Enhanced Growth and Osteogenic Differentiation of Human Osteoblast-Like Cells on Boron-Doped Nanocrystalline Diamond Thin Films

Intrinsic nanocrystalline diamond (NCD) films have been proven to be promising substrates for the adhesion, growth and osteogenic differentiation of bone-derived cells. To understand the role of various degrees of doping (semiconducting to metallic-like), the NCD films were deposited on silicon substrates by a microwave plasma-enhanced CVD process and their boron doping was achieved by adding trimethylboron to the CH4:H2 gas mixture, the B∶C ratio was 133, 1000 and 6700 ppm. The room temperature electrical resistivity of the films decreased from >10 MΩ (undoped films) to 55 kΩ, 0.6 kΩ, and 0.3 kΩ (doped films with 133, 1000 and 6700 ppm of B, respectively). The increase in the number of human osteoblast-like MG 63 cells in 7-day-old cultures on NCD films was most apparent on the NCD films doped with 133 and 1000 ppm of B (153,000±14,000 and 152,000±10,000 cells/cm2, respectively, compared to 113,000±10,000 cells/cm2 on undoped NCD films). As measured by ELISA per mg of total protein, the cells on NCD with 133 and 1000 ppm of B also contained the highest concentrations of collagen I and alkaline phosphatase, respectively. On the NCD films with 6700 ppm of B, the cells contained the highest concentration of focal adhesion protein vinculin, and the highest amount of collagen I was adsorbed. The concentration of osteocalcin also increased with increasing level of B doping. The cell viability on all tested NCD films was almost 100%. Measurements of the concentration of ICAM-1, i.e. an immunoglobuline adhesion molecule binding inflammatory cells, suggested that the cells on the NCD films did not undergo significant immune activation. Thus, the potential of NCD films for bone tissue regeneration can be further enhanced and tailored by B doping and that B doping up to metallic-like levels is not detrimental for cells

Smoothness Bias in Relevance Estimators for Feature Selection in Regression

Part 5: Machine Learning - Regression - ClassificationInternational audienceSelecting features from high-dimensional datasets is an important problem in machine learning. This paper shows that in the context of filter methods for feature selection, the estimator of the criterion used to select features plays an important role; in particular the estimators may suffer from a bias when comparing smooth and non-smooth features. This paper analyses the origin of such bias and investigates whether this bias influences the results of the feature selection process. Results show that non-smooth features tend to be penalised especially in small datasets