Abstract: In the early days of deciphering the injured neuronal tissues led to the realization that contrast is necessary to discern the parts of the recovering tissues from the damaged ones. Early attempts relied on available (and often naturally occurring) staining substances. Incidentally, the active ingredients of most of them were small molecules. With the advent of time, the knowledge of chemistry helped identify compounds and conditions for staining. The staining reagents were even found to enhance the visibility of the organelles. Silver impregnation identification of Golgi bodies was discovered in owl optic nerve. Staining reagents since the late 1800s were widely used across all disciplines and for nerve tissue and became a key contributor to advancement in nerve-related research. The use of these reagents provided insight into the organization of the neuronal tissues and helped distinguish nerve degeneration from regeneration. The neuronal staining reagents have played a fundamental role in the clinical research facilitating the identification of biological mechanisms underlying eye and neuropsychiatric diseases. We found a lack of systematic description of all staining reagents, whether they had been used historically or currently used. There is a lack of readily available information for optimal staining of different neuronal tissues for a given purpose. We present here a grouping of the reagents based on their target location: (I) the central nervous system (CNS), (II) the peripheral nervous system (PNS), or (III) both. The biochemical reactions of most of the staining reagents is based on acidic or basic pH and specific reaction partners such as organelle or biomolecules that exists within the given tissue type. We present here a summary of the chemical composition, optimal staining condition, use for given neuronal tissue and, where possible, historic usage. Several biomolecules such as lipids and metabolites lack specific antibodies. Despite being non-specific the reagents enhance contrast and provide corroboration about the microenvironment. In future, these reagents in combination with emerging techniques such as imaging mass spectrometry and kinetic histochemistry will validate or expand our understanding of localization of molecules within tissues or cells that are important for ophthalmology and vision science.

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.