Isolating stem cells from skin: designing a novel highly efficient non-enzymatic approach

Stem cells are undifferentiated elements capable to acquire a specific cellular phenotype under the influence of specific stimuli, thus being involved in tissue integrity and maintenance. In the skin tissue self-renewal and wound healing after injury is a complex process, especially in adulthood, due to the aging process and the continuous exposure to damaging agents. The importance of stem cells in regenerative medicine is well known and defining or improving their isolation methods is therefore a primary and crucial step. In the present paper we present a novel method to isolate stem cells from human skin, including the involvement of a novel medium for the maintenance and expansion of in vitro cultures. The biopsies were mechanically digested and put in culture. The migrating cells were positive selected with magnetic cell sorting, characterized by flow-cytometry analysis, and viability detected by MTT assay. Cells exhibited a mesenchymal phenotype, as demonstrated by the positive acquirement of an osteogenic or adipogenic phenotype when cultured in specific conditioned media. Taken together our results disclose a novel method for culturing and expanding stem cells from skin and pave the way for future clinical applications in tissue regeneration

Sparse Topologies with Small Spectrum Size

One of the fundamental properties of a graph is the number of distinct eigenvalues of its adjacency or Laplace matrix. Determining this number is of theoretical interest as well as of practical impact. Sparse graphs with small spectra exhibit excellent structural properties and can act as interconnection topologies. In this paper, for any n we present graphs, for which the product of their vertex degree and the number of dierent eigenvalues is small. It is known that load balancing can be performed on such graphs in a small number of steps

On Computation and Communication with Small Bias

Many models in theoretical computer science allow for computations or representations where the answer is only slightly biased in the right direction. The best-known of these is the complexity class PP, for “probabilistic polynomial time”. A language is in PP if there is a randomized polynomial-time Turing machine whose acceptance probability is greater than 1/2 if, and only if, its input is in the language. Most computational complexity classes have an analogous class in communication complexity. The class PP in fact has two, a version with weakly restricted bias called PPcc, and a version with unrestricted bias called UPPcc. Ever since their introduction by Babai, Frankl, and Simon in 1986, it has been open whether these classes are the same. We show that PPcc is strictly included in UPPcc. Our proof combines a query complexity separation due to Beigel with a technique of Razborov that translates the acceptance probability of quantum protocols to polynomials. We will discuss some complexity theoretical consequences of this separation. This presentation is bases on joined work with Nikolay Vereshchagin and Ronald de Wolf

On time versus size for monotone dynamic monopolies in regular topologies

Journal of Discrete Algorithm