Cardiac tissue engineering by electrical stimulation with subcutaneous and cardiac adipose-tissue derived progenitor cells (ATDPCs)

A major challenge of cardiac tissue engineering is directing cells to establish the physiological structure and function of the myocardium being replaced. In native heart, pacing cells generate electrical stimuli that spread throughout the heartcausing cell membrane depolarization and activation of contractile apparatus. We ought to examine whether electricalstimulation of adipose tissue-derived progenitor cells (ATDPCs) exerts phenotypic and genetic changes that enhance theircardiomyogenic potential.

Developmental and tissue-specific involvement of peroxisome proliferator-activated receptor-alpha in the control of mouse uncoupling protein-3 gene expression.

Uncoupling protein-3 (UCP3) is a member of the mitochondrial carrier family expressed preferentially in skeletal muscle and heart. It appears to be involved in metabolic handling of fatty acids in a way that minimizes excessive production of reactive oxygen species. Fatty acids are powerful regulators of UCP3 gene transcription. We have found that the role of peroxisome proliferator-activated receptor-α (PPARα) on the control of UCP3 gene expression depends on the tissue and developmental stage. In adults, UCP3 mRNA expression is unaltered in skeletal muscle from PPARα-null mice both in basal conditions and under the stimulus of starvation. In contrast, UCP3 mRNA is down-regulated in adult heart both in fed and fasted PPARα-null mice. This occurs despite the increased levels of free fatty acids caused by fasting in PPARα-null mice. In neonates, PPARα-null mice show impaired UCP3 mRNA expression in skeletal muscle in response to milk intake, and this is not a result of reduced free fatty acid levels. The murine UCP3 promoter is activated by fatty acids through either PPARα or PPARδ but not by PPARγ or retinoid X receptor alone. PPARδ-dependent activation could be a potential compensatory mechanism to ensure appropriate expression of UCP3 gene in adult skeletal muscle in the absence of PPARα. However, among transcripts from other PPARα and PPARδ target genes, only those acutely induced by milk intake in wild-type neonates were altered in muscle or heart from PPARα-null neonates. Thus, PPARα-dependent regulation is required for appropriate gene regulation of UCP3 as part of the subset of fatty-acid-responsive genes in neonatal muscle and heart.

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Study and modeling of the desintegration kinetics of coated paper

The disintegration of recovered paper is the first operation in the preparation of recycled pulp. It is known that the defibering process follows a first order kinetics from which it is possible to obtain the disintegration kinetic constant (KD) by means of different ways. The disintegration constant can be obtained from the Somerville index results (%lsv and from the dissipated energy per volume unit (Ss). The %slv is related to the quantity of non-defibrated paper, as a measure of the non-disintegrated fiber residual (percentage of flakes), which is expressed in disintegration time units. In this work, disintegration kinetics from recycled coated paper has been evaluated, working at 20 revise rotor speed and for different fiber consistency (6, 8, 10, 12 and 14%). The results showed that the values of experimental disintegration kinetic constant, Ko, through the analysis of Somerville index, as function of time. Increased, the disintegration time was drastically reduced. The calculation of the disintegration kinetic constant (modelled Ko), extracted from the Rayleigh’s dissipation function, showed a good correlation with the experimental values using the evolution of the Somerville index or with the dissipated energy

HIV-1 immune activation induces siglec-1 expression and enhances viral trans-infection in blood and tissue myeloid cells

Background: Myeloid cells are key players in the recognition and response of the host against invading viruses. Paradoxically, upon HIV-1 infection, myeloid cells might also promote viral pathogenesis through trans-infection, a mechanism that promotes HIV-1 transmission to target cells via viral capture and storage. The receptor Siglec-1 (CD169) potently enhances HIV-1 trans-infection and is regulated by immune activating signals present throughout the course of HIV-1 infection, such as interferon α (IFNα). Results: Here we show that IFNα-activated dendritic cells, monocytes and macrophages have an enhanced ability to capture and trans-infect HIV-1 via Siglec-1 recognition of viral membrane gangliosides. Monocytes from untreated HIV-1-infected individuals trans-infect HIV-1 via Siglec-1, but this capacity diminishes after effective antiretroviral treatment. Furthermore, Siglec-1 is expressed on myeloid cells residing in lymphoid tissues, where it can mediate viral trans-infection. Conclusions: Siglec-1 on myeloid cells could fuel novel CD4+ T-cell infections and contribute to HIV-1 dissemination in vivo.