Safety and efficacy of apixaban vs. enoxaparin in thromboprophylaxis after spinal stenosis and degenerative spine surgery.

INTRODUCTION: Degenerative spine surgeries often require postoperative immobilization or reduced mobility, predisposing patients to the formation of thrombosis and higher risk of thromboembolic complications. Despite the significance of this issue, there remains a lack of consensus on the optimal anticoagulant agent for postoperative thromboprophylaxis in spinal stenosis and degenerative spine surgeries. Low molecular weight heparins and direct Xa inhibitors represent two anticoagulant groups with high chemoprophylactic potential. METHODS: This study included a prospective cohort of patients undergoing posterior decompressive surgery with or without instrumentation for degenerative spine disease and/or spinal stenosis. Patients receiving postoperative prophylactic Enoxaparin and Apixaban were selected to evaluate the rate of complications, as assessed by Clavien-Dindo classification, thromboembolic events, and 30-day mortality, readmission, and reoperation rate between the two anticoagulants. RESULTS: 130 patients were included in the analysis. 65 patients received Apixaban and Enoxaparin in each group. Mean age of the participants was 57.6±11.0. 83.1% underwent laminectomy and posterior spinal fusion, while 22 patients underwent decompressive surgery only. The incidence of venous thromboembolism (P-value=0.403), deep vein thrombosis (p-value=0.999), hematoma formation (p-value=0.403), surgical site infection (p-value=0.901), readmission (p-value=0.545), reoperation (p=0.510), mortality (p=0.648), and complications rate (p-value=0.232) were not statistically different between Enoxaparin and Apixaban. DISCUSSION: Both Apixaban and Enoxaparin may be viable options for postoperative thromboprophylaxis in spine surgeries with comparable efficacy and safety profile. Future research endeavors should investigate the efficacy of these agents in comparison to placebo in a randomized setting.

A hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine.

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth.

Shape adaptive, robust iris feature extraction from noisy iris images.

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate.