Phantom Limb Pain: Low Frequency Repetitive Transcranial Magnetic Stimulation in Unaffected Hemisphere

Phantom limb pain is very common after limb amputation and is often difficult to treat. The motor cortex stimulation is a valid treatment for deafferentation pain that does not respond to conventional pain treatment, with relief for 50% to 70% of patients. This treatment is invasive as it uses implanted epidural electrodes. Cortical stimulation can be performed noninvasively by repetitive transcranial magnetic stimulation (rTMS). The stimulation of the hemisphere that isn't involved in phantom limb (unaffected hemisphere), remains unexplored. We report a case of phantom limb pain treated with 1 Hz rTMS stimulation over motor cortex in unaffected hemisphere. This stimulation produces a relevant clinical improvement of phantom limb pain; however, further studies are necessary to determine the efficacy of the method and the stimulation parameters

The Ionosphere Prediction Service for GNSS Users

Space weather events related to solar activity can affect both ground and space-based infrastructures, potentially resulting in failures or service disruptions across the globe and causing damage to equipment and systems. Global Navigation Satellite Systems (GNSS) represent one of such infrastructures that can suffer from electromagnetic phenomena in the atmosphere, in particular due to the interaction of the RF signals with the ionosphere. The Ionosphere Prediction Service (IPS) is a project funded by European Commission to provide a prototype platform for a monitoring and prediction service of potential ionosphere-related disturbances affecting GNSS user communities. It is designed to help these communities cope with the effects of the ionospheric activity and mitigate the impacts of these effects on the specific GNSS-based application/service. The IPS development has been conceived of two concurrent activities: the design and implementation of the prototype service and the research activity, which represents the scientific backbone of IPS and is at the base of all the models and algorithms used for the computation of the products. The products are the basic IPS output that translate the nowcasting or forecasting information from the whole IPS system down to the final user. They are fine-tuned to match the different needs of the communities (scientific, aviation, high accuracy, etc.) which the service is targeted to and to warn the GNSS users about possible performance degradations in the presence of anomalous solar and atmospheric phenomena. To achieve this overarching aim, four different blocks of products dealing with solar activity, ionospheric activity, GNSS receiver and system performance figures have been developed and integrated into a unique service chain. The service is available to a set of invited users since July 2018 through a web portal and its provision with all the necessary operations will last 6 months. The prototype will be also ported to the Joint Research Centre (JRC). This phase will be useful to further test the platform, and to assess whether and how a dedicated prediction service for International Technical Symposium on Navigation and Timing (ITSNT) 2018 13-16 Nov 2018 ENAC, Toulouse, France Galileo users is to be implemented as part of the service facilities of the Galileo infrastructure.Published2A. Fisica dell'alta atmosfera7SR AMBIENTE – Servizi e ricerca per la societàN/A or not JC

The ionosphere prediction service prototype for GNSS users

The effect of the Earth’s ionosphere represents the single largest contribution to the Global Navigation Satellite System (GNSS) error budget and abnormal ionospheric conditions can impose serious degradation on GNSS system functionality, including integrity, accuracy and availability. With the growing reliance on GNSS for many modern life applications, actionable ionospheric forecasts can contribute to the understanding and mitigation of the impact of the ionosphere on our technology based society. In this context, the Ionosphere Prediction Service (IPS) project was set up to design and develop a prototype platform to translate the forecast of the ionospheric effects into a service customized for specific GNSS user communities. To achieve this overarching aim, four different product groups dealing with solar activity, ionospheric activity, GNSS receiver performance and service performance have been developed and integrated into a service chain, which is made available through a web based platform. This paper provides an overview of the IPS project describing its overall architecture, products and web based platform.PublishedA412A. Fisica dell'alta atmosferaJCR Journa

The Ionosphere Prediction Service

The aim of the Ionosphere Prediction Service (IPS) project is to design and develop a prototype platform to translate the prediction and forecast of the ionosphere effects into a service customized for specific GNSS user communities. The project team is composed by Telespazio (coordinator), Nottingham Scientific Ltd, Telespazio Vega Deutschland, the University of Nottingham, the University of Rome “Tor Vergata” and the Italian Istituto Nazionale di Geofisica e Vulcanologia (INGV). The IPS development is conceived of two concurrent activities: prototype service design and development & research activity that will run along the whole project. Service design and development is conceived into four phases: user requirements collection, architecture specification, implementation and validation of the prototype. A sub-activity analyses also the integration feasibility in the Galileo Service center, located in Madrid. The research activity is the scientific backbone of IPS that will provide the models and algorithms for the forecasting products.PublishedUniversity of Exeter United Kingdom2A. Fisica dell'alta atmosfera7SR AMBIENTE – Servizi e ricerca per la societ

Usefulness of CGM with iPro2 in children with T1DM and correlations between Glucose Variability and metabolic control

Objectives: Primary aim of the study was to evaluate the effect of a single iPro2 CGM on 3-months. HbA1c. Secondary aims were the feasibility of iPro2 monitoring and the evaluation of different metabolic and risk indexes. Methods: Seventy pts with T1DM (age 13.8 ± 4.6 years, T1DM duration 7.4 ± 3.6 years, HbA1c 8.4% ± 1.3) treated with three different insulin regimens (three inj of premix ins. n = 6, MDI n = 45, CSII n = 19) wore iPro2 for 6 days. iPro2 was applied in pts with HbA1c >7% (n.59) despite optimized therapy, or with recurrent hypoglycemia and HbA1c <7% (n.11). HbA1c was tested before and 3 months. after CGM data were used for glucose variability (GV) indexes calculation (CV, Conga, MAGE, MODD, AUC) and glycemic risk (GR) assessment (LBGI, HBGI, BGRI, J index, ADRR and BG Rate). LBGI and HBGI were also tested for correlation with baseline (BL) parameters (HbA1c, age, BMI, pubertal stage, disease duration, therapeutic regimen). Results: No pts reported significant side effects. Three-month HbA1c decreased to 8.0% ± 1.0 (P = 0.04). In the pts with HbA1c >7% (n.51) HbA1c decreased from 8.8% ± 1.2 to 8.3% ± 0.94 (P = 0.008), while in the pts with HbA1c <7% (n.12) was unchanged 6.5% ± 0.4 of 6.7% ± 0.4 (NS). HBGI and LBGI didn’t significantly correlate with any BL parameter both in the univariate and multivariate logistic regression analysis. GV indexes were evaluated in pts with HbA1c increasing (n = 23, from 7.6 ± 1.1 to 8.1 ± 1.2) and decreasing (n = 47, from 8.8 ± 1.3 to 7.9 ± 0.9) founding no differences. Furthermore no significant differences were found between the therapy groups in GV indexes. Conclusions: iPro2 is feasible in pediatric patients and was helpful in improving HbA1c especially in patients with suboptimal glicemic control. No significant correlation was found between BL characteristics of pts and GR indexes (HBGI and LBGI). Since no significant difference was found considering HbA1c increasing and decreasing trend, it is confirmed the independent value of GV indexes in the assessment of metabolic control

Comparison Between Sensor-Augmented Insulin Therapy with Continuous Subcutaneous Insulin Infusion or Multiple Daily Injections in Everyday Life: 3-Day Analysis of Glucose Patterns and Sensor Accuracy in Children

BACKGROUND: Sensor-augmented continuous subcutaneous insulin infusion (CSII) therapy is superior to CSII therapy alone, but little is known on the effectiveness of sensor-augmented multiple daily injections (MDI) therapy. METHODS: We compared during everyday life mean glucose control and several variability indexes recorded for 3 days by a real-time glucose sensor (Medtronic, Northridge, CA) in two groups of children treated with either CSII or MDI. Fifty-five consecutive subjects were examined: 17 receiving CSII and 38 receiving MDI basal-bolus therapy (age range, 7-22 years). All subjects wore the sensor for 4 days, and 3 days were used for statistical analysis. Mean glucose and SD, coefficient of variation (CV), mean amplitude of glucose excursion (MAGE), mean of daily differences (MODD), continuous overall net glycemic action (CONGA) at 2 and 4 h, blood glucose (BG) rate, area under the curve (AUC) above 180 mg/dL and below 70 mg/dL, Low BG Index (LBGI), and High BG Index (HBGI) were calculated. RESULTS: Patients receiving CSII administered more daily boluses than patients receiving MDI (5.2\ub11.5 vs. 3.2\ub10.3, respectively; P=0.001). Mean glucose was lower in the CSII group. AUC above 180 mg/dL and HBGI were higher in the MDI group. CV, CONGA at 2\u2009h, CONGA at 2 h during the day, and HBGI were worse in the MDI group, whereas MODD, LBGI, BG rate, and MAGE were similar. A positive correlation (r=0.95; P<0.05) was found between the paired sensor-meter values. For the glucose values <70\u2009mg/dL, sensitivity was 40%, and specificity was 99%. CONCLUSIONS: In our pediatric patients during everyday life sensor-augmented CSII therapy seemed more effective than sensor-augmented MDI therapy, in terms both of glucose mean values and of intraday variability. Mild hypoglycemic episodes and indexes of low BG values were similar in the two groups, although the latter results may be inaccurate because of low sensor sensitivity at low glucose value