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Semantic Interoperability and Applications in Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 26296

Special Issue Editors


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Guest Editor
Institute of Informatics and Telematics, Italian National Research Council, Rome, Italy
Interests: semantic interoperability; terminologies and ontologies; eHealth; knowledge management and knowledge representation; decision support systems; term extraction; electronic health records; patient empowerment

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Guest Editor
Department of Basic and Applied Medical Sciences, University of Ghent, Gent, Belgium
Interests: clinical pharmacology; epidemiology; healthcare terminologies; semantic interoperability; eHealth; electronic health records; clinical models

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Guest Editor
Visiting Professor of Health Informatics, University of Gent, Gent, Belgium
Interests: electronic health records; information models and interoperability standards; clinical models and semantic interoperability; EHR data quality; data protection and information security safeguards; personal health records and patient empowerment; mHealth; standards development

Special Issue Information

Dear Colleagues,

Healthcare information is composed of many types of varying and heterogeneous data. Semantic interoperability in healthcare is especially important as all these different types of data need to be constantly exchanged between information systems. The use of semantic resources (e.g., clinical terminologies, biomedical ontologies, linguistic resources, clinical data models, etc.) and of knowledge engineering (KE) methodologies is essential for achieving semantic interoperability and thus improving communication in healthcare and ultimately benefiting patient care. The representation of complex medical information requires adequate structuring to foster correct sharing and interpretation. More specifically, coding and classification systems are essential instruments for the unambiguous identification of clinical concepts during the process of care and during the delivery of health services. The rapid evolution of health informatics and of eHealth platforms in the world, and in Europe in particular, has improved access to medical information and permits the elaboration of learning healthcare information systems.

Moreover, in recent years, more and more artificial intelligence (AI), natural language processing (NLP), and knowledge engineering applications in the medical domain have been proposed to facilitate clinical decision support, coding support, and electronic medical record (EMR) data extraction and alignment. EMRs record a huge amount of patient data, including episodes of care, clinical history, vaccinations, medications, adverse events, diagnoses, laboratory results, and observations. The analysis and interpretation of these data is of great importance for the community, especially considering the recent need for data sharing during the evolution of the global SARS-CoV-2 pandemic.

In this context, patient empowerment and the increasing demand for continuity of care requires the publication of numerous standards to govern and guide the electronic exchange and storage of medical information. Data captured by patients is a rapidly growing area, with the proposal of huge numbers of specific applications, but interoperability with data captured by clinicians still needs innovative solutions. The application of semantic resources and technologies together with the implementation of standards are challenges for improving the semantic interoperability of clinical information and can be useful at different levels (e.g., for clinical purposes, the exploitation of data for decision support, communication with the patient, information retrieval, research purposes, and public health policy). Finally, part of the semantic interoperability challenge is how to bridge between clinical research and clinical care.

We are pleased to invite you to submit original research work covering innovative methods and meaningful applications that can potentially lead to significant advances in the field.

This Special Issue aims to raise awareness for the need to guarantee semantic interoperability in healthcare and thus to integrate structured and unstructured data and encourage the research community to develop approaches and tools based on standards, AI and KE technologies to improve semantic interoperability, and data analysis and interpretation in the field of healthcare.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

  • Knowledge management of health data;
  • Terminology and ontology in the semantic interoperability of electronic health records (EHRs);
  • Biomedical knowledge representation and reasoning;
  • Semantic extraction and annotation of biomedical text and data;
  • Semantic and rule-based systems for healthcare data interpretation, coding, and presentation;
  • Implementation of standards and services for semantic interoperability;
  • AI methodologies for medical data analysis;
  • Medical data acquisition, cleaning, and integration using AI methodologies;
  • Natural language processing in medical documents;
  • Global identification of medicinal products.

We look forward to receiving your contributions.

Dr. Elena Cardillo
Prof. Dr. Robert Vander Stichele
Prof. Dr. Dipak Kalra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • semantic interoperability
  • learning healthcare systems
  • clinical models
  • terminologies
  • knowledge representation
  • decision support systems
  • patient empowerment and health literacy
  • AI application in healthcare
  • NLP application in healthcare

Published Papers (10 papers)

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Research

Jump to: Review

20 pages, 658 KiB  
Article
LUMA: A Mapping Assistant for Standardizing the Units of LOINC-Coded Laboratory Tests
by Kai Vogl, Josef Ingenerf, Jan Kramer, Christine Chantraine and Cora Drenkhahn
Appl. Sci. 2022, 12(12), 5848; https://doi.org/10.3390/app12125848 - 8 Jun 2022
Cited by 3 | Viewed by 2108
Abstract
The coding system Unified Code for Units of Measure (UCUM) serves the unambiguous electronic communication of physical quantities and their measurements and has faced a slow uptake. Despite being closely related to popular healthcare standards such as LOINC, laboratories still majorly report results [...] Read more.
The coding system Unified Code for Units of Measure (UCUM) serves the unambiguous electronic communication of physical quantities and their measurements and has faced a slow uptake. Despite being closely related to popular healthcare standards such as LOINC, laboratories still majorly report results using proprietary unit terms. Currently available methods helping users create mappings between their units and UCUM are not flexible and automated enough to be of great use in trying to remedy this. We propose the “LOINC to UCUM Mapping Assistant” (LUMA) as a tool able to overcome the drawbacks of existing approaches while being more accessible even to inexperienced users. By mapping LOINC’s Property axis to representations within UCUM reflecting its semantics, we were able to formalize the association between the two. An HL7 FHIR back-end provides LUMA with UCUM unit recommendations sourced from existing lookup tables simply by providing it with a LOINC code. Additionally, the mappings users created may be used to perform unit conversions from proprietary units to UCUM. The tool was evaluated with five participants from the LADR laboratory network in Germany, who valued the streamlined approach to creating the mappings and particularly emphasized the utility of being able to perform unit conversions within the tool. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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11 pages, 812 KiB  
Article
How Granular Can a Dose Form Be Described? Considering EDQM Standard Terms for a Global Terminology
by Robert H. Vander Stichele, Joseph Roumier and Dirk van Nimwegen
Appl. Sci. 2022, 12(9), 4337; https://doi.org/10.3390/app12094337 - 25 Apr 2022
Cited by 1 | Viewed by 2035
Abstract
The aim was (1) to analyse the features of the EDQM terminology, (2) to formulate proposals for minor changes and (3) to create a small ontology of dose forms, based on characteristics of EDQM, and suitable for alignment with other dose form terminologies. [...] Read more.
The aim was (1) to analyse the features of the EDQM terminology, (2) to formulate proposals for minor changes and (3) to create a small ontology of dose forms, based on characteristics of EDQM, and suitable for alignment with other dose form terminologies. The 428 Pharmaceutical Dose Forms (PDF) (“human and veterinary” only) were extracted from the EDQM Standard Terms database. A quantitative and qualitative analysis of the textual definitions of the terms was conducted. Through an analysis of unique combinations of different sets of descriptors and characteristics, a small ontology was built in three levels. For the 143 transformable PDFs, the administrable dose form was made explicit, with 121 requiring only one transformation and 22 multiple transformations, of which 10 include “no transformation”. Different levels of aggregations of the 428 PDFs were tested in 4 analyses, ranging from 206 to 383 unique combinations. An ontology in Webprotégé was created of 22 higher-level concepts (based on the intended site characteristics) and 69 intermediate-level terms (newly created) to accommodate the 428 PDFs of EDQM. EDQM Dose Form terminology is suitable terminology in terms of granularity, for defining dose forms of medicinal products, to enable fair comparison of similar medicinal products, and global identification of medicinal products (IDMP). Recommendations for minor improvements and a simple ontology for dose forms are proposed. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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20 pages, 4018 KiB  
Article
MedicalForms: Integrated Management of Semantics for Electronic Health Record Systems and Research Platforms
by Jesus Moreno-Conde, Samuel Salas-Fernandez and Alberto Moreno-Conde
Appl. Sci. 2022, 12(9), 4322; https://doi.org/10.3390/app12094322 - 25 Apr 2022
Cited by 1 | Viewed by 1691
Abstract
(1) Background: Clinical information modeling tools are software instruments designed to support the definition of semantic structures able to be implemented in health information systems. Based on the analysis of existing tools, this research developed a tool that proposes new approaches to promoting [...] Read more.
(1) Background: Clinical information modeling tools are software instruments designed to support the definition of semantic structures able to be implemented in health information systems. Based on the analysis of existing tools, this research developed a tool that proposes new approaches to promoting clinician involvement and supporting information modeling processes through mechanisms that ensure governance, information consistency and consensus building. (2) Method: This research developed the MedicalForms system, which is based on the requirements identified in both a Delphi study about tool requirements and the ISO/TS 13972 specifications. (3) Results: This system allows the management of projects, information structures and implementable forms related to clinical documentation. Users can easily define clinical documents in collaboration with the rest of the professionals in their team by being able to reuse previously defined forms, terminologies and information structures. The system is able to export the defined forms as interoperable specifications or as several implementable form formats compatible with multiple open source EHR systems and research platforms. End user perception of this tool was evaluated through the Technology Acceptance Questionnaire with satisfactory results. Finally, the system was applied to develop 12 research registries and 2 clinical trial research forms, 3 mobile applications and 1 decision support system. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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13 pages, 2917 KiB  
Article
Ensuring the Long-Term Preservation of and Access to the Italian Federated Electronic Health Record
by Maria Teresa Guaglianone, Giovanna Aracri, Maria Teresa Chiaravalloti, Elena Cardillo, Camillo Francesco Arena, Elisa Sorrentino and Anna Federica Spagnuolo
Appl. Sci. 2022, 12(7), 3304; https://doi.org/10.3390/app12073304 - 24 Mar 2022
Viewed by 2005
Abstract
The Italian Electronic Health Record (called the FSE) is based on a federated architectural model and involves various types of health data and documents deriving from public and private health stakeholders. Clinical documents are stored in repositories and indexed in a central regional [...] Read more.
The Italian Electronic Health Record (called the FSE) is based on a federated architectural model and involves various types of health data and documents deriving from public and private health stakeholders. Clinical documents are stored in repositories and indexed in a central regional index (registry) according to a defined metadata schema. The FSE repositories can be distributed in the regional network or centralized at the regional level, or regions can use subsidiarity services offered by the National Infrastructure for the regional FSEs Interoperability. In this scenario, this study addresses the open issue of the FSE documents’ long-term preservation and access over time, since the responsibility of their preservation is distributed among different stakeholders. As a consequence, the process traceability is necessary to ensure that a document indexed in an FSE is accessible over time, regardless of the document local discard policies applied for legal fulfilments. The results of this study show that the enhancement of metadata use could support the management and long-term preservation of the FSE documents. Addressing this issue is, finally, fundamental to guarantee the correct tracing and access to the clinical path of a patient and to ensure the efficiency of the entire care setting. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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28 pages, 2100 KiB  
Article
Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines
by Carlo Allocca, Samia Jilali, Rohit Ail, Jaehun Lee, Byungho Kim, Alessio Antonini, Enrico Motta, Julia Schellong, Lisa Stieler, Muhammad Salman Haleem, Eleni Georga, Leandro Pecchia, Eugenio Gaeta and Giuseppe Fico
Appl. Sci. 2022, 12(4), 1776; https://doi.org/10.3390/app12041776 - 9 Feb 2022
Cited by 2 | Viewed by 2890
Abstract
The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of [...] Read more.
The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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15 pages, 2122 KiB  
Article
How Can a Clinical Data Modelling Tool Be Used to Represent Data Items of Relevance to Paediatric Clinical Trials? Learning from the Conect4children (c4c) Consortium
by Chima Amadi, Rebecca Leary, Avril Palmeri, Victoria Hedley, Anando Sen, Rahil Qamar Siddiqui, Dipak Kalra and Volker Straub
Appl. Sci. 2022, 12(3), 1604; https://doi.org/10.3390/app12031604 - 2 Feb 2022
Cited by 3 | Viewed by 2233
Abstract
Data dictionaries for clinical trials are often created manually, with data structures and controlled vocabularies specific for a trial or family of trials within a sponsor’s portfolio. Microsoft Excel is commonly used to capture the representation of data dictionary items but has limited [...] Read more.
Data dictionaries for clinical trials are often created manually, with data structures and controlled vocabularies specific for a trial or family of trials within a sponsor’s portfolio. Microsoft Excel is commonly used to capture the representation of data dictionary items but has limited functionality for this purpose. The conect4children (c4c) network is piloting the Direcht clinical data modelling tool to model their Cross Cutting Paediatric Data Dictionary (CCPDD) in a more formalised way. The first pilot had the key objective of testing whether a clinical data modelling tool could be used to represent data items from the CCPDD. The key objective of the second pilot is to establish whether a small team with little or no experience of clinical data modelling can use Direcht to expand the CCPDD. Clinical modelling is the process of structuring clinical data so it can be understood by computer systems and humans. The model contains all of the elements that are needed to define the data item. Results from the pilots show that Direcht creates a structured environment to build data items into models that fit into the larger CCPDD. Models can be represented as an HTML document, mind map, or exported in various formats for import into a computer system. Challenges identified over the course of both pilots are being addressed with c4c partners and external stakeholders. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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12 pages, 1149 KiB  
Article
Using the Data Quality Dashboard to Improve the EHDEN Network
by Clair Blacketer, Erica A. Voss, Frank DeFalco, Nigel Hughes, Martijn J. Schuemie, Maxim Moinat and Peter R. Rijnbeek
Appl. Sci. 2021, 11(24), 11920; https://doi.org/10.3390/app112411920 - 15 Dec 2021
Cited by 3 | Viewed by 3564
Abstract
Federated networks of observational health databases have the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN [...] Read more.
Federated networks of observational health databases have the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN COVID-19 Rapid Collaboration Call presented the opportunity to assess how the newly developed open-source tool Data Quality Dashboard (DQD) informs the quality of data in a federated network. Fifteen Data Partners (DPs) from 10 different countries worked with the EHDEN taskforce to map their data to the OMOP CDM. Throughout the process at least two DQD results were collected and compared for each DP. All DPs showed an improvement in their data quality between the first and last run of the DQD. The DQD excelled at helping DPs identify and fix conformance issues but showed less of an impact on completeness and plausibility checks. This is the first study to apply the DQD on multiple, disparate databases across a network. While study-specific checks should still be run, we recommend that all data holders converting their data to the OMOP CDM use the DQD as it ensures conformance to the model specifications and that a database meets a baseline level of completeness and plausibility for use in research. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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10 pages, 3834 KiB  
Article
DCC Terminology Service—An Automated CI/CD Pipeline for Converting Clinical and Biomedical Terminologies in Graph Format for the Swiss Personalized Health Network
by Philip Krauss, Vasundra Touré, Kristin Gnodtke, Katrin Crameri and Sabine Österle
Appl. Sci. 2021, 11(23), 11311; https://doi.org/10.3390/app112311311 - 29 Nov 2021
Cited by 4 | Viewed by 2557
Abstract
One goal of the Swiss Personalized Health Network (SPHN) is to provide an infrastructure for FAIR (Findable, Accessible, Interoperable and Reusable) health-related data for research purposes. Semantic web technology and biomedical terminologies are key to achieving semantic interoperability. To enable the integrative use [...] Read more.
One goal of the Swiss Personalized Health Network (SPHN) is to provide an infrastructure for FAIR (Findable, Accessible, Interoperable and Reusable) health-related data for research purposes. Semantic web technology and biomedical terminologies are key to achieving semantic interoperability. To enable the integrative use of different terminologies, a terminology service is a important component of the SPHN Infrastructure for FAIR data. It provides both the current and historical versions of the terminologies in an SPHN-compliant graph format. To minimize the usually high maintenance effort of a terminology service, we developed an automated CI/CD pipeline for converting clinical and biomedical terminologies in an SPHN-compatible way. Hospitals, research infrastructure providers, as well as any other data providers, can download a terminology bundle (currently composed of SNOMED CT, LOINC, UCUM, ATC, ICD-10-GM, and CHOP) and deploy it in their local terminology service. The distributed service architecture allows each party to fulfill their local IT and security requirements, while still having an up-to-date interoperable stack of SPHN-compliant terminologies. In the future, more terminologies and mappings will be added to the terminology service according to the needs of the SPHN community. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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19 pages, 871 KiB  
Article
Information Extraction from German Clinical Care Documents in Context of Alzheimer’s Disease
by Lisa Langnickel, Kilian Krockauer, Mischa Uebachs, Sebastian Schaaf, Sumit Madan, Thomas Klockgether and Juliane Fluck
Appl. Sci. 2021, 11(22), 10717; https://doi.org/10.3390/app112210717 - 13 Nov 2021
Cited by 2 | Viewed by 2267
Abstract
Dementia affects approximately 50 million people in the world today, the majority suffering from Alzheimer’s disease (AD). The availability of long-term patient data is one of the most important prerequisites for a better understanding of diseases. Worldwide, many prospective, longitudinal cohort studies have [...] Read more.
Dementia affects approximately 50 million people in the world today, the majority suffering from Alzheimer’s disease (AD). The availability of long-term patient data is one of the most important prerequisites for a better understanding of diseases. Worldwide, many prospective, longitudinal cohort studies have been initiated to understand AD. However, this approach takes years to enroll and follow up with a substantial number of patients, resulting in a current lack of data. This raises the question of whether clinical routine datasets could be utilized to extend collected registry data. It is, therefore, necessary to assess what kind of information is available in memory clinic routine databases. We did exactly this based on the example of the University Hospital Bonn. Whereas a number of data items are available in machine readable formats, additional valuable information is stored in textual documents. The extraction of information from such documents is only applicable via text mining methods. Therefore, we set up modular, rule-based text mining workflows requiring minimal sets of training data. The system achieves F1-scores over 95% for the most relevant classes, i.e., memory disturbances from medical reports and quantitative scores from semi-structured neuropsychological test protocols. Thus, we created a machine-readable core dataset for over 8000 patient visits over a ten-year period. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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Review

Jump to: Research

14 pages, 347 KiB  
Review
Semantic Metadata Annotation Services in the Biomedical Domain—A Literature Review
by Julia Sasse, Johannes Darms and Juliane Fluck
Appl. Sci. 2022, 12(2), 796; https://doi.org/10.3390/app12020796 - 13 Jan 2022
Cited by 5 | Viewed by 2813
Abstract
For all research data collected, data descriptions and information about the corresponding variables are essential for data analysis and reuse. To enable cross-study comparisons and analyses, semantic interoperability of metadata is one of the most important requirements. In the area of clinical and [...] Read more.
For all research data collected, data descriptions and information about the corresponding variables are essential for data analysis and reuse. To enable cross-study comparisons and analyses, semantic interoperability of metadata is one of the most important requirements. In the area of clinical and epidemiological studies, data collection instruments such as case report forms (CRFs), data dictionaries and questionnaires are critical for metadata collection. Even though data collection instruments are often created in a digital form, they are mostly not machine readable; i.e., they are not semantically coded. As a result, the comparison between data collection instruments is complex. The German project NFDI4Health is dedicated to the development of national research data infrastructure for personal health data, and as such searches for ways to enhance semantic interoperability. Retrospective integration of semantic codes into study metadata is important, as ongoing or completed studies contain valuable information. However, this is labor intensive and should be eased by software. To understand the market and find out what techniques and technologies support retrospective semantic annotation/enrichment of metadata, we conducted a literature review. In NFDI4Health, we identified basic requirements for semantic metadata annotation software in the biomedical field and in the context of the FAIR principles. Ten relevant software systems were summarized and aligned with those requirements. We concluded that despite active research on semantic annotation systems, no system meets all requirements. Consequently, further research and software development in this area is needed, as interoperability of data dictionaries, questionnaires and data collection tools is key to reusing and combining results from independent research studies. Full article
(This article belongs to the Special Issue Semantic Interoperability and Applications in Healthcare)
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