Predictive Analysis of Dental Caries Risk via Rapid Urease Activity Evaluation in Saliva Using a ZIF-8 Nanoporous Membrane DOI
Bin Zhou,

Xiaoyan Shi,

Zhao-Ying Luo

et al.

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: May 21, 2025

Despite a decrease in the incidence of dental caries over past four decades, it remains widespread public health concern. The multifactorial etiology complicates effective prevention and early intervention efforts, underscoring need for development rapid predictive methods that account multiple factors. In this study, we selected activity urease secreted by Streptococcus salivarius as metabolic marker caries. This was quantified measuring diffusion hydroxide ions generated from catalytic reaction on urea across ZIF-8-modified nanoporous membrane. choice ZIF-8 based its preference transporting ions, enabling accurate detection at concentrations low 1 CFU/mL. Subsequently, collected 287 saliva samples to determine Michaelis constant (Km) using method. Logistic regression analysis revealed both Km frequency sugar intake are significant factors influencing Furthermore, developed machine learning methodology identifying caries, achieving an accuracy rate 81%. It is expected increasing sample size will further enhance model. innovative approach provides valuable insights into strategies fight against

Language: Английский

Predictive Analysis of Dental Caries Risk via Rapid Urease Activity Evaluation in Saliva Using a ZIF-8 Nanoporous Membrane DOI
Bin Zhou,

Xiaoyan Shi,

Zhao-Ying Luo

et al.

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: May 21, 2025

Despite a decrease in the incidence of dental caries over past four decades, it remains widespread public health concern. The multifactorial etiology complicates effective prevention and early intervention efforts, underscoring need for development rapid predictive methods that account multiple factors. In this study, we selected activity urease secreted by Streptococcus salivarius as metabolic marker caries. This was quantified measuring diffusion hydroxide ions generated from catalytic reaction on urea across ZIF-8-modified nanoporous membrane. choice ZIF-8 based its preference transporting ions, enabling accurate detection at concentrations low 1 CFU/mL. Subsequently, collected 287 saliva samples to determine Michaelis constant (Km) using method. Logistic regression analysis revealed both Km frequency sugar intake are significant factors influencing Furthermore, developed machine learning methodology identifying caries, achieving an accuracy rate 81%. It is expected increasing sample size will further enhance model. innovative approach provides valuable insights into strategies fight against

Language: Английский

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