Developing preclinical dog models for reconstructive severed spinal cord continuity via spinal cord fusion technique
Bibliographic record
Abstract
Background: Spinal cord injury (SCI) is a severe impairment of the central nervous system, leading to motor, sensory, and autonomic dysfunction. The present study investigates the efficacy of the polyethylene glycol (PEG)-mediated spinal cord fusion (SCF) techniques, demonstrating efficacious in various animal models with complete spinal cord transection at the T10 level. This research focuses on a comparative analysis of three SCF treatment models in beagles: spinal cord transection (SCT), vascular pedicle hemisected spinal cord transplantation (vSCT), and vascularized allograft spinal cord transplantation (vASCT) surgical model. Methods: Seven female beagles were included in the SCT surgical model, while four female dogs were enrolled in the vSCT surgical model. Additionally, twelve female dogs underwent vASCT in a paired donor-recipient setup. Three surgical model were evaluated and compared through electrophysiology, imaging and behavioral recovery. Results: >0.05). Neuroimaging analysis across the SCT, vSCT and vASCT surgical models revealed spinal cord graft survival and fiber regrowth across transection sites at 6 months postoperatively. Also, positive MEP waveforms were recorded in all three surgical models at 6-month post-surgery. Conclusion: The study underscores the clinical relevance of PEG-mediated SCF techniques in promoting nerve fusion, repair, and motor functional recovery in SCI. SCT, vSCT, and vASCT, tailored to specific clinical characteristics, demonstrated similar effective therapeutic outcomes.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".