The scaffold is a key element in the field of tissue engineering, especially when large defects or substitutions of pathological tissues or organs need to be clinically addressed. The expected outcome is strongly dependent on the cell–scaffold interaction and the integration with the surrounding biological tissue. Indeed, mimicking the natural extracellular matrix (ECM) of the tissue to be healed represents a further optimization that can limit a possible morphological mismatch between the scaffold and the tissue itself. For this aim, and referring to bone tissue engineering, polylactic acid (PLA) scaffolds were 3D printed with a microstructure inspired by the trabecular architecture and biologically evaluated by means of human osteosarcoma SAOS-2 cells. The cells were seeded on two types of scaffolds differing for the designed pore size (i.e., 400 and 600 µm), showing the same growth exponential trend found in the control and no significant alterations in the actin distribution. The microporous structure of the two tested samples enhanced the protein adsorption capability and mRNA expression of markers related to protein synthesis, proliferation, and osteoblast differentiation. Our findings demonstrate that 3D-printed scaffolds support the adhesion, growth, and differentiation of osteoblast-like cells and the microporous architecture, mimicking the natural bone hierarchical structure, and favoring greater bioactivity. These bioinspired scaffolds represent an interesting new tool for bone tissue engineering and regenerative medicine applications.

We exploit Surface-Enhanced Raman Scattering (SERS) to investigate aqueous droplets of genomic DNA deposited onto silver-coated silicon nanowires, and we show that it is possible to efficiently discriminate between spectra of tumoral and healthy cells. To assess the robustness of the proposed technique, we develop two different statistical approaches, one based on the Principal Components Analysis of spectral data and one based on the computation of the ${\ell }^2$ distance between spectra. Both methods prove to be highly efficient, and we test their accuracy via the Cohen’s $\kappa$ statistics. We show that the synergistic combination of the SERS spectroscopy and the statistical analysis methods leads to efficient and fast cancer diagnostic applications allowing rapid and unexpansive discrimination between healthy and tumoral genomic DNA alternative to the more complex and expensive DNA sequencing.