Biology subjects

Ross, I.

Publications and source records attributed to Ross, I..

3 recordsLinked to original sources

bioRxiv2023

Quantitative Three-dimensional Label-free Digital Holographic Imaging of Cardiomyocyte Size, Ploidy, and Cell Division

Cardiac regeneration in newborn rodents depends on the ability of pre-existing cardiomyocytes to proliferate and divide. This capacity is lost within the first week of postnatal development when these cells rapidly switch from hyperplasia to hypertrophy, withdraw from the cell cycle, become binucleated, and increase in size. How these dynamic changes in size and ploidy impact cardiomyocyte proliferative potential is not well understood. In this study, we innovate the application of a commercially available digital holographic imaging microscope, the Holomonitor M4, to evaluate the proliferative responses of mononucleated diploid and binucleated tetraploid cardiomyocytes. This instrument coupled with the powerful Holomonitor App Suite software enables long-term label-free quantitative three-dimensional tracking of primary cardiomyocyte dynamics in real-time with single-cell resolution. Our digital holographic imaging results provide direct evidence that mononucleated cardiomyocytes retain significant proliferative potential as most can successfully divide with high frequency. In contrast, binucleated cardiomyocytes exhibit a blunted response to a proliferative stimulus with the majority not attempting to divide at all. Nevertheless, some binucleated cardiomyocytes were capable of complete division, suggesting that these cells still do retain limited proliferative capacity. By quantitatively tracking cardiomyocyte volume dynamics during these proliferative responses, we reveal that both mononucleated and binucleated cells reach a unique size threshold prior to attempted cell division. The absolute threshold is increased by binucleation, which may limit the ability of binucleated cardiomyocytes to divide. By defining the interrelationship between cardiomyocyte size, ploidy, and cell cycle control, we will better understand the cellular mechanisms that drive the loss of mammalian cardiac regenerative capacity after birth.

cell biology↗

bioRxiv2023

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models

BackgroundThe PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. ResultsWe demonstrate SKiMs ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface (https://skim.morgridge.org) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. ConclusionsSKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

bioinformatics↗

bioRxiv2020

SKiM - A generalized literature-based discovery system for uncovering novel biomedical knowledge from PubMed

Literature-based discovery (LBD) uncovers undiscovered public knowledge by linking terms A to C via a B intermediate. Existing LBD systems are limited to process certain A, B, and C terms, and many are not maintained. We present SKiM (Serial KinderMiner), a generalized LBD system for processing any combination of A, Bs, and Cs. We evaluate SKiM via the rediscovery of discoveries by Don Swanson, who pioneered LBD. Using only literature from the 19th century up to a year before Swansons discoveries, SKiM uncovers all five discoveries. We apply SKiM to repurposing drugs for 26 conditions of high prevalence. Manual analysis confirmed 65 discoveries useful for four diseases from Swansons discoveries from one to 31 years prior to their first validation by clinical trials. SKiM predicts many new potential drug candidates representing prime targets for wet lab validation. SKiM can be applied to any biomedical inquiry sufficiently mentioned in the literature.

bioinformatics↗