{"title":"Predictive Analysis of Dental Caries Risk via Rapid Urease Activity Evaluation in Saliva Using a ZIF-8 Nanoporous Membrane.","authors":"Bao-Yi Zhou,Xiao-Yan Shi,Zhao-Ying Luo,Zhong-Qin Pan,Hai-Ying Gu,Yang Liu,Xin-He Shi,Zeng-Qiang Wu","doi":"10.1021/acssensors.4c03091","DOIUrl":null,"url":null,"abstract":"Despite a decrease in the incidence of dental caries over the past four decades, it remains a widespread public health concern. The multifactorial etiology of dental caries complicates effective prevention and early intervention efforts, underscoring the need for the development of rapid predictive methods that account for multiple factors. In this study, we selected the activity of urease secreted by Streptococcus salivarius as a metabolic marker for dental caries. This activity was quantified by measuring the diffusion of hydroxide ions generated from the urease catalytic reaction on urea across a ZIF-8-modified nanoporous membrane. The choice of ZIF-8 was based on its preference in transporting hydroxide ions, enabling the accurate detection of urease activity at concentrations as low as 1 CFU/mL. Subsequently, we collected 287 saliva samples to determine the Michaelis constant (Km) of urease using this method. Logistic regression analysis revealed that both the Km of urease and the frequency of sugar intake are significant factors influencing the development of dental caries. Furthermore, we developed a machine learning methodology for identifying dental caries, achieving an accuracy rate of 81%. It is expected that increasing the sample size will further enhance the predictive accuracy of the model. This innovative approach provides valuable insights into early intervention strategies in the fight against dental caries.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"18 1","pages":""},"PeriodicalIF":8.2000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Sensors","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acssensors.4c03091","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Despite a decrease in the incidence of dental caries over the past four decades, it remains a widespread public health concern. The multifactorial etiology of dental caries complicates effective prevention and early intervention efforts, underscoring the need for the development of rapid predictive methods that account for multiple factors. In this study, we selected the activity of urease secreted by Streptococcus salivarius as a metabolic marker for dental caries. This activity was quantified by measuring the diffusion of hydroxide ions generated from the urease catalytic reaction on urea across a ZIF-8-modified nanoporous membrane. The choice of ZIF-8 was based on its preference in transporting hydroxide ions, enabling the accurate detection of urease activity at concentrations as low as 1 CFU/mL. Subsequently, we collected 287 saliva samples to determine the Michaelis constant (Km) of urease using this method. Logistic regression analysis revealed that both the Km of urease and the frequency of sugar intake are significant factors influencing the development of dental caries. Furthermore, we developed a machine learning methodology for identifying dental caries, achieving an accuracy rate of 81%. It is expected that increasing the sample size will further enhance the predictive accuracy of the model. This innovative approach provides valuable insights into early intervention strategies in the fight against dental caries.
期刊介绍:
ACS Sensors is a peer-reviewed research journal that focuses on the dissemination of new and original knowledge in the field of sensor science, particularly those that selectively sense chemical or biological species or processes. The journal covers a broad range of topics, including but not limited to biosensors, chemical sensors, gas sensors, intracellular sensors, single molecule sensors, cell chips, and microfluidic devices. It aims to publish articles that address conceptual advances in sensing technology applicable to various types of analytes or application papers that report on the use of existing sensing concepts in new ways or for new analytes.