Search Results (4)

Background/Objectives: The present study aimed to test the efficacy of the chair-side rapid salivary C-reactive protein assay kit in differentiating oral potentially malignant disorders (OPMDs) and oral cancer from normal mucosa using whole salivary samples. Methods: In this study, unstimulated saliva samples of cases (OPMDs and oral cancer) and controls (systemically healthy subjects) were used to detect CRP levels using a novel colorimetric, quantitative rapid assay kit. Kruskal–Wallis ANOVA with a post hoc Dunn’s test were applied to determine the difference in the mean and SD values between the case and control groups. ROC analysis was performed to identify the positive and negative likelihood ratios. Results: The mean calculated salivary CRP level in the oral cancer group was 4.21 ng/mL, in the OPMD group it was 2.51 ng/mL and in the healthy controls it was 0.7 ng/mL. Post hoc tests showed that the salivary CRP levels were significantly higher in the oral cancer and OPMD groups than in the controls. Conclusions: The present study showed that the novel rapid salivary test kit could significantly differentiate between the salivary CRP values of cases and controls but there was no significant difference between the OPMD and malignancy groups. It also showed higher sensitivity values, confirming the efficacy of this kit as a screening tool. Full article

The present study explores the efficacy of Machine Learning and Artificial Neural Networks in age assessment using the root length of the second and third molar teeth. A dataset of 1000 panoramic radiographs with intact second and third molars ranging from 12 to 25 years was archived. The length of the mesial and distal roots was measured using ImageJ software. The dataset was classified in three ways based on the age distribution: 2–Class, 3–Class, and 5–Class. We used Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression models to train, test, and analyze the root length measurements. The mesial root of the third molar on the right side was a good predictor of age. The SVM showed the highest accuracy of 86.4% for 2–class, 66% for 3–class, and 42.8% for 5–Class. The RF showed the highest accuracy of 47.6% for 5–Class. Overall the present study demonstrated that the Deep Learning model (fully connected model) performed better than the Machine Learning models, and the mesial root length of the right third molar was a good predictor of age. Additionally, a combination of different root lengths could be informative while building a Machine Learning model. Full article

Cervical restoration of a premolar tooth is a challenging task as it involves structural modification to ensure the functional integrity of the tooth. The lack of retention in the cervical area, with the cavity margins on dentin and the nonavailability of enamel, makes it challenging for restoration. The high organic content of dentin, along with its tubular structure and outward flow of fluid, make dentin bonding difficult to attain. The objective of this study is to evaluate the impact of thermal and thermomechanical stimuli on the geometry of dental restorations in the cervical region. In the present study, a three-layered restorative material made of glass ionomer cement, hybrid layer, and composite resin is considered by varying the thickness of each layer. Group 1 of elliptical-shaped cavities generates von Mises stress of about 14.65 MPa (5 °C), 41.84 MPa (55 °C), 14.83 MPa (5 °C and 140 N), and 28.89 MPa (55 °C and 140 N), respectively, while the trapezoidal cavity showed higher stress of 36.27 MPa (5 °C), 74.44 MPa (55 °C), 34.14 MPa (5 °C and 140 N), and 75.57 MPa (55 °C and 140 N), which is comparable to the elliptical cavity. The result obtained from the analysis helps to identify the deformation and volume change that occurs due to various real-time conditions, such as temperature difference and thermal stress. The study provides insight into the behavior of novel restorative materials of varied thicknesses and temperature levels through simulation. Full article

Background: Wearable device technology has recently been involved in the healthcare industry substantially. India is the world’s third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries. Methods: This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above. Results: A total of 495 responses were obtained. In all, 50.3% were aged between 25–50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032). Conclusions: This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context. Full article