@article{ElHelouBiegnerBodeetal.2019, author = {El-Helou, Sabine M. and Biegner, Anika-Kerstin and Bode, Sebastian and Ehl, Stephan R. and Heeg, Maximilian and Maccari, Maria E. and Ritterbusch, Henrike and Speckmann, Carsten and Rusch, Stephan and Scheible, Raphael and Warnatz, Klaus and Atschekzei, Faranaz and Beider, Renata and Ernst, Diana and Gerschmann, Stev and Jablonka, Alexandra and Mielke, Gudrun and Schmidt, Reinhold E. and Sch{\"u}rmann, Gesine and Sogkas, Georgios and Baumann, Ulrich H. and Klemann, Christian and Viemann, Dorothee and Bernuth, Horst von and Kr{\"u}ger, Renate and Hanitsch, Leif G. and Scheibenbogen, Carmen M. and Wittke, Kirsten and Albert, Michael H. and Eichinger, Anna and Hauck, Fabian and Klein, Christoph and Rack-Hoch, Anita and Sollinger, Franz M. and Avila, Anne and Borte, Michael and Borte, Stephan and Fasshauer, Maria and Hauenherm, Anja and Kellner, Nils and M{\"u}ller, Anna H. and {\"U}lzen, Anett and Bader, Peter and Bakhtiar, Shahrzad and Lee, Jae-Yun and Heß, Ursula and Schubert, Ralf and W{\"o}lke, Sandra and Zielen, Stefan and Ghosh, Sujal and Laws, Hans-Juergen and Neubert, Jennifer and Oommen, Prasad T. and H{\"o}nig, Manfred and Schulz, Ansgar and Steinmann, Sandra and Klaus, Schwarz and D{\"u}ckers, Gregor and Lamers, Beate and Langemeyer, Vanessa and Niehues, Tim and Shai, Sonu and Graf, Dagmar and M{\"u}glich, Carmen and Schmalzing, Marc T. and Schwaneck, Eva C. and Tony, Hans-Peter and Dirks, Johannes and Haase, Gabriele and Liese, Johannes G. and Morbach, Henner and Foell, Dirk and Hellige, Antje and Wittkowski, Helmut and Masjosthusmann, Katja and Mohr, Michael and Geberzahn, Linda and Hedrich, Christian M. and M{\"u}ller, Christiane and R{\"o}sen-Wolff, Angela and Roesler, Joachim and Zimmermann, Antje and Behrends, Uta and Rieber, Nikolaus and Schauer, Uwe and Handgretinger, Rupert and Holzer, Ursula and Henes, J{\"o}rg and Kanz, Lothar and Boesecke, Christoph and Rockstroh, J{\"u}rgen K. and Schwarze-Zander, Carolynne and Wasmuth, Jan-Christian and Dilloo, Dagmar and H{\"u}lsmann, Brigitte and Sch{\"o}nberger, Stefan and Schreiber, Stefan and Zeuner, Rainald and Ankermann, Tobias and Bismarck, Philipp von and Huppertz, Hans-Iko and Kaiser-Labusch, Petra and Greil, Johann and Jakoby, Donate and Kulozik, Andreas E. and Metzler, Markus and Naumann-Bartsch, Nora and Sobik, Bettina and Graf, Norbert and Heine, Sabine and Kobbe, Robin and Lehmberg, Kai and M{\"u}ller, Ingo and Herrmann, Friedrich and Horneff, Gerd and Klein, Ariane and Peitz, Joachim and Schmidt, Nadine and Bielack, Stefan and Groß-Wieltsch, Ute and Classen, Carl F. and Klasen, Jessica and Deutz, Peter and Kamitz, Dirk and Lassy, Lisa and Tenbrock, Klaus and Wagner, Norbert and Bernbeck, Benedikt and Brummel, Bastian and Lara-Villacanas, Eusebia and M{\"u}nstermann, Esther and Schneider, Dominik T. and Tietsch, Nadine and Westkemper, Marco and Weiß, Michael and Kramm, Christof and K{\"u}hnle, Ingrid and Kullmann, Silke and Girschick, Hermann and Specker, Christof and Vinnemeier-Laubenthal, Elisabeth and Haenicke, Henriette and Schulz, Claudia and Schweigerer, Lothar and M{\"u}ller, Thomas G. and Stiefel, Martina and Belohradsky, Bernd H. and Soetedjo, Veronika and Kindle, Gerhard and Grimbacher, Bodo}, title = {The German national registry of primary immunodeficiencies (2012-2017)}, series = {Frontiers in Immunology}, volume = {10}, journal = {Frontiers in Immunology}, doi = {10.3389/fimmu.2019.01272}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-226629}, year = {2019}, abstract = {Introduction: The German PID-NET registry was founded in 2009, serving as the first national registry of patients with primary immunodeficiencies (PID) in Germany. It is part of the European Society for Immunodeficiencies (ESID) registry. The primary purpose of the registry is to gather data on the epidemiology, diagnostic delay, diagnosis, and treatment of PIDs. Methods: Clinical and laboratory data was collected from 2,453 patients from 36 German PID centres in an online registry. Data was analysed with the software Stata® and Excel. Results: The minimum prevalence of PID in Germany is 2.72 per 100,000 inhabitants. Among patients aged 1-25, there was a clear predominance of males. The median age of living patients ranged between 7 and 40 years, depending on the respective PID. Predominantly antibody disorders were the most prevalent group with 57\% of all 2,453 PID patients (including 728 CVID patients). A gene defect was identified in 36\% of patients. Familial cases were observed in 21\% of patients. The age of onset for presenting symptoms ranged from birth to late adulthood (range 0-88 years). Presenting symptoms comprised infections (74\%) and immune dysregulation (22\%). Ninety-three patients were diagnosed without prior clinical symptoms. Regarding the general and clinical diagnostic delay, no PID had undergone a slight decrease within the last decade. However, both, SCID and hyper IgE-syndrome showed a substantial improvement in shortening the time between onset of symptoms and genetic diagnosis. Regarding treatment, 49\% of all patients received immunoglobulin G (IgG) substitution (70\%-subcutaneous; 29\%-intravenous; 1\%-unknown). Three-hundred patients underwent at least one hematopoietic stem cell transplantation (HSCT). Five patients had gene therapy. Conclusion: The German PID-NET registry is a precious tool for physicians, researchers, the pharmaceutical industry, politicians, and ultimately the patients, for whom the outcomes will eventually lead to a more timely diagnosis and better treatment.}, language = {en} } @article{BerkhoutBodemErlweinetal.2014, author = {Berkhout, Ben and Bodem, Jochen and Erlwein, Otto and Herchenr{\"o}der, Ottmar and Khan, Arifa S. and Lever, Andrew M. L. and Lindemann, Dirk and Linial, Maxine L. and L{\"o}chelt, Martin and McClure, Myra O. and Scheller, Carsten and Weiss, Robin A.}, title = {Obituary: Axel Rethwilm (1959-2014)}, series = {Retrovirology}, volume = {11}, journal = {Retrovirology}, number = {85}, doi = {10.1186/s12977-014-0085-9}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-120781}, year = {2014}, abstract = {No abstract available}, language = {en} } @article{DirkFischerSchardtetal.2023, author = {Dirk, Robin and Fischer, Jonas L. and Schardt, Simon and Ankenbrand, Markus J. and Fischer, Sabine C.}, title = {Recognition and reconstruction of cell differentiation patterns with deep learning}, series = {PLoS Computational Biology}, volume = {19}, journal = {PLoS Computational Biology}, number = {10}, doi = {10.1371/journal.pcbi.1011582}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350167}, year = {2023}, abstract = {Abstract Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran's index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70\% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms. Author summary Mammalian embryo development relies on organized differentiation of stem cells into different lineages. Particularly at the early stages of embryogenesis, cells of different fates form three-dimensional spatial patterns that are difficult to identify by eye. Pattern quantification and mathematical modeling have produced first insights into potential mechanisms for the cell fate arrangements. However, these approaches have relied on classifications of the patterns such as inside-out or random, or used summary statistics such as pair correlation functions or cluster radii. Deep neural networks allow characterizing patterns directly. Since the tissue context can be readily reproduced by a graph, we implemented a graph neural network to characterize the patterns of embryonic stem cell organoids as a whole. In addition, we implemented a multilayer perceptron model to reconstruct the fate of a given cell based on its neighbors. To train and test the models, we used synthetic data generated by our mathematical model for cell-cell communication. This interplay of deep learning and mathematical modeling in combination with summary statistics allowed us to identify a potential mechanism for cell fate determination in mouse embryonic stem cells. Our results agree with a mechanism with a dispersion of the intercellular signal that links a cell's fate to those of the local neighborhood.}, language = {en} }