with intelligent service orchestration in dynamic and heterogeneous network environments.

STUDY QUESTIONCan patient-derived organoid models be reliably established from diverse surgical phenotypes of endometriosis, and how do clinical factors such as hormonal treatment affect their growth success and morphology?SUMMARY ANSWEREndometriosis organoids can be established across all major surgical phenotypes with variable efficiency, and hormonal treatment at the time of biospecimen collection significantly reduces organoid establishment success.WHAT IS KNOWN ALREADYOrganoid cultures have been developed from eutopic endometrium and select endometriosis tissue biospecimens previously, but their feasibility as pre-clinical models of endometriosis across diverse tissue types and clinical presentations remains unclear.STUDY DESIGN, SIZE, DURATIONTwenty-eight endometriosis tissue biospecimens were obtained from 23 patients undergoing surgery, with organoid cultures assessed through successive stages of establishment, passage, and cryopreservation.PARTICIPANTS/MATERIALS, SETTING, METHODSEndometriosis biospecimens, including deep infiltrating endometriosis (DIE), ovarian endometrioma (OMA), and superficial peritoneal (SUP) biospecimens, were processed into organoid cultures using a validated low-Wnt culture system. Organoid viability, morphology, hormone receptor expression, and cellular composition were evaluated by microscopy, immunohistochemistry, and quantitative morphometric analysis.MAIN RESULTS AND THE ROLE OF CHANCEOverall, 22/28 (78.6%) biospecimens established 3-dimensional structures, with 15/28 (53.6%) remaining viable after cryopreservation. Establishment success differed by phenotype (OMA 71.4%, DIE 63.6%, SUP 30%). Progesterone receptor expression was retained in SUP and DIE-derived organoids (7/7, 100%), while OMA-derived organoids showed substantial reductions (4/5 cases). Biospecimens from patients receiving hormonal treatment were smaller (P = 0.038) and had reduced organoid establishment success (3/13, 23.1% vs 12/15, 80.0%, P = 0.003). Organoids exhibited distinct morphological patterns correlating with disease phenotype.LIMITATIONS, REASONS FOR CAUTIONUniform culture conditions may limit growth of certain subtypes, and the in vitro organoid models may not fully represent in vivo tissue complexity. Sample sizes were modest, and pooling tissues from the same patient could mask intra-patient heterogeneity.WIDER IMPLICATIONS OF THE FINDINGSThese organoid models offer a promising platform for studying subtype-specific endometriosis biology, including hormone resistance mechanisms, and could inform personalized therapeutic development. The impact of hormonal treatment on organoid viability underscores the need to consider clinical context in pre-clinical models of endometriosis.STUDY FUNDING/COMPETING INTEREST(S)This work was supported by the National Endometriosis Clinical and Scientific Trials (NECST) Network, funded by the Australian Government Department of Health and Aged Care (Grant 4-I66SNMA), and by a research grant from Endometriosis Australia to C.E.F., D.L., and J.A.A. K.G. is supported by an Australian Government Research Training Program Scholarship and a NECST Network Top-Up Scholarship, which did not influence the conduct or outcomes of this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. J.A.A. has received consulting fees from Hologic, Gedeon Richter, and BD, personal payments from Hologic, Bayer, Organon, and Gedeon Richter, travel support from Gedeon Richter, and participated on data safety monitoring advisory boards for Hologic and Gideon Richter. He was the former chair of the Australian Endometriosis Guideline Committee and is the Co-Editor-in-Chief of the Journal of Minimally Invasive Gynaecology. All other authors declare no competing interests.TRIAL REGISTRATION NUMBERN/A.

The Modular Multilevel Converter (MMC) has gained significant popularity over the past decade due to its versatility. The MMC features have been leveraged in numerous fields, including high-voltage DC transmission, electric vehicle power trains, motor drives, and wind energy conversion. In controlling the MMC, the circulating current (i.e., the current flowing through both the upper and lower converter arms without delivering power to the load) has consistently been the most critical variable. In early applications, it was perceived as a source of losses, but more recently, it has become evident that injecting a specific current could reduce voltage and energy ripples. This paper presents a theoretical framework, based on time-scale analysis, useful for modeling and controlling MMCs. The new approach is adopted for generating the circulating current reference, which is expressed as a linear combination of orthogonal functions. The goals are to decouple the control of the voltages of the upper and lower converter arms and manage additional harmonic components of the circulating current for voltage ripple reduction on module capacitors. The simulations and experimental results demonstrate the effectiveness of the proposed control strategy.

Continuous-variable quantum key distribution (CV-QKD) maps information onto the quadrature components of electromagnetic waves, so that off-the-shelf wireless transceivers can be utilized. This motivates the move from optical to Terahertz (THz) bands. However, wireless THz channels suffer from severe path loss, while the mobility of wireless users imposes doubly selective fading. Against this background, we propose a new CV-QKD regime that relies on hybrid beamforming (HBF) assisted multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) system, where the channel’s transmissivity and robustness against double selectivity are overcome by HBF and OTFS, respectively. Secondly, in order to provide channel state information (CSI) for both the transmitter (CSI-T) and receiver (CSI-R), practical channel estimation methods are conceived. They operate in the time-frequency domain for OFDM and in the delay-Doppler domain for OTFS. Thirdly, soft-decision detection is devised for our MIMO OFDM/OTFS aided multi-dimensional reconciliation (MDR) scheme. Low-density parity-check (LDPC) coding is invoked for further improving secure CV-QKD transmission distance in the THz band. Our simulation results demonstrate that the proposed HBF MIMO OTFS-based CV-QKD system relying on realistic estimated CSI is capable of achieving an adequate secret key rate (SKR) and secure transmission distance in hostile doubly selective THz channels.