This project aims to automate and standardize the analysis required for a diagnostic test for cystic fibrosis.
Our partner Teresinha Leal from the UCLouvain in Brussels has established and improved a non-invasive version of the imaging based sweattest with a linear charateristic identifying heterozygous as half responders useful to study genotype-phenotype correlation of rare CFTR mutations.
Another advantage of the new image-based test is that it has proven to be able to detect improved levels of CFTR function in patients treated with CFTR modulators. However, a severe practical limitation of the test is the manual image analysis (80 images/test), which is labour-intensive and can take 6 hours per test.
The Automated Bubble Sweat Test Diagnostics – AutoBuSTeD aims at overcoming the practical limitation of the new sweat test due to the labour cost of its data analysis.
The software is being developed transnationally and tested by our project partner with a cohort from Brussels (ClinicalTrials.gov Identifier NCT03584841).
Department of Medical Bioinformatics (UMG) Dr. Manuel Nietert
Institut de Recherche Expérimentale et Clinique (IREC) Louvain centre for Toxicology and Applied Pharmacology (LTAP) Université catholique de Louvain (UCLouvain), Brussels, Belgium Prof. Dr. Teresinha Leal
Press release from the Mukoviszidose Institut – gemeinnützige Gesellschaft für Forschung und Therapieentwicklung mbH: