A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

Objectives: Current research aims to address the challenges of exchanging healthcare information, since when this information has to be shared, this happens by specifically designed medical applications or even by the patients themselves. Among the problems that the Health Information Exchange (HIE) initiative is facing are that (i) third party health data cannot be accessed without internet, (ii) there exist crucial delays in accessing citizens’ data, (iii) the direct HIE can only happen among Healthcare Institutions. Methods: Towards the solution of these issues, a Device-to-Device (D2D) protocol has been specified, running on top of the Bluetooth protocol for efficient data exchange. This research is focused on this D2D protocol, by comparing the different Bluetooth profiles that can be used for transmitting this data, based on specific metrics considering the probabilities of transferring erroneous data. Findings: An evaluation of three Bluetooth profiles takes place, concluding that two of the three profiles must be used to respect the D2D protocol nature and be fully supported by the main market vendors’ operating systems. Novelty:Based on this evaluation, the specified D2D protocol has been built on top of state-of-the-art short-range distance communication technologies, fully supporting the healthcare ecosystem towards the HIE paradigm. Doi: 10.28991/esj-2021-01276 Full Text: PD

Batch and Streaming Data Ingestion towards Creating Holistic Health Records

The healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PD

A Comparative Study of Collaborative Filtering in Product Recommendation

Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PD

Specificity and off-target effects of AAV8-TBG viral vectors for the manipulation of hepatocellular gene expression in mice

Mice are a widely used pre-clinical model system in large part due to their potential for genetic manipulation. The ability to manipulate gene expression in specific cells under temporal control is a powerful experimental tool. The liver is central to metabolic homeostasis and a site of many diseases, making the targeting of hepatocytes attractive. Adeno-associated virus 8 (AAV8) vectors are valuable instruments for the manipulation of hepatocellular gene expression. However, their off-target effects in mice have not been thoroughly explored. Here, we sought to identify the short-term off-target effects of AAV8 administration in mice. To do this, we injected C57BL/6J wild-type mice with either recombinant AAV8 vectors expressing Cre recombinase or control AAV8 vectors and characterised the changes in general health and in liver physiology, histology and transcriptomics compared to uninjected controls. We observed an acute and transient trend for reduction in homeostatic liver proliferation together with induction of the DNA damage marker γH2AX following AAV8 administration. The latter was enhanced upon Cre recombinase expression by the vector. Furthermore, we observed transcriptional changes in genes involved in circadian rhythm and response to infection. Notably, there were no additional transcriptomic changes upon expression of Cre recombinase by the AAV8 vector. Overall, there was no evidence of liver injury, and only mild T-cell infiltration was observed 14 days following AAV8 infection. These data advance the technique of hepatocellular genome editing through Cre-Lox recombination using Cre expressing AAV vectors, demonstrating their minimal effects on murine physiology and highlight the more subtle off target effects of these systems

Internet of Medical Things (IoMT): Acquiring and Transforming Data into HL7 FHIR through 5G Network Slicing

The Healthcare 4.0 era is surrounded by challenges varying from the Internet of Medical Things (IoMT) devices’ data collection, integration and interpretation. Several techniques have been developed that however do not propose solutions that can be applied to different scenarios or domains. When dealing with healthcare data, based on the severity and the application of their results, they should be provided almost in real-time, without any errors, inconsistencies or misunderstandings. Henceforth, in this manuscript a platform is proposed for efficiently managing healthcare data, by taking advantage of the latest techniques in Data Acquisition, 5G Network Slicing and Data Interoperability. In this platform, IoMT devices’ data and network specifications can be acquired and segmented in different 5G network slices according to the severity and the computation requirements of different medical scenarios. In sequel, transformations are performed on the data of each network slice to address data heterogeneity issues, and provide the data of the same network slices into HL7 FHIR-compliant format, for further analysis

p53-mediated redox control promotes liver regeneration and maintains liver function in response to CCl4

The p53 transcription factor coordinates wide-ranging responses to stress that contribute to its function as a tumour suppressor. The responses to p53 induction are complex and range from mediating the elimination of stressed or damaged cells to promoting survival and repair. These activities of p53 can modulate tumour development but may also play a role in pathological responses to stress such as tissue damage and repair. Using a p53 reporter mouse, we have previously detected strong induction of p53 activity in the liver of mice treated with the hepatotoxin carbon tetrachloride (CCl ). Here, we show that p53 functions to support repair and recovery from CCl -mediated liver damage, control reactive oxygen species (ROS) and limit the development of hepatocellular carcinoma (HCC), in part through the activation of a detoxification cytochrome P450, CYP2A5 (CYP2A6 in humans). Our work demonstrates an important role for p53-mediated redox control in facilitating the hepatic regenerative response after damage and identifies CYP2A5/CYP2A6 as a mediator of this pathway with potential prognostic utility in human HCC

Function Secret Sharing for PSI-CA: With Applications to Private Contact Tracing

In this work we describe a token-based solution to Contact Tracing via Distributed Point Functions (DPF) and, more generally, Function Secret Sharing (FSS). The key idea behind the solution is that FSS natively supports secure keyword search on raw sets of keywords without a need for processing the keyword sets via a data structure for set membership. Furthermore, the FSS functionality enables adding up numerical payloads associated with multiple matches without additional interaction. These features make FSS an attractive tool for lightweight privacy-preserving searching on a database of tokens belonging to infected individuals

Autophagy suppresses the formation of hepatocyte-derived cancer-initiating ductular progenitor cells in the liver

Hepatocellular carcinoma (HCC) is driven by repeated rounds of inflammation, leading to fibrosis, cirrhosis, and, ultimately, cancer. A critical step in HCC formation is the transition from fibrosis to cirrhosis, which is associated with a change in the liver parenchyma called ductular reaction. Here, we report a genetically engineered mouse model of HCC driven by loss of macroautophagy and hemizygosity of phosphatase and tensin homolog, which develops HCC involving ductular reaction. We show through lineage tracing that, following loss of autophagy, mature hepatocytes dedifferentiate into biliary-like liver progenitor cells (ductular reaction), giving rise to HCC. Furthermore, this change is associated with deregulation of yes-associated protein and transcriptional coactivator with PDZ-binding motif transcription factors, and the combined, but not individual, deletion of these factors completely reverses the dedifferentiation capacity and tumorigenesis. These findings therefore increase our understanding of the cell of origin of HCC development and highlight new potential points for therapeutic intervention

Absent expansion of AXIN2+ hepatocytes and altered physiology in Axin2CreERT2 mice challenges the role of pericentral hepatocytes in homeostatic liver regeneration

Background & Aims: Mouse models of lineage tracing have helped to describe the important subpopulations of hepatocytes responsible for liver regeneration. However, conflicting results have been obtained from different models. Herein, we aimed to reconcile these conflicting reports by repeating a key lineage-tracing study from pericentral hepatocytes and characterising this Axin2CreERT2 model in detail. Methods: We performed detailed characterisation of the labelled population in the Axin2CreERT2 model. We lineage traced this cell population, quantifying the labelled population over 1 year and performed in-depth phenotypic comparisons, including transcriptomics, metabolomics and analysis of proteins through immunohistochemistry, of Axin2CreERT2 mice to WT counterparts. Results: We found that after careful definition of a baseline population, there are marked differences in labelling between male and female mice. Upon induced lineage tracing there was no expansion of the labelled hepatocyte population in Axin2CreERT2 mice. We found substantial evidence of disrupted homeostasis in Axin2CreERT2 mice. Offspring are born with sub-Mendelian ratios and adult mice have perturbations of hepatic Wnt/β-catenin signalling and related metabolomic disturbance. Conclusions: We find no evidence of predominant expansion of the pericentral hepatocyte population during liver homeostatic regeneration. Our data highlight the importance of detailed preclinical model characterisation and the pitfalls which may occur when comparing across sexes and backgrounds of mice and the effects of genetic insertion into native loci. Impact and implications: Understanding the source of cells which regenerate the liver is crucial to harness their potential to regrow injured livers. Herein, we show that cells which were previously thought to repopulate the liver play only a limited role in physiological regeneration. Our data helps to reconcile differing conclusions drawn from results from a number of prior studies and highlights methodological challenges which are relevant to preclinical models more generally

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

Introduction: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. Aim: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. Methods: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. Results: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. Conclusions: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources