Occlussion and Other Factors of Importance for Temporomandibular Disorders/TMD

The relationship between occlusal factors and the health of the masticatory system has been one of the most controversial areas in dentistry. For many years the dental profession believed that occlusal interferences would lead to TMD. However, this opinion has gradually weakened since there is a lack of convincing evidence supporting this relationship. Several treatments not related to dental occlusion have also proved to be effective in management of TMD. At present, most so-called TMD experts deemphasize the importance of occlusion in the etiology of TMD, whereas a majority of practitioners still adhere to a concept focusing on occlusal factors in diagnosis and treatment of TMD. This controversy has sometimes become very dramatic, especially in the USA. It is well established that simple reversible therapy is efficient for helping a majority of TMD patients. Even if most studies have failed to find any close correlation between occlusal factors and TMD signs and symptoms, occlusion cannot be neglected as it plays an important role for comfort and function of the masticatory system. There is an obvious need for continuing research on the relationship between the occlusion and TMD using strict, evidencebased study methods in order to improve patient management. A search of the current literature on TMD will be presented

Roentgen cephalometric analysis of ridge resorption and changes in jaw and occlusal relationships in immediate complete denture wearers

In eighteen subjects assigned for immediate complete upper and lower dentures, roentgen cephalometric recordings were made before extraction of the residual anterior dentition and 3 weeks, 3 months, 6 months and 1 year after denture insertion. The cephalometric analysis was based on electronic measurements of linear and angular morphological variables and computer head plots generated from 177 reference points (Walker, 1967), derived for each subject for each of the five observation stages. The reduction of the alveolar ridges was most rapid during the first 3 months of denture wear and particularly during the post-extraction period of 3 weeks. The reduction in anterior height of the lower ridge was on average twice as great as that of the upper ridge. The ridge resorption and the accompanying settling of the dentures on the basal seats, measured from lead shots inserted in the dentures, brought about an upward rotation of the mandible with a resulting decrease in occlusal vertical dimension and reduction in overjet of the dentures. In accordance with the amount of ridge reduction, these changes showed great individual variation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73577/1/j.1365-2842.1980.tb01466.x.pd

Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis

Background: Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge. Methods and findings: The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2). Conclusions: TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients