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Models of Murine Vaginal Colonization by Anaerobically Grown Bacteria

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Published on: May 25, 2022

Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model.

Sunwha Park¹, Jeongsup Moon², Nayeon Kang²

¹Department of Obstetrics and Gynecology, College of Medicine, Ewha Medical Research Institute, Ewha Womans University, Seoul, South Korea.

Frontiers in Microbiology

|August 19, 2022

Summary

This study identifies key vaginal bacteria and clinical factors to predict preterm birth (PTB). Machine learning models combining microbiome data with white blood cell count and cervical length significantly improved PTB prediction accuracy.

Keywords:

16s ribosomal RNA metagenome sequencing cervicovaginal fluid machine learning microbial-marker pregnancy preterm birth vaginal microbiome

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Microbiology
Genetics
Obstetrics

Background:

The vaginal microbiome is linked to preterm birth (PTB), but prediction remains challenging due to high individual variability and low accuracy.
Existing methods struggle to reliably predict PTB using vaginal microbial profiles alone.

Purpose of the Study:

To identify specific vaginal microbiota markers that enhance PTB prediction.
To develop a machine learning (ML) algorithm combining microbiome data with clinical information for improved PTB prediction accuracy.

Main Methods:

A multicenter case-control study involving 150 Korean pregnant women (54 PTB, 96 full-term).
Collected cervicovaginal fluid for microbiome analysis and recorded demographic, white blood cell count, and cervical length data.
Employed machine learning models (logistic regression, random forest, etc.) with selected microbial markers and clinical data for prediction.

Main Results:

Selected markers included *Lactobacillus* spp., *Gardnerella vaginalis*, *Ureaplasma parvum*, *Atopobium vaginae*, *Prevotella timonensis*, and *Peptoniphilus grossensis*.
A logistic regression model with 17 markers achieved an AUC of 0.72.
Combining 7 microbiome markers with white blood cell count and cervical length in a random forest model yielded the highest AUC of 0.84.

Conclusions:

Specific vaginal bacteria like *P. timonensis* and *U. parvum* are associated with increased PTB risk.
Machine learning models integrating microbiome data with clinical factors (WBC, cervical length) significantly enhance preterm birth prediction accuracy.