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Background: Most studies support parafunctions play an important role in temporomandibular disorders (TMD), whereas the association between tooth wear and TMD remains controversial. Betel nut chewing as a parafunction is popular in South and Southeast Asia. We therefore investigated the association of severely worn dentition resulting from betel nut chewing with TMD. Methods: A cross-sectional analysis of 408 control participants (male: 380, female: 28, 43.62 ± 9.54 years) and 408 participants with betel nut chewing related severely worn dentition (male: 380, female: 28, 43.73 ± 8.93 years) who received dental and TMD checkup according to Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) in Health Management Center, Xiangya Hospital was performed. Betel nut chewing related severely worn dentition meant all the natural teeth had moderate to severe tooth wear [Tooth Wear Index (TWI) ≥ 2)] including ≥ 2 severe wear teeth (TWI ≥ 3) due to betel nut chewing. Multivariable logistic regression analysis was used. Results: After adjusting for age, gender, betel nut chewing related severely worn dentition, oral submucosal fibrosis, number of missing teeth, number of dental quadrants with missing teeth, visible third molar and orthodontic history, variables of age, gender and betel nut chewing related severely worn dentition were significant for overall TMD. Multivariable analysis showed betel nut chewing related severely worn dentition was significantly associated with intra-articular TMD [odds ratio and 95% confidence intervals: 1.689 (1.271–2.244), P = 0.001] in a betel nut chewing dose-dependent manner. Conclusion: Betel nut chewing related severely worn dentition was associated with intra-articular TMD. [ABSTRACT FROM AUTHOR]

Introduction: Quantitative electrocardiographic (ECG) waveform analysis provides a noninvasive reflection of the metabolic milieu of the myocardium during resuscitation and is a potentially useful tool to optimize the defibrillation strategy. However, whether combining multiple ECG features can improve the capability of defibrillation outcome prediction in comparison to single feature analysis is still uncertain.Methods: A total of 3828 defibrillations from 1617 patients who experienced out-of-hospital cardiac arrest were analyzed. A 2.048-s ECG trace prior to each defibrillation without chest compressions was used for the analysis. Sixteen predictive features were optimized through the training dataset that included 2447 shocks from 1050 patients. Logistic regression, neural network and support vector machine were used to combine multiple features for the prediction of defibrillation outcome. Performance between single and combined predictive features were compared by area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and prediction accuracy (PA) on a validation dataset that consisted of 1381 shocks from 567 patients.Results: Among the single features, mean slope (MS) outperformed other methods with an AUC of 0.876. Combination of complementary features using neural network resulted in the highest AUC of 0.874 among the multifeature-based methods. Compared to MS, no statistical difference was observed in AUC, sensitivity, specificity, PPV, NPV and PA when multiple features were considered.Conclusions: In this large dataset, the amplitude-related features achieved better defibrillation outcome prediction capability than other features. Combinations of multiple electrical features did not further improve prediction performance. [ABSTRACT FROM AUTHOR]