Robotic therapy for chronic stroke: general recovery of impairment or improved task-specific skill?

VS. Robotic therapy for chronic stroke: general recovery of impairment or improved task-specific skill

LNCaP Atlas: Gene expression associated with in vivo progression to castration-recurrent prostate cancer

<p>Abstract</p> <p>Background</p> <p>There is no cure for castration-recurrent prostate cancer (CRPC) and the mechanisms underlying this stage of the disease are unknown.</p> <p>Methods</p> <p>We analyzed the transcriptome of human LNCaP prostate cancer cells as they progress to CRPC <it>in vivo </it>using replicate LongSAGE libraries. We refer to these libraries as the LNCaP atlas and compared these gene expression profiles with current suggested models of CRPC.</p> <p>Results</p> <p>Three million tags were sequenced using <it>in vivo </it>samples at various stages of hormonal progression to reveal 96 novel genes differentially expressed in CRPC. Thirty-one genes encode proteins that are either secreted or are located at the plasma membrane, 21 genes changed levels of expression in response to androgen, and 8 genes have enriched expression in the prostate. Expression of 26, 6, 12, and 15 genes have previously been linked to prostate cancer, Gleason grade, progression, and metastasis, respectively. Expression profiles of genes in CRPC support a role for the transcriptional activity of the androgen receptor (<it>CCNH, CUEDC2, FLNA, PSMA7</it>), steroid synthesis and metabolism (<it>DHCR24, DHRS7</it>, <it>ELOVL5, HSD17B4</it>, <it>OPRK1</it>), neuroendocrine (<it>ENO2, MAOA, OPRK1, S100A10, TRPM8</it>), and proliferation (<it>GAS5</it>, <it>GNB2L1</it>, <it>MT-ND3</it>, <it>NKX3-1</it>, <it>PCGEM1</it>, <it>PTGFR</it>, <it>STEAP1</it>, <it>TMEM30A</it>), but neither supported nor discounted a role for cell survival genes.</p> <p>Conclusions</p> <p>The <it>in vivo </it>gene expression atlas for LNCaP was sequenced and support a role for the androgen receptor in CRPC.</p

Epigenetics of human cutaneous melanoma: setting the stage for new therapeutic strategies

Cutaneous melanoma is a very aggressive neoplasia of melanocytic origin with constantly growing incidence and mortality rates world-wide. Epigenetic modifications (i.e., alterations of genomic DNA methylation patterns, of post-translational modifications of histones, and of microRNA profiles) have been recently identified as playing an important role in melanoma development and progression by affecting key cellular pathways such as cell cycle regulation, cell signalling, differentiation, DNA repair, apoptosis, invasion and immune recognition. In this scenario, pharmacologic inhibition of DNA methyltransferases and/or of histone deacetylases were demonstrated to efficiently restore the expression of aberrantly-silenced genes, thus re-establishing pathway functions. In light of the pleiotropic activities of epigenetic drugs, their use alone or in combination therapies is being strongly suggested, and a particular clinical benefit might be expected from their synergistic activities with chemo-, radio-, and immuno-therapeutic approaches in melanoma patients. On this path, an important improvement would possibly derive from the development of new generation epigenetic drugs characterized by much reduced systemic toxicities, higher bioavailability, and more specific epigenetic effects

Self-assimilation for solving excessive information acquisition in potential learning

The present paper aims to propose a new computational method for potential learning to improve generalization and interpretation. Potential learning has been proposed to simplify the computational procedures of information maximization and to specify which neurons should be fired. However, it is often the case that potential learning sometimes absorbs too much information content on input patterns in the early stage of learning, which tends to degrade generalization performance. This can be solved by making potential learning as slow as possible. Accordingly, we here propose a procedure called “self-assimilation” in which connection weights are accentuated by their characteristics observed in the specific learning step. This makes it possible to predict future connection weights in the early stage of learning. Thus, it is possible to improve generalization by slow learning and at the same time to improve the interpretation of connection weights via the enhanced characteristics of the connection weights. The method was applied to an artificial data set, as well as a real data set of counter services at a local government office in the Tokyo metropolitan area. The results show that improved generalization was observed by making learning as slow as possible. In addition, the number of strong connection weights became smaller for better interpretation by self-assimilation