Ennio Tasciotti

TL;DR: This work presents an innovative technique for automated discovery of signatures optimized to characterize various cancer stages, and proves that these optimized signatures automatically select smaller sets of key biomarkers than the black-boxes generated by machine learning, and are much easier to interpret.

TL;DR: A hybrid material consisting of nanoporous silicon (pSi) and poly(lactic-co-glycolic acid) (PLGA) nanoparticles, loaded with antibiotics and pain relief medications, respectively, has the potential to dramatically affect the post-operative management of patients with an immediate improvement in post-operatively pain management, decreased PACU and hospital length of stays, with subsequently decreased hospital and surgical costs.

TL;DR: Pat-derived primary cells maintain their original phenotype when cultured on 3D scaffolds, and their in vivo behavior can be effectively studied with innovative zebrafish models.

TL;DR: This study confirmed MHA/Coll capabilities in promoting osteogenesis even in extreme long-term condition of MG, suggesting MG as an effective challenging model to apply in future studies to validate the ability of advanced scaffolds to counteract bone loss, facilitating their application in translational Regenerative Medicine and Tissue Engineering.

TL;DR: This work provides a set of biological parameters that can be used to assess cell stress related to nanoparticle exposure and the subsequent recovery of cell processes as a function of endosomal escape.