Fat mass as an important predictor of persistent hypertension in patients with primary aldosteronism after adrenalectomy.

Aldosterone excess is present in obesity and is associated with involvement in the pathogenesis of obesity. We evaluate the impact of body obesity as measured by body composition monitor (BCM) on clinical outcomes in patients with unilateral primary aldosteronism (uPA) after adrenalectomy. The BCM device was used to assess body composition before and after adrenalectomy. We used fat mass (FM) and body mass index (BMI) to classify obesity and divided obesity into three groups: clinical overweight (BMI (kg/m2) ≥25); normal weight obesity (NWO, FM (%) ≥ 35 for women, >25 for men & BMI < 25); and no obesity (FM < 35 for women, <25 for men & BMI < 25). A total of 130 unilateral PA (uPA) patients received adrenalectomy, and 27 EH patients were identified; uPA patients with hypertension remission were found to have lower FM (p = 0.046), BMI (p < 0.001), and lower prevalence of overweight (p = 0.001). In the logistic regression model, patients with clinical overweight (OR = 2.9, p = 0.007), NWO (OR = 3.04, p = 0.041) and longer HTN duration (years, OR = 1.065, p = 0.013) were at the risk of persistent hypertension after adrenalectomy. Obesity status was strongly associated with persistent hypertension in uPA patients after adrenalectomy. However, patients in the NWO group also carried higher risk of persistent hypertension. Therefore, assessment of pre-obesity and overweight in uPA patients are extremely important, especially in those who have normal BMI.

Robust face recognition with structurally incoherent low-rank matrix decomposition.

For the task of robust face recognition, we particularly focus on the scenario in which training and test image data are corrupted due to occlusion or disguise. Prior standard face recognition methods like Eigenfaces or state-of-the-art approaches such as sparse representation-based classification did not consider possible contamination of data during training, and thus their recognition performance on corrupted test data would be degraded. In this paper, we propose a novel face recognition algorithm based on low-rank matrix decomposition to address the aforementioned problem. Besides the capability of decomposing raw training data into a set of representative bases for better modeling the face images, we introduce a constraint of structural incoherence into the proposed algorithm, which enforces the bases learned for different classes to be as independent as possible. As a result, additional discriminating ability is added to the derived base matrices for improved recognition performance. Experimental results on different face databases with a variety of variations verify the effectiveness and robustness of our proposed method.