Fibrin Scaffolds Containing Ectomesenchymal Stem Cells Enhance Behavioral and Histological Improvement in a Rat Model of Spinal Cord Injury
Bibliographic record
Abstract
Fibrin has been widely used in wound healing. However, its benefit for spinal cord injury (SCI) is limited. In this study, we investigated the impact of fibrin scaffolds containing ectomesenchymal stem cells (EMSCs) on histological and behavioral recovery after SCI and compared it with fibrin alone. To achieve this, EMSCs derived from adult rat nasal respiratory mucosa were cultured, characterized and transfected with green fluorescent protein adenovirus before transplantation. Then, Sprague-Dawley host rats were randomly assigned into four groups: the control group (laminectomy); the SCI group (laminectomy and transection of spinal cords); the fibrin group (fibrin was transplanted immediately after SCI), and the fibrin cell (FC) group (fibrin scaffolds containing EMSCs were transplanted after SCI). Three days after the operation, a TUNEL assay indicated less apoptotic cells in the FC group than in the fibrin group. Two weeks after SCI, fluorescence staining demonstrated not only the survival and migration of EMSCs into the lesion sites, but also a higher number of nerve fibers in the FC group than in the fibrin group. Histological examination including immunohistochemistry and transmission electron microscopy 12 weeks after the operation showed more nerve fibers and a thicker myelin sheath in the FC group compared to the fibrin group. Western blotting confirmed these morphological results. Consistent with the histological results, Basso, Beattie and Bresnahan locomotor scores of the FC group were higher than those of the fibrin group. These results suggest that fibrin scaffolds containing EMSCs can improve the behavioral and histological recovery after SCI better than fibrin alone.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".