Fibroadhesive scarring of grafted collagen scaffolds interferes with implant–host neural tissue integration and bridging in experimental spinal cord injury
Bibliographic record
Abstract
Severe traumatic spinal cord injury (SCI) results in a devastating and permanent loss of function, and is currently an incurable condition. It is generally accepted that future intervention strategies will require combinational approaches, including bioengineered scaffolds, to support axon growth across tissue scarring and cystic cavitation. Previously, we demonstrated that implantation of a microporous type-I collagen scaffold into an experimental model of SCI was capable of supporting functional recovery in the absence of extensive implant-host neural tissue integration. Here, we demonstrate the reactive host cellular responses that may be detrimental to neural tissue integration after implantation of collagen scaffolds into unilateral resection injuries of the adult rat spinal cord. Immunohistochemistry demonstrated scattered fibroblast-like cell infiltration throughout the scaffolds as well as the presence of variable layers of densely packed cells, the fine processes of which extended along the graft-host interface. Few reactive astroglial or regenerating axonal profiles could be seen traversing this layer. Such encapsulation-type behaviour around bioengineered scaffolds impedes the integration of host neural tissues and reduces the intended bridging role of the implant. Characterization of the cellular and molecular mechanisms underpinning this behaviour will be pivotal in the future design of collagen-based bridging scaffolds intended for regenerative medicine.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".