The acute inflammatory response to copper(II)-doped biphasic calcium phosphates.

Infection and inflammation are two key features to consider to avoid septic or aseptic loosening of bone-implanted biomaterials. In this context, various approaches to fine-tune the biomaterial's properties have been studied in order to modulate the crosstalk between immune and skeletal cells. Cation-doping strategies for tuning of calcium phosphates properties has been evidenced as a promising way to control the biomaterial-induced inflammatory process, and thus improving their osteoimmunomodulatory properties. Copper(II) ions are recognized for their antibacterial potential, but the literature on their impact on particulate material-induced acute inflammation is scarce. We synthesized copper(II) ions-doped biphasic calcium phosphate (BCP), intended to exhibit osteoimmunomodulatory properties. We addressed in vitro, for the first time, the inflammatory response of human primary polymorphonuclear neutrophils (PMNs) to copper(II) ions-doped or undoped (BCP) powders, synthesized by an original and robust wet method, in the presence or absence of LPS as a costimulant to mimic an infectious environment. ELISA and zymography allowed us to evidence, in vitro, a specific increase in IL-8 and GRO-α secretion but not MIP-1ß, TNF-α, or MMP-9, by PMNs. To assess in vivo relevance of these findings, we used a mouse air pouch model. Thanks to flow cytometry analysis, we highlighted an increased PMN recruitment with the copper(II) ions-doped samples compared to undoped samples. The immunomodulatory effect of copper(II) ions-doped BCP powders and the consequent induced moderate level of inflammation may promote bacterial clearance by PMNs in addition to the antimicrobial potential of the material. Copper(II) doping provides new insights into calcium phosphate (CaP)-based biomaterials for prosthesis coating or bone reconstruction by effectively modulating the inflammatory environment.

Hidden Markov multiple event sequence models: A paradigm for the spatio-temporal analysis of fMRI data.

This paper presents a novel, completely unsupervised fMRI brain mapping method that addresses the three problems of hemodynamic response function (HRF) variability, hemodynamic event timing, and fMRI response non-linearity. Spatial and temporal information are directly taken into account into the core of the activation detection process. In practice, activation detection at voxel v is formulated in terms of temporal alignment between sequences of hemodynamic response onsets (HROs) detected in the fMRI signal at v and in the spatial neighborhood of v, and the input sequence of stimuli or stimulus onsets. Event-related and epoch paradigms are considered. The multiple event sequence alignment problem is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs), a new class of hidden Markov models. Results obtained on real and synthetic data significantly outperform those obtained with the popular statistical parametric mapping (SPM2) method without requiring any prior definition of the expected activation patterns, the HMMESM mapping approach being completely unsupervised.

Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring.

Information management for critical care monitoring is still a very difficult task. Medical staff is often overwhelmed by the amount of data provided by the increased number of specific monitoring devices and instrumentation, and the lack of an effective automated system. Specifically, a basic task such as arrhythmia detection still produce an important amount of undesirable alarms, due in part to the mechanistic approach of current monitoring systems. In this work, multisensor and multisource data fusion schemes to improve atrial and ventricular activity detection in critical care environments are presented. Applications of these schemes are quantitatively evaluated and compared with current methods, showing the potential advantages of data fusion techniques for event detection in noise corrupted signals.

Heart signal recognition by Hidden Markov Models: the ECG case.

Wave recognition in ECG signals by Hidden Markov Models (HMMs) relies on the stationary assumption for the set of parameters used to describe ECG waves. This approach seems unnatural and consequently generates severe errors in practice. A new class of HMMs called Modified Continuous Variable Duration HMMs is proposed to account for the specific properties of the ECG signal. An application of the latter, coupled with a multiresolution front-end analysis of the ECG is presented. Results show these methods can increase the performance of ECG recognition compared to classical HMMs.

Methodological approach towards the definition of new storage conditions for inert wastes.

In 1997, the French Ministry of Environment launched studies aiming to define a specific regulation concerning inert waste disposal in order to limit potential impact of such facilities on the environment by fixing minimum requirements. A model (chemical model/hydrodynamic model) was developed to determine dumping conditions. This model was then applied on two defined scenarios (landfill surface, effective rainfalls...) in order to study the sulphate concentrations in aquifer system immediately downstream from the storage facility. Results allow us to determine in which conditions the sulphates concentrations are compatibles with the potentially drinkable character of the groundwater. They more specifically concern the nature of the waste disposed of, the efficient rainfalls and the landfill area.