Spinal Cord Repair by Means of Tissue Engineered Scaffolds
Bibliographic record
Abstract
Spinal cord injury (SCI) leads to devastating and permanent loss of neurological function, affecting all levels below the site of trauma. The injured adult spinal cord has little self-regenerative capacity due to multifactorial reasons. Tissue engineered scaffolds have emerged as a promising approach to promote regeneration of the damaged spinal cord by providing guidance to the regrowing axons. Integration of different therapeutic strategies with scaffolds has achieved substantial reestablishment of functional neural connectivity, with some strategies now being considered for clinical trials. This chapter presents a comprehensive discussion on the development of scaffold-based strategies currently under investigation for spinal cord tissue regeneration. First is a discussion of spinal cord structure, the pathophysiology of spinal cord injury, and various SCI animal models for experimental studies. Second is a detailed literature review and discussion of scaffold biomaterials and widely used techniques for scaffold fabrication in the context of SCI repair. This chapter then examines various therapeutic strategies currently used to repair SCI, including cell therapy, extracellular matrix protein/peptide modification, gene therapy, and molecular therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".