Abstract: The tear film covers the anterior eye and the precise balance of its various constituting components is critical for maintaining ocular health. The composition of the tear film amphiphilic lipid sublayer, in particular, has largely remained a matter of contention. The limiting concentrations of lipid amphiphiles in tears have also posed considerable challenges to their detection and accurate quantitation. Using systematic and sensitive lipidomic approaches, we reported the most comprehensive human tear lipidome to date; and conferred novel insights to the compositional details of the existent tear film model, in particular the disputable amphiphilic lipid sublayer constituents, by demonstrating the presence of cholesteryl sulfate, O-acyl-ω-hydroxy fatty acids, and various sphingolipids and phospholipids in tears. Lipidomic analysis of human tear fluid from patients with various subtypes of dry eye syndrome (DES) revealed structure-specific lipid alterations in DES, which could potentially serve as unifying indicators of disease symptoms and signs.
The meibomian glands constitute the predominant source of lipid supply to the human tear fluid. Meibomian gland dysfunction (MGD) is a leading cause of evaporative dry eye and ocular discomfort, characterized by an unstable tear film principally attributed to afflicted delivery of lipids to the ocular surface. We investigated the longitudinal tear lipid alterations associated with disease alleviation and symptom improvement in a cohort of MGD patients undergoing eyelid-warming treatment for 12 weeks. Our preliminary data indicated that excess ocular surface phospholipase activity detrimental to tear film stability could be alleviated by eyelid warming alone without application of steroids and identify tear OAHFAs as suitable markers to monitor treatment response in MGD.
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.
