Osteonectin is a major glycoprotein of porcine and bovine bones and teeth that is found associated with hydroxylapatite crystal surfaces. From the ability of osteonectin to bind calcium ions, it has been proposed as a possible nucleator of hydroxylapatite crystal formation. Analysis of hydroxylapatite-bound proteins of rat bone and dentine, however, has revealed that osteonectin represents only 2.5 +/- 1.5% of the hydroxylapatite-bound protein in long bones, 0.9 +/- 0.5% in calvariae, and less than 0.1% in incisor dentine of animals of different ages. Further, in vivo pulse-chase studies carried out in young adult rats have shown osteonectin to be synthesized at low levels in these tissues. Similarly, low levels of osteonectin were synthesized by rat calvarial cells in vitro. In contrast, fibroblastic cells from periodontal ligament and gingiva synthesized significantly greater amounts of osteonectin. These studies indicate that the low quantities of osteonectin in rat mineralized tissues are a consequence of low rates of formation rather than being due to rapid turnover. The virtual absence of osteonectin in incisor dentine correlates with the lack of peritubular dentine in rat, whereas the low osteonectin content of rat bones may reflect differences in their structure and biophysical properties compared with bones of larger mammals.

BackgroundOral malodour is mainly caused by volatile sulphur compounds produced by bacteria and bacterial interactions. It is difficult to predict the presence or absence of oral malodour based on the abundances of specific species and their combinations. This paper presents an effective way of deep learning approach to predicting the oral malodour from salivary microbiota.MethodsThe 16S rRNA genes from saliva samples of 90 subjects (45 had no or weak oral malodour, and 45 had marked oral malodour) were amplified, and gene sequence analysis was carried out. Deep learning classified oral malodour and healthy breath based on the resultant abundances of operational taxonomic units (OTUs)ResultsA discrimination classifier model was constructed by profiling OTUs and calculating their relative abundance in saliva samples from 90 subjects. Our deep learning model achieved a predictive accuracy of 97%, compared to the 79% obtained with a support vector machine.ConclusionThis approach is expected to be useful in screening the saliva for prediction of oral malodour before visits to specialist clinics.Electronic supplementary materialThe online version of this article (10.1186/s12903-018-0591-6) contains supplementary material, which is available to authorized users.

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Optimal concentrations of the essential components for analyzing the activity of each enzyme associated with glycolysis and gluconeogenesis in rabbit periodontal ligament were examined, and enzyme assay systems for 15 enzymes including 22 reactions were established using triethanolamine buffer. Specific activities of all the enzymes, except for the gluconeogenic reaction of phosphoglycerate kinase, were systematically evaluated using the optimum buffer for each enzyme, since the activity of each enzyme varied depending on the buffer used. For glycolysis, the activity levels of hexokinase and 6-phosphofructokinase were very low, and consequently these enzyme reactions were inferred to be the rate-limiting steps. For gluconeogenesis, fructose 1,6-bisphosphatase and aldolase activities were extremely low, and the activities of glucose 6-phosphatase, phosphoenolpyruvate carboxykinase and pyruvate carboxylase were undetectable. These results suggest that the periodontal ligament may have no gluconeogenesis capability. With a rise in pH, the activities of the key enzymes of glycolysis gradually increased, and a specific "crossover" point was found between the activities of glyceraldehyde -phosphate dehydrogenase and phosphoglyceromutase. In addition, the activity of fructose 1,6-bisphosphatase, one of the key enzymes of gluconeogenesis, was markedly increased with a rise in pH, although pH changes had no effect on aldolase activity. Consequently, alkaline pH appeared to result in overall stimulation of glycolysis.