Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

Mesenchymal Stromal Cells (MSCs) and induced Plutipotent Stem Cells (iPSCs) in Domestic Animals: Characterization and Differentiation Potential

Derivation of stem cell lines from domesticated animals has been of great interest as it benefits translational medicine, clinical applications to improve human and animal health and biotechnology. The main types of stem cells studied are Embryonic Stem Cells (ESCs), induced Pluripotent Stem Cells (iPSCs) and Mesenchymal Stem/Stromal Cells (MSCs). This thesis had two main aims: (I) The isolation of bovine MSCs from amniotic fluid (AF) at different trimesters of pregnancy and their characterization to study pluripotency markers expression. Stemness markers were studied also in MSCs isolated from equine AF, Wharton’s jelly (WJ) and umbilical cord blood (UCB) as continuation of the characterization of these cells previously performed by our research group; (II) The establishment and characterization of iPSCs lines in two attractive large animal models for biomedical and biotechnology research such as the bovine and the swine, and the differentiation into the myogenic lineage of porcine iPSCs. It was observed that foetal tissues in domestic animals such as the bovine and the horse represent a source of MSCs able to differentiate into the mesodermal lineage but they do not proliferate indefinitely and they lack the expression of many pluripotency markers, making them an interesting source of cells for regenerative medicine, but not the best candidate to elucidate pluripotency networks. The protocol used to induce pluripotency in bovine fibroblasts did not work, as well as the chemical induction of pluripotency in porcine fibroblasts, while the reprogramming protocol used for porcine iPSCs was successful and the line generated was amenable to being differentiated into the myogenic lineage, demonstrating that they could be addressed into a desired lineage by genetic modification and appropriated culture conditions. Only a few cell types have been differentiated from domestic animal iPSCs to date, so the development of a reliable directed-differentiation protocol represents a very important result.

Algal wastewater treatment and biomass producing potential: nutrient removal efficiency and cell physiological responses

Microalgae are sun - light cell factories that convert carbon dioxide to biofuels, foods, feeds, and other bioproducts. The concept of microalgae cultivation as an integrated system in wastewater treatment has optimized the potential of the microalgae - based biofuel production. These microorganisms contains lipids, polysaccharides, proteins, pigments and other cell compounds, and their biomass can provide different kinds of biofuels such as biodiesel, biomethane and ethanol. The algal biomass application strongly depends on the cell composition and the production of biofuels appears to be economically convenient only in conjunction with wastewater treatment. The aim of this research thesis was to investigate a biological wastewater system on a laboratory scale growing a newly isolated freshwater microalgae, Desmodesmus communis, in effluents generated by a local wastewater reclamation facility in Cesena (Emilia Romagna, Italy) in batch and semi - continuous cultures. This work showed the potential utilization of this microorganism in an algae - based wastewater treatment; Desmodesmus communis had a great capacity to grow in the wastewater, competing with other microorganisms naturally present and adapting to various environmental conditions such as different irradiance levels and nutrient concentrations. The nutrient removal efficiency was characterized at different hydraulic retention times as well as the algal growth rate and biomass composition in terms of proteins, polysaccharides, total lipids and total fatty acids (TFAs) which are considered the substrate for biodiesel production. The biochemical analyses were coupled with the biomass elemental analysis which specified the amount of carbon and nitrogen in the algal biomass. Furthermore photosynthetic investigations were carried out to better correlate the environmental conditions with the physiology responses of the cells and consequently get more information to optimize the growth rate and the increase of TFAs and C/N ratio, cellular compounds and biomass parameter which are fundamental in the biomass energy recovery.