Although salivary liquid can degrade constituents in resin-based dental composites in short-term incubations, there is a knowledge gap on how longer-term aging impacts their bulk strength. We address this through extended aging studies with resin-based dental composites in different environments. Two commercial composites (FIL and AEL) were aged aseptically at 37 C in air (A, control), artificial saliva (AS), and esterase enzyme amended AS (EAS). Diametral and pushout strength were measured after periods of 120-180 days. At 120 days, the diametral strength of composites aged in air was 69.9 ± 11.0 and 57.7 ± 3.31 MPa in FIL and AEL, respectively. These were significantly greater compared to composites aged in AS (32.1 ± 7.01 and 46.2 ± 9.38 MPa in FIL and AEL, respectively) or EAS (36.7 ± 8.49 and 43.5 ± 5.51 MPa in FIL and AEL, respectively). In contrast, pushout strength for both composites were smaller in A compared to those aged in AS and EAS, results attributed to AS absorption and polymer expansion. No significant change in either diametral or pushout strength occurred after 120 days. There was no significant difference between aging in AS and EAS, suggesting that esterase did not significantly decrease the bulk material strength to a greater extent than AS under the test conditions. Aqueous diffusivities for the composites ranged from 8.4 to 11 Â 10 À13 m 2 /s, with associated porosities ranging from 0.06% to 0.10%. These results indicate that saturation of a typical dental composite occurs over a time frame of 4-5 months, longer than typical aging studies. Together, the results demonstrate the importance of aging time on composite strength.

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Compound identification by database searching that matches experimental with library mass spectra is commonly used in mass spectrometric (MS) data analysis. Vendor software often outputs scores that represent the quality of each spectral match for the identified compounds. However, software-generated identification results can differ drastically depending on the initial search parameters. Machine learning is applied here to provide a statistical evaluation of software-generated compound identification results from experimental tandem MS data. This task was accomplished using the logistic regression algorithm to assign an identification probability value to each identified compound. Logistic regression is usually used for classification, but here it is used to generate identification probabilities without setting a threshold for classification. Liquid chromatography coupled with quadrupole-time-of-flight tandem MS was used to analyze the organic monomers leached from resinbased dental composites in a simulated oral environment. The collected tandem MS data were processed with vendor software, followed by statistical evaluation of these results using logistic regression. The assigned identification probability to each compound provides more confidence in identification beyond solely by database matching. A total of 21 distinct monomers were identified among all samples, including five intact monomers and chemical degradation products of bisphenol A glycidyl methacrylate (BisGMA), oligomers of bisphenol-A ethoxylate methacrylate (BisEMA), triethylene glycol dimethacrylate (TEGDMA), and urethane dimethacrylate (UDMA). The logistic regression model can be used to evaluate any database-matched liquid chromatography-tandem MS result by training a new model using analytical standards of compounds present in a chosen database and then generating identification probabilities for candidates from unknown data using the new model.

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