Biomaterial Strategies for Delivering Stem Cells as a Treatment for Spinal Cord Injury
Bibliographic record
Abstract
Ongoing clinical trials are evaluating the use of stem cells as a way to treat traumatic spinal cord injury (SCI). However, the inhibitory environment present in the injured spinal cord makes it challenging to achieve the survival of these cells along with desired differentiation into the appropriate phenotypes necessary to regain function. Transplanting stem cells along with an instructive biomaterial scaffold can increase cell survival and improve differentiation efficiency. This study reviews the literature discussing different types of instructive biomaterial scaffolds developed for transplanting stem cells into the injured spinal cord. We have chosen to focus specifically on biomaterial scaffolds that direct the differentiation of neural stem cells and pluripotent stem cells since they offer the most promise for producing the cell phenotypes that could restore function after SCI. In terms of biomaterial scaffolds, this article reviews the literature associated with using hydrogels made from natural biomaterials and electrospun scaffolds for differentiating stem cells into neural phenotypes. It then presents new data showing how these different types of scaffolds can be combined for neural tissue engineering applications and provides directions for future studies.
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.
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 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".