Access to and quality of health and social care for rare diseases: patients' and caregivers' experiences.

People suffering from rare diseases, independently of the condition, often experience the same problems in receiving adequate health and social care. It is not clear how these problems differ in severity among different diseases and in different countries and how they change in time. In the framework of the NEPHIRD (Network of Public Health Institutions on Rare Diseases), a European project, funded by DG-SANCO (EU Commission), an effort was made to develop a simple but comprehensive tool to show patients' and/or caregivers' opinions about the quality and accessibility of health and social services. The self-filled questionnaire asks how often patients or caregivers had both negative and positive experiences about the quality and accessibility of health and social services and their opinion on their improvement, on 5-level scales. A pilot survey was carried out in several European Countries among members of Myasthenia Gravis, Neurofibromatosis, Prader Willi and Rett Syndrome volunteers' associations. Descriptive and comparative analyses were performed using Stata and Epi Info 2000. In total, 302 questionnaires were completed in France, Italy, Romania, Spain, Turkey and United Kingdom during 2004-05. In general, respondents thought that health care accessibility was worse than quality, and that social care and legal provisions were worse than health care, with some differences among countries. For all diseases, and for both patients and caregivers, the most frequent reported positive experiences were health professionals' kindness and readiness to help (all medians ranged from 3 to 5). As for the efforts for improvement made by public services in the last three years, the opinions were generally favourable. This study has several limitations. However the assessment tool that has been developed has some innovative and interesting features and may be considered a useful attempt to compare patients' and caregivers' experiences for a range of different diseases, countries and services, with respect to a rare disease programme.

Detection of incipient object slippage by skin-like sensing and neural network processing.

Detection of incipient slippage is of great importance in robotics for the control of grasping and manipulation tasks. Together with fine-form reconstruction and primitive recognition, it has to be the main feature of an artificial tactile system. The system presented here is based on a neural network used to detect incipient slippage and on a skin-like sensor sensible to normal and shear stresses. Normal and shear stresses components inside the sensor are the input data of the neural net. An important feature of the system is that the a priori knowledge of the friction coefficient between the sensor and the object being manipulated is not needed. To validate the method we worked on both simulated and experimental data. In the first case, the finite element method is used to solve the direct problem of elastic contact in its full nonlinearity by resorting to the lowest number of approximations regarding the real problem. Simulation has shown that the network learns and is robust to noise. Then an experimental test was carried out. Experimental results show that, in a simple case, the method is able to detect the insipiency of slippage between an object and the sensor.