Bioengineered Strategies for Spinal Cord Repair
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it