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Objectives To (1) introduce a novel machine learning method and (2) assess maxillary structure variation in unilateral canine impaction for advancing clinically viable information. Materials and Methods A machine learning algorithm utilizing Learning-based multi-source IntegratioN frameworK for Segmentation (LINKS) was used with cone-beam computed tomography (CBCT) images to quantify volumetric skeletal maxilla discrepancies of 30 study group (SG) patients with unilaterally impacted maxillary canines and 30 healthy control group (CG) subjects. Fully automatic segmentation was implemented for maxilla isolation, and maxillary volumetric and linear measurements were performed. Analysis of variance was used for statistical evaluation. Results Maxillary structure was successfully auto-segmented, with an average dice ratio of 0.80 for three-dimensional image segmentations and a minimal mean difference of two voxels on the midsagittal plane for digitized landmarks between the manually identified and the machine learning–based (LINKS) methods. No significant difference in bone volume was found between impaction ([2.37 ± 0.34] 104 mm3) and nonimpaction ([2.36 ± 0.35] 104 mm3) sides of SG. The SG maxillae had significantly smaller volumes, widths, heights, and depths (P < .05) than CG. Conclusions The data suggest that palatal expansion could be beneficial for those with unilateral canine impaction, as underdevelopment of the maxilla often accompanies that condition in the early teen years. Fast and efficient CBCT image segmentation will allow large clinical data sets to be analyzed effectively.

Dopamine (DA) is a well-known neurotransmitter and critical element in the mussel adhesive protein that has gained increasing attention for its role in cellular growth enhancement in biomaterials, including cellular adhesion improvement. As the mechanism underlying this remains unclear, the objective of this study was to explore the effects of DA on the adhesion properties of bone marrow derived rat mesenchymal stem cells (rMSCs) using an hydroxyapatite gelatin nanocomposite biomaterial and to test whether the effects are mediated through various endogenously expressed DA receptors. Primary rMSCs were pretreated with D1-like antagonist, D2-like antagonist, or a combination of these antagonists followed by treatment with 50 μM DA and cellular adhesion quantification at 0.5, 1, 2 and 4 hours post DA addition. DA was found to increase rMSC adhesion and spreading at the 0.5 hour time-point and the dopaminergic effect on cell adhesion was partially blocked by DA antagonists. In addition, the D1-like and D2-like antagonists appeared to have a similar effect on rMSCs. Immunofluorescent staining indicated that the rMSC spreading area was significantly increased in the DA treated group versus the control group. Treatment of the D1-like DA antagonists with DA revealed that the actin filaments of rMSCs could not connect the membrane with the nucleus. In summary, DA was found to enhance early rMSC adhesion partially via DA receptor activation.