Publications
The Fibrin-derived Peptide FX06 Protects Human Pulmonary Endothelial Cells Against the COVID-19-Triggered Cytokine Storm
Authors: Zhiran Wang, Dmitrii Lebedev, Simeng Li, Sudharshan Rao, Kevin Wu, Lorcan Doyle, Kieran Wynne, Alfonso Blanco, Margaritha Mysior, Jeremy C. Simpson, Dimitri Scholz, Petra Wülfroth, Kai P. Zacharowski, Walter Kolch, Vadim Zhernovkov, Guenther Eissner
Journal: Frontiers in Immunology
Published online: 19 June 2024
Lessons Learned from Model-based Economic Evaluations of COVID-19 Drug Treatments Under Pandemic Circumstances: Results from a Systematic Review
Authors: Clazinus Veijer, Marinus H. van Hulst, Benjamin Friedrichson, Maarten J. Postma & Antoinette D.I. van Asselt
Journal: PharmacoEconomics
Published online: 10 May 2024
Artificial intelligence research in the COVend COVID-19 clinical trial project
Authors: Alpo Olavi Värri, Antti Kallonen, Tunc Asuroglu, Mark van Gils
Journal: Finnish Journal of eHealth and eWelfare
Published online: 20 April 2023
Potential of FX06 to prevent disease progression in hospitalized non‑intubated COVID‑19 patients — the randomized, EU‑wide, placebo‑controlled, phase II study design of IXION
Authors: Jan Kloka, Benjamin Friedrichson, Stephanie Dauth, Ann Christina Foldenauer, Anita Bulczak-Schadendorf, Maria J. G. T. Vehreschild, Francisco Maio Matos, Antoni Riera-Mestre, Antoinette D. I. van Asselt, Edoardo De Robertis, Vilma Traskaite Juskeviciene, Patrick Meybohm, Dana Tomescu, Karine Lacombe, Coen D. A. Stehouwer & Kai Zacharowski on behalf of the IXION Collaboration Group
Journal: Trials
Published online: 19 August 2022
D6.8 Research data sets from the models
This repository contains synthetic and publicly available datasets created for the COVend research project. It includes generated synthetic patient data and logistic regression coefficients extracted using a large language model (LLM) to support the development of Bayesian models. These resources were specifically prepared to facilitate modelling and prediction of Acute Respiratory Distress Syndrome (ARDS) mortality in hospitalised patients.
Authors: Antti Kallonen, Francesco De Pretis
Platform: Zenodo
Published online: 4 July 2025
D6.5 Model toolbox for the research community
This repository contains the Model Toolbox developed for the research project COVend, designed to predict Acute Respiratory Distress Syndrome (ARDS) risk in hospitalised patients. The provided code includes temporal and multivariate modelling approaches, enabling prediction based on patient-specific time-series data. The toolbox is openly shared to support reproducibility and foster further research into predictive modelling for ARDS.
Authors: Antti Kallonen, Francesco De Pretis
Platform: Zenodo
Published online: 4 July 2025