Osteoblast and monocyte responses to 444 ferritic stainless steel intended for a Magneto-Mechanically Actuated Fibrous Scaffold

The rationale behind this work is to design an implant device, based on a ferromagnetic material, with the potential to deform in vivo promoting osseointegration through the growth of a healthy periprosthetic bone structure. One of the primary requirements for such a device is that the material should be non-inflammatory and non-cytotoxic. In the study described here, we assessed the short-term cellular response to 444 ferritic stainless steel; a steel, with a very low interstitial content and a small amount of strong carbide-forming elements to enhance intergranular corrosion resistance. Two different human cell types were used: (i) foetal osteoblasts and (ii) monocytes. Austenitic stainless steel 316L, currently utilised in many commercially available implant designs, and tissue culture plastic were used as the control surfaces. Cell viability, proliferation and alkaline phosphatase activity were measured. In addition, cells were stained with alizarin red and fluorescently-labelled phalloidin and examined using light, fluorescence and scanning electron microscopy. Results showed that the osteoblast cells exhibited a very similar degree of attachment, growth and osteogenic differentiation on all surfaces. Measurement of lactate dehydrogenase activity and tumour necrosis factor alpha protein released from human monocytes indicated that 444 stainless steel did not cause cytotoxic effects or any significant inflammatory response. Collectively, the results suggest that 444 ferritic stainless steel has the potential to be used in advanced bone implant designs. © 2011 Elsevier Ltd.

Enhanced poisson sum representation for alpha-stable processes

In this paper we present Poisson sum series representations for α-stable (αS) random variables and a-stable processes, in particular concentrating on continuous-time autoregressive (CAR) models driven by α-stable Lévy processes. Our representations aim to provide a conditionally Gaussian framework, which will allow parameter estimation using Rao-Blackwellised versions of state of the art Bayesian computational methods such as particle filters and Markov chain Monte Carlo (MCMC). To overcome the issues due to truncation of the series, novel residual approximations are developed. Simulations demonstrate the potential of these Poisson sum representations for inference in otherwise intractable α-stable models. © 2011 IEEE.