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Interactions between salivary agglutinin and the adhesin P1 of Streptococcus mutans contribute to bacterial aggregation and mediate sucrose-independent adherence to tooth surfaces. We have examined biofilm formation by S. mutans UA159, and derivative strains carrying mutations affecting the localization or expression of P1, in the presence of fluid-phase or adsorbed saliva or salivary agglutinin preparations. Whole saliva-and salivary agglutinin-induced aggregation of S. mutans was adversely affected by the loss of P1 and sortase (SrtA) but not by the loss of trigger factor (RopA). Fluid-phase salivary agglutinin and, to a lesser extent, immobilized agglutinin inhibited biofilm development by S. mutans in the absence of sucrose, and whole saliva was more effective at decreasing biofilm formation than salivary agglutinin. Inhibition of biofilm development by salivary agglutinin was differently influenced by particular mutations, with the P1-deficient strain displaying a greater inhibition of biofilm development than the SrtA-or RopA-deficient strains. As expected, biofilm-forming capacities of all strains in the presence of salivary preparations were markedly enhanced in the presence of sucrose, although biofilm formation by the mutants was less efficient than that by the parental strain. Aeration strongly inhibited biofilm development, and the presence of salivary components did not restore biofilm formation in aerated conditions. The results disclose a potent ability of salivary constituents to moderate biofilm formation by S. mutans through P1-dependent and P1-independent pathways.

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Diagnosis and treatment planning are the most important steps in the orthognathic surgery for the successful treatment. The purpose of this study was to develop a new artificial intelligent model for surgery/non-surgery decision and extraction determination, and to evaluate the performance of this model. The sample used in this study consisted of 316 patients in total. Of the total sample, 160 were planned with surgical treatment and 156 were planned with non-surgical treatment. The input values of artificial neural network were obtained from 12 measurement values of the lateral cephalogram and 6 additional indexes. The artificial intelligent model of machine learning consisted of 2-layer neural network with one hidden layer. The learning was carried out in 3 stages, and 4 best performing models were adopted. Using these models, decision-making success rates of surgery/non-surgery, surgery type, and extraction/non-extraction were calculated. The final diagnosis success rate was calculated by comparing the actual diagnosis with the diagnosis obtained by the artificial intelligent model. The success rate of the model showed 96% for the diagnosis of surgery/non-surgery decision, and showed 91% for the detailed diagnosis of surgery type and extraction decision. This study suggests the artificial intelligent model using neural network machine learning could be applied for the diagnosis of orthognathic surgery cases.