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Clinical Trial Summary

Objectives: to assess the relevance of the "RiboTaxa" algorithm coupled with neural network learning based on analysis of vaginal microbiota metagenomic sequencing data for predicting prematurity in an identified at-risk population. Study description: Longitudinal follow-up of a cohort of pregnant women, with collection of biological samples, and a posteriori case-control comparison based on the occurrence of an event (premature birth).


Clinical Trial Description

There is currently no reliable clinical or biological diagnosis to predict premature birth. Recent work using metagenomic data analysis coupled with artificial intelligence approaches suggests that there may be a vaginal microbiota signature during pregnancy that correlates with the occurrence of preterm birth. The aim of the study is to use biological samples to confirm the identification of these vaginal microbiota signatures as a means of predicting preterm birth. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT06265740
Study type Interventional
Source University Hospital, Clermont-Ferrand
Contact Lise Laclautre
Phone 334.73.754.963
Email promo_interne_drci@chu-clermontferrand.fr
Status Not yet recruiting
Phase N/A
Start date March 2024
Completion date November 2025

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