Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Eva C Schitter"'
Autor:
Kimberly G Lockwood, Priya R Kulkarni, Jason Paruthi, Lauren S Buch, Mathieu Chaffard, Eva C Schitter, OraLee H Branch, Sarah A Graham
Publikováno v:
JMIR Formative Research, Vol 8, p e50446 (2024)
BackgroundCardiovascular disease (CVD) is the leading cause of death in the United States, affecting a significant proportion of adults. Digital health lifestyle change programs have emerged as a promising method of CVD prevention, offering benefits
Externí odkaz:
https://doaj.org/article/cefb7391d6d743888be6475b0b29a960
Autor:
Sophie A. Herbst, Mattias Vesterlund, Alexander J. Helmboldt, Rozbeh Jafari, Ioannis Siavelis, Matthias Stahl, Eva C. Schitter, Nora Liebers, Berit J. Brinkmann, Felix Czernilofsky, Tobias Roider, Peter-Martin Bruch, Murat Iskar, Adam Kittai, Ying Huang, Junyan Lu, Sarah Richter, Georgios Mermelekas, Husen Muhammad Umer, Mareike Knoll, Carolin Kolb, Angela Lenze, Xiaofang Cao, Cecilia Österholm, Linus Wahnschaffe, Carmen Herling, Sebastian Scheinost, Matthias Ganzinger, Larry Mansouri, Katharina Kriegsmann, Mark Kriegsmann, Simon Anders, Marc Zapatka, Giovanni Del Poeta, Antonella Zucchetto, Riccardo Bomben, Valter Gattei, Peter Dreger, Jennifer Woyach, Marco Herling, Carsten Müller-Tidow, Richard Rosenquist, Stephan Stilgenbauer, Thorsten Zenz, Wolfgang Huber, Eugen Tausch, Janne Lehtiö, Sascha Dietrich
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-18 (2022)
Proteomics can be used to refine cancer classification. Here, the authors characterise chronic lymphocytic leukaemia patients by proteogenomics, and identified a subtype of patients with poor prognosis associated with aberrant B cell receptor signall
Externí odkaz:
https://doaj.org/article/91f1d089720c4b3a86f1c0a1195a3faa
Autor:
Angelika B. Riemer, Renata Blatnik, Eva C. Schitter, Marius D. Küpper, Christine Zeller, Diana Tichy, Jan Winter, Stephanie Hoppe, Maria Bonsack
This file contains the combined supplementary figures and tables. Table S1. Prediction methods used in this study. Table S2. In silico predicted and in vitro validated binding affinities of peptides derived from HPV16 E6 and E7 proteins for the HLA t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::296dfababe10c1bccaf50603ac7a0d81
https://doi.org/10.1158/2326-6066.22542396.v1
https://doi.org/10.1158/2326-6066.22542396.v1
Autor:
Angelika B. Riemer, Renata Blatnik, Eva C. Schitter, Marius D. Küpper, Christine Zeller, Diana Tichy, Jan Winter, Stephanie Hoppe, Maria Bonsack
Knowing whether a protein can be processed and the resulting peptides presented by major histocompatibility complex (MHC) is highly important for immunotherapy design. MHC ligands can be predicted by in silico peptide–MHC class-I binding prediction
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5f931ad4de8dc0ba2ea44e7a9433317
https://doi.org/10.1158/2326-6066.c.6549906
https://doi.org/10.1158/2326-6066.c.6549906