Impact Depth and the Interaction with Impact Speed Affect the Severity of Contusion Spinal Cord Injury in Rats
Bibliographic record
Abstract
Spinal cord injury (SCI) biomechanics suggest that the mechanical factors of impact depth and speed affect the severity of contusion injury, but their interaction is not well understood. The primary aim of this work was to examine both the individual and combined effects of impact depth and speed in contusion SCI on the cervical spinal cord. Spinal cord contusions between C5 and C6 were produced in anesthetized rats at impact speeds of 8, 80, or 800 mm/s with displacements of 0.9 or 1.5 mm (n=8/group). After 7 days postinjury, rats were assessed for open-field behavior, euthanized, and spinal cords were harvested. Spinal cord tissue sections were stained for demyelination (myelin-based protein) and tissue sparing (Luxol fast blue). In parallel, a finite element model of rat spinal cord was used to examine the resulting maximum principal strain in the spinal cord during impact. Increasing impact depth from 0.9 to 1.5 mm reduced open-field scores (p<0.01) above 80 mm/s, reduced gray (GM) and white matter (WM) sparing (p<0.01), and increased the amount of demyelination (p<0.01). Increasing impact speed showed similar results at the 1.5-mm impact depth, but not the 0.9-mm impact depth. Linear correlation analysis with finite element analysis strain showed correlations (p<0.001) with nerve fiber damage in the ventral (R(2)=0.86) and lateral (R(2)=0.74) regions of the spinal cord and with WM (R(2)=0.90) and GM (R(2)=0.76) sparing. The results demonstrate that impact depth is more important in determining the severity of SCI and that threshold interactions exist between impact depth and speed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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".