Developing preclinical dog models for reconstructive severed spinal cord continuity via spinal cord fusion technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it