Comparing natural hydrogels to self-assembling peptides in spinal cord injury treatment: a systematic review
Bibliographic record
Abstract
Background: In many cases, central nervous system (CNS) injury is unchanging due to the absence of neuronal regeneration and repair capabilities. In recent years, regenerative medicine, and especially hydrogels, has reached a significant amount of attention for their promising results for the treatment of spinal cord injury (SCI) currently considered permanent. Hydrogels are categorized based on their foundation: synthetic, natural, and combination. The objective of this study was to compare the properties and efficacy of commonly used hydrogels, like collagen, and other natural peptides with synthetic self-assembling peptide hydrogels in the treatment of SCI. Methods : Articles were searched in PubMed, Scopus, Web of Science, and Embase. All studies from 1985 until January 2020 were included in the primary search. Eligible articles were included based on the following criteria: administering hydrogels (both natural and synthetic) for SCI treatment, solely focusing on spinal cord injury treatment, and published in a peer-reviewed journal. Data on axonal regeneration, revascularization, elasticity, drug delivery efficacy, and porosity were extracted. Results: A total of 24 articles were included for full-text review and data extraction. There was only one experimental study comparing collagen I (natural hydrogel) and polyethylene glycol (PEG) in an in vitro setting. The included study suggested the behavior of cells with PEG is more expectable in the injury site, which makes it a more reliable scaffold for neurites. Conclusions: There is limited research comparing and evaluating both types of natural and self-assembling peptides (SAPs) in the same animal or in vitro study, despite its importance. Although we assume that the remodeling of natural scaffolds may lead to a stable hydrogel, there was not a definitive conclusion that synthetic hydrogels are more beneficial than natural hydrogels in neuronal regeneration.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".