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Related Experiment Video

Updated: May 22, 2025

Systematic Approach to Identify Novel Antimicrobial and Antibiofilm Molecules from Plants' Extracts and Fractions to Prevent Dental Caries
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Predictive Analysis of Dental Caries Risk via Rapid Urease Activity Evaluation in Saliva Using a ZIF-8 Nanoporous

Bao-Yi Zhou1, Xiao-Yan Shi2, Zhao-Ying Luo1

  • 1Institute for Applied Research in Public Health, School of Public Health, Nantong University, Nantong, Jiangsu 226019, China.

ACS Sensors
|May 21, 2025
PubMed
Summary
This summary is machine-generated.

Researchers identified urease activity from Streptococcus salivarius as a novel metabolic marker for predicting dental caries. This method, combined with machine learning, achieved 81% accuracy in identifying the disease.

Keywords:
ZIF-8-modified nanoporousdental cariesion transportlogistic regressionmachine learningurease activity

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Area of Science:

  • Biomedical Engineering
  • Microbiology
  • Public Health

Background:

  • Dental caries remains a significant public health issue despite declining incidence.
  • The complex causes of dental caries hinder effective prevention and early intervention.
  • Rapid predictive methods are needed to address multifactorial dental caries.

Purpose of the Study:

  • To establish urease activity from Streptococcus salivarius as a metabolic marker for dental caries.
  • To develop a novel method for quantifying urease activity using ZIF-8 nanoporous membranes.
  • To investigate the correlation between urease Michaelis constant (Km) and dental caries development.

Main Methods:

  • Quantified urease activity by measuring hydroxide ion diffusion across a ZIF-8 membrane.
  • Collected 287 saliva samples to determine urease Km.
  • Employed logistic regression and machine learning for caries prediction.

Main Results:

  • Detected urease activity at concentrations as low as 1 CFU/mL.
  • Identified urease Km and sugar intake frequency as significant factors in caries development.
  • Developed a machine learning model with 81% accuracy for dental caries identification.

Conclusions:

  • Urease activity is a viable metabolic marker for dental caries.
  • The developed method offers a sensitive approach for early detection.
  • Machine learning integration shows promise for enhanced predictive accuracy with larger sample sizes.