To Evaluate the Synergistic Effect of Different Crosslinking Strategies on Collagen Derivatives to Develop Funcional Biomimetic Scaffolds

The preliminary objective of this work was to study how the effect of different crosslinking methodologies can functionally modify various characteristics of biological macromolecules relevant for scaffold development in bone tissue engineering. The research study was classified and studied in three different phases: (i) different crosslinking strategies in gelatin functionalization, (ii) ribose mediated crosslinking in collagen-hydroxyapatite scaffold (iii) different crosslinking mechanisms in functional modification of bone-like scaffold. The obtained results were highly positive in all the three investigated studies. Though the core aim of this research was to explore the available crosslinking strategies in different biological macromolecules, the present study generated significant findings, largely contributing to provide optimum solutions in understanding how the crosslinking density can fine-tune the overall performance of a scaffold, relevant for its functioning in vivo. In particular, this study demonstrated that different crosslinkers at different conditions (pH and temperature) can modify the functional properties of the scaffolds differently, therefore this optimization strategies on these crosslinkers as obtained from this study results will help material scientists in the design and development of bioactive hybrid biomaterials for hard tissue regeneration.

New methodologies in CT perfusion and MRI analysis to develop cancer imaging biomarkers

Quantitative imaging in oncology aims at developing imaging biomarkers for diagnosis and prediction of cancer aggressiveness and therapy response before any morphological change become visible. This Thesis exploits Computed Tomography perfusion (CTp) and multiparametric Magnetic Resonance Imaging (mpMRI) for investigating diverse cancer features on different organs. I developed a voxel-based image analysis methodology in CTp and extended its use to mpMRI, for performing precise and accurate analyses at single-voxel level. This is expected to improve reproducibility of measurements and cancer mechanisms’ comprehension and clinical interpretability. CTp has not entered the clinical routine yet, although its usefulness in the monitoring of cancer angiogenesis, due to different perfusion computing methods yielding unreproducible results. Instead, machine learning applications in mpMRI, useful to detect imaging features representative of cancer heterogeneity, are mostly limited to clinical research, because of results’ variability and difficult interpretability, which make clinicians not confident in clinical applications. In hepatic CTp, I investigated whether, and under what conditions, two widely adopted perfusion methods, Maximum Slope (MS) and Deconvolution (DV), could yield reproducible parameters. To this end, I developed signal processing methods to model the first pass kinetics and remove any numerical cause hampering the reproducibility. In mpMRI, I proposed a new approach to extract local first-order features, aiming at preserving spatial reference and making their interpretation easier. In CTp, I found out the cause of MS and DV non-reproducibility: MS and DV represent two different states of the system. Transport delays invalidate MS assumptions and, by correcting MS formulation, I have obtained the voxel-based equivalence of the two methods. In mpMRI, the developed predictive models allowed (i) detecting rectal cancers responding to neoadjuvant chemoradiation showing, at pre-therapy, sparse coarse subregions with altered density, and (ii) predicting clinically significant prostate cancers stemming from the disproportion between high- and low- diffusivity gland components.