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Grafrath, Germany

Findeisen P.,Universitatsklinikum Mannheim | Hoffmann G.,Trillium GmbH | Kiehntopf M.,Universitatsklinikum Jena | Klein H.-G.,Zentrum For Humangenetik Und Laboratoriumsdiagnostik | And 2 more authors.
LaboratoriumsMedizin | Year: 2014

The 13th Annual Meeting of the Section of Molecular Diagnostics of the German Association of Clinical Chemistry and Laboratory Medicine (DGKL) was held under the main topic Omics-technologies in routine diagnostics and translational research on May 15th and 16th in Tutzing, Germany. In the past years, significant technological advances have been achieved in the examination of different compartments of the human body. These methods have now reached the threshold of entering clinical diagnostics. This year's meeting aimed to present and discuss the current state from the point of view of the sections' four working groups (genomics, bioinformatics, biobanking, proteomics, metabolomics). A key message of the meeting was a continuous opening of the gap between what is technologically feasible and the knowledge of the phenotype-genotype- relation. Consequently, the main challenge currently consists in an appropriate evaluation of the enormous amounts of data and the assessment of the associated ethical and legal implications. Source


Orth M.,Institute For Laboratoriumsmedizin | Aufenanger J.,Klinikum Ingolstadt GmbH | Hoffmann G.,Trillium GmbH | Hofmann W.,Stadtisches Klinikum Munich GmbH | And 6 more authors.
LaboratoriumsMedizin | Year: 2014

Eine labormedizinische Diagnostik ist bei vielen Patienten für eine erfolgreiche medizinische Behandlung notwendig. Sowohl aus ökonomischen als auch aus ethischen Gründen sollten labormedizinische Leistungen weder zu häufig (als .,Wiederholungsuntersuchungen") noch zu selten angefordert werden. Mit diesen Empfehlungen werden für eine Reihe von labormedizinischen Untersuchungen anhand von Studien, pathophysiologischen Zusammenhängen und Konsensus Empfehlungen für eine sinnvolle Wiederholungsfrequenz gegeben. Diese Empfehlungen betreffen das minimale Zeitintervall zwischen 2 Messungen sowie die Kriterien zur Durchführung einer Wiederholungsbestimmung. Ergänzt werden diese Empfehlungen mit grundsätzlichen Überlegungen zur Indikation und Untersuchungsfrequenz von labormedizinischen Untersuchungen. © 2014 by De Gruyter 2014. Source


Hoffmann G.,Trillium GmbH | Lichtinghagen R.,Hannover Medical School | Wosniok W.,University of Bremen
LaboratoriumsMedizin | Year: 2016

According to the recommendations of the IFCC and other organizations, medical laboratories should establish or at least adapt their own reference intervals, to make sure that they reflect the peculiar characteristics of the respective methods and patient collectives. In practice, however, this postulate is hard to fulfill. Therefore, two task forces of the DGKL ("AG Richtwerte" and "AG Bioinformatik") have developed methods for the estimation of reference intervals from routine laboratory data. Here we describe a visual procedure, which can be performed on an Excel sheet without any programming knowledge. Patient values are plotted against the quantiles of the standard normal distribution (so-called QQ plot) using the NORM. INV function of Excel. If the examined population contains mainly non-diseased persons with approximately normally distributed values, the respective dots form a straight line. Very often the values are rather lognormally distributed; in this case the straight line can be detected after logarithmic transformation of the original values. Values, which do not match with the assumed theoretical distribution, deviate from the linear shape and can easily be identified and eliminated. Using the reduced data set, the mean value and standard deviation are calculated and the reference interval (ì±2ó) is estimated. The method yields plausible results with simulated and real data. With the increasing number of results, which do not match with the model, it tends to underestimate the standard deviation. In all cases, where the QQ plot does not yield a substantial linear part, the proposed method is not applicable. © 2016 by De Gruyter. Source


Cullen P.,Mvz For Laboratoriumsmedizin | Cullen P.,Vorsitz der AG Chipdiagnostik | Hoffmann G.,Trillium GmbH | Hoffmann G.,Vorsitz der AG Bioinformatik | And 2 more authors.
LaboratoriumsMedizin | Year: 2010

The progress that has been made in the field of high-throughput sequencing is such that even experts in the field are impressed. At the time of writing, it is possible to sequence an entire genome within a few days for <€50,000. Thus, we are rapidly entering the era of the "thousand-Euro genome" or even the "hundred-Euro genome", a price at which whole-genome sequencing can be considered for inclusion in routine diagnostic workups. The current uses of high-throughput sequencing include research in epigenomics, population and medical genetics, tumor genetics, and in the analysis of gene expression. The emphasis of our conference in 2009 was on the possible uses of "next-generation" sequencing in medical diagnostics in the future. Other topics include the bioinformatic analysis of very large datasets and techniques in gene expression analysis. Other high-throughput methods of gene analysis, such as mass spectrometry, are also discussed. © 2010 by Walter de Gruyter Berlin New York. Source


Hoffmann G.,Trillium GmbH | Lichtinghagen R.,Institute For Klinische Chemie Der Medizinischen Hochschule Hanover Mhh | Wosniok W.,University of Bremen
LaboratoriumsMedizin | Year: 2015

According to the recommendations of the IFCC and other organizations, medical laboratories should establish or at least adapt their own reference intervals, to make sure that they reflect the peculiar characteristics of the respective methods and patient collectives. In practice, however, this postulate is hard to fulfill. Therefore, two task forces of the DGKL ("AG Richtwerte" and "AG Bioinformatik") have developed methods for the estimation of reference intervals from routine laboratory data. Here we describe a visual procedure, which can be performed on an Excel sheet without any programming knowledge. Patient values are plotted against the quantiles of the standard normal distribution (so-called QQ plot) using the NORM.INV function of Excel. If the examined population contains mainly non-diseased persons with approximately normally distributed values, the respective dots form a straight line. Quite often the values are rather lognormally distributed; in this case the straight line can be detected after logarithmic transformation of the original values. Values, which do not match with the assumed theoretical distribution, deviate from the linear shape and can easily be identified and eliminated. Using the reduced data set, the mean value and standard deviation are calculated and the reference interval (μ±2σ) is estimated. The method yields plausible results with simulated and real data. With increasing number of results, which do not match with the model, it tends to underestimate the standard deviation. In all cases, where the QQ plot does not yield a substantial linear part, the proposed method is not applicable. © 2015 by De Gruyter. Source

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