Relating Histopathology and Mechanical Strain in Experimental Contusion Spinal Cord Injury in a Rat Model
Bibliographic record
Abstract
During traumatic spinal cord injury (SCI), the spinal cord is subject to external displacements that result in damage of neural tissues. These displacements produce complex internal deformations, or strains, of the spinal cord parenchyma. The aim of this study is to determine a relationship between these internal strains during SCI and primary damage to spinal cord gray matter (GM) in an in vivo rat contusion model. Using magnetic resonance imaging and novel image registration methods, we measured three-dimensional (3D) mechanical strain in in vivo rat cervical spinal cord (n = 12) during an imposed contusion injury. We then assessed expression of the neuronal transcription factor, neuronal nuclei (NeuN), in ventral horns of GM (at the epicenter of injury as well as at intervals cranially and caudally), immediately post-injury. We found that minimum principal strain was most strongly correlated with loss of NeuN stain across all animals (R(2) = 0.19), but varied in strength between individual animals (R(2) = 0.06-0.52). Craniocaudal distribution of anatomical damage was similar to measured strain distribution. A Monte Carlo simulation was used to assess strain field error, and minimum principal strain (which ranged from 8% to 36% in GM ventral horns) exhibited a standard deviation of 2.6% attributed to the simulated error. This study is the first to measure 3D deformation of the spinal cord and relate it to patterns of ensuing tissue damage in an in vivo model. It provides a platform on which to build future studies addressing the tolerance of spinal cord tissue to mechanical deformation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".