Theme 1: Regenerative Medicine

AB004: Resuscitation of axon regenerative potential in mature retinal ganglion cells

2017,2(5):-
 

Abstract: Axon regeneration capacity declines in mature retinal ganglion cells (RGCs). While a number of transcription factors and signaling molecules have been implicated to the loss of regenerative potential of RGC axon, their upstream regulators are unclear. We investigated the association between developmental decline of RGC regenerative potential and age-related changes in microRNA (miRNA) expression and showed that loss of axon regenerative potential can be partially restored by upregulating miR-19a in RGCs in vitro and in vivo. Regulating miRNA expression represents a new potential therapeutic approach to resuscitate age-related loss of axon growth ability.

Original Article

RegenX: an NLP recommendation engine for neuroregeneration topics over time

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Background: In this investigation, we explore the literature regarding neuroregeneration from the 1700s to the present. The regeneration of central nervous system neurons or the regeneration of axons from cell bodies and their reconnection with other neurons remains a major hurdle. Injuries relating to war and accidents attracted medical professionals throughout early history to regenerate and reconnect nerves. Early literature till 1990 lacked specific molecular details and is likely provide some clues to conditions that promoted neuron and/or axon regeneration. This is an avenue for the application of natural language processing (NLP) to gain actionable intelligence. Post 1990 period saw an explosion of all molecular details. With the advent of genomic, transcriptomics, proteomics, and other omics—there is an emergence of big data sets and is another rich area for application of NLP. How the neuron and/or axon regeneration related keywords have changed over the years is a first step towards this endeavor.

Methods: Specifically, this article curates over 600 published works in the field of neuroregeneration. We then apply a dynamic topic modeling algorithm based on the Latent Dirichlet allocation (LDA) algorithm to assess how topics cluster based on topics.

Results: Based on how documents are assigned to topics, we then build a recommendation engine to assist researchers to access domain-specific literature based on how their search text matches to recommended document topics. The interface further includes interactive topic visualizations for researchers to understand how topics grow closer and further apart, and how intra-topic composition changes over time.

Conclusions: We present a recommendation engine and interactive interface that enables dynamic topic modeling for neuronal regeneration.

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    Executive director:Ministry of Education of the People's Republic of China
    Host: Sun Yat-sen University
    Undertake: Zhongshan Ophthalmic Center, Sun Yat-sen University
    Editors-in-Chief: 林浩添
    Executive director:Ministry of Education of the People's Republic of China
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    Executive director:Ministry of Education of the People's Republic of China
    Host: Sun Yat-sen University
    Undertake: Zhongshan Ophthalmic Center, Sun Yat-sen University
    Editors-in-Chief: 林浩添
    Executive director:Ministry of Education of the People's Republic of China
    Host: Sun Yat-sen University
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