Pain Sensitivity Questionnaire: Cross-cultural adaptation and validation of the Greek version.

Introduction: The Pain Sensitivity Questionnaire (PSQ) was developed to assess general pain sensitivity.

Objective: This study aimed to validate the Greek version of PSQ.

Methods: The questionnaire was translated into Greek (PSQ-GR) and piloted in a small sample of patients with chronic pain (n = 35).

Resveratrol and lycopene ameliorate contrast-induced nephropathy in a rabbit model.

Oxidative stress appears to possess a central role in CIN pathophysiology. Resveratrol (Res) and lycopene (Lyc) are strong natural antioxidants evaluated in a limited number of CIN animal studies in vivo. The aim of the study was to evaluate the potential renoprotective effects of Res/Lyc in a CIN rabbit model.

In Vivo Estimation of the Biological Effects of Endocrine Disruptors in Rabbits after Combined and Long-Term Exposure: Study Protocol.

Recently, an increasing number of chemical compounds are being characterized as endocrine disruptors since they have been proven to interact with the endocrine system, which plays a crucial role in the maintenance of homeostasis. Glyphosate is the active substance of the herbicide Roundup, bisphenol A (BPA) and di (2-ethylhexyl) phthalate (DEHP) are used as plasticizers, while triclosan (TCS), methyl (MePB), propyl (PrPB), and butyl (BuPB) parabens are used as antimicrobial agents and preservatives mainly in personal care products. Studies indicate that exposure to these substances can affect humans causing developmental problems and problems in the endocrine, reproductive, nervous, immune, and respiratory systems.

A Bayesian nonparametric approach to the approximation of the global stable manifold.

We propose a Bayesian nonparametric model based on Markov Chain Monte Carlo methods for unveiling the structure of the invariant global stable manifold from observed time-series data. The underlying unknown dynamical process could have been contaminated by additive noise. We introduce the Stable Manifold Geometric Stick Breaking Reconstruction model with which we reconstruct the unknown dynamic equations, while at the same time, we estimate the global structure of the perturbed stable manifold.

A Bayesian nonparametric approach to dynamical noise reduction.

We propose a Bayesian nonparametric approach for the noise reduction of a given chaotic time series contaminated by dynamical noise, based on Markov Chain Monte Carlo methods. The underlying unknown noise process (possibly) exhibits heavy tailed behavior. We introduce the Dynamic Noise Reduction Replicator model with which we reconstruct the unknown dynamic equations and in parallel we replicate the dynamics under reduced noise level dynamical perturbations.

A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems.

We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors.