ArticleJournal of experimental neuroscience2019
Quantification of surviving neurons after contusion, dislocation, and distraction spinal cord injuries using automated methods.
Article in Journal of experimental neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Reanalysis of Published Histological Data Can Help to Characterize Neuronal Death After Spinal Cord Injury.International journal of molecular sciences · 2025Article
- Neurophysiological, histological, and behavioral characterization of animal models of distraction spinal cord injury: a systematic review.Neural regeneration research · 2024Review
- Elucidating the Potential Mechanisms Underlying Distraction Spinal Cord Injury-Associated Neuroinflammation and Apoptosis.Frontiers in cell and developmental biology · 2022Article
- Walking Function After Cervical Contusion and Distraction Spinal Cord Injuries in Rats.Journal of experimental neuroscience · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes and validates an automated method for counting neurons in spinal cord injury (SCI) and then uses it to examine and compare the surviving cells in common types of SCI mechanisms. Moderate contusion, dislocation, and distraction SCIs were surgically induced in Sprague Dawley male rats (n = 6 for each type of injury). Their spinal cords were harvested 8 weeks post injury with 5 normal weight-matched rats. The spinal cords were cut, stained with anti-NeuN antibody and fluorescent Nissl, and imaged in the dorsal and ventral horns at various distances to the epicenter. Neurons in the images were automatically counted using an algorithm that was designed to filter non-soma-like objects based on morphological characteristics (size, solidity, circular pattern) and check the remaining objects for the double-stained nucleus/cell body features (brightness variation, brightness distribution, color). To validate the automated method, some of the images were randomly selected for manual counting. The number of surviving cells that were automatically measured by the algorithm was found to be correlated with the values that were manually measured by 2 observers (
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Registered trials
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