Minimal Symptom Expression' in Patients With Acetylcholine Receptor Antibody-Positive Refractory Generalized Myasthenia Gravis Treated With Eculizumab

The efficacy and tolerability of eculizumab were assessed in REGAIN, a 26-week, phase 3, randomized, double-blind, placebo-controlled study in anti-acetylcholine receptor antibody-positive (AChR+) refractory generalized myasthenia gravis (gMG), and its open-label extension

Post-intervention Status in Patients With Refractory Myasthenia Gravis Treated With Eculizumab During REGAIN and Its Open-Label Extension

OBJECTIVE: To evaluate whether eculizumab helps patients with anti-acetylcholine receptor-positive (AChR+) refractory generalized myasthenia gravis (gMG) achieve the Myasthenia Gravis Foundation of America (MGFA) post-intervention status of minimal manifestations (MM), we assessed patients' status throughout REGAIN (Safety and Efficacy of Eculizumab in AChR+ Refractory Generalized Myasthenia Gravis) and its open-label extension. METHODS: Patients who completed the REGAIN randomized controlled trial and continued into the open-label extension were included in this tertiary endpoint analysis. Patients were assessed for the MGFA post-intervention status of improved, unchanged, worse, MM, and pharmacologic remission at defined time points during REGAIN and through week 130 of the open-label study. RESULTS: A total of 117 patients completed REGAIN and continued into the open-label study (eculizumab/eculizumab: 56; placebo/eculizumab: 61). At week 26 of REGAIN, more eculizumab-treated patients than placebo-treated patients achieved a status of improved (60.7% vs 41.7%) or MM (25.0% vs 13.3%; common OR: 2.3; 95% CI: 1.1-4.5). After 130 weeks of eculizumab treatment, 88.0% of patients achieved improved status and 57.3% of patients achieved MM status. The safety profile of eculizumab was consistent with its known profile and no new safety signals were detected. CONCLUSION: Eculizumab led to rapid and sustained achievement of MM in patients with AChR+ refractory gMG. These findings support the use of eculizumab in this previously difficult-to-treat patient population. CLINICALTRIALSGOV IDENTIFIER: REGAIN, NCT01997229; REGAIN open-label extension, NCT02301624. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, after 26 weeks of eculizumab treatment, 25.0% of adults with AChR+ refractory gMG achieved MM, compared with 13.3% who received placebo

Localization in Wireless Sensor Network Using MDS

In this paper, determining the localization of nodes in a Wireless Sensor Network is a very important task, which involves collaboration between sensor nodes. Localization is a fundamental service since it is relevant to many applications and to the network main functions, such as: routing, communication, cluster creation, network coverage, etc. Collaboration is essential to self-localization, so that localization can be accomplished by the nodes themselves, without any human intervention. In this paper, we first analyze the key aspects that have to be considered when designing or choosing a solution for the localization problem. Then, we present MDS localization algorithm. With this analysis of results simulated. We identified the results in topologies by taking different cases and we have addresses shortcomings, which are caused by anisotropic network topology and complex terrain, of existing sensor positioning methods. Then, we explore the idea of using multidimensional scaling technique to compute relative positions of sensors in a wireless sensor network. A distributed sensor positioning method based on multidimensional scaling is proposed to get the accurate position estimation and reduce error cumulation. Comparing with other positioning methods, with very few anchors, our approach can accurately estimate the sensors’ positions in network with anisotropic topology and complex terrain as well as eliminate measurement error cumulation. We also propose an on demand position estimation method based on multidimensional scaling for one or several adjacent sensors positioning. Experimental results indicate that our distributed method for sensor position estimation is very effective and efficient

Localization in Wireless Sensor Network Using MDS

Abstract-In this paper, determining the localization of nodes in a Wireless Sensor Network is a very important task, which involves collaboration between sensor nodes. Localization is a fundamental service since it is relevant to many applications and to the network main functions, such as: routing, communication, cluster creation, network coverage, etc. Collaboration is essential to self-localization, so that localization can be accomplished by the nodes themselves, without any human intervention. In this paper, we first analyze the key aspects that have to be considered when designing or choosing a solution for the localization problem. Then, we present MDS localization algorithm. With this analysis of results simulated. We identified the results in topologies by taking different cases and we have addresses shortcomings, which are caused by anisotropic network topology and complex terrain, of existing sensor positioning methods. Then, we explore the idea of using multidimensional scaling technique to compute relative positions of sensors in a wireless sensor network. A distributed sensor positioning method based on multidimensional scaling is proposed to get the accurate position estimation and reduce error cumulation. Comparing with other positioning methods, with very few anchors, our approach can accurately estimate the sensors' positions in network with anisotropic topology and complex terrain as well as eliminate measurement error cumulation. We also propose an on demand position estimation method based on multidimensional scaling for one or several adjacent sensors positioning. Experimental results indicate that our distributed method for sensor position estimation is very effective and efficient

Localization in Wireless Sensor Network Using MDS

In this paper, determining the localization of nodes in a Wireless Sensor Network is a very important task, which involves collaboration between sensor nodes. Localization is a fundamental service since it is relevant to many applications and to the network main functions, such as: routing, communication, cluster creation, network coverage, etc. Collaboration is essential to self-localization, so that localization can be accomplished by the nodes themselves, without any human intervention. In this paper, we first analyze the key aspects that have to be considered when designing or choosing a solution for the localization problem. Then, we present MDS localization algorithm. With this analysis of results simulated. We identified the results in topologies by taking different cases and we have addresses shortcomings, which are caused by anisotropic network topology and complex terrain, of existing sensor positioning methods. Then, we explore the idea of using multidimensional scaling technique to compute relative positions of sensors in a wireless sensor network. A distributed sensor positioning method based on multidimensional scaling is proposed to get the accurate position estimation and reduce error cumulation. Comparing with other positioning methods, with very few anchors, our approach can accurately estimate the sensors’ positions in network with anisotropic topology and complex terrain as well as eliminate measurement error cumulation. We also propose an on demand position estimation method based on multidimensional scaling for one or several adjacent sensors positioning. Experimental results indicate that our distributed method for sensor position estimation is very effective and efficient.</jats:p