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Do 3D-Printed and Milled Denture Bases Differ in Microbial Activity and Adhesion? A Systematic Review and
Sarah Arzani1, Erfan Khorasani2, Aida Mokhlesi3
1Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran.
Abstract:
The increasing adoption of digital manufacturing in prosthodontics raises concerns about microbial metabolic activity and adhesion on denture bases fabricated via additive and subtractive methods. This systematic review aimed to compare microbial metabolic activity and microorganism adhesion between 3D-printed and milled complete denture bases. A systematic search of PubMed, Scopus, Web of Science, Embase, and the Cochrane Library was conducted up to December 2024. Studies were screened according to predefined eligibility criteria, and data were independently extracted by two reviewers. The standardized mean difference (SMD) was used to compare microbial metabolic activity and cell adhesion outcomes between groups. A random-effects model was applied for meta-analysis, and heterogeneity was assessed using Cochran's Q, I², and τ² indices. Publication bias was evaluated using funnel plots, Egger's test, and the Trim and Fill method. The Quality Assessment Tool for In Vitro Studies (QUIN) was used to assess the risk of bias. Twelve studies met the inclusion criteria. The meta-analysis revealed that microbial activity was significantly higher in 3D-printed compared to milled denture bases (SMD = 1.285, 95% CI: 0.810-1.760, P < .001). Similarly, the microbial cell count was significantly higher in the 3D-printed group (SMD = 1.050, 95% CI: 0.124-1.976, P = .026). Surface roughness was tested as a covariate in meta-regression and was not a significant predictor of microbial activity. 3D-printed denture bases exhibited higher microbial metabolic activity and adhesion than milled counterparts. However, the heterogeneity of studies and variations in fabrication parameters necessitate further research to optimize material selection and processing protocols.
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