Gattinger P, Niespodziana K, Stiasny K, Sahanic S, Tulaeva I, Borochova K, Dorofeeva Y, Schlederer T, Sonnweber T, Hofer G, Kiss R, Kratzer B, Trapin D, Tauber PA, Rottal A, Körmöczi U, Feichter M, Weber M, Focke-Tejkl M, Löffler-Ragg J, Mühl B, Kropfmüller A, Keller W, Stolz F, Henning R, Tancevski I, Puchhammer-Stöckl E, Pickl WF, Valenta R.
Allergy. 2021 Aug 28. doi: 10.1111/all.15066. Online ahead of print.
Martin Schepelmann, Nadja Kupper, Marta Sladczyk, Bethan Mansfield, Teresa Manhardt, Karina Piatek, Luca Iamartino, Daniela Riccardi, Benson M. Kariuki, Marcella Bassetto, and Enikö Kallay
Stereo-Specific Modulation of the Extracellular Calcium-Sensing Receptor in Colon Cancer Cells
Int. J. Mol. Sci. 2021, 22(18), 10124; https://doi.org/10.3390/ijms221810124
Foot ulcer is a common complication of diabetes mellitus; it is associated with substantial morbidity and mortality and remains a major risk factor for lower leg amputation. Extracting accurate morphological features from the foot wounds is crucial for proper treatment. Computer-mediated approaches enable segmentation of the lesions and extraction of related morphological features. Deep learning-based methods and more specifically convolutional neural networks (CNN) have shown excellent performances for various image segmentation tasks including medical image segmentation. In this work, Amirreza Mahbod, Rupert Ecker and Isabella Ellinger proposed an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and UNet, to perform foot ulcer segmentation. Our method achieved the first rank in the FUSeg challenge leaderboard.
Leaderboard link: https://uwm-bigdata.github.io/wound-segmentation/
Challenge link: https://fusc.grand-challenge.org/
Method description: https://arxiv.org/abs/2109.01408
Research team comprising Anastasia Meshcheryakova, Diana Mechtcheriakova and Peter Pietschmann from the Institute of Pathophysiology and Allergy Research has addressed the potential interrelations between AID/APOBECs and the SARS-CoV-2 virus, particularly in connection with the course of COVID-19 in different patients. This could provide a starting point for future clinical strategies to improve and strengthen individual antiviral response.
Anastasia Meshcheryakova, Felicitas Mungenast and Diana Mechtcheriakova (Research Group Molecular Systems Biology and Pathophysiology, Department of Pathophysiology and Allergy Research, Medical University of Vienna) – experts in computerized microscopy and digital image analysis, and their collaborative partner Rupert Ecker (TissueGnostics, Austria) are proud to announce the publication of the book "Imaging Modalities for Biological and Preclinical Research: A Compendium", with unique, comprehensive collection of about 100 state-of-the-art imaging technologies. The Team of authors contributed with the chapter "Tissue Image Cytometry".