This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects’ circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy.

This paper aims to propose using polylactic acid (PLA) as an alternative to nanocomposites in additive manufacturing processes in fusion deposition modelling (FDM) systems and describe its thermal and rheological conditions with multi-wall carbon nanotube (PLA/MWCNT) and halloysite nanotube (PLA/HNT) composites for possible applications in additive manufacturing processes. PLA/MWCNTs and PLA/HNTs were obtained through fusion in a co-rotating twin-screw extruder. PLA was mixed with different percentages of MWCNTs and HNTs at concentrations of 0.5 Wt.%, 0.75 Wt.% and 1 Wt.%. Differential scanning calorimetry (DSC) and capillary rheometry were used to characterise these products, together with an analysis of the melt flow index (MFI). The DSC data revealed that the nanocomposites had a glass transition temperature T_g = 65 ± 2°C and a melting temperature T_m = 169 ± 1°C. The crystallisation temperature of PLA/MWCNTs and PLA/HNTs was between 107 ± 2°C and 129°C, respectively. The viscosity data of PLA/MWCNTs and PLA/HNTs obtained by capillary rheometry indicated that the viscosity of the materials is the same as that of neat PLA. These results were confirmed by the higher fluidity index in the MFI analysis. This paper presents an alternative for the applications of nanocomposites in additive manufacturing processes in FDM systems.

Exploiting the capability of preparing nanostructured bifunctional terminated SrTiO₃ substrates (SrO and TiO₂), the surface properties have been locally tuned by employing a double bottom-up strategy which combines the use of chemically nanopatterned substrates with molecular self-assembly. The dynamics of surface diffusion that allows SrO and TiO₂ chemical-termination nano-patterning of the SrTiO₃ is first addressed. Second, termination-dependent heterogeneous nucleation is used to demonstrate that stearic acid selectively grows on the TiO₂ terminated terraces. This adsorption improves the frictional properties and modifies the surface contact potential. The possibility of simultaneously tailoring at the nanoscale different surface properties of widely employed oxide substrates is promising for building up new devices useful for emerging applications.