A Cervical Spinal Cord Hemi-Contusion Injury Model Based on Displacement Control in Non-Human Primates <i>(Macaca fascicularis)</i>
Bibliographic record
Abstract
Non-human primate (NHP) spinal cord injury (SCI) models can be informative in the evaluation of treatments that show promise in rodent models prior to translation to humans. In the present study, we aimed to establish a cervical spinal hemi-contusion model with controlled displacement and evaluate the abnormalities in behavior, electrophysiology, histology, and magnetic resonance imaging. Twelve adult NHPs were divided into an SCI group ( n = 8, 24 and 48 weeks) and a control group ( n = 4). An impactor (Φ = 4 mm) was driven to compress the left C5 cord at 800 mm/sec. The contusion displacement and peak force was 4.08 ± 0.17 mm and 19.8 ± 4.6 N. The behavioral assessment showed a consistent dysfunction below the wrist and spontaneous recovery of limb function after injury. Lesion length and lesion area at the epicenter based on T2 hyperintensity were 5.68 ± 0.47 mm and 5.99 ± 0.24 mm 2 at 24 weeks post-injury (wpi), and 5.29 ± 0.17 mm and 5.95 ± 0.24 mm 2 at 48 wpi. The spared spinal cord area immuno-positive for glial fibrillary acidic protein was significantly reduced, while the staining intensity increased at 24 wpi and 48 wpi, compared with the sham group. Ipsilateral somatosensory and motor evoked potentials were dynamic, increasing in latency and decreasing in amplitude compared with pre-operative values or the contralateral values, and correlated to varying degrees with behavioral outcomes. A shift in size–frequency distribution of sensory neurons of the dorsal root ganglia (DRG) was consistent with a loss of large-diameter cells. The present study demonstrated that the NHP SCI model resulted in consistent unilateral limb dysfunction and potential plasticity in the face of loss of spinal cord and DRG tissue.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".