Bioengineered Strategies for Spinal Cord Repair
Bibliographic record
Abstract
This article reviews bioengineered strategies for spinal cord repair using tissue engineered scaffolds and drug delivery systems. The pathophysiology of spinal cord injury (SCI) is multifactorial and multiphasic, and therefore, it is likely that effective treatments will require combinations of strategies such as neuroprotection to counteract secondary injury, provision of scaffolds to replace lost tissue, and methods to enhance axonal regrowth, synaptic plasticity, and inhibition of astrocytosis. Biomaterials have major advantages for spinal cord repair because of their structural and chemical versatility. To date, various degradable or non-degradable biomaterial polymers have been tested as guidance channels or delivery systems for cellular and non-cellular neuroprotective or neuroregenerative agents in experimental SCI. There is promise that bioengineering technology utilizing cellular treatment strategies, including Schwann cells, olfactory ensheathing glia, or neural stem cells, can promote repair of the injured spinal cord. This review is divided into three parts: (1) degradable and non-degradable biomaterials; (2) device design; and (3) combination strategies with scaffolds. We will show that bioengineering combinations of cellular and non-cellular strategies have enhanced the potential for experimental SCI repair, although further pre-clinical work is required before this technology can be translated to humans.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".