Macrophage Polarization in the Perivascular Fat Was Associated With Coronary Atherosclerosis.

Background Inflammation of the perivascular adipose tissue (PvAT) may be related to atherosclerosis; however, the association of polarized macrophages in the pericoronary PvAT with measurements of atherosclerosis components in humans has not been fully investigated. Methods and Results Coronary arteries were dissected with surrounding PvAT. We evaluated the percentage of arterial obstruction, intima-media thickness, fibrous cap thickness, plaque components, and the number of vasa vasorum. The number of proinflammatory (M1) and anti-inflammatory (M2) macrophages in the periplaque and control PvAT were evaluated using immunohistochemistry. Regression models adjusted for sociodemographic and clinical variables were used. In 319 segments from 82 individuals, we found a correlation of the M1/M2 macrophage density ratio with an increase in arterial obstruction (P=0.02) and lipid content (P=0.01), and a decrease in smooth muscle cells (P=0.02). M1 and the ratio of M1/M2 macrophages were associated with an increased risk of thrombosis (P=0.03). In plaques with thrombosis, M1 macrophages were correlated with a decrease in fibrous cap thickness (P=0.006), an increase in lipid content (P=0.008), and the number of vasa vasorum in the adventitia layer (P=0.001). M2 macrophages were correlated with increased arterial obstruction (P=0.01), calcification (P=0.02), necrosis (P=0.03) only in plaques without thrombosis, and decrease of the number of vasa vasorum in plaques with thrombosis (P=0.003). Conclusions M1 macrophages in the periplaque PvAT were associated with a higher risk of coronary thrombosis and were correlated with histological components of plaque progression and destabilization. M2 macrophages were correlated with plaque size, calcification, necrotic content, and a decrease in the number of vasa vasorum in the adventitia layer.

Evaluation of multi-class support-vector machines strategies and kernel adjustment levels in hand posture recognition by analyzing sEMG signals acquired from a wearable device.

One-vs-One (OVO) and One-vs-All (OVA) are decomposition methods for multi-class strategies used to allow binary Support-Vector Machines (SVM) to transform a given k-class problem into pairwise small problems. In this context, the present work proposes the analysis of these two decomposition methods applied to the hand posture recognition problem in which the sEMG data of eight participants were collected by means of an 8-channel armband bracelet located on the forearm. Linear, Polynomial and Radial Basis Function kernels functions and its adjustments level were implemented combined to the strategies OVO and OVA to compare the performance of the SVM when mapping posture data into the classification spaces spanned by the studied kernels. Acquired sEMG signals were segmented considering 0.16 s e 0.32 s time windows. Root Mean Square (RMS) feature was extracted from each time window of each posture and used for SVM training. The present work focused in investigating the relationship between the multi-class strategies combined to kernels adjustments levels and SVM classification performance. Promising results were observed using OVA strategy which presents a reduced number of binary SVM implementation achieved a mean accuracy of 97.63%.