Lipopolysaccharide inhibits or accelerates biomedical titanium corrosion depending on environmental acidity

Titanium and its alloys are routinely used as biomedical implants and are usually considered to be corrosion resistant under physiological conditions. However, during inflammation, chemical modifications of the peri-implant environment including acidification occur. In addition certain biomolecules including lipopolysaccharide (LPS), a component of Gram-negative bacterial cell walls and driver of inflammation have been shown to interact strongly with Ti and modify its corrosion resistance. Gram-negative microbes are abundant in biofilms which form on dental implants. The objective was to investigate the influence of LPS on the corrosion properties of relevant biomedical Ti substrates as a function of environmental acidity. Inductively coupled plasma mass spectrometry was used to quantify Ti dissolution following immersion testing in physiological saline for three common biomedical grades of Ti (ASTM Grade 2, Grade 4 and Grade 5). Complementary electrochemical tests including anodic and cathodic polarisation experiments and potentiostatic measurements were also conducted. All three Ti alloys were observed to behave similarly and ion release was sensitive to pH of the immersion solution. However, LPS significantly inhibited Ti release under the most acidic conditions (pH 2), which may develop in localized corrosion sites, but promoted dissolution at pH 4–7, which would be more commonly encountered physiologically. The observed pattern of sensitivity to environmental acidity of the effect of LPS on Ti corrosion has not previously been reported. LPS is found extensively on the surfaces of skin and mucosal penetrating Ti implants and the findings are therefore relevant when considering the chemical stability of Ti implant surfaces in vivo

Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

International audienceKnowledge Discovery in Databases (KDD) and especially pattern mining can be interpreted along several dimensions, namely data, knowledge, problem-solving and interactivity. These dimensions are not disconnected and have a direct impact on the quality, applicability, and efficiency of KDD. Accordingly, we discuss some objectives of KDD based on these dimensions, namely exploration, knowledge orientation, hybridization, and explanation. The data space and the pattern space can be explored in several ways, depending on specific evaluation functions and heuristics, possibly related to domain knowledge. Furthermore, numerical data are complex and supervised numerical machine learning methods are usually the best candidates for efficiently mining such data. However, the work and output of numerical methods are most of the time hard to understand, while symbolic methods are usually more intelligible. This calls for hybridization, combining numerical and symbolic mining methods to improve the applicability and interpretability of KDD. Moreover, suitable explanations about the operating models and possible subsequent decisions should complete KDD, and this is far from being the case at the moment. For illustrating these dimensions and objectives, we analyze a concrete case about the mining of biological data, where we characterize these dimensions and their connections. We also discuss dimensions and objectives in the framework of Formal Concept Analysis and we draw some perspectives for future research