Abstract: Tea is the second most popular beverage worldwide after water. Green tea has the highest nutraceutical values with well-established general health benefits and wide safety margins. Natural polyphenols found in green tea, including (+)-catechin (C), (–)-epicatechin (EC), (+)-gallocatechin (GC), (–)-epigallocatechin (EGC), (–)-epicatechin-3-gallate (ECG), (–)-gallocatechin-3-gallate (GCG) and (–)-epigallocatehin-3-gallate (EGCG). They have many potent biological properties and therapeutic effects in human health and diseases. These small molecules have high bioavailability and specific therapeutic potential in eye tissues. Recently some researchers studied the metabolomic responses to the green tea. In this talk, summary of these studies will be reviewed and its potential applications in the ocular research will be discussed.
Abstract: Retinal angiogenic diseases, such as diabetic retinopathy (DR) and age-related macular degeneration (AMD) represent the leading causes of vision impairment in developed countries. There is strong evidence that dysregulated metabolic pathways contribute to DR as known risk factors do not explain all cases and the phenomenon of metabolic memory persists for decades or longer. Some early studies also showed that changes of plasma metabolic profiles are associated with AMD. Metabolic abnormalities can be explored using the techniques of the new science of metabolomics. In this presentation, several metabolomics workflows as well as the application of data independent acquisition mass spectrometry (DIA-MS) in metabolomics will be discussed. Our recent findings from metabolomics studies on DR and AMD will be presented.
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.
