The Long COVID project, coordinated by Helsinki University Hospital (HUS), aims to understand mechanisms of LCS by combining front-line expertise from the fields of clinical medicine, virology, metabolism, and immunology.
The Long COVID project will also develop and apply a machine learning (ML) and artificial intelligence (AI)-informed Long Covid Prediction Support (LCPS) tool to predict and stratify the LSC patients.
To decipher the mechanisms underlying LSC, the project will study the pathogenesis of LCS (1) by conducting geographically diverse cohort and registry studies, (2) by conducting mechanistic studies, (3) by using novel high-throughput methods for biomarker analysis, and (4) by conducting interventional and follow-up studies on LCS patients.
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Additionally, an interactive graphic user interface infographic will be available to clinicians and patients; this will communicate novel and understandable information about LCS and recommendations for patient management.
Long COVID combines a broad range of scientific and technical approaches to empower clinicians to better manage patients with LCS at an early stage.
Photo credits: istockphoto.com
The cohort and clinical studies will be conducted at Helsinki University Hospital (HUS), University Hospital Basel (USB), and the University Medical Centre Groningen (UMCG):
Finland Cohort 1 – Registry linkage cohort from the Finnish National Centre for Health and Welfare (NCHW) Cohort 2 – LC policlinic – HUS Cohort 3 – ClinCOVID Cohort – UH Cohort 4 – ICU Cohort –UH
The Netherlands Cohort 5 – Lifelines – Dutch population cohort (UMCG – Groningen)
The mechanistic studies will be conducted at the University of Helsinki (UH), Finland and the University of Zurich (UZH), Switzerland. A variety of biomarkers will be analysed by several partners: lipidomics analyses will be conducted by Lipotype GmbH (Germany), genomics analyses by the Finnish Institute of Molecular Medicine (part of University of Helsinki, Finland), antibody epitope screening by Protobios Llc (Estonia), and metabolomics of the CSF and blood by University of Helsinki. Interventional studies on biopsychosocial parameters will be conducted at two sites, HUS and UNIBAS.
Nuromedia GmbH will perform data management and estimate ML models. NEC (Germany) will develop AI models as well as algorithms. Regulatory and GDPR-related issues will be solved by Chino.IO (Italy). The project outcome and results will be disseminated and exploited by Steinbeis Europa Zentrum GmbH (Germany) in close collaboration with preexisting EU-spanning networks. Finally, the project management will be performed by Spinverse Oy (Finland).
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Data & Methods
Platelet and coagulation activity
Invitro studies (animal modells & iPSCs)
HUS Digital Interventional study 1
UNIBAS Digital Interventional study 2 (VCS study)
ML & xAI Prediction Models
DATA COLLECTIOn, HARmoNIZATIOn & INTEGRATION
Long Covid Prediction Support
EOSC data sets
BIOMARKER PREDICTION – PATIENT STATIFICATION
Clinician – Patient work IDENTIFY LCS PATIENTS
Recommendations & Patents
Figure 1 Project scope & outcomes. This figure explains the overall project scope, data and methods used and the main outcomes. EOSC European open Science cloud.