Increased Adipogenesis of Human Adipose-Derived Stem Cells on Polycaprolactone Fiber Matrices

With accelerating rates of obesity and type 2 diabetes world-wide, interest in studying the adipocyte and adipose tissue is increasing. Human adipose derived stem cells differentiated to adipocytes in vitro - are frequently used as a model system for white adipocytes, as most of their pathways and functions resemble mature adipocytes in vivo. However, these cells are not completely like in vivo mature adipocytes. Hosting the cells in amore physiologically relevant environment compared to conventional two-dimensional cell culturing on plastic surfaces, can produce spatial cues that drive the cells towards a more mature state. We investigated the adipogenesis of adipose derived stem cells on electro spun polycaprolactone matrices and compared functionality to conventional two-dimensional cultures as well as to human primary mature adipocytes. To assess the degree of adipogenesis we measured cellular glucose-uptake and lipolysis and used a range of different methods to evaluate lipid accumulation. We compared the averaged results from a whole population with the single cell characteristics - studied by coherent anti-Stokes Raman scattering microscopy - to gain a comprehensive picture of the cell phenotypes. In adipose derived stem cells differentiated on a polycaprolactone-fiber matrix; an increased sensitivity in insulin-stimulated glucose uptake was detected when cells were grown on either aligned or random matrices. Furthermore, comparing differentiation of adipose derived stem cells on aligned polycaprolactone-fiber matrixes, to those differentiated in two-dimensional cultures showed, an increase in the cellular lipid accumulation, and hormone sensitive lipase content. In conclusion, we propose an adipocyte cell model created by differentiation of adipose derived stem cells on aligned polycaprolactone-fiber matrices which demonstrates increased maturity, compared to 2D cultured cells

Robust Bayes-Like Estimation: Rho-Bayes estimation

We consider the problem of estimating the joint distribution $P$ of $n$ independent random variables within the Bayes paradigm from a non-asymptotic point of view. Assuming that $P$ admits some density $s$ with respect to a given reference measure, we consider a density model $\overline S$ for $s$ that we endow with a prior distribution $\pi$ (with support $\overline S$) and we build a robust alternative to the classical Bayes posterior distribution which possesses similar concentration properties around $s$ whenever it belongs to the model $\overline S$. Furthermore, in density estimation, the Hellinger distance between the classical and the robust posterior distributions tends to 0, as the number of observations tends to infinity, under suitable assumptions on the model and the prior, provided that the model $\overline S$ contains the true density $s$. However, unlike what happens with the classical Bayes posterior distribution, we show that the concentration properties of this new posterior distribution are still preserved in the case of a misspecification of the model, that is when $s$ does not belong to $\overline S$ but is close enough to it with respect to the Hellinger distance.Comment: 68 page

Raman Spectroscopy and Regenerative Medicine: A Review

The field of regenerative medicine spans a wide area of the biomedical landscape—from single cell culture in laboratories to human whole-organ transplantation. To ensure that research is transferrable from bench to bedside, it is critical that we are able to assess regenerative processes in cells, tissues, organs and patients at a biochemical level. Regeneration relies on a large number of biological factors, which can be perturbed using conventional bioanalytical techniques. A versatile, non-invasive, non-destructive technique for biochemical analysis would be invaluable for the study of regeneration; and Raman spectroscopy is a potential solution. Raman spectroscopy is an analytical method by which chemical data are obtained through the inelastic scattering of light. Since its discovery in the 1920s, physicists and chemists have used Raman scattering to investigate the chemical composition of a vast range of both liquid and solid materials. However, only in the last two decades has this form of spectroscopy been employed in biomedical research. Particularly relevant to regenerative medicine are recent studies illustrating its ability to characterise and discriminate between healthy and disease states in cells, tissue biopsies and in patients. This review will briefly outline the principles behind Raman spectroscopy and its variants, describe key examples of its applications to biomedicine, and consider areas of regenerative medicine that would benefit from this non-invasive bioanalytical tool

Accelerated Monte Carlo models to simulate fluorescence spectra from layered tissues

Two efficient Monte Carlo models are described, facilitating predictions of complete time-resolved fluorescence spectra from a light-scattering and light-absorbing medium. These are compared with a third, conventional fluorescence Monte Carlo model in terms of accuracy, signal-to-noise statistics, and simulation time. The improved computation efficiency is achieved by means of a convolution technique, justified by the symmetry of the problem. Furthermore, the reciprocity principle for photon paths, employed in one of the accelerated models, is shown to simplify the computations of the distribution of the emitted fluorescence drastically. A so-called white Monte Carlo approach is finally suggested for efficient simulations of one excitation wavelength combined with a wide range of emission wavelengths. The fluorescence is simulated in a purely scattering medium, and the absorption properties are instead taken into account analytically afterward. This approach is applicable to the conventional model as well as to the two accelerated models. Essentially the same absolute values for the fluorescence integrated over the emitting surface and time are obtained for the three models within the accuracy of the simulations. The time-re solved and spatially resolved fluorescence exhibits a slight overestimation at short delay times close to the source corresponding to approximately two grid elements for the accelerated models, as a result of the discretization and the convolution. The improved efficiency is most prominent for the reverse-emission accelerated model, for which the simulation time can be reduced by up to two orders of magnitude. (C) 2003 Optical Society of America

Chemical imaging of lignocellulosic biomass by CARS microscopy

Chemical and structural composition of wood biomass is studied by label-free and chemically specific Coherent Anti-Stokes Raman Scattering (CARS) microscopy. A concept developed for assignment and semi-quantitative imaging of sample components; cellulose, hemicellulose, and lignin; by multiplex CARS microspectroscopy and subsequent data analysis is presented. Specific imaging without fluorescence backround is achieved an order of magnitude faster compared with conventional Raman microscopy. Laser polarization control yield information on molecular arrangement in wood fibers. Narrowband CARS excitation of single vibrations allows for three-dimensional volume imaging. Thus, CARS microscopy has potential as an important instrument for characterization of lignocellulosic materials

Chemical imaging of lignocellulosic biomass by CARS microscopy

Chemical and structural composition of wood biomass is studied by label-free and chemically specific Coherent Anti-Stokes Raman Scattering (CARS) microscopy. A concept developed for assignment and semi-quantitative imaging of sample components; cellulose, hemicellulose, and lignin; by multiplex CARS microspectroscopy and subsequent data analysis is presented. Specific imaging without fluorescence backround is achieved an order of magnitude faster compared with conventional Raman microscopy. Laser polarization control yield information on molecular arrangement in wood fibers. Narrowband CARS excitation of single vibrations allows for three-dimensional volume imaging. Thus, CARS microscopy has potential as an important instrument for characterization of lignocellulosic materials