A sensitive data access model in support of learning health systems

Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR

Hollow organosilica beads as reference particles for optical detection of extracellular vesicles

Background: The concentration of extracellular vesicles (EVs) in body fluids is a promising biomarker for disease, and flow cytometry remains the clinically most applicable method to identify the cellular origin of single EVs in suspension. To compare concentration measurements of EVs between flow cytometers, solid polystyrene reference beads and EVs were distributed in the first ISTH organized inter-laboratory comparison studies. The beads were used to set size gates based on light scatter, and the concentration of EVs was measured within the size gates. However, polystyrene beads lead to false size determination of EVs due to the mismatch in refractive index between beads and EVs. Moreover, polystyrene beads gate different EV sizes on different flow cytometers. Objective: To prepare, characterize and test hollow organosilica beads (HOBs) as reference beads to set EV size gates in flow cytometry investigations. Methods: HOBs were prepared by a hard template sol-gel method and extensively characterized for morphology, size and colloidal stability. The applicability of HOBs as reference particles was investigated by flow cytometry using HOBs and platelet-derived EVs. Results: HOBs proved monodisperse with homogeneous shell thickness. Two angle light scattering measurements by flow cytometry confirmed that HOBs have light scattering properties similar to platelet-derived EVs. Conclusions: Because HOBs resemble the structure and light scattering properties of EVs, HOBs with a given size will gate EVs of the same size. Therefore, HOBs are ideal reference beads to standardize optical measurements of the EV concentration within a predefined size range

Using an ontology to derive a sharable and interoperable relational data model for heterogeneous healthcare data and various applications

International audienceBackground. A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with its full semantic remains a challenging task. Objectives. This paper addresses the question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and its semantic to support various applications? Method. This paper describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing. Results. This paper (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents , a prototype developed to demonstrate the conversion rules. Conclusion. This work is a first step towards automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out

Size Measurement of Extracellular Vesicles and Synthetic Liposomes: The Impact of the Hydration Shell and the Protein Corona

Size characterization of extracellular vesicles (EVs) and drug delivery liposomes is of great importance in their applications in diagnosis and therapy of diseases. There are many different size characterization techniques used in the field, which often report different size values. Besides technological biases, these differences originate from the fact that various methods measure different physical quantities to determine particle size. In this study, the size of synthetic liposomes with nominal diameters of 50nm and 100nm, and red blood cell-derived EVs (REVs) were measured with established optical methods, such as dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA), and with emerging non-optical methods such as microfluidic resistive pulse sensing (MRPS) and very small-angle neutron scattering (VSANS). The comparison of the hydrodynamic sizes obtained by DLS and NTA with the sizes corresponding to the excluded volume of the particles by MRPS enabled the estimation of the thickness of the hydration shell of the particles. The comparison of diameter values corresponding to the boundary of the phospholipid bilayer obtained from VSANS measurements with MRPS size values revealed the thickness of the polyethylene glycol-layer in case of synthetic liposomes, and the thickness of the protein corona in case of REVs