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- Gaulton, Kyle J, et al.
(author)
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Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.
- 2015
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 47:12, s. 1415-1415
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Journal article (peer-reviewed)abstract
- We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.
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- Storm, Christian, et al.
(author)
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A survey on general and temperature management of post cardiac arrest patients in large teaching and university hospitals in 14 European countries-The SPAME trial results
- 2017
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In: Resuscitation. - : Elsevier BV. - 0300-9572. ; 116, s. 84-90
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Journal article (peer-reviewed)abstract
- Introduction: International guidelines recommend a bundle of care, including targeted temperature management (TTM), in post cardiac arrest survivors. Aside from a few small surveys in different European countries, adherence to the European Resuscitation Council (ERC) and European Society of Intensive Care Medicine (ESICM) recommendations are unknown. Methods: This international European telephone survey was conducted to provide an overview of current clinical practice of post cardiac arrest management with a main focus on TTM. We targeted large teaching and university hospitals within Europe as leading facilities and key opinion leaders in the field of post cardiac arrest care. Selected national principal investigators conducted the survey, which was based on a predefined questionnaire, between December 2014 and March 2015, before the publication of the ERC Guidelines 2015. Results: The return rate was 94% from 268 participating intensive care units (ICU). The majority had a predefined standard operating procedure (SOP) protocol for post cardiac arrest patients. Altogether, 68% of the ICUs provided TTM at a target temperature of 32-34. °C for 24. h, and 33% had changed the target temperature to 36. °C. The minority provided a written SOP for neurological prognostication, which was generally initiated 72. h after return of spontaneous circulation (ROSC). Electroencephalography and somatosensory evoked potentials were used by most ICUs for early prognostication. Treating more than fifty patients a year was significantly associated with providing written SOPs for TTM and prognostication (p. <. 0.01), as well as the use of a computer feedback device (p = 0.03) for TTM. Conclusion: This international European telephone survey revealed a high rate of implementation of TTM in post cardiac arrest patients in university and teaching hospitals. Most participants also provided a SOP, but only a minority had a SOP for neurological prognostication.
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- Voznuka, Natalja, et al.
(author)
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Report generation and data mining in the domain of thoracic surgery
- 2004
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In: Journal of medical systems. - 0148-5598 .- 1573-689X. ; 28:5, s. 497-509
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Journal article (peer-reviewed)abstract
- As a part of AssistMe system, the reporting system has been developed for the thoracic surgery domain. Reporting System is defined as software for dynamic report generation purpose and based on the data-mining techniques. The target users of the future reporting system - physicians, administrative staff, and patients - have been identified. Two major types of clinical reports have been found: predefined and customized. The decision of splitting reports into groups has been taken mainly because users were heterogeneous and had different access rights to the sensitive information. Data-mining process in the reporting system is based on descriptive statistics. It allows dynamically mined AssistMe databases and generates statistical reports about patient's morbidity, mortality, and comorbidity. Information is visualized in the chart way and can be also observed in tabular form. User interaction is also supported by the system.
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