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
• genetické markery genetika   MeSH
• genom lidský MeSH
• geny vázané na chromozom X * MeSH
• geny vázané na chromozom Y * MeSH
• jednonukleotidový polymorfismus genetika   MeSH
• lidé MeSH
• polymerázová řetězová reakce MeSH
• RNA genetika   MeSH
• sekvenční analýza RNA metody   MeSH
• software * MeSH
• vysoce účinné nukleotidové sekvenování metody   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem 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
• elektronické zdravotní záznamy * MeSH
• lidé MeSH
• sémantický web * MeSH
• software MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

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
• bio-ontologie MeSH
• biomedicínský výzkum MeSH
• internet MeSH
• lékařská informatika * metody   MeSH
• poskytování zdravotní péče MeSH
• přístup k informacím * MeSH
• sémantika MeSH
• ukládání a vyhledávání informací metody   MeSH
• Publikační typ
• přehledy MeSH

• MeSH
• informační management MeSH
• lékařská informatika MeSH
• sběr dat metody   MeSH
• systémy řízení databází MeSH
• Publikační typ
• kongresy MeSH
• souborné dílo MeSH

• Konspekt
• Informační věda
• NLK Obory
• lékařská informatika

• MeSH
• dokumentace MeSH
• dostupnost zdravotnických služeb MeSH
• důvěrnost informací MeSH
• ekonomika a organizace zdravotní péče MeSH
• integrované poskytování zdravotní péče MeSH
• osobní údaje MeSH
• veřejné zdravotnictví - informatika MeSH
• zdravotní politika MeSH
• zdravotnické informační systémy MeSH

• Konspekt
• Veřejné zdraví a hygiena
• NLK Obory
• veřejné zdravotnictví
• ekonomie, ekonomika, ekonomika zdravotnictví
• NLK Publikační typ
• studie

• MeSH
• longitudinální studie MeSH
• sociální vědy metody   MeSH
• výzkum MeSH
• výzkumný projekt MeSH
• Publikační typ
• monografie MeSH

• Konspekt
• Sociologie
• NLK Obory
• sociologie

In this chapter, we demonstrate the advantage of the simultaneous multicurve nonlinear least-squares analysis over that of the conventional single-curve analysis. Fitting results are subjected to thorough Monte Carlo analysis for rigorous assessment of confidence intervals and parameter correlations. The comparison is performed on a practical example of simulated steady-state reaction kinetics complemented with isothermal calorimetry (ITC) data resembling allosteric behavior of rabbit muscle pyruvate kinase (RMPK). Global analysis improves accuracy and confidence limits of model parameters. Cross-correlation between parameters is also reduced with accompanying enhancement of the model-testing power. This becomes especially important for validation of models with "difficult" highly cross-correlated parameters. We show how proper experimental design and critical evaluation of data can improve the chance of differentiating models.

• MeSH
• kalorimetrie MeSH
• králíci MeSH
• metoda Monte Carlo MeSH
• metoda nejmenších čtverců MeSH
• pyruvátkinasa chemie   metabolismus   MeSH
• zvířata MeSH
• Check Tag
• králíci MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

BACKGROUND: The capacity to use data linkage and artificial intelligence to estimate and predict health indicators varies across European countries. However, the estimation of health indicators from linked administrative data is challenging due to several reasons such as variability in data sources and data collection methods resulting in reduced interoperability at various levels and timeliness, availability of a large number of variables, lack of skills and capacity to link and analyze big data. The main objective of this study is to develop the methodological guidelines calculating population-based health indicators to guide European countries using linked data and/or machine learning (ML) techniques with new methods. METHOD: We have performed the following step-wise approach systematically to develop the methodological guidelines: i. Scientific literature review, ii. Identification of inspiring examples from European countries, and iii. Developing the checklist of guidelines contents. RESULTS: We have developed the methodological guidelines, which provide a systematic approach for studies using linked data and/or ML-techniques to produce population-based health indicators. These guidelines include a detailed checklist of the following items: rationale and objective of the study (i.e., research question), study design, linked data sources, study population/sample size, study outcomes, data preparation, data analysis (i.e., statistical techniques, sensitivity analysis and potential issues during data analysis) and study limitations. CONCLUSIONS: This is the first study to develop the methodological guidelines for studies focused on population health using linked data and/or machine learning techniques. These guidelines would support researchers to adopt and develop a systematic approach for high-quality research methods. There is a need for high-quality research methodologies using more linked data and ML-techniques to develop a structured cross-disciplinary approach for improving the population health information and thereby the population health.

• Publikační typ
• časopisecké články MeSH

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
• bio-ontologie * MeSH
• data management * MeSH
• řízený slovník MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem 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
• kontrola nákladů MeSH
• léčivé přípravky ekonomika   MeSH
• náklady a analýza nákladů ekonomika   MeSH
• náklady na léky * MeSH
• náklady na zdravotní péči MeSH
• sběr dat MeSH
• všeobecné zdravotní pojištění ekonomika   MeSH
• Publikační typ
• přehledy MeSH
• Geografické názvy
• Česká republika MeSH