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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. [ABSTRACT FROM AUTHOR]

Carbodiimide (EDC)–based dentin primers preserve hybrid layer (HL) integrity. However, aging >1 y has not been investigated. The present study examined whether the cross-linking effect of EDC was reflected in dentin bond strength, endogenous enzymatic activity, and the chemical profile of the HL after 5-y aging in artificial saliva. Noncarious human third molars (N = 42) were cut to expose middle/deep coronal dentin and treated as follows: group 1, dentin etched with 35% H3PO4, pretreated with a 0.3M aqueous EDC primer for 1 min and restored with XP Bond (Dentsply Sirona); group 2, as in group 1 but without EDC pretreatment; group 3, Clearfil SE Bond (Kuraray-Noritake) primer applied to dentin surface, followed by EDC pretreatment as in group 1 and application of bond; group 4, as in group 3 without EDC pretreatment. After composite buildup, the specimens were cut into sticks or slabs, depending on the experiment. All tests were performed at baseline (T0) and after 5 y of aging (T5) in artificial saliva at 37 °C. Microtensile bond strength (µTBS) was tested at a crosshead speed of 1 mm/min until failure. Endogenous enzymatic activity was investigated with in situ zymography. The chemical profile of HL was determined via Raman spectroscopy. Three-way analysis of variance and post hoc Tukey test were used to analyze µTBS and in situ zymography data (α = 0.05). EDC pretreatment and aging significantly influenced µTBS and in situ zymography results (P < 0.05). Higher bond strength and lower gelatinolytic activity were identified in the EDC-treated groups at T5 (P < 0.05), especially in the etch-and-rinse groups. Raman spectra revealed less defined amide III peaks in control specimens at T5. The EDC cross-linking effect persisted in the HL for 5 y in terms of bond strength, collagen structure preservation, and dentinal enzyme silencing. [ABSTRACT FROM AUTHOR]

This article presents a report of the Professional Development Committees's Mentorship Task Force on March 28, 2005. The concept of mentoring is elaborated. The characteristics and responsibilities of mentors and mentees are discussed. The advantages of mentoring are assessed.

Information on several activities during the conferences sponsored by the Art Libraries Society of North America (ARLIS/NA) is presented. Activities include a survey on mentoring, workshops, and evaluations on the society's status in communication, its goals, and the progress of relationship. The conferences featured several ARLIS/NA officials including Marilyn Nasserden, Jill Patrick, and other members of the Conference Planning Advisory Committee (CPAC).

A simple method was used to measure directly sodium and potassium currents underlying the action potential in single nerve fibres of Xenopus laevis. A short rectangular stimulus under current-clamp conditions elicited an action potential which was digitally stored and later used as command when voltageclamping the same fibre. The currents thus obtained nearly reproduced the original rectangular stimulus. Adding first 100 n M TTX and subsequently 100 n M TTX plus 10 m M TEA to the extracellular Ringer solution revealed the sodium and the potassium currents during an action potential. They were converted to permeabilities by use of the constant-field equation and are in good agreement with the curves which had been calculated from conventional voltage-clamp data. Thus experimentally determined currents and permeabilities are shown as they are changing during an action potential. [ABSTRACT FROM AUTHOR]