Non-invasive prediction of uterine contractions vies electronic nose and oral microbiota profiling

Background.Accurate and non-invasive prediction of uterine contractions is essential for optimizing obstetric care and improving perinatal outcomes. We aimed to develop and validate a diagnostic model that combines exhaled volatile organic compounds (VOCs) patterns and oral microbiota profiles to predict contraction status in term pregnancy.Methods. We prospectively enrolled 84 third-trimester pregnant women and analyzed exhaled breath samples by electronic nose (E-nose). A convolutional neural network (CNN) was used to develop a predictive model of uterine contraction status. Subsequently paired saliva samples from a subset of 15 participants underwent 16 S rRNA gene sequencing to explore potential microbial sources of breath VOCs patterns.Results. The CNN-based E-nose model achieved an area under the curve of 0.63 (95% CI: 0.56-0.71) for differentiating non-contractions from irregular contractions, with performance improving to 0.79 (95% CI: 0.72-0.89) for latent-phase contractions and 0.82 (95% CI: 0.77-0.87) during first-stage labor. Concordant with these findings, 16 S rRNA analysis revealed significant enrichment of specific oral taxa in the contraction group, includingStreptococcus sanguinis(P= 0.0016),Lactobacillus(P= 0.0263),Lautropia(P= 0.0453), andLachnospiraceae NK4A136 group(P= 0.0408). Bacterial interaction network analysis revealed enhanced synergistic relationships among microbial taxa.Conclusion.These preliminary findings suggest that E-nose profiling of exhaled breath may be capable of non-invasively predicting uterine contraction status in late pregnancy, pending validation in larger cohorts. The integration of microbial data provides hypothetical biological insights into the breath VOCs changes, suggesting that breath analysis could eventually support personalized obstetric monitoring after further investigation.