Antioxidant and inflammatory biomarkers in herpes zoster.

The risk of herpes zoster (HZ) increases as cell-mediated immunity declines with age. Even though oxidative stress plays a crucial role in the development of HZ, there are few serum biomarkers of the disease's antioxidant activity. The purpose of this study is to investigate the blood levels of major antioxidants in HZ patients. To the best of our knowledge, this is the first study on this issue in the literature. The serum levels of antioxidants including uric acid (UA), total bilirubin (TBIL), albumin (ALB), vitamin D levels, and inflammatory markers such as homocysteine (Hcy) and C-reactive protein (CRP) was retrospectively analyzed in 53 patients with HZ and 53 age-and sex-matched healthy controls (HCs). The relationships between these markers and postherpetic neuralgia (PHN) and the clinical severity of HZ were also evaluated. Serum levels of UA, TBIL, and ALB in patients with HZ were significantly lower than those in the HCs (p < 0.001), while no statistical differences were found in vitamin D levels between the groups. Hcy and CRP levels were significantly increased in HZ patients compared to HCs. Significant differences were observed in the serum levels of UA, Hcy, CRP, and vitamin D in the PHN group versus the non-PHN group (p < 0.001). The presence of inflammatory markers was found to be positively related to disease activity. Furthermore, when compared to the mild or moderate clinical types of HZ, these biomarkers were statistically significant in the severe clinical type. These results suggest that uncontrolled varicella-zoster virus reactivation, acute nerve damage, and PHN may all be associated with low antioxidant levels. These biomarkers may be a protective factor for HZ, but more research is needed to clarify the underlying mechanism.

Opening the Black Box: Hierarchical Sampling Optimization for Hand Pose Estimation.

Hand pose estimation, formulated as an inverse problem, is typically optimized by an energy function over pose parameters using a 'black box' image generation procedure, knowing little about either the relationships between the parameters or the form of the energy function. In this paper, we show significant improvement upon such black box optimization by exploiting high-level knowledge of the parameter structure and using a local surrogate energy function. Our new framework, hierarchical sampling optimization (HSO), consists of a sequence of discriminative predictors organized into a kinematic hierarchy. Each predictor is conditioned on its ancestors, and generates a set of samples over a subset of the pose parameters, with only one selected by the highly-efficient surrogate energy. The selected partial poses are concatenated to generate a full-pose hypothesis. Repeating the same process, several hypotheses are generated and the full energy function selects the best result. Under the same kinematic hierarchy, two methods based on decision forest and convolutional neural network are proposed to generate the samples and two optimization methods are studied when optimizing these samples. Experimental evaluations on three publicly available datasets show that our method is particularly impressive in low-compute scenarios where it significantly outperforms all other state-of-the-art methods.