Prevalence of temporomandibular joint disorder in people with Parkinson’s disease in a public university hospital

Evaluation of Temporomandibular Joint in Patients with Parkinson's Disease: A Comparative Study

(1) The aim of this study was to perform an evaluation of the temporomandibular joint (TMJ) in patients with Parkinson's disease (PD) and present the morphological differences of the TMJ between healthy subjects and patients with PD. (2) A total of 102 Caucasian subjects were divided equally into two groups. The study group consisted of patients with PD, while the control group comprised healthy subjects. Ten parameters, including anterior joint space (AJS), superior joint space (SJS), posterior joint space (PJS), condyle head length (CHL), condylar neck width (CNW), minor axis of the condyle (MAC), long axis of the condyle (LAC), condylar axis inclination (CI), medial joint space (MJS), and lateral joint space (LJS), were measured using magnetic resonance images. The data were statistically analyzed using paired samples t-test and Student's t-test, with a significance level set at p < 0.05. (3) In the PD group, all TMJ parameters showed a statistically significant difference between both sides of the face (p < 0.05). However, in the control group, AJS, SJS, PJS, CHL, CNW, MAC, CI, MJS, and LJS did not show a statistically significant difference between both sides of the face (p > 0.05), except for LAC (p < 0.05). The asymmetry index values of AJS, SJS, PJS, CHL, CNW, MAC, CI, MJS, and LJS demonstrated a statistically significant difference between the study and control groups (p < 0.05), except for LAC (p > 0.05). (4) Within the limitations of this retrospective study, the findings suggest that TMJ morphology and asymmetry could be associated with PD.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review

Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Sociodemographic profile and stages of Parkinson’s disease associated with predictors of Temporomandibular Disorder

ABSTRACT Purpose: to analyze the predictors of temporomandibular disorder in people with Parkinson’s disease, verifying their associations with sociodemographic aspects and stages of the disease. Methods: a study based on secondary data from research conducted in 2017 with 110 people with Parkinson’s disease. They were assessed with the Research Diagnostic Criteria for Temporomandibular Disorders and the Parkinson’s disease staging scale. The studied predictive variables for temporomandibular disorder were pain, crepitation, clicking, nighttime and daytime clenching/gnashing, uncomfortable/non-habitual bite, morning rigidity, and tinnitus. The sociodemographic aspects assessed were age, sex, schooling level, marital status, income, and stages 1 to 3 of the disease. The chi-squared odds ratio was used with a 95% confidence interval and significance level at p < 0.05. Results: an association was verified between nighttime clenching/gnashing and income (p = 0.006); tinnitus and income range from ½ to 3 (p = 0.003) and from 4 to 10 minimum wages (p = 0.004); and between tinnitus and stage 1 (p = 0.02). Conclusion: this study verified that the predictors associated with temporomandibular disorder in people with Parkinson’s disease were pain, clicking, crepitation, uncomfortable/non-habitual bite, and morning rigidity. It was verified that income and stage 1 of the disease had an association with nighttime clenching/gnashing and tinnitus.