Design of hydrogel-based scaffolds for the treatment of spinal cord injuries
Bibliographic record
Abstract
Spinal cord injury (SCI) is a traumatic lesion that diminishes sensory and/or motor neuronal functionality, directly affecting the quality of the patient's life. Due to the central nervous system's (CNS) inhibitory microenvironment that presents challenges in neuron repair and regeneration, tissue engineering strategies have received significant attention to improve the quality of a patient's life. In this regard, hydrogels are attractive SC scaffolds as they can provide not only an adjustable physiologically native-like microenvironment but also an appropriate matrix for cell delivery, drug delivery, and other bioactive molecule delivery at the lesion site. This systematic review characterizes the widely used biomaterials including natural polymers; protein- and polysaccharide-based synthetic polymers; methacrylate- and polyethylene glycol-based, and self-assembling (SA) peptides. In addition, synthesis routes of hydrogels are investigated. This review is complemented by the discussion of the various techniques utilized for hydrogel scaffold designs with their in vitro and in vivo outcomes and clinical trials. The existing challenges and opportunities for SC hydrogel scaffolds are mentioned towards the end of this review.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".