Dental adhesives provide retention to composite fillings in dental restorations. Microtensile bond strength (µTBS) test is the most used laboratory test to evaluate bonding performance of dental adhesives. The traditional approach for developing dental adhesives involves repetitive laboratory measurements, which consumes enormous time and resources. Machine learning (ML) is a promising tool for accelerating this process. This study aimed to develop ML models to predict the µTBS of dental adhesives using their chemical features and to identify important contributing factors for µTBS. Specifically, the chemical composition and µTBS information of 81 dental adhesives were collected from the manufacturers and the literature. The average µTBS value of each adhesive was labeled as either 0 (if <36 MPa) or 1 (if ≥36 MPa) to denote the low and high µTBS classes. The initial 9-feature data set comprised pH, HEMA, BisGMA, UDMA, MDP, PENTA, filler, fluoride, and organic solvent (OS) as input features. Nine ML algorithms, including logistic regression, k-nearest neighbor, support vector machine, decision trees and tree-based ensembles, and multilayer perceptron, were implemented for model development. Feature importance analysis identified MDP, pH, OS, and HEMA as the top 4 contributing features, which were used to construct a 4-feature data set. Grid search with stratified 10-fold cross-validation (CV) was employed for hyperparameter tunning and model performance evaluation using 2 metrics, the area under the receiver operating characteristic curve (AUC) and accuracy. The 4-feature data set generated slightly better performance than the 9-feature data set, with the highest AUC score of 0.90 and accuracy of 0.81 based on stratified CV. In conclusion, ML is an effective tool for predicting dental adhesives with low and high µTBS values and for identifying important chemical features contributing to the µTBS. The ML-based data-driven approach has great potential to accelerate the discovery of new dental adhesives and other dental materials.

Background: The use of Aspirin in the primary prevention of cardiovascular disease (CVD) is still a topic of debate, especially in patients with diabetes. The present meta-analysis aims to rule out the efficacy of Aspirin in patients with diabetes and to compare the effectiveness of Aspirin with a placebo (or no treatment) for the primary prevention of CVD and all-cause mortality events in people with diabetes. Materials and Methods: An extensive and systematic search was conducted in Medline (via PubMed), Cinahl (via Ebsco), Scopus, and Web of Sciences from 1988 to December 2020. A detailed literature search was conducted using Aspirin, cardiovascular disease (CVD), diabetes, and efficacy to identify trials of patients with diabetes who received Aspirin for primary prevention of CVD. Demographic details with the primary outcome of events and bleeding outcomes were analyzed. The risk of bias (RoB) in included studies was evaluated using the QUADAS-2 tool. Results: A total of 5 studies out of 13 were included with 23,570 diabetic patients; 11,738 allocated to Aspirin and 11,832 allocated to the placebo group. In patients with diabetes, there was no difference between Aspirin and placebo with respect to the risk of all-cause death with a confidence interval (CI) varying 0.63 to 1.17. In addition, there were no differences in the bleeding outcomes with an odds ratio of 1.4411 (CI 0.47 to 4.34). Conclusion: Aspirin has no significant risk on primary endpoints of cardiovascular events and the bleeding outcomes in diabetic patients compared to placebo. More research on the use of Aspirin alone or in combination with other antiplatelet drugs is required in patients with diabetes to supplement currently available research.