A Dynamic Analysis: the Smile and Display of Dentition during Speech

The objective of this retrospective study is to follow up on a previous Dynamic Smile Analysis and videographically analyze and develop averages for soft tissue norms with respect to the display of dentition during speech. These values would then be compared cross-sectionally across different age groups to see whether changes attributable to the aging process could be seen. A secondary objective was to compare averages for soft tissue norms in the display of dentition during speech to averages for soft tissue norms in the display of dentition during the smile. Materials and Method: Records from a previous study in which video equipment was used to capture video for 26 1 subjects were re-evaluated to find appropriate frames to analyze for speech. Two frames for each subject were selected; one frame representing the maximal display of maxillary incisors during speech and the second representing the widest transverse display of dentition during speech. After excluding 40 subjects the data for the remaining 221 subjects was analyzed. These averages were then compared to averages attained in the previous study to compare the display of the dentition during speech to the display of the dentition during smile. Results: On average, a difference in 1.29 mm was seen in the display of the maxillary incisors during speech at maximal display and during the smile. An average of 7.23 mm of maxillary incisors is readily visible during maximum display of maxillary incisors during speech, as compared to 8.52 mm during the smile. The constructed smile index was also smaller when measured during the speech when compared to the smile index by an average of 2.58 units. Conclusion: This study helps to establish age-related dynamic norms for the display of dentition during speech. The dynamic measures indicate that the display of dectition is greater, on average, during the smile than at speech.

Dynamic Smile Analysis : Changes with Age

Introduction: The objective of this study was to define age-related changes in the human smile. The areas of interest were: upper lip length at smile and repose, upper lip thickness at smile and repose, maxillary incisal display at smile, interlabial gap height at smile, smile index, percent buccal corridors, intercommissural width at rest, smile height, and smile arc. A secondary objective was to study the perioral changes from rest to smile and compare them on the basis of age. Materials and Method: Video equipment was used to capture video for 261 subjects. Two frames for each subject were selected; one frame representing the lips and rest and the second representing the widest smile. After excluding 40 subjects the data for the remaining 221 subjects was analyzed. Results: There was a decrease of 1.5 to 2 mm in the maxillary incisor display during smile, with increase in age. Smile index significantly increased with increase in age. Most (78%) subjects displayed an average smile height. No subjects in the 50 and over age group displayed a high smile while no subjects in the 15-19 year old age group presented with a low smile. All the dynamic measures indicated there was a pattern of decreasing change from rest to smile especially evident after the 30-39 year old age group. Conclusions: This study helps to establish age related dynamic norms. As the person ages the smile gets narrower vertically and wider transversely. The dynamic measures indicate that the muscles' ability to create a smile decreases with increasing age.

Biological Signatures of Vaccine Responses

The set of host- and pathogen-specific molecular features of a disease comprise its “signature”. We hypothesize that biological signatures enables distinctions between vaccinated vs. infected individuals. In our research, using porcine samples, protocols were developed that could also be used to identify biological signatures of human disease. Different classes of molecular features will be tested during this project, including indicators of basic immune capacity, which are being studied at this instance. These indicators of basic immune response such as porcine cytokines and antibodies were validated using Enzyme-linked immunosorbent assay (ELISA). This is an established method that detects antigens by their interaction with a specific antibody coupled to a polystyrene substrate. Serum from naïve and vaccinated pigs was tested for the presence of cytokines. We were able to differentiate the presence of porcine IL-6 in normal porcine serum with or without added porcine IL-6 by ELISA. In addition, four different cytokines were spotted on a grating-coupled surface plasmon resonance imaging system (GCSPRI) chip and antibody specific for IL-8 was run over the chip. Only the presence of IL-8 was detected; therefore, there was no cross-reactivity in this combination of antigens and antibodies. This system uses a multiplexed sensor chip to identify components of a sample run over it. The detection is accomplished by the change in refractive index caused by the interaction between the antibody spotted on the sensor chip and the antigen present in the sample. As the multiplexed GCSPRI is developed, we will need to optimize both sensitivity and specificity, minimizing the potential for cross-reactivity between individual analytes. The next step in this project is to increase the sensitivity of detection of the analytes. Currently, we are using two different antibodies (that recognize a different part of the antigen) to amplify the signal emitted by the interaction of antibody with its cognate antigen. The development of this sensor chip would not only allow to detect FMD virus, but also to differentiate between infected and vaccinated individuals, on location. Furthermore, the diagnosis of other diseases could be done with increased accuracy, and in less time due to the microarray approach.