@article{UysalFiratCreutzetal.2022,
  author    = {Uysal, Karya and Firat, Ipek Serat and Creutz, Till and Aydin, Inci Cansu and Artmann, Gerhard and Teusch, Nicole and Temiz Artmann, Ayseg{\"u}l},
  title     = {A novel in vitro wound healing assay using free-standing, ultra-thin PDMS membranes},
  series = {membranes},
  volume    = {2023},
  journal   = {membranes},
  number    = {13(1)},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/membranes13010022},
  pages     = {Artikel 22},
  year      = {2022},
  abstract  = {Advances in polymer science have significantly increased polymer applications in life sciences. We report the use of free-standing, ultra-thin polydimethylsiloxane (PDMS) membranes, called CellDrum, as cell culture substrates for an in vitro wound model. Dermal fibroblast monolayers from 28- and 88-year-old donors were cultured on CellDrums. By using stainless steel balls, circular cell-free areas were created in the cell layer (wounding). Sinusoidal strain of 1 Hz, 5\% strain, was applied to membranes for 30 min in 4 sessions. The gap circumference and closure rate of un-stretched samples (controls) and stretched samples were monitored over 4 days to investigate the effects of donor age and mechanical strain on wound closure. A significant decrease in gap circumference and an increase in gap closure rate were observed in trained samples from younger donors and control samples from older donors. In contrast, a significant decrease in gap closure rate and an increase in wound circumference were observed in the trained samples from older donors. Through these results, we propose the model of a cell monolayer on stretchable CellDrums as a practical tool for wound healing research. The combination of biomechanical cell loading in conjunction with analyses such as gene/protein expression seems promising beyond the scope published here.},
  language  = {en}
}
@inproceedings{WeldenSeverinsPoghossianetal.2022,
  author    = {Welden, Melanie and Severins, Robin and Poghossian, Arshak and Wege, Christina and Siegert, Petra and Keusgen, Michael and Sch{\"o}ning, Michael Josef},
  title     = {Studying the immobilization of acetoin reductase with Tobacco mosaic virus particles on capacitive field-effect sensors},
  series = {2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)},
  booktitle = {2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)},
  publisher = {IEEE},
  isbn      = {978-1-6654-5860-3 (Online)},
  doi       = {10.1109/ISOEN54820.2022.9789657},
  pages     = {4 Seiten},
  year      = {2022},
  abstract  = {A capacitive electrolyte-insulator-semiconductor (EISCAP) biosensor modified with Tobacco mosaic virus (TMV) particles for the detection of acetoin is presented. The enzyme acetoin reductase (AR) was immobilized on the surface of the EISCAP using TMV particles as nanoscaffolds. The study focused on the optimization of the TMV-assisted AR immobilization on the Ta 2 O 5 -gate EISCAP surface. The TMV-assisted acetoin EISCAPs were electrochemically characterized by means of leakage-current, capacitance-voltage, and constant-capacitance measurements. The TMV-modified transducer surface was studied via scanning electron microscopy.},
  language  = {en}
}
@article{WerfelGuenthnerHapfelmeieretal.2022,
  author    = {Werfel, Stanislas and G{\"u}nthner, Roman and Hapfelmeier, Alexander and Hanssen, Henner and Kotliar, Konstantin and Heemann, Uwe and Schmaderer, Christoph},
  title     = {Identification of cardiovascular high-risk groups from dynamic retinal vessel signals using untargeted machine learning},
  series = {Cardiovascular Research},
  volume    = {118},
  journal   = {Cardiovascular Research},
  number    = {2},
  editor    = {Guzik, Tomasz J.},
  publisher = {Oxford University Press},
  address   = {Oxford},
  issn      = {0008-6363},
  doi       = {10.1093/cvr/cvab040},
  pages     = {612 -- 621},
  year      = {2022},
  abstract  = {Dynamic retinal vessel analysis (DVA) provides a non-invasive way to assess microvascular function in patients and potentially to improve predictions of individual cardiovascular (CV) risk. The aim of our study was to use untargeted machine learning on DVA in order to improve CV mortality prediction and identify corresponding response alterations.},
  language  = {en}
}