A novel poly-ε-lysine based implant, Proliferate®, for promotion of CNS repair following spinal cord injury
Bibliographic record
Abstract
The limited regenerative capacity of the CNS poses formidable challenges to the repair of spinal cord injury (SCI). Two key barriers to repair are (i) the physical gap left by the injury, and (ii) the inhibitory milieu surrounding the injury, the glial scar. Biomaterial implantation into the injury site can fill the cavity, provide a substrate for cell migration, and potentially attenuate the glial scar. We investigated the biological viability of a biocompatible and biodegradable poly-ε-lysine based biomaterial, Proliferate®, in low and high cross-linked forms and when coated with IKVAV peptide, for SCI implantation. We demonstrate altered astrocyte morphology and nestin expression on Proliferate® compared to conventional glass cell coverslips suggesting a less reactive phenotype. Moreover Proliferate® supported myelination in vitro, with myelination observed sooner on IKVAV-coated constructs compared with uncoated Proliferate®, and delayed overall compared with maintenance on glass coverslips. For in vivo implantation, parallel-aligned channels were fabricated into Proliferate® to provide cell guidance cues. Extensive vascularisation and cellular infiltration were observed in constructs implanted in vivo, along with an astrocyte border and microglial response. Axonal ingrowth was observed at the construct border and inside implants in intact channels. We conclude that Proliferate® is a promising biomaterial for implantation following SCI.
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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".