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