The New Italian Health Care Reform: Introducing New Measures to Speed Up Market Access

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X-linked hypophosphatemic rickets: An Italian experts' opinion survey

Background: X-linked hypophosphatemic rickets (XLH) is the first cause of inherited hypophosphatemia and is caused by mutation in the PHEX gene, resulting in excessive expression of the phosphaturic factor FGF23. Symptoms are mainly related to rickets in children and osteomalacia in adults and cause several complications that can be highly invalidating. Due to its rarity, XLH is poorly known and diagnosis is frequently delayed. Conventional treatment is based on oral phosphate salts supplementation and activated vitamin D analogs, which however, cannot cure the disease in most cases. Objective: Due to the low prevalence of XLH, an experts' opinion survey was conducted across Italian centers to collect data on XLH and on its management. Methods: A questionnaire was developed by a group of experts to collect data on XLH epidemiology, diagnosis and treatment in Italy. Results: Data from 10 Italian centers (nine of which pediatric) on 175 patients, followed between 1998 and 2017, were included in the survey. Most patients were followed since childhood and 63 children became adults during the investigated period. The diagnosis was made before the age of 1 and between 1 and 5 years in 11 and 50% of cases, respectively. Clinically apparent bone deformities were present in 95% of patients. These were ranked moderate/severe in 75% of subjects and caused growth stunting in 67% of patients. Other frequent complications included bone pain (40%), dental abscesses (33%), and dental malpositions (53%). Treatment protocols varied substantially among centers. Nephrocalcinosis was observed in 34% of patients. Tertiary hyperparathyroidism developed in 6% of patients. Conclusions: XLH remains a severe condition with significant morbidities

A new method for detecting causality in fMRI data of cognitive processing.

One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data

A new method for detecting causality in fMRI data of cognitive processing

One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data