Long-term efficacy and safety results of taliglucerase alfa through 5years in adult treatment-naïve patients with Gaucher disease

Taliglucerase alfa, the first available plant cell-expressed recombinant therapeutic protein, is an enzyme replacement therapy approved for Gaucher disease (GD). PB-06-001, a pivotal phase 3, multicenter, randomized, double-blind, parallel-dose study investigated taliglucerase alfa 30 or 60U/kg every other week through 9months in treatment-naïve adults with GD; 30-month extension study PB-06-003 followed. Patients completing PB-06-001 and PB-06-003 could continue treatment in PB-06-007. Nineteen patients enrolled in PB-06-007 (30U/kg, n=8; 60U/kg, n=9; dose adjusted, n=2); 17 completed 5 total years of treatment. In these 3 groups, respectively, taliglucerase alfa resulted in mean decreases in spleen volume (-8.7, -6.9, -12.4 multiples of normal), liver volume (-0.6, -0.4, -0.5 multiples of normal), chitotriosidase activity (-83.1%, -93.4%, -87.9%), and chemokine (CC motif) ligand 18 concentration (-66.7%, -83.3%, -78.9%), as well as mean increases in hemoglobin concentration (+2.1, +2.1, +1.8mg/dL) and platelet count (+31,871, +106,800, +34,000/mm3). The most common adverse events were nasopharyngitis and arthralgia. Most adverse events were mild/moderate; no serious adverse events were considered treatment-related. These results demonstrate continued improvement of disease parameters during 5years of taliglucerase alfa therapy in 17 treatment-naive patients with no new safety concerns, extending the taliglucerase alfa clinical efficacy and safety dataset. This study was registered at www.clinicaltrials.gov as NCT01422187

Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

This paper presents a novel method for the automatic segmentation of coronary arteries in X-ray angiograms, based on multiscale analysis and neural networks. The multiscale analysis is performed by using Gaussian filters in the spatial domain and Gabor filters in the frequency domain, which are used as inputs by a multilayer perceptron (MLP) for the enhancement of vessel-like structures. The optimal design of the MLP is selected following a statistical comparative analysis, using a training set of 100 angiograms, and the area under the ROC curve ( A z ) for assessment of the detection performance. The detection results of the proposed method are compared with eleven state-of-the-art blood vessel enhancement methods, obtaining the highest performance of A z = 0.9775 , with a test set of 30 angiograms. The database of 130 X-ray coronary angiograms has been outlined by a specialist and approved by a medical ethics committee. On the other hand, the vessel extraction technique was selected from fourteen binary classification algorithms applied to the multiscale filter response. Finally, the proposed segmentation method is compared with twelve state-of-the-art vessel segmentation methods in terms of six binary evaluation metrics, where the proposed method provided the most accurate coronary arteries segmentation with a classification rate of 0.9698 and Dice coefficient of 0.6857 , using the test set of angiograms. In addition to the experimental results, the performance in the detection and segmentation steps of the proposed method have also shown that it can be highly suitable for systems that perform computer-aided diagnosis in X-ray imaging

Numerical Modeling of the Major Temporal Arcade Using BUMDA and Jacobi Polynomials

Within eye diseases, diabetic retinopathy and retinopathy of prematurity are considered one of the main causes of blindness in adults and children. In order to prevent the disease from reaching such an extreme, a timely diagnosis and effective treatment must be applied. Until now, the way to verify the state of the retina has been to make qualitative observations of fundus images, all carried out by an ophthalmological specialist; however, this is totally restricted to their experience, and some changes in the vascular structure of the retina could be omitted, in addition to the fact that very high resolution images would be needed to be able to detect significant changes. Accordingly, with the help of computational tools, this diagnostic/monitoring process can be improved. This paper presents a novel strategy for the modeling of the MTA by using an estimation of distribution algorithm (EDA) based on the probability density function in order to determine the coefficients and parameters (α,β) of a Jacobi polynomial series. A model using polynomials is the novel aspect of this work since in the literature there are no models of the MTA of this type, in addition to seeking to better cover the profile of the retinal vein. According to the experimental results, the proposed method presents the advantage to achieve superior performance in terms of the mean distance to the closest point (4.34 pixels), and the Hausdorff distance (14.43 pixels) with respect to different state-of-the-art methods of the numerical modeling of the retina, using the DRIVE database of retinal fundus images with a manual delineation of the MTA performed by an specialist

Automatic Feature Selection for Stenosis Detection in X-ray Coronary Angiograms

The automatic detection of coronary stenosis is a very important task in computer aided diagnosis systems in the cardiology area. The main contribution of this paper is the identification of a suitable subset of 20 features that allows for the classification of stenosis cases in X-ray coronary images with a high performance overcoming different state-of-the-art classification techniques including deep learning strategies. The automatic feature selection stage was driven by the Univariate Marginal Distribution Algorithm and carried out by statistical comparison between five metaheuristics in order to explore the search space, which is O(249) computational complexity. Moreover, the proposed method is compared with six state-of-the-art classification methods, probing its effectiveness in terms of the Accuracy and Jaccard Index evaluation metrics. All the experiments were performed using two X-ray image databases of coronary angiograms. The first database contains 500 instances and the second one 250 images. In the experimental results, the proposed method achieved an Accuracy rate of 0.89 and 0.88 and Jaccard Index of 0.80 and 0.79, respectively. Finally, the average computational time of the proposed method to classify stenosis cases was ≈0.02 s, which made it highly suitable to be used in clinical practice