Reconstruction of the transected cat spinal cord following NeuroGel™ implantation: axonal tracing, immunohistochemical and ultrastructural studies
Bibliographic record
Abstract
This study examined the ability of NeuroGel, a biocompatible porous poly [N-(2-hydroxypropyl) methacrylamide] hydrogel, to establish a permissive environment across a 3 mm gap in the cat spinal cord in order to promote tissue reconstitution and axonal regeneration across the lesion. Animals with NeuroGel implants were compared to transection-only controls and observed for 21 months. The hydrogel formed a stable bridge between the cord segments. Six months after reconstructive surgery, it was densely infiltrated by a reparative tissue composed of glial cells, capillary vessels and axonal fibres. Axonal labelling and double immunostaining for neurofilaments and myelin basic protein, showed that descending supraspinal axons of the ventral funiculus and afferent fibres of the dorsal column regenerated across the reconstructed lesion. Fifteen months after reconstructive surgery, axons had grown, at least, 12 mm into the distal cord tissue, and in the rostral cord there was labelling of neurons of the intermediate gray matter. Electron microscopy showed that after 9 months, most of the regenerating axons were myelinated, principally by Schwann cells. Newly formed neurons presumably from precursor cells of the ependyma and/or migrating neurons were observed within the reparative tissue after 21 months. Results indicate that functional deficit, as assessed by treadmill training, and morphological changes following double transection of the spinal cord can be modified by the implantation of NeuroGel. This technology offers the potential to promote the formation of a neural tissue equivalent via a reparative neohistogenesis process, that facilitates and supports regenerative growth of axons.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".