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. Although there are copious studies related to these substances, there are few in vivo studies related to combined exposure to these endocrine disruptors. The aim of the present pilot study is the investigation and assessment of the above substances’ toxicity in rabbits after twelve months of exposure to glyphosate (both pure and commercial form) and to a mixture of all the above substances at subtoxic levels. The lack of data from the literature concerning rabbits’ exposure to these substances and the restrictions of the 3Rs Principle will result in a limited number of animals available for use (four animals per group, twenty animals in total).

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. Twenty-four adult male New Zealand white rabbits were equally assigned into four groups: control (saline), CIN (intravenous iopromide; 7.5 g iodine/kg), Res + CIN (per os Res; 5 mg/kg), and Lyc + CIN (per os Lyc; 4 mg/kg). Serum Cr (sCr); symmetric/asymmetric dimethylarginine (SDMA/ADMA); oxidative stress biomarkers: malondialdehyde; total antioxidant capacity; catalase; glutathione) were evaluated in blood samples at three time points: right after (0 h); 24 h; 48 h after iopromide/saline administration. CD20⁺/CD3⁺ lymphocytes were determined (48 h). All animals were sacrificed at 48 h and both kidneys collected. Oxidative stress biomarkers were measured in renal tissue. sCr and SDMA/ADMA levels increased significantly in CIN compared to all groups. Oxidative stress secondary to CIN in blood/kidneys was suppressed by Res/Lyc. B and T lymphocytes decreased significantly in CIN compared to all groups. The present study provides emerging evidence that Res/Lyc ameliorate CIN by modulating oxidant/antioxidant balance in blood/renal tissue and by inhibiting vasoconstriction/blood cytotoxicity.

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. Our method works for noninvertible maps without modifications. The stable manifold estimation procedure is demonstrated specifically in the case of polynomial maps. Simulations based on synthetic time-series are presented.

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. The dynamic noise reduction procedure is demonstrated specifically in the case of polynomial maps. Simulations based on synthetic time series are presented.

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. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.