Next generation cellular systems are expected to entail a wide variety of wireless coverage zones, with cells of different sizes and capacities that can overlap in space and share the transmission resources. In this scenario, which is referred to as Heterogeneous Networks (HetNets), a fundamental challenge is the management of the handover process between macro, femto and pico cells. To limit the number of handovers and the signaling between the cells, it will hence be crucial to manage the user's mobility considering the context parameters, such as cells size, traffic loads, and user velocity. In this paper, we propose a theoretical model to characterize the performance of a mobile user in a HetNet scenario as a function of the user's mobility, the power profile of the neighboring cells, the handover parameters, and the traffic load of the different cells. We propose a Markov-based framework to model the handover process for the mobile user, and derive an optimal context-dependent handover criterion. The mathematical model is validated by means of simulations, comparing the performance of our strategy with conventional handover optimization techniques in different scenarios. Finally, we show the impact of the handover regulation on the users performance and how it is possible to improve the users capacity exploiting context information
Summary Objective Although many studies have attempted to describe treatment outcomes in patients with drug‐resistant epilepsy, results are often limited by the adoption of nonhomogeneous criteria and different definitions of seizure freedom. We sought to evaluate treatment outcomes with a newly administered antiepileptic drug (AED) in a large population of adults with drug‐resistant focal epilepsy according to the International League Against Epilepsy (ILAE) outcome criteria. Methods This is a multicenter, observational, prospective study of 1053 patients with focal epilepsy diagnosed as drug‐resistant by the investigators. Patients were assessed at baseline and 6, 12, and 18 months, for up to a maximum of 34 months after introducing another AED into their treatment regimen. Drug resistance status and treatment outcomes were rated according to ILAE criteria by the investigators and by at least two independent members of an external expert panel (EP). Results A seizure‐free outcome after a newly administered AED according to ILAE criteria ranged from 11.8% after two failed drugs to 2.6% for more than six failures. Significantly fewer patients were rated by the EP as having a “treatment failure” as compared to the judgment of the investigator (46.7% vs 62.9%, P < 0.001), because many more patients were rated as “undetermined outcome” (45.6% vs 27.7%, P < 0.001); 19.3% of the recruited patients were not considered drug‐resistant by the EP. Significance This study validates the use of ILAE treatment outcome criteria in a real‐life setting, providing validated estimates of seizure freedom in patients with drug‐resistant focal epilepsy in relation to the number of previously failed AEDs. Fewer than one in 10 patients achieved seizure freedom on a newly introduced AED over the study period. Pseudo drug resistance could be identified in one of five cases.
ObjectiveTo describe the clinical and paraclinical findings, treatment options and long-term outcomes in autoimmune encephalitis (AE), with a close look to epilepsy.MethodsIn this retrospective observational cohort study, we enrolled patients with new-onset seizures in the context of AE. We compared clinical and paraclinical findings in patients with and without evidence of antibodies.ResultsOverall, 263 patients (138 females; median age 55 years, range 4–86) were followed up for a median time of 30 months (range 12–120). Antineuronal antibodies were detected in 63.50%.Antibody-positive patients had multiple seizure types (p=0.01) and prevalent involvement of temporal regions (p=0.02). A higher prevalence of episodes of SE was found in the antibody-negative group (p<0.001).Immunotherapy was prescribed in 88.60%, and effective in 61.80%. Independent predictors of favourable outcome of the AE were early immunotherapy (p<0.001) and the detection of antineuronal surface antibodies (p=0.01).Autoimmune-associated epilepsy was the long-term sequela in 43.73%, associated with cognitive and psychiatric disturbances in 81.73%. Independent predictors of developing epilepsy were difficult to treat seizures at onset (p=0.04), a high number of antiseizure medications (p<0.001), persisting interictal epileptiform discharges at follow-up (p<0.001) and poor response to immunotherapy during the acute phase (p<0.001).ConclusionsThe recognition of seizures secondary to AE represents a rare chance for aetiology-driven seizures management. Early recognition and treatment at the pathogenic level may reduce the risk of long-term irreversible sequelae. However, the severity of seizures at onset is the major risk factor for the development of chronic epilepsy.This study provides class IV evidence for management recommendations.
The deployment of small cells in Heterogeneous Networks (HetNets) raises new challenges in relation to the Handover process and the mobility management. In fact, the performance of a mobile user within a HetNet scenario highly depends on the setting of the handover parameters in relation to other context parameters, such as the channel conditions and the user position and speed. In this paper, we derive a general theoretical analysis to characterize the user performance as a function of the mobility model, the power profile received from the neighboring cells, and the handover parameters. More in detail, we propose a Markov-based framework to model the user state during the handover process and, based on such model, we derive an optimal context-dependent handover criterion. The mathematical model is validated by means of simulations, showing that our strategy outperforms conventional handover optimization techniques by exploiting the context information
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