• MeSH
• Risk Assessment MeSH
• Uncertainty MeSH
• Data Collection standards   MeSH
• Environmental Exposure MeSH

• Conspectus
• Životní prostředí a jeho ochrana
• NML Fields
• environmentální vědy
• NML Publication type
• publikace WHO

Electroencephalography (EEG) is commonly used in epilepsy and neuroscience research to study brain activity. The principles of EEG recording such as signal acquisition, digitization, and conditioning share similarities between animal and clinical EEG systems. In contrast, preclinical EEG studies demonstrate more variability and diversity than clinical studies in the types and locations of EEG electrodes, methods of data analysis, and scoring of EEG patterns and associated behaviors. The TASK3 EEG working group of the International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force has developed a set of preclinical common data elements (CDEs) and case report forms (CRFs) for recording, analysis, and scoring of animal EEG studies. This companion document accompanies the first set of proposed preclinical EEG CRFs and is intended to clarify the CDEs included in these worksheets. We provide 7 CRF and accompanying CDE modules for use by the research community, covering video acquisition, electrode information, experimental scheduling, and scoring of EEG activity. For ease of use, all data elements and input ranges are defined in supporting Excel charts (Appendix S1).

• Publication type
• Journal Article MeSH

• MeSH
• Cell Biology MeSH
• Cells MeSH
• Molecular Biology MeSH
• Publication type
• Handbook MeSH

• Conspectus
• Buněčná biologie. Cytologie
• NML Fields
• cytologie, klinická cytologie

• MeSH
• Clinical Laboratory Techniques * MeSH
• Reference Values MeSH
• Publication type
• Handbook MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Fields
• laboratorní techniky a postupy

• MeSH
• Common Data Elements MeSH
• Clinical Laboratory Techniques methods   standards   MeSH
• Clinical Laboratory Information Systems * organization & administration   MeSH
• Data Collection MeSH

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.

• MeSH
• Basal Metabolism genetics   MeSH
• Genome-Wide Association Study MeSH
• Diabetes Mellitus, Type 2 genetics   MeSH
• Phenotype MeSH
• Gene Regulatory Networks MeSH
• Blood Glucose metabolism   MeSH
• Humans MeSH
• Metabolic Diseases genetics   MeSH
• Mice, Knockout MeSH
• Mice MeSH
• Obesity genetics   MeSH
• Area Under Curve MeSH
• High-Throughput Screening Assays MeSH
• Oxygen Consumption genetics   MeSH
• Body Weight genetics   MeSH
• Triglycerides metabolism   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

Having the administrative and clinical information concerning the patient presented in a comprehensible format, language, and terminology is valuable for any healthcare provider. In Europe, this type of information is represented by the Patient Summary Guideline and on the other side of the Atlantic by the Continuity of Care Document (CCD). Trillium Bridge is a project co-funded by the European Commission that “compares specifications of EU and US patient summaries with the aim of developing and testing common and consistent specifications and systems enabling interoperability of electronic health records across the Atlantic.” The objective of this article is to summarize the findings of the comparison between these two Patient Summaries. Both documents are using the same syntax, namely Clinical Document Architecture (CDA), making the comparison easier. The documents were compared from a clinical, syntactic, and terminological point of view focusing on semantic interoperability. A common denominator was found in terms of sections, data elements, and value sets. Comparing the value sets led the project team to assess available official maps such as the SNOMED CT and ICD-10 and determine their applicability. In some cases, such as the National Cancer Institute Thesaurus and the EDQM standard terms, no maps were found and the team proposed associations. The common denominator thus identified allows for significant parts of the data to be exchanged, setting the baseline for the transatlantic exchange of a meaningful set of patient summary data and establishing a springboard for an international patient summary standard.

• MeSH
• Medical Records Systems, Computerized MeSH
• Medical Record Linkage * MeSH
• Medical Records standards   MeSH
• Electronic Health Records * standards   MeSH
• European Union MeSH
• Clinical Coding MeSH
• Continuity of Patient Care MeSH
• Humans MeSH
• National Health Programs MeSH
• Vocabulary, Controlled MeSH
• Terminology as Topic * MeSH
• Records standards   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH
• Lecture MeSH
• Comparative Study MeSH
• Geographicals
• Europe MeSH
• United States MeSH

Genome-wide association studies (GWASs) based on common single nucleotide polymorphisms (SNPs) have identified several loci associated with the risk of monoclonal gammopathy of unknown significance (MGUS), a precursor condition for multiple myeloma (MM). We hypothesized that analyzing haplotypes might be more useful than analyzing individual SNPs, as it could identify functional chromosomal units that collectively contribute to MGUS risk. To test this hypothesis, we used data from our previous GWAS on 992 MGUS cases and 2910 controls from three European populations. We identified 23 haplotypes that were associated with the risk of MGUS at the genome-wide significance level (p < 5 × 10-8) and showed consistent results among all three populations. In 10 genomic regions, strong promoter, enhancer and regulatory element-related histone marks and their connections to target genes as well as genome segmentation data supported the importance of these regions in MGUS susceptibility. Several associated haplotypes affected pathways important for MM cell survival such as ubiquitin-proteasome system (RNF186, OTUD3), PI3K/AKT/mTOR (HINT3), innate immunity (SEC14L1, ZBP1), cell death regulation (BID) and NOTCH signaling (RBPJ). These pathways are important current therapeutic targets for MM, which may highlight the advantage of the haplotype approach homing to functional units.

• MeSH
• Genome-Wide Association Study * MeSH
• Genetic Predisposition to Disease * MeSH
• Haplotypes * MeSH
• Polymorphism, Single Nucleotide * MeSH
• Humans MeSH
• Multiple Myeloma genetics   MeSH
• Monoclonal Gammopathy of Undetermined Significance * genetics   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

• MeSH
• Dementia epidemiology   complications   mortality   MeSH
• Quality of Life MeSH
• Delivery of Health Care MeSH
• Managed Care Programs MeSH
• Translational Research, Biomedical MeSH
• Developed Countries MeSH
• Health Policy MeSH

• Conspectus
• Veřejné zdraví a hygiena
• NML Fields
• psychiatrie
• neurologie
• NML Publication type
• studie

V e-Health se často mluví o velkém potenciálu nestrukturovaných dat často v podobě volných textů lékařských zpráv a anamnéz. Z principu je tento potenciál však vždy menší než u zpracování těch samých dat ve strukturované podobě. Důvodem je, že strukturovaná data mají lépe definovaný odborný význam daný jejich strukturou. Podobně mají stejný význam pro autora i nestrukturovaná data, protože jim podvědomě svou vlastní strukturu přiřazuje. Bohužel výklad dané struktury a ve vzácném případě dokonce i vět nebo i termínů se může u různých odborníků lišit, pokud není standardizován. Proto se naskytuje otázka, jak zlepšit strukturální kvalitu těchto dat v e--Health. Ideálně již při jejich vzniku.

• MeSH
• Common Data Elements standards   MeSH
• Electronic Health Records * standards   MeSH
• Medical Informatics MeSH
• Humans MeSH
• Health Information Systems MeSH
• Check Tag
• Humans MeSH