A retrospective analysis of factors influencing the success of autotransplanted posterior teeth

BACKGROUND Survival and success rates of tooth transplantations even after long follow-up periods have been shown to be very high. Nevertheless, it is important to analyse factors potentially influencing these rates. The aim of this study was to assess the influence on success of potential factors. METHODS The research was based on a retrospective analysis of clinical and radiological data from a sample of 59 subjects (75 transplanted teeth). The follow-up period varied from 0.44 to 12.28 years (mean 3.95 years). Success rates were calculated and depicted with Kaplan-Meier plots. Log-rank tests were used to analyse the effect of root development stage, apex width, the use of enamel matrix proteins or the surgeon on success of transplantations. RESULTS Results for success of premolar transplantations were comparable with already published data, while molars performed worse than shown in other studies. The surgeon performing the transplantation (p = 0.001) and tooth type (p ≤ 0.001) were significantly associated with transplantation success. Use of enamel matrix proteins (p = 0.10), root development stage (p = 0.13), the recipient area (p = 0.48) and apex width (p = 0.59) were not significantly associated with success. CONCLUSIONS Molar transplantations were not as successful as premolar transplantations; however, success rates varied greatly depending on the surgeon's experience. The use of enamel matrix proteins as well as root development stage, the recipient area and apex width did not show significant associations with success of tooth transplantations.

Patterns of landscape form in the upper Rhône basin, Central Swiss Alps, predominantly show lithologic controls despite multiple glaciations and variations in rock uplift rates

The development of topography depends mainly on the interplay between uplift and erosion. These processes are controlled by various factors including climate, glaciers, lithology, seismic activity and short-term variables, such as anthropogenic impact. Many studies in orogens all over the world have shown how these controlling variables may affect the landscape's topography. In particular, it has been hypothesized that lithology exerts a dominant control on erosion rates and landscape morphology. However, clear demonstrations of this influence are rare and difficult to disentangle from the overprint of other signals such as climate or tectonics. In this study we focus on the upper Rhône Basin situated in the Central Swiss Alps in order to explore the relation between topography, possible controlling variables and lithology in particular. The Rhône Basin has been affected by spatially variable uplift, high orographically driven rainfalls and multiple glaciations. Furthermore, lithology and erodibility vary substantially within the basin. Thanks to high-resolution geological, climatic and topographic data, the Rhône Basin is a suitable laboratory to explore these complexities. Elevation, relief, slope and hypsometric data as well as river profile information from digital elevation models are used to characterize the landscape's topography of around 50 tributary basins. Additionally, uplift over different timescales, glacial inheritance, precipitation patterns and erodibility of the underlying bedrock are quantified for each basin. Results show that the chosen topographic and controlling variables vary remarkably between different tributary basins. We investigate the link between observed topographic differences and the possible controlling variables through statistical analyses. Variations of elevation, slope and relief seem to be linked to differences in long-term uplift rate, whereas elevation distributions (hypsometry) and river profile shapes may be related to glacial imprint. This confirms that the landscape of the Rhône Basin has been highly preconditioned by (past) uplift and glaciation. Linear discriminant analyses (LDAs), however, suggest a stronger link between observed topographic variations and differences in erodibility. We therefore conclude that despite evident glacial and tectonic conditioning, a lithologic control is still preserved and measurable in the landscape of the Rhône tributary basins.

Adjustment Disorders Are Uniquely Suited for eHealth Interventions: Concept and Case Study

Background: Adjustment disorders (also known as mental distress in response to a stressor) are among the most frequently diagnosed mental disorders in psychiatry and clinical psychology worldwide. They are also commonly diagnosed in clients engaging in deliberate self-harm and in those consulting general practitioners. However, their reputation in research-oriented mental health remains weak since they are largely underresearched. This may change when the International Statistical Classification of Diseases-11 (ICD-11) by the World Health Organization is introduced, including a new conceptualization of adjustment disorders as a stress-response disorder with positively defined core symptoms. Objective: This paper provides an overview of evidence-based interventions for adjustment disorders. Methods: We reviewed the new ICD-11 concept of adjustment disorder and discuss the the rationale and case study of an unguided self-help protocol for burglary victims with adjustment disorder, and its possible implementation as an eHealth intervention. Results: Overall, the treatment with the self-help manual reduced symptoms of adjustment disorder, namely preoccupation and failure to adapt, as well as symptoms of depression, anxiety, and stress. Conclusions: E-mental health options are considered uniquely suited for offering early intervention after the experiences of stressful life events that potentially trigger adjustment disorders.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.