Vascular wall stem cells. Selection and conditioning of progenitors useful for cell therapy. A pathological case study

The arterial wall contains MSCs with mesengenic and angiogenic abilities. These multipotent precursors have been isolated from variously-sized human adult segments, belying the notion that vessel wall is a relatively quiescent tissue. Recently, our group identified in normal human arteries a vasculogenic niche and subsequently isolated and characterized resident MSCs (VW-MSCs) with angiogenic ability and multilineage potential. To prove that VW-MSCs are involved in normal and pathological vascular remodeling, we used a long-term organ culture system; this method was of critical importance to follow spontaneous 3-D vascular remodeling without any influence of blood cells. Next we tried to identify and localize in situ the VW-MSCs and to understand their role in the vascular remodeling in failed arterial homografts. Subsequently, we isolated this cell population and tested in vitro their multilineage differentiation potential through immunohistochemical, immunofluorescence, RT-PCR and ultrastructural analysis. From 25-30cm2 of each vascular wall homograft sample, we isolated a cell population with MSCs properties; these cells expressed MSC lineage molecules (CD90, CD44, CD105, CD29, CD73), stemness (Notch-1, Oct-4, Sca-1, Stro-1) and pericyte markers (NG2) whilst were negative for hematopoietic and endothelial markers (CD34, CD133, CD45, KDR, CD146, CD31 and vWF). MSCs derived from failed homografts (H-MSCs) exhibited adipogenic, osteogenic and chondrogenic potential but scarce propensity to angiogenic and leiomyogenic differentiation. The present study demonstrates that failed homografts contain MSCs with morphological, phenotypic and functional MSCs properties; H-MSCs are long-lived in culture, highly proliferating and endowed with prompt ability to differentiate into adipocytes, osteocytes and chondrocytes; compared with VW-MSCs from normal arteries, H-MSCs show a failure in angiogenic and leiomyogenic differentiation. A switch in MSCs plasticity could be the basis of pathological remodeling and contribute to aneurysmal failure of arterial homografts. The study of VW-MSCs in a pathological setting indicate that additional mechanisms are involved in vascular diseases; their knowledge will be useful for opening new therapeutic options in cardiovascular diseases.

Efficient ultrasonic signal processing techniques for aided medical diagnostics

Ultrasound imaging is widely used in medical diagnostics as it is the fastest, least invasive, and least expensive imaging modality. However, ultrasound images are intrinsically difficult to be interpreted. In this scenario, Computer Aided Detection (CAD) systems can be used to support physicians during diagnosis providing them a second opinion. This thesis discusses efficient ultrasound processing techniques for computer aided medical diagnostics, focusing on two major topics: (i) Ultrasound Tissue Characterization (UTC), aimed at characterizing and differentiating between healthy and diseased tissue; (ii) Ultrasound Image Segmentation (UIS), aimed at detecting the boundaries of anatomical structures to automatically measure organ dimensions and compute clinically relevant functional indices. Research on UTC produced a CAD tool for Prostate Cancer detection to improve the biopsy protocol. In particular, this thesis contributes with: (i) the development of a robust classification system; (ii) the exploitation of parallel computing on GPU for real-time performance; (iii) the introduction of both an innovative Semi-Supervised Learning algorithm and a novel supervised/semi-supervised learning scheme for CAD system training that improve system performance reducing data collection effort and avoiding collected data wasting. The tool provides physicians a risk map highlighting suspect tissue areas, allowing them to perform a lesion-directed biopsy. Clinical validation demonstrated the system validity as a diagnostic support tool and its effectiveness at reducing the number of biopsy cores requested for an accurate diagnosis. For UIS the research developed a heart disease diagnostic tool based on Real-Time 3D Echocardiography. Thesis contributions to this application are: (i) the development of an automated GPU based level-set segmentation framework for 3D images; (ii) the application of this framework to the myocardium segmentation. Experimental results showed the high efficiency and flexibility of the proposed framework. Its effectiveness as a tool for quantitative analysis of 3D cardiac morphology and function was demonstrated through clinical validation.