A Graded Forceps Crush Spinal Cord Injury Model in Mice
Bibliographic record
Abstract
Given the rising availability and use of genetically modified animals in basic science research, it has become increasingly important to develop clinically relevant models for spinal cord injury (SCI) for use in mice. We developed a graded forceps crush model of SCI in mice that uses three different forceps with spacers of 0.25, 0.4, and 0.55 mm, to produce severe, moderate, and mild injuries, respectively. Briefly, each mouse was subjected to laminectomy of T5-T7, 15-second spinal cord crush using one of those forceps, behavioral assessments, and post-mortem neuroanatomical analyses. There were significant differences among the three injury severity groups on behavioral measures (Basso Mouse Score, footprint, and ladder analyses), demonstrating an increase in neurological deficits for groups with greater injury severity. Quantitative analysis of the lesion demonstrated that as injury severity increased, lesion size and GFAP negative area increased, and spared tissue, spinal cord cross-sectional area, spared grey matter and spared white matter decreased. These measures strongly correlated with the behavioral outcomes. Similar to other studies of SCI in mice, we report a dense laminin and fibronectin positive extracellular matrix in the lesion sites of injured mice, but unlike those previous studies, we also report the presence of numerous p75 positive Schwann cells in and around the lesion epicenter. These results provide evidence that the graded forceps crush model is an attractive alternative for the study of SCI and related therapeutic interventions. Because of its demonstrated consistency, ease of use, low cost, and clinical relevance, this graded forceps crush is an attractive alternative to the other mouse models of SCI currently available.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".