Gut Analysis Toolbox: A Tool for Analyzing Enteric Neurons
Author Information
Author(s): Sorensen Luke, Humenick Adam, Poon Sabrina S. B., Han Myat Noe, Mahdavian Narges S., Rowe Matthew C., Hamnett Ryan, Gómez-de-Mariscal Estibaliz, Neckel Peter H., Saito Ayame, Mutunduwe Keith, Glennan Christie, Haase Robert, McQuade Rachel M., Foong Jaime P. P., Brookes Simon J. H., Kaltschmidt Julia A., Muñoz-Barrutia Arrate, King Sebastian K., Veldhuis Nicholas A., Carbone Simona E., Poole Daniel P., Rajasekhar Pradeep
Primary Institution: Monash University
Hypothesis
The Gut Analysis Toolbox (GAT) can improve the analysis of enteric neurons and their distribution in the gastrointestinal tract.
Conclusion
GAT enhances the efficiency and accuracy of neuronal analysis in enteric nervous system research.
Supporting Evidence
- GAT provides rapid and accurate segmentation of enteric neurons.
- Proximal neighbor analysis reveals differences in cellular distribution across gut regions.
- GAT minimizes potential sampling errors and biases.
- Deep learning models were trained on diverse datasets to ensure generalizability.
- GAT allows for the analysis of larger tissue areas compared to manual methods.
- Statistical analysis showed significant improvements in segmentation accuracy.
- GAT is user-friendly and designed for researchers with minimal computational expertise.
- Documentation and tutorials are available to assist users in implementing GAT.
Takeaway
The Gut Analysis Toolbox helps scientists quickly and accurately study nerve cells in the gut, making it easier to understand how they work.
Methodology
GAT uses deep learning models for cell segmentation and spatial analysis of enteric neurons in 2D images.
Potential Biases
Potential operator bias during manual counting and tissue preparation.
Limitations
GAT is currently limited to 2D images and may not work well with images it hasn't encountered before.
Participant Demographics
Images collected from various species including mice, rats, and humans.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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