@article{NickersonAtalagdeBonoetal.2016,
  author    = {Nickerson, David and Atalag, Koray and de Bono, Bernard and Geiger, J{\"o}rg and Goble, Carole and Hollmann, Susanne and Lonien, Joachim and M{\"u}ller, Wolfgang and Regierer, Babette and Stanford, Natalie J. and Golebiewski, Martin and Hunter, Peter},
  title     = {The Human Physiome: how standards, software and innovative service infrastructures are providing the building blocks to make it achievable},
  series = {Interface Focus},
  volume    = {6},
  journal   = {Interface Focus},
  number    = {2},
  doi       = {10.1098/rsfs.2015.0103},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-189584},
  pages     = {13 Seiten},
  year      = {2016},
  abstract  = {Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome.},
  language  = {en}
}
@article{HeilingKnuttiScherretal.2021,
  author    = {Heiling, Sven and Knutti, Nadine and Scherr, Franziska and Geiger, J{\"o}rg and Weikert, Juliane and Rose, Michael and Jahns, Roland and Ceglarek, Uta and Scherag, Andr{\´e} and Kiehntopf, Michael},
  title     = {Metabolite ratios as quality indicators for pre-analytical variation in serum and EDTA plasma},
  series = {Metabolites},
  volume    = {11},
  journal   = {Metabolites},
  number    = {9},
  issn      = {2218-1989},
  doi       = {10.3390/metabo11090638},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246261},
  year      = {2021},
  abstract  = {In clinical diagnostics and research, blood samples are one of the most frequently used materials. Nevertheless, exploring the chemical composition of human plasma and serum is challenging due to the highly dynamic influence of pre-analytical variation. A prominent example is the variability in pre-centrifugation delay (time-to-centrifugation; TTC). Quality indicators (QI) reflecting sample TTC are of utmost importance in assessing sample history and resulting sample quality, which is essential for accurate diagnostics and conclusive, reproducible research. In the present study, we subjected human blood to varying TTCs at room temperature prior to processing for plasma or serum preparation. Potential sample QIs were identified by Ultra high pressure liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) based metabolite profiling in samples from healthy volunteers (n = 10). Selected QIs were validated by a targeted MS/MS approach in two independent sets of samples from patients (n = 40 and n = 70). In serum, the hypoxanthine/guanosine (HG) and hypoxanthine/inosine (HI) ratios demonstrated high diagnostic performance (Sensitivity/Specificity > 80\%) for the discrimination of samples with a TTC > 1 h. We identified several eicosanoids, such as 12-HETE, 15-(S)-HETE, 8-(S)-HETE, 12-oxo-HETE, (±)13-HODE and 12-(S)-HEPE as QIs for a pre-centrifugation delay > 2 h. 12-HETE, 12-oxo-HETE, 8-(S)-HETE, and 12-(S)-HEPE, and the HI- and HG-ratios could be validated in patient samples.},
  language  = {en}
}