Influence of the Type of Occlusion on the Occurrence of Noncarious Cervical Lesion

INTRODUCTION: The term \u27noncarious cervical lesions’ (NCCL) stands to indicate the loss of hard tissue at the tooth cervix. This loss can be caused by different physical and/or chemical agents. One of the causes of NCCL can be occlusal stress, which lead to toothflexure and loss of enamel at the cervical area. In different types of occlusion there are numerous lateral eccentric movements that can cause NCCL. The purpose of this study was to determine differences in the frequency of NCCLs between patients with different occlusal conception. METHODS AND RESULTS: The study involved 815 persons over the age of 10 years, chosen at random. The cervical third of the vestibular surface of the upper and lower teeth was clinically examined. NCCLs were measured with plus and minus. The type of occlusal conception was established by clinical examination and classified as canine guidance, group function and combined occlusion. The results showed that the NCCLs were equally participate in both sexes, and in all three types of occlusion (Chi-square values were 1.96, df=2, p>0.05). CONCLUSION: The results of the study indicate that there is no statistically significant difference in the frequency of NCCLs between patients with different occlusal

The Late-Effect Of X-Irradiation on the Mouse Submandibular Gland

INTRODUCTION: Life-long severe xerostomia is a common complication after radiotherapy of head and neck malignancy. It is a clinical entity which causes a great deal of suffering and disability for the patient. Saliva is an important factor for denture retention. Hyposalivation causes reduced retention of full dentures. The aim of the study was to determine late consequences of irradiation in the mouse submandibular gland. MATERIAL AND METHODS : Mouse submandibular glands were locally X-irradiated by single dose irradiation with 15Gy. Day 90 post-irradiation tissues were analyzed by morphology and morphometry. RESULTS: Strong vacuolization of almost all acini was noted. Kariopyknotic nuclei were found in numerous acini and the largest amount of acini was in the lysis. The epithelial cells of the granular convoluted tubule were degenerated and desquamated in the lumen, and some granular convoluted tubules were in the lysis. In the interstitial connective tissue disseminated focal mononuclear infiltrate was found. With respect to the control group a statistically significant decrease in the number of acinar cells (p<0.001) was determined, as well as a significant increase in the number of granular convoluted tubule cells (p<0.001). Whereas the number of intercalated duct cells was not different with respect to the control (p=0.10). CONCLUSION: The results of this study suggest that hypofunction in the late stage is a consequence of morphological changes and loss of acinar cells. The patients should use a saliva substitute to alleviate their symptoms easier

A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat

A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python