CD271-selected mesenchymal stem cells from adipose tissue enhance cartilage repair and are less angiogenic than plastic adherent mesenchymal stem cells

CD271 is a marker of bone marrow MSCs with enhanced differentiation capacity for bone or cartilage repair. However, the nature of CD271+ MSCs from adipose tissue (AT) is less well understood. Here, we investigated the differentiation, wound healing and angiogenic capacity of plastic adherent MSCs (PA MSCs) versus CD271+ MSCs from AT. There was no difference in the extent to which PA MSCs and CD271+ MSCs formed osteoblasts, adipocytes or chondrocytes in vitro. In contrast, CD271+ MSCs transplanted into athymic rats significantly enhanced osteochondral wound healing with reduced vascularisation in the repair tissue compared to PA MSCs and control animals; there was little histological evidence of mature articular cartilage formation in all animals. Conditioned medium from CD271+ MSC cultures was less angiogenic than PA MSC conditioned medium, and had little effect on endothelial cell migration or endothelial tubule formation in vitro. The low angiogenic activity of CD271+ MSCs and improved early stage tissue repair of osteochondral lesions when transplanted, along with a comparable differentiation capacity along mesenchymal lineages when induced, suggests that these selected cells are a better candidate than PA MSCs for the repair of cartilaginous tissue

On semi-supervised clustering

Due to its capability to exploit training datasets encompassing both labeled and unlabeled patterns, semi–supervised learning (SSL) has been receiving attention from the community throughout the last decade. Several SSL approaches to data clustering have been proposed and investigated, as well. Unlike typical SSL setups, in semi–supervised clustering (SSC) the partial supervision is generally not available in terms of class labels associated with a subset of the training sample. In fact, general SSC algorithms rely rather on additional constraints which bring some kind of a–priori, weak side–knowledge to the clustering process. Significant instances are: COP–COBWEB and COP k–means, HMRF k–means, seeded k–means, constrained k–means, and active fuzzy constrained clustering. This chapter is a survey of major SSC philosophies, setups, and techniques. It provides the reader with an insight into these notions, categorizing and reviewing the major state–of–the–art approaches to SSC