Diameter-dependent release of a cisplatin pro-drug from small and large functionalized carbon nanotubes

The use of platinum-based chemotherapeutic drugs in cancer therapy still suffers from severe disadvantages, such as lack of appropriate selectivity for tumor tissues and insurgence of multi-drug resistance. Moreover, drug efficacy can be attenuated by several mechanisms such as premature drug inactivation, reduced drug uptake inside cells and increased drug efflux once internalized. The use of functionalized carbon nanotubes (CNTs) as chemotherapeutic drug delivery systems is a promising strategy to overcome such limitations due to their ability to enhance cellular internalization of poorly permeable drugs and thus increase the drug bioavailability at the diseased site, compared to the free drug. Furthermore, the possibility to encapsulate agents in the nanotubes’ inner cavity can protect the drug from early inactivation and their external functionalizable surface is useful for selective targeting. In this study, a hydrophobic platinum(IV) complex was encapsulated within the inner space of two different diameter functionalized multi-walled CNTs (Pt(IV)@CNTs). The behavior of the complexes, compared to the free drug, was investigated on both HeLa human cancer cells and RAW 264.7 murine macrophages. Both CNT samples efficiently induced cell death in HeLa cancer cells 72 hours after the end of exposure to CNTs. Although the larger diameter CNTs were more cytotoxic on HeLa cells compared to both the free drug and the smaller diameter nanotubes, the latter allowed a prolonged release of the encapsulated drug, thus increasing its anticancer efficacy. In contrast, both Pt(IV)@CNT constructs were poorly cytotoxic on macrophages and induced negligible cell activation and no pro-inflammatory cytokine production. Both CNT samples were efficiently internalized by the two types of cells, as demonstrated by transmission electron microscopy observations and flow cytometry analysis. Finally, the platinum levels found in the cells after Pt(IV)@CNT exposure demonstrate that they can promote drug accumulation inside cells in comparison with treatment with the free complex. To conclude, our study shows that CNTs are promising nanocarriers to improve the accumulation of a chemotherapeutic drug and its slow release inside tumor cells, by tuning the CNT diameter, without inducing a high inflammatory response

Carnitine reduces the lipoperoxidative damage of the membrane and apoptosis after induction of cell stress in experimental glaucoma

The pathological damage caused by glaucoma is associated to a high intraocular pressure. The ocular hypertone is most likely due to a defective efflux of aqueous humor from the anterior chamber of the eye. Ocular hypertension causes apoptotic death of retinal ganglion cells and overexpression of molecular markers typical of cell stress response and apoptosis. In this work, we report on the neuroprotective, antiapoptotic and antioxidant action of a natural substance, -carnitine. This compound is known for its ability to improve the mitochondrial performance. We analyze a number of cellular and molecular markers, typical of ocular hypertension and, in general, of the cell stress response. In particular, -carnitine reduces the expression of glial fibrillary acidic protein, inducible nitric oxide synthase, ubiquitin and caspase 3 typical markers of cell stress. In addition, the morphological analysis of the optic nerve evidenced a reduction of the pathological excavation of the optic disk. This experimental hypertone protocol induces a severe lipoperoxidation, which is significantly reduced by -carnitine. The overall interpretation is that mortality of the retinal cells is due to membrane damage

Surfactant Mixtures: Performances vs. Aggregation States

Surfactants, Mixtures, interaction

A kernel-based approach to Hammerstein system identification

In this paper, we propose a novel algorithm for the identification of Hammerstein systems. Adopting a Bayesian approach, we model the impulse response of the unknown linear dynamic system as a realization of a zero-mean Gaussian process. The covariance matrix (or kernel) of this process is given by the recently introduced stable-spline kernel, which encodes information on the stability and regularity of the impulse response. The static nonlinearity of the model is identified using an Empirical Bayes approach, i.e. by maximizing the output marginal likelihood, which is obtained by integrating out the unknown impulse response. The related optimization problem is solved adopting a novel iterative scheme based on the Expectation-Maximization method, where each iteration consists in a simple sequence of update rules. Numerical experiments show that the proposed method compares favorably with a standard algorithm for Hammerstein system identification

Surfactant Mixtures: Performances vs. Aggregation States

Surfactants, Mixtures, interaction

Regeneration and DNA methylation do not trigger PDX-1 expression in rat liver

IN PRES

Approximate inference of nonparametric Hammerstein models

\u3cp\u3eWe propose a method for nonparametric identification of Hammerstein models with Gaussian-process models for the impulse response of the linear block and for the input nonlinearity. Interpreting the Gaussian-processes as prior distributions, we can estimate the unknowns using the posterior means given the data. To estimate the hyperparameters we set up an iterative scheme, reminiscent of the expectation-maximization method, where the posterior expectation of the complete likelihood is iteratively maximized. In the Hammerstein case, the posterior density is intractable because, in general, it does not admit a closed form expression. In this work, we propose two approximation approaches to estimate the posterior mean. In the first, we make a particle approximation of the posterior using Markov Chain Monte Carlo. In the second, we use a variational Bayes approach with a mean-field hypothesis. We validate the proposed methods on synthetic datasets of Hammerstein systems.\u3c/p\u3

Approximate maximum-likelihood identification of linear systems from quantized measurements\u3csup\u3e⁎\u3c/sup\u3e

\u3cp\u3eWe analyze likelihood-based identification of systems that are linear in the parameters from quantized output data; in particular, we propose a method to find approximate maximum-likelihood and maximum-a-posteriori solutions. The method consists of appropriate least-squares projections of the middle point of the active quantization intervals. We show that this approximation maximizes a variational approximation of the likelihood and we provide an upper bound for the approximation error. In a simulation study, we compare the proposed method with the true maximum-likelihood estimate of a finite impulse response model.\u3c/p\u3