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

作者：Y M Li, Chuyang Bao, Jiaming Liu, Biao Yu, Xue Du, Yali Bi, Xuefeng Hu, Qianqian Cui, Tengteng Li, Saihu Lu, Yi Yang, Cao Y, Wei Zhang, Zongzhi Yin · 发表于：Journal of Breath Research · 年份：2026 · DOI：10.1088/1752-7163/ae8326 · 研究领域：Advanced Chemical Sensor Technologies、Pregnancy and Medication Impact、Gut microbiota and health

Abstract 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, including Streptococcus sanguinis ( P = 0.0016), Lactobacillus ( P = 0.0263), Lautropia ( P = 0.0453), and Lachnospiraceae NK4A136 group ( P = 0.0408). Bacterial interaction network analysis revealed enhanced synergistic relationships among microbial taxa. Conclusion. These preliminary findings sugge...