Background: In the range of Semantic Web, the idea of linking and sharing the resources generated by different authors, like ontologies, knowledge bases, or datasets, is referred to “Linked data”. Then, an ambitious project within the “Linked Data” paradigm is the “Linking Open Data” community project. It aims at publishing open data sets on the Web and semantically connecting data items belonging to different data sources. Objectives: The purpose of this paper is to present a literature review on the subject of Linked Open Data in Health and Clinical Care. In fact, the availability of open data would increase evidence of the results of biomedical research, and consequently, of clinical practice. Methods: Selection criteria have been defined and searching in PubMed/Medline and Scopus citation databases - for all years the database were available - journals papers have been retrieved. Finally, an evaluation grid has been defined for analysing the retrieved papers, to answer some defined research questions. Results: Nine journal articles have been analysed according to the defined evaluation grid. In five out of nine papers, the main contributions are strategies and methodologies for the integration of systems, including bridging the information gap among forms for clinical research and the one for patient care. Then, in three papers the main contributions are the development of consistent triple stores according to the “Linked Data” paradigm. Finally, the last paper aims at building an open dataset for public health purposes. Conclusions: The review was able to answer the research questions, despite the limited number of included papers.

• MeSH
• Biological Ontologies MeSH
• Biomedical Research MeSH
• Internet MeSH
• Medical Informatics * methods   MeSH
• Delivery of Health Care MeSH
• Access to Information * MeSH
• Semantics MeSH
• Information Storage and Retrieval methods   MeSH
• Publication type
• Review MeSH

Electronic Health Record (EHR) systems currently in use are not designed for widely interoperable longitudinal health data. Therefore, EHR data cannot be properly shared, managed and analyzed. In this article, we propose two approaches to making EHR data more comprehensive and FAIR (Findable, Accessible, Interoperable, and Reusable) and thus more useful for diagnosis and clinical research. Firstly, the data modeling based on the LinkML framework makes the data interoperability more realistic in diverse environments with various experts involved. We show the first results of how diverse health data can be integrated based on an easy-to-understand data model and without loss of available clinical knowledge. Secondly, decentralizing EHRs contributes to the higher availability of comprehensive and consistent EHR data. We propose a technology stack for decentralized EHRs and the reasons behind this proposal. Moreover, the two proposed approaches empower patients because their EHR data can become more available, understandable, and usable for them, and they can share their data according to their needs and preferences. Finally, we explore how the users of the proposed solution could be involved in the process of its validation and adoption.

• MeSH
• Data Management MeSH
• Electronic Health Records * MeSH
• Humans MeSH
• Semantic Web * MeSH
• Software MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

• Publication type
• Overall MeSH

BACKGROUND: Sex chromosomes present a genomic region which to some extent, differs between the genders of a single species. Reliable high-throughput methods for detection of sex chromosomes specific markers are needed, especially in species where genome information is limited. Next generation sequencing (NGS) opens the door for identification of unique sequences or searching for nucleotide polymorphisms between datasets. A combination of classical genetic segregation analysis along with RNA-Seq data can present an ideal tool to map and identify sex chromosome-specific expressed markers. To address this challenge, we established genetic cross of dioecious plant Rumex acetosa and generated RNA-Seq data from both parental generation and male and female offspring. RESULTS: We present a pipeline for detection of sex linked genes based on nucleotide polymorphism analysis. In our approach, tracking of nucleotide polymorphisms is carried out using a cross of preferably distant populations. For this reason, only 4 datasets are needed - reads from high-throughput sequencing platforms for parent generation (mother and father) and F1 generation (male and female progeny). Our pipeline uses custom scripts together with external assembly, mapping and variant calling software. Given the resource-intensive nature of the computation, servers with high capacity are a requirement. Therefore, in order to keep this pipeline easily accessible and reproducible, we implemented it in Galaxy - an open, web-based platform for data-intensive biomedical research. Our tools are present in the Galaxy Tool Shed, from which they can be installed to any local Galaxy instance. As an output of the pipeline, user gets a FASTA file with candidate transcriptionally active sex-linked genes, sorted by their relevance. At the same time, a BAM file with identified genes and alignment of reads is also provided. Thus, polymorphisms following segregation pattern can be easily visualized, which significantly enhances primer design and subsequent steps of wet-lab verification. CONCLUSIONS: Our pipeline presents a simple and freely accessible software tool for identification of sex chromosome linked genes in species without an existing reference genome. Based on combination of genetic crosses and RNA-Seq data, we have designed a high-throughput, cost-effective approach for a broad community of scientists focused on sex chromosome structure and evolution.

• MeSH
• Genetic Markers genetics   MeSH
• Genome, Human MeSH
• Genes, X-Linked * MeSH
• Genes, Y-Linked * MeSH
• Polymorphism, Single Nucleotide genetics   MeSH
• Humans MeSH
• Polymerase Chain Reaction MeSH
• RNA genetics   MeSH
• Sequence Analysis, RNA methods   MeSH
• Software * MeSH
• High-Throughput Nucleotide Sequencing methods   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Information Management MeSH
• Medical Informatics MeSH
• Data Collection methods   MeSH
• Database Management Systems MeSH
• Publication type
• Congress MeSH
• Collected Work MeSH

• Conspectus
• Informační věda
• NML Fields
• lékařská informatika

• MeSH
• Documentation MeSH
• Health Services Accessibility MeSH
• Confidentiality MeSH
• Health Care Economics and Organizations MeSH
• Delivery of Health Care, Integrated MeSH
• Personally Identifiable Information MeSH
• Public Health Informatics MeSH
• Health Policy MeSH
• Health Information Systems MeSH

• Conspectus
• Veřejné zdraví a hygiena
• NML Fields
• veřejné zdravotnictví
• ekonomie, ekonomika, ekonomika zdravotnictví
• NML Publication type
• studie

• MeSH
• Longitudinal Studies MeSH
• Social Sciences methods   MeSH
• Research MeSH
• Research Design MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Sociologie
• NML Fields
• sociologie

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible but has no clear mechanism to distinguish between the core specification and its extensions.This paper reports on a community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard.

• MeSH
• Biological Ontologies * MeSH
• Data Management * MeSH
• Vocabulary, Controlled MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Výdaje zdravotních pojišťoven za zdravotní péči se každoročně pohybují ve výši 250 miliard korun českých. Významnou část tvoří položky za léčivé přípravky, a to více než 50 miliard korun českých. Tyto vysoké náklady jsou důvodem, proč plátci a regulátoři požadují před uhrazením nějaké nové zdravotní intervence důkazy prokazující dostatečnou klinickou účinnost, bezpečnost a nákladovou efektivitu. Data z randomizovaných klinických studií, která jsou standardně k dispozici v okamžiku vstupu nové molekuly na trh, nejsou často dostačující v té míře, aby poskytla požadované informace o tom, že nová terapie přináší požadovaný benefi t jak pro pacienty, tak i pro plátce. Studie přináší pouze parametry spojené s účinností, bezpečností a předpokládanou délkou léčby, které neposkytují plátcům velkou jistotu v odhadech týkajících se hodnoty nové terapie. Sběr dat v reálné klinické praxi může prokázat reálnou klinickou hodnotu nové zdravotní intervence, neboť zahrnuje větší soubory parametrů než v klinických studiích. Takový sběr přináší informace z různých subpopulací pacientů a jen tato data mohou identifi kovat efekt nové terapie za horizont registrační klinické studie. Sběr dat z reálné klinické praxe rovněž umožňuje kombinovat údaje z různých datových zdrojů, což může přinést novou kvalitu. Využitím údajů z reálné klinické praxe můžeme získat informace o epidemiologických trendech, dostupnosti zdravotní péče, obvyklých léčebných postupech, adherenci k léčbě a příležitostech na zlepšení zdravotní péče. Tato data mohou být ve spolupráci s plátci využita k identifi kaci neúčinné terapie nebo naopak k nalezení nových segmentů trhu, kde může být nová terapie uplatněna.

Total healthcare expenditures in the Czech Republic are around 250 billion CZK annualy. Drugs represent roughly 20 percent of these costs or around 50 billion CZK annually. That has led payers (HCIs) and regulators (MoH, SÚKL) to require data that demonstrate the cost-effectiveness of a medicine before agreeing to pay for it. Data from randomized clinical trials which are available at time of entry of new molecules to the market are not suffi cient to provide such an information, it may be diffi cult using conventional trial data alone to demonstrate that new expensive drugs provide real value relative to their cost. Payers concerns are related to the uncertainty in terms of effi cacy, safety and length of therapy. Real-world evidence (RWE) program can help to pharmaceutical companies, physicians, payers and healthcare providers to determine the effectiveness, safety and cost benefit of a medicine using the records of thousands of patients in real clinical practice. It makes use of very large data sets to identify how new drugs perform beyond the scope of clinical trials. RWE also allows providers, payers and pharma companies to integrate data from multiple sources, e.g. data from clinical registries, payers data, epidemiology data etc. Linking data from various databases can bring new insights and quality. Using RWE data we can gain insights into epidemiological trends, equity in terms of access to the therapy, treatment patterns, patient adherence and disease management opportunities. These can be used to find treatment inefficiencies and/or for new market segments identification. Using these data, pharmaceutical companies can work with stakeholders to determine exactly who benefits from new expensive therapies. When the evidence shows that a specific group of patients will have benefits from these medicines, then payers are much more willing to reimburse it.

• MeSH
• Cost Control MeSH
• Pharmaceutical Preparations economics   MeSH
• Costs and Cost Analysis economics   MeSH
• Drug Costs * MeSH
• Health Care Costs MeSH
• Data Collection MeSH
• Universal Health Insurance economics   MeSH
• Publication type
• Review MeSH
• Geographicals
• Czech Republic MeSH

BACKGROUND: Magnetic resonance spectroscopy provides metabolic information about living tissues in a non-invasive way. However, there are only few multi-centre clinical studies, mostly performed on a single scanner model or data format, as there is no flexible way of documenting and exchanging processed magnetic resonance spectroscopy data in digital format. This is because the DICOM standard for spectroscopy deals with unprocessed data. This paper proposes a plugin tool developed for jMRUI, namely jMRUI2XML, to tackle the latter limitation. jMRUI is a software tool for magnetic resonance spectroscopy data processing that is widely used in the magnetic resonance spectroscopy community and has evolved into a plugin platform allowing for implementation of novel features. RESULTS: jMRUI2XML is a Java solution that facilitates common preprocessing of magnetic resonance spectroscopy data across multiple scanners. Its main characteristics are: 1) it automates magnetic resonance spectroscopy preprocessing, and 2) it can be a platform for outputting exchangeable magnetic resonance spectroscopy data. The plugin works with any kind of data that can be opened by jMRUI and outputs in extensible markup language format. Data processing templates can be generated and saved for later use. The output format opens the way for easy data sharing- due to the documentation of the preprocessing parameters and the intrinsic anonymization--for example for performing pattern recognition analysis on multicentre/multi-manufacturer magnetic resonance spectroscopy data. CONCLUSIONS: jMRUI2XML provides a self-contained and self-descriptive format accounting for the most relevant information needed for exchanging magnetic resonance spectroscopy data in digital form, as well as for automating its processing. This allows for tracking the procedures the data has undergone, which makes the proposed tool especially useful when performing pattern recognition analysis. Moreover, this work constitutes a first proposal for a minimum amount of information that should accompany any magnetic resonance processed spectrum, towards the goal of achieving better transferability of magnetic resonance spectroscopy studies.

• MeSH
• Algorithms * MeSH
• Electronic Data Processing statistics & numerical data   MeSH
• Humans MeSH
• Magnetic Resonance Spectroscopy methods   MeSH
• Magnetic Resonance Imaging methods   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Software * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH