Project: Urinary peptidomic patterns of Long-COVID syndrome
| Acronym | UriCoV (Reference Number: ERAPERMED2022-270) |
| Duration | 01/03/2023 |
| Project Topic | Post acute sequelae of SARS-CoV-2 infection (PASC), also referred to as long COVID, is the most frequent, yet poorly characterized sequelae of the world´s most impactful pandemic during the last 100 years. It has tremendous, yet not even foreseeable consequences on personal health and socioeconomic status of affected individuals, and on global macro-economic issues. The consortium proposing this project presented and published a first-in-class urinary peptidomic classifier predicting the risk of severe COVID-19 course, enabling personalised patient management and intervention to save lives and reduce hospital costs. This classifier, CoV-50, is registered as IVD and in clinical use to personalise COVID-19 therapeutic intervention. The successful molecular tools developed and used shall now be applied with the aim to predict long COVID early after SARS-CoV-2 infection. If successful, this approach should allow personalised intervention to prevent or at least reduce long COVID, based on individual pathophysiological features and selection of matching available pharmaceuticals. In-silico drug repurposing with regard to COVID-19 has already been successfully applied by the proposing group. The study is based on molecular, clinical and demographic data from over 1000 COVID-19 patients collected during the European multicentre Crit-CoV-U study. The patients that took part in this study will be re-contacted to collect information on their health status, and long COVID symptoms. Collaboration with patient advocacy group and occupancy-insurance partner will provide strong links to patient´s viewpoints and socioeconomic dimensions of long COVID. Partners will incorporate proven expertise to approach patients and provide both clinical and molecular phenotyping. A RedCap, industry-standard software package will be used for GDPR compliant data retrieval and storage. Data analysis will combine machine learning approaches with conventional multivariable analysis of covariables. |
| Network | ERA PerMed |
| Call | 5th Joint Transnational Call for Proposals (2022) |
Project partner
| Number | Name | Role | Country |
|---|---|---|---|
| 1 | Mosaiques Diagnostics GmbH | Coordinator | Germany |
| 2 | Wroclaw Medical University | Partner | Poland |
| 3 | St. Georg Hospital Leipzig | Partner | Germany |
| 4 | Region Västra Götaland, Skaraborg Hospital, Skövde, Sweden | Partner | Sweden |
| 5 | ASSISTANCE PUBLIQUE – HÔPITAUX DE PARIS | Partner | France |
| 6 | Medical University of Vienna | Partner | Austria |
| 7 | University of Patients - Sorbonne | Partner | France |